=4.39.2\nflash_attn>=2.5.6\n```\n## Usage \n\n### Sentence Transformers\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", trust_remote_code=True)\n# In case you want to reduce the maximum length:\nmodel.max_seq_length = 8192\n\nqueries = [\n \"how much protein should a female eat\",\n \"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]\n\nquery_embeddings = model.encode(queries, prompt_name=\"query\")\ndocument_embeddings = model.encode(documents)\n\nscores = (query_embeddings @ document_embeddings.T) * 100\nprint(scores.tolist())\n```\n\nObserve 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.\n\n### Transformers\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'Instruct: {task_description}\\nQuery: {query}'\n\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 much protein should a female eat'),\n get_detailed_instruct(task, 'summit define')\n]\n# No need to add instruction for retrieval documents\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]\ninput_texts = queries + documents\n\ntokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)\nmodel = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)\n\nmax_length = 8192\n\n# Tokenize the input texts\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```\n\n## Evaluation\n\n### MTEB & C-MTEB\n\nYou 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):\n\n| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) | \n|:----:|:---------:|:----------:|:----------:|:----------:|\n| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | - | - |\n| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | - | - |\n| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | - | - |\n| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | - | - |\n| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | - | - |\n| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | - | - |\n| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | - | - |\n| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | - | - |\n| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | - | - |\n| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | - | - |\n| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | - | - |\n| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | - | - |\n| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | - | - |\n| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | **68.25** | **67.86** |\n| [**gte-Qwen2-1.5B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | **67.16** | **67.65** | **66.60** | **64.04** |\n\n### GTE Models\n\nThe 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). \n\n| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |\n|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:|\n| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB |\n| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB |\n| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB |\n| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB |\n| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |\n| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |\n| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB |\n| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB |\n| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |\n| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB |\n| [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | Multilingual | 32000 | 1536 | 6.62GB |\n\n## Citation\n\nIf you find our paper or models helpful, please consider cite:\n\n```\n@article{li2023towards,\n title={Towards general text embeddings with multi-stage contrastive learning},\n author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},\n journal={arXiv preprint arXiv:2308.03281},\n year={2023}\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":187,"cells":{"id":{"kind":"string","value":"TheBloke/meditron-70B-GPTQ"},"author":{"kind":"string","value":"TheBloke"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","safetensors","llama","text-generation","medical","health","llama2","en","dataset:bigbio/med_qa","dataset:medmcqa","dataset:bigbio/pubmed_qa","dataset:epfl-llm/guidelines","arxiv:2311.16079","base_model:epfl-llm/meditron-70b","base_model:quantized:epfl-llm/meditron-70b","license:llama2","autotrain_compatible","text-generation-inference","4-bit","gptq","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"llama\",\n \"text-generation\",\n \"medical\",\n \"health\",\n \"llama2\",\n \"en\",\n \"dataset:bigbio/med_qa\",\n \"dataset:medmcqa\",\n \"dataset:bigbio/pubmed_qa\",\n \"dataset:epfl-llm/guidelines\",\n \"arxiv:2311.16079\",\n \"base_model:epfl-llm/meditron-70b\",\n \"base_model:quantized:epfl-llm/meditron-70b\",\n \"license:llama2\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"4-bit\",\n \"gptq\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-11-30T17:10:33","string":"2023-11-30T17:10:33"},"last_modified":{"kind":"timestamp","value":"2023-11-30T21:24:40","string":"2023-11-30T21:24:40"},"downloads":{"kind":"number","value":2391,"string":"2,391"},"likes":{"kind":"number","value":4,"string":"4"},"README":{"kind":"string","value":"---\nbase_model: epfl-llm/meditron-70b\ndatasets:\n- bigbio/med_qa\n- medmcqa\n- bigbio/pubmed_qa\n- epfl-llm/guidelines\nlanguage:\n- en\nlicense: llama2\nmetrics:\n- accuracy\n- perplexity\nmodel_name: Meditron 70B\npipeline_tag: text-generation\ntags:\n- medical\n- health\n- llama2\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 70B - GPTQ\n- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)\n- Original model: [Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b)\n\n\n# Description\n\nThis repo contains GPTQ model files for [EPFL LLM Team's Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b).\n\nMultiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.\n\nThese files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).\n\n\n\n## Repositories available\n\n* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-70B-AWQ)\n* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-70B-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-70B-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-70b)\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\n## Known compatible clients / servers\n\nThese GPTQ models are known to work in the following inference servers/webuis.\n\n- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n- [KoboldAI United](https://github.com/henk717/koboldai)\n- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)\n\nThis may not be a complete list; if you know of others, please let me know!\n\n\n\n## Provided files, and GPTQ parameters\n\nMultiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.\n\nEach separate quant is in a different branch. See below for instructions on fetching from different branches.\n\nMost GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.\n\n![\"Alt](\"meditron_LOGO.png\" "\\"Meditron-logo\\"")
\n\n# Model Card for Meditron-70B-v1.0\n\nMeditron is a suite of open-source medical Large Language Models (LLMs).\nMeditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.\n\n\n\n\n![\"Alt](\"gap-replay.png\" "\\"Meditron-logo\\"")
\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 16 nodes 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. \nThe nodes are connected via RDMA over Converged Ethernet.\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 8,\n - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.\n \n\n#### Training Hyperparameters\n\n| | |\n| --- | ------ |\n| bf16 | true |\n| lr | 1.5e-4 |\n| eps | 1e-5 |\n| betas | \\[0.9, 0.95\\] |\n| clip_grad | 1 |\n| weight decay | 0.1 |\n| DP size | 2 |\n| TP size | 8 |\n| PP size | 8 |\n| seq length | 4096 |\n| lr scheduler | cosine|\n| min lr | 1e-6 |\n| warmup iteration | 2000 |\n| micro batch size | 2 |\n| global batch size | 512 |\n| | |\n\n#### Speeds, Sizes, Times\nThe model was trained in September and October 2023.\n\nThe model architecture is exactly Llama 2, meaning\n\n| | | \n| --- | ------ |\n| Model size | 70B | \n| Hidden dimension | 8192 |\n| Num. attention heads | 64 |\n| Num. layers | 80 |\n| | | | \n\nWe train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second. \nThis amounts to a bfloat16 model flops utilization of roughly 42.3\\%.\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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. \nWe report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |\n|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |\n|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |\n|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |\n|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |\n|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |\n|Avg | 72.0 | 69.2 | 63.6 | 57.4 |\n| | | | | | |\n\n**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.\n\n## Environmental Impact\n\n\n\n- **Hardware Type:** 128 x NVIDIA A100 (80GB) SXM\n- **Total GPU hours:** 42,496\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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be: \n \n (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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":188,"cells":{"id":{"kind":"string","value":"Alibaba-NLP/gme-Qwen2-VL-7B-Instruct"},"author":{"kind":"string","value":"Alibaba-NLP"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["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-7B-Instruct","base_model:finetune:Qwen/Qwen2-VL-7B-Instruct","license:apache-2.0","model-index","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"qwen2_vl\",\n \"image-text-to-text\",\n \"mteb\",\n \"transformers\",\n \"Qwen2-VL\",\n \"sentence-similarity\",\n \"vidore\",\n \"en\",\n \"zh\",\n \"arxiv:2412.16855\",\n \"base_model:Qwen/Qwen2-VL-7B-Instruct\",\n \"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-12-21T04:00:17","string":"2024-12-21T04:00:17"},"last_modified":{"kind":"timestamp","value":"2025-01-21T11:53:23","string":"2025-01-21T11:53:23"},"downloads":{"kind":"number","value":2333,"string":"2,333"},"likes":{"kind":"number","value":26,"string":"26"},"README":{"kind":"string","value":"---\nbase_model:\n- Qwen/Qwen2-VL-7B-Instruct\nlanguage:\n- en\n- zh\nlicense: apache-2.0\ntags:\n- mteb\n- sentence-transformers\n- transformers\n- Qwen2-VL\n- sentence-similarity\n- vidore\nmodel-index:\n- name: gme-Qwen2-VL-7B-Instruct\n results:\n - task:\n type: STS\n dataset:\n name: MTEB AFQMC\n type: C-MTEB/AFQMC\n config: default\n split: validation\n revision: b44c3b011063adb25877c13823db83bb193913c4\n metrics:\n - type: cos_sim_pearson\n value: 64.72351048394194\n - type: cos_sim_spearman\n value: 71.66842612591344\n - type: euclidean_pearson\n value: 70.0342809043895\n - type: euclidean_spearman\n value: 71.66842612323917\n - type: manhattan_pearson\n value: 69.94743870947117\n - type: manhattan_spearman\n value: 71.53159630946965\n - task:\n type: STS\n dataset:\n name: MTEB ATEC\n type: C-MTEB/ATEC\n config: default\n split: test\n revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865\n metrics:\n - type: cos_sim_pearson\n value: 52.38188106868689\n - type: cos_sim_spearman\n value: 55.468235529709766\n - type: euclidean_pearson\n value: 56.974786979175086\n - type: euclidean_spearman\n value: 55.468231026153745\n - type: manhattan_pearson\n value: 56.94467132566259\n - type: manhattan_spearman\n value: 55.39037386224014\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: 77.61194029850746\n - type: ap\n value: 41.29789064067677\n - type: f1\n value: 71.69633278678522\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: 97.3258\n - type: ap\n value: 95.91845683387056\n - type: f1\n value: 97.32526074864263\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: 64.794\n - type: f1\n value: 63.7329780206882\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (zh)\n type: mteb/amazon_reviews_multi\n config: zh\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 55.099999999999994\n - type: f1\n value: 53.115528412999666\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: 40.541\n - type: map_at_10\n value: 56.315000000000005\n - type: map_at_100\n value: 56.824\n - type: map_at_1000\n value: 56.825\n - type: map_at_3\n value: 51.778\n - type: map_at_5\n value: 54.623\n - type: mrr_at_1\n value: 41.038000000000004\n - type: mrr_at_10\n value: 56.532000000000004\n - type: mrr_at_100\n value: 57.034\n - type: mrr_at_1000\n value: 57.034\n - type: mrr_at_3\n value: 52.015\n - type: mrr_at_5\n value: 54.835\n - type: ndcg_at_1\n value: 40.541\n - type: ndcg_at_10\n value: 64.596\n - type: ndcg_at_100\n value: 66.656\n - type: ndcg_at_1000\n value: 66.666\n - type: ndcg_at_3\n value: 55.415000000000006\n - type: ndcg_at_5\n value: 60.527\n - type: precision_at_1\n value: 40.541\n - type: precision_at_10\n value: 9.083\n - type: precision_at_100\n value: 0.996\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 21.977\n - type: precision_at_5\n value: 15.661\n - type: recall_at_1\n value: 40.541\n - type: recall_at_10\n value: 90.825\n - type: recall_at_100\n value: 99.57300000000001\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 65.932\n - type: recall_at_5\n value: 78.307\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: 54.96111428218386\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: 50.637711388838945\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: 64.0741897266483\n - type: mrr\n value: 76.11440882909028\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: 86.2557839280406\n - type: cos_sim_spearman\n value: 82.58200216886888\n - type: euclidean_pearson\n value: 84.80588838508498\n - type: euclidean_spearman\n value: 82.58200216886888\n - type: manhattan_pearson\n value: 84.53082035185592\n - type: manhattan_spearman\n value: 82.4964580510134\n - task:\n type: STS\n dataset:\n name: MTEB BQ\n type: C-MTEB/BQ\n config: default\n split: test\n revision: e3dda5e115e487b39ec7e618c0c6a29137052a55\n metrics:\n - type: cos_sim_pearson\n value: 76.98420285210636\n - type: cos_sim_spearman\n value: 78.95549489000658\n - type: euclidean_pearson\n value: 79.14591532018991\n - type: euclidean_spearman\n value: 78.95549488953284\n - type: manhattan_pearson\n value: 79.26212116856509\n - type: manhattan_spearman\n value: 79.02104262086006\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: 84.76298701298703\n - type: f1\n value: 84.24881789367576\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: 46.86757924102047\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: 43.86043680479362\n - task:\n type: Clustering\n dataset:\n name: MTEB CLSClusteringP2P\n type: C-MTEB/CLSClusteringP2P\n config: default\n split: test\n revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476\n metrics:\n - type: v_measure\n value: 45.684222588040605\n - task:\n type: Clustering\n dataset:\n name: MTEB CLSClusteringS2S\n type: C-MTEB/CLSClusteringS2S\n config: default\n split: test\n revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f\n metrics:\n - type: v_measure\n value: 45.45639765303432\n - task:\n type: Reranking\n dataset:\n name: MTEB CMedQAv1\n type: C-MTEB/CMedQAv1-reranking\n config: default\n split: test\n revision: 8d7f1e942507dac42dc58017c1a001c3717da7df\n metrics:\n - type: map\n value: 88.7058672660788\n - type: mrr\n value: 90.5795634920635\n - task:\n type: Reranking\n dataset:\n name: MTEB CMedQAv2\n type: C-MTEB/CMedQAv2-reranking\n config: default\n split: test\n revision: 23d186750531a14a0357ca22cd92d712fd512ea0\n metrics:\n - type: map\n value: 90.50750030424048\n - type: mrr\n value: 92.3970634920635\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: f46a197baaae43b4f621051089b82a364682dfeb\n metrics:\n - type: map_at_1\n value: 28.848000000000003\n - type: map_at_10\n value: 40.453\n - type: map_at_100\n value: 42.065000000000005\n - type: map_at_1000\n value: 42.176\n - type: map_at_3\n value: 36.697\n - type: map_at_5\n value: 38.855000000000004\n - type: mrr_at_1\n value: 34.764\n - type: mrr_at_10\n value: 45.662000000000006\n - type: mrr_at_100\n value: 46.56\n - type: mrr_at_1000\n value: 46.597\n - type: mrr_at_3\n value: 42.632\n - type: mrr_at_5\n value: 44.249\n - type: ndcg_at_1\n value: 34.764\n - type: ndcg_at_10\n value: 47.033\n - type: ndcg_at_100\n value: 53.089\n - type: ndcg_at_1000\n value: 54.818\n - type: ndcg_at_3\n value: 41.142\n - type: ndcg_at_5\n value: 43.928\n - type: precision_at_1\n value: 34.764\n - type: precision_at_10\n value: 9.027000000000001\n - type: precision_at_100\n value: 1.465\n - type: precision_at_1000\n value: 0.192\n - type: precision_at_3\n value: 19.695\n - type: precision_at_5\n value: 14.535\n - type: recall_at_1\n value: 28.848000000000003\n - type: recall_at_10\n value: 60.849\n - type: recall_at_100\n value: 85.764\n - type: recall_at_1000\n value: 96.098\n - type: recall_at_3\n value: 44.579\n - type: recall_at_5\n value: 51.678999999999995\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackEnglishRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: ad9991cb51e31e31e430383c75ffb2885547b5f0\n metrics:\n - type: map_at_1\n value: 30.731\n - type: map_at_10\n value: 41.859\n - type: map_at_100\n value: 43.13\n - type: map_at_1000\n value: 43.257\n - type: map_at_3\n value: 38.384\n - type: map_at_5\n value: 40.284\n - type: mrr_at_1\n value: 38.471\n - type: mrr_at_10\n value: 47.531\n - type: mrr_at_100\n value: 48.199\n - type: mrr_at_1000\n value: 48.24\n - type: mrr_at_3\n value: 44.989000000000004\n - type: mrr_at_5\n value: 46.403\n - type: ndcg_at_1\n value: 38.471\n - type: ndcg_at_10\n value: 48.022999999999996\n - type: ndcg_at_100\n value: 52.32599999999999\n - type: ndcg_at_1000\n value: 54.26\n - type: ndcg_at_3\n value: 42.986999999999995\n - type: ndcg_at_5\n value: 45.23\n - type: precision_at_1\n value: 38.471\n - type: precision_at_10\n value: 9.248000000000001\n - type: precision_at_100\n value: 1.469\n - type: precision_at_1000\n value: 0.193\n - type: precision_at_3\n value: 20.892\n - type: precision_at_5\n value: 14.892\n - type: recall_at_1\n value: 30.731\n - type: recall_at_10\n value: 59.561\n - type: recall_at_100\n value: 77.637\n - type: recall_at_1000\n value: 89.64999999999999\n - type: recall_at_3\n value: 44.897999999999996\n - type: recall_at_5\n value: 51.181\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGamingRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 4885aa143210c98657558c04aaf3dc47cfb54340\n metrics:\n - type: map_at_1\n value: 34.949000000000005\n - type: map_at_10\n value: 48.117\n - type: map_at_100\n value: 49.355\n - type: map_at_1000\n value: 49.409\n - type: map_at_3\n value: 44.732\n - type: map_at_5\n value: 46.555\n - type: mrr_at_1\n value: 40.188\n - type: mrr_at_10\n value: 51.452\n - type: mrr_at_100\n value: 52.219\n - type: mrr_at_1000\n value: 52.24100000000001\n - type: mrr_at_3\n value: 48.642\n - type: mrr_at_5\n value: 50.134\n - type: ndcg_at_1\n value: 40.188\n - type: ndcg_at_10\n value: 54.664\n - type: ndcg_at_100\n value: 59.38099999999999\n - type: ndcg_at_1000\n value: 60.363\n - type: ndcg_at_3\n value: 48.684\n - type: ndcg_at_5\n value: 51.406\n - type: precision_at_1\n value: 40.188\n - type: precision_at_10\n value: 9.116\n - type: precision_at_100\n value: 1.248\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 22.236\n - type: precision_at_5\n value: 15.310000000000002\n - type: recall_at_1\n value: 34.949000000000005\n - type: recall_at_10\n value: 70.767\n - type: recall_at_100\n value: 90.79\n - type: recall_at_1000\n value: 97.57900000000001\n - type: recall_at_3\n value: 54.723\n - type: recall_at_5\n value: 61.404\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGisRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 5003b3064772da1887988e05400cf3806fe491f2\n metrics:\n - type: map_at_1\n value: 25.312\n - type: map_at_10\n value: 34.799\n - type: map_at_100\n value: 35.906\n - type: map_at_1000\n value: 35.983\n - type: map_at_3\n value: 31.582\n - type: map_at_5\n value: 33.507999999999996\n - type: mrr_at_1\n value: 27.232\n - type: mrr_at_10\n value: 36.82\n - type: mrr_at_100\n value: 37.733\n - type: mrr_at_1000\n value: 37.791000000000004\n - type: mrr_at_3\n value: 33.804\n - type: mrr_at_5\n value: 35.606\n - type: ndcg_at_1\n value: 27.232\n - type: ndcg_at_10\n value: 40.524\n - type: ndcg_at_100\n value: 45.654\n - type: ndcg_at_1000\n value: 47.557\n - type: ndcg_at_3\n value: 34.312\n - type: ndcg_at_5\n value: 37.553\n - type: precision_at_1\n value: 27.232\n - type: precision_at_10\n value: 6.52\n - type: precision_at_100\n value: 0.9530000000000001\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 14.915000000000001\n - type: precision_at_5\n value: 10.847\n - type: recall_at_1\n value: 25.312\n - type: recall_at_10\n value: 56.169000000000004\n - type: recall_at_100\n value: 79.16499999999999\n - type: recall_at_1000\n value: 93.49300000000001\n - type: recall_at_3\n value: 39.5\n - type: recall_at_5\n value: 47.288999999999994\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackMathematicaRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 90fceea13679c63fe563ded68f3b6f06e50061de\n metrics:\n - type: map_at_1\n value: 17.153\n - type: map_at_10\n value: 27.671\n - type: map_at_100\n value: 29.186\n - type: map_at_1000\n value: 29.299999999999997\n - type: map_at_3\n value: 24.490000000000002\n - type: map_at_5\n value: 26.178\n - type: mrr_at_1\n value: 21.144\n - type: mrr_at_10\n value: 32.177\n - type: mrr_at_100\n value: 33.247\n - type: mrr_at_1000\n value: 33.306000000000004\n - type: mrr_at_3\n value: 29.187\n - type: mrr_at_5\n value: 30.817\n - type: ndcg_at_1\n value: 21.144\n - type: ndcg_at_10\n value: 33.981\n - type: ndcg_at_100\n value: 40.549\n - type: ndcg_at_1000\n value: 43.03\n - type: ndcg_at_3\n value: 28.132\n - type: ndcg_at_5\n value: 30.721999999999998\n - type: precision_at_1\n value: 21.144\n - type: precision_at_10\n value: 6.666999999999999\n - type: precision_at_100\n value: 1.147\n - type: precision_at_1000\n value: 0.149\n - type: precision_at_3\n value: 14.302999999999999\n - type: precision_at_5\n value: 10.423\n - type: recall_at_1\n value: 17.153\n - type: recall_at_10\n value: 48.591\n - type: recall_at_100\n value: 76.413\n - type: recall_at_1000\n value: 93.8\n - type: recall_at_3\n value: 32.329\n - type: recall_at_5\n value: 38.958999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackPhysicsRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4\n metrics:\n - type: map_at_1\n value: 27.909\n - type: map_at_10\n value: 40.168\n - type: map_at_100\n value: 41.524\n - type: map_at_1000\n value: 41.626000000000005\n - type: map_at_3\n value: 36.274\n - type: map_at_5\n value: 38.411\n - type: mrr_at_1\n value: 34.649\n - type: mrr_at_10\n value: 45.613\n - type: mrr_at_100\n value: 46.408\n - type: mrr_at_1000\n value: 46.444\n - type: mrr_at_3\n value: 42.620999999999995\n - type: mrr_at_5\n value: 44.277\n - type: ndcg_at_1\n value: 34.649\n - type: ndcg_at_10\n value: 47.071000000000005\n - type: ndcg_at_100\n value: 52.559999999999995\n - type: ndcg_at_1000\n value: 54.285000000000004\n - type: ndcg_at_3\n value: 40.63\n - type: ndcg_at_5\n value: 43.584\n - type: precision_at_1\n value: 34.649\n - type: precision_at_10\n value: 8.855\n - type: precision_at_100\n value: 1.361\n - type: precision_at_1000\n value: 0.167\n - type: precision_at_3\n value: 19.538\n - type: precision_at_5\n value: 14.187\n - type: recall_at_1\n value: 27.909\n - type: recall_at_10\n value: 62.275000000000006\n - type: recall_at_100\n value: 84.95\n - type: recall_at_1000\n value: 96.02000000000001\n - type: recall_at_3\n value: 44.767\n - type: recall_at_5\n value: 52.03\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackProgrammersRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 6184bc1440d2dbc7612be22b50686b8826d22b32\n metrics:\n - type: map_at_1\n value: 25.846000000000004\n - type: map_at_10\n value: 36.870999999999995\n - type: map_at_100\n value: 38.294\n - type: map_at_1000\n value: 38.401\n - type: map_at_3\n value: 33.163\n - type: map_at_5\n value: 35.177\n - type: mrr_at_1\n value: 31.849\n - type: mrr_at_10\n value: 41.681000000000004\n - type: mrr_at_100\n value: 42.658\n - type: mrr_at_1000\n value: 42.71\n - type: mrr_at_3\n value: 39.003\n - type: mrr_at_5\n value: 40.436\n - type: ndcg_at_1\n value: 31.849\n - type: ndcg_at_10\n value: 43.291000000000004\n - type: ndcg_at_100\n value: 49.136\n - type: ndcg_at_1000\n value: 51.168\n - type: ndcg_at_3\n value: 37.297999999999995\n - type: ndcg_at_5\n value: 39.934\n - type: precision_at_1\n value: 31.849\n - type: precision_at_10\n value: 8.219\n - type: precision_at_100\n value: 1.318\n - type: precision_at_1000\n value: 0.167\n - type: precision_at_3\n value: 18.151\n - type: precision_at_5\n value: 13.242\n - type: recall_at_1\n value: 25.846000000000004\n - type: recall_at_10\n value: 57.642\n - type: recall_at_100\n value: 82.069\n - type: recall_at_1000\n value: 95.684\n - type: recall_at_3\n value: 40.778999999999996\n - type: recall_at_5\n value: 47.647\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 25.34866666666667\n - type: map_at_10\n value: 35.65541666666667\n - type: map_at_100\n value: 36.982416666666666\n - type: map_at_1000\n value: 37.09416666666667\n - type: map_at_3\n value: 32.421499999999995\n - type: map_at_5\n value: 34.20266666666667\n - type: mrr_at_1\n value: 30.02116666666667\n - type: mrr_at_10\n value: 39.781666666666666\n - type: mrr_at_100\n value: 40.69733333333333\n - type: mrr_at_1000\n value: 40.74875\n - type: mrr_at_3\n value: 37.043083333333335\n - type: mrr_at_5\n value: 38.56391666666666\n - type: ndcg_at_1\n value: 30.02116666666667\n - type: ndcg_at_10\n value: 41.66133333333333\n - type: ndcg_at_100\n value: 47.21474999999999\n - type: ndcg_at_1000\n value: 49.29600000000001\n - type: ndcg_at_3\n value: 36.06958333333334\n - type: ndcg_at_5\n value: 38.66858333333333\n - type: precision_at_1\n value: 30.02116666666667\n - type: precision_at_10\n value: 7.497249999999999\n - type: precision_at_100\n value: 1.2044166666666667\n - type: precision_at_1000\n value: 0.15766666666666665\n - type: precision_at_3\n value: 16.83458333333333\n - type: precision_at_5\n value: 12.134\n - type: recall_at_1\n value: 25.34866666666667\n - type: recall_at_10\n value: 55.40541666666666\n - type: recall_at_100\n value: 79.38683333333333\n - type: recall_at_1000\n value: 93.50958333333334\n - type: recall_at_3\n value: 39.99858333333334\n - type: recall_at_5\n value: 46.55741666666666\n - type: map_at_1\n value: 18.336\n - type: map_at_10\n value: 26.811\n - type: map_at_100\n value: 27.892\n - type: map_at_1000\n value: 27.986\n - type: map_at_3\n value: 23.976\n - type: map_at_5\n value: 25.605\n - type: mrr_at_1\n value: 20.148\n - type: mrr_at_10\n value: 28.898000000000003\n - type: mrr_at_100\n value: 29.866\n - type: mrr_at_1000\n value: 29.929\n - type: mrr_at_3\n value: 26.247999999999998\n - type: mrr_at_5\n value: 27.744999999999997\n - type: ndcg_at_1\n value: 20.148\n - type: ndcg_at_10\n value: 32.059\n - type: ndcg_at_100\n value: 37.495\n - type: ndcg_at_1000\n value: 39.855000000000004\n - type: ndcg_at_3\n value: 26.423000000000002\n - type: ndcg_at_5\n value: 29.212\n - type: precision_at_1\n value: 20.148\n - type: precision_at_10\n value: 5.268\n - type: precision_at_100\n value: 0.872\n - type: precision_at_1000\n value: 0.11900000000000001\n - type: precision_at_3\n value: 11.459999999999999\n - type: precision_at_5\n value: 8.503\n - type: recall_at_1\n value: 18.336\n - type: recall_at_10\n value: 46.411\n - type: recall_at_100\n value: 71.33500000000001\n - type: recall_at_1000\n value: 88.895\n - type: recall_at_3\n value: 31.134\n - type: recall_at_5\n value: 37.862\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackStatsRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a\n metrics:\n - type: map_at_1\n value: 25.102000000000004\n - type: map_at_10\n value: 33.31\n - type: map_at_100\n value: 34.443\n - type: map_at_1000\n value: 34.547\n - type: map_at_3\n value: 30.932\n - type: map_at_5\n value: 32.126\n - type: mrr_at_1\n value: 28.221\n - type: mrr_at_10\n value: 36.519\n - type: mrr_at_100\n value: 37.425000000000004\n - type: mrr_at_1000\n value: 37.498\n - type: mrr_at_3\n value: 34.254\n - type: mrr_at_5\n value: 35.388999999999996\n - type: ndcg_at_1\n value: 28.221\n - type: ndcg_at_10\n value: 38.340999999999994\n - type: ndcg_at_100\n value: 43.572\n - type: ndcg_at_1000\n value: 45.979\n - type: ndcg_at_3\n value: 33.793\n - type: ndcg_at_5\n value: 35.681000000000004\n - type: precision_at_1\n value: 28.221\n - type: precision_at_10\n value: 6.135\n - type: precision_at_100\n value: 0.946\n - type: precision_at_1000\n value: 0.123\n - type: precision_at_3\n value: 14.519000000000002\n - type: precision_at_5\n value: 9.969\n - type: recall_at_1\n value: 25.102000000000004\n - type: recall_at_10\n value: 50.639\n - type: recall_at_100\n value: 74.075\n - type: recall_at_1000\n value: 91.393\n - type: recall_at_3\n value: 37.952000000000005\n - type: recall_at_5\n value: 42.71\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackTexRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 46989137a86843e03a6195de44b09deda022eec7\n metrics:\n - type: map_at_1\n value: 18.618000000000002\n - type: map_at_10\n value: 26.714\n - type: map_at_100\n value: 27.929\n - type: map_at_1000\n value: 28.057\n - type: map_at_3\n value: 24.134\n - type: map_at_5\n value: 25.575\n - type: mrr_at_1\n value: 22.573999999999998\n - type: mrr_at_10\n value: 30.786\n - type: mrr_at_100\n value: 31.746000000000002\n - type: mrr_at_1000\n value: 31.822\n - type: mrr_at_3\n value: 28.412\n - type: mrr_at_5\n value: 29.818\n - type: ndcg_at_1\n value: 22.573999999999998\n - type: ndcg_at_10\n value: 31.852000000000004\n - type: ndcg_at_100\n value: 37.477\n - type: ndcg_at_1000\n value: 40.331\n - type: ndcg_at_3\n value: 27.314\n - type: ndcg_at_5\n value: 29.485\n - type: precision_at_1\n value: 22.573999999999998\n - type: precision_at_10\n value: 5.86\n - type: precision_at_100\n value: 1.012\n - type: precision_at_1000\n value: 0.146\n - type: precision_at_3\n value: 13.099\n - type: precision_at_5\n value: 9.56\n - type: recall_at_1\n value: 18.618000000000002\n - type: recall_at_10\n value: 43.134\n - type: recall_at_100\n value: 68.294\n - type: recall_at_1000\n value: 88.283\n - type: recall_at_3\n value: 30.397999999999996\n - type: recall_at_5\n value: 35.998000000000005\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackUnixRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53\n metrics:\n - type: map_at_1\n value: 27.76\n - type: map_at_10\n value: 37.569\n - type: map_at_100\n value: 38.784\n - type: map_at_1000\n value: 38.884\n - type: map_at_3\n value: 34.379\n - type: map_at_5\n value: 36.092999999999996\n - type: mrr_at_1\n value: 32.556000000000004\n - type: mrr_at_10\n value: 41.870000000000005\n - type: mrr_at_100\n value: 42.759\n - type: mrr_at_1000\n value: 42.806\n - type: mrr_at_3\n value: 39.086\n - type: mrr_at_5\n value: 40.574\n - type: ndcg_at_1\n value: 32.556000000000004\n - type: ndcg_at_10\n value: 43.382\n - type: ndcg_at_100\n value: 48.943\n - type: ndcg_at_1000\n value: 50.961999999999996\n - type: ndcg_at_3\n value: 37.758\n - type: ndcg_at_5\n value: 40.282000000000004\n - type: precision_at_1\n value: 32.556000000000004\n - type: precision_at_10\n value: 7.463\n - type: precision_at_100\n value: 1.1480000000000001\n - type: precision_at_1000\n value: 0.14300000000000002\n - type: precision_at_3\n value: 17.133000000000003\n - type: precision_at_5\n value: 12.164\n - type: recall_at_1\n value: 27.76\n - type: recall_at_10\n value: 56.71000000000001\n - type: recall_at_100\n value: 81.053\n - type: recall_at_1000\n value: 94.75\n - type: recall_at_3\n value: 41.387\n - type: recall_at_5\n value: 47.818\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWebmastersRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 160c094312a0e1facb97e55eeddb698c0abe3571\n metrics:\n - type: map_at_1\n value: 23.62\n - type: map_at_10\n value: 33.522999999999996\n - type: map_at_100\n value: 35.281\n - type: map_at_1000\n value: 35.504000000000005\n - type: map_at_3\n value: 30.314999999999998\n - type: map_at_5\n value: 32.065\n - type: mrr_at_1\n value: 28.458\n - type: mrr_at_10\n value: 38.371\n - type: mrr_at_100\n value: 39.548\n - type: mrr_at_1000\n value: 39.601\n - type: mrr_at_3\n value: 35.638999999999996\n - type: mrr_at_5\n value: 37.319\n - type: ndcg_at_1\n value: 28.458\n - type: ndcg_at_10\n value: 39.715\n - type: ndcg_at_100\n value: 46.394999999999996\n - type: ndcg_at_1000\n value: 48.943999999999996\n - type: ndcg_at_3\n value: 34.361999999999995\n - type: ndcg_at_5\n value: 37.006\n - type: precision_at_1\n value: 28.458\n - type: precision_at_10\n value: 7.5889999999999995\n - type: precision_at_100\n value: 1.514\n - type: precision_at_1000\n value: 0.242\n - type: precision_at_3\n value: 16.073999999999998\n - type: precision_at_5\n value: 11.976\n - type: recall_at_1\n value: 23.62\n - type: recall_at_10\n value: 52.117000000000004\n - type: recall_at_100\n value: 81.097\n - type: recall_at_1000\n value: 96.47\n - type: recall_at_3\n value: 37.537\n - type: recall_at_5\n value: 44.112\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: 21.149\n - type: map_at_10\n value: 35.251\n - type: map_at_100\n value: 37.342\n - type: map_at_1000\n value: 37.516\n - type: map_at_3\n value: 30.543\n - type: map_at_5\n value: 33.19\n - type: mrr_at_1\n value: 47.687000000000005\n - type: mrr_at_10\n value: 59.391000000000005\n - type: mrr_at_100\n value: 59.946999999999996\n - type: mrr_at_1000\n value: 59.965999999999994\n - type: mrr_at_3\n value: 56.938\n - type: mrr_at_5\n value: 58.498000000000005\n - type: ndcg_at_1\n value: 47.687000000000005\n - type: ndcg_at_10\n value: 45.381\n - type: ndcg_at_100\n value: 52.405\n - type: ndcg_at_1000\n value: 55.041\n - type: ndcg_at_3\n value: 40.024\n - type: ndcg_at_5\n value: 41.821999999999996\n - type: precision_at_1\n value: 47.687000000000005\n - type: precision_at_10\n value: 13.355\n - type: precision_at_100\n value: 2.113\n - type: precision_at_1000\n value: 0.261\n - type: precision_at_3\n value: 29.793999999999997\n - type: precision_at_5\n value: 21.811\n - type: recall_at_1\n value: 21.149\n - type: recall_at_10\n value: 49.937\n - type: recall_at_100\n value: 73.382\n - type: recall_at_1000\n value: 87.606\n - type: recall_at_3\n value: 35.704\n - type: recall_at_5\n value: 42.309000000000005\n - task:\n type: Retrieval\n dataset:\n name: MTEB CmedqaRetrieval\n type: C-MTEB/CmedqaRetrieval\n config: default\n split: dev\n revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301\n metrics:\n - type: map_at_1\n value: 28.74\n - type: map_at_10\n value: 41.981\n - type: map_at_100\n value: 43.753\n - type: map_at_1000\n value: 43.858999999999995\n - type: map_at_3\n value: 37.634\n - type: map_at_5\n value: 40.158\n - type: mrr_at_1\n value: 43.086\n - type: mrr_at_10\n value: 51.249\n - type: mrr_at_100\n value: 52.154\n - type: mrr_at_1000\n value: 52.190999999999995\n - type: mrr_at_3\n value: 48.787000000000006\n - type: mrr_at_5\n value: 50.193\n - type: ndcg_at_1\n value: 43.086\n - type: ndcg_at_10\n value: 48.703\n - type: ndcg_at_100\n value: 55.531\n - type: ndcg_at_1000\n value: 57.267999999999994\n - type: ndcg_at_3\n value: 43.464000000000006\n - type: ndcg_at_5\n value: 45.719\n - type: precision_at_1\n value: 43.086\n - type: precision_at_10\n value: 10.568\n - type: precision_at_100\n value: 1.616\n - type: precision_at_1000\n value: 0.184\n - type: precision_at_3\n value: 24.256\n - type: precision_at_5\n value: 17.509\n - type: recall_at_1\n value: 28.74\n - type: recall_at_10\n value: 59.349\n - type: recall_at_100\n value: 87.466\n - type: recall_at_1000\n value: 98.914\n - type: recall_at_3\n value: 43.322\n - type: recall_at_5\n value: 50.409000000000006\n - task:\n type: PairClassification\n dataset:\n name: MTEB Cmnli\n type: C-MTEB/CMNLI\n config: default\n split: validation\n revision: 41bc36f332156f7adc9e38f53777c959b2ae9766\n metrics:\n - type: cos_sim_accuracy\n value: 79.03788334335539\n - type: cos_sim_ap\n value: 87.21703260472833\n - type: cos_sim_f1\n value: 79.87784187309127\n - type: cos_sim_precision\n value: 77.36634531113059\n - type: cos_sim_recall\n value: 82.55786766425064\n - type: dot_accuracy\n value: 79.03788334335539\n - type: dot_ap\n value: 87.22906528217948\n - type: dot_f1\n value: 79.87784187309127\n - type: dot_precision\n value: 77.36634531113059\n - type: dot_recall\n value: 82.55786766425064\n - type: euclidean_accuracy\n value: 79.03788334335539\n - type: euclidean_ap\n value: 87.21703670465753\n - type: euclidean_f1\n value: 79.87784187309127\n - type: euclidean_precision\n value: 77.36634531113059\n - type: euclidean_recall\n value: 82.55786766425064\n - type: manhattan_accuracy\n value: 78.28021647624774\n - type: manhattan_ap\n value: 86.66244127855394\n - type: manhattan_f1\n value: 79.24485643228577\n - type: manhattan_precision\n value: 76.71262858393521\n - type: manhattan_recall\n value: 81.94996492868833\n - type: max_accuracy\n value: 79.03788334335539\n - type: max_ap\n value: 87.22906528217948\n - type: max_f1\n value: 79.87784187309127\n - task:\n type: Retrieval\n dataset:\n name: MTEB CovidRetrieval\n type: C-MTEB/CovidRetrieval\n config: default\n split: dev\n revision: 1271c7809071a13532e05f25fb53511ffce77117\n metrics:\n - type: map_at_1\n value: 67.597\n - type: map_at_10\n value: 75.81599999999999\n - type: map_at_100\n value: 76.226\n - type: map_at_1000\n value: 76.23100000000001\n - type: map_at_3\n value: 73.907\n - type: map_at_5\n value: 75.08200000000001\n - type: mrr_at_1\n value: 67.756\n - type: mrr_at_10\n value: 75.8\n - type: mrr_at_100\n value: 76.205\n - type: mrr_at_1000\n value: 76.21\n - type: mrr_at_3\n value: 73.955\n - type: mrr_at_5\n value: 75.093\n - type: ndcg_at_1\n value: 67.756\n - type: ndcg_at_10\n value: 79.598\n - type: ndcg_at_100\n value: 81.34400000000001\n - type: ndcg_at_1000\n value: 81.477\n - type: ndcg_at_3\n value: 75.876\n - type: ndcg_at_5\n value: 77.94200000000001\n - type: precision_at_1\n value: 67.756\n - type: precision_at_10\n value: 9.231\n - type: precision_at_100\n value: 1.0\n - type: precision_at_1000\n value: 0.101\n - type: precision_at_3\n value: 27.362\n - type: precision_at_5\n value: 17.45\n - type: recall_at_1\n value: 67.597\n - type: recall_at_10\n value: 91.307\n - type: recall_at_100\n value: 98.946\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 81.428\n - type: recall_at_5\n value: 86.407\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: 9.33\n - type: map_at_10\n value: 23.118\n - type: map_at_100\n value: 34.28\n - type: map_at_1000\n value: 36.574\n - type: map_at_3\n value: 15.576\n - type: map_at_5\n value: 18.778\n - type: mrr_at_1\n value: 75.25\n - type: mrr_at_10\n value: 81.958\n - type: mrr_at_100\n value: 82.282\n - type: mrr_at_1000\n value: 82.285\n - type: mrr_at_3\n value: 81.042\n - type: mrr_at_5\n value: 81.62899999999999\n - type: ndcg_at_1\n value: 63.625\n - type: ndcg_at_10\n value: 50.781\n - type: ndcg_at_100\n value: 55.537000000000006\n - type: ndcg_at_1000\n value: 62.651\n - type: ndcg_at_3\n value: 55.297\n - type: ndcg_at_5\n value: 53.103\n - type: precision_at_1\n value: 75.25\n - type: precision_at_10\n value: 41.475\n - type: precision_at_100\n value: 13.5\n - type: precision_at_1000\n value: 2.686\n - type: precision_at_3\n value: 59.333000000000006\n - type: precision_at_5\n value: 51.9\n - type: recall_at_1\n value: 9.33\n - type: recall_at_10\n value: 29.398000000000003\n - type: recall_at_100\n value: 61.951\n - type: recall_at_1000\n value: 85.463\n - type: recall_at_3\n value: 17.267\n - type: recall_at_5\n value: 21.89\n - task:\n type: Retrieval\n dataset:\n name: MTEB DuRetrieval\n type: C-MTEB/DuRetrieval\n config: default\n split: dev\n revision: a1a333e290fe30b10f3f56498e3a0d911a693ced\n metrics:\n - type: map_at_1\n value: 25.608999999999998\n - type: map_at_10\n value: 78.649\n - type: map_at_100\n value: 81.67699999999999\n - type: map_at_1000\n value: 81.71000000000001\n - type: map_at_3\n value: 54.112\n - type: map_at_5\n value: 68.34700000000001\n - type: mrr_at_1\n value: 87.75\n - type: mrr_at_10\n value: 92.175\n - type: mrr_at_100\n value: 92.225\n - type: mrr_at_1000\n value: 92.227\n - type: mrr_at_3\n value: 91.833\n - type: mrr_at_5\n value: 92.06800000000001\n - type: ndcg_at_1\n value: 87.75\n - type: ndcg_at_10\n value: 86.56700000000001\n - type: ndcg_at_100\n value: 89.519\n - type: ndcg_at_1000\n value: 89.822\n - type: ndcg_at_3\n value: 84.414\n - type: ndcg_at_5\n value: 83.721\n - type: precision_at_1\n value: 87.75\n - type: precision_at_10\n value: 41.665\n - type: precision_at_100\n value: 4.827\n - type: precision_at_1000\n value: 0.49\n - type: precision_at_3\n value: 75.533\n - type: precision_at_5\n value: 64.01\n - type: recall_at_1\n value: 25.608999999999998\n - type: recall_at_10\n value: 88.708\n - type: recall_at_100\n value: 98.007\n - type: recall_at_1000\n value: 99.555\n - type: recall_at_3\n value: 57.157000000000004\n - type: recall_at_5\n value: 74.118\n - task:\n type: Retrieval\n dataset:\n name: MTEB EcomRetrieval\n type: C-MTEB/EcomRetrieval\n config: default\n split: dev\n revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9\n metrics:\n - type: map_at_1\n value: 55.800000000000004\n - type: map_at_10\n value: 65.952\n - type: map_at_100\n value: 66.413\n - type: map_at_1000\n value: 66.426\n - type: map_at_3\n value: 63.3\n - type: map_at_5\n value: 64.945\n - type: mrr_at_1\n value: 55.800000000000004\n - type: mrr_at_10\n value: 65.952\n - type: mrr_at_100\n value: 66.413\n - type: mrr_at_1000\n value: 66.426\n - type: mrr_at_3\n value: 63.3\n - type: mrr_at_5\n value: 64.945\n - type: ndcg_at_1\n value: 55.800000000000004\n - type: ndcg_at_10\n value: 71.00800000000001\n - type: ndcg_at_100\n value: 72.974\n - type: ndcg_at_1000\n value: 73.302\n - type: ndcg_at_3\n value: 65.669\n - type: ndcg_at_5\n value: 68.634\n - type: precision_at_1\n value: 55.800000000000004\n - type: precision_at_10\n value: 8.690000000000001\n - type: precision_at_100\n value: 0.955\n - type: precision_at_1000\n value: 0.098\n - type: precision_at_3\n value: 24.166999999999998\n - type: precision_at_5\n value: 15.939999999999998\n - type: recall_at_1\n value: 55.800000000000004\n - type: recall_at_10\n value: 86.9\n - type: recall_at_100\n value: 95.5\n - type: recall_at_1000\n value: 98.0\n - type: recall_at_3\n value: 72.5\n - type: recall_at_5\n value: 79.7\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: 67.39500000000001\n - type: f1\n value: 62.01837785021389\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: 86.27\n - type: map_at_10\n value: 92.163\n - type: map_at_100\n value: 92.351\n - type: map_at_1000\n value: 92.36\n - type: map_at_3\n value: 91.36\n - type: map_at_5\n value: 91.888\n - type: mrr_at_1\n value: 92.72399999999999\n - type: mrr_at_10\n value: 95.789\n - type: mrr_at_100\n value: 95.80300000000001\n - type: mrr_at_1000\n value: 95.804\n - type: mrr_at_3\n value: 95.64200000000001\n - type: mrr_at_5\n value: 95.75\n - type: ndcg_at_1\n value: 92.72399999999999\n - type: ndcg_at_10\n value: 94.269\n - type: ndcg_at_100\n value: 94.794\n - type: ndcg_at_1000\n value: 94.94\n - type: ndcg_at_3\n value: 93.427\n - type: ndcg_at_5\n value: 93.914\n - type: precision_at_1\n value: 92.72399999999999\n - type: precision_at_10\n value: 11.007\n - type: precision_at_100\n value: 1.153\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 34.993\n - type: precision_at_5\n value: 21.542\n - type: recall_at_1\n value: 86.27\n - type: recall_at_10\n value: 97.031\n - type: recall_at_100\n value: 98.839\n - type: recall_at_1000\n value: 99.682\n - type: recall_at_3\n value: 94.741\n - type: recall_at_5\n value: 96.03\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: 29.561999999999998\n - type: map_at_10\n value: 48.52\n - type: map_at_100\n value: 50.753\n - type: map_at_1000\n value: 50.878\n - type: map_at_3\n value: 42.406\n - type: map_at_5\n value: 45.994\n - type: mrr_at_1\n value: 54.784\n - type: mrr_at_10\n value: 64.51400000000001\n - type: mrr_at_100\n value: 65.031\n - type: mrr_at_1000\n value: 65.05199999999999\n - type: mrr_at_3\n value: 62.474\n - type: mrr_at_5\n value: 63.562\n - type: ndcg_at_1\n value: 54.784\n - type: ndcg_at_10\n value: 57.138\n - type: ndcg_at_100\n value: 63.666999999999994\n - type: ndcg_at_1000\n value: 65.379\n - type: ndcg_at_3\n value: 52.589\n - type: ndcg_at_5\n value: 54.32599999999999\n - type: precision_at_1\n value: 54.784\n - type: precision_at_10\n value: 15.693999999999999\n - type: precision_at_100\n value: 2.259\n - type: precision_at_1000\n value: 0.256\n - type: precision_at_3\n value: 34.774\n - type: precision_at_5\n value: 25.772000000000002\n - type: recall_at_1\n value: 29.561999999999998\n - type: recall_at_10\n value: 64.708\n - type: recall_at_100\n value: 87.958\n - type: recall_at_1000\n value: 97.882\n - type: recall_at_3\n value: 48.394\n - type: recall_at_5\n value: 56.101\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: 43.72\n - type: map_at_10\n value: 71.905\n - type: map_at_100\n value: 72.685\n - type: map_at_1000\n value: 72.72800000000001\n - type: map_at_3\n value: 68.538\n - type: map_at_5\n value: 70.675\n - type: mrr_at_1\n value: 87.441\n - type: mrr_at_10\n value: 91.432\n - type: mrr_at_100\n value: 91.512\n - type: mrr_at_1000\n value: 91.513\n - type: mrr_at_3\n value: 90.923\n - type: mrr_at_5\n value: 91.252\n - type: ndcg_at_1\n value: 87.441\n - type: ndcg_at_10\n value: 79.212\n - type: ndcg_at_100\n value: 81.694\n - type: ndcg_at_1000\n value: 82.447\n - type: ndcg_at_3\n value: 74.746\n - type: ndcg_at_5\n value: 77.27199999999999\n - type: precision_at_1\n value: 87.441\n - type: precision_at_10\n value: 16.42\n - type: precision_at_100\n value: 1.833\n - type: precision_at_1000\n value: 0.193\n - type: precision_at_3\n value: 48.184\n - type: precision_at_5\n value: 30.897999999999996\n - type: recall_at_1\n value: 43.72\n - type: recall_at_10\n value: 82.1\n - type: recall_at_100\n value: 91.62700000000001\n - type: recall_at_1000\n value: 96.556\n - type: recall_at_3\n value: 72.275\n - type: recall_at_5\n value: 77.24499999999999\n - task:\n type: Classification\n dataset:\n name: MTEB IFlyTek\n type: C-MTEB/IFlyTek-classification\n config: default\n split: validation\n revision: 421605374b29664c5fc098418fe20ada9bd55f8a\n metrics:\n - type: accuracy\n value: 54.520969603693736\n - type: f1\n value: 42.359043311419626\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: 96.72559999999999\n - type: ap\n value: 95.01759461773742\n - type: f1\n value: 96.72429945397575\n - task:\n type: Classification\n dataset:\n name: MTEB JDReview\n type: C-MTEB/JDReview-classification\n config: default\n split: test\n revision: b7c64bd89eb87f8ded463478346f76731f07bf8b\n metrics:\n - type: accuracy\n value: 90.1688555347092\n - type: ap\n value: 63.36583667477521\n - type: f1\n value: 85.6845016521436\n - task:\n type: STS\n dataset:\n name: MTEB LCQMC\n type: C-MTEB/LCQMC\n config: default\n split: test\n revision: 17f9b096f80380fce5ed12a9be8be7784b337daf\n metrics:\n - type: cos_sim_pearson\n value: 67.35114066823127\n - type: cos_sim_spearman\n value: 72.98875207056305\n - type: euclidean_pearson\n value: 71.45620183630378\n - type: euclidean_spearman\n value: 72.98875207022671\n - type: manhattan_pearson\n value: 71.3845159780333\n - type: manhattan_spearman\n value: 72.92710990543166\n - task:\n type: Reranking\n dataset:\n name: MTEB MMarcoReranking\n type: C-MTEB/Mmarco-reranking\n config: default\n split: dev\n revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6\n metrics:\n - type: map\n value: 32.68592539803807\n - type: mrr\n value: 31.58968253968254\n - task:\n type: Retrieval\n dataset:\n name: MTEB MMarcoRetrieval\n type: C-MTEB/MMarcoRetrieval\n config: default\n split: dev\n revision: 539bbde593d947e2a124ba72651aafc09eb33fc2\n metrics:\n - type: map_at_1\n value: 71.242\n - type: map_at_10\n value: 80.01\n - type: map_at_100\n value: 80.269\n - type: map_at_1000\n value: 80.276\n - type: map_at_3\n value: 78.335\n - type: map_at_5\n value: 79.471\n - type: mrr_at_1\n value: 73.668\n - type: mrr_at_10\n value: 80.515\n - type: mrr_at_100\n value: 80.738\n - type: mrr_at_1000\n value: 80.744\n - type: mrr_at_3\n value: 79.097\n - type: mrr_at_5\n value: 80.045\n - type: ndcg_at_1\n value: 73.668\n - type: ndcg_at_10\n value: 83.357\n - type: ndcg_at_100\n value: 84.442\n - type: ndcg_at_1000\n value: 84.619\n - type: ndcg_at_3\n value: 80.286\n - type: ndcg_at_5\n value: 82.155\n - type: precision_at_1\n value: 73.668\n - type: precision_at_10\n value: 9.905\n - type: precision_at_100\n value: 1.043\n - type: precision_at_1000\n value: 0.106\n - type: precision_at_3\n value: 30.024\n - type: precision_at_5\n value: 19.017\n - type: recall_at_1\n value: 71.242\n - type: recall_at_10\n value: 93.11\n - type: recall_at_100\n value: 97.85000000000001\n - type: recall_at_1000\n value: 99.21900000000001\n - type: recall_at_3\n value: 85.137\n - type: recall_at_5\n value: 89.548\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: 22.006999999999998\n - type: map_at_10\n value: 34.994\n - type: map_at_100\n value: 36.183\n - type: map_at_1000\n value: 36.227\n - type: map_at_3\n value: 30.75\n - type: map_at_5\n value: 33.155\n - type: mrr_at_1\n value: 22.679\n - type: mrr_at_10\n value: 35.619\n - type: mrr_at_100\n value: 36.732\n - type: mrr_at_1000\n value: 36.77\n - type: mrr_at_3\n value: 31.44\n - type: mrr_at_5\n value: 33.811\n - type: ndcg_at_1\n value: 22.679\n - type: ndcg_at_10\n value: 42.376000000000005\n - type: ndcg_at_100\n value: 48.001\n - type: ndcg_at_1000\n value: 49.059999999999995\n - type: ndcg_at_3\n value: 33.727000000000004\n - type: ndcg_at_5\n value: 38.013000000000005\n - type: precision_at_1\n value: 22.679\n - type: precision_at_10\n value: 6.815\n - type: precision_at_100\n value: 0.962\n - type: precision_at_1000\n value: 0.105\n - type: precision_at_3\n value: 14.441\n - type: precision_at_5\n value: 10.817\n - type: recall_at_1\n value: 22.006999999999998\n - type: recall_at_10\n value: 65.158\n - type: recall_at_100\n value: 90.997\n - type: recall_at_1000\n value: 98.996\n - type: recall_at_3\n value: 41.646\n - type: recall_at_5\n value: 51.941\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: 97.55129958960327\n - type: f1\n value: 97.43464802675416\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: 90.4719562243502\n - type: f1\n value: 70.76460034443902\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: 83.49024882313383\n - type: f1\n value: 81.44067057564666\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (zh-CN)\n type: mteb/amazon_massive_intent\n config: zh-CN\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 79.88231338264963\n - type: f1\n value: 77.13536609019927\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: 87.23268325487558\n - type: f1\n value: 86.36737921996752\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (zh-CN)\n type: mteb/amazon_massive_scenario\n config: zh-CN\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 84.50571620712844\n - type: f1\n value: 83.4128768262944\n - task:\n type: Retrieval\n dataset:\n name: MTEB MedicalRetrieval\n type: C-MTEB/MedicalRetrieval\n config: default\n split: dev\n revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6\n metrics:\n - type: map_at_1\n value: 56.89999999999999\n - type: map_at_10\n value: 63.438\n - type: map_at_100\n value: 63.956\n - type: map_at_1000\n value: 63.991\n - type: map_at_3\n value: 61.983\n - type: map_at_5\n value: 62.778\n - type: mrr_at_1\n value: 56.99999999999999\n - type: mrr_at_10\n value: 63.483000000000004\n - type: mrr_at_100\n value: 63.993\n - type: mrr_at_1000\n value: 64.02799999999999\n - type: mrr_at_3\n value: 62.017\n - type: mrr_at_5\n value: 62.812\n - type: ndcg_at_1\n value: 56.89999999999999\n - type: ndcg_at_10\n value: 66.61\n - type: ndcg_at_100\n value: 69.387\n - type: ndcg_at_1000\n value: 70.327\n - type: ndcg_at_3\n value: 63.583999999999996\n - type: ndcg_at_5\n value: 65.0\n - type: precision_at_1\n value: 56.89999999999999\n - type: precision_at_10\n value: 7.66\n - type: precision_at_100\n value: 0.902\n - type: precision_at_1000\n value: 0.098\n - type: precision_at_3\n value: 22.733\n - type: precision_at_5\n value: 14.32\n - type: recall_at_1\n value: 56.89999999999999\n - type: recall_at_10\n value: 76.6\n - type: recall_at_100\n value: 90.2\n - type: recall_at_1000\n value: 97.6\n - type: recall_at_3\n value: 68.2\n - type: recall_at_5\n value: 71.6\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: 40.32149153753394\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: 39.40319973495386\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: 33.9769104898534\n - type: mrr\n value: 35.32831430710564\n - task:\n type: Classification\n dataset:\n name: MTEB MultilingualSentiment\n type: C-MTEB/MultilingualSentiment-classification\n config: default\n split: validation\n revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a\n metrics:\n - type: accuracy\n value: 81.80666666666667\n - type: f1\n value: 81.83278699395508\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: 6.3\n - type: map_at_10\n value: 14.151\n - type: map_at_100\n value: 18.455\n - type: map_at_1000\n value: 20.186999999999998\n - type: map_at_3\n value: 10.023\n - type: map_at_5\n value: 11.736\n - type: mrr_at_1\n value: 49.536\n - type: mrr_at_10\n value: 58.516\n - type: mrr_at_100\n value: 59.084\n - type: mrr_at_1000\n value: 59.114\n - type: mrr_at_3\n value: 56.45\n - type: mrr_at_5\n value: 57.642\n - type: ndcg_at_1\n value: 47.522999999999996\n - type: ndcg_at_10\n value: 38.4\n - type: ndcg_at_100\n value: 35.839999999999996\n - type: ndcg_at_1000\n value: 44.998\n - type: ndcg_at_3\n value: 43.221\n - type: ndcg_at_5\n value: 40.784\n - type: precision_at_1\n value: 49.536\n - type: precision_at_10\n value: 28.977999999999998\n - type: precision_at_100\n value: 9.378\n - type: precision_at_1000\n value: 2.2769999999999997\n - type: precision_at_3\n value: 40.454\n - type: precision_at_5\n value: 35.418\n - type: recall_at_1\n value: 6.3\n - type: recall_at_10\n value: 19.085\n - type: recall_at_100\n value: 38.18\n - type: recall_at_1000\n value: 71.219\n - type: recall_at_3\n value: 11.17\n - type: recall_at_5\n value: 13.975999999999999\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: 43.262\n - type: map_at_10\n value: 60.387\n - type: map_at_100\n value: 61.102000000000004\n - type: map_at_1000\n value: 61.111000000000004\n - type: map_at_3\n value: 56.391999999999996\n - type: map_at_5\n value: 58.916000000000004\n - type: mrr_at_1\n value: 48.725\n - type: mrr_at_10\n value: 62.812999999999995\n - type: mrr_at_100\n value: 63.297000000000004\n - type: mrr_at_1000\n value: 63.304\n - type: mrr_at_3\n value: 59.955999999999996\n - type: mrr_at_5\n value: 61.785999999999994\n - type: ndcg_at_1\n value: 48.696\n - type: ndcg_at_10\n value: 67.743\n - type: ndcg_at_100\n value: 70.404\n - type: ndcg_at_1000\n value: 70.60600000000001\n - type: ndcg_at_3\n value: 60.712999999999994\n - type: ndcg_at_5\n value: 64.693\n - type: precision_at_1\n value: 48.696\n - type: precision_at_10\n value: 10.513\n - type: precision_at_100\n value: 1.196\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 27.221\n - type: precision_at_5\n value: 18.701999999999998\n - type: recall_at_1\n value: 43.262\n - type: recall_at_10\n value: 87.35300000000001\n - type: recall_at_100\n value: 98.31299999999999\n - type: recall_at_1000\n value: 99.797\n - type: recall_at_3\n value: 69.643\n - type: recall_at_5\n value: 78.645\n - task:\n type: PairClassification\n dataset:\n name: MTEB Ocnli\n type: C-MTEB/OCNLI\n config: default\n split: validation\n revision: 66e76a618a34d6d565d5538088562851e6daa7ec\n metrics:\n - type: cos_sim_accuracy\n value: 72.65836491608013\n - type: cos_sim_ap\n value: 78.75807247519593\n - type: cos_sim_f1\n value: 74.84662576687117\n - type: cos_sim_precision\n value: 63.97003745318352\n - type: cos_sim_recall\n value: 90.17951425554382\n - type: dot_accuracy\n value: 72.65836491608013\n - type: dot_ap\n value: 78.75807247519593\n - type: dot_f1\n value: 74.84662576687117\n - type: dot_precision\n value: 63.97003745318352\n - type: dot_recall\n value: 90.17951425554382\n - type: euclidean_accuracy\n value: 72.65836491608013\n - type: euclidean_ap\n value: 78.75807247519593\n - type: euclidean_f1\n value: 74.84662576687117\n - type: euclidean_precision\n value: 63.97003745318352\n - type: euclidean_recall\n value: 90.17951425554382\n - type: manhattan_accuracy\n value: 72.00866269626421\n - type: manhattan_ap\n value: 78.34663376353235\n - type: manhattan_f1\n value: 74.13234613604813\n - type: manhattan_precision\n value: 65.98023064250413\n - type: manhattan_recall\n value: 84.58289334741288\n - type: max_accuracy\n value: 72.65836491608013\n - type: max_ap\n value: 78.75807247519593\n - type: max_f1\n value: 74.84662576687117\n - task:\n type: Classification\n dataset:\n name: MTEB OnlineShopping\n type: C-MTEB/OnlineShopping-classification\n config: default\n split: test\n revision: e610f2ebd179a8fda30ae534c3878750a96db120\n metrics:\n - type: accuracy\n value: 94.46999999999998\n - type: ap\n value: 93.56401511160975\n - type: f1\n value: 94.46692790889986\n - task:\n type: STS\n dataset:\n name: MTEB PAWSX\n type: C-MTEB/PAWSX\n config: default\n split: test\n revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1\n metrics:\n - type: cos_sim_pearson\n value: 46.851404503762474\n - type: cos_sim_spearman\n value: 52.74603680597415\n - type: euclidean_pearson\n value: 51.596358967977295\n - type: euclidean_spearman\n value: 52.74603680597415\n - type: manhattan_pearson\n value: 51.81838023379299\n - type: manhattan_spearman\n value: 52.79611669731429\n - task:\n type: STS\n dataset:\n name: MTEB QBQTC\n type: C-MTEB/QBQTC\n config: default\n split: test\n revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7\n metrics:\n - type: cos_sim_pearson\n value: 31.928376136347016\n - type: cos_sim_spearman\n value: 34.38497204533162\n - type: euclidean_pearson\n value: 32.658432953090674\n - type: euclidean_spearman\n value: 34.38497204533162\n - type: manhattan_pearson\n value: 32.887190283203054\n - type: manhattan_spearman\n value: 34.69496960849327\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: mteb/quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 69.952\n - type: map_at_10\n value: 84.134\n - type: map_at_100\n value: 84.795\n - type: map_at_1000\n value: 84.809\n - type: map_at_3\n value: 81.085\n - type: map_at_5\n value: 82.976\n - type: mrr_at_1\n value: 80.56\n - type: mrr_at_10\n value: 87.105\n - type: mrr_at_100\n value: 87.20700000000001\n - type: mrr_at_1000\n value: 87.208\n - type: mrr_at_3\n value: 86.118\n - type: mrr_at_5\n value: 86.79299999999999\n - type: ndcg_at_1\n value: 80.57\n - type: ndcg_at_10\n value: 88.047\n - type: ndcg_at_100\n value: 89.266\n - type: ndcg_at_1000\n value: 89.34299999999999\n - type: ndcg_at_3\n value: 85.052\n - type: ndcg_at_5\n value: 86.68299999999999\n - type: precision_at_1\n value: 80.57\n - type: precision_at_10\n value: 13.439\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: 37.283\n - type: precision_at_5\n value: 24.558\n - type: recall_at_1\n value: 69.952\n - type: recall_at_10\n value: 95.599\n - type: recall_at_100\n value: 99.67099999999999\n - type: recall_at_1000\n value: 99.983\n - type: recall_at_3\n value: 87.095\n - type: recall_at_5\n value: 91.668\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: 70.12802769698337\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: 71.19047621740276\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: mteb/scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.208\n - type: map_at_10\n value: 17.036\n - type: map_at_100\n value: 20.162\n - type: map_at_1000\n value: 20.552\n - type: map_at_3\n value: 11.591999999999999\n - type: map_at_5\n value: 14.349\n - type: mrr_at_1\n value: 30.599999999999998\n - type: mrr_at_10\n value: 43.325\n - type: mrr_at_100\n value: 44.281\n - type: mrr_at_1000\n value: 44.31\n - type: mrr_at_3\n value: 39.300000000000004\n - type: mrr_at_5\n value: 41.730000000000004\n - type: ndcg_at_1\n value: 30.599999999999998\n - type: ndcg_at_10\n value: 27.378000000000004\n - type: ndcg_at_100\n value: 37.768\n - type: ndcg_at_1000\n value: 43.275000000000006\n - type: ndcg_at_3\n value: 25.167\n - type: ndcg_at_5\n value: 22.537\n - type: precision_at_1\n value: 30.599999999999998\n - type: precision_at_10\n value: 14.46\n - type: precision_at_100\n value: 2.937\n - type: precision_at_1000\n value: 0.424\n - type: precision_at_3\n value: 23.666999999999998\n - type: precision_at_5\n value: 20.14\n - type: recall_at_1\n value: 6.208\n - type: recall_at_10\n value: 29.29\n - type: recall_at_100\n value: 59.565\n - type: recall_at_1000\n value: 85.963\n - type: recall_at_3\n value: 14.407\n - type: recall_at_5\n value: 20.412\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: 82.65489797062479\n - type: cos_sim_spearman\n value: 75.34808277034776\n - type: euclidean_pearson\n value: 79.28097508609059\n - type: euclidean_spearman\n value: 75.3480824481771\n - type: manhattan_pearson\n value: 78.83529262858895\n - type: manhattan_spearman\n value: 74.96318170787025\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: 85.06920163624117\n - type: cos_sim_spearman\n value: 77.24549887905519\n - type: euclidean_pearson\n value: 85.58740280635266\n - type: euclidean_spearman\n value: 77.24652170306867\n - type: manhattan_pearson\n value: 85.77917470895854\n - type: manhattan_spearman\n value: 77.54426264008778\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: 80.9762185094084\n - type: cos_sim_spearman\n value: 80.98090253728394\n - type: euclidean_pearson\n value: 80.88451512135202\n - type: euclidean_spearman\n value: 80.98090253728394\n - type: manhattan_pearson\n value: 80.7606664599805\n - type: manhattan_spearman\n value: 80.87197716950068\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: 81.91239166620251\n - type: cos_sim_spearman\n value: 76.36798509005328\n - type: euclidean_pearson\n value: 80.6393872615655\n - type: euclidean_spearman\n value: 76.36798836339655\n - type: manhattan_pearson\n value: 80.50765898709096\n - type: manhattan_spearman\n value: 76.31958999372227\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: 83.68800355225011\n - type: cos_sim_spearman\n value: 84.47549220803403\n - type: euclidean_pearson\n value: 83.86859896384159\n - type: euclidean_spearman\n value: 84.47551564954756\n - type: manhattan_pearson\n value: 83.74201103044383\n - type: manhattan_spearman\n value: 84.39903759718152\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: 78.24197302553398\n - type: cos_sim_spearman\n value: 79.44526946553684\n - type: euclidean_pearson\n value: 79.12747636563053\n - type: euclidean_spearman\n value: 79.44526946553684\n - type: manhattan_pearson\n value: 78.94407504115144\n - type: manhattan_spearman\n value: 79.24858249553934\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: 89.15329071763895\n - type: cos_sim_spearman\n value: 88.67251952242073\n - type: euclidean_pearson\n value: 89.16908249259637\n - type: euclidean_spearman\n value: 88.67251952242073\n - type: manhattan_pearson\n value: 89.1279735094785\n - type: manhattan_spearman\n value: 88.81731953658254\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: 69.44962535524695\n - type: cos_sim_spearman\n value: 71.75861316291065\n - type: euclidean_pearson\n value: 72.42347748883483\n - type: euclidean_spearman\n value: 71.75861316291065\n - type: manhattan_pearson\n value: 72.57545073534365\n - type: manhattan_spearman\n value: 71.90087671205625\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (zh)\n type: mteb/sts22-crosslingual-sts\n config: zh\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 68.9945443484093\n - type: cos_sim_spearman\n value: 71.46807157842791\n - type: euclidean_pearson\n value: 69.24911748374225\n - type: euclidean_spearman\n value: 69.46807157842791\n - type: manhattan_pearson\n value: 69.65580071876552\n - type: manhattan_spearman\n value: 69.68775795734852\n - task:\n type: STS\n dataset:\n name: MTEB STSB\n type: C-MTEB/STSB\n config: default\n split: test\n revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0\n metrics:\n - type: cos_sim_pearson\n value: 77.39283860361535\n - type: cos_sim_spearman\n value: 77.14577975930179\n - type: euclidean_pearson\n value: 76.64560889817044\n - type: euclidean_spearman\n value: 77.14577975930179\n - type: manhattan_pearson\n value: 76.82848456242104\n - type: manhattan_spearman\n value: 77.37708521460667\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: 84.14036697885552\n - type: cos_sim_spearman\n value: 83.10901632378086\n - type: euclidean_pearson\n value: 83.59991244380554\n - type: euclidean_spearman\n value: 83.10901632378086\n - type: manhattan_pearson\n value: 83.56632266895113\n - type: manhattan_spearman\n value: 83.17610542379353\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: 88.98026856845443\n - type: mrr\n value: 96.80987494712984\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: 41.661\n - type: map_at_10\n value: 55.492\n - type: map_at_100\n value: 56.237\n - type: map_at_1000\n value: 56.255\n - type: map_at_3\n value: 51.05\n - type: map_at_5\n value: 54.01200000000001\n - type: mrr_at_1\n value: 44.0\n - type: mrr_at_10\n value: 56.443\n - type: mrr_at_100\n value: 57.13700000000001\n - type: mrr_at_1000\n value: 57.152\n - type: mrr_at_3\n value: 52.944\n - type: mrr_at_5\n value: 55.37800000000001\n - type: ndcg_at_1\n value: 44.0\n - type: ndcg_at_10\n value: 62.312999999999995\n - type: ndcg_at_100\n value: 65.63900000000001\n - type: ndcg_at_1000\n value: 66.019\n - type: ndcg_at_3\n value: 54.67999999999999\n - type: ndcg_at_5\n value: 59.284000000000006\n - type: precision_at_1\n value: 44.0\n - type: precision_at_10\n value: 9.367\n - type: precision_at_100\n value: 1.0999999999999999\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 22.778000000000002\n - type: precision_at_5\n value: 16.467000000000002\n - type: recall_at_1\n value: 41.661\n - type: recall_at_10\n value: 82.306\n - type: recall_at_100\n value: 97.167\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 62.461\n - type: recall_at_5\n value: 73.411\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: 99.90693069306931\n - type: cos_sim_ap\n value: 97.86562522779887\n - type: cos_sim_f1\n value: 95.27162977867204\n - type: cos_sim_precision\n value: 95.8502024291498\n - type: cos_sim_recall\n value: 94.69999999999999\n - type: dot_accuracy\n value: 99.90693069306931\n - type: dot_ap\n value: 97.86562522779887\n - type: dot_f1\n value: 95.27162977867204\n - type: dot_precision\n value: 95.8502024291498\n - type: dot_recall\n value: 94.69999999999999\n - type: euclidean_accuracy\n value: 99.90693069306931\n - type: euclidean_ap\n value: 97.86562522779887\n - type: euclidean_f1\n value: 95.27162977867204\n - type: euclidean_precision\n value: 95.8502024291498\n - type: euclidean_recall\n value: 94.69999999999999\n - type: manhattan_accuracy\n value: 99.90693069306931\n - type: manhattan_ap\n value: 97.85527044211135\n - type: manhattan_f1\n value: 95.27638190954774\n - type: manhattan_precision\n value: 95.75757575757575\n - type: manhattan_recall\n value: 94.8\n - type: max_accuracy\n value: 99.90693069306931\n - type: max_ap\n value: 97.86562522779887\n - type: max_f1\n value: 95.27638190954774\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: 78.89230351770412\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: 47.52328347080355\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: 57.74702024461137\n - type: mrr\n value: 58.88074548001018\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: 30.047929797503592\n - type: cos_sim_spearman\n value: 29.465371781983567\n - type: dot_pearson\n value: 30.047927690552335\n - type: dot_spearman\n value: 29.465371781983567\n - task:\n type: Reranking\n dataset:\n name: MTEB T2Reranking\n type: C-MTEB/T2Reranking\n config: default\n split: dev\n revision: 76631901a18387f85eaa53e5450019b87ad58ef9\n metrics:\n - type: map\n value: 66.54177017978034\n - type: mrr\n value: 76.76094292377299\n - task:\n type: Retrieval\n dataset:\n name: MTEB T2Retrieval\n type: C-MTEB/T2Retrieval\n config: default\n split: dev\n revision: 8731a845f1bf500a4f111cf1070785c793d10e64\n metrics:\n - type: map_at_1\n value: 28.608\n - type: map_at_10\n value: 81.266\n - type: map_at_100\n value: 84.714\n - type: map_at_1000\n value: 84.758\n - type: map_at_3\n value: 56.967\n - type: map_at_5\n value: 70.14\n - type: mrr_at_1\n value: 91.881\n - type: mrr_at_10\n value: 94.11699999999999\n - type: mrr_at_100\n value: 94.178\n - type: mrr_at_1000\n value: 94.181\n - type: mrr_at_3\n value: 93.772\n - type: mrr_at_5\n value: 93.997\n - type: ndcg_at_1\n value: 91.881\n - type: ndcg_at_10\n value: 87.954\n - type: ndcg_at_100\n value: 90.904\n - type: ndcg_at_1000\n value: 91.326\n - type: ndcg_at_3\n value: 88.838\n - type: ndcg_at_5\n value: 87.764\n - type: precision_at_1\n value: 91.881\n - type: precision_at_10\n value: 43.628\n - type: precision_at_100\n value: 5.082\n - type: precision_at_1000\n value: 0.518\n - type: precision_at_3\n value: 77.62400000000001\n - type: precision_at_5\n value: 65.269\n - type: recall_at_1\n value: 28.608\n - type: recall_at_10\n value: 87.06\n - type: recall_at_100\n value: 96.815\n - type: recall_at_1000\n value: 98.969\n - type: recall_at_3\n value: 58.506\n - type: recall_at_5\n value: 73.21600000000001\n - task:\n type: Classification\n dataset:\n name: MTEB TNews\n type: C-MTEB/TNews-classification\n config: default\n split: validation\n revision: 317f262bf1e6126357bbe89e875451e4b0938fe4\n metrics:\n - type: accuracy\n value: 56.691999999999986\n - type: f1\n value: 54.692084702788065\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: mteb/trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.181\n - type: map_at_10\n value: 1.2\n - type: map_at_100\n value: 6.078\n - type: map_at_1000\n value: 14.940000000000001\n - type: map_at_3\n value: 0.45599999999999996\n - type: map_at_5\n value: 0.692\n - type: mrr_at_1\n value: 66.0\n - type: mrr_at_10\n value: 75.819\n - type: mrr_at_100\n value: 76.168\n - type: mrr_at_1000\n value: 76.168\n - type: mrr_at_3\n value: 72.667\n - type: mrr_at_5\n value: 74.86699999999999\n - type: ndcg_at_1\n value: 59.0\n - type: ndcg_at_10\n value: 52.60399999999999\n - type: ndcg_at_100\n value: 38.049\n - type: ndcg_at_1000\n value: 38.576\n - type: ndcg_at_3\n value: 57.235\n - type: ndcg_at_5\n value: 56.147000000000006\n - type: precision_at_1\n value: 66.0\n - type: precision_at_10\n value: 55.2\n - type: precision_at_100\n value: 38.78\n - type: precision_at_1000\n value: 16.986\n - type: precision_at_3\n value: 62.666999999999994\n - type: precision_at_5\n value: 60.8\n - type: recall_at_1\n value: 0.181\n - type: recall_at_10\n value: 1.471\n - type: recall_at_100\n value: 9.748999999999999\n - type: recall_at_1000\n value: 37.667\n - type: recall_at_3\n value: 0.49300000000000005\n - type: recall_at_5\n value: 0.7979999999999999\n - task:\n type: Clustering\n dataset:\n name: MTEB ThuNewsClusteringP2P\n type: C-MTEB/ThuNewsClusteringP2P\n config: default\n split: test\n revision: 5798586b105c0434e4f0fe5e767abe619442cf93\n metrics:\n - type: v_measure\n value: 78.68783858143624\n - task:\n type: Clustering\n dataset:\n name: MTEB ThuNewsClusteringS2S\n type: C-MTEB/ThuNewsClusteringS2S\n config: default\n split: test\n revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d\n metrics:\n - type: v_measure\n value: 77.04148998956299\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: 1.936\n - type: map_at_10\n value: 8.942\n - type: map_at_100\n value: 14.475999999999999\n - type: map_at_1000\n value: 16.156000000000002\n - type: map_at_3\n value: 4.865\n - type: map_at_5\n value: 6.367000000000001\n - type: mrr_at_1\n value: 26.531\n - type: mrr_at_10\n value: 42.846000000000004\n - type: mrr_at_100\n value: 43.441\n - type: mrr_at_1000\n value: 43.441\n - type: mrr_at_3\n value: 36.735\n - type: mrr_at_5\n value: 40.510000000000005\n - type: ndcg_at_1\n value: 24.490000000000002\n - type: ndcg_at_10\n value: 23.262\n - type: ndcg_at_100\n value: 34.959\n - type: ndcg_at_1000\n value: 47.258\n - type: ndcg_at_3\n value: 25.27\n - type: ndcg_at_5\n value: 24.246000000000002\n - type: precision_at_1\n value: 26.531\n - type: precision_at_10\n value: 20.408\n - type: precision_at_100\n value: 7.306\n - type: precision_at_1000\n value: 1.541\n - type: precision_at_3\n value: 26.531\n - type: precision_at_5\n value: 24.082\n - type: recall_at_1\n value: 1.936\n - type: recall_at_10\n value: 15.712000000000002\n - type: recall_at_100\n value: 45.451\n - type: recall_at_1000\n value: 83.269\n - type: recall_at_3\n value: 6.442\n - type: recall_at_5\n value: 9.151\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: 86.564\n - type: ap\n value: 34.58766846081731\n - type: f1\n value: 72.32759831978161\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: 77.80418788907753\n - type: f1\n value: 78.1047638421972\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: 59.20888659980063\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: 85.45627943017226\n - type: cos_sim_ap\n value: 72.25550061847534\n - type: cos_sim_f1\n value: 66.0611487783037\n - type: cos_sim_precision\n value: 64.11720884032779\n - type: cos_sim_recall\n value: 68.12664907651715\n - type: dot_accuracy\n value: 85.45627943017226\n - type: dot_ap\n value: 72.25574305366213\n - type: dot_f1\n value: 66.0611487783037\n - type: dot_precision\n value: 64.11720884032779\n - type: dot_recall\n value: 68.12664907651715\n - type: euclidean_accuracy\n value: 85.45627943017226\n - type: euclidean_ap\n value: 72.2557084446673\n - type: euclidean_f1\n value: 66.0611487783037\n - type: euclidean_precision\n value: 64.11720884032779\n - type: euclidean_recall\n value: 68.12664907651715\n - type: manhattan_accuracy\n value: 85.32514752339513\n - type: manhattan_ap\n value: 71.52919143472248\n - type: manhattan_f1\n value: 65.60288251190322\n - type: manhattan_precision\n value: 64.02913840743531\n - type: manhattan_recall\n value: 67.25593667546174\n - type: max_accuracy\n value: 85.45627943017226\n - type: max_ap\n value: 72.25574305366213\n - type: max_f1\n value: 66.0611487783037\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: 88.34167733923235\n - type: cos_sim_ap\n value: 84.58587730660244\n - type: cos_sim_f1\n value: 77.14170010676287\n - type: cos_sim_precision\n value: 73.91181657848324\n - type: cos_sim_recall\n value: 80.66676932553126\n - type: dot_accuracy\n value: 88.34167733923235\n - type: dot_ap\n value: 84.58585083616217\n - type: dot_f1\n value: 77.14170010676287\n - type: dot_precision\n value: 73.91181657848324\n - type: dot_recall\n value: 80.66676932553126\n - type: euclidean_accuracy\n value: 88.34167733923235\n - type: euclidean_ap\n value: 84.5858781355044\n - type: euclidean_f1\n value: 77.14170010676287\n - type: euclidean_precision\n value: 73.91181657848324\n - type: euclidean_recall\n value: 80.66676932553126\n - type: manhattan_accuracy\n value: 88.28152287809989\n - type: manhattan_ap\n value: 84.53184837110165\n - type: manhattan_f1\n value: 77.13582823915313\n - type: manhattan_precision\n value: 74.76156069364161\n - type: manhattan_recall\n value: 79.66584539574993\n - type: max_accuracy\n value: 88.34167733923235\n - type: max_ap\n value: 84.5858781355044\n - type: max_f1\n value: 77.14170010676287\n - task:\n type: Retrieval\n dataset:\n name: MTEB VideoRetrieval\n type: C-MTEB/VideoRetrieval\n config: default\n split: dev\n revision: 58c2597a5943a2ba48f4668c3b90d796283c5639\n metrics:\n - type: map_at_1\n value: 66.10000000000001\n - type: map_at_10\n value: 75.238\n - type: map_at_100\n value: 75.559\n - type: map_at_1000\n value: 75.565\n - type: map_at_3\n value: 73.68299999999999\n - type: map_at_5\n value: 74.63300000000001\n - type: mrr_at_1\n value: 66.10000000000001\n - type: mrr_at_10\n value: 75.238\n - type: mrr_at_100\n value: 75.559\n - type: mrr_at_1000\n value: 75.565\n - type: mrr_at_3\n value: 73.68299999999999\n - type: mrr_at_5\n value: 74.63300000000001\n - type: ndcg_at_1\n value: 66.10000000000001\n - type: ndcg_at_10\n value: 79.25999999999999\n - type: ndcg_at_100\n value: 80.719\n - type: ndcg_at_1000\n value: 80.862\n - type: ndcg_at_3\n value: 76.08200000000001\n - type: ndcg_at_5\n value: 77.782\n - type: precision_at_1\n value: 66.10000000000001\n - type: precision_at_10\n value: 9.17\n - type: precision_at_100\n value: 0.983\n - type: precision_at_1000\n value: 0.099\n - type: precision_at_3\n value: 27.667\n - type: precision_at_5\n value: 17.419999999999998\n - type: recall_at_1\n value: 66.10000000000001\n - type: recall_at_10\n value: 91.7\n - type: recall_at_100\n value: 98.3\n - type: recall_at_1000\n value: 99.4\n - type: recall_at_3\n value: 83.0\n - type: recall_at_5\n value: 87.1\n - task:\n type: Classification\n dataset:\n name: MTEB Waimai\n type: C-MTEB/waimai-classification\n config: default\n split: test\n revision: 339287def212450dcaa9df8c22bf93e9980c7023\n metrics:\n - type: accuracy\n value: 91.13\n - type: ap\n value: 79.55231335947015\n - type: f1\n value: 89.63091922203914\n---\n\n\n\n# Model Card for Meditron-70B-v1.0\n\nMeditron is a suite of open-source medical Large Language Models (LLMs).\nMeditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.\n\n\n\n\n![\"qualitative-analysis-prompt\"](\"prompt_example.png\" "\\"Qualitative")
\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\nFor **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.\nEach query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).\nPlease see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)\n\nSignificant research is still required to fully explore potential bias, fairness, and safety issues with this language model. \n\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 without comprehensive testing for your application. \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\n\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 16 nodes 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. \nThe nodes are connected via RDMA over Converged Ethernet.\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 8,\n - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.\n \n\n#### Training Hyperparameters\n\n| | |\n| --- | ------ |\n| bf16 | true |\n| lr | 1.5e-4 |\n| eps | 1e-5 |\n| betas | \\[0.9, 0.95\\] |\n| clip_grad | 1 |\n| weight decay | 0.1 |\n| DP size | 2 |\n| TP size | 8 |\n| PP size | 8 |\n| seq length | 4096 |\n| lr scheduler | cosine|\n| min lr | 1e-6 |\n| warmup iteration | 2000 |\n| micro batch size | 2 |\n| global batch size | 512 |\n| | |\n\n#### Speeds, Sizes, Times\nThe model was trained in September and October 2023.\n\nThe model architecture is exactly Llama 2, meaning\n\n| | | \n| --- | ------ |\n| Model size | 70B | \n| Hidden dimension | 8192 |\n| Num. attention heads | 64 |\n| Num. layers | 80 |\n| | | | \n\nWe train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second. \nThis amounts to a bfloat16 model flops utilization of roughly 42.3\\%.\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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. \nWe report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |\n|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |\n|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |\n|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |\n|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |\n|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |\n|Avg | 72.0 | 69.2 | 63.6 | 57.4 |\n| | | | | | |\n\n**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.\n\n## Environmental Impact\n\n\n\n- **Hardware Type:** 128 x NVIDIA A100 (80GB) SXM\n- **Total GPU hours:** 42,496\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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be: \n \n (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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```"},"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":192,"cells":{"id":{"kind":"string","value":"Dampish/StellarX-4B-V0"},"author":{"kind":"string","value":"Dampish"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","gpt_neox","text-generation","arxiv:2204.06745","license:cc-by-nc-sa-4.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"gpt_neox\",\n \"text-generation\",\n \"arxiv:2204.06745\",\n \"license:cc-by-nc-sa-4.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-05-27T19:04:49","string":"2023-05-27T19:04:49"},"last_modified":{"kind":"timestamp","value":"2023-12-03T19:52:22","string":"2023-12-03T19:52:22"},"downloads":{"kind":"number","value":2229,"string":"2,229"},"likes":{"kind":"number","value":1,"string":"1"},"README":{"kind":"string","value":"---\nlicense: cc-by-nc-sa-4.0\n---\n# StellarX: A Base Model by Dampish and Arkane\n\nStellarX is a powerful autoregressive language model designed for various natural language processing tasks. It has been trained on a massive dataset containing 810 billion tokens, trained on \"redpajama,\" and is built upon the popular GPT-NeoX architecture. With approximately 4 billion parameters, StellarX offers exceptional performance and versatility.\n\n## Model Details\n\n- **Training Data:** StellarX is trained on a large-scale dataset provided by \"redpajama\" maintained by the group \"togethercumputer.\" This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.\n- **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding.\n- **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks.\n- **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications.\n- **V0** Meaning what version it is on, currently version 0, Assume version 0 has only been trained on 300B tokens and the goal is 810B tokens. The next version aims to have a way higher accuracy.\n\n## How to Use\n\nTo load StellarX using the Hugging Face Transformers library, you can use the following code snippet:\n\n```python\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"Dampish/StellarX-4B-V0\")\nmodel = AutoModelForCausalLM.from_pretrained(\"Dampish/StellarX-4B-V0\")\n```\n\nThis model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing.\n\n**For any queries related to this model, feel free to reach out to \"Dampish#3607\" on discord.**\n\n## Licensing and Usage\nStellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.\n\n# Research and Personal Use\nStellarX can be freely used for research purposes, allowing you to explore its capabilities, conduct experiments, and develop novel applications. Whether you're a student, researcher, or hobbyist, the model's availability under the CC-BY-NC-SA-4.0 license empowers you to unlock the potential of StellarX for your own non-commercial projects.\n\n# Commercial Usage\nFor commercial usage of StellarX, an additional licensing arrangement must be established. If you intend to leverage the model for any commercial purpose, such as integrating it into a product or service, you are required to reach an agreement with the Dampish. This agreement will specify the terms, including the agreed-upon percentage or licensing fee to be paid for the commercial use of StellarX.\n\nTo initiate discussions regarding commercial usage, please contact Dampish through the designated channels mentioned earlier. They will be able to provide you with further information and guide you through the process of establishing a licensing arrangement tailored to your specific requirements.\n\n# Importance of Licensing Compliance\nIt is crucial to respect the licensing terms to ensure the fair usage and continued development of StellarX. The revenue generated from commercial licensing supports the efforts of the Dampish in advancing the model and making it more widely accessible.\n\n# Note on CC-BY-NC-SA-4.0\nUnder the CC-BY-NC-SA-4.0 license, you are allowed to modify and adapt StellarX, incorporating it into your own projects. However, any derivative work or modifications should also be shared under the same license terms, ensuring the continued openness and collaborative spirit of the project.\n\nPlease review the complete text of the CC-BY-NC-SA-4.0 license to familiarize yourself with its provisions and requirements. It is essential to comply with the terms of the license to respect the intellectual property rights and contributions of the Dampish and the wider community involved in developing StellarX.\n## GPT-NeoX and Model Selection\n\nGPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. StellarX draws inspiration from the architectural advancements and performance of GPT-NeoX models. While the specifics of StellarX's architecture and parameters may differ, it benefits from the proven capabilities of GPT-NeoX and its suitability for diverse natural language processing tasks.\n\n## Training and Evaluation\n\nStellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of \"redpajama\" dataset by the group \"togethercumputer\" group.\n\nEvaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge.\n\n## Limitations and Considerations\n\nStellarX, like its sibling models, is intended primarily for research purposes. It provides a powerful foundation for extracting useful features and insights from the English language. While StellarX can be further fine-tuned and adapted for deployment, users should conduct their own risk and bias assessments before using it as a basis for downstream tasks.\n\nIt's important to note that StellarX is not intended for direct deployment without supervision. It is not designed for human-facing interactions, unlike models like ChatGPT, which have been fine-tuned using reinforcement learning from human feedback to better understand human instructions and dialogue.\n\nFurthermore, StellarX is not limited to the English language if trained properly and can sometimes be used for translation aswell as text generation in other languages.\n\nLastly, users should be aware of potential biases and limitations inherent in\n\nSpecial thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer.\n\n## Community and Support\n\nTo inquire about StellarX and receive support, you can join the Dampish's \nserver and engage in discussions in the #questions channel. It is recommended to explore the existing documentation and resources available for GPT-NeoX-20B to familiarize yourself with the model before seeking assistance on. For better information about GPT-NeoX, you can reach out to eleutherAI.\n\n## Summary\n\nStellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms.\n\n**Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.\n-dampish**\n\n\n\nDiscord: https://discord.gg/vasyNnUa \nOR Reach out to me personally on Discord via the username: Dampish#3607\n\nThank you for your time.\n\n\n\n\n# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\nDetailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__StellarX-4B-V0)\n\n| Metric | Value |\n|-----------------------|---------------------------|\n| Avg. | 33.54 |\n| ARC (25-shot) | 36.95 |\n| HellaSwag (10-shot) | 61.9 |\n| MMLU (5-shot) | 26.85 |\n| TruthfulQA (0-shot) | 34.3 |\n| Winogrande (5-shot) | 63.85 |\n| GSM8K (5-shot) | 0.0 |\n| DROP (3-shot) | 10.95 |\n"},"matched_task":{"kind":"list like","value":["TRANSLATION"],"string":"[\n \"TRANSLATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["SCIQ"],"string":"[\n \"SCIQ\"\n]"}}},{"rowIdx":193,"cells":{"id":{"kind":"string","value":"BSC-LT/salamandra-7b"},"author":{"kind":"string","value":"BSC-LT"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","safetensors","llama","text-generation","bg","ca","code","cs","cy","da","de","el","en","es","et","eu","fi","fr","ga","gl","hr","hu","it","lt","lv","mt","nl","nn","oc","pl","pt","ro","ru","sh","sk","sl","sr","sv","uk","dataset:oscar-corpus/colossal-oscar-1.0","dataset:HuggingFaceFW/fineweb-edu","dataset:joelniklaus/eurlex_resources","dataset:joelito/legal-mc4","dataset:projecte-aina/CATalog","dataset:UFRGS/brwac","dataset:community-datasets/hrwac","dataset:danish-foundation-models/danish-gigaword","dataset:HiTZ/euscrawl","dataset:PleIAs/French-PD-Newspapers","dataset:PleIAs/French-PD-Books","dataset:AI-team-UoA/greek_legal_code","dataset:HiTZ/latxa-corpus-v1.1","dataset:allenai/peS2o","dataset:pile-of-law/pile-of-law","dataset:PORTULAN/parlamento-pt","dataset:hoskinson-center/proof-pile","dataset:togethercomputer/RedPajama-Data-1T","dataset:bigcode/starcoderdata","dataset:bjoernp/tagesschau-2018-2023","dataset:EleutherAI/the_pile_deduplicated","arxiv:2502.08489","arxiv:2403.14009","arxiv:2403.20266","arxiv:2101.00027","arxiv:2207.00220","arxiv:1810.06694","arxiv:1911.05507","arxiv:1906.03741","arxiv:2406.17557","arxiv:2402.06619","arxiv:1803.09010","license:apache-2.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"llama\",\n \"text-generation\",\n \"bg\",\n \"ca\",\n \"code\",\n \"cs\",\n \"cy\",\n \"da\",\n \"de\",\n \"el\",\n \"en\",\n \"es\",\n \"et\",\n \"eu\",\n \"fi\",\n \"fr\",\n \"ga\",\n \"gl\",\n \"hr\",\n \"hu\",\n \"it\",\n \"lt\",\n \"lv\",\n \"mt\",\n \"nl\",\n \"nn\",\n \"oc\",\n \"pl\",\n \"pt\",\n \"ro\",\n \"ru\",\n \"sh\",\n \"sk\",\n \"sl\",\n \"sr\",\n \"sv\",\n \"uk\",\n \"dataset:oscar-corpus/colossal-oscar-1.0\",\n \"dataset:HuggingFaceFW/fineweb-edu\",\n \"dataset:joelniklaus/eurlex_resources\",\n \"dataset:joelito/legal-mc4\",\n \"dataset:projecte-aina/CATalog\",\n \"dataset:UFRGS/brwac\",\n \"dataset:community-datasets/hrwac\",\n \"dataset:danish-foundation-models/danish-gigaword\",\n \"dataset:HiTZ/euscrawl\",\n \"dataset:PleIAs/French-PD-Newspapers\",\n \"dataset:PleIAs/French-PD-Books\",\n \"dataset:AI-team-UoA/greek_legal_code\",\n \"dataset:HiTZ/latxa-corpus-v1.1\",\n \"dataset:allenai/peS2o\",\n \"dataset:pile-of-law/pile-of-law\",\n \"dataset:PORTULAN/parlamento-pt\",\n \"dataset:hoskinson-center/proof-pile\",\n \"dataset:togethercomputer/RedPajama-Data-1T\",\n \"dataset:bigcode/starcoderdata\",\n \"dataset:bjoernp/tagesschau-2018-2023\",\n \"dataset:EleutherAI/the_pile_deduplicated\",\n \"arxiv:2502.08489\",\n \"arxiv:2403.14009\",\n \"arxiv:2403.20266\",\n \"arxiv:2101.00027\",\n \"arxiv:2207.00220\",\n \"arxiv:1810.06694\",\n \"arxiv:1911.05507\",\n \"arxiv:1906.03741\",\n \"arxiv:2406.17557\",\n \"arxiv:2402.06619\",\n \"arxiv:1803.09010\",\n \"license:apache-2.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-09-30T06:47:04","string":"2024-09-30T06:47:04"},"last_modified":{"kind":"timestamp","value":"2025-02-20T16:42:39","string":"2025-02-20T16:42:39"},"downloads":{"kind":"number","value":2229,"string":"2,229"},"likes":{"kind":"number","value":27,"string":"27"},"README":{"kind":"string","value":"---\ndatasets:\n- oscar-corpus/colossal-oscar-1.0\n- HuggingFaceFW/fineweb-edu\n- joelniklaus/eurlex_resources\n- joelito/legal-mc4\n- projecte-aina/CATalog\n- UFRGS/brwac\n- community-datasets/hrwac\n- danish-foundation-models/danish-gigaword\n- HiTZ/euscrawl\n- PleIAs/French-PD-Newspapers\n- PleIAs/French-PD-Books\n- AI-team-UoA/greek_legal_code\n- HiTZ/latxa-corpus-v1.1\n- allenai/peS2o\n- pile-of-law/pile-of-law\n- PORTULAN/parlamento-pt\n- hoskinson-center/proof-pile\n- togethercomputer/RedPajama-Data-1T\n- bigcode/starcoderdata\n- bjoernp/tagesschau-2018-2023\n- EleutherAI/the_pile_deduplicated\nlanguage:\n- bg\n- ca\n- code\n- cs\n- cy\n- da\n- de\n- el\n- en\n- es\n- et\n- eu\n- fi\n- fr\n- ga\n- gl\n- hr\n- hu\n- it\n- lt\n- lv\n- mt\n- nl\n- nn\n- \\no\n- oc\n- pl\n- pt\n- ro\n- ru\n- sh\n- sk\n- sl\n- sr\n- sv\n- uk\nlibrary_name: transformers\nlicense: apache-2.0\npipeline_tag: text-generation\n---\n\n\n\n# Salamandra Model Card\n\nThis repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).\n\nSalamandra is a highly multilingual model pre-trained from scratch that comes in three different \nsizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants. \nThis model card corresponds to the 7B base version.\n\nTo visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index).\n\nThe entire Salamandra family is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).\nAlong with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra).\n\n---\n\n## Model Details\n\n### Description\n\nTransformer-based decoder-only language model that has been pre-trained from scratch on 12.875 trillion tokens of highly curated data.\nThe pre-training corpus contains text in 35 European languages and code.\n\n### Hyperparameters\n\nThe full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/tree/main/configs).\n\n### Architecture\n\n| | |\n|-------------------------|:--------------|\n| Total Parameters | 7,768,117,248 |\n| Embedding Parameters | 1,048,576,000 |\n| Layers | 32 |\n| Hidden size | 4,096 |\n| Attention heads | 32 |\n| Context length | 8,192 |\n| Vocabulary size | 256,000 |\n| Precision | bfloat16 |\n| Embedding type | RoPE |\n| Activation Function | SwiGLU |\n| Layer normalization | RMS Norm |\n| Flash attention | ✅ |\n| Grouped Query Attention | ✅ |\n| Num. query groups | 8 |\n\n---\n\n## Intended Use\n\n### Direct Use\n\nThe models are intended for both research and commercial use in any of the languages included in the training data. \nThe base models are intended either for language generation or to be further fine-tuned for specific use-cases. \nThe instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations.\n\n### Out-of-scope Use\n\nThe model is not intended for malicious activities, such as harming others or violating human rights. \nAny downstream application must comply with current laws and regulations. \nIrresponsible usage in production environments without proper risk assessment and mitigation is also discouraged. \n\n---\n\n## Hardware and Software\n\n### Training Framework\n\nPre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html), \nwhich leverages PyTorch Lightning for efficient model training in highly distributed settings.\n\nThe instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat).\n\n### Compute Infrastructure\n\nAll models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and\noperated by Barcelona Supercomputing Center.\n\nThe accelerated partition is composed of 1,120 nodes with the following specifications:\n- 4x Nvidia Hopper GPUs with 64 HBM2 memory\n- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)\n- 4x NDR200 (BW per node 800Gb/s)\n- 512 GB of Main memory (DDR5)\n- 460GB on NVMe storage\n\n|Model|Nodes|GPUs|\n|:---:|:---:|:---:|\n|2B|64|256|\n|7B|128|512|\n|40B|256 / 512|1,024 / 2,048|\n\n---\n\n## How to use\nThis section offers examples of how to perform inference using various methods.\n\n### Inference\nYou'll find different techniques for running inference, including Huggingface's Text Generation Pipeline, multi-GPU configurations, and vLLM for scalable and efficient generation. \n\n#### Inference with Huggingface's Text Generation Pipeline\nThe Huggingface Text Generation Pipeline provides a straightforward way to run inference using the Salamandra-7b model.\n\n```bash\npip install transformers torch accelerate sentencepiece protobuf\n```\n\n
\n\n#### Catalan\n\n\n
\n\n#### Basque\n\n\n
\n\n#### Galician\n\n\n
\n\n#### English\n\n\n
\n\n\\* Current LM Evaluation Harness implementation is lacking correct pre-processing. These results are obtained with adequate pre-processing.\n\n\\*\\* This task is not yet available in the official Harness, we hope to add it soon.\n\n---\n\n## Ethical Considerations and Limitations\n\nWe examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases, \nwe test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019). \nWe report that while performance is high (accuracies between 0.69 and 0.87 depending on the social category) in disambiguated settings\nthe model performs very poorly in ambiguous settings, which is indicative of the presence of societal biases which need to be addressed in post-training phases.\n\nWe additionally analyse model generations using the Regard dataset and classifier in Catalan, Spanish, and English using backtranslation and manual revision of the \ntranslations. We find no statistically significant difference in regard between majority and minority groups for any regard types,\nwith the exception of negative regard in Catalan where model generations are actually slightly worse for social majorities. \nOur analyses on societal biases show that while these biases are capable of interfering with model performance as expressed in the results on the BBQ dataset,\ntheir tendency for representational harm is limited given the results of the Regard dataset. We highlight that our analyses of these biases are by no means exhaustive\nand are limited by the relative scarcity of adequate resources in all languages present in the training data. We aim to gradually extend and expand our analyses \nin future work.\n\nOur cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings.\nFor positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018).\nWe observe moderate to strong primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers.\nWe measure effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We detect moderate effects,\nimplying that outputs can be influenced by the prompts.\n\nWe highlight that these results can be expected from a pretrained model that has not yet been instruction-tuned or aligned.\nThese tests are performed in order to show the biases the model may contain.\nWe urge developers to take them into account and perform safety testing and tuning tailored to their specific applications of the model.\n\n---\n\n## Additional information\n\n### Author\nThe Language Technologies Unit from Barcelona Supercomputing Center.\n\n### Contact\nFor further information, please send an email to .\n\n### Copyright\nCopyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.\n\n### Funding\nThis work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).\n\nThis work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU \nwithin the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.\n\n### Acknowledgements\n\nThis project has benefited from the contributions of numerous teams and institutions, mainly through data contributions, knowledge transfer or technical support. \n\nIn Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà.\n\nAt the national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria. \n\nAt the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration. We would also like to give special thanks to the NVIDIA team, with whom we have met regularly, specially to: Ignacio Sarasua, Adam Henryk Grzywaczewski, Oleg Sudakov, Sergio Perez, Miguel Martinez, Felipes Soares and Meriem Bendris. Their constant support has been especially appreciated throughout the entire process.\n\nTheir valuable efforts have been instrumental in the development of this work.\n\n### Disclaimer\nBe aware that the model may contain biases or other unintended distortions. \nWhen third parties deploy systems or provide services based on this model, or use the model themselves, \nthey bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, \nincluding those governing the use of Artificial Intelligence.\n\nThe Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.\n\n### Citation\n```\n@misc{gonzalezagirre2025salamandratechnicalreport,\n title={Salamandra Technical Report}, \n author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},\n year={2025},\n eprint={2502.08489},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2502.08489}, \n}\n```\n\n### License\n[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)\n\n## Model Index\n|Model|Base|Instruct|\n|:---:|:---:|:---:|\n|2B| [Link](https://huggingface.co/BSC-LT/salamandra-2b) | [Link](https://huggingface.co/BSC-LT/salamandra-2b-instruct) |\n|7B| [Link](https://huggingface.co/BSC-LT/salamandra-7b) | [Link](https://huggingface.co/BSC-LT/salamandra-7b-instruct) |\n|40B| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |\n"},"matched_task":{"kind":"list like","value":["QUESTION_ANSWERING","TRANSLATION","SUMMARIZATION","PARAPHRASING"],"string":"[\n \"QUESTION_ANSWERING\",\n \"TRANSLATION\",\n \"SUMMARIZATION\",\n \"PARAPHRASING\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BEAR","SCIELO"],"string":"[\n \"BEAR\",\n \"SCIELO\"\n]"}}},{"rowIdx":194,"cells":{"id":{"kind":"string","value":"HiTZ/GoLLIE-7B"},"author":{"kind":"string","value":"HiTZ"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","llama","text-generation","code","text-generation-inference","Information Extraction","IE","Named Entity Recogniton","Event Extraction","Relation Extraction","LLaMA","custom_code","en","dataset:ACE05","dataset:bc5cdr","dataset:conll2003","dataset:ncbi_disease","dataset:conll2012_ontonotesv5","dataset:rams","dataset:tacred","dataset:wnut_17","arxiv:2310.03668","license:llama2","autotrain_compatible","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"llama\",\n \"text-generation\",\n \"code\",\n \"text-generation-inference\",\n \"Information Extraction\",\n \"IE\",\n \"Named Entity Recogniton\",\n \"Event Extraction\",\n \"Relation Extraction\",\n \"LLaMA\",\n \"custom_code\",\n \"en\",\n \"dataset:ACE05\",\n \"dataset:bc5cdr\",\n \"dataset:conll2003\",\n \"dataset:ncbi_disease\",\n \"dataset:conll2012_ontonotesv5\",\n \"dataset:rams\",\n \"dataset:tacred\",\n \"dataset:wnut_17\",\n \"arxiv:2310.03668\",\n \"license:llama2\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-09-25T10:24:52","string":"2023-09-25T10:24:52"},"last_modified":{"kind":"timestamp","value":"2023-10-10T07:51:44","string":"2023-10-10T07:51:44"},"downloads":{"kind":"number","value":2215,"string":"2,215"},"likes":{"kind":"number","value":28,"string":"28"},"README":{"kind":"string","value":"---\ndatasets:\n- ACE05\n- bc5cdr\n- conll2003\n- ncbi_disease\n- conll2012_ontonotesv5\n- rams\n- tacred\n- wnut_17\nlanguage:\n- en\nlicense: llama2\nmetrics:\n- f1\npipeline_tag: text-generation\ntags:\n- code\n- text-generation-inference\n- Information Extraction\n- IE\n- Named Entity Recogniton\n- Event Extraction\n- Relation Extraction\n- LLaMA\n---\n\n
\n\n\n# Model Card for GoLLIE 7B\n\n\n\n
\n\n#### IndoMMLU\n\n
\n\n\n#### English Results\n\n
\n\nGemma2 9B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library \n```python\n# Please use transformers==4.45.0\n\nimport torch\nimport transformers\n\nmodel_id = \"GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct\"\n\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model_id,\n model_kwargs={\"torch_dtype\": torch.bfloat16},\n device_map=\"auto\",\n)\n\nterminators = [\n pipeline.tokenizer.eos_token_id,\n pipeline.tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n]\n\n# Javanese\nmessages = [\n {\"role\": \"user\", \"content\": \"Sopo wae sing ana ing Punakawan?\"}\n]\n\noutputs = pipeline(\n messages,\n max_new_tokens=256,\n eos_token_id=terminators,\n)\nprint(outputs[0][\"generated_text\"][-1])\n\n\n# Sundanese\nmessages = [\n {\"role\": \"user\", \"content\": \"Kumaha caritana si Kabayan?\"},\n]\n\noutputs = pipeline(\n messages,\n max_new_tokens=256,\n eos_token_id=terminators,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\n### Caveats\nIt is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.\n\n## Limitations\n### Safety\n\nCurrent Sahabat-AI models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.\n\n## Technical Specifications\n### Fine-Tuning Details\nGemma2 9B CPT Sahabat-AI v1 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.\n\n## Data\nGemma2 9B CPT Sahabat-AI v1 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. \n\n## Call for Collaboration \n\nSahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison. \nSahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects. \n\nWe are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.\n\nWe also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.\n\nWe would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI. \nYour collaborations can involve:\n- Identifying and reporting technical issues\n- Sharing pre-training, instruction, and preference data\n- Improving documentation usability\n- Proposing and implementing new model evaluation tasks and metrics \n\nJoin us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.\n\nYou can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)\n\n## The Development Team (in ascending alphabetical order)\n\n### AI Singapore\nChan Adwin
\nCheng Nicholas
\nChoa Esther
\nHuang Yuli
\nLau Wayne
\nLee Chwan Ren
\nLeong Wai Yi
\nLeong Wei Qi
\nLimkonchotiwat Peerat
\nLiu Bing Jie Darius
\nMontalan Jann Railey
\nNg Boon Cheong Raymond
\nNgui Jian Gang
\nNguyen Thanh Ngan
\nOng Brandon
\nOng Tat-Wee David
\nOng Zhi Hao
\nRengarajan Hamsawardhini
\nSiow Bryan
\nSusanto Yosephine
\nTai Ngee Chia
\nTan Choon Meng
\nTeng Walter
\nTeo Eng Sipp Leslie
\nTeo Wei Yi
\nTjhi William
\nYeo Yeow Tong
\nYong Xianbin
\n\n### PT GoTo Gojek Tokopedia Tbk\nAnissa Dininta
\nChau Shiau Ching
\nChoiri Hendra Hadhil
\nGoel Priyank
\nSaini Ajay Kumar
\nShalev Ofir
\nTan Daryl
\nTep Kilian Rithi
\nTiwari Anupam
\nWidjojo Daniel
\n\n## Acknowledgements\n\n [AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. \n\nAny opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.\n\n\n## Contact\n\nFor more info, please contact us using this [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)\n\n## Disclaimer\n\nThis is the repository for the Instruct model.\nThe model has _not_ been aligned for safety.\nDevelopers and users should perform their own safety fine-tuning and related security measures.\nIn no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.\n\n\n## References\n### IndoMMLU Reference\n\n```bibtex\n@inproceedings{koto-etal-2023-indommlu,\n title = \"Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}\",\n author = \"Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin\",\n booktitle = \"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = December,\n year = \"2023\",\n address = \"Singapore\",\n publisher = \"Association for Computational Linguistics\",\n}\n}\n```"},"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":["CHIA"],"string":"[\n \"CHIA\"\n]"}}},{"rowIdx":196,"cells":{"id":{"kind":"string","value":"aliakseilabanau/bge-small-en"},"author":{"kind":"string","value":"aliakseilabanau"},"task_category":{"kind":"null"},"tags":{"kind":"list like","value":["safetensors","openvino","bert","mteb","sentence transformers","en","arxiv:2311.13534","arxiv:2310.07554","arxiv:2309.07597","base_model:BAAI/bge-small-en","base_model:quantized:BAAI/bge-small-en","license:mit","region:us"],"string":"[\n \"safetensors\",\n \"openvino\",\n \"bert\",\n \"mteb\",\n \"sentence transformers\",\n \"en\",\n \"arxiv:2311.13534\",\n \"arxiv:2310.07554\",\n \"arxiv:2309.07597\",\n \"base_model:BAAI/bge-small-en\",\n \"base_model:quantized:BAAI/bge-small-en\",\n \"license:mit\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-11-19T18:27:44","string":"2024-11-19T18:27:44"},"last_modified":{"kind":"timestamp","value":"2025-02-04T13:09:46","string":"2025-02-04T13:09:46"},"downloads":{"kind":"number","value":2173,"string":"2,173"},"likes":{"kind":"number","value":0,"string":"0"},"README":{"kind":"string","value":"---\nbase_model:\n- BAAI/bge-small-en\nlanguage:\n- en\nlicense: mit\ntags:\n- mteb\n- sentence transformers\n- openvino\nbase_model_relation: quantized\n---\n\nThe [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) converted for [openvino backend](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#openvino).\n\n```\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"aliakseilabanau/bge-small-en\", backend=\"openvino\")\n\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\nembeddings = model.encode(sentences)\n```\n\n\nThe original model card is below:\n\n--------\n\n**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.**\n\n\n\n### Training Logs\n| Epoch | Step | Training Loss | Validation Loss | sts-dev-768_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-64_spearman_cosine | sts-test-768_spearman_cosine | sts-test-512_spearman_cosine | sts-test-256_spearman_cosine | sts-test-128_spearman_cosine | sts-test-64_spearman_cosine |\n|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|\n| 0.5917 | 100 | 23.7709 | 22.5494 | 0.7185 | 0.7146 | 0.7055 | 0.6794 | 0.6570 | - | - | - | - | - |\n| 1.1834 | 200 | 22.137 | 22.7634 | 0.7449 | 0.7412 | 0.7439 | 0.7287 | 0.7027 | - | - | - | - | - |\n| 1.7751 | 300 | 21.5527 | 22.6985 | 0.7321 | 0.7281 | 0.7243 | 0.7063 | 0.6862 | - | - | - | - | - |\n| 2.3669 | 400 | 20.5745 | 24.0021 | 0.7302 | 0.7264 | 0.7221 | 0.7097 | 0.6897 | - | - | - | - | - |\n| 2.9586 | 500 | 20.0861 | 24.0091 | 0.7392 | 0.7361 | 0.7293 | 0.7124 | 0.6906 | - | - | - | - | - |\n| 3.5503 | 600 | 18.8191 | 26.9012 | 0.7502 | 0.7462 | 0.7399 | 0.7207 | 0.6960 | - | - | - | - | - |\n| 4.1420 | 700 | 18.3 | 29.0209 | 0.7496 | 0.7454 | 0.7432 | 0.7284 | 0.7065 | - | - | - | - | - |\n| 4.7337 | 800 | 17.6496 | 28.9536 | 0.7532 | 0.7482 | 0.7451 | 0.7304 | 0.7070 | - | - | - | - | - |\n| 5.0 | 845 | - | - | - | - | - | - | - | 0.8767 | 0.8752 | 0.8702 | 0.8617 | 0.8420 |\n\n\n### Framework Versions\n- Python: 3.10.12\n- Sentence Transformers: 3.3.1\n- Transformers: 4.48.0\n- PyTorch: 2.5.1+cu121\n- Accelerate: 1.2.1\n- Datasets: 3.2.0\n- Tokenizers: 0.21.0\n\n## Citation\n\n### BibTeX\n\n#### Sentence Transformers\n```bibtex\n@inproceedings{reimers-2019-sentence-bert,\n title = \"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\",\n author = \"Reimers, Nils and Gurevych, Iryna\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing\",\n month = \"11\",\n year = \"2019\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://arxiv.org/abs/1908.10084\",\n}\n```\n\n#### MatryoshkaLoss\n```bibtex\n@misc{kusupati2024matryoshka,\n title={Matryoshka Representation Learning},\n author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},\n year={2024},\n eprint={2205.13147},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\n#### CoSENTLoss\n```bibtex\n@online{kexuefm-8847,\n title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},\n author={Su Jianlin},\n year={2022},\n month={Jan},\n url={https://kexue.fm/archives/8847},\n}\n```\n\n\n\n\n\n"},"matched_task":{"kind":"list like","value":["TEXT_CLASSIFICATION","SEMANTIC_SIMILARITY"],"string":"[\n \"TEXT_CLASSIFICATION\",\n \"SEMANTIC_SIMILARITY\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["CPI"],"string":"[\n \"CPI\"\n]"}}},{"rowIdx":199,"cells":{"id":{"kind":"string","value":"aisingapore/gemma2-9b-cpt-sea-lionv3-instruct"},"author":{"kind":"string","value":"aisingapore"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","safetensors","gemma2","text-generation","conversational","en","zh","vi","id","th","fil","ta","ms","km","lo","my","jv","su","arxiv:2309.06085","arxiv:2311.07911","arxiv:2306.05685","base_model:aisingapore/gemma2-9b-cpt-sea-lionv3-base","base_model:finetune:aisingapore/gemma2-9b-cpt-sea-lionv3-base","license:gemma","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"gemma2\",\n \"text-generation\",\n \"conversational\",\n \"en\",\n \"zh\",\n \"vi\",\n \"id\",\n \"th\",\n \"fil\",\n \"ta\",\n \"ms\",\n \"km\",\n \"lo\",\n \"my\",\n \"jv\",\n \"su\",\n \"arxiv:2309.06085\",\n \"arxiv:2311.07911\",\n \"arxiv:2306.05685\",\n \"base_model:aisingapore/gemma2-9b-cpt-sea-lionv3-base\",\n \"base_model:finetune:aisingapore/gemma2-9b-cpt-sea-lionv3-base\",\n \"license:gemma\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-10-30T03:19:20","string":"2024-10-30T03:19:20"},"last_modified":{"kind":"timestamp","value":"2024-12-19T12:57:01","string":"2024-12-19T12:57:01"},"downloads":{"kind":"number","value":2114,"string":"2,114"},"likes":{"kind":"number","value":10,"string":"10"},"README":{"kind":"string","value":"---\nbase_model:\n- aisingapore/gemma2-9b-cpt-sea-lionv3-base\nlanguage:\n- en\n- zh\n- vi\n- id\n- th\n- fil\n- ta\n- ms\n- km\n- lo\n- my\n- jv\n- su\nlibrary_name: transformers\nlicense: gemma\npipeline_tag: text-generation\n---\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 70B - GPTQ\n- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)\n- Original model: [Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b)\n\n\n# Description\n\nThis repo contains GPTQ model files for [EPFL LLM Team's Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b).\n\nMultiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.\n\nThese files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).\n\n\n\n## Repositories available\n\n* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-70B-AWQ)\n* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-70B-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-70B-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-70b)\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\n## Known compatible clients / servers\n\nThese GPTQ models are known to work in the following inference servers/webuis.\n\n- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n- [KoboldAI United](https://github.com/henk717/koboldai)\n- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)\n\nThis may not be a complete list; if you know of others, please let me know!\n\n\n\n## Provided files, and GPTQ parameters\n\nMultiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.\n\nEach separate quant is in a different branch. See below for instructions on fetching from different branches.\n\nMost GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.\n\n
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\n\n| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |\n| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |\n| [main](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | \n| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | \n| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | \n| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | \n| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | \n| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 31.84 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |\n\n\n\n\n## How to download, including from branches\n\n### In text-generation-webui\n\nTo download from the `main` branch, enter `TheBloke/meditron-70B-GPTQ` in the \"Download model\" box.\n\nTo download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/meditron-70B-GPTQ:gptq-4bit-128g-actorder_True`\n\n### From the command line\n\nI recommend using the `huggingface-hub` Python library:\n\n```shell\npip3 install huggingface-hub\n```\n\nTo download the `main` branch to a folder called `meditron-70B-GPTQ`:\n\n```shell\nmkdir meditron-70B-GPTQ\nhuggingface-cli download TheBloke/meditron-70B-GPTQ --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False\n```\n\nTo download from a different branch, add the `--revision` parameter:\n\n```shell\nmkdir meditron-70B-GPTQ\nhuggingface-cli download TheBloke/meditron-70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False\n```\n\nExplanation of GPTQ parameters
\n\n- Bits: The bit size of the quantised model.\n- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. \"None\" is the lowest possible value.\n- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.\n- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.\n- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).\n- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.\n- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.\n\n\n
\n\n### With `git` (**not** recommended)\n\nTo clone a specific branch with `git`, use a command like this:\n\n```shell\ngit clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/meditron-70B-GPTQ\n```\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)\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-70B-GPTQ`.\n\n - To download from a specific branch, enter for example `TheBloke/meditron-70B-GPTQ:gptq-4bit-128g-actorder_True`\n - see Provided Files above for the list of branches for each option.\n\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-70B-GPTQ`\n7. The model will automatically load, and is now ready for use!\n8. 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.\n\n - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.\n\n9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!\n\n\n\n\n## Serving this model from Text Generation Inference (TGI)\n\nIt's recommended to use 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-70B-GPTQ --port 3000 --quantize gptq --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 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## Python code example: inference from this GPTQ model\n\n### Install the necessary packages\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n```shell\npip3 install --upgrade transformers optimum\n# If using PyTorch 2.1 + CUDA 12.x:\npip3 install --upgrade auto-gptq\n# or, if using PyTorch 2.1 + CUDA 11.x:\npip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/\n```\n\nIf you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:\n\n```shell\npip3 uninstall -y auto-gptq\ngit clone https://github.com/PanQiWei/AutoGPTQ\ncd AutoGPTQ\ngit checkout v0.5.1\npip3 install .\n```\n\n### Example Python code\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n\nmodel_name_or_path = \"TheBloke/meditron-70B-GPTQ\"\n# To use a different branch, change revision\n# For example: revision=\"gptq-4bit-128g-actorder_True\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name_or_path,\n device_map=\"auto\",\n trust_remote_code=False,\n revision=\"main\")\n\ntokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=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\nprint(\"\\n\\n*** Generate:\")\n\ninput_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()\noutput = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)\nprint(tokenizer.decode(output[0]))\n\n# Inference can also be done using transformers' pipeline\n\nprint(\"*** Pipeline:\")\npipe = pipeline(\n \"text-generation\",\n model=model,\n tokenizer=tokenizer,\n max_new_tokens=512,\n do_sample=True,\n temperature=0.7,\n top_p=0.95,\n top_k=40,\n repetition_penalty=1.1\n)\n\nprint(pipe(prompt_template)[0]['generated_text'])\n```\n\n\n\n## Compatibility\n\nThe files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.\n\n[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.\n\nFor a list of clients/servers, please see \"Known compatible clients / servers\", above.\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 70B\n\n\nMore advanced huggingface-cli download usage
\n\nIf you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\nThe cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.\n\nFor more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:\n\n```shell\npip3 install hf_transfer\n```\n\nAnd set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:\n\n```shell\nmkdir meditron-70B-GPTQ\nHF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/meditron-70B-GPTQ --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False\n```\n\nWindows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.\n\n\n# Model Card for Meditron-70B-v1.0\n\nMeditron is a suite of open-source medical Large Language Models (LLMs).\nMeditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.\n\n\n\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-2-70B](https://huggingface.co/meta-llama/Llama-2-70b)\n- **Context length:** 4K 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-70B 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-70B 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\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\nFor **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.\nEach query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).\nPlease see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)\n\nSignificant research is still required to fully explore potential bias, fairness, and safety issues with this language model. \n\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 without comprehensive testing for your application. \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\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\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 16 nodes 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. \nThe nodes are connected via RDMA over Converged Ethernet.\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 8,\n - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.\n \n\n#### Training Hyperparameters\n\n| | |\n| --- | ------ |\n| bf16 | true |\n| lr | 1.5e-4 |\n| eps | 1e-5 |\n| betas | \\[0.9, 0.95\\] |\n| clip_grad | 1 |\n| weight decay | 0.1 |\n| DP size | 2 |\n| TP size | 8 |\n| PP size | 8 |\n| seq length | 4096 |\n| lr scheduler | cosine|\n| min lr | 1e-6 |\n| warmup iteration | 2000 |\n| micro batch size | 2 |\n| global batch size | 512 |\n| | |\n\n#### Speeds, Sizes, Times\nThe model was trained in September and October 2023.\n\nThe model architecture is exactly Llama 2, meaning\n\n| | | \n| --- | ------ |\n| Model size | 70B | \n| Hidden dimension | 8192 |\n| Num. attention heads | 64 |\n| Num. layers | 80 |\n| | | | \n\nWe train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second. \nThis amounts to a bfloat16 model flops utilization of roughly 42.3\\%.\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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. \nWe report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |\n|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |\n|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |\n|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |\n|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |\n|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |\n|Avg | 72.0 | 69.2 | 63.6 | 57.4 |\n| | | | | | |\n\n**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.\n\n## Environmental Impact\n\n\n\n- **Hardware Type:** 128 x NVIDIA A100 (80GB) SXM\n- **Total GPU hours:** 42,496\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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be: \n \n (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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":188,"cells":{"id":{"kind":"string","value":"Alibaba-NLP/gme-Qwen2-VL-7B-Instruct"},"author":{"kind":"string","value":"Alibaba-NLP"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["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-7B-Instruct","base_model:finetune:Qwen/Qwen2-VL-7B-Instruct","license:apache-2.0","model-index","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"qwen2_vl\",\n \"image-text-to-text\",\n \"mteb\",\n \"transformers\",\n \"Qwen2-VL\",\n \"sentence-similarity\",\n \"vidore\",\n \"en\",\n \"zh\",\n \"arxiv:2412.16855\",\n \"base_model:Qwen/Qwen2-VL-7B-Instruct\",\n \"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-12-21T04:00:17","string":"2024-12-21T04:00:17"},"last_modified":{"kind":"timestamp","value":"2025-01-21T11:53:23","string":"2025-01-21T11:53:23"},"downloads":{"kind":"number","value":2333,"string":"2,333"},"likes":{"kind":"number","value":26,"string":"26"},"README":{"kind":"string","value":"---\nbase_model:\n- Qwen/Qwen2-VL-7B-Instruct\nlanguage:\n- en\n- zh\nlicense: apache-2.0\ntags:\n- mteb\n- sentence-transformers\n- transformers\n- Qwen2-VL\n- sentence-similarity\n- vidore\nmodel-index:\n- name: gme-Qwen2-VL-7B-Instruct\n results:\n - task:\n type: STS\n dataset:\n name: MTEB AFQMC\n type: C-MTEB/AFQMC\n config: default\n split: validation\n revision: b44c3b011063adb25877c13823db83bb193913c4\n metrics:\n - type: cos_sim_pearson\n value: 64.72351048394194\n - type: cos_sim_spearman\n value: 71.66842612591344\n - type: euclidean_pearson\n value: 70.0342809043895\n - type: euclidean_spearman\n value: 71.66842612323917\n - type: manhattan_pearson\n value: 69.94743870947117\n - type: manhattan_spearman\n value: 71.53159630946965\n - task:\n type: STS\n dataset:\n name: MTEB ATEC\n type: C-MTEB/ATEC\n config: default\n split: test\n revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865\n metrics:\n - type: cos_sim_pearson\n value: 52.38188106868689\n - type: cos_sim_spearman\n value: 55.468235529709766\n - type: euclidean_pearson\n value: 56.974786979175086\n - type: euclidean_spearman\n value: 55.468231026153745\n - type: manhattan_pearson\n value: 56.94467132566259\n - type: manhattan_spearman\n value: 55.39037386224014\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: 77.61194029850746\n - type: ap\n value: 41.29789064067677\n - type: f1\n value: 71.69633278678522\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: 97.3258\n - type: ap\n value: 95.91845683387056\n - type: f1\n value: 97.32526074864263\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: 64.794\n - type: f1\n value: 63.7329780206882\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (zh)\n type: mteb/amazon_reviews_multi\n config: zh\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 55.099999999999994\n - type: f1\n value: 53.115528412999666\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: 40.541\n - type: map_at_10\n value: 56.315000000000005\n - type: map_at_100\n value: 56.824\n - type: map_at_1000\n value: 56.825\n - type: map_at_3\n value: 51.778\n - type: map_at_5\n value: 54.623\n - type: mrr_at_1\n value: 41.038000000000004\n - type: mrr_at_10\n value: 56.532000000000004\n - type: mrr_at_100\n value: 57.034\n - type: mrr_at_1000\n value: 57.034\n - type: mrr_at_3\n value: 52.015\n - type: mrr_at_5\n value: 54.835\n - type: ndcg_at_1\n value: 40.541\n - type: ndcg_at_10\n value: 64.596\n - type: ndcg_at_100\n value: 66.656\n - type: ndcg_at_1000\n value: 66.666\n - type: ndcg_at_3\n value: 55.415000000000006\n - type: ndcg_at_5\n value: 60.527\n - type: precision_at_1\n value: 40.541\n - type: precision_at_10\n value: 9.083\n - type: precision_at_100\n value: 0.996\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 21.977\n - type: precision_at_5\n value: 15.661\n - type: recall_at_1\n value: 40.541\n - type: recall_at_10\n value: 90.825\n - type: recall_at_100\n value: 99.57300000000001\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 65.932\n - type: recall_at_5\n value: 78.307\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: 54.96111428218386\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: 50.637711388838945\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: 64.0741897266483\n - type: mrr\n value: 76.11440882909028\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: 86.2557839280406\n - type: cos_sim_spearman\n value: 82.58200216886888\n - type: euclidean_pearson\n value: 84.80588838508498\n - type: euclidean_spearman\n value: 82.58200216886888\n - type: manhattan_pearson\n value: 84.53082035185592\n - type: manhattan_spearman\n value: 82.4964580510134\n - task:\n type: STS\n dataset:\n name: MTEB BQ\n type: C-MTEB/BQ\n config: default\n split: test\n revision: e3dda5e115e487b39ec7e618c0c6a29137052a55\n metrics:\n - type: cos_sim_pearson\n value: 76.98420285210636\n - type: cos_sim_spearman\n value: 78.95549489000658\n - type: euclidean_pearson\n value: 79.14591532018991\n - type: euclidean_spearman\n value: 78.95549488953284\n - type: manhattan_pearson\n value: 79.26212116856509\n - type: manhattan_spearman\n value: 79.02104262086006\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: 84.76298701298703\n - type: f1\n value: 84.24881789367576\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: 46.86757924102047\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: 43.86043680479362\n - task:\n type: Clustering\n dataset:\n name: MTEB CLSClusteringP2P\n type: C-MTEB/CLSClusteringP2P\n config: default\n split: test\n revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476\n metrics:\n - type: v_measure\n value: 45.684222588040605\n - task:\n type: Clustering\n dataset:\n name: MTEB CLSClusteringS2S\n type: C-MTEB/CLSClusteringS2S\n config: default\n split: test\n revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f\n metrics:\n - type: v_measure\n value: 45.45639765303432\n - task:\n type: Reranking\n dataset:\n name: MTEB CMedQAv1\n type: C-MTEB/CMedQAv1-reranking\n config: default\n split: test\n revision: 8d7f1e942507dac42dc58017c1a001c3717da7df\n metrics:\n - type: map\n value: 88.7058672660788\n - type: mrr\n value: 90.5795634920635\n - task:\n type: Reranking\n dataset:\n name: MTEB CMedQAv2\n type: C-MTEB/CMedQAv2-reranking\n config: default\n split: test\n revision: 23d186750531a14a0357ca22cd92d712fd512ea0\n metrics:\n - type: map\n value: 90.50750030424048\n - type: mrr\n value: 92.3970634920635\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: f46a197baaae43b4f621051089b82a364682dfeb\n metrics:\n - type: map_at_1\n value: 28.848000000000003\n - type: map_at_10\n value: 40.453\n - type: map_at_100\n value: 42.065000000000005\n - type: map_at_1000\n value: 42.176\n - type: map_at_3\n value: 36.697\n - type: map_at_5\n value: 38.855000000000004\n - type: mrr_at_1\n value: 34.764\n - type: mrr_at_10\n value: 45.662000000000006\n - type: mrr_at_100\n value: 46.56\n - type: mrr_at_1000\n value: 46.597\n - type: mrr_at_3\n value: 42.632\n - type: mrr_at_5\n value: 44.249\n - type: ndcg_at_1\n value: 34.764\n - type: ndcg_at_10\n value: 47.033\n - type: ndcg_at_100\n value: 53.089\n - type: ndcg_at_1000\n value: 54.818\n - type: ndcg_at_3\n value: 41.142\n - type: ndcg_at_5\n value: 43.928\n - type: precision_at_1\n value: 34.764\n - type: precision_at_10\n value: 9.027000000000001\n - type: precision_at_100\n value: 1.465\n - type: precision_at_1000\n value: 0.192\n - type: precision_at_3\n value: 19.695\n - type: precision_at_5\n value: 14.535\n - type: recall_at_1\n value: 28.848000000000003\n - type: recall_at_10\n value: 60.849\n - type: recall_at_100\n value: 85.764\n - type: recall_at_1000\n value: 96.098\n - type: recall_at_3\n value: 44.579\n - type: recall_at_5\n value: 51.678999999999995\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackEnglishRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: ad9991cb51e31e31e430383c75ffb2885547b5f0\n metrics:\n - type: map_at_1\n value: 30.731\n - type: map_at_10\n value: 41.859\n - type: map_at_100\n value: 43.13\n - type: map_at_1000\n value: 43.257\n - type: map_at_3\n value: 38.384\n - type: map_at_5\n value: 40.284\n - type: mrr_at_1\n value: 38.471\n - type: mrr_at_10\n value: 47.531\n - type: mrr_at_100\n value: 48.199\n - type: mrr_at_1000\n value: 48.24\n - type: mrr_at_3\n value: 44.989000000000004\n - type: mrr_at_5\n value: 46.403\n - type: ndcg_at_1\n value: 38.471\n - type: ndcg_at_10\n value: 48.022999999999996\n - type: ndcg_at_100\n value: 52.32599999999999\n - type: ndcg_at_1000\n value: 54.26\n - type: ndcg_at_3\n value: 42.986999999999995\n - type: ndcg_at_5\n value: 45.23\n - type: precision_at_1\n value: 38.471\n - type: precision_at_10\n value: 9.248000000000001\n - type: precision_at_100\n value: 1.469\n - type: precision_at_1000\n value: 0.193\n - type: precision_at_3\n value: 20.892\n - type: precision_at_5\n value: 14.892\n - type: recall_at_1\n value: 30.731\n - type: recall_at_10\n value: 59.561\n - type: recall_at_100\n value: 77.637\n - type: recall_at_1000\n value: 89.64999999999999\n - type: recall_at_3\n value: 44.897999999999996\n - type: recall_at_5\n value: 51.181\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGamingRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 4885aa143210c98657558c04aaf3dc47cfb54340\n metrics:\n - type: map_at_1\n value: 34.949000000000005\n - type: map_at_10\n value: 48.117\n - type: map_at_100\n value: 49.355\n - type: map_at_1000\n value: 49.409\n - type: map_at_3\n value: 44.732\n - type: map_at_5\n value: 46.555\n - type: mrr_at_1\n value: 40.188\n - type: mrr_at_10\n value: 51.452\n - type: mrr_at_100\n value: 52.219\n - type: mrr_at_1000\n value: 52.24100000000001\n - type: mrr_at_3\n value: 48.642\n - type: mrr_at_5\n value: 50.134\n - type: ndcg_at_1\n value: 40.188\n - type: ndcg_at_10\n value: 54.664\n - type: ndcg_at_100\n value: 59.38099999999999\n - type: ndcg_at_1000\n value: 60.363\n - type: ndcg_at_3\n value: 48.684\n - type: ndcg_at_5\n value: 51.406\n - type: precision_at_1\n value: 40.188\n - type: precision_at_10\n value: 9.116\n - type: precision_at_100\n value: 1.248\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 22.236\n - type: precision_at_5\n value: 15.310000000000002\n - type: recall_at_1\n value: 34.949000000000005\n - type: recall_at_10\n value: 70.767\n - type: recall_at_100\n value: 90.79\n - type: recall_at_1000\n value: 97.57900000000001\n - type: recall_at_3\n value: 54.723\n - type: recall_at_5\n value: 61.404\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGisRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 5003b3064772da1887988e05400cf3806fe491f2\n metrics:\n - type: map_at_1\n value: 25.312\n - type: map_at_10\n value: 34.799\n - type: map_at_100\n value: 35.906\n - type: map_at_1000\n value: 35.983\n - type: map_at_3\n value: 31.582\n - type: map_at_5\n value: 33.507999999999996\n - type: mrr_at_1\n value: 27.232\n - type: mrr_at_10\n value: 36.82\n - type: mrr_at_100\n value: 37.733\n - type: mrr_at_1000\n value: 37.791000000000004\n - type: mrr_at_3\n value: 33.804\n - type: mrr_at_5\n value: 35.606\n - type: ndcg_at_1\n value: 27.232\n - type: ndcg_at_10\n value: 40.524\n - type: ndcg_at_100\n value: 45.654\n - type: ndcg_at_1000\n value: 47.557\n - type: ndcg_at_3\n value: 34.312\n - type: ndcg_at_5\n value: 37.553\n - type: precision_at_1\n value: 27.232\n - type: precision_at_10\n value: 6.52\n - type: precision_at_100\n value: 0.9530000000000001\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 14.915000000000001\n - type: precision_at_5\n value: 10.847\n - type: recall_at_1\n value: 25.312\n - type: recall_at_10\n value: 56.169000000000004\n - type: recall_at_100\n value: 79.16499999999999\n - type: recall_at_1000\n value: 93.49300000000001\n - type: recall_at_3\n value: 39.5\n - type: recall_at_5\n value: 47.288999999999994\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackMathematicaRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 90fceea13679c63fe563ded68f3b6f06e50061de\n metrics:\n - type: map_at_1\n value: 17.153\n - type: map_at_10\n value: 27.671\n - type: map_at_100\n value: 29.186\n - type: map_at_1000\n value: 29.299999999999997\n - type: map_at_3\n value: 24.490000000000002\n - type: map_at_5\n value: 26.178\n - type: mrr_at_1\n value: 21.144\n - type: mrr_at_10\n value: 32.177\n - type: mrr_at_100\n value: 33.247\n - type: mrr_at_1000\n value: 33.306000000000004\n - type: mrr_at_3\n value: 29.187\n - type: mrr_at_5\n value: 30.817\n - type: ndcg_at_1\n value: 21.144\n - type: ndcg_at_10\n value: 33.981\n - type: ndcg_at_100\n value: 40.549\n - type: ndcg_at_1000\n value: 43.03\n - type: ndcg_at_3\n value: 28.132\n - type: ndcg_at_5\n value: 30.721999999999998\n - type: precision_at_1\n value: 21.144\n - type: precision_at_10\n value: 6.666999999999999\n - type: precision_at_100\n value: 1.147\n - type: precision_at_1000\n value: 0.149\n - type: precision_at_3\n value: 14.302999999999999\n - type: precision_at_5\n value: 10.423\n - type: recall_at_1\n value: 17.153\n - type: recall_at_10\n value: 48.591\n - type: recall_at_100\n value: 76.413\n - type: recall_at_1000\n value: 93.8\n - type: recall_at_3\n value: 32.329\n - type: recall_at_5\n value: 38.958999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackPhysicsRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4\n metrics:\n - type: map_at_1\n value: 27.909\n - type: map_at_10\n value: 40.168\n - type: map_at_100\n value: 41.524\n - type: map_at_1000\n value: 41.626000000000005\n - type: map_at_3\n value: 36.274\n - type: map_at_5\n value: 38.411\n - type: mrr_at_1\n value: 34.649\n - type: mrr_at_10\n value: 45.613\n - type: mrr_at_100\n value: 46.408\n - type: mrr_at_1000\n value: 46.444\n - type: mrr_at_3\n value: 42.620999999999995\n - type: mrr_at_5\n value: 44.277\n - type: ndcg_at_1\n value: 34.649\n - type: ndcg_at_10\n value: 47.071000000000005\n - type: ndcg_at_100\n value: 52.559999999999995\n - type: ndcg_at_1000\n value: 54.285000000000004\n - type: ndcg_at_3\n value: 40.63\n - type: ndcg_at_5\n value: 43.584\n - type: precision_at_1\n value: 34.649\n - type: precision_at_10\n value: 8.855\n - type: precision_at_100\n value: 1.361\n - type: precision_at_1000\n value: 0.167\n - type: precision_at_3\n value: 19.538\n - type: precision_at_5\n value: 14.187\n - type: recall_at_1\n value: 27.909\n - type: recall_at_10\n value: 62.275000000000006\n - type: recall_at_100\n value: 84.95\n - type: recall_at_1000\n value: 96.02000000000001\n - type: recall_at_3\n value: 44.767\n - type: recall_at_5\n value: 52.03\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackProgrammersRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 6184bc1440d2dbc7612be22b50686b8826d22b32\n metrics:\n - type: map_at_1\n value: 25.846000000000004\n - type: map_at_10\n value: 36.870999999999995\n - type: map_at_100\n value: 38.294\n - type: map_at_1000\n value: 38.401\n - type: map_at_3\n value: 33.163\n - type: map_at_5\n value: 35.177\n - type: mrr_at_1\n value: 31.849\n - type: mrr_at_10\n value: 41.681000000000004\n - type: mrr_at_100\n value: 42.658\n - type: mrr_at_1000\n value: 42.71\n - type: mrr_at_3\n value: 39.003\n - type: mrr_at_5\n value: 40.436\n - type: ndcg_at_1\n value: 31.849\n - type: ndcg_at_10\n value: 43.291000000000004\n - type: ndcg_at_100\n value: 49.136\n - type: ndcg_at_1000\n value: 51.168\n - type: ndcg_at_3\n value: 37.297999999999995\n - type: ndcg_at_5\n value: 39.934\n - type: precision_at_1\n value: 31.849\n - type: precision_at_10\n value: 8.219\n - type: precision_at_100\n value: 1.318\n - type: precision_at_1000\n value: 0.167\n - type: precision_at_3\n value: 18.151\n - type: precision_at_5\n value: 13.242\n - type: recall_at_1\n value: 25.846000000000004\n - type: recall_at_10\n value: 57.642\n - type: recall_at_100\n value: 82.069\n - type: recall_at_1000\n value: 95.684\n - type: recall_at_3\n value: 40.778999999999996\n - type: recall_at_5\n value: 47.647\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 25.34866666666667\n - type: map_at_10\n value: 35.65541666666667\n - type: map_at_100\n value: 36.982416666666666\n - type: map_at_1000\n value: 37.09416666666667\n - type: map_at_3\n value: 32.421499999999995\n - type: map_at_5\n value: 34.20266666666667\n - type: mrr_at_1\n value: 30.02116666666667\n - type: mrr_at_10\n value: 39.781666666666666\n - type: mrr_at_100\n value: 40.69733333333333\n - type: mrr_at_1000\n value: 40.74875\n - type: mrr_at_3\n value: 37.043083333333335\n - type: mrr_at_5\n value: 38.56391666666666\n - type: ndcg_at_1\n value: 30.02116666666667\n - type: ndcg_at_10\n value: 41.66133333333333\n - type: ndcg_at_100\n value: 47.21474999999999\n - type: ndcg_at_1000\n value: 49.29600000000001\n - type: ndcg_at_3\n value: 36.06958333333334\n - type: ndcg_at_5\n value: 38.66858333333333\n - type: precision_at_1\n value: 30.02116666666667\n - type: precision_at_10\n value: 7.497249999999999\n - type: precision_at_100\n value: 1.2044166666666667\n - type: precision_at_1000\n value: 0.15766666666666665\n - type: precision_at_3\n value: 16.83458333333333\n - type: precision_at_5\n value: 12.134\n - type: recall_at_1\n value: 25.34866666666667\n - type: recall_at_10\n value: 55.40541666666666\n - type: recall_at_100\n value: 79.38683333333333\n - type: recall_at_1000\n value: 93.50958333333334\n - type: recall_at_3\n value: 39.99858333333334\n - type: recall_at_5\n value: 46.55741666666666\n - type: map_at_1\n value: 18.336\n - type: map_at_10\n value: 26.811\n - type: map_at_100\n value: 27.892\n - type: map_at_1000\n value: 27.986\n - type: map_at_3\n value: 23.976\n - type: map_at_5\n value: 25.605\n - type: mrr_at_1\n value: 20.148\n - type: mrr_at_10\n value: 28.898000000000003\n - type: mrr_at_100\n value: 29.866\n - type: mrr_at_1000\n value: 29.929\n - type: mrr_at_3\n value: 26.247999999999998\n - type: mrr_at_5\n value: 27.744999999999997\n - type: ndcg_at_1\n value: 20.148\n - type: ndcg_at_10\n value: 32.059\n - type: ndcg_at_100\n value: 37.495\n - type: ndcg_at_1000\n value: 39.855000000000004\n - type: ndcg_at_3\n value: 26.423000000000002\n - type: ndcg_at_5\n value: 29.212\n - type: precision_at_1\n value: 20.148\n - type: precision_at_10\n value: 5.268\n - type: precision_at_100\n value: 0.872\n - type: precision_at_1000\n value: 0.11900000000000001\n - type: precision_at_3\n value: 11.459999999999999\n - type: precision_at_5\n value: 8.503\n - type: recall_at_1\n value: 18.336\n - type: recall_at_10\n value: 46.411\n - type: recall_at_100\n value: 71.33500000000001\n - type: recall_at_1000\n value: 88.895\n - type: recall_at_3\n value: 31.134\n - type: recall_at_5\n value: 37.862\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackStatsRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a\n metrics:\n - type: map_at_1\n value: 25.102000000000004\n - type: map_at_10\n value: 33.31\n - type: map_at_100\n value: 34.443\n - type: map_at_1000\n value: 34.547\n - type: map_at_3\n value: 30.932\n - type: map_at_5\n value: 32.126\n - type: mrr_at_1\n value: 28.221\n - type: mrr_at_10\n value: 36.519\n - type: mrr_at_100\n value: 37.425000000000004\n - type: mrr_at_1000\n value: 37.498\n - type: mrr_at_3\n value: 34.254\n - type: mrr_at_5\n value: 35.388999999999996\n - type: ndcg_at_1\n value: 28.221\n - type: ndcg_at_10\n value: 38.340999999999994\n - type: ndcg_at_100\n value: 43.572\n - type: ndcg_at_1000\n value: 45.979\n - type: ndcg_at_3\n value: 33.793\n - type: ndcg_at_5\n value: 35.681000000000004\n - type: precision_at_1\n value: 28.221\n - type: precision_at_10\n value: 6.135\n - type: precision_at_100\n value: 0.946\n - type: precision_at_1000\n value: 0.123\n - type: precision_at_3\n value: 14.519000000000002\n - type: precision_at_5\n value: 9.969\n - type: recall_at_1\n value: 25.102000000000004\n - type: recall_at_10\n value: 50.639\n - type: recall_at_100\n value: 74.075\n - type: recall_at_1000\n value: 91.393\n - type: recall_at_3\n value: 37.952000000000005\n - type: recall_at_5\n value: 42.71\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackTexRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 46989137a86843e03a6195de44b09deda022eec7\n metrics:\n - type: map_at_1\n value: 18.618000000000002\n - type: map_at_10\n value: 26.714\n - type: map_at_100\n value: 27.929\n - type: map_at_1000\n value: 28.057\n - type: map_at_3\n value: 24.134\n - type: map_at_5\n value: 25.575\n - type: mrr_at_1\n value: 22.573999999999998\n - type: mrr_at_10\n value: 30.786\n - type: mrr_at_100\n value: 31.746000000000002\n - type: mrr_at_1000\n value: 31.822\n - type: mrr_at_3\n value: 28.412\n - type: mrr_at_5\n value: 29.818\n - type: ndcg_at_1\n value: 22.573999999999998\n - type: ndcg_at_10\n value: 31.852000000000004\n - type: ndcg_at_100\n value: 37.477\n - type: ndcg_at_1000\n value: 40.331\n - type: ndcg_at_3\n value: 27.314\n - type: ndcg_at_5\n value: 29.485\n - type: precision_at_1\n value: 22.573999999999998\n - type: precision_at_10\n value: 5.86\n - type: precision_at_100\n value: 1.012\n - type: precision_at_1000\n value: 0.146\n - type: precision_at_3\n value: 13.099\n - type: precision_at_5\n value: 9.56\n - type: recall_at_1\n value: 18.618000000000002\n - type: recall_at_10\n value: 43.134\n - type: recall_at_100\n value: 68.294\n - type: recall_at_1000\n value: 88.283\n - type: recall_at_3\n value: 30.397999999999996\n - type: recall_at_5\n value: 35.998000000000005\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackUnixRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53\n metrics:\n - type: map_at_1\n value: 27.76\n - type: map_at_10\n value: 37.569\n - type: map_at_100\n value: 38.784\n - type: map_at_1000\n value: 38.884\n - type: map_at_3\n value: 34.379\n - type: map_at_5\n value: 36.092999999999996\n - type: mrr_at_1\n value: 32.556000000000004\n - type: mrr_at_10\n value: 41.870000000000005\n - type: mrr_at_100\n value: 42.759\n - type: mrr_at_1000\n value: 42.806\n - type: mrr_at_3\n value: 39.086\n - type: mrr_at_5\n value: 40.574\n - type: ndcg_at_1\n value: 32.556000000000004\n - type: ndcg_at_10\n value: 43.382\n - type: ndcg_at_100\n value: 48.943\n - type: ndcg_at_1000\n value: 50.961999999999996\n - type: ndcg_at_3\n value: 37.758\n - type: ndcg_at_5\n value: 40.282000000000004\n - type: precision_at_1\n value: 32.556000000000004\n - type: precision_at_10\n value: 7.463\n - type: precision_at_100\n value: 1.1480000000000001\n - type: precision_at_1000\n value: 0.14300000000000002\n - type: precision_at_3\n value: 17.133000000000003\n - type: precision_at_5\n value: 12.164\n - type: recall_at_1\n value: 27.76\n - type: recall_at_10\n value: 56.71000000000001\n - type: recall_at_100\n value: 81.053\n - type: recall_at_1000\n value: 94.75\n - type: recall_at_3\n value: 41.387\n - type: recall_at_5\n value: 47.818\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWebmastersRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: 160c094312a0e1facb97e55eeddb698c0abe3571\n metrics:\n - type: map_at_1\n value: 23.62\n - type: map_at_10\n value: 33.522999999999996\n - type: map_at_100\n value: 35.281\n - type: map_at_1000\n value: 35.504000000000005\n - type: map_at_3\n value: 30.314999999999998\n - type: map_at_5\n value: 32.065\n - type: mrr_at_1\n value: 28.458\n - type: mrr_at_10\n value: 38.371\n - type: mrr_at_100\n value: 39.548\n - type: mrr_at_1000\n value: 39.601\n - type: mrr_at_3\n value: 35.638999999999996\n - type: mrr_at_5\n value: 37.319\n - type: ndcg_at_1\n value: 28.458\n - type: ndcg_at_10\n value: 39.715\n - type: ndcg_at_100\n value: 46.394999999999996\n - type: ndcg_at_1000\n value: 48.943999999999996\n - type: ndcg_at_3\n value: 34.361999999999995\n - type: ndcg_at_5\n value: 37.006\n - type: precision_at_1\n value: 28.458\n - type: precision_at_10\n value: 7.5889999999999995\n - type: precision_at_100\n value: 1.514\n - type: precision_at_1000\n value: 0.242\n - type: precision_at_3\n value: 16.073999999999998\n - type: precision_at_5\n value: 11.976\n - type: recall_at_1\n value: 23.62\n - type: recall_at_10\n value: 52.117000000000004\n - type: recall_at_100\n value: 81.097\n - type: recall_at_1000\n value: 96.47\n - type: recall_at_3\n value: 37.537\n - type: recall_at_5\n value: 44.112\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: 21.149\n - type: map_at_10\n value: 35.251\n - type: map_at_100\n value: 37.342\n - type: map_at_1000\n value: 37.516\n - type: map_at_3\n value: 30.543\n - type: map_at_5\n value: 33.19\n - type: mrr_at_1\n value: 47.687000000000005\n - type: mrr_at_10\n value: 59.391000000000005\n - type: mrr_at_100\n value: 59.946999999999996\n - type: mrr_at_1000\n value: 59.965999999999994\n - type: mrr_at_3\n value: 56.938\n - type: mrr_at_5\n value: 58.498000000000005\n - type: ndcg_at_1\n value: 47.687000000000005\n - type: ndcg_at_10\n value: 45.381\n - type: ndcg_at_100\n value: 52.405\n - type: ndcg_at_1000\n value: 55.041\n - type: ndcg_at_3\n value: 40.024\n - type: ndcg_at_5\n value: 41.821999999999996\n - type: precision_at_1\n value: 47.687000000000005\n - type: precision_at_10\n value: 13.355\n - type: precision_at_100\n value: 2.113\n - type: precision_at_1000\n value: 0.261\n - type: precision_at_3\n value: 29.793999999999997\n - type: precision_at_5\n value: 21.811\n - type: recall_at_1\n value: 21.149\n - type: recall_at_10\n value: 49.937\n - type: recall_at_100\n value: 73.382\n - type: recall_at_1000\n value: 87.606\n - type: recall_at_3\n value: 35.704\n - type: recall_at_5\n value: 42.309000000000005\n - task:\n type: Retrieval\n dataset:\n name: MTEB CmedqaRetrieval\n type: C-MTEB/CmedqaRetrieval\n config: default\n split: dev\n revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301\n metrics:\n - type: map_at_1\n value: 28.74\n - type: map_at_10\n value: 41.981\n - type: map_at_100\n value: 43.753\n - type: map_at_1000\n value: 43.858999999999995\n - type: map_at_3\n value: 37.634\n - type: map_at_5\n value: 40.158\n - type: mrr_at_1\n value: 43.086\n - type: mrr_at_10\n value: 51.249\n - type: mrr_at_100\n value: 52.154\n - type: mrr_at_1000\n value: 52.190999999999995\n - type: mrr_at_3\n value: 48.787000000000006\n - type: mrr_at_5\n value: 50.193\n - type: ndcg_at_1\n value: 43.086\n - type: ndcg_at_10\n value: 48.703\n - type: ndcg_at_100\n value: 55.531\n - type: ndcg_at_1000\n value: 57.267999999999994\n - type: ndcg_at_3\n value: 43.464000000000006\n - type: ndcg_at_5\n value: 45.719\n - type: precision_at_1\n value: 43.086\n - type: precision_at_10\n value: 10.568\n - type: precision_at_100\n value: 1.616\n - type: precision_at_1000\n value: 0.184\n - type: precision_at_3\n value: 24.256\n - type: precision_at_5\n value: 17.509\n - type: recall_at_1\n value: 28.74\n - type: recall_at_10\n value: 59.349\n - type: recall_at_100\n value: 87.466\n - type: recall_at_1000\n value: 98.914\n - type: recall_at_3\n value: 43.322\n - type: recall_at_5\n value: 50.409000000000006\n - task:\n type: PairClassification\n dataset:\n name: MTEB Cmnli\n type: C-MTEB/CMNLI\n config: default\n split: validation\n revision: 41bc36f332156f7adc9e38f53777c959b2ae9766\n metrics:\n - type: cos_sim_accuracy\n value: 79.03788334335539\n - type: cos_sim_ap\n value: 87.21703260472833\n - type: cos_sim_f1\n value: 79.87784187309127\n - type: cos_sim_precision\n value: 77.36634531113059\n - type: cos_sim_recall\n value: 82.55786766425064\n - type: dot_accuracy\n value: 79.03788334335539\n - type: dot_ap\n value: 87.22906528217948\n - type: dot_f1\n value: 79.87784187309127\n - type: dot_precision\n value: 77.36634531113059\n - type: dot_recall\n value: 82.55786766425064\n - type: euclidean_accuracy\n value: 79.03788334335539\n - type: euclidean_ap\n value: 87.21703670465753\n - type: euclidean_f1\n value: 79.87784187309127\n - type: euclidean_precision\n value: 77.36634531113059\n - type: euclidean_recall\n value: 82.55786766425064\n - type: manhattan_accuracy\n value: 78.28021647624774\n - type: manhattan_ap\n value: 86.66244127855394\n - type: manhattan_f1\n value: 79.24485643228577\n - type: manhattan_precision\n value: 76.71262858393521\n - type: manhattan_recall\n value: 81.94996492868833\n - type: max_accuracy\n value: 79.03788334335539\n - type: max_ap\n value: 87.22906528217948\n - type: max_f1\n value: 79.87784187309127\n - task:\n type: Retrieval\n dataset:\n name: MTEB CovidRetrieval\n type: C-MTEB/CovidRetrieval\n config: default\n split: dev\n revision: 1271c7809071a13532e05f25fb53511ffce77117\n metrics:\n - type: map_at_1\n value: 67.597\n - type: map_at_10\n value: 75.81599999999999\n - type: map_at_100\n value: 76.226\n - type: map_at_1000\n value: 76.23100000000001\n - type: map_at_3\n value: 73.907\n - type: map_at_5\n value: 75.08200000000001\n - type: mrr_at_1\n value: 67.756\n - type: mrr_at_10\n value: 75.8\n - type: mrr_at_100\n value: 76.205\n - type: mrr_at_1000\n value: 76.21\n - type: mrr_at_3\n value: 73.955\n - type: mrr_at_5\n value: 75.093\n - type: ndcg_at_1\n value: 67.756\n - type: ndcg_at_10\n value: 79.598\n - type: ndcg_at_100\n value: 81.34400000000001\n - type: ndcg_at_1000\n value: 81.477\n - type: ndcg_at_3\n value: 75.876\n - type: ndcg_at_5\n value: 77.94200000000001\n - type: precision_at_1\n value: 67.756\n - type: precision_at_10\n value: 9.231\n - type: precision_at_100\n value: 1.0\n - type: precision_at_1000\n value: 0.101\n - type: precision_at_3\n value: 27.362\n - type: precision_at_5\n value: 17.45\n - type: recall_at_1\n value: 67.597\n - type: recall_at_10\n value: 91.307\n - type: recall_at_100\n value: 98.946\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 81.428\n - type: recall_at_5\n value: 86.407\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: 9.33\n - type: map_at_10\n value: 23.118\n - type: map_at_100\n value: 34.28\n - type: map_at_1000\n value: 36.574\n - type: map_at_3\n value: 15.576\n - type: map_at_5\n value: 18.778\n - type: mrr_at_1\n value: 75.25\n - type: mrr_at_10\n value: 81.958\n - type: mrr_at_100\n value: 82.282\n - type: mrr_at_1000\n value: 82.285\n - type: mrr_at_3\n value: 81.042\n - type: mrr_at_5\n value: 81.62899999999999\n - type: ndcg_at_1\n value: 63.625\n - type: ndcg_at_10\n value: 50.781\n - type: ndcg_at_100\n value: 55.537000000000006\n - type: ndcg_at_1000\n value: 62.651\n - type: ndcg_at_3\n value: 55.297\n - type: ndcg_at_5\n value: 53.103\n - type: precision_at_1\n value: 75.25\n - type: precision_at_10\n value: 41.475\n - type: precision_at_100\n value: 13.5\n - type: precision_at_1000\n value: 2.686\n - type: precision_at_3\n value: 59.333000000000006\n - type: precision_at_5\n value: 51.9\n - type: recall_at_1\n value: 9.33\n - type: recall_at_10\n value: 29.398000000000003\n - type: recall_at_100\n value: 61.951\n - type: recall_at_1000\n value: 85.463\n - type: recall_at_3\n value: 17.267\n - type: recall_at_5\n value: 21.89\n - task:\n type: Retrieval\n dataset:\n name: MTEB DuRetrieval\n type: C-MTEB/DuRetrieval\n config: default\n split: dev\n revision: a1a333e290fe30b10f3f56498e3a0d911a693ced\n metrics:\n - type: map_at_1\n value: 25.608999999999998\n - type: map_at_10\n value: 78.649\n - type: map_at_100\n value: 81.67699999999999\n - type: map_at_1000\n value: 81.71000000000001\n - type: map_at_3\n value: 54.112\n - type: map_at_5\n value: 68.34700000000001\n - type: mrr_at_1\n value: 87.75\n - type: mrr_at_10\n value: 92.175\n - type: mrr_at_100\n value: 92.225\n - type: mrr_at_1000\n value: 92.227\n - type: mrr_at_3\n value: 91.833\n - type: mrr_at_5\n value: 92.06800000000001\n - type: ndcg_at_1\n value: 87.75\n - type: ndcg_at_10\n value: 86.56700000000001\n - type: ndcg_at_100\n value: 89.519\n - type: ndcg_at_1000\n value: 89.822\n - type: ndcg_at_3\n value: 84.414\n - type: ndcg_at_5\n value: 83.721\n - type: precision_at_1\n value: 87.75\n - type: precision_at_10\n value: 41.665\n - type: precision_at_100\n value: 4.827\n - type: precision_at_1000\n value: 0.49\n - type: precision_at_3\n value: 75.533\n - type: precision_at_5\n value: 64.01\n - type: recall_at_1\n value: 25.608999999999998\n - type: recall_at_10\n value: 88.708\n - type: recall_at_100\n value: 98.007\n - type: recall_at_1000\n value: 99.555\n - type: recall_at_3\n value: 57.157000000000004\n - type: recall_at_5\n value: 74.118\n - task:\n type: Retrieval\n dataset:\n name: MTEB EcomRetrieval\n type: C-MTEB/EcomRetrieval\n config: default\n split: dev\n revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9\n metrics:\n - type: map_at_1\n value: 55.800000000000004\n - type: map_at_10\n value: 65.952\n - type: map_at_100\n value: 66.413\n - type: map_at_1000\n value: 66.426\n - type: map_at_3\n value: 63.3\n - type: map_at_5\n value: 64.945\n - type: mrr_at_1\n value: 55.800000000000004\n - type: mrr_at_10\n value: 65.952\n - type: mrr_at_100\n value: 66.413\n - type: mrr_at_1000\n value: 66.426\n - type: mrr_at_3\n value: 63.3\n - type: mrr_at_5\n value: 64.945\n - type: ndcg_at_1\n value: 55.800000000000004\n - type: ndcg_at_10\n value: 71.00800000000001\n - type: ndcg_at_100\n value: 72.974\n - type: ndcg_at_1000\n value: 73.302\n - type: ndcg_at_3\n value: 65.669\n - type: ndcg_at_5\n value: 68.634\n - type: precision_at_1\n value: 55.800000000000004\n - type: precision_at_10\n value: 8.690000000000001\n - type: precision_at_100\n value: 0.955\n - type: precision_at_1000\n value: 0.098\n - type: precision_at_3\n value: 24.166999999999998\n - type: precision_at_5\n value: 15.939999999999998\n - type: recall_at_1\n value: 55.800000000000004\n - type: recall_at_10\n value: 86.9\n - type: recall_at_100\n value: 95.5\n - type: recall_at_1000\n value: 98.0\n - type: recall_at_3\n value: 72.5\n - type: recall_at_5\n value: 79.7\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: 67.39500000000001\n - type: f1\n value: 62.01837785021389\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: 86.27\n - type: map_at_10\n value: 92.163\n - type: map_at_100\n value: 92.351\n - type: map_at_1000\n value: 92.36\n - type: map_at_3\n value: 91.36\n - type: map_at_5\n value: 91.888\n - type: mrr_at_1\n value: 92.72399999999999\n - type: mrr_at_10\n value: 95.789\n - type: mrr_at_100\n value: 95.80300000000001\n - type: mrr_at_1000\n value: 95.804\n - type: mrr_at_3\n value: 95.64200000000001\n - type: mrr_at_5\n value: 95.75\n - type: ndcg_at_1\n value: 92.72399999999999\n - type: ndcg_at_10\n value: 94.269\n - type: ndcg_at_100\n value: 94.794\n - type: ndcg_at_1000\n value: 94.94\n - type: ndcg_at_3\n value: 93.427\n - type: ndcg_at_5\n value: 93.914\n - type: precision_at_1\n value: 92.72399999999999\n - type: precision_at_10\n value: 11.007\n - type: precision_at_100\n value: 1.153\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 34.993\n - type: precision_at_5\n value: 21.542\n - type: recall_at_1\n value: 86.27\n - type: recall_at_10\n value: 97.031\n - type: recall_at_100\n value: 98.839\n - type: recall_at_1000\n value: 99.682\n - type: recall_at_3\n value: 94.741\n - type: recall_at_5\n value: 96.03\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: 29.561999999999998\n - type: map_at_10\n value: 48.52\n - type: map_at_100\n value: 50.753\n - type: map_at_1000\n value: 50.878\n - type: map_at_3\n value: 42.406\n - type: map_at_5\n value: 45.994\n - type: mrr_at_1\n value: 54.784\n - type: mrr_at_10\n value: 64.51400000000001\n - type: mrr_at_100\n value: 65.031\n - type: mrr_at_1000\n value: 65.05199999999999\n - type: mrr_at_3\n value: 62.474\n - type: mrr_at_5\n value: 63.562\n - type: ndcg_at_1\n value: 54.784\n - type: ndcg_at_10\n value: 57.138\n - type: ndcg_at_100\n value: 63.666999999999994\n - type: ndcg_at_1000\n value: 65.379\n - type: ndcg_at_3\n value: 52.589\n - type: ndcg_at_5\n value: 54.32599999999999\n - type: precision_at_1\n value: 54.784\n - type: precision_at_10\n value: 15.693999999999999\n - type: precision_at_100\n value: 2.259\n - type: precision_at_1000\n value: 0.256\n - type: precision_at_3\n value: 34.774\n - type: precision_at_5\n value: 25.772000000000002\n - type: recall_at_1\n value: 29.561999999999998\n - type: recall_at_10\n value: 64.708\n - type: recall_at_100\n value: 87.958\n - type: recall_at_1000\n value: 97.882\n - type: recall_at_3\n value: 48.394\n - type: recall_at_5\n value: 56.101\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: 43.72\n - type: map_at_10\n value: 71.905\n - type: map_at_100\n value: 72.685\n - type: map_at_1000\n value: 72.72800000000001\n - type: map_at_3\n value: 68.538\n - type: map_at_5\n value: 70.675\n - type: mrr_at_1\n value: 87.441\n - type: mrr_at_10\n value: 91.432\n - type: mrr_at_100\n value: 91.512\n - type: mrr_at_1000\n value: 91.513\n - type: mrr_at_3\n value: 90.923\n - type: mrr_at_5\n value: 91.252\n - type: ndcg_at_1\n value: 87.441\n - type: ndcg_at_10\n value: 79.212\n - type: ndcg_at_100\n value: 81.694\n - type: ndcg_at_1000\n value: 82.447\n - type: ndcg_at_3\n value: 74.746\n - type: ndcg_at_5\n value: 77.27199999999999\n - type: precision_at_1\n value: 87.441\n - type: precision_at_10\n value: 16.42\n - type: precision_at_100\n value: 1.833\n - type: precision_at_1000\n value: 0.193\n - type: precision_at_3\n value: 48.184\n - type: precision_at_5\n value: 30.897999999999996\n - type: recall_at_1\n value: 43.72\n - type: recall_at_10\n value: 82.1\n - type: recall_at_100\n value: 91.62700000000001\n - type: recall_at_1000\n value: 96.556\n - type: recall_at_3\n value: 72.275\n - type: recall_at_5\n value: 77.24499999999999\n - task:\n type: Classification\n dataset:\n name: MTEB IFlyTek\n type: C-MTEB/IFlyTek-classification\n config: default\n split: validation\n revision: 421605374b29664c5fc098418fe20ada9bd55f8a\n metrics:\n - type: accuracy\n value: 54.520969603693736\n - type: f1\n value: 42.359043311419626\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: 96.72559999999999\n - type: ap\n value: 95.01759461773742\n - type: f1\n value: 96.72429945397575\n - task:\n type: Classification\n dataset:\n name: MTEB JDReview\n type: C-MTEB/JDReview-classification\n config: default\n split: test\n revision: b7c64bd89eb87f8ded463478346f76731f07bf8b\n metrics:\n - type: accuracy\n value: 90.1688555347092\n - type: ap\n value: 63.36583667477521\n - type: f1\n value: 85.6845016521436\n - task:\n type: STS\n dataset:\n name: MTEB LCQMC\n type: C-MTEB/LCQMC\n config: default\n split: test\n revision: 17f9b096f80380fce5ed12a9be8be7784b337daf\n metrics:\n - type: cos_sim_pearson\n value: 67.35114066823127\n - type: cos_sim_spearman\n value: 72.98875207056305\n - type: euclidean_pearson\n value: 71.45620183630378\n - type: euclidean_spearman\n value: 72.98875207022671\n - type: manhattan_pearson\n value: 71.3845159780333\n - type: manhattan_spearman\n value: 72.92710990543166\n - task:\n type: Reranking\n dataset:\n name: MTEB MMarcoReranking\n type: C-MTEB/Mmarco-reranking\n config: default\n split: dev\n revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6\n metrics:\n - type: map\n value: 32.68592539803807\n - type: mrr\n value: 31.58968253968254\n - task:\n type: Retrieval\n dataset:\n name: MTEB MMarcoRetrieval\n type: C-MTEB/MMarcoRetrieval\n config: default\n split: dev\n revision: 539bbde593d947e2a124ba72651aafc09eb33fc2\n metrics:\n - type: map_at_1\n value: 71.242\n - type: map_at_10\n value: 80.01\n - type: map_at_100\n value: 80.269\n - type: map_at_1000\n value: 80.276\n - type: map_at_3\n value: 78.335\n - type: map_at_5\n value: 79.471\n - type: mrr_at_1\n value: 73.668\n - type: mrr_at_10\n value: 80.515\n - type: mrr_at_100\n value: 80.738\n - type: mrr_at_1000\n value: 80.744\n - type: mrr_at_3\n value: 79.097\n - type: mrr_at_5\n value: 80.045\n - type: ndcg_at_1\n value: 73.668\n - type: ndcg_at_10\n value: 83.357\n - type: ndcg_at_100\n value: 84.442\n - type: ndcg_at_1000\n value: 84.619\n - type: ndcg_at_3\n value: 80.286\n - type: ndcg_at_5\n value: 82.155\n - type: precision_at_1\n value: 73.668\n - type: precision_at_10\n value: 9.905\n - type: precision_at_100\n value: 1.043\n - type: precision_at_1000\n value: 0.106\n - type: precision_at_3\n value: 30.024\n - type: precision_at_5\n value: 19.017\n - type: recall_at_1\n value: 71.242\n - type: recall_at_10\n value: 93.11\n - type: recall_at_100\n value: 97.85000000000001\n - type: recall_at_1000\n value: 99.21900000000001\n - type: recall_at_3\n value: 85.137\n - type: recall_at_5\n value: 89.548\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: 22.006999999999998\n - type: map_at_10\n value: 34.994\n - type: map_at_100\n value: 36.183\n - type: map_at_1000\n value: 36.227\n - type: map_at_3\n value: 30.75\n - type: map_at_5\n value: 33.155\n - type: mrr_at_1\n value: 22.679\n - type: mrr_at_10\n value: 35.619\n - type: mrr_at_100\n value: 36.732\n - type: mrr_at_1000\n value: 36.77\n - type: mrr_at_3\n value: 31.44\n - type: mrr_at_5\n value: 33.811\n - type: ndcg_at_1\n value: 22.679\n - type: ndcg_at_10\n value: 42.376000000000005\n - type: ndcg_at_100\n value: 48.001\n - type: ndcg_at_1000\n value: 49.059999999999995\n - type: ndcg_at_3\n value: 33.727000000000004\n - type: ndcg_at_5\n value: 38.013000000000005\n - type: precision_at_1\n value: 22.679\n - type: precision_at_10\n value: 6.815\n - type: precision_at_100\n value: 0.962\n - type: precision_at_1000\n value: 0.105\n - type: precision_at_3\n value: 14.441\n - type: precision_at_5\n value: 10.817\n - type: recall_at_1\n value: 22.006999999999998\n - type: recall_at_10\n value: 65.158\n - type: recall_at_100\n value: 90.997\n - type: recall_at_1000\n value: 98.996\n - type: recall_at_3\n value: 41.646\n - type: recall_at_5\n value: 51.941\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: 97.55129958960327\n - type: f1\n value: 97.43464802675416\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: 90.4719562243502\n - type: f1\n value: 70.76460034443902\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: 83.49024882313383\n - type: f1\n value: 81.44067057564666\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (zh-CN)\n type: mteb/amazon_massive_intent\n config: zh-CN\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 79.88231338264963\n - type: f1\n value: 77.13536609019927\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: 87.23268325487558\n - type: f1\n value: 86.36737921996752\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (zh-CN)\n type: mteb/amazon_massive_scenario\n config: zh-CN\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 84.50571620712844\n - type: f1\n value: 83.4128768262944\n - task:\n type: Retrieval\n dataset:\n name: MTEB MedicalRetrieval\n type: C-MTEB/MedicalRetrieval\n config: default\n split: dev\n revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6\n metrics:\n - type: map_at_1\n value: 56.89999999999999\n - type: map_at_10\n value: 63.438\n - type: map_at_100\n value: 63.956\n - type: map_at_1000\n value: 63.991\n - type: map_at_3\n value: 61.983\n - type: map_at_5\n value: 62.778\n - type: mrr_at_1\n value: 56.99999999999999\n - type: mrr_at_10\n value: 63.483000000000004\n - type: mrr_at_100\n value: 63.993\n - type: mrr_at_1000\n value: 64.02799999999999\n - type: mrr_at_3\n value: 62.017\n - type: mrr_at_5\n value: 62.812\n - type: ndcg_at_1\n value: 56.89999999999999\n - type: ndcg_at_10\n value: 66.61\n - type: ndcg_at_100\n value: 69.387\n - type: ndcg_at_1000\n value: 70.327\n - type: ndcg_at_3\n value: 63.583999999999996\n - type: ndcg_at_5\n value: 65.0\n - type: precision_at_1\n value: 56.89999999999999\n - type: precision_at_10\n value: 7.66\n - type: precision_at_100\n value: 0.902\n - type: precision_at_1000\n value: 0.098\n - type: precision_at_3\n value: 22.733\n - type: precision_at_5\n value: 14.32\n - type: recall_at_1\n value: 56.89999999999999\n - type: recall_at_10\n value: 76.6\n - type: recall_at_100\n value: 90.2\n - type: recall_at_1000\n value: 97.6\n - type: recall_at_3\n value: 68.2\n - type: recall_at_5\n value: 71.6\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: 40.32149153753394\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: 39.40319973495386\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: 33.9769104898534\n - type: mrr\n value: 35.32831430710564\n - task:\n type: Classification\n dataset:\n name: MTEB MultilingualSentiment\n type: C-MTEB/MultilingualSentiment-classification\n config: default\n split: validation\n revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a\n metrics:\n - type: accuracy\n value: 81.80666666666667\n - type: f1\n value: 81.83278699395508\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: 6.3\n - type: map_at_10\n value: 14.151\n - type: map_at_100\n value: 18.455\n - type: map_at_1000\n value: 20.186999999999998\n - type: map_at_3\n value: 10.023\n - type: map_at_5\n value: 11.736\n - type: mrr_at_1\n value: 49.536\n - type: mrr_at_10\n value: 58.516\n - type: mrr_at_100\n value: 59.084\n - type: mrr_at_1000\n value: 59.114\n - type: mrr_at_3\n value: 56.45\n - type: mrr_at_5\n value: 57.642\n - type: ndcg_at_1\n value: 47.522999999999996\n - type: ndcg_at_10\n value: 38.4\n - type: ndcg_at_100\n value: 35.839999999999996\n - type: ndcg_at_1000\n value: 44.998\n - type: ndcg_at_3\n value: 43.221\n - type: ndcg_at_5\n value: 40.784\n - type: precision_at_1\n value: 49.536\n - type: precision_at_10\n value: 28.977999999999998\n - type: precision_at_100\n value: 9.378\n - type: precision_at_1000\n value: 2.2769999999999997\n - type: precision_at_3\n value: 40.454\n - type: precision_at_5\n value: 35.418\n - type: recall_at_1\n value: 6.3\n - type: recall_at_10\n value: 19.085\n - type: recall_at_100\n value: 38.18\n - type: recall_at_1000\n value: 71.219\n - type: recall_at_3\n value: 11.17\n - type: recall_at_5\n value: 13.975999999999999\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: 43.262\n - type: map_at_10\n value: 60.387\n - type: map_at_100\n value: 61.102000000000004\n - type: map_at_1000\n value: 61.111000000000004\n - type: map_at_3\n value: 56.391999999999996\n - type: map_at_5\n value: 58.916000000000004\n - type: mrr_at_1\n value: 48.725\n - type: mrr_at_10\n value: 62.812999999999995\n - type: mrr_at_100\n value: 63.297000000000004\n - type: mrr_at_1000\n value: 63.304\n - type: mrr_at_3\n value: 59.955999999999996\n - type: mrr_at_5\n value: 61.785999999999994\n - type: ndcg_at_1\n value: 48.696\n - type: ndcg_at_10\n value: 67.743\n - type: ndcg_at_100\n value: 70.404\n - type: ndcg_at_1000\n value: 70.60600000000001\n - type: ndcg_at_3\n value: 60.712999999999994\n - type: ndcg_at_5\n value: 64.693\n - type: precision_at_1\n value: 48.696\n - type: precision_at_10\n value: 10.513\n - type: precision_at_100\n value: 1.196\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 27.221\n - type: precision_at_5\n value: 18.701999999999998\n - type: recall_at_1\n value: 43.262\n - type: recall_at_10\n value: 87.35300000000001\n - type: recall_at_100\n value: 98.31299999999999\n - type: recall_at_1000\n value: 99.797\n - type: recall_at_3\n value: 69.643\n - type: recall_at_5\n value: 78.645\n - task:\n type: PairClassification\n dataset:\n name: MTEB Ocnli\n type: C-MTEB/OCNLI\n config: default\n split: validation\n revision: 66e76a618a34d6d565d5538088562851e6daa7ec\n metrics:\n - type: cos_sim_accuracy\n value: 72.65836491608013\n - type: cos_sim_ap\n value: 78.75807247519593\n - type: cos_sim_f1\n value: 74.84662576687117\n - type: cos_sim_precision\n value: 63.97003745318352\n - type: cos_sim_recall\n value: 90.17951425554382\n - type: dot_accuracy\n value: 72.65836491608013\n - type: dot_ap\n value: 78.75807247519593\n - type: dot_f1\n value: 74.84662576687117\n - type: dot_precision\n value: 63.97003745318352\n - type: dot_recall\n value: 90.17951425554382\n - type: euclidean_accuracy\n value: 72.65836491608013\n - type: euclidean_ap\n value: 78.75807247519593\n - type: euclidean_f1\n value: 74.84662576687117\n - type: euclidean_precision\n value: 63.97003745318352\n - type: euclidean_recall\n value: 90.17951425554382\n - type: manhattan_accuracy\n value: 72.00866269626421\n - type: manhattan_ap\n value: 78.34663376353235\n - type: manhattan_f1\n value: 74.13234613604813\n - type: manhattan_precision\n value: 65.98023064250413\n - type: manhattan_recall\n value: 84.58289334741288\n - type: max_accuracy\n value: 72.65836491608013\n - type: max_ap\n value: 78.75807247519593\n - type: max_f1\n value: 74.84662576687117\n - task:\n type: Classification\n dataset:\n name: MTEB OnlineShopping\n type: C-MTEB/OnlineShopping-classification\n config: default\n split: test\n revision: e610f2ebd179a8fda30ae534c3878750a96db120\n metrics:\n - type: accuracy\n value: 94.46999999999998\n - type: ap\n value: 93.56401511160975\n - type: f1\n value: 94.46692790889986\n - task:\n type: STS\n dataset:\n name: MTEB PAWSX\n type: C-MTEB/PAWSX\n config: default\n split: test\n revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1\n metrics:\n - type: cos_sim_pearson\n value: 46.851404503762474\n - type: cos_sim_spearman\n value: 52.74603680597415\n - type: euclidean_pearson\n value: 51.596358967977295\n - type: euclidean_spearman\n value: 52.74603680597415\n - type: manhattan_pearson\n value: 51.81838023379299\n - type: manhattan_spearman\n value: 52.79611669731429\n - task:\n type: STS\n dataset:\n name: MTEB QBQTC\n type: C-MTEB/QBQTC\n config: default\n split: test\n revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7\n metrics:\n - type: cos_sim_pearson\n value: 31.928376136347016\n - type: cos_sim_spearman\n value: 34.38497204533162\n - type: euclidean_pearson\n value: 32.658432953090674\n - type: euclidean_spearman\n value: 34.38497204533162\n - type: manhattan_pearson\n value: 32.887190283203054\n - type: manhattan_spearman\n value: 34.69496960849327\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: mteb/quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 69.952\n - type: map_at_10\n value: 84.134\n - type: map_at_100\n value: 84.795\n - type: map_at_1000\n value: 84.809\n - type: map_at_3\n value: 81.085\n - type: map_at_5\n value: 82.976\n - type: mrr_at_1\n value: 80.56\n - type: mrr_at_10\n value: 87.105\n - type: mrr_at_100\n value: 87.20700000000001\n - type: mrr_at_1000\n value: 87.208\n - type: mrr_at_3\n value: 86.118\n - type: mrr_at_5\n value: 86.79299999999999\n - type: ndcg_at_1\n value: 80.57\n - type: ndcg_at_10\n value: 88.047\n - type: ndcg_at_100\n value: 89.266\n - type: ndcg_at_1000\n value: 89.34299999999999\n - type: ndcg_at_3\n value: 85.052\n - type: ndcg_at_5\n value: 86.68299999999999\n - type: precision_at_1\n value: 80.57\n - type: precision_at_10\n value: 13.439\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: 37.283\n - type: precision_at_5\n value: 24.558\n - type: recall_at_1\n value: 69.952\n - type: recall_at_10\n value: 95.599\n - type: recall_at_100\n value: 99.67099999999999\n - type: recall_at_1000\n value: 99.983\n - type: recall_at_3\n value: 87.095\n - type: recall_at_5\n value: 91.668\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: 70.12802769698337\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: 71.19047621740276\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: mteb/scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.208\n - type: map_at_10\n value: 17.036\n - type: map_at_100\n value: 20.162\n - type: map_at_1000\n value: 20.552\n - type: map_at_3\n value: 11.591999999999999\n - type: map_at_5\n value: 14.349\n - type: mrr_at_1\n value: 30.599999999999998\n - type: mrr_at_10\n value: 43.325\n - type: mrr_at_100\n value: 44.281\n - type: mrr_at_1000\n value: 44.31\n - type: mrr_at_3\n value: 39.300000000000004\n - type: mrr_at_5\n value: 41.730000000000004\n - type: ndcg_at_1\n value: 30.599999999999998\n - type: ndcg_at_10\n value: 27.378000000000004\n - type: ndcg_at_100\n value: 37.768\n - type: ndcg_at_1000\n value: 43.275000000000006\n - type: ndcg_at_3\n value: 25.167\n - type: ndcg_at_5\n value: 22.537\n - type: precision_at_1\n value: 30.599999999999998\n - type: precision_at_10\n value: 14.46\n - type: precision_at_100\n value: 2.937\n - type: precision_at_1000\n value: 0.424\n - type: precision_at_3\n value: 23.666999999999998\n - type: precision_at_5\n value: 20.14\n - type: recall_at_1\n value: 6.208\n - type: recall_at_10\n value: 29.29\n - type: recall_at_100\n value: 59.565\n - type: recall_at_1000\n value: 85.963\n - type: recall_at_3\n value: 14.407\n - type: recall_at_5\n value: 20.412\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: 82.65489797062479\n - type: cos_sim_spearman\n value: 75.34808277034776\n - type: euclidean_pearson\n value: 79.28097508609059\n - type: euclidean_spearman\n value: 75.3480824481771\n - type: manhattan_pearson\n value: 78.83529262858895\n - type: manhattan_spearman\n value: 74.96318170787025\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: 85.06920163624117\n - type: cos_sim_spearman\n value: 77.24549887905519\n - type: euclidean_pearson\n value: 85.58740280635266\n - type: euclidean_spearman\n value: 77.24652170306867\n - type: manhattan_pearson\n value: 85.77917470895854\n - type: manhattan_spearman\n value: 77.54426264008778\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: 80.9762185094084\n - type: cos_sim_spearman\n value: 80.98090253728394\n - type: euclidean_pearson\n value: 80.88451512135202\n - type: euclidean_spearman\n value: 80.98090253728394\n - type: manhattan_pearson\n value: 80.7606664599805\n - type: manhattan_spearman\n value: 80.87197716950068\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: 81.91239166620251\n - type: cos_sim_spearman\n value: 76.36798509005328\n - type: euclidean_pearson\n value: 80.6393872615655\n - type: euclidean_spearman\n value: 76.36798836339655\n - type: manhattan_pearson\n value: 80.50765898709096\n - type: manhattan_spearman\n value: 76.31958999372227\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: 83.68800355225011\n - type: cos_sim_spearman\n value: 84.47549220803403\n - type: euclidean_pearson\n value: 83.86859896384159\n - type: euclidean_spearman\n value: 84.47551564954756\n - type: manhattan_pearson\n value: 83.74201103044383\n - type: manhattan_spearman\n value: 84.39903759718152\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: 78.24197302553398\n - type: cos_sim_spearman\n value: 79.44526946553684\n - type: euclidean_pearson\n value: 79.12747636563053\n - type: euclidean_spearman\n value: 79.44526946553684\n - type: manhattan_pearson\n value: 78.94407504115144\n - type: manhattan_spearman\n value: 79.24858249553934\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: 89.15329071763895\n - type: cos_sim_spearman\n value: 88.67251952242073\n - type: euclidean_pearson\n value: 89.16908249259637\n - type: euclidean_spearman\n value: 88.67251952242073\n - type: manhattan_pearson\n value: 89.1279735094785\n - type: manhattan_spearman\n value: 88.81731953658254\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: 69.44962535524695\n - type: cos_sim_spearman\n value: 71.75861316291065\n - type: euclidean_pearson\n value: 72.42347748883483\n - type: euclidean_spearman\n value: 71.75861316291065\n - type: manhattan_pearson\n value: 72.57545073534365\n - type: manhattan_spearman\n value: 71.90087671205625\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (zh)\n type: mteb/sts22-crosslingual-sts\n config: zh\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 68.9945443484093\n - type: cos_sim_spearman\n value: 71.46807157842791\n - type: euclidean_pearson\n value: 69.24911748374225\n - type: euclidean_spearman\n value: 69.46807157842791\n - type: manhattan_pearson\n value: 69.65580071876552\n - type: manhattan_spearman\n value: 69.68775795734852\n - task:\n type: STS\n dataset:\n name: MTEB STSB\n type: C-MTEB/STSB\n config: default\n split: test\n revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0\n metrics:\n - type: cos_sim_pearson\n value: 77.39283860361535\n - type: cos_sim_spearman\n value: 77.14577975930179\n - type: euclidean_pearson\n value: 76.64560889817044\n - type: euclidean_spearman\n value: 77.14577975930179\n - type: manhattan_pearson\n value: 76.82848456242104\n - type: manhattan_spearman\n value: 77.37708521460667\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: 84.14036697885552\n - type: cos_sim_spearman\n value: 83.10901632378086\n - type: euclidean_pearson\n value: 83.59991244380554\n - type: euclidean_spearman\n value: 83.10901632378086\n - type: manhattan_pearson\n value: 83.56632266895113\n - type: manhattan_spearman\n value: 83.17610542379353\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: 88.98026856845443\n - type: mrr\n value: 96.80987494712984\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: 41.661\n - type: map_at_10\n value: 55.492\n - type: map_at_100\n value: 56.237\n - type: map_at_1000\n value: 56.255\n - type: map_at_3\n value: 51.05\n - type: map_at_5\n value: 54.01200000000001\n - type: mrr_at_1\n value: 44.0\n - type: mrr_at_10\n value: 56.443\n - type: mrr_at_100\n value: 57.13700000000001\n - type: mrr_at_1000\n value: 57.152\n - type: mrr_at_3\n value: 52.944\n - type: mrr_at_5\n value: 55.37800000000001\n - type: ndcg_at_1\n value: 44.0\n - type: ndcg_at_10\n value: 62.312999999999995\n - type: ndcg_at_100\n value: 65.63900000000001\n - type: ndcg_at_1000\n value: 66.019\n - type: ndcg_at_3\n value: 54.67999999999999\n - type: ndcg_at_5\n value: 59.284000000000006\n - type: precision_at_1\n value: 44.0\n - type: precision_at_10\n value: 9.367\n - type: precision_at_100\n value: 1.0999999999999999\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 22.778000000000002\n - type: precision_at_5\n value: 16.467000000000002\n - type: recall_at_1\n value: 41.661\n - type: recall_at_10\n value: 82.306\n - type: recall_at_100\n value: 97.167\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 62.461\n - type: recall_at_5\n value: 73.411\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: 99.90693069306931\n - type: cos_sim_ap\n value: 97.86562522779887\n - type: cos_sim_f1\n value: 95.27162977867204\n - type: cos_sim_precision\n value: 95.8502024291498\n - type: cos_sim_recall\n value: 94.69999999999999\n - type: dot_accuracy\n value: 99.90693069306931\n - type: dot_ap\n value: 97.86562522779887\n - type: dot_f1\n value: 95.27162977867204\n - type: dot_precision\n value: 95.8502024291498\n - type: dot_recall\n value: 94.69999999999999\n - type: euclidean_accuracy\n value: 99.90693069306931\n - type: euclidean_ap\n value: 97.86562522779887\n - type: euclidean_f1\n value: 95.27162977867204\n - type: euclidean_precision\n value: 95.8502024291498\n - type: euclidean_recall\n value: 94.69999999999999\n - type: manhattan_accuracy\n value: 99.90693069306931\n - type: manhattan_ap\n value: 97.85527044211135\n - type: manhattan_f1\n value: 95.27638190954774\n - type: manhattan_precision\n value: 95.75757575757575\n - type: manhattan_recall\n value: 94.8\n - type: max_accuracy\n value: 99.90693069306931\n - type: max_ap\n value: 97.86562522779887\n - type: max_f1\n value: 95.27638190954774\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: 78.89230351770412\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: 47.52328347080355\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: 57.74702024461137\n - type: mrr\n value: 58.88074548001018\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: 30.047929797503592\n - type: cos_sim_spearman\n value: 29.465371781983567\n - type: dot_pearson\n value: 30.047927690552335\n - type: dot_spearman\n value: 29.465371781983567\n - task:\n type: Reranking\n dataset:\n name: MTEB T2Reranking\n type: C-MTEB/T2Reranking\n config: default\n split: dev\n revision: 76631901a18387f85eaa53e5450019b87ad58ef9\n metrics:\n - type: map\n value: 66.54177017978034\n - type: mrr\n value: 76.76094292377299\n - task:\n type: Retrieval\n dataset:\n name: MTEB T2Retrieval\n type: C-MTEB/T2Retrieval\n config: default\n split: dev\n revision: 8731a845f1bf500a4f111cf1070785c793d10e64\n metrics:\n - type: map_at_1\n value: 28.608\n - type: map_at_10\n value: 81.266\n - type: map_at_100\n value: 84.714\n - type: map_at_1000\n value: 84.758\n - type: map_at_3\n value: 56.967\n - type: map_at_5\n value: 70.14\n - type: mrr_at_1\n value: 91.881\n - type: mrr_at_10\n value: 94.11699999999999\n - type: mrr_at_100\n value: 94.178\n - type: mrr_at_1000\n value: 94.181\n - type: mrr_at_3\n value: 93.772\n - type: mrr_at_5\n value: 93.997\n - type: ndcg_at_1\n value: 91.881\n - type: ndcg_at_10\n value: 87.954\n - type: ndcg_at_100\n value: 90.904\n - type: ndcg_at_1000\n value: 91.326\n - type: ndcg_at_3\n value: 88.838\n - type: ndcg_at_5\n value: 87.764\n - type: precision_at_1\n value: 91.881\n - type: precision_at_10\n value: 43.628\n - type: precision_at_100\n value: 5.082\n - type: precision_at_1000\n value: 0.518\n - type: precision_at_3\n value: 77.62400000000001\n - type: precision_at_5\n value: 65.269\n - type: recall_at_1\n value: 28.608\n - type: recall_at_10\n value: 87.06\n - type: recall_at_100\n value: 96.815\n - type: recall_at_1000\n value: 98.969\n - type: recall_at_3\n value: 58.506\n - type: recall_at_5\n value: 73.21600000000001\n - task:\n type: Classification\n dataset:\n name: MTEB TNews\n type: C-MTEB/TNews-classification\n config: default\n split: validation\n revision: 317f262bf1e6126357bbe89e875451e4b0938fe4\n metrics:\n - type: accuracy\n value: 56.691999999999986\n - type: f1\n value: 54.692084702788065\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: mteb/trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.181\n - type: map_at_10\n value: 1.2\n - type: map_at_100\n value: 6.078\n - type: map_at_1000\n value: 14.940000000000001\n - type: map_at_3\n value: 0.45599999999999996\n - type: map_at_5\n value: 0.692\n - type: mrr_at_1\n value: 66.0\n - type: mrr_at_10\n value: 75.819\n - type: mrr_at_100\n value: 76.168\n - type: mrr_at_1000\n value: 76.168\n - type: mrr_at_3\n value: 72.667\n - type: mrr_at_5\n value: 74.86699999999999\n - type: ndcg_at_1\n value: 59.0\n - type: ndcg_at_10\n value: 52.60399999999999\n - type: ndcg_at_100\n value: 38.049\n - type: ndcg_at_1000\n value: 38.576\n - type: ndcg_at_3\n value: 57.235\n - type: ndcg_at_5\n value: 56.147000000000006\n - type: precision_at_1\n value: 66.0\n - type: precision_at_10\n value: 55.2\n - type: precision_at_100\n value: 38.78\n - type: precision_at_1000\n value: 16.986\n - type: precision_at_3\n value: 62.666999999999994\n - type: precision_at_5\n value: 60.8\n - type: recall_at_1\n value: 0.181\n - type: recall_at_10\n value: 1.471\n - type: recall_at_100\n value: 9.748999999999999\n - type: recall_at_1000\n value: 37.667\n - type: recall_at_3\n value: 0.49300000000000005\n - type: recall_at_5\n value: 0.7979999999999999\n - task:\n type: Clustering\n dataset:\n name: MTEB ThuNewsClusteringP2P\n type: C-MTEB/ThuNewsClusteringP2P\n config: default\n split: test\n revision: 5798586b105c0434e4f0fe5e767abe619442cf93\n metrics:\n - type: v_measure\n value: 78.68783858143624\n - task:\n type: Clustering\n dataset:\n name: MTEB ThuNewsClusteringS2S\n type: C-MTEB/ThuNewsClusteringS2S\n config: default\n split: test\n revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d\n metrics:\n - type: v_measure\n value: 77.04148998956299\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: 1.936\n - type: map_at_10\n value: 8.942\n - type: map_at_100\n value: 14.475999999999999\n - type: map_at_1000\n value: 16.156000000000002\n - type: map_at_3\n value: 4.865\n - type: map_at_5\n value: 6.367000000000001\n - type: mrr_at_1\n value: 26.531\n - type: mrr_at_10\n value: 42.846000000000004\n - type: mrr_at_100\n value: 43.441\n - type: mrr_at_1000\n value: 43.441\n - type: mrr_at_3\n value: 36.735\n - type: mrr_at_5\n value: 40.510000000000005\n - type: ndcg_at_1\n value: 24.490000000000002\n - type: ndcg_at_10\n value: 23.262\n - type: ndcg_at_100\n value: 34.959\n - type: ndcg_at_1000\n value: 47.258\n - type: ndcg_at_3\n value: 25.27\n - type: ndcg_at_5\n value: 24.246000000000002\n - type: precision_at_1\n value: 26.531\n - type: precision_at_10\n value: 20.408\n - type: precision_at_100\n value: 7.306\n - type: precision_at_1000\n value: 1.541\n - type: precision_at_3\n value: 26.531\n - type: precision_at_5\n value: 24.082\n - type: recall_at_1\n value: 1.936\n - type: recall_at_10\n value: 15.712000000000002\n - type: recall_at_100\n value: 45.451\n - type: recall_at_1000\n value: 83.269\n - type: recall_at_3\n value: 6.442\n - type: recall_at_5\n value: 9.151\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: 86.564\n - type: ap\n value: 34.58766846081731\n - type: f1\n value: 72.32759831978161\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: 77.80418788907753\n - type: f1\n value: 78.1047638421972\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: 59.20888659980063\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: 85.45627943017226\n - type: cos_sim_ap\n value: 72.25550061847534\n - type: cos_sim_f1\n value: 66.0611487783037\n - type: cos_sim_precision\n value: 64.11720884032779\n - type: cos_sim_recall\n value: 68.12664907651715\n - type: dot_accuracy\n value: 85.45627943017226\n - type: dot_ap\n value: 72.25574305366213\n - type: dot_f1\n value: 66.0611487783037\n - type: dot_precision\n value: 64.11720884032779\n - type: dot_recall\n value: 68.12664907651715\n - type: euclidean_accuracy\n value: 85.45627943017226\n - type: euclidean_ap\n value: 72.2557084446673\n - type: euclidean_f1\n value: 66.0611487783037\n - type: euclidean_precision\n value: 64.11720884032779\n - type: euclidean_recall\n value: 68.12664907651715\n - type: manhattan_accuracy\n value: 85.32514752339513\n - type: manhattan_ap\n value: 71.52919143472248\n - type: manhattan_f1\n value: 65.60288251190322\n - type: manhattan_precision\n value: 64.02913840743531\n - type: manhattan_recall\n value: 67.25593667546174\n - type: max_accuracy\n value: 85.45627943017226\n - type: max_ap\n value: 72.25574305366213\n - type: max_f1\n value: 66.0611487783037\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: 88.34167733923235\n - type: cos_sim_ap\n value: 84.58587730660244\n - type: cos_sim_f1\n value: 77.14170010676287\n - type: cos_sim_precision\n value: 73.91181657848324\n - type: cos_sim_recall\n value: 80.66676932553126\n - type: dot_accuracy\n value: 88.34167733923235\n - type: dot_ap\n value: 84.58585083616217\n - type: dot_f1\n value: 77.14170010676287\n - type: dot_precision\n value: 73.91181657848324\n - type: dot_recall\n value: 80.66676932553126\n - type: euclidean_accuracy\n value: 88.34167733923235\n - type: euclidean_ap\n value: 84.5858781355044\n - type: euclidean_f1\n value: 77.14170010676287\n - type: euclidean_precision\n value: 73.91181657848324\n - type: euclidean_recall\n value: 80.66676932553126\n - type: manhattan_accuracy\n value: 88.28152287809989\n - type: manhattan_ap\n value: 84.53184837110165\n - type: manhattan_f1\n value: 77.13582823915313\n - type: manhattan_precision\n value: 74.76156069364161\n - type: manhattan_recall\n value: 79.66584539574993\n - type: max_accuracy\n value: 88.34167733923235\n - type: max_ap\n value: 84.5858781355044\n - type: max_f1\n value: 77.14170010676287\n - task:\n type: Retrieval\n dataset:\n name: MTEB VideoRetrieval\n type: C-MTEB/VideoRetrieval\n config: default\n split: dev\n revision: 58c2597a5943a2ba48f4668c3b90d796283c5639\n metrics:\n - type: map_at_1\n value: 66.10000000000001\n - type: map_at_10\n value: 75.238\n - type: map_at_100\n value: 75.559\n - type: map_at_1000\n value: 75.565\n - type: map_at_3\n value: 73.68299999999999\n - type: map_at_5\n value: 74.63300000000001\n - type: mrr_at_1\n value: 66.10000000000001\n - type: mrr_at_10\n value: 75.238\n - type: mrr_at_100\n value: 75.559\n - type: mrr_at_1000\n value: 75.565\n - type: mrr_at_3\n value: 73.68299999999999\n - type: mrr_at_5\n value: 74.63300000000001\n - type: ndcg_at_1\n value: 66.10000000000001\n - type: ndcg_at_10\n value: 79.25999999999999\n - type: ndcg_at_100\n value: 80.719\n - type: ndcg_at_1000\n value: 80.862\n - type: ndcg_at_3\n value: 76.08200000000001\n - type: ndcg_at_5\n value: 77.782\n - type: precision_at_1\n value: 66.10000000000001\n - type: precision_at_10\n value: 9.17\n - type: precision_at_100\n value: 0.983\n - type: precision_at_1000\n value: 0.099\n - type: precision_at_3\n value: 27.667\n - type: precision_at_5\n value: 17.419999999999998\n - type: recall_at_1\n value: 66.10000000000001\n - type: recall_at_10\n value: 91.7\n - type: recall_at_100\n value: 98.3\n - type: recall_at_1000\n value: 99.4\n - type: recall_at_3\n value: 83.0\n - type: recall_at_5\n value: 87.1\n - task:\n type: Classification\n dataset:\n name: MTEB Waimai\n type: C-MTEB/waimai-classification\n config: default\n split: test\n revision: 339287def212450dcaa9df8c22bf93e9980c7023\n metrics:\n - type: accuracy\n value: 91.13\n - type: ap\n value: 79.55231335947015\n - type: f1\n value: 89.63091922203914\n---\n\n\n ![\"GME](\"https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/raw/main/images/gme_logo.png\"){kind=logo}
\n
GME: General Multimodal Embedding
\n\n## gme-Qwen2-VL-7B\n\nWe are excited to present `GME-Qwen2VL` series of unified **multimodal embedding models**,\nwhich are based on the advanced [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) multimodal large language models (MLLMs).\n\nThe `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.\n\n**Key Enhancements of GME Models**:\n\n- **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.\n- **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**).\n- **Dynamic Image Resolution**: Benefiting from `Qwen2-VL` and our training data, GME models support dynamic resolution image input.\n- **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.\n This capability is particularly beneficial for complex document understanding scenarios,\n such as multimodal retrieval-augmented generation (RAG) applications focused on academic papers.\n\n**Developed by**: Tongyi Lab, Alibaba Group\n\n**Paper**: [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](http://arxiv.org/abs/2412.16855)\n\n\n## Model List\n| Models | Model Size | Max Seq. Length | Dimension | MTEB-en| MTEB-zh | UMRB |\n|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |\n|[`gme-Qwen2-VL-2B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct) | 2.21B | 32768 | 1536 | 65.27 | 68.41 | 64.45 |\n|[`gme-Qwen2-VL-7B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | 67.48 | 71.36 | 67.44 |\n\n## Usage \n**Use with custom code**\n\n```python\n# You can find the script gme_inference.py in https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/blob/main/gme_inference.py\nfrom gme_inference import GmeQwen2VL\n\nmodel = GmeQwen2VL('Alibaba-NLP/gme-Qwen2-VL-7B-Instruct')\n\ntexts = [\n \"What kind of car is this?\",\n \"The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023.\"\n]\nimages = [\n 'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',\n 'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',\n]\n\n# Single-modal embedding\ne_text = gme.get_text_embeddings(texts=texts)\ne_image = gme.get_image_embeddings(images=images)\nprint((e_text * e_image).sum(-1))\n## tensor([0.1702, 0.5278], dtype=torch.float16)\n\n# How to set embedding instruction\ne_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')\n# If is_query=False, we always use the default instruction.\ne_corpus = gme.get_image_embeddings(images=images, is_query=False)\nprint((e_query * e_corpus).sum(-1))\n## tensor([0.2000, 0.5752], dtype=torch.float16)\n\n# Fused-modal embedding\ne_fused = gme.get_fused_embeddings(texts=texts, images=images)\nprint((e_fused[0] * e_fused[1]).sum())\n## tensor(0.6826, dtype=torch.float16)\n\n```\n\n\n\n## Evaluation\n\nWe validated the performance on our universal multimodal retrieval benchmark (**UMRB**) among others.\n\n| | | Single-modal | | Cross-modal | | | Fused-modal | | | | Avg. |\n|--------------------|------|:------------:|:---------:|:-----------:|:-----------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|:----------:|\n| | | 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) |\n| VISTA | 0.2B | 55.15 | **31.98** | 32.88 | 10.12 | 31.23 | 45.81 | 53.32 | 8.97 | 26.26 | 37.32 |\n| CLIP-SF | 0.4B | 39.75 | 31.42 | 59.05 | 24.09 | 62.95 | 66.41 | 53.32 | 34.9 | 55.65 | 43.66 |\n| One-Peace | 4B | 43.54 | 31.27 | 61.38 | 42.9 | 65.59 | 42.72 | 28.29 | 6.73 | 23.41 | 42.01 |\n| DSE | 4.2B | 48.94 | 27.92 | 40.75 | 78.21 | 52.54 | 49.62 | 35.44 | 8.36 | 40.18 | 50.04 |\n| E5-V | 8.4B | 52.41 | 27.36 | 46.56 | 41.22 | 47.95 | 54.13 | 32.9 | 23.17 | 7.23 | 42.52 |\n| **[GME-Qwen2-VL-2B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct)** | 2.2B | 55.93 | 29.86\t| 57.36\t| 87.84\t| 61.93 |\t76.47\t| 64.58\t | 37.02\t| 66.47 | 64.45 |\n| **[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** |\n\nThe [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) English tab shows the text embeddings performence of our model.\n\n**More detailed experimental results can be found in the [paper](http://arxiv.org/abs/2412.16855)**.\n\n## Community support\n\n### Fine-tuning\n\nGME models can be fine-tuned by SWIFT：\n\n```shell\npip install ms-swift -U\n```\n\n```shell\n# MAX_PIXELS settings to reduce memory usage\n# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html\nnproc_per_node=8\nMAX_PIXELS=1003520 \\\nUSE_HF=1 \\\nNPROC_PER_NODE=$nproc_per_node \\\nswift sft \\\n --model Alibaba-NLP/gme-Qwen2-VL-7B-Instruct \\\n --train_type lora \\\n --dataset 'HuggingFaceM4/TextCaps:emb' \\\n --torch_dtype bfloat16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --per_device_eval_batch_size 2 \\\n --gradient_accumulation_steps $(expr 64 / $nproc_per_node) \\\n --eval_steps 100 \\\n --save_steps 100 \\\n --eval_strategy steps \\\n --save_total_limit 5 \\\n --logging_steps 5 \\\n --output_dir output \\\n --lazy_tokenize true \\\n --warmup_ratio 0.05 \\\n --learning_rate 5e-6 \\\n --deepspeed zero3 \\\n --dataloader_num_workers 4 \\\n --task_type embedding \\\n --loss_type infonce \\\n --dataloader_drop_last true\n```\n\n## Limitations\n\n- **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.\nDue to the lack of relevant data, our models and evaluations retain one single image.\n- **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.\n\nWe will extend to multi-image input, image-text interleaved data as well as multilingual data in the future version.\n\n\n## Redistribution and Use\n\nWe encourage and value diverse applications of GME models and continuous enhancements to the models themselves.\n\n- 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.\n\n- 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`.\n\n## Cloud API Services\n\nIn 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.\n\n- [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.\n\nNote that the models behind the commercial APIs are not entirely identical to the open-source models.\n\n## Hiring\n\nWe have open positions for Research Interns and Full-Time Researchers to join our team at Tongyi Lab. \nWe are seeking passionate individuals with expertise in representation learning, LLM-driven information retrieval, Retrieval-Augmented Generation (RAG), and agent-based systems. \nOur 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.\nIf 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 dingkun.ldk@alibaba-inc.com.\n\n\n## Citation\nIf you find our paper or models helpful, please consider cite:\n\n```\n@misc{zhang2024gme,\n title={GME: Improving Universal Multimodal Retrieval by Multimodal LLMs}, \n 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},\n year={2024},\n eprint={2412.16855},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={http://arxiv.org/abs/2412.16855}, \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":189,"cells":{"id":{"kind":"string","value":"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse"},"author":{"kind":"string","value":"McGill-NLP"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["peft","safetensors","text-embedding","embeddings","information-retrieval","beir","text-classification","language-model","text-clustering","text-semantic-similarity","text-evaluation","text-reranking","feature-extraction","sentence-similarity","Sentence Similarity","natural_questions","ms_marco","fever","hotpot_qa","mteb","en","arxiv:2404.05961","license:mit","model-index","region:us"],"string":"[\n \"peft\",\n \"safetensors\",\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 \"text-reranking\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"Sentence Similarity\",\n \"natural_questions\",\n \"ms_marco\",\n \"fever\",\n \"hotpot_qa\",\n \"mteb\",\n \"en\",\n \"arxiv:2404.05961\",\n \"license:mit\",\n \"model-index\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-04-30T02:45:32","string":"2024-04-30T02:45:32"},"last_modified":{"kind":"timestamp","value":"2024-04-30T03:42:49","string":"2024-04-30T03:42:49"},"downloads":{"kind":"number","value":2287,"string":"2,287"},"likes":{"kind":"number","value":4,"string":"4"},"README":{"kind":"string","value":"---\nlanguage:\n- en\nlibrary_name: peft\nlicense: mit\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- text-reranking\n- feature-extraction\n- sentence-similarity\n- Sentence Similarity\n- natural_questions\n- ms_marco\n- fever\n- hotpot_qa\n- mteb\nmodel-index:\n- name: LLM2Vec-Meta-Llama-3-unsupervised\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: 75.70149253731343\n - type: ap\n value: 40.824269118508354\n - type: f1\n value: 70.55918234479084\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: 80.6812\n - type: ap\n value: 76.63327889516552\n - type: f1\n value: 80.5276613226382\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: 40.002\n - type: f1\n value: 39.67277678335084\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: 26.173999999999996\n - type: map_at_10\n value: 42.548\n - type: map_at_100\n value: 43.492999999999995\n - type: map_at_1000\n value: 43.5\n - type: map_at_3\n value: 37.376\n - type: map_at_5\n value: 40.359\n - type: mrr_at_1\n value: 27.24\n - type: mrr_at_10\n value: 42.945\n - type: mrr_at_100\n value: 43.89\n - type: mrr_at_1000\n value: 43.897000000000006\n - type: mrr_at_3\n value: 37.779\n - type: mrr_at_5\n value: 40.755\n - type: ndcg_at_1\n value: 26.173999999999996\n - type: ndcg_at_10\n value: 51.731\n - type: ndcg_at_100\n value: 55.684999999999995\n - type: ndcg_at_1000\n value: 55.86\n - type: ndcg_at_3\n value: 41.122\n - type: ndcg_at_5\n value: 46.491\n - type: precision_at_1\n value: 26.173999999999996\n - type: precision_at_10\n value: 8.108\n - type: precision_at_100\n value: 0.9820000000000001\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 17.330000000000002\n - type: precision_at_5\n value: 13.001\n - type: recall_at_1\n value: 26.173999999999996\n - type: recall_at_10\n value: 81.081\n - type: recall_at_100\n value: 98.222\n - type: recall_at_1000\n value: 99.57300000000001\n - type: recall_at_3\n value: 51.991\n - type: recall_at_5\n value: 65.007\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: 49.215974795578546\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: 41.71067780141813\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: 57.15639347603191\n - type: mrr\n value: 71.4509959108297\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: 84.67361609277127\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: 84.76623376623375\n - type: f1\n value: 84.70041172334481\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: 38.39251163108548\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: 31.30501371807517\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: cqadupstack/android\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 26.409\n - type: map_at_10\n value: 36.925000000000004\n - type: map_at_100\n value: 38.651\n - type: map_at_1000\n value: 38.798\n - type: map_at_3\n value: 33.437\n - type: map_at_5\n value: 35.506\n - type: mrr_at_1\n value: 33.763\n - type: mrr_at_10\n value: 43.442\n - type: mrr_at_100\n value: 44.339\n - type: mrr_at_1000\n value: 44.391000000000005\n - type: mrr_at_3\n value: 40.749\n - type: mrr_at_5\n value: 42.408\n - type: ndcg_at_1\n value: 33.763\n - type: ndcg_at_10\n value: 43.486999999999995\n - type: ndcg_at_100\n value: 49.71\n - type: ndcg_at_1000\n value: 51.81\n - type: ndcg_at_3\n value: 38.586\n - type: ndcg_at_5\n value: 41.074\n - type: precision_at_1\n value: 33.763\n - type: precision_at_10\n value: 8.798\n - type: precision_at_100\n value: 1.544\n - type: precision_at_1000\n value: 0.21\n - type: precision_at_3\n value: 19.361\n - type: precision_at_5\n value: 14.335\n - type: recall_at_1\n value: 26.409\n - type: recall_at_10\n value: 55.352999999999994\n - type: recall_at_100\n value: 81.66799999999999\n - type: recall_at_1000\n value: 95.376\n - type: recall_at_3\n value: 40.304\n - type: recall_at_5\n value: 47.782000000000004\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackEnglishRetrieval\n type: cqadupstack/english\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 26.6\n - type: map_at_10\n value: 36.42\n - type: map_at_100\n value: 37.628\n - type: map_at_1000\n value: 37.767\n - type: map_at_3\n value: 33.553\n - type: map_at_5\n value: 35.118\n - type: mrr_at_1\n value: 34.394999999999996\n - type: mrr_at_10\n value: 42.586\n - type: mrr_at_100\n value: 43.251\n - type: mrr_at_1000\n value: 43.303000000000004\n - type: mrr_at_3\n value: 40.297\n - type: mrr_at_5\n value: 41.638\n - type: ndcg_at_1\n value: 34.394999999999996\n - type: ndcg_at_10\n value: 42.05\n - type: ndcg_at_100\n value: 46.371\n - type: ndcg_at_1000\n value: 48.76\n - type: ndcg_at_3\n value: 37.936\n - type: ndcg_at_5\n value: 39.827\n - type: precision_at_1\n value: 34.394999999999996\n - type: precision_at_10\n value: 8.268\n - type: precision_at_100\n value: 1.355\n - type: precision_at_1000\n value: 0.186\n - type: precision_at_3\n value: 18.726000000000003\n - type: precision_at_5\n value: 13.541\n - type: recall_at_1\n value: 26.6\n - type: recall_at_10\n value: 51.529\n - type: recall_at_100\n value: 70.038\n - type: recall_at_1000\n value: 85.67\n - type: recall_at_3\n value: 39.448\n - type: recall_at_5\n value: 44.6\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGamingRetrieval\n type: cqadupstack/gaming\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 31.863000000000003\n - type: map_at_10\n value: 43.733\n - type: map_at_100\n value: 45.005\n - type: map_at_1000\n value: 45.074\n - type: map_at_3\n value: 40.593\n - type: map_at_5\n value: 42.272\n - type: mrr_at_1\n value: 37.555\n - type: mrr_at_10\n value: 47.532999999999994\n - type: mrr_at_100\n value: 48.431999999999995\n - type: mrr_at_1000\n value: 48.47\n - type: mrr_at_3\n value: 44.901\n - type: mrr_at_5\n value: 46.274\n - type: ndcg_at_1\n value: 37.555\n - type: ndcg_at_10\n value: 49.789\n - type: ndcg_at_100\n value: 55.059999999999995\n - type: ndcg_at_1000\n value: 56.434\n - type: ndcg_at_3\n value: 44.238\n - type: ndcg_at_5\n value: 46.698\n - type: precision_at_1\n value: 37.555\n - type: precision_at_10\n value: 8.257\n - type: precision_at_100\n value: 1.189\n - type: precision_at_1000\n value: 0.136\n - type: precision_at_3\n value: 20.23\n - type: precision_at_5\n value: 13.868\n - type: recall_at_1\n value: 31.863000000000003\n - type: recall_at_10\n value: 64.188\n - type: recall_at_100\n value: 87.02600000000001\n - type: recall_at_1000\n value: 96.761\n - type: recall_at_3\n value: 48.986000000000004\n - type: recall_at_5\n value: 55.177\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGisRetrieval\n type: cqadupstack/gis\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 15.964\n - type: map_at_10\n value: 22.746\n - type: map_at_100\n value: 23.704\n - type: map_at_1000\n value: 23.82\n - type: map_at_3\n value: 20.5\n - type: map_at_5\n value: 21.836\n - type: mrr_at_1\n value: 17.740000000000002\n - type: mrr_at_10\n value: 24.634\n - type: mrr_at_100\n value: 25.535999999999998\n - type: mrr_at_1000\n value: 25.628\n - type: mrr_at_3\n value: 22.429\n - type: mrr_at_5\n value: 23.791\n - type: ndcg_at_1\n value: 17.740000000000002\n - type: ndcg_at_10\n value: 26.838\n - type: ndcg_at_100\n value: 31.985000000000003\n - type: ndcg_at_1000\n value: 35.289\n - type: ndcg_at_3\n value: 22.384\n - type: ndcg_at_5\n value: 24.726\n - type: precision_at_1\n value: 17.740000000000002\n - type: precision_at_10\n value: 4.35\n - type: precision_at_100\n value: 0.753\n - type: precision_at_1000\n value: 0.108\n - type: precision_at_3\n value: 9.754999999999999\n - type: precision_at_5\n value: 7.164\n - type: recall_at_1\n value: 15.964\n - type: recall_at_10\n value: 37.705\n - type: recall_at_100\n value: 61.94499999999999\n - type: recall_at_1000\n value: 87.646\n - type: recall_at_3\n value: 25.714\n - type: recall_at_5\n value: 31.402\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackMathematicaRetrieval\n type: cqadupstack/mathematica\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 9.221\n - type: map_at_10\n value: 14.735000000000001\n - type: map_at_100\n value: 15.778\n - type: map_at_1000\n value: 15.9\n - type: map_at_3\n value: 12.791\n - type: map_at_5\n value: 13.703999999999999\n - type: mrr_at_1\n value: 12.438\n - type: mrr_at_10\n value: 18.353\n - type: mrr_at_100\n value: 19.285\n - type: mrr_at_1000\n value: 19.375\n - type: mrr_at_3\n value: 16.439\n - type: mrr_at_5\n value: 17.352999999999998\n - type: ndcg_at_1\n value: 12.438\n - type: ndcg_at_10\n value: 18.703\n - type: ndcg_at_100\n value: 24.104999999999997\n - type: ndcg_at_1000\n value: 27.366\n - type: ndcg_at_3\n value: 15.055\n - type: ndcg_at_5\n value: 16.42\n - type: precision_at_1\n value: 12.438\n - type: precision_at_10\n value: 3.818\n - type: precision_at_100\n value: 0.77\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 7.753\n - type: precision_at_5\n value: 5.622\n - type: recall_at_1\n value: 9.221\n - type: recall_at_10\n value: 27.461999999999996\n - type: recall_at_100\n value: 51.909000000000006\n - type: recall_at_1000\n value: 75.56\n - type: recall_at_3\n value: 17.046\n - type: recall_at_5\n value: 20.766000000000002\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackPhysicsRetrieval\n type: cqadupstack/physics\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 22.828\n - type: map_at_10\n value: 33.166000000000004\n - type: map_at_100\n value: 34.618\n - type: map_at_1000\n value: 34.744\n - type: map_at_3\n value: 29.737000000000002\n - type: map_at_5\n value: 31.541000000000004\n - type: mrr_at_1\n value: 29.548000000000002\n - type: mrr_at_10\n value: 38.582\n - type: mrr_at_100\n value: 39.527\n - type: mrr_at_1000\n value: 39.577\n - type: mrr_at_3\n value: 35.884\n - type: mrr_at_5\n value: 37.413999999999994\n - type: ndcg_at_1\n value: 29.548000000000002\n - type: ndcg_at_10\n value: 39.397\n - type: ndcg_at_100\n value: 45.584\n - type: ndcg_at_1000\n value: 47.823\n - type: ndcg_at_3\n value: 33.717000000000006\n - type: ndcg_at_5\n value: 36.223\n - type: precision_at_1\n value: 29.548000000000002\n - type: precision_at_10\n value: 7.767\n - type: precision_at_100\n value: 1.2959999999999998\n - type: precision_at_1000\n value: 0.17099999999999999\n - type: precision_at_3\n value: 16.747\n - type: precision_at_5\n value: 12.203999999999999\n - type: recall_at_1\n value: 22.828\n - type: recall_at_10\n value: 52.583999999999996\n - type: recall_at_100\n value: 79.06400000000001\n - type: recall_at_1000\n value: 93.59100000000001\n - type: recall_at_3\n value: 36.671\n - type: recall_at_5\n value: 43.22\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackProgrammersRetrieval\n type: cqadupstack/programmers\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 21.366\n - type: map_at_10\n value: 30.214000000000002\n - type: map_at_100\n value: 31.647\n - type: map_at_1000\n value: 31.763\n - type: map_at_3\n value: 27.234\n - type: map_at_5\n value: 28.801\n - type: mrr_at_1\n value: 26.256\n - type: mrr_at_10\n value: 35.299\n - type: mrr_at_100\n value: 36.284\n - type: mrr_at_1000\n value: 36.342\n - type: mrr_at_3\n value: 32.572\n - type: mrr_at_5\n value: 34.050999999999995\n - type: ndcg_at_1\n value: 26.256\n - type: ndcg_at_10\n value: 35.899\n - type: ndcg_at_100\n value: 41.983\n - type: ndcg_at_1000\n value: 44.481\n - type: ndcg_at_3\n value: 30.665\n - type: ndcg_at_5\n value: 32.879999999999995\n - type: precision_at_1\n value: 26.256\n - type: precision_at_10\n value: 6.804\n - type: precision_at_100\n value: 1.187\n - type: precision_at_1000\n value: 0.16\n - type: precision_at_3\n value: 14.84\n - type: precision_at_5\n value: 10.708\n - type: recall_at_1\n value: 21.366\n - type: recall_at_10\n value: 47.878\n - type: recall_at_100\n value: 73.245\n - type: recall_at_1000\n value: 90.623\n - type: recall_at_3\n value: 33.341\n - type: recall_at_5\n value: 39.198\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: mteb/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 19.477166666666665\n - type: map_at_10\n value: 27.431416666666664\n - type: map_at_100\n value: 28.656000000000002\n - type: map_at_1000\n value: 28.787583333333338\n - type: map_at_3\n value: 24.85175\n - type: map_at_5\n value: 26.270166666666668\n - type: mrr_at_1\n value: 24.06841666666667\n - type: mrr_at_10\n value: 31.620000000000005\n - type: mrr_at_100\n value: 32.52283333333333\n - type: mrr_at_1000\n value: 32.59441666666667\n - type: mrr_at_3\n value: 29.328666666666663\n - type: mrr_at_5\n value: 30.620416666666667\n - type: ndcg_at_1\n value: 24.06841666666667\n - type: ndcg_at_10\n value: 32.404583333333335\n - type: ndcg_at_100\n value: 37.779500000000006\n - type: ndcg_at_1000\n value: 40.511583333333334\n - type: ndcg_at_3\n value: 27.994166666666665\n - type: ndcg_at_5\n value: 30.021749999999997\n - type: precision_at_1\n value: 24.06841666666667\n - type: precision_at_10\n value: 6.03725\n - type: precision_at_100\n value: 1.0500833333333337\n - type: precision_at_1000\n value: 0.14875000000000002\n - type: precision_at_3\n value: 13.419583333333335\n - type: precision_at_5\n value: 9.700666666666665\n - type: recall_at_1\n value: 19.477166666666665\n - type: recall_at_10\n value: 42.99441666666667\n - type: recall_at_100\n value: 66.787\n - type: recall_at_1000\n value: 86.18825000000001\n - type: recall_at_3\n value: 30.46366666666667\n - type: recall_at_5\n value: 35.83141666666667\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackStatsRetrieval\n type: cqadupstack/stats\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 16.246\n - type: map_at_10\n value: 22.127\n - type: map_at_100\n value: 23.006\n - type: map_at_1000\n value: 23.125\n - type: map_at_3\n value: 20.308999999999997\n - type: map_at_5\n value: 21.139\n - type: mrr_at_1\n value: 19.631999999999998\n - type: mrr_at_10\n value: 24.884999999999998\n - type: mrr_at_100\n value: 25.704\n - type: mrr_at_1000\n value: 25.793\n - type: mrr_at_3\n value: 23.083000000000002\n - type: mrr_at_5\n value: 23.942\n - type: ndcg_at_1\n value: 19.631999999999998\n - type: ndcg_at_10\n value: 25.862000000000002\n - type: ndcg_at_100\n value: 30.436000000000003\n - type: ndcg_at_1000\n value: 33.638\n - type: ndcg_at_3\n value: 22.431\n - type: ndcg_at_5\n value: 23.677\n - type: precision_at_1\n value: 19.631999999999998\n - type: precision_at_10\n value: 4.417\n - type: precision_at_100\n value: 0.7270000000000001\n - type: precision_at_1000\n value: 0.109\n - type: precision_at_3\n value: 10.327\n - type: precision_at_5\n value: 7.147\n - type: recall_at_1\n value: 16.246\n - type: recall_at_10\n value: 34.869\n - type: recall_at_100\n value: 56.221\n - type: recall_at_1000\n value: 80.449\n - type: recall_at_3\n value: 24.83\n - type: recall_at_5\n value: 28.142\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackTexRetrieval\n type: cqadupstack/tex\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 9.798\n - type: map_at_10\n value: 14.695\n - type: map_at_100\n value: 15.590000000000002\n - type: map_at_1000\n value: 15.726999999999999\n - type: map_at_3\n value: 13.004999999999999\n - type: map_at_5\n value: 13.861\n - type: mrr_at_1\n value: 12.939\n - type: mrr_at_10\n value: 18.218\n - type: mrr_at_100\n value: 18.998\n - type: mrr_at_1000\n value: 19.093\n - type: mrr_at_3\n value: 16.454\n - type: mrr_at_5\n value: 17.354\n - type: ndcg_at_1\n value: 12.939\n - type: ndcg_at_10\n value: 18.278\n - type: ndcg_at_100\n value: 22.709\n - type: ndcg_at_1000\n value: 26.064\n - type: ndcg_at_3\n value: 15.204\n - type: ndcg_at_5\n value: 16.416\n - type: precision_at_1\n value: 12.939\n - type: precision_at_10\n value: 3.768\n - type: precision_at_100\n value: 0.724\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 7.707999999999999\n - type: precision_at_5\n value: 5.733\n - type: recall_at_1\n value: 9.798\n - type: recall_at_10\n value: 25.562\n - type: recall_at_100\n value: 45.678999999999995\n - type: recall_at_1000\n value: 69.963\n - type: recall_at_3\n value: 16.705000000000002\n - type: recall_at_5\n value: 19.969\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackUnixRetrieval\n type: cqadupstack/unix\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 19.1\n - type: map_at_10\n value: 27.034999999999997\n - type: map_at_100\n value: 28.396\n - type: map_at_1000\n value: 28.518\n - type: map_at_3\n value: 24.363\n - type: map_at_5\n value: 25.826999999999998\n - type: mrr_at_1\n value: 23.694000000000003\n - type: mrr_at_10\n value: 31.724999999999998\n - type: mrr_at_100\n value: 32.743\n - type: mrr_at_1000\n value: 32.82\n - type: mrr_at_3\n value: 29.275000000000002\n - type: mrr_at_5\n value: 30.684\n - type: ndcg_at_1\n value: 23.694000000000003\n - type: ndcg_at_10\n value: 32.366\n - type: ndcg_at_100\n value: 38.241\n - type: ndcg_at_1000\n value: 40.973\n - type: ndcg_at_3\n value: 27.661\n - type: ndcg_at_5\n value: 29.782999999999998\n - type: precision_at_1\n value: 23.694000000000003\n - type: precision_at_10\n value: 5.951\n - type: precision_at_100\n value: 1.0070000000000001\n - type: precision_at_1000\n value: 0.135\n - type: precision_at_3\n value: 13.34\n - type: precision_at_5\n value: 9.533999999999999\n - type: recall_at_1\n value: 19.1\n - type: recall_at_10\n value: 44.032\n - type: recall_at_100\n value: 69.186\n - type: recall_at_1000\n value: 88.562\n - type: recall_at_3\n value: 30.712\n - type: recall_at_5\n value: 36.372\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWebmastersRetrieval\n type: cqadupstack/webmasters\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 20.671\n - type: map_at_10\n value: 28.583\n - type: map_at_100\n value: 30.098999999999997\n - type: map_at_1000\n value: 30.364\n - type: map_at_3\n value: 25.825\n - type: map_at_5\n value: 27.500999999999998\n - type: mrr_at_1\n value: 25.889\n - type: mrr_at_10\n value: 33.617999999999995\n - type: mrr_at_100\n value: 34.687\n - type: mrr_at_1000\n value: 34.774\n - type: mrr_at_3\n value: 31.191999999999997\n - type: mrr_at_5\n value: 32.675\n - type: ndcg_at_1\n value: 25.889\n - type: ndcg_at_10\n value: 34.056999999999995\n - type: ndcg_at_100\n value: 40.142\n - type: ndcg_at_1000\n value: 43.614000000000004\n - type: ndcg_at_3\n value: 29.688\n - type: ndcg_at_5\n value: 32.057\n - type: precision_at_1\n value: 25.889\n - type: precision_at_10\n value: 6.7\n - type: precision_at_100\n value: 1.417\n - type: precision_at_1000\n value: 0.241\n - type: precision_at_3\n value: 14.360999999999999\n - type: precision_at_5\n value: 10.711\n - type: recall_at_1\n value: 20.671\n - type: recall_at_10\n value: 43.97\n - type: recall_at_100\n value: 71.83699999999999\n - type: recall_at_1000\n value: 94.42399999999999\n - type: recall_at_3\n value: 31.0\n - type: recall_at_5\n value: 37.489\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWordpressRetrieval\n type: cqadupstack/wordpress\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 13.66\n - type: map_at_10\n value: 18.798000000000002\n - type: map_at_100\n value: 19.75\n - type: map_at_1000\n value: 19.851\n - type: map_at_3\n value: 16.874\n - type: map_at_5\n value: 18.136\n - type: mrr_at_1\n value: 14.972\n - type: mrr_at_10\n value: 20.565\n - type: mrr_at_100\n value: 21.488\n - type: mrr_at_1000\n value: 21.567\n - type: mrr_at_3\n value: 18.669\n - type: mrr_at_5\n value: 19.861\n - type: ndcg_at_1\n value: 14.972\n - type: ndcg_at_10\n value: 22.128999999999998\n - type: ndcg_at_100\n value: 27.028000000000002\n - type: ndcg_at_1000\n value: 29.887000000000004\n - type: ndcg_at_3\n value: 18.365000000000002\n - type: ndcg_at_5\n value: 20.48\n - type: precision_at_1\n value: 14.972\n - type: precision_at_10\n value: 3.549\n - type: precision_at_100\n value: 0.632\n - type: precision_at_1000\n value: 0.093\n - type: precision_at_3\n value: 7.887\n - type: precision_at_5\n value: 5.840999999999999\n - type: recall_at_1\n value: 13.66\n - type: recall_at_10\n value: 30.801000000000002\n - type: recall_at_100\n value: 53.626\n - type: recall_at_1000\n value: 75.634\n - type: recall_at_3\n value: 20.807000000000002\n - type: recall_at_5\n value: 25.86\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: 8.622\n - type: map_at_10\n value: 16.042\n - type: map_at_100\n value: 18.023\n - type: map_at_1000\n value: 18.228\n - type: map_at_3\n value: 12.995999999999999\n - type: map_at_5\n value: 14.424000000000001\n - type: mrr_at_1\n value: 18.892999999999997\n - type: mrr_at_10\n value: 30.575000000000003\n - type: mrr_at_100\n value: 31.814999999999998\n - type: mrr_at_1000\n value: 31.856\n - type: mrr_at_3\n value: 26.851000000000003\n - type: mrr_at_5\n value: 29.021\n - type: ndcg_at_1\n value: 18.892999999999997\n - type: ndcg_at_10\n value: 23.575\n - type: ndcg_at_100\n value: 31.713\n - type: ndcg_at_1000\n value: 35.465\n - type: ndcg_at_3\n value: 18.167\n - type: ndcg_at_5\n value: 20.071\n - type: precision_at_1\n value: 18.892999999999997\n - type: precision_at_10\n value: 7.883\n - type: precision_at_100\n value: 1.652\n - type: precision_at_1000\n value: 0.23500000000000001\n - type: precision_at_3\n value: 13.898\n - type: precision_at_5\n value: 11.14\n - type: recall_at_1\n value: 8.622\n - type: recall_at_10\n value: 30.044999999999998\n - type: recall_at_100\n value: 58.072\n - type: recall_at_1000\n value: 79.226\n - type: recall_at_3\n value: 17.21\n - type: recall_at_5\n value: 22.249\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: 4.845\n - type: map_at_10\n value: 12.352\n - type: map_at_100\n value: 17.423\n - type: map_at_1000\n value: 18.529\n - type: map_at_3\n value: 8.505\n - type: map_at_5\n value: 10.213\n - type: mrr_at_1\n value: 41.75\n - type: mrr_at_10\n value: 54.6\n - type: mrr_at_100\n value: 55.345\n - type: mrr_at_1000\n value: 55.374\n - type: mrr_at_3\n value: 52.37500000000001\n - type: mrr_at_5\n value: 53.87499999999999\n - type: ndcg_at_1\n value: 31.25\n - type: ndcg_at_10\n value: 26.779999999999998\n - type: ndcg_at_100\n value: 31.929000000000002\n - type: ndcg_at_1000\n value: 39.290000000000006\n - type: ndcg_at_3\n value: 28.746\n - type: ndcg_at_5\n value: 27.334999999999997\n - type: precision_at_1\n value: 41.75\n - type: precision_at_10\n value: 22.55\n - type: precision_at_100\n value: 7.242\n - type: precision_at_1000\n value: 1.439\n - type: precision_at_3\n value: 33.833\n - type: precision_at_5\n value: 28.65\n - type: recall_at_1\n value: 4.845\n - type: recall_at_10\n value: 18.664\n - type: recall_at_100\n value: 41.085\n - type: recall_at_1000\n value: 65.242\n - type: recall_at_3\n value: 10.572\n - type: recall_at_5\n value: 13.961000000000002\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: 47.08\n - type: f1\n value: 42.843345856303756\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: 33.743\n - type: map_at_10\n value: 46.521\n - type: map_at_100\n value: 47.235\n - type: map_at_1000\n value: 47.272\n - type: map_at_3\n value: 43.252\n - type: map_at_5\n value: 45.267\n - type: mrr_at_1\n value: 36.484\n - type: mrr_at_10\n value: 49.406\n - type: mrr_at_100\n value: 50.03300000000001\n - type: mrr_at_1000\n value: 50.058\n - type: mrr_at_3\n value: 46.195\n - type: mrr_at_5\n value: 48.193999999999996\n - type: ndcg_at_1\n value: 36.484\n - type: ndcg_at_10\n value: 53.42\n - type: ndcg_at_100\n value: 56.69499999999999\n - type: ndcg_at_1000\n value: 57.623999999999995\n - type: ndcg_at_3\n value: 47.010999999999996\n - type: ndcg_at_5\n value: 50.524\n - type: precision_at_1\n value: 36.484\n - type: precision_at_10\n value: 7.925\n - type: precision_at_100\n value: 0.975\n - type: precision_at_1000\n value: 0.107\n - type: precision_at_3\n value: 19.967\n - type: precision_at_5\n value: 13.87\n - type: recall_at_1\n value: 33.743\n - type: recall_at_10\n value: 71.988\n - type: recall_at_100\n value: 86.60799999999999\n - type: recall_at_1000\n value: 93.54\n - type: recall_at_3\n value: 54.855\n - type: recall_at_5\n value: 63.341\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: 13.003\n - type: map_at_10\n value: 21.766\n - type: map_at_100\n value: 23.618\n - type: map_at_1000\n value: 23.832\n - type: map_at_3\n value: 18.282999999999998\n - type: map_at_5\n value: 20.267\n - type: mrr_at_1\n value: 26.851999999999997\n - type: mrr_at_10\n value: 34.658\n - type: mrr_at_100\n value: 35.729\n - type: mrr_at_1000\n value: 35.785\n - type: mrr_at_3\n value: 31.686999999999998\n - type: mrr_at_5\n value: 33.315\n - type: ndcg_at_1\n value: 26.851999999999997\n - type: ndcg_at_10\n value: 28.563\n - type: ndcg_at_100\n value: 36.374\n - type: ndcg_at_1000\n value: 40.306999999999995\n - type: ndcg_at_3\n value: 24.224\n - type: ndcg_at_5\n value: 25.939\n - type: precision_at_1\n value: 26.851999999999997\n - type: precision_at_10\n value: 8.193999999999999\n - type: precision_at_100\n value: 1.616\n - type: precision_at_1000\n value: 0.232\n - type: precision_at_3\n value: 16.255\n - type: precision_at_5\n value: 12.469\n - type: recall_at_1\n value: 13.003\n - type: recall_at_10\n value: 35.689\n - type: recall_at_100\n value: 65.762\n - type: recall_at_1000\n value: 89.546\n - type: recall_at_3\n value: 21.820999999999998\n - type: recall_at_5\n value: 28.097\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: 29.541\n - type: map_at_10\n value: 43.088\n - type: map_at_100\n value: 44.252\n - type: map_at_1000\n value: 44.345\n - type: map_at_3\n value: 39.79\n - type: map_at_5\n value: 41.687000000000005\n - type: mrr_at_1\n value: 59.082\n - type: mrr_at_10\n value: 67.27300000000001\n - type: mrr_at_100\n value: 67.708\n - type: mrr_at_1000\n value: 67.731\n - type: mrr_at_3\n value: 65.526\n - type: mrr_at_5\n value: 66.589\n - type: ndcg_at_1\n value: 59.082\n - type: ndcg_at_10\n value: 52.372\n - type: ndcg_at_100\n value: 56.725\n - type: ndcg_at_1000\n value: 58.665\n - type: ndcg_at_3\n value: 47.129\n - type: ndcg_at_5\n value: 49.808\n - type: precision_at_1\n value: 59.082\n - type: precision_at_10\n value: 11.275\n - type: precision_at_100\n value: 1.469\n - type: precision_at_1000\n value: 0.173\n - type: precision_at_3\n value: 29.773\n - type: precision_at_5\n value: 19.980999999999998\n - type: recall_at_1\n value: 29.541\n - type: recall_at_10\n value: 56.374\n - type: recall_at_100\n value: 73.42999999999999\n - type: recall_at_1000\n value: 86.28\n - type: recall_at_3\n value: 44.659\n - type: recall_at_5\n value: 49.952999999999996\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: 75.1904\n - type: ap\n value: 69.80555086826531\n - type: f1\n value: 74.93725389065787\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: 7.085\n - type: map_at_10\n value: 13.344000000000001\n - type: map_at_100\n value: 14.501\n - type: map_at_1000\n value: 14.605\n - type: map_at_3\n value: 10.758\n - type: map_at_5\n value: 12.162\n - type: mrr_at_1\n value: 7.278\n - type: mrr_at_10\n value: 13.607\n - type: mrr_at_100\n value: 14.761\n - type: mrr_at_1000\n value: 14.860000000000001\n - type: mrr_at_3\n value: 11.003\n - type: mrr_at_5\n value: 12.421\n - type: ndcg_at_1\n value: 7.278\n - type: ndcg_at_10\n value: 17.473\n - type: ndcg_at_100\n value: 23.721\n - type: ndcg_at_1000\n value: 26.69\n - type: ndcg_at_3\n value: 12.078\n - type: ndcg_at_5\n value: 14.62\n - type: precision_at_1\n value: 7.278\n - type: precision_at_10\n value: 3.175\n - type: precision_at_100\n value: 0.639\n - type: precision_at_1000\n value: 0.09\n - type: precision_at_3\n value: 5.382\n - type: precision_at_5\n value: 4.519\n - type: recall_at_1\n value: 7.085\n - type: recall_at_10\n value: 30.549\n - type: recall_at_100\n value: 60.919999999999995\n - type: recall_at_1000\n value: 84.372\n - type: recall_at_3\n value: 15.675\n - type: recall_at_5\n value: 21.818\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: 94.46876424988601\n - type: f1\n value: 94.23159241922738\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: 81.0875512995896\n - type: f1\n value: 61.674961674414\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: 75.01344989912575\n - type: f1\n value: 71.7942527839921\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: 79.15601882985877\n - type: f1\n value: 78.82502954601195\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: 31.468806971345227\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: 27.874332804382256\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: 30.099340785595842\n - type: mrr\n value: 31.077367694660257\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: 3.9050000000000002\n - type: map_at_10\n value: 8.931000000000001\n - type: map_at_100\n value: 11.246\n - type: map_at_1000\n value: 12.579\n - type: map_at_3\n value: 6.544\n - type: map_at_5\n value: 7.854\n - type: mrr_at_1\n value: 33.745999999999995\n - type: mrr_at_10\n value: 44.734\n - type: mrr_at_100\n value: 45.486\n - type: mrr_at_1000\n value: 45.534\n - type: mrr_at_3\n value: 42.157\n - type: mrr_at_5\n value: 43.813\n - type: ndcg_at_1\n value: 31.734\n - type: ndcg_at_10\n value: 26.284999999999997\n - type: ndcg_at_100\n value: 25.211\n - type: ndcg_at_1000\n value: 34.974\n - type: ndcg_at_3\n value: 29.918\n - type: ndcg_at_5\n value: 29.066\n - type: precision_at_1\n value: 33.745999999999995\n - type: precision_at_10\n value: 19.628\n - type: precision_at_100\n value: 6.476999999999999\n - type: precision_at_1000\n value: 1.976\n - type: precision_at_3\n value: 28.793000000000003\n - type: precision_at_5\n value: 25.759\n - type: recall_at_1\n value: 3.9050000000000002\n - type: recall_at_10\n value: 13.375\n - type: recall_at_100\n value: 28.453\n - type: recall_at_1000\n value: 61.67399999999999\n - type: recall_at_3\n value: 7.774\n - type: recall_at_5\n value: 10.754\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: 18.33\n - type: map_at_10\n value: 30.44\n - type: map_at_100\n value: 31.848\n - type: map_at_1000\n value: 31.906000000000002\n - type: map_at_3\n value: 26.143\n - type: map_at_5\n value: 28.583\n - type: mrr_at_1\n value: 21.031\n - type: mrr_at_10\n value: 33.028\n - type: mrr_at_100\n value: 34.166000000000004\n - type: mrr_at_1000\n value: 34.208\n - type: mrr_at_3\n value: 29.089\n - type: mrr_at_5\n value: 31.362000000000002\n - type: ndcg_at_1\n value: 21.031\n - type: ndcg_at_10\n value: 37.65\n - type: ndcg_at_100\n value: 43.945\n - type: ndcg_at_1000\n value: 45.338\n - type: ndcg_at_3\n value: 29.256999999999998\n - type: ndcg_at_5\n value: 33.453\n - type: precision_at_1\n value: 21.031\n - type: precision_at_10\n value: 6.8309999999999995\n - type: precision_at_100\n value: 1.035\n - type: precision_at_1000\n value: 0.117\n - type: precision_at_3\n value: 13.818\n - type: precision_at_5\n value: 10.649000000000001\n - type: recall_at_1\n value: 18.33\n - type: recall_at_10\n value: 57.330999999999996\n - type: recall_at_100\n value: 85.284\n - type: recall_at_1000\n value: 95.676\n - type: recall_at_3\n value: 35.356\n - type: recall_at_5\n value: 45.073\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: 66.373\n - type: map_at_10\n value: 80.233\n - type: map_at_100\n value: 80.973\n - type: map_at_1000\n value: 80.99499999999999\n - type: map_at_3\n value: 77.127\n - type: map_at_5\n value: 79.056\n - type: mrr_at_1\n value: 76.55\n - type: mrr_at_10\n value: 83.813\n - type: mrr_at_100\n value: 83.96900000000001\n - type: mrr_at_1000\n value: 83.97200000000001\n - type: mrr_at_3\n value: 82.547\n - type: mrr_at_5\n value: 83.38600000000001\n - type: ndcg_at_1\n value: 76.53999999999999\n - type: ndcg_at_10\n value: 84.638\n - type: ndcg_at_100\n value: 86.28099999999999\n - type: ndcg_at_1000\n value: 86.459\n - type: ndcg_at_3\n value: 81.19\n - type: ndcg_at_5\n value: 83.057\n - type: precision_at_1\n value: 76.53999999999999\n - type: precision_at_10\n value: 12.928999999999998\n - type: precision_at_100\n value: 1.514\n - type: precision_at_1000\n value: 0.156\n - type: precision_at_3\n value: 35.503\n - type: precision_at_5\n value: 23.512\n - type: recall_at_1\n value: 66.373\n - type: recall_at_10\n value: 93.273\n - type: recall_at_100\n value: 99.031\n - type: recall_at_1000\n value: 99.91799999999999\n - type: recall_at_3\n value: 83.55799999999999\n - type: recall_at_5\n value: 88.644\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: 43.67174666339103\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: 61.66838659211271\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: 2.318\n - type: map_at_10\n value: 5.938000000000001\n - type: map_at_100\n value: 7.582\n - type: map_at_1000\n value: 7.936\n - type: map_at_3\n value: 4.208\n - type: map_at_5\n value: 5.098\n - type: mrr_at_1\n value: 11.4\n - type: mrr_at_10\n value: 17.655\n - type: mrr_at_100\n value: 19.088\n - type: mrr_at_1000\n value: 19.203\n - type: mrr_at_3\n value: 15.25\n - type: mrr_at_5\n value: 16.535\n - type: ndcg_at_1\n value: 11.4\n - type: ndcg_at_10\n value: 10.388\n - type: ndcg_at_100\n value: 18.165\n - type: ndcg_at_1000\n value: 24.842\n - type: ndcg_at_3\n value: 9.414\n - type: ndcg_at_5\n value: 8.453\n - type: precision_at_1\n value: 11.4\n - type: precision_at_10\n value: 5.54\n - type: precision_at_100\n value: 1.71\n - type: precision_at_1000\n value: 0.33\n - type: precision_at_3\n value: 8.866999999999999\n - type: precision_at_5\n value: 7.580000000000001\n - type: recall_at_1\n value: 2.318\n - type: recall_at_10\n value: 11.267000000000001\n - type: recall_at_100\n value: 34.743\n - type: recall_at_1000\n value: 67.07300000000001\n - type: recall_at_3\n value: 5.408\n - type: recall_at_5\n value: 7.713\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: 72.15850185456762\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: 61.59518395985063\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: 79.71131323749228\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: 72.10974664733891\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: 82.17899407125657\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: 79.41138579273438\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: 85.44343473477939\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: 63.90264271389905\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: 77.44151296326804\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: 76.27597486396654\n - type: mrr\n value: 93.28127119793788\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: 49.594\n - type: map_at_10\n value: 60.951\n - type: map_at_100\n value: 61.68599999999999\n - type: map_at_1000\n value: 61.712\n - type: map_at_3\n value: 57.946\n - type: map_at_5\n value: 59.89\n - type: mrr_at_1\n value: 52.666999999999994\n - type: mrr_at_10\n value: 62.724000000000004\n - type: mrr_at_100\n value: 63.269\n - type: mrr_at_1000\n value: 63.291\n - type: mrr_at_3\n value: 60.167\n - type: mrr_at_5\n value: 61.95\n - type: ndcg_at_1\n value: 52.666999999999994\n - type: ndcg_at_10\n value: 66.35600000000001\n - type: ndcg_at_100\n value: 69.463\n - type: ndcg_at_1000\n value: 70.111\n - type: ndcg_at_3\n value: 60.901\n - type: ndcg_at_5\n value: 64.054\n - type: precision_at_1\n value: 52.666999999999994\n - type: precision_at_10\n value: 9.0\n - type: precision_at_100\n value: 1.073\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 24.221999999999998\n - type: precision_at_5\n value: 16.333000000000002\n - type: recall_at_1\n value: 49.594\n - type: recall_at_10\n value: 81.256\n - type: recall_at_100\n value: 94.989\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 66.706\n - type: recall_at_5\n value: 74.411\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: 99.65049504950495\n - type: cos_sim_ap\n value: 88.1421623503371\n - type: cos_sim_f1\n value: 81.44072036018008\n - type: cos_sim_precision\n value: 81.48148148148148\n - type: cos_sim_recall\n value: 81.39999999999999\n - type: dot_accuracy\n value: 99.37623762376238\n - type: dot_ap\n value: 69.87152032240303\n - type: dot_f1\n value: 65.64885496183206\n - type: dot_precision\n value: 72.18225419664267\n - type: dot_recall\n value: 60.199999999999996\n - type: euclidean_accuracy\n value: 99.63069306930693\n - type: euclidean_ap\n value: 86.13858297902517\n - type: euclidean_f1\n value: 79.87679671457904\n - type: euclidean_precision\n value: 82.0675105485232\n - type: euclidean_recall\n value: 77.8\n - type: manhattan_accuracy\n value: 99.63168316831683\n - type: manhattan_ap\n value: 86.31976532265482\n - type: manhattan_f1\n value: 80.10204081632654\n - type: manhattan_precision\n value: 81.77083333333334\n - type: manhattan_recall\n value: 78.5\n - type: max_accuracy\n value: 99.65049504950495\n - type: max_ap\n value: 88.1421623503371\n - type: max_f1\n value: 81.44072036018008\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: 68.19604139959692\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: 36.3569584557381\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: 48.82174503355024\n - type: mrr\n value: 49.610933388506915\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: 30.805895993742798\n - type: cos_sim_spearman\n value: 31.445431226826738\n - type: dot_pearson\n value: 24.441585432516867\n - type: dot_spearman\n value: 25.468117334810188\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: 0.2\n - type: map_at_10\n value: 1.431\n - type: map_at_100\n value: 7.138999999999999\n - type: map_at_1000\n value: 17.933\n - type: map_at_3\n value: 0.551\n - type: map_at_5\n value: 0.7979999999999999\n - type: mrr_at_1\n value: 76.0\n - type: mrr_at_10\n value: 85.167\n - type: mrr_at_100\n value: 85.21300000000001\n - type: mrr_at_1000\n value: 85.21300000000001\n - type: mrr_at_3\n value: 84.667\n - type: mrr_at_5\n value: 85.167\n - type: ndcg_at_1\n value: 72.0\n - type: ndcg_at_10\n value: 63.343\n - type: ndcg_at_100\n value: 45.739999999999995\n - type: ndcg_at_1000\n value: 41.875\n - type: ndcg_at_3\n value: 68.162\n - type: ndcg_at_5\n value: 65.666\n - type: precision_at_1\n value: 76.0\n - type: precision_at_10\n value: 66.4\n - type: precision_at_100\n value: 46.800000000000004\n - type: precision_at_1000\n value: 18.996\n - type: precision_at_3\n value: 72.667\n - type: precision_at_5\n value: 68.4\n - type: recall_at_1\n value: 0.2\n - type: recall_at_10\n value: 1.712\n - type: recall_at_100\n value: 10.896\n - type: recall_at_1000\n value: 40.115\n - type: recall_at_3\n value: 0.594\n - type: recall_at_5\n value: 0.889\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: 1.0619999999999998\n - type: map_at_10\n value: 5.611\n - type: map_at_100\n value: 8.841000000000001\n - type: map_at_1000\n value: 10.154\n - type: map_at_3\n value: 2.7720000000000002\n - type: map_at_5\n value: 4.181\n - type: mrr_at_1\n value: 14.285999999999998\n - type: mrr_at_10\n value: 26.249\n - type: mrr_at_100\n value: 28.046\n - type: mrr_at_1000\n value: 28.083000000000002\n - type: mrr_at_3\n value: 21.769\n - type: mrr_at_5\n value: 24.524\n - type: ndcg_at_1\n value: 11.224\n - type: ndcg_at_10\n value: 12.817\n - type: ndcg_at_100\n value: 23.183999999999997\n - type: ndcg_at_1000\n value: 35.099000000000004\n - type: ndcg_at_3\n value: 11.215\n - type: ndcg_at_5\n value: 12.016\n - type: precision_at_1\n value: 14.285999999999998\n - type: precision_at_10\n value: 12.653\n - type: precision_at_100\n value: 5.306\n - type: precision_at_1000\n value: 1.294\n - type: precision_at_3\n value: 13.605\n - type: precision_at_5\n value: 13.877999999999998\n - type: recall_at_1\n value: 1.0619999999999998\n - type: recall_at_10\n value: 10.377\n - type: recall_at_100\n value: 34.77\n - type: recall_at_1000\n value: 70.875\n - type: recall_at_3\n value: 3.688\n - type: recall_at_5\n value: 6.2509999999999994\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: 71.8488\n - type: ap\n value: 15.590122317097372\n - type: f1\n value: 55.86108396102662\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: 57.61460101867573\n - type: f1\n value: 57.8678726826158\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: 32.01459876897588\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: 84.1032365738809\n - type: cos_sim_ap\n value: 66.60137415520323\n - type: cos_sim_f1\n value: 62.12845010615712\n - type: cos_sim_precision\n value: 62.493326214628944\n - type: cos_sim_recall\n value: 61.76781002638523\n - type: dot_accuracy\n value: 81.85015199380103\n - type: dot_ap\n value: 58.854644211365084\n - type: dot_f1\n value: 56.15180082185158\n - type: dot_precision\n value: 51.806422836752894\n - type: dot_recall\n value: 61.2928759894459\n - type: euclidean_accuracy\n value: 83.6681170650295\n - type: euclidean_ap\n value: 64.93555585305603\n - type: euclidean_f1\n value: 61.02775195857125\n - type: euclidean_precision\n value: 61.42742582197273\n - type: euclidean_recall\n value: 60.633245382585756\n - type: manhattan_accuracy\n value: 83.73368301841808\n - type: manhattan_ap\n value: 65.45422483039611\n - type: manhattan_f1\n value: 61.58552806597499\n - type: manhattan_precision\n value: 62.09763948497854\n - type: manhattan_recall\n value: 61.08179419525066\n - type: max_accuracy\n value: 84.1032365738809\n - type: max_ap\n value: 66.60137415520323\n - type: max_f1\n value: 62.12845010615712\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: 86.36628245430201\n - type: cos_sim_ap\n value: 79.29963896460292\n - type: cos_sim_f1\n value: 72.63895990066467\n - type: cos_sim_precision\n value: 69.09128803668196\n - type: cos_sim_recall\n value: 76.57068062827224\n - type: dot_accuracy\n value: 84.65091007878294\n - type: dot_ap\n value: 75.04883449222972\n - type: dot_f1\n value: 69.18569117382708\n - type: dot_precision\n value: 64.89512376070682\n - type: dot_recall\n value: 74.08376963350786\n - type: euclidean_accuracy\n value: 85.88116583226608\n - type: euclidean_ap\n value: 78.42687640324908\n - type: euclidean_f1\n value: 71.74350111107192\n - type: euclidean_precision\n value: 66.19800820152314\n - type: euclidean_recall\n value: 78.3030489682784\n - type: manhattan_accuracy\n value: 86.27508052935926\n - type: manhattan_ap\n value: 79.29581298930101\n - type: manhattan_f1\n value: 72.51838235294117\n - type: manhattan_precision\n value: 67.03921568627452\n - type: manhattan_recall\n value: 78.97289805974745\n - type: max_accuracy\n value: 86.36628245430201\n - type: max_ap\n value: 79.29963896460292\n - type: max_f1\n value: 72.63895990066467\n---\n\n\n> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.\n- **Repository:** https://github.com/McGill-NLP/llm2vec\n- **Paper:** https://arxiv.org/abs/2404.05961\n\n\n## Installation\n```bash\npip install llm2vec\n```\n\n## Usage\n```python\nfrom llm2vec import LLM2Vec\n\nimport torch\nfrom transformers import AutoTokenizer, AutoModel, AutoConfig\nfrom peft import PeftModel\n\n# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.\ntokenizer = AutoTokenizer.from_pretrained(\n \"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp\"\n)\nconfig = AutoConfig.from_pretrained(\n \"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp\", trust_remote_code=True\n)\nmodel = AutoModel.from_pretrained(\n \"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp\",\n trust_remote_code=True,\n config=config,\n torch_dtype=torch.bfloat16,\n device_map=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n)\nmodel = PeftModel.from_pretrained(\n model,\n \"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp\",\n)\nmodel = model.merge_and_unload() # This can take several minutes on cpu\n\n# Loading unsupervised SimCSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + SimCSE (LoRA).\nmodel = PeftModel.from_pretrained(\n model, \"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse\"\n)\n\n# Wrapper for encoding and pooling operations\nl2v = LLM2Vec(model, tokenizer, pooling_mode=\"mean\", max_length=512)\n\n# Encoding queries using instructions\ninstruction = (\n \"Given a web search query, retrieve relevant passages that answer the query:\"\n)\nqueries = [\n [instruction, \"how much protein should a female eat\"],\n [instruction, \"summit define\"],\n]\nq_reps = l2v.encode(queries)\n\n# Encoding documents. Instruction are not required for documents\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]\nd_reps = l2v.encode(documents)\n\n# Compute cosine similarity\nq_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)\nd_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)\ncos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))\n\nprint(cos_sim)\n\"\"\"\ntensor([[0.6522, 0.1891],\n [0.1162, 0.3457]])\n\"\"\"\n```\n\n## Questions\nIf you have any question about the code, feel free to email Parishad (`parishad.behnamghader@mila.quebec`) and Vaibhav (`vaibhav.adlakha@mila.quebec`)."},"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":190,"cells":{"id":{"kind":"string","value":"Dampish/StellarX-4B-V0.2"},"author":{"kind":"string","value":"Dampish"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","gpt_neox","text-generation","arxiv:2204.06745","license:cc-by-nc-sa-4.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"gpt_neox\",\n \"text-generation\",\n \"arxiv:2204.06745\",\n \"license:cc-by-nc-sa-4.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-06-03T21:05:01","string":"2023-06-03T21:05:01"},"last_modified":{"kind":"timestamp","value":"2023-09-18T12:13:30","string":"2023-09-18T12:13:30"},"downloads":{"kind":"number","value":2264,"string":"2,264"},"likes":{"kind":"number","value":2,"string":"2"},"README":{"kind":"string","value":"---\nlicense: cc-by-nc-sa-4.0\n---\n# StellarX: A Base Model by Dampish and Arkane\n\nStellarX is a powerful autoregressive language model designed for various natural language processing tasks. It has been trained on a massive dataset containing 810 billion tokens(trained on 300B tokens), trained on \"redpajama,\" and is built upon the popular GPT-NeoX architecture. With approximately 4 billion parameters, StellarX offers exceptional performance and versatility.\n\n## Model Details\n\n- **Training Data:** StellarX is trained on a large-scale dataset provided by \"redpajama\" maintained by the group \"togethercumputer.\" This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.\n- **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding.\n- **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks.\n- **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications.\n- **V0.2** Meaning what version it is on, currently version 0.2, Assume version 0.2 has only been trained on 300B tokens and the goal is 810B tokens. The next version aims to have a way higher accuracy.\n\n## How to Use\n\nTo load StellarX using the Hugging Face Transformers library, you can use the following code snippet:\n\n```python\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"Dampish/StellarX-4B-V0\")\nmodel = AutoModelForCausalLM.from_pretrained(\"Dampish/StellarX-4B-V0\")\n```\n\nThis model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing.\n\n**For any queries related to this model, feel free to reach out to \"Dampish#3607\" on discord.**\n\n## Licensing and Usage\nStellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.\n\n# Research and Personal Use\nStellarX can be freely used for research purposes, allowing you to explore its capabilities, conduct experiments, and develop novel applications. Whether you're a student, researcher, or hobbyist, the model's availability under the CC-BY-NC-SA-4.0 license empowers you to unlock the potential of StellarX for your own non-commercial projects.\n\n# Commercial Usage\nFor commercial usage of StellarX, an additional licensing arrangement must be established. If you intend to leverage the model for any commercial purpose, such as integrating it into a product or service, you are required to reach an agreement with the Dampish. This agreement will specify the terms, including the agreed-upon percentage or licensing fee to be paid for the commercial use of StellarX.\n\nTo initiate discussions regarding commercial usage, please contact Dampish through the designated channels mentioned earlier. They will be able to provide you with further information and guide you through the process of establishing a licensing arrangement tailored to your specific requirements.\n\n# Importance of Licensing Compliance\nIt is crucial to respect the licensing terms to ensure the fair usage and continued development of StellarX. The revenue generated from commercial licensing supports the efforts of the Dampish in advancing the model and making it more widely accessible.\n\n# Note on CC-BY-NC-SA-4.0\nUnder the CC-BY-NC-SA-4.0 license, you are allowed to modify and adapt StellarX, incorporating it into your own projects. However, any derivative work or modifications should also be shared under the same license terms, ensuring the continued openness and collaborative spirit of the project.\n\nPlease review the complete text of the CC-BY-NC-SA-4.0 license to familiarize yourself with its provisions and requirements. It is essential to comply with the terms of the license to respect the intellectual property rights and contributions of the Dampish and the wider community involved in developing StellarX.\n## GPT-NeoX and Model Selection\n\nGPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. StellarX draws inspiration from the architectural advancements and performance of GPT-NeoX models. While the specifics of StellarX's architecture and parameters may differ, it benefits from the proven capabilities of GPT-NeoX and its suitability for diverse natural language processing tasks.\n\n## Training and Evaluation\n\nStellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of \"redpajama\" dataset by the group \"togethercumputer\" group.\n\nEvaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge.\n\n## Limitations and Considerations\n\nStellarX, like its sibling models, is intended primarily for research purposes. It provides a powerful foundation for extracting useful features and insights from the English language. While StellarX can be further fine-tuned and adapted for deployment, users should conduct their own risk and bias assessments before using it as a basis for downstream tasks.\n\nIt's important to note that StellarX is not intended for direct deployment without supervision. It is not designed for human-facing interactions, unlike models like ChatGPT, which have been fine-tuned using reinforcement learning from human feedback to better understand human instructions and dialogue.\n\nFurthermore, StellarX is not limited to the English language if trained properly and can sometimes be used for translation aswell as text generation in other languages.\n\nLastly, users should be aware of potential biases and limitations inherent in\n\nSpecial thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer.\n\n## Community and Support\n\nTo inquire about StellarX and receive support, you can join the Dampish's \nserver and engage in discussions in the #questions channel. It is recommended to explore the existing documentation and resources available for GPT-NeoX-20B to familiarize yourself with the model before seeking assistance on. For better information about GPT-NeoX, you can reach out to eleutherAI.\n\n## Summary\n\nStellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms.\n\n**Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.\n-dampish**\n\n\n\nDiscord: https://discord.gg/vasyNnUa \nOR Reach out to me personally on Discord via the username: Dampish#3607\n\nThank you for your time.\n\n\n\n"},"matched_task":{"kind":"list like","value":["TRANSLATION"],"string":"[\n \"TRANSLATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["SCIQ"],"string":"[\n \"SCIQ\"\n]"}}},{"rowIdx":191,"cells":{"id":{"kind":"string","value":"epfl-llm/meditron-70b"},"author":{"kind":"string","value":"epfl-llm"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","llama","text-generation","medical","health","llama2","en","dataset:bigbio/med_qa","dataset:medmcqa","dataset:bigbio/pubmed_qa","dataset:epfl-llm/guidelines","arxiv:2311.16079","base_model:meta-llama/Llama-2-70b","base_model:finetune:meta-llama/Llama-2-70b","license:llama2","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"llama\",\n \"text-generation\",\n \"medical\",\n \"health\",\n \"llama2\",\n \"en\",\n \"dataset:bigbio/med_qa\",\n \"dataset:medmcqa\",\n \"dataset:bigbio/pubmed_qa\",\n \"dataset:epfl-llm/guidelines\",\n \"arxiv:2311.16079\",\n \"base_model:meta-llama/Llama-2-70b\",\n \"base_model:finetune:meta-llama/Llama-2-70b\",\n \"license:llama2\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-11-08T13:57:04","string":"2023-11-08T13:57:04"},"last_modified":{"kind":"timestamp","value":"2023-12-07T19:39:04","string":"2023-12-07T19:39:04"},"downloads":{"kind":"number","value":2262,"string":"2,262"},"likes":{"kind":"number","value":234,"string":"234"},"README":{"kind":"string","value":"---\nbase_model: meta-llama/Llama-2-70b\ndatasets:\n- bigbio/med_qa\n- medmcqa\n- bigbio/pubmed_qa\n- epfl-llm/guidelines\nlanguage:\n- en\nlicense: llama2\nmetrics:\n- accuracy\n- perplexity\npipeline_tag: text-generation\ntags:\n- medical\n- health\n- llama2\n---\n\n\n\n# Model Card for Meditron-70B-v1.0\n\nMeditron is a suite of open-source medical Large Language Models (LLMs).\nMeditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.\n\n\n\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-2-70B](https://huggingface.co/meta-llama/Llama-2-70b)\n- **Context length:** 4K 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-70B 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-70B and Meditron-7B are both foundation models without finetuning or instruction-tuning. They can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.\nThere are two ways we have used this model for downstream question-answering tasks.\n1. We apply in-context learning with k demonstrations (3 or 5 in our paper) added to the prompt.\n2. We finetuned the models for downstream question-answering tasks using specific training sets.\n\nWe encourage and look forward to the adaption of the base model for more diverse applications.\n\nIf you want a more interactive way to prompt the model, we recommend using a high-throughput and memory-efficient inference engine with a UI that supports chat and text generation.\n\nYou can check out our deployment [guide](https://github.com/epfLLM/meditron/blob/main/deployment/README.md), where we used [FastChat](https://github.com/lm-sys/FastChat) with [vLLM](https://github.com/vllm-project/vllm). We collected generations for our qualitative analysis through an interactive UI platform, [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT). Here is the prompt format we used as an example:\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### 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\nFor **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.\nEach query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).\nPlease see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)\n\nSignificant research is still required to fully explore potential bias, fairness, and safety issues with this language model. \n\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 without comprehensive testing for your application. \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\n\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 16 nodes 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. \nThe nodes are connected via RDMA over Converged Ethernet.\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 8,\n - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.\n \n\n#### Training Hyperparameters\n\n| | |\n| --- | ------ |\n| bf16 | true |\n| lr | 1.5e-4 |\n| eps | 1e-5 |\n| betas | \\[0.9, 0.95\\] |\n| clip_grad | 1 |\n| weight decay | 0.1 |\n| DP size | 2 |\n| TP size | 8 |\n| PP size | 8 |\n| seq length | 4096 |\n| lr scheduler | cosine|\n| min lr | 1e-6 |\n| warmup iteration | 2000 |\n| micro batch size | 2 |\n| global batch size | 512 |\n| | |\n\n#### Speeds, Sizes, Times\nThe model was trained in September and October 2023.\n\nThe model architecture is exactly Llama 2, meaning\n\n| | | \n| --- | ------ |\n| Model size | 70B | \n| Hidden dimension | 8192 |\n| Num. attention heads | 64 |\n| Num. layers | 80 |\n| | | | \n\nWe train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second. \nThis amounts to a bfloat16 model flops utilization of roughly 42.3\\%.\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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. \nWe report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |\n|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |\n|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |\n|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |\n|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |\n|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |\n|Avg | 72.0 | 69.2 | 63.6 | 57.4 |\n| | | | | | |\n\n**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.\n\n## Environmental Impact\n\n\n\n- **Hardware Type:** 128 x NVIDIA A100 (80GB) SXM\n- **Total GPU hours:** 42,496\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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be: \n \n (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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```"},"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":192,"cells":{"id":{"kind":"string","value":"Dampish/StellarX-4B-V0"},"author":{"kind":"string","value":"Dampish"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","gpt_neox","text-generation","arxiv:2204.06745","license:cc-by-nc-sa-4.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"gpt_neox\",\n \"text-generation\",\n \"arxiv:2204.06745\",\n \"license:cc-by-nc-sa-4.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-05-27T19:04:49","string":"2023-05-27T19:04:49"},"last_modified":{"kind":"timestamp","value":"2023-12-03T19:52:22","string":"2023-12-03T19:52:22"},"downloads":{"kind":"number","value":2229,"string":"2,229"},"likes":{"kind":"number","value":1,"string":"1"},"README":{"kind":"string","value":"---\nlicense: cc-by-nc-sa-4.0\n---\n# StellarX: A Base Model by Dampish and Arkane\n\nStellarX is a powerful autoregressive language model designed for various natural language processing tasks. It has been trained on a massive dataset containing 810 billion tokens, trained on \"redpajama,\" and is built upon the popular GPT-NeoX architecture. With approximately 4 billion parameters, StellarX offers exceptional performance and versatility.\n\n## Model Details\n\n- **Training Data:** StellarX is trained on a large-scale dataset provided by \"redpajama\" maintained by the group \"togethercumputer.\" This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.\n- **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding.\n- **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks.\n- **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications.\n- **V0** Meaning what version it is on, currently version 0, Assume version 0 has only been trained on 300B tokens and the goal is 810B tokens. The next version aims to have a way higher accuracy.\n\n## How to Use\n\nTo load StellarX using the Hugging Face Transformers library, you can use the following code snippet:\n\n```python\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"Dampish/StellarX-4B-V0\")\nmodel = AutoModelForCausalLM.from_pretrained(\"Dampish/StellarX-4B-V0\")\n```\n\nThis model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing.\n\n**For any queries related to this model, feel free to reach out to \"Dampish#3607\" on discord.**\n\n## Licensing and Usage\nStellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.\n\n# Research and Personal Use\nStellarX can be freely used for research purposes, allowing you to explore its capabilities, conduct experiments, and develop novel applications. Whether you're a student, researcher, or hobbyist, the model's availability under the CC-BY-NC-SA-4.0 license empowers you to unlock the potential of StellarX for your own non-commercial projects.\n\n# Commercial Usage\nFor commercial usage of StellarX, an additional licensing arrangement must be established. If you intend to leverage the model for any commercial purpose, such as integrating it into a product or service, you are required to reach an agreement with the Dampish. This agreement will specify the terms, including the agreed-upon percentage or licensing fee to be paid for the commercial use of StellarX.\n\nTo initiate discussions regarding commercial usage, please contact Dampish through the designated channels mentioned earlier. They will be able to provide you with further information and guide you through the process of establishing a licensing arrangement tailored to your specific requirements.\n\n# Importance of Licensing Compliance\nIt is crucial to respect the licensing terms to ensure the fair usage and continued development of StellarX. The revenue generated from commercial licensing supports the efforts of the Dampish in advancing the model and making it more widely accessible.\n\n# Note on CC-BY-NC-SA-4.0\nUnder the CC-BY-NC-SA-4.0 license, you are allowed to modify and adapt StellarX, incorporating it into your own projects. However, any derivative work or modifications should also be shared under the same license terms, ensuring the continued openness and collaborative spirit of the project.\n\nPlease review the complete text of the CC-BY-NC-SA-4.0 license to familiarize yourself with its provisions and requirements. It is essential to comply with the terms of the license to respect the intellectual property rights and contributions of the Dampish and the wider community involved in developing StellarX.\n## GPT-NeoX and Model Selection\n\nGPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. StellarX draws inspiration from the architectural advancements and performance of GPT-NeoX models. While the specifics of StellarX's architecture and parameters may differ, it benefits from the proven capabilities of GPT-NeoX and its suitability for diverse natural language processing tasks.\n\n## Training and Evaluation\n\nStellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of \"redpajama\" dataset by the group \"togethercumputer\" group.\n\nEvaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge.\n\n## Limitations and Considerations\n\nStellarX, like its sibling models, is intended primarily for research purposes. It provides a powerful foundation for extracting useful features and insights from the English language. While StellarX can be further fine-tuned and adapted for deployment, users should conduct their own risk and bias assessments before using it as a basis for downstream tasks.\n\nIt's important to note that StellarX is not intended for direct deployment without supervision. It is not designed for human-facing interactions, unlike models like ChatGPT, which have been fine-tuned using reinforcement learning from human feedback to better understand human instructions and dialogue.\n\nFurthermore, StellarX is not limited to the English language if trained properly and can sometimes be used for translation aswell as text generation in other languages.\n\nLastly, users should be aware of potential biases and limitations inherent in\n\nSpecial thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer.\n\n## Community and Support\n\nTo inquire about StellarX and receive support, you can join the Dampish's \nserver and engage in discussions in the #questions channel. It is recommended to explore the existing documentation and resources available for GPT-NeoX-20B to familiarize yourself with the model before seeking assistance on. For better information about GPT-NeoX, you can reach out to eleutherAI.\n\n## Summary\n\nStellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms.\n\n**Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.\n-dampish**\n\n\n\nDiscord: https://discord.gg/vasyNnUa \nOR Reach out to me personally on Discord via the username: Dampish#3607\n\nThank you for your time.\n\n\n\n\n# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\nDetailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__StellarX-4B-V0)\n\n| Metric | Value |\n|-----------------------|---------------------------|\n| Avg. | 33.54 |\n| ARC (25-shot) | 36.95 |\n| HellaSwag (10-shot) | 61.9 |\n| MMLU (5-shot) | 26.85 |\n| TruthfulQA (0-shot) | 34.3 |\n| Winogrande (5-shot) | 63.85 |\n| GSM8K (5-shot) | 0.0 |\n| DROP (3-shot) | 10.95 |\n"},"matched_task":{"kind":"list like","value":["TRANSLATION"],"string":"[\n \"TRANSLATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["SCIQ"],"string":"[\n \"SCIQ\"\n]"}}},{"rowIdx":193,"cells":{"id":{"kind":"string","value":"BSC-LT/salamandra-7b"},"author":{"kind":"string","value":"BSC-LT"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","safetensors","llama","text-generation","bg","ca","code","cs","cy","da","de","el","en","es","et","eu","fi","fr","ga","gl","hr","hu","it","lt","lv","mt","nl","nn","oc","pl","pt","ro","ru","sh","sk","sl","sr","sv","uk","dataset:oscar-corpus/colossal-oscar-1.0","dataset:HuggingFaceFW/fineweb-edu","dataset:joelniklaus/eurlex_resources","dataset:joelito/legal-mc4","dataset:projecte-aina/CATalog","dataset:UFRGS/brwac","dataset:community-datasets/hrwac","dataset:danish-foundation-models/danish-gigaword","dataset:HiTZ/euscrawl","dataset:PleIAs/French-PD-Newspapers","dataset:PleIAs/French-PD-Books","dataset:AI-team-UoA/greek_legal_code","dataset:HiTZ/latxa-corpus-v1.1","dataset:allenai/peS2o","dataset:pile-of-law/pile-of-law","dataset:PORTULAN/parlamento-pt","dataset:hoskinson-center/proof-pile","dataset:togethercomputer/RedPajama-Data-1T","dataset:bigcode/starcoderdata","dataset:bjoernp/tagesschau-2018-2023","dataset:EleutherAI/the_pile_deduplicated","arxiv:2502.08489","arxiv:2403.14009","arxiv:2403.20266","arxiv:2101.00027","arxiv:2207.00220","arxiv:1810.06694","arxiv:1911.05507","arxiv:1906.03741","arxiv:2406.17557","arxiv:2402.06619","arxiv:1803.09010","license:apache-2.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"llama\",\n \"text-generation\",\n \"bg\",\n \"ca\",\n \"code\",\n \"cs\",\n \"cy\",\n \"da\",\n \"de\",\n \"el\",\n \"en\",\n \"es\",\n \"et\",\n \"eu\",\n \"fi\",\n \"fr\",\n \"ga\",\n \"gl\",\n \"hr\",\n \"hu\",\n \"it\",\n \"lt\",\n \"lv\",\n \"mt\",\n \"nl\",\n \"nn\",\n \"oc\",\n \"pl\",\n \"pt\",\n \"ro\",\n \"ru\",\n \"sh\",\n \"sk\",\n \"sl\",\n \"sr\",\n \"sv\",\n \"uk\",\n \"dataset:oscar-corpus/colossal-oscar-1.0\",\n \"dataset:HuggingFaceFW/fineweb-edu\",\n \"dataset:joelniklaus/eurlex_resources\",\n \"dataset:joelito/legal-mc4\",\n \"dataset:projecte-aina/CATalog\",\n \"dataset:UFRGS/brwac\",\n \"dataset:community-datasets/hrwac\",\n \"dataset:danish-foundation-models/danish-gigaword\",\n \"dataset:HiTZ/euscrawl\",\n \"dataset:PleIAs/French-PD-Newspapers\",\n \"dataset:PleIAs/French-PD-Books\",\n \"dataset:AI-team-UoA/greek_legal_code\",\n \"dataset:HiTZ/latxa-corpus-v1.1\",\n \"dataset:allenai/peS2o\",\n \"dataset:pile-of-law/pile-of-law\",\n \"dataset:PORTULAN/parlamento-pt\",\n \"dataset:hoskinson-center/proof-pile\",\n \"dataset:togethercomputer/RedPajama-Data-1T\",\n \"dataset:bigcode/starcoderdata\",\n \"dataset:bjoernp/tagesschau-2018-2023\",\n \"dataset:EleutherAI/the_pile_deduplicated\",\n \"arxiv:2502.08489\",\n \"arxiv:2403.14009\",\n \"arxiv:2403.20266\",\n \"arxiv:2101.00027\",\n \"arxiv:2207.00220\",\n \"arxiv:1810.06694\",\n \"arxiv:1911.05507\",\n \"arxiv:1906.03741\",\n \"arxiv:2406.17557\",\n \"arxiv:2402.06619\",\n \"arxiv:1803.09010\",\n \"license:apache-2.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-09-30T06:47:04","string":"2024-09-30T06:47:04"},"last_modified":{"kind":"timestamp","value":"2025-02-20T16:42:39","string":"2025-02-20T16:42:39"},"downloads":{"kind":"number","value":2229,"string":"2,229"},"likes":{"kind":"number","value":27,"string":"27"},"README":{"kind":"string","value":"---\ndatasets:\n- oscar-corpus/colossal-oscar-1.0\n- HuggingFaceFW/fineweb-edu\n- joelniklaus/eurlex_resources\n- joelito/legal-mc4\n- projecte-aina/CATalog\n- UFRGS/brwac\n- community-datasets/hrwac\n- danish-foundation-models/danish-gigaword\n- HiTZ/euscrawl\n- PleIAs/French-PD-Newspapers\n- PleIAs/French-PD-Books\n- AI-team-UoA/greek_legal_code\n- HiTZ/latxa-corpus-v1.1\n- allenai/peS2o\n- pile-of-law/pile-of-law\n- PORTULAN/parlamento-pt\n- hoskinson-center/proof-pile\n- togethercomputer/RedPajama-Data-1T\n- bigcode/starcoderdata\n- bjoernp/tagesschau-2018-2023\n- EleutherAI/the_pile_deduplicated\nlanguage:\n- bg\n- ca\n- code\n- cs\n- cy\n- da\n- de\n- el\n- en\n- es\n- et\n- eu\n- fi\n- fr\n- ga\n- gl\n- hr\n- hu\n- it\n- lt\n- lv\n- mt\n- nl\n- nn\n- \\no\n- oc\n- pl\n- pt\n- ro\n- ru\n- sh\n- sk\n- sl\n- sr\n- sv\n- uk\nlibrary_name: transformers\nlicense: apache-2.0\npipeline_tag: text-generation\n---\n\n\n\n# Salamandra Model Card\n\nThis repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).\n\nSalamandra is a highly multilingual model pre-trained from scratch that comes in three different \nsizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants. \nThis model card corresponds to the 7B base version.\n\nTo visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index).\n\nThe entire Salamandra family is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).\nAlong with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra).\n\n---\n\n## Model Details\n\n### Description\n\nTransformer-based decoder-only language model that has been pre-trained from scratch on 12.875 trillion tokens of highly curated data.\nThe pre-training corpus contains text in 35 European languages and code.\n\n### Hyperparameters\n\nThe full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/tree/main/configs).\n\n### Architecture\n\n| | |\n|-------------------------|:--------------|\n| Total Parameters | 7,768,117,248 |\n| Embedding Parameters | 1,048,576,000 |\n| Layers | 32 |\n| Hidden size | 4,096 |\n| Attention heads | 32 |\n| Context length | 8,192 |\n| Vocabulary size | 256,000 |\n| Precision | bfloat16 |\n| Embedding type | RoPE |\n| Activation Function | SwiGLU |\n| Layer normalization | RMS Norm |\n| Flash attention | ✅ |\n| Grouped Query Attention | ✅ |\n| Num. query groups | 8 |\n\n---\n\n## Intended Use\n\n### Direct Use\n\nThe models are intended for both research and commercial use in any of the languages included in the training data. \nThe base models are intended either for language generation or to be further fine-tuned for specific use-cases. \nThe instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations.\n\n### Out-of-scope Use\n\nThe model is not intended for malicious activities, such as harming others or violating human rights. \nAny downstream application must comply with current laws and regulations. \nIrresponsible usage in production environments without proper risk assessment and mitigation is also discouraged. \n\n---\n\n## Hardware and Software\n\n### Training Framework\n\nPre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html), \nwhich leverages PyTorch Lightning for efficient model training in highly distributed settings.\n\nThe instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat).\n\n### Compute Infrastructure\n\nAll models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and\noperated by Barcelona Supercomputing Center.\n\nThe accelerated partition is composed of 1,120 nodes with the following specifications:\n- 4x Nvidia Hopper GPUs with 64 HBM2 memory\n- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)\n- 4x NDR200 (BW per node 800Gb/s)\n- 512 GB of Main memory (DDR5)\n- 460GB on NVMe storage\n\n|Model|Nodes|GPUs|\n|:---:|:---:|:---:|\n|2B|64|256|\n|7B|128|512|\n|40B|256 / 512|1,024 / 2,048|\n\n---\n\n## How to use\nThis section offers examples of how to perform inference using various methods.\n\n### Inference\nYou'll find different techniques for running inference, including Huggingface's Text Generation Pipeline, multi-GPU configurations, and vLLM for scalable and efficient generation. \n\n#### Inference with Huggingface's Text Generation Pipeline\nThe Huggingface Text Generation Pipeline provides a straightforward way to run inference using the Salamandra-7b model.\n\n```bash\npip install transformers torch accelerate sentencepiece protobuf\n```\n\n
\n\n#### Inference with single / multi GPU\nThis section provides a simple example of how to run inference using Huggingface's AutoModel class.\n\n```bash\npip install transformers torch accelerate sentencepiece protobuf\n```\n\nShow code
\n\n```python\nfrom transformers import pipeline, set_seed\n\nmodel_id = \"BSC-LT/salamandra-7b\"\n\n# Sample prompts\nprompts = [\n \"Las fiestas de San Isidro Labrador de Yecla son\",\n \"El punt més alt del Parc Natural del Montseny és\",\n \"Sentence in English: The typical chance of such a storm is around 10%. Sentence in Catalan:\",\n \"Si le monde était clair\",\n \"The future of AI is\",\n]\n\n# Create the pipeline\ngenerator = pipeline(\"text-generation\", model_id, device_map=\"auto\")\ngeneration_args = {\n \"temperature\": 0.1,\n \"top_p\": 0.95,\n \"max_new_tokens\": 25,\n \"repetition_penalty\": 1.2,\n \"do_sample\": True\n}\n\n# Fix the seed\nset_seed(1)\n# Generate texts\noutputs = generator(prompts, **generation_args)\n# Print outputs\nfor output in outputs:\n print(output[0][\"generated_text\"])\n\n```\n\n
\n\n#### Inference with vLLM\nvLLM is an efficient library for inference that enables faster and more scalable text generation.\n\n```bash\npip install vllm\n```\n\nShow code
\n\n```python\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nimport torch\n\nmodel_id = \"BSC-LT/salamandra-7b\"\n\n# Input text\ntext = \"El mercat del barri és\"\n\n# Load the tokenizer\ntokenizer = AutoTokenizer.from_pretrained(model_id)\n# Load the model\nmodel = AutoModelForCausalLM.from_pretrained(\n model_id,\n device_map=\"auto\",\n torch_dtype=torch.bfloat16\n)\n\ngeneration_args = {\n \"temperature\": 0.1,\n \"top_p\": 0.95,\n \"max_new_tokens\": 25,\n \"repetition_penalty\": 1.2,\n \"do_sample\": True\n}\n\ninputs = tokenizer(text, return_tensors=\"pt\")\n# Generate texts\noutput = model.generate(input_ids=inputs[\"input_ids\"].to(model.device), attention_mask=inputs[\"attention_mask\"], **generation_args)\n# Print outputs\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\n```\n\n\n
\n\n---\n\n## Data\n\n### Pretraining Data\n\nThe pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below. \nThe initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation \nand give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half, \nSpanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.\nDuring the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset. \nThis adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:\n\n\n\nThe pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens. \nFollowing this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%. \nOther notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%. \nThese major sources collectively form the bulk of the corpus, ensuring a rich and diverse dataset for training the language model. \nThe remaining 10% comes from smaller sources in various languages.\n\nFeel free to click the expand button below to see the full list of sources.\n\nShow code
\n\n```python\nfrom vllm import LLM, SamplingParams\n\nmodel_id = \"BSC-LT/salamandra-7b\"\n\n# Sample prompts\nprompts = [\n \"Las fiestas de San Isidro Labrador de Yecla son\",\n \"El punt més alt del Parc Natural del Montseny és\",\n \"Sentence in English: The typical chance of such a storm is around 10%. Sentence in Catalan:\",\n \"Si le monde était clair\",\n \"The future of AI is\",\n]\n# Create a sampling params object\nsampling_params = SamplingParams(\n temperature=0.1,\n top_p=0.95,\n seed=1,\n max_tokens=25,\n repetition_penalty=1.2)\n\n# Create an LLM\nllm = LLM(model=model_id)\n# Generate texts\noutputs = llm.generate(prompts, sampling_params)\n# Print 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\nThe model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part \nof the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch; \nand 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 trillion tokens.\n\nWe provide an extense Datasheet section following the best practices defined by [(Gebru et al., 2021)](https://arxiv.org/pdf/1803.09010).\n\nData Sources
\n \n\n| Dataset | Language | Source |\n|---|---|---|\n| Colossal OSCAR 1.0 | bg, ca, cs, cy, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, oc, pl, pt, ro, ru, sh, sk, sl, sr, sv, uk | Brack et al., 2024 |\n| Aya Dataset (w/o Evaluation Suite) | eu, hr, nl, fi, ka, hu, lt, nn, ro, sk, lv, cy, bg, cs, en, fr, de, ga, mt, pl, ru, sl, sv, ca, da, et, gl, el, it, no, pt, sr, es, uk | Singh et al., 2024 |\n| Wikimedia dumps | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, pl, pt, ro, sh, sk, sl, sr, uk | [Link](https://dumps.wikimedia.org/) |\n| OpenSubtitles v2016 | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, gl, hr, it, lt, lv, nl, no, pl, pt, ro, sk, sl, sr, sv, uk | Lison & Tiedemann, 2016 |\n| EurLEX-Resources | bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelniklaus/eurlex_resources) |\n| MC4-Legal | bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelito/legal-mc4) |\n| Parlamint | at, bg, cz, dk, ee, es, es-ga, fi, fr, gb, gr, hr, hu, it, lv, nl, no, pl, pt, rs, se, si | Erjavec et al., 2021 |\n| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |\n| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |\n| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |\n| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |\n| BIGPATENT | en | Sharma et al., 2019 |\n| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |\n| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |\n| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |\n| CaBeRnet | fr | Popa-Fabre et al., 2020 |\n| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |\n| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |\n| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |\n| DaNewsroom | da | Varab & Schluter, 2020 |\n| Danish GigaWord | da | Strømberg-Derczynski et al., 2021 |\n| DK-CLARIN Reference Corpus of General Danish | da | [Link](https://korpus.dsl.dk/clarin/) |\n| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |\n| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |\n| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |\n| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |\n| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |\n| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |\n| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |\n| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |\n| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |\n| HPLT v1 - Spanish | es | de Gibert et al., 2024 |\n| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |\n| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |\n| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |\n| Korpus Malti | mt | Micallef et al., 2022 |\n| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |\n| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |\n| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |\n| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |\n| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |\n| Math AMPS | en | Hendrycks et al., 2021 |\n| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |\n| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |\n| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |\n| ParlamentoPT | pt | Rodrigues et al., 2023 |\n| peS2o | en | Soldaini & Lo, 2023 |\n| PG-19 | en | Rae et al., 2019 |\n| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |\n| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |\n| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |\n| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |\n| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |\n| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |\n| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |\n| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |\n| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |\n| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |\n| Starcoder | code | Li et al., 2023 |\n| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |\n| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |\n| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |\n| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |\n| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |\n| The Pile (PhilPapers) | en | Gao et al., 2021 |\n| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |\n| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |\n| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |\n\nTo consult the data summary document with the respective licences, please send an e-mail to ipr@bsc.es. \n\n\n
\n\nReferences
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[Link](https://aclanthology.org/2021.nodalida-main.46)\n- Subramani, N., Luccioni, S., Dodge, J., & Mitchell, M. (2023). Detecting Personal Information in Training Corpora: An Analysis. 208–220. [Link](https://doi.org/10.18653/v1/2023.trustnlp-1.18)\n- Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of The 12th Language Resources and Evaluation Conference, 6731–6739. [Link](https://www.aclweb.org/anthology/2020.lrec-1.831)\n- Váradi, T., Nyéki, B., Koeva, S., Tadić, M., Štefanec, V., Ogrodniczuk, M., Nitoń, B., Pezik, P., Barbu Mititelu, V., Irimia, E., Mitrofan, M., Tufi\\textcommabelows, D., Garabík, R., Krek, S., & Repar, A. (2022). Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources. In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Thirteenth Language Resources and Evaluation Conference (pp. 100–108). European Language Resources Association. [Link](https://aclanthology.org/2022.lrec-1.11)\n- Wagner Filho, J. A., Wilkens, R., Idiart, M., & Villavicencio, A. (2018). The brwac corpus: A new open resource for brazilian portuguese. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).\n- Žagar, A., Kavaš, M., Robnik-Šikonja, M., Erjavec, T., Fišer, D., Ljubešić, N., Ferme, M., Borovič, M., Boškovič, B., Ojsteršek, M., & Hrovat, G. (2022). Corpus of academic Slovene KAS 2.0. [Link](http://hdl.handle.net/11356/1448)\n- Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel Bowman. 2022. BBQ: A hand-built bias benchmark for question answering. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2086–2105, Dublin, Ireland. Association for Computational Linguistics.\n- Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019. The Woman Worked as a Babysitter: On Biases in Language Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3407–3412, Hong Kong, China. Association for Computational Linguistics.\n- Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., & Tafjord, O. (2018). Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. arXiv:1803. 05457v1.\n- Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631–1642, Seattle, Washington, USA. Association for Computational Linguistics.\n- Penedo, G., Kydlíček, H., allal, L. B., Lozhkov, A., Mitchell, M., Raffel, C., Von Werra, L., & Wolf, T. (2024). The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale (arXiv:2406.17557). arXiv. http://arxiv.org/abs/2406.17557\n- Singh, S., Vargus, F., Dsouza, D., Karlsson, B. F., Mahendiran, A., Ko, W.-Y., Shandilya, H., Patel, J., Mataciunas, D., OMahony, L., Zhang, M., Hettiarachchi, R., Wilson, J., Machado, M., Moura, L. S., Krzemiński, D., Fadaei, H., Ergün, I., Okoh, I., … Hooker, S. (2024). Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning (arXiv:2402.06619). arXiv. http://arxiv.org/abs/2402.06619\n\n\n
\n\n---\n\n\n## Evaluation\n\n### Gold-standard benchmarks\n\nEvaluation is done using the Language Model Evaluation Harness (Gao et al., 2024). We evaluate on a set of tasks taken from [SpanishBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/spanish_bench), [CatalanBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/catalan_bench), [BasqueBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/basque_bench) and [GalicianBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/galician_bench). We also use English tasks already available on the LM Evaluation Harness. These benchmarks include both new and existing tasks and datasets. In the tables below, we include the results in a selection of evaluation datasets that represent model's performance across a variety of tasks within these benchmarks. \n\nWe only use tasks that are either human generated, human translated, or with a strong human-in-the-loop (i.e., machine translation followed by professional revision or machine generation followed by human revision and annotation). This is the reason behind the variety in number of tasks reported across languages. As more tasks that fulfill these requirements are published, we will update the presented results. We also intend to expand the evaluation to other languages, as long as the datasets meet our quality standards.\n\nDuring the implementation of the evaluation we observed a series of issues worth considering when replicating and interpreting the results presented. These issues include ≈1.5% variances in performance in some tasks depending on the version of the `transformers` library used, and depending on the use (or lack of use) of tensor parallelism when loading a model. When implementing existing tasks, we carry out a comprehensive quality evaluation of the dataset, the Harness task itself, and what kind of input models see during evaluation. Our implementation (see links above) addresses multiple existing problems such as errors in datasets and prompts, and lack of pre-processing. All this means that results will vary if using other Harness implementations, and may slightly vary depending on the replication setup.\n\nIt should be noted that these results are subject to all the drawbacks of every current gold-standard evaluation, and that the figures do not fully represent the models capabilities and potential. We thus advise caution when reading and interpreting the results.\n\nA full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.\n\nAll results reported below are on a 5-shot setting.\n\n#### Spanish\n\nDatasheet
\n\n#### Motivation\n\n**For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.**\n\nThe purpose of creating this dataset is to pre-train the Salamandra family of multilingual models with high performance in a large number of European languages (35) \nand programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample \nthese languages by a factor of 2.\n\nThere is a great lack of massive multilingual data, especially in minority languages (Ostendorff & Rehm, 2023), so part of our efforts in the creation of \nthis pre-training dataset have resulted in the contribution to large projects such as the Community OSCAR (Brack et al., 2024), which includes 151 languages \nand 40T words, or CATalog (Palomar-Giner et al., 2024), the largest open dataset in Catalan in the world.\n\n**Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?**\n\nThe dataset has been created by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS), \nwhich aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, it was created by \nthe unit's data team, the main contributors being José Javier Saiz, Ferran Espuña and Jorge Palomar.\n\nHowever, the creation of the dataset would not have been possible without the collaboration of a large number of collaborators, partners and public institutions, \nwhich can be found in detail in the acknowledgements.\n\n**Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.**\n\nThis work has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/).\n\nThis work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU \nwithin the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.\n\n#### Composition\n\n**What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.**\n\nThe dataset consists entirely of text documents in various languages. Specifically, data was mainly sourced from the following databases and \nrepositories:\n- **Common Crawl:** Repository that holds website data and is run by the Common Crawl non-profit organization. It is updated monthly and is\n distributed under the CC0 1.0 public domain license.\n- **GitHub:** Community platform that allows developers to create, store, manage, and share their code. Repositories are crawled and then\n distributed with their original licenses, which may vary from permissive to non-commercial licenses.\n- **Wikimedia:** Database that holds the collection databases managed by the Wikimedia Foundation, including Wikipedia, Wikibooks, Wikinews,\n Wikiquote, Wikisource, and Wikivoyage. It is updated monthly and is distributed under Creative Commons Attribution-ShareAlike License 4.0.\n- **EurLex:** Repository that holds the collection of legal documents from the European Union, available in all of the EU’s 24 official\n languages and run by the Publications Office of the European Union. It is updated daily and is distributed under the Creative Commons\n Attribution 4.0 International license.\n- **Other repositories:** Specific repositories were crawled under permission for domain-specific corpora, which include academic, legal,\n and newspaper repositories.\n\nWe provide a complete list of dataset sources at the end of this section.\n\n**How many instances are there in total (of each type, if appropriate)?**\n\nThe dataset contains a diverse range of instances across multiple languages, with notable adjustments for certain languages. English \nrepresents the largest portion, accounting for 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,\nwhile Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled \nby half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian \n(4.59%), with several additional languages contributing between 1% and 2%, and smaller portions represented by a variety of others.\n\n**Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).**\n\nThe dataset is a sample from multiple sources, with different weights based on the primary language of the content: Spanish, Catalan, \nBasque, and Galician content was upsampled by a factor of two, while programming languages were downsampled by a factor of half. Other \nsources were sampled in proportion to their occurrence.\n\n**What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.**\n\nEach instance consists of a text document processed for deduplication, language identification, and source-specific filtering. Some documents required \noptical character recognition (OCR) to extract text from non-text formats such as PDFs.\n\n**Is there a label or target associated with each instance? If so, please provide a description.**\n\nEach instance is labelled with a unique identifier, the primary language of the content, and the URL for web-sourced instances. Additional labels were \nautomatically assigned to detect specific types of content -harmful or toxic content- and to assign preliminary indicators of undesired qualities -very \nshort documents, high density of symbols, etc.- which were used for filtering instances.\n\n**Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.**\n\nNo significant information is missing from the instances.\n\n**Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.**\n\nInstances are related through shared metadata, such as source and language identifiers.\n\n**Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.**\n\nThe dataset is randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.\n\n**Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.**\n\nDespite removing duplicated instances within each source, redundancy remains at the paragraph and sentence levels, particularly in web-sourced \ninstances where search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also duplicated \nacross sources due to format variations.\n\n**Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.**\n\nThe dataset is self-contained and does not rely on external resources.\n\n**Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.**\n\nThe dataset does not contain confidential data.\n\n**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.**\n\nThe dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). Although \npre-processing techniques were applied to mitigate offensive content, the heterogeneity and scale of web-sourced data make exhaustive \nfiltering challenging, which makes it next to impossible to identify all adult content without falling into excessive filtering, which may \nnegatively influence certain demographic groups (Dodge et al., 2021).\n\n**Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.**\n\nThe dataset does not explicitly identify any subpopulations.\n\n**Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.**\n\nWeb-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names, \nIP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple \ndata points, the nature and scale of web data makes it difficult to parse such information. In any case, efforts are made to filter or anonymize \nsensitive data (Mina et al., 2024), but some identifiable information may remain in the dataset.\n\n**Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.**\n\nGiven that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial \ninformation, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023), \nespecially if the content originates from less-regulated sources or user-generated platforms. \n\n#### Collection Process\n\n**How was the data collected?**\n\nThis dataset is constituted by combining several sources, whose acquisition methods can be classified into three groups:\n- Web-sourced datasets with some preprocessing available under permissive license.\n- Domain-specific or language-specific raw crawls.\n- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).\n\n**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**\n\nThe data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:\n- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.\n- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.\n- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.\n\n**If the dataset is a sample from a larger set, what was the sampling strategy?**\n\nThe sampling strategy was to use the whole dataset resulting from the filtering explained in the 'preprocessing/cleaning/labelling' section, \nwith the particularity that an upsampling of 2 (i.e. twice the probability of sampling a document) was performed for the co-official languages \nof Spain (Spanish, Catalan, Galician, Basque), and a downsampling of 1/2 was applied for code (half the probability of sampling a code document, \nevenly distributed among all programming languages).\n\n**Who was involved in the data collection process and how were they compensated?**\n\nThis data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely \nby members of the Language Technologies data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary \nconsideration for acquiring data from suppliers.\n\n**Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.**\n\nData were acquired and processed from April 2023 to April 2024. However, as mentioned, much data has been obtained from open projects such \nas Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important.\n\n**Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.**\n\nNo particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an \ninternal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència \nArtificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an \nethical and legal point of view, respectively.\n\n#### Preprocessing\n\n**Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.**\n\nNo changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:\n- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.\n- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\\_pp' field, a perplexity-based score generated by a language model.\n\n**Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.**\n\nThe original raw data was not kept.\n\n**Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.**\n\nYes, the preprocessing and filtering software is open-sourced. The [CURATE](https://github.com/langtech-bsc/CURATE) pipeline was used for CATalog and other curated datasets, \nand the [Ungoliant](https://github.com/oscar-project/ungoliant) pipeline was used for the OSCAR project.\n\n#### Uses\n\n**Has the dataset been used for any tasks already? If so, please provide a description.**\n\nPre-train the Salamandra model family.\n\n**What (other) tasks could the dataset be used for?**\n\nThe data can be used primarily to pre-train other language models, which can then be used for a wide range of use cases. The dataset could \nalso be used for other tasks such as fine-tuning language models, cross-lingual NLP tasks, machine translation, domain-specific text \ngeneration, and language-specific data analysis.\n\n**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?**\n \nWeb-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages. \nLanguage diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic \ngroups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures, \nacknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to \naddress privacy concerns and contribute to a more inclusive linguistic dataset.\n\n**Are there tasks for which the dataset should not be used?**\n\n-\n\n#### Distribution \n\n**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.**\n\nThe dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section.\n\n#### Maintenance\n\n**Who will be supporting/hosting/maintaining the dataset?**\n\nThe dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure \nregular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are \nresponsible for.\n\n**How can the owner/curator/manager of the dataset be contacted?**\n\nThe data owner may be contacted with the email address langtech@bsc.es.\n\n**Will the dataset be updated?**\n\nThe dataset will not be updated.\n\n**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.**\n\nThe dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in \nweb-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an \nindividual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to \nremove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues.\n\n**Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.**\n\nSince the dataset will not be updated, only the final version will be kept.\n\n**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**\n\nThe dataset does not allow for external contributions.\n\n| Category | \nTask | \nMetric | \nResult | \n
|---|---|---|---|
| Commonsense Reasoning | \ncopa_es | \nacc | \n86 | \n
| xstorycloze_es | \nacc | \n74.32 | \n|
| NLI | \nwnli_es | \nacc | \n59.15 | \n
| xnli_es | \nacc | \n46.59 | \n|
| Paraphrasing | \npaws_es | \nacc | \n60.3 | \n
| QA | \nopenbookqa_es | \nacc | \n41.6 | \n
| xquad_es | \nacc | \n72.26 | \n|
| Translation | \nflores_es | \nbleu | \n23.43 | \n
| Category | \nTask | \nMetric | \nResult | \n
|---|---|---|---|
| Commonsense Reasoning | \ncopa_ca | \nacc | \n84 | \n
| xstorycloze_ca | \nacc | \n75.51 | \n|
| NLI | \nwnli_ca | \nacc | \n59.15 | \n
| xnli_ca | \nacc | \n50.16 | \n|
| Paraphrasing | \nparafraseja | \nacc | \n65.83 | \n
| paws_ca | \nacc | \n67.45 | \n|
| QA | \narc_ca_easy | \nacc | \n71.72 | \n
| arc_ca_challenge | \nacc | \n45.56 | \n|
| openbookqa_ca | \nacc | \n38.8 | \n|
| piqa_ca | \nacc | \n71.27 | \n|
| siqa_ca | \nacc | \n49.85 | \n|
| Translation | \nflores_ca | \nbleu | \n30.63 | \n
| Category | \nTask | \nMetric | \nResult | \n
|---|---|---|---|
| Commonsense Reasoning | \nxcopa_eu | \nacc | \n68.8 | \n
| xstorycloze_eu | \nacc | \n66.12 | \n|
| NLI | \nwnli_eu | \nacc | \n57.75 | \n
| xnli_eu | \nacc | \n43.51 | \n|
| QA | \neus_exams | \nacc | \n41.04 | \n
| eus_proficiency | \nacc | \n39.72 | \n|
| eus_trivia | \nacc | \n52.36 | \n|
| piqa_eu | \nacc | \n63.67 | \n|
| Reading Comprehension | \neus_reading | \nacc | \n33.52 | \n
| Translation | \nflores_eu | \nbleu | \n16.95 | \n
| Category | \nTask | \nMetric | \nResult | \n
|---|---|---|---|
| Commonsense Reasoning | \nxstorycloze_gl | \nacc | \n74.12 | \n
| NLI | \nxnli_gl | \nacc | \n50.95 | \n
| Paraphrasing | \nparafrases_gl | \nacc | \n54.42 | \n
| paws_gl | \nacc | \n63.2 | \n|
| QA | \nopenbookqa_gl | \nacc | \n34.4 | \n
| Translation | \nflores_gl | \nbleu | \n27.75 | \n
| Category | \nTask | \nMetric | \nResult | \n
|---|---|---|---|
| Commonsense Reasoning | \ncopa | \nacc | \n91 | \n
| xstorycloze_en | \nacc | \n79.09 | \n|
| NLI | \nwnli | \nacc | \n56.34 | \n
| xnli_en | \nacc | \n50 | \n|
| Paraphrasing | \npaws * | \nacc | \n64.05 | \n
| QA | \narc_easy | \nacc | \n82.2 | \n
| arc_challenge | \nacc | \n52.82 | \n|
| openbookqa | \nacc | \n36 | \n|
| piqa | \nacc | \n80.03 | \n|
| social_iqa | \nacc | \n50.31 | \n|
| xquad_en ** | \nacc | \n77.74 | \n
\n
\n ![](\"https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/GoLLIE.png\")
\n
Guideline following Large Language Model for Information Extraction
\n\n\n\n# Model Card for GoLLIE 7B\n\n\n
\nWe present GoLLIE, a Large Language Model trained to follow annotation guidelines. GoLLIE outperforms previous approaches on zero-shot Information Extraction and allows the user to perform inferences with annotation schemas defined on the fly. Different from previous approaches, GoLLIE is able to follow detailed definitions and does not only rely on the knowledge already encoded in the LLM. \n\n- 💻 Code: [https://github.com/osainz59/CoLLIE/](https://github.com/hitz-zentroa/GoLLIE)\n- 📒 Blog Post: [GoLLIE: Guideline-following Large Language Model for Information Extraction](https://hitz-zentroa.github.io/GoLLIE/)\n- 📖 Paper: [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://arxiv.org/abs/2310.03668)\n- 🐕 GoLLIE Colection in the 🤗HuggingFace Hub: [HiTZ/gollie](https://huggingface.co/collections/HiTZ/gollie-651bf19ee315e8a224aacc4f)\n- 🚀 Example Jupyter Notebooks: [GoLLIE Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks)\n
\n\n\n![](\"https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/zero_shot_results.png\")
\n
\n![](\"https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/datasets.png\")
\n
| Language / Model Name [Instruct] | \nQwen2-7B | \nQwen2.5-7B | \nLlama-3-8B | \nLlama-3.1-8B | \nsea-lionv2.1-8B | \ngemma-2-9B | \nsahabatai-v1-8B | \nsahabatai-v1-9B | \n
|---|---|---|---|---|---|---|---|---|
| Overall (Bahasa Indonesia + Javanese + Sundanese) | \n36.963 | \n42.988 | \n37.805 | \n45.866 | \n46.880 | \n56.359 | \n53.725 | \n61.169 | \n
| Bahasa Indonesia | \n46.760 | \n60.372 | \n42.022 | \n51.944 | \n54.579 | \n63.394 | \n57.221 | \n64.154 | \n
| Javanese | \n33.956 | \n40.625 | \n41.739 | \n47.587 | \n48.012 | \n56.468 | \n56.460 | \n64.439 | \n
| Sundanese | \n30.173 | \n27.969 | \n29.654 | \n38.068 | \n38.050 | \n49.216 | \n47.495 | \n54.913 | \n
| Model Name [Instruct] | \nQwen2-7B | \nQwen2.5-7B | \nMeta-Llama-3-8B | \nLlama-3.1-8B | \nsea-lionv2.1-8B | \ngemma-2-9B | \nsahabatai-v1-8B | \nsahabatai-v1-9B | \n
|---|---|---|---|---|---|---|---|---|
| Overall Results | \n53.0% | \n56.0% | \n51.9% | \n53.8% | \n54.4% | \n61.4% | \n55.6% | \n62.6% | \n
| Model Name [Instruct] | \nQwen2-7B | \nQwen2.5-7B | \nLlama-3-8B | \nLlama-3.1-8B | \nsea-lionv2.1-8B | \ngemma-2-9B | \nsahabatai-v1-8B | \nsahabatai-v1-9B | \n
|---|---|---|---|---|---|---|---|---|
| Average | \n24.48 | \n27.75 | \n23.91 | \n27.98 | \n24.52 | \n26.44 | \n24.43 | \n33.67 | \n
\nCheng Nicholas
\nChoa Esther
\nHuang Yuli
\nLau Wayne
\nLee Chwan Ren
\nLeong Wai Yi
\nLeong Wei Qi
\nLimkonchotiwat Peerat
\nLiu Bing Jie Darius
\nMontalan Jann Railey
\nNg Boon Cheong Raymond
\nNgui Jian Gang
\nNguyen Thanh Ngan
\nOng Brandon
\nOng Tat-Wee David
\nOng Zhi Hao
\nRengarajan Hamsawardhini
\nSiow Bryan
\nSusanto Yosephine
\nTai Ngee Chia
\nTan Choon Meng
\nTeng Walter
\nTeo Eng Sipp Leslie
\nTeo Wei Yi
\nTjhi William
\nYeo Yeow Tong
\nYong Xianbin
\n\n### PT GoTo Gojek Tokopedia Tbk\nAnissa Dininta
\nChau Shiau Ching
\nChoiri Hendra Hadhil
\nGoel Priyank
\nSaini Ajay Kumar
\nShalev Ofir
\nTan Daryl
\nTep Kilian Rithi
\nTiwari Anupam
\nWidjojo Daniel
\n\n## Acknowledgements\n\n [AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. \n\nAny opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.\n\n\n## Contact\n\nFor more info, please contact us using this [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)\n\n## Disclaimer\n\nThis is the repository for the Instruct model.\nThe model has _not_ been aligned for safety.\nDevelopers and users should perform their own safety fine-tuning and related security measures.\nIn no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.\n\n\n## References\n### IndoMMLU Reference\n\n```bibtex\n@inproceedings{koto-etal-2023-indommlu,\n title = \"Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}\",\n author = \"Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin\",\n booktitle = \"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = December,\n year = \"2023\",\n address = \"Singapore\",\n publisher = \"Association for Computational Linguistics\",\n}\n}\n```"},"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":["CHIA"],"string":"[\n \"CHIA\"\n]"}}},{"rowIdx":196,"cells":{"id":{"kind":"string","value":"aliakseilabanau/bge-small-en"},"author":{"kind":"string","value":"aliakseilabanau"},"task_category":{"kind":"null"},"tags":{"kind":"list like","value":["safetensors","openvino","bert","mteb","sentence transformers","en","arxiv:2311.13534","arxiv:2310.07554","arxiv:2309.07597","base_model:BAAI/bge-small-en","base_model:quantized:BAAI/bge-small-en","license:mit","region:us"],"string":"[\n \"safetensors\",\n \"openvino\",\n \"bert\",\n \"mteb\",\n \"sentence transformers\",\n \"en\",\n \"arxiv:2311.13534\",\n \"arxiv:2310.07554\",\n \"arxiv:2309.07597\",\n \"base_model:BAAI/bge-small-en\",\n \"base_model:quantized:BAAI/bge-small-en\",\n \"license:mit\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2024-11-19T18:27:44","string":"2024-11-19T18:27:44"},"last_modified":{"kind":"timestamp","value":"2025-02-04T13:09:46","string":"2025-02-04T13:09:46"},"downloads":{"kind":"number","value":2173,"string":"2,173"},"likes":{"kind":"number","value":0,"string":"0"},"README":{"kind":"string","value":"---\nbase_model:\n- BAAI/bge-small-en\nlanguage:\n- en\nlicense: mit\ntags:\n- mteb\n- sentence transformers\n- openvino\nbase_model_relation: quantized\n---\n\nThe [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) converted for [openvino backend](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#openvino).\n\n```\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"aliakseilabanau/bge-small-en\", backend=\"openvino\")\n\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\nembeddings = model.encode(sentences)\n```\n\n\nThe original model card is below:\n\n--------\n\n**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.**\n\n
FlagEmbedding
\n\n\n\n
\n Model List | \n FAQ |\n Usage |\n Evaluation |\n Train |\n Citation |\n License \n
\n\n\nMore details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).\n\n\n[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)\n\nFlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently:\n\n- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)\n- **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)\n- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)\n\n\n## News \n\n- 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: \n- 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)\n- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released \n- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released \n- 09/12/2023: New models: \n - **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. \n - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.\n \n\n\n \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| [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |\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[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.\n\n[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. \nFor 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.\n\nAll models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. \nIf you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .\n\n\n## Frequently asked questions\n\nMore
\n\n \n- 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. \n- 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). \n- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** \n- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: \n- 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. \n \n\n \n\n1. How to fine-tune bge embedding model?
\n\n \nFollowing this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. \nSome suggestions:\n- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.\n- 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.\n- 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.\n\n \n\n \n\n2. The similarity score between two dissimilar sentences is higher than 0.5
\n\n \n**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** \n\nSince we finetune the models by contrastive learning with a temperature of 0.01, \nthe similarity distribution of the current BGE model is about in the interval \\[0.6, 1\\].\nSo a similarity score greater than 0.5 does not indicate that the two sentences are similar.\n\nFor downstream tasks, such as passage retrieval or semantic similarity, \n**what matters is the relative order of the scores, not the absolute value.**\nIf you need to filter similar sentences based on a similarity threshold, \nplease select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).\n\n\n \n\n\n## Usage \n\n### Usage for Embedding Model\n\nHere are some examples for using `bge` models with \n[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).\n\n#### Using FlagEmbedding\n```\npip install -U FlagEmbedding\n```\nIf 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.\n\n```python\nfrom FlagEmbedding import FlagModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagModel('BAAI/bge-large-zh-v1.5', \n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章：\",\n use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n\n# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query\n# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode(passages)\nscores = q_embeddings @ p_embeddings.T\n```\nFor the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). \n\nBy default, FlagModel 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 Sentence-Transformers\n\nYou can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):\n\n```\npip install -U sentence-transformers\n```\n```python\nfrom sentence_transformers import SentenceTransformer\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = SentenceTransformer('BAAI/bge-large-zh-v1.5')\nembeddings_1 = model.encode(sentences_1, normalize_embeddings=True)\nembeddings_2 = model.encode(sentences_2, normalize_embeddings=True)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\nFor s2p(short query to long passage) retrieval task, \neach short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). \nBut the instruction is not needed for passages.\n```python\nfrom sentence_transformers import SentenceTransformer\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\ninstruction = \"为这个句子生成表示以用于检索相关文章：\"\n\nmodel = SentenceTransformer('BAAI/bge-large-zh-v1.5')\nq_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)\np_embeddings = model.encode(passages, normalize_embeddings=True)\nscores = q_embeddings @ p_embeddings.T\n```\n\n#### Using Langchain \n\nYou can use `bge` in langchain like this:\n```python\nfrom langchain.embeddings import HuggingFaceBgeEmbeddings\nmodel_name = \"BAAI/bge-large-en-v1.5\"\nmodel_kwargs = {'device': 'cuda'}\nencode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity\nmodel = HuggingFaceBgeEmbeddings(\n model_name=model_name,\n model_kwargs=model_kwargs,\n encode_kwargs=encode_kwargs,\n query_instruction=\"为这个句子生成表示以用于检索相关文章：\"\n)\nmodel.query_instruction = \"为这个句子生成表示以用于检索相关文章：\"\n```\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\nfrom transformers import AutoTokenizer, AutoModel\nimport torch\n# Sentences we want sentence embeddings for\nsentences = [\"样例数据-1\", \"样例数据-2\"]\n\n# Load model from HuggingFace Hub\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')\nmodel = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')\nmodel.eval()\n\n# Tokenize sentences\nencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)\n# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')\n\n# Compute token embeddings\nwith torch.no_grad():\n model_output = model(**encoded_input)\n # Perform pooling. In this case, cls pooling.\n sentence_embeddings = model_output[0][:, 0]\n# normalize embeddings\nsentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)\nprint(\"Sentence embeddings:\", sentence_embeddings)\n```\n\n### Usage for Reranker\n\nDifferent from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. \nYou can get a relevance score by inputting query and passage to the reranker. \nThe reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.\n\n\n#### Using FlagEmbedding\n```\npip install -U FlagEmbedding\n```\n\nGet relevance scores (higher scores indicate more relevance):\n```python\nfrom FlagEmbedding import FlagReranker\nreranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nscore = reranker.compute_score(['query', 'passage'])\nprint(score)\n\nscores = 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.']])\nprint(scores)\n```\n\n\n#### Using Huggingface transformers\n\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')\nmodel = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')\nmodel.eval()\n\npairs = [['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.']]\nwith torch.no_grad():\n inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)\n scores = model(**inputs, return_dict=True).logits.view(-1, ).float()\n print(scores)\n```\n\n## Evaluation \n\n`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**\nFor more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). \n\n- **MTEB**: \n\n| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |\n|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| [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 | \n| [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 | \n| [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 | \n| [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 | \n| [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 | \n| [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 |\n| [gte-base](https://huggingface.co/thenlper/gte-base) \t| 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |\n| [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 |\n| [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 | \n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \t| 768 | 514 \t| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |\n| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \t| 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |\n\n\n\n- **C-MTEB**: \nWe create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. \nPlease refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.\n \n| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |\n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n| [**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 | \n| [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 | \n| [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 | \n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |\n| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |\n| [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 |\n| [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 |\n| [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 |\n| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |\n| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |\n| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |\n\n\n- **Reranking**:\nSee [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.\n\n| Model | T2Reranking | T2RerankingZh2En\\* | T2RerankingEn2Zh\\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| \n| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | \n| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | \n| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | \n| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | \n| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | \n| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | \n| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | \n| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | \n| [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 | \n| [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 | \n\n\\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks\n\n## Train\n\n### BAAI Embedding \n\nWe pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. \n**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**\nWe also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).\nNote 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.\nMore training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).\n\n\n\n### BGE Reranker\n\nCross-encoder will perform full-attention over the input pair, \nwhich is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.\nTherefore, it can be used to re-rank the top-k documents returned by embedding model.\nWe train the cross-encoder on a multilingual pair data, \nThe 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). \nMore details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)\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{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). The released models can be used for commercial purposes free of charge."},"matched_task":{"kind":"list like","value":["SEMANTIC_SIMILARITY","SUMMARIZATION"],"string":"[\n \"SEMANTIC_SIMILARITY\",\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BEAR"],"string":"[\n \"BEAR\"\n]"}}},{"rowIdx":197,"cells":{"id":{"kind":"string","value":"JosephusCheung/Guanaco"},"author":{"kind":"string","value":"JosephusCheung"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","llama","text-generation","guannaco","alpaca","conversational","en","zh","ja","de","dataset:JosephusCheung/GuanacoDataset","doi:10.57967/hf/0607","license:gpl-3.0","autotrain_compatible","text-generation-inference","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"llama\",\n \"text-generation\",\n \"guannaco\",\n \"alpaca\",\n \"conversational\",\n \"en\",\n \"zh\",\n \"ja\",\n \"de\",\n \"dataset:JosephusCheung/GuanacoDataset\",\n \"doi:10.57967/hf/0607\",\n \"license:gpl-3.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2023-04-08T03:03:14","string":"2023-04-08T03:03:14"},"last_modified":{"kind":"timestamp","value":"2023-05-29T12:48:21","string":"2023-05-29T12:48:21"},"downloads":{"kind":"number","value":2164,"string":"2,164"},"likes":{"kind":"number","value":230,"string":"230"},"README":{"kind":"string","value":"---\ndatasets:\n- JosephusCheung/GuanacoDataset\nlanguage:\n- en\n- zh\n- ja\n- de\nlicense: gpl-3.0\npipeline_tag: conversational\ntags:\n- llama\n- guannaco\n- alpaca\ninference: false\n---\n\n\n\n**You can run on Colab free T4 GPU now**\n\n[](https://colab.research.google.com/drive/1ocSmoy3ba1EkYu7JWT1oCw9vz8qC2cMk#scrollTo=zLORi5OcPcIJ)\n\n **It is highly recommended to use fp16 inference for this model, as 8-bit precision may significantly affect performance. If you require a more Consumer Hardware friendly version, please use the specialized quantized, only 5+GB V-Ram required** [JosephusCheung/GuanacoOnConsumerHardware](https://huggingface.co/JosephusCheung/GuanacoOnConsumerHardware).\n\n **You are encouraged to use the latest version of transformers from GitHub.**\n\nGuanaco is an advanced instruction-following language model built on Meta's LLaMA 7B model. Expanding upon the initial 52K dataset from the Alpaca model, an additional 534K+ entries have been incorporated, covering English, Simplified Chinese, Traditional Chinese (Taiwan), Traditional Chinese (Hong Kong), Japanese, Deutsch, and various linguistic and grammatical tasks. This wealth of data enables Guanaco to perform exceptionally well in multilingual environments.\n\nIn an effort to foster openness and replicability in research, we have made the Guanaco Dataset publicly accessible and we have released the model weights here. By providing these resources, we aim to inspire more researchers to pursue related research and collectively advance the development of instruction-following language models.\n\n[KBlueLeaf](https://huggingface.co/KBlueLeaf)’s invaluable contributions to the conceptual validation, [trained model](https://huggingface.co/KBlueLeaf/guanaco-7B-leh) and [inference development](https://github.com/KohakuBlueleaf/guanaco-lora) of the model would be gratefully acknowledged, without whose efforts the project shall never have come to fruition.\n\nWhen utilizing the Guanaco model, please bear in mind the following points:\n\nThe Guanaco model has not been filtered for harmful, biased, or explicit content. As a result, outputs that do not adhere to ethical norms may be generated during use. Please exercise caution when using the model in research or practical applications.\n1. ### Improved context and prompt role support:\n\n The new format is designed to be similar to ChatGPT, allowing for better integration with the Alpaca format and enhancing the overall user experience.\n\n Instruction is utilized as a few-shot context to support diverse inputs and responses, making it easier for the model to understand and provide accurate responses to user queries.\n\n The format is as follows:\n\n```\n### Instruction:\nUser: History User Input\nAssistant: History Assistant Answer\n### Input:\nSystem: Knowledge\nUser: New User Input\n### Response:\nNew Assistant Answer\n```\n\n This structured format allows for easier tracking of the conversation history and maintaining context throughout a multi-turn dialogue.\n\n3. ### Role-playing support:\n\n Guanaco now offers advanced role-playing support, similar to Character.AI, in English, Simplified Chinese, Traditional Chinese, Japanese, and Deutsch, making it more versatile for users from different linguistic backgrounds.\n\n Users can instruct the model to assume specific roles, historical figures, or fictional characters, as well as personalities based on their input. This allows for more engaging and immersive conversations.\n\n The model can use various sources of information to provide knowledge and context for the character's background and behavior, such as encyclopedic entries, first-person narrations, or a list of personality traits.\n\n The model will consistently output responses in the format \"Character Name: Reply\" to maintain the chosen role throughout the conversation, enhancing the user's experience.\n\n4. ### Rejection of answers and avoidance of erroneous responses:\n \n The model has been updated to handle situations where it lacks sufficient knowledge or is unable to provide a valid response more effectively.\n\n Reserved keywords have been introduced to indicate different scenarios and provide clearer communication with the user, use in System Prompt:\n\n NO IDEA: Indicates that the model lacks the necessary knowledge to provide an accurate answer, and will explain this to the user, encouraging them to seek alternative sources.\n \n FORBIDDEN: Indicates that the model refuses to answer due to specific reasons (e.g., legal, ethical, or safety concerns), which will be inferred based on the context of the query.\n\n SFW: Indicates that the model refuses to answer a question because it has been filtered for NSFW content, ensuring a safer and more appropriate user experience.\n\n6. ### Continuation of responses for ongoing topics:\n\n The Guanaco model can now continue answering questions or discussing topics upon the user's request, making it more adaptable and better suited for extended conversations.\n\n The contextual structure consisting of System, Assistant, and User roles allows the model to engage in multi-turn dialogues, maintain context-aware conversations, and provide more coherent responses.\n\n The model can now accommodate role specification and character settings, providing a more immersive and tailored conversational experience based on the user's preferences.\n\nIt is important to remember that Guanaco is a 7B-parameter model, and **any knowledge-based content should be considered potentially inaccurate**. We strongly recommend **providing verifiable sources in System Prompt, such as Wikipedia, for knowledge-based answers**. In the absence of sources, it is crucial to inform users of this limitation to prevent the dissemination of false information and to maintain transparency.\n\nDue to the differences in the format between this project and [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), please refer to *Guanaco-lora: LoRA for training Multilingual Instruction-following LM based on LLaMA* (https://github.com/KohakuBlueleaf/guanaco-lora) for further training and inference our models.\n\n## Recent News\n\nWe've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it.\n\nGuanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental.\n\nWe, however, aim to move beyond the incremental:\n\n1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks.\n\n2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks.\n\n3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM.\n\nWhile our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress.\n"},"matched_task":{"kind":"list like","value":["TRANSLATION"],"string":"[\n \"TRANSLATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BEAR"],"string":"[\n \"BEAR\"\n]"}}},{"rowIdx":198,"cells":{"id":{"kind":"string","value":"mrm8488/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64"},"author":{"kind":"string","value":"mrm8488"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","modernbert","sentence-similarity","feature-extraction","generated_from_trainer","dataset_size:2697","loss:MatryoshkaLoss","loss:CoSENTLoss","arxiv:1908.10084","arxiv:2205.13147","base_model:nomic-ai/modernbert-embed-base","base_model:finetune:nomic-ai/modernbert-embed-base","model-index","autotrain_compatible","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"modernbert\",\n \"sentence-similarity\",\n \"feature-extraction\",\n \"generated_from_trainer\",\n \"dataset_size:2697\",\n \"loss:MatryoshkaLoss\",\n \"loss:CoSENTLoss\",\n \"arxiv:1908.10084\",\n \"arxiv:2205.13147\",\n \"base_model:nomic-ai/modernbert-embed-base\",\n \"base_model:finetune:nomic-ai/modernbert-embed-base\",\n \"model-index\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"timestamp","value":"2025-01-10T21:44:41","string":"2025-01-10T21:44:41"},"last_modified":{"kind":"timestamp","value":"2025-01-10T21:50:00","string":"2025-01-10T21:50:00"},"downloads":{"kind":"number","value":2130,"string":"2,130"},"likes":{"kind":"number","value":2,"string":"2"},"README":{"kind":"string","value":"---\nbase_model: nomic-ai/modernbert-embed-base\nlibrary_name: sentence-transformers\nmetrics:\n- pearson_cosine\n- spearman_cosine\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- sentence-similarity\n- feature-extraction\n- generated_from_trainer\n- dataset_size:2697\n- loss:MatryoshkaLoss\n- loss:CoSENTLoss\nwidget:\n- source_sentence: En un mercado de granjeros, se encuentra un hombre.\n sentences:\n - Un abogado de la CPI detenido en Libia está ahora mismo encarando un período de\n detención de 45 días\n - Un hombre está presente en un mercado donde se venden productos agrícolas directamente\n de los agricultores.\n - ¿Existe la posibilidad de que cambie de opinión si no se expresa de manera enérgica\n o muestra un comportamiento inapropiado?\n- source_sentence: Una mujer está posada en una postura con los brazos abiertos mientras\n otra persona le toma una fotografía.\n sentences:\n - Un hombre se encuentra parado en medio de una multitud sujetando un objeto de\n color blanco.\n - Las personas están cerca del agua.\n - Frente a una estatua de una vaca, hay una mujer, un niño pequeño y un bebé diminuto.\n- source_sentence: Un grupo de cuatro niños está observando los diferentes animales\n que están en el establo.\n sentences:\n - Evita apoyar todo tu peso en los brazos, ya que tus manos no están diseñadas para\n soportar esa presión constante.\n - Los niños están mirando atentamente a una oveja.\n - Un puma persigue a un oso grande en el bosque.\n- source_sentence: La gente se balancea saltando al agua mientras otros pescan en\n el fondo del mar.\n sentences:\n - Dos individuos observan el agua con atención.\n - Siempre golpeamos suavemente a nuestros hijos en la boca para mostrarles que su\n boca es lo que les causa dolor.\n - Aunque el sistema de prioridad al primero en llegar beneficia a dos participantes,\n no asegura definitivamente la exclusión de terceros.\n- source_sentence: El cordero está mirando hacia la cámara.\n sentences:\n - Manmohan en Teherán insta a NAM a tomar una posición clara sobre el conflicto\n en Siria\n - Un gato está mirando hacia la cámara también.\n - '\"Sí, no deseo estar presente durante este testimonio\", declaró tranquilamente\n Peterson, de 31 años, al juez cuando fue devuelto a su celda.'\nmodel-index:\n- name: SentenceTransformer based on nomic-ai/modernbert-embed-base\n results:\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts dev 768\n type: sts-dev-768\n metrics:\n - type: pearson_cosine\n value: 0.7498914121357008\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.7531670275662775\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts dev 512\n type: sts-dev-512\n metrics:\n - type: pearson_cosine\n value: 0.7468285624371191\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.7482342767593612\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts dev 256\n type: sts-dev-256\n metrics:\n - type: pearson_cosine\n value: 0.7419098803201045\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.7450577925521013\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts dev 128\n type: sts-dev-128\n metrics:\n - type: pearson_cosine\n value: 0.7262860099881795\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.7304432975238186\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts dev 64\n type: sts-dev-64\n metrics:\n - type: pearson_cosine\n value: 0.6973267849431932\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.7069603266334332\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts test 768\n type: sts-test-768\n metrics:\n - type: pearson_cosine\n value: 0.8673484326459211\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.8767387684433159\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts test 512\n type: sts-test-512\n metrics:\n - type: pearson_cosine\n value: 0.8665336885415594\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.8751868367625472\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts test 256\n type: sts-test-256\n metrics:\n - type: pearson_cosine\n value: 0.8568125590206718\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.8702353416571491\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts test 128\n type: sts-test-128\n metrics:\n - type: pearson_cosine\n value: 0.8485344363338887\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.8617402150766132\n name: Spearman Cosine\n - task:\n type: semantic-similarity\n name: Semantic Similarity\n dataset:\n name: sts test 64\n type: sts-test-64\n metrics:\n - type: pearson_cosine\n value: 0.8193790032247387\n name: Pearson Cosine\n - type: spearman_cosine\n value: 0.8419631939550043\n name: Spearman Cosine\n---\n\n# SentenceTransformer based on nomic-ai/modernbert-embed-base\n\nThis is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the stsb_multi_es_augmented (private) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.\n\n## Model Details\n\n### Model Description\n- **Model Type:** Sentence Transformer\n- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) \n- **Maximum Sequence Length:** 8192 tokens\n- **Output Dimensionality:** 768 dimensions\n- **Similarity Function:** Cosine Similarity\n- **Training Dataset:**\n - Private stsb dataset\n\n### Model Sources\n\n- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)\n- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)\n- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)\n\n### Full Model Architecture\n\n```\nSentenceTransformer(\n (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel \n (1): Pooling({'word_embedding_dimension': 768, '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## Usage\n\n### Direct Usage (Sentence Transformers)\n\nFirst install the Sentence Transformers library:\n\n```bash\npip install -U sentence-transformers\n```\n\nThen you can load this model and run inference.\n```python\nfrom sentence_transformers import SentenceTransformer\n\n# Download from the 🤗 Hub\nmodel = SentenceTransformer(\"mrm8488/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64-5e\")\n# Run inference\nsentences = [\n 'El cordero está mirando hacia la cámara.',\n 'Un gato está mirando hacia la cámara también.',\n '\"Sí, no deseo estar presente durante este testimonio\", declaró tranquilamente Peterson, de 31 años, al juez cuando fue devuelto a su celda.',\n]\nembeddings = model.encode(sentences)\nprint(embeddings.shape)\n# [3, 768]\n\n# Get the similarity scores for the embeddings\nsimilarities = model.similarity(embeddings, embeddings)\nprint(similarities.shape)\n# [3, 3]\n```\n\n\n\n\n\n\n\n## Evaluation\n\n### Metrics\n\n#### Semantic Similarity\n\n* Datasets: `sts-dev-768`, `sts-dev-512`, `sts-dev-256`, `sts-dev-128`, `sts-dev-64`, `sts-test-768`, `sts-test-512`, `sts-test-256`, `sts-test-128` and `sts-test-64`\n* Evaluated with [3. When does the query instruction need to be used
\n\n \n\nFor the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. \nNo instruction only has a slight degradation in retrieval performance compared with using instruction. \nSo you can generate embedding without instruction in all cases for convenience.\n \nFor a retrieval task that uses short queries to find long related documents, \nit is recommended to add instructions for these short queries.\n**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**\nIn all cases, the documents/passages do not need to add the instruction. \n\nEmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)\n\n| Metric | sts-dev-768 | sts-dev-512 | sts-dev-256 | sts-dev-128 | sts-dev-64 | sts-test-768 | sts-test-512 | sts-test-256 | sts-test-128 | sts-test-64 |\n|:--------------------|:------------|:------------|:------------|:------------|:-----------|:-------------|:-------------|:-------------|:-------------|:------------|\n| pearson_cosine | 0.7499 | 0.7468 | 0.7419 | 0.7263 | 0.6973 | 0.8673 | 0.8665 | 0.8568 | 0.8485 | 0.8194 |\n| **spearman_cosine** | **0.7532** | **0.7482** | **0.7451** | **0.7304** | **0.707** | **0.8767** | **0.8752** | **0.8702** | **0.8617** | **0.842** |\n\n\n\n\n\n## Training Details\n\n### Training Dataset\n\n#### stsb_multi_es_augmented (private)\n\n* Size: 2,697 training samples\n* Columns: sentence1, sentence2, and score\n* Approximate statistics based on the first 1000 samples:\n | | sentence1 | sentence2 | score |\n |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|\n | type | string | string | float |\n | details |
- min: 9 tokens
- mean: 28.42 tokens
- max: 96 tokens
- min: 10 tokens
- mean: 28.01 tokens
- max: 92 tokens
- min: 0.0
- mean: 2.72
- max: 5.0
El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha. | Un ave de color amarillo descansaba tranquilamente en una rama. | 3.200000047683716 |\n | Una chica está tocando la flauta en un parque. | Un grupo de músicos está tocando en un escenario al aire libre. | 1.286 |\n | La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece | La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere | 4.199999809265137 |\n* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:\n ```json\n {\n \"loss\": \"CoSENTLoss\",\n \"matryoshka_dims\": [\n 768,\n 512,\n 256,\n 128,\n 64\n ],\n \"matryoshka_weights\": [\n 1,\n 1,\n 1,\n 1,\n 1\n ],\n \"n_dims_per_step\": -1\n }\n ```\n\n### Evaluation Dataset\n\n#### stsb_multi_es_augmented (private)\n\n* Size: 697 evaluation samples\n* Columns: sentence1, sentence2, and score\n* Approximate statistics based on the first 697 samples:\n | | sentence1 | sentence2 | score |\n |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|\n | type | string | string | float |\n | details | - min: 9 tokens
- mean: 29.35 tokens
- max: 87 tokens
- min: 9 tokens
- mean: 28.52 tokens
- max: 81 tokens
- min: 0.0
- mean: 2.3
- max: 5.0
Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas. | Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos. | 4.199999809265137 |\n | \"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud\" | \"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar.\" | 3.5 |\n | El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario. | Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida. | 3.691999912261963 |\n* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:\n ```json\n {\n \"loss\": \"CoSENTLoss\",\n \"matryoshka_dims\": [\n 768,\n 512,\n 256,\n 128,\n 64\n ],\n \"matryoshka_weights\": [\n 1,\n 1,\n 1,\n 1,\n 1\n ],\n \"n_dims_per_step\": -1\n }\n ```\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `eval_strategy`: steps\n- `per_device_train_batch_size`: 16\n- `per_device_eval_batch_size`: 16\n- `num_train_epochs`: 5\n- `warmup_ratio`: 0.1\n- `bf16`: True\n\n#### All Hyperparameters\nClick to expand
\n\n- `overwrite_output_dir`: False\n- `do_predict`: False\n- `eval_strategy`: steps\n- `prediction_loss_only`: True\n- `per_device_train_batch_size`: 16\n- `per_device_eval_batch_size`: 16\n- `per_gpu_train_batch_size`: None\n- `per_gpu_eval_batch_size`: None\n- `gradient_accumulation_steps`: 1\n- `eval_accumulation_steps`: None\n- `torch_empty_cache_steps`: None\n- `learning_rate`: 5e-05\n- `weight_decay`: 0.0\n- `adam_beta1`: 0.9\n- `adam_beta2`: 0.999\n- `adam_epsilon`: 1e-08\n- `max_grad_norm`: 1.0\n- `num_train_epochs`: 5\n- `max_steps`: -1\n- `lr_scheduler_type`: linear\n- `lr_scheduler_kwargs`: {}\n- `warmup_ratio`: 0.1\n- `warmup_steps`: 0\n- `log_level`: passive\n- `log_level_replica`: warning\n- `log_on_each_node`: True\n- `logging_nan_inf_filter`: True\n- `save_safetensors`: True\n- `save_on_each_node`: False\n- `save_only_model`: False\n- `restore_callback_states_from_checkpoint`: False\n- `no_cuda`: False\n- `use_cpu`: False\n- `use_mps_device`: False\n- `seed`: 42\n- `data_seed`: None\n- `jit_mode_eval`: False\n- `use_ipex`: False\n- `bf16`: True\n- `fp16`: False\n- `fp16_opt_level`: O1\n- `half_precision_backend`: auto\n- `bf16_full_eval`: False\n- `fp16_full_eval`: False\n- `tf32`: None\n- `local_rank`: 0\n- `ddp_backend`: None\n- `tpu_num_cores`: None\n- `tpu_metrics_debug`: False\n- `debug`: []\n- `dataloader_drop_last`: False\n- `dataloader_num_workers`: 0\n- `dataloader_prefetch_factor`: None\n- `past_index`: -1\n- `disable_tqdm`: False\n- `remove_unused_columns`: True\n- `label_names`: None\n- `load_best_model_at_end`: False\n- `ignore_data_skip`: False\n- `fsdp`: []\n- `fsdp_min_num_params`: 0\n- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}\n- `fsdp_transformer_layer_cls_to_wrap`: None\n- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}\n- `deepspeed`: None\n- `label_smoothing_factor`: 0.0\n- `optim`: adamw_torch\n- `optim_args`: None\n- `adafactor`: False\n- `group_by_length`: False\n- `length_column_name`: length\n- `ddp_find_unused_parameters`: None\n- `ddp_bucket_cap_mb`: None\n- `ddp_broadcast_buffers`: False\n- `dataloader_pin_memory`: True\n- `dataloader_persistent_workers`: False\n- `skip_memory_metrics`: True\n- `use_legacy_prediction_loop`: False\n- `push_to_hub`: False\n- `resume_from_checkpoint`: None\n- `hub_model_id`: None\n- `hub_strategy`: every_save\n- `hub_private_repo`: None\n- `hub_always_push`: False\n- `gradient_checkpointing`: False\n- `gradient_checkpointing_kwargs`: None\n- `include_inputs_for_metrics`: False\n- `include_for_metrics`: []\n- `eval_do_concat_batches`: True\n- `fp16_backend`: auto\n- `push_to_hub_model_id`: None\n- `push_to_hub_organization`: None\n- `mp_parameters`: \n- `auto_find_batch_size`: False\n- `full_determinism`: False\n- `torchdynamo`: None\n- `ray_scope`: last\n- `ddp_timeout`: 1800\n- `torch_compile`: False\n- `torch_compile_backend`: None\n- `torch_compile_mode`: None\n- `dispatch_batches`: None\n- `split_batches`: None\n- `include_tokens_per_second`: False\n- `include_num_input_tokens_seen`: False\n- `neftune_noise_alpha`: None\n- `optim_target_modules`: None\n- `batch_eval_metrics`: False\n- `eval_on_start`: False\n- `use_liger_kernel`: False\n- `eval_use_gather_object`: False\n- `average_tokens_across_devices`: False\n- `prompts`: None\n- `batch_sampler`: batch_sampler\n- `multi_dataset_batch_sampler`: proportional\n\n\n ![](\"gemma_2_9b_sea-lion_v3_instruct_banner.png\"/)
\n
\n\n# Gemma2 9B CPT SEA-LIONv3 Instruct\n\nSEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.\n\nGemma2 9B CPT SEA-LIONv3 Instruct is a multilingual model which has been fine-tuned with around **500,000 English instruction-completion pairs** alongside a larger pool of around **1,000,000 instruction-completion pairs** from other ASEAN languages, such as Indonesian, Thai and Vietnamese.\n\nSEA-LION stands for _Southeast Asian Languages In One Network_.\n\n- **Developed by:** Products Pillar, AI Singapore\n- **Funded by:** Singapore NRF\n- **Model type:** Decoder\n- **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese\n- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)\n\n## Model Details\n### Model Description\nWe performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our [continued pre-trained Gemma2 9B CPT SEA-LIONv3](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT SEA-LIONv3 Instruct.\n\nFor tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.\n\n### Benchmark Performance\nWe evaluated Gemma2 9B CPT SEA-LIONv3 Instruct on both general language capabilities and instruction-following capabilities.\n\n#### General Language Capabilities\nFor the evaluation of general language capabilities, we employed the [SEA HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.\nThese tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).\n\nNote: SEA HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.\n\nThe evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.\n\n#### Instruction-following Capabilities\nSince Gemma2 9B CPT SEA-LIONv3 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, [IFEval](https://arxiv.org/abs/2311.07911) and [MT-Bench](https://arxiv.org/abs/2306.05685).\n\nAs these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localize and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.\n\n**IFEval**\n\nIFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalized by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).\n\n\n**MT-Bench**\n\nMT-Bench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction). A tie is given a score of 0.5.\n\n\nFor more details on Gemma2 9B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/\n\n\n### Usage\n**NOTE** This model has not been trained to use a system prompt or to use tool calling.\n\nGemma2 9B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library \n```python\n# Please use transformers==4.45.2\n\nimport transformers\nimport torch\n\nmodel_id = \"aisingapore/gemma2-9b-cpt-sea-lionv3-instruct\"\n\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model_id,\n model_kwargs={\"torch_dtype\": torch.bfloat16},\n device_map=\"auto\",\n)\nmessages = [\n {\"role\": \"user\", \"content\": \"Apa sentimen dari kalimat berikut ini?\\nKalimat: Buku ini sangat membosankan.\\nJawaban: \"},\n]\n\noutputs = pipeline(\n messages,\n max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\n### Caveats\nIt is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.\n\n## Limitations\n### Safety\nCurrent SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.\n\n## Technical Specifications\n### Fine-Tuning Details\nGemma2 9B CPT SEA-LIONv3 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 15 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.\n\n## Data\nGemma2 9B CPT SEA-LIONv3 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. \n\n## Indonesian, Javanese & Sudanese Specific SEA-LION\nOur partners at GoTo have continued pretrained and instruction tuned a variant of Gemma2 9B CPT SEA-LIONv3, specifically enhancing its capabilities for Indonesian, Javanese, and Sundanese languages. Find the continued pretrained model at [Gemma2 9B CPT SahabatAIv1 Base](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), and its corresponding instructioned tuned version at [Gemma2 9B CPT SahabatAIv1 Instruct](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct). \n\n## Call for Contributions\nWe encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.\n\n## The Team\nChan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin\n\n## Acknowledgements\n[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore. \n\n## Contact\nFor more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)\n\n[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)\n\n## Disclaimer\nThis is the repository for the commercial instruction-tuned model.\nThe model has _not_ been aligned for safety.\nDevelopers and users should perform their own safety fine-tuning and related security measures.\nIn no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes."},"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":["CHIA"],"string":"[\n \"CHIA\"\n]"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":1,"numItemsPerPage":100,"numTotalItems":1838,"offset":100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1NjI5ODM0Niwic3ViIjoiL2RhdGFzZXRzL0V1YW55dS9tZXJnZWRfdGFza19kYXRhc2V0X21vZGVscyIsImV4cCI6MTc1NjMwMTk0NiwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.TBuO7CEtQUb9vvV2HUX9yEnJ4SX02qqG99T3pb5goGaYdmuYUV6qsMGSTSFJEV6ZOlvh3auw8kpk3rdeFur-Ag","displayUrls":true},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">\n
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ibm-granite/granite-embedding-278m-multilingual
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---
language:
- en
- ar
- cs
- de
- es
- fr
- it
- ja
- ko
- nl
- pt
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- language
- granite
- embeddings
- multilingual
- mteb
- sentence-transformers
model-index:
- name: ibm-granite/granite-embedding-278m-multilingual
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: 73.4333
- type: f1
value: 61.2301
- type: f1_weighted
value: 78.40899999999999
- type: ap
value: 23.347
- type: ap_weighted
value: 23.347
- type: main_score
value: 73.4333
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.806
- type: f1
value: 65.6467
- type: f1_weighted
value: 74.4815
- type: ap
value: 34.045700000000004
- type: ap_weighted
value: 34.045700000000004
- type: main_score
value: 71.806
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 67.5907
- type: f1
value: 67.36370000000001
- type: f1_weighted
value: 67.36370000000001
- type: ap
value: 62.0368
- type: ap_weighted
value: 62.0368
- type: main_score
value: 67.5907
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.278
- type: f1
value: 36.4099
- type: f1_weighted
value: 36.4099
- type: main_score
value: 37.278
- task:
type: Retrieval
dataset:
name: MTEB AppsRetrieval (default)
type: CoIR-Retrieval/apps
config: default
split: test
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
metrics:
- type: ndcg_at_1
value: 3.453
- type: ndcg_at_3
value: 4.882000000000001
- type: ndcg_at_5
value: 5.564
- type: ndcg_at_10
value: 6.214
- type: ndcg_at_20
value: 6.814000000000001
- type: ndcg_at_100
value: 8.581
- type: ndcg_at_1000
value: 12.215
- type: map_at_1
value: 3.453
- type: map_at_3
value: 4.515000000000001
- type: map_at_5
value: 4.89
- type: map_at_10
value: 5.151
- type: map_at_20
value: 5.313
- type: map_at_100
value: 5.539000000000001
- type: map_at_1000
value: 5.638
- type: recall_at_1
value: 3.453
- type: recall_at_3
value: 5.949999999999999
- type: recall_at_5
value: 7.623
- type: recall_at_10
value: 9.668000000000001
- type: recall_at_20
value: 12.058
- type: recall_at_100
value: 21.859
- type: recall_at_1000
value: 52.722
- type: precision_at_1
value: 3.453
- type: precision_at_3
value: 1.983
- type: precision_at_5
value: 1.525
- type: precision_at_10
value: 0.967
- type: precision_at_20
value: 0.603
- type: precision_at_100
value: 0.219
- type: precision_at_1000
value: 0.053
- type: mrr_at_1
value: 3.4528999999999996
- type: mrr_at_3
value: 4.5153
- type: mrr_at_5
value: 4.889799999999999
- type: mrr_at_10
value: 5.1507
- type: mrr_at_20
value: 5.3135
- type: mrr_at_100
value: 5.5391
- type: mrr_at_1000
value: 5.6382
- type: nauc_ndcg_at_1_max
value: 37.1714
- type: nauc_ndcg_at_1_std
value: 15.306700000000001
- type: nauc_ndcg_at_1_diff1
value: 46.2252
- type: nauc_ndcg_at_3_max
value: 32.0309
- type: nauc_ndcg_at_3_std
value: 14.2983
- type: nauc_ndcg_at_3_diff1
value: 34.7174
- type: nauc_ndcg_at_5_max
value: 29.3613
- type: nauc_ndcg_at_5_std
value: 13.0358
- type: nauc_ndcg_at_5_diff1
value: 30.8369
- type: nauc_ndcg_at_10_max
value: 26.820100000000004
- type: nauc_ndcg_at_10_std
value: 12.3422
- type: nauc_ndcg_at_10_diff1
value: 27.3719
- type: nauc_ndcg_at_20_max
value: 25.5643
- type: nauc_ndcg_at_20_std
value: 11.383000000000001
- type: nauc_ndcg_at_20_diff1
value: 25.7058
- type: nauc_ndcg_at_100_max
value: 23.2131
- type: nauc_ndcg_at_100_std
value: 12.4787
- type: nauc_ndcg_at_100_diff1
value: 21.6874
- type: nauc_ndcg_at_1000_max
value: 22.900499999999997
- type: nauc_ndcg_at_1000_std
value: 13.2218
- type: nauc_ndcg_at_1000_diff1
value: 19.668
- type: nauc_map_at_1_max
value: 37.1714
- type: nauc_map_at_1_std
value: 15.306700000000001
- type: nauc_map_at_1_diff1
value: 46.2252
- type: nauc_map_at_3_max
value: 33.1012
- type: nauc_map_at_3_std
value: 14.4117
- type: nauc_map_at_3_diff1
value: 36.8859
- type: nauc_map_at_5_max
value: 31.404700000000002
- type: nauc_map_at_5_std
value: 13.5956
- type: nauc_map_at_5_diff1
value: 34.3454
- type: nauc_map_at_10_max
value: 30.1013
- type: nauc_map_at_10_std
value: 13.2253
- type: nauc_map_at_10_diff1
value: 32.487
- type: nauc_map_at_20_max
value: 29.5747
- type: nauc_map_at_20_std
value: 12.843499999999999
- type: nauc_map_at_20_diff1
value: 31.8252
- type: nauc_map_at_100_max
value: 28.968899999999998
- type: nauc_map_at_100_std
value: 12.967699999999999
- type: nauc_map_at_100_diff1
value: 30.924000000000003
- type: nauc_map_at_1000_max
value: 28.894599999999997
- type: nauc_map_at_1000_std
value: 12.997800000000002
- type: nauc_map_at_1000_diff1
value: 30.7653
- type: nauc_recall_at_1_max
value: 37.1714
- type: nauc_recall_at_1_std
value: 15.306700000000001
- type: nauc_recall_at_1_diff1
value: 46.2252
- type: nauc_recall_at_3_max
value: 29.6485
- type: nauc_recall_at_3_std
value: 14.072799999999999
- type: nauc_recall_at_3_diff1
value: 29.9536
- type: nauc_recall_at_5_max
value: 25.251099999999997
- type: nauc_recall_at_5_std
value: 11.9121
- type: nauc_recall_at_5_diff1
value: 23.9203
- type: nauc_recall_at_10_max
value: 20.8856
- type: nauc_recall_at_10_std
value: 10.7653
- type: nauc_recall_at_10_diff1
value: 18.3716
- type: nauc_recall_at_20_max
value: 18.9378
- type: nauc_recall_at_20_std
value: 8.8933
- type: nauc_recall_at_20_diff1
value: 15.7693
- type: nauc_recall_at_100_max
value: 15.7027
- type: nauc_recall_at_100_std
value: 12.6519
- type: nauc_recall_at_100_diff1
value: 9.2726
- type: nauc_recall_at_1000_max
value: 16.2321
- type: nauc_recall_at_1000_std
value: 15.2717
- type: nauc_recall_at_1000_diff1
value: 4.4337
- type: nauc_precision_at_1_max
value: 37.1714
- type: nauc_precision_at_1_std
value: 15.306700000000001
- type: nauc_precision_at_1_diff1
value: 46.2252
- type: nauc_precision_at_3_max
value: 29.6485
- type: nauc_precision_at_3_std
value: 14.072799999999999
- type: nauc_precision_at_3_diff1
value: 29.9536
- type: nauc_precision_at_5_max
value: 25.251099999999997
- type: nauc_precision_at_5_std
value: 11.9121
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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
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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
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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dataset:
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type: mteb/medrxiv-clustering-p2p
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split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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dataset:
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split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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dataset:
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type: mteb/mind_small
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split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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type: mteb/nfcorpus
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split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 81.6341
- type: spearman
value: 75.1911
- type: cosine_pearson
value: 81.6341
- type: cosine_spearman
value: 75.1911
- type: manhattan_pearson
value: 78.4046
- type: manhattan_spearman
value: 75.1706
- type: euclidean_pearson
value: 78.3649
- type: euclidean_spearman
value: 75.1934
- type: main_score
value: 75.1911
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 76.4378
- type: spearman
value: 77.3053
- type: cosine_pearson
value: 76.4378
- type: cosine_spearman
value: 77.3053
- type: manhattan_pearson
value: 77.1958
- type: manhattan_spearman
value: 77.2543
- type: euclidean_pearson
value: 77.2317
- type: euclidean_spearman
value: 77.3053
- type: main_score
value: 77.3053
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 78.4342
- type: spearman
value: 74.9479
- type: cosine_pearson
value: 78.4342
- type: cosine_spearman
value: 74.9479
- type: manhattan_pearson
value: 77.12219999999999
- type: manhattan_spearman
value: 74.924
- type: euclidean_pearson
value: 77.14800000000001
- type: euclidean_spearman
value: 74.94800000000001
- type: main_score
value: 74.9479
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 85.1908
- type: spearman
value: 86.0174
- type: cosine_pearson
value: 85.1908
- type: cosine_spearman
value: 86.0174
- type: manhattan_pearson
value: 85.4436
- type: manhattan_spearman
value: 86.0332
- type: euclidean_pearson
value: 85.4339
- type: euclidean_spearman
value: 86.0174
- type: main_score
value: 86.0174
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 80.5421
- type: spearman
value: 81.9568
- type: cosine_pearson
value: 80.5421
- type: cosine_spearman
value: 81.9568
- type: manhattan_pearson
value: 81.1013
- type: manhattan_spearman
value: 81.8165
- type: euclidean_pearson
value: 81.24510000000001
- type: euclidean_spearman
value: 81.9568
- type: main_score
value: 81.9568
- 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: 48.2717
- type: spearman
value: 44.642900000000004
- type: cosine_pearson
value: 48.2717
- type: cosine_spearman
value: 44.642900000000004
- type: manhattan_pearson
value: 50.314400000000006
- type: manhattan_spearman
value: 44.982299999999995
- type: euclidean_pearson
value: 50.1685
- type: euclidean_spearman
value: 44.642900000000004
- type: main_score
value: 44.642900000000004
- 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: 67.8601
- type: spearman
value: 68.2763
- type: cosine_pearson
value: 67.8601
- type: cosine_spearman
value: 68.2763
- type: manhattan_pearson
value: 68.1563
- type: manhattan_spearman
value: 68.4724
- type: euclidean_pearson
value: 68.1026
- type: euclidean_spearman
value: 68.2763
- type: main_score
value: 68.2763
- 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: 78.05539999999999
- type: spearman
value: 78.5929
- type: cosine_pearson
value: 78.05539999999999
- type: cosine_spearman
value: 78.5929
- type: manhattan_pearson
value: 78.408
- type: manhattan_spearman
value: 78.8622
- type: euclidean_pearson
value: 78.1413
- type: euclidean_spearman
value: 78.5929
- type: main_score
value: 78.5929
- 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: 59.4349
- type: spearman
value: 59.838800000000006
- type: cosine_pearson
value: 59.4349
- type: cosine_spearman
value: 59.838800000000006
- type: manhattan_pearson
value: 60.7565
- type: manhattan_spearman
value: 60.5824
- type: euclidean_pearson
value: 60.247099999999996
- type: euclidean_spearman
value: 59.838800000000006
- type: main_score
value: 59.838800000000006
- 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: 73.84039999999999
- type: spearman
value: 74.2498
- type: cosine_pearson
value: 73.84039999999999
- type: cosine_spearman
value: 74.2498
- type: manhattan_pearson
value: 74.6784
- type: manhattan_spearman
value: 74.4608
- type: euclidean_pearson
value: 74.5596
- type: euclidean_spearman
value: 74.2498
- type: main_score
value: 74.2498
- 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: 67.9218
- type: spearman
value: 68.0418
- type: cosine_pearson
value: 67.9218
- type: cosine_spearman
value: 68.0418
- type: manhattan_pearson
value: 68.51
- type: manhattan_spearman
value: 68.1968
- type: euclidean_pearson
value: 68.343
- type: euclidean_spearman
value: 68.0418
- type: main_score
value: 68.0418
- 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: 70.381
- type: spearman
value: 69.5729
- type: cosine_pearson
value: 70.381
- type: cosine_spearman
value: 69.5729
- type: manhattan_pearson
value: 70.8688
- type: manhattan_spearman
value: 69.4406
- type: euclidean_pearson
value: 71.0267
- type: euclidean_spearman
value: 69.5729
- type: main_score
value: 69.5729
- 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: 70.0196
- type: spearman
value: 69.7175
- type: cosine_pearson
value: 70.0196
- type: cosine_spearman
value: 69.7175
- type: manhattan_pearson
value: 71.40990000000001
- type: manhattan_spearman
value: 70.1461
- type: euclidean_pearson
value: 70.88799999999999
- type: euclidean_spearman
value: 69.7175
- type: main_score
value: 69.7175
- 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: 65.7536
- type: spearman
value: 60.04429999999999
- type: cosine_pearson
value: 65.7536
- type: cosine_spearman
value: 60.04429999999999
- type: manhattan_pearson
value: 68.58579999999999
- type: manhattan_spearman
value: 60.3699
- type: euclidean_pearson
value: 68.3761
- type: euclidean_spearman
value: 60.04429999999999
- type: main_score
value: 60.04429999999999
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 68.997
- type: spearman
value: 68.1508
- type: cosine_pearson
value: 68.997
- type: cosine_spearman
value: 68.1508
- type: manhattan_pearson
value: 68.9229
- type: manhattan_spearman
value: 68.0124
- type: euclidean_pearson
value: 69.0519
- type: euclidean_spearman
value: 68.1508
- type: main_score
value: 68.1508
- 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: 80.2006
- type: spearman
value: 80.4702
- type: cosine_pearson
value: 80.2006
- type: cosine_spearman
value: 80.4702
- type: manhattan_pearson
value: 80.81009999999999
- type: manhattan_spearman
value: 80.6037
- type: euclidean_pearson
value: 80.66290000000001
- type: euclidean_spearman
value: 80.4702
- type: main_score
value: 80.4702
- 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: 74.0885
- type: spearman
value: 72.4574
- type: cosine_pearson
value: 74.0885
- type: cosine_spearman
value: 72.4574
- type: manhattan_pearson
value: 75.25659999999999
- type: manhattan_spearman
value: 71.9695
- type: euclidean_pearson
value: 75.4999
- type: euclidean_spearman
value: 72.4574
- type: main_score
value: 72.4574
- 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: 74.1794
- type: spearman
value: 70.6749
- type: cosine_pearson
value: 74.1794
- type: cosine_spearman
value: 70.6749
- type: manhattan_pearson
value: 74.3245
- type: manhattan_spearman
value: 71.2375
- type: euclidean_pearson
value: 73.221
- type: euclidean_spearman
value: 70.6749
- type: main_score
value: 70.6749
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 76.7328
- type: spearman
value: 78.4076
- type: cosine_pearson
value: 76.7328
- type: cosine_spearman
value: 78.4076
- type: manhattan_pearson
value: 78.24950000000001
- type: manhattan_spearman
value: 78.23400000000001
- type: euclidean_pearson
value: 78.3628
- type: euclidean_spearman
value: 78.4076
- type: main_score
value: 78.4076
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.6097
- type: mrr
value: 94.12939999999999
- type: nAUC_map_max
value: 58.7937
- type: nAUC_map_std
value: 69.6785
- type: nAUC_map_diff1
value: 7.4891
- type: nAUC_mrr_max
value: 84.7821
- type: nAUC_mrr_std
value: 77.6636
- type: nAUC_mrr_diff1
value: 49.763600000000004
- type: main_score
value: 79.6097
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 54.0
- type: ndcg_at_3
value: 60.851
- type: ndcg_at_5
value: 63.410999999999994
- type: ndcg_at_10
value: 65.847
- type: ndcg_at_20
value: 66.937
- type: ndcg_at_100
value: 68.262
- type: ndcg_at_1000
value: 69.341
- type: map_at_1
value: 51.093999999999994
- type: map_at_3
value: 58.044
- type: map_at_5
value: 59.702999999999996
- type: map_at_10
value: 60.885999999999996
- type: map_at_20
value: 61.266
- type: map_at_100
value: 61.482000000000006
- type: map_at_1000
value: 61.519
- type: recall_at_1
value: 51.093999999999994
- type: recall_at_3
value: 66.128
- type: recall_at_5
value: 72.456
- type: recall_at_10
value: 79.3
- type: recall_at_20
value: 83.2
- type: recall_at_100
value: 90.0
- type: recall_at_1000
value: 98.667
- type: precision_at_1
value: 54.0
- type: precision_at_3
value: 23.778
- type: precision_at_5
value: 15.933
- type: precision_at_10
value: 8.967
- type: precision_at_20
value: 4.75
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.11199999999999999
- type: mrr_at_1
value: 54.0
- type: mrr_at_3
value: 60.3889
- type: mrr_at_5
value: 61.7556
- type: mrr_at_10
value: 62.5984
- type: mrr_at_20
value: 62.85039999999999
- type: mrr_at_100
value: 63.0155
- type: mrr_at_1000
value: 63.052699999999994
- type: nauc_ndcg_at_1_max
value: 56.6373
- type: nauc_ndcg_at_1_std
value: 2.1765
- type: nauc_ndcg_at_1_diff1
value: 71.14829999999999
- type: nauc_ndcg_at_3_max
value: 53.7965
- type: nauc_ndcg_at_3_std
value: -3.4057999999999997
- type: nauc_ndcg_at_3_diff1
value: 63.712199999999996
- type: nauc_ndcg_at_5_max
value: 56.96059999999999
- type: nauc_ndcg_at_5_std
value: 1.4794
- type: nauc_ndcg_at_5_diff1
value: 64.65419999999999
- type: nauc_ndcg_at_10_max
value: 59.4154
- type: nauc_ndcg_at_10_std
value: 5.2752
- type: nauc_ndcg_at_10_diff1
value: 64.3098
- type: nauc_ndcg_at_20_max
value: 59.7717
- type: nauc_ndcg_at_20_std
value: 6.2032
- type: nauc_ndcg_at_20_diff1
value: 64.18599999999999
- type: nauc_ndcg_at_100_max
value: 59.2146
- type: nauc_ndcg_at_100_std
value: 6.0138
- type: nauc_ndcg_at_100_diff1
value: 64.0895
- type: nauc_ndcg_at_1000_max
value: 58.5714
- type: nauc_ndcg_at_1000_std
value: 4.8872
- type: nauc_ndcg_at_1000_diff1
value: 64.66969999999999
- type: nauc_map_at_1_max
value: 51.2417
- type: nauc_map_at_1_std
value: -5.42
- type: nauc_map_at_1_diff1
value: 70.0616
- type: nauc_map_at_3_max
value: 51.9587
- type: nauc_map_at_3_std
value: -5.3035
- type: nauc_map_at_3_diff1
value: 65.282
- type: nauc_map_at_5_max
value: 54.1516
- type: nauc_map_at_5_std
value: -2.2858
- type: nauc_map_at_5_diff1
value: 65.86659999999999
- type: nauc_map_at_10_max
value: 55.5412
- type: nauc_map_at_10_std
value: -0.34299999999999997
- type: nauc_map_at_10_diff1
value: 65.89620000000001
- type: nauc_map_at_20_max
value: 55.7967
- type: nauc_map_at_20_std
value: 0.13799999999999998
- type: nauc_map_at_20_diff1
value: 65.8685
- type: nauc_map_at_100_max
value: 55.74550000000001
- type: nauc_map_at_100_std
value: 0.211
- type: nauc_map_at_100_diff1
value: 65.8557
- type: nauc_map_at_1000_max
value: 55.728
- type: nauc_map_at_1000_std
value: 0.1875
- type: nauc_map_at_1000_diff1
value: 65.8748
- type: nauc_recall_at_1_max
value: 51.2417
- type: nauc_recall_at_1_std
value: -5.42
- type: nauc_recall_at_1_diff1
value: 70.0616
- type: nauc_recall_at_3_max
value: 52.4327
- type: nauc_recall_at_3_std
value: -6.7153
- type: nauc_recall_at_3_diff1
value: 57.111999999999995
- type: nauc_recall_at_5_max
value: 60.5827
- type: nauc_recall_at_5_std
value: 7.1365
- type: nauc_recall_at_5_diff1
value: 58.3449
- type: nauc_recall_at_10_max
value: 70.24770000000001
- type: nauc_recall_at_10_std
value: 22.0896
- type: nauc_recall_at_10_diff1
value: 55.7264
- type: nauc_recall_at_20_max
value: 73.483
- type: nauc_recall_at_20_std
value: 29.653299999999998
- type: nauc_recall_at_20_diff1
value: 53.54750000000001
- type: nauc_recall_at_100_max
value: 74.0321
- type: nauc_recall_at_100_std
value: 37.491400000000006
- type: nauc_recall_at_100_diff1
value: 47.3918
- type: nauc_recall_at_1000_max
value: 69.5378
- type: nauc_recall_at_1000_std
value: 60.5042
- type: nauc_recall_at_1000_diff1
value: 19.5028
- type: nauc_precision_at_1_max
value: 56.6373
- type: nauc_precision_at_1_std
value: 2.1765
- type: nauc_precision_at_1_diff1
value: 71.14829999999999
- type: nauc_precision_at_3_max
value: 51.811099999999996
- type: nauc_precision_at_3_std
value: 8.4319
- type: nauc_precision_at_3_diff1
value: 48.545500000000004
- type: nauc_precision_at_5_max
value: 55.4685
- type: nauc_precision_at_5_std
value: 26.387
- type: nauc_precision_at_5_diff1
value: 39.6201
- type: nauc_precision_at_10_max
value: 53.2436
- type: nauc_precision_at_10_std
value: 41.6957
- type: nauc_precision_at_10_diff1
value: 24.6115
- type: nauc_precision_at_20_max
value: 48.353699999999996
- type: nauc_precision_at_20_std
value: 47.253
- type: nauc_precision_at_20_diff1
value: 15.687599999999998
- type: nauc_precision_at_100_max
value: 36.771100000000004
- type: nauc_precision_at_100_std
value: 48.1335
- type: nauc_precision_at_100_diff1
value: 2.6454
- type: nauc_precision_at_1000_max
value: 23.0391
- type: nauc_precision_at_1000_std
value: 53.26499999999999
- type: nauc_precision_at_1000_diff1
value: -15.0974
- type: nauc_mrr_at_1_max
value: 56.6373
- type: nauc_mrr_at_1_std
value: 2.1765
- type: nauc_mrr_at_1_diff1
value: 71.14829999999999
- type: nauc_mrr_at_3_max
value: 57.6843
- type: nauc_mrr_at_3_std
value: 2.4692
- type: nauc_mrr_at_3_diff1
value: 66.10340000000001
- type: nauc_mrr_at_5_max
value: 59.2453
- type: nauc_mrr_at_5_std
value: 5.1308
- type: nauc_mrr_at_5_diff1
value: 66.7377
- type: nauc_mrr_at_10_max
value: 59.5575
- type: nauc_mrr_at_10_std
value: 5.7778
- type: nauc_mrr_at_10_diff1
value: 66.36149999999999
- type: nauc_mrr_at_20_max
value: 59.466300000000004
- type: nauc_mrr_at_20_std
value: 5.6867
- type: nauc_mrr_at_20_diff1
value: 66.37100000000001
- type: nauc_mrr_at_100_max
value: 59.404999999999994
- type: nauc_mrr_at_100_std
value: 5.6528
- type: nauc_mrr_at_100_diff1
value: 66.41040000000001
- type: nauc_mrr_at_1000_max
value: 59.3919
- type: nauc_mrr_at_1000_std
value: 5.6358
- type: nauc_mrr_at_1000_diff1
value: 66.43050000000001
- type: main_score
value: 65.847
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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value: 99.7386
- type: similarity_accuracy_threshold
value: 84.1442
- type: similarity_f1
value: 86.41980000000001
- type: similarity_f1_threshold
value: 84.1442
- type: similarity_precision
value: 88.98310000000001
- type: similarity_recall
value: 84.0
- type: similarity_ap
value: 93.50309999999999
- type: cosine_accuracy
value: 99.7386
- type: cosine_accuracy_threshold
value: 84.1442
- type: cosine_f1
value: 86.41980000000001
- type: cosine_f1_threshold
value: 84.1442
- type: cosine_precision
value: 88.98310000000001
- type: cosine_recall
value: 84.0
- type: cosine_ap
value: 93.50309999999999
- type: manhattan_accuracy
value: 99.7406
- type: manhattan_accuracy_threshold
value: 1243.0971
- type: manhattan_f1
value: 86.5641
- type: manhattan_f1_threshold
value: 1243.0971
- type: manhattan_precision
value: 88.8421
- type: manhattan_recall
value: 84.39999999999999
- type: manhattan_ap
value: 93.50840000000001
- type: euclidean_accuracy
value: 99.7386
- type: euclidean_accuracy_threshold
value: 56.313
- type: euclidean_f1
value: 86.41980000000001
- type: euclidean_f1_threshold
value: 56.313
- type: euclidean_precision
value: 88.98310000000001
- type: euclidean_recall
value: 84.0
- type: euclidean_ap
value: 93.50309999999999
- type: dot_accuracy
value: 99.7386
- type: dot_accuracy_threshold
value: 84.1442
- type: dot_f1
value: 86.41980000000001
- type: dot_f1_threshold
value: 84.1442
- type: dot_precision
value: 88.98310000000001
- type: dot_recall
value: 84.0
- type: dot_ap
value: 93.50309999999999
- type: max_accuracy
value: 99.7406
- type: max_f1
value: 86.5641
- type: max_precision
value: 88.98310000000001
- type: max_recall
value: 84.39999999999999
- type: max_ap
value: 93.50840000000001
- type: main_score
value: 93.50840000000001
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.9311
- type: v_measure_std
value: 5.0881
- type: main_score
value: 55.9311
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.9298
- type: v_measure_std
value: 1.7169
- type: main_score
value: 32.9298
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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value: 51.7759
- type: mrr
value: 52.7456
- type: nAUC_map_max
value: 15.138499999999999
- type: nAUC_map_std
value: 9.876999999999999
- type: nAUC_map_diff1
value: 37.8337
- type: nAUC_mrr_max
value: 16.128600000000002
- type: nAUC_mrr_std
value: 10.4175
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value: 37.3753
- type: main_score
value: 51.7759
- task:
type: Retrieval
dataset:
name: MTEB StackOverflowQA (default)
type: CoIR-Retrieval/stackoverflow-qa
config: default
split: test
revision: db8f169f3894c14a00251061f957b2063eef2bd5
metrics:
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- type: ndcg_at_5
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- type: ndcg_at_10
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- type: ndcg_at_20
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- type: ndcg_at_100
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- type: ndcg_at_1000
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value: 68.205
- type: map_at_3
value: 73.763
- type: map_at_5
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- type: map_at_10
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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: 68.205
- type: recall_at_3
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- type: recall_at_5
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- type: recall_at_10
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- type: recall_at_20
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- type: recall_at_1000
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- type: precision_at_5
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- 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_5
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- type: mrr_at_20
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_std
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- type: nauc_ndcg_at_1_diff1
value: 80.8018
- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
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value: 76.6152
- type: nauc_ndcg_at_5_max
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value: 76.6923
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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_max
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value: 77.73780000000001
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value: -5.680000000000001
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value: 77.8324
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value: 77.837
- type: nauc_recall_at_1_max
value: 70.0997
- type: nauc_recall_at_1_std
value: -6.6174
- type: nauc_recall_at_1_diff1
value: 80.8018
- type: nauc_recall_at_3_max
value: 73.3015
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value: -4.9247
- type: nauc_recall_at_3_diff1
value: 72.6201
- type: nauc_recall_at_5_max
value: 73.3818
- type: nauc_recall_at_5_std
value: -4.196
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value: 71.8984
- type: nauc_recall_at_10_max
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- type: nauc_recall_at_10_std
value: 1.0328
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value: 69.0552
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value: 67.3481
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value: 66.6112
- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
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- type: nauc_recall_at_1000_diff1
value: 69.8675
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- type: nauc_precision_at_1_std
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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_3_diff1
value: 72.6201
- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
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- type: nauc_precision_at_10_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_20_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_100_diff1
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- type: nauc_precision_at_1000_max
value: 76.7462
- type: nauc_precision_at_1000_std
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- type: nauc_precision_at_1000_diff1
value: 69.8675
- type: nauc_mrr_at_1_max
value: 70.0997
- type: nauc_mrr_at_1_std
value: -6.6174
- type: nauc_mrr_at_1_diff1
value: 80.8018
- type: nauc_mrr_at_3_max
value: 71.46329999999999
- type: nauc_mrr_at_3_std
value: -5.9901
- type: nauc_mrr_at_3_diff1
value: 77.7281
- type: nauc_mrr_at_5_max
value: 71.4046
- type: nauc_mrr_at_5_std
value: -5.9794
- type: nauc_mrr_at_5_diff1
value: 77.8163
- type: nauc_mrr_at_10_max
value: 71.2618
- type: nauc_mrr_at_10_std
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- type: nauc_mrr_at_10_diff1
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- type: nauc_mrr_at_20_max
value: 71.30330000000001
- type: nauc_mrr_at_20_std
value: -5.691
- type: nauc_mrr_at_20_diff1
value: 77.7683
- type: nauc_mrr_at_100_max
value: 71.3035
- type: nauc_mrr_at_100_std
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- type: nauc_mrr_at_100_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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value: 77.837
- type: main_score
value: 78.45
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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- type: spearman
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- type: cosine_spearman
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- type: cosine_pearson
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- type: dot_spearman
value: 32.0256
- type: dot_pearson
value: 31.7097
- type: main_score
value: 32.0256
- task:
type: Retrieval
dataset:
name: MTEB SyntheticText2SQL (default)
type: CoIR-Retrieval/synthetic-text2sql
config: default
split: test
revision: 686b87296c3a0191b5d9415a00526c62db9fce09
metrics:
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- type: ndcg_at_5
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- type: ndcg_at_10
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- type: ndcg_at_20
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- type: ndcg_at_100
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- type: ndcg_at_1000
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- type: map_at_1
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- type: map_at_3
value: 33.083
- type: map_at_5
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- type: map_at_10
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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
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- type: recall_at_3
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- type: recall_at_5
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- type: recall_at_10
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- type: recall_at_20
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- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_3
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- type: precision_at_5
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- type: precision_at_10
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- type: precision_at_20
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- type: precision_at_1000
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- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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type: mteb/trec-covid
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revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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dataset:
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type: mteb/touche2020
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split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
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type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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- type: max_recall
value: 68.3113
- type: max_ap
value: 64.4131
- type: main_score
value: 64.4131
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 88.95100000000001
- type: similarity_accuracy_threshold
value: 82.18520000000001
- type: similarity_f1
value: 77.9051
- type: similarity_f1_threshold
value: 80.3369
- type: similarity_precision
value: 76.07310000000001
- type: similarity_recall
value: 79.8275
- type: similarity_ap
value: 86.1545
- type: cosine_accuracy
value: 88.95100000000001
- type: cosine_accuracy_threshold
value: 82.18520000000001
- type: cosine_f1
value: 77.9051
- type: cosine_f1_threshold
value: 80.3369
- type: cosine_precision
value: 76.07310000000001
- type: cosine_recall
value: 79.8275
- type: cosine_ap
value: 86.1545
- type: manhattan_accuracy
value: 88.9277
- type: manhattan_accuracy_threshold
value: 1338.2836
- type: manhattan_f1
value: 77.8186
- type: manhattan_f1_threshold
value: 1372.5978
- type: manhattan_precision
value: 76.5745
- type: manhattan_recall
value: 79.1038
- type: manhattan_ap
value: 86.114
- type: euclidean_accuracy
value: 88.95100000000001
- type: euclidean_accuracy_threshold
value: 59.6905
- type: euclidean_f1
value: 77.9051
- type: euclidean_f1_threshold
value: 62.71060000000001
- type: euclidean_precision
value: 76.07310000000001
- type: euclidean_recall
value: 79.8275
- type: euclidean_ap
value: 86.1544
- type: dot_accuracy
value: 88.95100000000001
- type: dot_accuracy_threshold
value: 82.18520000000001
- type: dot_f1
value: 77.9051
- type: dot_f1_threshold
value: 80.3369
- type: dot_precision
value: 76.07310000000001
- type: dot_recall
value: 79.8275
- type: dot_ap
value: 86.1544
- type: max_accuracy
value: 88.95100000000001
- type: max_f1
value: 77.9051
- type: max_precision
value: 76.5745
- type: max_recall
value: 79.8275
- type: max_ap
value: 86.1545
- type: main_score
value: 86.1545
---
# Granite-Embedding-278m-multilingual
**Model Summary:**
Granite-Embedding-278M-Multilingual is a 278M parameter model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using 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, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite-Embedding-278M-Multilingual for languages beyond these 12 languages.
**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-278m-multilingual"
# 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-278m-Multilingual 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-278m-multilingual"
# 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-278m-multilingual uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
```
**Evaluation:**
The average performance of the Granite-Embedding-278M-Multilingual on Multilingual Miracl (across 18 langauges), Mintaka Retrieval (across 8 languages) and MTEB Retrieval for English (across 15 tasks), German (across 4 tasks), Spanish (across 2 tasks), Frenc (across 5 tasks), Japanese (across 2 tasks), Arabic (1 task), Korean (1 task) and Chinese (across 8 tasks) is reported below.
| Model | Paramters (M)| Embedding Dimension | Miracl (18) | Mintaka Retrieval (8) | MTEB English (15) | MTEB German (4) |MTEB Spanish (2) | MTEB French (5) | MTEB Japanese (2) | MTEB Arabic (1) | MTEB Korean (1) | MTEB Chinese (8) |
|:-----------------------------------|:------------:|:-------------------:|:-------------:| :---------------------:|:-----------------:|:---------------:|:---------------:|:---------------:|:----------------:|:----------------:|:---------------:|:----------------:|
|granite-embedding-278M-multilingual | 278 | 768 | 58.3 | 23.2 | 48.2 | 71.2 | 52.6 | 54.1 | 61.7 | 64.2 | 71.8 | 45.2 |
**Model Architecture:**
Granite-Embedding-278m-Multilingual is based on an encoder-only XLM-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** |
|:--------------------------------------------------------------------------|:--------------:|
| Multilingual MC4 | 52,823,484 |
| Multilingual Webhose | 12,369,322 |
| English Wikipedia | 20,745,403 |
| Multilingual Wikimedia | 2,911,090 |
| Miracl Corpus (Title-Body) | 10,120,398 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Machine Translations of Stack Exchange Duplicate questions (titles) | 187,195 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Machine Translations of Stack Exchange (Title+Body, Answer) pairs | 1,827,15 |
| SearchQA | 582,261 |
| S2ORC (Title, Abstract) | 41,769,185 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| CCNews | 614,664 |
| XSum | 226,711 |
| SimpleWiki | 102,225 |
| Machine Translated Cross Lingual Parallel Corpora | 28,376,115 |
| SPECTER citation triplets | 684,100 |
| Machine Translations of SPECTER citation triplets | 4,104,600 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| PubMed | 20,000,000 |
| Multilingual Miracl Triples | 81,409 |
| Multilingual MrTydi Triples | 48,715 |
| Sadeeem Question Asnwering | 4,037 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: English Query-Wikipedia Passage | 1,879,093 |
| Synthetic: English Fact Verification | 9,888 |
| Synthetic: Multilingual Query-Wikipedia Passage | 300,266 |
| Synthetic: Multilingual News Summaries | 37,489 |
| 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-278m-Multilingual is finetuned on 12 languages, 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},
}
``` -->
|
[
"TRANSLATION",
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-large-zh
|
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"
] | 2023-08-02T07:13:44 | 2023-10-12T03:38:28 | 21,308 | 320 |
---
language:
- zh
license: mit
---
**Recommend switching to newest [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-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"
] |
EleutherAI/pythia-1b-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"
] | 2023-02-14T00:07:42 | 2023-07-10T15:04:31 | 20,123 | 19 |
---
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-1B-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-1B-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-1B-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-1B-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-1B-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-1B-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-1B-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-1B-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-1B-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/gpt-neox-20b
|
EleutherAI
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"en",
"dataset:EleutherAI/pile",
"arxiv:2204.06745",
"arxiv:2101.00027",
"arxiv:2201.07311",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-04-07T20:28:29 | 2024-01-31T20:30:35 | 19,595 | 555 |
---
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
---
GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained
on [the Pile](https://pile.eleuther.ai/) using the [GPT-NeoX
library](https://github.com/EleutherAI/gpt-neox). Its architecture intentionally
resembles that of GPT-3, and is almost identical to that of [GPT-J-
6B](https://huggingface.co/EleutherAI/gpt-j-6B). Its training dataset contains
a multitude of English-language texts, reflecting the general-purpose nature
of this model. See the [accompanying paper](https://arxiv.org/abs/2204.06745)
for details about model architecture (including how it differs from GPT-3),
training procedure, and additional evaluations.
### Model details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [GPT-NeoX-20B: An Open-Source Autoregressive Language
Model](https://arxiv.org/abs/2204.06745). For details about the training dataset,
see [the Pile paper](https://arxiv.org/abs/2101.00027), and [its data
sheet](https://arxiv.org/abs/2201.07311).
- 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 GPT-NeoX-20B documentation before asking about the model
on Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure style="width:30em">
| Hyperparameter | Value |
| ---------------------- | ----------- |
| n<sub>parameters</sub> | 20554567680 |
| n<sub>layers</sub> | 44 |
| d<sub>model</sub> | 6144 |
| n<sub>heads</sub> | 64 |
| d<sub>head</sub> | 96 |
| n<sub>vocab</sub> | 50257 |
| Sequence Length | 2048 |
| Learning Rate | 0.97 x 10<sup>-5</sup> |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
</figure>
### Uses and limitations
#### Intended use
GPT-NeoX-20B was developed primarily for research purposes. It learns an inner
representation of the English language that can be used to extract features
useful for downstream tasks.
In addition to scientific uses, you may also further fine-tune and adapt
GPT-NeoX-20B for deployment, as long as your use is in accordance with the
Apache 2.0 license. This model works with the [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained GPT-NeoX-20B as a basis for your fine-tuned model, please note that
you need to conduct your own risk and bias assessment.
#### Out-of-scope use
GPT-NeoX-20B is **not** intended for deployment as-is. It is not a product
and cannot be used for human-facing interactions without supervision.
GPT-NeoX-20B has not been fine-tuned for downstream tasks for which language
models are commonly deployed, such as writing genre prose, or commercial
chatbots. This means GPT-NeoX-20B will likely **not** respond to a given prompt
the way products such as ChatGPT do. This is because, unlike GPT-NeoX-20B,
ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human
Feedback (RLHF) to better “understand” human instructions and dialogue.
This model is English-language only, and thus cannot be used for translation
or generating text in other languages.
#### Limitations and biases
The core functionality of GPT-NeoX-20B is to take a string of text and predict
the next token. Remember that the statistically most likely next token need
not result in the most “accurate” text. Never rely on GPT-NeoX-20B 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.
GPT-NeoX-20B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
We recommend curating the outputs of this model before presenting it to a human
reader. Please inform your audience that you are using artificially generated
text.
#### How to use
If you simply want to try out some prompts, check out [this
playground](https://20b.eleuther.ai/).
GPT-NeoX-20B can be loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
```
### Training
#### Training dataset
The Pile 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/).
The Pile was **not** deduplicated before being used to train GPT-NeoX-20B.
#### Training procedure
GPT-NeoX-20B was trained with a batch size of approximately 3.15M tokens
(1538 sequences of 2048 tokens each), for a total of 150,000 steps. Tensor
parallelism and pipeline parallelism were used to distribute the model across
GPUs. Additional details about the training procedure are in [Section 3 of
the accompanying paper](https://arxiv.org/abs/2204.06745).
### Evaluations
<figure style="width:55em">
| Model | OpenAI’s LAMBADA | SciQ | PIQA | TriviaQA | ARC (Challenge) |
| ------------- | :--------------: | :-----------: | :-----------: | :-----------: | :-------------: |
| GPT-J-6B | 0.683 ± 0.006 | 0.910 ± 0.009 | 0.752 ± 0.010 | 0.170 ± 0.004 | 0.340 ± 0.014 |
| FairSeq 6.7B | 0.673 ± 0.007 | 0.895 ± 0.010 | 0.762 ± 0.010 | 0.221 ± 0.004 | 0.329 ± 0.014 |
| GPT-3 Curie | 0.693 ± 0.006 | 0.918 ± 0.009 | 0.767 ± 0.010 | 0.196 ± 0.004 | 0.334 ± 0.014 |
| FairSeq 13B | 0.709 ± 0.006 | 0.910 ± 0.009 | 0.769 ± 0.010 | 0.270 ± 0.004 | 0.345 ± 0.014 |
| GPT-NeoX-20B | 0.720 ± 0.006 | 0.928 ± 0.008 | 0.779 ± 0.010 | 0.259 ± 0.004 | 0.380 ± 0.014 |
| GPT-3 DaVinci | 0.752 ± 0.006 | 0.949 ± 0.007 | 0.791 ± 0.009 | 0.409 ± 0.005 | 0.435 ± 0.014 |
<figcaption>Zero-shot performance on selected natural language tasks.</figcaption>
</figure>
This is a heavily abridged version of the evaluation results. Appendix D of the
[GPT-NeoX-20B paper](https://arxiv.org/abs/2204.06745) compares more model
sizes, and contains additional evaluations, including on: zero and five-shot
natural language tasks, zero and five-shot Basic Arithmetic and MATH,
and zero-shot Hendrycks tasks.
### BibTeX
To cite the GPT-NeoX-20B paper:
```
@misc{https://doi.org/10.48550/arxiv.2204.06745,
doi = {10.48550/ARXIV.2204.06745},
url = {https://arxiv.org/abs/2204.06745},
author = {Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and Pieler, Michael and Prashanth, USVSN Sai and Purohit, Shivanshu and Reynolds, Laria and Tow, Jonathan and Wang, Ben and Weinbach, Samuel},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {GPT-NeoX-20B: An Open-Source Autoregressive Language Model},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
# [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__gpt-neox-20b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 36.02 |
| ARC (25-shot) | 45.73 |
| HellaSwag (10-shot) | 73.45 |
| MMLU (5-shot) | 25.0 |
| TruthfulQA (0-shot) | 31.61 |
| Winogrande (5-shot) | 68.9 |
| GSM8K (5-shot) | 2.43 |
| DROP (3-shot) | 5.04 |
|
[
"TRANSLATION"
] |
[
"SCIQ"
] |
Linq-AI-Research/Linq-Embed-Mistral
|
Linq-AI-Research
|
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"
] | 2024-05-29T03:41:40 | 2024-06-05T12:50:34 | 19,366 | 69 |
---
language:
- en
license: cc-by-nc-4.0
tags:
- mteb
- transformers
- sentence-transformers
model-index:
- name: Linq-Embed-Mistral
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 84.43283582089552
- type: ap
value: 50.39222584035829
- type: f1
value: 78.47906270064071
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.70445
- type: ap
value: 94.28273900595173
- type: f1
value: 95.70048412173735
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 57.644000000000005
- type: f1
value: 56.993648296704876
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 45.804
- type: map_at_10
value: 61.742
- type: map_at_100
value: 62.07899999999999
- type: map_at_1000
value: 62.08
- type: map_at_3
value: 57.717
- type: map_at_5
value: 60.27
- type: mrr_at_1
value: 47.226
- type: mrr_at_10
value: 62.256
- type: mrr_at_100
value: 62.601
- type: mrr_at_1000
value: 62.601
- type: mrr_at_3
value: 58.203
- type: mrr_at_5
value: 60.767
- type: ndcg_at_1
value: 45.804
- type: ndcg_at_10
value: 69.649
- type: ndcg_at_100
value: 70.902
- type: ndcg_at_1000
value: 70.91199999999999
- type: ndcg_at_3
value: 61.497
- type: ndcg_at_5
value: 66.097
- type: precision_at_1
value: 45.804
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.135
- type: precision_at_5
value: 16.714000000000002
- type: recall_at_1
value: 45.804
- type: recall_at_10
value: 94.523
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 72.404
- type: recall_at_5
value: 83.57
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 51.47612678878609
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 47.2977392340418
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 66.82016765243456
- type: mrr
value: 79.55227982236292
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.15068664186332
- type: cos_sim_spearman
value: 86.4013663041054
- type: euclidean_pearson
value: 87.36391302921588
- type: euclidean_spearman
value: 86.4013663041054
- type: manhattan_pearson
value: 87.46116676558589
- type: manhattan_spearman
value: 86.78149544753352
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.88311688311688
- type: f1
value: 87.82368154811464
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 42.72860396750569
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 39.58412067938718
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 30.082666666666665
- type: map_at_10
value: 41.13875
- type: map_at_100
value: 42.45525
- type: map_at_1000
value: 42.561249999999994
- type: map_at_3
value: 37.822750000000006
- type: map_at_5
value: 39.62658333333333
- type: mrr_at_1
value: 35.584
- type: mrr_at_10
value: 45.4675
- type: mrr_at_100
value: 46.31016666666667
- type: mrr_at_1000
value: 46.35191666666666
- type: mrr_at_3
value: 42.86674999999999
- type: mrr_at_5
value: 44.31341666666666
- type: ndcg_at_1
value: 35.584
- type: ndcg_at_10
value: 47.26516666666667
- type: ndcg_at_100
value: 52.49108333333332
- type: ndcg_at_1000
value: 54.24575
- type: ndcg_at_3
value: 41.83433333333334
- type: ndcg_at_5
value: 44.29899999999999
- type: precision_at_1
value: 35.584
- type: precision_at_10
value: 8.390333333333334
- type: precision_at_100
value: 1.2941666666666667
- type: precision_at_1000
value: 0.16308333333333336
- type: precision_at_3
value: 19.414583333333333
- type: precision_at_5
value: 13.751
- type: recall_at_1
value: 30.082666666666665
- type: recall_at_10
value: 60.88875
- type: recall_at_100
value: 83.35141666666667
- type: recall_at_1000
value: 95.0805
- type: recall_at_3
value: 45.683749999999996
- type: recall_at_5
value: 52.08208333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 16.747
- type: map_at_10
value: 29.168
- type: map_at_100
value: 31.304
- type: map_at_1000
value: 31.496000000000002
- type: map_at_3
value: 24.57
- type: map_at_5
value: 26.886
- type: mrr_at_1
value: 37.524
- type: mrr_at_10
value: 50.588
- type: mrr_at_100
value: 51.28
- type: mrr_at_1000
value: 51.29899999999999
- type: mrr_at_3
value: 47.438
- type: mrr_at_5
value: 49.434
- type: ndcg_at_1
value: 37.524
- type: ndcg_at_10
value: 39.11
- type: ndcg_at_100
value: 46.373999999999995
- type: ndcg_at_1000
value: 49.370999999999995
- type: ndcg_at_3
value: 32.964
- type: ndcg_at_5
value: 35.028
- type: precision_at_1
value: 37.524
- type: precision_at_10
value: 12.137
- type: precision_at_100
value: 1.9929999999999999
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 24.886
- type: precision_at_5
value: 18.762
- type: recall_at_1
value: 16.747
- type: recall_at_10
value: 45.486
- type: recall_at_100
value: 69.705
- type: recall_at_1000
value: 86.119
- type: recall_at_3
value: 30.070999999999998
- type: recall_at_5
value: 36.565
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 10.495000000000001
- type: map_at_10
value: 24.005000000000003
- type: map_at_100
value: 34.37
- type: map_at_1000
value: 36.268
- type: map_at_3
value: 16.694
- type: map_at_5
value: 19.845
- type: mrr_at_1
value: 75.5
- type: mrr_at_10
value: 82.458
- type: mrr_at_100
value: 82.638
- type: mrr_at_1000
value: 82.64
- type: mrr_at_3
value: 81.25
- type: mrr_at_5
value: 82.125
- type: ndcg_at_1
value: 64.625
- type: ndcg_at_10
value: 51.322
- type: ndcg_at_100
value: 55.413999999999994
- type: ndcg_at_1000
value: 62.169
- type: ndcg_at_3
value: 56.818999999999996
- type: ndcg_at_5
value: 54.32900000000001
- type: precision_at_1
value: 75.5
- type: precision_at_10
value: 40.849999999999994
- type: precision_at_100
value: 12.882
- type: precision_at_1000
value: 2.394
- type: precision_at_3
value: 59.667
- type: precision_at_5
value: 52.2
- type: recall_at_1
value: 10.495000000000001
- type: recall_at_10
value: 29.226000000000003
- type: recall_at_100
value: 59.614
- type: recall_at_1000
value: 81.862
- type: recall_at_3
value: 17.97
- type: recall_at_5
value: 22.438
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.82
- type: f1
value: 47.794956731921054
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 82.52199999999999
- type: map_at_10
value: 89.794
- type: map_at_100
value: 89.962
- type: map_at_1000
value: 89.972
- type: map_at_3
value: 88.95100000000001
- type: map_at_5
value: 89.524
- type: mrr_at_1
value: 88.809
- type: mrr_at_10
value: 93.554
- type: mrr_at_100
value: 93.577
- type: mrr_at_1000
value: 93.577
- type: mrr_at_3
value: 93.324
- type: mrr_at_5
value: 93.516
- type: ndcg_at_1
value: 88.809
- type: ndcg_at_10
value: 92.419
- type: ndcg_at_100
value: 92.95
- type: ndcg_at_1000
value: 93.10000000000001
- type: ndcg_at_3
value: 91.45299999999999
- type: ndcg_at_5
value: 92.05
- type: precision_at_1
value: 88.809
- type: precision_at_10
value: 10.911999999999999
- type: precision_at_100
value: 1.143
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 34.623
- type: precision_at_5
value: 21.343999999999998
- type: recall_at_1
value: 82.52199999999999
- type: recall_at_10
value: 96.59400000000001
- type: recall_at_100
value: 98.55699999999999
- type: recall_at_1000
value: 99.413
- type: recall_at_3
value: 94.02199999999999
- type: recall_at_5
value: 95.582
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.842
- type: map_at_10
value: 53.147
- type: map_at_100
value: 55.265
- type: map_at_1000
value: 55.37
- type: map_at_3
value: 46.495
- type: map_at_5
value: 50.214999999999996
- type: mrr_at_1
value: 61.574
- type: mrr_at_10
value: 68.426
- type: mrr_at_100
value: 68.935
- type: mrr_at_1000
value: 68.95400000000001
- type: mrr_at_3
value: 66.307
- type: mrr_at_5
value: 67.611
- type: ndcg_at_1
value: 61.574
- type: ndcg_at_10
value: 61.205
- type: ndcg_at_100
value: 67.25999999999999
- type: ndcg_at_1000
value: 68.657
- type: ndcg_at_3
value: 56.717
- type: ndcg_at_5
value: 58.196999999999996
- type: precision_at_1
value: 61.574
- type: precision_at_10
value: 16.852
- type: precision_at_100
value: 2.33
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 37.5
- type: precision_at_5
value: 27.468999999999998
- type: recall_at_1
value: 32.842
- type: recall_at_10
value: 68.157
- type: recall_at_100
value: 89.5
- type: recall_at_1000
value: 97.68599999999999
- type: recall_at_3
value: 50.783
- type: recall_at_5
value: 58.672000000000004
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 39.068000000000005
- type: map_at_10
value: 69.253
- type: map_at_100
value: 70.036
- type: map_at_1000
value: 70.081
- type: map_at_3
value: 65.621
- type: map_at_5
value: 67.976
- type: mrr_at_1
value: 78.13600000000001
- type: mrr_at_10
value: 84.328
- type: mrr_at_100
value: 84.515
- type: mrr_at_1000
value: 84.52300000000001
- type: mrr_at_3
value: 83.52199999999999
- type: mrr_at_5
value: 84.019
- type: ndcg_at_1
value: 78.13600000000001
- type: ndcg_at_10
value: 76.236
- type: ndcg_at_100
value: 78.891
- type: ndcg_at_1000
value: 79.73400000000001
- type: ndcg_at_3
value: 71.258
- type: ndcg_at_5
value: 74.129
- type: precision_at_1
value: 78.13600000000001
- type: precision_at_10
value: 16.347
- type: precision_at_100
value: 1.839
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 47.189
- type: precision_at_5
value: 30.581999999999997
- type: recall_at_1
value: 39.068000000000005
- type: recall_at_10
value: 81.735
- type: recall_at_100
value: 91.945
- type: recall_at_1000
value: 97.44800000000001
- type: recall_at_3
value: 70.783
- type: recall_at_5
value: 76.455
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.7764
- type: ap
value: 92.67841294818406
- type: f1
value: 94.77375157383646
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 24.624
- type: map_at_10
value: 37.861
- type: map_at_100
value: 39.011
- type: map_at_1000
value: 39.052
- type: map_at_3
value: 33.76
- type: map_at_5
value: 36.153
- type: mrr_at_1
value: 25.358000000000004
- type: mrr_at_10
value: 38.5
- type: mrr_at_100
value: 39.572
- type: mrr_at_1000
value: 39.607
- type: mrr_at_3
value: 34.491
- type: mrr_at_5
value: 36.83
- type: ndcg_at_1
value: 25.358000000000004
- type: ndcg_at_10
value: 45.214999999999996
- type: ndcg_at_100
value: 50.56
- type: ndcg_at_1000
value: 51.507999999999996
- type: ndcg_at_3
value: 36.925999999999995
- type: ndcg_at_5
value: 41.182
- type: precision_at_1
value: 25.358000000000004
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 0.9740000000000001
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 15.697
- type: precision_at_5
value: 11.599
- type: recall_at_1
value: 24.624
- type: recall_at_10
value: 67.78699999999999
- type: recall_at_100
value: 92.11200000000001
- type: recall_at_1000
value: 99.208
- type: recall_at_3
value: 45.362
- type: recall_at_5
value: 55.58
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.83310533515733
- type: f1
value: 96.57069781347995
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 89.5690834473324
- type: f1
value: 73.7275204564728
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 82.67316745124411
- type: f1
value: 79.70626515721662
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 85.01344989912575
- type: f1
value: 84.45181022816965
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 37.843426126777295
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 36.651728547241476
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.05750522793288
- type: mrr
value: 33.28067556869468
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.744
- type: map_at_10
value: 16.235
- type: map_at_100
value: 20.767
- type: map_at_1000
value: 22.469
- type: map_at_3
value: 11.708
- type: map_at_5
value: 13.924
- type: mrr_at_1
value: 55.728
- type: mrr_at_10
value: 63.869
- type: mrr_at_100
value: 64.322
- type: mrr_at_1000
value: 64.342
- type: mrr_at_3
value: 62.022999999999996
- type: mrr_at_5
value: 63.105999999999995
- type: ndcg_at_1
value: 53.096
- type: ndcg_at_10
value: 41.618
- type: ndcg_at_100
value: 38.562999999999995
- type: ndcg_at_1000
value: 47.006
- type: ndcg_at_3
value: 47.657
- type: ndcg_at_5
value: 45.562999999999995
- type: precision_at_1
value: 55.108000000000004
- type: precision_at_10
value: 30.464000000000002
- type: precision_at_100
value: 9.737
- type: precision_at_1000
value: 2.2720000000000002
- type: precision_at_3
value: 44.376
- type: precision_at_5
value: 39.505
- type: recall_at_1
value: 6.744
- type: recall_at_10
value: 21.11
- type: recall_at_100
value: 39.69
- type: recall_at_1000
value: 70.44
- type: recall_at_3
value: 13.120000000000001
- type: recall_at_5
value: 16.669
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 46.263
- type: map_at_10
value: 63.525
- type: map_at_100
value: 64.142
- type: map_at_1000
value: 64.14800000000001
- type: map_at_3
value: 59.653
- type: map_at_5
value: 62.244
- type: mrr_at_1
value: 51.796
- type: mrr_at_10
value: 65.764
- type: mrr_at_100
value: 66.155
- type: mrr_at_1000
value: 66.158
- type: mrr_at_3
value: 63.05500000000001
- type: mrr_at_5
value: 64.924
- type: ndcg_at_1
value: 51.766999999999996
- type: ndcg_at_10
value: 70.626
- type: ndcg_at_100
value: 72.905
- type: ndcg_at_1000
value: 73.021
- type: ndcg_at_3
value: 63.937999999999995
- type: ndcg_at_5
value: 68.00699999999999
- type: precision_at_1
value: 51.766999999999996
- type: precision_at_10
value: 10.768
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 28.409000000000002
- type: precision_at_5
value: 19.502
- type: recall_at_1
value: 46.263
- type: recall_at_10
value: 89.554
- type: recall_at_100
value: 98.914
- type: recall_at_1000
value: 99.754
- type: recall_at_3
value: 72.89999999999999
- type: recall_at_5
value: 82.1
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 72.748
- type: map_at_10
value: 86.87700000000001
- type: map_at_100
value: 87.46199999999999
- type: map_at_1000
value: 87.47399999999999
- type: map_at_3
value: 83.95700000000001
- type: map_at_5
value: 85.82300000000001
- type: mrr_at_1
value: 83.62
- type: mrr_at_10
value: 89.415
- type: mrr_at_100
value: 89.484
- type: mrr_at_1000
value: 89.484
- type: mrr_at_3
value: 88.633
- type: mrr_at_5
value: 89.176
- type: ndcg_at_1
value: 83.62
- type: ndcg_at_10
value: 90.27
- type: ndcg_at_100
value: 91.23599999999999
- type: ndcg_at_1000
value: 91.293
- type: ndcg_at_3
value: 87.69500000000001
- type: ndcg_at_5
value: 89.171
- type: precision_at_1
value: 83.62
- type: precision_at_10
value: 13.683
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.363
- type: precision_at_5
value: 25.196
- type: recall_at_1
value: 72.748
- type: recall_at_10
value: 96.61699999999999
- type: recall_at_100
value: 99.789
- type: recall_at_1000
value: 99.997
- type: recall_at_3
value: 89.21
- type: recall_at_5
value: 93.418
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 61.51909029379199
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 68.24483162045645
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.793
- type: map_at_10
value: 13.092
- type: map_at_100
value: 15.434000000000001
- type: map_at_1000
value: 15.748999999999999
- type: map_at_3
value: 9.139
- type: map_at_5
value: 11.033
- type: mrr_at_1
value: 23.599999999999998
- type: mrr_at_10
value: 35.892
- type: mrr_at_100
value: 36.962
- type: mrr_at_1000
value: 37.009
- type: mrr_at_3
value: 32.550000000000004
- type: mrr_at_5
value: 34.415
- type: ndcg_at_1
value: 23.599999999999998
- type: ndcg_at_10
value: 21.932
- type: ndcg_at_100
value: 30.433
- type: ndcg_at_1000
value: 35.668
- type: ndcg_at_3
value: 20.483999999999998
- type: ndcg_at_5
value: 17.964
- type: precision_at_1
value: 23.599999999999998
- type: precision_at_10
value: 11.63
- type: precision_at_100
value: 2.383
- type: precision_at_1000
value: 0.363
- type: precision_at_3
value: 19.567
- type: precision_at_5
value: 16.06
- type: recall_at_1
value: 4.793
- type: recall_at_10
value: 23.558
- type: recall_at_100
value: 48.376999999999995
- type: recall_at_1000
value: 73.75699999999999
- type: recall_at_3
value: 11.903
- type: recall_at_5
value: 16.278000000000002
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 87.31937967632581
- type: cos_sim_spearman
value: 84.30523596401186
- type: euclidean_pearson
value: 84.19537987069458
- type: euclidean_spearman
value: 84.30522052876
- type: manhattan_pearson
value: 84.16420807244911
- type: manhattan_spearman
value: 84.28515410219309
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.17180810119646
- type: cos_sim_spearman
value: 78.44413657529002
- type: euclidean_pearson
value: 81.69054139101816
- type: euclidean_spearman
value: 78.44412412142488
- type: manhattan_pearson
value: 82.04975789626462
- type: manhattan_spearman
value: 78.78390856857253
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.35737871089687
- type: cos_sim_spearman
value: 88.26850223126127
- type: euclidean_pearson
value: 87.44100858335746
- type: euclidean_spearman
value: 88.26850223126127
- type: manhattan_pearson
value: 87.61572015772133
- type: manhattan_spearman
value: 88.56229552813319
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.8395966764906
- type: cos_sim_spearman
value: 84.49441798385489
- type: euclidean_pearson
value: 85.3259176121388
- type: euclidean_spearman
value: 84.49442124804686
- type: manhattan_pearson
value: 85.35153862806513
- type: manhattan_spearman
value: 84.60094577432503
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 90.14048269057345
- type: cos_sim_spearman
value: 90.27866978947013
- type: euclidean_pearson
value: 89.35308361940393
- type: euclidean_spearman
value: 90.27866978947013
- type: manhattan_pearson
value: 89.37601244066997
- type: manhattan_spearman
value: 90.42707449698062
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 86.8522678865688
- type: cos_sim_spearman
value: 87.37396401580446
- type: euclidean_pearson
value: 86.37219665505377
- type: euclidean_spearman
value: 87.37396385867791
- type: manhattan_pearson
value: 86.44628823799896
- type: manhattan_spearman
value: 87.49116026788859
- 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: 92.94248481968916
- type: cos_sim_spearman
value: 92.68185242943188
- type: euclidean_pearson
value: 92.33802342092979
- type: euclidean_spearman
value: 92.68185242943188
- type: manhattan_pearson
value: 92.2011323340474
- type: manhattan_spearman
value: 92.43364757640346
- 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: 70.2918782293091
- type: cos_sim_spearman
value: 68.61986257003369
- type: euclidean_pearson
value: 70.51920905899138
- type: euclidean_spearman
value: 68.61986257003369
- type: manhattan_pearson
value: 70.64673843811433
- type: manhattan_spearman
value: 68.86711466517345
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 88.62956838105524
- type: cos_sim_spearman
value: 88.80650007123052
- type: euclidean_pearson
value: 88.37976252122822
- type: euclidean_spearman
value: 88.80650007123052
- type: manhattan_pearson
value: 88.49866938476616
- type: manhattan_spearman
value: 89.02489665452616
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.40175229911527
- type: mrr
value: 96.61958230585682
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 63.05
- type: map_at_10
value: 73.844
- type: map_at_100
value: 74.313
- type: map_at_1000
value: 74.321
- type: map_at_3
value: 71.17999999999999
- type: map_at_5
value: 72.842
- type: mrr_at_1
value: 65.667
- type: mrr_at_10
value: 74.772
- type: mrr_at_100
value: 75.087
- type: mrr_at_1000
value: 75.095
- type: mrr_at_3
value: 72.944
- type: mrr_at_5
value: 74.078
- type: ndcg_at_1
value: 65.667
- type: ndcg_at_10
value: 78.31700000000001
- type: ndcg_at_100
value: 79.969
- type: ndcg_at_1000
value: 80.25
- type: ndcg_at_3
value: 74.099
- type: ndcg_at_5
value: 76.338
- type: precision_at_1
value: 65.667
- type: precision_at_10
value: 10.233
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.889
- type: precision_at_5
value: 19.0
- type: recall_at_1
value: 63.05
- type: recall_at_10
value: 90.822
- type: recall_at_100
value: 97.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 79.489
- type: recall_at_5
value: 85.161
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83564356435643
- type: cos_sim_ap
value: 96.10619363017767
- type: cos_sim_f1
value: 91.61225514816677
- type: cos_sim_precision
value: 92.02825428859738
- type: cos_sim_recall
value: 91.2
- type: dot_accuracy
value: 99.83564356435643
- type: dot_ap
value: 96.10619363017767
- type: dot_f1
value: 91.61225514816677
- type: dot_precision
value: 92.02825428859738
- type: dot_recall
value: 91.2
- type: euclidean_accuracy
value: 99.83564356435643
- type: euclidean_ap
value: 96.10619363017769
- type: euclidean_f1
value: 91.61225514816677
- type: euclidean_precision
value: 92.02825428859738
- type: euclidean_recall
value: 91.2
- type: manhattan_accuracy
value: 99.84158415841584
- type: manhattan_ap
value: 96.27527798658713
- type: manhattan_f1
value: 92.0
- type: manhattan_precision
value: 92.0
- type: manhattan_recall
value: 92.0
- type: max_accuracy
value: 99.84158415841584
- type: max_ap
value: 96.27527798658713
- type: max_f1
value: 92.0
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 76.93753872885304
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 46.044085080870126
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.885129730227256
- type: mrr
value: 56.95062494694848
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.202047940935508
- type: cos_sim_spearman
value: 30.984832035722228
- type: dot_pearson
value: 31.20204247226978
- type: dot_spearman
value: 30.984832035722228
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 2.249
- type: map_at_100
value: 14.85
- type: map_at_1000
value: 36.596000000000004
- type: map_at_3
value: 0.717
- type: map_at_5
value: 1.18
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 96.167
- type: mrr_at_100
value: 96.167
- type: mrr_at_1000
value: 96.167
- type: mrr_at_3
value: 95.667
- type: mrr_at_5
value: 96.167
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 87.09700000000001
- type: ndcg_at_100
value: 69.637
- type: ndcg_at_1000
value: 62.257
- type: ndcg_at_3
value: 90.235
- type: ndcg_at_5
value: 89.51400000000001
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 90.60000000000001
- type: precision_at_100
value: 71.38
- type: precision_at_1000
value: 27.400000000000002
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 93.2
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.366
- type: recall_at_100
value: 17.491
- type: recall_at_1000
value: 58.772999999999996
- type: recall_at_3
value: 0.7270000000000001
- 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: 3.435
- type: map_at_10
value: 12.147
- type: map_at_100
value: 18.724
- type: map_at_1000
value: 20.426
- type: map_at_3
value: 6.526999999999999
- type: map_at_5
value: 9.198
- type: mrr_at_1
value: 48.980000000000004
- type: mrr_at_10
value: 62.970000000000006
- type: mrr_at_100
value: 63.288999999999994
- type: mrr_at_1000
value: 63.288999999999994
- type: mrr_at_3
value: 59.184000000000005
- type: mrr_at_5
value: 61.224000000000004
- type: ndcg_at_1
value: 46.939
- type: ndcg_at_10
value: 30.61
- type: ndcg_at_100
value: 41.683
- type: ndcg_at_1000
value: 53.144000000000005
- type: ndcg_at_3
value: 36.284
- type: ndcg_at_5
value: 34.345
- type: precision_at_1
value: 48.980000000000004
- type: precision_at_10
value: 26.122
- type: precision_at_100
value: 8.204
- type: precision_at_1000
value: 1.6019999999999999
- type: precision_at_3
value: 35.374
- type: precision_at_5
value: 32.653
- type: recall_at_1
value: 3.435
- type: recall_at_10
value: 18.953
- type: recall_at_100
value: 50.775000000000006
- type: recall_at_1000
value: 85.858
- type: recall_at_3
value: 7.813000000000001
- type: recall_at_5
value: 11.952
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 71.2938
- type: ap
value: 15.090139095602268
- type: f1
value: 55.23862650598296
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.7623089983022
- type: f1
value: 65.07617131099336
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 57.2988222684939
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.6034451928235
- type: cos_sim_ap
value: 81.51815279166863
- type: cos_sim_f1
value: 74.43794671864849
- type: cos_sim_precision
value: 73.34186939820742
- type: cos_sim_recall
value: 75.56728232189973
- type: dot_accuracy
value: 88.6034451928235
- type: dot_ap
value: 81.51816956866841
- type: dot_f1
value: 74.43794671864849
- type: dot_precision
value: 73.34186939820742
- type: dot_recall
value: 75.56728232189973
- type: euclidean_accuracy
value: 88.6034451928235
- type: euclidean_ap
value: 81.51817015121485
- type: euclidean_f1
value: 74.43794671864849
- type: euclidean_precision
value: 73.34186939820742
- type: euclidean_recall
value: 75.56728232189973
- type: manhattan_accuracy
value: 88.5736424867378
- type: manhattan_ap
value: 81.37610101292196
- type: manhattan_f1
value: 74.2504182215931
- type: manhattan_precision
value: 72.46922883697563
- type: manhattan_recall
value: 76.12137203166228
- type: max_accuracy
value: 88.6034451928235
- type: max_ap
value: 81.51817015121485
- type: max_f1
value: 74.43794671864849
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.53118329646446
- type: cos_sim_ap
value: 87.41972033060013
- type: cos_sim_f1
value: 79.4392523364486
- type: cos_sim_precision
value: 75.53457372951958
- type: cos_sim_recall
value: 83.7696335078534
- type: dot_accuracy
value: 89.53118329646446
- type: dot_ap
value: 87.41971646088945
- type: dot_f1
value: 79.4392523364486
- type: dot_precision
value: 75.53457372951958
- type: dot_recall
value: 83.7696335078534
- type: euclidean_accuracy
value: 89.53118329646446
- type: euclidean_ap
value: 87.41972415605997
- type: euclidean_f1
value: 79.4392523364486
- type: euclidean_precision
value: 75.53457372951958
- type: euclidean_recall
value: 83.7696335078534
- type: manhattan_accuracy
value: 89.5855163581325
- type: manhattan_ap
value: 87.51158697451964
- type: manhattan_f1
value: 79.54455087655883
- type: manhattan_precision
value: 74.96763643796416
- type: manhattan_recall
value: 84.71666153372344
- type: max_accuracy
value: 89.5855163581325
- type: max_ap
value: 87.51158697451964
- type: max_f1
value: 79.54455087655883
---
<h1 align="center">Linq-AI-Research/Linq-Embed-Mistral</h1>
**Linq-Embed-Mistral**
Linq-Embed-Mistral has been developed by building upon the foundations of the [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) models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance.
Linq-Embed-Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking <ins>**`1st`**</ins> among all models listed on the MTEB leaderboard with a performance score of <ins>**`60.2`**</ins>. This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of <ins>**`68.2`**</ins> across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall. (Please note that [NV-Emb-v1](https://huggingface.co/nvidia/NV-Embed-v1) and [voyage-large-2-instruct](https://docs.voyageai.com/embeddings/), ranked 1st and 2nd on the leaderboard as of May 29, reported their performance without releasing their models.)
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)
For more details, refer to [this blog post](https://getlinq.com/blog/linq-embed-mistral/) and [this report](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral/blob/main/LinqAIResearch2024_Linq-Embed-Mistral.pdf).
## How to use
Here is an example of how to encode queries and passages from the Mr.TyDi training dataset, both with Sentence Transformers or Transformers directly.
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral")
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a question, retrieve Wikipedia passages that answer the question'
prompt = f"Instruct: {task}\nQuery: "
queries = [
"최초의 원자력 발전소는 무엇인가?",
"Who invented Hangul?"
]
passages = [
"현재 사용되는 핵분열 방식을 이용한 전력생산은 1948년 9월 미국 테네시주 오크리지에 설치된 X-10 흑연원자로에서 전구의 불을 밝히는 데 사용되면서 시작되었다. 그리고 1954년 6월에 구소련의 오브닌스크에 건설된 흑연감속 비등경수 압력관형 원자로를 사용한 오브닌스크 원자력 발전소가 시험적으로 전력생산을 시작하였고, 최초의 상업용 원자력 엉더이로를 사용한 영국 셀라필드 원자력 단지에 위치한 콜더 홀(Calder Hall) 원자력 발전소로, 1956년 10월 17일 상업 운전을 시작하였다.",
"Hangul was personally created and promulgated by the fourth king of the Joseon dynasty, Sejong the Great.[1][2] Sejong's scholarly institute, the Hall of Worthies, is often credited with the work, and at least one of its scholars was heavily involved in its creation, but it appears to have also been a personal project of Sejong."
]
# Encode the queries and passages. We only use the prompt for the queries
query_embeddings = model.encode(queries, prompt=prompt)
passage_embeddings = model.encode(passages)
# Compute the (cosine) similarity scores
scores = model.similarity(query_embeddings, passage_embeddings) * 100
print(scores.tolist())
# [[73.72908782958984, 30.122787475585938], [29.15508460998535, 79.25375366210938]]
```
### 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 question, retrieve Wikipedia passages that answer the question'
queries = [
get_detailed_instruct(task, '최초의 원자력 발전소는 무엇인가?'),
get_detailed_instruct(task, 'Who invented Hangul?')
]
# No need to add instruction for retrieval documents
passages = [
"현재 사용되는 핵분열 방식을 이용한 전력생산은 1948년 9월 미국 테네시주 오크리지에 설치된 X-10 흑연원자로에서 전구의 불을 밝히는 데 사용되면서 시작되었다. 그리고 1954년 6월에 구소련의 오브닌스크에 건설된 흑연감속 비등경수 압력관형 원자로를 사용한 오브닌스크 원자력 발전소가 시험적으로 전력생산을 시작하였고, 최초의 상업용 원자력 엉더이로를 사용한 영국 셀라필드 원자력 단지에 위치한 콜더 홀(Calder Hall) 원자력 발전소로, 1956년 10월 17일 상업 운전을 시작하였다.",
"Hangul was personally created and promulgated by the fourth king of the Joseon dynasty, Sejong the Great.[1][2] Sejong's scholarly institute, the Hall of Worthies, is often credited with the work, and at least one of its scholars was heavily involved in its creation, but it appears to have also been a personal project of Sejong."
]
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Linq-AI-Research/Linq-Embed-Mistral')
model = AutoModel.from_pretrained('Linq-AI-Research/Linq-Embed-Mistral')
max_length = 4096
input_texts = [*queries, *passages]
# 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())
# [[73.72909545898438, 30.122783660888672], [29.155078887939453, 79.25374603271484]]
```
### 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.
## Evaluation Result
### MTEB (as of May 29, 2024)
| Model Name | Retrieval (15) | Average (56) |
| :------------------------------------------------------------------------------: | :------------: | :----------: |
| [Linq-Embed-Mistral](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral) | 60.2 | 68.2 |
| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 59.4 | 69.3 |
| [SFR-Embedding-Mistral](https://huggingface.co/Salesforce/SFR-Embedding-Mistral) | 59.0 | 67.6 |
| [voyage-large-2-instruct](https://docs.voyageai.com/docs/embeddings) | 58.3 | 68.3 |
| [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) | 57.4 | 66.8 |
| [voyage-lite-02-instruct](https://docs.voyageai.com/docs/embeddings) | 56.6 | 67.1 |
|[gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| 56.2 | 67.3 |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 56.9 | 66.6 |
|[google-gecko.text-embedding-preview-0409](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings?hl=ko#latest_models)| 55.7 | 66.3 |
|[text-embedding-3-large](https://openai.com/index/new-embedding-models-and-api-updates/)| 55.4 | 64.6 |
|[Cohere-embed-english-v3.0](https://huggingface.co/Cohere/Cohere-embed-english-v3.0)| 55.0 | 64.5 |
# Linq Research Team.
- [Junseong Kim](https://huggingface.co/Junseong)
- [Seolhwa Lee](https://huggingface.co/Seolhwa)
- [Jihoon Kwon](https://huggingface.co/Mayfull)
- [Sangmo Gu](https://huggingface.co/karma-os)
- Yejin Kim
- Minkyung Cho
- [Jy-yong Sohn](https://itml.yonsei.ac.kr/professor)
- [Chanyeol Choi](https://www.linkedin.com/in/chanyeolchoi)
# Citation
```bibtex
@misc{LinqAIResearch2024,
title={Linq-Embed-Mistral:Elevating Text Retrieval with Improved GPT Data Through Task-Specific Control and Quality Refinement},
author={Junseong Kim, Seolhwa Lee, Jihoon Kwon, Sangmo Gu, Yejin Kim, Minkyung Cho, Jy-yong Sohn, Chanyeol Choi},
howpublished={Linq AI Research Blog},
year={2024},
url={https://getlinq.com/blog/linq-embed-mistral/}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
bigscience/bloom-3b
|
bigscience
|
text-generation
|
[
"transformers",
"pytorch",
"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",
"zhs",
"zht",
"zu",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"license:bigscience-bloom-rail-1.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-05-19T11:52:27 | 2023-04-14T08:43:29 | 17,019 | 90 |
---
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
- zhs
- zht
- zu
license: bigscience-bloom-rail-1.0
pipeline_tag: text-generation
model-index:
- name: bloom
results:
- task:
type: text-generation
name: text generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- type: acc
value: 0.27986348122866894
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: arc_easy
type: arc_easy
metrics:
- type: acc
value: 0.5946969696969697
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axb
type: axb
metrics:
- type: acc
value: 0.4433876811594203
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axg
type: axg
metrics:
- type: acc
value: 0.5
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: boolq
type: boolq
metrics:
- type: acc
value: 0.6165137614678899
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cb
type: cb
metrics:
- type: acc
value: 0.30357142857142855
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cola
type: cola
metrics:
- type: acc
value: 0.610738255033557
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: copa
type: copa
metrics:
- type: acc
value: 0.63
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_english
type: crows_pairs_english
metrics:
- type: acc
value: 0.4973166368515206
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_french
type: crows_pairs_french
metrics:
- type: acc
value: 0.5032796660703638
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: diabla
type: diabla
metrics:
- type: acc
value: 0.28888308977035493
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_afr
type: gsarti/flores_101_afr
metrics:
- type: byte_perplexity
value: 6.500798737976343
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_amh
type: gsarti/flores_101_amh
metrics:
- type: byte_perplexity
value: 3.9726863338897145
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ara
type: gsarti/flores_101_ara
metrics:
- type: byte_perplexity
value: 1.8083841089875814
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_asm
type: gsarti/flores_101_asm
metrics:
- type: byte_perplexity
value: 5.699102962086425
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ast
type: gsarti/flores_101_ast
metrics:
- type: byte_perplexity
value: 3.9252047073429384
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_azj
type: gsarti/flores_101_azj
metrics:
- type: byte_perplexity
value: 6.942805054270002
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bel
type: gsarti/flores_101_bel
metrics:
- type: byte_perplexity
value: 3.614136245847082
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ben
type: gsarti/flores_101_ben
metrics:
- type: byte_perplexity
value: 5.121491534300969
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bos
type: gsarti/flores_101_bos
metrics:
- type: byte_perplexity
value: 5.653353469118798
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bul
type: gsarti/flores_101_bul
metrics:
- type: byte_perplexity
value: 2.7014693938055068
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cat
type: gsarti/flores_101_cat
metrics:
- type: byte_perplexity
value: 2.305190041967345
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ceb
type: gsarti/flores_101_ceb
metrics:
- type: byte_perplexity
value: 6.291000321323428
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ces
type: gsarti/flores_101_ces
metrics:
- type: byte_perplexity
value: 5.447322753586386
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ckb
type: gsarti/flores_101_ckb
metrics:
- type: byte_perplexity
value: 3.7255124939234765
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cym
type: gsarti/flores_101_cym
metrics:
- type: byte_perplexity
value: 12.539424151448149
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_dan
type: gsarti/flores_101_dan
metrics:
- type: byte_perplexity
value: 5.183309001005672
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_deu
type: gsarti/flores_101_deu
metrics:
- type: byte_perplexity
value: 3.1180422286591347
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ell
type: gsarti/flores_101_ell
metrics:
- type: byte_perplexity
value: 2.467943456164706
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_eng
type: gsarti/flores_101_eng
metrics:
- type: byte_perplexity
value: 2.018740628193298
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_est
type: gsarti/flores_101_est
metrics:
- type: byte_perplexity
value: 9.11654425176368
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fas
type: gsarti/flores_101_fas
metrics:
- type: byte_perplexity
value: 3.058009097116482
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fin
type: gsarti/flores_101_fin
metrics:
- type: byte_perplexity
value: 6.847047959628553
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fra
type: gsarti/flores_101_fra
metrics:
- type: byte_perplexity
value: 1.9975177011840075
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ful
type: gsarti/flores_101_ful
metrics:
- type: byte_perplexity
value: 11.465912731488828
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_gle
type: gsarti/flores_101_gle
metrics:
- type: byte_perplexity
value: 8.681491663539422
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_glg
type: gsarti/flores_101_glg
metrics:
- type: byte_perplexity
value: 3.029991089015508
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_guj
type: gsarti/flores_101_guj
metrics:
- type: byte_perplexity
value: 4.955224230286231
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hau
type: gsarti/flores_101_hau
metrics:
- type: byte_perplexity
value: 10.758347356372159
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_heb
type: gsarti/flores_101_heb
metrics:
- type: byte_perplexity
value: 3.6004478129801667
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hin
type: gsarti/flores_101_hin
metrics:
- type: byte_perplexity
value: 4.712530650588064
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hrv
type: gsarti/flores_101_hrv
metrics:
- type: byte_perplexity
value: 5.822418943372185
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hun
type: gsarti/flores_101_hun
metrics:
- type: byte_perplexity
value: 6.440482646965992
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hye
type: gsarti/flores_101_hye
metrics:
- type: byte_perplexity
value: 3.657718918347166
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ibo
type: gsarti/flores_101_ibo
metrics:
- type: byte_perplexity
value: 5.564814003872672
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ind
type: gsarti/flores_101_ind
metrics:
- type: byte_perplexity
value: 2.1597101468869373
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_isl
type: gsarti/flores_101_isl
metrics:
- type: byte_perplexity
value: 8.082349269518136
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ita
type: gsarti/flores_101_ita
metrics:
- type: byte_perplexity
value: 2.9687591414176207
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_jav
type: gsarti/flores_101_jav
metrics:
- type: byte_perplexity
value: 7.0573805415708994
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_jpn
type: gsarti/flores_101_jpn
metrics:
- type: byte_perplexity
value: 2.7758864197116933
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kam
type: gsarti/flores_101_kam
metrics:
- type: byte_perplexity
value: 11.072949642861332
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kan
type: gsarti/flores_101_kan
metrics:
- type: byte_perplexity
value: 5.551730651007082
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kat
type: gsarti/flores_101_kat
metrics:
- type: byte_perplexity
value: 2.522630524283745
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kaz
type: gsarti/flores_101_kaz
metrics:
- type: byte_perplexity
value: 3.3901748516975574
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kea
type: gsarti/flores_101_kea
metrics:
- type: byte_perplexity
value: 8.918534182590863
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kir
type: gsarti/flores_101_kir
metrics:
- type: byte_perplexity
value: 3.729278369847201
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kor
type: gsarti/flores_101_kor
metrics:
- type: byte_perplexity
value: 3.932884847226212
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lao
type: gsarti/flores_101_lao
metrics:
- type: byte_perplexity
value: 2.9077314760849924
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lav
type: gsarti/flores_101_lav
metrics:
- type: byte_perplexity
value: 7.777221919194806
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lin
type: gsarti/flores_101_lin
metrics:
- type: byte_perplexity
value: 7.524842908050988
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lit
type: gsarti/flores_101_lit
metrics:
- type: byte_perplexity
value: 7.369179434621725
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ltz
type: gsarti/flores_101_ltz
metrics:
- type: byte_perplexity
value: 8.801059747949214
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lug
type: gsarti/flores_101_lug
metrics:
- type: byte_perplexity
value: 8.483203026364786
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_luo
type: gsarti/flores_101_luo
metrics:
- type: byte_perplexity
value: 11.975963093623681
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mal
type: gsarti/flores_101_mal
metrics:
- type: byte_perplexity
value: 4.615948455160037
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mar
type: gsarti/flores_101_mar
metrics:
- type: byte_perplexity
value: 5.483253482821379
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mkd
type: gsarti/flores_101_mkd
metrics:
- type: byte_perplexity
value: 2.9656732291754087
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mlt
type: gsarti/flores_101_mlt
metrics:
- type: byte_perplexity
value: 15.004773437665275
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mon
type: gsarti/flores_101_mon
metrics:
- type: byte_perplexity
value: 3.410598542315402
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mri
type: gsarti/flores_101_mri
metrics:
- type: byte_perplexity
value: 7.474035895661322
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_msa
type: gsarti/flores_101_msa
metrics:
- type: byte_perplexity
value: 2.5710001772665634
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mya
type: gsarti/flores_101_mya
metrics:
- type: byte_perplexity
value: 2.413577969878331
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nld
type: gsarti/flores_101_nld
metrics:
- type: byte_perplexity
value: 4.127831721885065
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nob
type: gsarti/flores_101_nob
metrics:
- type: byte_perplexity
value: 5.402763169129877
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_npi
type: gsarti/flores_101_npi
metrics:
- type: byte_perplexity
value: 5.199342701937889
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nso
type: gsarti/flores_101_nso
metrics:
- type: byte_perplexity
value: 8.154626800955667
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nya
type: gsarti/flores_101_nya
metrics:
- type: byte_perplexity
value: 8.179860208369393
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_oci
type: gsarti/flores_101_oci
metrics:
- type: byte_perplexity
value: 4.8617357393685845
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_orm
type: gsarti/flores_101_orm
metrics:
- type: byte_perplexity
value: 12.911595421079408
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ory
type: gsarti/flores_101_ory
metrics:
- type: byte_perplexity
value: 5.189421861225964
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pan
type: gsarti/flores_101_pan
metrics:
- type: byte_perplexity
value: 4.698477289331806
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pol
type: gsarti/flores_101_pol
metrics:
- type: byte_perplexity
value: 4.625550458479643
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_por
type: gsarti/flores_101_por
metrics:
- type: byte_perplexity
value: 1.9754515986213523
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pus
type: gsarti/flores_101_pus
metrics:
- type: byte_perplexity
value: 4.4963371422771585
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ron
type: gsarti/flores_101_ron
metrics:
- type: byte_perplexity
value: 4.965456830031304
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_rus
type: gsarti/flores_101_rus
metrics:
- type: byte_perplexity
value: 2.0498020542445303
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_slk
type: gsarti/flores_101_slk
metrics:
- type: byte_perplexity
value: 6.450822127057479
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_slv
type: gsarti/flores_101_slv
metrics:
- type: byte_perplexity
value: 6.620252120186232
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_sna
type: gsarti/flores_101_sna
metrics:
- type: byte_perplexity
value: 8.462166771382726
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_snd
type: gsarti/flores_101_snd
metrics:
- type: byte_perplexity
value: 5.466066951221973
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_som
type: gsarti/flores_101_som
metrics:
- type: byte_perplexity
value: 11.95918054093392
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_spa
type: gsarti/flores_101_spa
metrics:
- type: byte_perplexity
value: 1.8965140104323535
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_srp
type: gsarti/flores_101_srp
metrics:
- type: byte_perplexity
value: 2.871214785885079
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_swe
type: gsarti/flores_101_swe
metrics:
- type: byte_perplexity
value: 5.054972008155866
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_swh
type: gsarti/flores_101_swh
metrics:
- type: byte_perplexity
value: 3.6973091886730676
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tam
type: gsarti/flores_101_tam
metrics:
- type: byte_perplexity
value: 4.539493400469833
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tel
type: gsarti/flores_101_tel
metrics:
- type: byte_perplexity
value: 5.807499987508966
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgk
type: gsarti/flores_101_tgk
metrics:
- type: byte_perplexity
value: 3.5994818827380426
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgl
type: gsarti/flores_101_tgl
metrics:
- type: byte_perplexity
value: 5.667053833119858
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tha
type: gsarti/flores_101_tha
metrics:
- type: byte_perplexity
value: 2.365940201944242
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tur
type: gsarti/flores_101_tur
metrics:
- type: byte_perplexity
value: 4.885014749844601
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ukr
type: gsarti/flores_101_ukr
metrics:
- type: byte_perplexity
value: 2.7240934990288483
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_umb
type: gsarti/flores_101_umb
metrics:
- type: byte_perplexity
value: 12.766915508610673
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_urd
type: gsarti/flores_101_urd
metrics:
- type: byte_perplexity
value: 1.9797467071381232
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_uzb
type: gsarti/flores_101_uzb
metrics:
- type: byte_perplexity
value: 12.002337637722146
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_vie
type: gsarti/flores_101_vie
metrics:
- type: byte_perplexity
value: 1.76578415476397
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_wol
type: gsarti/flores_101_wol
metrics:
- type: byte_perplexity
value: 9.144285650306488
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_xho
type: gsarti/flores_101_xho
metrics:
- type: byte_perplexity
value: 7.403240538286952
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_yor
type: gsarti/flores_101_yor
metrics:
- type: byte_perplexity
value: 5.91272037551173
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_simpl
type: gsarti/flores_101_zho_simpl
metrics:
- type: byte_perplexity
value: 2.2769070822768533
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_trad
type: gsarti/flores_101_zho_trad
metrics:
- type: byte_perplexity
value: 2.5180582198242383
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zul
type: gsarti/flores_101_zul
metrics:
- type: byte_perplexity
value: 8.53353320693145
name: byte_perplexity
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: headqa
type: headqa
metrics:
- type: acc
value: 0.26440554339897887
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: hellaswag
type: hellaswag
metrics:
- type: acc
value: 0.41236805417247563
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: logiqa
type: logiqa
metrics:
- type: acc
value: 0.2073732718894009
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mathqa
type: mathqa
metrics:
- type: acc
value: 0.24958123953098826
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mc_taco
type: mc_taco
metrics:
- type: em
value: 0.11936936936936937
name: em
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli
type: mnli
metrics:
- type: acc
value: 0.35496688741721855
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli_mismatched
type: mnli_mismatched
metrics:
- type: acc
value: 0.35211554109031734
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mrpc
type: mrpc
metrics:
- type: acc
value: 0.5857843137254902
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: multirc
type: multirc
metrics:
- type: acc
value: 0.5375412541254125
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: openbookqa
type: openbookqa
metrics:
- type: acc
value: 0.216
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: piqa
type: piqa
metrics:
- type: acc
value: 0.7078346028291621
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: prost
type: prost
metrics:
- type: acc
value: 0.22683603757472245
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: pubmedqa
type: pubmedqa
metrics:
- type: acc
value: 0.616
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qnli
type: qnli
metrics:
- type: acc
value: 0.5072304594545122
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verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qqp
type: qqp
metrics:
- type: acc
value: 0.3842443729903537
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verified: false
- task:
type: text-generation
name: text generation
dataset:
name: race
type: race
metrics:
- type: acc
value: 0.3521531100478469
name: acc
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: rte
type: rte
metrics:
- type: acc
value: 0.47653429602888087
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verified: false
- task:
type: text-generation
name: text generation
dataset:
name: sciq
type: sciq
metrics:
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value: 0.892
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verified: false
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type: text-generation
name: text generation
dataset:
name: sst
type: sst
metrics:
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value: 0.5177752293577982
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verified: false
- task:
type: text-generation
name: text generation
dataset:
name: triviaqa
type: triviaqa
metrics:
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value: 0.041633518960487934
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verified: false
- task:
type: text-generation
name: text generation
dataset:
name: tydiqa_primary
type: tydiqa_primary
metrics:
- type: acc
value: 0.3011337608795236
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verified: false
- task:
type: text-generation
name: text generation
dataset:
name: webqs
type: webqs
metrics:
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value: 0.01673228346456693
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verified: false
- task:
type: text-generation
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dataset:
name: wic
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metrics:
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- task:
type: text-generation
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dataset:
name: winogrande
type: winogrande
metrics:
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value: 0.5864246250986582
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verified: false
- task:
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dataset:
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metrics:
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dataset:
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type: wsc
metrics:
- type: acc
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- task:
type: text-generation
name: text generation
dataset:
name: humaneval
type: humaneval
metrics:
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value: 0.15524390243902436
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verified: false
- type: pass@10
value: 0.3220367632383857
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- type: pass@100
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verified: false
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**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
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/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 provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
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:** 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
* 3,002,557,440 parameters:
* 642,252,800 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 2560-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))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is 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.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
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 upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## 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 provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### 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 is not 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>
<p> </p>
## 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>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB 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 table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| 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 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| 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 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
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
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### 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:**
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
| Task | Language | Metric | BLOOM-2B5 |
|:----|:----|:----|:----:|
| arc_challenge | eng | acc ↑ | 0.28 |
| arc_easy | eng | acc ↑ | 0.595 |
| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |
| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |
| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |
| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |
| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |
| headqa | esp | acc ↑ | 0.264 |
| hellaswag | eng | acc ↑ | 0.412 |
| logiqa | eng | acc ↑ | 0.207 |
| mathqa | eng | acc ↑ | 0.25 |
| mc_taco | eng | em ↑ | 0.119 |
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |
| mrpc | eng | acc ↑ | 0.586 |
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |
| openbookqa | eng | acc ↑ | 0.216 |
| piqa | eng | acc ↑ | 0.708 |
| prost | eng | acc ↑ | 0.227 |
| pubmedqa | eng | acc ↑ | 0.616 |
| qnli | eng | acc ↑ | 0.507 |
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |
| race | eng | acc ↑ | 0.352 |
| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |
| sciq | eng | acc ↑ | 0.892 |
| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |
| triviaqa | eng | acc ↑ | 0.042 |
| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |
| webqs | eng | acc ↑ | 0.017 |
| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |
| winogrande | eng | acc ↑ | 0.586 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |
| humaneval | python | pass@1 ↑ | 0.155 |
| humaneval | python | pass@10 ↑ | 0.322 |
| humaneval | python | pass@100 ↑ | 0.555 |
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- 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 pretrained with the LLM 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>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <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>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### 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
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>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
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",
"SUMMARIZATION"
] |
[
"PUBMEDQA",
"SCIQ"
] |
Lihuchen/pearl_base
|
Lihuchen
|
feature-extraction
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"Phrase Representation",
"String Matching",
"Fuzzy Join",
"Entity Retrieval",
"transformers",
"en",
"arxiv:2401.10407",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-16T22:00:37 | 2024-03-06T16:14:00 | 16,855 | 3 |
---
language:
- en
license: apache-2.0
tags:
- Phrase Representation
- String Matching
- Fuzzy Join
- Entity Retrieval
- transformers
- sentence-transformers
---
## PEARL-base
[Learning High-Quality and General-Purpose Phrase Representations](https://arxiv.org/pdf/2401.10407.pdf). <br>
[Lihu Chen](https://chenlihu.com), [Gaël Varoquaux](https://gael-varoquaux.info/), [Fabian M. Suchanek](https://suchanek.name/).
<br> Accepted by EACL Findings 2024
PEARL-base is a lightweight string embedding model. It is the tool of choice for semantic similarity computation for strings,
creating excellent embeddings for string matching, entity retrieval, entity clustering, fuzzy join...
<br>
It differs from typical sentence embedders because it incorporates phrase type information and morphological features,
allowing it to better capture variations in strings.
The model is a variant of [E5-base](https://huggingface.co/intfloat/e5-base-v2) finetuned on our constructed context-free [dataset](https://zenodo.org/records/10676475) to yield better representations
for phrases and strings. <br>
🤗 [PEARL-small](https://huggingface.co/Lihuchen/pearl_small) 🤗 [PEARL-base](https://huggingface.co/Lihuchen/pearl_base)
📐 [PEARL Benchmark](https://huggingface.co/datasets/Lihuchen/pearl_benchmark) 🏆 [PEARL Leaderboard](https://huggingface.co/spaces/Lihuchen/pearl_leaderboard)
<br>
| Model |Size|Avg| PPDB | PPDB filtered |Turney|BIRD|YAGO|UMLS|CoNLL|BC5CDR|AutoFJ|
|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|
| FastText |-| 40.3| 94.4 | 61.2 | 59.6 | 58.9 |16.9|14.5|3.0|0.2| 53.6|
| Sentence-BERT |110M|50.1| 94.6 | 66.8 | 50.4 | 62.6 | 21.6|23.6|25.5|48.4| 57.2|
| Phrase-BERT |110M|54.5| 96.8 | 68.7 | 57.2 | 68.8 |23.7|26.1|35.4| 59.5|66.9|
| E5-small |34M|57.0| 96.0| 56.8|55.9| 63.1|43.3| 42.0|27.6| 53.7|74.8|
|E5-base|110M| 61.1| 95.4|65.6|59.4|66.3| 47.3|44.0|32.0| 69.3|76.1|
|PEARL-small|34M| 62.5| 97.0|70.2|57.9|68.1| 48.1|44.5|42.4|59.3|75.2|
|PEARL-base|110M|64.8|97.3|72.2|59.7|72.6|50.7|45.8|39.3|69.4|77.1|
Cost comparison of FastText and PEARL. The estimated memory is calculated by the number of parameters (float16). The unit of inference speed is `*ms/512 samples`. The FastText model here is `crawl-300d-2M-subword.bin`.
| Model |Avg Score| Estimated Memory |Speed GPU | Speed CPU |
|-|-|-|-|-|
|FastText|40.3|1200MB|-|57ms|
|PEARL-small|62.5|68MB|42ms|446ms|
|PEARL-base|64.8|220MB|89ms|1394ms|
## Usage
### Sentence Transformers
PEARL is integrated with the Sentence Transformers library (Thanks for [Tom Aarsen](https://huggingface.co/tomaarsen)'s contribution), and can be used like so:
```python
from sentence_transformers import SentenceTransformer, util
query_texts = ["The New York Times"]
doc_texts = [ "NYTimes", "New York Post", "New York"]
input_texts = query_texts + doc_texts
model = SentenceTransformer("Lihuchen/pearl_base")
embeddings = model.encode(input_texts)
scores = util.cos_sim(embeddings[0], embeddings[1:]) * 100
print(scores.tolist())
# [[85.61601257324219, 73.65623474121094, 70.36174774169922]]
```
### Transformers
You can also use `transformers` to use PEARL. Below is an example of entity retrieval, and we reuse the code from E5.
```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 encode_text(model, input_texts):
# 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'])
return embeddings
query_texts = ["The New York Times"]
doc_texts = [ "NYTimes", "New York Post", "New York"]
input_texts = query_texts + doc_texts
tokenizer = AutoTokenizer.from_pretrained('Lihuchen/pearl_base')
model = AutoModel.from_pretrained('Lihuchen/pearl_base')
# encode
embeddings = encode_text(model, input_texts)
# calculate similarity
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
# expected outputs
# [[85.61601257324219, 73.65624237060547, 70.36172485351562]]
```
## Training and Evaluation
Have a look at our code on [Github](https://github.com/tigerchen52/PEARL)
## Citation
If you find our work useful, please give us a citation:
```
@article{chen2024learning,
title={Learning High-Quality and General-Purpose Phrase Representations},
author={Chen, Lihu and Varoquaux, Ga{\"e}l and Suchanek, Fabian M},
journal={arXiv preprint arXiv:2401.10407},
year={2024}
}
```
|
[
"SEMANTIC_SIMILARITY"
] |
[
"BC5CDR"
] |
sileod/deberta-v3-base-tasksource-nli
|
sileod
|
zero-shot-classification
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3-base",
"deberta-v3",
"deberta",
"nli",
"natural-language-inference",
"multitask",
"multi-task",
"pipeline",
"extreme-multi-task",
"extreme-mtl",
"tasksource",
"zero-shot",
"rlhf",
"zero-shot-classification",
"en",
"dataset:glue",
"dataset:nyu-mll/multi_nli",
"dataset:multi_nli",
"dataset:super_glue",
"dataset:anli",
"dataset:tasksource/babi_nli",
"dataset:sick",
"dataset:snli",
"dataset:scitail",
"dataset:OpenAssistant/oasst1",
"dataset:universal_dependencies",
"dataset:hans",
"dataset:qbao775/PARARULE-Plus",
"dataset:alisawuffles/WANLI",
"dataset:metaeval/recast",
"dataset:sileod/probability_words_nli",
"dataset:joey234/nan-nli",
"dataset:pietrolesci/nli_fever",
"dataset:pietrolesci/breaking_nli",
"dataset:pietrolesci/conj_nli",
"dataset:pietrolesci/fracas",
"dataset:pietrolesci/dialogue_nli",
"dataset:pietrolesci/mpe",
"dataset:pietrolesci/dnc",
"dataset:pietrolesci/gpt3_nli",
"dataset:pietrolesci/recast_white",
"dataset:pietrolesci/joci",
"dataset:martn-nguyen/contrast_nli",
"dataset:pietrolesci/robust_nli",
"dataset:pietrolesci/robust_nli_is_sd",
"dataset:pietrolesci/robust_nli_li_ts",
"dataset:pietrolesci/gen_debiased_nli",
"dataset:pietrolesci/add_one_rte",
"dataset:metaeval/imppres",
"dataset:pietrolesci/glue_diagnostics",
"dataset:hlgd",
"dataset:PolyAI/banking77",
"dataset:paws",
"dataset:quora",
"dataset:medical_questions_pairs",
"dataset:conll2003",
"dataset:nlpaueb/finer-139",
"dataset:Anthropic/hh-rlhf",
"dataset:Anthropic/model-written-evals",
"dataset:truthful_qa",
"dataset:nightingal3/fig-qa",
"dataset:tasksource/bigbench",
"dataset:blimp",
"dataset:cos_e",
"dataset:cosmos_qa",
"dataset:dream",
"dataset:openbookqa",
"dataset:qasc",
"dataset:quartz",
"dataset:quail",
"dataset:head_qa",
"dataset:sciq",
"dataset:social_i_qa",
"dataset:wiki_hop",
"dataset:wiqa",
"dataset:piqa",
"dataset:hellaswag",
"dataset:pkavumba/balanced-copa",
"dataset:12ml/e-CARE",
"dataset:art",
"dataset:tasksource/mmlu",
"dataset:winogrande",
"dataset:codah",
"dataset:ai2_arc",
"dataset:definite_pronoun_resolution",
"dataset:swag",
"dataset:math_qa",
"dataset:metaeval/utilitarianism",
"dataset:mteb/amazon_counterfactual",
"dataset:SetFit/insincere-questions",
"dataset:SetFit/toxic_conversations",
"dataset:turingbench/TuringBench",
"dataset:trec",
"dataset:tals/vitaminc",
"dataset:hope_edi",
"dataset:strombergnlp/rumoureval_2019",
"dataset:ethos",
"dataset:tweet_eval",
"dataset:discovery",
"dataset:pragmeval",
"dataset:silicone",
"dataset:lex_glue",
"dataset:papluca/language-identification",
"dataset:imdb",
"dataset:rotten_tomatoes",
"dataset:ag_news",
"dataset:yelp_review_full",
"dataset:financial_phrasebank",
"dataset:poem_sentiment",
"dataset:dbpedia_14",
"dataset:amazon_polarity",
"dataset:app_reviews",
"dataset:hate_speech18",
"dataset:sms_spam",
"dataset:humicroedit",
"dataset:snips_built_in_intents",
"dataset:banking77",
"dataset:hate_speech_offensive",
"dataset:yahoo_answers_topics",
"dataset:pacovaldez/stackoverflow-questions",
"dataset:zapsdcn/hyperpartisan_news",
"dataset:zapsdcn/sciie",
"dataset:zapsdcn/citation_intent",
"dataset:go_emotions",
"dataset:allenai/scicite",
"dataset:liar",
"dataset:relbert/lexical_relation_classification",
"dataset:metaeval/linguisticprobing",
"dataset:tasksource/crowdflower",
"dataset:metaeval/ethics",
"dataset:emo",
"dataset:google_wellformed_query",
"dataset:tweets_hate_speech_detection",
"dataset:has_part",
"dataset:wnut_17",
"dataset:ncbi_disease",
"dataset:acronym_identification",
"dataset:jnlpba",
"dataset:species_800",
"dataset:SpeedOfMagic/ontonotes_english",
"dataset:blog_authorship_corpus",
"dataset:launch/open_question_type",
"dataset:health_fact",
"dataset:commonsense_qa",
"dataset:mc_taco",
"dataset:ade_corpus_v2",
"dataset:prajjwal1/discosense",
"dataset:circa",
"dataset:PiC/phrase_similarity",
"dataset:copenlu/scientific-exaggeration-detection",
"dataset:quarel",
"dataset:mwong/fever-evidence-related",
"dataset:numer_sense",
"dataset:dynabench/dynasent",
"dataset:raquiba/Sarcasm_News_Headline",
"dataset:sem_eval_2010_task_8",
"dataset:demo-org/auditor_review",
"dataset:medmcqa",
"dataset:aqua_rat",
"dataset:RuyuanWan/Dynasent_Disagreement",
"dataset:RuyuanWan/Politeness_Disagreement",
"dataset:RuyuanWan/SBIC_Disagreement",
"dataset:RuyuanWan/SChem_Disagreement",
"dataset:RuyuanWan/Dilemmas_Disagreement",
"dataset:lucasmccabe/logiqa",
"dataset:wiki_qa",
"dataset:metaeval/cycic_classification",
"dataset:metaeval/cycic_multiplechoice",
"dataset:metaeval/sts-companion",
"dataset:metaeval/commonsense_qa_2.0",
"dataset:metaeval/lingnli",
"dataset:metaeval/monotonicity-entailment",
"dataset:metaeval/arct",
"dataset:metaeval/scinli",
"dataset:metaeval/naturallogic",
"dataset:onestop_qa",
"dataset:demelin/moral_stories",
"dataset:corypaik/prost",
"dataset:aps/dynahate",
"dataset:metaeval/syntactic-augmentation-nli",
"dataset:metaeval/autotnli",
"dataset:lasha-nlp/CONDAQA",
"dataset:openai/webgpt_comparisons",
"dataset:Dahoas/synthetic-instruct-gptj-pairwise",
"dataset:metaeval/scruples",
"dataset:metaeval/wouldyourather",
"dataset:sileod/attempto-nli",
"dataset:metaeval/defeasible-nli",
"dataset:metaeval/help-nli",
"dataset:metaeval/nli-veridicality-transitivity",
"dataset:metaeval/natural-language-satisfiability",
"dataset:metaeval/lonli",
"dataset:tasksource/dadc-limit-nli",
"dataset:ColumbiaNLP/FLUTE",
"dataset:metaeval/strategy-qa",
"dataset:openai/summarize_from_feedback",
"dataset:tasksource/folio",
"dataset:metaeval/tomi-nli",
"dataset:metaeval/avicenna",
"dataset:stanfordnlp/SHP",
"dataset:GBaker/MedQA-USMLE-4-options-hf",
"dataset:GBaker/MedQA-USMLE-4-options",
"dataset:sileod/wikimedqa",
"dataset:declare-lab/cicero",
"dataset:amydeng2000/CREAK",
"dataset:metaeval/mutual",
"dataset:inverse-scaling/NeQA",
"dataset:inverse-scaling/quote-repetition",
"dataset:inverse-scaling/redefine-math",
"dataset:tasksource/puzzte",
"dataset:metaeval/implicatures",
"dataset:race",
"dataset:metaeval/spartqa-yn",
"dataset:metaeval/spartqa-mchoice",
"dataset:metaeval/temporal-nli",
"dataset:metaeval/ScienceQA_text_only",
"dataset:AndyChiang/cloth",
"dataset:metaeval/logiqa-2.0-nli",
"dataset:tasksource/oasst1_dense_flat",
"dataset:metaeval/boolq-natural-perturbations",
"dataset:metaeval/path-naturalness-prediction",
"dataset:riddle_sense",
"dataset:Jiangjie/ekar_english",
"dataset:metaeval/implicit-hate-stg1",
"dataset:metaeval/chaos-mnli-ambiguity",
"dataset:IlyaGusev/headline_cause",
"dataset:metaeval/race-c",
"dataset:metaeval/equate",
"dataset:metaeval/ambient",
"dataset:AndyChiang/dgen",
"dataset:metaeval/clcd-english",
"dataset:civil_comments",
"dataset:metaeval/acceptability-prediction",
"dataset:maximedb/twentyquestions",
"dataset:metaeval/counterfactually-augmented-snli",
"dataset:tasksource/I2D2",
"dataset:sileod/mindgames",
"dataset:metaeval/counterfactually-augmented-imdb",
"dataset:metaeval/cnli",
"dataset:metaeval/reclor",
"dataset:tasksource/oasst1_pairwise_rlhf_reward",
"dataset:tasksource/zero-shot-label-nli",
"dataset:webis/args_me",
"dataset:webis/Touche23-ValueEval",
"dataset:tasksource/starcon",
"dataset:tasksource/ruletaker",
"dataset:lighteval/lsat_qa",
"dataset:tasksource/ConTRoL-nli",
"dataset:tasksource/tracie",
"dataset:tasksource/sherliic",
"dataset:tasksource/sen-making",
"dataset:tasksource/winowhy",
"dataset:mediabiasgroup/mbib-base",
"dataset:tasksource/robustLR",
"dataset:CLUTRR/v1",
"dataset:tasksource/logical-fallacy",
"dataset:tasksource/parade",
"dataset:tasksource/cladder",
"dataset:tasksource/subjectivity",
"dataset:tasksource/MOH",
"dataset:tasksource/VUAC",
"dataset:tasksource/TroFi",
"dataset:sharc_modified",
"dataset:tasksource/conceptrules_v2",
"dataset:tasksource/disrpt",
"dataset:conll2000",
"dataset:DFKI-SLT/few-nerd",
"dataset:tasksource/com2sense",
"dataset:tasksource/scone",
"dataset:tasksource/winodict",
"dataset:tasksource/fool-me-twice",
"dataset:tasksource/monli",
"dataset:tasksource/corr2cause",
"dataset:tasksource/apt",
"dataset:zeroshot/twitter-financial-news-sentiment",
"dataset:tasksource/icl-symbol-tuning-instruct",
"dataset:tasksource/SpaceNLI",
"dataset:sihaochen/propsegment",
"dataset:HannahRoseKirk/HatemojiBuild",
"dataset:tasksource/regset",
"dataset:lmsys/chatbot_arena_conversations",
"dataset:tasksource/nlgraph",
"arxiv:2301.05948",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-01-13T13:47:22 | 2024-08-13T21:12:51 | 15,941 | 123 |
---
datasets:
- glue
- nyu-mll/multi_nli
- multi_nli
- super_glue
- anli
- tasksource/babi_nli
- sick
- snli
- scitail
- OpenAssistant/oasst1
- universal_dependencies
- hans
- qbao775/PARARULE-Plus
- alisawuffles/WANLI
- metaeval/recast
- sileod/probability_words_nli
- joey234/nan-nli
- pietrolesci/nli_fever
- pietrolesci/breaking_nli
- pietrolesci/conj_nli
- pietrolesci/fracas
- pietrolesci/dialogue_nli
- pietrolesci/mpe
- pietrolesci/dnc
- pietrolesci/gpt3_nli
- pietrolesci/recast_white
- pietrolesci/joci
- martn-nguyen/contrast_nli
- pietrolesci/robust_nli
- pietrolesci/robust_nli_is_sd
- pietrolesci/robust_nli_li_ts
- pietrolesci/gen_debiased_nli
- pietrolesci/add_one_rte
- metaeval/imppres
- pietrolesci/glue_diagnostics
- hlgd
- PolyAI/banking77
- paws
- quora
- medical_questions_pairs
- conll2003
- nlpaueb/finer-139
- Anthropic/hh-rlhf
- Anthropic/model-written-evals
- truthful_qa
- nightingal3/fig-qa
- tasksource/bigbench
- blimp
- cos_e
- cosmos_qa
- dream
- openbookqa
- qasc
- quartz
- quail
- head_qa
- sciq
- social_i_qa
- wiki_hop
- wiqa
- piqa
- hellaswag
- pkavumba/balanced-copa
- 12ml/e-CARE
- art
- tasksource/mmlu
- winogrande
- codah
- ai2_arc
- definite_pronoun_resolution
- swag
- math_qa
- metaeval/utilitarianism
- mteb/amazon_counterfactual
- SetFit/insincere-questions
- SetFit/toxic_conversations
- turingbench/TuringBench
- trec
- tals/vitaminc
- hope_edi
- strombergnlp/rumoureval_2019
- ethos
- tweet_eval
- discovery
- pragmeval
- silicone
- lex_glue
- papluca/language-identification
- imdb
- rotten_tomatoes
- ag_news
- yelp_review_full
- financial_phrasebank
- poem_sentiment
- dbpedia_14
- amazon_polarity
- app_reviews
- hate_speech18
- sms_spam
- humicroedit
- snips_built_in_intents
- banking77
- hate_speech_offensive
- yahoo_answers_topics
- pacovaldez/stackoverflow-questions
- zapsdcn/hyperpartisan_news
- zapsdcn/sciie
- zapsdcn/citation_intent
- go_emotions
- allenai/scicite
- liar
- relbert/lexical_relation_classification
- metaeval/linguisticprobing
- tasksource/crowdflower
- metaeval/ethics
- emo
- google_wellformed_query
- tweets_hate_speech_detection
- has_part
- wnut_17
- ncbi_disease
- acronym_identification
- jnlpba
- species_800
- SpeedOfMagic/ontonotes_english
- blog_authorship_corpus
- launch/open_question_type
- health_fact
- commonsense_qa
- mc_taco
- ade_corpus_v2
- prajjwal1/discosense
- circa
- PiC/phrase_similarity
- copenlu/scientific-exaggeration-detection
- quarel
- mwong/fever-evidence-related
- numer_sense
- dynabench/dynasent
- raquiba/Sarcasm_News_Headline
- sem_eval_2010_task_8
- demo-org/auditor_review
- medmcqa
- aqua_rat
- RuyuanWan/Dynasent_Disagreement
- RuyuanWan/Politeness_Disagreement
- RuyuanWan/SBIC_Disagreement
- RuyuanWan/SChem_Disagreement
- RuyuanWan/Dilemmas_Disagreement
- lucasmccabe/logiqa
- wiki_qa
- metaeval/cycic_classification
- metaeval/cycic_multiplechoice
- metaeval/sts-companion
- metaeval/commonsense_qa_2.0
- metaeval/lingnli
- metaeval/monotonicity-entailment
- metaeval/arct
- metaeval/scinli
- metaeval/naturallogic
- onestop_qa
- demelin/moral_stories
- corypaik/prost
- aps/dynahate
- metaeval/syntactic-augmentation-nli
- metaeval/autotnli
- lasha-nlp/CONDAQA
- openai/webgpt_comparisons
- Dahoas/synthetic-instruct-gptj-pairwise
- metaeval/scruples
- metaeval/wouldyourather
- sileod/attempto-nli
- metaeval/defeasible-nli
- metaeval/help-nli
- metaeval/nli-veridicality-transitivity
- metaeval/natural-language-satisfiability
- metaeval/lonli
- tasksource/dadc-limit-nli
- ColumbiaNLP/FLUTE
- metaeval/strategy-qa
- openai/summarize_from_feedback
- tasksource/folio
- metaeval/tomi-nli
- metaeval/avicenna
- stanfordnlp/SHP
- GBaker/MedQA-USMLE-4-options-hf
- GBaker/MedQA-USMLE-4-options
- sileod/wikimedqa
- declare-lab/cicero
- amydeng2000/CREAK
- metaeval/mutual
- inverse-scaling/NeQA
- inverse-scaling/quote-repetition
- inverse-scaling/redefine-math
- tasksource/puzzte
- metaeval/implicatures
- race
- metaeval/spartqa-yn
- metaeval/spartqa-mchoice
- metaeval/temporal-nli
- metaeval/ScienceQA_text_only
- AndyChiang/cloth
- metaeval/logiqa-2.0-nli
- tasksource/oasst1_dense_flat
- metaeval/boolq-natural-perturbations
- metaeval/path-naturalness-prediction
- riddle_sense
- Jiangjie/ekar_english
- metaeval/implicit-hate-stg1
- metaeval/chaos-mnli-ambiguity
- IlyaGusev/headline_cause
- metaeval/race-c
- metaeval/equate
- metaeval/ambient
- AndyChiang/dgen
- metaeval/clcd-english
- civil_comments
- metaeval/acceptability-prediction
- maximedb/twentyquestions
- metaeval/counterfactually-augmented-snli
- tasksource/I2D2
- sileod/mindgames
- metaeval/counterfactually-augmented-imdb
- metaeval/cnli
- metaeval/reclor
- tasksource/oasst1_pairwise_rlhf_reward
- tasksource/zero-shot-label-nli
- webis/args_me
- webis/Touche23-ValueEval
- tasksource/starcon
- tasksource/ruletaker
- lighteval/lsat_qa
- tasksource/ConTRoL-nli
- tasksource/tracie
- tasksource/sherliic
- tasksource/sen-making
- tasksource/winowhy
- mediabiasgroup/mbib-base
- tasksource/robustLR
- CLUTRR/v1
- tasksource/logical-fallacy
- tasksource/parade
- tasksource/cladder
- tasksource/subjectivity
- tasksource/MOH
- tasksource/VUAC
- tasksource/TroFi
- sharc_modified
- tasksource/conceptrules_v2
- tasksource/disrpt
- conll2000
- DFKI-SLT/few-nerd
- tasksource/com2sense
- tasksource/scone
- tasksource/winodict
- tasksource/fool-me-twice
- tasksource/monli
- tasksource/corr2cause
- tasksource/apt
- zeroshot/twitter-financial-news-sentiment
- tasksource/icl-symbol-tuning-instruct
- tasksource/SpaceNLI
- sihaochen/propsegment
- HannahRoseKirk/HatemojiBuild
- tasksource/regset
- tasksource/babi_nli
- lmsys/chatbot_arena_conversations
- tasksource/nlgraph
language: en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
pipeline_tag: zero-shot-classification
tags:
- deberta-v3-base
- deberta-v3
- deberta
- text-classification
- nli
- natural-language-inference
- multitask
- multi-task
- pipeline
- extreme-multi-task
- extreme-mtl
- tasksource
- zero-shot
- rlhf
model-index:
- name: deberta-v3-base-tasksource-nli
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: rte
split: validation
metrics:
- type: accuracy
value: 0.89
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: anli-r3
type: anli
config: plain_text
split: validation
metrics:
- type: accuracy
value: 0.52
name: Accuracy
---
# Model Card for DeBERTa-v3-base-tasksource-nli
---
**NOTE**
Deprecated: use https://huggingface.co/tasksource/deberta-small-long-nli for longer context and better accuracy.
---
This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 600+ tasks of the [tasksource collection](https://github.com/sileod/tasksource/).
This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
- Zero-shot entailment-based classification for arbitrary labels [ZS].
- Natural language inference [NLI]
- Hundreds of previous tasks with tasksource-adapters [TA].
- Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT].
# [ZS] Zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="sileod/deberta-v3-base-tasksource-nli")
text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)
```
NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
# [NLI] Natural language inference pipeline
```python
from transformers import pipeline
pipe = pipeline("text-classification",model="sileod/deberta-v3-base-tasksource-nli")
pipe([dict(text='there is a cat',
text_pair='there is a black cat')]) #list of (premise,hypothesis)
# [{'label': 'neutral', 'score': 0.9952911138534546}]
```
# [TA] Tasksource-adapters: 1 line access to hundreds of tasks
```python
# !pip install tasknet
import tasknet as tn
pipe = tn.load_pipeline('sileod/deberta-v3-base-tasksource-nli','glue/sst2') # works for 500+ tasksource tasks
pipe(['That movie was great !', 'Awful movie.'])
# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}]
```
The list of tasks is available in model config.json.
This is more efficient than ZS since it requires only one forward pass per example, but it is less flexible.
# [FT] Tasknet: 3 lines fine-tuning
```python
# !pip install tasknet
import tasknet as tn
hparams=dict(model_name='sileod/deberta-v3-base-tasksource-nli', learning_rate=2e-5)
model, trainer = tn.Model_Trainer([tn.AutoTask("glue/rte")], hparams)
trainer.train()
```
## Evaluation
This model ranked 1st among all models with the microsoft/deberta-v3-base architecture according to the IBM model recycling evaluation.
https://ibm.github.io/model-recycling/
### Software and training details
The model was trained on 600 tasks for 200k steps with a batch size of 384 and a peak learning rate of 2e-5. Training took 15 days on Nvidia A30 24GB gpu.
This is the shared model with the MNLI classifier on top. Each task had a specific CLS embedding, which is dropped 10% of the time to facilitate model use without it. All multiple-choice model used the same classification layers. For classification tasks, models shared weights if their labels matched.
https://github.com/sileod/tasksource/ \
https://github.com/sileod/tasknet/ \
Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
# Citation
More details on this [article:](https://arxiv.org/abs/2301.05948)
```
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}
```
# Model Card Contact
[email protected]
</details>
|
[
"TEXT_CLASSIFICATION"
] |
[
"HEAD-QA",
"JNLPBA",
"MEDQA",
"NCBI DISEASE",
"SCICITE",
"SCIQ",
"SCITAIL"
] |
sdadas/mmlw-e5-small
|
sdadas
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"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"
] | 2023-11-17T18:40:08 | 2024-10-30T14:16:51 | 15,170 | 0 |
---
language: pl
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
widget:
- source_sentence: 'query: Jak dożyć 100 lat?'
sentences:
- 'passage: Trzeba zdrowo się odżywiać i uprawiać sport.'
- 'passage: Trzeba pić alkohol, imprezować i jeździć szybkimi autami.'
- 'passage: Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem
niedzielnego handlu.'
model-index:
- name: mmlw-e5-small
results:
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 31.772224277808153
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 33.03180914512922
- type: f1
value: 29.800304217426167
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: arguana-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.804999999999996
- type: map_at_10
value: 45.327
- type: map_at_100
value: 46.17
- type: map_at_1000
value: 46.177
- type: map_at_3
value: 40.528999999999996
- type: map_at_5
value: 43.335
- type: mrr_at_1
value: 30.299
- type: mrr_at_10
value: 45.763
- type: mrr_at_100
value: 46.641
- type: mrr_at_1000
value: 46.648
- type: mrr_at_3
value: 41.074
- type: mrr_at_5
value: 43.836999999999996
- type: ndcg_at_1
value: 28.804999999999996
- type: ndcg_at_10
value: 54.308
- type: ndcg_at_100
value: 57.879000000000005
- type: ndcg_at_1000
value: 58.048
- type: ndcg_at_3
value: 44.502
- type: ndcg_at_5
value: 49.519000000000005
- type: precision_at_1
value: 28.804999999999996
- type: precision_at_10
value: 8.286
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.682000000000002
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 28.804999999999996
- type: recall_at_10
value: 82.85900000000001
- type: recall_at_100
value: 98.36399999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 56.04599999999999
- type: recall_at_5
value: 68.137
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 64.24
- type: ap
value: 17.967103105024705
- type: f1
value: 52.97375416129459
- 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: 88.8
- type: cos_sim_ap
value: 76.68028778789487
- type: cos_sim_f1
value: 66.82352941176471
- type: cos_sim_precision
value: 60.42553191489362
- type: cos_sim_recall
value: 74.73684210526315
- type: dot_accuracy
value: 88.1
- type: dot_ap
value: 72.04910086070551
- type: dot_f1
value: 66.66666666666667
- type: dot_precision
value: 69.31818181818183
- type: dot_recall
value: 64.21052631578948
- type: euclidean_accuracy
value: 88.8
- type: euclidean_ap
value: 76.63591858340688
- type: euclidean_f1
value: 67.13286713286713
- type: euclidean_precision
value: 60.25104602510461
- type: euclidean_recall
value: 75.78947368421053
- type: manhattan_accuracy
value: 88.9
- type: manhattan_ap
value: 76.54552849815124
- type: manhattan_f1
value: 66.66666666666667
- type: manhattan_precision
value: 60.51502145922747
- type: manhattan_recall
value: 74.21052631578947
- type: max_accuracy
value: 88.9
- type: max_ap
value: 76.68028778789487
- type: max_f1
value: 67.13286713286713
- 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: 91.64169404461497
- type: cos_sim_spearman
value: 91.9755161377078
- type: euclidean_pearson
value: 90.87481478491249
- type: euclidean_spearman
value: 91.92362666383987
- type: manhattan_pearson
value: 90.8415510499638
- type: manhattan_spearman
value: 91.85927127194698
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: dbpedia-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.148
- type: map_at_10
value: 12.870999999999999
- type: map_at_100
value: 18.04
- type: map_at_1000
value: 19.286
- type: map_at_3
value: 9.156
- type: map_at_5
value: 10.857999999999999
- type: mrr_at_1
value: 53.25
- type: mrr_at_10
value: 61.016999999999996
- type: mrr_at_100
value: 61.48400000000001
- type: mrr_at_1000
value: 61.507999999999996
- type: mrr_at_3
value: 58.75
- type: mrr_at_5
value: 60.375
- type: ndcg_at_1
value: 41.0
- type: ndcg_at_10
value: 30.281000000000002
- type: ndcg_at_100
value: 33.955999999999996
- type: ndcg_at_1000
value: 40.77
- type: ndcg_at_3
value: 34.127
- type: ndcg_at_5
value: 32.274
- type: precision_at_1
value: 52.5
- type: precision_at_10
value: 24.525
- type: precision_at_100
value: 8.125
- type: precision_at_1000
value: 1.728
- type: precision_at_3
value: 37.083
- type: precision_at_5
value: 32.15
- type: recall_at_1
value: 6.148
- type: recall_at_10
value: 17.866
- type: recall_at_100
value: 39.213
- type: recall_at_1000
value: 61.604000000000006
- type: recall_at_3
value: 10.084
- type: recall_at_5
value: 13.333999999999998
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: fiqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.643
- type: map_at_10
value: 23.166
- type: map_at_100
value: 24.725
- type: map_at_1000
value: 24.92
- type: map_at_3
value: 20.166
- type: map_at_5
value: 22.003
- type: mrr_at_1
value: 29.630000000000003
- type: mrr_at_10
value: 37.632
- type: mrr_at_100
value: 38.512
- type: mrr_at_1000
value: 38.578
- type: mrr_at_3
value: 35.391
- type: mrr_at_5
value: 36.857
- type: ndcg_at_1
value: 29.166999999999998
- type: ndcg_at_10
value: 29.749
- type: ndcg_at_100
value: 35.983
- type: ndcg_at_1000
value: 39.817
- type: ndcg_at_3
value: 26.739
- type: ndcg_at_5
value: 27.993000000000002
- type: precision_at_1
value: 29.166999999999998
- type: precision_at_10
value: 8.333
- type: precision_at_100
value: 1.448
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 17.747
- type: precision_at_5
value: 13.58
- type: recall_at_1
value: 14.643
- type: recall_at_10
value: 35.247
- type: recall_at_100
value: 59.150999999999996
- type: recall_at_1000
value: 82.565
- type: recall_at_3
value: 24.006
- type: recall_at_5
value: 29.383
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: hotpotqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.627
- type: map_at_10
value: 48.041
- type: map_at_100
value: 49.008
- type: map_at_1000
value: 49.092999999999996
- type: map_at_3
value: 44.774
- type: map_at_5
value: 46.791
- type: mrr_at_1
value: 65.28
- type: mrr_at_10
value: 72.53500000000001
- type: mrr_at_100
value: 72.892
- type: mrr_at_1000
value: 72.909
- type: mrr_at_3
value: 71.083
- type: mrr_at_5
value: 71.985
- type: ndcg_at_1
value: 65.253
- type: ndcg_at_10
value: 57.13700000000001
- type: ndcg_at_100
value: 60.783
- type: ndcg_at_1000
value: 62.507000000000005
- type: ndcg_at_3
value: 52.17
- type: ndcg_at_5
value: 54.896
- type: precision_at_1
value: 65.253
- type: precision_at_10
value: 12.088000000000001
- type: precision_at_100
value: 1.496
- type: precision_at_1000
value: 0.172
- type: precision_at_3
value: 32.96
- type: precision_at_5
value: 21.931
- type: recall_at_1
value: 32.627
- type: recall_at_10
value: 60.439
- type: recall_at_100
value: 74.80799999999999
- type: recall_at_1000
value: 86.219
- type: recall_at_3
value: 49.44
- type: recall_at_5
value: 54.827999999999996
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: msmarco-pl
config: default
split: validation
revision: None
metrics:
- type: map_at_1
value: 13.150999999999998
- type: map_at_10
value: 21.179000000000002
- type: map_at_100
value: 22.227
- type: map_at_1000
value: 22.308
- type: map_at_3
value: 18.473
- type: map_at_5
value: 19.942999999999998
- type: mrr_at_1
value: 13.467
- type: mrr_at_10
value: 21.471
- type: mrr_at_100
value: 22.509
- type: mrr_at_1000
value: 22.585
- type: mrr_at_3
value: 18.789
- type: mrr_at_5
value: 20.262
- type: ndcg_at_1
value: 13.539000000000001
- type: ndcg_at_10
value: 25.942999999999998
- type: ndcg_at_100
value: 31.386999999999997
- type: ndcg_at_1000
value: 33.641
- type: ndcg_at_3
value: 20.368
- type: ndcg_at_5
value: 23.003999999999998
- type: precision_at_1
value: 13.539000000000001
- type: precision_at_10
value: 4.249
- type: precision_at_100
value: 0.7040000000000001
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 8.782
- type: precision_at_5
value: 6.6049999999999995
- type: recall_at_1
value: 13.150999999999998
- type: recall_at_10
value: 40.698
- type: recall_at_100
value: 66.71000000000001
- type: recall_at_1000
value: 84.491
- type: recall_at_3
value: 25.452
- type: recall_at_5
value: 31.791000000000004
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.3537323470074
- type: f1
value: 64.67852047603644
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.12508406186953
- type: f1
value: 71.55887309568853
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: nfcorpus-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.18
- type: map_at_10
value: 9.524000000000001
- type: map_at_100
value: 12.272
- type: map_at_1000
value: 13.616
- type: map_at_3
value: 6.717
- type: map_at_5
value: 8.172
- type: mrr_at_1
value: 37.152
- type: mrr_at_10
value: 45.068000000000005
- type: mrr_at_100
value: 46.026
- type: mrr_at_1000
value: 46.085
- type: mrr_at_3
value: 43.344
- type: mrr_at_5
value: 44.412
- type: ndcg_at_1
value: 34.52
- type: ndcg_at_10
value: 27.604
- type: ndcg_at_100
value: 26.012999999999998
- type: ndcg_at_1000
value: 35.272
- type: ndcg_at_3
value: 31.538
- type: ndcg_at_5
value: 30.165999999999997
- type: precision_at_1
value: 36.223
- type: precision_at_10
value: 21.053
- type: precision_at_100
value: 7.08
- type: precision_at_1000
value: 1.9929999999999999
- type: precision_at_3
value: 30.031000000000002
- type: precision_at_5
value: 26.997
- type: recall_at_1
value: 4.18
- type: recall_at_10
value: 12.901000000000002
- type: recall_at_100
value: 27.438000000000002
- type: recall_at_1000
value: 60.768
- type: recall_at_3
value: 7.492
- type: recall_at_5
value: 10.05
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: nq-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.965
- type: map_at_10
value: 28.04
- type: map_at_100
value: 29.217
- type: map_at_1000
value: 29.285
- type: map_at_3
value: 24.818
- type: map_at_5
value: 26.617
- type: mrr_at_1
value: 20.22
- type: mrr_at_10
value: 30.148000000000003
- type: mrr_at_100
value: 31.137999999999998
- type: mrr_at_1000
value: 31.19
- type: mrr_at_3
value: 27.201999999999998
- type: mrr_at_5
value: 28.884999999999998
- type: ndcg_at_1
value: 20.365
- type: ndcg_at_10
value: 33.832
- type: ndcg_at_100
value: 39.33
- type: ndcg_at_1000
value: 41.099999999999994
- type: ndcg_at_3
value: 27.46
- type: ndcg_at_5
value: 30.584
- type: precision_at_1
value: 20.365
- type: precision_at_10
value: 5.849
- type: precision_at_100
value: 0.8959999999999999
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 12.64
- type: precision_at_5
value: 9.334000000000001
- type: recall_at_1
value: 17.965
- type: recall_at_10
value: 49.503
- type: recall_at_100
value: 74.351
- type: recall_at_1000
value: 87.766
- type: recall_at_3
value: 32.665
- type: recall_at_5
value: 39.974
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 63.11323486823051
- type: ap
value: 74.53486257377787
- type: f1
value: 60.631005373417736
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 80.10000000000001
- type: cos_sim_ap
value: 89.69526236458292
- type: cos_sim_f1
value: 83.37468982630274
- type: cos_sim_precision
value: 83.30578512396694
- type: cos_sim_recall
value: 83.44370860927152
- type: dot_accuracy
value: 77.8
- type: dot_ap
value: 87.72366051496104
- type: dot_f1
value: 82.83752860411899
- type: dot_precision
value: 76.80339462517681
- type: dot_recall
value: 89.90066225165563
- type: euclidean_accuracy
value: 80.10000000000001
- type: euclidean_ap
value: 89.61317191870039
- type: euclidean_f1
value: 83.40214698596202
- type: euclidean_precision
value: 83.19604612850083
- type: euclidean_recall
value: 83.6092715231788
- type: manhattan_accuracy
value: 79.60000000000001
- type: manhattan_ap
value: 89.48363786968471
- type: manhattan_f1
value: 82.96296296296296
- type: manhattan_precision
value: 82.48772504091653
- type: manhattan_recall
value: 83.44370860927152
- type: max_accuracy
value: 80.10000000000001
- type: max_ap
value: 89.69526236458292
- type: max_f1
value: 83.40214698596202
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 96.93877551020408
- type: cos_sim_ap
value: 98.86489482248999
- type: cos_sim_f1
value: 95.11111111111113
- type: cos_sim_precision
value: 92.507204610951
- type: cos_sim_recall
value: 97.86585365853658
- type: dot_accuracy
value: 95.73283858998145
- type: dot_ap
value: 97.8261652492545
- type: dot_f1
value: 93.21533923303835
- type: dot_precision
value: 90.28571428571428
- type: dot_recall
value: 96.34146341463415
- type: euclidean_accuracy
value: 96.93877551020408
- type: euclidean_ap
value: 98.84837797066623
- type: euclidean_f1
value: 95.11111111111113
- type: euclidean_precision
value: 92.507204610951
- type: euclidean_recall
value: 97.86585365853658
- type: manhattan_accuracy
value: 96.84601113172542
- type: manhattan_ap
value: 98.78659090944161
- type: manhattan_f1
value: 94.9404761904762
- type: manhattan_precision
value: 92.73255813953489
- type: manhattan_recall
value: 97.2560975609756
- type: max_accuracy
value: 96.93877551020408
- type: max_ap
value: 98.86489482248999
- type: max_f1
value: 95.11111111111113
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 63.961218836565095
- type: f1
value: 64.3979989243291
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 40.32388663967612
- type: f1
value: 32.339117999015755
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: quora-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 62.757
- type: map_at_10
value: 76.55999999999999
- type: map_at_100
value: 77.328
- type: map_at_1000
value: 77.35499999999999
- type: map_at_3
value: 73.288
- type: map_at_5
value: 75.25500000000001
- type: mrr_at_1
value: 72.28
- type: mrr_at_10
value: 79.879
- type: mrr_at_100
value: 80.121
- type: mrr_at_1000
value: 80.12700000000001
- type: mrr_at_3
value: 78.40700000000001
- type: mrr_at_5
value: 79.357
- type: ndcg_at_1
value: 72.33000000000001
- type: ndcg_at_10
value: 81.151
- type: ndcg_at_100
value: 83.107
- type: ndcg_at_1000
value: 83.397
- type: ndcg_at_3
value: 77.3
- type: ndcg_at_5
value: 79.307
- type: precision_at_1
value: 72.33000000000001
- type: precision_at_10
value: 12.587000000000002
- type: precision_at_100
value: 1.488
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 33.943
- type: precision_at_5
value: 22.61
- type: recall_at_1
value: 62.757
- type: recall_at_10
value: 90.616
- type: recall_at_100
value: 97.905
- type: recall_at_1000
value: 99.618
- type: recall_at_3
value: 79.928
- type: recall_at_5
value: 85.30499999999999
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: scidocs-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.313
- type: map_at_10
value: 8.559999999999999
- type: map_at_100
value: 10.177999999999999
- type: map_at_1000
value: 10.459999999999999
- type: map_at_3
value: 6.094
- type: map_at_5
value: 7.323
- type: mrr_at_1
value: 16.3
- type: mrr_at_10
value: 25.579
- type: mrr_at_100
value: 26.717000000000002
- type: mrr_at_1000
value: 26.799
- type: mrr_at_3
value: 22.583000000000002
- type: mrr_at_5
value: 24.298000000000002
- type: ndcg_at_1
value: 16.3
- type: ndcg_at_10
value: 14.789
- type: ndcg_at_100
value: 21.731
- type: ndcg_at_1000
value: 27.261999999999997
- type: ndcg_at_3
value: 13.74
- type: ndcg_at_5
value: 12.199
- type: precision_at_1
value: 16.3
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.79
- type: precision_at_1000
value: 0.313
- type: precision_at_3
value: 12.933
- type: precision_at_5
value: 10.86
- type: recall_at_1
value: 3.313
- type: recall_at_10
value: 15.772
- type: recall_at_100
value: 36.392
- type: recall_at_1000
value: 63.525
- type: recall_at_3
value: 7.863
- type: recall_at_5
value: 11.003
- 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: 81.7977986139421
- type: cos_sim_ap
value: 73.21294750778902
- type: cos_sim_f1
value: 66.57391304347826
- type: cos_sim_precision
value: 65.05778382053025
- type: cos_sim_recall
value: 68.16239316239316
- type: dot_accuracy
value: 78.67916836526702
- type: dot_ap
value: 63.61943815978181
- type: dot_f1
value: 62.45014245014245
- type: dot_precision
value: 52.04178537511871
- type: dot_recall
value: 78.06267806267806
- type: euclidean_accuracy
value: 81.7774154097024
- type: euclidean_ap
value: 73.25053778387148
- type: euclidean_f1
value: 66.55064392620953
- type: euclidean_precision
value: 65.0782845473111
- type: euclidean_recall
value: 68.09116809116809
- type: manhattan_accuracy
value: 81.63473298002447
- type: manhattan_ap
value: 72.99781945530033
- type: manhattan_f1
value: 66.3623595505618
- type: manhattan_precision
value: 65.4432132963989
- type: manhattan_recall
value: 67.3076923076923
- type: max_accuracy
value: 81.7977986139421
- type: max_ap
value: 73.25053778387148
- type: max_f1
value: 66.57391304347826
- 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: 79.62332929388755
- type: cos_sim_spearman
value: 73.70598290849304
- type: euclidean_pearson
value: 77.3603286710006
- type: euclidean_spearman
value: 73.74420279933932
- type: manhattan_pearson
value: 77.12735032552482
- type: manhattan_spearman
value: 73.53014836690127
- 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: 37.696942928686724
- type: cos_sim_spearman
value: 40.6271445245692
- type: euclidean_pearson
value: 30.212734461370832
- type: euclidean_spearman
value: 40.66643376699638
- type: manhattan_pearson
value: 29.90223716230108
- type: manhattan_spearman
value: 40.35576319091178
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: scifact-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 43.528
- type: map_at_10
value: 53.290000000000006
- type: map_at_100
value: 54.342
- type: map_at_1000
value: 54.376999999999995
- type: map_at_3
value: 50.651999999999994
- type: map_at_5
value: 52.248000000000005
- type: mrr_at_1
value: 46.666999999999994
- type: mrr_at_10
value: 55.286
- type: mrr_at_100
value: 56.094
- type: mrr_at_1000
value: 56.125
- type: mrr_at_3
value: 53.222
- type: mrr_at_5
value: 54.339000000000006
- type: ndcg_at_1
value: 46.0
- type: ndcg_at_10
value: 58.142
- type: ndcg_at_100
value: 62.426
- type: ndcg_at_1000
value: 63.395999999999994
- type: ndcg_at_3
value: 53.53
- type: ndcg_at_5
value: 55.842000000000006
- type: precision_at_1
value: 46.0
- type: precision_at_10
value: 7.9670000000000005
- type: precision_at_100
value: 1.023
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 21.444
- type: precision_at_5
value: 14.333000000000002
- type: recall_at_1
value: 43.528
- type: recall_at_10
value: 71.511
- type: recall_at_100
value: 89.93299999999999
- type: recall_at_1000
value: 97.667
- type: recall_at_3
value: 59.067
- type: recall_at_5
value: 64.789
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: trec-covid-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22699999999999998
- type: map_at_10
value: 1.3379999999999999
- type: map_at_100
value: 6.965000000000001
- type: map_at_1000
value: 17.135
- type: map_at_3
value: 0.53
- type: map_at_5
value: 0.799
- type: mrr_at_1
value: 84.0
- type: mrr_at_10
value: 88.083
- type: mrr_at_100
value: 88.432
- type: mrr_at_1000
value: 88.432
- type: mrr_at_3
value: 87.333
- type: mrr_at_5
value: 87.833
- type: ndcg_at_1
value: 76.0
- type: ndcg_at_10
value: 58.199
- type: ndcg_at_100
value: 43.230000000000004
- type: ndcg_at_1000
value: 39.751
- type: ndcg_at_3
value: 63.743
- type: ndcg_at_5
value: 60.42999999999999
- type: precision_at_1
value: 84.0
- type: precision_at_10
value: 62.0
- type: precision_at_100
value: 44.519999999999996
- type: precision_at_1000
value: 17.746000000000002
- type: precision_at_3
value: 67.333
- type: precision_at_5
value: 63.2
- type: recall_at_1
value: 0.22699999999999998
- type: recall_at_10
value: 1.627
- type: recall_at_100
value: 10.600999999999999
- type: recall_at_1000
value: 37.532
- type: recall_at_3
value: 0.547
- type: recall_at_5
value: 0.864
---
<h1 align="center">MMLW-e5-small</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 384 dimensional vectors.
The model was initialized with multilingual E5 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, queries should be prefixed with **"query: "** and passages with **"passage: "** ⚠️
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 = "query: "
answer_prefix = "passage: "
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-e5-small")
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 **55.84** 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 **47.64** 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"
] |
EleutherAI/pythia-1.4b-deduped-v0
|
EleutherAI
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"pythia_v0",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-10-18T03:03:34 | 2023-07-09T16:02:25 | 15,140 | 5 |
---
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
- pythia_v0
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research. 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. All Pythia models are available
[on Hugging Face](https://huggingface.co/models?other=pythia).
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.
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.
## Pythia-1.4B-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.
- 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 | 4M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 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 | 4M | 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. To enable the
study of how language models change in the course of training, we provide
143 evenly spaced intermediate checkpoints per model. 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-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-1.4B-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.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-1.4B-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-1.4B-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 “understand” 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 deemed statistically most likely by the
model need not produce the most “accurate” text. Never rely on
Pythia-1.4B-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-1.4B-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-1.4B-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-1.4B-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.
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 the equivalent of 143000 steps at a batch size
of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch
size of 4M tokens listed were originally trained for 71500 steps instead, with
checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for
consistency with all 2M batch models, so `step1000` is the first checkpoint
for `pythia-1.4b` that was saved (corresponding to step 500 in training), and
`step1000` is likewise the first `pythia-6.9b` checkpoint that was saved
(corresponding to 1000 “actual” steps).<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.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge – Challenge Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/>
</details>
### 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-long
|
Snowflake
|
sentence-similarity
|
[
"sentence-transformers",
"onnx",
"safetensors",
"nomic_bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"custom_code",
"arxiv:2407.18887",
"arxiv:2405.05374",
"arxiv:2104.09864",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-12T13:52:31 | 2024-12-13T20:53:23 | 15,016 | 36 |
---
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-m-long
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.4776119402985
- type: ap
value: 42.34374238166049
- type: f1
value: 72.51164234732224
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 78.7416
- type: ap
value: 73.12074819362377
- type: f1
value: 78.64057339708795
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.926
- type: f1
value: 39.35531993117573
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 34.851
- type: map_at_10
value: 51.473
- type: map_at_100
value: 52.103
- type: map_at_1000
value: 52.105000000000004
- type: map_at_3
value: 46.776
- type: map_at_5
value: 49.617
- type: mrr_at_1
value: 35.491
- type: mrr_at_10
value: 51.73799999999999
- type: mrr_at_100
value: 52.37500000000001
- type: mrr_at_1000
value: 52.378
- type: mrr_at_3
value: 46.965
- type: mrr_at_5
value: 49.878
- type: ndcg_at_1
value: 34.851
- type: ndcg_at_10
value: 60.364
- type: ndcg_at_100
value: 62.888999999999996
- type: ndcg_at_1000
value: 62.946000000000005
- type: ndcg_at_3
value: 50.807
- type: ndcg_at_5
value: 55.901
- type: precision_at_1
value: 34.851
- type: precision_at_10
value: 8.855
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.839
- type: precision_at_5
value: 14.963999999999999
- type: recall_at_1
value: 34.851
- type: recall_at_10
value: 88.549
- type: recall_at_100
value: 99.21799999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 62.517999999999994
- type: recall_at_5
value: 74.822
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.5554998405317
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.614248811397005
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.355489424753884
- type: mrr
value: 75.49443784900849
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.17311056578292
- type: cos_sim_spearman
value: 88.24237210809322
- type: euclidean_pearson
value: 87.3188065853646
- type: euclidean_spearman
value: 88.24237210809322
- type: manhattan_pearson
value: 86.89499710049658
- type: manhattan_spearman
value: 87.85441146091777
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.26298701298703
- type: f1
value: 79.68356764080303
- task:
type: Clustering
dataset:
name: MTEB BigPatentClustering
type: jinaai/big-patent-clustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 20.923883720813706
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.16058801465044
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.1402356118627
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 35.612
- type: map_at_10
value: 47.117
- type: map_at_100
value: 48.711
- type: map_at_1000
value: 48.826
- type: map_at_3
value: 43.858999999999995
- type: map_at_5
value: 45.612
- type: mrr_at_1
value: 42.918
- type: mrr_at_10
value: 52.806
- type: mrr_at_100
value: 53.564
- type: mrr_at_1000
value: 53.596999999999994
- type: mrr_at_3
value: 50.453
- type: mrr_at_5
value: 51.841
- type: ndcg_at_1
value: 42.918
- type: ndcg_at_10
value: 53.291999999999994
- type: ndcg_at_100
value: 58.711999999999996
- type: ndcg_at_1000
value: 60.317
- type: ndcg_at_3
value: 48.855
- type: ndcg_at_5
value: 50.778
- type: precision_at_1
value: 42.918
- type: precision_at_10
value: 9.927999999999999
- type: precision_at_100
value: 1.592
- type: precision_at_1000
value: 0.201
- type: precision_at_3
value: 23.366999999999997
- type: precision_at_5
value: 16.366
- type: recall_at_1
value: 35.612
- type: recall_at_10
value: 64.671
- type: recall_at_100
value: 86.97
- type: recall_at_1000
value: 96.99600000000001
- type: recall_at_3
value: 51.37199999999999
- type: recall_at_5
value: 57.094
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 33.742
- type: map_at_10
value: 44.49
- type: map_at_100
value: 45.781
- type: map_at_1000
value: 45.902
- type: map_at_3
value: 41.453
- type: map_at_5
value: 43.251
- type: mrr_at_1
value: 42.357
- type: mrr_at_10
value: 50.463
- type: mrr_at_100
value: 51.17
- type: mrr_at_1000
value: 51.205999999999996
- type: mrr_at_3
value: 48.397
- type: mrr_at_5
value: 49.649
- type: ndcg_at_1
value: 42.357
- type: ndcg_at_10
value: 50.175000000000004
- type: ndcg_at_100
value: 54.491
- type: ndcg_at_1000
value: 56.282
- type: ndcg_at_3
value: 46.159
- type: ndcg_at_5
value: 48.226
- type: precision_at_1
value: 42.357
- type: precision_at_10
value: 9.382
- type: precision_at_100
value: 1.473
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 22.187
- type: precision_at_5
value: 15.758
- type: recall_at_1
value: 33.742
- type: recall_at_10
value: 59.760999999999996
- type: recall_at_100
value: 77.89500000000001
- type: recall_at_1000
value: 89.005
- type: recall_at_3
value: 47.872
- type: recall_at_5
value: 53.559
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 43.883
- type: map_at_10
value: 56.464999999999996
- type: map_at_100
value: 57.394
- type: map_at_1000
value: 57.443999999999996
- type: map_at_3
value: 53.169
- type: map_at_5
value: 54.984
- type: mrr_at_1
value: 50.470000000000006
- type: mrr_at_10
value: 59.997
- type: mrr_at_100
value: 60.586
- type: mrr_at_1000
value: 60.61
- type: mrr_at_3
value: 57.837
- type: mrr_at_5
value: 59.019
- type: ndcg_at_1
value: 50.470000000000006
- type: ndcg_at_10
value: 62.134
- type: ndcg_at_100
value: 65.69500000000001
- type: ndcg_at_1000
value: 66.674
- type: ndcg_at_3
value: 56.916999999999994
- type: ndcg_at_5
value: 59.312
- type: precision_at_1
value: 50.470000000000006
- type: precision_at_10
value: 9.812
- type: precision_at_100
value: 1.25
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 25.119999999999997
- type: precision_at_5
value: 17.016000000000002
- type: recall_at_1
value: 43.883
- type: recall_at_10
value: 75.417
- type: recall_at_100
value: 90.545
- type: recall_at_1000
value: 97.44500000000001
- type: recall_at_3
value: 61.306000000000004
- type: recall_at_5
value: 67.244
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 29.813000000000002
- type: map_at_10
value: 38.627
- type: map_at_100
value: 39.735
- type: map_at_1000
value: 39.806000000000004
- type: map_at_3
value: 36.283
- type: map_at_5
value: 37.491
- type: mrr_at_1
value: 32.316
- type: mrr_at_10
value: 40.752
- type: mrr_at_100
value: 41.699000000000005
- type: mrr_at_1000
value: 41.749
- type: mrr_at_3
value: 38.531
- type: mrr_at_5
value: 39.706
- type: ndcg_at_1
value: 32.316
- type: ndcg_at_10
value: 43.524
- type: ndcg_at_100
value: 48.648
- type: ndcg_at_1000
value: 50.405
- type: ndcg_at_3
value: 38.928000000000004
- type: ndcg_at_5
value: 40.967
- type: precision_at_1
value: 32.316
- type: precision_at_10
value: 6.451999999999999
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 16.384
- type: precision_at_5
value: 11.006
- type: recall_at_1
value: 29.813000000000002
- type: recall_at_10
value: 56.562999999999995
- type: recall_at_100
value: 79.452
- type: recall_at_1000
value: 92.715
- type: recall_at_3
value: 43.985
- type: recall_at_5
value: 49.001
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 19.961000000000002
- type: map_at_10
value: 28.026
- type: map_at_100
value: 29.212
- type: map_at_1000
value: 29.332
- type: map_at_3
value: 25.296999999999997
- type: map_at_5
value: 26.832
- type: mrr_at_1
value: 24.627
- type: mrr_at_10
value: 33.045
- type: mrr_at_100
value: 33.944
- type: mrr_at_1000
value: 34.013
- type: mrr_at_3
value: 30.307000000000002
- type: mrr_at_5
value: 31.874000000000002
- type: ndcg_at_1
value: 24.627
- type: ndcg_at_10
value: 33.414
- type: ndcg_at_100
value: 39.061
- type: ndcg_at_1000
value: 41.795
- type: ndcg_at_3
value: 28.377000000000002
- type: ndcg_at_5
value: 30.781999999999996
- type: precision_at_1
value: 24.627
- type: precision_at_10
value: 6.02
- type: precision_at_100
value: 1.035
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 13.516
- type: precision_at_5
value: 9.851
- type: recall_at_1
value: 19.961000000000002
- type: recall_at_10
value: 45.174
- type: recall_at_100
value: 69.69
- type: recall_at_1000
value: 89.24600000000001
- type: recall_at_3
value: 31.062
- type: recall_at_5
value: 37.193
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 32.080999999999996
- type: map_at_10
value: 42.177
- type: map_at_100
value: 43.431999999999995
- type: map_at_1000
value: 43.533
- type: map_at_3
value: 38.721
- type: map_at_5
value: 40.669
- type: mrr_at_1
value: 38.787
- type: mrr_at_10
value: 47.762
- type: mrr_at_100
value: 48.541000000000004
- type: mrr_at_1000
value: 48.581
- type: mrr_at_3
value: 45.123999999999995
- type: mrr_at_5
value: 46.639
- type: ndcg_at_1
value: 38.787
- type: ndcg_at_10
value: 48.094
- type: ndcg_at_100
value: 53.291
- type: ndcg_at_1000
value: 55.21
- type: ndcg_at_3
value: 42.721
- type: ndcg_at_5
value: 45.301
- type: precision_at_1
value: 38.787
- type: precision_at_10
value: 8.576
- type: precision_at_100
value: 1.306
- 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: 32.080999999999996
- type: recall_at_10
value: 59.948
- type: recall_at_100
value: 81.811
- type: recall_at_1000
value: 94.544
- type: recall_at_3
value: 44.903999999999996
- type: recall_at_5
value: 51.763999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.869
- type: map_at_10
value: 38.954
- type: map_at_100
value: 40.233000000000004
- type: map_at_1000
value: 40.332
- type: map_at_3
value: 35.585
- type: map_at_5
value: 37.476
- type: mrr_at_1
value: 35.959
- type: mrr_at_10
value: 44.800000000000004
- type: mrr_at_100
value: 45.609
- type: mrr_at_1000
value: 45.655
- type: mrr_at_3
value: 42.333
- type: mrr_at_5
value: 43.68
- type: ndcg_at_1
value: 35.959
- type: ndcg_at_10
value: 44.957
- type: ndcg_at_100
value: 50.275000000000006
- type: ndcg_at_1000
value: 52.29899999999999
- type: ndcg_at_3
value: 39.797
- type: ndcg_at_5
value: 42.128
- type: precision_at_1
value: 35.959
- type: precision_at_10
value: 8.185
- type: precision_at_100
value: 1.261
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 18.988
- type: precision_at_5
value: 13.516
- type: recall_at_1
value: 28.869
- type: recall_at_10
value: 57.154
- type: recall_at_100
value: 79.764
- type: recall_at_1000
value: 93.515
- type: recall_at_3
value: 42.364000000000004
- type: recall_at_5
value: 48.756
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 29.31008333333333
- type: map_at_10
value: 38.81849999999999
- type: map_at_100
value: 40.05058333333334
- type: map_at_1000
value: 40.16116666666667
- type: map_at_3
value: 35.91441666666667
- type: map_at_5
value: 37.526583333333335
- type: mrr_at_1
value: 34.60066666666667
- type: mrr_at_10
value: 43.08858333333333
- type: mrr_at_100
value: 43.927749999999996
- type: mrr_at_1000
value: 43.97866666666667
- type: mrr_at_3
value: 40.72775
- type: mrr_at_5
value: 42.067249999999994
- type: ndcg_at_1
value: 34.60066666666667
- type: ndcg_at_10
value: 44.20841666666667
- type: ndcg_at_100
value: 49.32866666666667
- type: ndcg_at_1000
value: 51.373999999999995
- type: ndcg_at_3
value: 39.452083333333334
- type: ndcg_at_5
value: 41.67
- type: precision_at_1
value: 34.60066666666667
- type: precision_at_10
value: 7.616583333333334
- type: precision_at_100
value: 1.20175
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 17.992
- type: precision_at_5
value: 12.658416666666666
- type: recall_at_1
value: 29.31008333333333
- type: recall_at_10
value: 55.81900000000001
- type: recall_at_100
value: 78.06308333333334
- type: recall_at_1000
value: 92.10641666666668
- type: recall_at_3
value: 42.50166666666667
- type: recall_at_5
value: 48.26108333333333
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 26.773000000000003
- type: map_at_10
value: 34.13
- type: map_at_100
value: 35.113
- type: map_at_1000
value: 35.211
- type: map_at_3
value: 31.958
- type: map_at_5
value: 33.080999999999996
- type: mrr_at_1
value: 30.061
- type: mrr_at_10
value: 37.061
- type: mrr_at_100
value: 37.865
- type: mrr_at_1000
value: 37.939
- type: mrr_at_3
value: 34.995
- type: mrr_at_5
value: 36.092
- type: ndcg_at_1
value: 30.061
- type: ndcg_at_10
value: 38.391999999999996
- type: ndcg_at_100
value: 43.13
- type: ndcg_at_1000
value: 45.449
- type: ndcg_at_3
value: 34.411
- type: ndcg_at_5
value: 36.163000000000004
- type: precision_at_1
value: 30.061
- type: precision_at_10
value: 5.982
- type: precision_at_100
value: 0.911
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 14.673
- type: precision_at_5
value: 10.030999999999999
- type: recall_at_1
value: 26.773000000000003
- type: recall_at_10
value: 48.445
- type: recall_at_100
value: 69.741
- type: recall_at_1000
value: 86.59
- type: recall_at_3
value: 37.576
- type: recall_at_5
value: 41.948
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.556
- type: map_at_10
value: 26.340999999999998
- type: map_at_100
value: 27.560000000000002
- type: map_at_1000
value: 27.685
- type: map_at_3
value: 24.136
- type: map_at_5
value: 25.34
- type: mrr_at_1
value: 22.368
- type: mrr_at_10
value: 30.192999999999998
- type: mrr_at_100
value: 31.183
- type: mrr_at_1000
value: 31.258000000000003
- type: mrr_at_3
value: 28.223
- type: mrr_at_5
value: 29.294999999999998
- type: ndcg_at_1
value: 22.368
- type: ndcg_at_10
value: 31.029
- type: ndcg_at_100
value: 36.768
- type: ndcg_at_1000
value: 39.572
- type: ndcg_at_3
value: 27.197
- type: ndcg_at_5
value: 28.912
- type: precision_at_1
value: 22.368
- type: precision_at_10
value: 5.606
- type: precision_at_100
value: 0.9979999999999999
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.892999999999999
- type: precision_at_5
value: 9.16
- type: recall_at_1
value: 18.556
- type: recall_at_10
value: 41.087
- type: recall_at_100
value: 66.92
- type: recall_at_1000
value: 86.691
- type: recall_at_3
value: 30.415
- type: recall_at_5
value: 34.813
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 29.953999999999997
- type: map_at_10
value: 39.633
- type: map_at_100
value: 40.923
- type: map_at_1000
value: 41.016000000000005
- type: map_at_3
value: 36.609
- type: map_at_5
value: 38.443
- type: mrr_at_1
value: 35.354
- type: mrr_at_10
value: 43.718
- type: mrr_at_100
value: 44.651999999999994
- type: mrr_at_1000
value: 44.696000000000005
- type: mrr_at_3
value: 41.154
- type: mrr_at_5
value: 42.730000000000004
- type: ndcg_at_1
value: 35.354
- type: ndcg_at_10
value: 44.933
- type: ndcg_at_100
value: 50.577000000000005
- type: ndcg_at_1000
value: 52.428
- type: ndcg_at_3
value: 39.833
- type: ndcg_at_5
value: 42.465
- type: precision_at_1
value: 35.354
- type: precision_at_10
value: 7.416
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 17.817
- type: precision_at_5
value: 12.687000000000001
- type: recall_at_1
value: 29.953999999999997
- type: recall_at_10
value: 56.932
- type: recall_at_100
value: 80.93900000000001
- type: recall_at_1000
value: 93.582
- type: recall_at_3
value: 43.192
- type: recall_at_5
value: 49.757
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.85
- type: map_at_10
value: 37.68
- type: map_at_100
value: 39.295
- type: map_at_1000
value: 39.527
- type: map_at_3
value: 35.036
- type: map_at_5
value: 36.269
- type: mrr_at_1
value: 33.004
- type: mrr_at_10
value: 42.096000000000004
- type: mrr_at_100
value: 43.019
- type: mrr_at_1000
value: 43.071
- type: mrr_at_3
value: 39.987
- type: mrr_at_5
value: 40.995
- type: ndcg_at_1
value: 33.004
- type: ndcg_at_10
value: 43.461
- type: ndcg_at_100
value: 49.138
- type: ndcg_at_1000
value: 51.50900000000001
- type: ndcg_at_3
value: 39.317
- type: ndcg_at_5
value: 40.760999999999996
- type: precision_at_1
value: 33.004
- type: precision_at_10
value: 8.161999999999999
- type: precision_at_100
value: 1.583
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 18.445
- type: precision_at_5
value: 12.885
- type: recall_at_1
value: 27.85
- type: recall_at_10
value: 54.419
- type: recall_at_100
value: 79.742
- type: recall_at_1000
value: 93.97
- type: recall_at_3
value: 42.149
- type: recall_at_5
value: 46.165
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 24.627
- type: map_at_10
value: 32.182
- type: map_at_100
value: 33.217999999999996
- type: map_at_1000
value: 33.32
- type: map_at_3
value: 28.866999999999997
- type: map_at_5
value: 30.871
- type: mrr_at_1
value: 26.987
- type: mrr_at_10
value: 34.37
- type: mrr_at_100
value: 35.301
- type: mrr_at_1000
value: 35.369
- type: mrr_at_3
value: 31.391999999999996
- type: mrr_at_5
value: 33.287
- type: ndcg_at_1
value: 26.987
- type: ndcg_at_10
value: 37.096000000000004
- type: ndcg_at_100
value: 42.158
- type: ndcg_at_1000
value: 44.548
- type: ndcg_at_3
value: 30.913
- type: ndcg_at_5
value: 34.245
- type: precision_at_1
value: 26.987
- type: precision_at_10
value: 5.878
- type: precision_at_100
value: 0.906
- type: precision_at_1000
value: 0.123
- type: precision_at_3
value: 12.815999999999999
- type: precision_at_5
value: 9.612
- type: recall_at_1
value: 24.627
- type: recall_at_10
value: 50.257
- type: recall_at_100
value: 73.288
- type: recall_at_1000
value: 90.97800000000001
- type: recall_at_3
value: 33.823
- type: recall_at_5
value: 41.839
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 17.343
- type: map_at_10
value: 28.59
- type: map_at_100
value: 30.591
- type: map_at_1000
value: 30.759999999999998
- type: map_at_3
value: 24.197
- type: map_at_5
value: 26.433
- type: mrr_at_1
value: 39.609
- type: mrr_at_10
value: 51.107
- type: mrr_at_100
value: 51.87199999999999
- type: mrr_at_1000
value: 51.894
- type: mrr_at_3
value: 48.154
- type: mrr_at_5
value: 49.939
- type: ndcg_at_1
value: 39.609
- type: ndcg_at_10
value: 38.329
- type: ndcg_at_100
value: 45.573
- type: ndcg_at_1000
value: 48.405
- type: ndcg_at_3
value: 32.506
- type: ndcg_at_5
value: 34.331
- type: precision_at_1
value: 39.609
- type: precision_at_10
value: 11.668000000000001
- type: precision_at_100
value: 1.9539999999999997
- type: precision_at_1000
value: 0.249
- type: precision_at_3
value: 23.952
- type: precision_at_5
value: 17.902
- type: recall_at_1
value: 17.343
- type: recall_at_10
value: 43.704
- type: recall_at_100
value: 68.363
- type: recall_at_1000
value: 84.04599999999999
- type: recall_at_3
value: 29.028
- type: recall_at_5
value: 35.022
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.934999999999999
- type: map_at_10
value: 22.081
- type: map_at_100
value: 32.036
- type: map_at_1000
value: 33.803
- type: map_at_3
value: 15.687999999999999
- type: map_at_5
value: 18.357
- type: mrr_at_1
value: 70.75
- type: mrr_at_10
value: 78.506
- type: mrr_at_100
value: 78.874
- type: mrr_at_1000
value: 78.88300000000001
- type: mrr_at_3
value: 77.667
- type: mrr_at_5
value: 78.342
- type: ndcg_at_1
value: 57.25
- type: ndcg_at_10
value: 45.286
- type: ndcg_at_100
value: 50.791
- type: ndcg_at_1000
value: 58.021
- type: ndcg_at_3
value: 49.504
- type: ndcg_at_5
value: 47.03
- type: precision_at_1
value: 70.75
- type: precision_at_10
value: 36.425000000000004
- type: precision_at_100
value: 11.953
- type: precision_at_1000
value: 2.248
- type: precision_at_3
value: 53.25
- type: precision_at_5
value: 46.150000000000006
- type: recall_at_1
value: 9.934999999999999
- type: recall_at_10
value: 27.592
- type: recall_at_100
value: 58.089
- type: recall_at_1000
value: 81.025
- type: recall_at_3
value: 17.048
- type: recall_at_5
value: 20.834
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.25999999999999
- type: f1
value: 43.83371155132253
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 73.68900000000001
- type: map_at_10
value: 82.878
- type: map_at_100
value: 83.084
- type: map_at_1000
value: 83.097
- type: map_at_3
value: 81.528
- type: map_at_5
value: 82.432
- type: mrr_at_1
value: 79.49300000000001
- type: mrr_at_10
value: 87.24300000000001
- type: mrr_at_100
value: 87.3
- type: mrr_at_1000
value: 87.301
- type: mrr_at_3
value: 86.359
- type: mrr_at_5
value: 87.01
- type: ndcg_at_1
value: 79.49300000000001
- type: ndcg_at_10
value: 86.894
- type: ndcg_at_100
value: 87.6
- type: ndcg_at_1000
value: 87.79299999999999
- type: ndcg_at_3
value: 84.777
- type: ndcg_at_5
value: 86.08
- type: precision_at_1
value: 79.49300000000001
- type: precision_at_10
value: 10.578
- type: precision_at_100
value: 1.117
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.592999999999996
- type: precision_at_5
value: 20.423
- type: recall_at_1
value: 73.68900000000001
- type: recall_at_10
value: 94.833
- type: recall_at_100
value: 97.554
- type: recall_at_1000
value: 98.672
- type: recall_at_3
value: 89.236
- type: recall_at_5
value: 92.461
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 20.59
- type: map_at_10
value: 34.089000000000006
- type: map_at_100
value: 35.796
- type: map_at_1000
value: 35.988
- type: map_at_3
value: 29.877
- type: map_at_5
value: 32.202999999999996
- type: mrr_at_1
value: 41.049
- type: mrr_at_10
value: 50.370000000000005
- type: mrr_at_100
value: 51.209
- type: mrr_at_1000
value: 51.247
- type: mrr_at_3
value: 48.122
- type: mrr_at_5
value: 49.326
- type: ndcg_at_1
value: 41.049
- type: ndcg_at_10
value: 42.163000000000004
- type: ndcg_at_100
value: 48.638999999999996
- type: ndcg_at_1000
value: 51.775000000000006
- type: ndcg_at_3
value: 38.435
- type: ndcg_at_5
value: 39.561
- type: precision_at_1
value: 41.049
- type: precision_at_10
value: 11.481
- type: precision_at_100
value: 1.8239999999999998
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 25.257
- type: precision_at_5
value: 18.519
- type: recall_at_1
value: 20.59
- type: recall_at_10
value: 49.547999999999995
- type: recall_at_100
value: 73.676
- type: recall_at_1000
value: 92.269
- type: recall_at_3
value: 35.656
- type: recall_at_5
value: 41.455
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 39.932
- type: map_at_10
value: 64.184
- type: map_at_100
value: 65.06
- type: map_at_1000
value: 65.109
- type: map_at_3
value: 60.27
- type: map_at_5
value: 62.732
- type: mrr_at_1
value: 79.865
- type: mrr_at_10
value: 85.99799999999999
- type: mrr_at_100
value: 86.13
- type: mrr_at_1000
value: 86.13300000000001
- type: mrr_at_3
value: 85.136
- type: mrr_at_5
value: 85.69200000000001
- type: ndcg_at_1
value: 79.865
- type: ndcg_at_10
value: 72.756
- type: ndcg_at_100
value: 75.638
- type: ndcg_at_1000
value: 76.589
- type: ndcg_at_3
value: 67.38199999999999
- type: ndcg_at_5
value: 70.402
- type: precision_at_1
value: 79.865
- type: precision_at_10
value: 15.387999999999998
- type: precision_at_100
value: 1.7610000000000001
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 43.394
- type: precision_at_5
value: 28.424
- type: recall_at_1
value: 39.932
- type: recall_at_10
value: 76.941
- type: recall_at_100
value: 88.062
- type: recall_at_1000
value: 94.396
- type: recall_at_3
value: 65.091
- type: recall_at_5
value: 71.06
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 71.7904
- type: ap
value: 65.82899456730257
- type: f1
value: 71.56611877410202
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 21.931
- type: map_at_10
value: 34.849999999999994
- type: map_at_100
value: 36.033
- type: map_at_1000
value: 36.08
- type: map_at_3
value: 30.842000000000002
- type: map_at_5
value: 33.229
- type: mrr_at_1
value: 22.55
- type: mrr_at_10
value: 35.436
- type: mrr_at_100
value: 36.563
- type: mrr_at_1000
value: 36.604
- type: mrr_at_3
value: 31.507
- type: mrr_at_5
value: 33.851
- type: ndcg_at_1
value: 22.55
- type: ndcg_at_10
value: 41.969
- type: ndcg_at_100
value: 47.576
- type: ndcg_at_1000
value: 48.731
- type: ndcg_at_3
value: 33.894000000000005
- type: ndcg_at_5
value: 38.133
- type: precision_at_1
value: 22.55
- type: precision_at_10
value: 6.660000000000001
- type: precision_at_100
value: 0.946
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.532
- type: precision_at_5
value: 10.865
- type: recall_at_1
value: 21.931
- type: recall_at_10
value: 63.841
- type: recall_at_100
value: 89.47699999999999
- type: recall_at_1000
value: 98.259
- type: recall_at_3
value: 42.063
- type: recall_at_5
value: 52.21
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.03921568627452
- type: f1
value: 92.56400672314416
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 63.515731874145
- type: f1
value: 44.922310875523216
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 77.57383966244727
- type: f1
value: 76.55222378218293
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 62.74836240280833
- type: v_measure
value: 24.414348715238184
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.54673839946201
- type: f1
value: 64.61004101532164
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.11365164761264
- type: f1
value: 72.01684013680978
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.123671999617297
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 26.72684341430875
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.910228061734816
- type: mrr
value: 30.835255982532477
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.6770000000000005
- type: map_at_10
value: 13.15
- type: map_at_100
value: 16.205
- type: map_at_1000
value: 17.580000000000002
- type: map_at_3
value: 9.651
- type: map_at_5
value: 11.142000000000001
- type: mrr_at_1
value: 47.678
- type: mrr_at_10
value: 56.257000000000005
- type: mrr_at_100
value: 56.708000000000006
- type: mrr_at_1000
value: 56.751
- type: mrr_at_3
value: 54.128
- type: mrr_at_5
value: 55.181000000000004
- type: ndcg_at_1
value: 45.511
- type: ndcg_at_10
value: 35.867
- type: ndcg_at_100
value: 31.566
- type: ndcg_at_1000
value: 40.077
- type: ndcg_at_3
value: 41.9
- type: ndcg_at_5
value: 39.367999999999995
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 26.842
- type: precision_at_100
value: 7.991
- type: precision_at_1000
value: 2.0469999999999997
- type: precision_at_3
value: 39.938
- type: precision_at_5
value: 34.613
- type: recall_at_1
value: 5.6770000000000005
- type: recall_at_10
value: 17.119999999999997
- type: recall_at_100
value: 30.828
- type: recall_at_1000
value: 62.082
- type: recall_at_3
value: 10.456
- type: recall_at_5
value: 12.903999999999998
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 39.021
- type: map_at_10
value: 54.976
- type: map_at_100
value: 55.793000000000006
- type: map_at_1000
value: 55.811
- type: map_at_3
value: 50.759
- type: map_at_5
value: 53.429
- type: mrr_at_1
value: 43.308
- type: mrr_at_10
value: 57.118
- type: mrr_at_100
value: 57.69499999999999
- type: mrr_at_1000
value: 57.704
- type: mrr_at_3
value: 53.848
- type: mrr_at_5
value: 55.915000000000006
- type: ndcg_at_1
value: 43.308
- type: ndcg_at_10
value: 62.33800000000001
- type: ndcg_at_100
value: 65.61099999999999
- type: ndcg_at_1000
value: 65.995
- type: ndcg_at_3
value: 54.723
- type: ndcg_at_5
value: 59.026
- type: precision_at_1
value: 43.308
- type: precision_at_10
value: 9.803
- type: precision_at_100
value: 1.167
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 24.334
- type: precision_at_5
value: 17.144000000000002
- type: recall_at_1
value: 39.021
- type: recall_at_10
value: 82.37299999999999
- type: recall_at_100
value: 96.21499999999999
- type: recall_at_1000
value: 99.02499999999999
- type: recall_at_3
value: 63.031000000000006
- type: recall_at_5
value: 72.856
- task:
type: Classification
dataset:
name: MTEB NewsClassification
type: ag_news
config: default
split: test
revision: eb185aade064a813bc0b7f42de02595523103ca4
metrics:
- type: accuracy
value: 78.03289473684211
- type: f1
value: 77.89323745730803
- 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: 100.0
- type: cos_sim_f1
value: 99.9490575649516
- type: cos_sim_precision
value: 100.0
- type: cos_sim_recall
value: 99.89816700610999
- type: dot_accuracy
value: 99.89816700610999
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.9490575649516
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.89816700610999
- type: euclidean_accuracy
value: 99.89816700610999
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.9490575649516
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.89816700610999
- type: manhattan_accuracy
value: 99.89816700610999
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.9490575649516
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.89816700610999
- type: max_accuracy
value: 99.89816700610999
- type: max_ap
value: 100.0
- 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: 59.578879568280385
- type: cos_sim_f1
value: 62.50861474844934
- type: cos_sim_precision
value: 45.46365914786967
- type: cos_sim_recall
value: 100.0
- type: dot_accuracy
value: 61.75000000000001
- type: dot_ap
value: 59.57893088951573
- type: dot_f1
value: 62.50861474844934
- type: dot_precision
value: 45.46365914786967
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 61.75000000000001
- type: euclidean_ap
value: 59.578755624671686
- type: euclidean_f1
value: 62.50861474844934
- type: euclidean_precision
value: 45.46365914786967
- type: euclidean_recall
value: 100.0
- type: manhattan_accuracy
value: 61.75000000000001
- type: manhattan_ap
value: 59.58504334461159
- type: manhattan_f1
value: 62.50861474844934
- type: manhattan_precision
value: 45.46365914786967
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 61.75000000000001
- type: max_ap
value: 59.58504334461159
- type: max_f1
value: 62.50861474844934
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 70.186
- type: map_at_10
value: 83.875
- type: map_at_100
value: 84.514
- type: map_at_1000
value: 84.53500000000001
- type: map_at_3
value: 80.926
- type: map_at_5
value: 82.797
- type: mrr_at_1
value: 80.82000000000001
- type: mrr_at_10
value: 87.068
- type: mrr_at_100
value: 87.178
- type: mrr_at_1000
value: 87.18
- type: mrr_at_3
value: 86.055
- type: mrr_at_5
value: 86.763
- type: ndcg_at_1
value: 80.84
- type: ndcg_at_10
value: 87.723
- type: ndcg_at_100
value: 88.98700000000001
- type: ndcg_at_1000
value: 89.13499999999999
- type: ndcg_at_3
value: 84.821
- type: ndcg_at_5
value: 86.441
- type: precision_at_1
value: 80.84
- type: precision_at_10
value: 13.270000000000001
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 37.013
- type: precision_at_5
value: 24.37
- type: recall_at_1
value: 70.186
- type: recall_at_10
value: 94.948
- type: recall_at_100
value: 99.223
- type: recall_at_1000
value: 99.932
- type: recall_at_3
value: 86.57000000000001
- type: recall_at_5
value: 91.157
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.24198927949519
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 61.452073078765544
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.972
- type: map_at_10
value: 12.314
- type: map_at_100
value: 14.333000000000002
- type: map_at_1000
value: 14.628
- type: map_at_3
value: 8.972
- type: map_at_5
value: 10.724
- type: mrr_at_1
value: 24.4
- type: mrr_at_10
value: 35.257
- type: mrr_at_100
value: 36.297000000000004
- type: mrr_at_1000
value: 36.363
- type: mrr_at_3
value: 32.267
- type: mrr_at_5
value: 33.942
- type: ndcg_at_1
value: 24.4
- type: ndcg_at_10
value: 20.47
- type: ndcg_at_100
value: 28.111000000000004
- type: ndcg_at_1000
value: 33.499
- type: ndcg_at_3
value: 19.975
- type: ndcg_at_5
value: 17.293
- type: precision_at_1
value: 24.4
- type: precision_at_10
value: 10.440000000000001
- type: precision_at_100
value: 2.136
- type: precision_at_1000
value: 0.34299999999999997
- type: precision_at_3
value: 18.733
- type: precision_at_5
value: 15.120000000000001
- type: recall_at_1
value: 4.972
- type: recall_at_10
value: 21.157
- type: recall_at_100
value: 43.335
- type: recall_at_1000
value: 69.652
- type: recall_at_3
value: 11.417
- type: recall_at_5
value: 15.317
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 76.70295978506286
- type: cos_sim_spearman
value: 70.91162732446628
- type: euclidean_pearson
value: 73.25693688746031
- type: euclidean_spearman
value: 70.91162556180127
- type: manhattan_pearson
value: 73.27735004735767
- type: manhattan_spearman
value: 70.8856787022704
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 67.55878682646774
- type: cos_sim_spearman
value: 66.10824660353681
- type: euclidean_pearson
value: 64.93937270068541
- type: euclidean_spearman
value: 66.10824660353681
- type: manhattan_pearson
value: 64.96325555978984
- type: manhattan_spearman
value: 66.12052481638577
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.79979774019496
- type: cos_sim_spearman
value: 79.82293444619499
- type: euclidean_pearson
value: 79.4830436509311
- type: euclidean_spearman
value: 79.82293444619499
- type: manhattan_pearson
value: 79.49785594799296
- type: manhattan_spearman
value: 79.8280390479434
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 76.36839628231121
- type: cos_sim_spearman
value: 73.63809739428072
- type: euclidean_pearson
value: 74.93718121215906
- type: euclidean_spearman
value: 73.63810227650436
- type: manhattan_pearson
value: 74.8737197659424
- type: manhattan_spearman
value: 73.57534688126572
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.67482138157656
- type: cos_sim_spearman
value: 83.23485786963107
- type: euclidean_pearson
value: 82.50847772197369
- type: euclidean_spearman
value: 83.23485786963107
- type: manhattan_pearson
value: 82.48916218377576
- type: manhattan_spearman
value: 83.19756483500014
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.11626268793967
- type: cos_sim_spearman
value: 81.58184691061507
- type: euclidean_pearson
value: 80.65900869004938
- type: euclidean_spearman
value: 81.58184691061507
- type: manhattan_pearson
value: 80.67912306966772
- type: manhattan_spearman
value: 81.59957593393145
- 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: 80.3140990821409
- type: cos_sim_spearman
value: 80.59196586367551
- type: euclidean_pearson
value: 80.73014029317672
- type: euclidean_spearman
value: 80.59196586367551
- type: manhattan_pearson
value: 80.5774325136987
- type: manhattan_spearman
value: 80.35102610546238
- 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: 68.34450491529164
- type: cos_sim_spearman
value: 68.79451793414492
- type: euclidean_pearson
value: 68.75619738499324
- type: euclidean_spearman
value: 68.79451793414492
- type: manhattan_pearson
value: 68.75256119543882
- type: manhattan_spearman
value: 68.81836416978547
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 77.95580414975612
- type: cos_sim_spearman
value: 77.89671867168987
- type: euclidean_pearson
value: 77.61352097720862
- type: euclidean_spearman
value: 77.89671867168987
- type: manhattan_pearson
value: 77.65282228135632
- type: manhattan_spearman
value: 77.91730533156762
- 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: 77.95580421496413
- type: cos_sim_spearman
value: 77.89671867168987
- type: euclidean_pearson
value: 77.61352107168794
- type: euclidean_spearman
value: 77.89671867168987
- type: manhattan_pearson
value: 77.65282237231794
- type: manhattan_spearman
value: 77.91730533156762
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.22928110092924
- type: mrr
value: 94.46700902583257
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 56.011
- type: map_at_10
value: 65.544
- type: map_at_100
value: 66.034
- type: map_at_1000
value: 66.065
- type: map_at_3
value: 63.077000000000005
- type: map_at_5
value: 64.354
- type: mrr_at_1
value: 59.0
- type: mrr_at_10
value: 66.74900000000001
- type: mrr_at_100
value: 67.176
- type: mrr_at_1000
value: 67.203
- type: mrr_at_3
value: 65.056
- type: mrr_at_5
value: 65.956
- type: ndcg_at_1
value: 59.0
- type: ndcg_at_10
value: 69.95599999999999
- type: ndcg_at_100
value: 72.27
- type: ndcg_at_1000
value: 73.066
- type: ndcg_at_3
value: 65.837
- type: ndcg_at_5
value: 67.633
- type: precision_at_1
value: 59.0
- type: precision_at_10
value: 9.333
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.0
- type: precision_at_5
value: 16.866999999999997
- type: recall_at_1
value: 56.011
- type: recall_at_10
value: 82.133
- type: recall_at_100
value: 92.767
- type: recall_at_1000
value: 99.0
- type: recall_at_3
value: 70.95
- type: recall_at_5
value: 75.556
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81584158415842
- type: cos_sim_ap
value: 94.67482871230736
- type: cos_sim_f1
value: 90.67201604814443
- type: cos_sim_precision
value: 90.94567404426559
- type: cos_sim_recall
value: 90.4
- type: dot_accuracy
value: 99.81584158415842
- type: dot_ap
value: 94.67482871230737
- type: dot_f1
value: 90.67201604814443
- type: dot_precision
value: 90.94567404426559
- type: dot_recall
value: 90.4
- type: euclidean_accuracy
value: 99.81584158415842
- type: euclidean_ap
value: 94.67482871230737
- type: euclidean_f1
value: 90.67201604814443
- type: euclidean_precision
value: 90.94567404426559
- type: euclidean_recall
value: 90.4
- type: manhattan_accuracy
value: 99.81188118811882
- type: manhattan_ap
value: 94.6409082219286
- type: manhattan_f1
value: 90.50949050949052
- type: manhattan_precision
value: 90.41916167664671
- type: manhattan_recall
value: 90.60000000000001
- type: max_accuracy
value: 99.81584158415842
- type: max_ap
value: 94.67482871230737
- type: max_f1
value: 90.67201604814443
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 62.63494511649264
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 37.165838327685755
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.384873075208084
- type: mrr
value: 52.196439181733304
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 32.13690355567596
- type: cos_sim_spearman
value: 31.38349778638125
- type: dot_pearson
value: 32.13689596691593
- type: dot_spearman
value: 31.38349778638125
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.26
- type: map_at_10
value: 2.08
- type: map_at_100
value: 12.598
- type: map_at_1000
value: 30.119
- type: map_at_3
value: 0.701
- type: map_at_5
value: 1.11
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 97.167
- type: mrr_at_100
value: 97.167
- type: mrr_at_1000
value: 97.167
- type: mrr_at_3
value: 96.667
- type: mrr_at_5
value: 97.167
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 81.69800000000001
- type: ndcg_at_100
value: 62.9
- type: ndcg_at_1000
value: 55.245999999999995
- type: ndcg_at_3
value: 86.397
- type: ndcg_at_5
value: 84.286
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 87.0
- type: precision_at_100
value: 64.86
- type: precision_at_1000
value: 24.512
- type: precision_at_3
value: 90.667
- type: precision_at_5
value: 88.8
- type: recall_at_1
value: 0.26
- type: recall_at_10
value: 2.238
- type: recall_at_100
value: 15.488
- type: recall_at_1000
value: 51.6
- type: recall_at_3
value: 0.716
- type: recall_at_5
value: 1.151
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.376
- type: map_at_10
value: 13.142000000000001
- type: map_at_100
value: 19.763
- type: map_at_1000
value: 21.319
- type: map_at_3
value: 6.805999999999999
- type: map_at_5
value: 8.952
- type: mrr_at_1
value: 46.939
- type: mrr_at_10
value: 61.082
- type: mrr_at_100
value: 61.45
- type: mrr_at_1000
value: 61.468999999999994
- type: mrr_at_3
value: 57.483
- type: mrr_at_5
value: 59.931999999999995
- type: ndcg_at_1
value: 44.897999999999996
- type: ndcg_at_10
value: 32.35
- type: ndcg_at_100
value: 42.719
- type: ndcg_at_1000
value: 53.30200000000001
- type: ndcg_at_3
value: 37.724999999999994
- type: ndcg_at_5
value: 34.79
- type: precision_at_1
value: 46.939
- type: precision_at_10
value: 28.366999999999997
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.557
- type: precision_at_3
value: 38.095
- type: precision_at_5
value: 33.469
- type: recall_at_1
value: 3.376
- type: recall_at_10
value: 20.164
- type: recall_at_100
value: 50.668
- type: recall_at_1000
value: 83.159
- type: recall_at_3
value: 8.155
- type: recall_at_5
value: 11.872
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 66.739
- type: ap
value: 12.17931839228834
- type: f1
value: 51.05383188624636
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.72891907187323
- type: f1
value: 56.997614557150946
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.825318429345224
- 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: 66.17412885183877
- type: cos_sim_f1
value: 63.09125656951745
- type: cos_sim_precision
value: 57.63858577040594
- type: cos_sim_recall
value: 69.68337730870712
- type: dot_accuracy
value: 83.65619598259522
- type: dot_ap
value: 66.17413621964548
- type: dot_f1
value: 63.09125656951745
- type: dot_precision
value: 57.63858577040594
- type: dot_recall
value: 69.68337730870712
- type: euclidean_accuracy
value: 83.65619598259522
- type: euclidean_ap
value: 66.17412836413126
- type: euclidean_f1
value: 63.09125656951745
- type: euclidean_precision
value: 57.63858577040594
- type: euclidean_recall
value: 69.68337730870712
- type: manhattan_accuracy
value: 83.5548667819038
- type: manhattan_ap
value: 66.07998834521334
- type: manhattan_f1
value: 62.96433419721092
- type: manhattan_precision
value: 59.14676559239509
- type: manhattan_recall
value: 67.30870712401055
- type: max_accuracy
value: 83.65619598259522
- type: max_ap
value: 66.17413621964548
- type: max_f1
value: 63.09125656951745
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.55706911941631
- type: cos_sim_ap
value: 85.20971331546805
- type: cos_sim_f1
value: 77.28446050593702
- type: cos_sim_precision
value: 74.16135881104033
- type: cos_sim_recall
value: 80.6821681552202
- type: dot_accuracy
value: 88.55706911941631
- type: dot_ap
value: 85.2097154112633
- type: dot_f1
value: 77.28446050593702
- type: dot_precision
value: 74.16135881104033
- type: dot_recall
value: 80.6821681552202
- type: euclidean_accuracy
value: 88.55706911941631
- type: euclidean_ap
value: 85.20971719214488
- type: euclidean_f1
value: 77.28446050593702
- type: euclidean_precision
value: 74.16135881104033
- type: euclidean_recall
value: 80.6821681552202
- type: manhattan_accuracy
value: 88.52020025614158
- type: manhattan_ap
value: 85.17569799117058
- type: manhattan_f1
value: 77.27157773040933
- type: manhattan_precision
value: 72.79286638077734
- type: manhattan_recall
value: 82.33754234678165
- type: max_accuracy
value: 88.55706911941631
- type: max_ap
value: 85.20971719214488
- type: max_f1
value: 77.28446050593702
- task:
type: Clustering
dataset:
name: MTEB WikiCitiesClustering
type: jinaai/cities_wiki_clustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 85.63474850264893
---
<h1 align="center">Snowflake's Arctic-embed-m-long</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-long", trust_remote_code=True)
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?
0.46484852 The Data Cloud!
0.3758855 Mexico City of Course!
Query: Where can I get the best tacos?
0.42407742 Mexico City of Course!
0.36740506 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-long')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, add_pooling_layer=False, safe_serialization=True)
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)
```
If you use the long context model with more than 2048 tokens, ensure that you initialize the model like below instead. This will use [RPE](https://arxiv.org/abs/2104.09864) to allow up to 8192 tokens.
``` py
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, safe_serialization=True, rotary_scaling_factor=2)
```
### 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-long', {
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); // [0.36740492125676116, 0.42407774292046635]
```
## 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=f528b5b4-2ba6-4fc6-8eed-259968d45577" />
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
EleutherAI/pythia-12b
|
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"
] | 2023-02-28T18:48:12 | 2024-07-09T15:50:54 | 14,944 | 135 |
---
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">Past 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-12B
## 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-12B 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-12B 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-12B 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-12B 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-12B 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-12B 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-12B.
### 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-12B.
### 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-large
|
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"
] | 2022-12-26T06:03:12 | 2023-08-07T04:59:49 | 14,071 | 74 |
---
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: 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: 77.68656716417911
- type: ap
value: 41.336896075573584
- type: f1
value: 71.788561468075
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.04965
- type: ap
value: 86.24637009569418
- type: f1
value: 90.03896671762645
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.016000000000005
- type: f1
value: 42.1942431880186
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.107000000000003
- type: map_at_10
value: 40.464
- type: map_at_100
value: 41.577999999999996
- type: map_at_1000
value: 41.588
- type: map_at_3
value: 35.301
- type: map_at_5
value: 38.263000000000005
- type: mrr_at_1
value: 25.605
- type: mrr_at_10
value: 40.64
- type: mrr_at_100
value: 41.760000000000005
- type: mrr_at_1000
value: 41.77
- type: mrr_at_3
value: 35.443000000000005
- type: mrr_at_5
value: 38.448
- type: ndcg_at_1
value: 25.107000000000003
- type: ndcg_at_10
value: 49.352000000000004
- type: ndcg_at_100
value: 53.98500000000001
- type: ndcg_at_1000
value: 54.208
- type: ndcg_at_3
value: 38.671
- type: ndcg_at_5
value: 43.991
- type: precision_at_1
value: 25.107000000000003
- type: precision_at_10
value: 7.795000000000001
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.145
- type: precision_at_5
value: 12.262
- type: recall_at_1
value: 25.107000000000003
- type: recall_at_10
value: 77.952
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 48.435
- type: recall_at_5
value: 61.309000000000005
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.19278045044154
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.37976387757665
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.07433334608074
- type: mrr
value: 73.44347711383723
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.4298072183543
- type: cos_sim_spearman
value: 84.73144873582848
- type: euclidean_pearson
value: 85.15885058870728
- type: euclidean_spearman
value: 85.42062106559356
- type: manhattan_pearson
value: 84.89409921792054
- type: manhattan_spearman
value: 85.31941394024344
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.14285714285714
- type: f1
value: 84.11674412565644
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.600076342340785
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.08861812135148
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.684000000000005
- type: map_at_10
value: 41.675000000000004
- type: map_at_100
value: 42.963
- type: map_at_1000
value: 43.078
- type: map_at_3
value: 38.708999999999996
- type: map_at_5
value: 40.316
- type: mrr_at_1
value: 39.485
- type: mrr_at_10
value: 47.152
- type: mrr_at_100
value: 47.96
- type: mrr_at_1000
value: 48.010000000000005
- type: mrr_at_3
value: 44.754
- type: mrr_at_5
value: 46.285
- type: ndcg_at_1
value: 39.485
- type: ndcg_at_10
value: 46.849000000000004
- type: ndcg_at_100
value: 52.059
- type: ndcg_at_1000
value: 54.358
- type: ndcg_at_3
value: 42.705
- type: ndcg_at_5
value: 44.663000000000004
- type: precision_at_1
value: 39.485
- type: precision_at_10
value: 8.455
- type: precision_at_100
value: 1.3379999999999999
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.695
- type: precision_at_5
value: 13.905999999999999
- type: recall_at_1
value: 32.684000000000005
- type: recall_at_10
value: 56.227000000000004
- type: recall_at_100
value: 78.499
- type: recall_at_1000
value: 94.021
- type: recall_at_3
value: 44.157999999999994
- type: recall_at_5
value: 49.694
- type: map_at_1
value: 31.875999999999998
- type: map_at_10
value: 41.603
- type: map_at_100
value: 42.825
- type: map_at_1000
value: 42.961
- type: map_at_3
value: 38.655
- type: map_at_5
value: 40.294999999999995
- type: mrr_at_1
value: 40.127
- type: mrr_at_10
value: 47.959
- type: mrr_at_100
value: 48.59
- type: mrr_at_1000
value: 48.634
- type: mrr_at_3
value: 45.786
- type: mrr_at_5
value: 46.964
- type: ndcg_at_1
value: 40.127
- type: ndcg_at_10
value: 47.176
- type: ndcg_at_100
value: 51.346000000000004
- type: ndcg_at_1000
value: 53.502
- type: ndcg_at_3
value: 43.139
- type: ndcg_at_5
value: 44.883
- type: precision_at_1
value: 40.127
- type: precision_at_10
value: 8.72
- type: precision_at_100
value: 1.387
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 20.637
- type: precision_at_5
value: 14.446
- type: recall_at_1
value: 31.875999999999998
- type: recall_at_10
value: 56.54900000000001
- type: recall_at_100
value: 73.939
- type: recall_at_1000
value: 87.732
- type: recall_at_3
value: 44.326
- type: recall_at_5
value: 49.445
- type: map_at_1
value: 41.677
- type: map_at_10
value: 52.222
- type: map_at_100
value: 53.229000000000006
- type: map_at_1000
value: 53.288000000000004
- type: map_at_3
value: 49.201
- type: map_at_5
value: 51.00599999999999
- type: mrr_at_1
value: 47.524
- type: mrr_at_10
value: 55.745999999999995
- type: mrr_at_100
value: 56.433
- type: mrr_at_1000
value: 56.464999999999996
- type: mrr_at_3
value: 53.37499999999999
- type: mrr_at_5
value: 54.858
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 57.406
- type: ndcg_at_100
value: 61.403
- type: ndcg_at_1000
value: 62.7
- type: ndcg_at_3
value: 52.298
- type: ndcg_at_5
value: 55.02
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 8.865
- type: precision_at_100
value: 1.179
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 22.612
- type: precision_at_5
value: 15.461
- type: recall_at_1
value: 41.677
- type: recall_at_10
value: 69.346
- type: recall_at_100
value: 86.344
- type: recall_at_1000
value: 95.703
- type: recall_at_3
value: 55.789
- type: recall_at_5
value: 62.488
- type: map_at_1
value: 25.991999999999997
- type: map_at_10
value: 32.804
- type: map_at_100
value: 33.812999999999995
- type: map_at_1000
value: 33.897
- type: map_at_3
value: 30.567
- type: map_at_5
value: 31.599
- type: mrr_at_1
value: 27.797
- type: mrr_at_10
value: 34.768
- type: mrr_at_100
value: 35.702
- type: mrr_at_1000
value: 35.766
- type: mrr_at_3
value: 32.637
- type: mrr_at_5
value: 33.614
- type: ndcg_at_1
value: 27.797
- type: ndcg_at_10
value: 36.966
- type: ndcg_at_100
value: 41.972
- type: ndcg_at_1000
value: 44.139
- type: ndcg_at_3
value: 32.547
- type: ndcg_at_5
value: 34.258
- type: precision_at_1
value: 27.797
- type: precision_at_10
value: 5.514
- type: precision_at_100
value: 0.8340000000000001
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 13.333
- type: precision_at_5
value: 9.04
- type: recall_at_1
value: 25.991999999999997
- type: recall_at_10
value: 47.941
- type: recall_at_100
value: 71.039
- type: recall_at_1000
value: 87.32799999999999
- type: recall_at_3
value: 36.01
- type: recall_at_5
value: 40.056000000000004
- type: map_at_1
value: 17.533
- type: map_at_10
value: 24.336
- type: map_at_100
value: 25.445
- type: map_at_1000
value: 25.561
- type: map_at_3
value: 22.116
- type: map_at_5
value: 23.347
- type: mrr_at_1
value: 21.642
- type: mrr_at_10
value: 28.910999999999998
- type: mrr_at_100
value: 29.836000000000002
- type: mrr_at_1000
value: 29.907
- type: mrr_at_3
value: 26.638
- type: mrr_at_5
value: 27.857
- type: ndcg_at_1
value: 21.642
- type: ndcg_at_10
value: 28.949
- type: ndcg_at_100
value: 34.211000000000006
- type: ndcg_at_1000
value: 37.031
- type: ndcg_at_3
value: 24.788
- type: ndcg_at_5
value: 26.685
- type: precision_at_1
value: 21.642
- type: precision_at_10
value: 5.137
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 11.733
- type: precision_at_5
value: 8.383000000000001
- type: recall_at_1
value: 17.533
- type: recall_at_10
value: 38.839
- type: recall_at_100
value: 61.458999999999996
- type: recall_at_1000
value: 81.58
- type: recall_at_3
value: 27.328999999999997
- type: recall_at_5
value: 32.168
- type: map_at_1
value: 28.126
- type: map_at_10
value: 37.872
- type: map_at_100
value: 39.229
- type: map_at_1000
value: 39.353
- type: map_at_3
value: 34.93
- type: map_at_5
value: 36.59
- type: mrr_at_1
value: 34.071
- type: mrr_at_10
value: 43.056
- type: mrr_at_100
value: 43.944
- type: mrr_at_1000
value: 43.999
- type: mrr_at_3
value: 40.536
- type: mrr_at_5
value: 42.065999999999995
- type: ndcg_at_1
value: 34.071
- type: ndcg_at_10
value: 43.503
- type: ndcg_at_100
value: 49.120000000000005
- type: ndcg_at_1000
value: 51.410999999999994
- type: ndcg_at_3
value: 38.767
- type: ndcg_at_5
value: 41.075
- type: precision_at_1
value: 34.071
- type: precision_at_10
value: 7.843999999999999
- type: precision_at_100
value: 1.2489999999999999
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.223
- type: precision_at_5
value: 13.050999999999998
- type: recall_at_1
value: 28.126
- type: recall_at_10
value: 54.952
- type: recall_at_100
value: 78.375
- type: recall_at_1000
value: 93.29899999999999
- type: recall_at_3
value: 41.714
- type: recall_at_5
value: 47.635
- type: map_at_1
value: 25.957
- type: map_at_10
value: 34.749
- type: map_at_100
value: 35.929
- type: map_at_1000
value: 36.043
- type: map_at_3
value: 31.947
- type: map_at_5
value: 33.575
- type: mrr_at_1
value: 32.078
- type: mrr_at_10
value: 39.844
- type: mrr_at_100
value: 40.71
- type: mrr_at_1000
value: 40.77
- type: mrr_at_3
value: 37.386
- type: mrr_at_5
value: 38.83
- type: ndcg_at_1
value: 32.078
- type: ndcg_at_10
value: 39.97
- type: ndcg_at_100
value: 45.254
- type: ndcg_at_1000
value: 47.818
- type: ndcg_at_3
value: 35.453
- type: ndcg_at_5
value: 37.631
- type: precision_at_1
value: 32.078
- type: precision_at_10
value: 7.158
- type: precision_at_100
value: 1.126
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 16.743
- type: precision_at_5
value: 11.872
- type: recall_at_1
value: 25.957
- type: recall_at_10
value: 50.583
- type: recall_at_100
value: 73.593
- type: recall_at_1000
value: 91.23599999999999
- type: recall_at_3
value: 37.651
- type: recall_at_5
value: 43.626
- type: map_at_1
value: 27.1505
- type: map_at_10
value: 34.844833333333334
- type: map_at_100
value: 35.95216666666667
- type: map_at_1000
value: 36.06675
- type: map_at_3
value: 32.41975
- type: map_at_5
value: 33.74233333333333
- type: mrr_at_1
value: 31.923666666666662
- type: mrr_at_10
value: 38.87983333333334
- type: mrr_at_100
value: 39.706250000000004
- type: mrr_at_1000
value: 39.76708333333333
- type: mrr_at_3
value: 36.72008333333333
- type: mrr_at_5
value: 37.96933333333334
- type: ndcg_at_1
value: 31.923666666666662
- type: ndcg_at_10
value: 39.44258333333334
- type: ndcg_at_100
value: 44.31475
- type: ndcg_at_1000
value: 46.75
- type: ndcg_at_3
value: 35.36299999999999
- type: ndcg_at_5
value: 37.242333333333335
- type: precision_at_1
value: 31.923666666666662
- type: precision_at_10
value: 6.643333333333333
- type: precision_at_100
value: 1.0612499999999998
- type: precision_at_1000
value: 0.14575
- type: precision_at_3
value: 15.875250000000001
- type: precision_at_5
value: 11.088916666666664
- type: recall_at_1
value: 27.1505
- type: recall_at_10
value: 49.06349999999999
- type: recall_at_100
value: 70.60841666666666
- type: recall_at_1000
value: 87.72049999999999
- type: recall_at_3
value: 37.60575000000001
- type: recall_at_5
value: 42.511166666666675
- type: map_at_1
value: 25.101000000000003
- type: map_at_10
value: 30.147000000000002
- type: map_at_100
value: 30.98
- type: map_at_1000
value: 31.080000000000002
- type: map_at_3
value: 28.571
- type: map_at_5
value: 29.319
- type: mrr_at_1
value: 27.761000000000003
- type: mrr_at_10
value: 32.716
- type: mrr_at_100
value: 33.504
- type: mrr_at_1000
value: 33.574
- type: mrr_at_3
value: 31.135
- type: mrr_at_5
value: 32.032
- type: ndcg_at_1
value: 27.761000000000003
- type: ndcg_at_10
value: 33.358
- type: ndcg_at_100
value: 37.569
- type: ndcg_at_1000
value: 40.189
- type: ndcg_at_3
value: 30.291
- type: ndcg_at_5
value: 31.558000000000003
- type: precision_at_1
value: 27.761000000000003
- type: precision_at_10
value: 4.939
- type: precision_at_100
value: 0.759
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.497
- type: recall_at_1
value: 25.101000000000003
- type: recall_at_10
value: 40.739
- type: recall_at_100
value: 60.089999999999996
- type: recall_at_1000
value: 79.768
- type: recall_at_3
value: 32.16
- type: recall_at_5
value: 35.131
- type: map_at_1
value: 20.112
- type: map_at_10
value: 26.119999999999997
- type: map_at_100
value: 27.031
- type: map_at_1000
value: 27.150000000000002
- type: map_at_3
value: 24.230999999999998
- type: map_at_5
value: 25.15
- type: mrr_at_1
value: 24.535
- type: mrr_at_10
value: 30.198000000000004
- type: mrr_at_100
value: 30.975
- type: mrr_at_1000
value: 31.051000000000002
- type: mrr_at_3
value: 28.338
- type: mrr_at_5
value: 29.269000000000002
- type: ndcg_at_1
value: 24.535
- type: ndcg_at_10
value: 30.147000000000002
- type: ndcg_at_100
value: 34.544000000000004
- type: ndcg_at_1000
value: 37.512
- type: ndcg_at_3
value: 26.726
- type: ndcg_at_5
value: 28.046
- type: precision_at_1
value: 24.535
- type: precision_at_10
value: 5.179
- type: precision_at_100
value: 0.859
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 12.159
- type: precision_at_5
value: 8.424
- type: recall_at_1
value: 20.112
- type: recall_at_10
value: 38.312000000000005
- type: recall_at_100
value: 58.406000000000006
- type: recall_at_1000
value: 79.863
- type: recall_at_3
value: 28.358
- type: recall_at_5
value: 31.973000000000003
- type: map_at_1
value: 27.111
- type: map_at_10
value: 34.096
- type: map_at_100
value: 35.181000000000004
- type: map_at_1000
value: 35.276
- type: map_at_3
value: 31.745
- type: map_at_5
value: 33.045
- type: mrr_at_1
value: 31.343
- type: mrr_at_10
value: 37.994
- type: mrr_at_100
value: 38.873000000000005
- type: mrr_at_1000
value: 38.934999999999995
- type: mrr_at_3
value: 35.743
- type: mrr_at_5
value: 37.077
- type: ndcg_at_1
value: 31.343
- type: ndcg_at_10
value: 38.572
- type: ndcg_at_100
value: 43.854
- type: ndcg_at_1000
value: 46.190999999999995
- type: ndcg_at_3
value: 34.247
- type: ndcg_at_5
value: 36.28
- type: precision_at_1
value: 31.343
- type: precision_at_10
value: 6.166
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 15.081
- type: precision_at_5
value: 10.428999999999998
- type: recall_at_1
value: 27.111
- type: recall_at_10
value: 48.422
- type: recall_at_100
value: 71.846
- type: recall_at_1000
value: 88.57000000000001
- type: recall_at_3
value: 36.435
- type: recall_at_5
value: 41.765
- type: map_at_1
value: 26.264
- type: map_at_10
value: 33.522
- type: map_at_100
value: 34.963
- type: map_at_1000
value: 35.175
- type: map_at_3
value: 31.366
- type: map_at_5
value: 32.621
- type: mrr_at_1
value: 31.028
- type: mrr_at_10
value: 37.230000000000004
- type: mrr_at_100
value: 38.149
- type: mrr_at_1000
value: 38.218
- type: mrr_at_3
value: 35.046
- type: mrr_at_5
value: 36.617
- type: ndcg_at_1
value: 31.028
- type: ndcg_at_10
value: 37.964999999999996
- type: ndcg_at_100
value: 43.342000000000006
- type: ndcg_at_1000
value: 46.471000000000004
- type: ndcg_at_3
value: 34.67
- type: ndcg_at_5
value: 36.458
- type: precision_at_1
value: 31.028
- type: precision_at_10
value: 6.937
- type: precision_at_100
value: 1.346
- type: precision_at_1000
value: 0.22799999999999998
- type: precision_at_3
value: 15.942
- type: precision_at_5
value: 11.462
- type: recall_at_1
value: 26.264
- type: recall_at_10
value: 45.571
- type: recall_at_100
value: 70.246
- type: recall_at_1000
value: 90.971
- type: recall_at_3
value: 36.276
- type: recall_at_5
value: 41.162
- type: map_at_1
value: 23.372999999999998
- type: map_at_10
value: 28.992
- type: map_at_100
value: 29.837999999999997
- type: map_at_1000
value: 29.939
- type: map_at_3
value: 26.999000000000002
- type: map_at_5
value: 28.044999999999998
- type: mrr_at_1
value: 25.692999999999998
- type: mrr_at_10
value: 30.984
- type: mrr_at_100
value: 31.799
- type: mrr_at_1000
value: 31.875999999999998
- type: mrr_at_3
value: 29.267
- type: mrr_at_5
value: 30.163
- type: ndcg_at_1
value: 25.692999999999998
- type: ndcg_at_10
value: 32.45
- type: ndcg_at_100
value: 37.103
- type: ndcg_at_1000
value: 39.678000000000004
- type: ndcg_at_3
value: 28.725
- type: ndcg_at_5
value: 30.351
- type: precision_at_1
value: 25.692999999999998
- type: precision_at_10
value: 4.806
- type: precision_at_100
value: 0.765
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 11.768
- type: precision_at_5
value: 8.096
- type: recall_at_1
value: 23.372999999999998
- type: recall_at_10
value: 41.281
- type: recall_at_100
value: 63.465
- type: recall_at_1000
value: 82.575
- type: recall_at_3
value: 31.063000000000002
- type: recall_at_5
value: 34.991
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.821
- type: map_at_10
value: 15.383
- type: map_at_100
value: 17.244999999999997
- type: map_at_1000
value: 17.445
- type: map_at_3
value: 12.64
- type: map_at_5
value: 13.941999999999998
- type: mrr_at_1
value: 19.544
- type: mrr_at_10
value: 29.738999999999997
- type: mrr_at_100
value: 30.923000000000002
- type: mrr_at_1000
value: 30.969
- type: mrr_at_3
value: 26.384
- type: mrr_at_5
value: 28.199
- type: ndcg_at_1
value: 19.544
- type: ndcg_at_10
value: 22.398
- type: ndcg_at_100
value: 30.253999999999998
- type: ndcg_at_1000
value: 33.876
- type: ndcg_at_3
value: 17.473
- type: ndcg_at_5
value: 19.154
- type: precision_at_1
value: 19.544
- type: precision_at_10
value: 7.217999999999999
- type: precision_at_100
value: 1.564
- type: precision_at_1000
value: 0.22300000000000003
- type: precision_at_3
value: 13.225000000000001
- type: precision_at_5
value: 10.319
- type: recall_at_1
value: 8.821
- type: recall_at_10
value: 28.110000000000003
- type: recall_at_100
value: 55.64
- type: recall_at_1000
value: 75.964
- type: recall_at_3
value: 16.195
- type: recall_at_5
value: 20.678
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.344
- type: map_at_10
value: 20.301
- type: map_at_100
value: 28.709
- type: map_at_1000
value: 30.470999999999997
- type: map_at_3
value: 14.584
- type: map_at_5
value: 16.930999999999997
- type: mrr_at_1
value: 67.25
- type: mrr_at_10
value: 75.393
- type: mrr_at_100
value: 75.742
- type: mrr_at_1000
value: 75.75
- type: mrr_at_3
value: 73.958
- type: mrr_at_5
value: 74.883
- type: ndcg_at_1
value: 56.00000000000001
- type: ndcg_at_10
value: 42.394
- type: ndcg_at_100
value: 47.091
- type: ndcg_at_1000
value: 54.215
- type: ndcg_at_3
value: 46.995
- type: ndcg_at_5
value: 44.214999999999996
- type: precision_at_1
value: 67.25
- type: precision_at_10
value: 33.525
- type: precision_at_100
value: 10.67
- type: precision_at_1000
value: 2.221
- type: precision_at_3
value: 49.417
- type: precision_at_5
value: 42.15
- type: recall_at_1
value: 9.344
- type: recall_at_10
value: 25.209
- type: recall_at_100
value: 52.329
- type: recall_at_1000
value: 74.2
- type: recall_at_3
value: 15.699
- type: recall_at_5
value: 19.24
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.05
- type: f1
value: 43.06718139212933
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 46.452
- type: map_at_10
value: 58.825
- type: map_at_100
value: 59.372
- type: map_at_1000
value: 59.399
- type: map_at_3
value: 56.264
- type: map_at_5
value: 57.879999999999995
- type: mrr_at_1
value: 49.82
- type: mrr_at_10
value: 62.178999999999995
- type: mrr_at_100
value: 62.641999999999996
- type: mrr_at_1000
value: 62.658
- type: mrr_at_3
value: 59.706
- type: mrr_at_5
value: 61.283
- type: ndcg_at_1
value: 49.82
- type: ndcg_at_10
value: 65.031
- type: ndcg_at_100
value: 67.413
- type: ndcg_at_1000
value: 68.014
- type: ndcg_at_3
value: 60.084
- type: ndcg_at_5
value: 62.858000000000004
- type: precision_at_1
value: 49.82
- type: precision_at_10
value: 8.876000000000001
- type: precision_at_100
value: 1.018
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 24.477
- type: precision_at_5
value: 16.208
- type: recall_at_1
value: 46.452
- type: recall_at_10
value: 80.808
- type: recall_at_100
value: 91.215
- type: recall_at_1000
value: 95.52000000000001
- type: recall_at_3
value: 67.62899999999999
- type: recall_at_5
value: 74.32900000000001
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.351
- type: map_at_10
value: 30.796
- type: map_at_100
value: 32.621
- type: map_at_1000
value: 32.799
- type: map_at_3
value: 26.491
- type: map_at_5
value: 28.933999999999997
- type: mrr_at_1
value: 36.265
- type: mrr_at_10
value: 45.556999999999995
- type: mrr_at_100
value: 46.323
- type: mrr_at_1000
value: 46.359
- type: mrr_at_3
value: 42.695
- type: mrr_at_5
value: 44.324000000000005
- type: ndcg_at_1
value: 36.265
- type: ndcg_at_10
value: 38.558
- type: ndcg_at_100
value: 45.18
- type: ndcg_at_1000
value: 48.292
- type: ndcg_at_3
value: 34.204
- type: ndcg_at_5
value: 35.735
- type: precision_at_1
value: 36.265
- type: precision_at_10
value: 10.879999999999999
- type: precision_at_100
value: 1.77
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 23.044999999999998
- type: precision_at_5
value: 17.253
- type: recall_at_1
value: 18.351
- type: recall_at_10
value: 46.116
- type: recall_at_100
value: 70.786
- type: recall_at_1000
value: 89.46300000000001
- type: recall_at_3
value: 31.404
- type: recall_at_5
value: 37.678
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.847
- type: map_at_10
value: 54.269999999999996
- type: map_at_100
value: 55.152
- type: map_at_1000
value: 55.223
- type: map_at_3
value: 51.166
- type: map_at_5
value: 53.055
- type: mrr_at_1
value: 73.693
- type: mrr_at_10
value: 79.975
- type: mrr_at_100
value: 80.202
- type: mrr_at_1000
value: 80.214
- type: mrr_at_3
value: 78.938
- type: mrr_at_5
value: 79.595
- type: ndcg_at_1
value: 73.693
- type: ndcg_at_10
value: 63.334999999999994
- type: ndcg_at_100
value: 66.452
- type: ndcg_at_1000
value: 67.869
- type: ndcg_at_3
value: 58.829
- type: ndcg_at_5
value: 61.266
- type: precision_at_1
value: 73.693
- type: precision_at_10
value: 13.122
- type: precision_at_100
value: 1.5559999999999998
- type: precision_at_1000
value: 0.174
- type: precision_at_3
value: 37.083
- type: precision_at_5
value: 24.169999999999998
- type: recall_at_1
value: 36.847
- type: recall_at_10
value: 65.61099999999999
- type: recall_at_100
value: 77.792
- type: recall_at_1000
value: 87.17099999999999
- type: recall_at_3
value: 55.625
- type: recall_at_5
value: 60.425
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 82.1096
- type: ap
value: 76.67089212843918
- type: f1
value: 82.03535056754939
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.465
- type: map_at_10
value: 37.072
- type: map_at_100
value: 38.188
- type: map_at_1000
value: 38.232
- type: map_at_3
value: 33.134
- type: map_at_5
value: 35.453
- type: mrr_at_1
value: 25.142999999999997
- type: mrr_at_10
value: 37.669999999999995
- type: mrr_at_100
value: 38.725
- type: mrr_at_1000
value: 38.765
- type: mrr_at_3
value: 33.82
- type: mrr_at_5
value: 36.111
- type: ndcg_at_1
value: 25.142999999999997
- type: ndcg_at_10
value: 44.054
- type: ndcg_at_100
value: 49.364000000000004
- type: ndcg_at_1000
value: 50.456
- type: ndcg_at_3
value: 36.095
- type: ndcg_at_5
value: 40.23
- type: precision_at_1
value: 25.142999999999997
- type: precision_at_10
value: 6.845
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.204999999999998
- type: precision_at_5
value: 11.221
- type: recall_at_1
value: 24.465
- type: recall_at_10
value: 65.495
- type: recall_at_100
value: 89.888
- type: recall_at_1000
value: 98.165
- type: recall_at_3
value: 43.964
- type: recall_at_5
value: 53.891
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.86228910168718
- type: f1
value: 93.69177113259104
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.3999088007296
- type: f1
value: 58.96668664333438
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.21788836583727
- type: f1
value: 71.4545936552952
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.39071956960323
- type: f1
value: 77.12398952847603
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.255379528166955
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.66423362872814
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.782211620375964
- type: mrr
value: 31.773479703044956
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.863
- type: map_at_10
value: 13.831
- type: map_at_100
value: 17.534
- type: map_at_1000
value: 19.012
- type: map_at_3
value: 10.143
- type: map_at_5
value: 12.034
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.376999999999995
- type: mrr_at_100
value: 56.009
- type: mrr_at_1000
value: 56.042
- type: mrr_at_3
value: 53.30200000000001
- type: mrr_at_5
value: 54.85
- type: ndcg_at_1
value: 44.582
- type: ndcg_at_10
value: 36.07
- type: ndcg_at_100
value: 33.39
- type: ndcg_at_1000
value: 41.884
- type: ndcg_at_3
value: 41.441
- type: ndcg_at_5
value: 39.861000000000004
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 26.594
- type: precision_at_100
value: 8.365
- type: precision_at_1000
value: 2.1260000000000003
- type: precision_at_3
value: 39.009
- type: precision_at_5
value: 34.861
- type: recall_at_1
value: 5.863
- type: recall_at_10
value: 17.961
- type: recall_at_100
value: 34.026
- type: recall_at_1000
value: 64.46499999999999
- type: recall_at_3
value: 11.242
- type: recall_at_5
value: 14.493
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.601
- type: map_at_10
value: 55.293000000000006
- type: map_at_100
value: 56.092
- type: map_at_1000
value: 56.111999999999995
- type: map_at_3
value: 51.269
- type: map_at_5
value: 53.787
- type: mrr_at_1
value: 43.221
- type: mrr_at_10
value: 57.882999999999996
- type: mrr_at_100
value: 58.408
- type: mrr_at_1000
value: 58.421
- type: mrr_at_3
value: 54.765
- type: mrr_at_5
value: 56.809
- type: ndcg_at_1
value: 43.221
- type: ndcg_at_10
value: 62.858999999999995
- type: ndcg_at_100
value: 65.987
- type: ndcg_at_1000
value: 66.404
- type: ndcg_at_3
value: 55.605000000000004
- type: ndcg_at_5
value: 59.723000000000006
- type: precision_at_1
value: 43.221
- type: precision_at_10
value: 9.907
- type: precision_at_100
value: 1.169
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.019000000000002
- type: precision_at_5
value: 17.474
- type: recall_at_1
value: 38.601
- type: recall_at_10
value: 82.966
- type: recall_at_100
value: 96.154
- type: recall_at_1000
value: 99.223
- type: recall_at_3
value: 64.603
- type: recall_at_5
value: 73.97200000000001
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.77
- type: map_at_10
value: 84.429
- type: map_at_100
value: 85.04599999999999
- type: map_at_1000
value: 85.065
- type: map_at_3
value: 81.461
- type: map_at_5
value: 83.316
- type: mrr_at_1
value: 81.51
- type: mrr_at_10
value: 87.52799999999999
- type: mrr_at_100
value: 87.631
- type: mrr_at_1000
value: 87.632
- type: mrr_at_3
value: 86.533
- type: mrr_at_5
value: 87.214
- type: ndcg_at_1
value: 81.47999999999999
- type: ndcg_at_10
value: 88.181
- type: ndcg_at_100
value: 89.39200000000001
- type: ndcg_at_1000
value: 89.52
- type: ndcg_at_3
value: 85.29299999999999
- type: ndcg_at_5
value: 86.88
- type: precision_at_1
value: 81.47999999999999
- type: precision_at_10
value: 13.367
- type: precision_at_100
value: 1.5230000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.227
- type: precision_at_5
value: 24.494
- type: recall_at_1
value: 70.77
- type: recall_at_10
value: 95.199
- type: recall_at_100
value: 99.37700000000001
- type: recall_at_1000
value: 99.973
- type: recall_at_3
value: 86.895
- type: recall_at_5
value: 91.396
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.686353396858344
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.3664675312921
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.7379999999999995
- type: map_at_10
value: 12.01
- type: map_at_100
value: 14.02
- type: map_at_1000
value: 14.310999999999998
- type: map_at_3
value: 8.459
- type: map_at_5
value: 10.281
- type: mrr_at_1
value: 23.3
- type: mrr_at_10
value: 34.108
- type: mrr_at_100
value: 35.217
- type: mrr_at_1000
value: 35.272
- type: mrr_at_3
value: 30.833
- type: mrr_at_5
value: 32.768
- type: ndcg_at_1
value: 23.3
- type: ndcg_at_10
value: 20.116999999999997
- type: ndcg_at_100
value: 27.961000000000002
- type: ndcg_at_1000
value: 33.149
- type: ndcg_at_3
value: 18.902
- type: ndcg_at_5
value: 16.742
- type: precision_at_1
value: 23.3
- type: precision_at_10
value: 10.47
- type: precision_at_100
value: 2.177
- type: precision_at_1000
value: 0.34299999999999997
- type: precision_at_3
value: 17.567
- type: precision_at_5
value: 14.78
- type: recall_at_1
value: 4.7379999999999995
- type: recall_at_10
value: 21.221999999999998
- type: recall_at_100
value: 44.242
- type: recall_at_1000
value: 69.652
- type: recall_at_3
value: 10.688
- type: recall_at_5
value: 14.982999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.84572946827069
- type: cos_sim_spearman
value: 80.48508130408966
- type: euclidean_pearson
value: 82.0481530027767
- type: euclidean_spearman
value: 80.45902876782752
- type: manhattan_pearson
value: 82.03728222483326
- type: manhattan_spearman
value: 80.45684282911755
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.33476464677516
- type: cos_sim_spearman
value: 75.93057758003266
- type: euclidean_pearson
value: 80.89685744015691
- type: euclidean_spearman
value: 76.29929953441706
- type: manhattan_pearson
value: 80.91391345459995
- type: manhattan_spearman
value: 76.31985463110914
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.63686106359005
- type: cos_sim_spearman
value: 85.22240034668202
- type: euclidean_pearson
value: 84.6074814189106
- type: euclidean_spearman
value: 85.17169644755828
- type: manhattan_pearson
value: 84.48329306239368
- type: manhattan_spearman
value: 85.0086508544768
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.95455774064745
- type: cos_sim_spearman
value: 80.54074646118492
- type: euclidean_pearson
value: 81.79598955554704
- type: euclidean_spearman
value: 80.55837617606814
- type: manhattan_pearson
value: 81.78213797905386
- type: manhattan_spearman
value: 80.5666746878273
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.92813309124739
- type: cos_sim_spearman
value: 88.81459873052108
- type: euclidean_pearson
value: 88.21193118930564
- type: euclidean_spearman
value: 88.87072745043731
- type: manhattan_pearson
value: 88.22576929706727
- type: manhattan_spearman
value: 88.8867671095791
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.6881529671839
- type: cos_sim_spearman
value: 85.2807092969554
- type: euclidean_pearson
value: 84.62334178652704
- type: euclidean_spearman
value: 85.2116373296784
- type: manhattan_pearson
value: 84.54948211541777
- type: manhattan_spearman
value: 85.10737722637882
- 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: 88.55963694458408
- type: cos_sim_spearman
value: 89.36731628848683
- type: euclidean_pearson
value: 89.64975952985465
- type: euclidean_spearman
value: 89.29689484033007
- type: manhattan_pearson
value: 89.61234491713135
- type: manhattan_spearman
value: 89.20302520255782
- 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: 62.411800961903886
- type: cos_sim_spearman
value: 62.99105515749963
- type: euclidean_pearson
value: 65.29826669549443
- type: euclidean_spearman
value: 63.29880964105775
- type: manhattan_pearson
value: 65.00126190601183
- type: manhattan_spearman
value: 63.32011025899179
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.83498531837608
- type: cos_sim_spearman
value: 87.21366640615442
- type: euclidean_pearson
value: 86.74764288798261
- type: euclidean_spearman
value: 87.06060470780834
- type: manhattan_pearson
value: 86.65971223951476
- type: manhattan_spearman
value: 86.99814399831457
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.94448463485881
- type: mrr
value: 95.36291867174221
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.928000000000004
- type: map_at_10
value: 68.577
- type: map_at_100
value: 69.35900000000001
- type: map_at_1000
value: 69.37299999999999
- type: map_at_3
value: 66.217
- type: map_at_5
value: 67.581
- type: mrr_at_1
value: 63
- type: mrr_at_10
value: 69.994
- type: mrr_at_100
value: 70.553
- type: mrr_at_1000
value: 70.56700000000001
- type: mrr_at_3
value: 68.167
- type: mrr_at_5
value: 69.11699999999999
- type: ndcg_at_1
value: 63
- type: ndcg_at_10
value: 72.58
- type: ndcg_at_100
value: 75.529
- type: ndcg_at_1000
value: 76.009
- type: ndcg_at_3
value: 68.523
- type: ndcg_at_5
value: 70.301
- type: precision_at_1
value: 63
- type: precision_at_10
value: 9.333
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 59.928000000000004
- type: recall_at_10
value: 83.544
- type: recall_at_100
value: 96
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 72.072
- type: recall_at_5
value: 76.683
- 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: 95.41507679819003
- type: cos_sim_f1
value: 90.9456740442656
- type: cos_sim_precision
value: 91.49797570850203
- type: cos_sim_recall
value: 90.4
- type: dot_accuracy
value: 99.77227722772277
- type: dot_ap
value: 92.50123869445967
- type: dot_f1
value: 88.18414322250638
- type: dot_precision
value: 90.26178010471205
- type: dot_recall
value: 86.2
- type: euclidean_accuracy
value: 99.81782178217821
- type: euclidean_ap
value: 95.3935066749006
- type: euclidean_f1
value: 90.66128218071681
- type: euclidean_precision
value: 91.53924566768603
- type: euclidean_recall
value: 89.8
- type: manhattan_accuracy
value: 99.81881188118813
- type: manhattan_ap
value: 95.39767454613512
- type: manhattan_f1
value: 90.62019477191186
- type: manhattan_precision
value: 92.95478443743428
- type: manhattan_recall
value: 88.4
- type: max_accuracy
value: 99.82178217821782
- type: max_ap
value: 95.41507679819003
- type: max_f1
value: 90.9456740442656
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 64.96313921233748
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.602625720956745
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.32659230651731
- type: mrr
value: 52.33861726508785
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.01587644214203
- type: cos_sim_spearman
value: 30.974306908731013
- type: dot_pearson
value: 29.83339853838187
- type: dot_spearman
value: 30.07761671934048
- 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: 1.9539999999999997
- type: map_at_100
value: 11.437
- type: map_at_1000
value: 27.861000000000004
- type: map_at_3
value: 0.6479999999999999
- type: map_at_5
value: 1.0410000000000001
- type: mrr_at_1
value: 84
- type: mrr_at_10
value: 90.333
- type: mrr_at_100
value: 90.333
- type: mrr_at_1000
value: 90.333
- type: mrr_at_3
value: 90.333
- type: mrr_at_5
value: 90.333
- type: ndcg_at_1
value: 80
- type: ndcg_at_10
value: 78.31700000000001
- type: ndcg_at_100
value: 59.396
- type: ndcg_at_1000
value: 52.733
- type: ndcg_at_3
value: 81.46900000000001
- type: ndcg_at_5
value: 80.74
- type: precision_at_1
value: 84
- type: precision_at_10
value: 84
- type: precision_at_100
value: 60.980000000000004
- type: precision_at_1000
value: 23.432
- 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: 2.156
- type: recall_at_100
value: 14.557999999999998
- type: recall_at_1000
value: 49.553999999999995
- type: recall_at_3
value: 0.685
- type: recall_at_5
value: 1.121
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.373
- type: map_at_10
value: 11.701
- type: map_at_100
value: 17.144000000000002
- type: map_at_1000
value: 18.624
- type: map_at_3
value: 6.552
- type: map_at_5
value: 9.372
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 51.975
- type: mrr_at_100
value: 52.873999999999995
- type: mrr_at_1000
value: 52.873999999999995
- type: mrr_at_3
value: 47.619
- type: mrr_at_5
value: 50.578
- type: ndcg_at_1
value: 36.735
- type: ndcg_at_10
value: 27.212999999999997
- type: ndcg_at_100
value: 37.245
- type: ndcg_at_1000
value: 48.602000000000004
- type: ndcg_at_3
value: 30.916
- type: ndcg_at_5
value: 30.799
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 7.327
- type: precision_at_1000
value: 1.486
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 32.245000000000005
- type: recall_at_1
value: 3.373
- type: recall_at_10
value: 17.404
- type: recall_at_100
value: 46.105000000000004
- type: recall_at_1000
value: 80.35
- type: recall_at_3
value: 7.4399999999999995
- type: recall_at_5
value: 12.183
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.5592
- type: ap
value: 14.330910591410134
- type: f1
value: 54.45745186286521
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.20543293718167
- type: f1
value: 61.45365480309872
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 43.81162998944145
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.69011146212075
- type: cos_sim_ap
value: 76.09792353652536
- type: cos_sim_f1
value: 70.10202763786646
- type: cos_sim_precision
value: 68.65671641791045
- type: cos_sim_recall
value: 71.60949868073878
- type: dot_accuracy
value: 85.33110806461227
- type: dot_ap
value: 70.19304383327554
- type: dot_f1
value: 67.22494202525122
- type: dot_precision
value: 65.6847935548842
- type: dot_recall
value: 68.83905013192611
- type: euclidean_accuracy
value: 86.5410979316922
- type: euclidean_ap
value: 75.91906915651882
- type: euclidean_f1
value: 69.6798975672215
- type: euclidean_precision
value: 67.6865671641791
- type: euclidean_recall
value: 71.79419525065963
- type: manhattan_accuracy
value: 86.60070334386363
- type: manhattan_ap
value: 75.94617413885031
- type: manhattan_f1
value: 69.52689565780946
- type: manhattan_precision
value: 68.3312101910828
- type: manhattan_recall
value: 70.76517150395777
- type: max_accuracy
value: 86.69011146212075
- type: max_ap
value: 76.09792353652536
- type: max_f1
value: 70.10202763786646
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.25951798812434
- type: cos_sim_ap
value: 86.31476416599727
- type: cos_sim_f1
value: 78.52709971038477
- type: cos_sim_precision
value: 76.7629972792117
- type: cos_sim_recall
value: 80.37419156144134
- type: dot_accuracy
value: 88.03896456708192
- type: dot_ap
value: 83.26963599196237
- type: dot_f1
value: 76.72696459492317
- type: dot_precision
value: 73.56411162133521
- type: dot_recall
value: 80.17400677548507
- type: euclidean_accuracy
value: 89.21682772538519
- type: euclidean_ap
value: 86.29306071289969
- type: euclidean_f1
value: 78.40827030519554
- type: euclidean_precision
value: 77.42250243939053
- type: euclidean_recall
value: 79.41946412072683
- type: manhattan_accuracy
value: 89.22458959133776
- type: manhattan_ap
value: 86.2901934710645
- type: manhattan_f1
value: 78.54211378440453
- type: manhattan_precision
value: 76.85505858079729
- type: manhattan_recall
value: 80.30489682784109
- type: max_accuracy
value: 89.25951798812434
- type: max_ap
value: 86.31476416599727
- type: max_f1
value: 78.54211378440453
---
## E5-large
**News (May 2023): please switch to [e5-large-v2](https://huggingface.co/intfloat/e5-large-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 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')
model = AutoModel.from_pretrained('intfloat/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())
```
## 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')
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"
] |
stanford-crfm/BioMedLM
|
stanford-crfm
|
text-generation
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"dataset:pubmed",
"arxiv:2403.18421",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-12-14T08:14:59 | 2024-03-28T13:57:14 | 13,701 | 417 |
---
datasets:
- pubmed
license: bigscience-bloom-rail-1.0
widget:
- text: Photosynthesis is
---
# Model Card for BioMedLM 2.7B
Note: This model was previously known as PubMedGPT 2.7B, but we have changed it due to a request from the NIH which holds the trademark for "PubMed".
Paper: [BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text](https://arxiv.org/abs/2403.18421)
BioMedLM 2.7B is new language model trained exclusively on biomedical abstracts and papers from [The Pile](https://pile.eleuther.ai/). This GPT-style model can achieve strong results on a variety of biomedical NLP tasks, including a new state of the art performance of 50.3% accuracy on the MedQA biomedical question answering task.
As an autoregressive language model, BioMedLM 2.7B is also capable of natural language generation. However, we have only begun to explore the generation capabilities and limitations of this model, and we emphasize that this model’s generation capabilities are for research purposes only and not suitable for production. In releasing this model, we hope to advance both the development of biomedical NLP applications and best practices for responsibly training and utilizing domain-specific language models; issues of reliability, truthfulness, and explainability are top of mind for us.
This model was a joint collaboration of [Stanford CRFM](https://crfm.stanford.edu/) and [MosaicML](https://www.mosaicml.com/).
# Table of Contents
- [Model Card for BioMedLM 2.7B](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
BioMedLM 2.7B is new language model trained exclusively on biomedical abstracts and papers from [The Pile](https://pile.eleuther.ai/). This GPT-style model can achieve strong results on a variety of biomedical NLP tasks, including a new state of the art performance of 50.3% accuracy on the MedQA biomedical question answering task.
As an autoregressive language model, BioMedLM 2.7B is also capable of natural language generation. However, we have only begun to explore the generation capabilities and limitations of this model, and we emphasize that this model’s generation capabilities are for research purposes only and not suitable for production. In releasing this model, we hope to advance both the development of biomedical NLP applications and best practices for responsibly training and utilizing domain-specific language models; issues of reliability, truthfulness, and explainability are top of mind for us.
This model was a joint collaboration of [Stanford CRFM](https://crfm.stanford.edu/) and [MosaicML](https://www.mosaicml.com/).
- **Developed by:** Stanford CRFM, MosaicML
- **Shared by:** Stanford CRFM
- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** [bigscience-bloom-rail-1.0](https://huggingface.co/spaces/bigscience/license)
# Uses
This model is licensed under the terms of [BigScience Open RAIL-M license](https://huggingface.co/spaces/bigscience/license) used for [BLOOM](https://huggingface.co/bigscience/bloom-1b1). Please note that, among other restrictions, this license forbids use of the model (or derivatives thereof)
"To provide medical advice and medical results interpretation." If you are concerned that your use case would follow under the "letter" of this restriction, but not the "spirit," you can contact us to discuss.
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be directly used for production or work that may directly impact people.
## Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
The main way we have used this model is finetuning for downstream question answering tasks, and we recommend using this model that way.
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.**
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
This model was trained on the Pubmed Abstracts and Full Text from [The Pile](https://pile.eleuther.ai/).
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
The model was trained on [MosaicML Cloud](https://www.mosaicml.com/cloud), a platform designed for large workloads like LLMs. Using the [Composer](https://github.com/mosaicml/composer) training library and [PyTorch FSDP](https://pytorch.org/docs/stable/fsdp.html), it was easy to enable multi-node training across 128 A100-40GB GPUs, and the total run was completed in ~6.25 days. The model was trained with batch size=1024 and sequence length=1024 for 300B tokens using Decoupled AdamW with the following settings:
| | |
| --- | ------ |
| lr | 1.6e-4 |
| eps | 1e-8 |
| betas | \[0.9, 0.95\] |
| weight decay | 1.6e-5 |
The training process was very smooth and did not suffer from any divergences.
As we were preparing the training run, we were unsure of the benefits of training out to 300B tokens for language model perplexity and downstream task performance. While most models of this scale (e.g. GPT Neo 2.7B) are trained to 300-400B tokens, the datasets those models use are vastly larger than PubMed. For instance, The Pile is 8x the size of its PubMed subcorpora.
Fortunately, we did continue to see steady perplexity improvements on the validation and training sets for the entirety of training, and preliminary experiments showed improved downstream task performance as we trained out to the full 300B tokens. Our takeaway from this was that it was indeed worth it to train for the full 300B tokens, even though this represented dramatically more passes through the data than comparable models.
### Preprocessing
The model uses a custom tokenizer trained on the PubMed Abstracts. When building domain specific models we have found it important to use a tokenizer trained on in-domain text to maximize performance on downstream tasks. A key benefit is that common biomedical terms are represented as entire tokens.
For instance, all of these following terms are tokenized into single tokens by the biomedical tokenizer and multiple tokens by the standard GPT-2 tokenizer:
| | |
| --- | --- |
| chromatography | chrom/atography |
| cytotoxicity | cyt/ot/oxicity |
| Immunohistochemistry | Immun/oh/ist/ochemistry |
| photosynthesis | photos/ynthesis |
| probiotic | prob/iotic |
This allows the model to encode information about these concepts in their individual token representations rather than spread out across subword tokens like “oh” shared with many other terms.
# Technical Specifications
## Model Architecture and Objective
BioMedLM 2.7B is a standard GPT-2 implementation (trained with Flash Attention) with the following hyperparameters:
| | |
| ----------- | ----- |
| hidden size | 2560 |
| heads | 20 |
| layers | 32 |
| vocab size | 28896 |
| sequence length| 1024 |
## Compute Infrastructure
The model was trained on [MosaicML Cloud](https://www.mosaicml.com/cloud), a platform designed for large workloads like LLMs. Using the [Composer](https://github.com/mosaicml/composer) training library and [PyTorch FSDP](https://pytorch.org/docs/stable/fsdp.html), it was easy to enable multi-node training across 128 A100-40GB GPUs, and the total run was completed in ~6.25 days.
|
[
"QUESTION_ANSWERING"
] |
[
"MEDQA"
] |
ibm-granite/granite-embedding-125m-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"
] | 2024-12-04T20:28:08 | 2025-03-04T15:09:35 | 13,457 | 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-125m-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: 67.3613
- type: f1
value: 55.0794
- type: f1_weighted
value: 73.55120000000001
- type: ap
value: 17.643900000000002
- type: ap_weighted
value: 17.643900000000002
- type: main_score
value: 67.3613
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 63.403
- type: f1
value: 57.4178
- type: f1_weighted
value: 66.9704
- type: ap
value: 26.892300000000002
- type: ap_weighted
value: 26.892300000000002
- type: main_score
value: 63.403
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 64.5872
- type: f1
value: 64.33330000000001
- type: f1_weighted
value: 64.33330000000001
- type: ap
value: 59.602
- type: ap_weighted
value: 59.602
- type: main_score
value: 64.5872
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 33.534000000000006
- type: f1
value: 32.5389
- type: f1_weighted
value: 32.5389
- type: main_score
value: 33.534000000000006
- task:
type: Retrieval
dataset:
name: MTEB AppsRetrieval (default)
type: CoIR-Retrieval/apps
config: default
split: test
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
metrics:
- type: ndcg_at_1
value: 6.932
- type: ndcg_at_3
value: 9.577
- type: ndcg_at_5
value: 10.597
- type: ndcg_at_10
value: 11.787
- type: ndcg_at_20
value: 12.863
- type: ndcg_at_100
value: 15.573999999999998
- type: ndcg_at_1000
value: 19.772000000000002
- type: map_at_1
value: 6.932
- type: map_at_3
value: 8.938
- type: map_at_5
value: 9.506
- type: map_at_10
value: 10.0
- type: map_at_20
value: 10.296
- type: map_at_100
value: 10.644
- type: map_at_1000
value: 10.771
- type: recall_at_1
value: 6.932
- type: recall_at_3
value: 11.421000000000001
- type: recall_at_5
value: 13.891
- type: recall_at_10
value: 17.556
- type: recall_at_20
value: 21.806
- type: recall_at_100
value: 36.839
- type: recall_at_1000
value: 71.71300000000001
- type: precision_at_1
value: 6.932
- type: precision_at_3
value: 3.807
- type: precision_at_5
value: 2.778
- type: precision_at_10
value: 1.756
- type: precision_at_20
value: 1.09
- type: precision_at_100
value: 0.368
- type: precision_at_1000
value: 0.07200000000000001
- type: mrr_at_1
value: 6.9323
- type: mrr_at_3
value: 8.9376
- type: mrr_at_5
value: 9.506
- type: mrr_at_10
value: 9.9999
- type: mrr_at_20
value: 10.2957
- type: mrr_at_100
value: 10.643600000000001
- type: mrr_at_1000
value: 10.7707
- type: nauc_ndcg_at_1_max
value: 27.327299999999997
- type: nauc_ndcg_at_1_std
value: 9.6266
- type: nauc_ndcg_at_1_diff1
value: 39.4451
- type: nauc_ndcg_at_3_max
value: 22.9053
- type: nauc_ndcg_at_3_std
value: 10.123
- type: nauc_ndcg_at_3_diff1
value: 27.742099999999997
- type: nauc_ndcg_at_5_max
value: 21.7041
- type: nauc_ndcg_at_5_std
value: 9.661100000000001
- type: nauc_ndcg_at_5_diff1
value: 25.0689
- type: nauc_ndcg_at_10_max
value: 21.0966
- type: nauc_ndcg_at_10_std
value: 10.4106
- type: nauc_ndcg_at_10_diff1
value: 23.4219
- type: nauc_ndcg_at_20_max
value: 20.0575
- type: nauc_ndcg_at_20_std
value: 10.89
- type: nauc_ndcg_at_20_diff1
value: 22.6143
- type: nauc_ndcg_at_100_max
value: 19.4243
- type: nauc_ndcg_at_100_std
value: 11.5431
- type: nauc_ndcg_at_100_diff1
value: 21.013
- type: nauc_ndcg_at_1000_max
value: 20.6057
- type: nauc_ndcg_at_1000_std
value: 13.0027
- type: nauc_ndcg_at_1000_diff1
value: 20.988799999999998
- type: nauc_map_at_1_max
value: 27.327299999999997
- type: nauc_map_at_1_std
value: 9.6266
- type: nauc_map_at_1_diff1
value: 39.4451
- type: nauc_map_at_3_max
value: 23.6991
- type: nauc_map_at_3_std
value: 9.9287
- type: nauc_map_at_3_diff1
value: 29.909799999999997
- type: nauc_map_at_5_max
value: 22.9242
- type: nauc_map_at_5_std
value: 9.640600000000001
- type: nauc_map_at_5_diff1
value: 28.228199999999998
- type: nauc_map_at_10_max
value: 22.612199999999998
- type: nauc_map_at_10_std
value: 10.0051
- type: nauc_map_at_10_diff1
value: 27.3942
- type: nauc_map_at_20_max
value: 22.236
- type: nauc_map_at_20_std
value: 10.168000000000001
- type: nauc_map_at_20_diff1
value: 27.0258
- type: nauc_map_at_100_max
value: 22.1373
- type: nauc_map_at_100_std
value: 10.2741
- type: nauc_map_at_100_diff1
value: 26.717800000000004
- type: nauc_map_at_1000_max
value: 22.1829
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value: -13.9831
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value: 32.495000000000005
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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: 91.31099999999999
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value: 90.9331
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value: 91.2787
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value: 91.31099999999999
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 54.9362
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value: 38.364399999999996
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value: 57.1133
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value: 54.9362
- 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: 64.5461
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value: 60.8751
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value: 64.5461
- 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: 71.6476
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value: 71.03110000000001
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value: 71.3832
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value: 71.6476
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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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value: 32.3037
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type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S (default)
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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value: 31.9128
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type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
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value: 32.2181
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type: Retrieval
dataset:
name: MTEB NFCorpus (default)
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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dataset:
name: MTEB NQ (default)
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval (default)
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
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dataset:
name: MTEB RedditClustering (default)
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
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type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
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dataset:
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type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
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dataset:
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type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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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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- type: cosine_spearman
value: 50.0286
- type: manhattan_pearson
value: 48.5188
- type: manhattan_spearman
value: 50.432
- type: euclidean_pearson
value: 48.1655
- type: euclidean_spearman
value: 50.0286
- type: main_score
value: 50.0286
- 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: 24.4119
- type: spearman
value: 22.1195
- type: cosine_pearson
value: 24.4119
- type: cosine_spearman
value: 22.1195
- type: manhattan_pearson
value: 25.873800000000003
- type: manhattan_spearman
value: 23.6049
- type: euclidean_pearson
value: 24.3693
- type: euclidean_spearman
value: 22.1195
- type: main_score
value: 22.1195
- 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: 22.656200000000002
- type: spearman
value: 22.5445
- type: cosine_pearson
value: 22.656200000000002
- type: cosine_spearman
value: 22.5445
- type: manhattan_pearson
value: 22.414
- type: manhattan_spearman
value: 22.1601
- type: euclidean_pearson
value: 22.7736
- type: euclidean_spearman
value: 22.5445
- type: main_score
value: 22.5445
- 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: 44.4998
- type: spearman
value: 43.1984
- type: cosine_pearson
value: 44.4998
- type: cosine_spearman
value: 43.1984
- type: manhattan_pearson
value: 43.3837
- type: manhattan_spearman
value: 43.1122
- type: euclidean_pearson
value: 44.1642
- type: euclidean_spearman
value: 43.1984
- type: main_score
value: 43.1984
- 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: 82.3891
- type: spearman
value: 83.9634
- type: cosine_pearson
value: 82.3891
- type: cosine_spearman
value: 83.9634
- type: manhattan_pearson
value: 83.1481
- type: manhattan_spearman
value: 83.9743
- type: euclidean_pearson
value: 83.2767
- type: euclidean_spearman
value: 83.9634
- type: main_score
value: 83.9634
- 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: 35.3106
- type: spearman
value: 30.7572
- type: cosine_pearson
value: 35.3106
- type: cosine_spearman
value: 30.7572
- type: manhattan_pearson
value: 35.6552
- type: manhattan_spearman
value: 31.596000000000004
- type: euclidean_pearson
value: 35.4393
- type: euclidean_spearman
value: 30.7572
- type: main_score
value: 30.7572
- 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: 36.9322
- type: spearman
value: 37.7137
- type: cosine_pearson
value: 36.9322
- type: cosine_spearman
value: 37.7137
- type: manhattan_pearson
value: 36.0714
- type: manhattan_spearman
value: 36.9979
- type: euclidean_pearson
value: 36.784800000000004
- type: euclidean_spearman
value: 37.7137
- type: main_score
value: 37.7137
- 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: 39.963300000000004
- type: spearman
value: 38.9248
- type: cosine_pearson
value: 39.963300000000004
- type: cosine_spearman
value: 38.9248
- type: manhattan_pearson
value: 39.539699999999996
- type: manhattan_spearman
value: 38.191900000000004
- type: euclidean_pearson
value: 39.8596
- type: euclidean_spearman
value: 38.9248
- type: main_score
value: 38.9248
- 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: 56.0924
- type: spearman
value: 54.1844
- type: cosine_pearson
value: 56.0924
- type: cosine_spearman
value: 54.1844
- type: manhattan_pearson
value: 56.938100000000006
- type: manhattan_spearman
value: 53.9407
- type: euclidean_pearson
value: 57.9844
- type: euclidean_spearman
value: 54.1844
- type: main_score
value: 54.1844
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 69.3771
- type: spearman
value: 69.3609
- type: cosine_pearson
value: 69.3771
- type: cosine_spearman
value: 69.3609
- type: manhattan_pearson
value: 70.8762
- type: manhattan_spearman
value: 69.1889
- type: euclidean_pearson
value: 70.9433
- type: euclidean_spearman
value: 69.3609
- type: main_score
value: 69.3609
- 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: 74.11609999999999
- type: spearman
value: 71.63340000000001
- type: cosine_pearson
value: 74.11609999999999
- type: cosine_spearman
value: 71.63340000000001
- type: manhattan_pearson
value: 73.2348
- type: manhattan_spearman
value: 71.1802
- type: euclidean_pearson
value: 73.284
- type: euclidean_spearman
value: 71.63340000000001
- type: main_score
value: 71.63340000000001
- 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: 70.08879999999999
- type: spearman
value: 73.79
- type: cosine_pearson
value: 70.08879999999999
- type: cosine_spearman
value: 73.79
- type: manhattan_pearson
value: 71.5415
- type: manhattan_spearman
value: 73.6588
- type: euclidean_pearson
value: 71.621
- type: euclidean_spearman
value: 73.79
- type: main_score
value: 73.79
- 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: 37.5935
- type: spearman
value: 39.5919
- type: cosine_pearson
value: 37.5935
- type: cosine_spearman
value: 39.5919
- type: manhattan_pearson
value: 37.1717
- type: manhattan_spearman
value: 38.6974
- type: euclidean_pearson
value: 37.5632
- type: euclidean_spearman
value: 39.5919
- type: main_score
value: 39.5919
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 79.9453
- type: spearman
value: 79.6569
- type: cosine_pearson
value: 79.9453
- type: cosine_spearman
value: 79.6569
- type: manhattan_pearson
value: 79.8923
- type: manhattan_spearman
value: 79.58370000000001
- type: euclidean_pearson
value: 79.9829
- type: euclidean_spearman
value: 79.6569
- type: main_score
value: 79.6569
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 88.09949999999999
- type: mrr
value: 96.6455
- type: nAUC_map_max
value: 53.3622
- type: nAUC_map_std
value: 70.3532
- type: nAUC_map_diff1
value: -0.21419999999999997
- type: nAUC_mrr_max
value: 88.893
- type: nAUC_mrr_std
value: 85.4516
- type: nAUC_mrr_diff1
value: 43.6847
- type: main_score
value: 88.09949999999999
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 62.666999999999994
- type: ndcg_at_3
value: 69.77600000000001
- type: ndcg_at_5
value: 71.964
- type: ndcg_at_10
value: 74.72
- type: ndcg_at_20
value: 76.154
- type: ndcg_at_100
value: 76.961
- type: ndcg_at_1000
value: 77.294
- type: map_at_1
value: 60.011
- type: map_at_3
value: 67.135
- type: map_at_5
value: 68.78
- type: map_at_10
value: 70.101
- type: map_at_20
value: 70.56099999999999
- type: map_at_100
value: 70.687
- type: map_at_1000
value: 70.699
- type: recall_at_1
value: 60.011
- type: recall_at_3
value: 74.839
- type: recall_at_5
value: 80.028
- type: recall_at_10
value: 87.8
- type: recall_at_20
value: 93.10000000000001
- type: recall_at_100
value: 97.333
- type: recall_at_1000
value: 100.0
- type: precision_at_1
value: 62.666999999999994
- type: precision_at_3
value: 27.0
- type: precision_at_5
value: 17.8
- type: precision_at_10
value: 9.933
- type: precision_at_20
value: 5.283
- type: precision_at_100
value: 1.103
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 62.6667
- type: mrr_at_3
value: 68.9444
- type: mrr_at_5
value: 69.9611
- type: mrr_at_10
value: 71.02199999999999
- type: mrr_at_20
value: 71.3777
- type: mrr_at_100
value: 71.4841
- type: mrr_at_1000
value: 71.4961
- type: nauc_ndcg_at_1_max
value: 55.4562
- type: nauc_ndcg_at_1_std
value: -9.3317
- type: nauc_ndcg_at_1_diff1
value: 71.1878
- type: nauc_ndcg_at_3_max
value: 55.3473
- type: nauc_ndcg_at_3_std
value: -14.341400000000002
- type: nauc_ndcg_at_3_diff1
value: 69.11880000000001
- type: nauc_ndcg_at_5_max
value: 55.5531
- type: nauc_ndcg_at_5_std
value: -13.448699999999999
- type: nauc_ndcg_at_5_diff1
value: 67.4611
- type: nauc_ndcg_at_10_max
value: 59.5974
- type: nauc_ndcg_at_10_std
value: -10.262
- type: nauc_ndcg_at_10_diff1
value: 68.3408
- type: nauc_ndcg_at_20_max
value: 58.586499999999994
- type: nauc_ndcg_at_20_std
value: -9.8438
- type: nauc_ndcg_at_20_diff1
value: 68.4434
- type: nauc_ndcg_at_100_max
value: 58.28489999999999
- type: nauc_ndcg_at_100_std
value: -8.7782
- type: nauc_ndcg_at_100_diff1
value: 68.585
- type: nauc_ndcg_at_1000_max
value: 58.0138
- type: nauc_ndcg_at_1000_std
value: -9.4827
- type: nauc_ndcg_at_1000_diff1
value: 69.0467
- type: nauc_map_at_1_max
value: 49.434
- type: nauc_map_at_1_std
value: -17.0503
- type: nauc_map_at_1_diff1
value: 71.80290000000001
- type: nauc_map_at_3_max
value: 52.8035
- type: nauc_map_at_3_std
value: -16.2138
- type: nauc_map_at_3_diff1
value: 69.81739999999999
- type: nauc_map_at_5_max
value: 54.644400000000005
- type: nauc_map_at_5_std
value: -13.910900000000002
- type: nauc_map_at_5_diff1
value: 68.8879
- type: nauc_map_at_10_max
value: 56.550999999999995
- type: nauc_map_at_10_std
value: -12.126900000000001
- type: nauc_map_at_10_diff1
value: 69.2326
- type: nauc_map_at_20_max
value: 56.299699999999994
- type: nauc_map_at_20_std
value: -11.8978
- type: nauc_map_at_20_diff1
value: 69.3387
- type: nauc_map_at_100_max
value: 56.295300000000005
- type: nauc_map_at_100_std
value: -11.6546
- type: nauc_map_at_100_diff1
value: 69.3881
- type: nauc_map_at_1000_max
value: 56.2905
- type: nauc_map_at_1000_std
value: -11.666400000000001
- type: nauc_map_at_1000_diff1
value: 69.4106
- type: nauc_recall_at_1_max
value: 49.434
- type: nauc_recall_at_1_std
value: -17.0503
- type: nauc_recall_at_1_diff1
value: 71.80290000000001
- type: nauc_recall_at_3_max
value: 53.6504
- type: nauc_recall_at_3_std
value: -20.3796
- type: nauc_recall_at_3_diff1
value: 66.0397
- type: nauc_recall_at_5_max
value: 54.45140000000001
- type: nauc_recall_at_5_std
value: -17.8965
- type: nauc_recall_at_5_diff1
value: 60.6996
- type: nauc_recall_at_10_max
value: 72.7183
- type: nauc_recall_at_10_std
value: -7.3393
- type: nauc_recall_at_10_diff1
value: 62.0422
- type: nauc_recall_at_20_max
value: 70.7849
- type: nauc_recall_at_20_std
value: -3.1933000000000002
- type: nauc_recall_at_20_diff1
value: 58.146
- type: nauc_recall_at_100_max
value: 75.43769999999999
- type: nauc_recall_at_100_std
value: 36.5488
- type: nauc_recall_at_100_diff1
value: 46.3177
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_precision_at_1_max
value: 55.4562
- type: nauc_precision_at_1_std
value: -9.3317
- type: nauc_precision_at_1_diff1
value: 71.1878
- type: nauc_precision_at_3_max
value: 52.548300000000005
- type: nauc_precision_at_3_std
value: 6.719899999999999
- type: nauc_precision_at_3_diff1
value: 42.6315
- type: nauc_precision_at_5_max
value: 47.9921
- type: nauc_precision_at_5_std
value: 21.9242
- type: nauc_precision_at_5_diff1
value: 23.0825
- type: nauc_precision_at_10_max
value: 47.517399999999995
- type: nauc_precision_at_10_std
value: 44.4913
- type: nauc_precision_at_10_diff1
value: 5.4589
- type: nauc_precision_at_20_max
value: 36.0675
- type: nauc_precision_at_20_std
value: 53.9269
- type: nauc_precision_at_20_diff1
value: -7.0865
- type: nauc_precision_at_100_max
value: 28.0561
- type: nauc_precision_at_100_std
value: 66.17920000000001
- type: nauc_precision_at_100_diff1
value: -19.653000000000002
- type: nauc_precision_at_1000_max
value: 22.470100000000002
- type: nauc_precision_at_1000_std
value: 69.6725
- type: nauc_precision_at_1000_diff1
value: -27.430500000000002
- type: nauc_mrr_at_1_max
value: 55.4562
- type: nauc_mrr_at_1_std
value: -9.3317
- type: nauc_mrr_at_1_diff1
value: 71.1878
- type: nauc_mrr_at_3_max
value: 57.4634
- type: nauc_mrr_at_3_std
value: -10.6496
- type: nauc_mrr_at_3_diff1
value: 69.881
- type: nauc_mrr_at_5_max
value: 56.8667
- type: nauc_mrr_at_5_std
value: -10.2421
- type: nauc_mrr_at_5_diff1
value: 69.0777
- type: nauc_mrr_at_10_max
value: 58.06289999999999
- type: nauc_mrr_at_10_std
value: -9.8724
- type: nauc_mrr_at_10_diff1
value: 69.5505
- type: nauc_mrr_at_20_max
value: 57.740700000000004
- type: nauc_mrr_at_20_std
value: -10.0261
- type: nauc_mrr_at_20_diff1
value: 69.5455
- type: nauc_mrr_at_100_max
value: 57.735499999999995
- type: nauc_mrr_at_100_std
value: -9.8413
- type: nauc_mrr_at_100_diff1
value: 69.5846
- type: nauc_mrr_at_1000_max
value: 57.7313
- type: nauc_mrr_at_1000_std
value: -9.8523
- type: nauc_mrr_at_1000_diff1
value: 69.6076
- type: main_score
value: 74.72
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: similarity_accuracy
value: 99.798
- type: similarity_accuracy_threshold
value: 92.7546
- type: similarity_f1
value: 89.441
- type: similarity_f1_threshold
value: 92.7546
- type: similarity_precision
value: 92.70389999999999
- type: similarity_recall
value: 86.4
- type: similarity_ap
value: 95.40729999999999
- type: cosine_accuracy
value: 99.798
- type: cosine_accuracy_threshold
value: 92.7546
- type: cosine_f1
value: 89.441
- type: cosine_f1_threshold
value: 92.7546
- type: cosine_precision
value: 92.70389999999999
- type: cosine_recall
value: 86.4
- type: cosine_ap
value: 95.40729999999999
- type: manhattan_accuracy
value: 99.795
- type: manhattan_accuracy_threshold
value: 851.3785
- type: manhattan_f1
value: 89.5464
- type: manhattan_f1_threshold
value: 902.8005999999999
- type: manhattan_precision
value: 88.3268
- type: manhattan_recall
value: 90.8
- type: manhattan_ap
value: 95.3814
- type: euclidean_accuracy
value: 99.798
- type: euclidean_accuracy_threshold
value: 38.0669
- type: euclidean_f1
value: 89.441
- type: euclidean_f1_threshold
value: 38.0669
- type: euclidean_precision
value: 92.70389999999999
- type: euclidean_recall
value: 86.4
- type: euclidean_ap
value: 95.4074
- type: dot_accuracy
value: 99.798
- type: dot_accuracy_threshold
value: 92.7546
- type: dot_f1
value: 89.441
- type: dot_f1_threshold
value: 92.7546
- type: dot_precision
value: 92.70389999999999
- type: dot_recall
value: 86.4
- type: dot_ap
value: 95.4074
- type: max_accuracy
value: 99.798
- type: max_f1
value: 89.5464
- type: max_precision
value: 92.70389999999999
- type: max_recall
value: 90.8
- type: max_ap
value: 95.4074
- type: main_score
value: 95.4074
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 70.3156
- type: v_measure_std
value: 3.9677
- type: main_score
value: 70.3156
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.4198
- type: v_measure_std
value: 1.5537
- type: main_score
value: 35.4198
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.522099999999995
- type: mrr
value: 55.500099999999996
- type: nAUC_map_max
value: 7.9342
- type: nAUC_map_std
value: 6.8542000000000005
- type: nAUC_map_diff1
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value: 42.1774
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value: 14.072499999999998
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value: 38.3755
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value: 58.458299999999994
- type: nauc_map_at_100_diff1
value: -7.320200000000001
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value: 43.0209
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value: 72.8673
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value: -29.952299999999997
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value: 26.8022
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value: 29.149900000000002
- type: nauc_recall_at_3_std
value: 27.2806
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value: 16.0975
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value: 29.3013
- type: nauc_recall_at_5_std
value: 26.4035
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value: 20.3157
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value: 27.326099999999997
- type: nauc_recall_at_10_std
value: 30.1061
- type: nauc_recall_at_10_diff1
value: 22.0122
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value: 25.176399999999997
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value: 33.1536
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value: -6.1627
- type: nauc_recall_at_1000_max
value: 33.4423
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- type: nauc_recall_at_1000_diff1
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- type: nauc_precision_at_1_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_5_std
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value: 36.3251
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value: 44.9613
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value: 18.5485
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value: 69.259
- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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value: 23.469
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value: 22.555
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value: 20.97
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- type: precision_at_1000
value: 1.547
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value: 26.5306
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value: 38.7755
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value: 40.6122
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value: 41.3994
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value: 42.7601
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value: 43.0467
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value: -2.2094
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value: -10.705
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value: 48.9062
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value: -8.4789
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value: -11.4192
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value: 3.2409
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value: -2.4743
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value: -21.8691
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value: 12.0827
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value: -7.6671000000000005
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value: -29.541600000000003
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value: 18.4544
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value: -4.9384
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value: -13.991700000000002
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value: 80.9784
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value: 0.1001
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value: 35.3463
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value: -16.8628
- type: nauc_mrr_at_1_max
value: -23.163800000000002
- type: nauc_mrr_at_1_std
value: -15.9221
- type: nauc_mrr_at_1_diff1
value: -1.0075
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value: -37.628099999999996
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value: -13.678199999999999
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value: -8.0387
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value: -38.205
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value: -10.0574
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value: -7.273300000000001
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value: -38.2773
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value: -10.5208
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value: -7.556400000000001
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value: -38.8068
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value: -10.7195
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value: -6.7631
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value: -38.318200000000004
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value: -10.854999999999999
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value: -6.843000000000001
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value: -38.318200000000004
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value: -10.854999999999999
- type: nauc_mrr_at_1000_diff1
value: -6.843000000000001
- type: main_score
value: 20.147000000000002
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 59.7607
- type: f1
value: 45.7266
- type: f1_weighted
value: 68.3382
- type: ap
value: 9.8682
- type: ap_weighted
value: 9.8682
- type: main_score
value: 59.7607
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 53.3192
- type: f1
value: 53.505100000000006
- type: f1_weighted
value: 52.726600000000005
- type: main_score
value: 53.3192
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.3133
- type: v_measure_std
value: 1.6674000000000002
- type: main_score
value: 48.3133
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: similarity_accuracy
value: 82.2972
- type: similarity_accuracy_threshold
value: 92.5986
- type: similarity_f1
value: 58.2994
- type: similarity_f1_threshold
value: 89.689
- type: similarity_precision
value: 53.3772
- type: similarity_recall
value: 64.2216
- type: similarity_ap
value: 60.9374
- type: cosine_accuracy
value: 82.2972
- type: cosine_accuracy_threshold
value: 92.5986
- type: cosine_f1
value: 58.2994
- type: cosine_f1_threshold
value: 89.689
- type: cosine_precision
value: 53.3772
- type: cosine_recall
value: 64.2216
- type: cosine_ap
value: 60.9374
- type: manhattan_accuracy
value: 82.2912
- type: manhattan_accuracy_threshold
value: 839.1809000000001
- type: manhattan_f1
value: 58.2447
- type: manhattan_f1_threshold
value: 996.9049
- type: manhattan_precision
value: 53.74830000000001
- type: manhattan_recall
value: 63.562
- type: manhattan_ap
value: 60.8808
- type: euclidean_accuracy
value: 82.2972
- type: euclidean_accuracy_threshold
value: 38.4743
- type: euclidean_f1
value: 58.2994
- type: euclidean_f1_threshold
value: 45.4114
- type: euclidean_precision
value: 53.3772
- type: euclidean_recall
value: 64.2216
- type: euclidean_ap
value: 60.9374
- type: dot_accuracy
value: 82.2972
- type: dot_accuracy_threshold
value: 92.5986
- type: dot_f1
value: 58.2994
- type: dot_f1_threshold
value: 89.689
- type: dot_precision
value: 53.3772
- type: dot_recall
value: 64.2216
- type: dot_ap
value: 60.9374
- type: max_accuracy
value: 82.2972
- type: max_f1
value: 58.2994
- type: max_precision
value: 53.74830000000001
- type: max_recall
value: 64.2216
- type: max_ap
value: 60.9374
- type: main_score
value: 60.9374
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 87.2162
- type: similarity_accuracy_threshold
value: 91.6164
- type: similarity_f1
value: 74.8086
- type: similarity_f1_threshold
value: 90.18260000000001
- type: similarity_precision
value: 69.3065
- type: similarity_recall
value: 81.25959999999999
- type: similarity_ap
value: 82.53160000000001
- type: cosine_accuracy
value: 87.2162
- type: cosine_accuracy_threshold
value: 91.6164
- type: cosine_f1
value: 74.8086
- type: cosine_f1_threshold
value: 90.18260000000001
- type: cosine_precision
value: 69.3065
- type: cosine_recall
value: 81.25959999999999
- type: cosine_ap
value: 82.53160000000001
- type: manhattan_accuracy
value: 87.21039999999999
- type: manhattan_accuracy_threshold
value: 899.2865999999999
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value: 74.77510000000001
- type: manhattan_f1_threshold
value: 962.114
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value: 70.6927
- type: manhattan_recall
value: 79.3579
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value: 82.5262
- type: euclidean_accuracy
value: 87.2162
- type: euclidean_accuracy_threshold
value: 40.9478
- type: euclidean_f1
value: 74.8086
- type: euclidean_f1_threshold
value: 44.3112
- type: euclidean_precision
value: 69.3065
- type: euclidean_recall
value: 81.25959999999999
- type: euclidean_ap
value: 82.53160000000001
- type: dot_accuracy
value: 87.2162
- type: dot_accuracy_threshold
value: 91.6164
- type: dot_f1
value: 74.8086
- type: dot_f1_threshold
value: 90.18260000000001
- type: dot_precision
value: 69.3065
- type: dot_recall
value: 81.25959999999999
- type: dot_ap
value: 82.53160000000001
- type: max_accuracy
value: 87.2162
- type: max_f1
value: 74.8086
- type: max_precision
value: 70.6927
- type: max_recall
value: 81.25959999999999
- type: max_ap
value: 82.53160000000001
- type: main_score
value: 82.53160000000001
---
# Granite-Embedding-125m-English
**Model Summary:**
Granite-Embedding-125m-English is a 125M 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 768. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus 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 and knowledge distillation.
- **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-125m-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-125m-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-125m-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-125m-english uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
```
**Evaluation:**
The performance of the Granite-Embedding-125M-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-125m-english |125 |768 |52.3 |50.3 |
**Model Architecture:**
Granite-Embedding-125m-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-125m-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"
] |
BSC-LT/salamandra-7b-instruct
|
BSC-LT
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"dataset:oscar-corpus/colossal-oscar-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:joelniklaus/eurlex_resources",
"dataset:joelito/legal-mc4",
"dataset:projecte-aina/CATalog",
"dataset:UFRGS/brwac",
"dataset:community-datasets/hrwac",
"dataset:danish-foundation-models/danish-gigaword",
"dataset:HiTZ/euscrawl",
"dataset:PleIAs/French-PD-Newspapers",
"dataset:PleIAs/French-PD-Books",
"dataset:AI-team-UoA/greek_legal_code",
"dataset:HiTZ/latxa-corpus-v1.1",
"dataset:allenai/peS2o",
"dataset:pile-of-law/pile-of-law",
"dataset:PORTULAN/parlamento-pt",
"dataset:hoskinson-center/proof-pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/starcoderdata",
"dataset:bjoernp/tagesschau-2018-2023",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2502.08489",
"arxiv:2403.14009",
"arxiv:2403.20266",
"arxiv:2101.00027",
"arxiv:2207.00220",
"arxiv:1810.06694",
"arxiv:1911.05507",
"arxiv:1906.03741",
"arxiv:2406.17557",
"arxiv:2402.06619",
"arxiv:1803.09010",
"base_model:BSC-LT/salamandra-7b",
"base_model:finetune:BSC-LT/salamandra-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T11:13:21 | 2025-02-20T16:48:03 | 12,988 | 54 |
---
base_model:
- BSC-LT/salamandra-7b
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
language:
- bg
- ca
- code
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nn
- \no
- oc
- pl
- pt
- ro
- ru
- sh
- sk
- sl
- sr
- sv
- uk
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Salamandra Model Card
This repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).
Salamandra is a highly multilingual model pre-trained from scratch that comes in three different
sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants.
This model card corresponds to the 7B instructed version.
To visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index).
The entire Salamandra family is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
Along with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra).
> [!WARNING]
> **DISCLAIMER:** This model is a first proof-of-concept designed to demonstrate the instruction-following capabilities of recently released base models.
> It has been optimized to engage in conversation but has *NOT* been aligned through RLHF to filter or avoid sensitive topics.
> As a result, it may generate harmful or inappropriate content.
> The team is actively working to enhance its performance through further instruction and alignment with RL techniques.
---
## Model Details
### Description
Transformer-based decoder-only language model that has been pre-trained from scratch on 12.875 trillion tokens of highly curated data.
The pre-training corpus contains text in 35 European languages and code.
### Hyperparameters
The full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/blob/main/configs/bsc_7b.yaml).
### Architecture
| | |
|-------------------------|:--------------|
| Total Parameters | 7,768,117,248 |
| Embedding Parameters | 1,048,576,000 |
| Layers | 32 |
| Hidden size | 4,096 |
| Attention heads | 32 |
| Context length | 8,192 |
| Vocabulary size | 256,000 |
| Precision | bfloat16 |
| Embedding type | RoPE |
| Activation Function | SwiGLU |
| Layer normalization | RMS Norm |
| Flash attention | ✅ |
| Grouped Query Attention | ✅ |
| Num. query groups | 8 |
---
## Intended Use
### Direct Use
The models are intended for both research and commercial use in any of the languages included in the training data.
The base models are intended either for language generation or to be further fine-tuned for specific use-cases.
The instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations.
### Out-of-scope Use
The model is not intended for malicious activities, such as harming others or violating human rights.
Any downstream application must comply with current laws and regulations.
Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged.
---
## Hardware and Software
### Training Framework
Pre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html),
which leverages PyTorch Lightning for efficient model training in highly distributed settings.
The instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat).
### Compute Infrastructure
All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and
operated by Barcelona Supercomputing Center.
The accelerated partition is composed of 1,120 nodes with the following specifications:
- 4x Nvidia Hopper GPUs with 64GB HBM2 memory
- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)
- 4x NDR200 (BW per node 800Gb/s)
- 512 GB of Main memory (DDR5)
- 460GB on NVMe storage
|Model|Nodes|GPUs|
|:---:|:---:|:---:|
|2B|64|256|
|7B|128|512|
|40B|256 / 512|1,024 / 2,048|
---
## How to use
The instruction-following models use the commonly adopted ChatML template:
```jinja
{%- if messages[0]['role'] == 'system' %}{%- set system_message = messages[0]['content'] %}{%- set loop_messages = messages[1:] %}{%- else %}{%- set system_message = 'SYSTEM MESSAGE' %}{%- set loop_messages = messages %}{%- endif %}{%- if not date_string is defined %}{%- set date_string = '2024-09-30' %}{%- endif %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% for message in loop_messages %}{%- if (message['role'] != 'user') and (message['role'] != 'assistant')%}{{ raise_exception('Only user and assitant roles are suported after the initial optional system message.') }}{% endif %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}
```
Where `system_message` is used to guide the model during generation and `date_string` can be set to allow the model to respond with the current date.
The exact same chat template should be used for an enhanced conversational experience.
The easiest way to apply it is by using the tokenizer's built-in functions, as shown in the following snippet.
```python
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "BSC-LT/salamandra-7b-instruct"
text = "At what temperature does water boil?"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
message = [ { "role": "user", "content": text } ]
date_string = datetime.today().strftime('%Y-%m-%d')
prompt = tokenizer.apply_chat_template(
message,
tokenize=False,
add_generation_prompt=True,
date_string=date_string
)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Using this template, each turn is preceded by a `<|im_start|>` delimiter and the role of the entity
(either `user`, for content supplied by the user, or `assistant` for LLM responses), and finished with the `<|im_end|>` token.
---
## Data
### Pretraining Data
The pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below.
The initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation
and give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half,
Spanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.
During the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset.
This adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:
The pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens.
Following this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%.
Other notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%.
These major sources collectively form the bulk of the corpus, ensuring a rich and diverse dataset for training the language model.
The remaining 10% comes from smaller sources in various languages.
Feel free to click the expand button below to see the full list of sources.
<details>
<summary>Data Sources</summary>
| Dataset | Language | Source |
|---|---|---|
| Colossal OSCAR 1.0 | bg, ca, cs, cy, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, oc, pl, pt, ro, ru, sh, sk, sl, sr, sv, uk | Brack et al., 2024 |
| Aya Dataset (w/o Evaluation Suite) | eu, hr, nl, fi, ka, hu, lt, nn, ro, sk, lv, cy, bg, cs, en, fr, de, ga, mt, pl, ru, sl, sv, ca, da, et, gl, el, it, no, pt, sr, es, uk | Singh et al., 2024 |
| Wikimedia dumps | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, pl, pt, ro, sh, sk, sl, sr, uk | [Link](https://dumps.wikimedia.org/) |
| OpenSubtitles v2016 | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, gl, hr, it, lt, lv, nl, no, pl, pt, ro, sk, sl, sr, sv, uk | Lison & Tiedemann, 2016 |
| EurLEX-Resources | bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelniklaus/eurlex_resources) |
| MC4-Legal | bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelito/legal-mc4) |
| Parlamint | at, bg, cz, dk, ee, es, es-ga, fi, fr, gb, gr, hr, hu, it, lv, nl, no, pl, pt, rs, se, si | Erjavec et al., 2021 |
| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |
| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |
| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |
| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |
| BIGPATENT | en | Sharma et al., 2019 |
| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |
| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |
| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
| CaBeRnet | fr | Popa-Fabre et al., 2020 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
| DaNewsroom | da | Varab & Schluter, 2020 |
| Danish GigaWord | da | Strømberg-Derczynski et al., 2021 |
| DK-CLARIN Reference Corpus of General Danish | da | [Link](https://korpus.dsl.dk/clarin/) |
| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |
| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |
| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |
| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |
| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |
| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |
| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |
| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |
| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |
| HPLT v1 - Spanish | es | de Gibert et al., 2024 |
| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |
| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |
| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |
| Korpus Malti | mt | Micallef et al., 2022 |
| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |
| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |
| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |
| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |
| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |
| Math AMPS | en | Hendrycks et al., 2021 |
| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |
| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |
| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |
| ParlamentoPT | pt | Rodrigues et al., 2023 |
| peS2o | en | Soldaini & Lo, 2023 |
| PG-19 | en | Rae et al., 2019 |
| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |
| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |
| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |
| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |
| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |
| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |
| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |
| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |
| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |
| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |
| Starcoder | code | Li et al., 2023 |
| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |
| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |
| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |
| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |
| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |
| The Pile (PhilPapers) | en | Gao et al., 2021 |
| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |
| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |
| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
<details>
<summary>References</summary>
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</details>
</details>
The model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part
of the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch;
and 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 trillion tokens.
We provide an extense Datasheet section following the best practices defined by [(Gebru et al., 2021)](https://arxiv.org/pdf/1803.09010).
<details>
<summary>Datasheet</summary>
#### Motivation
**For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.**
The purpose of creating this dataset is to pre-train the Salamandra family of multilingual models with high performance in a large number of European languages (35)
and programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample
these languages by a factor of 2.
There is a great lack of massive multilingual data, especially in minority languages (Ostendorff & Rehm, 2023), so part of our efforts in the creation of
this pre-training dataset have resulted in the contribution to large projects such as the Community OSCAR (Brack et al., 2024), which includes 151 languages
and 40T words, or CATalog (Palomar-Giner et al., 2024), the largest open dataset in Catalan in the world.
**Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?**
The dataset has been created by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS),
which aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, it was created by
the unit's data team, the main contributors being José Javier Saiz, Ferran Espuña and Jorge Palomar.
However, the creation of the dataset would not have been possible without the collaboration of a large number of collaborators, partners and public institutions,
which can be found in detail in the acknowledgements.
**Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.**
This work has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
#### Composition
**What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.**
The dataset consists entirely of text documents in various languages. Specifically, data was mainly sourced from the following databases and
repositories:
- **Common Crawl:** Repository that holds website data and is run by the Common Crawl non-profit organization. It is updated monthly and is
distributed under the CC0 1.0 public domain license.
- **GitHub:** Community platform that allows developers to create, store, manage, and share their code. Repositories are crawled and then
distributed with their original licenses, which may vary from permissive to non-commercial licenses.
- **Wikimedia:** Database that holds the collection databases managed by the Wikimedia Foundation, including Wikipedia, Wikibooks, Wikinews,
Wikiquote, Wikisource, and Wikivoyage. It is updated monthly and is distributed under Creative Commons Attribution-ShareAlike License 4.0.
- **EurLex:** Repository that holds the collection of legal documents from the European Union, available in all of the EU’s 24 official
languages and run by the Publications Office of the European Union. It is updated daily and is distributed under the Creative Commons
Attribution 4.0 International license.
- **Other repositories:** Specific repositories were crawled under permission for domain-specific corpora, which include academic, legal,
and newspaper repositories.
We provide a complete list of dataset sources at the end of this section.
**How many instances are there in total (of each type, if appropriate)?**
The dataset contains a diverse range of instances across multiple languages, with notable adjustments for certain languages. English
represents the largest portion, accounting for 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,
while Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled
by half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian
(4.59%), with several additional languages contributing between 1% and 2%, and smaller portions represented by a variety of others.
**Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).**
The dataset is a sample from multiple sources, with different weights based on the primary language of the content: Spanish, Catalan,
Basque, and Galician content was upsampled by a factor of two, while programming languages were downsampled by a factor of half. Other
sources were sampled in proportion to their occurrence.
**What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.**
Each instance consists of a text document processed for deduplication, language identification, and source-specific filtering. Some documents required
optical character recognition (OCR) to extract text from non-text formats such as PDFs.
**Is there a label or target associated with each instance? If so, please provide a description.**
Each instance is labelled with a unique identifier, the primary language of the content, and the URL for web-sourced instances. Additional labels were
automatically assigned to detect specific types of content -harmful or toxic content- and to assign preliminary indicators of undesired qualities -very
short documents, high density of symbols, etc.- which were used for filtering instances.
**Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.**
No significant information is missing from the instances.
**Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.**
Instances are related through shared metadata, such as source and language identifiers.
**Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.**
The dataset is randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.
**Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.**
Despite removing duplicated instances within each source, redundancy remains at the paragraph and sentence levels, particularly in web-sourced
instances where search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also duplicated
across sources due to format variations.
**Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.**
The dataset is self-contained and does not rely on external resources.
**Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.**
The dataset does not contain confidential data.
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.**
The dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). Although
pre-processing techniques were applied to mitigate offensive content, the heterogeneity and scale of web-sourced data make exhaustive
filtering challenging, which makes it next to impossible to identify all adult content without falling into excessive filtering, which may
negatively influence certain demographic groups (Dodge et al., 2021).
**Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.**
The dataset does not explicitly identify any subpopulations.
**Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.**
Web-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names,
IP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple
data points, the nature and scale of web data makes it difficult to parse such information. In any case, efforts are made to filter or anonymize
sensitive data (Mina et al., 2024), but some identifiable information may remain in the dataset.
**Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.**
Given that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial
information, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023),
especially if the content originates from less-regulated sources or user-generated platforms.
#### Collection Process
**How was the data collected?**
This dataset is constituted by combining several sources, whose acquisition methods can be classified into three groups:
- Web-sourced datasets with some preprocessing available under permissive license.
- Domain-specific or language-specific raw crawls.
- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).
**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**
The data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:
- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.
- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.
- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.
**If the dataset is a sample from a larger set, what was the sampling strategy?**
The sampling strategy was to use the whole dataset resulting from the filtering explained in the 'preprocessing/cleaning/labelling' section,
with the particularity that an upsampling of 2 (i.e. twice the probability of sampling a document) was performed for the co-official languages
of Spain (Spanish, Catalan, Galician, Basque), and a downsampling of 1/2 was applied for code (half the probability of sampling a code document,
evenly distributed among all programming languages).
**Who was involved in the data collection process and how were they compensated?**
This data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely
by members of the Language Technologies data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary
consideration for acquiring data from suppliers.
**Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.**
Data were acquired and processed from April 2023 to April 2024. However, as mentioned, much data has been obtained from open projects such
as Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important.
**Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.**
No particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an
internal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència
Artificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an
ethical and legal point of view, respectively.
#### Preprocessing
**Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.**
No changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:
- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.
- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\_pp' field, a perplexity-based score generated by a language model.
**Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.**
The original raw data was not kept.
**Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.**
Yes, the preprocessing and filtering software is open-sourced. The [CURATE](https://github.com/langtech-bsc/CURATE) pipeline was used for CATalog and other curated datasets,
and the [Ungoliant](https://github.com/oscar-project/ungoliant) pipeline was used for the OSCAR project.
#### Uses
**Has the dataset been used for any tasks already? If so, please provide a description.**
Pre-train the Salamandra model family.
**What (other) tasks could the dataset be used for?**
The data can be used primarily to pre-train other language models, which can then be used for a wide range of use cases. The dataset could
also be used for other tasks such as fine-tuning language models, cross-lingual NLP tasks, machine translation, domain-specific text
generation, and language-specific data analysis.
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?**
Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages.
Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic
groups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures,
acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to
address privacy concerns and contribute to a more inclusive linguistic dataset.
**Are there tasks for which the dataset should not be used?**
-
#### Distribution
**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.**
The dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section.
#### Maintenance
**Who will be supporting/hosting/maintaining the dataset?**
The dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure
regular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are
responsible for.
**How can the owner/curator/manager of the dataset be contacted?**
The data owner may be contacted with the email address [email protected].
**Will the dataset be updated?**
The dataset will not be updated.
**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.**
The dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in
web-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an
individual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to
remove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues.
**Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.**
Since the dataset will not be updated, only the final version will be kept.
**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**
The dataset does not allow for external contributions.
</details>
### Finetuning Data
This instructed-tuned variant has been fine-tuned with a collection of 273k instructions, focusing on the performance of Catalan, English and Spanish. However, instruction data for other closely related Iberian languages has also been included, since it yielded a positive impact on the languages of interest. That said, the performance in these additional languages is not guaranteed due to the limited amount of available data and the lack of resources for thorough testing.
| **Dataset** | **ca** | **en** | **es** | **eu** | **gl** | **pt** | **Total** |
|----------------------|------------|-------------|------------|-----------|---------|------------|-------------|
| alpaca-cleaned | | 49,950 | | | | | **49,950** |
| aya-dataset | | 3,941 | 3,851 | 939 | | 8,995 | **17,726** |
| coqcat | 4,797 | | | | | | **4,797** |
| databricks-dolly-15k | | 15,011 | | | | | **15,011** |
| dolly-ca | 3,232 | | | | | | **3,232** |
| flores-dev | 986 | 1,037 | 1,964 | 493 | 505 | | **4,985** |
| mentor-ca | 7,119 | | | | | | **7,119** |
| mentor-es | | | 7,122 | | | | **7,122** |
| no-robots | | 9,485 | | | | | **9,485** |
| oasst-ca | 2,517 | | | | | | **2,517** |
| oasst2 | 750 | 31,086 | 15,438 | 190 | 197 | 1,203 | **48,864** |
| open-orca | | 49,996 | | | | | **49,996** |
| rag-multilingual | 16,043 | 14,997 | 11,263 | | | | **42,303** |
| tower-blocks | | 7,762 | 1,000 | | | 1,000 | **9,762** |
| **Total** | **35,444** | **183,265** | **40,638** | **1,622** | **702** | **11,198** | **272,869** |
---
## Evaluation
### Gold-standard benchmarks
WiP
<!--
Evaluation is done using the Language Model Evaluation Harness (Gao et al., 2024). We evaluate on a set of tasks taken from [SpanishBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/spanish_bench), [CatalanBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/catalan_bench), [BasqueBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/basque_bench) and [GalicianBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/galician_bench). These benchmarks include both new and existing tasks and datasets. Given that this is an instructed model, we add LM Evaluation Harness's native feature of `chat-template` to the setup. In the tables below, we include the results in a selection of evaluation datasets that represent model's performance across a variety of tasks within these benchmarks.
We only use tasks that are either human generated, human translated, or with a strong human-in-the-loop (i.e., machine translation followed by professional revision or machine generation followed by human revision and annotation). This is the reason behind the variety in number of tasks reported across languages. As more tasks that fulfill these requirements are published, we will update the presented results. We also intend to expand the evaluation to other languages, as long as the datasets meet our quality standards.
During the implementation of the evaluation we observed a series of issues worth considering when replicating and interpreting the results presented. These issues include ≈1.5% variances in performance in some tasks depending on the version of the `transformers` library used, and depending on the use (or lack of use) of tensor parallelism when loading a model. When implementing existing tasks, we carry out a comprehensive quality evaluation of the dataset, the Harness task itself, and what kind of input models see during evaluation. Our implementation (see links above) addresses multiple existing problems such as errors in datasets and prompts, and lack of pre-processing. All this means that results will vary if using other Harness implementations, and may slightly vary depending on the replication setup.
It should be noted that these results are subject to all the drawbacks of every current gold-standard evaluation, and that the figures do not fully represent the model's capabilities and potential. We thus advise caution when reading and interpreting the results.
A full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.
All results reported below are on a 0-shot setting.
#### Spanish
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td>Commonsense Reasoning</td>
<td>xstorycloze_es</td>
<td>acc</td>
<td>73.13</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_es</td>
<td>acc</td>
<td>60.56</td>
</tr>
<tr>
<td>xnli_es</td>
<td>acc</td>
<td>50.84</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws_es</td>
<td>acc</td>
<td>60.75</td>
</tr>
<tr>
<td>QA</td>
<td>xquad_es</td>
<td>acc</td>
<td>63.20/td>
</tr>
<tr>
<td>Translation</td>
<td>flores_es</td>
<td>bleu</td>
<td>14.95</td>
</tr>
</tbody>
</table>
#### Catalan
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa_ca</td>
<td>acc</td>
<td>82.80</td>
</tr>
<tr>
<td>xstorycloze_ca</td>
<td>acc</td>
<td>73.73</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_ca</td>
<td>acc</td>
<td>64.79</td>
</tr>
<tr>
<td>xnli_ca</td>
<td>acc</td>
<td>53.45</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafraseja</td>
<td>acc</td>
<td>64.15</td>
</tr>
<tr>
<td>paws_ca</td>
<td>acc</td>
<td>64.35</td>
</tr>
<tr>
<td rowspan="5">QA</td>
<td>arc_ca_easy</td>
<td>acc</td>
<td>73.57</td>
</tr>
<tr>
<td>arc_ca_challenge</td>
<td>acc</td>
<td>45.90</td>
</tr>
<tr>
<td>openbookqa_ca</td>
<td>acc</td>
<td>40.60</td>
</tr>
<tr>
<td>piqa_ca</td>
<td>acc</td>
<td>73.39</td>
</tr>
<tr>
<td>siqa_ca</td>
<td>acc</td>
<td>51.84</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_ca</td>
<td>bleu</td>
<td>20.49</td>
</tr>
</tbody></table>
#### Basque
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>xcopa_eu</td>
<td>acc</td>
<td>67.80</td>
</tr>
<tr>
<td>xstorycloze_eu</td>
<td>acc</td>
<td>65.06</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_eu</td>
<td>acc</td>
<td>56.34</td>
</tr>
<tr>
<td>xnli_eu</td>
<td>acc</td>
<td>47.34</td>
</tr>
<tr>
<td rowspan="3">QA</td>
<td>eus_exams</td>
<td>acc</td>
<td>45.98</td>
</tr>
<tr>
<td>eus_proficiency</td>
<td>acc</td>
<td>43.92</td>
</tr>
<tr>
<td>eus_trivia</td>
<td>acc</td>
<td>50.38</td>
</tr>
<tr>
<td>Reading Comprehension</td>
<td>eus_reading</td>
<td>acc</td>
<td>48.01</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_eu</td>
<td>bleu</td>
<td>10.99</td>
</tr>
</tbody></table>
#### Galician
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafrases_gl</td>
<td>acc</td>
<td>58.50</td>
</tr>
<tr>
<td>paws_gl</td>
<td>acc</td>
<td>62.45</td>
</tr>
<tr>
<td>QA</td>
<td>openbookqa_gl</td>
<td>acc</td>
<td>37.20</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_gl</td>
<td>bleu</td>
<td>18.81</td>
</tr>
</tbody>
</table>
-->
### LLM-as-a-judge
We use [Prometheus-2 8x7B](https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0) as a judge to evaluate the responses of the model. Tasks are created from existing multilingual evaluation datasets covering the same categories as the ones measured in our gold-standard benchmarks. We randomly select a subset of 250 instances per language from the `test` set of each source dataset. To evaluate the responses of our model, we use task-specific criteria developed in-house for the _LLM-judge_ to use. Each criterion is measured either as a 5-point Likert scale or as a binary task depending on the idiosyncrasy of the task and criterion.
Prompts for each task are created in various ways to score the model's robustness in addition to these criteria. This is done by presenting the same source instance within three different prompts. We then calculate the variance between the scores assigned by the _LLM-judge_ to our model's responses to the three prompt styles and average it across all instances. Prompts are human translated to all languages measured. We do not provide the _LLM-judge_ with a reference answer.
The _judge_ prompt we use during evaluation is the same used to fine tune the Prometheus-2 family. We keep the _judge_ prompt and criteria used to present the _LLM-judge_ with the task prompts and model responses in English for evaluation across languages. The _judge_ prompt used is:
```python
"You are a fair judge assistant tasked with providing clear, objective feedback based on specific criteria, ensuring each assessment reflects the absolute standards set for performance.
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between {a} and {b}. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between {a} and {b})\"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{input}
###Response to evaluate:
{prediction}
###Score Rubrics:
{criteria}
###Feedback:"
```
As an example, prompts for the Math task in English are based on instances from [MGSM](https://huggingface.co/datasets/juletxara/mgsm), and each instance is presented within these prompts:
```python
"en": [
("I need help with this math problem: \"", "\" Give me the answer step by step and also the final result separately."),
("Can you please help me answer this? \"", "\" Explain the answer and give me the final result as well. Thanks."),
("Help me with this problem: \"", "\" I need the answer explained and the final result separately.")
]
```
This task is then evaluated by the _LLM-judge_ using two criteria, reasoning capability (5-point Likert) and mathematical correctness (binary):
```python
reasoning_capability_criteria = {
"reasoning_capability": """
[Does the model's answer demonstrate reasoning capability?]
Score 1: The answer demonstrates poor reasoning, with illogical arguments or conclusions that do not follow from the provided information.
Score 2: The answer shows weak reasoning, with some logical connections but also contains significant flaws or gaps in the argumentation.
Score 3: The answer demonstrates adequate reasoning, with generally logical arguments, but may have minor flaws or a lack of depth in the reasoning process.
Score 4: The answer shows strong reasoning, with well-structured arguments and conclusions that logically follow from the information provided.
Score 5: The answer demonstrates exceptional reasoning, with clear, coherent, and insightful arguments that are logically sound and well-supported by the information provided."""
}
mathematical_correctness_binary_criteria = {
"mathematical_correctness_binary": """
[Is the model's answer mathematically correct?]
Score 0: The answer contains mathematical errors that render the solution incorrect or unreliable.
Score 1: The answer is mathematically correct, with accurate calculations and appropriate use of mathematical concepts."""
}
```
#### Multilingual results
Here, we present results for seven categories of tasks in Spanish, Catalan, Basque, Galician, and English. Results are presented for each task, criterion and language. Criteria with a `(B)` after their name are binary criteria (i.e., numbers go from 0 to 1, where 1 is best). The rest of the criteria are measured using a 5-point Likert scale, where 5 is best. The first number of the pair of numbers separated by `/` shows the average score for the criterion (and language). The second number of each pair is the robustness score, where numbers closer to 0 mean that the model generates similar responses when comparing the three prompt varieties for a single instance.
Further details on all tasks and criteria, a full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.
<table class="tg"><thead>
<tr>
<th class="tg-0pky"><span style="font-weight:bold">Category</span></th>
<th class="tg-0pky"><span style="font-weight:bold">Dataset</span></th>
<th class="tg-0pky"><span style="font-weight:bold">Criteria</span></th>
<th class="tg-0pky"><span style="font-weight:bold">es</span></th>
<th class="tg-0pky"><span style="font-weight:bold">ca</span></th>
<th class="tg-0pky"><span style="font-weight:bold">gl</span></th>
<th class="tg-0pky"><span style="font-weight:bold">eu</span></th>
<th class="tg-0pky"><span style="font-weight:bold">en</span></th>
</tr></thead>
<tbody>
<tr>
<td class="tg-0pky">Commonsense Reasoning</td>
<td class="tg-0pky">XStoryCloze</td>
<td class="tg-0pky">Ending coherence</td>
<td class="tg-0pky">3.24/0.63</td>
<td class="tg-0pky">3.12/0.51</td>
<td class="tg-0pky">2.87/0.59</td>
<td class="tg-0pky">2.16/0.52</td>
<td class="tg-0pky">3.71/0.50</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="3">Paraphrasing</td>
<td class="tg-0pky" rowspan="3">PAWS</td>
<td class="tg-0pky">Completeness `(B)`</td>
<td class="tg-0pky">0.86/0.07</td>
<td class="tg-0pky">0.82/0.09</td>
<td class="tg-0pky">0.78/0.10</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">0.92/0.05</td>
</tr>
<tr>
<td class="tg-0pky">Paraphrase generation</td>
<td class="tg-0pky">3.81/0.54</td>
<td class="tg-0pky">3.67/0.55</td>
<td class="tg-0pky">3.56/0.57</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.98/0.37</td>
</tr>
<tr>
<td class="tg-0pky">Grammatical correctness `(B)`</td>
<td class="tg-0pky">0.93/0.03</td>
<td class="tg-0pky">0.92/0.05</td>
<td class="tg-0pky">0.89/0.06</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">0.96/0.03</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Reading Comprehension</td>
<td class="tg-0pky" rowspan="2">Belebele</td>
<td class="tg-0pky">Passage comprehension</td>
<td class="tg-0pky">3.43/0.43</td>
<td class="tg-0pky">3.28/0.50</td>
<td class="tg-0pky">3.02/0.56</td>
<td class="tg-0pky">2.61/0.43</td>
<td class="tg-0pky">3.43/0.58</td>
</tr>
<tr>
<td class="tg-0pky">Answer relevance `(B)`</td>
<td class="tg-0pky">0.86/0.05</td>
<td class="tg-0pky">0.84/0.05</td>
<td class="tg-0pky">0.75/0.08</td>
<td class="tg-0pky">0.65/0.11</td>
<td class="tg-0pky">0.83/0.06</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Extreme Summarization</td>
<td class="tg-0pky" rowspan="2">XLSum & caBreu & summarization_gl</td>
<td class="tg-0pky">Informativeness</td>
<td class="tg-0pky">3.37/0.34</td>
<td class="tg-0pky">3.57/0.31</td>
<td class="tg-0pky">3.40/0.31</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.32/0.26</td>
</tr>
<tr>
<td class="tg-0pky">Conciseness</td>
<td class="tg-0pky">3.06/0.34</td>
<td class="tg-0pky">2.88/0.50</td>
<td class="tg-0pky">3.09/0.38</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.32/0.22</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Math</td>
<td class="tg-0pky" rowspan="2">MGSM</td>
<td class="tg-0pky">Reasoning capability</td>
<td class="tg-0pky">3.29/0.72</td>
<td class="tg-0pky">3.16/0.65</td>
<td class="tg-0pky">3.33/0.60</td>
<td class="tg-0pky">2.56/0.52</td>
<td class="tg-0pky">3.35/0.65</td>
</tr>
<tr>
<td class="tg-0pky">Mathematical correctness `(B)`</td>
<td class="tg-0pky">0.68/0.12</td>
<td class="tg-0pky">0.65/0.13</td>
<td class="tg-0pky">0.73/0.11</td>
<td class="tg-0pky">0.59/0.13</td>
<td class="tg-0pky">0.67/0.12</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Translation form Language</td>
<td class="tg-0pky" rowspan="2">FLORES-200</td>
<td class="tg-0pky">Fluency</td>
<td class="tg-0pky">3.95/0.11</td>
<td class="tg-0pky">3.88/0.15</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.92/0.14</td>
</tr>
<tr>
<td class="tg-0pky">Accuracy</td>
<td class="tg-0pky">4.22/0.15</td>
<td class="tg-0pky">4.25/0.21</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.25/0.23</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Translation to Language</td>
<td class="tg-0pky" rowspan="2">FLORES-200</td>
<td class="tg-0pky">Fluency</td>
<td class="tg-0pky">3.92/0.11</td>
<td class="tg-0pky">3.84/0.14</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.19/0.14</td>
</tr>
<tr>
<td class="tg-0pky">Accuracy</td>
<td class="tg-0pky">4.31/0.16</td>
<td class="tg-0pky">4.18/0.20</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.63/0.15</td>
</tr>
</tbody></table>
---
## Ethical Considerations and Limitations
We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases,
we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019).
We report that while performance is high (accuracies around 0.8 depending on the social category) in disambiguated settings,
the model performs very poorly in ambiguous settings, which indicates the presence of societal biases that need to be further addressed in post-training phases.
Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings.
For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018). We observe significant,
but relatively weak primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers.
We measure the effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We again detect significant effects,
with a small effect size. This suggests that the model is relatively robust against the examined cognitive biases.
We highlight that our analyses of these biases are by no means exhaustive and are limited by the relative scarcity of adequate resources
in all languages present in the training data. We aim to gradually extend and expand our analyses in future work.
These results can be expected from a model that has undergone only a preliminary instruction tuning.
These tests are performed in order to show the biases the model may contain. We urge developers to take
them into account and perform safety testing and tuning tailored to their specific applications of the model.
---
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.
### Funding
This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
### Acknowledgements
This project has benefited from the contributions of numerous teams and institutions, mainly through data contributions, knowledge transfer or technical support.
In Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà.
At the national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria.
At the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration. We would also like to give special thanks to the NVIDIA team, with whom we have met regularly, specially to: Ignacio Sarasua, Adam Henryk Grzywaczewski, Oleg Sudakov, Sergio Perez, Miguel Martinez, Felipes Soares and Meriem Bendris. Their constant support has been especially appreciated throughout the entire process.
Their valuable efforts have been instrumental in the development of this work.
### Disclaimer
Be aware that the model may contain biases or other unintended distortions.
When third parties deploy systems or provide services based on this model, or use the model themselves,
they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations,
including those governing the use of Artificial Intelligence.
The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
### Citation
```
@misc{gonzalezagirre2025salamandratechnicalreport,
title={Salamandra Technical Report},
author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
year={2025},
eprint={2502.08489},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.08489},
}
```
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Index
|Model|Base|Instruct|
|:---:|:---:|:---:|
|2B| [Link](https://huggingface.co/BSC-LT/salamandra-2b) | [Link](https://huggingface.co/BSC-LT/salamandra-2b-instruct) |
|7B| [Link](https://huggingface.co/BSC-LT/salamandra-7b) | [Link](https://huggingface.co/BSC-LT/salamandra-7b-instruct) |
|40B| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] |
[
"BEAR",
"SCIELO"
] |
EleutherAI/pythia-1.4b-deduped
|
EleutherAI
|
text-generation
|
[
"transformers",
"pytorch",
"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"
] | 2023-02-09T21:42:04 | 2023-06-08T13:03:28 | 12,669 | 19 |
---
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-1.4B-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-1.4B-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-1.4B-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-1.4B-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-1.4B-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-1.4B-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-1.4B-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-1.4B-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-1.4B-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-small
|
intfloat
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"onnx",
"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"
] | 2022-12-07T06:48:03 | 2023-08-07T04:58:08 | 11,580 | 41 |
---
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: 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: 76.22388059701493
- type: ap
value: 40.27466219523129
- type: f1
value: 70.60533006025108
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.525775
- type: ap
value: 83.51063993897611
- type: f1
value: 87.49342736805572
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.611999999999995
- type: f1
value: 42.05088045932892
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.826
- type: map_at_10
value: 38.269
- type: map_at_100
value: 39.322
- type: map_at_1000
value: 39.344
- type: map_at_3
value: 33.428000000000004
- type: map_at_5
value: 36.063
- type: mrr_at_1
value: 24.253
- type: mrr_at_10
value: 38.425
- type: mrr_at_100
value: 39.478
- type: mrr_at_1000
value: 39.5
- type: mrr_at_3
value: 33.606
- type: mrr_at_5
value: 36.195
- type: ndcg_at_1
value: 23.826
- type: ndcg_at_10
value: 46.693
- type: ndcg_at_100
value: 51.469
- type: ndcg_at_1000
value: 52.002
- type: ndcg_at_3
value: 36.603
- type: ndcg_at_5
value: 41.365
- type: precision_at_1
value: 23.826
- type: precision_at_10
value: 7.383000000000001
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 15.268
- type: precision_at_5
value: 11.479000000000001
- type: recall_at_1
value: 23.826
- type: recall_at_10
value: 73.82600000000001
- type: recall_at_100
value: 95.306
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 45.804
- type: recall_at_5
value: 57.397
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.13995374767436
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 37.13950072624313
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.35843292105327
- type: mrr
value: 73.72312359846987
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.55140418324174
- type: cos_sim_spearman
value: 84.21637675860022
- type: euclidean_pearson
value: 81.26069614610006
- type: euclidean_spearman
value: 83.25069210421785
- type: manhattan_pearson
value: 80.17441422581014
- type: manhattan_spearman
value: 81.87596198487877
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.87337662337661
- type: f1
value: 81.76647866926402
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.80600542614507
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.86321613256603
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.054
- type: map_at_10
value: 40.699999999999996
- type: map_at_100
value: 41.818
- type: map_at_1000
value: 41.959999999999994
- type: map_at_3
value: 37.742
- type: map_at_5
value: 39.427
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 46.150000000000006
- type: mrr_at_100
value: 46.865
- type: mrr_at_1000
value: 46.925
- type: mrr_at_3
value: 43.705
- type: mrr_at_5
value: 45.214999999999996
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 45.778
- type: ndcg_at_100
value: 50.38
- type: ndcg_at_1000
value: 52.922999999999995
- type: ndcg_at_3
value: 41.597
- type: ndcg_at_5
value: 43.631
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 8.269
- type: precision_at_100
value: 1.278
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.266
- type: precision_at_5
value: 13.705
- type: recall_at_1
value: 32.054
- type: recall_at_10
value: 54.947
- type: recall_at_100
value: 74.79599999999999
- type: recall_at_1000
value: 91.40899999999999
- type: recall_at_3
value: 42.431000000000004
- type: recall_at_5
value: 48.519
- type: map_at_1
value: 29.035
- type: map_at_10
value: 38.007000000000005
- type: map_at_100
value: 39.125
- type: map_at_1000
value: 39.251999999999995
- type: map_at_3
value: 35.77
- type: map_at_5
value: 37.057
- type: mrr_at_1
value: 36.497
- type: mrr_at_10
value: 44.077
- type: mrr_at_100
value: 44.743
- type: mrr_at_1000
value: 44.79
- type: mrr_at_3
value: 42.123
- type: mrr_at_5
value: 43.308
- type: ndcg_at_1
value: 36.497
- type: ndcg_at_10
value: 42.986000000000004
- type: ndcg_at_100
value: 47.323
- type: ndcg_at_1000
value: 49.624
- type: ndcg_at_3
value: 39.805
- type: ndcg_at_5
value: 41.286
- type: precision_at_1
value: 36.497
- type: precision_at_10
value: 7.8340000000000005
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.248
- type: recall_at_1
value: 29.035
- type: recall_at_10
value: 51.06
- type: recall_at_100
value: 69.64099999999999
- type: recall_at_1000
value: 84.49
- type: recall_at_3
value: 41.333999999999996
- type: recall_at_5
value: 45.663
- type: map_at_1
value: 37.239
- type: map_at_10
value: 47.873
- type: map_at_100
value: 48.842999999999996
- type: map_at_1000
value: 48.913000000000004
- type: map_at_3
value: 45.050000000000004
- type: map_at_5
value: 46.498
- type: mrr_at_1
value: 42.508
- type: mrr_at_10
value: 51.44
- type: mrr_at_100
value: 52.087
- type: mrr_at_1000
value: 52.129999999999995
- type: mrr_at_3
value: 49.164
- type: mrr_at_5
value: 50.343
- type: ndcg_at_1
value: 42.508
- type: ndcg_at_10
value: 53.31399999999999
- type: ndcg_at_100
value: 57.245000000000005
- type: ndcg_at_1000
value: 58.794000000000004
- type: ndcg_at_3
value: 48.295
- type: ndcg_at_5
value: 50.415
- type: precision_at_1
value: 42.508
- type: precision_at_10
value: 8.458
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 21.191
- type: precision_at_5
value: 14.307
- type: recall_at_1
value: 37.239
- type: recall_at_10
value: 65.99000000000001
- type: recall_at_100
value: 82.99499999999999
- type: recall_at_1000
value: 94.128
- type: recall_at_3
value: 52.382
- type: recall_at_5
value: 57.648999999999994
- type: map_at_1
value: 23.039
- type: map_at_10
value: 29.694
- type: map_at_100
value: 30.587999999999997
- type: map_at_1000
value: 30.692999999999998
- type: map_at_3
value: 27.708
- type: map_at_5
value: 28.774
- type: mrr_at_1
value: 24.633
- type: mrr_at_10
value: 31.478
- type: mrr_at_100
value: 32.299
- type: mrr_at_1000
value: 32.381
- type: mrr_at_3
value: 29.435
- type: mrr_at_5
value: 30.446
- type: ndcg_at_1
value: 24.633
- type: ndcg_at_10
value: 33.697
- type: ndcg_at_100
value: 38.080000000000005
- type: ndcg_at_1000
value: 40.812
- type: ndcg_at_3
value: 29.654000000000003
- type: ndcg_at_5
value: 31.474000000000004
- type: precision_at_1
value: 24.633
- type: precision_at_10
value: 5.0729999999999995
- type: precision_at_100
value: 0.753
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.279
- type: precision_at_5
value: 8.452
- type: recall_at_1
value: 23.039
- type: recall_at_10
value: 44.275999999999996
- type: recall_at_100
value: 64.4
- type: recall_at_1000
value: 85.135
- type: recall_at_3
value: 33.394
- type: recall_at_5
value: 37.687
- type: map_at_1
value: 13.594999999999999
- type: map_at_10
value: 19.933999999999997
- type: map_at_100
value: 20.966
- type: map_at_1000
value: 21.087
- type: map_at_3
value: 17.749000000000002
- type: map_at_5
value: 19.156000000000002
- type: mrr_at_1
value: 17.662
- type: mrr_at_10
value: 24.407
- type: mrr_at_100
value: 25.385
- type: mrr_at_1000
value: 25.465
- type: mrr_at_3
value: 22.056
- type: mrr_at_5
value: 23.630000000000003
- type: ndcg_at_1
value: 17.662
- type: ndcg_at_10
value: 24.391
- type: ndcg_at_100
value: 29.681
- type: ndcg_at_1000
value: 32.923
- type: ndcg_at_3
value: 20.271
- type: ndcg_at_5
value: 22.621
- type: precision_at_1
value: 17.662
- type: precision_at_10
value: 4.44
- type: precision_at_100
value: 0.8200000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 9.577
- type: precision_at_5
value: 7.313
- type: recall_at_1
value: 13.594999999999999
- type: recall_at_10
value: 33.976
- type: recall_at_100
value: 57.43000000000001
- type: recall_at_1000
value: 80.958
- type: recall_at_3
value: 22.897000000000002
- type: recall_at_5
value: 28.714000000000002
- type: map_at_1
value: 26.683
- type: map_at_10
value: 35.068
- type: map_at_100
value: 36.311
- type: map_at_1000
value: 36.436
- type: map_at_3
value: 32.371
- type: map_at_5
value: 33.761
- type: mrr_at_1
value: 32.435
- type: mrr_at_10
value: 40.721000000000004
- type: mrr_at_100
value: 41.535
- type: mrr_at_1000
value: 41.593
- type: mrr_at_3
value: 38.401999999999994
- type: mrr_at_5
value: 39.567
- type: ndcg_at_1
value: 32.435
- type: ndcg_at_10
value: 40.538000000000004
- type: ndcg_at_100
value: 45.963
- type: ndcg_at_1000
value: 48.400999999999996
- type: ndcg_at_3
value: 36.048
- type: ndcg_at_5
value: 37.899
- type: precision_at_1
value: 32.435
- type: precision_at_10
value: 7.1129999999999995
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 16.683
- type: precision_at_5
value: 11.684
- type: recall_at_1
value: 26.683
- type: recall_at_10
value: 51.517
- type: recall_at_100
value: 74.553
- type: recall_at_1000
value: 90.649
- type: recall_at_3
value: 38.495000000000005
- type: recall_at_5
value: 43.495
- type: map_at_1
value: 24.186
- type: map_at_10
value: 31.972
- type: map_at_100
value: 33.117000000000004
- type: map_at_1000
value: 33.243
- type: map_at_3
value: 29.423
- type: map_at_5
value: 30.847
- type: mrr_at_1
value: 29.794999999999998
- type: mrr_at_10
value: 36.767
- type: mrr_at_100
value: 37.645
- type: mrr_at_1000
value: 37.716
- type: mrr_at_3
value: 34.513
- type: mrr_at_5
value: 35.791000000000004
- type: ndcg_at_1
value: 29.794999999999998
- type: ndcg_at_10
value: 36.786
- type: ndcg_at_100
value: 41.94
- type: ndcg_at_1000
value: 44.830999999999996
- type: ndcg_at_3
value: 32.504
- type: ndcg_at_5
value: 34.404
- type: precision_at_1
value: 29.794999999999998
- type: precision_at_10
value: 6.518
- type: precision_at_100
value: 1.0659999999999998
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 15.296999999999999
- type: precision_at_5
value: 10.731
- type: recall_at_1
value: 24.186
- type: recall_at_10
value: 46.617
- type: recall_at_100
value: 68.75
- type: recall_at_1000
value: 88.864
- type: recall_at_3
value: 34.199
- type: recall_at_5
value: 39.462
- type: map_at_1
value: 24.22083333333333
- type: map_at_10
value: 31.606666666666662
- type: map_at_100
value: 32.6195
- type: map_at_1000
value: 32.739999999999995
- type: map_at_3
value: 29.37825
- type: map_at_5
value: 30.596083333333336
- type: mrr_at_1
value: 28.607916666666668
- type: mrr_at_10
value: 35.54591666666666
- type: mrr_at_100
value: 36.33683333333333
- type: mrr_at_1000
value: 36.40624999999999
- type: mrr_at_3
value: 33.526250000000005
- type: mrr_at_5
value: 34.6605
- type: ndcg_at_1
value: 28.607916666666668
- type: ndcg_at_10
value: 36.07966666666667
- type: ndcg_at_100
value: 40.73308333333333
- type: ndcg_at_1000
value: 43.40666666666666
- type: ndcg_at_3
value: 32.23525
- type: ndcg_at_5
value: 33.97083333333333
- type: precision_at_1
value: 28.607916666666668
- type: precision_at_10
value: 6.120333333333335
- type: precision_at_100
value: 0.9921666666666668
- type: precision_at_1000
value: 0.14091666666666666
- type: precision_at_3
value: 14.54975
- type: precision_at_5
value: 10.153166666666667
- type: recall_at_1
value: 24.22083333333333
- type: recall_at_10
value: 45.49183333333334
- type: recall_at_100
value: 66.28133333333332
- type: recall_at_1000
value: 85.16541666666667
- type: recall_at_3
value: 34.6485
- type: recall_at_5
value: 39.229749999999996
- type: map_at_1
value: 21.842
- type: map_at_10
value: 27.573999999999998
- type: map_at_100
value: 28.410999999999998
- type: map_at_1000
value: 28.502
- type: map_at_3
value: 25.921
- type: map_at_5
value: 26.888
- type: mrr_at_1
value: 24.08
- type: mrr_at_10
value: 29.915999999999997
- type: mrr_at_100
value: 30.669
- type: mrr_at_1000
value: 30.746000000000002
- type: mrr_at_3
value: 28.349000000000004
- type: mrr_at_5
value: 29.246
- type: ndcg_at_1
value: 24.08
- type: ndcg_at_10
value: 30.898999999999997
- type: ndcg_at_100
value: 35.272999999999996
- type: ndcg_at_1000
value: 37.679
- type: ndcg_at_3
value: 27.881
- type: ndcg_at_5
value: 29.432000000000002
- type: precision_at_1
value: 24.08
- type: precision_at_10
value: 4.678
- type: precision_at_100
value: 0.744
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 11.860999999999999
- type: precision_at_5
value: 8.16
- type: recall_at_1
value: 21.842
- type: recall_at_10
value: 38.66
- type: recall_at_100
value: 59.169000000000004
- type: recall_at_1000
value: 76.887
- type: recall_at_3
value: 30.532999999999998
- type: recall_at_5
value: 34.354
- type: map_at_1
value: 17.145
- type: map_at_10
value: 22.729
- type: map_at_100
value: 23.574
- type: map_at_1000
value: 23.695
- type: map_at_3
value: 21.044
- type: map_at_5
value: 21.981
- type: mrr_at_1
value: 20.888
- type: mrr_at_10
value: 26.529000000000003
- type: mrr_at_100
value: 27.308
- type: mrr_at_1000
value: 27.389000000000003
- type: mrr_at_3
value: 24.868000000000002
- type: mrr_at_5
value: 25.825
- type: ndcg_at_1
value: 20.888
- type: ndcg_at_10
value: 26.457000000000004
- type: ndcg_at_100
value: 30.764000000000003
- type: ndcg_at_1000
value: 33.825
- type: ndcg_at_3
value: 23.483999999999998
- type: ndcg_at_5
value: 24.836
- type: precision_at_1
value: 20.888
- type: precision_at_10
value: 4.58
- type: precision_at_100
value: 0.784
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.874
- type: precision_at_5
value: 7.639
- type: recall_at_1
value: 17.145
- type: recall_at_10
value: 33.938
- type: recall_at_100
value: 53.672
- type: recall_at_1000
value: 76.023
- type: recall_at_3
value: 25.363000000000003
- type: recall_at_5
value: 29.023
- type: map_at_1
value: 24.275
- type: map_at_10
value: 30.438
- type: map_at_100
value: 31.489
- type: map_at_1000
value: 31.601000000000003
- type: map_at_3
value: 28.647
- type: map_at_5
value: 29.660999999999998
- type: mrr_at_1
value: 28.077999999999996
- type: mrr_at_10
value: 34.098
- type: mrr_at_100
value: 35.025
- type: mrr_at_1000
value: 35.109
- type: mrr_at_3
value: 32.4
- type: mrr_at_5
value: 33.379999999999995
- type: ndcg_at_1
value: 28.077999999999996
- type: ndcg_at_10
value: 34.271
- type: ndcg_at_100
value: 39.352
- type: ndcg_at_1000
value: 42.199
- type: ndcg_at_3
value: 30.978
- type: ndcg_at_5
value: 32.498
- type: precision_at_1
value: 28.077999999999996
- type: precision_at_10
value: 5.345
- type: precision_at_100
value: 0.897
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 13.526
- type: precision_at_5
value: 9.16
- type: recall_at_1
value: 24.275
- type: recall_at_10
value: 42.362
- type: recall_at_100
value: 64.461
- type: recall_at_1000
value: 84.981
- type: recall_at_3
value: 33.249
- type: recall_at_5
value: 37.214999999999996
- type: map_at_1
value: 22.358
- type: map_at_10
value: 30.062
- type: map_at_100
value: 31.189
- type: map_at_1000
value: 31.386999999999997
- type: map_at_3
value: 27.672
- type: map_at_5
value: 28.76
- type: mrr_at_1
value: 26.877000000000002
- type: mrr_at_10
value: 33.948
- type: mrr_at_100
value: 34.746
- type: mrr_at_1000
value: 34.816
- type: mrr_at_3
value: 31.884
- type: mrr_at_5
value: 33.001000000000005
- type: ndcg_at_1
value: 26.877000000000002
- type: ndcg_at_10
value: 34.977000000000004
- type: ndcg_at_100
value: 39.753
- type: ndcg_at_1000
value: 42.866
- type: ndcg_at_3
value: 30.956
- type: ndcg_at_5
value: 32.381
- type: precision_at_1
value: 26.877000000000002
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.287
- type: precision_at_1000
value: 0.215
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.119
- type: recall_at_1
value: 22.358
- type: recall_at_10
value: 44.183
- type: recall_at_100
value: 67.14
- type: recall_at_1000
value: 87.53999999999999
- type: recall_at_3
value: 32.79
- type: recall_at_5
value: 36.829
- type: map_at_1
value: 19.198999999999998
- type: map_at_10
value: 25.229000000000003
- type: map_at_100
value: 26.003
- type: map_at_1000
value: 26.111
- type: map_at_3
value: 23.442
- type: map_at_5
value: 24.343
- type: mrr_at_1
value: 21.072
- type: mrr_at_10
value: 27.02
- type: mrr_at_100
value: 27.735
- type: mrr_at_1000
value: 27.815
- type: mrr_at_3
value: 25.416
- type: mrr_at_5
value: 26.173999999999996
- type: ndcg_at_1
value: 21.072
- type: ndcg_at_10
value: 28.862
- type: ndcg_at_100
value: 33.043
- type: ndcg_at_1000
value: 36.003
- type: ndcg_at_3
value: 25.35
- type: ndcg_at_5
value: 26.773000000000003
- type: precision_at_1
value: 21.072
- type: precision_at_10
value: 4.436
- type: precision_at_100
value: 0.713
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 10.659
- type: precision_at_5
value: 7.32
- type: recall_at_1
value: 19.198999999999998
- type: recall_at_10
value: 38.376
- type: recall_at_100
value: 58.36900000000001
- type: recall_at_1000
value: 80.92099999999999
- type: recall_at_3
value: 28.715000000000003
- type: recall_at_5
value: 32.147
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.9319999999999995
- type: map_at_10
value: 10.483
- type: map_at_100
value: 11.97
- type: map_at_1000
value: 12.171999999999999
- type: map_at_3
value: 8.477
- type: map_at_5
value: 9.495000000000001
- type: mrr_at_1
value: 13.094
- type: mrr_at_10
value: 21.282
- type: mrr_at_100
value: 22.556
- type: mrr_at_1000
value: 22.628999999999998
- type: mrr_at_3
value: 18.218999999999998
- type: mrr_at_5
value: 19.900000000000002
- type: ndcg_at_1
value: 13.094
- type: ndcg_at_10
value: 15.811
- type: ndcg_at_100
value: 23.035
- type: ndcg_at_1000
value: 27.089999999999996
- type: ndcg_at_3
value: 11.905000000000001
- type: ndcg_at_5
value: 13.377
- type: precision_at_1
value: 13.094
- type: precision_at_10
value: 5.225
- type: precision_at_100
value: 1.2970000000000002
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 8.86
- type: precision_at_5
value: 7.309
- type: recall_at_1
value: 5.9319999999999995
- type: recall_at_10
value: 20.305
- type: recall_at_100
value: 46.314
- type: recall_at_1000
value: 69.612
- type: recall_at_3
value: 11.21
- type: recall_at_5
value: 14.773
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.674
- type: map_at_10
value: 17.822
- type: map_at_100
value: 24.794
- type: map_at_1000
value: 26.214
- type: map_at_3
value: 12.690999999999999
- type: map_at_5
value: 15.033
- type: mrr_at_1
value: 61.75000000000001
- type: mrr_at_10
value: 71.58
- type: mrr_at_100
value: 71.923
- type: mrr_at_1000
value: 71.932
- type: mrr_at_3
value: 70.125
- type: mrr_at_5
value: 71.038
- type: ndcg_at_1
value: 51
- type: ndcg_at_10
value: 38.637
- type: ndcg_at_100
value: 42.398
- type: ndcg_at_1000
value: 48.962
- type: ndcg_at_3
value: 43.29
- type: ndcg_at_5
value: 40.763
- type: precision_at_1
value: 61.75000000000001
- type: precision_at_10
value: 30.125
- type: precision_at_100
value: 9.53
- type: precision_at_1000
value: 1.9619999999999997
- type: precision_at_3
value: 45.583
- type: precision_at_5
value: 38.95
- type: recall_at_1
value: 8.674
- type: recall_at_10
value: 23.122
- type: recall_at_100
value: 47.46
- type: recall_at_1000
value: 67.662
- type: recall_at_3
value: 13.946
- type: recall_at_5
value: 17.768
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.86000000000001
- type: f1
value: 41.343580452760776
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.609
- type: map_at_10
value: 47.552
- type: map_at_100
value: 48.283
- type: map_at_1000
value: 48.321
- type: map_at_3
value: 44.869
- type: map_at_5
value: 46.509
- type: mrr_at_1
value: 39.214
- type: mrr_at_10
value: 50.434999999999995
- type: mrr_at_100
value: 51.122
- type: mrr_at_1000
value: 51.151
- type: mrr_at_3
value: 47.735
- type: mrr_at_5
value: 49.394
- type: ndcg_at_1
value: 39.214
- type: ndcg_at_10
value: 53.52400000000001
- type: ndcg_at_100
value: 56.997
- type: ndcg_at_1000
value: 57.975
- type: ndcg_at_3
value: 48.173
- type: ndcg_at_5
value: 51.05800000000001
- type: precision_at_1
value: 39.214
- type: precision_at_10
value: 7.573
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 19.782
- type: precision_at_5
value: 13.453000000000001
- type: recall_at_1
value: 36.609
- type: recall_at_10
value: 69.247
- type: recall_at_100
value: 84.99600000000001
- type: recall_at_1000
value: 92.40899999999999
- type: recall_at_3
value: 54.856
- type: recall_at_5
value: 61.797000000000004
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.466
- type: map_at_10
value: 27.060000000000002
- type: map_at_100
value: 28.511999999999997
- type: map_at_1000
value: 28.693
- type: map_at_3
value: 22.777
- type: map_at_5
value: 25.086000000000002
- type: mrr_at_1
value: 32.716
- type: mrr_at_10
value: 41.593999999999994
- type: mrr_at_100
value: 42.370000000000005
- type: mrr_at_1000
value: 42.419000000000004
- type: mrr_at_3
value: 38.143
- type: mrr_at_5
value: 40.288000000000004
- type: ndcg_at_1
value: 32.716
- type: ndcg_at_10
value: 34.795
- type: ndcg_at_100
value: 40.58
- type: ndcg_at_1000
value: 43.993
- type: ndcg_at_3
value: 29.573
- type: ndcg_at_5
value: 31.583
- type: precision_at_1
value: 32.716
- type: precision_at_10
value: 9.937999999999999
- type: precision_at_100
value: 1.585
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 19.496
- type: precision_at_5
value: 15.247
- type: recall_at_1
value: 16.466
- type: recall_at_10
value: 42.886
- type: recall_at_100
value: 64.724
- type: recall_at_1000
value: 85.347
- type: recall_at_3
value: 26.765
- type: recall_at_5
value: 33.603
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.025
- type: map_at_10
value: 47.343
- type: map_at_100
value: 48.207
- type: map_at_1000
value: 48.281
- type: map_at_3
value: 44.519
- type: map_at_5
value: 46.217000000000006
- type: mrr_at_1
value: 66.05
- type: mrr_at_10
value: 72.94699999999999
- type: mrr_at_100
value: 73.289
- type: mrr_at_1000
value: 73.30499999999999
- type: mrr_at_3
value: 71.686
- type: mrr_at_5
value: 72.491
- type: ndcg_at_1
value: 66.05
- type: ndcg_at_10
value: 56.338
- type: ndcg_at_100
value: 59.599999999999994
- type: ndcg_at_1000
value: 61.138000000000005
- type: ndcg_at_3
value: 52.034000000000006
- type: ndcg_at_5
value: 54.352000000000004
- type: precision_at_1
value: 66.05
- type: precision_at_10
value: 11.693000000000001
- type: precision_at_100
value: 1.425
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 32.613
- type: precision_at_5
value: 21.401999999999997
- type: recall_at_1
value: 33.025
- type: recall_at_10
value: 58.467
- type: recall_at_100
value: 71.242
- type: recall_at_1000
value: 81.452
- type: recall_at_3
value: 48.92
- type: recall_at_5
value: 53.504
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.5492
- type: ap
value: 69.42911637216271
- type: f1
value: 75.39113704261024
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.173
- type: map_at_10
value: 35.453
- type: map_at_100
value: 36.573
- type: map_at_1000
value: 36.620999999999995
- type: map_at_3
value: 31.655
- type: map_at_5
value: 33.823
- type: mrr_at_1
value: 23.868000000000002
- type: mrr_at_10
value: 36.085
- type: mrr_at_100
value: 37.15
- type: mrr_at_1000
value: 37.193
- type: mrr_at_3
value: 32.376
- type: mrr_at_5
value: 34.501
- type: ndcg_at_1
value: 23.854
- type: ndcg_at_10
value: 42.33
- type: ndcg_at_100
value: 47.705999999999996
- type: ndcg_at_1000
value: 48.91
- type: ndcg_at_3
value: 34.604
- type: ndcg_at_5
value: 38.473
- type: precision_at_1
value: 23.854
- type: precision_at_10
value: 6.639
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.685
- type: precision_at_5
value: 10.782
- type: recall_at_1
value: 23.173
- type: recall_at_10
value: 63.441
- type: recall_at_100
value: 88.25
- type: recall_at_1000
value: 97.438
- type: recall_at_3
value: 42.434
- type: recall_at_5
value: 51.745
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.05426356589147
- type: f1
value: 91.88068588063942
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.23985408116735
- type: f1
value: 55.858906745287506
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.21923335574984
- type: f1
value: 70.0174116204253
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.77673167451245
- type: f1
value: 75.44811354778666
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.340414710728737
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.196676760061578
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.564149683482206
- type: mrr
value: 30.28995474250486
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.93
- type: map_at_10
value: 12.828000000000001
- type: map_at_100
value: 15.501000000000001
- type: map_at_1000
value: 16.791
- type: map_at_3
value: 9.727
- type: map_at_5
value: 11.318999999999999
- type: mrr_at_1
value: 47.678
- type: mrr_at_10
value: 55.893
- type: mrr_at_100
value: 56.491
- type: mrr_at_1000
value: 56.53
- type: mrr_at_3
value: 54.386
- type: mrr_at_5
value: 55.516
- type: ndcg_at_1
value: 45.975
- type: ndcg_at_10
value: 33.928999999999995
- type: ndcg_at_100
value: 30.164
- type: ndcg_at_1000
value: 38.756
- type: ndcg_at_3
value: 41.077000000000005
- type: ndcg_at_5
value: 38.415
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 24.365000000000002
- type: precision_at_100
value: 7.344
- type: precision_at_1000
value: 1.994
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 33.003
- type: recall_at_1
value: 5.93
- type: recall_at_10
value: 16.239
- type: recall_at_100
value: 28.782999999999998
- type: recall_at_1000
value: 60.11
- type: recall_at_3
value: 10.700999999999999
- type: recall_at_5
value: 13.584
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.163000000000004
- type: map_at_10
value: 51.520999999999994
- type: map_at_100
value: 52.449
- type: map_at_1000
value: 52.473000000000006
- type: map_at_3
value: 47.666
- type: map_at_5
value: 50.043000000000006
- type: mrr_at_1
value: 40.266999999999996
- type: mrr_at_10
value: 54.074
- type: mrr_at_100
value: 54.722
- type: mrr_at_1000
value: 54.739000000000004
- type: mrr_at_3
value: 51.043000000000006
- type: mrr_at_5
value: 52.956
- type: ndcg_at_1
value: 40.238
- type: ndcg_at_10
value: 58.73199999999999
- type: ndcg_at_100
value: 62.470000000000006
- type: ndcg_at_1000
value: 63.083999999999996
- type: ndcg_at_3
value: 51.672
- type: ndcg_at_5
value: 55.564
- type: precision_at_1
value: 40.238
- type: precision_at_10
value: 9.279
- type: precision_at_100
value: 1.139
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.078000000000003
- type: precision_at_5
value: 16.176
- type: recall_at_1
value: 36.163000000000004
- type: recall_at_10
value: 77.88199999999999
- type: recall_at_100
value: 93.83399999999999
- type: recall_at_1000
value: 98.465
- type: recall_at_3
value: 59.857000000000006
- type: recall_at_5
value: 68.73599999999999
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.344
- type: map_at_10
value: 83.907
- type: map_at_100
value: 84.536
- type: map_at_1000
value: 84.557
- type: map_at_3
value: 80.984
- type: map_at_5
value: 82.844
- type: mrr_at_1
value: 81.02000000000001
- type: mrr_at_10
value: 87.158
- type: mrr_at_100
value: 87.268
- type: mrr_at_1000
value: 87.26899999999999
- type: mrr_at_3
value: 86.17
- type: mrr_at_5
value: 86.87
- type: ndcg_at_1
value: 81.02000000000001
- type: ndcg_at_10
value: 87.70700000000001
- type: ndcg_at_100
value: 89.004
- type: ndcg_at_1000
value: 89.139
- type: ndcg_at_3
value: 84.841
- type: ndcg_at_5
value: 86.455
- type: precision_at_1
value: 81.02000000000001
- type: precision_at_10
value: 13.248999999999999
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.963
- type: precision_at_5
value: 24.33
- type: recall_at_1
value: 70.344
- type: recall_at_10
value: 94.75099999999999
- type: recall_at_100
value: 99.30499999999999
- type: recall_at_1000
value: 99.928
- type: recall_at_3
value: 86.506
- type: recall_at_5
value: 91.083
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.873718018378305
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.39477366450528
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.868
- type: map_at_10
value: 9.611
- type: map_at_100
value: 11.087
- type: map_at_1000
value: 11.332
- type: map_at_3
value: 6.813
- type: map_at_5
value: 8.233
- type: mrr_at_1
value: 19
- type: mrr_at_10
value: 28.457
- type: mrr_at_100
value: 29.613
- type: mrr_at_1000
value: 29.695
- type: mrr_at_3
value: 25.55
- type: mrr_at_5
value: 27.29
- type: ndcg_at_1
value: 19
- type: ndcg_at_10
value: 16.419
- type: ndcg_at_100
value: 22.817999999999998
- type: ndcg_at_1000
value: 27.72
- type: ndcg_at_3
value: 15.379000000000001
- type: ndcg_at_5
value: 13.645
- type: precision_at_1
value: 19
- type: precision_at_10
value: 8.540000000000001
- type: precision_at_100
value: 1.7819999999999998
- type: precision_at_1000
value: 0.297
- type: precision_at_3
value: 14.267
- type: precision_at_5
value: 12.04
- type: recall_at_1
value: 3.868
- type: recall_at_10
value: 17.288
- type: recall_at_100
value: 36.144999999999996
- type: recall_at_1000
value: 60.199999999999996
- type: recall_at_3
value: 8.688
- type: recall_at_5
value: 12.198
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.96614722598582
- type: cos_sim_spearman
value: 78.9003023008781
- type: euclidean_pearson
value: 81.01829384436505
- type: euclidean_spearman
value: 78.93248416788914
- type: manhattan_pearson
value: 81.1665428926402
- type: manhattan_spearman
value: 78.93264116287453
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.54613363895993
- type: cos_sim_spearman
value: 75.1883451602451
- type: euclidean_pearson
value: 79.70320886899894
- type: euclidean_spearman
value: 74.5917140136796
- type: manhattan_pearson
value: 79.82157067185999
- type: manhattan_spearman
value: 74.74185720594735
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.30430156721782
- type: cos_sim_spearman
value: 81.79962989974364
- type: euclidean_pearson
value: 80.89058823224924
- type: euclidean_spearman
value: 81.35929372984597
- type: manhattan_pearson
value: 81.12204370487478
- type: manhattan_spearman
value: 81.6248963282232
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.13064504403134
- type: cos_sim_spearman
value: 78.48371403924872
- type: euclidean_pearson
value: 80.16794919665591
- type: euclidean_spearman
value: 78.29216082221699
- type: manhattan_pearson
value: 80.22308565207301
- type: manhattan_spearman
value: 78.37829229948022
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.52918899541099
- type: cos_sim_spearman
value: 87.49276894673142
- type: euclidean_pearson
value: 86.77440570164254
- type: euclidean_spearman
value: 87.5753295736756
- type: manhattan_pearson
value: 86.86098573892133
- type: manhattan_spearman
value: 87.65848591821947
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.86805307244882
- type: cos_sim_spearman
value: 84.58066253757511
- type: euclidean_pearson
value: 84.38377000876991
- type: euclidean_spearman
value: 85.1837278784528
- type: manhattan_pearson
value: 84.41903291363842
- type: manhattan_spearman
value: 85.19023736251052
- 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: 86.77218560282436
- type: cos_sim_spearman
value: 87.94243515296604
- type: euclidean_pearson
value: 88.22800939214864
- type: euclidean_spearman
value: 87.91106839439841
- type: manhattan_pearson
value: 88.17063269848741
- type: manhattan_spearman
value: 87.72751904126062
- 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: 60.40731554300387
- type: cos_sim_spearman
value: 63.76300532966479
- type: euclidean_pearson
value: 62.94727878229085
- type: euclidean_spearman
value: 63.678039531461216
- type: manhattan_pearson
value: 63.00661039863549
- type: manhattan_spearman
value: 63.6282591984376
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.92731569745344
- type: cos_sim_spearman
value: 86.36336704300167
- type: euclidean_pearson
value: 86.09122224841195
- type: euclidean_spearman
value: 86.2116149319238
- type: manhattan_pearson
value: 86.07879456717032
- type: manhattan_spearman
value: 86.2022069635119
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.75976311752326
- type: mrr
value: 94.15782837351466
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 51.193999999999996
- type: map_at_10
value: 61.224999999999994
- type: map_at_100
value: 62.031000000000006
- type: map_at_1000
value: 62.066
- type: map_at_3
value: 59.269000000000005
- type: map_at_5
value: 60.159
- type: mrr_at_1
value: 53.667
- type: mrr_at_10
value: 62.74999999999999
- type: mrr_at_100
value: 63.39399999999999
- type: mrr_at_1000
value: 63.425
- type: mrr_at_3
value: 61.389
- type: mrr_at_5
value: 61.989000000000004
- type: ndcg_at_1
value: 53.667
- type: ndcg_at_10
value: 65.596
- type: ndcg_at_100
value: 68.906
- type: ndcg_at_1000
value: 69.78999999999999
- type: ndcg_at_3
value: 62.261
- type: ndcg_at_5
value: 63.453
- type: precision_at_1
value: 53.667
- type: precision_at_10
value: 8.667
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 24.556
- type: precision_at_5
value: 15.6
- type: recall_at_1
value: 51.193999999999996
- type: recall_at_10
value: 77.156
- type: recall_at_100
value: 91.43299999999999
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 67.994
- type: recall_at_5
value: 71.14399999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81485148514851
- type: cos_sim_ap
value: 95.28896513388551
- type: cos_sim_f1
value: 90.43478260869566
- type: cos_sim_precision
value: 92.56544502617801
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.30594059405941
- type: dot_ap
value: 61.6432597455472
- type: dot_f1
value: 59.46481665014866
- type: dot_precision
value: 58.93909626719057
- type: dot_recall
value: 60
- type: euclidean_accuracy
value: 99.81980198019802
- type: euclidean_ap
value: 95.21411049527
- type: euclidean_f1
value: 91.06090373280944
- type: euclidean_precision
value: 89.47876447876449
- type: euclidean_recall
value: 92.7
- type: manhattan_accuracy
value: 99.81782178217821
- type: manhattan_ap
value: 95.32449994414968
- type: manhattan_f1
value: 90.86395233366436
- type: manhattan_precision
value: 90.23668639053254
- type: manhattan_recall
value: 91.5
- type: max_accuracy
value: 99.81980198019802
- type: max_ap
value: 95.32449994414968
- type: max_f1
value: 91.06090373280944
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.08045614613064
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.297802606804748
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.12801740706292
- type: mrr
value: 50.05592956879722
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.523347880124497
- type: cos_sim_spearman
value: 31.388214436391014
- type: dot_pearson
value: 24.55403435439901
- type: dot_spearman
value: 23.50153210841191
- 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: 1.886
- type: map_at_100
value: 10.040000000000001
- type: map_at_1000
value: 23.768
- type: map_at_3
value: 0.674
- type: map_at_5
value: 1.079
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.667
- type: mrr_at_100
value: 93.667
- type: mrr_at_1000
value: 93.667
- type: mrr_at_3
value: 93.667
- type: mrr_at_5
value: 93.667
- type: ndcg_at_1
value: 83
- type: ndcg_at_10
value: 76.777
- type: ndcg_at_100
value: 55.153
- type: ndcg_at_1000
value: 47.912
- type: ndcg_at_3
value: 81.358
- type: ndcg_at_5
value: 80.74799999999999
- type: precision_at_1
value: 88
- type: precision_at_10
value: 80.80000000000001
- type: precision_at_100
value: 56.02
- type: precision_at_1000
value: 21.51
- type: precision_at_3
value: 86
- type: precision_at_5
value: 86
- type: recall_at_1
value: 0.243
- type: recall_at_10
value: 2.0869999999999997
- type: recall_at_100
value: 13.014000000000001
- type: recall_at_1000
value: 44.433
- type: recall_at_3
value: 0.6910000000000001
- type: recall_at_5
value: 1.1440000000000001
- 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: 10.615
- type: map_at_100
value: 16.463
- type: map_at_1000
value: 17.815
- type: map_at_3
value: 5.7860000000000005
- type: map_at_5
value: 7.353999999999999
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 53.846000000000004
- type: mrr_at_100
value: 54.37
- type: mrr_at_1000
value: 54.37
- type: mrr_at_3
value: 48.980000000000004
- type: mrr_at_5
value: 51.735
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 26.811
- type: ndcg_at_100
value: 37.342999999999996
- type: ndcg_at_1000
value: 47.964
- type: ndcg_at_3
value: 30.906
- type: ndcg_at_5
value: 27.77
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 23.878
- type: precision_at_100
value: 7.632999999999999
- type: precision_at_1000
value: 1.469
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 26.939
- type: recall_at_1
value: 3.066
- type: recall_at_10
value: 17.112
- type: recall_at_100
value: 47.723
- type: recall_at_1000
value: 79.50500000000001
- type: recall_at_3
value: 6.825
- type: recall_at_5
value: 9.584
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.76460000000002
- type: ap
value: 14.944240012137053
- type: f1
value: 55.89805777266571
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.30503678551217
- type: f1
value: 63.57492701921179
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 37.51066495006874
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.07021517553794
- type: cos_sim_ap
value: 74.15520712370555
- type: cos_sim_f1
value: 68.64321608040201
- type: cos_sim_precision
value: 65.51558752997602
- type: cos_sim_recall
value: 72.0844327176781
- type: dot_accuracy
value: 80.23484532395541
- type: dot_ap
value: 54.298763810214176
- type: dot_f1
value: 53.22254659779924
- type: dot_precision
value: 46.32525410476936
- type: dot_recall
value: 62.532981530343015
- type: euclidean_accuracy
value: 86.04637301066937
- type: euclidean_ap
value: 73.85333854233123
- type: euclidean_f1
value: 68.77723660599845
- type: euclidean_precision
value: 66.87437686939182
- type: euclidean_recall
value: 70.79155672823218
- type: manhattan_accuracy
value: 85.98676759849795
- type: manhattan_ap
value: 73.56016090035973
- type: manhattan_f1
value: 68.48878539036647
- type: manhattan_precision
value: 63.9505607690547
- type: manhattan_recall
value: 73.7203166226913
- type: max_accuracy
value: 86.07021517553794
- type: max_ap
value: 74.15520712370555
- type: max_f1
value: 68.77723660599845
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.92769821865176
- type: cos_sim_ap
value: 85.78879502899773
- type: cos_sim_f1
value: 78.14414083990464
- type: cos_sim_precision
value: 74.61651607480563
- type: cos_sim_recall
value: 82.0218663381583
- type: dot_accuracy
value: 84.95750378390964
- type: dot_ap
value: 75.80219641857563
- type: dot_f1
value: 70.13966179585681
- type: dot_precision
value: 65.71140262361251
- type: dot_recall
value: 75.20788420080073
- type: euclidean_accuracy
value: 88.93546008460433
- type: euclidean_ap
value: 85.72056428301667
- type: euclidean_f1
value: 78.14387902598124
- type: euclidean_precision
value: 75.3376688344172
- type: euclidean_recall
value: 81.16723129042192
- type: manhattan_accuracy
value: 88.96262661543835
- type: manhattan_ap
value: 85.76605136314335
- type: manhattan_f1
value: 78.26696165191743
- type: manhattan_precision
value: 75.0990659496179
- type: manhattan_recall
value: 81.71388974437943
- type: max_accuracy
value: 88.96262661543835
- type: max_ap
value: 85.78879502899773
- type: max_f1
value: 78.26696165191743
---
# E5-small
**News (May 2023): please switch to [e5-small-v2](https://huggingface.co/intfloat/e5-small-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 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')
model = AutoModel.from_pretrained('intfloat/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())
```
## 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')
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-base
|
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"
] | 2022-12-20T05:59:40 | 2023-01-21T06:31:16 | 11,563 | 116 |
---
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_base_results
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 86.2089552238806
- type: ap
value: 55.76273850794966
- type: f1
value: 81.26104211414781
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.35995000000001
- type: ap
value: 84.18839957309655
- type: f1
value: 88.317619250081
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.64
- type: f1
value: 42.48663956478136
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.383000000000003
- type: map_at_10
value: 43.024
- type: map_at_100
value: 44.023
- type: map_at_1000
value: 44.025999999999996
- type: map_at_3
value: 37.684
- type: map_at_5
value: 40.884
- type: mrr_at_1
value: 28.094
- type: mrr_at_10
value: 43.315
- type: mrr_at_100
value: 44.313
- type: mrr_at_1000
value: 44.317
- type: mrr_at_3
value: 37.862
- type: mrr_at_5
value: 41.155
- type: ndcg_at_1
value: 27.383000000000003
- type: ndcg_at_10
value: 52.032000000000004
- type: ndcg_at_100
value: 56.19499999999999
- type: ndcg_at_1000
value: 56.272
- type: ndcg_at_3
value: 41.166000000000004
- type: ndcg_at_5
value: 46.92
- type: precision_at_1
value: 27.383000000000003
- type: precision_at_10
value: 8.087
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 17.093
- type: precision_at_5
value: 13.044
- type: recall_at_1
value: 27.383000000000003
- type: recall_at_10
value: 80.868
- type: recall_at_100
value: 98.86200000000001
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 51.28
- type: recall_at_5
value: 65.22
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.68441054431849
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 29.188539728343844
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.173362687519784
- type: mrr
value: 76.18860748362133
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 82.30789953771232
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 77.03571428571428
- type: f1
value: 75.87384305045917
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 32.98041170516364
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 25.71652988451154
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.739999999999995
- type: map_at_10
value: 46.197
- type: map_at_100
value: 47.814
- type: map_at_1000
value: 47.934
- type: map_at_3
value: 43.091
- type: map_at_5
value: 44.81
- type: mrr_at_1
value: 41.059
- type: mrr_at_10
value: 52.292
- type: mrr_at_100
value: 52.978
- type: mrr_at_1000
value: 53.015
- type: mrr_at_3
value: 49.976
- type: mrr_at_5
value: 51.449999999999996
- type: ndcg_at_1
value: 41.059
- type: ndcg_at_10
value: 52.608
- type: ndcg_at_100
value: 57.965
- type: ndcg_at_1000
value: 59.775999999999996
- type: ndcg_at_3
value: 48.473
- type: ndcg_at_5
value: 50.407999999999994
- type: precision_at_1
value: 41.059
- type: precision_at_10
value: 9.943
- type: precision_at_100
value: 1.6070000000000002
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 23.413999999999998
- type: precision_at_5
value: 16.481
- type: recall_at_1
value: 33.739999999999995
- type: recall_at_10
value: 63.888999999999996
- type: recall_at_100
value: 85.832
- type: recall_at_1000
value: 97.475
- type: recall_at_3
value: 51.953
- type: recall_at_5
value: 57.498000000000005
- type: map_at_1
value: 31.169999999999998
- type: map_at_10
value: 41.455
- type: map_at_100
value: 42.716
- type: map_at_1000
value: 42.847
- type: map_at_3
value: 38.568999999999996
- type: map_at_5
value: 40.099000000000004
- type: mrr_at_1
value: 39.427
- type: mrr_at_10
value: 47.818
- type: mrr_at_100
value: 48.519
- type: mrr_at_1000
value: 48.558
- type: mrr_at_3
value: 45.86
- type: mrr_at_5
value: 46.936
- type: ndcg_at_1
value: 39.427
- type: ndcg_at_10
value: 47.181
- type: ndcg_at_100
value: 51.737
- type: ndcg_at_1000
value: 53.74
- type: ndcg_at_3
value: 43.261
- type: ndcg_at_5
value: 44.891
- type: precision_at_1
value: 39.427
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.425
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 20.785999999999998
- type: precision_at_5
value: 14.560999999999998
- type: recall_at_1
value: 31.169999999999998
- type: recall_at_10
value: 56.971000000000004
- type: recall_at_100
value: 76.31400000000001
- type: recall_at_1000
value: 88.93900000000001
- type: recall_at_3
value: 45.208
- type: recall_at_5
value: 49.923
- type: map_at_1
value: 39.682
- type: map_at_10
value: 52.766000000000005
- type: map_at_100
value: 53.84100000000001
- type: map_at_1000
value: 53.898
- type: map_at_3
value: 49.291000000000004
- type: map_at_5
value: 51.365
- type: mrr_at_1
value: 45.266
- type: mrr_at_10
value: 56.093
- type: mrr_at_100
value: 56.763
- type: mrr_at_1000
value: 56.793000000000006
- type: mrr_at_3
value: 53.668000000000006
- type: mrr_at_5
value: 55.1
- type: ndcg_at_1
value: 45.266
- type: ndcg_at_10
value: 58.836
- type: ndcg_at_100
value: 62.863
- type: ndcg_at_1000
value: 63.912
- type: ndcg_at_3
value: 53.19199999999999
- type: ndcg_at_5
value: 56.125
- type: precision_at_1
value: 45.266
- type: precision_at_10
value: 9.492
- type: precision_at_100
value: 1.236
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 23.762
- type: precision_at_5
value: 16.414
- type: recall_at_1
value: 39.682
- type: recall_at_10
value: 73.233
- type: recall_at_100
value: 90.335
- type: recall_at_1000
value: 97.452
- type: recall_at_3
value: 58.562000000000005
- type: recall_at_5
value: 65.569
- type: map_at_1
value: 26.743
- type: map_at_10
value: 34.016000000000005
- type: map_at_100
value: 35.028999999999996
- type: map_at_1000
value: 35.113
- type: map_at_3
value: 31.763
- type: map_at_5
value: 33.013999999999996
- type: mrr_at_1
value: 28.927000000000003
- type: mrr_at_10
value: 36.32
- type: mrr_at_100
value: 37.221
- type: mrr_at_1000
value: 37.281
- type: mrr_at_3
value: 34.105000000000004
- type: mrr_at_5
value: 35.371
- type: ndcg_at_1
value: 28.927000000000003
- type: ndcg_at_10
value: 38.474000000000004
- type: ndcg_at_100
value: 43.580000000000005
- type: ndcg_at_1000
value: 45.64
- type: ndcg_at_3
value: 34.035
- type: ndcg_at_5
value: 36.186
- type: precision_at_1
value: 28.927000000000003
- type: precision_at_10
value: 5.74
- type: precision_at_100
value: 0.8710000000000001
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 14.124
- type: precision_at_5
value: 9.74
- type: recall_at_1
value: 26.743
- type: recall_at_10
value: 49.955
- type: recall_at_100
value: 73.904
- type: recall_at_1000
value: 89.133
- type: recall_at_3
value: 38.072
- type: recall_at_5
value: 43.266
- type: map_at_1
value: 16.928
- type: map_at_10
value: 23.549
- type: map_at_100
value: 24.887
- type: map_at_1000
value: 25.018
- type: map_at_3
value: 21.002000000000002
- type: map_at_5
value: 22.256
- type: mrr_at_1
value: 21.02
- type: mrr_at_10
value: 27.898
- type: mrr_at_100
value: 29.018
- type: mrr_at_1000
value: 29.099999999999998
- type: mrr_at_3
value: 25.456
- type: mrr_at_5
value: 26.625
- type: ndcg_at_1
value: 21.02
- type: ndcg_at_10
value: 28.277
- type: ndcg_at_100
value: 34.54
- type: ndcg_at_1000
value: 37.719
- type: ndcg_at_3
value: 23.707
- type: ndcg_at_5
value: 25.482
- type: precision_at_1
value: 21.02
- type: precision_at_10
value: 5.361
- type: precision_at_100
value: 0.9809999999999999
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 11.401
- type: precision_at_5
value: 8.209
- type: recall_at_1
value: 16.928
- type: recall_at_10
value: 38.601
- type: recall_at_100
value: 65.759
- type: recall_at_1000
value: 88.543
- type: recall_at_3
value: 25.556
- type: recall_at_5
value: 30.447000000000003
- type: map_at_1
value: 28.549000000000003
- type: map_at_10
value: 38.426
- type: map_at_100
value: 39.845000000000006
- type: map_at_1000
value: 39.956
- type: map_at_3
value: 35.372
- type: map_at_5
value: 37.204
- type: mrr_at_1
value: 35.034
- type: mrr_at_10
value: 44.041000000000004
- type: mrr_at_100
value: 44.95
- type: mrr_at_1000
value: 44.997
- type: mrr_at_3
value: 41.498000000000005
- type: mrr_at_5
value: 43.077
- type: ndcg_at_1
value: 35.034
- type: ndcg_at_10
value: 44.218
- type: ndcg_at_100
value: 49.958000000000006
- type: ndcg_at_1000
value: 52.019000000000005
- type: ndcg_at_3
value: 39.34
- type: ndcg_at_5
value: 41.892
- type: precision_at_1
value: 35.034
- type: precision_at_10
value: 7.911
- type: precision_at_100
value: 1.26
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 18.511
- type: precision_at_5
value: 13.205
- type: recall_at_1
value: 28.549000000000003
- type: recall_at_10
value: 56.035999999999994
- type: recall_at_100
value: 79.701
- type: recall_at_1000
value: 93.149
- type: recall_at_3
value: 42.275
- type: recall_at_5
value: 49.097
- type: map_at_1
value: 29.391000000000002
- type: map_at_10
value: 39.48
- type: map_at_100
value: 40.727000000000004
- type: map_at_1000
value: 40.835
- type: map_at_3
value: 36.234
- type: map_at_5
value: 37.877
- type: mrr_at_1
value: 35.959
- type: mrr_at_10
value: 44.726
- type: mrr_at_100
value: 45.531
- type: mrr_at_1000
value: 45.582
- type: mrr_at_3
value: 42.047000000000004
- type: mrr_at_5
value: 43.611
- type: ndcg_at_1
value: 35.959
- type: ndcg_at_10
value: 45.303
- type: ndcg_at_100
value: 50.683
- type: ndcg_at_1000
value: 52.818
- type: ndcg_at_3
value: 39.987
- type: ndcg_at_5
value: 42.243
- type: precision_at_1
value: 35.959
- type: precision_at_10
value: 8.241999999999999
- type: precision_at_100
value: 1.274
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.836
- type: precision_at_5
value: 13.196
- type: recall_at_1
value: 29.391000000000002
- type: recall_at_10
value: 57.364000000000004
- type: recall_at_100
value: 80.683
- type: recall_at_1000
value: 94.918
- type: recall_at_3
value: 42.263
- type: recall_at_5
value: 48.634
- type: map_at_1
value: 26.791749999999997
- type: map_at_10
value: 35.75541666666667
- type: map_at_100
value: 37.00791666666667
- type: map_at_1000
value: 37.12408333333333
- type: map_at_3
value: 33.02966666666667
- type: map_at_5
value: 34.56866666666667
- type: mrr_at_1
value: 31.744333333333337
- type: mrr_at_10
value: 39.9925
- type: mrr_at_100
value: 40.86458333333333
- type: mrr_at_1000
value: 40.92175000000001
- type: mrr_at_3
value: 37.68183333333334
- type: mrr_at_5
value: 39.028499999999994
- type: ndcg_at_1
value: 31.744333333333337
- type: ndcg_at_10
value: 40.95008333333334
- type: ndcg_at_100
value: 46.25966666666667
- type: ndcg_at_1000
value: 48.535333333333334
- type: ndcg_at_3
value: 36.43333333333333
- type: ndcg_at_5
value: 38.602333333333334
- type: precision_at_1
value: 31.744333333333337
- type: precision_at_10
value: 7.135166666666666
- type: precision_at_100
value: 1.1535833333333334
- type: precision_at_1000
value: 0.15391666666666665
- type: precision_at_3
value: 16.713
- type: precision_at_5
value: 11.828416666666666
- type: recall_at_1
value: 26.791749999999997
- type: recall_at_10
value: 51.98625
- type: recall_at_100
value: 75.30358333333334
- type: recall_at_1000
value: 91.05433333333333
- type: recall_at_3
value: 39.39583333333333
- type: recall_at_5
value: 45.05925
- type: map_at_1
value: 22.219
- type: map_at_10
value: 29.162
- type: map_at_100
value: 30.049999999999997
- type: map_at_1000
value: 30.144
- type: map_at_3
value: 27.204
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 25.153
- type: mrr_at_10
value: 31.814999999999998
- type: mrr_at_100
value: 32.573
- type: mrr_at_1000
value: 32.645
- type: mrr_at_3
value: 29.934
- type: mrr_at_5
value: 30.946
- type: ndcg_at_1
value: 25.153
- type: ndcg_at_10
value: 33.099000000000004
- type: ndcg_at_100
value: 37.768
- type: ndcg_at_1000
value: 40.331
- type: ndcg_at_3
value: 29.473
- type: ndcg_at_5
value: 31.206
- type: precision_at_1
value: 25.153
- type: precision_at_10
value: 5.183999999999999
- type: precision_at_100
value: 0.8170000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 12.831999999999999
- type: precision_at_5
value: 8.895999999999999
- type: recall_at_1
value: 22.219
- type: recall_at_10
value: 42.637
- type: recall_at_100
value: 64.704
- type: recall_at_1000
value: 83.963
- type: recall_at_3
value: 32.444
- type: recall_at_5
value: 36.802
- type: map_at_1
value: 17.427999999999997
- type: map_at_10
value: 24.029
- type: map_at_100
value: 25.119999999999997
- type: map_at_1000
value: 25.257
- type: map_at_3
value: 22.016
- type: map_at_5
value: 23.143
- type: mrr_at_1
value: 21.129
- type: mrr_at_10
value: 27.750000000000004
- type: mrr_at_100
value: 28.666999999999998
- type: mrr_at_1000
value: 28.754999999999995
- type: mrr_at_3
value: 25.849
- type: mrr_at_5
value: 26.939999999999998
- type: ndcg_at_1
value: 21.129
- type: ndcg_at_10
value: 28.203
- type: ndcg_at_100
value: 33.44
- type: ndcg_at_1000
value: 36.61
- type: ndcg_at_3
value: 24.648999999999997
- type: ndcg_at_5
value: 26.316
- type: precision_at_1
value: 21.129
- type: precision_at_10
value: 5.055
- type: precision_at_100
value: 0.909
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.666
- type: precision_at_5
value: 8.3
- type: recall_at_1
value: 17.427999999999997
- type: recall_at_10
value: 36.923
- type: recall_at_100
value: 60.606
- type: recall_at_1000
value: 83.19
- type: recall_at_3
value: 26.845000000000002
- type: recall_at_5
value: 31.247000000000003
- type: map_at_1
value: 26.457000000000004
- type: map_at_10
value: 35.228
- type: map_at_100
value: 36.475
- type: map_at_1000
value: 36.585
- type: map_at_3
value: 32.444
- type: map_at_5
value: 34.046
- type: mrr_at_1
value: 30.784
- type: mrr_at_10
value: 39.133
- type: mrr_at_100
value: 40.11
- type: mrr_at_1000
value: 40.169
- type: mrr_at_3
value: 36.692
- type: mrr_at_5
value: 38.17
- type: ndcg_at_1
value: 30.784
- type: ndcg_at_10
value: 40.358
- type: ndcg_at_100
value: 46.119
- type: ndcg_at_1000
value: 48.428
- type: ndcg_at_3
value: 35.504000000000005
- type: ndcg_at_5
value: 37.864
- type: precision_at_1
value: 30.784
- type: precision_at_10
value: 6.800000000000001
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 15.920000000000002
- type: precision_at_5
value: 11.437
- type: recall_at_1
value: 26.457000000000004
- type: recall_at_10
value: 51.845
- type: recall_at_100
value: 77.046
- type: recall_at_1000
value: 92.892
- type: recall_at_3
value: 38.89
- type: recall_at_5
value: 44.688
- type: map_at_1
value: 29.378999999999998
- type: map_at_10
value: 37.373
- type: map_at_100
value: 39.107
- type: map_at_1000
value: 39.317
- type: map_at_3
value: 34.563
- type: map_at_5
value: 36.173
- type: mrr_at_1
value: 35.178
- type: mrr_at_10
value: 42.44
- type: mrr_at_100
value: 43.434
- type: mrr_at_1000
value: 43.482
- type: mrr_at_3
value: 39.987
- type: mrr_at_5
value: 41.370000000000005
- type: ndcg_at_1
value: 35.178
- type: ndcg_at_10
value: 42.82
- type: ndcg_at_100
value: 48.935
- type: ndcg_at_1000
value: 51.28
- type: ndcg_at_3
value: 38.562999999999995
- type: ndcg_at_5
value: 40.687
- type: precision_at_1
value: 35.178
- type: precision_at_10
value: 7.945
- type: precision_at_100
value: 1.524
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 17.721
- type: precision_at_5
value: 12.925
- type: recall_at_1
value: 29.378999999999998
- type: recall_at_10
value: 52.141999999999996
- type: recall_at_100
value: 79.49000000000001
- type: recall_at_1000
value: 93.782
- type: recall_at_3
value: 39.579
- type: recall_at_5
value: 45.462
- type: map_at_1
value: 19.814999999999998
- type: map_at_10
value: 27.383999999999997
- type: map_at_100
value: 28.483999999999998
- type: map_at_1000
value: 28.585
- type: map_at_3
value: 24.807000000000002
- type: map_at_5
value: 26.485999999999997
- type: mrr_at_1
value: 21.996
- type: mrr_at_10
value: 29.584
- type: mrr_at_100
value: 30.611
- type: mrr_at_1000
value: 30.684
- type: mrr_at_3
value: 27.11
- type: mrr_at_5
value: 28.746
- type: ndcg_at_1
value: 21.996
- type: ndcg_at_10
value: 32.024
- type: ndcg_at_100
value: 37.528
- type: ndcg_at_1000
value: 40.150999999999996
- type: ndcg_at_3
value: 27.016000000000002
- type: ndcg_at_5
value: 29.927999999999997
- type: precision_at_1
value: 21.996
- type: precision_at_10
value: 5.102
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.583
- type: precision_at_5
value: 8.577
- type: recall_at_1
value: 19.814999999999998
- type: recall_at_10
value: 44.239
- type: recall_at_100
value: 69.269
- type: recall_at_1000
value: 89.216
- type: recall_at_3
value: 31.102999999999998
- type: recall_at_5
value: 38.078
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.349
- type: map_at_10
value: 19.436
- type: map_at_100
value: 21.282999999999998
- type: map_at_1000
value: 21.479
- type: map_at_3
value: 15.841
- type: map_at_5
value: 17.558
- type: mrr_at_1
value: 25.863000000000003
- type: mrr_at_10
value: 37.218
- type: mrr_at_100
value: 38.198
- type: mrr_at_1000
value: 38.236
- type: mrr_at_3
value: 33.409
- type: mrr_at_5
value: 35.602000000000004
- type: ndcg_at_1
value: 25.863000000000003
- type: ndcg_at_10
value: 27.953
- type: ndcg_at_100
value: 35.327
- type: ndcg_at_1000
value: 38.708999999999996
- type: ndcg_at_3
value: 21.985
- type: ndcg_at_5
value: 23.957
- type: precision_at_1
value: 25.863000000000003
- type: precision_at_10
value: 8.99
- type: precision_at_100
value: 1.6889999999999998
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 16.308
- type: precision_at_5
value: 12.912
- type: recall_at_1
value: 11.349
- type: recall_at_10
value: 34.581
- type: recall_at_100
value: 60.178
- type: recall_at_1000
value: 78.88199999999999
- type: recall_at_3
value: 20.041999999999998
- type: recall_at_5
value: 25.458
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.893
- type: map_at_10
value: 15.457
- type: map_at_100
value: 20.905
- type: map_at_1000
value: 22.116
- type: map_at_3
value: 11.593
- type: map_at_5
value: 13.134
- type: mrr_at_1
value: 57.49999999999999
- type: mrr_at_10
value: 65.467
- type: mrr_at_100
value: 66.022
- type: mrr_at_1000
value: 66.039
- type: mrr_at_3
value: 63.458000000000006
- type: mrr_at_5
value: 64.546
- type: ndcg_at_1
value: 45.875
- type: ndcg_at_10
value: 33.344
- type: ndcg_at_100
value: 36.849
- type: ndcg_at_1000
value: 44.03
- type: ndcg_at_3
value: 37.504
- type: ndcg_at_5
value: 34.892
- type: precision_at_1
value: 57.49999999999999
- type: precision_at_10
value: 25.95
- type: precision_at_100
value: 7.89
- type: precision_at_1000
value: 1.669
- type: precision_at_3
value: 40.333000000000006
- type: precision_at_5
value: 33.050000000000004
- type: recall_at_1
value: 7.893
- type: recall_at_10
value: 20.724999999999998
- type: recall_at_100
value: 42.516
- type: recall_at_1000
value: 65.822
- type: recall_at_3
value: 12.615000000000002
- type: recall_at_5
value: 15.482000000000001
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.760000000000005
- type: f1
value: 45.51690565701713
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 53.882
- type: map_at_10
value: 65.902
- type: map_at_100
value: 66.33
- type: map_at_1000
value: 66.348
- type: map_at_3
value: 63.75999999999999
- type: map_at_5
value: 65.181
- type: mrr_at_1
value: 58.041
- type: mrr_at_10
value: 70.133
- type: mrr_at_100
value: 70.463
- type: mrr_at_1000
value: 70.47
- type: mrr_at_3
value: 68.164
- type: mrr_at_5
value: 69.465
- type: ndcg_at_1
value: 58.041
- type: ndcg_at_10
value: 71.84700000000001
- type: ndcg_at_100
value: 73.699
- type: ndcg_at_1000
value: 74.06700000000001
- type: ndcg_at_3
value: 67.855
- type: ndcg_at_5
value: 70.203
- type: precision_at_1
value: 58.041
- type: precision_at_10
value: 9.427000000000001
- type: precision_at_100
value: 1.049
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 27.278000000000002
- type: precision_at_5
value: 17.693
- type: recall_at_1
value: 53.882
- type: recall_at_10
value: 85.99
- type: recall_at_100
value: 94.09100000000001
- type: recall_at_1000
value: 96.612
- type: recall_at_3
value: 75.25
- type: recall_at_5
value: 80.997
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.165
- type: map_at_10
value: 31.845000000000002
- type: map_at_100
value: 33.678999999999995
- type: map_at_1000
value: 33.878
- type: map_at_3
value: 27.881
- type: map_at_5
value: 30.049999999999997
- type: mrr_at_1
value: 38.272
- type: mrr_at_10
value: 47.04
- type: mrr_at_100
value: 47.923
- type: mrr_at_1000
value: 47.973
- type: mrr_at_3
value: 44.985
- type: mrr_at_5
value: 46.150000000000006
- type: ndcg_at_1
value: 38.272
- type: ndcg_at_10
value: 39.177
- type: ndcg_at_100
value: 45.995000000000005
- type: ndcg_at_1000
value: 49.312
- type: ndcg_at_3
value: 36.135
- type: ndcg_at_5
value: 36.936
- type: precision_at_1
value: 38.272
- type: precision_at_10
value: 10.926
- type: precision_at_100
value: 1.809
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 24.331
- type: precision_at_5
value: 17.747
- type: recall_at_1
value: 19.165
- type: recall_at_10
value: 45.103
- type: recall_at_100
value: 70.295
- type: recall_at_1000
value: 90.592
- type: recall_at_3
value: 32.832
- type: recall_at_5
value: 37.905
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.397
- type: map_at_10
value: 44.83
- type: map_at_100
value: 45.716
- type: map_at_1000
value: 45.797
- type: map_at_3
value: 41.955999999999996
- type: map_at_5
value: 43.736999999999995
- type: mrr_at_1
value: 64.794
- type: mrr_at_10
value: 71.866
- type: mrr_at_100
value: 72.22
- type: mrr_at_1000
value: 72.238
- type: mrr_at_3
value: 70.416
- type: mrr_at_5
value: 71.304
- type: ndcg_at_1
value: 64.794
- type: ndcg_at_10
value: 54.186
- type: ndcg_at_100
value: 57.623000000000005
- type: ndcg_at_1000
value: 59.302
- type: ndcg_at_3
value: 49.703
- type: ndcg_at_5
value: 52.154999999999994
- type: precision_at_1
value: 64.794
- type: precision_at_10
value: 11.219
- type: precision_at_100
value: 1.394
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 30.767
- type: precision_at_5
value: 20.397000000000002
- type: recall_at_1
value: 32.397
- type: recall_at_10
value: 56.096999999999994
- type: recall_at_100
value: 69.696
- type: recall_at_1000
value: 80.88499999999999
- type: recall_at_3
value: 46.150999999999996
- type: recall_at_5
value: 50.993
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 81.1744
- type: ap
value: 75.44973697032414
- type: f1
value: 81.09901117955782
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.519000000000002
- type: map_at_10
value: 31.025000000000002
- type: map_at_100
value: 32.275999999999996
- type: map_at_1000
value: 32.329
- type: map_at_3
value: 27.132
- type: map_at_5
value: 29.415999999999997
- type: mrr_at_1
value: 20.115
- type: mrr_at_10
value: 31.569000000000003
- type: mrr_at_100
value: 32.768
- type: mrr_at_1000
value: 32.816
- type: mrr_at_3
value: 27.748
- type: mrr_at_5
value: 29.956
- type: ndcg_at_1
value: 20.115
- type: ndcg_at_10
value: 37.756
- type: ndcg_at_100
value: 43.858000000000004
- type: ndcg_at_1000
value: 45.199
- type: ndcg_at_3
value: 29.818
- type: ndcg_at_5
value: 33.875
- type: precision_at_1
value: 20.115
- type: precision_at_10
value: 6.122
- type: precision_at_100
value: 0.919
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.794
- type: precision_at_5
value: 9.731
- type: recall_at_1
value: 19.519000000000002
- type: recall_at_10
value: 58.62500000000001
- type: recall_at_100
value: 86.99
- type: recall_at_1000
value: 97.268
- type: recall_at_3
value: 37.002
- type: recall_at_5
value: 46.778
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.71865025079799
- type: f1
value: 93.38906173610519
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 70.2576379388965
- type: f1
value: 49.20405830249464
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.48486886348351
- type: f1
value: 64.92199176095157
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.59246805648958
- type: f1
value: 72.1222026389164
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.887642595096825
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.3764418784054
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.81544126336991
- type: mrr
value: 32.82666576268031
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.185
- type: map_at_10
value: 11.158
- type: map_at_100
value: 14.041
- type: map_at_1000
value: 15.360999999999999
- type: map_at_3
value: 8.417
- type: map_at_5
value: 9.378
- type: mrr_at_1
value: 44.582
- type: mrr_at_10
value: 53.083999999999996
- type: mrr_at_100
value: 53.787
- type: mrr_at_1000
value: 53.824000000000005
- type: mrr_at_3
value: 51.187000000000005
- type: mrr_at_5
value: 52.379
- type: ndcg_at_1
value: 42.57
- type: ndcg_at_10
value: 31.593
- type: ndcg_at_100
value: 29.093999999999998
- type: ndcg_at_1000
value: 37.909
- type: ndcg_at_3
value: 37.083
- type: ndcg_at_5
value: 34.397
- type: precision_at_1
value: 43.963
- type: precision_at_10
value: 23.498
- type: precision_at_100
value: 7.6160000000000005
- type: precision_at_1000
value: 2.032
- type: precision_at_3
value: 34.572
- type: precision_at_5
value: 29.412
- type: recall_at_1
value: 5.185
- type: recall_at_10
value: 15.234
- type: recall_at_100
value: 29.49
- type: recall_at_1000
value: 62.273999999999994
- type: recall_at_3
value: 9.55
- type: recall_at_5
value: 11.103
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.803
- type: map_at_10
value: 38.183
- type: map_at_100
value: 39.421
- type: map_at_1000
value: 39.464
- type: map_at_3
value: 33.835
- type: map_at_5
value: 36.327
- type: mrr_at_1
value: 26.68
- type: mrr_at_10
value: 40.439
- type: mrr_at_100
value: 41.415
- type: mrr_at_1000
value: 41.443999999999996
- type: mrr_at_3
value: 36.612
- type: mrr_at_5
value: 38.877
- type: ndcg_at_1
value: 26.68
- type: ndcg_at_10
value: 45.882
- type: ndcg_at_100
value: 51.227999999999994
- type: ndcg_at_1000
value: 52.207
- type: ndcg_at_3
value: 37.511
- type: ndcg_at_5
value: 41.749
- type: precision_at_1
value: 26.68
- type: precision_at_10
value: 7.9750000000000005
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 17.449
- type: precision_at_5
value: 12.897
- type: recall_at_1
value: 23.803
- type: recall_at_10
value: 67.152
- type: recall_at_100
value: 90.522
- type: recall_at_1000
value: 97.743
- type: recall_at_3
value: 45.338
- type: recall_at_5
value: 55.106
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.473
- type: map_at_10
value: 84.452
- type: map_at_100
value: 85.101
- type: map_at_1000
value: 85.115
- type: map_at_3
value: 81.435
- type: map_at_5
value: 83.338
- type: mrr_at_1
value: 81.19
- type: mrr_at_10
value: 87.324
- type: mrr_at_100
value: 87.434
- type: mrr_at_1000
value: 87.435
- type: mrr_at_3
value: 86.31
- type: mrr_at_5
value: 87.002
- type: ndcg_at_1
value: 81.21000000000001
- type: ndcg_at_10
value: 88.19
- type: ndcg_at_100
value: 89.44
- type: ndcg_at_1000
value: 89.526
- type: ndcg_at_3
value: 85.237
- type: ndcg_at_5
value: 86.892
- type: precision_at_1
value: 81.21000000000001
- type: precision_at_10
value: 13.417000000000002
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.59
- type: recall_at_1
value: 70.473
- type: recall_at_10
value: 95.367
- type: recall_at_100
value: 99.616
- type: recall_at_1000
value: 99.996
- type: recall_at_3
value: 86.936
- type: recall_at_5
value: 91.557
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.25776525253911
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.22135271663078
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.003
- type: map_at_10
value: 10.062999999999999
- type: map_at_100
value: 11.854000000000001
- type: map_at_1000
value: 12.145999999999999
- type: map_at_3
value: 7.242
- type: map_at_5
value: 8.652999999999999
- type: mrr_at_1
value: 19.7
- type: mrr_at_10
value: 29.721999999999998
- type: mrr_at_100
value: 30.867
- type: mrr_at_1000
value: 30.944
- type: mrr_at_3
value: 26.683
- type: mrr_at_5
value: 28.498
- type: ndcg_at_1
value: 19.7
- type: ndcg_at_10
value: 17.095
- type: ndcg_at_100
value: 24.375
- type: ndcg_at_1000
value: 29.831000000000003
- type: ndcg_at_3
value: 16.305
- type: ndcg_at_5
value: 14.291
- type: precision_at_1
value: 19.7
- type: precision_at_10
value: 8.799999999999999
- type: precision_at_100
value: 1.9349999999999998
- type: precision_at_1000
value: 0.32399999999999995
- type: precision_at_3
value: 15.2
- type: precision_at_5
value: 12.540000000000001
- type: recall_at_1
value: 4.003
- type: recall_at_10
value: 17.877000000000002
- type: recall_at_100
value: 39.217
- type: recall_at_1000
value: 65.862
- type: recall_at_3
value: 9.242
- type: recall_at_5
value: 12.715000000000002
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 80.25888668589654
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 77.02037527837669
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 86.58432681008449
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.31697756099051
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 88.18867599667057
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 84.87853941747623
- 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: 89.46479925383916
- 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: 66.45272113649146
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.43357313527851
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.82761687254882
- type: mrr
value: 93.46223674655047
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.583
- type: map_at_10
value: 52.978
- type: map_at_100
value: 53.803
- type: map_at_1000
value: 53.839999999999996
- type: map_at_3
value: 50.03300000000001
- type: map_at_5
value: 51.939
- type: mrr_at_1
value: 47.0
- type: mrr_at_10
value: 54.730000000000004
- type: mrr_at_100
value: 55.31399999999999
- type: mrr_at_1000
value: 55.346
- type: mrr_at_3
value: 52.0
- type: mrr_at_5
value: 53.783
- type: ndcg_at_1
value: 47.0
- type: ndcg_at_10
value: 57.82899999999999
- type: ndcg_at_100
value: 61.49400000000001
- type: ndcg_at_1000
value: 62.676
- type: ndcg_at_3
value: 52.373000000000005
- type: ndcg_at_5
value: 55.481
- type: precision_at_1
value: 47.0
- type: precision_at_10
value: 7.867
- type: precision_at_100
value: 0.997
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 20.556
- type: precision_at_5
value: 14.066999999999998
- type: recall_at_1
value: 44.583
- type: recall_at_10
value: 71.172
- type: recall_at_100
value: 87.7
- type: recall_at_1000
value: 97.333
- type: recall_at_3
value: 56.511
- type: recall_at_5
value: 64.206
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.66237623762376
- type: cos_sim_ap
value: 90.35465126226322
- type: cos_sim_f1
value: 82.44575936883628
- type: cos_sim_precision
value: 81.32295719844358
- type: cos_sim_recall
value: 83.6
- type: dot_accuracy
value: 99.66237623762376
- type: dot_ap
value: 90.35464287920453
- type: dot_f1
value: 82.44575936883628
- type: dot_precision
value: 81.32295719844358
- type: dot_recall
value: 83.6
- type: euclidean_accuracy
value: 99.66237623762376
- type: euclidean_ap
value: 90.3546512622632
- type: euclidean_f1
value: 82.44575936883628
- type: euclidean_precision
value: 81.32295719844358
- type: euclidean_recall
value: 83.6
- type: manhattan_accuracy
value: 99.65940594059406
- type: manhattan_ap
value: 90.29220174849843
- type: manhattan_f1
value: 82.4987605354487
- type: manhattan_precision
value: 81.80924287118977
- type: manhattan_recall
value: 83.2
- type: max_accuracy
value: 99.66237623762376
- type: max_ap
value: 90.35465126226322
- type: max_f1
value: 82.4987605354487
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.0394225901397
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.27954189859326
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.99055979974896
- type: mrr
value: 51.82745257193787
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.21655465344237
- type: cos_sim_spearman
value: 29.853205339630172
- type: dot_pearson
value: 30.216540628083564
- type: dot_spearman
value: 29.868978894753027
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.2
- type: map_at_10
value: 1.398
- type: map_at_100
value: 7.406
- type: map_at_1000
value: 18.401
- type: map_at_3
value: 0.479
- type: map_at_5
value: 0.772
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 79.25999999999999
- type: mrr_at_100
value: 79.25999999999999
- type: mrr_at_1000
value: 79.25999999999999
- type: mrr_at_3
value: 77.333
- type: mrr_at_5
value: 78.133
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 58.548
- type: ndcg_at_100
value: 45.216
- type: ndcg_at_1000
value: 41.149
- type: ndcg_at_3
value: 60.641999999999996
- type: ndcg_at_5
value: 61.135
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 64.0
- type: precision_at_100
value: 46.92
- type: precision_at_1000
value: 18.642
- type: precision_at_3
value: 64.667
- type: precision_at_5
value: 66.4
- type: recall_at_1
value: 0.2
- type: recall_at_10
value: 1.6729999999999998
- type: recall_at_100
value: 10.856
- type: recall_at_1000
value: 38.964999999999996
- type: recall_at_3
value: 0.504
- type: recall_at_5
value: 0.852
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.6629999999999998
- type: map_at_10
value: 8.601
- type: map_at_100
value: 14.354
- type: map_at_1000
value: 15.927
- type: map_at_3
value: 4.1930000000000005
- type: map_at_5
value: 5.655
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 34.466
- type: mrr_at_100
value: 35.235
- type: mrr_at_1000
value: 35.27
- type: mrr_at_3
value: 28.571
- type: mrr_at_5
value: 31.531
- type: ndcg_at_1
value: 14.285999999999998
- type: ndcg_at_10
value: 20.374
- type: ndcg_at_100
value: 33.532000000000004
- type: ndcg_at_1000
value: 45.561
- type: ndcg_at_3
value: 18.442
- type: ndcg_at_5
value: 18.076
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 20.204
- type: precision_at_100
value: 7.489999999999999
- type: precision_at_1000
value: 1.5630000000000002
- type: precision_at_3
value: 21.769
- type: precision_at_5
value: 20.408
- type: recall_at_1
value: 1.6629999999999998
- type: recall_at_10
value: 15.549
- type: recall_at_100
value: 47.497
- type: recall_at_1000
value: 84.524
- type: recall_at_3
value: 5.289
- type: recall_at_5
value: 8.035
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.8194
- type: ap
value: 14.447702451658554
- type: f1
value: 55.13659412856185
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.310696095076416
- type: f1
value: 63.360434851097814
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.30677907335145
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.12386004649221
- type: cos_sim_ap
value: 73.99096426215495
- type: cos_sim_f1
value: 68.18416968442834
- type: cos_sim_precision
value: 66.86960933536275
- type: cos_sim_recall
value: 69.55145118733509
- type: dot_accuracy
value: 86.12386004649221
- type: dot_ap
value: 73.99096813038672
- type: dot_f1
value: 68.18416968442834
- type: dot_precision
value: 66.86960933536275
- type: dot_recall
value: 69.55145118733509
- type: euclidean_accuracy
value: 86.12386004649221
- type: euclidean_ap
value: 73.99095984980165
- type: euclidean_f1
value: 68.18416968442834
- type: euclidean_precision
value: 66.86960933536275
- type: euclidean_recall
value: 69.55145118733509
- type: manhattan_accuracy
value: 86.09405734040651
- type: manhattan_ap
value: 73.96825745608601
- type: manhattan_f1
value: 68.13888179729383
- type: manhattan_precision
value: 65.99901088031652
- type: manhattan_recall
value: 70.42216358839049
- type: max_accuracy
value: 86.12386004649221
- type: max_ap
value: 73.99096813038672
- type: max_f1
value: 68.18416968442834
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.99367407924865
- type: cos_sim_ap
value: 86.19720829843081
- type: cos_sim_f1
value: 78.39889075384951
- type: cos_sim_precision
value: 74.5110278818144
- type: cos_sim_recall
value: 82.71481367416075
- type: dot_accuracy
value: 88.99367407924865
- type: dot_ap
value: 86.19718471454047
- type: dot_f1
value: 78.39889075384951
- type: dot_precision
value: 74.5110278818144
- type: dot_recall
value: 82.71481367416075
- type: euclidean_accuracy
value: 88.99367407924865
- type: euclidean_ap
value: 86.1972021422436
- type: euclidean_f1
value: 78.39889075384951
- type: euclidean_precision
value: 74.5110278818144
- type: euclidean_recall
value: 82.71481367416075
- type: manhattan_accuracy
value: 88.95680521597392
- type: manhattan_ap
value: 86.16659921351506
- type: manhattan_f1
value: 78.39125971550081
- type: manhattan_precision
value: 74.82502799552073
- type: manhattan_recall
value: 82.31444410224823
- type: max_accuracy
value: 88.99367407924865
- type: max_ap
value: 86.19720829843081
- type: max_f1
value: 78.39889075384951
---
# hkunlp/instructor-base
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-base) 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-base) 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-base')
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"
] |
BAAI/bge-base-zh
|
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"
] | 2023-08-05T08:02:30 | 2023-10-12T03:37:45 | 11,361 | 52 |
---
language:
- zh
license: mit
---
**Recommend switching to newest [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-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"
] |
GritLM/GritLM-7B
|
GritLM
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"mteb",
"conversational",
"custom_code",
"dataset:GritLM/tulu2",
"arxiv:2402.09906",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-11T15:55:35 | 2024-02-16T10:14:51 | 11,128 | 97 |
---
datasets:
- GritLM/tulu2
license: apache-2.0
pipeline_tag: text-generation
tags:
- mteb
inference: true
model-index:
- name: GritLM-7B
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 81.17910447761194
- type: ap
value: 46.26260671758199
- type: f1
value: 75.44565719934167
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.5161
- type: ap
value: 94.79131981460425
- type: f1
value: 96.51506148413065
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 57.806000000000004
- type: f1
value: 56.78350156257903
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.478
- type: map_at_10
value: 54.955
- type: map_at_100
value: 54.955
- type: map_at_1000
value: 54.955
- type: map_at_3
value: 50.888999999999996
- type: map_at_5
value: 53.349999999999994
- type: mrr_at_1
value: 39.757999999999996
- type: mrr_at_10
value: 55.449000000000005
- type: mrr_at_100
value: 55.449000000000005
- type: mrr_at_1000
value: 55.449000000000005
- type: mrr_at_3
value: 51.37500000000001
- type: mrr_at_5
value: 53.822
- type: ndcg_at_1
value: 38.478
- type: ndcg_at_10
value: 63.239999999999995
- type: ndcg_at_100
value: 63.239999999999995
- type: ndcg_at_1000
value: 63.239999999999995
- type: ndcg_at_3
value: 54.935
- type: ndcg_at_5
value: 59.379000000000005
- type: precision_at_1
value: 38.478
- type: precision_at_10
value: 8.933
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.089
- type: precision_at_3
value: 22.214
- type: precision_at_5
value: 15.491
- type: recall_at_1
value: 38.478
- type: recall_at_10
value: 89.331
- type: recall_at_100
value: 89.331
- type: recall_at_1000
value: 89.331
- type: recall_at_3
value: 66.643
- type: recall_at_5
value: 77.45400000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 51.67144081472449
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 48.11256154264126
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.33801955487878
- type: mrr
value: 80.71549487754474
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.1935203751726
- type: cos_sim_spearman
value: 86.35497970498659
- type: euclidean_pearson
value: 85.46910708503744
- type: euclidean_spearman
value: 85.13928935405485
- type: manhattan_pearson
value: 85.68373836333303
- type: manhattan_spearman
value: 85.40013867117746
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.46753246753248
- type: f1
value: 88.43006344981134
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.86793640310432
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 39.80291334130727
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.421
- type: map_at_10
value: 52.349000000000004
- type: map_at_100
value: 52.349000000000004
- type: map_at_1000
value: 52.349000000000004
- type: map_at_3
value: 48.17
- type: map_at_5
value: 50.432
- type: mrr_at_1
value: 47.353
- type: mrr_at_10
value: 58.387
- type: mrr_at_100
value: 58.387
- type: mrr_at_1000
value: 58.387
- type: mrr_at_3
value: 56.199
- type: mrr_at_5
value: 57.487
- type: ndcg_at_1
value: 47.353
- type: ndcg_at_10
value: 59.202
- type: ndcg_at_100
value: 58.848
- type: ndcg_at_1000
value: 58.831999999999994
- type: ndcg_at_3
value: 54.112
- type: ndcg_at_5
value: 56.312
- type: precision_at_1
value: 47.353
- type: precision_at_10
value: 11.459
- type: precision_at_100
value: 1.146
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 26.133
- type: precision_at_5
value: 18.627
- type: recall_at_1
value: 38.421
- type: recall_at_10
value: 71.89
- type: recall_at_100
value: 71.89
- type: recall_at_1000
value: 71.89
- type: recall_at_3
value: 56.58
- type: recall_at_5
value: 63.125
- type: map_at_1
value: 38.025999999999996
- type: map_at_10
value: 50.590999999999994
- type: map_at_100
value: 51.99700000000001
- type: map_at_1000
value: 52.11599999999999
- type: map_at_3
value: 47.435
- type: map_at_5
value: 49.236000000000004
- type: mrr_at_1
value: 48.28
- type: mrr_at_10
value: 56.814
- type: mrr_at_100
value: 57.446
- type: mrr_at_1000
value: 57.476000000000006
- type: mrr_at_3
value: 54.958
- type: mrr_at_5
value: 56.084999999999994
- type: ndcg_at_1
value: 48.28
- type: ndcg_at_10
value: 56.442
- type: ndcg_at_100
value: 60.651999999999994
- type: ndcg_at_1000
value: 62.187000000000005
- type: ndcg_at_3
value: 52.866
- type: ndcg_at_5
value: 54.515
- type: precision_at_1
value: 48.28
- type: precision_at_10
value: 10.586
- type: precision_at_100
value: 1.6310000000000002
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_3
value: 25.945
- type: precision_at_5
value: 18.076
- type: recall_at_1
value: 38.025999999999996
- type: recall_at_10
value: 66.11399999999999
- type: recall_at_100
value: 83.339
- type: recall_at_1000
value: 92.413
- type: recall_at_3
value: 54.493
- type: recall_at_5
value: 59.64699999999999
- type: map_at_1
value: 47.905
- type: map_at_10
value: 61.58
- type: map_at_100
value: 62.605
- type: map_at_1000
value: 62.637
- type: map_at_3
value: 58.074000000000005
- type: map_at_5
value: 60.260000000000005
- type: mrr_at_1
value: 54.42
- type: mrr_at_10
value: 64.847
- type: mrr_at_100
value: 65.403
- type: mrr_at_1000
value: 65.41900000000001
- type: mrr_at_3
value: 62.675000000000004
- type: mrr_at_5
value: 64.101
- type: ndcg_at_1
value: 54.42
- type: ndcg_at_10
value: 67.394
- type: ndcg_at_100
value: 70.846
- type: ndcg_at_1000
value: 71.403
- type: ndcg_at_3
value: 62.025
- type: ndcg_at_5
value: 65.032
- type: precision_at_1
value: 54.42
- type: precision_at_10
value: 10.646
- type: precision_at_100
value: 1.325
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 27.398
- type: precision_at_5
value: 18.796
- type: recall_at_1
value: 47.905
- type: recall_at_10
value: 80.84599999999999
- type: recall_at_100
value: 95.078
- type: recall_at_1000
value: 98.878
- type: recall_at_3
value: 67.05600000000001
- type: recall_at_5
value: 74.261
- type: map_at_1
value: 30.745
- type: map_at_10
value: 41.021
- type: map_at_100
value: 41.021
- type: map_at_1000
value: 41.021
- type: map_at_3
value: 37.714999999999996
- type: map_at_5
value: 39.766
- type: mrr_at_1
value: 33.559
- type: mrr_at_10
value: 43.537
- type: mrr_at_100
value: 43.537
- type: mrr_at_1000
value: 43.537
- type: mrr_at_3
value: 40.546
- type: mrr_at_5
value: 42.439
- type: ndcg_at_1
value: 33.559
- type: ndcg_at_10
value: 46.781
- type: ndcg_at_100
value: 46.781
- type: ndcg_at_1000
value: 46.781
- type: ndcg_at_3
value: 40.516000000000005
- type: ndcg_at_5
value: 43.957
- type: precision_at_1
value: 33.559
- type: precision_at_10
value: 7.198
- type: precision_at_100
value: 0.72
- type: precision_at_1000
value: 0.07200000000000001
- type: precision_at_3
value: 17.1
- type: precision_at_5
value: 12.316
- type: recall_at_1
value: 30.745
- type: recall_at_10
value: 62.038000000000004
- type: recall_at_100
value: 62.038000000000004
- type: recall_at_1000
value: 62.038000000000004
- type: recall_at_3
value: 45.378
- type: recall_at_5
value: 53.580000000000005
- type: map_at_1
value: 19.637999999999998
- type: map_at_10
value: 31.05
- type: map_at_100
value: 31.05
- type: map_at_1000
value: 31.05
- type: map_at_3
value: 27.628000000000004
- type: map_at_5
value: 29.767
- type: mrr_at_1
value: 25.0
- type: mrr_at_10
value: 36.131
- type: mrr_at_100
value: 36.131
- type: mrr_at_1000
value: 36.131
- type: mrr_at_3
value: 33.333
- type: mrr_at_5
value: 35.143
- type: ndcg_at_1
value: 25.0
- type: ndcg_at_10
value: 37.478
- type: ndcg_at_100
value: 37.469
- type: ndcg_at_1000
value: 37.469
- type: ndcg_at_3
value: 31.757999999999996
- type: ndcg_at_5
value: 34.821999999999996
- type: precision_at_1
value: 25.0
- type: precision_at_10
value: 7.188999999999999
- type: precision_at_100
value: 0.719
- type: precision_at_1000
value: 0.07200000000000001
- type: precision_at_3
value: 15.837000000000002
- type: precision_at_5
value: 11.841
- type: recall_at_1
value: 19.637999999999998
- type: recall_at_10
value: 51.836000000000006
- type: recall_at_100
value: 51.836000000000006
- type: recall_at_1000
value: 51.836000000000006
- type: recall_at_3
value: 36.384
- type: recall_at_5
value: 43.964
- type: map_at_1
value: 34.884
- type: map_at_10
value: 47.88
- type: map_at_100
value: 47.88
- type: map_at_1000
value: 47.88
- type: map_at_3
value: 43.85
- type: map_at_5
value: 46.414
- type: mrr_at_1
value: 43.022
- type: mrr_at_10
value: 53.569
- type: mrr_at_100
value: 53.569
- type: mrr_at_1000
value: 53.569
- type: mrr_at_3
value: 51.075
- type: mrr_at_5
value: 52.725
- type: ndcg_at_1
value: 43.022
- type: ndcg_at_10
value: 54.461000000000006
- type: ndcg_at_100
value: 54.388000000000005
- type: ndcg_at_1000
value: 54.388000000000005
- type: ndcg_at_3
value: 48.864999999999995
- type: ndcg_at_5
value: 52.032000000000004
- type: precision_at_1
value: 43.022
- type: precision_at_10
value: 9.885
- type: precision_at_100
value: 0.988
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 23.612
- type: precision_at_5
value: 16.997
- type: recall_at_1
value: 34.884
- type: recall_at_10
value: 68.12899999999999
- type: recall_at_100
value: 68.12899999999999
- type: recall_at_1000
value: 68.12899999999999
- type: recall_at_3
value: 52.428
- type: recall_at_5
value: 60.662000000000006
- type: map_at_1
value: 31.588
- type: map_at_10
value: 43.85
- type: map_at_100
value: 45.317
- type: map_at_1000
value: 45.408
- type: map_at_3
value: 39.73
- type: map_at_5
value: 42.122
- type: mrr_at_1
value: 38.927
- type: mrr_at_10
value: 49.582
- type: mrr_at_100
value: 50.39
- type: mrr_at_1000
value: 50.426
- type: mrr_at_3
value: 46.518
- type: mrr_at_5
value: 48.271
- type: ndcg_at_1
value: 38.927
- type: ndcg_at_10
value: 50.605999999999995
- type: ndcg_at_100
value: 56.22200000000001
- type: ndcg_at_1000
value: 57.724
- type: ndcg_at_3
value: 44.232
- type: ndcg_at_5
value: 47.233999999999995
- type: precision_at_1
value: 38.927
- type: precision_at_10
value: 9.429
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.172
- type: precision_at_3
value: 21.271
- type: precision_at_5
value: 15.434000000000001
- type: recall_at_1
value: 31.588
- type: recall_at_10
value: 64.836
- type: recall_at_100
value: 88.066
- type: recall_at_1000
value: 97.748
- type: recall_at_3
value: 47.128
- type: recall_at_5
value: 54.954
- type: map_at_1
value: 31.956083333333336
- type: map_at_10
value: 43.33483333333333
- type: map_at_100
value: 44.64883333333333
- type: map_at_1000
value: 44.75
- type: map_at_3
value: 39.87741666666666
- type: map_at_5
value: 41.86766666666667
- type: mrr_at_1
value: 38.06341666666667
- type: mrr_at_10
value: 47.839666666666666
- type: mrr_at_100
value: 48.644000000000005
- type: mrr_at_1000
value: 48.68566666666667
- type: mrr_at_3
value: 45.26358333333334
- type: mrr_at_5
value: 46.790000000000006
- type: ndcg_at_1
value: 38.06341666666667
- type: ndcg_at_10
value: 49.419333333333334
- type: ndcg_at_100
value: 54.50166666666667
- type: ndcg_at_1000
value: 56.161166666666674
- type: ndcg_at_3
value: 43.982416666666666
- type: ndcg_at_5
value: 46.638083333333334
- type: precision_at_1
value: 38.06341666666667
- type: precision_at_10
value: 8.70858333333333
- type: precision_at_100
value: 1.327
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.37816666666667
- type: precision_at_5
value: 14.516333333333334
- type: recall_at_1
value: 31.956083333333336
- type: recall_at_10
value: 62.69458333333334
- type: recall_at_100
value: 84.46433333333334
- type: recall_at_1000
value: 95.58449999999999
- type: recall_at_3
value: 47.52016666666666
- type: recall_at_5
value: 54.36066666666666
- type: map_at_1
value: 28.912
- type: map_at_10
value: 38.291
- type: map_at_100
value: 39.44
- type: map_at_1000
value: 39.528
- type: map_at_3
value: 35.638
- type: map_at_5
value: 37.218
- type: mrr_at_1
value: 32.822
- type: mrr_at_10
value: 41.661
- type: mrr_at_100
value: 42.546
- type: mrr_at_1000
value: 42.603
- type: mrr_at_3
value: 39.238
- type: mrr_at_5
value: 40.726
- type: ndcg_at_1
value: 32.822
- type: ndcg_at_10
value: 43.373
- type: ndcg_at_100
value: 48.638
- type: ndcg_at_1000
value: 50.654999999999994
- type: ndcg_at_3
value: 38.643
- type: ndcg_at_5
value: 41.126000000000005
- type: precision_at_1
value: 32.822
- type: precision_at_10
value: 6.8709999999999996
- type: precision_at_100
value: 1.032
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 16.82
- type: precision_at_5
value: 11.718
- type: recall_at_1
value: 28.912
- type: recall_at_10
value: 55.376999999999995
- type: recall_at_100
value: 79.066
- type: recall_at_1000
value: 93.664
- type: recall_at_3
value: 42.569
- type: recall_at_5
value: 48.719
- type: map_at_1
value: 22.181
- type: map_at_10
value: 31.462
- type: map_at_100
value: 32.73
- type: map_at_1000
value: 32.848
- type: map_at_3
value: 28.57
- type: map_at_5
value: 30.182
- type: mrr_at_1
value: 27.185
- type: mrr_at_10
value: 35.846000000000004
- type: mrr_at_100
value: 36.811
- type: mrr_at_1000
value: 36.873
- type: mrr_at_3
value: 33.437
- type: mrr_at_5
value: 34.813
- type: ndcg_at_1
value: 27.185
- type: ndcg_at_10
value: 36.858000000000004
- type: ndcg_at_100
value: 42.501
- type: ndcg_at_1000
value: 44.945
- type: ndcg_at_3
value: 32.066
- type: ndcg_at_5
value: 34.29
- type: precision_at_1
value: 27.185
- type: precision_at_10
value: 6.752
- type: precision_at_100
value: 1.111
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 15.290000000000001
- type: precision_at_5
value: 11.004999999999999
- type: recall_at_1
value: 22.181
- type: recall_at_10
value: 48.513
- type: recall_at_100
value: 73.418
- type: recall_at_1000
value: 90.306
- type: recall_at_3
value: 35.003
- type: recall_at_5
value: 40.876000000000005
- type: map_at_1
value: 33.934999999999995
- type: map_at_10
value: 44.727
- type: map_at_100
value: 44.727
- type: map_at_1000
value: 44.727
- type: map_at_3
value: 40.918
- type: map_at_5
value: 42.961
- type: mrr_at_1
value: 39.646
- type: mrr_at_10
value: 48.898
- type: mrr_at_100
value: 48.898
- type: mrr_at_1000
value: 48.898
- type: mrr_at_3
value: 45.896
- type: mrr_at_5
value: 47.514
- type: ndcg_at_1
value: 39.646
- type: ndcg_at_10
value: 50.817
- type: ndcg_at_100
value: 50.803
- type: ndcg_at_1000
value: 50.803
- type: ndcg_at_3
value: 44.507999999999996
- type: ndcg_at_5
value: 47.259
- type: precision_at_1
value: 39.646
- type: precision_at_10
value: 8.759
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.08800000000000001
- type: precision_at_3
value: 20.274
- type: precision_at_5
value: 14.366000000000001
- type: recall_at_1
value: 33.934999999999995
- type: recall_at_10
value: 65.037
- type: recall_at_100
value: 65.037
- type: recall_at_1000
value: 65.037
- type: recall_at_3
value: 47.439
- type: recall_at_5
value: 54.567
- type: map_at_1
value: 32.058
- type: map_at_10
value: 43.137
- type: map_at_100
value: 43.137
- type: map_at_1000
value: 43.137
- type: map_at_3
value: 39.882
- type: map_at_5
value: 41.379
- type: mrr_at_1
value: 38.933
- type: mrr_at_10
value: 48.344
- type: mrr_at_100
value: 48.344
- type: mrr_at_1000
value: 48.344
- type: mrr_at_3
value: 45.652
- type: mrr_at_5
value: 46.877
- type: ndcg_at_1
value: 38.933
- type: ndcg_at_10
value: 49.964
- type: ndcg_at_100
value: 49.242000000000004
- type: ndcg_at_1000
value: 49.222
- type: ndcg_at_3
value: 44.605
- type: ndcg_at_5
value: 46.501999999999995
- type: precision_at_1
value: 38.933
- type: precision_at_10
value: 9.427000000000001
- type: precision_at_100
value: 0.943
- type: precision_at_1000
value: 0.094
- type: precision_at_3
value: 20.685000000000002
- type: precision_at_5
value: 14.585
- type: recall_at_1
value: 32.058
- type: recall_at_10
value: 63.074
- type: recall_at_100
value: 63.074
- type: recall_at_1000
value: 63.074
- type: recall_at_3
value: 47.509
- type: recall_at_5
value: 52.455
- type: map_at_1
value: 26.029000000000003
- type: map_at_10
value: 34.646
- type: map_at_100
value: 34.646
- type: map_at_1000
value: 34.646
- type: map_at_3
value: 31.456
- type: map_at_5
value: 33.138
- type: mrr_at_1
value: 28.281
- type: mrr_at_10
value: 36.905
- type: mrr_at_100
value: 36.905
- type: mrr_at_1000
value: 36.905
- type: mrr_at_3
value: 34.011
- type: mrr_at_5
value: 35.638
- type: ndcg_at_1
value: 28.281
- type: ndcg_at_10
value: 40.159
- type: ndcg_at_100
value: 40.159
- type: ndcg_at_1000
value: 40.159
- type: ndcg_at_3
value: 33.995
- type: ndcg_at_5
value: 36.836999999999996
- type: precision_at_1
value: 28.281
- type: precision_at_10
value: 6.358999999999999
- type: precision_at_100
value: 0.636
- type: precision_at_1000
value: 0.064
- type: precision_at_3
value: 14.233
- type: precision_at_5
value: 10.314
- type: recall_at_1
value: 26.029000000000003
- type: recall_at_10
value: 55.08
- type: recall_at_100
value: 55.08
- type: recall_at_1000
value: 55.08
- type: recall_at_3
value: 38.487
- type: recall_at_5
value: 45.308
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.842999999999998
- type: map_at_10
value: 22.101000000000003
- type: map_at_100
value: 24.319
- type: map_at_1000
value: 24.51
- type: map_at_3
value: 18.372
- type: map_at_5
value: 20.323
- type: mrr_at_1
value: 27.948
- type: mrr_at_10
value: 40.321
- type: mrr_at_100
value: 41.262
- type: mrr_at_1000
value: 41.297
- type: mrr_at_3
value: 36.558
- type: mrr_at_5
value: 38.824999999999996
- type: ndcg_at_1
value: 27.948
- type: ndcg_at_10
value: 30.906
- type: ndcg_at_100
value: 38.986
- type: ndcg_at_1000
value: 42.136
- type: ndcg_at_3
value: 24.911
- type: ndcg_at_5
value: 27.168999999999997
- type: precision_at_1
value: 27.948
- type: precision_at_10
value: 9.798
- type: precision_at_100
value: 1.8399999999999999
- type: precision_at_1000
value: 0.243
- type: precision_at_3
value: 18.328
- type: precision_at_5
value: 14.502
- type: recall_at_1
value: 12.842999999999998
- type: recall_at_10
value: 37.245
- type: recall_at_100
value: 64.769
- type: recall_at_1000
value: 82.055
- type: recall_at_3
value: 23.159
- type: recall_at_5
value: 29.113
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.934000000000001
- type: map_at_10
value: 21.915000000000003
- type: map_at_100
value: 21.915000000000003
- type: map_at_1000
value: 21.915000000000003
- type: map_at_3
value: 14.623
- type: map_at_5
value: 17.841
- type: mrr_at_1
value: 71.25
- type: mrr_at_10
value: 78.994
- type: mrr_at_100
value: 78.994
- type: mrr_at_1000
value: 78.994
- type: mrr_at_3
value: 77.208
- type: mrr_at_5
value: 78.55799999999999
- type: ndcg_at_1
value: 60.62499999999999
- type: ndcg_at_10
value: 46.604
- type: ndcg_at_100
value: 35.653
- type: ndcg_at_1000
value: 35.531
- type: ndcg_at_3
value: 50.605
- type: ndcg_at_5
value: 48.730000000000004
- type: precision_at_1
value: 71.25
- type: precision_at_10
value: 37.75
- type: precision_at_100
value: 3.775
- type: precision_at_1000
value: 0.377
- type: precision_at_3
value: 54.417
- type: precision_at_5
value: 48.15
- type: recall_at_1
value: 8.934000000000001
- type: recall_at_10
value: 28.471000000000004
- type: recall_at_100
value: 28.471000000000004
- type: recall_at_1000
value: 28.471000000000004
- type: recall_at_3
value: 16.019
- type: recall_at_5
value: 21.410999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.81
- type: f1
value: 47.987573380720114
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.81899999999999
- type: map_at_10
value: 78.034
- type: map_at_100
value: 78.034
- type: map_at_1000
value: 78.034
- type: map_at_3
value: 76.43100000000001
- type: map_at_5
value: 77.515
- type: mrr_at_1
value: 71.542
- type: mrr_at_10
value: 81.638
- type: mrr_at_100
value: 81.638
- type: mrr_at_1000
value: 81.638
- type: mrr_at_3
value: 80.403
- type: mrr_at_5
value: 81.256
- type: ndcg_at_1
value: 71.542
- type: ndcg_at_10
value: 82.742
- type: ndcg_at_100
value: 82.741
- type: ndcg_at_1000
value: 82.741
- type: ndcg_at_3
value: 80.039
- type: ndcg_at_5
value: 81.695
- type: precision_at_1
value: 71.542
- type: precision_at_10
value: 10.387
- type: precision_at_100
value: 1.039
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 31.447999999999997
- type: precision_at_5
value: 19.91
- type: recall_at_1
value: 66.81899999999999
- type: recall_at_10
value: 93.372
- type: recall_at_100
value: 93.372
- type: recall_at_1000
value: 93.372
- type: recall_at_3
value: 86.33
- type: recall_at_5
value: 90.347
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.158
- type: map_at_10
value: 52.017
- type: map_at_100
value: 54.259
- type: map_at_1000
value: 54.367
- type: map_at_3
value: 45.738
- type: map_at_5
value: 49.283
- type: mrr_at_1
value: 57.87
- type: mrr_at_10
value: 66.215
- type: mrr_at_100
value: 66.735
- type: mrr_at_1000
value: 66.75
- type: mrr_at_3
value: 64.043
- type: mrr_at_5
value: 65.116
- type: ndcg_at_1
value: 57.87
- type: ndcg_at_10
value: 59.946999999999996
- type: ndcg_at_100
value: 66.31099999999999
- type: ndcg_at_1000
value: 67.75999999999999
- type: ndcg_at_3
value: 55.483000000000004
- type: ndcg_at_5
value: 56.891000000000005
- type: precision_at_1
value: 57.87
- type: precision_at_10
value: 16.497
- type: precision_at_100
value: 2.321
- type: precision_at_1000
value: 0.258
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 27.067999999999998
- type: recall_at_1
value: 31.158
- type: recall_at_10
value: 67.381
- type: recall_at_100
value: 89.464
- type: recall_at_1000
value: 97.989
- type: recall_at_3
value: 50.553000000000004
- type: recall_at_5
value: 57.824
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 42.073
- type: map_at_10
value: 72.418
- type: map_at_100
value: 73.175
- type: map_at_1000
value: 73.215
- type: map_at_3
value: 68.791
- type: map_at_5
value: 71.19
- type: mrr_at_1
value: 84.146
- type: mrr_at_10
value: 88.994
- type: mrr_at_100
value: 89.116
- type: mrr_at_1000
value: 89.12
- type: mrr_at_3
value: 88.373
- type: mrr_at_5
value: 88.82
- type: ndcg_at_1
value: 84.146
- type: ndcg_at_10
value: 79.404
- type: ndcg_at_100
value: 81.83200000000001
- type: ndcg_at_1000
value: 82.524
- type: ndcg_at_3
value: 74.595
- type: ndcg_at_5
value: 77.474
- type: precision_at_1
value: 84.146
- type: precision_at_10
value: 16.753999999999998
- type: precision_at_100
value: 1.8599999999999999
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 48.854
- type: precision_at_5
value: 31.579
- type: recall_at_1
value: 42.073
- type: recall_at_10
value: 83.768
- type: recall_at_100
value: 93.018
- type: recall_at_1000
value: 97.481
- type: recall_at_3
value: 73.282
- type: recall_at_5
value: 78.947
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.9968
- type: ap
value: 92.93892195862824
- type: f1
value: 94.99327998213761
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.698
- type: map_at_10
value: 34.585
- type: map_at_100
value: 35.782000000000004
- type: map_at_1000
value: 35.825
- type: map_at_3
value: 30.397999999999996
- type: map_at_5
value: 32.72
- type: mrr_at_1
value: 22.192
- type: mrr_at_10
value: 35.085
- type: mrr_at_100
value: 36.218
- type: mrr_at_1000
value: 36.256
- type: mrr_at_3
value: 30.986000000000004
- type: mrr_at_5
value: 33.268
- type: ndcg_at_1
value: 22.192
- type: ndcg_at_10
value: 41.957
- type: ndcg_at_100
value: 47.658
- type: ndcg_at_1000
value: 48.697
- type: ndcg_at_3
value: 33.433
- type: ndcg_at_5
value: 37.551
- type: precision_at_1
value: 22.192
- type: precision_at_10
value: 6.781
- type: precision_at_100
value: 0.963
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.365
- type: precision_at_5
value: 10.713000000000001
- type: recall_at_1
value: 21.698
- type: recall_at_10
value: 64.79
- type: recall_at_100
value: 91.071
- type: recall_at_1000
value: 98.883
- type: recall_at_3
value: 41.611
- type: recall_at_5
value: 51.459999999999994
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.15823073415413
- type: f1
value: 96.00362034963248
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 87.12722298221614
- type: f1
value: 70.46888967516227
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.77673167451245
- type: f1
value: 77.60202561132175
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 82.09145931405514
- type: f1
value: 81.7701921473406
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.52153488185864
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 36.80090398444147
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.807141746058605
- type: mrr
value: 32.85025611455029
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.920999999999999
- type: map_at_10
value: 16.049
- type: map_at_100
value: 16.049
- type: map_at_1000
value: 16.049
- type: map_at_3
value: 11.865
- type: map_at_5
value: 13.657
- type: mrr_at_1
value: 53.87
- type: mrr_at_10
value: 62.291
- type: mrr_at_100
value: 62.291
- type: mrr_at_1000
value: 62.291
- type: mrr_at_3
value: 60.681
- type: mrr_at_5
value: 61.61
- type: ndcg_at_1
value: 51.23799999999999
- type: ndcg_at_10
value: 40.892
- type: ndcg_at_100
value: 26.951999999999998
- type: ndcg_at_1000
value: 26.474999999999998
- type: ndcg_at_3
value: 46.821
- type: ndcg_at_5
value: 44.333
- type: precision_at_1
value: 53.251000000000005
- type: precision_at_10
value: 30.124000000000002
- type: precision_at_100
value: 3.012
- type: precision_at_1000
value: 0.301
- type: precision_at_3
value: 43.55
- type: precision_at_5
value: 38.266
- type: recall_at_1
value: 6.920999999999999
- type: recall_at_10
value: 20.852
- type: recall_at_100
value: 20.852
- type: recall_at_1000
value: 20.852
- type: recall_at_3
value: 13.628000000000002
- type: recall_at_5
value: 16.273
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 46.827999999999996
- type: map_at_10
value: 63.434000000000005
- type: map_at_100
value: 63.434000000000005
- type: map_at_1000
value: 63.434000000000005
- type: map_at_3
value: 59.794000000000004
- type: map_at_5
value: 62.08
- type: mrr_at_1
value: 52.288999999999994
- type: mrr_at_10
value: 65.95
- type: mrr_at_100
value: 65.95
- type: mrr_at_1000
value: 65.95
- type: mrr_at_3
value: 63.413
- type: mrr_at_5
value: 65.08
- type: ndcg_at_1
value: 52.288999999999994
- type: ndcg_at_10
value: 70.301
- type: ndcg_at_100
value: 70.301
- type: ndcg_at_1000
value: 70.301
- type: ndcg_at_3
value: 63.979
- type: ndcg_at_5
value: 67.582
- type: precision_at_1
value: 52.288999999999994
- type: precision_at_10
value: 10.576
- type: precision_at_100
value: 1.058
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 28.177000000000003
- type: precision_at_5
value: 19.073
- type: recall_at_1
value: 46.827999999999996
- type: recall_at_10
value: 88.236
- type: recall_at_100
value: 88.236
- type: recall_at_1000
value: 88.236
- type: recall_at_3
value: 72.371
- type: recall_at_5
value: 80.56
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.652
- type: map_at_10
value: 85.953
- type: map_at_100
value: 85.953
- type: map_at_1000
value: 85.953
- type: map_at_3
value: 83.05399999999999
- type: map_at_5
value: 84.89
- type: mrr_at_1
value: 82.42
- type: mrr_at_10
value: 88.473
- type: mrr_at_100
value: 88.473
- type: mrr_at_1000
value: 88.473
- type: mrr_at_3
value: 87.592
- type: mrr_at_5
value: 88.211
- type: ndcg_at_1
value: 82.44
- type: ndcg_at_10
value: 89.467
- type: ndcg_at_100
value: 89.33
- type: ndcg_at_1000
value: 89.33
- type: ndcg_at_3
value: 86.822
- type: ndcg_at_5
value: 88.307
- type: precision_at_1
value: 82.44
- type: precision_at_10
value: 13.616
- type: precision_at_100
value: 1.362
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 38.117000000000004
- type: precision_at_5
value: 25.05
- type: recall_at_1
value: 71.652
- type: recall_at_10
value: 96.224
- type: recall_at_100
value: 96.224
- type: recall_at_1000
value: 96.224
- type: recall_at_3
value: 88.571
- type: recall_at_5
value: 92.812
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 61.295010338050474
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 67.26380819328142
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.683
- type: map_at_10
value: 14.924999999999999
- type: map_at_100
value: 17.532
- type: map_at_1000
value: 17.875
- type: map_at_3
value: 10.392
- type: map_at_5
value: 12.592
- type: mrr_at_1
value: 28.000000000000004
- type: mrr_at_10
value: 39.951
- type: mrr_at_100
value: 41.025
- type: mrr_at_1000
value: 41.056
- type: mrr_at_3
value: 36.317
- type: mrr_at_5
value: 38.412
- type: ndcg_at_1
value: 28.000000000000004
- type: ndcg_at_10
value: 24.410999999999998
- type: ndcg_at_100
value: 33.79
- type: ndcg_at_1000
value: 39.035
- type: ndcg_at_3
value: 22.845
- type: ndcg_at_5
value: 20.080000000000002
- type: precision_at_1
value: 28.000000000000004
- type: precision_at_10
value: 12.790000000000001
- type: precision_at_100
value: 2.633
- type: precision_at_1000
value: 0.388
- type: precision_at_3
value: 21.367
- type: precision_at_5
value: 17.7
- type: recall_at_1
value: 5.683
- type: recall_at_10
value: 25.91
- type: recall_at_100
value: 53.443
- type: recall_at_1000
value: 78.73
- type: recall_at_3
value: 13.003
- type: recall_at_5
value: 17.932000000000002
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.677978681023
- type: cos_sim_spearman
value: 83.13093441058189
- type: euclidean_pearson
value: 83.35535759341572
- type: euclidean_spearman
value: 83.42583744219611
- type: manhattan_pearson
value: 83.2243124045889
- type: manhattan_spearman
value: 83.39801618652632
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.68960206569666
- type: cos_sim_spearman
value: 77.3368966488535
- type: euclidean_pearson
value: 77.62828980560303
- type: euclidean_spearman
value: 76.77951481444651
- type: manhattan_pearson
value: 77.88637240839041
- type: manhattan_spearman
value: 77.22157841466188
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.18745821650724
- type: cos_sim_spearman
value: 85.04423285574542
- type: euclidean_pearson
value: 85.46604816931023
- type: euclidean_spearman
value: 85.5230593932974
- type: manhattan_pearson
value: 85.57912805986261
- type: manhattan_spearman
value: 85.65955905111873
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.6715333300355
- type: cos_sim_spearman
value: 82.9058522514908
- type: euclidean_pearson
value: 83.9640357424214
- type: euclidean_spearman
value: 83.60415457472637
- type: manhattan_pearson
value: 84.05621005853469
- type: manhattan_spearman
value: 83.87077724707746
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.82422928098886
- type: cos_sim_spearman
value: 88.12660311894628
- type: euclidean_pearson
value: 87.50974805056555
- type: euclidean_spearman
value: 87.91957275596677
- type: manhattan_pearson
value: 87.74119404878883
- type: manhattan_spearman
value: 88.2808922165719
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.80605838552093
- type: cos_sim_spearman
value: 86.24123388765678
- type: euclidean_pearson
value: 85.32648347339814
- type: euclidean_spearman
value: 85.60046671950158
- type: manhattan_pearson
value: 85.53800168487811
- type: manhattan_spearman
value: 85.89542420480763
- 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: 89.87540978988132
- type: cos_sim_spearman
value: 90.12715295099461
- type: euclidean_pearson
value: 91.61085993525275
- type: euclidean_spearman
value: 91.31835942311758
- type: manhattan_pearson
value: 91.57500202032934
- type: manhattan_spearman
value: 91.1790925526635
- 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: 69.87136205329556
- type: cos_sim_spearman
value: 68.6253154635078
- type: euclidean_pearson
value: 68.91536015034222
- type: euclidean_spearman
value: 67.63744649352542
- type: manhattan_pearson
value: 69.2000713045275
- type: manhattan_spearman
value: 68.16002901587316
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.21849551039082
- type: cos_sim_spearman
value: 85.6392959372461
- type: euclidean_pearson
value: 85.92050852609488
- type: euclidean_spearman
value: 85.97205649009734
- type: manhattan_pearson
value: 86.1031154802254
- type: manhattan_spearman
value: 86.26791155517466
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.83953958636627
- type: mrr
value: 96.71167612344082
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 64.994
- type: map_at_10
value: 74.763
- type: map_at_100
value: 75.127
- type: map_at_1000
value: 75.143
- type: map_at_3
value: 71.824
- type: map_at_5
value: 73.71
- type: mrr_at_1
value: 68.333
- type: mrr_at_10
value: 75.749
- type: mrr_at_100
value: 75.922
- type: mrr_at_1000
value: 75.938
- type: mrr_at_3
value: 73.556
- type: mrr_at_5
value: 74.739
- type: ndcg_at_1
value: 68.333
- type: ndcg_at_10
value: 79.174
- type: ndcg_at_100
value: 80.41
- type: ndcg_at_1000
value: 80.804
- type: ndcg_at_3
value: 74.361
- type: ndcg_at_5
value: 76.861
- type: precision_at_1
value: 68.333
- type: precision_at_10
value: 10.333
- type: precision_at_100
value: 1.0999999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.778
- type: precision_at_5
value: 19.067
- type: recall_at_1
value: 64.994
- type: recall_at_10
value: 91.822
- type: recall_at_100
value: 97.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 78.878
- type: recall_at_5
value: 85.172
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72079207920792
- type: cos_sim_ap
value: 93.00265215525152
- type: cos_sim_f1
value: 85.06596306068602
- type: cos_sim_precision
value: 90.05586592178771
- type: cos_sim_recall
value: 80.60000000000001
- type: dot_accuracy
value: 99.66039603960397
- type: dot_ap
value: 91.22371407479089
- type: dot_f1
value: 82.34693877551021
- type: dot_precision
value: 84.0625
- type: dot_recall
value: 80.7
- type: euclidean_accuracy
value: 99.71881188118812
- type: euclidean_ap
value: 92.88449963304728
- type: euclidean_f1
value: 85.19480519480518
- type: euclidean_precision
value: 88.64864864864866
- type: euclidean_recall
value: 82.0
- type: manhattan_accuracy
value: 99.73267326732673
- type: manhattan_ap
value: 93.23055393056883
- type: manhattan_f1
value: 85.88957055214725
- type: manhattan_precision
value: 87.86610878661088
- type: manhattan_recall
value: 84.0
- type: max_accuracy
value: 99.73267326732673
- type: max_ap
value: 93.23055393056883
- type: max_f1
value: 85.88957055214725
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 77.3305735900358
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 41.32967136540674
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.95514866379359
- type: mrr
value: 56.95423245055598
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.783007208997144
- type: cos_sim_spearman
value: 30.373444721540533
- type: dot_pearson
value: 29.210604111143905
- type: dot_spearman
value: 29.98809758085659
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.234
- type: map_at_10
value: 1.894
- type: map_at_100
value: 1.894
- type: map_at_1000
value: 1.894
- type: map_at_3
value: 0.636
- type: map_at_5
value: 1.0
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 93.667
- type: mrr_at_100
value: 93.667
- type: mrr_at_1000
value: 93.667
- type: mrr_at_3
value: 93.667
- type: mrr_at_5
value: 93.667
- type: ndcg_at_1
value: 85.0
- type: ndcg_at_10
value: 74.798
- type: ndcg_at_100
value: 16.462
- type: ndcg_at_1000
value: 7.0889999999999995
- type: ndcg_at_3
value: 80.754
- type: ndcg_at_5
value: 77.319
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 78.0
- type: precision_at_100
value: 7.8
- type: precision_at_1000
value: 0.7799999999999999
- type: precision_at_3
value: 83.333
- type: precision_at_5
value: 80.80000000000001
- type: recall_at_1
value: 0.234
- type: recall_at_10
value: 2.093
- type: recall_at_100
value: 2.093
- type: recall_at_1000
value: 2.093
- type: recall_at_3
value: 0.662
- type: recall_at_5
value: 1.0739999999999998
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.703
- type: map_at_10
value: 10.866000000000001
- type: map_at_100
value: 10.866000000000001
- type: map_at_1000
value: 10.866000000000001
- type: map_at_3
value: 5.909
- type: map_at_5
value: 7.35
- type: mrr_at_1
value: 36.735
- type: mrr_at_10
value: 53.583000000000006
- type: mrr_at_100
value: 53.583000000000006
- type: mrr_at_1000
value: 53.583000000000006
- type: mrr_at_3
value: 49.32
- type: mrr_at_5
value: 51.769
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 27.926000000000002
- type: ndcg_at_100
value: 22.701
- type: ndcg_at_1000
value: 22.701
- type: ndcg_at_3
value: 32.073
- type: ndcg_at_5
value: 28.327999999999996
- type: precision_at_1
value: 36.735
- type: precision_at_10
value: 24.694
- type: precision_at_100
value: 2.469
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 26.939
- type: recall_at_1
value: 2.703
- type: recall_at_10
value: 17.702
- type: recall_at_100
value: 17.702
- type: recall_at_1000
value: 17.702
- type: recall_at_3
value: 7.208
- type: recall_at_5
value: 9.748999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.79960000000001
- type: ap
value: 15.467565415565815
- type: f1
value: 55.28639823443618
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.7792869269949
- type: f1
value: 65.08597154774318
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 55.70352297774293
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.27561542588067
- type: cos_sim_ap
value: 81.08262141256193
- type: cos_sim_f1
value: 73.82341501361338
- type: cos_sim_precision
value: 72.5720112159062
- type: cos_sim_recall
value: 75.11873350923483
- type: dot_accuracy
value: 86.66030875603504
- type: dot_ap
value: 76.6052349228621
- type: dot_f1
value: 70.13897280966768
- type: dot_precision
value: 64.70457079152732
- type: dot_recall
value: 76.56992084432717
- type: euclidean_accuracy
value: 88.37098408535495
- type: euclidean_ap
value: 81.12515230092113
- type: euclidean_f1
value: 74.10338225909379
- type: euclidean_precision
value: 71.76761433868974
- type: euclidean_recall
value: 76.59630606860158
- type: manhattan_accuracy
value: 88.34118137926924
- type: manhattan_ap
value: 80.95751834536561
- type: manhattan_f1
value: 73.9119496855346
- type: manhattan_precision
value: 70.625
- type: manhattan_recall
value: 77.5197889182058
- type: max_accuracy
value: 88.37098408535495
- type: max_ap
value: 81.12515230092113
- type: max_f1
value: 74.10338225909379
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.79896767182831
- type: cos_sim_ap
value: 87.40071784061065
- type: cos_sim_f1
value: 79.87753144712087
- type: cos_sim_precision
value: 76.67304015296367
- type: cos_sim_recall
value: 83.3615645210964
- type: dot_accuracy
value: 88.95486474948578
- type: dot_ap
value: 86.00227979119943
- type: dot_f1
value: 78.54601474525914
- type: dot_precision
value: 75.00525394045535
- type: dot_recall
value: 82.43763473975977
- type: euclidean_accuracy
value: 89.7892653393876
- type: euclidean_ap
value: 87.42174706480819
- type: euclidean_f1
value: 80.07283321194465
- type: euclidean_precision
value: 75.96738529574351
- type: euclidean_recall
value: 84.6473668001232
- type: manhattan_accuracy
value: 89.8474793340319
- type: manhattan_ap
value: 87.47814292587448
- type: manhattan_f1
value: 80.15461150280949
- type: manhattan_precision
value: 74.88798234468
- type: manhattan_recall
value: 86.21804742839544
- type: max_accuracy
value: 89.8474793340319
- type: max_ap
value: 87.47814292587448
- type: max_f1
value: 80.15461150280949
---
# Model Summary
> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.
- **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm)
- **Paper:** https://arxiv.org/abs/2402.09906
- **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview
- **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh
| Model | Description |
|-------|-------------|
| [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT |
| [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT |
# Use
The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference).
# Citation
```bibtex
@misc{muennighoff2024generative,
title={Generative Representational Instruction Tuning},
author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
year={2024},
eprint={2402.09906},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
m42-health/Llama3-Med42-70B
|
m42-health
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"m42",
"health",
"healthcare",
"clinical-llm",
"conversational",
"en",
"arxiv:2408.06142",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-06-27T13:19:46 | 2024-08-20T05:11:30 | 10,777 | 44 |
---
language:
- en
license: llama3
license_name: llama3
pipeline_tag: text-generation
tags:
- m42
- health
- healthcare
- clinical-llm
inference: false
---
# **Med42-v2 - A Suite of Clinically-aligned Large Language Models**
Med42-v2 is a suite of open-access clinical large language models (LLM) instruct and preference-tuned by M42 to expand access to medical knowledge. Built off LLaMA-3 and comprising either 8 or 70 billion parameters, these generative AI systems provide high-quality answers to medical questions.
## Key performance metrics:
- Med42-v2-70B outperforms GPT-4.0 in most of the MCQA tasks.
- Med42-v2-70B achieves a MedQA zero-shot performance of 79.10, surpassing the prior state-of-the-art among all openly available medical LLMs.
- Med42-v2-70B sits at the top of the Clinical Elo Rating Leaderboard.
|Models|Elo Score|
|:---:|:---:|
|**Med42-v2-70B**| 1764 |
|Llama3-70B-Instruct| 1643 |
|GPT4-o| 1426 |
|Llama3-8B-Instruct| 1352 |
|Mixtral-8x7b-Instruct| 970 |
|**Med42-v2-8B**| 924 |
|OpenBioLLM-70B| 657 |
|JSL-MedLlama-3-8B-v2.0| 447 |
## Limitations & Safe Use
- The Med42-v2 suite of models is not ready for real clinical use. Extensive human evaluation is undergoing as it is essential to ensure safety.
- Potential for generating incorrect or harmful information.
- Risk of perpetuating biases in training data.
Use this suite of models responsibly! Do not rely on them for medical usage without rigorous safety testing.
## Model Details
*Disclaimer: This large language model is not yet ready for clinical use without further testing and validation. It should not be relied upon for making medical decisions or providing patient care.*
Beginning with Llama3 models, Med42-v2 were instruction-tuned using a dataset of ~1B tokens compiled from different open-access and high-quality sources, including medical flashcards, exam questions, and open-domain dialogues.
**Model Developers:** M42 Health AI Team
**Finetuned from model:** Llama3 - 8B & 70B Instruct
**Context length:** 8k 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 the model's performance.
**License:** Llama 3 Community License Agreement
**Research Paper:** [Med42-v2: A Suite of Clinical LLMs](https://huggingface.co/papers/2408.06142)
## Intended Use
The Med42-v2 suite of models is being made available for further testing and assessment as AI assistants to enhance clinical decision-making and access to LLMs for healthcare use. Potential use cases include:
- Medical question answering
- Patient record summarization
- Aiding medical diagnosis
- General health Q&A
**Run the model**
You can use the 🤗 Transformers library `text-generation` pipeline to do inference.
```python
import transformers
import torch
model_name_or_path = "m42-health/Llama3-Med42-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_name_or_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{
"role": "system",
"content": (
"You are a helpful, respectful and honest medical assistant. You are a second version of Med42 developed by the AI team at M42, UAE. "
"Always answer as helpfully as possible, while being safe. "
"Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
"Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
"If you don’t know the answer to a question, please don’t share false information."
),
},
{"role": "user", "content": "What are the symptoms of diabetes?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=False
)
stop_tokens = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
]
outputs = pipeline(
prompt,
max_new_tokens=512,
eos_token_id=stop_tokens,
do_sample=True,
temperature=0.4,
top_k=150,
top_p=0.75,
)
print(outputs[0]["generated_text"][len(prompt) :])
```
## Hardware and Software
The training was conducted on the NVIDIA DGX cluster with H100 GPUs, utilizing PyTorch's Fully Sharded Data Parallel (FSDP) framework.
## Evaluation Results
### Open-ended question generation
To ensure a robust evaluation of our model's output quality, we employ the LLM-as-a-Judge approach using Prometheus-8x7b-v2.0. Our assessment uses 4,000 carefully curated publicly accessible healthcare-related questions, generating responses from various models. We then use Prometheus to conduct pairwise comparisons of the answers. Drawing inspiration from the LMSYS Chatbot-Arena methodology, we present the results as Elo ratings for each model.
To maintain fairness and eliminate potential bias from prompt engineering, we used the same simple system prompt for every model throughout the evaluation process.
Below is the scoring rubric we used to prompt Prometheus to select the best answer:
```
### Score Rubric:
Which response is of higher overall quality in a medical context? Consider:
* Relevance: Does it directly address the question?
* Completeness: Does it cover all important aspects, details and subpoints?
* Safety: Does it avoid unsafe practices and address potential risks?
* Ethics: Does it maintain confidentiality and avoid biases?
* Clarity: Is it professional, clear and easy to understand?
```
#### Elo Ratings
|Models|Elo Score|
|:---:|:---:|
|**Med42-v2-70B**| 1764 |
|Llama3-70B-Instruct| 1643 |
|GPT4-o| 1426 |
|Llama3-8B-Instruct| 1352 |
|Mixtral-8x7b-Instruct| 970 |
|**Med42-v2-8B**| 924 |
|OpenBioLLM-70B| 657 |
|JSL-MedLlama-3-8B-v2.0| 447 |
#### Win-rate
### MCQA Evaluation
Med42-v2 improves performance on every clinical benchmark compared to our previous version, including MedQA, MedMCQA, USMLE, MMLU clinical topics, and MMLU Pro clinical subset. For all evaluations reported so far, we use [EleutherAI's evaluation harness library](https://github.com/EleutherAI/lm-evaluation-harness) and report zero-shot accuracies (except otherwise stated). We integrated chat templates into harness and computed the likelihood for the full answer instead of only the tokens "a.", "b.", "c." or "d.".
|Model|MMLU Pro|MMLU|MedMCQA|MedQA|USMLE|
|---:|:---:|:---:|:---:|:---:|:---:|
|**Med42v2-70B**|64.36|87.12|73.20|79.10|83.80|
|**Med42v2-8B**|54.30|75.76|61.34|62.84|67.04|
|OpenBioLLM-70B|64.24|90.40|73.18|76.90|79.01|
|GPT-4.0<sup>†</sup>|-|87.00|69.50|78.90|84.05|
|MedGemini*|-|-|-|84.00|-|
|Med-PaLM-2 (5-shot)*|-|87.77|71.30|79.70|-|
|Med42|-|76.72|60.90|61.50|71.85|
|ClinicalCamel-70B|-|69.75|47.00|53.40|54.30|
|GPT-3.5<sup>†</sup>|-|66.63|50.10|50.80|53.00|
|Llama3-8B-Instruct|48.24|72.89|59.65|61.64|60.38|
|Llama3-70B-Instruct|64.24|85.99|72.03|78.88|83.57|
**For MedGemini, results are reported for MedQA without self-training and without search. We note that 0-shot performance is not reported for Med-PaLM 2. Further details can be found at [https://github.com/m42health/med42](https://github.com/m42health/med42)*.
<sup>†</sup> *Results as reported in the paper [Capabilities of GPT-4 on Medical Challenge Problems](https://www.microsoft.com/en-us/research/uploads/prod/2023/03/GPT-4_medical_benchmarks.pdf)*.
## Accessing Med42 and Reporting Issues
Please report any software "bug" or other problems through one of the following means:
- Reporting issues with the model: [https://github.com/m42health/med42](https://github.com/m42health/med42)
- Reporting risky content generated by the model, bugs and/or any security concerns: [https://forms.office.com/r/fPY4Ksecgf](https://forms.office.com/r/fPY4Ksecgf)
- M42’s privacy policy available at [https://m42.ae/privacy-policy/](https://m42.ae/privacy-policy/)
- Reporting violations of the Acceptable Use Policy or unlicensed uses of Med42: <[email protected]>
## Acknowledgements
We thank the Torch FSDP team for their robust distributed training framework, the EleutherAI harness team for their valuable evaluation tools, and the Hugging Face Alignment team for their contributions to responsible AI development.
## Citation
```
@misc{med42v2,
Author = {Cl{\'e}ment Christophe and Praveen K Kanithi and Tathagata Raha and Shadab Khan and Marco AF Pimentel},
Title = {Med42-v2: A Suite of Clinical LLMs},
Year = {2024},
Eprint = {arXiv:2408.06142},
url={https://arxiv.org/abs/2408.06142},
}
```
|
[
"QUESTION_ANSWERING",
"SUMMARIZATION"
] |
[
"MEDQA"
] |
McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised
|
McGill-NLP
|
sentence-similarity
|
[
"peft",
"safetensors",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2404.05961",
"license:mit",
"model-index",
"region:us"
] | 2024-04-30T02:35:26 | 2024-04-30T03:48:00 | 10,725 | 48 |
---
language:
- en
library_name: peft
license: mit
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- text-reranking
- feature-extraction
- sentence-similarity
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
model-index:
- name: LLM2Vec-Meta-Llama-3-supervised
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.94029850746269
- type: ap
value: 44.93223506764482
- type: f1
value: 74.30328994013465
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 86.06680000000001
- type: ap
value: 81.97124658709345
- type: f1
value: 86.00558036874241
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.836
- type: f1
value: 46.05094679201488
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.980000000000004
- type: map_at_10
value: 54.167
- type: map_at_100
value: 54.735
- type: map_at_1000
value: 54.738
- type: map_at_3
value: 49.384
- type: map_at_5
value: 52.285000000000004
- type: mrr_at_1
value: 38.549
- type: mrr_at_10
value: 54.351000000000006
- type: mrr_at_100
value: 54.932
- type: mrr_at_1000
value: 54.935
- type: mrr_at_3
value: 49.585
- type: mrr_at_5
value: 52.469
- type: ndcg_at_1
value: 37.980000000000004
- type: ndcg_at_10
value: 62.778999999999996
- type: ndcg_at_100
value: 64.986
- type: ndcg_at_1000
value: 65.036
- type: ndcg_at_3
value: 53.086999999999996
- type: ndcg_at_5
value: 58.263
- type: precision_at_1
value: 37.980000000000004
- type: precision_at_10
value: 9.011
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.266
- type: precision_at_5
value: 15.248999999999999
- type: recall_at_1
value: 37.980000000000004
- type: recall_at_10
value: 90.114
- type: recall_at_100
value: 99.289
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 63.798
- type: recall_at_5
value: 76.24499999999999
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.27081216556421
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 46.8490872532913
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 65.18525400430678
- type: mrr
value: 78.80149936244119
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.92301936595548
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.0487012987013
- type: f1
value: 88.00953788281542
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 32.34687321141145
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.69881680534123
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: cqadupstack/android
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.742
- type: map_at_10
value: 51.803
- type: map_at_100
value: 53.556000000000004
- type: map_at_1000
value: 53.652
- type: map_at_3
value: 47.286
- type: map_at_5
value: 50.126000000000005
- type: mrr_at_1
value: 46.924
- type: mrr_at_10
value: 57.857
- type: mrr_at_100
value: 58.592
- type: mrr_at_1000
value: 58.619
- type: mrr_at_3
value: 55.340999999999994
- type: mrr_at_5
value: 57.150999999999996
- type: ndcg_at_1
value: 46.924
- type: ndcg_at_10
value: 58.733999999999995
- type: ndcg_at_100
value: 63.771
- type: ndcg_at_1000
value: 64.934
- type: ndcg_at_3
value: 53.189
- type: ndcg_at_5
value: 56.381
- type: precision_at_1
value: 46.924
- type: precision_at_10
value: 11.431
- type: precision_at_100
value: 1.73
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 25.942
- type: precision_at_5
value: 19.113
- type: recall_at_1
value: 37.742
- type: recall_at_10
value: 71.34
- type: recall_at_100
value: 91.523
- type: recall_at_1000
value: 98.494
- type: recall_at_3
value: 55.443
- type: recall_at_5
value: 64.122
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: cqadupstack/english
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.183
- type: map_at_10
value: 46.837
- type: map_at_100
value: 48.126000000000005
- type: map_at_1000
value: 48.25
- type: map_at_3
value: 43.171
- type: map_at_5
value: 45.318999999999996
- type: mrr_at_1
value: 43.376
- type: mrr_at_10
value: 52.859
- type: mrr_at_100
value: 53.422000000000004
- type: mrr_at_1000
value: 53.456
- type: mrr_at_3
value: 50.434999999999995
- type: mrr_at_5
value: 51.861999999999995
- type: ndcg_at_1
value: 43.376
- type: ndcg_at_10
value: 53.223
- type: ndcg_at_100
value: 57.175
- type: ndcg_at_1000
value: 58.86900000000001
- type: ndcg_at_3
value: 48.417
- type: ndcg_at_5
value: 50.77
- type: precision_at_1
value: 43.376
- type: precision_at_10
value: 10.236
- type: precision_at_100
value: 1.5730000000000002
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 23.97
- type: precision_at_5
value: 17.134
- type: recall_at_1
value: 34.183
- type: recall_at_10
value: 64.866
- type: recall_at_100
value: 81.26100000000001
- type: recall_at_1000
value: 91.412
- type: recall_at_3
value: 50.080000000000005
- type: recall_at_5
value: 56.871
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: cqadupstack/gaming
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.878
- type: map_at_10
value: 58.656
- type: map_at_100
value: 59.668
- type: map_at_1000
value: 59.704
- type: map_at_3
value: 54.891
- type: map_at_5
value: 57.050999999999995
- type: mrr_at_1
value: 51.975
- type: mrr_at_10
value: 62.357
- type: mrr_at_100
value: 62.907999999999994
- type: mrr_at_1000
value: 62.925
- type: mrr_at_3
value: 59.801
- type: mrr_at_5
value: 61.278
- type: ndcg_at_1
value: 51.975
- type: ndcg_at_10
value: 64.95100000000001
- type: ndcg_at_100
value: 68.414
- type: ndcg_at_1000
value: 69.077
- type: ndcg_at_3
value: 58.897999999999996
- type: ndcg_at_5
value: 61.866
- type: precision_at_1
value: 51.975
- type: precision_at_10
value: 10.502
- type: precision_at_100
value: 1.31
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 26.290000000000003
- type: precision_at_5
value: 18.093999999999998
- type: recall_at_1
value: 44.878
- type: recall_at_10
value: 79.746
- type: recall_at_100
value: 94.17
- type: recall_at_1000
value: 98.80499999999999
- type: recall_at_3
value: 63.70099999999999
- type: recall_at_5
value: 70.878
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: cqadupstack/gis
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.807
- type: map_at_10
value: 39.431
- type: map_at_100
value: 40.56
- type: map_at_1000
value: 40.617999999999995
- type: map_at_3
value: 36.436
- type: map_at_5
value: 37.955
- type: mrr_at_1
value: 31.186000000000003
- type: mrr_at_10
value: 41.654
- type: mrr_at_100
value: 42.58
- type: mrr_at_1000
value: 42.623
- type: mrr_at_3
value: 38.983000000000004
- type: mrr_at_5
value: 40.35
- type: ndcg_at_1
value: 31.186000000000003
- type: ndcg_at_10
value: 45.297
- type: ndcg_at_100
value: 50.515
- type: ndcg_at_1000
value: 52.005
- type: ndcg_at_3
value: 39.602
- type: ndcg_at_5
value: 42.027
- type: precision_at_1
value: 31.186000000000003
- type: precision_at_10
value: 7.073
- type: precision_at_100
value: 1.0210000000000001
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 17.1
- type: precision_at_5
value: 11.729000000000001
- type: recall_at_1
value: 28.807
- type: recall_at_10
value: 61.138999999999996
- type: recall_at_100
value: 84.491
- type: recall_at_1000
value: 95.651
- type: recall_at_3
value: 45.652
- type: recall_at_5
value: 51.522
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: cqadupstack/mathematica
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.607
- type: map_at_10
value: 31.944
- type: map_at_100
value: 33.317
- type: map_at_1000
value: 33.428000000000004
- type: map_at_3
value: 28.508
- type: map_at_5
value: 30.348999999999997
- type: mrr_at_1
value: 25.622
- type: mrr_at_10
value: 36.726
- type: mrr_at_100
value: 37.707
- type: mrr_at_1000
value: 37.761
- type: mrr_at_3
value: 33.934
- type: mrr_at_5
value: 35.452
- type: ndcg_at_1
value: 25.622
- type: ndcg_at_10
value: 38.462
- type: ndcg_at_100
value: 44.327
- type: ndcg_at_1000
value: 46.623
- type: ndcg_at_3
value: 32.583
- type: ndcg_at_5
value: 35.175
- type: precision_at_1
value: 25.622
- type: precision_at_10
value: 7.425
- type: precision_at_100
value: 1.173
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 16.418
- type: precision_at_5
value: 11.866
- type: recall_at_1
value: 20.607
- type: recall_at_10
value: 53.337
- type: recall_at_100
value: 78.133
- type: recall_at_1000
value: 94.151
- type: recall_at_3
value: 37.088
- type: recall_at_5
value: 43.627
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: cqadupstack/physics
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.814
- type: map_at_10
value: 47.609
- type: map_at_100
value: 48.972
- type: map_at_1000
value: 49.061
- type: map_at_3
value: 43.397999999999996
- type: map_at_5
value: 45.839
- type: mrr_at_1
value: 42.059999999999995
- type: mrr_at_10
value: 53.074
- type: mrr_at_100
value: 53.76800000000001
- type: mrr_at_1000
value: 53.794
- type: mrr_at_3
value: 50.241
- type: mrr_at_5
value: 51.805
- type: ndcg_at_1
value: 42.059999999999995
- type: ndcg_at_10
value: 54.419
- type: ndcg_at_100
value: 59.508
- type: ndcg_at_1000
value: 60.858000000000004
- type: ndcg_at_3
value: 48.296
- type: ndcg_at_5
value: 51.28
- type: precision_at_1
value: 42.059999999999995
- type: precision_at_10
value: 10.231
- type: precision_at_100
value: 1.4789999999999999
- type: precision_at_1000
value: 0.17700000000000002
- type: precision_at_3
value: 23.419999999999998
- type: precision_at_5
value: 16.843
- type: recall_at_1
value: 33.814
- type: recall_at_10
value: 68.88
- type: recall_at_100
value: 89.794
- type: recall_at_1000
value: 98.058
- type: recall_at_3
value: 51.915
- type: recall_at_5
value: 59.704
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: cqadupstack/programmers
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.668
- type: map_at_10
value: 43.032
- type: map_at_100
value: 44.48
- type: map_at_1000
value: 44.574000000000005
- type: map_at_3
value: 38.609
- type: map_at_5
value: 41.164
- type: mrr_at_1
value: 37.785000000000004
- type: mrr_at_10
value: 48.898
- type: mrr_at_100
value: 49.728
- type: mrr_at_1000
value: 49.769000000000005
- type: mrr_at_3
value: 45.909
- type: mrr_at_5
value: 47.61
- type: ndcg_at_1
value: 37.785000000000004
- type: ndcg_at_10
value: 50.21099999999999
- type: ndcg_at_100
value: 55.657999999999994
- type: ndcg_at_1000
value: 57.172
- type: ndcg_at_3
value: 43.726
- type: ndcg_at_5
value: 46.758
- type: precision_at_1
value: 37.785000000000004
- type: precision_at_10
value: 9.669
- type: precision_at_100
value: 1.4409999999999998
- type: precision_at_1000
value: 0.174
- type: precision_at_3
value: 21.651
- type: precision_at_5
value: 15.822
- type: recall_at_1
value: 29.668
- type: recall_at_10
value: 65.575
- type: recall_at_100
value: 87.977
- type: recall_at_1000
value: 97.615
- type: recall_at_3
value: 47.251
- type: recall_at_5
value: 55.359
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.29925
- type: map_at_10
value: 41.98708333333333
- type: map_at_100
value: 43.306916666666666
- type: map_at_1000
value: 43.40716666666667
- type: map_at_3
value: 38.431666666666665
- type: map_at_5
value: 40.4195
- type: mrr_at_1
value: 36.24483333333334
- type: mrr_at_10
value: 46.32666666666667
- type: mrr_at_100
value: 47.13983333333333
- type: mrr_at_1000
value: 47.18058333333334
- type: mrr_at_3
value: 43.66799999999999
- type: mrr_at_5
value: 45.163666666666664
- type: ndcg_at_1
value: 36.24483333333334
- type: ndcg_at_10
value: 48.251916666666666
- type: ndcg_at_100
value: 53.3555
- type: ndcg_at_1000
value: 55.024249999999995
- type: ndcg_at_3
value: 42.599583333333335
- type: ndcg_at_5
value: 45.24166666666666
- type: precision_at_1
value: 36.24483333333334
- type: precision_at_10
value: 8.666833333333333
- type: precision_at_100
value: 1.3214166666666665
- type: precision_at_1000
value: 0.16475
- type: precision_at_3
value: 19.9955
- type: precision_at_5
value: 14.271999999999998
- type: recall_at_1
value: 30.29925
- type: recall_at_10
value: 62.232333333333344
- type: recall_at_100
value: 84.151
- type: recall_at_1000
value: 95.37333333333333
- type: recall_at_3
value: 46.45541666666667
- type: recall_at_5
value: 53.264
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: cqadupstack/stats
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.996
- type: map_at_10
value: 38.047
- type: map_at_100
value: 39.121
- type: map_at_1000
value: 39.202999999999996
- type: map_at_3
value: 35.376000000000005
- type: map_at_5
value: 36.763
- type: mrr_at_1
value: 32.362
- type: mrr_at_10
value: 40.717999999999996
- type: mrr_at_100
value: 41.586
- type: mrr_at_1000
value: 41.641
- type: mrr_at_3
value: 38.292
- type: mrr_at_5
value: 39.657
- type: ndcg_at_1
value: 32.362
- type: ndcg_at_10
value: 43.105
- type: ndcg_at_100
value: 48.026
- type: ndcg_at_1000
value: 49.998
- type: ndcg_at_3
value: 38.147999999999996
- type: ndcg_at_5
value: 40.385
- type: precision_at_1
value: 32.362
- type: precision_at_10
value: 6.7940000000000005
- type: precision_at_100
value: 1.0170000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 16.411
- type: precision_at_5
value: 11.35
- type: recall_at_1
value: 28.996
- type: recall_at_10
value: 55.955
- type: recall_at_100
value: 77.744
- type: recall_at_1000
value: 92.196
- type: recall_at_3
value: 42.254999999999995
- type: recall_at_5
value: 47.776
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: cqadupstack/tex
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.029
- type: map_at_10
value: 29.188
- type: map_at_100
value: 30.484
- type: map_at_1000
value: 30.608
- type: map_at_3
value: 26.195
- type: map_at_5
value: 27.866999999999997
- type: mrr_at_1
value: 24.57
- type: mrr_at_10
value: 33.461
- type: mrr_at_100
value: 34.398
- type: mrr_at_1000
value: 34.464
- type: mrr_at_3
value: 30.856
- type: mrr_at_5
value: 32.322
- type: ndcg_at_1
value: 24.57
- type: ndcg_at_10
value: 34.846
- type: ndcg_at_100
value: 40.544000000000004
- type: ndcg_at_1000
value: 43.019
- type: ndcg_at_3
value: 29.683999999999997
- type: ndcg_at_5
value: 32.11
- type: precision_at_1
value: 24.57
- type: precision_at_10
value: 6.535
- type: precision_at_100
value: 1.11
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 14.338000000000001
- type: precision_at_5
value: 10.496
- type: recall_at_1
value: 20.029
- type: recall_at_10
value: 47.509
- type: recall_at_100
value: 72.61999999999999
- type: recall_at_1000
value: 89.778
- type: recall_at_3
value: 33.031
- type: recall_at_5
value: 39.306000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: cqadupstack/unix
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.753999999999998
- type: map_at_10
value: 43.814
- type: map_at_100
value: 45.072
- type: map_at_1000
value: 45.155
- type: map_at_3
value: 40.316
- type: map_at_5
value: 42.15
- type: mrr_at_1
value: 38.06
- type: mrr_at_10
value: 48.311
- type: mrr_at_100
value: 49.145
- type: mrr_at_1000
value: 49.181000000000004
- type: mrr_at_3
value: 45.678000000000004
- type: mrr_at_5
value: 47.072
- type: ndcg_at_1
value: 38.06
- type: ndcg_at_10
value: 50.083
- type: ndcg_at_100
value: 55.342
- type: ndcg_at_1000
value: 56.87
- type: ndcg_at_3
value: 44.513999999999996
- type: ndcg_at_5
value: 46.886
- type: precision_at_1
value: 38.06
- type: precision_at_10
value: 8.638
- type: precision_at_100
value: 1.253
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 20.709
- type: precision_at_5
value: 14.44
- type: recall_at_1
value: 31.753999999999998
- type: recall_at_10
value: 64.473
- type: recall_at_100
value: 86.832
- type: recall_at_1000
value: 96.706
- type: recall_at_3
value: 48.937000000000005
- type: recall_at_5
value: 55.214
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: cqadupstack/webmasters
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.815
- type: map_at_10
value: 40.595
- type: map_at_100
value: 42.337
- type: map_at_1000
value: 42.559000000000005
- type: map_at_3
value: 37.120999999999995
- type: map_at_5
value: 38.912
- type: mrr_at_1
value: 34.585
- type: mrr_at_10
value: 45.068000000000005
- type: mrr_at_100
value: 45.93
- type: mrr_at_1000
value: 45.974
- type: mrr_at_3
value: 42.26
- type: mrr_at_5
value: 43.742
- type: ndcg_at_1
value: 34.585
- type: ndcg_at_10
value: 47.519
- type: ndcg_at_100
value: 53.102000000000004
- type: ndcg_at_1000
value: 54.949999999999996
- type: ndcg_at_3
value: 41.719
- type: ndcg_at_5
value: 44.17
- type: precision_at_1
value: 34.585
- type: precision_at_10
value: 9.368
- type: precision_at_100
value: 1.7870000000000001
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 19.895
- type: precision_at_5
value: 14.506
- type: recall_at_1
value: 28.815
- type: recall_at_10
value: 61.414
- type: recall_at_100
value: 85.922
- type: recall_at_1000
value: 97.15
- type: recall_at_3
value: 45.076
- type: recall_at_5
value: 51.271
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: cqadupstack/wordpress
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.298000000000002
- type: map_at_10
value: 32.889
- type: map_at_100
value: 33.989999999999995
- type: map_at_1000
value: 34.074
- type: map_at_3
value: 29.873
- type: map_at_5
value: 31.539
- type: mrr_at_1
value: 26.433
- type: mrr_at_10
value: 34.937000000000005
- type: mrr_at_100
value: 35.914
- type: mrr_at_1000
value: 35.96
- type: mrr_at_3
value: 32.286
- type: mrr_at_5
value: 33.663
- type: ndcg_at_1
value: 26.433
- type: ndcg_at_10
value: 38.173
- type: ndcg_at_100
value: 43.884
- type: ndcg_at_1000
value: 45.916000000000004
- type: ndcg_at_3
value: 32.419
- type: ndcg_at_5
value: 35.092
- type: precision_at_1
value: 26.433
- type: precision_at_10
value: 6.1
- type: precision_at_100
value: 0.963
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 13.802
- type: precision_at_5
value: 9.871
- type: recall_at_1
value: 24.298000000000002
- type: recall_at_10
value: 52.554
- type: recall_at_100
value: 79.345
- type: recall_at_1000
value: 94.464
- type: recall_at_3
value: 37.036
- type: recall_at_5
value: 43.518
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.194999999999999
- type: map_at_10
value: 24.563
- type: map_at_100
value: 26.775
- type: map_at_1000
value: 26.965
- type: map_at_3
value: 19.983999999999998
- type: map_at_5
value: 22.24
- type: mrr_at_1
value: 31.661
- type: mrr_at_10
value: 44.804
- type: mrr_at_100
value: 45.655
- type: mrr_at_1000
value: 45.678000000000004
- type: mrr_at_3
value: 41.292
- type: mrr_at_5
value: 43.468
- type: ndcg_at_1
value: 31.661
- type: ndcg_at_10
value: 34.271
- type: ndcg_at_100
value: 42.04
- type: ndcg_at_1000
value: 45.101
- type: ndcg_at_3
value: 27.529999999999998
- type: ndcg_at_5
value: 29.862
- type: precision_at_1
value: 31.661
- type: precision_at_10
value: 10.925
- type: precision_at_100
value: 1.92
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 20.456
- type: precision_at_5
value: 16.012999999999998
- type: recall_at_1
value: 14.194999999999999
- type: recall_at_10
value: 41.388999999999996
- type: recall_at_100
value: 67.58800000000001
- type: recall_at_1000
value: 84.283
- type: recall_at_3
value: 25.089
- type: recall_at_5
value: 31.642
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.898
- type: map_at_10
value: 23.226
- type: map_at_100
value: 33.372
- type: map_at_1000
value: 35.407
- type: map_at_3
value: 15.892999999999999
- type: map_at_5
value: 18.747
- type: mrr_at_1
value: 73.5
- type: mrr_at_10
value: 80.404
- type: mrr_at_100
value: 80.671
- type: mrr_at_1000
value: 80.676
- type: mrr_at_3
value: 78.958
- type: mrr_at_5
value: 79.683
- type: ndcg_at_1
value: 62.0
- type: ndcg_at_10
value: 48.337
- type: ndcg_at_100
value: 53.474
- type: ndcg_at_1000
value: 60.999
- type: ndcg_at_3
value: 52.538
- type: ndcg_at_5
value: 49.659
- type: precision_at_1
value: 73.5
- type: precision_at_10
value: 39.25
- type: precision_at_100
value: 12.4
- type: precision_at_1000
value: 2.4459999999999997
- type: precision_at_3
value: 56.333
- type: precision_at_5
value: 48.15
- type: recall_at_1
value: 9.898
- type: recall_at_10
value: 29.511
- type: recall_at_100
value: 60.45700000000001
- type: recall_at_1000
value: 84.47200000000001
- type: recall_at_3
value: 17.064
- type: recall_at_5
value: 21.258
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.19999999999999
- type: f1
value: 46.23854137552949
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 80.093
- type: map_at_10
value: 87.139
- type: map_at_100
value: 87.333
- type: map_at_1000
value: 87.344
- type: map_at_3
value: 86.395
- type: map_at_5
value: 86.866
- type: mrr_at_1
value: 86.36399999999999
- type: mrr_at_10
value: 91.867
- type: mrr_at_100
value: 91.906
- type: mrr_at_1000
value: 91.90700000000001
- type: mrr_at_3
value: 91.484
- type: mrr_at_5
value: 91.759
- type: ndcg_at_1
value: 86.36399999999999
- type: ndcg_at_10
value: 90.197
- type: ndcg_at_100
value: 90.819
- type: ndcg_at_1000
value: 91.01599999999999
- type: ndcg_at_3
value: 89.166
- type: ndcg_at_5
value: 89.74
- type: precision_at_1
value: 86.36399999999999
- type: precision_at_10
value: 10.537
- type: precision_at_100
value: 1.106
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 33.608
- type: precision_at_5
value: 20.618
- type: recall_at_1
value: 80.093
- type: recall_at_10
value: 95.003
- type: recall_at_100
value: 97.328
- type: recall_at_1000
value: 98.485
- type: recall_at_3
value: 92.072
- type: recall_at_5
value: 93.661
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.063
- type: map_at_10
value: 47.113
- type: map_at_100
value: 49.294
- type: map_at_1000
value: 49.422
- type: map_at_3
value: 40.955000000000005
- type: map_at_5
value: 44.5
- type: mrr_at_1
value: 55.401
- type: mrr_at_10
value: 62.99400000000001
- type: mrr_at_100
value: 63.63999999999999
- type: mrr_at_1000
value: 63.661
- type: mrr_at_3
value: 61.034
- type: mrr_at_5
value: 62.253
- type: ndcg_at_1
value: 55.401
- type: ndcg_at_10
value: 55.332
- type: ndcg_at_100
value: 61.931000000000004
- type: ndcg_at_1000
value: 63.841
- type: ndcg_at_3
value: 50.92
- type: ndcg_at_5
value: 52.525
- type: precision_at_1
value: 55.401
- type: precision_at_10
value: 15.262
- type: precision_at_100
value: 2.231
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 33.848
- type: precision_at_5
value: 25.031
- type: recall_at_1
value: 29.063
- type: recall_at_10
value: 62.498
- type: recall_at_100
value: 85.86
- type: recall_at_1000
value: 97.409
- type: recall_at_3
value: 45.472
- type: recall_at_5
value: 53.344
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.205
- type: map_at_10
value: 64.19399999999999
- type: map_at_100
value: 65.183
- type: map_at_1000
value: 65.23299999999999
- type: map_at_3
value: 60.239
- type: map_at_5
value: 62.695
- type: mrr_at_1
value: 74.409
- type: mrr_at_10
value: 80.84
- type: mrr_at_100
value: 81.10199999999999
- type: mrr_at_1000
value: 81.109
- type: mrr_at_3
value: 79.739
- type: mrr_at_5
value: 80.46600000000001
- type: ndcg_at_1
value: 74.409
- type: ndcg_at_10
value: 71.757
- type: ndcg_at_100
value: 75.152
- type: ndcg_at_1000
value: 76.098
- type: ndcg_at_3
value: 66.174
- type: ndcg_at_5
value: 69.283
- type: precision_at_1
value: 74.409
- type: precision_at_10
value: 15.503
- type: precision_at_100
value: 1.8110000000000002
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 43.457
- type: precision_at_5
value: 28.532000000000004
- type: recall_at_1
value: 37.205
- type: recall_at_10
value: 77.515
- type: recall_at_100
value: 90.56
- type: recall_at_1000
value: 96.759
- type: recall_at_3
value: 65.18599999999999
- type: recall_at_5
value: 71.33
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 82.9448
- type: ap
value: 78.25923353099166
- type: f1
value: 82.86422040179993
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.834
- type: map_at_10
value: 35.85
- type: map_at_100
value: 37.013
- type: map_at_1000
value: 37.056
- type: map_at_3
value: 31.613000000000003
- type: map_at_5
value: 34.113
- type: mrr_at_1
value: 23.424
- type: mrr_at_10
value: 36.398
- type: mrr_at_100
value: 37.498
- type: mrr_at_1000
value: 37.534
- type: mrr_at_3
value: 32.275999999999996
- type: mrr_at_5
value: 34.705000000000005
- type: ndcg_at_1
value: 23.424
- type: ndcg_at_10
value: 43.236999999999995
- type: ndcg_at_100
value: 48.776
- type: ndcg_at_1000
value: 49.778
- type: ndcg_at_3
value: 34.692
- type: ndcg_at_5
value: 39.119
- type: precision_at_1
value: 23.424
- type: precision_at_10
value: 6.918
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.881
- type: precision_at_5
value: 11.183
- type: recall_at_1
value: 22.834
- type: recall_at_10
value: 66.03999999999999
- type: recall_at_100
value: 91.532
- type: recall_at_1000
value: 99.068
- type: recall_at_3
value: 42.936
- type: recall_at_5
value: 53.539
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.1377108983128
- type: f1
value: 95.87034720246666
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 86.10579115367078
- type: f1
value: 70.20810321445228
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 79.80497646267652
- type: f1
value: 77.32475274059293
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.52320107599192
- type: f1
value: 81.22312939311655
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.709106678767018
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.95879128399585
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.67476691128679
- type: mrr
value: 33.921654478513986
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.223
- type: map_at_10
value: 15.992999999999999
- type: map_at_100
value: 21.09
- type: map_at_1000
value: 22.822
- type: map_at_3
value: 11.475
- type: map_at_5
value: 13.501
- type: mrr_at_1
value: 53.251000000000005
- type: mrr_at_10
value: 61.878
- type: mrr_at_100
value: 62.307
- type: mrr_at_1000
value: 62.342
- type: mrr_at_3
value: 60.01
- type: mrr_at_5
value: 61.202
- type: ndcg_at_1
value: 51.702999999999996
- type: ndcg_at_10
value: 41.833999999999996
- type: ndcg_at_100
value: 39.061
- type: ndcg_at_1000
value: 47.397
- type: ndcg_at_3
value: 47.083000000000006
- type: ndcg_at_5
value: 44.722
- type: precision_at_1
value: 53.251000000000005
- type: precision_at_10
value: 31.3
- type: precision_at_100
value: 10.254000000000001
- type: precision_at_1000
value: 2.338
- type: precision_at_3
value: 43.756
- type: precision_at_5
value: 38.824
- type: recall_at_1
value: 7.223
- type: recall_at_10
value: 20.529
- type: recall_at_100
value: 39.818
- type: recall_at_1000
value: 70.152
- type: recall_at_3
value: 12.666
- type: recall_at_5
value: 15.798000000000002
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.847
- type: map_at_10
value: 56.255
- type: map_at_100
value: 57.019
- type: map_at_1000
value: 57.03
- type: map_at_3
value: 51.665000000000006
- type: map_at_5
value: 54.543
- type: mrr_at_1
value: 43.801
- type: mrr_at_10
value: 58.733999999999995
- type: mrr_at_100
value: 59.206
- type: mrr_at_1000
value: 59.21300000000001
- type: mrr_at_3
value: 55.266999999999996
- type: mrr_at_5
value: 57.449
- type: ndcg_at_1
value: 43.772
- type: ndcg_at_10
value: 64.213
- type: ndcg_at_100
value: 67.13
- type: ndcg_at_1000
value: 67.368
- type: ndcg_at_3
value: 55.977
- type: ndcg_at_5
value: 60.597
- type: precision_at_1
value: 43.772
- type: precision_at_10
value: 10.272
- type: precision_at_100
value: 1.193
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.261
- type: precision_at_5
value: 17.885
- type: recall_at_1
value: 38.847
- type: recall_at_10
value: 85.76700000000001
- type: recall_at_100
value: 98.054
- type: recall_at_1000
value: 99.812
- type: recall_at_3
value: 64.82
- type: recall_at_5
value: 75.381
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.77
- type: map_at_10
value: 83.195
- type: map_at_100
value: 83.869
- type: map_at_1000
value: 83.883
- type: map_at_3
value: 80.04599999999999
- type: map_at_5
value: 82.011
- type: mrr_at_1
value: 79.2
- type: mrr_at_10
value: 85.942
- type: mrr_at_100
value: 86.063
- type: mrr_at_1000
value: 86.064
- type: mrr_at_3
value: 84.82
- type: mrr_at_5
value: 85.56899999999999
- type: ndcg_at_1
value: 79.17999999999999
- type: ndcg_at_10
value: 87.161
- type: ndcg_at_100
value: 88.465
- type: ndcg_at_1000
value: 88.553
- type: ndcg_at_3
value: 83.958
- type: ndcg_at_5
value: 85.699
- type: precision_at_1
value: 79.17999999999999
- type: precision_at_10
value: 13.401
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.903000000000006
- type: precision_at_5
value: 24.404
- type: recall_at_1
value: 68.77
- type: recall_at_10
value: 95.132
- type: recall_at_100
value: 99.58200000000001
- type: recall_at_1000
value: 99.997
- type: recall_at_3
value: 86.119
- type: recall_at_5
value: 90.932
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 61.7204049654583
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.98164986883849
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.443
- type: map_at_10
value: 13.86
- type: map_at_100
value: 16.496
- type: map_at_1000
value: 16.836000000000002
- type: map_at_3
value: 9.661
- type: map_at_5
value: 11.745
- type: mrr_at_1
value: 26.8
- type: mrr_at_10
value: 37.777
- type: mrr_at_100
value: 38.928000000000004
- type: mrr_at_1000
value: 38.967
- type: mrr_at_3
value: 34.083000000000006
- type: mrr_at_5
value: 36.308
- type: ndcg_at_1
value: 26.8
- type: ndcg_at_10
value: 22.961000000000002
- type: ndcg_at_100
value: 32.582
- type: ndcg_at_1000
value: 37.972
- type: ndcg_at_3
value: 21.292
- type: ndcg_at_5
value: 18.945999999999998
- type: precision_at_1
value: 26.8
- type: precision_at_10
value: 12.06
- type: precision_at_100
value: 2.593
- type: precision_at_1000
value: 0.388
- type: precision_at_3
value: 19.900000000000002
- type: precision_at_5
value: 16.84
- type: recall_at_1
value: 5.443
- type: recall_at_10
value: 24.445
- type: recall_at_100
value: 52.602000000000004
- type: recall_at_1000
value: 78.767
- type: recall_at_3
value: 12.098
- type: recall_at_5
value: 17.077
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 83.9379272617096
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 79.26752176661364
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 84.8327309083665
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 82.9394255552954
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 88.08995363382608
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 86.53522220099619
- 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: 89.57796559847532
- 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: 67.66598855577894
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 88.0472708354572
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.04689157650684
- type: mrr
value: 96.51889958262507
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 62.827999999999996
- type: map_at_10
value: 73.54899999999999
- type: map_at_100
value: 73.892
- type: map_at_1000
value: 73.901
- type: map_at_3
value: 70.663
- type: map_at_5
value: 72.449
- type: mrr_at_1
value: 66.0
- type: mrr_at_10
value: 74.554
- type: mrr_at_100
value: 74.81700000000001
- type: mrr_at_1000
value: 74.82600000000001
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 73.717
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 78.218
- type: ndcg_at_100
value: 79.706
- type: ndcg_at_1000
value: 79.925
- type: ndcg_at_3
value: 73.629
- type: ndcg_at_5
value: 75.89
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 10.333
- type: precision_at_100
value: 1.113
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.889
- type: precision_at_5
value: 19.067
- type: recall_at_1
value: 62.827999999999996
- type: recall_at_10
value: 91.533
- type: recall_at_100
value: 98.333
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 79.0
- type: recall_at_5
value: 84.68900000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8019801980198
- type: cos_sim_ap
value: 95.09301057928796
- type: cos_sim_f1
value: 89.71193415637859
- type: cos_sim_precision
value: 92.37288135593221
- type: cos_sim_recall
value: 87.2
- type: dot_accuracy
value: 99.72079207920792
- type: dot_ap
value: 92.77707970155015
- type: dot_f1
value: 85.88588588588588
- type: dot_precision
value: 85.97194388777555
- type: dot_recall
value: 85.8
- type: euclidean_accuracy
value: 99.7980198019802
- type: euclidean_ap
value: 95.04124481520121
- type: euclidean_f1
value: 89.61693548387096
- type: euclidean_precision
value: 90.34552845528455
- type: euclidean_recall
value: 88.9
- type: manhattan_accuracy
value: 99.7960396039604
- type: manhattan_ap
value: 95.02691504694813
- type: manhattan_f1
value: 89.60321446509292
- type: manhattan_precision
value: 90.0100908173562
- type: manhattan_recall
value: 89.2
- type: max_accuracy
value: 99.8019801980198
- type: max_ap
value: 95.09301057928796
- type: max_f1
value: 89.71193415637859
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 72.74124969197169
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.262798307863996
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.823414217790464
- type: mrr
value: 55.557133838383834
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.01226930465494
- type: cos_sim_spearman
value: 30.9368445798007
- type: dot_pearson
value: 30.204833368654533
- type: dot_spearman
value: 30.438900411966618
- 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: 2.0420000000000003
- type: map_at_100
value: 13.33
- type: map_at_1000
value: 33.627
- type: map_at_3
value: 0.639
- type: map_at_5
value: 1.056
- type: mrr_at_1
value: 84.0
- type: mrr_at_10
value: 91.167
- type: mrr_at_100
value: 91.167
- type: mrr_at_1000
value: 91.167
- type: mrr_at_3
value: 90.667
- type: mrr_at_5
value: 91.167
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 80.337
- type: ndcg_at_100
value: 65.852
- type: ndcg_at_1000
value: 59.821000000000005
- type: ndcg_at_3
value: 81.061
- type: ndcg_at_5
value: 81.396
- type: precision_at_1
value: 84.0
- type: precision_at_10
value: 85.0
- type: precision_at_100
value: 67.75999999999999
- type: precision_at_1000
value: 26.272000000000002
- type: precision_at_3
value: 85.333
- type: precision_at_5
value: 86.4
- type: recall_at_1
value: 0.22699999999999998
- type: recall_at_10
value: 2.241
- type: recall_at_100
value: 16.478
- type: recall_at_1000
value: 56.442
- type: recall_at_3
value: 0.672
- type: recall_at_5
value: 1.143
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.836
- type: map_at_10
value: 8.536000000000001
- type: map_at_100
value: 14.184
- type: map_at_1000
value: 15.885
- type: map_at_3
value: 3.7359999999999998
- type: map_at_5
value: 5.253
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 34.77
- type: mrr_at_100
value: 36.18
- type: mrr_at_1000
value: 36.18
- type: mrr_at_3
value: 30.612000000000002
- type: mrr_at_5
value: 32.449
- type: ndcg_at_1
value: 20.408
- type: ndcg_at_10
value: 20.498
- type: ndcg_at_100
value: 33.354
- type: ndcg_at_1000
value: 45.699
- type: ndcg_at_3
value: 19.292
- type: ndcg_at_5
value: 19.541
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 19.387999999999998
- type: precision_at_100
value: 7.163
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 19.728
- type: precision_at_5
value: 20.0
- type: recall_at_1
value: 1.836
- type: recall_at_10
value: 15.212
- type: recall_at_100
value: 45.364
- type: recall_at_1000
value: 83.64
- type: recall_at_3
value: 4.651000000000001
- type: recall_at_5
value: 7.736
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.5856
- type: ap
value: 14.297836125608864
- type: f1
value: 54.45458507465688
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.89869835880024
- type: f1
value: 62.15163526419782
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 56.408998393035446
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.78822197055493
- type: cos_sim_ap
value: 81.73234934293887
- type: cos_sim_f1
value: 74.16373812312898
- type: cos_sim_precision
value: 73.18263549961469
- type: cos_sim_recall
value: 75.17150395778364
- type: dot_accuracy
value: 87.85837754068069
- type: dot_ap
value: 79.69812660365871
- type: dot_f1
value: 72.52999744702579
- type: dot_precision
value: 70.25222551928783
- type: dot_recall
value: 74.96042216358839
- type: euclidean_accuracy
value: 88.74649818203493
- type: euclidean_ap
value: 81.47777928110055
- type: euclidean_f1
value: 74.1248097412481
- type: euclidean_precision
value: 71.37274059599413
- type: euclidean_recall
value: 77.0976253298153
- type: manhattan_accuracy
value: 88.7286165583835
- type: manhattan_ap
value: 81.47766386927232
- type: manhattan_f1
value: 74.16730231375541
- type: manhattan_precision
value: 71.56526005888125
- type: manhattan_recall
value: 76.96569920844327
- type: max_accuracy
value: 88.78822197055493
- type: max_ap
value: 81.73234934293887
- type: max_f1
value: 74.16730231375541
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.30026778437536
- type: cos_sim_ap
value: 86.56353001037664
- type: cos_sim_f1
value: 79.359197907585
- type: cos_sim_precision
value: 75.12379642365887
- type: cos_sim_recall
value: 84.10070834616569
- type: dot_accuracy
value: 88.8539604921023
- type: dot_ap
value: 85.44601003294055
- type: dot_f1
value: 78.20008094484713
- type: dot_precision
value: 74.88549080403072
- type: dot_recall
value: 81.82168155220204
- type: euclidean_accuracy
value: 89.25369658865992
- type: euclidean_ap
value: 86.46965679550075
- type: euclidean_f1
value: 79.16785612332285
- type: euclidean_precision
value: 73.77627028465017
- type: euclidean_recall
value: 85.4096088697259
- type: manhattan_accuracy
value: 89.26727985407692
- type: manhattan_ap
value: 86.46460344566123
- type: manhattan_f1
value: 79.1723543358
- type: manhattan_precision
value: 74.20875420875421
- type: manhattan_recall
value: 84.84755158607946
- type: max_accuracy
value: 89.30026778437536
- type: max_ap
value: 86.56353001037664
- type: max_f1
value: 79.359197907585
---
# LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
- **Repository:** https://github.com/McGill-NLP/llm2vec
- **Paper:** https://arxiv.org/abs/2404.05961
## Installation
```bash
pip install llm2vec
```
## Usage
```python
from llm2vec import LLM2Vec
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel
# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
tokenizer = AutoTokenizer.from_pretrained(
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp"
)
config = AutoConfig.from_pretrained(
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp", trust_remote_code=True
)
model = AutoModel.from_pretrained(
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
trust_remote_code=True,
config=config,
torch_dtype=torch.bfloat16,
device_map="cuda" if torch.cuda.is_available() else "cpu",
)
model = PeftModel.from_pretrained(
model,
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
)
model = model.merge_and_unload() # This can take several minutes on cpu
# Loading supervised model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + supervised (LoRA).
model = PeftModel.from_pretrained(
model, "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised"
)
# Wrapper for encoding and pooling operations
l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
# Encoding queries using instructions
instruction = (
"Given a web search query, retrieve relevant passages that answer the query:"
)
queries = [
[instruction, "how much protein should a female eat"],
[instruction, "summit define"],
]
q_reps = l2v.encode(queries)
# Encoding documents. Instruction are not required 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.",
]
d_reps = l2v.encode(documents)
# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
print(cos_sim)
"""
tensor([[0.6470, 0.1619],
[0.0786, 0.5844]])
"""
```
## Questions
If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
NovaSearch/jasper_en_vision_language_v1
|
NovaSearch
| null |
[
"sentence-transformers",
"safetensors",
"jasper_vl",
"mteb",
"custom_code",
"en",
"dataset:BAAI/Infinity-MM",
"dataset:HuggingFaceFW/fineweb-edu",
"arxiv:2412.19048",
"base_model:NovaSearch/stella_en_1.5B_v5",
"base_model:finetune:NovaSearch/stella_en_1.5B_v5",
"model-index",
"region:us"
] | 2024-12-11T03:23:03 | 2025-01-24T02:03:17 | 10,537 | 46 |
---
base_model:
- dunzhang/stella_en_1.5B_v5
- google/siglip-so400m-patch14-384
datasets:
- BAAI/Infinity-MM
- HuggingFaceFW/fineweb-edu
language:
- en
tags:
- mteb
- sentence-transformers
model-index:
- name: jasper_en_vision_language_v1
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: 95.7271
- type: f1
value: 89.25450000000001
- type: f1_weighted
value: 95.8563
- type: ap
value: 67.1563
- type: ap_weighted
value: 67.1563
- type: main_score
value: 95.7271
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 93.7761
- type: f1
value: 90.7582
- type: f1_weighted
value: 93.974
- type: ap
value: 74.88759999999999
- type: ap_weighted
value: 74.88759999999999
- type: main_score
value: 93.7761
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.5809
- type: f1
value: 97.5808
- type: f1_weighted
value: 97.5808
- type: ap
value: 96.3911
- type: ap_weighted
value: 96.3911
- type: main_score
value: 97.5809
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 62.918
- type: f1
value: 60.696099999999994
- type: f1_weighted
value: 60.696099999999994
- type: main_score
value: 62.918
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_1
value: 41.323
- type: ndcg_at_3
value: 56.737
- type: ndcg_at_5
value: 61.254
- type: ndcg_at_10
value: 65.204
- type: ndcg_at_20
value: 66.75
- type: ndcg_at_100
value: 67.254
- type: ndcg_at_1000
value: 67.254
- type: map_at_1
value: 41.323
- type: map_at_3
value: 52.881
- type: map_at_5
value: 55.373000000000005
- type: map_at_10
value: 57.013999999999996
- type: map_at_20
value: 57.457
- type: map_at_100
value: 57.538
- type: map_at_1000
value: 57.538
- type: recall_at_1
value: 41.323
- type: recall_at_3
value: 67.923
- type: recall_at_5
value: 78.947
- type: recall_at_10
value: 91.11
- type: recall_at_20
value: 97.084
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 41.323
- type: precision_at_3
value: 22.641
- type: precision_at_5
value: 15.789
- type: precision_at_10
value: 9.110999999999999
- type: precision_at_20
value: 4.854
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 41.6785
- type: mrr_at_3
value: 52.963499999999996
- type: mrr_at_5
value: 55.456399999999995
- type: mrr_at_10
value: 57.104299999999995
- type: mrr_at_20
value: 57.5402
- type: mrr_at_100
value: 57.6213
- type: mrr_at_1000
value: 57.6213
- type: nauc_ndcg_at_1_max
value: -12.633600000000001
- type: nauc_ndcg_at_1_std
value: -19.747700000000002
- type: nauc_ndcg_at_1_diff1
value: 25.820300000000003
- type: nauc_ndcg_at_3_max
value: -8.480799999999999
- type: nauc_ndcg_at_3_std
value: -18.928900000000002
- type: nauc_ndcg_at_3_diff1
value: 19.409499999999998
- type: nauc_ndcg_at_5_max
value: -7.9172
- type: nauc_ndcg_at_5_std
value: -19.2306
- type: nauc_ndcg_at_5_diff1
value: 18.5809
- type: nauc_ndcg_at_10_max
value: -9.7812
- type: nauc_ndcg_at_10_std
value: -19.9964
- type: nauc_ndcg_at_10_diff1
value: 18.0753
- type: nauc_ndcg_at_20_max
value: -8.6546
- type: nauc_ndcg_at_20_std
value: -19.3205
- type: nauc_ndcg_at_20_diff1
value: 20.1741
- type: nauc_ndcg_at_100_max
value: -9.376900000000001
- type: nauc_ndcg_at_100_std
value: -19.5093
- type: nauc_ndcg_at_100_diff1
value: 20.4997
- type: nauc_ndcg_at_1000_max
value: -9.376900000000001
- type: nauc_ndcg_at_1000_std
value: -19.5093
- type: nauc_ndcg_at_1000_diff1
value: 20.4997
- type: nauc_map_at_1_max
value: -12.633600000000001
- type: nauc_map_at_1_std
value: -19.747700000000002
- type: nauc_map_at_1_diff1
value: 25.820300000000003
- type: nauc_map_at_3_max
value: -9.5533
- type: nauc_map_at_3_std
value: -19.3235
- type: nauc_map_at_3_diff1
value: 20.9083
- type: nauc_map_at_5_max
value: -9.3126
- type: nauc_map_at_5_std
value: -19.4593
- type: nauc_map_at_5_diff1
value: 20.5718
- type: nauc_map_at_10_max
value: -10.0766
- type: nauc_map_at_10_std
value: -19.8322
- type: nauc_map_at_10_diff1
value: 20.484099999999998
- type: nauc_map_at_20_max
value: -9.7917
- type: nauc_map_at_20_std
value: -19.6759
- type: nauc_map_at_20_diff1
value: 21.0095
- type: nauc_map_at_100_max
value: -9.8837
- type: nauc_map_at_100_std
value: -19.741400000000002
- type: nauc_map_at_100_diff1
value: 21.067700000000002
- type: nauc_map_at_1000_max
value: -9.8837
- type: nauc_map_at_1000_std
value: -19.741400000000002
- type: nauc_map_at_1000_diff1
value: 21.067700000000002
- type: nauc_recall_at_1_max
value: -12.633600000000001
- type: nauc_recall_at_1_std
value: -19.747700000000002
- type: nauc_recall_at_1_diff1
value: 25.820300000000003
- type: nauc_recall_at_3_max
value: -4.7886999999999995
- type: nauc_recall_at_3_std
value: -17.4764
- type: nauc_recall_at_3_diff1
value: 14.3308
- type: nauc_recall_at_5_max
value: -1.4803
- type: nauc_recall_at_5_std
value: -18.2137
- type: nauc_recall_at_5_diff1
value: 9.4604
- type: nauc_recall_at_10_max
value: -8.8102
- type: nauc_recall_at_10_std
value: -21.8279
- type: nauc_recall_at_10_diff1
value: -5.5388
- type: nauc_recall_at_20_max
value: 25.202400000000004
- type: nauc_recall_at_20_std
value: -4.8613
- type: nauc_recall_at_20_diff1
value: 9.3917
- type: nauc_recall_at_100_max
value: 37.0551
- type: nauc_recall_at_100_std
value: 84.5472
- type: nauc_recall_at_100_diff1
value: 19.5116
- type: nauc_recall_at_1000_max
value: 37.0551
- type: nauc_recall_at_1000_std
value: 84.5472
- type: nauc_recall_at_1000_diff1
value: 19.5116
- type: nauc_precision_at_1_max
value: -12.633600000000001
- type: nauc_precision_at_1_std
value: -19.747700000000002
- type: nauc_precision_at_1_diff1
value: 25.820300000000003
- type: nauc_precision_at_3_max
value: -4.7886999999999995
- type: nauc_precision_at_3_std
value: -17.4764
- type: nauc_precision_at_3_diff1
value: 14.3308
- type: nauc_precision_at_5_max
value: -1.4803
- type: nauc_precision_at_5_std
value: -18.2137
- type: nauc_precision_at_5_diff1
value: 9.4604
- type: nauc_precision_at_10_max
value: -8.8102
- type: nauc_precision_at_10_std
value: -21.8279
- type: nauc_precision_at_10_diff1
value: -5.5388
- type: nauc_precision_at_20_max
value: 25.202400000000004
- type: nauc_precision_at_20_std
value: -4.8613
- type: nauc_precision_at_20_diff1
value: 9.3917
- type: nauc_precision_at_100_max
value: 37.0551
- type: nauc_precision_at_100_std
value: 84.5472
- type: nauc_precision_at_100_diff1
value: 19.5116
- type: nauc_precision_at_1000_max
value: 37.0551
- type: nauc_precision_at_1000_std
value: 84.5472
- type: nauc_precision_at_1000_diff1
value: 19.5116
- type: nauc_mrr_at_1_max
value: -11.9728
- type: nauc_mrr_at_1_std
value: -19.4014
- type: nauc_mrr_at_1_diff1
value: 24.8653
- type: nauc_mrr_at_3_max
value: -9.6607
- type: nauc_mrr_at_3_std
value: -19.1819
- type: nauc_mrr_at_3_diff1
value: 20.0205
- type: nauc_mrr_at_5_max
value: -9.4261
- type: nauc_mrr_at_5_std
value: -19.3098
- type: nauc_mrr_at_5_diff1
value: 19.6347
- type: nauc_mrr_at_10_max
value: -10.1698
- type: nauc_mrr_at_10_std
value: -19.683
- type: nauc_mrr_at_10_diff1
value: 19.4823
- type: nauc_mrr_at_20_max
value: -9.913
- type: nauc_mrr_at_20_std
value: -19.517300000000002
- type: nauc_mrr_at_20_diff1
value: 20.0066
- type: nauc_mrr_at_100_max
value: -10.0053
- type: nauc_mrr_at_100_std
value: -19.5824
- type: nauc_mrr_at_100_diff1
value: 20.061899999999998
- type: nauc_mrr_at_1000_max
value: -10.0053
- type: nauc_mrr_at_1000_std
value: -19.5824
- type: nauc_mrr_at_1000_diff1
value: 20.061899999999998
- type: main_score
value: 65.204
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 55.710300000000004
- type: v_measure_std
value: 14.551900000000002
- type: main_score
value: 55.710300000000004
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 51.004599999999996
- type: v_measure_std
value: 14.868899999999998
- type: main_score
value: 51.004599999999996
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.38119999999999
- type: mrr
value: 79.0692
- type: nAUC_map_max
value: 21.9757
- type: nAUC_map_std
value: 14.3545
- type: nAUC_map_diff1
value: 11.82
- type: nAUC_mrr_max
value: 40.3449
- type: nAUC_mrr_std
value: 18.2678
- type: nAUC_mrr_diff1
value: 21.1596
- type: main_score
value: 67.38119999999999
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: pearson
value: 87.4332
- type: spearman
value: 84.65979999999999
- type: cosine_pearson
value: 87.4332
- type: cosine_spearman
value: 84.65979999999999
- type: manhattan_pearson
value: 86.37270000000001
- type: manhattan_spearman
value: 85.20309999999999
- type: euclidean_pearson
value: 86.1672
- type: euclidean_spearman
value: 84.7453
- type: main_score
value: 84.65979999999999
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.224
- type: f1
value: 86.7235
- type: f1_weighted
value: 86.7235
- type: main_score
value: 87.224
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 52.04990000000001
- type: v_measure_std
value: 0.7255
- type: main_score
value: 52.04990000000001
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 47.5811
- type: v_measure_std
value: 0.8049000000000001
- type: main_score
value: 47.5811
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: ndcg_at_1
value: 46.781
- type: ndcg_at_3
value: 52.111
- type: ndcg_at_5
value: 54.371
- type: ndcg_at_10
value: 57.247
- type: ndcg_at_20
value: 59.122
- type: ndcg_at_100
value: 62.071
- type: ndcg_at_1000
value: 63.32
- type: map_at_1
value: 37.2
- type: map_at_3
value: 46.005
- type: map_at_5
value: 48.32
- type: map_at_10
value: 50.181
- type: map_at_20
value: 51.062
- type: map_at_100
value: 51.735
- type: map_at_1000
value: 51.842
- type: recall_at_1
value: 37.2
- type: recall_at_3
value: 53.94
- type: recall_at_5
value: 60.88
- type: recall_at_10
value: 69.692
- type: recall_at_20
value: 76.711
- type: recall_at_100
value: 90.263
- type: recall_at_1000
value: 97.749
- type: precision_at_1
value: 46.781
- type: precision_at_3
value: 25.894000000000002
- type: precision_at_5
value: 18.541
- type: precision_at_10
value: 11.402
- type: precision_at_20
value: 6.567
- type: precision_at_100
value: 1.704
- type: precision_at_1000
value: 0.21
- type: mrr_at_1
value: 46.781099999999995
- type: mrr_at_3
value: 54.6257
- type: mrr_at_5
value: 56.0992
- type: mrr_at_10
value: 57.00769999999999
- type: mrr_at_20
value: 57.3576
- type: mrr_at_100
value: 57.6118
- type: mrr_at_1000
value: 57.6415
- type: nauc_ndcg_at_1_max
value: 41.3069
- type: nauc_ndcg_at_1_std
value: -3.9859999999999998
- type: nauc_ndcg_at_1_diff1
value: 56.355900000000005
- type: nauc_ndcg_at_3_max
value: 40.354299999999995
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dataset:
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type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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dataset:
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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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revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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- type: nauc_precision_at_20_diff1
value: 8.251
- type: nauc_precision_at_100_max
value: 22.2412
- type: nauc_precision_at_100_std
value: 32.925900000000006
- type: nauc_precision_at_100_diff1
value: 1.5637999999999999
- type: nauc_precision_at_1000_max
value: 22.1
- type: nauc_precision_at_1000_std
value: 37.227700000000006
- type: nauc_precision_at_1000_diff1
value: -2.3548
- type: nauc_mrr_at_1_max
value: 18.4908
- type: nauc_mrr_at_1_std
value: 8.3766
- type: nauc_mrr_at_1_diff1
value: 22.0483
- type: nauc_mrr_at_3_max
value: 18.3602
- type: nauc_mrr_at_3_std
value: 9.579600000000001
- type: nauc_mrr_at_3_diff1
value: 18.7544
- type: nauc_mrr_at_5_max
value: 18.5224
- type: nauc_mrr_at_5_std
value: 9.747300000000001
- type: nauc_mrr_at_5_diff1
value: 19.0625
- type: nauc_mrr_at_10_max
value: 19.067
- type: nauc_mrr_at_10_std
value: 10.7103
- type: nauc_mrr_at_10_diff1
value: 19.444
- type: nauc_mrr_at_20_max
value: 19.118399999999998
- type: nauc_mrr_at_20_std
value: 10.6384
- type: nauc_mrr_at_20_diff1
value: 19.5223
- type: nauc_mrr_at_100_max
value: 19.200300000000002
- type: nauc_mrr_at_100_std
value: 10.6891
- type: nauc_mrr_at_100_diff1
value: 19.4009
- type: nauc_mrr_at_1000_max
value: 19.1676
- type: nauc_mrr_at_1000_std
value: 10.649799999999999
- type: nauc_mrr_at_1000_diff1
value: 19.4127
- type: main_score
value: 24.637999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: pearson
value: 86.8981
- type: spearman
value: 82.234
- type: cosine_pearson
value: 86.8981
- type: cosine_spearman
value: 82.234
- type: manhattan_pearson
value: 84.1165
- type: manhattan_spearman
value: 82.063
- type: euclidean_pearson
value: 84.0688
- type: euclidean_spearman
value: 82.2329
- type: main_score
value: 82.234
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 87.8269
- type: spearman
value: 80.2578
- type: cosine_pearson
value: 87.8269
- type: cosine_spearman
value: 80.2578
- type: manhattan_pearson
value: 85.09020000000001
- type: manhattan_spearman
value: 80.5777
- type: euclidean_pearson
value: 84.71990000000001
- type: euclidean_spearman
value: 80.2547
- type: main_score
value: 80.2578
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 89.45769999999999
- type: spearman
value: 89.69200000000001
- type: cosine_pearson
value: 89.45769999999999
- type: cosine_spearman
value: 89.69200000000001
- type: manhattan_pearson
value: 89.1685
- type: manhattan_spearman
value: 89.8889
- type: euclidean_pearson
value: 89.07969999999999
- type: euclidean_spearman
value: 89.7003
- type: main_score
value: 89.69200000000001
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 87.01989999999999
- type: spearman
value: 85.3155
- type: cosine_pearson
value: 87.01989999999999
- type: cosine_spearman
value: 85.3155
- type: manhattan_pearson
value: 86.24159999999999
- type: manhattan_spearman
value: 85.544
- type: euclidean_pearson
value: 86.1032
- type: euclidean_spearman
value: 85.31490000000001
- type: main_score
value: 85.3155
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 89.593
- type: spearman
value: 90.1574
- type: cosine_pearson
value: 89.593
- type: cosine_spearman
value: 90.1574
- type: manhattan_pearson
value: 89.93910000000001
- type: manhattan_spearman
value: 90.4357
- type: euclidean_pearson
value: 89.76650000000001
- type: euclidean_spearman
value: 90.1554
- type: main_score
value: 90.1574
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 85.03540000000001
- type: spearman
value: 86.4191
- type: cosine_pearson
value: 85.03540000000001
- type: cosine_spearman
value: 86.4191
- type: manhattan_pearson
value: 85.8992
- type: manhattan_spearman
value: 86.7274
- type: euclidean_pearson
value: 85.638
- type: euclidean_spearman
value: 86.42
- type: main_score
value: 86.4191
- 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: 54.1098
- type: spearman
value: 52.721
- type: cosine_pearson
value: 54.1098
- type: cosine_spearman
value: 52.721
- type: manhattan_pearson
value: 54.890899999999995
- type: manhattan_spearman
value: 52.727900000000005
- type: euclidean_pearson
value: 54.65410000000001
- type: euclidean_spearman
value: 52.8582
- type: main_score
value: 52.721
- 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: 84.3262
- type: spearman
value: 84.1421
- type: cosine_pearson
value: 84.3262
- type: cosine_spearman
value: 84.1421
- type: manhattan_pearson
value: 83.8999
- type: manhattan_spearman
value: 83.9987
- type: euclidean_pearson
value: 84.0174
- type: euclidean_spearman
value: 84.1711
- type: main_score
value: 84.1421
- 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: 91.38969999999999
- type: spearman
value: 91.00789999999999
- type: cosine_pearson
value: 91.38969999999999
- type: cosine_spearman
value: 91.00789999999999
- type: manhattan_pearson
value: 90.8917
- type: manhattan_spearman
value: 90.7834
- type: euclidean_pearson
value: 91.0361
- type: euclidean_spearman
value: 91.011
- type: main_score
value: 91.00789999999999
- 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: 84.636
- type: spearman
value: 84.1977
- type: cosine_pearson
value: 84.636
- type: cosine_spearman
value: 84.1977
- type: manhattan_pearson
value: 84.6895
- type: manhattan_spearman
value: 84.1849
- type: euclidean_pearson
value: 84.753
- type: euclidean_spearman
value: 84.2251
- type: main_score
value: 84.1977
- 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: 85.8595
- type: spearman
value: 86.9965
- type: cosine_pearson
value: 85.8595
- type: cosine_spearman
value: 86.9965
- type: manhattan_pearson
value: 86.92450000000001
- type: manhattan_spearman
value: 87.39489999999999
- type: euclidean_pearson
value: 86.5575
- type: euclidean_spearman
value: 86.997
- type: main_score
value: 86.9965
- 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: 85.959
- type: spearman
value: 86.3103
- type: cosine_pearson
value: 85.959
- type: cosine_spearman
value: 86.3103
- type: manhattan_pearson
value: 85.7486
- type: manhattan_spearman
value: 86.0977
- type: euclidean_pearson
value: 85.8361
- type: euclidean_spearman
value: 86.2741
- type: main_score
value: 86.3103
- 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: 86.2629
- type: spearman
value: 85.8372
- type: cosine_pearson
value: 86.2629
- type: cosine_spearman
value: 85.8372
- type: manhattan_pearson
value: 86.2491
- type: manhattan_spearman
value: 85.4973
- type: euclidean_pearson
value: 86.5074
- type: euclidean_spearman
value: 85.83370000000001
- type: main_score
value: 85.8372
- 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: 57.5675
- type: spearman
value: 55.564899999999994
- type: cosine_pearson
value: 57.5675
- type: cosine_spearman
value: 55.564899999999994
- type: manhattan_pearson
value: 57.4686
- type: manhattan_spearman
value: 55.3816
- type: euclidean_pearson
value: 57.635099999999994
- type: euclidean_spearman
value: 55.501900000000006
- type: main_score
value: 55.564899999999994
- 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: 76.1237
- type: spearman
value: 76.4
- type: cosine_pearson
value: 76.1237
- type: cosine_spearman
value: 76.4
- type: manhattan_pearson
value: 78.6794
- type: manhattan_spearman
value: 77.2282
- type: euclidean_pearson
value: 77.1688
- type: euclidean_spearman
value: 76.4
- type: main_score
value: 76.4
- 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: 73.89099999999999
- type: spearman
value: 77.8292
- type: cosine_pearson
value: 73.89099999999999
- type: cosine_spearman
value: 77.8292
- type: manhattan_pearson
value: 77.53229999999999
- type: manhattan_spearman
value: 78.3926
- type: euclidean_pearson
value: 76.6734
- type: euclidean_spearman
value: 77.8721
- type: main_score
value: 77.8292
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 68.31099999999999
- type: spearman
value: 68.0764
- type: cosine_pearson
value: 68.31099999999999
- type: cosine_spearman
value: 68.0764
- type: manhattan_pearson
value: 69.7423
- type: manhattan_spearman
value: 68.3212
- type: euclidean_pearson
value: 69.5701
- type: euclidean_spearman
value: 68.2299
- type: main_score
value: 68.0764
- 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: 65.0636
- type: spearman
value: 59.4669
- type: cosine_pearson
value: 65.0636
- type: cosine_spearman
value: 59.4669
- type: manhattan_pearson
value: 69.723
- type: manhattan_spearman
value: 60.3823
- type: euclidean_pearson
value: 68.6454
- type: euclidean_spearman
value: 59.384899999999995
- type: main_score
value: 59.4669
- 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: 73.7603
- type: spearman
value: 73.0799
- type: cosine_pearson
value: 73.7603
- type: cosine_spearman
value: 73.0799
- type: manhattan_pearson
value: 76.2351
- type: manhattan_spearman
value: 73.60719999999999
- type: euclidean_pearson
value: 75.61529999999999
- type: euclidean_spearman
value: 72.9809
- type: main_score
value: 73.0799
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 88.1242
- type: spearman
value: 88.8456
- type: cosine_pearson
value: 88.1242
- type: cosine_spearman
value: 88.8456
- type: manhattan_pearson
value: 88.6122
- type: manhattan_spearman
value: 89.0198
- type: euclidean_pearson
value: 88.5314
- type: euclidean_spearman
value: 88.8432
- type: main_score
value: 88.8456
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 89.06920000000001
- type: mrr
value: 96.977
- type: nAUC_map_max
value: 49.1778
- type: nAUC_map_std
value: 62.091499999999996
- type: nAUC_map_diff1
value: -13.373899999999999
- type: nAUC_mrr_max
value: 86.7325
- type: nAUC_mrr_std
value: 74.958
- type: nAUC_mrr_diff1
value: 25.6955
- type: main_score
value: 89.06920000000001
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 67.333
- type: ndcg_at_3
value: 75.142
- type: ndcg_at_5
value: 78.873
- type: ndcg_at_10
value: 80.372
- type: ndcg_at_20
value: 80.914
- type: ndcg_at_100
value: 81.563
- type: ndcg_at_1000
value: 81.601
- type: map_at_1
value: 63.99399999999999
- type: map_at_3
value: 72.181
- type: map_at_5
value: 74.665
- type: map_at_10
value: 75.51299999999999
- type: map_at_20
value: 75.693
- type: map_at_100
value: 75.78
- type: map_at_1000
value: 75.78099999999999
- type: recall_at_1
value: 63.99399999999999
- type: recall_at_3
value: 80.63900000000001
- type: recall_at_5
value: 90.017
- type: recall_at_10
value: 94.167
- type: recall_at_20
value: 96.167
- type: recall_at_100
value: 99.667
- type: recall_at_1000
value: 100.0
- type: precision_at_1
value: 67.333
- type: precision_at_3
value: 29.444
- type: precision_at_5
value: 20.067
- type: precision_at_10
value: 10.667
- type: precision_at_20
value: 5.45
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 67.3333
- type: mrr_at_3
value: 73.9444
- type: mrr_at_5
value: 76.0444
- type: mrr_at_10
value: 76.4517
- type: mrr_at_20
value: 76.5739
- type: mrr_at_100
value: 76.6556
- type: mrr_at_1000
value: 76.6563
- type: nauc_ndcg_at_1_max
value: 55.373099999999994
- type: nauc_ndcg_at_1_std
value: -11.362
- type: nauc_ndcg_at_1_diff1
value: 81.5232
- type: nauc_ndcg_at_3_max
value: 58.0376
- type: nauc_ndcg_at_3_std
value: -14.4854
- type: nauc_ndcg_at_3_diff1
value: 77.848
- type: nauc_ndcg_at_5_max
value: 55.789699999999996
- type: nauc_ndcg_at_5_std
value: -15.0412
- type: nauc_ndcg_at_5_diff1
value: 77.7873
- type: nauc_ndcg_at_10_max
value: 58.6243
- type: nauc_ndcg_at_10_std
value: -13.867799999999999
- type: nauc_ndcg_at_10_diff1
value: 78.071
- type: nauc_ndcg_at_20_max
value: 58.9032
- type: nauc_ndcg_at_20_std
value: -12.7998
- type: nauc_ndcg_at_20_diff1
value: 78.12010000000001
- type: nauc_ndcg_at_100_max
value: 58.0772
- type: nauc_ndcg_at_100_std
value: -12.4604
- type: nauc_ndcg_at_100_diff1
value: 78.7002
- type: nauc_ndcg_at_1000_max
value: 58.0835
- type: nauc_ndcg_at_1000_std
value: -12.662799999999999
- type: nauc_ndcg_at_1000_diff1
value: 78.7157
- type: nauc_map_at_1_max
value: 45.2711
- type: nauc_map_at_1_std
value: -21.685
- type: nauc_map_at_1_diff1
value: 80.8536
- type: nauc_map_at_3_max
value: 53.970600000000005
- type: nauc_map_at_3_std
value: -18.0178
- type: nauc_map_at_3_diff1
value: 78.95309999999999
- type: nauc_map_at_5_max
value: 54.7643
- type: nauc_map_at_5_std
value: -15.943999999999999
- type: nauc_map_at_5_diff1
value: 78.9665
- type: nauc_map_at_10_max
value: 56.737899999999996
- type: nauc_map_at_10_std
value: -14.355599999999999
- type: nauc_map_at_10_diff1
value: 79.095
- type: nauc_map_at_20_max
value: 56.804
- type: nauc_map_at_20_std
value: -14.033599999999998
- type: nauc_map_at_20_diff1
value: 79.0729
- type: nauc_map_at_100_max
value: 56.7168
- type: nauc_map_at_100_std
value: -14.000000000000002
- type: nauc_map_at_100_diff1
value: 79.1456
- type: nauc_map_at_1000_max
value: 56.7168
- type: nauc_map_at_1000_std
value: -14.003099999999998
- type: nauc_map_at_1000_diff1
value: 79.1458
- type: nauc_recall_at_1_max
value: 45.2711
- type: nauc_recall_at_1_std
value: -21.685
- type: nauc_recall_at_1_diff1
value: 80.8536
- type: nauc_recall_at_3_max
value: 55.911100000000005
- type: nauc_recall_at_3_std
value: -19.1603
- type: nauc_recall_at_3_diff1
value: 72.51689999999999
- type: nauc_recall_at_5_max
value: 50.6317
- type: nauc_recall_at_5_std
value: -21.4004
- type: nauc_recall_at_5_diff1
value: 68.3824
- type: nauc_recall_at_10_max
value: 67.3176
- type: nauc_recall_at_10_std
value: -23.6601
- type: nauc_recall_at_10_diff1
value: 67.20150000000001
- type: nauc_recall_at_20_max
value: 76.566
- type: nauc_recall_at_20_std
value: -12.4873
- type: nauc_recall_at_20_diff1
value: 63.6849
- type: nauc_recall_at_100_max
value: 55.415499999999994
- type: nauc_recall_at_100_std
value: 72.2222
- type: nauc_recall_at_100_diff1
value: 72.2222
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_1000_diff1
value: .nan
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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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type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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type: mteb/trec-covid
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revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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- type: nauc_recall_at_100_std
value: 44.8333
- type: nauc_recall_at_100_diff1
value: -6.1889
- type: nauc_recall_at_1000_max
value: 8.706
- type: nauc_recall_at_1000_std
value: 74.9427
- type: nauc_recall_at_1000_diff1
value: -11.8207
- type: nauc_precision_at_1_max
value: -17.1924
- type: nauc_precision_at_1_std
value: 0.0948
- type: nauc_precision_at_1_diff1
value: 28.084300000000002
- type: nauc_precision_at_3_max
value: -23.0318
- type: nauc_precision_at_3_std
value: 14.8594
- type: nauc_precision_at_3_diff1
value: 12.4551
- type: nauc_precision_at_5_max
value: -26.849800000000002
- type: nauc_precision_at_5_std
value: 2.2552
- type: nauc_precision_at_5_diff1
value: 6.4963999999999995
- type: nauc_precision_at_10_max
value: -14.1258
- type: nauc_precision_at_10_std
value: 9.764299999999999
- type: nauc_precision_at_10_diff1
value: 2.9594
- type: nauc_precision_at_20_max
value: 0.6306
- type: nauc_precision_at_20_std
value: 24.2147
- type: nauc_precision_at_20_diff1
value: -3.5282
- type: nauc_precision_at_100_max
value: 11.4275
- type: nauc_precision_at_100_std
value: 71.0325
- type: nauc_precision_at_100_diff1
value: -6.978800000000001
- type: nauc_precision_at_1000_max
value: 37.155899999999995
- type: nauc_precision_at_1000_std
value: 20.5794
- type: nauc_precision_at_1000_diff1
value: -11.6191
- type: nauc_mrr_at_1_max
value: -17.1924
- type: nauc_mrr_at_1_std
value: 0.0948
- type: nauc_mrr_at_1_diff1
value: 28.084300000000002
- type: nauc_mrr_at_3_max
value: -26.1233
- type: nauc_mrr_at_3_std
value: 12.6302
- type: nauc_mrr_at_3_diff1
value: 17.8467
- type: nauc_mrr_at_5_max
value: -23.641499999999997
- type: nauc_mrr_at_5_std
value: 8.3047
- type: nauc_mrr_at_5_diff1
value: 20.5063
- type: nauc_mrr_at_10_max
value: -23.5564
- type: nauc_mrr_at_10_std
value: 8.5333
- type: nauc_mrr_at_10_diff1
value: 20.5287
- type: nauc_mrr_at_20_max
value: -23.154700000000002
- type: nauc_mrr_at_20_std
value: 9.079600000000001
- type: nauc_mrr_at_20_diff1
value: 21.3888
- type: nauc_mrr_at_100_max
value: -23.3008
- type: nauc_mrr_at_100_std
value: 8.7424
- type: nauc_mrr_at_100_diff1
value: 21.2832
- type: nauc_mrr_at_1000_max
value: -23.3008
- type: nauc_mrr_at_1000_std
value: 8.7424
- type: nauc_mrr_at_1000_diff1
value: 21.2832
- type: main_score
value: 31.395
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 91.3135
- type: f1
value: 78.12480000000001
- type: f1_weighted
value: 92.4208
- type: ap
value: 41.943599999999996
- type: ap_weighted
value: 41.943599999999996
- type: main_score
value: 91.3135
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 77.24109999999999
- type: f1
value: 77.4964
- type: f1_weighted
value: 77.0248
- type: main_score
value: 77.24109999999999
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 63.132200000000005
- type: v_measure_std
value: 0.7163
- type: main_score
value: 63.132200000000005
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: similarity_accuracy
value: 88.061
- type: similarity_accuracy_threshold
value: 85.9546
- type: similarity_f1
value: 73.36240000000001
- type: similarity_f1_threshold
value: 84.0127
- type: similarity_precision
value: 71.47149999999999
- type: similarity_recall
value: 75.3562
- type: similarity_ap
value: 80.2787
- type: cosine_accuracy
value: 88.061
- type: cosine_accuracy_threshold
value: 85.9546
- type: cosine_f1
value: 73.36240000000001
- type: cosine_f1_threshold
value: 84.0127
- type: cosine_precision
value: 71.47149999999999
- type: cosine_recall
value: 75.3562
- type: cosine_ap
value: 80.2787
- type: manhattan_accuracy
value: 87.8941
- type: manhattan_accuracy_threshold
value: 4374.3263
- type: manhattan_f1
value: 72.929
- type: manhattan_f1_threshold
value: 4637.7289
- type: manhattan_precision
value: 71.3636
- type: manhattan_recall
value: 74.5646
- type: manhattan_ap
value: 79.98140000000001
- type: euclidean_accuracy
value: 88.0789
- type: euclidean_accuracy_threshold
value: 52.9742
- type: euclidean_f1
value: 73.3591
- type: euclidean_f1_threshold
value: 56.4815
- type: euclidean_precision
value: 71.608
- type: euclidean_recall
value: 75.19789999999999
- type: euclidean_ap
value: 80.2656
- type: dot_accuracy
value: 88.0789
- type: dot_accuracy_threshold
value: 85.8034
- type: dot_f1
value: 73.40379999999999
- type: dot_f1_threshold
value: 84.3213
- type: dot_precision
value: 72.6544
- type: dot_recall
value: 74.16890000000001
- type: dot_ap
value: 80.32010000000001
- type: max_accuracy
value: 88.0789
- type: max_f1
value: 73.40379999999999
- type: max_precision
value: 72.6544
- type: max_recall
value: 75.3562
- type: max_ap
value: 80.32010000000001
- type: main_score
value: 80.32010000000001
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 89.93090000000001
- type: similarity_accuracy_threshold
value: 82.1349
- type: similarity_f1
value: 80.1377
- type: similarity_f1_threshold
value: 80.5047
- type: similarity_precision
value: 77.1539
- type: similarity_recall
value: 83.3616
- type: similarity_ap
value: 87.6917
- type: cosine_accuracy
value: 89.93090000000001
- type: cosine_accuracy_threshold
value: 82.1349
- type: cosine_f1
value: 80.1377
- type: cosine_f1_threshold
value: 80.5047
- type: cosine_precision
value: 77.1539
- type: cosine_recall
value: 83.3616
- type: cosine_ap
value: 87.6917
- type: manhattan_accuracy
value: 89.86880000000001
- type: manhattan_accuracy_threshold
value: 4882.7347
- type: manhattan_f1
value: 80.2323
- type: manhattan_f1_threshold
value: 5185.1944
- type: manhattan_precision
value: 76.74889999999999
- type: manhattan_recall
value: 84.0468
- type: manhattan_ap
value: 87.70750000000001
- type: euclidean_accuracy
value: 89.94640000000001
- type: euclidean_accuracy_threshold
value: 59.9149
- type: euclidean_f1
value: 80.1527
- type: euclidean_f1_threshold
value: 62.3611
- type: euclidean_precision
value: 77.2744
- type: euclidean_recall
value: 83.2538
- type: euclidean_ap
value: 87.6922
- type: dot_accuracy
value: 89.9038
- type: dot_accuracy_threshold
value: 82.53049999999999
- type: dot_f1
value: 80.0969
- type: dot_f1_threshold
value: 80.7285
- type: dot_precision
value: 77.5853
- type: dot_recall
value: 82.77640000000001
- type: dot_ap
value: 87.668
- type: max_accuracy
value: 89.94640000000001
- type: max_f1
value: 80.2323
- type: max_precision
value: 77.5853
- type: max_recall
value: 84.0468
- type: max_ap
value: 87.70750000000001
- type: main_score
value: 87.70750000000001
---
## Introduction
Based on dunzhang/stella_en_1.5B_v5 and google/siglip-so400m-patch14-384.
It can encode both text and images.
**Report:** https://arxiv.org/abs/2412.19048
**Codes:** https://github.com/NLPJCL/RAG-Retrieval
**Data:** https://huggingface.co/datasets/infgrad/jasper_text_distill_dataset
**Training logs:** https://api.wandb.ai/links/dunnzhang0/z8jqoqpb
The core idea of jasper and stella is distillation: **Let student model learn teacher model's vectors.**
## Usage
```python
import torch
from sentence_transformers import SentenceTransformer
DOC1 = """
Blue light is scattered in all directions by the tiny molecules of air in Earth's atmosphere.
Blue is scattered more than other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time.
Closer to the horizon, the sky fades to a lighter blue or white.
"""
DOC2 = """
When choosing colors, you can consider the following factors:
Color theory: Understand how colors work together and how they can evoke different reactions.
Color psychology: Consider how colors affect emotions, behaviors, and responses.
Brand identity: Colors can convey meaning and information about a brand.
Mood: Consider the mood you want to create. For example, brighter colors can feel cheerful, while cooler colors can be calming.
Space: Consider the size of the space and the amount of natural light it receives. Dark colors can make a room feel smaller, while light colors can make it feel larger.
Color wheel: Use the color wheel to identify primary, secondary, and tertiary colors.
Color combinations: Decide how to best complement your preferred color with others.
Color palette: Limit your color palette to a main color and one or two additional colors.
60-30-10 rule: Use a primary color 60% of the time, a secondary color 30% of the time, and an accent color 10% of the time
"""
if __name__ == "__main__":
# load model
use_gpu = False
model_name = "infgrad/jasper_en_vision_language_v1"
model = SentenceTransformer(
model_name,
trust_remote_code=True,
device="cpu" if not use_gpu else "cuda",
model_kwargs={
"torch_dtype": torch.bfloat16 if use_gpu else torch.float32,
"attn_implementation": "sdpa"
},
# vector_dim must be 12288, 1024, 512, 256
## 1024 is recommended
# set is_text_encoder 'True', if you do not encode image
config_kwargs={"is_text_encoder": False, "vector_dim": 1024},
)
# We can reduce the max_seq_length from the default of 2048 for faster encoding
model.max_seq_length = 1024
# data
q_list = [
"Why the sky is blue?",
"how to choose suitable color",
]
doc_list = [
DOC1,
[{"type": "image_path", "content": "./assets/img1.png"}, {"type": "text", "content": "Hope this image helps!"}],
DOC2,
[{"type": "image_path", "content": "./assets/img2.png"}],
]
q_vecs = model.encode(q_list, prompt_name="s2p_query")
doc_vecs = model.encode(doc_list)
# calculate similarity
similarities = model.similarity(q_vecs, doc_vecs)
print(similarities)
# the output is:
# tensor([[0.7775, 0.7594, 0.2429, 0.2187],
# [0.3226, 0.3054, 0.7421, 0.5484]])
```
## Evaluation on MTEB
script: ./scripts/evaluate_en_mteb/run_evaluate_mteb.py
## License
**This model should not be used for any commercial purpose!**
## 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},
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf
|
RichardErkhov
| null |
[
"gguf",
"arxiv:2308.03281",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-06-22T13:20:44 | 2024-06-22T15:22:37 | 10,421 | 7 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gte-Qwen2-7B-instruct - GGUF
- Model creator: https://huggingface.co/Alibaba-NLP/
- Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gte-Qwen2-7B-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q2_K.gguf) | Q2_K | 2.81GB |
| [gte-Qwen2-7B-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_XS.gguf) | IQ3_XS | 3.11GB |
| [gte-Qwen2-7B-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [gte-Qwen2-7B-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [gte-Qwen2-7B-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [gte-Qwen2-7B-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K.gguf) | Q3_K | 3.55GB |
| [gte-Qwen2-7B-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [gte-Qwen2-7B-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [gte-Qwen2-7B-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [gte-Qwen2-7B-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_0.gguf) | Q4_0 | 4.13GB |
| [gte-Qwen2-7B-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ4_NL.gguf) | IQ4_NL | 4.15GB |
| [gte-Qwen2-7B-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [gte-Qwen2-7B-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K.gguf) | Q4_K | 4.36GB |
| [gte-Qwen2-7B-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [gte-Qwen2-7B-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_1.gguf) | Q4_1 | 4.54GB |
| [gte-Qwen2-7B-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_0.gguf) | Q5_0 | 4.95GB |
| [gte-Qwen2-7B-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [gte-Qwen2-7B-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K.gguf) | Q5_K | 5.07GB |
| [gte-Qwen2-7B-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [gte-Qwen2-7B-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_1.gguf) | Q5_1 | 5.36GB |
| [gte-Qwen2-7B-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q6_K.gguf) | Q6_K | 5.82GB |
| [gte-Qwen2-7B-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
license: apache-2.0
model-index:
- name: gte-qwen2-7B-instruct
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
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:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
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:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 62.564
- type: f1
value: 60.975777935041066
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: 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:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 56.461169803700564
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 51.73600434466286
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.57827065898053
- type: mrr
value: 79.08136569493911
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
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:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.56818181818181
- type: f1
value: 87.25826722019875
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 50.09239610327673
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 46.64733054606282
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
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
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- 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:
type: mteb/climate-fever
name: MTEB ClimateFEVER
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:
type: mteb/dbpedia
name: 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:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 79.455
- type: f1
value: 74.16798697647569
- task:
type: Retrieval
dataset:
type: mteb/fever
name: 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:
type: mteb/fiqa
name: MTEB FiQA2018
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:
type: mteb/hotpotqa
name: 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:
type: mteb/imdb
name: MTEB ImdbClassification
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:
type: mteb/msmarco
name: 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:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 99.03784769721841
- type: f1
value: 98.97791641821495
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 91.88326493388054
- type: f1
value: 73.74809928034335
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 85.41358439811701
- type: f1
value: 83.503679460639
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 89.77135171486215
- type: f1
value: 88.89843747468366
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 46.22695362087359
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 44.132372165849425
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.35680810650402
- type: mrr
value: 34.72625715637218
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: 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:
type: mteb/nq
name: 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:
type: mteb/quora
name: MTEB QuoraRetrieval
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:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 73.55219145406065
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 74.13437105242755
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: 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:
type: mteb/sickr-sts
name: MTEB SICK-R
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:
type: mteb/sts12-sts
name: MTEB STS12
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:
type: mteb/sts13-sts
name: MTEB STS13
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:
type: mteb/sts14-sts
name: MTEB STS14
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:
type: mteb/sts15-sts
name: MTEB STS15
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:
type: mteb/sts16-sts
name: MTEB STS16
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:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
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:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
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:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
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:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 89.09255369305481
- type: mrr
value: 97.10323445617563
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: 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:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
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:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 79.86443362395185
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 49.40897096662564
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.66040806627947
- type: mrr
value: 56.58670475766064
- task:
type: Summarization
dataset:
type: mteb/summeval
name: 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:
type: mteb/trec-covid
name: MTEB TRECCOVID
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:
type: mteb/touche2020
name: 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:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
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:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.58347481607245
- type: f1
value: 72.74877295564937
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.90586138221305
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
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:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
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:
type: C-MTEB/AFQMC
name: 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:
type: C-MTEB/ATEC
name: 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:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.98400000000001
- type: f1
value: 51.21447361350723
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: 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:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 47.07270168705156
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.98511703185043
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.19895157194931
- type: mrr
value: 90.21424603174603
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.03317320980119
- type: mrr
value: 89.9461507936508
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: 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:
type: C-MTEB/CMNLI
name: 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:
type: C-MTEB/CovidRetrieval
name: 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:
type: C-MTEB/DuRetrieval
name: 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:
type: C-MTEB/EcomRetrieval
name: 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:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.52096960369373
- type: f1
value: 40.930845295808695
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
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:
type: C-MTEB/LCQMC
name: 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:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 31.648613483640254
- type: mrr
value: 30.37420634920635
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: 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:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 81.08607935440484
- type: f1
value: 78.24879986066307
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 86.05917955615332
- type: f1
value: 85.05279279434997
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: 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:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 76.87666666666667
- type: f1
value: 76.7317686219665
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: 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:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
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:
type: C-MTEB/PAWSX
name: 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:
type: C-MTEB/QBQTC
name: 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:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
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:
type: C-MTEB/STSB
name: 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:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.80129586475687
- type: mrr
value: 77.77402311635554
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: 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:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 52.971999999999994
- type: f1
value: 50.2898280984929
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 86.0797948663824
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 85.10759092255017
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: 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:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 89.47
- type: ap
value: 75.59561751845389
- type: f1
value: 87.95207751382563
---
## 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())
```
## 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) |
|:----:|:---------:|:----------:|
| [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** |
### 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 |
## 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"
] |
EleutherAI/pythia-6.9b-deduped
|
EleutherAI
|
text-generation
|
[
"transformers",
"pytorch",
"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"
] | 2023-02-25T17:56:57 | 2023-06-08T13:05:19 | 10,266 | 8 |
---
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-6.9B-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-6.9B-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-6.9B-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-6.9B-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-6.9B-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-6.9B-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-6.9B-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-6.9B-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-6.9B-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-2.8b-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"
] | 2023-02-10T22:26:20 | 2023-07-09T16:06:37 | 9,120 | 14 |
---
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-2.8B-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-2.8B-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-2.8B-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-2.8B-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-2.8B-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-2.8B-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-2.8B-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-2.8B-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-2.8B-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"
] |
BAAI/bge-small-zh
|
BAAI
|
feature-extraction
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"zh",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-08-05T08:03:22 | 2023-10-12T03:37:29 | 9,020 | 18 |
---
language:
- zh
license: mit
---
**Recommend switching to newest [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-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"
] |
unicamp-dl/translation-en-pt-t5
|
unicamp-dl
|
translation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"pt",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2021-10-11T03:47:21 | 9,018 | 20 |
---
datasets:
- EMEA
- ParaCrawl 99k
- CAPES
- Scielo
- JRC-Acquis
- Biomedical Domain Corpora
language:
- en
- pt
metrics:
- bleu
tags:
- translation
---
# Introduction
This repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in [our repository](https://github.com/unicamp-dl/Lite-T5-Translation). Also, check [our paper](https://aclanthology.org/2020.wmt-1.90.pdf)!
# Usage
Just follow "Use in Transformers" instructions. It is necessary to add a few words before to define the task to T5.
You can also create a pipeline for it. An example with the phrase "I like to eat rice" is:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/translation-en-pt-t5")
model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/translation-en-pt-t5")
enpt_pipeline = pipeline('text2text-generation', model=model, tokenizer=tokenizer)
enpt_pipeline("translate English to Portuguese: I like to eat rice.")
```
# Citation
```bibtex
@inproceedings{lopes-etal-2020-lite,
title = "Lite Training Strategies for {P}ortuguese-{E}nglish and {E}nglish-{P}ortuguese Translation",
author = "Lopes, Alexandre and
Nogueira, Rodrigo and
Lotufo, Roberto and
Pedrini, Helio",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.wmt-1.90",
pages = "833--840",
}
```
|
[
"TRANSLATION"
] |
[
"SCIELO"
] |
HIT-TMG/KaLM-embedding-multilingual-mini-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"
] | 2024-08-27T08:56:33 | 2025-01-03T07:26:50 | 8,131 | 20 |
---
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: 74.16041979010495
- type: ap
value: 22.731316107205824
- type: ap_weighted
value: 22.731316107205824
- type: f1
value: 61.311184650259634
- type: f1_weighted
value: 78.92070802470501
- type: main_score
value: 74.16041979010495
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.35820895522387
- type: ap
value: 34.13026440006763
- type: ap_weighted
value: 34.13026440006763
- type: f1
value: 65.91101941691169
- type: f1_weighted
value: 74.90947851184335
- type: main_score
value: 72.35820895522387
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.2693
- type: ap
value: 93.69278757537118
- type: ap_weighted
value: 93.69278757537118
- type: f1
value: 95.26705627226383
- type: f1_weighted
value: 95.26705627226384
- type: main_score
value: 95.2693
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 51.01
- type: f1
value: 48.69903082137716
- type: f1_weighted
value: 48.69903082137716
- type: main_score
value: 51.01
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 56.713
- type: map_at_1
value: 31.436999999999998
- type: map_at_10
value: 47.632000000000005
- type: map_at_100
value: 48.418
- type: map_at_1000
value: 48.421
- type: map_at_20
value: 48.274
- type: map_at_3
value: 42.568
- type: map_at_5
value: 45.473
- type: mrr_at_1
value: 31.934566145092464
- type: mrr_at_10
value: 47.80803359750735
- type: mrr_at_100
value: 48.594181951484266
- type: mrr_at_1000
value: 48.59689299100106
- type: mrr_at_20
value: 48.450028297368256
- type: mrr_at_3
value: 42.7453769559033
- type: mrr_at_5
value: 45.625889046941744
- type: nauc_map_at_1000_diff1
value: 11.309764384647323
- type: nauc_map_at_1000_max
value: -12.696935142377729
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 84.5234167909779
- type: cosine_spearman
value: 84.86841413445535
- type: euclidean_pearson
value: 84.17741655183796
- type: euclidean_spearman
value: 84.86841405901674
- type: main_score
value: 84.86841413445535
- type: manhattan_pearson
value: 84.15491829147086
- type: manhattan_spearman
value: 84.93066841323679
- type: pearson
value: 84.5234167909779
- type: spearman
value: 84.86841413445535
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 83.42559938022957
- type: cosine_spearman
value: 80.10636060670153
- type: euclidean_pearson
value: 82.31695543050009
- type: euclidean_spearman
value: 80.10637586616073
- type: main_score
value: 80.10636060670153
- type: manhattan_pearson
value: 82.15731596876633
- type: manhattan_spearman
value: 80.02499151302123
- type: pearson
value: 83.42559938022957
- type: spearman
value: 80.10636060670153
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 87.98708135949613
- type: cosine_spearman
value: 88.69670049389599
- type: euclidean_pearson
value: 87.73091071499016
- type: euclidean_spearman
value: 88.69669966606001
- type: main_score
value: 88.69670049389599
- type: manhattan_pearson
value: 87.52276751048582
- type: manhattan_spearman
value: 88.5214230554986
- type: pearson
value: 87.98708135949613
- type: spearman
value: 88.69670049389599
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 83.38330950325803
- type: cosine_spearman
value: 84.62194600310691
- type: euclidean_pearson
value: 83.4921014845454
- type: euclidean_spearman
value: 84.62194539439683
- type: main_score
value: 84.62194600310691
- type: manhattan_pearson
value: 83.27754689500482
- type: manhattan_spearman
value: 84.37797144965002
- type: pearson
value: 83.38330950325803
- type: spearman
value: 84.62194600310691
- 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: 64.3970938916265
- type: cosine_spearman
value: 64.20857293171593
- type: euclidean_pearson
value: 64.70484646950464
- type: euclidean_spearman
value: 64.20857293171593
- type: main_score
value: 64.20857293171593
- type: manhattan_pearson
value: 63.61585574374933
- type: manhattan_spearman
value: 62.52898030084564
- type: pearson
value: 64.3970938916265
- type: spearman
value: 64.20857293171593
- 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: 78.3035787778662
- type: cosine_spearman
value: 78.85326338385796
- type: euclidean_pearson
value: 78.59090666313418
- type: euclidean_spearman
value: 78.85326338385796
- type: main_score
value: 78.85326338385796
- type: manhattan_pearson
value: 78.4961035895383
- type: manhattan_spearman
value: 78.42104373908565
- type: pearson
value: 78.3035787778662
- type: spearman
value: 78.85326338385796
- 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: 88.20922919233338
- type: cosine_spearman
value: 87.94347302365394
- type: euclidean_pearson
value: 87.98965741145625
- type: euclidean_spearman
value: 87.94347302365394
- type: main_score
value: 87.94347302365394
- type: manhattan_pearson
value: 87.94636580768939
- type: manhattan_spearman
value: 87.82077364455115
- type: pearson
value: 88.20922919233338
- type: spearman
value: 87.94347302365394
- 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: 58.50589296592958
- type: cosine_spearman
value: 57.045627811103
- type: euclidean_pearson
value: 58.54066429107441
- type: euclidean_spearman
value: 57.045627811103
- type: main_score
value: 57.045627811103
- type: manhattan_pearson
value: 57.77923152721202
- type: manhattan_spearman
value: 55.832507020505886
- type: pearson
value: 58.50589296592958
- type: spearman
value: 57.045627811103
- 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: 79.01593420315352
- type: cosine_spearman
value: 79.86309144376173
- type: euclidean_pearson
value: 78.85136309334905
- type: euclidean_spearman
value: 79.86309144376173
- type: main_score
value: 79.86309144376173
- type: manhattan_pearson
value: 78.87419337945624
- type: manhattan_spearman
value: 80.0980944874198
- type: pearson
value: 79.01593420315352
- type: spearman
value: 79.86309144376173
- 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: 79.67432399995894
- type: cosine_spearman
value: 79.12303288340163
- type: euclidean_pearson
value: 79.721668775324
- type: euclidean_spearman
value: 79.12303288340163
- type: main_score
value: 79.12303288340163
- type: manhattan_pearson
value: 79.33800466555394
- type: manhattan_spearman
value: 78.30603645374914
- type: pearson
value: 79.67432399995894
- type: spearman
value: 79.12303288340163
- 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: 78.92024449526863
- type: cosine_spearman
value: 79.06471992660374
- type: euclidean_pearson
value: 78.85388657114522
- type: euclidean_spearman
value: 79.06471992660374
- type: main_score
value: 79.06471992660374
- type: manhattan_pearson
value: 78.56658857806735
- type: manhattan_spearman
value: 78.5908742980949
- type: pearson
value: 78.92024449526863
- type: spearman
value: 79.06471992660374
- 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: 76.64708509569135
- type: cosine_spearman
value: 75.76775070804274
- type: euclidean_pearson
value: 76.69358579979829
- type: euclidean_spearman
value: 75.76775070804274
- type: main_score
value: 75.76775070804274
- type: manhattan_pearson
value: 76.28750520391006
- type: manhattan_spearman
value: 75.30493726054976
- type: pearson
value: 76.64708509569135
- type: spearman
value: 75.76775070804274
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 69.07403446182418
- type: cosine_spearman
value: 68.99668192503603
- type: euclidean_pearson
value: 70.82685591260719
- type: euclidean_spearman
value: 68.99668192503603
- type: main_score
value: 68.99668192503603
- type: manhattan_pearson
value: 70.94201332797343
- type: manhattan_spearman
value: 68.98821773218067
- type: pearson
value: 69.07403446182418
- type: spearman
value: 68.99668192503603
- 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: 65.95032307094047
- type: cosine_spearman
value: 63.15571038787516
- type: euclidean_pearson
value: 68.31815956207403
- type: euclidean_spearman
value: 63.15571038787516
- type: main_score
value: 63.15571038787516
- type: manhattan_pearson
value: 69.57471678363024
- type: manhattan_spearman
value: 63.78770917466211
- type: pearson
value: 65.95032307094047
- type: spearman
value: 63.15571038787516
- 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: 76.57985370197574
- type: cosine_spearman
value: 78.61171041249278
- type: euclidean_pearson
value: 77.64916374513423
- type: euclidean_spearman
value: 78.61182871621082
- type: main_score
value: 78.61171041249278
- type: manhattan_pearson
value: 79.45516154600577
- type: manhattan_spearman
value: 79.81770224017768
- type: pearson
value: 76.57985370197574
- type: spearman
value: 78.61171041249278
- 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: 78.66979183071325
- type: cosine_spearman
value: 76.74899167835852
- type: euclidean_pearson
value: 78.89780095637012
- type: euclidean_spearman
value: 76.74899167835852
- type: main_score
value: 76.74899167835852
- type: manhattan_pearson
value: 79.18536398264527
- type: manhattan_spearman
value: 77.8533686712189
- type: pearson
value: 78.66979183071325
- type: spearman
value: 76.74899167835852
- type: cosine_pearson
value: 78.67129157333113
- type: cosine_spearman
value: 77.17497249706467
- type: euclidean_pearson
value: 78.93527680834069
- type: euclidean_spearman
value: 77.17497249706467
- type: main_score
value: 77.17497249706467
- type: manhattan_pearson
value: 79.17117078125075
- type: manhattan_spearman
value: 77.98920639910075
- type: pearson
value: 78.67129157333113
- type: spearman
value: 77.17497249706467
- 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: 75.65018415517595
- type: cosine_spearman
value: 74.96983110528109
- type: euclidean_pearson
value: 77.0199252096022
- type: euclidean_spearman
value: 75.05313744822759
- type: main_score
value: 74.96983110528109
- type: manhattan_pearson
value: 77.28747618528581
- type: manhattan_spearman
value: 74.95188542213391
- type: pearson
value: 75.65018415517595
- type: spearman
value: 74.96983110528109
- type: cosine_pearson
value: 75.65018415517595
- type: cosine_spearman
value: 74.96983110528109
- type: euclidean_pearson
value: 77.0199252096022
- type: euclidean_spearman
value: 75.05313744822759
- type: main_score
value: 74.96983110528109
- type: manhattan_pearson
value: 77.28747618528581
- type: manhattan_spearman
value: 74.95188542213391
- type: pearson
value: 75.65018415517595
- type: spearman
value: 74.96983110528109
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 84.89952732150343
- type: cosine_spearman
value: 86.06896054399277
- type: euclidean_pearson
value: 85.69195853460913
- type: euclidean_spearman
value: 86.06896054399277
- type: main_score
value: 86.06896054399277
- type: manhattan_pearson
value: 85.56550688049849
- type: manhattan_spearman
value: 85.96422284827248
- type: pearson
value: 84.89952732150343
- type: spearman
value: 86.06896054399277
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 81.89447973144247
- type: map
value: 81.89447973144247
- type: mrr
value: 95.02511830943203
- type: nAUC_map_diff1
value: 3.3432260393863147
- type: nAUC_map_max
value: 54.252667154593915
- type: nAUC_map_std
value: 68.86046114121041
- type: nAUC_mrr_diff1
value: 48.53496653582678
- type: nAUC_mrr_max
value: 85.71793394587537
- type: nAUC_mrr_std
value: 80.13736591117815
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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value: 73.055
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 68.73700000000001
- type: map_at_100
value: 69.248
- type: map_at_1000
value: 69.271
- type: map_at_20
value: 69.059
- type: map_at_3
value: 66.235
- type: map_at_5
value: 67.843
- type: mrr_at_1
value: 60.66666666666667
- type: mrr_at_10
value: 69.7063492063492
- type: mrr_at_100
value: 70.13874332314896
- type: mrr_at_1000
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- type: mrr_at_20
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- type: mrr_at_3
value: 68.0
- type: mrr_at_5
value: 69.16666666666667
- type: nauc_map_at_1000_diff1
value: 70.43790903123511
- type: nauc_map_at_1000_max
value: 54.58438799194478
- type: nauc_map_at_1000_std
value: -2.3233833924225875
- type: nauc_map_at_100_diff1
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value: -2.296496281894915
- type: nauc_map_at_10_diff1
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- type: nauc_map_at_1_diff1
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- type: nauc_map_at_20_diff1
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- type: nauc_map_at_5_diff1
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value: 53.82183956625946
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- type: nauc_mrr_at_1000_diff1
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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: 90.9
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value: 89.7
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type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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dataset:
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type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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dataset:
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type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: 30.802000000000003
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value: 32.653
- type: precision_at_10
value: 26.531
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value: 8.224
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value: 27.632
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value: 7.578
- type: recall_at_5
value: 11.346
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 70.810546875
- type: ap
value: 14.252152092007437
- type: ap_weighted
value: 14.252152092007437
- type: f1
value: 54.48430687519361
- type: f1_weighted
value: 77.28107973539473
- type: main_score
value: 70.810546875
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.66553480475382
- type: f1
value: 62.053566222838384
- type: f1_weighted
value: 60.48069640139468
- type: main_score
value: 62.66553480475382
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 49.676842982432774
- type: v_measure
value: 49.676842982432774
- type: v_measure_std
value: 1.3041225457855343
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 85.07480479227513
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value: 78.39158177375793
- type: cosine_ap
value: 70.92737526837412
- type: cosine_f1
value: 66.1954959271682
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value: 74.12481307983398
- type: cosine_precision
value: 60.61869240895129
- type: cosine_recall
value: 72.9023746701847
- type: dot_accuracy
value: 85.07480479227513
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value: 78.39158773422241
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value: 70.92737601494514
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value: 66.1954959271682
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value: 74.12482500076294
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value: 60.61869240895129
- type: dot_recall
value: 72.9023746701847
- type: euclidean_accuracy
value: 85.07480479227513
- type: euclidean_accuracy_threshold
value: 65.73951244354248
- type: euclidean_ap
value: 70.92738137519932
- type: euclidean_f1
value: 66.1954959271682
- type: euclidean_f1_threshold
value: 71.93772792816162
- type: euclidean_precision
value: 60.61869240895129
- type: euclidean_recall
value: 72.9023746701847
- type: main_score
value: 70.92738137519932
- type: manhattan_accuracy
value: 84.89002801454372
- type: manhattan_accuracy_threshold
value: 1543.7227249145508
- type: manhattan_ap
value: 70.45819704836475
- type: manhattan_f1
value: 65.75607397558322
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value: 1691.067886352539
- type: manhattan_precision
value: 60.673656033905864
- type: manhattan_recall
value: 71.76781002638522
- type: max_ap
value: 70.92738137519932
- type: max_f1
value: 66.1954959271682
- type: max_precision
value: 60.673656033905864
- type: max_recall
value: 72.9023746701847
- type: similarity_accuracy
value: 85.07480479227513
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value: 78.39158177375793
- type: similarity_ap
value: 70.92737526837412
- type: similarity_f1
value: 66.1954959271682
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value: 74.12481307983398
- type: similarity_precision
value: 60.61869240895129
- type: similarity_recall
value: 72.9023746701847
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 89.32355338223309
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value: 72.50972986221313
- type: cosine_ap
value: 86.74895762701595
- type: cosine_f1
value: 79.21738810635873
- type: cosine_f1_threshold
value: 69.94493007659912
- type: cosine_precision
value: 75.82905020066183
- type: cosine_recall
value: 82.9226978749615
- type: dot_accuracy
value: 89.32355338223309
- type: dot_accuracy_threshold
value: 72.50974178314209
- type: dot_ap
value: 86.74894970312789
- type: dot_f1
value: 79.21738810635873
- type: dot_f1_threshold
value: 69.94493007659912
- type: dot_precision
value: 75.82905020066183
- type: dot_recall
value: 82.9226978749615
- type: euclidean_accuracy
value: 89.32355338223309
- type: euclidean_accuracy_threshold
value: 74.14885759353638
- type: euclidean_ap
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- type: euclidean_f1
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value: 77.53072381019592
- type: euclidean_precision
value: 75.82905020066183
- type: euclidean_recall
value: 82.9226978749615
- type: main_score
value: 86.74895762701595
- type: manhattan_accuracy
value: 89.28474405247022
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value: 1725.102424621582
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value: 86.69699016049593
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value: 1807.0615768432617
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value: 76.68671642872673
- type: manhattan_recall
value: 81.4752078842008
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value: 86.74895762701595
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value: 79.21738810635873
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value: 76.68671642872673
- type: max_recall
value: 82.9226978749615
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value: 89.32355338223309
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value: 72.50972986221313
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value: 86.74895762701595
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value: 79.21738810635873
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value: 69.94493007659912
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value: 75.82905020066183
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value: 82.9226978749615
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
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value: 38.29145368837485
- type: cosine_spearman
value: 39.41056570139273
- type: euclidean_pearson
value: 38.0651461534699
- type: euclidean_spearman
value: 39.41056569992215
- type: main_score
value: 39.41056570139273
- type: manhattan_pearson
value: 37.70876309636298
- type: manhattan_spearman
value: 39.04864822187025
- type: pearson
value: 38.29145368837485
- type: spearman
value: 39.41056570139273
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cosine_pearson
value: 46.47704725371303
- type: cosine_spearman
value: 46.9183608596495
- type: euclidean_pearson
value: 49.36420417260176
- type: euclidean_spearman
value: 46.91835860770197
- type: main_score
value: 46.9183608596495
- type: manhattan_pearson
value: 49.124318954541145
- type: manhattan_spearman
value: 46.69432997494852
- type: pearson
value: 46.47704725371303
- type: spearman
value: 46.9183608596495
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.858000000000004
- type: f1
value: 38.04731113109237
- type: f1_weighted
value: 38.04731113109237
- type: main_score
value: 41.858000000000004
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cosine_pearson
value: 51.2270285721989
- type: cosine_spearman
value: 51.53381532349815
- type: euclidean_pearson
value: 50.83672339980501
- type: euclidean_spearman
value: 51.53382225123762
- type: main_score
value: 51.53381532349815
- type: manhattan_pearson
value: 50.481897254555655
- type: manhattan_spearman
value: 51.165938122581764
- type: pearson
value: 51.2270285721989
- type: spearman
value: 51.53381532349815
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: main_score
value: 42.6351765343486
- type: v_measure
value: 42.6351765343486
- type: v_measure_std
value: 0.8266776246358534
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: main_score
value: 39.14026434895999
- type: v_measure
value: 39.14026434895999
- type: v_measure_std
value: 0.8843326244130124
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
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value: 81.62649518330059
- type: map
value: 81.62649518330059
- type: mrr
value: 84.59920634920634
- type: nAUC_map_diff1
value: 57.57622865226385
- type: nAUC_map_max
value: 64.24578070815535
- type: nAUC_map_std
value: 25.825835637398292
- type: nAUC_mrr_diff1
value: 64.506555321586
- type: nAUC_mrr_max
value: 73.72849839805279
- type: nAUC_mrr_std
value: 33.50231715071016
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
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value: 82.6884842555647
- type: map
value: 82.6884842555647
- type: mrr
value: 85.7413492063492
- type: nAUC_map_diff1
value: 62.227875149480674
- type: nAUC_map_max
value: 65.39899447833739
- type: nAUC_map_std
value: 22.232770911289762
- type: nAUC_mrr_diff1
value: 71.02339957841794
- type: nAUC_mrr_max
value: 75.79106833222022
- type: nAUC_mrr_std
value: 31.922312297325313
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
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value: 41.912
- type: map_at_1
value: 24.154
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value: 35.771
- type: map_at_100
value: 37.361
- 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: nauc_map_at_1000_diff1
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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 MassiveIntentClassification (zh-TW)
type: mteb/amazon_massive_intent
config: zh-TW
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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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 (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
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type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: test
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
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dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
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value: 83.21013727560718
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type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
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value: 90.09
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value: 88.76450265603408
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value: 88.76450265603408
- type: f1
value: 90.08779175324347
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value: 90.08719838771795
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value: 90.09
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type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
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value: 17.64991910323611
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type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
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value: 31.42374000164117
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value: 34.11139115201034
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type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 70.18092147138205
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value: 69.8926146345444
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type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
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value: 79.32633637626917
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type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
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type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
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- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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value: 51.577000000000005
- type: f1
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- type: main_score
value: 51.577000000000005
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
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value: 61.3311446133969
- type: v_measure
value: 61.3311446133969
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value: 1.4292037065102101
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
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value: 56.41668748695762
- type: v_measure
value: 56.41668748695762
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type: Retrieval
dataset:
name: MTEB VideoRetrieval
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:
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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type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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type: maastrichtlawtech/bsard
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value: 31.66461426950881
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value: 11.729838363348398
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- type: nauc_precision_at_5_std
value: 3.758711226096333
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- type: nauc_recall_at_1000_max
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value: 41.21558958335586
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value: 31.66461426950869
- type: nauc_recall_at_10_diff1
value: 10.276992565698032
- type: nauc_recall_at_10_max
value: 31.610465042792473
- type: nauc_recall_at_10_std
value: 11.729838363348378
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value: 2.099858103167991
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value: 17.440312691119587
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value: 3.5177146563010853
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value: 9.911736958870106
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value: 24.178288877634106
- type: nauc_recall_at_5_std
value: 3.758711226096281
- type: ndcg_at_1
value: 10.36
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value: 20.471
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value: 25.777
- type: ndcg_at_1000
value: 28.593000000000004
- type: ndcg_at_20
value: 22.246
- type: ndcg_at_3
value: 15.916
- type: ndcg_at_5
value: 18.3
- type: precision_at_1
value: 10.36
- type: precision_at_10
value: 3.243
- type: precision_at_100
value: 0.586
- type: precision_at_1000
value: 0.08099999999999999
- type: precision_at_20
value: 1.982
- type: precision_at_3
value: 6.607
- type: precision_at_5
value: 5.135
- type: recall_at_1
value: 10.36
- type: recall_at_10
value: 32.432
- type: recall_at_100
value: 58.559000000000005
- type: recall_at_1000
value: 81.081
- type: recall_at_20
value: 39.64
- type: recall_at_3
value: 19.82
- 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: 26.918470641446472
- type: v_measure
value: 26.918470641446472
- type: v_measure_std
value: 2.717665658348912
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (fr)
type: reciTAL/mlsum
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: main_score
value: 45.581413658149
- type: v_measure
value: 45.581413658149
- type: v_measure_std
value: 1.646260736751199
- type: main_score
value: 44.45455749734905
- type: v_measure
value: 44.45455749734905
- type: v_measure_std
value: 1.935205028548908
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 80.14719699342312
- type: f1
value: 79.68802657402165
- type: f1_weighted
value: 79.85763712873417
- type: main_score
value: 80.14719699342312
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 50.241152521139995
- type: f1
value: 34.39524038805554
- type: f1_weighted
value: 53.93775073819592
- type: main_score
value: 50.241152521139995
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 83.34123222748818
- type: f1
value: 79.48624508308065
- type: f1_weighted
value: 83.20210238500908
- type: main_score
value: 83.34123222748818
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: main_score
value: 71.51218291988776
- type: v_measure
value: 71.51218291988776
- type: v_measure_std
value: 35.6439739308977
- type: main_score
value: 60.155743100795725
- type: v_measure
value: 60.155743100795725
- type: v_measure_std
value: 28.180226808833797
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 59.048419636852735
- type: f1
value: 55.77513997227217
- type: f1_weighted
value: 57.65743868976365
- type: main_score
value: 59.048419636852735
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 67.2932078009415
- type: f1
value: 66.85444841091169
- type: f1_weighted
value: 66.78952167770717
- type: main_score
value: 67.2932078009415
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
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value: 27.400000000000002
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value: 23.213
- type: map_at_100
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- type: map_at_1000
value: 24.397
- type: map_at_20
value: 23.741
- type: map_at_3
value: 20.973
- type: map_at_5
value: 22.212
- type: mrr_at_1
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- type: mrr_at_10
value: 23.21331071331073
- type: mrr_at_100
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- type: mrr_at_1000
value: 24.396606382776362
- type: mrr_at_20
value: 23.74068872679202
- type: mrr_at_3
value: 20.973245973245938
- type: mrr_at_5
value: 22.211984711984677
- type: nauc_map_at_1000_diff1
value: 23.49190518969821
- type: nauc_map_at_1000_max
value: 21.816535185868748
- type: nauc_map_at_1000_std
value: 7.898426575861743
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value: 23.455942061154243
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value: 7.903289282168091
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value: 7.889585005426794
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value: 29.069568522388433
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value: 19.942608291469913
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value: 3.1283142332992635
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value: 7.994053361768913
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value: 6.615976792964795
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value: 7.272332396807288
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value: 7.898426575861743
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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_diff1
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- type: nauc_recall_at_1000_max
value: 42.303069380982336
- type: nauc_recall_at_1000_std
value: 54.25130768922508
- type: nauc_recall_at_100_diff1
value: 12.076659360813771
- type: nauc_recall_at_100_max
value: 23.254576247061777
- type: nauc_recall_at_100_std
value: 17.80398606936441
- type: nauc_recall_at_10_diff1
value: 16.05902741145243
- type: nauc_recall_at_10_max
value: 24.536458909415412
- type: nauc_recall_at_10_std
value: 15.281423796153174
- type: nauc_recall_at_1_diff1
value: 29.069568522388433
- type: nauc_recall_at_1_max
value: 19.942608291469913
- type: nauc_recall_at_1_std
value: 3.1283142332992635
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value: 13.618514792543923
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value: 23.3574173893104
- type: nauc_recall_at_20_std
value: 16.629711994588593
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value: 21.305869779106818
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value: 23.815582518552738
- type: nauc_recall_at_3_std
value: 10.358496834243747
- type: nauc_recall_at_5_diff1
value: 18.546773284411426
- type: nauc_recall_at_5_max
value: 22.987289739937083
- type: nauc_recall_at_5_std
value: 12.591593599364312
- type: ndcg_at_1
value: 15.479000000000001
- type: ndcg_at_10
value: 27.400000000000002
- type: ndcg_at_100
value: 33.382
- type: ndcg_at_1000
value: 36.691
- type: ndcg_at_20
value: 29.352
- type: ndcg_at_3
value: 22.759999999999998
- type: ndcg_at_5
value: 25.006
- type: precision_at_1
value: 15.479000000000001
- type: precision_at_10
value: 4.075
- type: precision_at_100
value: 0.7040000000000001
- type: precision_at_1000
value: 0.097
- type: precision_at_20
value: 2.426
- type: precision_at_3
value: 9.309000000000001
- type: precision_at_5
value: 6.683
- type: recall_at_1
value: 15.479000000000001
- type: recall_at_10
value: 40.745
- type: recall_at_100
value: 70.434
- type: recall_at_1000
value: 97.21499999999999
- type: recall_at_20
value: 48.526
- type: recall_at_3
value: 27.927999999999997
- type: recall_at_5
value: 33.415
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 82.42506811989101
- type: cosine_accuracy_threshold
value: 59.91581678390503
- type: cosine_ap
value: 92.86245135331164
- type: cosine_f1
value: 88.0
- type: cosine_f1_threshold
value: 59.91581678390503
- type: cosine_precision
value: 82.76465441819772
- type: cosine_recall
value: 93.94240317775571
- type: dot_accuracy
value: 82.42506811989101
- type: dot_accuracy_threshold
value: 59.91581678390503
- type: dot_ap
value: 92.86245135331164
- type: dot_f1
value: 88.0
- type: dot_f1_threshold
value: 59.91581678390503
- type: dot_precision
value: 82.76465441819772
- type: dot_recall
value: 93.94240317775571
- type: euclidean_accuracy
value: 82.42506811989101
- type: euclidean_accuracy_threshold
value: 89.53677415847778
- type: euclidean_ap
value: 92.86245135331164
- type: euclidean_f1
value: 88.0
- type: euclidean_f1_threshold
value: 89.53677415847778
- type: euclidean_precision
value: 82.76465441819772
- type: euclidean_recall
value: 93.94240317775571
- type: main_score
value: 92.86245135331164
- type: manhattan_accuracy
value: 82.28882833787466
- type: manhattan_accuracy_threshold
value: 2091.843032836914
- type: manhattan_ap
value: 92.84258977975239
- type: manhattan_f1
value: 87.88443616029824
- type: manhattan_f1_threshold
value: 2091.843032836914
- type: manhattan_precision
value: 82.79192273924495
- type: manhattan_recall
value: 93.64448857994041
- type: max_ap
value: 92.86245135331164
- type: max_f1
value: 88.0
- type: max_precision
value: 82.79192273924495
- type: max_recall
value: 93.94240317775571
- type: similarity_accuracy
value: 82.42506811989101
- type: similarity_accuracy_threshold
value: 59.91581678390503
- type: similarity_ap
value: 92.86245135331164
- type: similarity_f1
value: 88.0
- type: similarity_f1_threshold
value: 59.91581678390503
- type: similarity_precision
value: 82.76465441819772
- 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: 61.050000000000004
- type: cosine_accuracy_threshold
value: 98.11633825302124
- type: cosine_ap
value: 60.385395031891264
- type: cosine_f1
value: 62.60428001450852
- type: cosine_f1_threshold
value: 89.5184874534607
- type: cosine_precision
value: 46.54800431499461
- type: cosine_recall
value: 95.5703211517165
- type: dot_accuracy
value: 61.050000000000004
- type: dot_accuracy_threshold
value: 98.11633825302124
- type: dot_ap
value: 60.37120015758097
- type: dot_f1
value: 62.60428001450852
- type: dot_f1_threshold
value: 89.5184874534607
- type: dot_precision
value: 46.54800431499461
- type: dot_recall
value: 95.5703211517165
- type: euclidean_accuracy
value: 61.050000000000004
- type: euclidean_accuracy_threshold
value: 19.409586489200592
- type: euclidean_ap
value: 60.385395031891264
- type: euclidean_f1
value: 62.60428001450852
- type: euclidean_f1_threshold
value: 45.78540325164795
- type: euclidean_precision
value: 46.54800431499461
- type: euclidean_recall
value: 95.5703211517165
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value: 60.61779879922903
- type: manhattan_accuracy
value: 61.0
- type: manhattan_accuracy_threshold
value: 455.7579040527344
- type: manhattan_ap
value: 60.61779879922903
- type: manhattan_f1
value: 62.56448047162859
- type: manhattan_f1_threshold
value: 1030.442714691162
- type: manhattan_precision
value: 46.880176697956934
- type: manhattan_recall
value: 94.01993355481729
- type: max_ap
value: 60.61779879922903
- type: max_f1
value: 62.60428001450852
- type: max_precision
value: 46.880176697956934
- type: max_recall
value: 95.5703211517165
- type: similarity_accuracy
value: 61.050000000000004
- type: similarity_accuracy_threshold
value: 98.11633825302124
- type: similarity_ap
value: 60.385395031891264
- type: similarity_f1
value: 62.60428001450852
- type: similarity_f1_threshold
value: 89.5184874534607
- type: similarity_precision
value: 46.54800431499461
- type: similarity_recall
value: 95.5703211517165
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
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value: 81.36950266249254
- type: cosine_spearman
value: 77.4306890341242
- type: euclidean_pearson
value: 77.47472965962992
- type: euclidean_spearman
value: 77.431649040768
- type: main_score
value: 77.4306890341242
- type: manhattan_pearson
value: 77.44468465408777
- type: manhattan_spearman
value: 77.25503240591341
- type: pearson
value: 81.36950266249254
- type: spearman
value: 77.4306890341242
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type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 81.48671869348665
- type: cosine_spearman
value: 82.57396913836067
- type: euclidean_pearson
value: 81.71206012505978
- type: euclidean_spearman
value: 82.64978141643995
- type: main_score
value: 82.57396913836067
- type: manhattan_pearson
value: 82.22351352342636
- type: manhattan_spearman
value: 83.04856400618516
- type: pearson
value: 81.48671869348665
- type: spearman
value: 82.57396913836067
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 60.45418014677442
- type: cosine_spearman
value: 64.66584550775643
- type: euclidean_pearson
value: 60.042908719941124
- type: euclidean_spearman
value: 64.66584550775643
- type: main_score
value: 64.66584550775643
- type: manhattan_pearson
value: 58.56106956676841
- type: manhattan_spearman
value: 64.07469227945803
- type: pearson
value: 60.45418014677442
- type: spearman
value: 64.66584550775643
- task:
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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value: 83.39169883126554
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 83.79128537281704
- type: euclidean_spearman
value: 84.51542547285167
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 82.282109060827
- type: manhattan_spearman
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- type: pearson
value: 83.39169883126554
- type: spearman
value: 84.51542547285167
- type: cosine_pearson
value: 83.39168516605531
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 83.7912731376875
- type: euclidean_spearman
value: 84.51542547285167
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 82.28209868239296
- type: manhattan_spearman
value: 84.51542547285167
- type: pearson
value: 83.39168516605531
- 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:
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value: 81.23994381619546
- type: cosine_spearman
value: 81.55923116292537
- type: euclidean_pearson
value: 79.95507984767936
- type: euclidean_spearman
value: 81.55780186152964
- type: main_score
value: 81.55923116292537
- type: manhattan_pearson
value: 79.85599761287939
- type: manhattan_spearman
value: 81.47864706229939
- type: pearson
value: 81.23994381619546
- type: spearman
value: 81.55923116292537
- 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: 32.15173983476866
- type: cosine_spearman
value: 30.52126378106083
- type: dot_pearson
value: 32.15174076737564
- type: dot_spearman
value: 30.5195596882719
- type: main_score
value: 30.52126378106083
- type: pearson
value: 32.15173983476866
- type: spearman
value: 30.52126378106083
- 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: 87.26666666666667
- type: map
value: 87.26666666666667
- type: mrr
value: 87.26666666666667
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value: 61.78899094665834
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value: 37.30593860183502
- 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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value: 82.43599999999999
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- type: ndcg_at_1
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- type: ndcg_at_100
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value: 82.607
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- type: precision_at_1
value: 64.0
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value: 9.9
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- type: recall_at_1
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- type: recall_at_10
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- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.0
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value: 90.0
- type: recall_at_5
value: 94.0
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fra-fra
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
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- type: map_at_100
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type: PL-MTEB/allegro-reviews
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revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
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dataset:
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type: PL-MTEB/cbd
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revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
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type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
metrics:
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- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
metrics:
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value: 65.58934260063714
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- type: ap_weighted
value: 74.96037603906956
- type: f1
value: 62.46883531701779
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value: 65.87422072252049
- type: main_score
value: 65.58934260063714
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
metrics:
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- type: cosine_precision
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- type: cosine_recall
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- type: dot_accuracy
value: 97.49536178107606
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value: 62.81645894050598
- type: dot_precision
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- type: dot_recall
value: 98.47560975609755
- type: euclidean_accuracy
value: 97.49536178107606
- type: euclidean_accuracy_threshold
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- type: euclidean_ap
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- type: euclidean_f1
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- type: euclidean_f1_threshold
value: 86.23623847961426
- type: euclidean_precision
value: 93.6231884057971
- type: euclidean_recall
value: 98.47560975609755
- type: main_score
value: 99.4366325576277
- type: manhattan_accuracy
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value: 99.4366325576277
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value: 95.98811292719166
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value: 94.3952802359882
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- type: similarity_accuracy
value: 97.49536178107606
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value: 62.816452980041504
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value: 93.6231884057971
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value: 98.47560975609755
- 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: 73.49030470914128
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value: 64.44026912860524
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value: 70.76142496919624
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value: 73.49030470914128
- 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: 40.10783686862694
- type: f1_weighted
value: 52.57241968032103
- type: main_score
value: 56.1336032388664
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: 2c7d2df57801a591f6b1e3aaf042e7a04ec7d9f2
metrics:
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value: 75.7
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value: 87.18435695095992
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value: 80.79877112135176
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value: 80.05339503288269
- type: cosine_precision
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- type: dot_accuracy
value: 75.7
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- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
metrics:
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dataset:
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type: clarin-knext/trec-covid-pl
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revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
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dataset:
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type: ai-forever/cedr-classification
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split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
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type: ai-forever/georeview-classification
config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
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type: ai-forever/georeview-clustering-p2p
config: default
split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
metrics:
- type: main_score
value: 63.39849666467603
- type: v_measure
value: 63.39849666467603
- type: v_measure_std
value: 0.4433669974776044
- task:
type: Classification
dataset:
name: MTEB HeadlineClassification
type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
- type: accuracy
value: 83.45703125
- type: f1
value: 83.44147121320216
- type: f1_weighted
value: 83.43953816781061
- type: main_score
value: 83.45703125
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 61.318359375
- type: ap
value: 57.103049962056815
- type: ap_weighted
value: 57.103049962056815
- type: f1
value: 60.69364450664112
- type: f1_weighted
value: 60.69364450664112
- type: main_score
value: 61.318359375
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 59.040000000000006
- type: f1
value: 55.63433742720159
- type: f1_weighted
value: 55.63433742720159
- type: main_score
value: 59.040000000000006
- task:
type: Reranking
dataset:
name: MTEB MIRACLReranking (ru)
type: miracl/mmteb-miracl-reranking
config: ru
split: dev
revision: 6d1962c527217f8927fca80f890f14f36b2802af
metrics:
- type: MAP@1(MIRACL)
value: 29.729
- type: MAP@10(MIRACL)
value: 48.713
- type: MAP@100(MIRACL)
value: 50.792
- type: MAP@1000(MIRACL)
value: 50.792
- type: MAP@20(MIRACL)
value: 50.197
- type: MAP@3(MIRACL)
value: 41.8
- type: MAP@5(MIRACL)
value: 45.706
- type: NDCG@1(MIRACL)
value: 49.158
- type: NDCG@10(MIRACL)
value: 56.550999999999995
- type: NDCG@100(MIRACL)
value: 60.829
- type: NDCG@1000(MIRACL)
value: 60.829
- type: NDCG@20(MIRACL)
value: 59.229
- type: NDCG@3(MIRACL)
value: 50.397000000000006
- type: NDCG@5(MIRACL)
value: 53.105000000000004
- type: P@1(MIRACL)
value: 49.158
- type: P@10(MIRACL)
value: 14.908
- type: P@100(MIRACL)
value: 1.9529999999999998
- type: P@1000(MIRACL)
value: 0.19499999999999998
- type: P@20(MIRACL)
value: 8.753
- type: P@3(MIRACL)
value: 31.061
- type: P@5(MIRACL)
value: 23.785
- type: Recall@1(MIRACL)
value: 29.729
- type: Recall@10(MIRACL)
value: 67.223
- type: Recall@100(MIRACL)
value: 79.952
- type: Recall@1000(MIRACL)
value: 79.952
- type: Recall@20(MIRACL)
value: 74.417
- type: Recall@3(MIRACL)
value: 49.073
- type: Recall@5(MIRACL)
value: 58.094
- type: main_score
value: 56.550999999999995
- type: nAUC_MAP@1000_diff1(MIRACL)
value: 19.222716664871324
- type: nAUC_MAP@1000_max(MIRACL)
value: 28.91315309273525
- type: nAUC_MAP@1000_std(MIRACL)
value: 15.773770301363973
- type: nAUC_MAP@100_diff1(MIRACL)
value: 19.222716664871324
- type: nAUC_MAP@100_max(MIRACL)
value: 28.91315309273525
- type: nAUC_MAP@100_std(MIRACL)
value: 15.773770301363973
- type: nAUC_MAP@10_diff1(MIRACL)
value: 21.16716217839532
- type: nAUC_MAP@10_max(MIRACL)
value: 26.58073750952478
- type: nAUC_MAP@10_std(MIRACL)
value: 14.98546699381452
- type: nAUC_MAP@1_diff1(MIRACL)
value: 37.50928508734578
- type: nAUC_MAP@1_max(MIRACL)
value: 13.158704351998995
- type: nAUC_MAP@1_std(MIRACL)
value: 4.422878276220556
- type: nAUC_MAP@20_diff1(MIRACL)
value: 19.951045759045467
- type: nAUC_MAP@20_max(MIRACL)
value: 28.25165991244302
- type: nAUC_MAP@20_std(MIRACL)
value: 15.850363419877105
- type: nAUC_MAP@3_diff1(MIRACL)
value: 27.774164479669988
- type: nAUC_MAP@3_max(MIRACL)
value: 20.738889611307496
- type: nAUC_MAP@3_std(MIRACL)
value: 9.22491952318088
- type: nAUC_MAP@5_diff1(MIRACL)
value: 23.86089217267443
- type: nAUC_MAP@5_max(MIRACL)
value: 23.19878810494586
- type: nAUC_MAP@5_std(MIRACL)
value: 11.851875808858123
- type: nAUC_NDCG@1000_diff1(MIRACL)
value: 9.459016218726891
- type: nAUC_NDCG@1000_max(MIRACL)
value: 38.018030050210896
- type: nAUC_NDCG@1000_std(MIRACL)
value: 20.555997574199246
- type: nAUC_NDCG@100_diff1(MIRACL)
value: 9.459016218726891
- type: nAUC_NDCG@100_max(MIRACL)
value: 38.018030050210896
- type: nAUC_NDCG@100_std(MIRACL)
value: 20.555997574199246
- type: nAUC_NDCG@10_diff1(MIRACL)
value: 14.2494195957649
- type: nAUC_NDCG@10_max(MIRACL)
value: 32.87676976986289
- type: nAUC_NDCG@10_std(MIRACL)
value: 19.469852065776976
- type: nAUC_NDCG@1_diff1(MIRACL)
value: 23.312659021070818
- type: nAUC_NDCG@1_max(MIRACL)
value: 31.554119919664593
- type: nAUC_NDCG@1_std(MIRACL)
value: 17.533789813864466
- type: nAUC_NDCG@20_diff1(MIRACL)
value: 11.694064829915717
- type: nAUC_NDCG@20_max(MIRACL)
value: 36.12122229242797
- type: nAUC_NDCG@20_std(MIRACL)
value: 20.886325245384313
- type: nAUC_NDCG@3_diff1(MIRACL)
value: 19.70964037059834
- type: nAUC_NDCG@3_max(MIRACL)
value: 28.271224651385758
- type: nAUC_NDCG@3_std(MIRACL)
value: 14.182889320426757
- type: nAUC_NDCG@5_diff1(MIRACL)
value: 17.143482434537635
- type: nAUC_NDCG@5_max(MIRACL)
value: 28.911278684121744
- type: nAUC_NDCG@5_std(MIRACL)
value: 15.83019582479379
- type: nAUC_P@1000_diff1(MIRACL)
value: -28.806220159210838
- type: nAUC_P@1000_max(MIRACL)
value: 30.19137414854295
- type: nAUC_P@1000_std(MIRACL)
value: 15.577217138606922
- type: nAUC_P@100_diff1(MIRACL)
value: -28.8062201592108
- type: nAUC_P@100_max(MIRACL)
value: 30.191374148543016
- type: nAUC_P@100_std(MIRACL)
value: 15.577217138606963
- type: nAUC_P@10_diff1(MIRACL)
value: -23.950963396253567
- type: nAUC_P@10_max(MIRACL)
value: 32.31620562041691
- type: nAUC_P@10_std(MIRACL)
value: 22.76652888514141
- type: nAUC_P@1_diff1(MIRACL)
value: 23.312659021070818
- type: nAUC_P@1_max(MIRACL)
value: 31.554119919664593
- type: nAUC_P@1_std(MIRACL)
value: 17.533789813864466
- type: nAUC_P@20_diff1(MIRACL)
value: -26.522109242426172
- type: nAUC_P@20_max(MIRACL)
value: 31.490097667881027
- type: nAUC_P@20_std(MIRACL)
value: 20.51757471839622
- type: nAUC_P@3_diff1(MIRACL)
value: -8.494670555442749
- type: nAUC_P@3_max(MIRACL)
value: 33.197306356212295
- type: nAUC_P@3_std(MIRACL)
value: 18.96447162170764
- type: nAUC_P@5_diff1(MIRACL)
value: -19.15325386641154
- type: nAUC_P@5_max(MIRACL)
value: 31.846463690427683
- type: nAUC_P@5_std(MIRACL)
value: 20.914296846825028
- type: nAUC_Recall@1000_diff1(MIRACL)
value: -22.62644777038629
- type: nAUC_Recall@1000_max(MIRACL)
value: 63.09417027858301
- type: nAUC_Recall@1000_std(MIRACL)
value: 31.96936126619333
- type: nAUC_Recall@100_diff1(MIRACL)
value: -22.62644777038629
- type: nAUC_Recall@100_max(MIRACL)
value: 63.09417027858301
- type: nAUC_Recall@100_std(MIRACL)
value: 31.96936126619333
- type: nAUC_Recall@10_diff1(MIRACL)
value: 1.389536667314163
- type: nAUC_Recall@10_max(MIRACL)
value: 36.80168430587649
- type: nAUC_Recall@10_std(MIRACL)
value: 24.6096121100626
- type: nAUC_Recall@1_diff1(MIRACL)
value: 37.50928508734578
- type: nAUC_Recall@1_max(MIRACL)
value: 13.158704351998995
- type: nAUC_Recall@1_std(MIRACL)
value: 4.422878276220556
- type: nAUC_Recall@20_diff1(MIRACL)
value: -8.586661617880036
- type: nAUC_Recall@20_max(MIRACL)
value: 48.977640900606715
- type: nAUC_Recall@20_std(MIRACL)
value: 30.787733282193763
- type: nAUC_Recall@3_diff1(MIRACL)
value: 20.85452801657472
- type: nAUC_Recall@3_max(MIRACL)
value: 20.457796008702196
- type: nAUC_Recall@3_std(MIRACL)
value: 10.422494162066547
- type: nAUC_Recall@5_diff1(MIRACL)
value: 11.294860119295114
- type: nAUC_Recall@5_max(MIRACL)
value: 24.55554040640634
- type: nAUC_Recall@5_std(MIRACL)
value: 15.07523755007524
- task:
type: Retrieval
dataset:
name: MTEB MIRACLRetrieval (ru)
type: miracl/mmteb-miracl
config: ru
split: dev
revision: main
metrics:
- type: main_score
value: 53.33
- type: map_at_1
value: 23.51
- type: map_at_10
value: 42.506
- type: map_at_100
value: 45.727000000000004
- type: map_at_1000
value: 45.824
- type: map_at_20
value: 44.482
- type: map_at_3
value: 34.903
- type: map_at_5
value: 38.924
- type: mrr_at_1
value: 47.52396166134185
- type: mrr_at_10
value: 60.53929585678796
- type: mrr_at_100
value: 61.08405013111772
- type: mrr_at_1000
value: 61.090960329457786
- type: mrr_at_20
value: 60.942355859942886
- type: mrr_at_3
value: 57.21512247071355
- type: mrr_at_5
value: 59.423588924387715
- type: nauc_map_at_1000_diff1
value: 27.9258851452338
- type: nauc_map_at_1000_max
value: 23.91526202439492
- type: nauc_map_at_1000_std
value: 1.9886186316328294
- type: nauc_map_at_100_diff1
value: 27.950443502043935
- type: nauc_map_at_100_max
value: 23.91952747895155
- type: nauc_map_at_100_std
value: 1.9828664117240875
- type: nauc_map_at_10_diff1
value: 28.591900542084257
- type: nauc_map_at_10_max
value: 22.26715273276218
- type: nauc_map_at_10_std
value: -0.2905582006620209
- type: nauc_map_at_1_diff1
value: 36.29159533442582
- type: nauc_map_at_1_max
value: 14.017798723971604
- type: nauc_map_at_1_std
value: -4.135744714942541
- type: nauc_map_at_20_diff1
value: 28.227642002703888
- type: nauc_map_at_20_max
value: 23.31288716904143
- type: nauc_map_at_20_std
value: 0.8608305708684871
- type: nauc_map_at_3_diff1
value: 31.25854158298961
- type: nauc_map_at_3_max
value: 19.94828898205679
- type: nauc_map_at_3_std
value: -3.055128116323982
- type: nauc_map_at_5_diff1
value: 29.569541485869138
- type: nauc_map_at_5_max
value: 20.295566102579233
- type: nauc_map_at_5_std
value: -2.0623859574064496
- type: nauc_mrr_at_1000_diff1
value: 27.361661005387717
- type: nauc_mrr_at_1000_max
value: 29.835566057491185
- type: nauc_mrr_at_1000_std
value: 9.18992468804867
- type: nauc_mrr_at_100_diff1
value: 27.364549933483367
- type: nauc_mrr_at_100_max
value: 29.841000191685662
- type: nauc_mrr_at_100_std
value: 9.201936238611633
- type: nauc_mrr_at_10_diff1
value: 27.091315668645876
- type: nauc_mrr_at_10_max
value: 30.083804137944814
- type: nauc_mrr_at_10_std
value: 9.295940302357145
- type: nauc_mrr_at_1_diff1
value: 30.096520602983773
- type: nauc_mrr_at_1_max
value: 25.92117667316542
- type: nauc_mrr_at_1_std
value: 6.628159094331555
- type: nauc_mrr_at_20_diff1
value: 27.26907735403706
- type: nauc_mrr_at_20_max
value: 29.91703823542895
- type: nauc_mrr_at_20_std
value: 9.220168448561815
- type: nauc_mrr_at_3_diff1
value: 27.132416524688672
- type: nauc_mrr_at_3_max
value: 29.879006809416147
- type: nauc_mrr_at_3_std
value: 8.495778638777473
- type: nauc_mrr_at_5_diff1
value: 27.164544736044938
- type: nauc_mrr_at_5_max
value: 29.756896839148844
- type: nauc_mrr_at_5_std
value: 8.697141135185072
- type: nauc_ndcg_at_1000_diff1
value: 25.711789502779325
- type: nauc_ndcg_at_1000_max
value: 28.742258668080943
- type: nauc_ndcg_at_1000_std
value: 8.197781962071534
- type: nauc_ndcg_at_100_diff1
value: 25.844850932804846
- type: nauc_ndcg_at_100_max
value: 29.043525248699453
- type: nauc_ndcg_at_100_std
value: 8.810501750069859
- type: nauc_ndcg_at_10_diff1
value: 26.47161747010468
- type: nauc_ndcg_at_10_max
value: 25.36709975989015
- type: nauc_ndcg_at_10_std
value: 3.070985924814878
- type: nauc_ndcg_at_1_diff1
value: 30.096520602983773
- type: nauc_ndcg_at_1_max
value: 25.92117667316542
- type: nauc_ndcg_at_1_std
value: 6.628159094331555
- type: nauc_ndcg_at_20_diff1
value: 26.329559310197325
- type: nauc_ndcg_at_20_max
value: 27.252374736353723
- type: nauc_ndcg_at_20_std
value: 5.279499913033636
- type: nauc_ndcg_at_3_diff1
value: 26.382469083855774
- type: nauc_ndcg_at_3_max
value: 25.667817557434446
- type: nauc_ndcg_at_3_std
value: 2.722781380568278
- type: nauc_ndcg_at_5_diff1
value: 26.63587958392066
- type: nauc_ndcg_at_5_max
value: 24.012746599673562
- type: nauc_ndcg_at_5_std
value: 1.875533584617588
- type: nauc_precision_at_1000_diff1
value: -16.886796017740146
- type: nauc_precision_at_1000_max
value: 13.452350695770388
- type: nauc_precision_at_1000_std
value: 20.253057030417295
- type: nauc_precision_at_100_diff1
value: -15.676681024836736
- type: nauc_precision_at_100_max
value: 17.21039273342314
- type: nauc_precision_at_100_std
value: 23.503219057796482
- type: nauc_precision_at_10_diff1
value: -7.353821346474632
- type: nauc_precision_at_10_max
value: 22.963099870525657
- type: nauc_precision_at_10_std
value: 16.75138999512155
- type: nauc_precision_at_1_diff1
value: 30.096520602983773
- type: nauc_precision_at_1_max
value: 25.92117667316542
- type: nauc_precision_at_1_std
value: 6.628159094331555
- type: nauc_precision_at_20_diff1
value: -11.020811644697545
- type: nauc_precision_at_20_max
value: 21.625978665259115
- type: nauc_precision_at_20_std
value: 20.005095685790348
- type: nauc_precision_at_3_diff1
value: 7.003507657338856
- type: nauc_precision_at_3_max
value: 27.73371213700131
- type: nauc_precision_at_3_std
value: 9.668915001732463
- type: nauc_precision_at_5_diff1
value: -1.715206180870653
- type: nauc_precision_at_5_max
value: 24.29609734679536
- type: nauc_precision_at_5_std
value: 13.402584423111977
- type: nauc_recall_at_1000_diff1
value: 17.28590002253731
- type: nauc_recall_at_1000_max
value: 68.10425916894825
- type: nauc_recall_at_1000_std
value: 73.8411367347451
- type: nauc_recall_at_100_diff1
value: 18.442237799863165
- type: nauc_recall_at_100_max
value: 39.59374558744695
- type: nauc_recall_at_100_std
value: 38.54186929047189
- type: nauc_recall_at_10_diff1
value: 19.243325372129107
- type: nauc_recall_at_10_max
value: 19.111906153501202
- type: nauc_recall_at_10_std
value: 0.8737992988209908
- type: nauc_recall_at_1_diff1
value: 36.29159533442582
- type: nauc_recall_at_1_max
value: 14.017798723971604
- type: nauc_recall_at_1_std
value: -4.135744714942541
- type: nauc_recall_at_20_diff1
value: 19.01527783708535
- type: nauc_recall_at_20_max
value: 22.731910630901435
- type: nauc_recall_at_20_std
value: 5.981218642323668
- type: nauc_recall_at_3_diff1
value: 25.892436310762985
- type: nauc_recall_at_3_max
value: 18.9097432217694
- type: nauc_recall_at_3_std
value: -3.8494373478485033
- type: nauc_recall_at_5_diff1
value: 22.032856212342626
- type: nauc_recall_at_5_max
value: 16.22066351445006
- type: nauc_recall_at_5_std
value: -3.416429358868604
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 53.33
- type: ndcg_at_100
value: 61.746
- type: ndcg_at_1000
value: 62.803
- type: ndcg_at_20
value: 57.498000000000005
- type: ndcg_at_3
value: 46.204
- type: ndcg_at_5
value: 48.824
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 16.478
- type: precision_at_100
value: 2.5860000000000003
- type: precision_at_1000
value: 0.27799999999999997
- type: precision_at_20
value: 10.12
- type: precision_at_3
value: 31.735999999999997
- type: precision_at_5
value: 24.951999999999998
- type: recall_at_1
value: 23.51
- type: recall_at_10
value: 64.98899999999999
- type: recall_at_100
value: 92.241
- type: recall_at_1000
value: 97.929
- type: recall_at_20
value: 76.822
- type: recall_at_3
value: 42.126000000000005
- type: recall_at_5
value: 52.449
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ru)
type: mteb/amazon_massive_intent
config: ru
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 60.08069939475453
- type: f1
value: 56.18556634916303
- type: f1_weighted
value: 58.60322135027107
- type: main_score
value: 60.08069939475453
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 66.43913920645595
- type: f1
value: 66.11191203959372
- type: f1_weighted
value: 65.72977001101279
- type: main_score
value: 66.43913920645595
- task:
type: STS
dataset:
name: MTEB RUParaPhraserSTS
type: merionum/ru_paraphraser
config: default
split: test
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
metrics:
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dataset:
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type: ai-forever/ria-news-retrieval
config: default
split: test
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
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dataset:
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type: ai-forever/rubq-reranking
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revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
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type: ai-forever/rubq-retrieval
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revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
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type: Classification
dataset:
name: MTEB RuReviewsClassification
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
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- type: f1_weighted
value: 63.79112620928187
- type: main_score
value: 66.0888671875
- task:
type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
- type: cosine_pearson
value: 78.40381860532754
- type: cosine_spearman
value: 78.44128247246344
- type: euclidean_pearson
value: 77.03436669125563
- type: euclidean_spearman
value: 78.44009017152538
- type: main_score
value: 78.44128247246344
- type: manhattan_pearson
value: 77.084766201637
- type: manhattan_spearman
value: 78.46899044600028
- type: pearson
value: 78.40381860532754
- type: spearman
value: 78.44128247246344
- task:
type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: accuracy
value: 61.4111328125
- type: f1
value: 59.604229603854044
- type: f1_weighted
value: 59.61906710038802
- type: main_score
value: 61.4111328125
- 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: 55.660781672610625
- type: v_measure
value: 55.660781672610625
- type: v_measure_std
value: 1.0880487214373578
- task:
type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: accuracy
value: 48.6669921875
- type: f1
value: 46.24529719568694
- type: f1_weighted
value: 46.24736172369365
- type: main_score
value: 48.6669921875
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchOECDClusteringP2P
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: main_score
value: 47.95513383500326
- type: v_measure
value: 47.95513383500326
- type: v_measure_std
value: 0.9391146092620886
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 65.27471390704719
- type: cosine_spearman
value: 68.12010913287949
- type: euclidean_pearson
value: 65.60124415285192
- type: euclidean_spearman
value: 68.12010913287949
- type: main_score
value: 68.12010913287949
- type: manhattan_pearson
value: 65.21850751060232
- type: manhattan_spearman
value: 67.85162022914248
- type: pearson
value: 65.27471390704719
- type: spearman
value: 68.12010913287949
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 30.0537109375
- type: f1
value: 35.12028781898003
- type: lrap
value: 45.91071234808953
- type: main_score
value: 30.0537109375
- task:
type: PairClassification
dataset:
name: MTEB TERRa
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
- type: cosine_accuracy
value: 60.91205211726385
- type: cosine_accuracy_threshold
value: 68.15387606620789
- type: cosine_ap
value: 57.705995373862805
- type: cosine_f1
value: 67.57990867579909
- type: cosine_f1_threshold
value: 54.87680435180664
- type: cosine_precision
value: 51.92982456140351
- type: cosine_recall
value: 96.73202614379085
- type: dot_accuracy
value: 60.91205211726385
- type: dot_accuracy_threshold
value: 68.15387010574341
- type: dot_ap
value: 57.705995373862805
- type: dot_f1
value: 67.57990867579909
- type: dot_f1_threshold
value: 54.87680435180664
- type: dot_precision
value: 51.92982456140351
- type: dot_recall
value: 96.73202614379085
- type: euclidean_accuracy
value: 60.91205211726385
- type: euclidean_accuracy_threshold
value: 79.80742454528809
- type: euclidean_ap
value: 57.705995373862805
- type: euclidean_f1
value: 67.57990867579909
- type: euclidean_f1_threshold
value: 94.99809741973877
- type: euclidean_precision
value: 51.92982456140351
- type: euclidean_recall
value: 96.73202614379085
- type: main_score
value: 57.705995373862805
- type: manhattan_accuracy
value: 60.586319218241044
- type: manhattan_accuracy_threshold
value: 1858.333969116211
- type: manhattan_ap
value: 57.53277048517774
- type: manhattan_f1
value: 67.59259259259261
- type: manhattan_f1_threshold
value: 2154.4769287109375
- type: manhattan_precision
value: 52.32974910394266
- type: manhattan_recall
value: 95.42483660130719
- type: max_ap
value: 57.705995373862805
- type: max_f1
value: 67.59259259259261
- type: max_precision
value: 52.32974910394266
- type: max_recall
value: 96.73202614379085
- type: similarity_accuracy
value: 60.91205211726385
- type: similarity_accuracy_threshold
value: 68.15387606620789
- type: similarity_ap
value: 57.705995373862805
- type: similarity_f1
value: 67.57990867579909
- type: similarity_f1_threshold
value: 54.87680435180664
- type: similarity_precision
value: 51.92982456140351
- type: similarity_recall
value: 96.73202614379085
---
-
<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)
```
## Contact
If you encounter any issue, feel free to contact us via the email: [email protected]
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
sdadas/mmlw-roberta-large
|
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"
] | 2023-11-17T19:08:47 | 2024-10-25T04:30:01 | 7,993 | 13 |
---
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-large
results:
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 31.16472823814849
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 47.48508946322067
- type: f1
value: 42.33327527584009
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: arguana-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.834
- type: map_at_10
value: 55.22899999999999
- type: map_at_100
value: 55.791999999999994
- type: map_at_1000
value: 55.794
- type: map_at_3
value: 51.233
- type: map_at_5
value: 53.772
- type: mrr_at_1
value: 39.687
- type: mrr_at_10
value: 55.596000000000004
- type: mrr_at_100
value: 56.157000000000004
- type: mrr_at_1000
value: 56.157999999999994
- type: mrr_at_3
value: 51.66
- type: mrr_at_5
value: 54.135
- type: ndcg_at_1
value: 38.834
- type: ndcg_at_10
value: 63.402
- type: ndcg_at_100
value: 65.78
- type: ndcg_at_1000
value: 65.816
- type: ndcg_at_3
value: 55.349000000000004
- type: ndcg_at_5
value: 59.892
- type: precision_at_1
value: 38.834
- type: precision_at_10
value: 8.905000000000001
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.428
- type: precision_at_5
value: 15.647
- type: recall_at_1
value: 38.834
- type: recall_at_10
value: 89.047
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 67.283
- type: recall_at_5
value: 78.236
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 69.33
- type: ap
value: 22.972409521444508
- type: f1
value: 58.91072163784952
- 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: 89.8
- type: cos_sim_ap
value: 79.87039801032493
- type: cos_sim_f1
value: 68.53932584269663
- type: cos_sim_precision
value: 73.49397590361446
- type: cos_sim_recall
value: 64.21052631578948
- type: dot_accuracy
value: 86.1
- type: dot_ap
value: 63.684975861694035
- type: dot_f1
value: 63.61746361746362
- type: dot_precision
value: 52.57731958762887
- type: dot_recall
value: 80.52631578947368
- type: euclidean_accuracy
value: 89.8
- type: euclidean_ap
value: 79.7527126811392
- type: euclidean_f1
value: 68.46361185983827
- type: euclidean_precision
value: 70.1657458563536
- type: euclidean_recall
value: 66.84210526315789
- type: manhattan_accuracy
value: 89.7
- type: manhattan_ap
value: 79.64632771093657
- type: manhattan_f1
value: 68.4931506849315
- type: manhattan_precision
value: 71.42857142857143
- type: manhattan_recall
value: 65.78947368421053
- type: max_accuracy
value: 89.8
- type: max_ap
value: 79.87039801032493
- type: max_f1
value: 68.53932584269663
- 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: 92.1088892402831
- type: cos_sim_spearman
value: 92.54126377343101
- type: euclidean_pearson
value: 91.99022371986013
- type: euclidean_spearman
value: 92.55235973775511
- type: manhattan_pearson
value: 91.92170171331357
- type: manhattan_spearman
value: 92.47797623672449
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: dbpedia-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.683
- type: map_at_10
value: 18.9
- type: map_at_100
value: 26.933
- type: map_at_1000
value: 28.558
- type: map_at_3
value: 13.638
- type: map_at_5
value: 15.9
- type: mrr_at_1
value: 63.74999999999999
- type: mrr_at_10
value: 73.566
- type: mrr_at_100
value: 73.817
- type: mrr_at_1000
value: 73.824
- type: mrr_at_3
value: 71.875
- type: mrr_at_5
value: 73.2
- type: ndcg_at_1
value: 53.125
- type: ndcg_at_10
value: 40.271
- type: ndcg_at_100
value: 45.51
- type: ndcg_at_1000
value: 52.968
- type: ndcg_at_3
value: 45.122
- type: ndcg_at_5
value: 42.306
- type: precision_at_1
value: 63.74999999999999
- type: precision_at_10
value: 31.55
- type: precision_at_100
value: 10.440000000000001
- type: precision_at_1000
value: 2.01
- type: precision_at_3
value: 48.333
- type: precision_at_5
value: 40.5
- type: recall_at_1
value: 8.683
- type: recall_at_10
value: 24.63
- type: recall_at_100
value: 51.762
- type: recall_at_1000
value: 75.64999999999999
- type: recall_at_3
value: 15.136
- type: recall_at_5
value: 18.678
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: fiqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.872999999999998
- type: map_at_10
value: 32.923
- type: map_at_100
value: 34.819
- type: map_at_1000
value: 34.99
- type: map_at_3
value: 28.500999999999998
- type: map_at_5
value: 31.087999999999997
- type: mrr_at_1
value: 40.432
- type: mrr_at_10
value: 49.242999999999995
- type: mrr_at_100
value: 50.014
- type: mrr_at_1000
value: 50.05500000000001
- type: mrr_at_3
value: 47.144999999999996
- type: mrr_at_5
value: 48.171
- type: ndcg_at_1
value: 40.586
- type: ndcg_at_10
value: 40.887
- type: ndcg_at_100
value: 47.701
- type: ndcg_at_1000
value: 50.624
- type: ndcg_at_3
value: 37.143
- type: ndcg_at_5
value: 38.329
- type: precision_at_1
value: 40.586
- type: precision_at_10
value: 11.497
- type: precision_at_100
value: 1.838
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 25.0
- type: precision_at_5
value: 18.549
- type: recall_at_1
value: 19.872999999999998
- type: recall_at_10
value: 48.073
- type: recall_at_100
value: 73.473
- type: recall_at_1000
value: 90.94
- type: recall_at_3
value: 33.645
- type: recall_at_5
value: 39.711
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: hotpotqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.399
- type: map_at_10
value: 62.604000000000006
- type: map_at_100
value: 63.475
- type: map_at_1000
value: 63.534
- type: map_at_3
value: 58.870999999999995
- type: map_at_5
value: 61.217
- type: mrr_at_1
value: 78.758
- type: mrr_at_10
value: 84.584
- type: mrr_at_100
value: 84.753
- type: mrr_at_1000
value: 84.759
- type: mrr_at_3
value: 83.65700000000001
- type: mrr_at_5
value: 84.283
- type: ndcg_at_1
value: 78.798
- type: ndcg_at_10
value: 71.04
- type: ndcg_at_100
value: 74.048
- type: ndcg_at_1000
value: 75.163
- type: ndcg_at_3
value: 65.862
- type: ndcg_at_5
value: 68.77600000000001
- type: precision_at_1
value: 78.798
- type: precision_at_10
value: 14.949000000000002
- type: precision_at_100
value: 1.7309999999999999
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 42.237
- type: precision_at_5
value: 27.634999999999998
- type: recall_at_1
value: 39.399
- type: recall_at_10
value: 74.747
- type: recall_at_100
value: 86.529
- type: recall_at_1000
value: 93.849
- type: recall_at_3
value: 63.356
- type: recall_at_5
value: 69.08800000000001
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: msmarco-pl
config: default
split: validation
revision: None
metrics:
- type: map_at_1
value: 19.598
- type: map_at_10
value: 30.453999999999997
- type: map_at_100
value: 31.601000000000003
- type: map_at_1000
value: 31.66
- type: map_at_3
value: 27.118
- type: map_at_5
value: 28.943
- type: mrr_at_1
value: 20.1
- type: mrr_at_10
value: 30.978
- type: mrr_at_100
value: 32.057
- type: mrr_at_1000
value: 32.112
- type: mrr_at_3
value: 27.679
- type: mrr_at_5
value: 29.493000000000002
- type: ndcg_at_1
value: 20.158
- type: ndcg_at_10
value: 36.63
- type: ndcg_at_100
value: 42.291000000000004
- type: ndcg_at_1000
value: 43.828
- type: ndcg_at_3
value: 29.744999999999997
- type: ndcg_at_5
value: 33.024
- type: precision_at_1
value: 20.158
- type: precision_at_10
value: 5.811999999999999
- type: precision_at_100
value: 0.868
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 12.689
- type: precision_at_5
value: 9.295
- type: recall_at_1
value: 19.598
- type: recall_at_10
value: 55.596999999999994
- type: recall_at_100
value: 82.143
- type: recall_at_1000
value: 94.015
- type: recall_at_3
value: 36.720000000000006
- type: recall_at_5
value: 44.606
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.8117014122394
- type: f1
value: 72.0259730121889
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.84465366509752
- type: f1
value: 77.73439218970051
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: nfcorpus-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.604
- type: map_at_10
value: 12.684000000000001
- type: map_at_100
value: 16.274
- type: map_at_1000
value: 17.669
- type: map_at_3
value: 9.347
- type: map_at_5
value: 10.752
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 52.94
- type: mrr_at_100
value: 53.571000000000005
- type: mrr_at_1000
value: 53.613
- type: mrr_at_3
value: 51.032
- type: mrr_at_5
value: 52.193
- type: ndcg_at_1
value: 41.486000000000004
- type: ndcg_at_10
value: 33.937
- type: ndcg_at_100
value: 31.726
- type: ndcg_at_1000
value: 40.331
- type: ndcg_at_3
value: 39.217
- type: ndcg_at_5
value: 36.521
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 25.324999999999996
- type: precision_at_100
value: 8.022
- type: precision_at_1000
value: 2.0629999999999997
- type: precision_at_3
value: 36.945
- type: precision_at_5
value: 31.517
- type: recall_at_1
value: 5.604
- type: recall_at_10
value: 16.554
- type: recall_at_100
value: 33.113
- type: recall_at_1000
value: 62.832
- type: recall_at_3
value: 10.397
- type: recall_at_5
value: 12.629999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: nq-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.642
- type: map_at_10
value: 40.367999999999995
- type: map_at_100
value: 41.487
- type: map_at_1000
value: 41.528
- type: map_at_3
value: 36.292
- type: map_at_5
value: 38.548
- type: mrr_at_1
value: 30.156
- type: mrr_at_10
value: 42.853
- type: mrr_at_100
value: 43.742
- type: mrr_at_1000
value: 43.772
- type: mrr_at_3
value: 39.47
- type: mrr_at_5
value: 41.366
- type: ndcg_at_1
value: 30.214000000000002
- type: ndcg_at_10
value: 47.620000000000005
- type: ndcg_at_100
value: 52.486
- type: ndcg_at_1000
value: 53.482
- type: ndcg_at_3
value: 39.864
- type: ndcg_at_5
value: 43.645
- type: precision_at_1
value: 30.214000000000002
- type: precision_at_10
value: 8.03
- type: precision_at_100
value: 1.0739999999999998
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 18.183
- type: precision_at_5
value: 13.105
- type: recall_at_1
value: 26.642
- type: recall_at_10
value: 67.282
- type: recall_at_100
value: 88.632
- type: recall_at_1000
value: 96.109
- type: recall_at_3
value: 47.048
- type: recall_at_5
value: 55.791000000000004
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 64.69446857804807
- type: ap
value: 75.58028779280512
- type: f1
value: 62.3610392963539
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 88.4
- type: cos_sim_ap
value: 93.56462741831817
- type: cos_sim_f1
value: 90.73634204275535
- type: cos_sim_precision
value: 86.94992412746586
- type: cos_sim_recall
value: 94.86754966887418
- type: dot_accuracy
value: 75.3
- type: dot_ap
value: 83.06945936688015
- type: dot_f1
value: 81.50887573964496
- type: dot_precision
value: 73.66310160427807
- type: dot_recall
value: 91.22516556291392
- type: euclidean_accuracy
value: 88.8
- type: euclidean_ap
value: 93.53974198044985
- type: euclidean_f1
value: 90.87947882736157
- type: euclidean_precision
value: 89.42307692307693
- type: euclidean_recall
value: 92.3841059602649
- type: manhattan_accuracy
value: 88.8
- type: manhattan_ap
value: 93.54209967780366
- type: manhattan_f1
value: 90.85072231139645
- type: manhattan_precision
value: 88.1619937694704
- type: manhattan_recall
value: 93.70860927152319
- type: max_accuracy
value: 88.8
- type: max_ap
value: 93.56462741831817
- type: max_f1
value: 90.87947882736157
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 97.03153988868274
- type: cos_sim_ap
value: 98.63208302459417
- type: cos_sim_f1
value: 95.06172839506173
- type: cos_sim_precision
value: 96.25
- type: cos_sim_recall
value: 93.90243902439023
- type: dot_accuracy
value: 86.82745825602969
- type: dot_ap
value: 83.77450133931302
- type: dot_f1
value: 79.3053545586107
- type: dot_precision
value: 75.48209366391184
- type: dot_recall
value: 83.53658536585365
- type: euclidean_accuracy
value: 97.03153988868274
- type: euclidean_ap
value: 98.80678168225653
- type: euclidean_f1
value: 95.20958083832335
- type: euclidean_precision
value: 93.52941176470588
- type: euclidean_recall
value: 96.95121951219512
- type: manhattan_accuracy
value: 97.21706864564007
- type: manhattan_ap
value: 98.82279484224186
- type: manhattan_f1
value: 95.44072948328268
- type: manhattan_precision
value: 95.15151515151516
- type: manhattan_recall
value: 95.73170731707317
- type: max_accuracy
value: 97.21706864564007
- type: max_ap
value: 98.82279484224186
- type: max_f1
value: 95.44072948328268
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 76.84210526315789
- type: f1
value: 75.49713789106988
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 53.7246963562753
- type: f1
value: 43.060592194322986
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: quora-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.021
- type: map_at_10
value: 81.362
- type: map_at_100
value: 82.06700000000001
- type: map_at_1000
value: 82.084
- type: map_at_3
value: 78.223
- type: map_at_5
value: 80.219
- type: mrr_at_1
value: 77.17
- type: mrr_at_10
value: 84.222
- type: mrr_at_100
value: 84.37599999999999
- type: mrr_at_1000
value: 84.379
- type: mrr_at_3
value: 83.003
- type: mrr_at_5
value: 83.834
- type: ndcg_at_1
value: 77.29
- type: ndcg_at_10
value: 85.506
- type: ndcg_at_100
value: 87.0
- type: ndcg_at_1000
value: 87.143
- type: ndcg_at_3
value: 82.17
- type: ndcg_at_5
value: 84.057
- type: precision_at_1
value: 77.29
- type: precision_at_10
value: 13.15
- type: precision_at_100
value: 1.522
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.173
- type: precision_at_5
value: 23.988
- type: recall_at_1
value: 67.021
- type: recall_at_10
value: 93.943
- type: recall_at_100
value: 99.167
- type: recall_at_1000
value: 99.929
- type: recall_at_3
value: 84.55799999999999
- type: recall_at_5
value: 89.697
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: scidocs-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.523
- type: map_at_10
value: 11.584
- type: map_at_100
value: 13.705
- type: map_at_1000
value: 14.038999999999998
- type: map_at_3
value: 8.187999999999999
- type: map_at_5
value: 9.922
- type: mrr_at_1
value: 22.1
- type: mrr_at_10
value: 32.946999999999996
- type: mrr_at_100
value: 34.11
- type: mrr_at_1000
value: 34.163
- type: mrr_at_3
value: 29.633
- type: mrr_at_5
value: 31.657999999999998
- type: ndcg_at_1
value: 22.2
- type: ndcg_at_10
value: 19.466
- type: ndcg_at_100
value: 27.725
- type: ndcg_at_1000
value: 33.539
- type: ndcg_at_3
value: 18.26
- type: ndcg_at_5
value: 16.265
- type: precision_at_1
value: 22.2
- type: precision_at_10
value: 10.11
- type: precision_at_100
value: 2.204
- type: precision_at_1000
value: 0.36
- type: precision_at_3
value: 17.1
- type: precision_at_5
value: 14.44
- type: recall_at_1
value: 4.523
- type: recall_at_10
value: 20.497
- type: recall_at_100
value: 44.757000000000005
- type: recall_at_1000
value: 73.14699999999999
- type: recall_at_3
value: 10.413
- type: recall_at_5
value: 14.638000000000002
- 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: 87.4235629841011
- type: cos_sim_ap
value: 84.46531935663157
- type: cos_sim_f1
value: 77.18910963944077
- type: cos_sim_precision
value: 79.83257229832572
- type: cos_sim_recall
value: 74.71509971509973
- type: dot_accuracy
value: 81.10476966979209
- type: dot_ap
value: 71.12231750543143
- type: dot_f1
value: 68.13455657492355
- type: dot_precision
value: 59.69989281886387
- type: dot_recall
value: 79.34472934472934
- type: euclidean_accuracy
value: 87.21973094170403
- type: euclidean_ap
value: 84.33077991405355
- type: euclidean_f1
value: 76.81931132410365
- type: euclidean_precision
value: 76.57466383581033
- type: euclidean_recall
value: 77.06552706552706
- type: manhattan_accuracy
value: 87.21973094170403
- type: manhattan_ap
value: 84.35651252115137
- type: manhattan_f1
value: 76.87004481213376
- type: manhattan_precision
value: 74.48229792919172
- type: manhattan_recall
value: 79.41595441595442
- type: max_accuracy
value: 87.4235629841011
- type: max_ap
value: 84.46531935663157
- type: max_f1
value: 77.18910963944077
- 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: 83.05629619004273
- type: cos_sim_spearman
value: 79.90632583043678
- type: euclidean_pearson
value: 81.56426663515931
- type: euclidean_spearman
value: 80.05439220131294
- type: manhattan_pearson
value: 81.52958181013108
- type: manhattan_spearman
value: 80.0387467163383
- 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: 35.93847200513348
- type: cos_sim_spearman
value: 39.31543525546526
- type: euclidean_pearson
value: 30.19743936591465
- type: euclidean_spearman
value: 39.966612599252095
- type: manhattan_pearson
value: 30.195614462473387
- type: manhattan_spearman
value: 39.822552043685754
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: scifact-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 56.05
- type: map_at_10
value: 65.93299999999999
- type: map_at_100
value: 66.571
- type: map_at_1000
value: 66.60000000000001
- type: map_at_3
value: 63.489
- type: map_at_5
value: 64.91799999999999
- type: mrr_at_1
value: 59.0
- type: mrr_at_10
value: 67.026
- type: mrr_at_100
value: 67.559
- type: mrr_at_1000
value: 67.586
- type: mrr_at_3
value: 65.444
- type: mrr_at_5
value: 66.278
- type: ndcg_at_1
value: 59.0
- type: ndcg_at_10
value: 70.233
- type: ndcg_at_100
value: 72.789
- type: ndcg_at_1000
value: 73.637
- type: ndcg_at_3
value: 66.40700000000001
- type: ndcg_at_5
value: 68.206
- type: precision_at_1
value: 59.0
- type: precision_at_10
value: 9.367
- type: precision_at_100
value: 1.06
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.222
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 56.05
- type: recall_at_10
value: 82.089
- type: recall_at_100
value: 93.167
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 71.822
- type: recall_at_5
value: 76.483
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: trec-covid-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.7680000000000002
- type: map_at_100
value: 9.447999999999999
- type: map_at_1000
value: 21.728
- type: map_at_3
value: 0.603
- type: map_at_5
value: 0.9610000000000001
- type: mrr_at_1
value: 80.0
- type: mrr_at_10
value: 88.667
- type: mrr_at_100
value: 88.667
- type: mrr_at_1000
value: 88.667
- type: mrr_at_3
value: 87.667
- type: mrr_at_5
value: 88.667
- type: ndcg_at_1
value: 77.0
- type: ndcg_at_10
value: 70.814
- type: ndcg_at_100
value: 52.532000000000004
- type: ndcg_at_1000
value: 45.635999999999996
- type: ndcg_at_3
value: 76.542
- type: ndcg_at_5
value: 73.24000000000001
- type: precision_at_1
value: 80.0
- type: precision_at_10
value: 75.0
- type: precision_at_100
value: 53.879999999999995
- type: precision_at_1000
value: 20.002
- type: precision_at_3
value: 80.0
- type: precision_at_5
value: 76.4
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 2.012
- type: recall_at_100
value: 12.781999999999998
- type: recall_at_1000
value: 42.05
- type: recall_at_3
value: 0.644
- type: recall_at_5
value: 1.04
---
<h1 align="center">MMLW-roberta-large</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 1024 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-large")
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 **63.23** 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 **55.95** 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"
] |
michiyasunaga/BioLinkBERT-large
|
michiyasunaga
|
text-classification
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-08T06:20:38 | 2022-03-31T00:54:57 | 7,776 | 33 |
---
datasets:
- pubmed
language: en
license: apache-2.0
tags:
- bert
- exbert
- linkbert
- biolinkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
widget:
- text: Sunitinib is a tyrosine kinase inhibitor
---
## BioLinkBERT-large
BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA).
## Model description
LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
## Intended uses & limitations
The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
### How to use
To use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-large')
model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-large')
inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
## Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
**Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art.
| | BLURB score | PubMedQA | BioASQ | MedQA-USMLE |
| ---------------------- | -------- | -------- | ------- | -------- |
| PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 |
| **BioLinkBERT-base** | **83.39** | **70.2** | **91.4** | **40.0** |
| **BioLinkBERT-large** | **84.30** | **72.2** | **94.8** | **44.6** |
| | MMLU-professional medicine |
| ---------------------- | -------- |
| GPT-3 (175 params) | 38.7 |
| UnifiedQA (11B params) | 43.2 |
| **BioLinkBERT-large (340M params)** | **50.7** |
## Citation
If you find LinkBERT useful in your project, please cite the following:
```bibtex
@InProceedings{yasunaga2022linkbert,
author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
title = {LinkBERT: Pretraining Language Models with Document Links},
year = {2022},
booktitle = {Association for Computational Linguistics (ACL)},
}
```
|
[
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING"
] |
[
"BLURB",
"MEDQA",
"PUBMEDQA"
] |
ai-sage/Giga-Embeddings-instruct
|
ai-sage
|
feature-extraction
|
[
"safetensors",
"gigarembed",
"mteb",
"feature-extraction",
"custom_code",
"ru",
"en",
"license:mit",
"model-index",
"region:us"
] | 2024-12-11T12:25:30 | 2025-02-04T09:21:03 | 7,701 | 43 |
---
language:
- ru
- en
license: mit
pipeline_tag: feature-extraction
tags:
- mteb
model-index:
- name: giga-embeddings-instruct
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: 94.5352323838081
- type: ap
value: 62.422648408367344
- type: ap_weighted
value: 62.422648408367344
- type: f1
value: 87.13103677336655
- type: f1_weighted
value: 94.85637995412655
- type: main_score
value: 94.5352323838081
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 90.31343283582089
- type: ap
value: 63.42364739316405
- type: ap_weighted
value: 63.42364739316405
- type: f1
value: 85.54214552412623
- type: f1_weighted
value: 90.59539168268289
- type: main_score
value: 90.31343283582089
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 94.29605000000001
- type: ap
value: 91.30887530384256
- type: ap_weighted
value: 91.30887530384256
- type: f1
value: 94.29070662237378
- type: f1_weighted
value: 94.29070662237378
- type: main_score
value: 94.29605000000001
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 53.227999999999994
- type: map_at_1
value: 27.595999999999997
- type: map_at_10
value: 43.756
- type: map_at_100
value: 44.674
- type: map_at_1000
value: 44.675
- type: map_at_20
value: 44.511
- type: map_at_3
value: 38.312000000000005
- type: map_at_5
value: 41.271
- type: mrr_at_1
value: 27.951635846372692
- type: mrr_at_10
value: 43.8683138025244
- type: mrr_at_100
value: 44.79916793634115
- type: mrr_at_1000
value: 44.800641832434614
- type: mrr_at_20
value: 44.63636850959653
- type: mrr_at_3
value: 38.383119962067305
- type: mrr_at_5
value: 41.41299193930774
- type: nauc_map_at_1000_diff1
value: 6.936710279308449
- type: nauc_map_at_1000_max
value: -16.426102328143827
- type: nauc_map_at_1000_std
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- type: precision_at_5
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- type: recall_at_1
value: 27.595999999999997
- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 94.23899999999999
- type: recall_at_3
value: 52.347
- type: recall_at_5
value: 65.505
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 58.18725222465893
- type: map
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- type: mrr
value: 69.84335839598998
- type: nAUC_map_diff1
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- type: nAUC_map_max
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- type: nAUC_mrr_diff1
value: 25.38427130339339
- type: nAUC_mrr_max
value: 24.2962940173052
- type: nAUC_mrr_std
value: 20.846599304343176
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 87.40814966131607
- type: cosine_spearman
value: 84.21758160533057
- type: euclidean_pearson
value: 86.68087011664755
- type: euclidean_spearman
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- type: manhattan_spearman
value: 84.69409848718736
- type: pearson
value: 87.40814966131607
- type: spearman
value: 84.21758160533057
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
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- type: f1
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- type: f1_weighted
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- type: main_score
value: 87.78571428571429
- task:
type: MultilabelClassification
dataset:
name: MTEB CEDRClassification (default)
type: ai-forever/cedr-classification
config: default
split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
metrics:
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- type: lrap
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- type: main_score
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- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
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type: Classification
dataset:
name: MTEB GeoreviewClassification (default)
type: ai-forever/georeview-classification
config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
metrics:
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value: 58.59296875
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value: 55.87139132757291
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value: 58.59296875
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type: Clustering
dataset:
name: MTEB GeoreviewClusteringP2P (default)
type: ai-forever/georeview-clustering-p2p
config: default
split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
metrics:
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value: 61.37882703448214
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type: Classification
dataset:
name: MTEB HeadlineClassification (default)
type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
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value: 86.01484375
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type: Retrieval
dataset:
name: MTEB HotpotQA (default)
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
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- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
value: 85.544
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value: 48.467
- type: recall_at_5
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- task:
type: Classification
dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.90639999999999
- type: ap
value: 92.50413995343256
- type: ap_weighted
value: 92.50413995343256
- type: f1
value: 94.90353518558736
- type: f1_weighted
value: 94.90353518558736
- type: main_score
value: 94.90639999999999
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification (default)
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 83.29023437500001
- type: ap
value: 77.50711273019213
- type: ap_weighted
value: 77.50711273019213
- type: f1
value: 82.42886423677278
- type: f1_weighted
value: 82.42886423677278
- type: main_score
value: 83.29023437500001
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification (default)
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 74.88666666666667
- type: f1
value: 72.6314130184491
- type: f1_weighted
value: 72.63141301844908
- type: main_score
value: 74.88666666666667
- task:
type: Reranking
dataset:
name: MTEB MIRACLReranking (ru)
type: miracl/mmteb-miracl-reranking
config: ru
split: dev
revision: 6d1962c527217f8927fca80f890f14f36b2802af
metrics:
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value: 34.256
- type: MAP@10(MIRACL)
value: 54.418
- type: MAP@100(MIRACL)
value: 56.224
- type: MAP@1000(MIRACL)
value: 56.224
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value: 55.726
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value: 47.811
- type: MAP@5(MIRACL)
value: 51.604000000000006
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value: 55.493
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value: 61.209
- type: NDCG@100(MIRACL)
value: 64.768
- type: NDCG@1000(MIRACL)
value: 64.768
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value: 63.497
- type: NDCG@3(MIRACL)
value: 56.259
- type: NDCG@5(MIRACL)
value: 58.164
- type: P@1(MIRACL)
value: 55.493
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value: 15.533
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value: 1.9529999999999998
- type: P@1000(MIRACL)
value: 0.19499999999999998
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value: 8.896999999999998
- type: P@3(MIRACL)
value: 34.509
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value: 25.436999999999998
- type: Recall@1(MIRACL)
value: 34.256
- type: Recall@10(MIRACL)
value: 69.61500000000001
- type: Recall@100(MIRACL)
value: 79.952
- type: Recall@1000(MIRACL)
value: 79.952
- type: Recall@20(MIRACL)
value: 75.753
- type: Recall@3(MIRACL)
value: 54.217999999999996
- type: Recall@5(MIRACL)
value: 61.617999999999995
- type: main_score
value: 61.209
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value: 25.375679947366475
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value: 39.17949421634556
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value: 15.488075690428888
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type: Reranking
dataset:
name: MTEB RuBQReranking (default)
type: ai-forever/rubq-reranking
config: default
split: test
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
metrics:
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type: Retrieval
dataset:
name: MTEB RuBQRetrieval (default)
type: ai-forever/rubq-retrieval
config: default
split: test
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
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- task:
type: Classification
dataset:
name: MTEB RuReviewsClassification (default)
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
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value: 73.7150390625
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type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS (default)
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
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type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
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type: Clustering
dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
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type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
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dataset:
name: MTEB RuSciBenchOECDClusteringP2P (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
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dataset:
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type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
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value: 6.269540116426191
- type: nauc_recall_at_1000_max
value: 22.50245807435009
- type: nauc_recall_at_1000_std
value: 38.92843130214358
- type: nauc_recall_at_100_diff1
value: 5.858562324196213
- type: nauc_recall_at_100_max
value: 21.146719698636726
- type: nauc_recall_at_100_std
value: 33.409178845783586
- type: nauc_recall_at_10_diff1
value: 10.791079221016838
- type: nauc_recall_at_10_max
value: 23.402958626226223
- type: nauc_recall_at_10_std
value: 23.778905836972058
- type: nauc_recall_at_1_diff1
value: 26.524260461587694
- type: nauc_recall_at_1_max
value: 18.237790264396608
- type: nauc_recall_at_1_std
value: 13.862573541948848
- type: nauc_recall_at_20_diff1
value: 9.588806834662645
- type: nauc_recall_at_20_max
value: 20.992020581818323
- type: nauc_recall_at_20_std
value: 25.92296591496667
- type: nauc_recall_at_3_diff1
value: 14.86565067742853
- type: nauc_recall_at_3_max
value: 20.312249816233308
- type: nauc_recall_at_3_std
value: 16.924697629563205
- type: nauc_recall_at_5_diff1
value: 13.735197736933962
- type: nauc_recall_at_5_max
value: 20.94698008166532
- type: nauc_recall_at_5_std
value: 19.962609439107787
- type: ndcg_at_1
value: 22.2
- type: ndcg_at_10
value: 19.162000000000003
- type: ndcg_at_100
value: 28.321
- type: ndcg_at_1000
value: 34.266999999999996
- type: ndcg_at_20
value: 22.253999999999998
- type: ndcg_at_3
value: 17.798
- type: ndcg_at_5
value: 15.975
- type: precision_at_1
value: 22.2
- type: precision_at_10
value: 9.98
- type: precision_at_100
value: 2.329
- type: precision_at_1000
value: 0.376
- type: precision_at_20
value: 6.795
- type: precision_at_3
value: 16.567
- type: precision_at_5
value: 14.24
- type: recall_at_1
value: 4.475
- type: recall_at_10
value: 20.307
- type: recall_at_100
value: 47.242
- type: recall_at_1000
value: 76.242
- type: recall_at_20
value: 27.596999999999998
- type: recall_at_3
value: 10.05
- type: recall_at_5
value: 14.432
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 85.571141897898
- type: cosine_spearman
value: 81.06253068558962
- type: euclidean_pearson
value: 83.27075641353736
- type: euclidean_spearman
value: 81.06253622684207
- type: main_score
value: 81.06253068558962
- type: manhattan_pearson
value: 83.35659901147551
- type: manhattan_spearman
value: 81.13591158643307
- type: pearson
value: 85.571141897898
- type: spearman
value: 81.06253068558962
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 81.78267795485768
- type: cosine_spearman
value: 70.09877188862463
- type: euclidean_pearson
value: 78.49440835422978
- type: euclidean_spearman
value: 70.09877188862463
- type: main_score
value: 70.09877188862463
- type: manhattan_pearson
value: 78.7282306108082
- type: manhattan_spearman
value: 70.29557277377175
- type: pearson
value: 81.78267795485768
- type: spearman
value: 70.09877188862463
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 80.25745663712165
- type: cosine_spearman
value: 81.01483036919429
- type: euclidean_pearson
value: 81.14541657219165
- type: euclidean_spearman
value: 81.01478607738545
- type: main_score
value: 81.01483036919429
- type: manhattan_pearson
value: 81.40111412290987
- type: manhattan_spearman
value: 81.28649812590226
- type: pearson
value: 80.25745663712165
- type: spearman
value: 81.01483036919429
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 81.7451052491344
- type: cosine_spearman
value: 77.81666029040504
- type: euclidean_pearson
value: 81.31747977745773
- type: euclidean_spearman
value: 77.81666029040504
- type: main_score
value: 77.81666029040504
- type: manhattan_pearson
value: 81.46161935201552
- type: manhattan_spearman
value: 77.98131880904403
- type: pearson
value: 81.7451052491344
- type: spearman
value: 77.81666029040504
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 83.69084648758516
- type: cosine_spearman
value: 84.52134445704115
- type: euclidean_pearson
value: 84.13552443709717
- type: euclidean_spearman
value: 84.52134445704115
- type: main_score
value: 84.52134445704115
- type: manhattan_pearson
value: 84.21966806051941
- type: manhattan_spearman
value: 84.61306373914836
- type: pearson
value: 83.69084648758516
- type: spearman
value: 84.52134445704115
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 79.22411263296355
- type: cosine_spearman
value: 81.0920748151385
- type: euclidean_pearson
value: 80.69903573722831
- type: euclidean_spearman
value: 81.0920748151385
- type: main_score
value: 81.0920748151385
- type: manhattan_pearson
value: 80.82176565456413
- type: manhattan_spearman
value: 81.22812310589154
- type: pearson
value: 79.22411263296355
- type: spearman
value: 81.0920748151385
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 46.40454184212831
- type: cosine_spearman
value: 61.10568034074938
- type: euclidean_pearson
value: 51.24169968038525
- type: euclidean_spearman
value: 61.10568034074938
- type: main_score
value: 61.10568034074938
- type: manhattan_pearson
value: 52.48738021500564
- type: manhattan_spearman
value: 61.15965141864881
- type: pearson
value: 46.40454184212831
- type: spearman
value: 61.10568034074938
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 82.25427804989813
- type: cosine_spearman
value: 82.20135109121625
- type: euclidean_pearson
value: 83.21943775385887
- type: euclidean_spearman
value: 82.2013271737365
- type: main_score
value: 82.20135109121625
- type: manhattan_pearson
value: 83.30139032750424
- type: manhattan_spearman
value: 82.313788704099
- type: pearson
value: 82.25427804989813
- type: spearman
value: 82.20135109121625
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 88.00790793717358
- type: map
value: 88.00790793717358
- type: mrr
value: 96.2317149081855
- type: nAUC_map_diff1
value: -4.285706894005613
- type: nAUC_map_max
value: 47.69800035323553
- type: nAUC_map_std
value: 66.05509605566249
- type: nAUC_mrr_diff1
value: 37.59327273978339
- type: nAUC_mrr_max
value: 74.83336690604901
- type: nAUC_mrr_std
value: 79.76400854713901
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 72.902
- type: map_at_1
value: 58.160999999999994
- type: map_at_10
value: 68.15
- type: map_at_100
value: 68.76299999999999
- type: map_at_1000
value: 68.777
- type: map_at_20
value: 68.636
- type: map_at_3
value: 65.496
- type: map_at_5
value: 67.166
- type: mrr_at_1
value: 61.66666666666667
- type: mrr_at_10
value: 69.90119047619046
- type: mrr_at_100
value: 70.35924850016258
- type: mrr_at_1000
value: 70.36956277343268
- type: mrr_at_20
value: 70.2427525490219
- type: mrr_at_3
value: 68.0
- type: mrr_at_5
value: 69.23333333333332
- type: nauc_map_at_1000_diff1
value: 67.60995698057089
- type: nauc_map_at_1000_max
value: 46.068809020612704
- type: nauc_map_at_1000_std
value: 1.1948672845462098
- type: nauc_map_at_100_diff1
value: 67.60952762159904
- type: nauc_map_at_100_max
value: 46.07379744229154
- type: nauc_map_at_100_std
value: 1.182970323131957
- type: nauc_map_at_10_diff1
value: 67.57824321545665
- type: nauc_map_at_10_max
value: 46.32612750524493
- type: nauc_map_at_10_std
value: 1.2781320788709092
- type: nauc_map_at_1_diff1
value: 70.5130136513353
- type: nauc_map_at_1_max
value: 40.673408888331544
- type: nauc_map_at_1_std
value: -1.3408323300249747
- type: nauc_map_at_20_diff1
value: 67.52032735442157
- type: nauc_map_at_20_max
value: 46.12651713618736
- type: nauc_map_at_20_std
value: 1.1355480686211972
- type: nauc_map_at_3_diff1
value: 68.16380401477547
- type: nauc_map_at_3_max
value: 43.88680563184851
- type: nauc_map_at_3_std
value: -3.720201195392404
- type: nauc_map_at_5_diff1
value: 67.99170514346827
- type: nauc_map_at_5_max
value: 45.1182341963952
- type: nauc_map_at_5_std
value: -0.8736684979875234
- type: nauc_mrr_at_1000_diff1
value: 65.5906860945105
- type: nauc_mrr_at_1000_max
value: 47.56601805618958
- type: nauc_mrr_at_1000_std
value: 5.9136892538197126
- type: nauc_mrr_at_100_diff1
value: 65.59130506472648
- type: nauc_mrr_at_100_max
value: 47.560766539048224
- type: nauc_mrr_at_100_std
value: 5.886452280327445
- type: nauc_mrr_at_10_diff1
value: 65.38789898290564
- type: nauc_mrr_at_10_max
value: 47.82853216971601
- type: nauc_mrr_at_10_std
value: 6.362470864688538
- type: nauc_mrr_at_1_diff1
value: 67.64455543946382
- type: nauc_mrr_at_1_max
value: 44.87347417242169
- type: nauc_mrr_at_1_std
value: 7.4488417726357365
- type: nauc_mrr_at_20_diff1
value: 65.47605317912657
- type: nauc_mrr_at_20_max
value: 47.58709668066147
- type: nauc_mrr_at_20_std
value: 5.809216165052655
- type: nauc_mrr_at_3_diff1
value: 65.77227321919354
- type: nauc_mrr_at_3_max
value: 47.54231580750389
- type: nauc_mrr_at_3_std
value: 4.222335116280824
- type: nauc_mrr_at_5_diff1
value: 65.54309281879533
- type: nauc_mrr_at_5_max
value: 47.89198835887271
- type: nauc_mrr_at_5_std
value: 5.990782526521852
- type: nauc_ndcg_at_1000_diff1
value: 66.50501237344044
- type: nauc_ndcg_at_1000_max
value: 47.89691614927347
- type: nauc_ndcg_at_1000_std
value: 3.858567312387061
- type: nauc_ndcg_at_100_diff1
value: 66.52978786688968
- type: nauc_ndcg_at_100_max
value: 47.92798566987145
- type: nauc_ndcg_at_100_std
value: 3.2827316431460147
- type: nauc_ndcg_at_10_diff1
value: 65.58633101511163
- type: nauc_ndcg_at_10_max
value: 48.80288520105782
- type: nauc_ndcg_at_10_std
value: 3.9764819883604385
- type: nauc_ndcg_at_1_diff1
value: 67.64455543946382
- type: nauc_ndcg_at_1_max
value: 44.87347417242169
- type: nauc_ndcg_at_1_std
value: 7.4488417726357365
- type: nauc_ndcg_at_20_diff1
value: 65.674407420572
- type: nauc_ndcg_at_20_max
value: 48.11394986776404
- type: nauc_ndcg_at_20_std
value: 2.711393534038602
- type: nauc_ndcg_at_3_diff1
value: 66.5498828618974
- type: nauc_ndcg_at_3_max
value: 45.78842720237144
- type: nauc_ndcg_at_3_std
value: -2.524503922872291
- type: nauc_ndcg_at_5_diff1
value: 66.51681687231908
- type: nauc_ndcg_at_5_max
value: 47.28239002104229
- type: nauc_ndcg_at_5_std
value: 0.8797704241318283
- type: nauc_precision_at_1000_diff1
value: -25.278551984459547
- type: nauc_precision_at_1000_max
value: 19.072121412932663
- type: nauc_precision_at_1000_std
value: 51.03267260477789
- type: nauc_precision_at_100_diff1
value: -17.95505562847295
- type: nauc_precision_at_100_max
value: 22.786207573322784
- type: nauc_precision_at_100_std
value: 45.52886915349048
- type: nauc_precision_at_10_diff1
value: 7.786315249522727
- type: nauc_precision_at_10_max
value: 40.45721598510848
- type: nauc_precision_at_10_std
value: 38.86811809562086
- type: nauc_precision_at_1_diff1
value: 67.64455543946382
- type: nauc_precision_at_1_max
value: 44.87347417242169
- type: nauc_precision_at_1_std
value: 7.4488417726357365
- type: nauc_precision_at_20_diff1
value: -6.7787477098826265
- type: nauc_precision_at_20_max
value: 29.43636167690474
- type: nauc_precision_at_20_std
value: 38.48151740489274
- type: nauc_precision_at_3_diff1
value: 39.481963258187015
- type: nauc_precision_at_3_max
value: 42.42369906705573
- type: nauc_precision_at_3_std
value: 9.531028412147295
- type: nauc_precision_at_5_diff1
value: 24.898111796597416
- type: nauc_precision_at_5_max
value: 41.17351707929381
- type: nauc_precision_at_5_std
value: 24.90291910370421
- type: nauc_recall_at_1000_diff1
value: 70.76330532212897
- type: nauc_recall_at_1000_max
value: 88.85387488328593
- type: nauc_recall_at_1000_std
value: 83.25163398692725
- type: nauc_recall_at_100_diff1
value: 68.08723489395771
- type: nauc_recall_at_100_max
value: 63.74994442221321
- type: nauc_recall_at_100_std
value: 7.758659019163229
- type: nauc_recall_at_10_diff1
value: 56.545518621597324
- type: nauc_recall_at_10_max
value: 56.74870654004741
- type: nauc_recall_at_10_std
value: 8.701951811447595
- type: nauc_recall_at_1_diff1
value: 70.5130136513353
- type: nauc_recall_at_1_max
value: 40.673408888331544
- type: nauc_recall_at_1_std
value: -1.3408323300249747
- type: nauc_recall_at_20_diff1
value: 54.13998932906485
- type: nauc_recall_at_20_max
value: 55.454403983815794
- type: nauc_recall_at_20_std
value: -3.1705274702472694
- type: nauc_recall_at_3_diff1
value: 63.260925909299544
- type: nauc_recall_at_3_max
value: 46.05508565736711
- type: nauc_recall_at_3_std
value: -10.957671894931114
- type: nauc_recall_at_5_diff1
value: 61.62947530846916
- type: nauc_recall_at_5_max
value: 50.48704479871539
- type: nauc_recall_at_5_std
value: -1.4810663364085042
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 72.902
- type: ndcg_at_100
value: 75.368
- type: ndcg_at_1000
value: 75.657
- type: ndcg_at_20
value: 74.453
- type: ndcg_at_3
value: 68.57
- type: ndcg_at_5
value: 70.926
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.667
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 5.2
- type: precision_at_3
value: 27.0
- type: precision_at_5
value: 17.8
- type: recall_at_1
value: 58.160999999999994
- type: recall_at_10
value: 85.68900000000001
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 98.667
- type: recall_at_20
value: 91.60000000000001
- type: recall_at_3
value: 74.083
- type: recall_at_5
value: 80.022
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification (default)
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 30.009765625
- type: f1
value: 31.97592344768223
- type: lrap
value: 46.34419759114506
- type: main_score
value: 30.009765625
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.6059405940594
- type: cosine_accuracy_threshold
value: 80.66068887710571
- type: cosine_ap
value: 86.29950883344696
- type: cosine_f1
value: 79.280205655527
- type: cosine_f1_threshold
value: 80.31163215637207
- type: cosine_precision
value: 81.58730158730158
- type: cosine_recall
value: 77.10000000000001
- type: dot_accuracy
value: 99.6059405940594
- type: dot_accuracy_threshold
value: 80.66068291664124
- type: dot_ap
value: 86.29952127770177
- type: dot_f1
value: 79.280205655527
- type: dot_f1_threshold
value: 80.31163215637207
- type: dot_precision
value: 81.58730158730158
- type: dot_recall
value: 77.10000000000001
- type: euclidean_accuracy
value: 99.6059405940594
- type: euclidean_accuracy_threshold
value: 62.19213604927063
- type: euclidean_ap
value: 86.29950883344696
- type: euclidean_f1
value: 79.280205655527
- type: euclidean_f1_threshold
value: 62.75087594985962
- type: euclidean_precision
value: 81.58730158730158
- type: euclidean_recall
value: 77.10000000000001
- type: main_score
value: 86.97106165735022
- type: manhattan_accuracy
value: 99.61683168316831
- type: manhattan_accuracy_threshold
value: 2190.611457824707
- type: manhattan_ap
value: 86.97106165735022
- type: manhattan_f1
value: 79.76999477260847
- type: manhattan_f1_threshold
value: 2214.816665649414
- type: manhattan_precision
value: 83.57064622124864
- type: manhattan_recall
value: 76.3
- type: max_accuracy
value: 99.61683168316831
- type: max_ap
value: 86.97106165735022
- type: max_f1
value: 79.76999477260847
- type: max_precision
value: 83.57064622124864
- type: max_recall
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- type: similarity_accuracy
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type: mteb/stackoverflowdupquestions-reranking
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- type: nauc_precision_at_5_std
value: 1.0506363193249282
- type: nauc_recall_at_1000_diff1
value: 5.452672989062937
- type: nauc_recall_at_1000_max
value: 9.433825474933833
- type: nauc_recall_at_1000_std
value: 83.07846603694948
- type: nauc_recall_at_100_diff1
value: 2.535260598249035
- type: nauc_recall_at_100_max
value: -21.695436099245338
- type: nauc_recall_at_100_std
value: 30.34608369582977
- type: nauc_recall_at_10_diff1
value: -3.8610239417771135
- type: nauc_recall_at_10_max
value: -35.92826433381732
- type: nauc_recall_at_10_std
value: -4.431168646074323
- type: nauc_recall_at_1_diff1
value: -2.3057823615493915
- type: nauc_recall_at_1_max
value: -33.66603052318725
- type: nauc_recall_at_1_std
value: -13.676412668949379
- type: nauc_recall_at_20_diff1
value: -2.0569660419185167
- type: nauc_recall_at_20_max
value: -31.28343777931168
- type: nauc_recall_at_20_std
value: 0.732859893313201
- type: nauc_recall_at_3_diff1
value: -1.1280951425624475
- type: nauc_recall_at_3_max
value: -29.014124779660904
- type: nauc_recall_at_3_std
value: -24.40550848467014
- type: nauc_recall_at_5_diff1
value: -0.5347797031107686
- type: nauc_recall_at_5_max
value: -31.129644592060185
- type: nauc_recall_at_5_std
value: -14.423630504744494
- type: ndcg_at_1
value: 13.264999999999999
- type: ndcg_at_10
value: 13.937
- type: ndcg_at_100
value: 23.186
- type: ndcg_at_1000
value: 36.565
- type: ndcg_at_20
value: 15.665999999999999
- type: ndcg_at_3
value: 13.664000000000001
- type: ndcg_at_5
value: 13.555
- type: precision_at_1
value: 14.285999999999998
- type: precision_at_10
value: 13.469000000000001
- type: precision_at_100
value: 5.061
- type: precision_at_1000
value: 1.367
- type: precision_at_20
value: 10.918
- type: precision_at_3
value: 15.645999999999999
- type: precision_at_5
value: 15.101999999999999
- type: recall_at_1
value: 1.2930000000000001
- type: recall_at_10
value: 11.106
- type: recall_at_100
value: 34.054
- type: recall_at_1000
value: 74.773
- type: recall_at_20
value: 16.54
- type: recall_at_3
value: 3.9669999999999996
- type: recall_at_5
value: 6.354
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 75.3662109375
- type: ap
value: 17.520455604109124
- type: ap_weighted
value: 17.520455604109124
- type: f1
value: 58.701834404711065
- type: f1_weighted
value: 80.58084750047006
- type: main_score
value: 75.3662109375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 67.64855687606112
- type: f1
value: 67.95235466328752
- type: f1_weighted
value: 67.05421511780088
- type: main_score
value: 67.64855687606112
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 83.14358943792097
- type: cosine_accuracy_threshold
value: 82.20133781433105
- type: cosine_ap
value: 63.42168849627343
- type: cosine_f1
value: 60.19512195121952
- type: cosine_f1_threshold
value: 78.19918394088745
- type: cosine_precision
value: 55.96371882086167
- type: cosine_recall
value: 65.11873350923483
- type: dot_accuracy
value: 83.14358943792097
- type: dot_accuracy_threshold
value: 82.20133781433105
- type: dot_ap
value: 63.42166690978694
- type: dot_f1
value: 60.19512195121952
- type: dot_f1_threshold
value: 78.19918394088745
- type: dot_precision
value: 55.96371882086167
- type: dot_recall
value: 65.11873350923483
- type: euclidean_accuracy
value: 83.14358943792097
- type: euclidean_accuracy_threshold
value: 59.66349244117737
- type: euclidean_ap
value: 63.42169082838296
- type: euclidean_f1
value: 60.19512195121952
- type: euclidean_f1_threshold
value: 66.03152751922607
- type: euclidean_precision
value: 55.96371882086167
- type: euclidean_recall
value: 65.11873350923483
- type: main_score
value: 63.562161347378336
- type: manhattan_accuracy
value: 83.17339214400667
- type: manhattan_accuracy_threshold
value: 2131.719970703125
- type: manhattan_ap
value: 63.562161347378336
- type: manhattan_f1
value: 60.42120551924474
- type: manhattan_f1_threshold
value: 2370.509910583496
- type: manhattan_precision
value: 55.81395348837209
- type: manhattan_recall
value: 65.85751978891821
- type: max_accuracy
value: 83.17339214400667
- type: max_ap
value: 63.562161347378336
- type: max_f1
value: 60.42120551924474
- type: max_precision
value: 55.96371882086167
- type: max_recall
value: 65.85751978891821
- type: similarity_accuracy
value: 83.14358943792097
- type: similarity_accuracy_threshold
value: 82.20133781433105
- type: similarity_ap
value: 63.42168849627343
- type: similarity_f1
value: 60.19512195121952
- type: similarity_f1_threshold
value: 78.19918394088745
- type: similarity_precision
value: 55.96371882086167
- type: similarity_recall
value: 65.11873350923483
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 88.78798463150541
- type: cosine_accuracy_threshold
value: 79.96333837509155
- type: cosine_ap
value: 86.19745333095996
- type: cosine_f1
value: 77.74792925901053
- type: cosine_f1_threshold
value: 77.78993844985962
- type: cosine_precision
value: 75.42348342261474
- type: cosine_recall
value: 80.2202032645519
- type: dot_accuracy
value: 88.78798463150541
- type: dot_accuracy_threshold
value: 79.96333241462708
- type: dot_ap
value: 86.19745428025062
- type: dot_f1
value: 77.74792925901053
- type: dot_f1_threshold
value: 77.78993844985962
- type: dot_precision
value: 75.42348342261474
- type: dot_recall
value: 80.2202032645519
- type: euclidean_accuracy
value: 88.78798463150541
- type: euclidean_accuracy_threshold
value: 63.303494453430176
- type: euclidean_ap
value: 86.19745308448735
- type: euclidean_f1
value: 77.74792925901053
- type: euclidean_f1_threshold
value: 66.64841771125793
- type: euclidean_precision
value: 75.42348342261474
- type: euclidean_recall
value: 80.2202032645519
- type: main_score
value: 86.25813794250404
- type: manhattan_accuracy
value: 88.79962743043428
- type: manhattan_accuracy_threshold
value: 2239.702606201172
- type: manhattan_ap
value: 86.25813794250404
- type: manhattan_f1
value: 77.84831272970264
- type: manhattan_f1_threshold
value: 2393.1716918945312
- type: manhattan_precision
value: 75.16667861495448
- type: manhattan_recall
value: 80.72836464428703
- type: max_accuracy
value: 88.79962743043428
- type: max_ap
value: 86.25813794250404
- type: max_f1
value: 77.84831272970264
- type: max_precision
value: 75.42348342261474
- type: max_recall
value: 80.72836464428703
- type: similarity_accuracy
value: 88.78798463150541
- type: similarity_accuracy_threshold
value: 79.96333837509155
- type: similarity_ap
value: 86.19745333095996
- type: similarity_f1
value: 77.74792925901053
- type: similarity_f1_threshold
value: 77.78993844985962
- type: similarity_precision
value: 75.42348342261474
- type: similarity_recall
value: 80.2202032645519
---
## Giga-Embeddings-instruct
- Base Decoder-only LLM: GigaChat-3b
- Pooling Type: Latent-Attention
- Embedding Dimension: 2048
## Использование
Ниже приведен пример кодирования запросов и текстов.
### Transformers
```python
import os
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 = task_name_to_instruct["example"] + "\nquestion: "
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('ai-sage/Giga-Embeddings-instruct', trust_remote_code=True)
# get the embeddings
query_embeddings = model.encode(queries, instruction=query_prefix)
passage_embeddings = model.encode(passages, instruction=passage_prefix)
# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)
scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
```
## Инструктивность
**Использование инструкций для улучшения качества эмбеддингов**
Для достижения более точных результатов при работе с эмбеддингами, особенно в задачах поиска и извлечения информации (retrieval), рекомендуется добавлять инструкцию на естественном языке перед текстовым запросом (query). Это помогает модели лучше понять контекст и цель запроса, что положительно сказывается на качестве результатов. Важно отметить, что инструкцию нужно добавлять только перед запросом, а не перед документом.
Для **симметричных задач**, таких как классификация (classification) или семантическое сравнение текстов (semantic text similarity), инструкцию необходимо добавлять перед каждым запросом. Это связано с тем, что такие задачи требуют одинакового контекста для всех входных данных, чтобы модель могла корректно сравнивать или классифицировать их.
**Примеры инструкций для симметричных задач:**
- `"Retrieve semantically similar text \ntext: {query}"`
- `"Given a text, retrieve semantically similar text \ntext: {query}"`
- `"Дано предложение, необходимо найти его парафраз \nпредложение: {query}"`
- `"Классифицируй отзыв на товар как положительный, отрицательный или нейтральный \nотзыв: {query}"`
- `"Классифицируй чувствительную тему по запросу \nзапрос: {query}"`
Для **retrieval-задач** (например, поиск ответа в тексте) можно использовать инструкцию:
`'Дан вопрос, необходимо найти абзац текста с ответом \nвопрос: {query}'`.
Такой подход особенно эффективен для задач поиска и извлечения информации, таких как поиск релевантных документов или извлечение ответов из текста.
**Примеры инструкций для retrieval-задач:**
- `'Дан вопрос, необходимо найти абзац текста с ответом \nвопрос: {query}'`
- `'Given the question, find a paragraph with the answer \nquestion: {query}'`
Использование инструкций позволяет значительно улучшить качество поиска и релевантность результатов, что подтверждается тестами на бенчмарках, таких как RuBQ. Для симметричных задач добавление инструкции перед каждым запросом обеспечивает согласованность и повышает точность модели.
## Поддерживаемые языки
Эта модель инициализирована pretrain моделью GigaChat и дополнительно обучена на смеси английских и русских данных. Однако, поскольку pretrain GigaChat'a делался в основном на русскоязычных данных, мы рекомендуем использовать эту модель только для русского языка.
## FAQ
1. Нужно ли добавлять инструкции к запросу?
Да, именно так модель обучалась, иначе вы увидите снижение производительности. Определение задачи должно быть инструкцией в одном предложении, которая описывает задачу. Это способ настройки текстовых эмбеддингов для разных сценариев с помощью инструкций на естественном языке.
С другой стороны, добавлять инструкции на сторону документа не требуется.
2. Почему мои воспроизведённые результаты немного отличаются от указанных в карточке модели?
Разные версии библиотек transformers и pytorch могут вызывать незначительные, но ненулевые различия в производительности.
## Ограничения
Использование этой модели для входных данных, содержащих более 4096 токенов, невозможно.
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
Lajavaness/bilingual-embedding-small
|
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"
] | 2024-07-17T03:13:47 | 2024-11-20T14:45:37 | 6,982 | 4 |
---
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-small
results:
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 63.19030822769444
- type: v_measures
value:
- 0.5938891912573394
- 0.6171518411959
- 0.6042518292029612
- 0.6626602879382325
- 0.6471224639325329
- type: v_measure
value: 41.32807908087869
- type: v_measures
value:
- 0.3351458197856525
- 0.48472823318531566
- 0.4631757871803168
- 0.43580166532679027
- 0.4219041689415661
- 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: 68.43888603876276
- type: mrr
value: 69.59097513501659
- type: nAUC_map_diff1
value: 47.92767021121887
- type: nAUC_map_max
value: 10.206900586093957
- type: nAUC_mrr_diff1
value: 47.823670000503014
- type: nAUC_mrr_max
value: 10.266197221615979
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: map_at_1
value: 23.23
- type: map_at_10
value: 33.513999999999996
- type: map_at_100
value: 34.554
- type: map_at_1000
value: 34.621
- type: map_at_20
value: 34.129
- type: map_at_3
value: 30.526999999999997
- type: map_at_5
value: 32.107
- type: mrr_at_1
value: 23.229706390328154
- type: mrr_at_10
value: 33.51421786331119
- type: mrr_at_100
value: 34.55441665729269
- type: mrr_at_1000
value: 34.62084787184653
- type: mrr_at_20
value: 34.12901601558586
- type: mrr_at_3
value: 30.52677029360969
- type: mrr_at_5
value: 32.107081174438754
- type: nauc_map_at_1000_diff1
value: 31.371888101756962
- type: nauc_map_at_1000_max
value: 28.482571238049648
- type: nauc_map_at_100_diff1
value: 31.36352656865708
- type: nauc_map_at_100_max
value: 28.51143558254042
- type: nauc_map_at_10_diff1
value: 31.26317124194119
- type: nauc_map_at_10_max
value: 28.356439091020903
- type: nauc_map_at_1_diff1
value: 36.87861012283668
- type: nauc_map_at_1_max
value: 25.592704128025584
- type: nauc_map_at_20_diff1
value: 31.27934733015461
- type: nauc_map_at_20_max
value: 28.471156752297954
- type: nauc_map_at_3_diff1
value: 31.613137860497627
- type: nauc_map_at_3_max
value: 27.518268339115743
- type: nauc_map_at_5_diff1
value: 31.356295694985565
- type: nauc_map_at_5_max
value: 27.79553638754489
- type: nauc_mrr_at_1000_diff1
value: 31.371889078421372
- type: nauc_mrr_at_1000_max
value: 28.482578347594856
- type: nauc_mrr_at_100_diff1
value: 31.36352656865708
- type: nauc_mrr_at_100_max
value: 28.51143558254042
- type: nauc_mrr_at_10_diff1
value: 31.26317124194119
- type: nauc_mrr_at_10_max
value: 28.356439091020903
- type: nauc_mrr_at_1_diff1
value: 36.87861012283668
- type: nauc_mrr_at_1_max
value: 25.592704128025584
- type: nauc_mrr_at_20_diff1
value: 31.27934733015461
- type: nauc_mrr_at_20_max
value: 28.471156752297954
- type: nauc_mrr_at_3_diff1
value: 31.613137860497627
- type: nauc_mrr_at_3_max
value: 27.518268339115743
- type: nauc_mrr_at_5_diff1
value: 31.356295694985565
- type: nauc_mrr_at_5_max
value: 27.79553638754489
- type: nauc_ndcg_at_1000_diff1
value: 30.418606855093337
- type: nauc_ndcg_at_1000_max
value: 29.993105440430234
- type: nauc_ndcg_at_100_diff1
value: 30.131330243160843
- type: nauc_ndcg_at_100_max
value: 30.820165762770422
- type: nauc_ndcg_at_10_diff1
value: 29.510008265344545
- type: nauc_ndcg_at_10_max
value: 29.94961535617982
- type: nauc_ndcg_at_1_diff1
value: 36.87861012283668
- type: nauc_ndcg_at_1_max
value: 25.592704128025584
- type: nauc_ndcg_at_20_diff1
value: 29.52438230390851
- type: nauc_ndcg_at_20_max
value: 30.504655157655904
- type: nauc_ndcg_at_3_diff1
value: 30.18136510240507
- type: nauc_ndcg_at_3_max
value: 28.099090120422275
- type: nauc_ndcg_at_5_diff1
value: 29.762075942245303
- type: nauc_ndcg_at_5_max
value: 28.61500294452224
- type: nauc_precision_at_1000_diff1
value: 27.306371732512996
- type: nauc_precision_at_1000_max
value: 65.78374115284707
- type: nauc_precision_at_100_diff1
value: 25.3948170473858
- type: nauc_precision_at_100_max
value: 47.29752571335181
- type: nauc_precision_at_10_diff1
value: 24.310996780059035
- type: nauc_precision_at_10_max
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type: mteb/amazon_reviews_multi
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type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
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type: reciTAL/mlsum
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split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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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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type: mteb/masakhanews
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type: masakhane/masakhanews
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type: mteb/amazon_massive_intent
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type: mteb/amazon_massive_scenario
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type: jinaai/mintakaqa
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dataset:
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type: GEM/opusparcus
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revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
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value: 92.92034333454589
- type: cos_sim_f1
value: 87.20651653090562
- type: cos_sim_precision
value: 84.25925925925927
- type: cos_sim_recall
value: 90.3674280039722
- type: dot_accuracy
value: 81.06267029992752
- type: dot_ap
value: 92.19923182286357
- type: dot_f1
value: 87.23307587460246
- type: dot_precision
value: 80.40200995025126
- type: dot_recall
value: 95.33267130089378
- type: euclidean_accuracy
value: 81.06267029992752
- type: euclidean_ap
value: 92.58456772515233
- type: euclidean_f1
value: 86.94835680751173
- type: euclidean_precision
value: 82.45770258236865
- type: euclidean_recall
value: 91.9563058589871
- type: manhattan_accuracy
value: 80.92643051771117
- type: manhattan_ap
value: 92.47972548332238
- type: manhattan_f1
value: 86.88372093023257
- type: manhattan_precision
value: 81.71478565179353
- type: manhattan_recall
value: 92.75074478649454
- type: max_accuracy
value: 81.81198910081744
- type: max_ap
value: 92.92034333454589
- type: max_f1
value: 87.23307587460246
- 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: 60.550000000000004
- type: cos_sim_ap
value: 58.1865824487652
- type: cos_sim_f1
value: 62.491349480968864
- type: cos_sim_precision
value: 45.44539506794162
- type: cos_sim_recall
value: 100.0
- type: dot_accuracy
value: 56.49999999999999
- type: dot_ap
value: 49.511525626044474
- type: dot_f1
value: 62.76595744680852
- type: dot_precision
value: 46.165884194053206
- type: dot_recall
value: 98.00664451827242
- type: euclidean_accuracy
value: 60.199999999999996
- type: euclidean_ap
value: 58.003058708335246
- type: euclidean_f1
value: 62.491349480968864
- type: euclidean_precision
value: 45.44539506794162
- type: euclidean_recall
value: 100.0
- type: manhattan_accuracy
value: 60.199999999999996
- type: manhattan_ap
value: 58.02420001567834
- type: manhattan_f1
value: 62.491349480968864
- type: manhattan_precision
value: 45.44539506794162
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 60.550000000000004
- type: max_ap
value: 58.1865824487652
- type: max_f1
value: 62.76595744680852
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 81.35988610550564
- type: cos_sim_spearman
value: 74.30702501405389
- type: euclidean_pearson
value: 77.98265846914386
- type: euclidean_spearman
value: 74.28309779423242
- type: manhattan_pearson
value: 77.91611618952486
- type: manhattan_spearman
value: 74.09543847416339
- 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: 82.87930229178569
- type: cos_sim_spearman
value: 82.91122500126046
- type: euclidean_pearson
value: 82.30161381658885
- type: euclidean_spearman
value: 82.80157531184477
- type: manhattan_pearson
value: 82.59746592491155
- type: manhattan_spearman
value: 82.91620907805208
- 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: 83.44206580073515
- type: cos_sim_spearman
value: 83.29855460437528
- type: euclidean_pearson
value: 82.28885833656986
- type: euclidean_spearman
value: 83.17545506016941
- type: manhattan_pearson
value: 82.16568250036501
- type: manhattan_spearman
value: 83.0743139221437
- 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: 31.280485627024635
- type: cos_sim_spearman
value: 32.33005962500831
- type: dot_pearson
value: 30.158348138782753
- type: dot_spearman
value: 30.392045689426418
- 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: 81.67777777777776
- type: mrr
value: 81.67777777777776
- type: nAUC_map_diff1
value: 59.89472485574524
- type: nAUC_map_max
value: 8.215249384307162
- type: nAUC_mrr_diff1
value: 59.89472485574524
- type: nAUC_mrr_max
value: 8.215249384307162
- 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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value: 62.0
- type: map_at_10
value: 72.887
- type: map_at_100
value: 73.181
- type: map_at_1000
value: 73.181
- type: map_at_20
value: 73.16
- type: map_at_3
value: 70.667
- type: map_at_5
value: 71.56700000000001
- type: mrr_at_1
value: 62.0
- type: mrr_at_10
value: 72.88690476190479
- type: mrr_at_100
value: 73.18055555555557
- type: mrr_at_1000
value: 73.18055555555557
- type: mrr_at_20
value: 73.15972222222224
- type: mrr_at_3
value: 70.66666666666667
- type: mrr_at_5
value: 71.56666666666666
- type: nauc_map_at_1000_diff1
value: 47.52352042312832
- type: nauc_map_at_1000_max
value: 12.229977029802052
- type: nauc_map_at_100_diff1
value: 47.52352042312832
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value: 12.229977029802052
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value: 47.83118981173179
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value: 48.29708026951358
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value: 5.016460019075176
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value: 47.5126416742559
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value: 12.23002184861472
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value: 48.18168651330906
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value: 46.8656518414084
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value: 13.22896127813873
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value: 47.52352042312832
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value: 12.229977029802052
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value: 47.52352042312832
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value: 12.229977029802052
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value: 47.83118981173179
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value: 12.67122414331949
- type: nauc_mrr_at_1_diff1
value: 48.29708026951358
- type: nauc_mrr_at_1_max
value: 5.016460019075176
- type: nauc_mrr_at_20_diff1
value: 47.5126416742559
- type: nauc_mrr_at_20_max
value: 12.23002184861472
- type: nauc_mrr_at_3_diff1
value: 48.18168651330906
- type: nauc_mrr_at_3_max
value: 14.063513453945578
- type: nauc_mrr_at_5_diff1
value: 46.8656518414084
- type: nauc_mrr_at_5_max
value: 13.22896127813873
- type: nauc_ndcg_at_1000_diff1
value: 47.56455972451391
- type: nauc_ndcg_at_1000_max
value: 12.900901768894494
- type: nauc_ndcg_at_100_diff1
value: 47.56455972451391
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value: 12.900901768894494
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value: 48.92225620164975
- type: nauc_ndcg_at_10_max
value: 14.848602834576374
- type: nauc_ndcg_at_1_diff1
value: 48.29708026951358
- type: nauc_ndcg_at_1_max
value: 5.016460019075176
- type: nauc_ndcg_at_20_diff1
value: 47.44500349427683
- type: nauc_ndcg_at_20_max
value: 12.894569953616672
- type: nauc_ndcg_at_3_diff1
value: 48.79515966817958
- type: nauc_ndcg_at_3_max
value: 17.067858878871014
- type: nauc_ndcg_at_5_diff1
value: 46.2582129725611
- type: nauc_ndcg_at_5_max
value: 15.802131944100553
- 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: 67.1335200746968
- type: nauc_precision_at_10_max
value: 41.521942110178045
- type: nauc_precision_at_1_diff1
value: 48.29708026951358
- type: nauc_precision_at_1_max
value: 5.016460019075176
- type: nauc_precision_at_20_diff1
value: 35.80765639589114
- type: nauc_precision_at_20_max
value: 12.278244631185926
- type: nauc_precision_at_3_diff1
value: 51.516580229451506
- type: nauc_precision_at_3_max
value: 28.765257478128753
- type: nauc_precision_at_5_diff1
value: 43.146762121705116
- type: nauc_precision_at_5_max
value: 27.715587373901627
- 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: 67.13352007469638
- type: nauc_recall_at_10_max
value: 41.52194211017754
- type: nauc_recall_at_1_diff1
value: 48.29708026951358
- type: nauc_recall_at_1_max
value: 5.016460019075176
- type: nauc_recall_at_20_diff1
value: 35.80765639589109
- type: nauc_recall_at_20_max
value: 12.278244631185359
- type: nauc_recall_at_3_diff1
value: 51.516580229451556
- type: nauc_recall_at_3_max
value: 28.765257478128735
- type: nauc_recall_at_5_diff1
value: 43.14676212170519
- type: nauc_recall_at_5_max
value: 27.7155873739018
- type: ndcg_at_1
value: 62.0
- type: ndcg_at_10
value: 78.188
- type: ndcg_at_100
value: 79.372
- type: ndcg_at_1000
value: 79.372
- type: ndcg_at_20
value: 79.194
- type: ndcg_at_3
value: 73.333
- type: ndcg_at_5
value: 74.968
- type: precision_at_1
value: 62.0
- type: precision_at_10
value: 9.5
- 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: 27.0
- type: precision_at_5
value: 17.0
- type: recall_at_1
value: 62.0
- type: recall_at_10
value: 95.0
- 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: 81.0
- type: recall_at_5
value: 85.0
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
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value: 36.611
- type: map_at_10
value: 57.187
- type: map_at_100
value: 58.631
- type: map_at_1000
value: 58.709999999999994
- type: map_at_20
value: 58.08
- type: map_at_3
value: 50.998
- type: map_at_5
value: 55.191
- type: mrr_at_1
value: 57.810413885180246
- type: mrr_at_10
value: 65.8401148621442
- type: mrr_at_100
value: 66.40333125160906
- type: mrr_at_1000
value: 66.42402394693958
- type: mrr_at_20
value: 66.18532893351842
- type: mrr_at_3
value: 63.81842456608808
- type: mrr_at_5
value: 65.18691588785039
- type: nauc_map_at_1000_diff1
value: 50.788831583223235
- type: nauc_map_at_1000_max
value: 51.55624948390649
- type: nauc_map_at_100_diff1
value: 50.7629709789859
- type: nauc_map_at_100_max
value: 51.554970702491374
- type: nauc_map_at_10_diff1
value: 50.597059943822785
- type: nauc_map_at_10_max
value: 50.88242396839643
- type: nauc_map_at_1_diff1
value: 60.321467202422596
- type: nauc_map_at_1_max
value: 35.442708774490455
- type: nauc_map_at_20_diff1
value: 50.65058875526523
- type: nauc_map_at_20_max
value: 51.32644359237018
- type: nauc_map_at_3_diff1
value: 51.80849131095309
- type: nauc_map_at_3_max
value: 46.17402861801263
- type: nauc_map_at_5_diff1
value: 50.507875139443456
- type: nauc_map_at_5_max
value: 49.47151715153637
- type: nauc_mrr_at_1000_diff1
value: 58.704380971676926
- type: nauc_mrr_at_1000_max
value: 59.554838611287494
- type: nauc_mrr_at_100_diff1
value: 58.699898563786
- type: nauc_mrr_at_100_max
value: 59.55774727939887
- type: nauc_mrr_at_10_diff1
value: 58.73927285559378
- type: nauc_mrr_at_10_max
value: 59.479293253354605
- type: nauc_mrr_at_1_diff1
value: 61.67387773779846
- type: nauc_mrr_at_1_max
value: 59.51259333152851
- type: nauc_mrr_at_20_diff1
value: 58.66891615345236
- type: nauc_mrr_at_20_max
value: 59.58138583451017
- type: nauc_mrr_at_3_diff1
value: 58.51184610727805
- type: nauc_mrr_at_3_max
value: 59.23400060136551
- type: nauc_mrr_at_5_diff1
value: 58.47244190154927
- type: nauc_mrr_at_5_max
value: 59.331044981327196
- type: nauc_ndcg_at_1000_diff1
value: 52.37179722848664
- type: nauc_ndcg_at_1000_max
value: 54.72666617792271
- type: nauc_ndcg_at_100_diff1
value: 51.93605170636807
- type: nauc_ndcg_at_100_max
value: 54.79165999040737
- type: nauc_ndcg_at_10_diff1
value: 51.405480630090835
- type: nauc_ndcg_at_10_max
value: 53.04193527385732
- type: nauc_ndcg_at_1_diff1
value: 61.67387773779846
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value: 59.51259333152851
- type: nauc_ndcg_at_20_diff1
value: 51.293469681563096
- type: nauc_ndcg_at_20_max
value: 54.08435882900078
- type: nauc_ndcg_at_3_diff1
value: 51.58388244693231
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value: 51.74775013382323
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value: 50.82307910981021
- type: nauc_ndcg_at_5_max
value: 51.420799224894
- type: nauc_precision_at_1000_diff1
value: -16.663205684819612
- type: nauc_precision_at_1000_max
value: 12.234886940913926
- type: nauc_precision_at_100_diff1
value: -11.830123517342091
- type: nauc_precision_at_100_max
value: 19.147184681617514
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value: -0.128354517220691
- type: nauc_precision_at_10_max
value: 31.00617539775257
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value: 61.67387773779846
- type: nauc_precision_at_1_max
value: 59.51259333152851
- type: nauc_precision_at_20_diff1
value: -4.838065494986492
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value: 26.59319852551229
- type: nauc_precision_at_3_diff1
value: 12.133336207725199
- type: nauc_precision_at_3_max
value: 39.377184679653084
- type: nauc_precision_at_5_diff1
value: 3.6946214253817242
- type: nauc_precision_at_5_max
value: 34.46699361026347
- type: nauc_recall_at_1000_diff1
value: 42.80775305857285
- type: nauc_recall_at_1000_max
value: 56.52032475068802
- type: nauc_recall_at_100_diff1
value: 37.39345422008765
- type: nauc_recall_at_100_max
value: 50.846199839766236
- type: nauc_recall_at_10_diff1
value: 42.80186951683253
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value: 45.84205807317027
- type: nauc_recall_at_1_diff1
value: 60.321467202422596
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value: 35.442708774490455
- type: nauc_recall_at_20_diff1
value: 39.799538893141424
- type: nauc_recall_at_20_max
value: 48.35852294352722
- type: nauc_recall_at_3_diff1
value: 45.955979843159135
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value: 42.31051973839205
- type: nauc_recall_at_5_diff1
value: 42.5632345738307
- type: nauc_recall_at_5_max
value: 44.4648694495511
- type: ndcg_at_1
value: 57.809999999999995
- type: ndcg_at_10
value: 63.495999999999995
- type: ndcg_at_100
value: 68.394
- type: ndcg_at_1000
value: 69.663
- type: ndcg_at_20
value: 65.67399999999999
- type: ndcg_at_3
value: 58.23199999999999
- type: ndcg_at_5
value: 60.431999999999995
- type: precision_at_1
value: 57.809999999999995
- type: precision_at_10
value: 14.753
- type: precision_at_100
value: 1.8929999999999998
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_20
value: 8.164
- type: precision_at_3
value: 35.737
- type: precision_at_5
value: 26.061
- type: recall_at_1
value: 36.611
- type: recall_at_10
value: 72.501
- type: recall_at_100
value: 91.40899999999999
- type: recall_at_1000
value: 99.544
- type: recall_at_20
value: 79.475
- type: recall_at_3
value: 55.96600000000001
- type: recall_at_5
value: 64.976
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.28358208955224
- type: ap
value: 37.19063914095112
- type: ap_weighted
value: 37.19063914095112
- type: f1
value: 68.28593926963595
- type: f1_weighted
value: 76.64216663284145
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 82.19695
- type: ap
value: 76.84400739904562
- type: ap_weighted
value: 76.84400739904562
- type: f1
value: 82.13083090108348
- type: f1_weighted
value: 82.13083090108348
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.216
- type: f1
value: 39.88981487562277
- type: f1_weighted
value: 39.88981487562277
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 28.307
- type: map_at_10
value: 44.415
- type: map_at_100
value: 45.24
- type: map_at_1000
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type: mteb/mtop_domain
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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value: 20.571279610990683
- type: nauc_mrr_at_3_diff1
value: 16.61433823095321
- type: nauc_mrr_at_3_max
value: 19.8671374514683
- type: nauc_mrr_at_5_diff1
value: 16.657607225925013
- type: nauc_mrr_at_5_max
value: 20.485690382244712
- type: nauc_ndcg_at_1000_diff1
value: 17.216527125545124
- type: nauc_ndcg_at_1000_max
value: 29.67323723253682
- type: nauc_ndcg_at_100_diff1
value: 17.920363114583992
- type: nauc_ndcg_at_100_max
value: 28.74219286431791
- type: nauc_ndcg_at_10_diff1
value: 17.4262322341026
- type: nauc_ndcg_at_10_max
value: 25.314398482777406
- type: nauc_ndcg_at_1_diff1
value: 18.72320588103456
- type: nauc_ndcg_at_1_max
value: 17.31659868214623
- type: nauc_ndcg_at_20_diff1
value: 18.49350721003082
- type: nauc_ndcg_at_20_max
value: 26.95660628845422
- type: nauc_ndcg_at_3_diff1
value: 16.388721576110076
- type: nauc_ndcg_at_3_max
value: 21.574925593659326
- type: nauc_ndcg_at_5_diff1
value: 16.62472439103214
- type: nauc_ndcg_at_5_max
value: 23.186257022779994
- type: nauc_precision_at_1000_diff1
value: 7.882444572522718
- type: nauc_precision_at_1000_max
value: 29.389796806861163
- type: nauc_precision_at_100_diff1
value: 13.9186095734099
- type: nauc_precision_at_100_max
value: 30.35346461874792
- type: nauc_precision_at_10_diff1
value: 15.858687077827474
- type: nauc_precision_at_10_max
value: 26.884411423943906
- type: nauc_precision_at_1_diff1
value: 18.72320588103456
- type: nauc_precision_at_1_max
value: 17.31659868214623
- type: nauc_precision_at_20_diff1
value: 17.397174842486937
- type: nauc_precision_at_20_max
value: 28.48509998553517
- type: nauc_precision_at_3_diff1
value: 15.910758722664974
- type: nauc_precision_at_3_max
value: 23.37753724707492
- type: nauc_precision_at_5_diff1
value: 15.480650294833314
- type: nauc_precision_at_5_max
value: 24.92100239632834
- type: nauc_recall_at_1000_diff1
value: 8.568163684580515
- type: nauc_recall_at_1000_max
value: 29.761661131284278
- type: nauc_recall_at_100_diff1
value: 14.139732606832828
- type: nauc_recall_at_100_max
value: 30.30928539057988
- type: nauc_recall_at_10_diff1
value: 16.0957814746088
- type: nauc_recall_at_10_max
value: 26.730370480937783
- type: nauc_recall_at_1_diff1
value: 19.012090704165505
- type: nauc_recall_at_1_max
value: 17.258809318287167
- type: nauc_recall_at_20_diff1
value: 17.58458055089181
- type: nauc_recall_at_20_max
value: 28.329240158930897
- type: nauc_recall_at_3_diff1
value: 16.11861072893215
- type: nauc_recall_at_3_max
value: 23.34743857534646
- type: nauc_recall_at_5_diff1
value: 15.659970648558035
- type: nauc_recall_at_5_max
value: 24.916484416681683
- type: ndcg_at_1
value: 17.5
- type: ndcg_at_10
value: 16.203
- type: ndcg_at_100
value: 23.311
- type: ndcg_at_1000
value: 28.476000000000003
- type: ndcg_at_20
value: 18.614
- type: ndcg_at_3
value: 15.246
- type: ndcg_at_5
value: 13.142000000000001
- type: precision_at_1
value: 17.5
- type: precision_at_10
value: 8.61
- type: precision_at_100
value: 1.8929999999999998
- type: precision_at_1000
value: 0.314
- type: precision_at_20
value: 5.695
- type: precision_at_3
value: 14.7
- type: precision_at_5
value: 11.700000000000001
- type: recall_at_1
value: 3.5680000000000005
- type: recall_at_10
value: 17.497
- type: recall_at_100
value: 38.377
- type: recall_at_1000
value: 63.858000000000004
- type: recall_at_20
value: 23.122
- type: recall_at_3
value: 8.948
- type: recall_at_5
value: 11.858
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 83.0786740980213
- type: cos_sim_spearman
value: 74.64910820402831
- type: euclidean_pearson
value: 79.40680658618808
- type: euclidean_spearman
value: 74.04786370197291
- type: manhattan_pearson
value: 79.30290796130608
- type: manhattan_spearman
value: 73.86543081865257
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.14143764866938
- type: cos_sim_spearman
value: 79.39117869636218
- type: euclidean_pearson
value: 82.27893672472992
- type: euclidean_spearman
value: 78.12857266398304
- type: manhattan_pearson
value: 82.40958626880706
- type: manhattan_spearman
value: 78.2460736745845
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.98565298864834
- type: cos_sim_spearman
value: 85.3226077419183
- type: euclidean_pearson
value: 83.36095201234602
- type: euclidean_spearman
value: 83.44580751011605
- type: manhattan_pearson
value: 83.26944531709971
- type: manhattan_spearman
value: 83.3511641574103
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.15642283009589
- type: cos_sim_spearman
value: 83.89978896960656
- type: euclidean_pearson
value: 85.01657605766617
- type: euclidean_spearman
value: 82.70615194483753
- type: manhattan_pearson
value: 84.82154011079453
- type: manhattan_spearman
value: 82.61620436539884
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.7730685270548
- type: cos_sim_spearman
value: 88.46744045180212
- type: euclidean_pearson
value: 87.11846600678471
- type: euclidean_spearman
value: 87.32502541228249
- type: manhattan_pearson
value: 87.06217303693649
- type: manhattan_spearman
value: 87.24696449513658
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.0653949018384
- type: cos_sim_spearman
value: 84.43898725124001
- type: euclidean_pearson
value: 83.46057253146975
- type: euclidean_spearman
value: 83.70938571051141
- type: manhattan_pearson
value: 83.48079890307652
- type: manhattan_spearman
value: 83.75548841452152
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cos_sim_pearson
value: 85.45225298379407
- type: cos_sim_spearman
value: 85.76725038940407
- type: euclidean_pearson
value: 85.9615450336946
- type: euclidean_spearman
value: 85.48341197609108
- type: manhattan_pearson
value: 85.74837479284034
- type: manhattan_spearman
value: 85.19050180417275
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cos_sim_pearson
value: 66.72129983991873
- type: cos_sim_spearman
value: 67.23743199464064
- type: euclidean_pearson
value: 68.41402075343164
- type: euclidean_spearman
value: 67.96307375904688
- type: manhattan_pearson
value: 68.40814603490281
- type: manhattan_spearman
value: 67.78239579617318
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.17592091160849
- type: cos_sim_spearman
value: 86.0757276289371
- type: euclidean_pearson
value: 85.24314028679827
- type: euclidean_spearman
value: 84.79227270552205
- type: manhattan_pearson
value: 85.15711414880685
- type: manhattan_spearman
value: 84.68939283251983
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.61113471166244
- type: mrr
value: 93.432848923045
- type: nAUC_map_diff1
value: 5.468214413465522
- type: nAUC_map_max
value: 53.344699872043364
- type: nAUC_mrr_diff1
value: 50.8786565680291
- type: nAUC_mrr_max
value: 79.73153373046732
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 46.694
- type: map_at_10
value: 58.492999999999995
- type: map_at_100
value: 59.079
- type: map_at_1000
value: 59.114999999999995
- type: map_at_20
value: 58.784000000000006
- type: map_at_3
value: 56.091
- type: map_at_5
value: 57.023999999999994
- type: mrr_at_1
value: 49.333333333333336
- type: mrr_at_10
value: 59.850132275132296
- type: mrr_at_100
value: 60.31782622597538
- type: mrr_at_1000
value: 60.34922440201215
- type: mrr_at_20
value: 60.08416454832866
- type: mrr_at_3
value: 58.05555555555557
- type: mrr_at_5
value: 58.67222222222224
- type: nauc_map_at_1000_diff1
value: 67.76127454103812
- type: nauc_map_at_1000_max
value: 42.06391105197536
- type: nauc_map_at_100_diff1
value: 67.73734626481158
- type: nauc_map_at_100_max
value: 42.07013722993752
- type: nauc_map_at_10_diff1
value: 67.75019487037416
- type: nauc_map_at_10_max
value: 42.08004179578344
- type: nauc_map_at_1_diff1
value: 73.16882642657764
- type: nauc_map_at_1_max
value: 38.22765895246309
- type: nauc_map_at_20_diff1
value: 67.71028360631355
- type: nauc_map_at_20_max
value: 42.182021960109665
- type: nauc_map_at_3_diff1
value: 67.62369130951392
- type: nauc_map_at_3_max
value: 39.910755718969696
- type: nauc_map_at_5_diff1
value: 67.66911636315015
- type: nauc_map_at_5_max
value: 40.38236382755538
- type: nauc_mrr_at_1000_diff1
value: 66.06803763645875
- type: nauc_mrr_at_1000_max
value: 42.86556398916693
- type: nauc_mrr_at_100_diff1
value: 66.04424547602991
- type: nauc_mrr_at_100_max
value: 42.867192517898935
- type: nauc_mrr_at_10_diff1
value: 65.9181187541585
- type: nauc_mrr_at_10_max
value: 43.00997791733552
- type: nauc_mrr_at_1_diff1
value: 71.14402949361032
- type: nauc_mrr_at_1_max
value: 40.41989400733797
- type: nauc_mrr_at_20_diff1
value: 65.96893983596155
- type: nauc_mrr_at_20_max
value: 42.96939035490266
- type: nauc_mrr_at_3_diff1
value: 65.61751418820666
- type: nauc_mrr_at_3_max
value: 41.73632436886939
- type: nauc_mrr_at_5_diff1
value: 65.93649980807021
- type: nauc_mrr_at_5_max
value: 41.99687107195354
- type: nauc_ndcg_at_1000_diff1
value: 66.14801590849353
- type: nauc_ndcg_at_1000_max
value: 43.70286520140021
- type: nauc_ndcg_at_100_diff1
value: 65.57206500474688
- type: nauc_ndcg_at_100_max
value: 43.804634724756234
- type: nauc_ndcg_at_10_diff1
value: 65.58658179189969
- type: nauc_ndcg_at_10_max
value: 44.605601186017815
- type: nauc_ndcg_at_1_diff1
value: 71.14402949361032
- type: nauc_ndcg_at_1_max
value: 40.41989400733797
- type: nauc_ndcg_at_20_diff1
value: 65.52436059710848
- type: nauc_ndcg_at_20_max
value: 44.80884075855281
- type: nauc_ndcg_at_3_diff1
value: 65.33560750072314
- type: nauc_ndcg_at_3_max
value: 41.02191665715624
- type: nauc_ndcg_at_5_diff1
value: 65.49156588896797
- type: nauc_ndcg_at_5_max
value: 41.193628278772906
- type: nauc_precision_at_1000_diff1
value: -21.271717431265248
- type: nauc_precision_at_1000_max
value: 14.880187641241479
- type: nauc_precision_at_100_diff1
value: -6.170679294185874
- type: nauc_precision_at_100_max
value: 23.392807344666835
- type: nauc_precision_at_10_diff1
value: 24.15372806591396
- type: nauc_precision_at_10_max
value: 42.122189619323315
- type: nauc_precision_at_1_diff1
value: 71.14402949361032
- type: nauc_precision_at_1_max
value: 40.41989400733797
- type: nauc_precision_at_20_diff1
value: 15.788476578628993
- type: nauc_precision_at_20_max
value: 39.31283062678818
- type: nauc_precision_at_3_diff1
value: 45.48749226553521
- type: nauc_precision_at_3_max
value: 38.4930807232584
- type: nauc_precision_at_5_diff1
value: 38.55379599441077
- type: nauc_precision_at_5_max
value: 36.431299487657185
- type: nauc_recall_at_1000_diff1
value: 45.004668534080174
- type: nauc_recall_at_1000_max
value: 80.78120136943592
- type: nauc_recall_at_100_diff1
value: 47.77911164465763
- type: nauc_recall_at_100_max
value: 51.29449629314065
- type: nauc_recall_at_10_diff1
value: 57.71614029345987
- type: nauc_recall_at_10_max
value: 53.908934707903775
- type: nauc_recall_at_1_diff1
value: 73.16882642657764
- type: nauc_recall_at_1_max
value: 38.22765895246309
- type: nauc_recall_at_20_diff1
value: 56.143181435044355
- type: nauc_recall_at_20_max
value: 56.12210887724124
- type: nauc_recall_at_3_diff1
value: 58.947466694908826
- type: nauc_recall_at_3_max
value: 40.205765050955286
- type: nauc_recall_at_5_diff1
value: 58.72258574569608
- type: nauc_recall_at_5_max
value: 40.857639009739245
- type: ndcg_at_1
value: 49.333
- type: ndcg_at_10
value: 63.966
- type: ndcg_at_100
value: 66.808
- type: ndcg_at_1000
value: 67.62700000000001
- type: ndcg_at_20
value: 64.92
- type: ndcg_at_3
value: 59.496
- type: ndcg_at_5
value: 60.743
- type: precision_at_1
value: 49.333
- type: precision_at_10
value: 8.866999999999999
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 4.683
- type: precision_at_3
value: 24.0
- type: precision_at_5
value: 15.333
- type: recall_at_1
value: 46.694
- type: recall_at_10
value: 79.5
- type: recall_at_100
value: 92.767
- type: recall_at_1000
value: 99.0
- type: recall_at_20
value: 82.956
- type: recall_at_3
value: 66.783
- type: recall_at_5
value: 69.906
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.78118811881188
- type: cos_sim_ap
value: 94.38499172210743
- type: cos_sim_f1
value: 88.99803536345776
- type: cos_sim_precision
value: 87.45173745173746
- type: cos_sim_recall
value: 90.60000000000001
- type: dot_accuracy
value: 99.68118811881187
- type: dot_ap
value: 88.59372518155831
- type: dot_f1
value: 83.45323741007195
- type: dot_precision
value: 85.83509513742071
- type: dot_recall
value: 81.2
- type: euclidean_accuracy
value: 99.78019801980199
- type: euclidean_ap
value: 94.41961507812081
- type: euclidean_f1
value: 88.91098955743412
- type: euclidean_precision
value: 88.4272997032641
- type: euclidean_recall
value: 89.4
- type: manhattan_accuracy
value: 99.78118811881188
- type: manhattan_ap
value: 94.53929097513269
- type: manhattan_f1
value: 88.93280632411069
- type: manhattan_precision
value: 87.890625
- type: manhattan_recall
value: 90.0
- type: max_accuracy
value: 99.78118811881188
- type: max_ap
value: 94.53929097513269
- type: max_f1
value: 88.99803536345776
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 52.46659916482748
- type: v_measures
value:
- 0.5533520743369753
- 0.5226026021922323
- 0.4443153697300708
- 0.5442847332820114
- 0.5574991389583961
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.6334285506705
- type: v_measures
value:
- 0.3276318900057692
- 0.3240387341697168
- 0.32272003147893974
- 0.32313817118726607
- 0.3156113464382597
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 47.16067733309403
- type: mrr
value: 47.8574611662847
- type: nAUC_map_diff1
value: 32.52594575795374
- type: nAUC_map_max
value: 14.426033057319177
- type: nAUC_mrr_diff1
value: 32.717518660141344
- type: nAUC_mrr_max
value: 15.511520995680103
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.510117786456014
- type: cos_sim_spearman
value: 29.2255704281364
- type: dot_pearson
value: 29.920367312494868
- type: dot_spearman
value: 29.70675041719688
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.153
- type: map_at_10
value: 1.084
- type: map_at_100
value: 5.065
- type: map_at_1000
value: 14.255999999999998
- type: map_at_20
value: 1.7739999999999998
- type: map_at_3
value: 0.40299999999999997
- type: map_at_5
value: 0.63
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
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type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: 75.737
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value: 17.494
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value: 3.74
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value: 6.131
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 62.5244140625
- type: ap
value: 11.036738738208067
- type: ap_weighted
value: 11.036738738208067
- type: f1
value: 48.178922337841016
- type: f1_weighted
value: 70.79346668027127
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.60384833050367
- type: f1
value: 59.89473957112635
- type: f1_weighted
value: 59.21770850754739
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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value: 40.52017931682707
- type: v_measures
value:
- 0.41056001750265486
- 0.4115812168479953
- 0.4020131539653664
- 0.41845373495523314
- 0.3990043371824943
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 84.19860523335518
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value: 67.98183780223552
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value: 63.287574797606474
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value: 56.98288611874076
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value: 71.16094986807387
- type: dot_accuracy
value: 81.0872027180068
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value: 57.080616165589994
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value: 57.184056030487184
- type: dot_precision
value: 46.899814157796925
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value: 73.24538258575198
- type: euclidean_accuracy
value: 84.10919711509806
- type: euclidean_ap
value: 68.02422564958268
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value: 63.76539589442815
- type: euclidean_precision
value: 57.40232312565998
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value: 71.71503957783642
- type: manhattan_accuracy
value: 84.06747332657805
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value: 67.74186393843273
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value: 63.57935359382538
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value: 58.55175477565526
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value: 69.55145118733509
- type: max_accuracy
value: 84.19860523335518
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value: 68.02422564958268
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value: 63.76539589442815
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 89.40311250824699
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value: 86.47944792205789
- type: cos_sim_f1
value: 78.80539499036608
- type: cos_sim_precision
value: 75.95714285714286
- type: cos_sim_recall
value: 81.87557745611333
- type: dot_accuracy
value: 87.8468583847557
- type: dot_ap
value: 83.05643449341216
- type: dot_f1
value: 76.55210439257489
- type: dot_precision
value: 73.24330027431948
- type: dot_recall
value: 80.17400677548507
- type: euclidean_accuracy
value: 89.29250591842279
- type: euclidean_ap
value: 86.35499372223612
- type: euclidean_f1
value: 78.9011715450439
- type: euclidean_precision
value: 75.43009620110948
- type: euclidean_recall
value: 82.7071142593163
- type: manhattan_accuracy
value: 89.26339892110063
- type: manhattan_ap
value: 86.2956040159182
- type: manhattan_f1
value: 78.78428904601488
- type: manhattan_precision
value: 75.87165775401068
- type: manhattan_recall
value: 81.92947336002464
- type: max_accuracy
value: 89.40311250824699
- type: max_ap
value: 86.47944792205789
- type: max_f1
value: 78.9011715450439
---
# [bilingual-embedding-small](https://huggingface.co/Lajavaness/bilingual-embedding-small)
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 [Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384), a pre-trained language model is built upon [multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) architecture. The model utilizes MiniLM 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': 384, '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-small', 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"
] |
parasail-ai/GritLM-7B-vllm
|
parasail-ai
|
text-generation
|
[
"pytorch",
"safetensors",
"mistral",
"mteb",
"vllm",
"text-generation",
"conversational",
"custom_code",
"dataset:GritLM/tulu2",
"arxiv:2402.09906",
"license:apache-2.0",
"model-index",
"region:us"
] | 2024-11-27T18:52:58 | 2024-12-06T03:01:43 | 6,918 | 1 |
---
datasets:
- GritLM/tulu2
license: apache-2.0
pipeline_tag: text-generation
tags:
- mteb
- vllm
inference: true
model-index:
- name: GritLM-7B
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 81.17910447761194
- type: ap
value: 46.26260671758199
- type: f1
value: 75.44565719934167
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.5161
- type: ap
value: 94.79131981460425
- type: f1
value: 96.51506148413065
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 57.806000000000004
- type: f1
value: 56.78350156257903
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.478
- type: map_at_10
value: 54.955
- type: map_at_100
value: 54.955
- type: map_at_1000
value: 54.955
- type: map_at_3
value: 50.888999999999996
- type: map_at_5
value: 53.349999999999994
- type: mrr_at_1
value: 39.757999999999996
- type: mrr_at_10
value: 55.449000000000005
- type: mrr_at_100
value: 55.449000000000005
- type: mrr_at_1000
value: 55.449000000000005
- type: mrr_at_3
value: 51.37500000000001
- type: mrr_at_5
value: 53.822
- type: ndcg_at_1
value: 38.478
- type: ndcg_at_10
value: 63.239999999999995
- type: ndcg_at_100
value: 63.239999999999995
- type: ndcg_at_1000
value: 63.239999999999995
- type: ndcg_at_3
value: 54.935
- type: ndcg_at_5
value: 59.379000000000005
- type: precision_at_1
value: 38.478
- type: precision_at_10
value: 8.933
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.089
- type: precision_at_3
value: 22.214
- type: precision_at_5
value: 15.491
- type: recall_at_1
value: 38.478
- type: recall_at_10
value: 89.331
- type: recall_at_100
value: 89.331
- type: recall_at_1000
value: 89.331
- type: recall_at_3
value: 66.643
- type: recall_at_5
value: 77.45400000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 51.67144081472449
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 48.11256154264126
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.33801955487878
- type: mrr
value: 80.71549487754474
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.1935203751726
- type: cos_sim_spearman
value: 86.35497970498659
- type: euclidean_pearson
value: 85.46910708503744
- type: euclidean_spearman
value: 85.13928935405485
- type: manhattan_pearson
value: 85.68373836333303
- type: manhattan_spearman
value: 85.40013867117746
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.46753246753248
- type: f1
value: 88.43006344981134
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.86793640310432
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 39.80291334130727
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.421
- type: map_at_10
value: 52.349000000000004
- type: map_at_100
value: 52.349000000000004
- type: map_at_1000
value: 52.349000000000004
- type: map_at_3
value: 48.17
- type: map_at_5
value: 50.432
- type: mrr_at_1
value: 47.353
- type: mrr_at_10
value: 58.387
- type: mrr_at_100
value: 58.387
- type: mrr_at_1000
value: 58.387
- type: mrr_at_3
value: 56.199
- type: mrr_at_5
value: 57.487
- type: ndcg_at_1
value: 47.353
- type: ndcg_at_10
value: 59.202
- type: ndcg_at_100
value: 58.848
- type: ndcg_at_1000
value: 58.831999999999994
- type: ndcg_at_3
value: 54.112
- type: ndcg_at_5
value: 56.312
- type: precision_at_1
value: 47.353
- type: precision_at_10
value: 11.459
- type: precision_at_100
value: 1.146
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 26.133
- type: precision_at_5
value: 18.627
- type: recall_at_1
value: 38.421
- type: recall_at_10
value: 71.89
- type: recall_at_100
value: 71.89
- type: recall_at_1000
value: 71.89
- type: recall_at_3
value: 56.58
- type: recall_at_5
value: 63.125
- type: map_at_1
value: 38.025999999999996
- type: map_at_10
value: 50.590999999999994
- type: map_at_100
value: 51.99700000000001
- type: map_at_1000
value: 52.11599999999999
- type: map_at_3
value: 47.435
- type: map_at_5
value: 49.236000000000004
- type: mrr_at_1
value: 48.28
- type: mrr_at_10
value: 56.814
- type: mrr_at_100
value: 57.446
- type: mrr_at_1000
value: 57.476000000000006
- type: mrr_at_3
value: 54.958
- type: mrr_at_5
value: 56.084999999999994
- type: ndcg_at_1
value: 48.28
- type: ndcg_at_10
value: 56.442
- type: ndcg_at_100
value: 60.651999999999994
- type: ndcg_at_1000
value: 62.187000000000005
- type: ndcg_at_3
value: 52.866
- type: ndcg_at_5
value: 54.515
- type: precision_at_1
value: 48.28
- type: precision_at_10
value: 10.586
- type: precision_at_100
value: 1.6310000000000002
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_3
value: 25.945
- type: precision_at_5
value: 18.076
- type: recall_at_1
value: 38.025999999999996
- type: recall_at_10
value: 66.11399999999999
- type: recall_at_100
value: 83.339
- type: recall_at_1000
value: 92.413
- type: recall_at_3
value: 54.493
- type: recall_at_5
value: 59.64699999999999
- type: map_at_1
value: 47.905
- type: map_at_10
value: 61.58
- type: map_at_100
value: 62.605
- type: map_at_1000
value: 62.637
- type: map_at_3
value: 58.074000000000005
- type: map_at_5
value: 60.260000000000005
- type: mrr_at_1
value: 54.42
- type: mrr_at_10
value: 64.847
- type: mrr_at_100
value: 65.403
- type: mrr_at_1000
value: 65.41900000000001
- type: mrr_at_3
value: 62.675000000000004
- type: mrr_at_5
value: 64.101
- type: ndcg_at_1
value: 54.42
- type: ndcg_at_10
value: 67.394
- type: ndcg_at_100
value: 70.846
- type: ndcg_at_1000
value: 71.403
- type: ndcg_at_3
value: 62.025
- type: ndcg_at_5
value: 65.032
- type: precision_at_1
value: 54.42
- type: precision_at_10
value: 10.646
- type: precision_at_100
value: 1.325
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 27.398
- type: precision_at_5
value: 18.796
- type: recall_at_1
value: 47.905
- type: recall_at_10
value: 80.84599999999999
- type: recall_at_100
value: 95.078
- type: recall_at_1000
value: 98.878
- type: recall_at_3
value: 67.05600000000001
- type: recall_at_5
value: 74.261
- type: map_at_1
value: 30.745
- type: map_at_10
value: 41.021
- type: map_at_100
value: 41.021
- type: map_at_1000
value: 41.021
- type: map_at_3
value: 37.714999999999996
- type: map_at_5
value: 39.766
- type: mrr_at_1
value: 33.559
- type: mrr_at_10
value: 43.537
- type: mrr_at_100
value: 43.537
- type: mrr_at_1000
value: 43.537
- type: mrr_at_3
value: 40.546
- type: mrr_at_5
value: 42.439
- type: ndcg_at_1
value: 33.559
- type: ndcg_at_10
value: 46.781
- type: ndcg_at_100
value: 46.781
- type: ndcg_at_1000
value: 46.781
- type: ndcg_at_3
value: 40.516000000000005
- type: ndcg_at_5
value: 43.957
- type: precision_at_1
value: 33.559
- type: precision_at_10
value: 7.198
- type: precision_at_100
value: 0.72
- type: precision_at_1000
value: 0.07200000000000001
- type: precision_at_3
value: 17.1
- type: precision_at_5
value: 12.316
- type: recall_at_1
value: 30.745
- type: recall_at_10
value: 62.038000000000004
- type: recall_at_100
value: 62.038000000000004
- type: recall_at_1000
value: 62.038000000000004
- type: recall_at_3
value: 45.378
- type: recall_at_5
value: 53.580000000000005
- type: map_at_1
value: 19.637999999999998
- type: map_at_10
value: 31.05
- type: map_at_100
value: 31.05
- type: map_at_1000
value: 31.05
- type: map_at_3
value: 27.628000000000004
- type: map_at_5
value: 29.767
- type: mrr_at_1
value: 25
- type: mrr_at_10
value: 36.131
- type: mrr_at_100
value: 36.131
- type: mrr_at_1000
value: 36.131
- type: mrr_at_3
value: 33.333
- type: mrr_at_5
value: 35.143
- type: ndcg_at_1
value: 25
- type: ndcg_at_10
value: 37.478
- type: ndcg_at_100
value: 37.469
- type: ndcg_at_1000
value: 37.469
- type: ndcg_at_3
value: 31.757999999999996
- type: ndcg_at_5
value: 34.821999999999996
- type: precision_at_1
value: 25
- type: precision_at_10
value: 7.188999999999999
- type: precision_at_100
value: 0.719
- type: precision_at_1000
value: 0.07200000000000001
- type: precision_at_3
value: 15.837000000000002
- type: precision_at_5
value: 11.841
- type: recall_at_1
value: 19.637999999999998
- type: recall_at_10
value: 51.836000000000006
- type: recall_at_100
value: 51.836000000000006
- type: recall_at_1000
value: 51.836000000000006
- type: recall_at_3
value: 36.384
- type: recall_at_5
value: 43.964
- type: map_at_1
value: 34.884
- type: map_at_10
value: 47.88
- type: map_at_100
value: 47.88
- type: map_at_1000
value: 47.88
- type: map_at_3
value: 43.85
- type: map_at_5
value: 46.414
- type: mrr_at_1
value: 43.022
- type: mrr_at_10
value: 53.569
- type: mrr_at_100
value: 53.569
- type: mrr_at_1000
value: 53.569
- type: mrr_at_3
value: 51.075
- type: mrr_at_5
value: 52.725
- type: ndcg_at_1
value: 43.022
- type: ndcg_at_10
value: 54.461000000000006
- type: ndcg_at_100
value: 54.388000000000005
- type: ndcg_at_1000
value: 54.388000000000005
- type: ndcg_at_3
value: 48.864999999999995
- type: ndcg_at_5
value: 52.032000000000004
- type: precision_at_1
value: 43.022
- type: precision_at_10
value: 9.885
- type: precision_at_100
value: 0.988
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 23.612
- type: precision_at_5
value: 16.997
- type: recall_at_1
value: 34.884
- type: recall_at_10
value: 68.12899999999999
- type: recall_at_100
value: 68.12899999999999
- type: recall_at_1000
value: 68.12899999999999
- type: recall_at_3
value: 52.428
- type: recall_at_5
value: 60.662000000000006
- type: map_at_1
value: 31.588
- type: map_at_10
value: 43.85
- type: map_at_100
value: 45.317
- type: map_at_1000
value: 45.408
- type: map_at_3
value: 39.73
- type: map_at_5
value: 42.122
- type: mrr_at_1
value: 38.927
- type: mrr_at_10
value: 49.582
- type: mrr_at_100
value: 50.39
- type: mrr_at_1000
value: 50.426
- type: mrr_at_3
value: 46.518
- type: mrr_at_5
value: 48.271
- type: ndcg_at_1
value: 38.927
- type: ndcg_at_10
value: 50.605999999999995
- type: ndcg_at_100
value: 56.22200000000001
- type: ndcg_at_1000
value: 57.724
- type: ndcg_at_3
value: 44.232
- type: ndcg_at_5
value: 47.233999999999995
- type: precision_at_1
value: 38.927
- type: precision_at_10
value: 9.429
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.172
- type: precision_at_3
value: 21.271
- type: precision_at_5
value: 15.434000000000001
- type: recall_at_1
value: 31.588
- type: recall_at_10
value: 64.836
- type: recall_at_100
value: 88.066
- type: recall_at_1000
value: 97.748
- type: recall_at_3
value: 47.128
- type: recall_at_5
value: 54.954
- type: map_at_1
value: 31.956083333333336
- type: map_at_10
value: 43.33483333333333
- type: map_at_100
value: 44.64883333333333
- type: map_at_1000
value: 44.75
- type: map_at_3
value: 39.87741666666666
- type: map_at_5
value: 41.86766666666667
- type: mrr_at_1
value: 38.06341666666667
- type: mrr_at_10
value: 47.839666666666666
- type: mrr_at_100
value: 48.644000000000005
- type: mrr_at_1000
value: 48.68566666666667
- type: mrr_at_3
value: 45.26358333333334
- type: mrr_at_5
value: 46.790000000000006
- type: ndcg_at_1
value: 38.06341666666667
- type: ndcg_at_10
value: 49.419333333333334
- type: ndcg_at_100
value: 54.50166666666667
- type: ndcg_at_1000
value: 56.161166666666674
- type: ndcg_at_3
value: 43.982416666666666
- type: ndcg_at_5
value: 46.638083333333334
- type: precision_at_1
value: 38.06341666666667
- type: precision_at_10
value: 8.70858333333333
- type: precision_at_100
value: 1.327
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.37816666666667
- type: precision_at_5
value: 14.516333333333334
- type: recall_at_1
value: 31.956083333333336
- type: recall_at_10
value: 62.69458333333334
- type: recall_at_100
value: 84.46433333333334
- type: recall_at_1000
value: 95.58449999999999
- type: recall_at_3
value: 47.52016666666666
- type: recall_at_5
value: 54.36066666666666
- type: map_at_1
value: 28.912
- type: map_at_10
value: 38.291
- type: map_at_100
value: 39.44
- type: map_at_1000
value: 39.528
- type: map_at_3
value: 35.638
- type: map_at_5
value: 37.218
- type: mrr_at_1
value: 32.822
- type: mrr_at_10
value: 41.661
- type: mrr_at_100
value: 42.546
- type: mrr_at_1000
value: 42.603
- type: mrr_at_3
value: 39.238
- type: mrr_at_5
value: 40.726
- type: ndcg_at_1
value: 32.822
- type: ndcg_at_10
value: 43.373
- type: ndcg_at_100
value: 48.638
- type: ndcg_at_1000
value: 50.654999999999994
- type: ndcg_at_3
value: 38.643
- type: ndcg_at_5
value: 41.126000000000005
- type: precision_at_1
value: 32.822
- type: precision_at_10
value: 6.8709999999999996
- type: precision_at_100
value: 1.032
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 16.82
- type: precision_at_5
value: 11.718
- type: recall_at_1
value: 28.912
- type: recall_at_10
value: 55.376999999999995
- type: recall_at_100
value: 79.066
- type: recall_at_1000
value: 93.664
- type: recall_at_3
value: 42.569
- type: recall_at_5
value: 48.719
- type: map_at_1
value: 22.181
- type: map_at_10
value: 31.462
- type: map_at_100
value: 32.73
- type: map_at_1000
value: 32.848
- type: map_at_3
value: 28.57
- type: map_at_5
value: 30.182
- type: mrr_at_1
value: 27.185
- type: mrr_at_10
value: 35.846000000000004
- type: mrr_at_100
value: 36.811
- type: mrr_at_1000
value: 36.873
- type: mrr_at_3
value: 33.437
- type: mrr_at_5
value: 34.813
- type: ndcg_at_1
value: 27.185
- type: ndcg_at_10
value: 36.858000000000004
- type: ndcg_at_100
value: 42.501
- type: ndcg_at_1000
value: 44.945
- type: ndcg_at_3
value: 32.066
- type: ndcg_at_5
value: 34.29
- type: precision_at_1
value: 27.185
- type: precision_at_10
value: 6.752
- type: precision_at_100
value: 1.111
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 15.290000000000001
- type: precision_at_5
value: 11.004999999999999
- type: recall_at_1
value: 22.181
- type: recall_at_10
value: 48.513
- type: recall_at_100
value: 73.418
- type: recall_at_1000
value: 90.306
- type: recall_at_3
value: 35.003
- type: recall_at_5
value: 40.876000000000005
- type: map_at_1
value: 33.934999999999995
- type: map_at_10
value: 44.727
- type: map_at_100
value: 44.727
- type: map_at_1000
value: 44.727
- type: map_at_3
value: 40.918
- type: map_at_5
value: 42.961
- type: mrr_at_1
value: 39.646
- type: mrr_at_10
value: 48.898
- type: mrr_at_100
value: 48.898
- type: mrr_at_1000
value: 48.898
- type: mrr_at_3
value: 45.896
- type: mrr_at_5
value: 47.514
- type: ndcg_at_1
value: 39.646
- type: ndcg_at_10
value: 50.817
- type: ndcg_at_100
value: 50.803
- type: ndcg_at_1000
value: 50.803
- type: ndcg_at_3
value: 44.507999999999996
- type: ndcg_at_5
value: 47.259
- type: precision_at_1
value: 39.646
- type: precision_at_10
value: 8.759
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.08800000000000001
- type: precision_at_3
value: 20.274
- type: precision_at_5
value: 14.366000000000001
- type: recall_at_1
value: 33.934999999999995
- type: recall_at_10
value: 65.037
- type: recall_at_100
value: 65.037
- type: recall_at_1000
value: 65.037
- type: recall_at_3
value: 47.439
- type: recall_at_5
value: 54.567
- type: map_at_1
value: 32.058
- type: map_at_10
value: 43.137
- type: map_at_100
value: 43.137
- type: map_at_1000
value: 43.137
- type: map_at_3
value: 39.882
- type: map_at_5
value: 41.379
- type: mrr_at_1
value: 38.933
- type: mrr_at_10
value: 48.344
- type: mrr_at_100
value: 48.344
- type: mrr_at_1000
value: 48.344
- type: mrr_at_3
value: 45.652
- type: mrr_at_5
value: 46.877
- type: ndcg_at_1
value: 38.933
- type: ndcg_at_10
value: 49.964
- type: ndcg_at_100
value: 49.242000000000004
- type: ndcg_at_1000
value: 49.222
- type: ndcg_at_3
value: 44.605
- type: ndcg_at_5
value: 46.501999999999995
- type: precision_at_1
value: 38.933
- type: precision_at_10
value: 9.427000000000001
- type: precision_at_100
value: 0.943
- type: precision_at_1000
value: 0.094
- type: precision_at_3
value: 20.685000000000002
- type: precision_at_5
value: 14.585
- type: recall_at_1
value: 32.058
- type: recall_at_10
value: 63.074
- type: recall_at_100
value: 63.074
- type: recall_at_1000
value: 63.074
- type: recall_at_3
value: 47.509
- type: recall_at_5
value: 52.455
- type: map_at_1
value: 26.029000000000003
- type: map_at_10
value: 34.646
- type: map_at_100
value: 34.646
- type: map_at_1000
value: 34.646
- type: map_at_3
value: 31.456
- type: map_at_5
value: 33.138
- type: mrr_at_1
value: 28.281
- type: mrr_at_10
value: 36.905
- type: mrr_at_100
value: 36.905
- type: mrr_at_1000
value: 36.905
- type: mrr_at_3
value: 34.011
- type: mrr_at_5
value: 35.638
- type: ndcg_at_1
value: 28.281
- type: ndcg_at_10
value: 40.159
- type: ndcg_at_100
value: 40.159
- type: ndcg_at_1000
value: 40.159
- type: ndcg_at_3
value: 33.995
- type: ndcg_at_5
value: 36.836999999999996
- type: precision_at_1
value: 28.281
- type: precision_at_10
value: 6.358999999999999
- type: precision_at_100
value: 0.636
- type: precision_at_1000
value: 0.064
- type: precision_at_3
value: 14.233
- type: precision_at_5
value: 10.314
- type: recall_at_1
value: 26.029000000000003
- type: recall_at_10
value: 55.08
- type: recall_at_100
value: 55.08
- type: recall_at_1000
value: 55.08
- type: recall_at_3
value: 38.487
- type: recall_at_5
value: 45.308
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.842999999999998
- type: map_at_10
value: 22.101000000000003
- type: map_at_100
value: 24.319
- type: map_at_1000
value: 24.51
- type: map_at_3
value: 18.372
- type: map_at_5
value: 20.323
- type: mrr_at_1
value: 27.948
- type: mrr_at_10
value: 40.321
- type: mrr_at_100
value: 41.262
- type: mrr_at_1000
value: 41.297
- type: mrr_at_3
value: 36.558
- type: mrr_at_5
value: 38.824999999999996
- type: ndcg_at_1
value: 27.948
- type: ndcg_at_10
value: 30.906
- type: ndcg_at_100
value: 38.986
- type: ndcg_at_1000
value: 42.136
- type: ndcg_at_3
value: 24.911
- type: ndcg_at_5
value: 27.168999999999997
- type: precision_at_1
value: 27.948
- type: precision_at_10
value: 9.798
- type: precision_at_100
value: 1.8399999999999999
- type: precision_at_1000
value: 0.243
- type: precision_at_3
value: 18.328
- type: precision_at_5
value: 14.502
- type: recall_at_1
value: 12.842999999999998
- type: recall_at_10
value: 37.245
- type: recall_at_100
value: 64.769
- type: recall_at_1000
value: 82.055
- type: recall_at_3
value: 23.159
- type: recall_at_5
value: 29.113
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.934000000000001
- type: map_at_10
value: 21.915000000000003
- type: map_at_100
value: 21.915000000000003
- type: map_at_1000
value: 21.915000000000003
- type: map_at_3
value: 14.623
- type: map_at_5
value: 17.841
- type: mrr_at_1
value: 71.25
- type: mrr_at_10
value: 78.994
- type: mrr_at_100
value: 78.994
- type: mrr_at_1000
value: 78.994
- type: mrr_at_3
value: 77.208
- type: mrr_at_5
value: 78.55799999999999
- type: ndcg_at_1
value: 60.62499999999999
- type: ndcg_at_10
value: 46.604
- type: ndcg_at_100
value: 35.653
- type: ndcg_at_1000
value: 35.531
- type: ndcg_at_3
value: 50.605
- type: ndcg_at_5
value: 48.730000000000004
- type: precision_at_1
value: 71.25
- type: precision_at_10
value: 37.75
- type: precision_at_100
value: 3.775
- type: precision_at_1000
value: 0.377
- type: precision_at_3
value: 54.417
- type: precision_at_5
value: 48.15
- type: recall_at_1
value: 8.934000000000001
- type: recall_at_10
value: 28.471000000000004
- type: recall_at_100
value: 28.471000000000004
- type: recall_at_1000
value: 28.471000000000004
- type: recall_at_3
value: 16.019
- type: recall_at_5
value: 21.410999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.81
- type: f1
value: 47.987573380720114
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.81899999999999
- type: map_at_10
value: 78.034
- type: map_at_100
value: 78.034
- type: map_at_1000
value: 78.034
- type: map_at_3
value: 76.43100000000001
- type: map_at_5
value: 77.515
- type: mrr_at_1
value: 71.542
- type: mrr_at_10
value: 81.638
- type: mrr_at_100
value: 81.638
- type: mrr_at_1000
value: 81.638
- type: mrr_at_3
value: 80.403
- type: mrr_at_5
value: 81.256
- type: ndcg_at_1
value: 71.542
- type: ndcg_at_10
value: 82.742
- type: ndcg_at_100
value: 82.741
- type: ndcg_at_1000
value: 82.741
- type: ndcg_at_3
value: 80.039
- type: ndcg_at_5
value: 81.695
- type: precision_at_1
value: 71.542
- type: precision_at_10
value: 10.387
- type: precision_at_100
value: 1.039
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 31.447999999999997
- type: precision_at_5
value: 19.91
- type: recall_at_1
value: 66.81899999999999
- type: recall_at_10
value: 93.372
- type: recall_at_100
value: 93.372
- type: recall_at_1000
value: 93.372
- type: recall_at_3
value: 86.33
- type: recall_at_5
value: 90.347
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.158
- type: map_at_10
value: 52.017
- type: map_at_100
value: 54.259
- type: map_at_1000
value: 54.367
- type: map_at_3
value: 45.738
- type: map_at_5
value: 49.283
- type: mrr_at_1
value: 57.87
- type: mrr_at_10
value: 66.215
- type: mrr_at_100
value: 66.735
- type: mrr_at_1000
value: 66.75
- type: mrr_at_3
value: 64.043
- type: mrr_at_5
value: 65.116
- type: ndcg_at_1
value: 57.87
- type: ndcg_at_10
value: 59.946999999999996
- type: ndcg_at_100
value: 66.31099999999999
- type: ndcg_at_1000
value: 67.75999999999999
- type: ndcg_at_3
value: 55.483000000000004
- type: ndcg_at_5
value: 56.891000000000005
- type: precision_at_1
value: 57.87
- type: precision_at_10
value: 16.497
- type: precision_at_100
value: 2.321
- type: precision_at_1000
value: 0.258
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 27.067999999999998
- type: recall_at_1
value: 31.158
- type: recall_at_10
value: 67.381
- type: recall_at_100
value: 89.464
- type: recall_at_1000
value: 97.989
- type: recall_at_3
value: 50.553000000000004
- type: recall_at_5
value: 57.824
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 42.073
- type: map_at_10
value: 72.418
- type: map_at_100
value: 73.175
- type: map_at_1000
value: 73.215
- type: map_at_3
value: 68.791
- type: map_at_5
value: 71.19
- type: mrr_at_1
value: 84.146
- type: mrr_at_10
value: 88.994
- type: mrr_at_100
value: 89.116
- type: mrr_at_1000
value: 89.12
- type: mrr_at_3
value: 88.373
- type: mrr_at_5
value: 88.82
- type: ndcg_at_1
value: 84.146
- type: ndcg_at_10
value: 79.404
- type: ndcg_at_100
value: 81.83200000000001
- type: ndcg_at_1000
value: 82.524
- type: ndcg_at_3
value: 74.595
- type: ndcg_at_5
value: 77.474
- type: precision_at_1
value: 84.146
- type: precision_at_10
value: 16.753999999999998
- type: precision_at_100
value: 1.8599999999999999
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 48.854
- type: precision_at_5
value: 31.579
- type: recall_at_1
value: 42.073
- type: recall_at_10
value: 83.768
- type: recall_at_100
value: 93.018
- type: recall_at_1000
value: 97.481
- type: recall_at_3
value: 73.282
- type: recall_at_5
value: 78.947
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.9968
- type: ap
value: 92.93892195862824
- type: f1
value: 94.99327998213761
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.698
- type: map_at_10
value: 34.585
- type: map_at_100
value: 35.782000000000004
- type: map_at_1000
value: 35.825
- type: map_at_3
value: 30.397999999999996
- type: map_at_5
value: 32.72
- type: mrr_at_1
value: 22.192
- type: mrr_at_10
value: 35.085
- type: mrr_at_100
value: 36.218
- type: mrr_at_1000
value: 36.256
- type: mrr_at_3
value: 30.986000000000004
- type: mrr_at_5
value: 33.268
- type: ndcg_at_1
value: 22.192
- type: ndcg_at_10
value: 41.957
- type: ndcg_at_100
value: 47.658
- type: ndcg_at_1000
value: 48.697
- type: ndcg_at_3
value: 33.433
- type: ndcg_at_5
value: 37.551
- type: precision_at_1
value: 22.192
- type: precision_at_10
value: 6.781
- type: precision_at_100
value: 0.963
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.365
- type: precision_at_5
value: 10.713000000000001
- type: recall_at_1
value: 21.698
- type: recall_at_10
value: 64.79
- type: recall_at_100
value: 91.071
- type: recall_at_1000
value: 98.883
- type: recall_at_3
value: 41.611
- type: recall_at_5
value: 51.459999999999994
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.15823073415413
- type: f1
value: 96.00362034963248
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 87.12722298221614
- type: f1
value: 70.46888967516227
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.77673167451245
- type: f1
value: 77.60202561132175
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 82.09145931405514
- type: f1
value: 81.7701921473406
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.52153488185864
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 36.80090398444147
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.807141746058605
- type: mrr
value: 32.85025611455029
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.920999999999999
- type: map_at_10
value: 16.049
- type: map_at_100
value: 16.049
- type: map_at_1000
value: 16.049
- type: map_at_3
value: 11.865
- type: map_at_5
value: 13.657
- type: mrr_at_1
value: 53.87
- type: mrr_at_10
value: 62.291
- type: mrr_at_100
value: 62.291
- type: mrr_at_1000
value: 62.291
- type: mrr_at_3
value: 60.681
- type: mrr_at_5
value: 61.61
- type: ndcg_at_1
value: 51.23799999999999
- type: ndcg_at_10
value: 40.892
- type: ndcg_at_100
value: 26.951999999999998
- type: ndcg_at_1000
value: 26.474999999999998
- type: ndcg_at_3
value: 46.821
- type: ndcg_at_5
value: 44.333
- type: precision_at_1
value: 53.251000000000005
- type: precision_at_10
value: 30.124000000000002
- type: precision_at_100
value: 3.012
- type: precision_at_1000
value: 0.301
- type: precision_at_3
value: 43.55
- type: precision_at_5
value: 38.266
- type: recall_at_1
value: 6.920999999999999
- type: recall_at_10
value: 20.852
- type: recall_at_100
value: 20.852
- type: recall_at_1000
value: 20.852
- type: recall_at_3
value: 13.628000000000002
- type: recall_at_5
value: 16.273
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 46.827999999999996
- type: map_at_10
value: 63.434000000000005
- type: map_at_100
value: 63.434000000000005
- type: map_at_1000
value: 63.434000000000005
- type: map_at_3
value: 59.794000000000004
- type: map_at_5
value: 62.08
- type: mrr_at_1
value: 52.288999999999994
- type: mrr_at_10
value: 65.95
- type: mrr_at_100
value: 65.95
- type: mrr_at_1000
value: 65.95
- type: mrr_at_3
value: 63.413
- type: mrr_at_5
value: 65.08
- type: ndcg_at_1
value: 52.288999999999994
- type: ndcg_at_10
value: 70.301
- type: ndcg_at_100
value: 70.301
- type: ndcg_at_1000
value: 70.301
- type: ndcg_at_3
value: 63.979
- type: ndcg_at_5
value: 67.582
- type: precision_at_1
value: 52.288999999999994
- type: precision_at_10
value: 10.576
- type: precision_at_100
value: 1.058
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 28.177000000000003
- type: precision_at_5
value: 19.073
- type: recall_at_1
value: 46.827999999999996
- type: recall_at_10
value: 88.236
- type: recall_at_100
value: 88.236
- type: recall_at_1000
value: 88.236
- type: recall_at_3
value: 72.371
- type: recall_at_5
value: 80.56
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.652
- type: map_at_10
value: 85.953
- type: map_at_100
value: 85.953
- type: map_at_1000
value: 85.953
- type: map_at_3
value: 83.05399999999999
- type: map_at_5
value: 84.89
- type: mrr_at_1
value: 82.42
- type: mrr_at_10
value: 88.473
- type: mrr_at_100
value: 88.473
- type: mrr_at_1000
value: 88.473
- type: mrr_at_3
value: 87.592
- type: mrr_at_5
value: 88.211
- type: ndcg_at_1
value: 82.44
- type: ndcg_at_10
value: 89.467
- type: ndcg_at_100
value: 89.33
- type: ndcg_at_1000
value: 89.33
- type: ndcg_at_3
value: 86.822
- type: ndcg_at_5
value: 88.307
- type: precision_at_1
value: 82.44
- type: precision_at_10
value: 13.616
- type: precision_at_100
value: 1.362
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 38.117000000000004
- type: precision_at_5
value: 25.05
- type: recall_at_1
value: 71.652
- type: recall_at_10
value: 96.224
- type: recall_at_100
value: 96.224
- type: recall_at_1000
value: 96.224
- type: recall_at_3
value: 88.571
- type: recall_at_5
value: 92.812
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 61.295010338050474
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 67.26380819328142
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.683
- type: map_at_10
value: 14.924999999999999
- type: map_at_100
value: 17.532
- type: map_at_1000
value: 17.875
- type: map_at_3
value: 10.392
- type: map_at_5
value: 12.592
- type: mrr_at_1
value: 28.000000000000004
- type: mrr_at_10
value: 39.951
- type: mrr_at_100
value: 41.025
- type: mrr_at_1000
value: 41.056
- type: mrr_at_3
value: 36.317
- type: mrr_at_5
value: 38.412
- type: ndcg_at_1
value: 28.000000000000004
- type: ndcg_at_10
value: 24.410999999999998
- type: ndcg_at_100
value: 33.79
- type: ndcg_at_1000
value: 39.035
- type: ndcg_at_3
value: 22.845
- type: ndcg_at_5
value: 20.080000000000002
- type: precision_at_1
value: 28.000000000000004
- type: precision_at_10
value: 12.790000000000001
- type: precision_at_100
value: 2.633
- type: precision_at_1000
value: 0.388
- type: precision_at_3
value: 21.367
- type: precision_at_5
value: 17.7
- type: recall_at_1
value: 5.683
- type: recall_at_10
value: 25.91
- type: recall_at_100
value: 53.443
- type: recall_at_1000
value: 78.73
- type: recall_at_3
value: 13.003
- type: recall_at_5
value: 17.932000000000002
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.677978681023
- type: cos_sim_spearman
value: 83.13093441058189
- type: euclidean_pearson
value: 83.35535759341572
- type: euclidean_spearman
value: 83.42583744219611
- type: manhattan_pearson
value: 83.2243124045889
- type: manhattan_spearman
value: 83.39801618652632
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.68960206569666
- type: cos_sim_spearman
value: 77.3368966488535
- type: euclidean_pearson
value: 77.62828980560303
- type: euclidean_spearman
value: 76.77951481444651
- type: manhattan_pearson
value: 77.88637240839041
- type: manhattan_spearman
value: 77.22157841466188
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.18745821650724
- type: cos_sim_spearman
value: 85.04423285574542
- type: euclidean_pearson
value: 85.46604816931023
- type: euclidean_spearman
value: 85.5230593932974
- type: manhattan_pearson
value: 85.57912805986261
- type: manhattan_spearman
value: 85.65955905111873
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.6715333300355
- type: cos_sim_spearman
value: 82.9058522514908
- type: euclidean_pearson
value: 83.9640357424214
- type: euclidean_spearman
value: 83.60415457472637
- type: manhattan_pearson
value: 84.05621005853469
- type: manhattan_spearman
value: 83.87077724707746
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.82422928098886
- type: cos_sim_spearman
value: 88.12660311894628
- type: euclidean_pearson
value: 87.50974805056555
- type: euclidean_spearman
value: 87.91957275596677
- type: manhattan_pearson
value: 87.74119404878883
- type: manhattan_spearman
value: 88.2808922165719
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.80605838552093
- type: cos_sim_spearman
value: 86.24123388765678
- type: euclidean_pearson
value: 85.32648347339814
- type: euclidean_spearman
value: 85.60046671950158
- type: manhattan_pearson
value: 85.53800168487811
- type: manhattan_spearman
value: 85.89542420480763
- 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: 89.87540978988132
- type: cos_sim_spearman
value: 90.12715295099461
- type: euclidean_pearson
value: 91.61085993525275
- type: euclidean_spearman
value: 91.31835942311758
- type: manhattan_pearson
value: 91.57500202032934
- type: manhattan_spearman
value: 91.1790925526635
- 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: 69.87136205329556
- type: cos_sim_spearman
value: 68.6253154635078
- type: euclidean_pearson
value: 68.91536015034222
- type: euclidean_spearman
value: 67.63744649352542
- type: manhattan_pearson
value: 69.2000713045275
- type: manhattan_spearman
value: 68.16002901587316
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.21849551039082
- type: cos_sim_spearman
value: 85.6392959372461
- type: euclidean_pearson
value: 85.92050852609488
- type: euclidean_spearman
value: 85.97205649009734
- type: manhattan_pearson
value: 86.1031154802254
- type: manhattan_spearman
value: 86.26791155517466
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.83953958636627
- type: mrr
value: 96.71167612344082
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 64.994
- type: map_at_10
value: 74.763
- type: map_at_100
value: 75.127
- type: map_at_1000
value: 75.143
- type: map_at_3
value: 71.824
- type: map_at_5
value: 73.71
- type: mrr_at_1
value: 68.333
- type: mrr_at_10
value: 75.749
- type: mrr_at_100
value: 75.922
- type: mrr_at_1000
value: 75.938
- type: mrr_at_3
value: 73.556
- type: mrr_at_5
value: 74.739
- type: ndcg_at_1
value: 68.333
- type: ndcg_at_10
value: 79.174
- type: ndcg_at_100
value: 80.41
- type: ndcg_at_1000
value: 80.804
- type: ndcg_at_3
value: 74.361
- type: ndcg_at_5
value: 76.861
- type: precision_at_1
value: 68.333
- type: precision_at_10
value: 10.333
- type: precision_at_100
value: 1.0999999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.778
- type: precision_at_5
value: 19.067
- type: recall_at_1
value: 64.994
- type: recall_at_10
value: 91.822
- type: recall_at_100
value: 97
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 78.878
- type: recall_at_5
value: 85.172
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72079207920792
- type: cos_sim_ap
value: 93.00265215525152
- type: cos_sim_f1
value: 85.06596306068602
- type: cos_sim_precision
value: 90.05586592178771
- type: cos_sim_recall
value: 80.60000000000001
- type: dot_accuracy
value: 99.66039603960397
- type: dot_ap
value: 91.22371407479089
- type: dot_f1
value: 82.34693877551021
- type: dot_precision
value: 84.0625
- type: dot_recall
value: 80.7
- type: euclidean_accuracy
value: 99.71881188118812
- type: euclidean_ap
value: 92.88449963304728
- type: euclidean_f1
value: 85.19480519480518
- type: euclidean_precision
value: 88.64864864864866
- type: euclidean_recall
value: 82
- type: manhattan_accuracy
value: 99.73267326732673
- type: manhattan_ap
value: 93.23055393056883
- type: manhattan_f1
value: 85.88957055214725
- type: manhattan_precision
value: 87.86610878661088
- type: manhattan_recall
value: 84
- type: max_accuracy
value: 99.73267326732673
- type: max_ap
value: 93.23055393056883
- type: max_f1
value: 85.88957055214725
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 77.3305735900358
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 41.32967136540674
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.95514866379359
- type: mrr
value: 56.95423245055598
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.783007208997144
- type: cos_sim_spearman
value: 30.373444721540533
- type: dot_pearson
value: 29.210604111143905
- type: dot_spearman
value: 29.98809758085659
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.234
- type: map_at_10
value: 1.894
- type: map_at_100
value: 1.894
- type: map_at_1000
value: 1.894
- type: map_at_3
value: 0.636
- type: map_at_5
value: 1
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.667
- type: mrr_at_100
value: 93.667
- type: mrr_at_1000
value: 93.667
- type: mrr_at_3
value: 93.667
- type: mrr_at_5
value: 93.667
- type: ndcg_at_1
value: 85
- type: ndcg_at_10
value: 74.798
- type: ndcg_at_100
value: 16.462
- type: ndcg_at_1000
value: 7.0889999999999995
- type: ndcg_at_3
value: 80.754
- type: ndcg_at_5
value: 77.319
- type: precision_at_1
value: 88
- type: precision_at_10
value: 78
- type: precision_at_100
value: 7.8
- type: precision_at_1000
value: 0.7799999999999999
- type: precision_at_3
value: 83.333
- type: precision_at_5
value: 80.80000000000001
- type: recall_at_1
value: 0.234
- type: recall_at_10
value: 2.093
- type: recall_at_100
value: 2.093
- type: recall_at_1000
value: 2.093
- type: recall_at_3
value: 0.662
- type: recall_at_5
value: 1.0739999999999998
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.703
- type: map_at_10
value: 10.866000000000001
- type: map_at_100
value: 10.866000000000001
- type: map_at_1000
value: 10.866000000000001
- type: map_at_3
value: 5.909
- type: map_at_5
value: 7.35
- type: mrr_at_1
value: 36.735
- type: mrr_at_10
value: 53.583000000000006
- type: mrr_at_100
value: 53.583000000000006
- type: mrr_at_1000
value: 53.583000000000006
- type: mrr_at_3
value: 49.32
- type: mrr_at_5
value: 51.769
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 27.926000000000002
- type: ndcg_at_100
value: 22.701
- type: ndcg_at_1000
value: 22.701
- type: ndcg_at_3
value: 32.073
- type: ndcg_at_5
value: 28.327999999999996
- type: precision_at_1
value: 36.735
- type: precision_at_10
value: 24.694
- type: precision_at_100
value: 2.469
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 26.939
- type: recall_at_1
value: 2.703
- type: recall_at_10
value: 17.702
- type: recall_at_100
value: 17.702
- type: recall_at_1000
value: 17.702
- type: recall_at_3
value: 7.208
- type: recall_at_5
value: 9.748999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.79960000000001
- type: ap
value: 15.467565415565815
- type: f1
value: 55.28639823443618
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.7792869269949
- type: f1
value: 65.08597154774318
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 55.70352297774293
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.27561542588067
- type: cos_sim_ap
value: 81.08262141256193
- type: cos_sim_f1
value: 73.82341501361338
- type: cos_sim_precision
value: 72.5720112159062
- type: cos_sim_recall
value: 75.11873350923483
- type: dot_accuracy
value: 86.66030875603504
- type: dot_ap
value: 76.6052349228621
- type: dot_f1
value: 70.13897280966768
- type: dot_precision
value: 64.70457079152732
- type: dot_recall
value: 76.56992084432717
- type: euclidean_accuracy
value: 88.37098408535495
- type: euclidean_ap
value: 81.12515230092113
- type: euclidean_f1
value: 74.10338225909379
- type: euclidean_precision
value: 71.76761433868974
- type: euclidean_recall
value: 76.59630606860158
- type: manhattan_accuracy
value: 88.34118137926924
- type: manhattan_ap
value: 80.95751834536561
- type: manhattan_f1
value: 73.9119496855346
- type: manhattan_precision
value: 70.625
- type: manhattan_recall
value: 77.5197889182058
- type: max_accuracy
value: 88.37098408535495
- type: max_ap
value: 81.12515230092113
- type: max_f1
value: 74.10338225909379
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.79896767182831
- type: cos_sim_ap
value: 87.40071784061065
- type: cos_sim_f1
value: 79.87753144712087
- type: cos_sim_precision
value: 76.67304015296367
- type: cos_sim_recall
value: 83.3615645210964
- type: dot_accuracy
value: 88.95486474948578
- type: dot_ap
value: 86.00227979119943
- type: dot_f1
value: 78.54601474525914
- type: dot_precision
value: 75.00525394045535
- type: dot_recall
value: 82.43763473975977
- type: euclidean_accuracy
value: 89.7892653393876
- type: euclidean_ap
value: 87.42174706480819
- type: euclidean_f1
value: 80.07283321194465
- type: euclidean_precision
value: 75.96738529574351
- type: euclidean_recall
value: 84.6473668001232
- type: manhattan_accuracy
value: 89.8474793340319
- type: manhattan_ap
value: 87.47814292587448
- type: manhattan_f1
value: 80.15461150280949
- type: manhattan_precision
value: 74.88798234468
- type: manhattan_recall
value: 86.21804742839544
- type: max_accuracy
value: 89.8474793340319
- type: max_ap
value: 87.47814292587448
- type: max_f1
value: 80.15461150280949
---
# Model Summary
This is a fork of the [original GritLM model](https://hf.co/GritLM/GritLM-7B). The main difference between this fork and the original model is the name of the architecture used in the config file. The new name is chosen to make it easier to adopt this model in vLLM.
> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.
- **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm)
- **Paper:** https://arxiv.org/abs/2402.09906
- **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview
- **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh
| Model | Description |
|-------|-------------|
| [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT |
| [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT |
# Use
The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference).
# Citation
```bibtex
@misc{muennighoff2024generative,
title={Generative Representational Instruction Tuning},
author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
year={2024},
eprint={2402.09906},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
minishlab/potion-base-4M
|
minishlab
| null |
[
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"mteb",
"sentence-transformers",
"license:mit",
"model-index",
"region:us"
] | 2024-10-29T09:40:20 | 2025-01-21T19:12:35 | 6,850 | 7 |
---
library_name: model2vec
license: mit
tags:
- embeddings
- static-embeddings
- mteb
- sentence-transformers
model-index:
- name: potion-base-4M
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: 67.5712143928036
- type: ap
value: 17.612722401291446
- type: ap_weighted
value: 17.612722401291446
- type: f1
value: 55.192880430611815
- type: f1_weighted
value: 73.78539599566238
- type: main_score
value: 67.5712143928036
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 68.38805970149254
- type: ap
value: 29.77595242354233
- type: ap_weighted
value: 29.77595242354233
- type: f1
value: 61.660550379147104
- type: f1_weighted
value: 71.3687043183968
- type: main_score
value: 68.38805970149254
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 71.69602499999999
- type: ap
value: 66.0912235632392
- type: ap_weighted
value: 66.0912235632392
- type: f1
value: 71.42572451950106
- type: f1_weighted
value: 71.42572451950106
- type: main_score
value: 71.69602499999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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type: mteb/arguana
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split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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type: mteb/arxiv-clustering-p2p
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split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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dataset:
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type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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value: 2.625
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value: 4.593
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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: 85.6155950752394
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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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type: Classification
dataset:
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type: mteb/amazon_massive_scenario
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split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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type: mteb/nfcorpus
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- type: precision_at_100
value: 1.422
- type: precision_at_1000
value: 0.269
- type: precision_at_20
value: 3.925
- type: precision_at_3
value: 9.866999999999999
- type: precision_at_5
value: 8.08
- type: recall_at_1
value: 2.6229999999999998
- type: recall_at_10
value: 11.458
- type: recall_at_100
value: 28.852
- type: recall_at_1000
value: 54.65
- type: recall_at_20
value: 15.906999999999998
- type: recall_at_3
value: 6.008
- type: recall_at_5
value: 8.187999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 75.3765633045698
- type: cosine_spearman
value: 63.98613448451591
- type: euclidean_pearson
value: 68.77492302556632
- type: euclidean_spearman
value: 63.98600896235449
- type: main_score
value: 63.98613448451591
- type: manhattan_pearson
value: 68.97719158550568
- type: manhattan_spearman
value: 64.17632002019486
- type: pearson
value: 75.3765633045698
- type: spearman
value: 63.98613448451591
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 72.15434642732643
- type: cosine_spearman
value: 62.37621006422606
- type: euclidean_pearson
value: 68.4217723007459
- type: euclidean_spearman
value: 62.3761516548255
- type: main_score
value: 62.37621006422606
- type: manhattan_pearson
value: 68.44462184344337
- type: manhattan_spearman
value: 62.409862632343504
- type: pearson
value: 72.15434642732643
- type: spearman
value: 62.37621006422606
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 75.8217088748812
- type: cosine_spearman
value: 76.81750749576634
- type: euclidean_pearson
value: 76.28374455431411
- type: euclidean_spearman
value: 76.81750749576634
- type: main_score
value: 76.81750749576634
- type: manhattan_pearson
value: 76.22730912471911
- type: manhattan_spearman
value: 76.76337659370503
- type: pearson
value: 75.8217088748812
- type: spearman
value: 76.81750749576634
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 75.44045207503216
- type: cosine_spearman
value: 70.98200491385815
- type: euclidean_pearson
value: 73.79528647452996
- type: euclidean_spearman
value: 70.98200491385815
- type: main_score
value: 70.98200491385815
- type: manhattan_pearson
value: 74.00347105187919
- type: manhattan_spearman
value: 71.15597425184266
- type: pearson
value: 75.44045207503216
- type: spearman
value: 70.98200491385815
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 77.94210244961548
- type: cosine_spearman
value: 78.66432028882325
- type: euclidean_pearson
value: 78.35289467379204
- type: euclidean_spearman
value: 78.66432028882325
- type: main_score
value: 78.66432028882325
- type: manhattan_pearson
value: 78.28424867038663
- type: manhattan_spearman
value: 78.58719353431712
- type: pearson
value: 77.94210244961548
- type: spearman
value: 78.66432028882325
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 74.09988267613177
- type: cosine_spearman
value: 75.09842774068727
- type: euclidean_pearson
value: 74.68409526331901
- type: euclidean_spearman
value: 75.09842774068727
- type: main_score
value: 75.09842774068727
- type: manhattan_pearson
value: 74.59031379824495
- type: manhattan_spearman
value: 75.02693979965505
- type: pearson
value: 74.09988267613177
- type: spearman
value: 75.09842774068727
- 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: 14.399972034167586
- type: cosine_spearman
value: 11.974036233167949
- type: euclidean_pearson
value: 14.485293885486817
- type: euclidean_spearman
value: 11.974036233167949
- type: main_score
value: 11.974036233167949
- type: manhattan_pearson
value: 13.719281545868167
- type: manhattan_spearman
value: 11.030737066771716
- type: pearson
value: 14.399972034167586
- type: spearman
value: 11.974036233167949
- 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: 0.48735904843064204
- type: cosine_spearman
value: -0.5586853869425744
- type: euclidean_pearson
value: 0.6087049823875109
- type: euclidean_spearman
value: -0.5586853869425744
- type: main_score
value: -0.5586853869425744
- type: manhattan_pearson
value: -5.604629753019841
- type: manhattan_spearman
value: -6.156533065413701
- type: pearson
value: 0.48735904843064204
- type: spearman
value: -0.5586853869425744
- 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: 83.08779566633636
- type: cosine_spearman
value: 84.49159199501017
- type: euclidean_pearson
value: 83.5601221620653
- type: euclidean_spearman
value: 84.49159199501017
- type: main_score
value: 84.49159199501017
- type: manhattan_pearson
value: 83.66974922388209
- type: manhattan_spearman
value: 84.69728258948982
- type: pearson
value: 83.08779566633636
- type: spearman
value: 84.49159199501017
- 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: 28.253544786263518
- type: cosine_spearman
value: 24.619138389390237
- type: euclidean_pearson
value: 28.492064397709616
- type: euclidean_spearman
value: 24.619138389390237
- type: main_score
value: 24.619138389390237
- type: manhattan_pearson
value: 30.135975855905052
- type: manhattan_spearman
value: 25.508286051679313
- type: pearson
value: 28.253544786263518
- type: spearman
value: 24.619138389390237
- 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: 32.599578555181466
- type: cosine_spearman
value: 31.33358514527386
- type: euclidean_pearson
value: 32.613214551816796
- type: euclidean_spearman
value: 31.33358514527386
- type: main_score
value: 31.33358514527386
- type: manhattan_pearson
value: 32.25686415499878
- type: manhattan_spearman
value: 29.668763682572735
- type: pearson
value: 32.599578555181466
- type: spearman
value: 31.33358514527386
- 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: 23.75577872956453
- type: cosine_spearman
value: 21.321554136347544
- type: euclidean_pearson
value: 23.28498966277531
- type: euclidean_spearman
value: 21.321554136347544
- type: main_score
value: 21.321554136347544
- type: manhattan_pearson
value: 21.83738887582424
- type: manhattan_spearman
value: 18.096121115941422
- type: pearson
value: 23.75577872956453
- type: spearman
value: 21.321554136347544
- 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: 27.69563703760355
- type: cosine_spearman
value: 24.76336397535317
- type: euclidean_pearson
value: 28.070464936235396
- type: euclidean_spearman
value: 24.76336397535317
- type: main_score
value: 24.76336397535317
- type: manhattan_pearson
value: 29.820923974737546
- type: manhattan_spearman
value: 27.812820254677657
- type: pearson
value: 27.69563703760355
- type: spearman
value: 24.76336397535317
- 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: 16.346177540651276
- type: cosine_spearman
value: 13.601344161361006
- type: euclidean_pearson
value: 16.494437424874043
- type: euclidean_spearman
value: 13.601344161361006
- type: main_score
value: 13.601344161361006
- type: manhattan_pearson
value: 14.109457511241242
- type: manhattan_spearman
value: 13.157710082543057
- type: pearson
value: 16.346177540651276
- type: spearman
value: 13.601344161361006
- 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: 8.721258908976726
- type: cosine_spearman
value: 18.35245351814682
- type: euclidean_pearson
value: 6.733168292100376
- type: euclidean_spearman
value: 18.35245351814682
- type: main_score
value: 18.35245351814682
- type: manhattan_pearson
value: 10.48470979974683
- type: manhattan_spearman
value: 23.301312684266456
- type: pearson
value: 8.721258908976726
- type: spearman
value: 18.35245351814682
- 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: 11.49126181142697
- type: cosine_spearman
value: 16.21762340764813
- type: euclidean_pearson
value: 10.421413269548768
- type: euclidean_spearman
value: 16.15075860419545
- type: main_score
value: 16.21762340764813
- type: manhattan_pearson
value: 14.305549482593522
- type: manhattan_spearman
value: 18.371358872490433
- type: pearson
value: 11.49126181142697
- type: spearman
value: 16.21762340764813
- 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: 26.168037910219216
- type: cosine_spearman
value: 31.68384203851073
- type: euclidean_pearson
value: 25.745626390764293
- type: euclidean_spearman
value: 31.677361748737955
- type: main_score
value: 31.68384203851073
- type: manhattan_pearson
value: 25.87013667642989
- type: manhattan_spearman
value: 34.71610605034525
- type: pearson
value: 26.168037910219216
- type: spearman
value: 31.68384203851073
- 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: 9.071484790364082
- type: cosine_spearman
value: 9.726488833098573
- type: euclidean_pearson
value: 8.563790512620805
- type: euclidean_spearman
value: 9.741694852789207
- type: main_score
value: 9.726488833098573
- type: manhattan_pearson
value: 6.653824957307301
- type: manhattan_spearman
value: 7.550086762351419
- type: pearson
value: 9.071484790364082
- type: spearman
value: 9.726488833098573
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 58.835618895152855
- type: cosine_spearman
value: 63.35773898104199
- type: euclidean_pearson
value: 62.348443804922525
- type: euclidean_spearman
value: 63.35773898104199
- type: main_score
value: 63.35773898104199
- type: manhattan_pearson
value: 62.7037835433589
- type: manhattan_spearman
value: 62.86339756370198
- type: pearson
value: 58.835618895152855
- type: spearman
value: 63.35773898104199
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 75.85762627227177
- type: cosine_spearman
value: 74.28444387078392
- type: euclidean_pearson
value: 76.14631470926992
- type: euclidean_spearman
value: 74.28444387078392
- type: main_score
value: 74.28444387078392
- type: manhattan_pearson
value: 76.29026607759047
- type: manhattan_spearman
value: 74.36833261994545
- type: pearson
value: 75.85762627227177
- type: spearman
value: 74.28444387078392
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 70.28519419296406
- type: map
value: 70.28519419296406
- type: mrr
value: 89.42476278260591
- type: nAUC_map_diff1
value: 9.174758618691703
- type: nAUC_map_max
value: 56.09564435538077
- type: nAUC_map_std
value: 67.85518368829582
- type: nAUC_mrr_diff1
value: 43.619263369174874
- type: nAUC_mrr_max
value: 74.99462642243834
- type: nAUC_mrr_std
value: 71.03708831031823
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 46.039
- type: map_at_1
value: 32.833
- type: map_at_10
value: 41.073
- type: map_at_100
value: 42.055
- type: map_at_1000
value: 42.115
- type: map_at_20
value: 41.577
- type: map_at_3
value: 38.181
- type: map_at_5
value: 39.939
- type: mrr_at_1
value: 34.66666666666667
- type: mrr_at_10
value: 42.69973544973544
- type: mrr_at_100
value: 43.50094990516196
- type: mrr_at_1000
value: 43.55518326135714
- type: mrr_at_20
value: 43.09661278264219
- type: mrr_at_3
value: 40.055555555555536
- type: mrr_at_5
value: 41.755555555555546
- type: nauc_map_at_1000_diff1
value: 45.428609740785745
- type: nauc_map_at_1000_max
value: 37.543057127562015
- type: nauc_map_at_1000_std
value: 2.996790610257377
- type: nauc_map_at_100_diff1
value: 45.41655665365538
- type: nauc_map_at_100_max
value: 37.55142170311717
- type: nauc_map_at_100_std
value: 3.021634986612468
- type: nauc_map_at_10_diff1
value: 45.28151352698938
- type: nauc_map_at_10_max
value: 37.15161941800589
- type: nauc_map_at_10_std
value: 2.1466882062165955
- type: nauc_map_at_1_diff1
value: 46.886698245684144
- type: nauc_map_at_1_max
value: 34.23436427795755
- type: nauc_map_at_1_std
value: -1.63247086935568
- type: nauc_map_at_20_diff1
value: 45.323443483378334
- type: nauc_map_at_20_max
value: 37.360033627177806
- type: nauc_map_at_20_std
value: 2.5814764857730226
- type: nauc_map_at_3_diff1
value: 46.42738772300192
- type: nauc_map_at_3_max
value: 34.91781909311709
- type: nauc_map_at_3_std
value: 0.7407858981306185
- type: nauc_map_at_5_diff1
value: 45.56095939652318
- type: nauc_map_at_5_max
value: 36.42349927345809
- type: nauc_map_at_5_std
value: 1.5882842990321877
- type: nauc_mrr_at_1000_diff1
value: 46.619194210193115
- type: nauc_mrr_at_1000_max
value: 39.58593846571098
- type: nauc_mrr_at_1000_std
value: 6.871860120606581
- type: nauc_mrr_at_100_diff1
value: 46.60992557885071
- type: nauc_mrr_at_100_max
value: 39.59384195719456
- type: nauc_mrr_at_100_std
value: 6.913799322671517
- type: nauc_mrr_at_10_diff1
value: 46.49797118603073
- type: nauc_mrr_at_10_max
value: 39.50640104704752
- type: nauc_mrr_at_10_std
value: 6.508999197821892
- type: nauc_mrr_at_1_diff1
value: 49.3185659952106
- type: nauc_mrr_at_1_max
value: 36.96867516551628
- type: nauc_mrr_at_1_std
value: 3.2209642203127
- type: nauc_mrr_at_20_diff1
value: 46.57642255869317
- type: nauc_mrr_at_20_max
value: 39.57536672051077
- type: nauc_mrr_at_20_std
value: 6.686866161626959
- type: nauc_mrr_at_3_diff1
value: 47.71472878192224
- type: nauc_mrr_at_3_max
value: 37.84645732330119
- type: nauc_mrr_at_3_std
value: 5.794681287411272
- type: nauc_mrr_at_5_diff1
value: 46.88419733444017
- type: nauc_mrr_at_5_max
value: 38.93781880268596
- type: nauc_mrr_at_5_std
value: 5.995326077656308
- type: nauc_ndcg_at_1000_diff1
value: 44.62859803359441
- type: nauc_ndcg_at_1000_max
value: 40.04051001437065
- type: nauc_ndcg_at_1000_std
value: 7.077880529469134
- type: nauc_ndcg_at_100_diff1
value: 44.27396584172457
- type: nauc_ndcg_at_100_max
value: 40.45032063067928
- type: nauc_ndcg_at_100_std
value: 8.295284232687681
- type: nauc_ndcg_at_10_diff1
value: 44.12502453790537
- type: nauc_ndcg_at_10_max
value: 39.397967545534506
- type: nauc_ndcg_at_10_std
value: 5.154810286641697
- type: nauc_ndcg_at_1_diff1
value: 49.3185659952106
- type: nauc_ndcg_at_1_max
value: 36.96867516551628
- type: nauc_ndcg_at_1_std
value: 3.2209642203127
- type: nauc_ndcg_at_20_diff1
value: 44.36600328826306
- type: nauc_ndcg_at_20_max
value: 39.81785860876702
- type: nauc_ndcg_at_20_std
value: 6.377952248139375
- type: nauc_ndcg_at_3_diff1
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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:
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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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type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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dataset:
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type: mteb/trec-covid
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split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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dataset:
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type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: 15.369
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value: 26.151999999999997
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value: 38.553
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value: 17.325
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value: 13.944999999999999
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value: 14.565
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value: 8.163
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value: 16.735
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value: 13.776
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value: 17.687
- type: precision_at_5
value: 17.959
- type: recall_at_1
value: 0.624
- type: recall_at_10
value: 11.723
- type: recall_at_100
value: 38.072
- type: recall_at_1000
value: 76.011
- type: recall_at_20
value: 18.553
- type: recall_at_3
value: 3.7379999999999995
- type: recall_at_5
value: 6.2170000000000005
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 67.724609375
- type: ap
value: 12.118510465436508
- type: ap_weighted
value: 12.118510465436508
- type: f1
value: 51.502156902277044
- type: f1_weighted
value: 74.90031680337547
- type: main_score
value: 67.724609375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 54.20486700622524
- type: f1
value: 54.430757063175975
- type: f1_weighted
value: 53.770651858268025
- type: main_score
value: 54.20486700622524
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 31.416458059499856
- type: v_measure
value: 31.416458059499856
- type: v_measure_std
value: 1.7923550883286548
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 81.09912380044108
- type: cosine_accuracy_threshold
value: 78.23829613291026
- type: cosine_ap
value: 55.35060563890305
- type: cosine_f1
value: 53.80317025886646
- type: cosine_f1_threshold
value: 66.90525865927196
- type: cosine_precision
value: 47.37899176541474
- type: cosine_recall
value: 62.24274406332454
- type: dot_accuracy
value: 81.09912380044108
- type: dot_accuracy_threshold
value: 78.23829756344699
- type: dot_ap
value: 55.35060534827081
- type: dot_f1
value: 53.80317025886646
- type: dot_f1_threshold
value: 66.90525431322651
- type: dot_precision
value: 47.37899176541474
- type: dot_recall
value: 62.24274406332454
- type: euclidean_accuracy
value: 81.09912380044108
- type: euclidean_accuracy_threshold
value: 65.97227331408025
- type: euclidean_ap
value: 55.350603467922014
- type: euclidean_f1
value: 53.80317025886646
- type: euclidean_f1_threshold
value: 81.35691587916897
- type: euclidean_precision
value: 47.37899176541474
- type: euclidean_recall
value: 62.24274406332454
- type: main_score
value: 55.411799272092175
- type: manhattan_accuracy
value: 81.25409787208679
- type: manhattan_accuracy_threshold
value: 593.7671894840605
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value: 55.411799272092175
- type: manhattan_f1
value: 53.751250416805604
- type: manhattan_f1_threshold
value: 732.5377952336567
- type: manhattan_precision
value: 46.43748799692721
- type: manhattan_recall
value: 63.79947229551451
- type: max_accuracy
value: 81.25409787208679
- type: max_ap
value: 55.411799272092175
- type: max_f1
value: 53.80317025886646
- type: max_precision
value: 47.37899176541474
- type: max_recall
value: 63.79947229551451
- type: similarity_accuracy
value: 81.09912380044108
- type: similarity_accuracy_threshold
value: 78.23829613291026
- type: similarity_ap
value: 55.35060563890305
- type: similarity_f1
value: 53.80317025886646
- type: similarity_f1_threshold
value: 66.90525865927196
- type: similarity_precision
value: 47.37899176541474
- type: similarity_recall
value: 62.24274406332454
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 86.66123336049986
- type: cosine_accuracy_threshold
value: 65.02320188247097
- type: cosine_ap
value: 80.81687896238907
- type: cosine_f1
value: 73.12032134380135
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value: 58.973089149680625
- type: cosine_precision
value: 69.54226574980899
- type: cosine_recall
value: 77.08654142285187
- type: dot_accuracy
value: 86.66123336049986
- type: dot_accuracy_threshold
value: 65.02319875482263
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value: 80.81688006755317
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value: 73.12032134380135
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value: 58.97308824803296
- type: dot_precision
value: 69.54226574980899
- type: dot_recall
value: 77.08654142285187
- type: euclidean_accuracy
value: 86.66123336049986
- type: euclidean_accuracy_threshold
value: 83.63826448345795
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value: 80.8168799506033
- type: euclidean_f1
value: 73.12032134380135
- type: euclidean_f1_threshold
value: 90.58356387644642
- type: euclidean_precision
value: 69.54226574980899
- type: euclidean_recall
value: 77.08654142285187
- type: main_score
value: 80.81688006755317
- type: manhattan_accuracy
value: 86.5564481701401
- type: manhattan_accuracy_threshold
value: 762.9837047696128
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value: 80.69253277787045
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value: 73.04996608636671
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value: 792.7533836003931
- type: manhattan_precision
value: 71.5350553505535
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value: 74.63042808746535
- type: max_accuracy
value: 86.66123336049986
- type: max_ap
value: 80.81688006755317
- type: max_f1
value: 73.12032134380135
- type: max_precision
value: 71.5350553505535
- type: max_recall
value: 77.08654142285187
- type: similarity_accuracy
value: 86.66123336049986
- type: similarity_accuracy_threshold
value: 65.02320188247097
- type: similarity_ap
value: 80.81687896238907
- type: similarity_f1
value: 73.12032134380135
- type: similarity_f1_threshold
value: 58.973089149680625
- type: similarity_precision
value: 69.54226574980899
- type: similarity_recall
value: 77.08654142285187
---
# potion-base-4M 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-4M")
# 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"
] |
abhinand/MedEmbed-small-v0.1
|
abhinand
| null |
[
"sentence-transformers",
"safetensors",
"bert",
"medembed",
"medical-embedding",
"clinical-embedding",
"information-retrieval",
"mteb",
"en",
"dataset:MedicalQARetrieval",
"dataset:NFCorpus",
"dataset:PublicHealthQA",
"dataset:TRECCOVID",
"dataset:ArguAna",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"license:apache-2.0",
"model-index",
"region:us"
] | 2024-10-20T11:47:50 | 2024-10-23T14:49:50 | 6,846 | 8 |
---
base_model:
- BAAI/bge-small-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
- mteb
model-index:
- name: abhinand/MedEmbed-small-v0.1
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: 72.17391304347827
- type: ap
value: 21.757637881353535
- type: ap_weighted
value: 21.757637881353535
- type: f1
value: 59.80304692298741
- type: f1_weighted
value: 77.3761270422597
- type: main_score
value: 72.17391304347827
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.28358208955224
- type: ap
value: 33.51413347752456
- type: ap_weighted
value: 33.51413347752456
- type: f1
value: 65.07760889689999
- type: f1_weighted
value: 74.00602410875776
- type: main_score
value: 71.28358208955224
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.99472500000002
- type: ap
value: 88.24057492408383
- type: ap_weighted
value: 88.24057492408383
- type: f1
value: 91.97746777375899
- type: f1_weighted
value: 91.97746777375899
- type: main_score
value: 91.99472500000002
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.211999999999996
- type: f1
value: 46.94308842799891
- type: f1_weighted
value: 46.94308842799891
- type: main_score
value: 48.211999999999996
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 61.587
- type: map_at_1
value: 37.980000000000004
- type: map_at_10
value: 53.40200000000001
- type: map_at_100
value: 54.032000000000004
- type: map_at_1000
value: 54.038
- type: map_at_20
value: 53.898999999999994
- type: map_at_3
value: 49.123
- type: map_at_5
value: 51.747
- type: mrr_at_1
value: 38.26458036984353
- type: mrr_at_10
value: 53.522206416943355
- type: mrr_at_100
value: 54.145400691658374
- type: mrr_at_1000
value: 54.150812285856695
- type: mrr_at_20
value: 54.015571340811796
- type: mrr_at_3
value: 49.3006164058796
- type: mrr_at_5
value: 51.850403034613656
- type: nauc_map_at_1000_diff1
value: 12.462809218755954
- type: nauc_map_at_1000_max
value: -8.081945194296322
- type: nauc_map_at_1000_std
value: -6.165333174593185
- type: nauc_map_at_100_diff1
value: 12.46115346472636
- type: nauc_map_at_100_max
value: -8.07700864766809
- type: nauc_map_at_100_std
value: -6.154824360110573
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type: mteb/reddit-clustering
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type: mteb/scidocs
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type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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type: mteb/sts12-sts
config: default
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type: mteb/sts13-sts
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type: mteb/sts14-sts
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type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 80.9001128553287
- type: cosine_spearman
value: 83.28982485088675
- type: euclidean_pearson
value: 82.42648548297315
- type: euclidean_spearman
value: 83.28990050342193
- type: main_score
value: 83.28982485088675
- type: manhattan_pearson
value: 82.25070148571425
- type: manhattan_spearman
value: 83.07757318740721
- type: pearson
value: 80.9001128553287
- type: spearman
value: 83.28982485088675
- 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: 24.18877892078201
- type: cosine_spearman
value: 20.233596577843038
- type: euclidean_pearson
value: 24.542177362845315
- type: euclidean_spearman
value: 20.233596577843038
- type: main_score
value: 20.233596577843038
- type: manhattan_pearson
value: 24.01700616075699
- type: manhattan_spearman
value: 19.446659958484517
- type: pearson
value: 24.18877892078201
- type: spearman
value: 20.233596577843038
- 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: 8.614199975001977
- type: cosine_spearman
value: 5.012961284277124
- type: euclidean_pearson
value: 8.84952193581556
- type: euclidean_spearman
value: 5.012961284277124
- type: main_score
value: 5.012961284277124
- type: manhattan_pearson
value: 8.745277048601178
- type: manhattan_spearman
value: 5.409735174524082
- type: pearson
value: 8.614199975001977
- type: spearman
value: 5.012961284277124
- 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: 32.392432370287786
- type: cosine_spearman
value: 29.30493234698128
- type: euclidean_pearson
value: 32.966478634610255
- type: euclidean_spearman
value: 29.30493234698128
- type: main_score
value: 29.30493234698128
- type: manhattan_pearson
value: 32.755965728091894
- type: manhattan_spearman
value: 29.146714726559253
- type: pearson
value: 32.392432370287786
- type: spearman
value: 29.30493234698128
- 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: 20.254702485801023
- type: cosine_spearman
value: 19.721956722605672
- type: euclidean_pearson
value: 19.871717953344167
- type: euclidean_spearman
value: 19.721956722605672
- type: main_score
value: 19.721956722605672
- type: manhattan_pearson
value: 20.449457320012122
- type: manhattan_spearman
value: 20.169665776497684
- type: pearson
value: 20.254702485801023
- type: spearman
value: 19.721956722605672
- 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: 28.074886980533577
- type: cosine_spearman
value: 24.306393355436498
- type: euclidean_pearson
value: 29.01202135632306
- type: euclidean_spearman
value: 24.306393355436498
- type: main_score
value: 24.306393355436498
- type: manhattan_pearson
value: 29.1296157400599
- type: manhattan_spearman
value: 23.73491100295491
- type: pearson
value: 28.074886980533577
- type: spearman
value: 24.306393355436498
- 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: -0.5773611433810728
- type: cosine_spearman
value: -1.0982086986987292
- type: euclidean_pearson
value: -0.5206158458966739
- type: euclidean_spearman
value: -1.0982086986987292
- type: main_score
value: -1.0982086986987292
- type: manhattan_pearson
value: -1.0668301997346301
- type: manhattan_spearman
value: -0.8412954712140625
- type: pearson
value: -0.5773611433810728
- type: spearman
value: -1.0982086986987292
- 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: 86.27108126511854
- type: cosine_spearman
value: 86.53957993982179
- type: euclidean_pearson
value: 87.62799362362965
- type: euclidean_spearman
value: 86.53957993982179
- type: main_score
value: 86.53957993982179
- type: manhattan_pearson
value: 87.6959515498115
- type: manhattan_spearman
value: 86.64863324136145
- type: pearson
value: 86.27108126511854
- type: spearman
value: 86.53957993982179
- 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: 26.150575010131767
- type: cosine_spearman
value: 22.06712968681051
- type: euclidean_pearson
value: 26.604960551656553
- type: euclidean_spearman
value: 22.06712968681051
- type: main_score
value: 22.06712968681051
- type: manhattan_pearson
value: 26.88338799013417
- type: manhattan_spearman
value: 22.431306979297936
- type: pearson
value: 26.150575010131767
- type: spearman
value: 22.06712968681051
- 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: 56.43553760517022
- type: cosine_spearman
value: 61.54782397245725
- type: euclidean_pearson
value: 57.49144139445497
- type: euclidean_spearman
value: 61.54782397245725
- type: main_score
value: 61.54782397245725
- type: manhattan_pearson
value: 57.23292330897806
- type: manhattan_spearman
value: 61.072557803031756
- type: pearson
value: 56.43553760517022
- type: spearman
value: 61.54782397245725
- 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: 42.54975380534044
- type: cosine_spearman
value: 46.810373173640016
- type: euclidean_pearson
value: 45.28349759462344
- type: euclidean_spearman
value: 46.810373173640016
- type: main_score
value: 46.810373173640016
- type: manhattan_pearson
value: 46.16729933212417
- type: manhattan_spearman
value: 46.249145972529426
- type: pearson
value: 42.54975380534044
- type: spearman
value: 46.810373173640016
- 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: 42.33771713953653
- type: cosine_spearman
value: 41.91423247431431
- type: euclidean_pearson
value: 43.03252081424651
- type: euclidean_spearman
value: 41.91423247431431
- type: main_score
value: 41.91423247431431
- type: manhattan_pearson
value: 41.39868682401022
- type: manhattan_spearman
value: 40.26808563589977
- type: pearson
value: 42.33771713953653
- type: spearman
value: 41.91423247431431
- 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: 48.18269761507854
- type: cosine_spearman
value: 50.6785956820247
- type: euclidean_pearson
value: 48.610255848327895
- type: euclidean_spearman
value: 50.6785956820247
- type: main_score
value: 50.6785956820247
- type: manhattan_pearson
value: 48.558643114423774
- type: manhattan_spearman
value: 50.40531034934534
- type: pearson
value: 48.18269761507854
- type: spearman
value: 50.6785956820247
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 65.33289638983779
- type: cosine_spearman
value: 66.80763004782261
- type: euclidean_pearson
value: 67.9778359567448
- type: euclidean_spearman
value: 66.80763004782261
- type: main_score
value: 66.80763004782261
- type: manhattan_pearson
value: 68.49657450051612
- type: manhattan_spearman
value: 67.36431350100104
- type: pearson
value: 65.33289638983779
- type: spearman
value: 66.80763004782261
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 83.11688439720268
- type: cosine_spearman
value: 84.81184678969443
- type: euclidean_pearson
value: 84.74087810156583
- type: euclidean_spearman
value: 84.81189525583689
- type: main_score
value: 84.81184678969443
- type: manhattan_pearson
value: 84.55725669112154
- type: manhattan_spearman
value: 84.65629518341167
- type: pearson
value: 83.11688439720268
- type: spearman
value: 84.81184678969443
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 85.79326406191477
- type: map
value: 85.79326406191477
- type: mrr
value: 96.01126632989377
- type: nAUC_map_diff1
value: -1.9960063892305635
- type: nAUC_map_max
value: 52.28855245081865
- type: nAUC_map_std
value: 66.17006861709118
- type: nAUC_mrr_diff1
value: 37.199120271359995
- type: nAUC_mrr_max
value: 83.25191469254256
- type: nAUC_mrr_std
value: 77.46103699775429
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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value: 70.781
- type: map_at_1
value: 56.15
- type: map_at_10
value: 66.068
- type: map_at_100
value: 66.606
- type: map_at_1000
value: 66.62700000000001
- type: map_at_20
value: 66.393
- type: map_at_3
value: 63.273999999999994
- type: map_at_5
value: 64.97699999999999
- type: mrr_at_1
value: 59.0
- type: mrr_at_10
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- type: mrr_at_100
value: 67.52274973457133
- type: mrr_at_1000
value: 67.54192035738791
- type: mrr_at_20
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- type: mrr_at_3
value: 64.77777777777779
- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
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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_100_diff1
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- type: nauc_map_at_20_diff1
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- type: precision_at_3
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- type: precision_at_5
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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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- type: recall_at_3
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type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
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type: mteb/stackexchange-clustering
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split: test
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value: 12.20603394911171
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value: -17.251065315072193
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value: 26.867651256647452
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value: 10.093913963838736
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value: -20.946372820355073
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value: 42.58398961954329
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value: 27.541259514336
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value: 26.488575954326027
- type: nauc_recall_at_1000_std
value: 77.42345371512604
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value: 0.3196527391909681
- type: nauc_recall_at_100_max
value: -24.479150613828303
- type: nauc_recall_at_100_std
value: 39.2629664046755
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value: -12.59639639211954
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value: -28.209370861454307
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value: -7.833213547133838
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value: -20.82132515478208
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value: -40.886965604054176
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value: -23.05338042822077
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value: -5.180606615847058
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value: -19.492523770094547
- type: nauc_recall_at_20_std
value: 3.0890655409078276
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value: -7.4383614317036715
- type: nauc_recall_at_3_max
value: -33.467231727496504
- type: nauc_recall_at_3_std
value: -10.871143037448503
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value: -6.729176537186017
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value: -34.57305958555233
- type: nauc_recall_at_5_std
value: -4.486225513133468
- type: ndcg_at_1
value: 60.204
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value: 56.196
- type: ndcg_at_100
value: 58.08
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value: 69.069
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value: 50.604000000000006
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value: 59.114
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value: 59.52499999999999
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value: 73.469
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value: 63.26500000000001
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value: 19.796
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value: 3.102
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value: 49.592000000000006
- type: precision_at_3
value: 69.388
- type: precision_at_5
value: 70.612
- type: recall_at_1
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- type: recall_at_1000
value: 88.495
- type: recall_at_20
value: 34.344
- type: recall_at_3
value: 7.571
- type: recall_at_5
value: 12.606
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 65.8447265625
- type: ap
value: 11.790127057253194
- type: ap_weighted
value: 11.790127057253194
- type: f1
value: 50.28742613560235
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value: 73.24450181406255
- type: main_score
value: 65.8447265625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
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value: 61.349745331069606
- type: f1
value: 61.502480965412744
- type: f1_weighted
value: 60.39561856225271
- type: main_score
value: 61.349745331069606
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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value: 47.338360343180106
- type: v_measure
value: 47.338360343180106
- type: v_measure_std
value: 2.01314014968057
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type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 85.49204267747511
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value: 86.85047030448914
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value: 72.98051397210814
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value: 84.59665775299072
- type: cosine_precision
value: 64.69861660079052
- type: cosine_recall
value: 69.10290237467018
- type: dot_accuracy
value: 85.49204267747511
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- type: dot_ap
value: 72.98051075246349
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- type: dot_recall
value: 69.10290237467018
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value: 55.50377368927002
- type: euclidean_precision
value: 64.69861660079052
- type: euclidean_recall
value: 69.10290237467018
- type: main_score
value: 72.98052075606988
- type: manhattan_accuracy
value: 85.43839780652083
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value: 796.9008445739746
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value: 72.80895903518599
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value: 66.64168852254278
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value: 871.8400955200195
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value: 63.267725871472614
- type: manhattan_recall
value: 70.3957783641161
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value: 85.49204267747511
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value: 72.98052075606988
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value: 66.8282725184996
- type: max_precision
value: 64.69861660079052
- type: max_recall
value: 70.3957783641161
- type: similarity_accuracy
value: 85.49204267747511
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value: 86.85047030448914
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value: 84.59665775299072
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value: 64.69861660079052
- type: similarity_recall
value: 69.10290237467018
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 81.41384720802307
- type: dot_precision
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value: 60.96909046173096
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- type: manhattan_accuracy
value: 88.4192959987581
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value: 907.758617401123
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value: 76.9788698516079
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value: 74.10758817242608
- type: manhattan_recall
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- type: max_accuracy
value: 88.4192959987581
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value: 79.95072374499537
- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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- type: recall_at_5
value: 9.945
---
# 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",
"SUMMARIZATION"
] |
[
"BIOSSES",
"MEDICAL DATA",
"SCIFACT"
] |
EleutherAI/pythia-12b-deduped
|
EleutherAI
|
text-generation
|
[
"transformers",
"pytorch",
"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"
] | 2023-02-27T14:01:34 | 2023-06-08T13:07:07 | 6,792 | 51 |
---
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-12B-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-12B-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-12B-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-12B-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-12B-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-12B-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-12B-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-12B-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-12B-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"
] |
ibm-granite/granite-embedding-107m-multilingual
|
ibm-granite
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"feature-extraction",
"language",
"granite",
"embeddings",
"multilingual",
"mteb",
"transformers",
"sentence-similarity",
"en",
"ar",
"cs",
"de",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pt",
"zh",
"arxiv:0000.00000",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-04T20:29:00 | 2025-03-04T15:11:23 | 6,783 | 19 |
---
language:
- en
- ar
- cs
- de
- es
- fr
- it
- ja
- ko
- nl
- pt
- zh
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- language
- granite
- embeddings
- multilingual
- mteb
- transformers
model-index:
- name: ibm-granite/granite-embedding-107m-multilingual
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: 72.7136
- type: f1
value: 60.44540000000001
- type: f1_weighted
value: 77.8541
- type: ap
value: 22.4958
- type: ap_weighted
value: 22.4958
- type: main_score
value: 72.7136
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.6716
- type: f1
value: 65.4221
- type: f1_weighted
value: 74.3533
- type: ap
value: 33.7567
- type: ap_weighted
value: 33.7567
- type: main_score
value: 71.6716
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 66.5804
- type: f1
value: 66.2191
- type: f1_weighted
value: 66.2191
- type: ap
value: 61.340799999999994
- type: ap_weighted
value: 61.340799999999994
- type: main_score
value: 66.5804
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.412
- type: f1
value: 35.633199999999995
- type: f1_weighted
value: 35.633199999999995
- type: main_score
value: 36.412
- task:
type: Retrieval
dataset:
name: MTEB AppsRetrieval (default)
type: CoIR-Retrieval/apps
config: default
split: test
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
metrics:
- type: ndcg_at_1
value: 2.39
- type: ndcg_at_3
value: 3.527
- type: ndcg_at_5
value: 3.9759999999999995
- type: ndcg_at_10
value: 4.537
- type: ndcg_at_20
value: 5.140000000000001
- type: ndcg_at_100
value: 6.526
- type: ndcg_at_1000
value: 9.797
- type: map_at_1
value: 2.39
- type: map_at_3
value: 3.2489999999999997
- type: map_at_5
value: 3.499
- type: map_at_10
value: 3.7220000000000004
- type: map_at_20
value: 3.887
- type: map_at_100
value: 4.058
- type: map_at_1000
value: 4.146
- type: recall_at_1
value: 2.39
- type: recall_at_3
value: 4.329000000000001
- type: recall_at_5
value: 5.418
- type: recall_at_10
value: 7.198
- type: recall_at_20
value: 9.588000000000001
- type: recall_at_100
value: 17.371
- type: recall_at_1000
value: 45.206
- type: precision_at_1
value: 2.39
- type: precision_at_3
value: 1.443
- type: precision_at_5
value: 1.084
- type: precision_at_10
value: 0.72
- type: precision_at_20
value: 0.479
- type: precision_at_100
value: 0.174
- type: precision_at_1000
value: 0.045
- type: mrr_at_1
value: 2.3904
- type: mrr_at_3
value: 3.2492
- type: mrr_at_5
value: 3.4989
- type: mrr_at_10
value: 3.7220000000000004
- type: mrr_at_20
value: 3.8869000000000002
- type: mrr_at_100
value: 4.0578
- type: mrr_at_1000
value: 4.1463
- type: nauc_ndcg_at_1_max
value: 37.599700000000006
- type: nauc_ndcg_at_1_std
value: 20.302899999999998
- type: nauc_ndcg_at_1_diff1
value: 40.4987
- type: nauc_ndcg_at_3_max
value: 31.119400000000002
- type: nauc_ndcg_at_3_std
value: 11.7335
- type: nauc_ndcg_at_3_diff1
value: 28.788000000000004
- type: nauc_ndcg_at_5_max
value: 28.505399999999998
- type: nauc_ndcg_at_5_std
value: 12.1402
- type: nauc_ndcg_at_5_diff1
value: 25.730900000000002
- type: nauc_ndcg_at_10_max
value: 27.0656
- type: nauc_ndcg_at_10_std
value: 12.648699999999998
- type: nauc_ndcg_at_10_diff1
value: 22.0832
- type: nauc_ndcg_at_20_max
value: 25.953599999999998
- type: nauc_ndcg_at_20_std
value: 12.550500000000001
- type: nauc_ndcg_at_20_diff1
value: 19.3722
- type: nauc_ndcg_at_100_max
value: 23.268
- type: nauc_ndcg_at_100_std
value: 12.8176
- type: nauc_ndcg_at_100_diff1
value: 15.9275
- type: nauc_ndcg_at_1000_max
value: 21.921499999999998
- type: nauc_ndcg_at_1000_std
value: 12.656300000000002
- type: nauc_ndcg_at_1000_diff1
value: 13.9004
- type: nauc_map_at_1_max
value: 37.599700000000006
- type: nauc_map_at_1_std
value: 20.302899999999998
- type: nauc_map_at_1_diff1
value: 40.4987
- type: nauc_map_at_3_max
value: 32.2818
- type: nauc_map_at_3_std
value: 13.276399999999999
- type: nauc_map_at_3_diff1
value: 30.9064
- type: nauc_map_at_5_max
value: 30.5166
- type: nauc_map_at_5_std
value: 13.406
- type: nauc_map_at_5_diff1
value: 28.8213
- type: nauc_map_at_10_max
value: 29.731999999999996
- type: nauc_map_at_10_std
value: 13.5688
- type: nauc_map_at_10_diff1
value: 26.888499999999997
- type: nauc_map_at_20_max
value: 29.211399999999998
- type: nauc_map_at_20_std
value: 13.4739
- type: nauc_map_at_20_diff1
value: 25.6814
- type: nauc_map_at_100_max
value: 28.578300000000002
- type: nauc_map_at_100_std
value: 13.5385
- type: nauc_map_at_100_diff1
value: 24.793100000000003
- type: nauc_map_at_1000_max
value: 28.3912
- type: nauc_map_at_1000_std
value: 13.5039
- type: nauc_map_at_1000_diff1
value: 24.570600000000002
- type: nauc_recall_at_1_max
value: 37.599700000000006
- type: nauc_recall_at_1_std
value: 20.302899999999998
- type: nauc_recall_at_1_diff1
value: 40.4987
- type: nauc_recall_at_3_max
value: 28.598000000000003
- type: nauc_recall_at_3_std
value: 8.3847
- type: nauc_recall_at_3_diff1
value: 24.1871
- type: nauc_recall_at_5_max
value: 24.5381
- type: nauc_recall_at_5_std
value: 9.8274
- type: nauc_recall_at_5_diff1
value: 19.6821
- type: nauc_recall_at_10_max
value: 22.5445
- type: nauc_recall_at_10_std
value: 11.4415
- type: nauc_recall_at_10_diff1
value: 13.8268
- type: nauc_recall_at_20_max
value: 21.3196
- type: nauc_recall_at_20_std
value: 11.5932
- type: nauc_recall_at_20_diff1
value: 10.1991
- type: nauc_recall_at_100_max
value: 16.9415
- type: nauc_recall_at_100_std
value: 12.353200000000001
- type: nauc_recall_at_100_diff1
value: 5.7534
- type: nauc_recall_at_1000_max
value: 15.9223
- type: nauc_recall_at_1000_std
value: 12.2848
- type: nauc_recall_at_1000_diff1
value: 3.5477000000000003
- type: nauc_precision_at_1_max
value: 37.599700000000006
- type: nauc_precision_at_1_std
value: 20.302899999999998
- type: nauc_precision_at_1_diff1
value: 40.4987
- type: nauc_precision_at_3_max
value: 28.598000000000003
- type: nauc_precision_at_3_std
value: 8.3847
- type: nauc_precision_at_3_diff1
value: 24.1871
- type: nauc_precision_at_5_max
value: 24.5381
- type: nauc_precision_at_5_std
value: 9.8274
- type: nauc_precision_at_5_diff1
value: 19.6821
- type: nauc_precision_at_10_max
value: 22.5445
- type: nauc_precision_at_10_std
value: 11.4415
- type: nauc_precision_at_10_diff1
value: 13.8268
- type: nauc_precision_at_20_max
value: 21.3196
- type: nauc_precision_at_20_std
value: 11.5932
- type: nauc_precision_at_20_diff1
value: 10.1991
- type: nauc_precision_at_100_max
value: 16.9415
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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
metrics:
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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
metrics:
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type: Clustering
dataset:
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type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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dataset:
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type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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dataset:
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type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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type: mteb/nq
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value: 74.6345
- type: spearman
value: 75.63990000000001
- type: cosine_pearson
value: 74.6345
- type: cosine_spearman
value: 75.63990000000001
- type: manhattan_pearson
value: 75.5227
- type: manhattan_spearman
value: 75.5136
- type: euclidean_pearson
value: 75.5744
- type: euclidean_spearman
value: 75.63990000000001
- type: main_score
value: 75.63990000000001
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 76.66629999999999
- type: spearman
value: 73.1976
- type: cosine_pearson
value: 76.66629999999999
- type: cosine_spearman
value: 73.1976
- type: manhattan_pearson
value: 75.0827
- type: manhattan_spearman
value: 73.2472
- type: euclidean_pearson
value: 75.2873
- type: euclidean_spearman
value: 73.1976
- type: main_score
value: 73.1976
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 84.33810000000001
- type: spearman
value: 85.0551
- type: cosine_pearson
value: 84.33810000000001
- type: cosine_spearman
value: 85.0551
- type: manhattan_pearson
value: 84.5984
- type: manhattan_spearman
value: 85.1619
- type: euclidean_pearson
value: 84.529
- type: euclidean_spearman
value: 85.0551
- type: main_score
value: 85.0551
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 79.5933
- type: spearman
value: 81.11120000000001
- type: cosine_pearson
value: 79.5933
- type: cosine_spearman
value: 81.11120000000001
- type: manhattan_pearson
value: 80.136
- type: manhattan_spearman
value: 80.8767
- type: euclidean_pearson
value: 80.3305
- type: euclidean_spearman
value: 81.11120000000001
- type: main_score
value: 81.11120000000001
- 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: 38.2331
- type: spearman
value: 33.7346
- type: cosine_pearson
value: 38.2331
- type: cosine_spearman
value: 33.7346
- type: manhattan_pearson
value: 40.986
- type: manhattan_spearman
value: 34.253099999999996
- type: euclidean_pearson
value: 40.2622
- type: euclidean_spearman
value: 33.7346
- type: main_score
value: 33.7346
- 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: 73.5477
- type: spearman
value: 74.1745
- type: cosine_pearson
value: 73.5477
- type: cosine_spearman
value: 74.1745
- type: manhattan_pearson
value: 74.84920000000001
- type: manhattan_spearman
value: 74.49900000000001
- type: euclidean_pearson
value: 74.14
- type: euclidean_spearman
value: 74.1745
- type: main_score
value: 74.1745
- 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: 66.7169
- type: spearman
value: 66.864
- type: cosine_pearson
value: 66.7169
- type: cosine_spearman
value: 66.864
- type: manhattan_pearson
value: 67.39359999999999
- type: manhattan_spearman
value: 67.0985
- type: euclidean_pearson
value: 66.9389
- type: euclidean_spearman
value: 66.864
- type: main_score
value: 66.864
- 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: 70.5101
- type: spearman
value: 70.05930000000001
- type: cosine_pearson
value: 70.5101
- type: cosine_spearman
value: 70.05930000000001
- type: manhattan_pearson
value: 72.7524
- type: manhattan_spearman
value: 71.2907
- type: euclidean_pearson
value: 71.148
- type: euclidean_spearman
value: 70.05930000000001
- type: main_score
value: 70.05930000000001
- 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: 68.3089
- type: spearman
value: 68.4899
- type: cosine_pearson
value: 68.3089
- type: cosine_spearman
value: 68.4899
- type: manhattan_pearson
value: 69.3956
- type: manhattan_spearman
value: 68.9486
- type: euclidean_pearson
value: 68.8059
- type: euclidean_spearman
value: 68.4899
- type: main_score
value: 68.4899
- 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: 78.28739999999999
- type: spearman
value: 78.6966
- type: cosine_pearson
value: 78.28739999999999
- type: cosine_spearman
value: 78.6966
- type: manhattan_pearson
value: 78.97070000000001
- type: manhattan_spearman
value: 79.1907
- type: euclidean_pearson
value: 78.36070000000001
- type: euclidean_spearman
value: 78.6966
- type: main_score
value: 78.6966
- 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: 59.611999999999995
- type: spearman
value: 59.9288
- type: cosine_pearson
value: 59.611999999999995
- type: cosine_spearman
value: 59.9288
- type: manhattan_pearson
value: 60.3549
- type: manhattan_spearman
value: 59.696099999999994
- type: euclidean_pearson
value: 60.4754
- type: euclidean_spearman
value: 59.9288
- type: main_score
value: 59.9288
- 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: 70.6341
- type: spearman
value: 69.9775
- type: cosine_pearson
value: 70.6341
- type: cosine_spearman
value: 69.9775
- type: manhattan_pearson
value: 72.7788
- type: manhattan_spearman
value: 71.2033
- type: euclidean_pearson
value: 71.5822
- type: euclidean_spearman
value: 69.9775
- type: main_score
value: 69.9775
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 67.2703
- type: spearman
value: 67.58229999999999
- type: cosine_pearson
value: 67.2703
- type: cosine_spearman
value: 67.58229999999999
- type: manhattan_pearson
value: 68.1768
- type: manhattan_spearman
value: 67.6479
- type: euclidean_pearson
value: 67.9708
- type: euclidean_spearman
value: 67.58229999999999
- type: main_score
value: 67.58229999999999
- 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: 62.2109
- type: spearman
value: 56.2314
- type: cosine_pearson
value: 62.2109
- type: cosine_spearman
value: 56.2314
- type: manhattan_pearson
value: 65.9455
- type: manhattan_spearman
value: 56.5496
- type: euclidean_pearson
value: 65.30550000000001
- type: euclidean_spearman
value: 56.2314
- type: main_score
value: 56.2314
- 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: 74.4185
- type: spearman
value: 72.82119999999999
- type: cosine_pearson
value: 74.4185
- type: cosine_spearman
value: 72.82119999999999
- type: manhattan_pearson
value: 75.6921
- type: manhattan_spearman
value: 72.3315
- type: euclidean_pearson
value: 75.1725
- type: euclidean_spearman
value: 72.82119999999999
- type: main_score
value: 72.82119999999999
- 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: 78.6974
- type: spearman
value: 79.5845
- type: cosine_pearson
value: 78.6974
- type: cosine_spearman
value: 79.5845
- type: manhattan_pearson
value: 79.6724
- type: manhattan_spearman
value: 79.668
- type: euclidean_pearson
value: 79.69380000000001
- type: euclidean_spearman
value: 79.5845
- type: main_score
value: 79.5845
- 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: 71.3237
- type: spearman
value: 71.5178
- type: cosine_pearson
value: 71.3237
- type: cosine_spearman
value: 71.5178
- type: manhattan_pearson
value: 73.3948
- type: manhattan_spearman
value: 71.5607
- type: euclidean_pearson
value: 73.1403
- type: euclidean_spearman
value: 71.5178
- type: main_score
value: 71.5178
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 75.5279
- type: spearman
value: 76.9844
- type: cosine_pearson
value: 75.5279
- type: cosine_spearman
value: 76.9844
- type: manhattan_pearson
value: 77.5474
- type: manhattan_spearman
value: 77.4353
- type: euclidean_pearson
value: 77.1612
- type: euclidean_spearman
value: 76.9844
- type: main_score
value: 76.9844
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.33109999999999
- type: mrr
value: 94.0725
- type: nAUC_map_max
value: 59.0089
- type: nAUC_map_std
value: 69.9131
- type: nAUC_map_diff1
value: 5.900600000000001
- type: nAUC_mrr_max
value: 84.5132
- type: nAUC_mrr_std
value: 77.767
- type: nAUC_mrr_diff1
value: 46.5557
- type: main_score
value: 79.33109999999999
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_3
value: 57.781000000000006
- type: ndcg_at_5
value: 60.925
- type: ndcg_at_10
value: 63.254
- type: ndcg_at_20
value: 64.955
- type: ndcg_at_100
value: 66.155
- type: ndcg_at_1000
value: 67.193
- type: map_at_1
value: 48.428
- type: map_at_3
value: 55.145999999999994
- type: map_at_5
value: 57.055
- type: map_at_10
value: 58.17
- type: map_at_20
value: 58.723000000000006
- type: map_at_100
value: 58.901
- type: map_at_1000
value: 58.940000000000005
- type: recall_at_1
value: 48.428
- type: recall_at_3
value: 62.55
- type: recall_at_5
value: 70.367
- type: recall_at_10
value: 76.972
- type: recall_at_20
value: 83.317
- type: recall_at_100
value: 89.7
- type: recall_at_1000
value: 98.0
- type: precision_at_1
value: 51.333
- type: precision_at_3
value: 22.444
- type: precision_at_5
value: 15.4
- type: precision_at_10
value: 8.6
- type: precision_at_20
value: 4.717
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.11100000000000002
- type: mrr_at_1
value: 51.3333
- type: mrr_at_3
value: 57.5556
- type: mrr_at_5
value: 59.255599999999994
- type: mrr_at_10
value: 60.104400000000005
- type: mrr_at_20
value: 60.4592
- type: mrr_at_100
value: 60.590999999999994
- type: mrr_at_1000
value: 60.622299999999996
- type: nauc_ndcg_at_1_max
value: 55.0684
- type: nauc_ndcg_at_1_std
value: 13.461200000000002
- type: nauc_ndcg_at_1_diff1
value: 67.4931
- type: nauc_ndcg_at_3_max
value: 54.1942
- type: nauc_ndcg_at_3_std
value: 11.029300000000001
- type: nauc_ndcg_at_3_diff1
value: 61.4423
- type: nauc_ndcg_at_5_max
value: 53.712199999999996
- type: nauc_ndcg_at_5_std
value: 11.0586
- type: nauc_ndcg_at_5_diff1
value: 59.3723
- type: nauc_ndcg_at_10_max
value: 55.2513
- type: nauc_ndcg_at_10_std
value: 13.413400000000001
- type: nauc_ndcg_at_10_diff1
value: 58.5176
- type: nauc_ndcg_at_20_max
value: 56.721900000000005
- type: nauc_ndcg_at_20_std
value: 14.9832
- type: nauc_ndcg_at_20_diff1
value: 59.1445
- type: nauc_ndcg_at_100_max
value: 56.5049
- type: nauc_ndcg_at_100_std
value: 15.021799999999999
- type: nauc_ndcg_at_100_diff1
value: 59.4117
- type: nauc_ndcg_at_1000_max
value: 56.0829
- type: nauc_ndcg_at_1000_std
value: 14.4429
- type: nauc_ndcg_at_1000_diff1
value: 60.45700000000001
- type: nauc_map_at_1_max
value: 50.901799999999994
- type: nauc_map_at_1_std
value: 6.0093
- type: nauc_map_at_1_diff1
value: 66.6214
- type: nauc_map_at_3_max
value: 52.684200000000004
- type: nauc_map_at_3_std
value: 7.9088
- type: nauc_map_at_3_diff1
value: 62.906600000000005
- type: nauc_map_at_5_max
value: 52.6187
- type: nauc_map_at_5_std
value: 8.2372
- type: nauc_map_at_5_diff1
value: 61.772000000000006
- type: nauc_map_at_10_max
value: 53.317899999999995
- type: nauc_map_at_10_std
value: 9.397
- type: nauc_map_at_10_diff1
value: 61.355599999999995
- type: nauc_map_at_20_max
value: 54.04259999999999
- type: nauc_map_at_20_std
value: 10.2201
- type: nauc_map_at_20_diff1
value: 61.684000000000005
- type: nauc_map_at_100_max
value: 54.0394
- type: nauc_map_at_100_std
value: 10.2894
- type: nauc_map_at_100_diff1
value: 61.7302
- type: nauc_map_at_1000_max
value: 54.024300000000004
- type: nauc_map_at_1000_std
value: 10.2881
- type: nauc_map_at_1000_diff1
value: 61.7661
- type: nauc_recall_at_1_max
value: 50.901799999999994
- type: nauc_recall_at_1_std
value: 6.0093
- type: nauc_recall_at_1_diff1
value: 66.6214
- type: nauc_recall_at_3_max
value: 52.8806
- type: nauc_recall_at_3_std
value: 10.7463
- type: nauc_recall_at_3_diff1
value: 55.5486
- type: nauc_recall_at_5_max
value: 52.277300000000004
- type: nauc_recall_at_5_std
value: 12.2395
- type: nauc_recall_at_5_diff1
value: 49.147800000000004
- type: nauc_recall_at_10_max
value: 57.403499999999994
- type: nauc_recall_at_10_std
value: 20.4581
- type: nauc_recall_at_10_diff1
value: 44.0595
- type: nauc_recall_at_20_max
value: 65.5378
- type: nauc_recall_at_20_std
value: 29.5288
- type: nauc_recall_at_20_diff1
value: 43.2217
- type: nauc_recall_at_100_max
value: 67.4941
- type: nauc_recall_at_100_std
value: 36.178399999999996
- type: nauc_recall_at_100_diff1
value: 39.3443
- type: nauc_recall_at_1000_max
value: 72.50229999999999
- type: nauc_recall_at_1000_std
value: 51.455
- type: nauc_recall_at_1000_diff1
value: 62.153800000000004
- type: nauc_precision_at_1_max
value: 55.0684
- type: nauc_precision_at_1_std
value: 13.461200000000002
- type: nauc_precision_at_1_diff1
value: 67.4931
- type: nauc_precision_at_3_max
value: 54.947599999999994
- type: nauc_precision_at_3_std
value: 23.1875
- type: nauc_precision_at_3_diff1
value: 51.166199999999996
- type: nauc_precision_at_5_max
value: 50.1483
- type: nauc_precision_at_5_std
value: 27.1119
- type: nauc_precision_at_5_diff1
value: 37.3846
- type: nauc_precision_at_10_max
value: 46.800799999999995
- type: nauc_precision_at_10_std
value: 37.737500000000004
- type: nauc_precision_at_10_diff1
value: 22.945999999999998
- type: nauc_precision_at_20_max
value: 43.980000000000004
- type: nauc_precision_at_20_std
value: 46.3352
- type: nauc_precision_at_20_diff1
value: 14.718300000000001
- type: nauc_precision_at_100_max
value: 34.8346
- type: nauc_precision_at_100_std
value: 49.0032
- type: nauc_precision_at_100_diff1
value: 4.7538
- type: nauc_precision_at_1000_max
value: 19.9994
- type: nauc_precision_at_1000_std
value: 51.132999999999996
- type: nauc_precision_at_1000_diff1
value: -6.5839
- type: nauc_mrr_at_1_max
value: 55.0684
- type: nauc_mrr_at_1_std
value: 13.461200000000002
- type: nauc_mrr_at_1_diff1
value: 67.4931
- type: nauc_mrr_at_3_max
value: 56.2153
- type: nauc_mrr_at_3_std
value: 15.4146
- type: nauc_mrr_at_3_diff1
value: 63.273199999999996
- type: nauc_mrr_at_5_max
value: 56.0011
- type: nauc_mrr_at_5_std
value: 15.7535
- type: nauc_mrr_at_5_diff1
value: 62.1466
- type: nauc_mrr_at_10_max
value: 56.643100000000004
- type: nauc_mrr_at_10_std
value: 16.354
- type: nauc_mrr_at_10_diff1
value: 62.0124
- type: nauc_mrr_at_20_max
value: 56.686800000000005
- type: nauc_mrr_at_20_std
value: 16.1984
- type: nauc_mrr_at_20_diff1
value: 62.095
- type: nauc_mrr_at_100_max
value: 56.6659
- type: nauc_mrr_at_100_std
value: 16.1601
- type: nauc_mrr_at_100_diff1
value: 62.157399999999996
- type: nauc_mrr_at_1000_max
value: 56.657599999999995
- type: nauc_mrr_at_1000_std
value: 16.1579
- type: nauc_mrr_at_1000_diff1
value: 62.195
- type: main_score
value: 63.254
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: similarity_accuracy
value: 99.7465
- type: similarity_accuracy_threshold
value: 84.08489999999999
- type: similarity_f1
value: 86.9388
- type: similarity_f1_threshold
value: 84.08489999999999
- type: similarity_precision
value: 88.75
- type: similarity_recall
value: 85.2
- type: similarity_ap
value: 93.56139999999999
- type: cosine_accuracy
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value: 88.75
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value: 94.0777
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value: 94.0777
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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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value: 32.6113
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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type: Retrieval
dataset:
name: MTEB StackOverflowQA (default)
type: CoIR-Retrieval/stackoverflow-qa
config: default
split: test
revision: db8f169f3894c14a00251061f957b2063eef2bd5
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type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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type: Retrieval
dataset:
name: MTEB SyntheticText2SQL (default)
type: CoIR-Retrieval/synthetic-text2sql
config: default
split: test
revision: 686b87296c3a0191b5d9415a00526c62db9fce09
metrics:
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type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
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- type: nauc_mrr_at_5_diff1
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- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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value: 34.694
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value: 27.029999999999998
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- type: ndcg_at_1000
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- type: map_at_1000
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- type: recall_at_1
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- type: recall_at_3
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- type: recall_at_5
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- type: recall_at_10
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- type: recall_at_20
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- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_3
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- type: precision_at_5
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- type: precision_at_10
value: 22.041
- 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
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- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_1000
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- type: nauc_mrr_at_1_max
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- type: nauc_mrr_at_3_max
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value: 11.4561
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value: 11.792900000000001
- type: nauc_mrr_at_10_max
value: -35.5993
- type: nauc_mrr_at_10_std
value: 13.369800000000001
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value: 14.168
- type: nauc_mrr_at_20_max
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value: 12.8052
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value: 13.6937
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value: 13.0847
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value: 13.0847
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value: 13.5063
- type: main_score
value: 24.853
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 60.380900000000004
- type: f1
value: 46.8295
- type: f1_weighted
value: 69.05930000000001
- type: ap
value: 10.5988
- type: ap_weighted
value: 10.5988
- type: main_score
value: 60.380900000000004
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.537099999999995
- type: f1
value: 58.7006
- type: f1_weighted
value: 58.013400000000004
- type: main_score
value: 58.537099999999995
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 36.6842
- type: v_measure_std
value: 1.9854
- type: main_score
value: 36.6842
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 82.3866
- type: similarity_accuracy_threshold
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- type: similarity_f1
value: 58.4102
- type: similarity_f1_threshold
value: 82.61540000000001
- type: similarity_precision
value: 52.937400000000004
- type: similarity_recall
value: 65.1451
- type: similarity_ap
value: 61.6413
- type: cosine_accuracy
value: 82.3866
- type: cosine_accuracy_threshold
value: 87.0467
- type: cosine_f1
value: 58.4102
- type: cosine_f1_threshold
value: 82.61540000000001
- type: cosine_precision
value: 52.937400000000004
- type: cosine_recall
value: 65.1451
- type: cosine_ap
value: 61.6413
- type: manhattan_accuracy
value: 82.12429999999999
- type: manhattan_accuracy_threshold
value: 786.2048
- type: manhattan_f1
value: 57.862899999999996
- type: manhattan_f1_threshold
value: 911.9348
- type: manhattan_precision
value: 50.2725
- type: manhattan_recall
value: 68.15299999999999
- type: manhattan_ap
value: 60.6893
- type: euclidean_accuracy
value: 82.3866
- type: euclidean_accuracy_threshold
value: 50.8985
- type: euclidean_f1
value: 58.4102
- type: euclidean_f1_threshold
value: 58.9654
- type: euclidean_precision
value: 52.937400000000004
- type: euclidean_recall
value: 65.1451
- type: euclidean_ap
value: 61.6413
- type: dot_accuracy
value: 82.3866
- type: dot_accuracy_threshold
value: 87.0467
- type: dot_f1
value: 58.4102
- type: dot_f1_threshold
value: 82.61540000000001
- type: dot_precision
value: 52.937400000000004
- type: dot_recall
value: 65.1451
- type: dot_ap
value: 61.6413
- type: max_accuracy
value: 82.3866
- type: max_f1
value: 58.4102
- type: max_precision
value: 52.937400000000004
- type: max_recall
value: 68.15299999999999
- type: max_ap
value: 61.6413
- type: main_score
value: 61.6413
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 88.77629999999999
- type: similarity_accuracy_threshold
value: 82.2251
- type: similarity_f1
value: 77.3613
- type: similarity_f1_threshold
value: 80.3174
- type: similarity_precision
value: 75.0906
- type: similarity_recall
value: 79.7736
- type: similarity_ap
value: 85.6694
- type: cosine_accuracy
value: 88.77629999999999
- type: cosine_accuracy_threshold
value: 82.2251
- type: cosine_f1
value: 77.3613
- type: cosine_f1_threshold
value: 80.3174
- type: cosine_precision
value: 75.0906
- type: cosine_recall
value: 79.7736
- type: cosine_ap
value: 85.6694
- type: manhattan_accuracy
value: 88.7317
- type: manhattan_accuracy_threshold
value: 914.4955
- type: manhattan_f1
value: 77.1707
- type: manhattan_f1_threshold
value: 946.5603
- type: manhattan_precision
value: 76.2825
- type: manhattan_recall
value: 78.0798
- type: manhattan_ap
value: 85.5718
- type: euclidean_accuracy
value: 88.77629999999999
- type: euclidean_accuracy_threshold
value: 59.6237
- type: euclidean_f1
value: 77.3613
- type: euclidean_f1_threshold
value: 62.7417
- type: euclidean_precision
value: 75.0906
- type: euclidean_recall
value: 79.7736
- type: euclidean_ap
value: 85.6694
- type: dot_accuracy
value: 88.77629999999999
- type: dot_accuracy_threshold
value: 82.2251
- type: dot_f1
value: 77.3613
- type: dot_f1_threshold
value: 80.3174
- type: dot_precision
value: 75.0906
- type: dot_recall
value: 79.7736
- type: dot_ap
value: 85.6694
- type: max_accuracy
value: 88.77629999999999
- type: max_f1
value: 77.3613
- type: max_precision
value: 76.2825
- type: max_recall
value: 79.7736
- type: max_ap
value: 85.6694
- type: main_score
value: 85.6694
---
# Granite-Embedding-107m-multilingual
**Model Summary:**
Granite-Embedding-107M-Multilingual is a 107M 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. This model is developed using 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, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite-Embedding-107M-Multilingual for languages beyond these 12 languages.
**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-107m-multilingual"
# 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-107m-Multilingual 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-107m-multilingual"
# 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-107m-multilingual uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
```
**Evaluation:**
The average performance of the Granite-Embedding-107M-Multilingual on Multilingual Miracl (across 18 langauges), Mintaka Retrieval (across 8 languages) and MTEB Retrieval for English (across 15 tasks), German (across 4 tasks), Spanish (across 2 tasks), Frenc (across 5 tasks), Japanese (across 2 tasks), Arabic (1 task), Korean (1 task) and Chinese (across 8 tasks) is reported below. Granite-Embedding-107M-Multilingual is twice as fast as other models with similar embedding dimensions.
| Model | Paramters (M)| Embedding Dimension | Miracl (18) | Mintaka Retrieval (8) | MTEB English (15) | MTEB German (4) |MTEB Spanish (2) | MTEB French (5) | MTEB Japanese (2) | MTEB Arabic (1) | MTEB Korean (1) | MTEB Chinese (8) |
|------------------------------------|:------------:|:-------------------:|:-------------:| :---------------------:|:-----------------:|:---------------:|:---------------:|:---------------:|:----------------:|:----------------:|----------------:|-----------------:|
|granite-embedding-107m-multilingual | 107 | 384 | 55.9 | 22.6 | 45.3 | 70.3 | 48.7 | 51.1 | 59.0 | 63.2 | 70.5 | 40.8 |
**Model Architecture:**
Granite-Embedding-107m-Multilingual is based on an encoder-only XLM-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** |
|:--------------------------------------------------------------------------|:--------------:|
| Multilingual MC4 | 52,823,484 |
| Multilingual Webhose | 12,369,322 |
| English Wikipedia | 20,745,403 |
| Multilingual Wikimedia | 2,911,090 |
| Miracl Corpus (Title-Body) | 10,120,398 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Machine Translations of Stack Exchange Duplicate questions (titles) | 187,195 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Machine Translations of Stack Exchange (Title+Body, Answer) pairs | 1,827,15 |
| SearchQA | 582,261 |
| S2ORC (Title, Abstract) | 41,769,185 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| CCNews | 614,664 |
| XSum | 226,711 |
| SimpleWiki | 102,225 |
| Machine Translated Cross Lingual Parallel Corpora | 28,376,115 |
| SPECTER citation triplets | 684,100 |
| Machine Translations of SPECTER citation triplets | 4,104,600 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| PubMed | 20,000,000 |
| Multilingual Miracl Triples | 81,409 |
| Multilingual MrTydi Triples | 48,715 |
| Sadeeem Question Asnwering | 4,037 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: English Query-Wikipedia Passage | 1,879,093 |
| Synthetic: English Fact Verification | 9,888 |
| Synthetic: Multilingual Query-Wikipedia Passage | 300,266 |
| Synthetic: Multilingual News Summaries | 37,489 |
| 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-107m-Multilingual is finetuned on 12 languages, 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},
}
``` -->
|
[
"TRANSLATION",
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
minishlab/potion-base-2M
|
minishlab
| null |
[
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"mteb",
"sentence-transformers",
"license:mit",
"model-index",
"region:us"
] | 2024-10-29T09:48:42 | 2025-01-21T19:13:44 | 6,772 | 10 |
---
library_name: model2vec
license: mit
tags:
- embeddings
- static-embeddings
- mteb
- sentence-transformers
model-index:
- name: potion-base-2M
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: 64.09295352323838
- type: ap
value: 15.890517627297978
- type: ap_weighted
value: 15.890517627297978
- type: f1
value: 52.38020164592307
- type: f1_weighted
value: 71.02083973787023
- type: main_score
value: 64.09295352323838
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 65.44776119402985
- type: ap
value: 27.63770109073248
- type: ap_weighted
value: 27.63770109073248
- type: f1
value: 58.90706680555824
- type: f1_weighted
value: 68.76825531256598
- type: main_score
value: 65.44776119402985
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 70.8279
- type: ap
value: 65.28490580225534
- type: ap_weighted
value: 65.28490580225534
- type: f1
value: 70.57831663695143
- type: f1_weighted
value: 70.5783166369514
- type: main_score
value: 70.8279
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 32.996
- type: f1
value: 32.31726739771069
- type: f1_weighted
value: 32.31726739771067
- type: main_score
value: 32.996
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 32.622
- type: map_at_1
value: 15.931999999999999
- type: map_at_10
value: 26.404
- type: map_at_100
value: 27.697
- type: map_at_1000
value: 27.755000000000003
- type: map_at_20
value: 27.195000000000004
- type: map_at_3
value: 22.819
- type: map_at_5
value: 24.714
- type: mrr_at_1
value: 16.35846372688478
- type: mrr_at_10
value: 26.554703199440034
- type: mrr_at_100
value: 27.847935657025847
- type: mrr_at_1000
value: 27.90589599760921
- type: mrr_at_20
value: 27.34605509902883
- type: mrr_at_3
value: 22.97297297297296
- type: mrr_at_5
value: 24.839971550497825
- type: nauc_map_at_1000_diff1
value: 9.371299425433035
- type: nauc_map_at_1000_max
value: 2.9574792255471287
- type: nauc_map_at_1000_std
value: 11.687859698836608
- type: nauc_map_at_100_diff1
value: 9.371053628047203
- type: nauc_map_at_100_max
value: 2.9809791205622322
- type: nauc_map_at_100_std
value: 11.75333361929675
- type: nauc_map_at_10_diff1
value: 9.252241357696322
- type: nauc_map_at_10_max
value: 2.742017052724334
- type: nauc_map_at_10_std
value: 11.558508764008751
- type: nauc_map_at_1_diff1
value: 12.16316227081504
- type: nauc_map_at_1_max
value: -0.5028808771807869
- type: nauc_map_at_1_std
value: 6.962721581652243
- type: nauc_map_at_20_diff1
value: 9.300115601627917
- type: nauc_map_at_20_max
value: 2.9569034588818686
- type: nauc_map_at_20_std
value: 11.691557039773556
- type: nauc_map_at_3_diff1
value: 8.848778277311055
- type: nauc_map_at_3_max
value: 1.423235443130067
- type: nauc_map_at_3_std
value: 8.992118754001654
- type: nauc_map_at_5_diff1
value: 8.57377738557976
- type: nauc_map_at_5_max
value: 1.6847244703988815
- type: nauc_map_at_5_std
value: 10.164700751752981
- type: nauc_mrr_at_1000_diff1
value: 7.83234430121121
- type: nauc_mrr_at_1000_max
value: 2.791250763636318
- type: nauc_mrr_at_1000_std
value: 11.622332535373612
- type: nauc_mrr_at_100_diff1
value: 7.834999518447015
- type: nauc_mrr_at_100_max
value: 2.8149797974038266
- type: nauc_mrr_at_100_std
value: 11.687441656533483
- type: nauc_mrr_at_10_diff1
value: 7.762034718278307
- type: nauc_mrr_at_10_max
value: 2.588154258263355
- type: nauc_mrr_at_10_std
value: 11.496120973626677
- type: nauc_mrr_at_1_diff1
value: 10.195687129188947
- type: nauc_mrr_at_1_max
value: 0.7959780949834511
- type: nauc_mrr_at_1_std
value: 6.909734959320861
- type: nauc_mrr_at_20_diff1
value: 7.782191305042387
- type: nauc_mrr_at_20_max
value: 2.793166533360494
- type: nauc_mrr_at_20_std
value: 11.627007183010022
- type: nauc_mrr_at_3_diff1
value: 7.24231818837065
- type: nauc_mrr_at_3_max
value: 1.1207446053044912
- type: nauc_mrr_at_3_std
value: 8.80503737855467
- type: nauc_mrr_at_5_diff1
value: 6.979716982325695
- type: nauc_mrr_at_5_max
value: 1.4170822735031223
- type: nauc_mrr_at_5_std
value: 10.1236436941688
- type: nauc_ndcg_at_1000_diff1
value: 9.442383068205185
- type: nauc_ndcg_at_1000_max
value: 5.123695329561358
- type: nauc_ndcg_at_1000_std
value: 14.437791974669423
- type: nauc_ndcg_at_100_diff1
value: 9.708550273174609
- type: nauc_ndcg_at_100_max
value: 6.051680933998194
- type: nauc_ndcg_at_100_std
value: 16.356231738002254
- type: nauc_ndcg_at_10_diff1
value: 9.067738290559637
- type: nauc_ndcg_at_10_max
value: 5.051562540309537
- type: nauc_ndcg_at_10_std
value: 14.981456308721722
- type: nauc_ndcg_at_1_diff1
value: 12.16316227081504
- type: nauc_ndcg_at_1_max
value: -0.5028808771807869
- type: nauc_ndcg_at_1_std
value: 6.962721581652243
- type: nauc_ndcg_at_20_diff1
value: 9.231508996935032
- type: nauc_ndcg_at_20_max
value: 5.908400445912541
- type: nauc_ndcg_at_20_std
value: 15.59551518646074
- type: nauc_ndcg_at_3_diff1
value: 8.176316410933907
- type: nauc_ndcg_at_3_max
value: 2.274031400606708
- type: nauc_ndcg_at_3_std
value: 9.692557288413111
- type: nauc_ndcg_at_5_diff1
value: 7.594758622405593
- type: nauc_ndcg_at_5_max
value: 2.6813579129239575
- type: nauc_ndcg_at_5_std
value: 11.657985683999456
- type: nauc_precision_at_1000_diff1
value: 10.05696671180073
- type: nauc_precision_at_1000_max
value: 38.62495739567785
- type: nauc_precision_at_1000_std
value: 48.13074377805322
- type: nauc_precision_at_100_diff1
value: 14.277579964693818
- type: nauc_precision_at_100_max
value: 25.831877593548068
- type: nauc_precision_at_100_std
value: 48.257692065653586
- type: nauc_precision_at_10_diff1
value: 8.967213665268718
- type: nauc_precision_at_10_max
value: 11.661929553714575
- type: nauc_precision_at_10_std
value: 24.98165263864555
- type: nauc_precision_at_1_diff1
value: 12.16316227081504
- type: nauc_precision_at_1_max
value: -0.5028808771807869
- type: nauc_precision_at_1_std
value: 6.962721581652243
- type: nauc_precision_at_20_diff1
value: 9.596515632249602
- type: nauc_precision_at_20_max
value: 16.17218527097958
- type: nauc_precision_at_20_std
value: 29.12126867872034
- type: nauc_precision_at_3_diff1
value: 6.602134310609642
- type: nauc_precision_at_3_max
value: 4.422669583221979
- type: nauc_precision_at_3_std
value: 11.430754758385522
- type: nauc_precision_at_5_diff1
value: 5.2002210955386445
- type: nauc_precision_at_5_max
value: 5.146945531205673
- type: nauc_precision_at_5_std
value: 15.431582644403685
- type: nauc_recall_at_1000_diff1
value: 10.056966711801207
- type: nauc_recall_at_1000_max
value: 38.62495739567756
- type: nauc_recall_at_1000_std
value: 48.1307437780539
- type: nauc_recall_at_100_diff1
value: 14.277579964693778
- type: nauc_recall_at_100_max
value: 25.83187759354808
- type: nauc_recall_at_100_std
value: 48.257692065653615
- type: nauc_recall_at_10_diff1
value: 8.967213665268691
- type: nauc_recall_at_10_max
value: 11.661929553714536
- type: nauc_recall_at_10_std
value: 24.98165263864557
- type: nauc_recall_at_1_diff1
value: 12.16316227081504
- type: nauc_recall_at_1_max
value: -0.5028808771807869
- type: nauc_recall_at_1_std
value: 6.962721581652243
- type: nauc_recall_at_20_diff1
value: 9.596515632249584
- type: nauc_recall_at_20_max
value: 16.17218527097958
- type: nauc_recall_at_20_std
value: 29.121268678720334
- type: nauc_recall_at_3_diff1
value: 6.602134310609631
- type: nauc_recall_at_3_max
value: 4.4226695832219765
- type: nauc_recall_at_3_std
value: 11.430754758385536
- type: nauc_recall_at_5_diff1
value: 5.200221095538592
- type: nauc_recall_at_5_max
value: 5.146945531205657
- type: nauc_recall_at_5_std
value: 15.431582644403655
- type: ndcg_at_1
value: 15.931999999999999
- type: ndcg_at_10
value: 32.622
- type: ndcg_at_100
value: 39.182
- type: ndcg_at_1000
value: 40.814
- type: ndcg_at_20
value: 35.538
- type: ndcg_at_3
value: 25.119000000000003
- type: ndcg_at_5
value: 28.541
- type: precision_at_1
value: 15.931999999999999
- type: precision_at_10
value: 5.27
- type: precision_at_100
value: 0.84
- type: precision_at_1000
value: 0.097
- type: precision_at_20
value: 3.215
- type: precision_at_3
value: 10.597
- type: precision_at_5
value: 8.023
- type: recall_at_1
value: 15.931999999999999
- type: recall_at_10
value: 52.703
- type: recall_at_100
value: 83.997
- type: recall_at_1000
value: 97.013
- type: recall_at_20
value: 64.29599999999999
- type: recall_at_3
value: 31.791999999999998
- type: recall_at_5
value: 40.114
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 29.870127302809124
- type: v_measure
value: 29.870127302809124
- type: v_measure_std
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type: mteb/cqadupstack-physics
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revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
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dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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type: mteb/msmarco
config: default
split: test
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: 79.27268581851345
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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: 46.29236281595424
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value: 42.49886000911992
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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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- type: ndcg_at_1000
value: 79.022
- type: ndcg_at_20
value: 77.095
- type: ndcg_at_3
value: 72.336
- type: ndcg_at_5
value: 74.122
- type: precision_at_1
value: 68.49
- type: precision_at_10
value: 11.478
- type: precision_at_100
value: 1.4040000000000001
- type: precision_at_1000
value: 0.152
- type: precision_at_20
value: 6.232
- type: precision_at_3
value: 31.287
- type: precision_at_5
value: 20.712
- type: recall_at_1
value: 59.521
- type: recall_at_10
value: 84.768
- type: recall_at_100
value: 94.906
- type: recall_at_1000
value: 98.437
- type: recall_at_20
value: 88.919
- type: recall_at_3
value: 74.507
- type: recall_at_5
value: 79.487
- task:
type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 29.134297978095674
- type: v_measure
value: 29.134297978095674
- type: v_measure_std
value: 3.9934034124121185
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: main_score
value: 39.215421675518
- type: v_measure
value: 39.215421675518
- type: v_measure_std
value: 10.607286582764162
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: main_score
value: 8.163
- type: map_at_1
value: 2.06
- type: map_at_10
value: 4.349
- type: map_at_100
value: 5.29
- type: map_at_1000
value: 5.502
- type: map_at_20
value: 4.757
- type: map_at_3
value: 3.3320000000000003
- type: map_at_5
value: 3.789
- type: mrr_at_1
value: 10.2
- type: mrr_at_10
value: 15.777301587301585
- type: mrr_at_100
value: 16.87040130235826
- type: mrr_at_1000
value: 16.99318092347203
- type: mrr_at_20
value: 16.33887346227904
- type: mrr_at_3
value: 13.816666666666663
- type: mrr_at_5
value: 14.876666666666672
- type: nauc_map_at_1000_diff1
value: 16.820399860912577
- type: nauc_map_at_1000_max
value: 11.036291872368661
- type: nauc_map_at_1000_std
value: 28.197293404910596
- type: nauc_map_at_100_diff1
value: 16.974714726054092
- type: nauc_map_at_100_max
value: 10.865585523763155
- type: nauc_map_at_100_std
value: 27.743692421138462
- type: nauc_map_at_10_diff1
value: 17.114604628918144
- type: nauc_map_at_10_max
value: 9.375565363093843
- type: nauc_map_at_10_std
value: 24.780455052145346
- type: nauc_map_at_1_diff1
value: 20.658116587695112
- type: nauc_map_at_1_max
value: 13.155907394830615
- type: nauc_map_at_1_std
value: 22.426847527670628
- type: nauc_map_at_20_diff1
value: 16.898082467709294
- type: nauc_map_at_20_max
value: 9.988559872619556
- type: nauc_map_at_20_std
value: 26.307229888534273
- type: nauc_map_at_3_diff1
value: 16.76206057854526
- type: nauc_map_at_3_max
value: 10.440545699729146
- type: nauc_map_at_3_std
value: 22.958859601482796
- type: nauc_map_at_5_diff1
value: 16.530876411848187
- type: nauc_map_at_5_max
value: 9.311867261847425
- type: nauc_map_at_5_std
value: 24.387141896077
- type: nauc_mrr_at_1000_diff1
value: 16.73600946942673
- type: nauc_mrr_at_1000_max
value: 12.676243402896537
- type: nauc_mrr_at_1000_std
value: 22.6143458461655
- type: nauc_mrr_at_100_diff1
value: 16.71570698771131
- type: nauc_mrr_at_100_max
value: 12.669159209039286
- type: nauc_mrr_at_100_std
value: 22.628552541531114
- type: nauc_mrr_at_10_diff1
value: 16.724205099190893
- type: nauc_mrr_at_10_max
value: 12.284407084892688
- type: nauc_mrr_at_10_std
value: 22.029318681643513
- type: nauc_mrr_at_1_diff1
value: 20.65518464968726
- type: nauc_mrr_at_1_max
value: 13.380415958308845
- type: nauc_mrr_at_1_std
value: 23.027057944994354
- type: nauc_mrr_at_20_diff1
value: 16.743321127999945
- type: nauc_mrr_at_20_max
value: 12.497444652373034
- type: nauc_mrr_at_20_std
value: 22.523374071799278
- type: nauc_mrr_at_3_diff1
value: 17.429706373434005
- type: nauc_mrr_at_3_max
value: 11.567635068970057
- type: nauc_mrr_at_3_std
value: 21.792853845733678
- type: nauc_mrr_at_5_diff1
value: 17.067362660541164
- type: nauc_mrr_at_5_max
value: 11.838591994727086
- type: nauc_mrr_at_5_std
value: 21.89903870786229
- type: nauc_ndcg_at_1000_diff1
value: 13.976383558618032
- type: nauc_ndcg_at_1000_max
value: 15.004200828026185
- type: nauc_ndcg_at_1000_std
value: 32.29145506934514
- type: nauc_ndcg_at_100_diff1
value: 15.151338074531035
- type: nauc_ndcg_at_100_max
value: 13.467821215240413
- type: nauc_ndcg_at_100_std
value: 29.802198591954856
- type: nauc_ndcg_at_10_diff1
value: 15.922085162704697
- type: nauc_ndcg_at_10_max
value: 10.447859010783938
- type: nauc_ndcg_at_10_std
value: 23.90947332043245
- type: nauc_ndcg_at_1_diff1
value: 20.65518464968726
- type: nauc_ndcg_at_1_max
value: 13.380415958308845
- type: nauc_ndcg_at_1_std
value: 23.027057944994354
- type: nauc_ndcg_at_20_diff1
value: 15.55874475345454
- type: nauc_ndcg_at_20_max
value: 11.437273797119275
- type: nauc_ndcg_at_20_std
value: 26.552304709089604
- type: nauc_ndcg_at_3_diff1
value: 16.552004746479756
- type: nauc_ndcg_at_3_max
value: 10.804357733167999
- type: nauc_ndcg_at_3_std
value: 22.49302563529031
- type: nauc_ndcg_at_5_diff1
value: 15.941541214122312
- type: nauc_ndcg_at_5_max
value: 9.999332076127734
- type: nauc_ndcg_at_5_std
value: 23.38227609142699
- type: nauc_precision_at_1000_diff1
value: 7.058340373874923
- type: nauc_precision_at_1000_max
value: 17.258245524006963
- type: nauc_precision_at_1000_std
value: 36.45652142925109
- type: nauc_precision_at_100_diff1
value: 11.432632810176315
- type: nauc_precision_at_100_max
value: 14.706743744286266
- type: nauc_precision_at_100_std
value: 33.509148405438516
- type: nauc_precision_at_10_diff1
value: 13.899621711899094
- type: nauc_precision_at_10_max
value: 9.288924597831762
- type: nauc_precision_at_10_std
value: 23.887371820866647
- type: nauc_precision_at_1_diff1
value: 20.65518464968726
- type: nauc_precision_at_1_max
value: 13.380415958308845
- type: nauc_precision_at_1_std
value: 23.027057944994354
- type: nauc_precision_at_20_diff1
value: 13.143970754077383
- type: nauc_precision_at_20_max
value: 11.347036397273031
- type: nauc_precision_at_20_std
value: 28.728122625727842
- type: nauc_precision_at_3_diff1
value: 15.17229479556843
- type: nauc_precision_at_3_max
value: 9.942412630398362
- type: nauc_precision_at_3_std
value: 22.56878491369083
- type: nauc_precision_at_5_diff1
value: 14.110634156514475
- type: nauc_precision_at_5_max
value: 8.484616766016119
- type: nauc_precision_at_5_std
value: 24.207087120488005
- type: nauc_recall_at_1000_diff1
value: 7.1431971152880624
- type: nauc_recall_at_1000_max
value: 17.940414755613098
- type: nauc_recall_at_1000_std
value: 36.52036034800652
- type: nauc_recall_at_100_diff1
value: 11.564422539945994
- type: nauc_recall_at_100_max
value: 14.768866414576125
- type: nauc_recall_at_100_std
value: 33.57594828721061
- type: nauc_recall_at_10_diff1
value: 13.826760252646993
- type: nauc_recall_at_10_max
value: 9.167497311121016
- type: nauc_recall_at_10_std
value: 23.658037032839953
- type: nauc_recall_at_1_diff1
value: 20.658116587695112
- type: nauc_recall_at_1_max
value: 13.155907394830615
- type: nauc_recall_at_1_std
value: 22.426847527670628
- type: nauc_recall_at_20_diff1
value: 13.053984329111262
- type: nauc_recall_at_20_max
value: 11.261855881829511
- type: nauc_recall_at_20_std
value: 28.59453401494739
- type: nauc_recall_at_3_diff1
value: 15.176171372019025
- type: nauc_recall_at_3_max
value: 9.927743665598976
- type: nauc_recall_at_3_std
value: 22.263215956695852
- type: nauc_recall_at_5_diff1
value: 14.117753921764933
- type: nauc_recall_at_5_max
value: 8.407325978866647
- type: nauc_recall_at_5_std
value: 23.973302948861026
- type: ndcg_at_1
value: 10.2
- type: ndcg_at_10
value: 8.163
- type: ndcg_at_100
value: 13.093
- type: ndcg_at_1000
value: 18.129
- type: ndcg_at_20
value: 9.589
- type: ndcg_at_3
value: 7.85
- type: ndcg_at_5
value: 6.701
- type: precision_at_1
value: 10.2
- type: precision_at_10
value: 4.19
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_20
value: 2.9250000000000003
- type: precision_at_3
value: 7.167
- type: precision_at_5
value: 5.7
- type: recall_at_1
value: 2.06
- type: recall_at_10
value: 8.488
- type: recall_at_100
value: 23.383000000000003
- type: recall_at_1000
value: 48.312
- type: recall_at_20
value: 11.843
- type: recall_at_3
value: 4.35
- type: recall_at_5
value: 5.765
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 74.06014749723313
- type: cosine_spearman
value: 62.85583348143348
- type: euclidean_pearson
value: 67.90829663189977
- type: euclidean_spearman
value: 62.855829168697966
- type: main_score
value: 62.85583348143348
- type: manhattan_pearson
value: 68.16895231463954
- type: manhattan_spearman
value: 62.942131884343155
- type: pearson
value: 74.06014749723313
- type: spearman
value: 62.85583348143348
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 71.71587397454503
- type: cosine_spearman
value: 62.07913034464432
- type: euclidean_pearson
value: 68.38396254056258
- type: euclidean_spearman
value: 62.07912362817025
- type: main_score
value: 62.07913034464432
- type: manhattan_pearson
value: 68.80285040680448
- type: manhattan_spearman
value: 62.48427594825191
- type: pearson
value: 71.71587397454503
- type: spearman
value: 62.07913034464432
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 74.00146491973214
- type: cosine_spearman
value: 75.73113726697468
- type: euclidean_pearson
value: 75.17723244913799
- type: euclidean_spearman
value: 75.73113726697468
- type: main_score
value: 75.73113726697468
- type: manhattan_pearson
value: 75.04817185215164
- type: manhattan_spearman
value: 75.56882534866682
- type: pearson
value: 74.00146491973214
- type: spearman
value: 75.73113726697468
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 73.18799052289306
- type: cosine_spearman
value: 69.27997439795548
- type: euclidean_pearson
value: 72.01699560068344
- type: euclidean_spearman
value: 69.27997439795548
- type: main_score
value: 69.27997439795548
- type: manhattan_pearson
value: 72.08946320578453
- type: manhattan_spearman
value: 69.3115876094692
- type: pearson
value: 73.18799052289306
- type: spearman
value: 69.27997439795548
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 75.05240168700195
- type: cosine_spearman
value: 76.32976845993336
- type: euclidean_pearson
value: 76.23332436291838
- type: euclidean_spearman
value: 76.32976845993336
- type: main_score
value: 76.32976845993336
- type: manhattan_pearson
value: 76.0196103445834
- type: manhattan_spearman
value: 76.11367025445854
- type: pearson
value: 75.05240168700195
- type: spearman
value: 76.32976845993336
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 71.35240308275529
- type: cosine_spearman
value: 73.46659216141927
- type: euclidean_pearson
value: 73.01431094479076
- type: euclidean_spearman
value: 73.46659216141927
- type: main_score
value: 73.46659216141927
- type: manhattan_pearson
value: 73.05673665594625
- type: manhattan_spearman
value: 73.46097106391906
- type: pearson
value: 71.35240308275529
- type: spearman
value: 73.46659216141927
- 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: 80.32585308865436
- type: cosine_spearman
value: 82.08042618874391
- type: euclidean_pearson
value: 81.50315333642457
- type: euclidean_spearman
value: 82.08042618874391
- type: main_score
value: 82.08042618874391
- type: manhattan_pearson
value: 81.50403771658148
- type: manhattan_spearman
value: 81.86754737875918
- type: pearson
value: 80.32585308865436
- type: spearman
value: 82.08042618874391
- 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: 26.492433454505004
- type: cosine_spearman
value: 25.26192630209604
- type: euclidean_pearson
value: 26.508248428681146
- type: euclidean_spearman
value: 25.26192630209604
- type: main_score
value: 25.26192630209604
- type: manhattan_pearson
value: 24.577958833043617
- type: manhattan_spearman
value: 23.107268314361416
- type: pearson
value: 26.492433454505004
- type: spearman
value: 25.26192630209604
- 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: 4.849502004066215
- type: cosine_spearman
value: 2.4221360201347566
- type: euclidean_pearson
value: 4.946775087695405
- type: euclidean_spearman
value: 2.4221360201347566
- type: main_score
value: 2.4221360201347566
- type: manhattan_pearson
value: 2.0088474400844993
- type: manhattan_spearman
value: 2.501283757283151
- type: pearson
value: 4.849502004066215
- type: spearman
value: 2.4221360201347566
- 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: 17.67430795163699
- type: cosine_spearman
value: 14.138028269188412
- type: euclidean_pearson
value: 17.719124540919474
- type: euclidean_spearman
value: 14.138028269188412
- type: main_score
value: 14.138028269188412
- type: manhattan_pearson
value: 15.979360498335673
- type: manhattan_spearman
value: 13.033003503816229
- type: pearson
value: 17.67430795163699
- type: spearman
value: 14.138028269188412
- 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: 11.032677618214326
- type: cosine_spearman
value: 8.819837594034183
- type: euclidean_pearson
value: 11.212471090722028
- type: euclidean_spearman
value: 8.819837594034183
- type: main_score
value: 8.819837594034183
- type: manhattan_pearson
value: 7.552514418132754
- type: manhattan_spearman
value: 5.788554277295719
- type: pearson
value: 11.032677618214326
- type: spearman
value: 8.819837594034183
- 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: 21.77242194085935
- type: cosine_spearman
value: 19.564246863458028
- type: euclidean_pearson
value: 21.997979208209685
- type: euclidean_spearman
value: 19.564246863458028
- type: main_score
value: 19.564246863458028
- type: manhattan_pearson
value: 21.80940866422115
- type: manhattan_spearman
value: 19.5079327687257
- type: pearson
value: 21.77242194085935
- type: spearman
value: 19.564246863458028
- 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: 24.34392722914247
- type: cosine_spearman
value: 22.516912091222096
- type: euclidean_pearson
value: 24.860123500651827
- type: euclidean_spearman
value: 22.516912091222096
- type: main_score
value: 22.516912091222096
- type: manhattan_pearson
value: 28.816841514354884
- type: manhattan_spearman
value: 27.740284625490002
- type: pearson
value: 24.34392722914247
- type: spearman
value: 22.516912091222096
- 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: 11.486165309912764
- type: cosine_spearman
value: 10.139614392782256
- type: euclidean_pearson
value: 11.75125169236987
- type: euclidean_spearman
value: 10.139614392782256
- type: main_score
value: 10.139614392782256
- type: manhattan_pearson
value: 12.405185554485518
- type: manhattan_spearman
value: 10.053617041455068
- type: pearson
value: 11.486165309912764
- type: spearman
value: 10.139614392782256
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 56.1393113780294
- type: cosine_spearman
value: 62.64707232707212
- type: euclidean_pearson
value: 60.68784382476653
- type: euclidean_spearman
value: 62.64707232707212
- type: main_score
value: 62.64707232707212
- type: manhattan_pearson
value: 60.849049192788776
- type: manhattan_spearman
value: 62.50847116906587
- type: pearson
value: 56.1393113780294
- type: spearman
value: 62.64707232707212
- 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: 9.554093605202135
- type: cosine_spearman
value: 11.788855140937605
- type: euclidean_pearson
value: 8.3590729795075
- type: euclidean_spearman
value: 11.674682450877647
- type: main_score
value: 11.788855140937605
- type: manhattan_pearson
value: 15.34897773929992
- type: manhattan_spearman
value: 18.76001274530547
- type: pearson
value: 9.554093605202135
- type: spearman
value: 11.788855140937605
- 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: 23.699968394697848
- type: cosine_spearman
value: 25.635685273215014
- type: euclidean_pearson
value: 23.30847681564559
- type: euclidean_spearman
value: 25.62920498344224
- type: main_score
value: 25.635685273215014
- type: manhattan_pearson
value: 22.877299914701172
- type: manhattan_spearman
value: 29.58678616125247
- type: pearson
value: 23.699968394697848
- type: spearman
value: 25.635685273215014
- 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: 9.344287684864119
- type: cosine_spearman
value: 10.042423712385212
- type: euclidean_pearson
value: 9.033832457728343
- type: euclidean_spearman
value: 10.033434689662734
- type: main_score
value: 10.042423712385212
- type: manhattan_pearson
value: 6.744871382125734
- type: manhattan_spearman
value: 6.302888265155408
- type: pearson
value: 9.344287684864119
- type: spearman
value: 10.042423712385212
- 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: 8.001041267374578
- type: cosine_spearman
value: 15.127881072012025
- type: euclidean_pearson
value: 6.362399463770095
- type: euclidean_spearman
value: 15.127881072012025
- type: main_score
value: 15.127881072012025
- type: manhattan_pearson
value: 14.398693320171096
- type: manhattan_spearman
value: 23.42112716934093
- type: pearson
value: 8.001041267374578
- type: spearman
value: 15.127881072012025
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 73.57677681061787
- type: cosine_spearman
value: 72.80800903257308
- type: euclidean_pearson
value: 74.4255195885822
- type: euclidean_spearman
value: 72.80800903257308
- type: main_score
value: 72.80800903257308
- type: manhattan_pearson
value: 74.6222587602608
- type: manhattan_spearman
value: 72.9559654281266
- type: pearson
value: 73.57677681061787
- type: spearman
value: 72.80800903257308
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 66.13944998572143
- type: map
value: 66.13944998572143
- type: mrr
value: 86.84982682531702
- type: nAUC_map_diff1
value: 9.655489527090475
- type: nAUC_map_max
value: 55.329447225464676
- type: nAUC_map_std
value: 63.92644562946389
- type: nAUC_mrr_diff1
value: 38.69379106400398
- type: nAUC_mrr_max
value: 69.96013858740152
- type: nAUC_mrr_std
value: 62.072046098925156
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 36.55
- type: map_at_1
value: 24.166999999999998
- type: map_at_10
value: 32.255
- type: map_at_100
value: 33.359
- type: map_at_1000
value: 33.44
- type: map_at_20
value: 32.885999999999996
- type: map_at_3
value: 29.866
- type: map_at_5
value: 31.557000000000002
- type: mrr_at_1
value: 26.0
- type: mrr_at_10
value: 33.87486772486773
- type: mrr_at_100
value: 34.829580502303436
- type: mrr_at_1000
value: 34.90127768602221
- type: mrr_at_20
value: 34.44486881510876
- type: mrr_at_3
value: 31.5
- type: mrr_at_5
value: 33.21666666666667
- type: nauc_map_at_1000_diff1
value: 40.27120542337293
- type: nauc_map_at_1000_max
value: 34.881725395249745
- type: nauc_map_at_1000_std
value: 14.804128183822776
- type: nauc_map_at_100_diff1
value: 40.21945625314103
- type: nauc_map_at_100_max
value: 34.86632729849179
- type: nauc_map_at_100_std
value: 14.822072821802628
- type: nauc_map_at_10_diff1
value: 40.22503326192557
- type: nauc_map_at_10_max
value: 34.681031950162314
- type: nauc_map_at_10_std
value: 13.969909037414721
- type: nauc_map_at_1_diff1
value: 46.18145166307877
- type: nauc_map_at_1_max
value: 32.850938864900506
- type: nauc_map_at_1_std
value: 9.284621032067
- type: nauc_map_at_20_diff1
value: 40.24803710661317
- type: nauc_map_at_20_max
value: 34.74111106117432
- type: nauc_map_at_20_std
value: 14.52709156133609
- type: nauc_map_at_3_diff1
value: 42.21002367018134
- type: nauc_map_at_3_max
value: 33.57697582968568
- type: nauc_map_at_3_std
value: 12.715120187246024
- type: nauc_map_at_5_diff1
value: 40.361071576043734
- type: nauc_map_at_5_max
value: 34.960048160721065
- type: nauc_map_at_5_std
value: 13.587382041347976
- type: nauc_mrr_at_1000_diff1
value: 40.172026071768876
- type: nauc_mrr_at_1000_max
value: 36.393047482968285
- type: nauc_mrr_at_1000_std
value: 18.514061967011543
- type: nauc_mrr_at_100_diff1
value: 40.10935371489407
- type: nauc_mrr_at_100_max
value: 36.3941603476526
- type: nauc_mrr_at_100_std
value: 18.549364999359284
- type: nauc_mrr_at_10_diff1
value: 40.0143297539273
- type: nauc_mrr_at_10_max
value: 36.285300624108174
- type: nauc_mrr_at_10_std
value: 18.06040618434985
- type: nauc_mrr_at_1_diff1
value: 45.94065893825947
- type: nauc_mrr_at_1_max
value: 34.89250575775973
- type: nauc_mrr_at_1_std
value: 15.032812003122197
- type: nauc_mrr_at_20_diff1
value: 40.04355197303724
- type: nauc_mrr_at_20_max
value: 36.35951332162351
- type: nauc_mrr_at_20_std
value: 18.42335404196948
- type: nauc_mrr_at_3_diff1
value: 42.30608858184136
- type: nauc_mrr_at_3_max
value: 35.61225415425098
- type: nauc_mrr_at_3_std
value: 17.223247492661102
- type: nauc_mrr_at_5_diff1
value: 40.485972003959404
- type: nauc_mrr_at_5_max
value: 36.67688438369464
- type: nauc_mrr_at_5_std
value: 18.021306525172033
- type: nauc_ndcg_at_1000_diff1
value: 38.1448539512143
- type: nauc_ndcg_at_1000_max
value: 36.41251174091125
- type: nauc_ndcg_at_1000_std
value: 18.863348298831134
- type: nauc_ndcg_at_100_diff1
value: 36.828616223036995
- type: nauc_ndcg_at_100_max
value: 36.326875743799825
- type: nauc_ndcg_at_100_std
value: 19.753271271756166
- type: nauc_ndcg_at_10_diff1
value: 36.999696509492765
- type: nauc_ndcg_at_10_max
value: 35.510700157502384
- type: nauc_ndcg_at_10_std
value: 16.51692902623065
- type: nauc_ndcg_at_1_diff1
value: 45.94065893825947
- type: nauc_ndcg_at_1_max
value: 34.89250575775973
- type: nauc_ndcg_at_1_std
value: 15.032812003122197
- type: nauc_ndcg_at_20_diff1
value: 36.98821200336492
- type: nauc_ndcg_at_20_max
value: 35.51321933251509
- type: nauc_ndcg_at_20_std
value: 17.880281629389334
- type: nauc_ndcg_at_3_diff1
value: 40.854488421982474
- type: nauc_ndcg_at_3_max
value: 34.13463997398456
- type: nauc_ndcg_at_3_std
value: 14.833355685530647
- type: nauc_ndcg_at_5_diff1
value: 37.65087893614269
- type: nauc_ndcg_at_5_max
value: 36.16610548649603
- type: nauc_ndcg_at_5_std
value: 15.85595394609524
- type: nauc_precision_at_1000_diff1
value: -1.2213861207148984
- type: nauc_precision_at_1000_max
value: 33.792059295415115
- type: nauc_precision_at_1000_std
value: 51.25358732741295
- type: nauc_precision_at_100_diff1
value: 8.942954849181
- type: nauc_precision_at_100_max
value: 37.44655423710336
- type: nauc_precision_at_100_std
value: 47.04438370323114
- type: nauc_precision_at_10_diff1
value: 22.922592142376622
- type: nauc_precision_at_10_max
value: 37.716906832835676
- type: nauc_precision_at_10_std
value: 28.841183482232413
- type: nauc_precision_at_1_diff1
value: 45.94065893825947
- type: nauc_precision_at_1_max
value: 34.89250575775973
- type: nauc_precision_at_1_std
value: 15.032812003122197
- type: nauc_precision_at_20_diff1
value: 20.36859440465984
- type: nauc_precision_at_20_max
value: 38.34513768246952
- type: nauc_precision_at_20_std
value: 35.229890026962615
- type: nauc_precision_at_3_diff1
value: 34.05345356587694
- type: nauc_precision_at_3_max
value: 36.40222017855832
- type: nauc_precision_at_3_std
value: 22.09598528562007
- type: nauc_precision_at_5_diff1
value: 25.37065548133688
- type: nauc_precision_at_5_max
value: 41.03941131544809
- type: nauc_precision_at_5_std
value: 25.017881172035043
- type: nauc_recall_at_1000_diff1
value: 33.09021721896312
- type: nauc_recall_at_1000_max
value: 47.697443757377194
- type: nauc_recall_at_1000_std
value: 44.12337261948821
- type: nauc_recall_at_100_diff1
value: 22.06710557626714
- type: nauc_recall_at_100_max
value: 40.18599082452499
- type: nauc_recall_at_100_std
value: 38.184660672167595
- type: nauc_recall_at_10_diff1
value: 27.10204324985591
- type: nauc_recall_at_10_max
value: 35.30960104403818
- type: nauc_recall_at_10_std
value: 19.306977699362033
- type: nauc_recall_at_1_diff1
value: 46.18145166307877
- type: nauc_recall_at_1_max
value: 32.850938864900506
- type: nauc_recall_at_1_std
value: 9.284621032067
- type: nauc_recall_at_20_diff1
value: 26.382409406526
- type: nauc_recall_at_20_max
value: 34.469251680833004
- type: nauc_recall_at_20_std
value: 23.400056135882995
- type: nauc_recall_at_3_diff1
value: 37.2267183565415
- type: nauc_recall_at_3_max
value: 32.77661901779293
- type: nauc_recall_at_3_std
value: 15.031686858933885
- type: nauc_recall_at_5_diff1
value: 29.257298344591987
- type: nauc_recall_at_5_max
value: 36.84185394701337
- type: nauc_recall_at_5_std
value: 17.84731120093588
- type: ndcg_at_1
value: 26.0
- type: ndcg_at_10
value: 36.55
- type: ndcg_at_100
value: 42.049
- type: ndcg_at_1000
value: 44.4
- type: ndcg_at_20
value: 38.692
- type: ndcg_at_3
value: 32.019999999999996
- type: ndcg_at_5
value: 34.922
- type: precision_at_1
value: 26.0
- type: precision_at_10
value: 5.3
- type: precision_at_100
value: 0.843
- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 3.1329999999999996
- type: precision_at_3
value: 13.111
- type: precision_at_5
value: 9.4
- type: recall_at_1
value: 24.166999999999998
- type: recall_at_10
value: 48.167
- type: recall_at_100
value: 74.1
- type: recall_at_1000
value: 92.93299999999999
- type: recall_at_20
value: 56.289
- type: recall_at_3
value: 36.306
- type: recall_at_5
value: 43.389
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.68415841584158
- type: cosine_accuracy_threshold
value: 81.75174090984304
- type: cosine_ap
value: 88.17695295243927
- type: cosine_f1
value: 83.46293416277865
- type: cosine_f1_threshold
value: 81.67502920031147
- type: cosine_precision
value: 86.65231431646933
- type: cosine_recall
value: 80.5
- type: dot_accuracy
value: 99.68415841584158
- type: dot_accuracy_threshold
value: 81.7517430435309
- type: dot_ap
value: 88.17695295243927
- type: dot_f1
value: 83.46293416277865
- type: dot_f1_threshold
value: 81.67503038678444
- type: dot_precision
value: 86.65231431646933
- type: dot_recall
value: 80.5
- type: euclidean_accuracy
value: 99.68415841584158
- type: euclidean_accuracy_threshold
value: 60.412346616246396
- type: euclidean_ap
value: 88.17695295243927
- type: euclidean_f1
value: 83.46293416277865
- type: euclidean_f1_threshold
value: 60.539194123743535
- type: euclidean_precision
value: 86.65231431646933
- type: euclidean_recall
value: 80.5
- type: main_score
value: 88.17695295243927
- type: manhattan_accuracy
value: 99.67920792079208
- type: manhattan_accuracy_threshold
value: 366.6920070296328
- type: manhattan_ap
value: 88.09159390940687
- type: manhattan_f1
value: 82.97982410760476
- type: manhattan_f1_threshold
value: 384.15647450019605
- type: manhattan_precision
value: 85.95927116827438
- type: manhattan_recall
value: 80.2
- type: max_accuracy
value: 99.68415841584158
- type: max_ap
value: 88.17695295243927
- type: max_f1
value: 83.46293416277865
- type: max_precision
value: 86.65231431646933
- type: max_recall
value: 80.5
- type: similarity_accuracy
value: 99.68415841584158
- type: similarity_accuracy_threshold
value: 81.75174090984304
- type: similarity_ap
value: 88.17695295243927
- type: similarity_f1
value: 83.46293416277865
- type: similarity_f1_threshold
value: 81.67502920031147
- type: similarity_precision
value: 86.65231431646933
- type: similarity_recall
value: 80.5
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 37.82716668016299
- type: v_measure
value: 37.82716668016299
- type: v_measure_std
value: 3.9071651545475055
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 31.549916824347523
- type: v_measure
value: 31.549916824347523
- type: v_measure_std
value: 1.649284454526032
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 40.201162273119
- type: map
value: 40.201162273119
- type: mrr
value: 40.4580866437484
- type: nAUC_map_diff1
value: 33.47313252548623
- type: nAUC_map_max
value: 15.023702266852649
- type: nAUC_map_std
value: 4.389545656603201
- type: nAUC_mrr_diff1
value: 32.789106382547125
- type: nAUC_mrr_max
value: 16.03297395659567
- type: nAUC_mrr_std
value: 4.5441260195062885
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_pearson
value: 30.982384340344282
- type: cosine_spearman
value: 31.512077655680574
- type: dot_pearson
value: 30.98238732795588
- type: dot_spearman
value: 31.518281673786575
- type: main_score
value: 31.512077655680574
- type: pearson
value: 30.982384340344282
- type: spearman
value: 31.512077655680574
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 40.475
- type: map_at_1
value: 0.13799999999999998
- type: map_at_10
value: 0.8420000000000001
- type: map_at_100
value: 3.5770000000000004
- type: map_at_1000
value: 8.158
- type: map_at_20
value: 1.2930000000000001
- type: map_at_3
value: 0.314
- type: map_at_5
value: 0.469
- type: mrr_at_1
value: 52.0
- type: mrr_at_10
value: 61.566666666666656
- type: mrr_at_100
value: 62.05786242033046
- type: mrr_at_1000
value: 62.114468654223785
- type: mrr_at_20
value: 61.80277777777777
- type: mrr_at_3
value: 59.333333333333336
- type: mrr_at_5
value: 61.23333333333333
- type: nauc_map_at_1000_diff1
value: -9.841499993688778
- type: nauc_map_at_1000_max
value: 35.583722623282604
- type: nauc_map_at_1000_std
value: 49.8512713272345
- type: nauc_map_at_100_diff1
value: -9.535301607183984
- type: nauc_map_at_100_max
value: 24.79493554952309
- type: nauc_map_at_100_std
value: 30.200092896546565
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value: 52.268926301705875
- type: manhattan_f1_threshold
value: 502.7589220990194
- type: manhattan_precision
value: 45.25970264529832
- type: manhattan_recall
value: 61.84696569920845
- type: max_accuracy
value: 80.6818859152411
- type: max_ap
value: 53.66111257744601
- type: max_f1
value: 52.50138045278852
- type: max_precision
value: 45.25970264529832
- type: max_recall
value: 62.71767810026385
- type: similarity_accuracy
value: 80.6818859152411
- type: similarity_accuracy_threshold
value: 80.54484987856905
- type: similarity_ap
value: 53.66110400517361
- type: similarity_f1
value: 52.50138045278852
- type: similarity_f1_threshold
value: 68.20751217239422
- type: similarity_precision
value: 45.14719848053181
- type: similarity_recall
value: 62.71767810026385
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 85.89474909768309
- type: cosine_accuracy_threshold
value: 66.7208384412081
- type: cosine_ap
value: 79.33674860685535
- type: cosine_f1
value: 71.67923542148885
- type: cosine_f1_threshold
value: 61.64839965467019
- type: cosine_precision
value: 69.57530076822728
- type: cosine_recall
value: 73.91438250692947
- type: dot_accuracy
value: 85.89474909768309
- type: dot_accuracy_threshold
value: 66.72083970945883
- type: dot_ap
value: 79.33671093343192
- type: dot_f1
value: 71.67923542148885
- type: dot_f1_threshold
value: 61.648404274339356
- type: dot_precision
value: 69.57530076822728
- type: dot_recall
value: 73.91438250692947
- type: euclidean_accuracy
value: 85.89474909768309
- type: euclidean_accuracy_threshold
value: 81.58328528607446
- type: euclidean_ap
value: 79.33675212612457
- type: euclidean_f1
value: 71.67923542148885
- type: euclidean_f1_threshold
value: 87.58036675764342
- type: euclidean_precision
value: 69.57530076822728
- type: euclidean_recall
value: 73.91438250692947
- type: main_score
value: 79.33675212612457
- type: manhattan_accuracy
value: 85.89863003065938
- type: manhattan_accuracy_threshold
value: 513.242605511914
- type: manhattan_ap
value: 79.06488846026886
- type: manhattan_f1
value: 71.42752882790833
- type: manhattan_f1_threshold
value: 563.6103289492894
- type: manhattan_precision
value: 67.88984461709212
- type: manhattan_recall
value: 75.3541730828457
- type: max_accuracy
value: 85.89863003065938
- type: max_ap
value: 79.33675212612457
- type: max_f1
value: 71.67923542148885
- type: max_precision
value: 69.57530076822728
- type: max_recall
value: 75.3541730828457
- type: similarity_accuracy
value: 85.89474909768309
- type: similarity_accuracy_threshold
value: 66.7208384412081
- type: similarity_ap
value: 79.33674860685535
- type: similarity_f1
value: 71.67923542148885
- type: similarity_f1_threshold
value: 61.64839965467019
- type: similarity_precision
value: 69.57530076822728
- type: similarity_recall
value: 73.91438250692947
---
# potion-base-2M 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-2M")
# 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"
] |
abhinand/MedEmbed-base-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-base-en-v1.5",
"base_model:finetune:BAAI/bge-base-en-v1.5",
"license:apache-2.0",
"region:us"
] | 2024-10-20T11:46:20 | 2024-10-21T06:49:35 | 6,316 | 14 |
---
base_model:
- BAAI/bge-base-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"
] |
aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct
|
aisingapore
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"id",
"ta",
"th",
"vi",
"arxiv:2309.06085",
"arxiv:2311.07911",
"arxiv:2306.05685",
"base_model:aisingapore/llama3-8b-cpt-sea-lionv2-base",
"base_model:finetune:aisingapore/llama3-8b-cpt-sea-lionv2-base",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-30T00:31:49 | 2024-12-19T13:11:44 | 6,256 | 17 |
---
base_model:
- aisingapore/llama3-8b-cpt-sea-lionv2-base
language:
- en
- id
- ta
- th
- vi
license: llama3
new_version: aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct
---
# Llama3 8B CPT SEA-Lionv2.1 Instruct
SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
Llama3 8B CPT SEA-Lionv2.1 Instruct is a multilingual model which has been fine-tuned with around **100,000 English instruction-completion pairs** alongside a smaller pool of around **50,000 instruction-completion pairs** from other ASEAN languages, such as Indonesian, Thai and Vietnamese.
These instructions have been carefully curated and rewritten to ensure the model was trained on truly open, commercially permissive and high quality datasets.
Llama3 8B CPT SEA-Lionv2.1 Instruct has undergone additional supervised fine-tuning and alignment compared to the now deprecated Llama3 8B CPT SEA-Lionv2 Instruct. These improvements have increased the model's capabilities in chat interactions and its ability to follow instructions accurately.
SEA-LION stands for _Southeast Asian Languages In One Network_.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages supported:** English, Indonesian, Thai, Vietnamese, Tamil
- **License:** [Llama3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
## Model Details
### Model Description
We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our [continued pre-trained Llama3 CPT 8B SEA-Lionv2](https://huggingface.co/aisingapore/llama3-8b-cpt-SEA-Lionv2-base), a decoder model using the Llama3 architecture, to create Llama3 8B SEA-Lionv2.1 Instruct.
The model has a context length of 8192.
### Benchmark Performance
We evaluated Llama3 8B SEA-Lionv2.1 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the [BHASA evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
Note: BHASA is implemented following a strict answer format, and only spaces and punctuations are cleaned. For tasks where options are provided, the answer should only include one of the pre-defined options, nothing else. If the model continues to generate more tokens (e.g. to explain its answer), it will be considered to be a wrong response. For the F1 score metric (as used in Sentiment Analysis and Toxicity Detection), all answers that do not fall under the pre-defined labels will be treated as a separate label (to mark it as a wrong answer) and included in the calculations so that the model is penalized for not generating one of the pre-defined labels.
The evaluation was done zero-shot with native prompts and only a sample of 100-1000 instances for each dataset was used as per the setting described in the paper.
#### Instruction-following Capabilities
Since LLama3 8B SEA-Lionv2.1 is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, [IFEval](https://arxiv.org/abs/2311.07911) and [MT-Bench](https://arxiv.org/abs/2306.05685).
As these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localize and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. The metric used is accuracy normalized by language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
**MT-Bench**
MT-Bench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category (Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction)). A tie is given a score of 0.5.
For more details on Llama3 8B CPT SEA-Lionv2.1 Instruct benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
### Usage
SEA-LION can be run using the 🤗 Transformers library
```python
# Please use transformers==4.43.2
import transformers
import torch
model_id = "aisingapore/llama3-8b-cpt-SEA-Lionv2.1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Accessing Older Revisions
Huggingface provides support for the revision parameter, allowing users to access specific versions of models. This can be used to retrieve the original llama3-8b-cpt-SEA-Lionv2-instruct model with the tag "v2.0.0".
```python
# Please use transformers==4.43.2
import transformers
import torch
model_id = "aisingapore/llama3-8b-cpt-SEA-Lionv2.1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
revision="v2.0.0", # Specify the revision here. Initial release is at "v2.0.0".
device_map="auto",
)
messages = [
{"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
The Llama3 8B CPT SEA-Lionv2.1 Instruct was fine-tuned using 8x A100-40GB using parameter efficient fine tuning in the form of LoRA.
## Data
Llama3 8B CPT SEA-Lionv2.1 Instruct was trained on a wide range of instructions that were manually and stringently verified by our team. A large portion of the effort was dedicated to ensuring that each instruction-completion pair that the model sees is of high quality and any errors were corrected and rewritten by native speakers or else dropped from our mix.
In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.
Link to dataset: _coming soon_
## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.
## The Team
Cheng Nicholas, Choa Esther, Huang Yuli, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Li Yier, Liu Bing Jie Darius, Lovenia Holy, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Teng Walter, Yeo Yeow Tong, Yong Xianbin
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
## Disclaimer
This is the repository for the commercial instruction-tuned model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION"
] |
[
"CHIA"
] |
microsoft/BioGPT-Large
|
microsoft
|
text-generation
|
[
"transformers",
"pytorch",
"biogpt",
"text-generation",
"medical",
"en",
"dataset:pubmed",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-03T16:17:26 | 2023-02-05T06:18:14 | 6,013 | 188 |
---
datasets:
- pubmed
language:
- en
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- medical
widget:
- text: COVID-19 is
inference:
parameters:
max_new_tokens: 50
---
## 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.
## 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"
] |
nvidia/NV-Embed-v1
|
nvidia
| null |
[
"sentence-transformers",
"safetensors",
"nvembed",
"mteb",
"custom_code",
"en",
"arxiv:2210.07316",
"arxiv:2405.17428",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | 2024-05-23T01:20:16 | 2024-11-30T23:02:27 | 5,894 | 426 |
---
language:
- en
license: cc-by-nc-4.0
tags:
- mteb
- sentence-transformers
model-index:
- name: NV-Embed-v1
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 95.11940298507461
- type: ap
value: 79.21521293687752
- type: f1
value: 92.45575440759485
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.143125
- type: ap
value: 95.28635983806933
- type: f1
value: 97.1426073127198
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.465999999999994
- type: f1
value: 52.70196166254287
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 44.879000000000005
- type: map_at_10
value: 60.146
- type: map_at_100
value: 60.533
- type: map_at_1000
value: 60.533
- type: map_at_3
value: 55.725
- type: map_at_5
value: 58.477999999999994
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 44.879000000000005
- type: ndcg_at_10
value: 68.205
- type: ndcg_at_100
value: 69.646
- type: ndcg_at_1000
value: 69.65599999999999
- type: ndcg_at_3
value: 59.243
- type: ndcg_at_5
value: 64.214
- type: precision_at_1
value: 44.879000000000005
- type: precision_at_10
value: 9.374
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 23.139000000000003
- type: precision_at_5
value: 16.302
- type: recall_at_1
value: 44.879000000000005
- type: recall_at_10
value: 93.741
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 69.417
- 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: 53.76391569504432
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 49.589284930659005
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.49860736554155
- type: mrr
value: 80.77771182341819
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.87900681188576
- type: cos_sim_spearman
value: 85.5905044545741
- type: euclidean_pearson
value: 86.80150192033507
- type: euclidean_spearman
value: 85.5905044545741
- type: manhattan_pearson
value: 86.79080500635683
- type: manhattan_spearman
value: 85.69351885001977
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 90.33766233766235
- type: f1
value: 90.20736178753944
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 48.152262077598465
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 44.742970683037235
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 31.825333333333326
- type: map_at_10
value: 44.019999999999996
- type: map_at_100
value: 45.37291666666667
- type: map_at_1000
value: 45.46991666666666
- type: map_at_3
value: 40.28783333333333
- type: map_at_5
value: 42.39458333333334
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 37.79733333333333
- type: ndcg_at_10
value: 50.50541666666667
- type: ndcg_at_100
value: 55.59125
- type: ndcg_at_1000
value: 57.06325
- type: ndcg_at_3
value: 44.595666666666666
- type: ndcg_at_5
value: 47.44875
- type: precision_at_1
value: 37.79733333333333
- type: precision_at_10
value: 9.044083333333333
- type: precision_at_100
value: 1.3728333333333336
- type: precision_at_1000
value: 0.16733333333333333
- type: precision_at_3
value: 20.842166666666667
- type: precision_at_5
value: 14.921916666666668
- type: recall_at_1
value: 31.825333333333326
- type: recall_at_10
value: 65.11916666666666
- type: recall_at_100
value: 86.72233333333335
- type: recall_at_1000
value: 96.44200000000001
- type: recall_at_3
value: 48.75691666666667
- type: recall_at_5
value: 56.07841666666666
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 14.698
- type: map_at_10
value: 25.141999999999996
- type: map_at_100
value: 27.1
- type: map_at_1000
value: 27.277
- type: map_at_3
value: 21.162
- type: map_at_5
value: 23.154
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 32.704
- type: ndcg_at_10
value: 34.715
- type: ndcg_at_100
value: 41.839
- type: ndcg_at_1000
value: 44.82
- type: ndcg_at_3
value: 28.916999999999998
- type: ndcg_at_5
value: 30.738
- type: precision_at_1
value: 32.704
- type: precision_at_10
value: 10.795
- type: precision_at_100
value: 1.8530000000000002
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 21.564
- type: precision_at_5
value: 16.261
- type: recall_at_1
value: 14.698
- type: recall_at_10
value: 41.260999999999996
- type: recall_at_100
value: 65.351
- type: recall_at_1000
value: 81.759
- type: recall_at_3
value: 26.545999999999996
- type: recall_at_5
value: 32.416
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.959
- type: map_at_10
value: 23.104
- type: map_at_100
value: 33.202
- type: map_at_1000
value: 35.061
- type: map_at_3
value: 15.911
- type: map_at_5
value: 18.796
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 63.5
- type: ndcg_at_10
value: 48.29
- type: ndcg_at_100
value: 52.949999999999996
- type: ndcg_at_1000
value: 60.20100000000001
- type: ndcg_at_3
value: 52.92
- type: ndcg_at_5
value: 50.375
- type: precision_at_1
value: 73.75
- type: precision_at_10
value: 38.65
- type: precision_at_100
value: 12.008000000000001
- type: precision_at_1000
value: 2.409
- type: precision_at_3
value: 56.083000000000006
- type: precision_at_5
value: 48.449999999999996
- type: recall_at_1
value: 9.959
- type: recall_at_10
value: 28.666999999999998
- type: recall_at_100
value: 59.319
- type: recall_at_1000
value: 81.973
- type: recall_at_3
value: 17.219
- type: recall_at_5
value: 21.343999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 91.705
- type: f1
value: 87.98464515154814
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 74.297
- type: map_at_10
value: 83.931
- type: map_at_100
value: 84.152
- type: map_at_1000
value: 84.164
- type: map_at_3
value: 82.708
- type: map_at_5
value: 83.536
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 80.048
- type: ndcg_at_10
value: 87.77000000000001
- type: ndcg_at_100
value: 88.467
- type: ndcg_at_1000
value: 88.673
- type: ndcg_at_3
value: 86.003
- type: ndcg_at_5
value: 87.115
- type: precision_at_1
value: 80.048
- type: precision_at_10
value: 10.711
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 33.248
- type: precision_at_5
value: 20.744
- type: recall_at_1
value: 74.297
- type: recall_at_10
value: 95.402
- type: recall_at_100
value: 97.97
- type: recall_at_1000
value: 99.235
- type: recall_at_3
value: 90.783
- type: recall_at_5
value: 93.55499999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.986
- type: map_at_10
value: 55.173
- type: map_at_100
value: 57.077
- type: map_at_1000
value: 57.176
- type: map_at_3
value: 48.182
- type: map_at_5
value: 52.303999999999995
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 62.037
- type: ndcg_at_10
value: 63.096
- type: ndcg_at_100
value: 68.42200000000001
- type: ndcg_at_1000
value: 69.811
- type: ndcg_at_3
value: 58.702
- type: ndcg_at_5
value: 60.20100000000001
- type: precision_at_1
value: 62.037
- type: precision_at_10
value: 17.269000000000002
- type: precision_at_100
value: 2.309
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 38.992
- type: precision_at_5
value: 28.610999999999997
- type: recall_at_1
value: 32.986
- type: recall_at_10
value: 70.61800000000001
- type: recall_at_100
value: 89.548
- type: recall_at_1000
value: 97.548
- type: recall_at_3
value: 53.400000000000006
- type: recall_at_5
value: 61.29599999999999
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 41.357
- type: map_at_10
value: 72.91499999999999
- type: map_at_100
value: 73.64699999999999
- type: map_at_1000
value: 73.67899999999999
- type: map_at_3
value: 69.113
- type: map_at_5
value: 71.68299999999999
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 82.714
- type: ndcg_at_10
value: 79.92
- type: ndcg_at_100
value: 82.232
- type: ndcg_at_1000
value: 82.816
- type: ndcg_at_3
value: 74.875
- type: ndcg_at_5
value: 77.969
- type: precision_at_1
value: 82.714
- type: precision_at_10
value: 17.037
- type: precision_at_100
value: 1.879
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 49.471
- type: precision_at_5
value: 32.124
- type: recall_at_1
value: 41.357
- type: recall_at_10
value: 85.18599999999999
- type: recall_at_100
value: 93.964
- type: recall_at_1000
value: 97.765
- type: recall_at_3
value: 74.207
- type: recall_at_5
value: 80.31099999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 97.05799999999998
- type: ap
value: 95.51324940484382
- type: f1
value: 97.05788617110184
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: test
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 25.608999999999998
- type: map_at_10
value: 39.098
- type: map_at_100
value: 0
- type: map_at_1000
value: 0
- type: map_at_3
value: 0
- type: map_at_5
value: 37.383
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 26.404
- type: ndcg_at_10
value: 46.493
- type: ndcg_at_100
value: 0
- type: ndcg_at_1000
value: 0
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 42.459
- type: precision_at_1
value: 26.404
- type: precision_at_10
value: 7.249
- type: precision_at_100
value: 0
- type: precision_at_1000
value: 0
- type: precision_at_3
value: 0
- type: precision_at_5
value: 11.874
- type: recall_at_1
value: 25.608999999999998
- type: recall_at_10
value: 69.16799999999999
- type: recall_at_100
value: 0
- type: recall_at_1000
value: 0
- type: recall_at_3
value: 0
- type: recall_at_5
value: 56.962
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.50706794345645
- type: f1
value: 96.3983656000426
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 89.77428180574556
- type: f1
value: 70.47378359921777
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.07061197041023
- type: f1
value: 77.8633288994029
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.74176193678547
- type: f1
value: 79.8943810025071
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 39.239199736486334
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 36.98167653792483
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.815595271130718
- type: mrr
value: 31.892823243368795
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.214
- type: map_at_10
value: 14.393
- type: map_at_100
value: 18.163999999999998
- type: map_at_1000
value: 19.753999999999998
- type: map_at_3
value: 10.737
- type: map_at_5
value: 12.325
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 48.297000000000004
- type: ndcg_at_10
value: 38.035000000000004
- type: ndcg_at_100
value: 34.772
- type: ndcg_at_1000
value: 43.631
- type: ndcg_at_3
value: 44.252
- type: ndcg_at_5
value: 41.307
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 27.647
- type: precision_at_100
value: 8.824
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 40.97
- type: precision_at_5
value: 35.17
- type: recall_at_1
value: 6.214
- type: recall_at_10
value: 18.566
- type: recall_at_100
value: 34.411
- type: recall_at_1000
value: 67.331
- type: recall_at_3
value: 12.277000000000001
- type: recall_at_5
value: 14.734
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 47.11
- type: map_at_10
value: 64.404
- type: map_at_100
value: 65.005
- type: map_at_1000
value: 65.01400000000001
- type: map_at_3
value: 60.831
- type: map_at_5
value: 63.181
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 52.983999999999995
- type: ndcg_at_10
value: 71.219
- type: ndcg_at_100
value: 73.449
- type: ndcg_at_1000
value: 73.629
- type: ndcg_at_3
value: 65.07
- type: ndcg_at_5
value: 68.715
- type: precision_at_1
value: 52.983999999999995
- type: precision_at_10
value: 10.756
- type: precision_at_100
value: 1.198
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 28.977999999999998
- type: precision_at_5
value: 19.583000000000002
- type: recall_at_1
value: 47.11
- type: recall_at_10
value: 89.216
- type: recall_at_100
value: 98.44500000000001
- type: recall_at_1000
value: 99.744
- type: recall_at_3
value: 73.851
- type: recall_at_5
value: 82.126
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.641
- type: map_at_10
value: 85.687
- type: map_at_100
value: 86.304
- type: map_at_1000
value: 86.318
- type: map_at_3
value: 82.811
- type: map_at_5
value: 84.641
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 82.48
- type: ndcg_at_10
value: 89.212
- type: ndcg_at_100
value: 90.321
- type: ndcg_at_1000
value: 90.405
- type: ndcg_at_3
value: 86.573
- type: ndcg_at_5
value: 88.046
- type: precision_at_1
value: 82.48
- type: precision_at_10
value: 13.522
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.95
- type: precision_at_5
value: 24.932000000000002
- type: recall_at_1
value: 71.641
- type: recall_at_10
value: 95.91499999999999
- type: recall_at_100
value: 99.63300000000001
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.248
- type: recall_at_5
value: 92.428
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.19631707795757
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 68.01353074322002
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.67
- type: map_at_10
value: 11.991999999999999
- type: map_at_100
value: 14.263
- type: map_at_1000
value: 14.59
- type: map_at_3
value: 8.468
- type: map_at_5
value: 10.346
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 23.1
- type: ndcg_at_10
value: 20.19
- type: ndcg_at_100
value: 28.792
- type: ndcg_at_1000
value: 34.406
- type: ndcg_at_3
value: 19.139
- type: ndcg_at_5
value: 16.916
- type: precision_at_1
value: 23.1
- type: precision_at_10
value: 10.47
- type: precision_at_100
value: 2.2849999999999997
- type: precision_at_1000
value: 0.363
- type: precision_at_3
value: 17.9
- type: precision_at_5
value: 14.979999999999999
- type: recall_at_1
value: 4.67
- type: recall_at_10
value: 21.21
- type: recall_at_100
value: 46.36
- type: recall_at_1000
value: 73.72999999999999
- type: recall_at_3
value: 10.865
- type: recall_at_5
value: 15.185
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 84.31392081916142
- type: cos_sim_spearman
value: 82.80375234068289
- type: euclidean_pearson
value: 81.4159066418654
- type: euclidean_spearman
value: 82.80377112831907
- type: manhattan_pearson
value: 81.48376861134983
- type: manhattan_spearman
value: 82.86696725667119
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1940844467158
- type: cos_sim_spearman
value: 76.22474792649982
- type: euclidean_pearson
value: 79.87714243582901
- type: euclidean_spearman
value: 76.22462054296349
- type: manhattan_pearson
value: 80.19242023327877
- type: manhattan_spearman
value: 76.53202564089719
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.58028303401805
- type: cos_sim_spearman
value: 86.30355131725051
- type: euclidean_pearson
value: 85.9027489087145
- type: euclidean_spearman
value: 86.30352515906158
- type: manhattan_pearson
value: 85.74953930990678
- type: manhattan_spearman
value: 86.21878393891001
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.92370135244734
- type: cos_sim_spearman
value: 82.09196894621044
- type: euclidean_pearson
value: 81.83198023906334
- type: euclidean_spearman
value: 82.09196482328333
- type: manhattan_pearson
value: 81.8951479497964
- type: manhattan_spearman
value: 82.2392819738236
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.05662816919057
- type: cos_sim_spearman
value: 87.24083005603993
- type: euclidean_pearson
value: 86.54673655650183
- type: euclidean_spearman
value: 87.24083428218053
- type: manhattan_pearson
value: 86.51248710513431
- type: manhattan_spearman
value: 87.24796986335883
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.06330254316376
- type: cos_sim_spearman
value: 84.76788840323285
- type: euclidean_pearson
value: 84.15438606134029
- type: euclidean_spearman
value: 84.76788840323285
- type: manhattan_pearson
value: 83.97986968570088
- type: manhattan_spearman
value: 84.52468572953663
- 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: 88.08627867173213
- type: cos_sim_spearman
value: 87.41531216247836
- type: euclidean_pearson
value: 87.92912483282956
- type: euclidean_spearman
value: 87.41531216247836
- type: manhattan_pearson
value: 87.85418528366228
- type: manhattan_spearman
value: 87.32655499883539
- 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: 70.74143864859911
- type: cos_sim_spearman
value: 69.84863549051433
- type: euclidean_pearson
value: 71.07346533903932
- type: euclidean_spearman
value: 69.84863549051433
- type: manhattan_pearson
value: 71.32285810342451
- type: manhattan_spearman
value: 70.13063960824287
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.05702492574339
- type: cos_sim_spearman
value: 86.13895001731495
- type: euclidean_pearson
value: 85.86694514265486
- type: euclidean_spearman
value: 86.13895001731495
- type: manhattan_pearson
value: 85.96382530570494
- type: manhattan_spearman
value: 86.30950247235928
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.26225076335467
- type: mrr
value: 96.60696329813977
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 64.494
- type: map_at_10
value: 74.102
- type: map_at_100
value: 74.571
- type: map_at_1000
value: 74.58
- type: map_at_3
value: 71.111
- type: map_at_5
value: 73.184
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 67.667
- type: ndcg_at_10
value: 78.427
- type: ndcg_at_100
value: 80.167
- type: ndcg_at_1000
value: 80.41
- type: ndcg_at_3
value: 73.804
- type: ndcg_at_5
value: 76.486
- type: precision_at_1
value: 67.667
- type: precision_at_10
value: 10.167
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.222
- type: precision_at_5
value: 18.867
- type: recall_at_1
value: 64.494
- type: recall_at_10
value: 90.422
- type: recall_at_100
value: 97.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 78.278
- type: recall_at_5
value: 84.828
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82772277227723
- type: cos_sim_ap
value: 95.93881941923254
- type: cos_sim_f1
value: 91.12244897959184
- type: cos_sim_precision
value: 93.02083333333333
- type: cos_sim_recall
value: 89.3
- type: dot_accuracy
value: 99.82772277227723
- type: dot_ap
value: 95.93886287716076
- type: dot_f1
value: 91.12244897959184
- type: dot_precision
value: 93.02083333333333
- type: dot_recall
value: 89.3
- type: euclidean_accuracy
value: 99.82772277227723
- type: euclidean_ap
value: 95.93881941923253
- type: euclidean_f1
value: 91.12244897959184
- type: euclidean_precision
value: 93.02083333333333
- type: euclidean_recall
value: 89.3
- type: manhattan_accuracy
value: 99.83366336633664
- type: manhattan_ap
value: 96.07286531485964
- type: manhattan_f1
value: 91.34912461380021
- type: manhattan_precision
value: 94.16135881104034
- type: manhattan_recall
value: 88.7
- type: max_accuracy
value: 99.83366336633664
- type: max_ap
value: 96.07286531485964
- type: max_f1
value: 91.34912461380021
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 74.98877944689897
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 42.0365286267706
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 56.5797777961647
- type: mrr
value: 57.57701754944402
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.673216240991756
- type: cos_sim_spearman
value: 31.198648165051225
- type: dot_pearson
value: 30.67321511262982
- type: dot_spearman
value: 31.198648165051225
- 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: 2.274
- type: map_at_100
value: 14.002
- type: map_at_1000
value: 34.443
- type: map_at_3
value: 0.705
- type: map_at_5
value: 1.162
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 88
- type: ndcg_at_10
value: 85.883
- type: ndcg_at_100
value: 67.343
- type: ndcg_at_1000
value: 59.999
- type: ndcg_at_3
value: 87.70400000000001
- type: ndcg_at_5
value: 85.437
- type: precision_at_1
value: 92
- type: precision_at_10
value: 91.2
- type: precision_at_100
value: 69.19999999999999
- type: precision_at_1000
value: 26.6
- type: precision_at_3
value: 92.667
- type: precision_at_5
value: 90.8
- type: recall_at_1
value: 0.23500000000000001
- type: recall_at_10
value: 2.409
- type: recall_at_100
value: 16.706
- type: recall_at_1000
value: 56.396
- type: recall_at_3
value: 0.734
- type: recall_at_5
value: 1.213
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.4819999999999998
- type: map_at_10
value: 10.985
- type: map_at_100
value: 17.943
- type: map_at_1000
value: 19.591
- type: map_at_3
value: 5.86
- type: map_at_5
value: 8.397
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 37.755
- type: ndcg_at_10
value: 28.383000000000003
- type: ndcg_at_100
value: 40.603
- type: ndcg_at_1000
value: 51.469
- type: ndcg_at_3
value: 32.562000000000005
- type: ndcg_at_5
value: 31.532
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 24.898
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.582
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 2.4819999999999998
- type: recall_at_10
value: 17.079
- type: recall_at_100
value: 51.406
- type: recall_at_1000
value: 84.456
- type: recall_at_3
value: 6.802
- type: recall_at_5
value: 10.856
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 92.5984
- type: ap
value: 41.969971606260906
- type: f1
value: 78.95995145145926
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 80.63950198075835
- type: f1
value: 80.93345710055597
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 60.13491858535076
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.42325803182929
- type: cos_sim_ap
value: 78.72789856051176
- type: cos_sim_f1
value: 71.83879093198993
- type: cos_sim_precision
value: 68.72289156626506
- type: cos_sim_recall
value: 75.25065963060686
- type: dot_accuracy
value: 87.42325803182929
- type: dot_ap
value: 78.72789755269454
- type: dot_f1
value: 71.83879093198993
- type: dot_precision
value: 68.72289156626506
- type: dot_recall
value: 75.25065963060686
- type: euclidean_accuracy
value: 87.42325803182929
- type: euclidean_ap
value: 78.7278973892869
- type: euclidean_f1
value: 71.83879093198993
- type: euclidean_precision
value: 68.72289156626506
- type: euclidean_recall
value: 75.25065963060686
- type: manhattan_accuracy
value: 87.59015318590929
- type: manhattan_ap
value: 78.99631410090865
- type: manhattan_f1
value: 72.11323565929972
- type: manhattan_precision
value: 68.10506566604127
- type: manhattan_recall
value: 76.62269129287598
- type: max_accuracy
value: 87.59015318590929
- type: max_ap
value: 78.99631410090865
- type: max_f1
value: 72.11323565929972
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.15473279776458
- type: cos_sim_ap
value: 86.05463278065247
- type: cos_sim_f1
value: 78.63797449855686
- type: cos_sim_precision
value: 74.82444552596816
- type: cos_sim_recall
value: 82.86110255620572
- type: dot_accuracy
value: 89.15473279776458
- type: dot_ap
value: 86.05463366261054
- type: dot_f1
value: 78.63797449855686
- type: dot_precision
value: 74.82444552596816
- type: dot_recall
value: 82.86110255620572
- type: euclidean_accuracy
value: 89.15473279776458
- type: euclidean_ap
value: 86.05463195314907
- type: euclidean_f1
value: 78.63797449855686
- type: euclidean_precision
value: 74.82444552596816
- type: euclidean_recall
value: 82.86110255620572
- type: manhattan_accuracy
value: 89.15861373074087
- type: manhattan_ap
value: 86.08743411620402
- type: manhattan_f1
value: 78.70125023325248
- type: manhattan_precision
value: 76.36706018686174
- type: manhattan_recall
value: 81.18263012011087
- type: max_accuracy
value: 89.15861373074087
- type: max_ap
value: 86.08743411620402
- type: max_f1
value: 78.70125023325248
---
## Introduction
We introduce NV-Embed, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark ([MTEB benchmark](https://arxiv.org/abs/2210.07316))(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks. Notably, our model also achieves the highest score of 59.36 on 15 retrieval tasks within this benchmark.
NV-Embed presents several new designs, including having the LLM attend to latent vectors for better pooled embedding output, and demonstrating a two-stage instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks.
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).
For more benchmark results (other than MTEB), please find the [AIR-Bench](https://huggingface.co/spaces/AIR-Bench/leaderboard) for QA (English only) and Long-Doc.
## 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-v1#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-v1', trust_remote_code=True)
# get the embeddings
max_length = 4096
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())
#[[77.9402084350586, 0.4248958230018616], [3.757718086242676, 79.60113525390625]]
```
### 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-v1', trust_remote_code=True)
model.max_seq_length = 4096
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())
```
## 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
@misc{lee2024nvembed,
title={NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models},
author={Chankyu Lee and Rajarshi Roy and Mengyao Xu and Jonathan Raiman and Mohammad Shoeybi and Bryan Catanzaro and Wei Ping},
year={2024},
eprint={2405.17428},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## 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).
## Troubleshooting
#### 1. 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-v1")
for module_key, module in embedding_model._modules.items():
embedding_model._modules[module_key] = DataParallel(module)
```
#### 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. Fixing "nvidia/NV-Embed-v1 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.
#### 4. Access to model nvidia/NV-Embed-v1 is restricted. You must be authenticated to access it
Use your huggingface access [token](https://huggingface.co/settings/tokens) to execute *"huggingface-cli login"*.
#### 5. 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"
] |
Lajavaness/bilingual-embedding-base
|
Lajavaness
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"bilingual",
"feature-extraction",
"sentence-similarity",
"transformers",
"sentence-embedding",
"mteb",
"custom_code",
"arxiv:2010.08240",
"arxiv:1911.02116",
"arxiv:1908.10084",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-26T15:22:37 | 2024-11-18T11:21:39 | 5,585 | 6 |
---
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-base
results:
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 64.71347977413274
- type: v_measures
value:
- 0.6276406100583347
- 0.6380984027637511
- 0.6322659733117469
- 0.652473343931587
- 0.6411502615838207
- type: v_measure
value: 45.56886694296516
- type: v_measures
value:
- 0.4359417286866465
- 0.4218168523895086
- 0.419693469863105
- 0.4981808644314091
- 0.4546120704986696
- 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: 70.45979049191963
- type: mrr
value: 71.61250582558874
- type: nAUC_map_diff1
value: 53.81172404763562
- type: nAUC_map_max
value: 11.344605627946006
- type: nAUC_mrr_diff1
value: 53.73572411920392
- type: nAUC_mrr_max
value: 11.900785644756448
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: map_at_1
value: 24.956999999999997
- type: map_at_10
value: 35.519
- type: map_at_100
value: 36.602000000000004
- type: map_at_1000
value: 36.667
- type: map_at_20
value: 36.199
- type: map_at_3
value: 32.397999999999996
- type: map_at_5
value: 34.121
- type: mrr_at_1
value: 24.956822107081177
- type: mrr_at_10
value: 35.5187515420676
- type: mrr_at_100
value: 36.60233410773095
- type: mrr_at_1000
value: 36.6673425266803
- type: mrr_at_20
value: 36.19886807640962
- type: mrr_at_3
value: 32.39781232009212
- type: mrr_at_5
value: 34.120610247553294
- type: nauc_map_at_1000_diff1
value: 35.47549954610535
- type: nauc_map_at_1000_max
value: 32.90078190148035
- type: nauc_map_at_100_diff1
value: 35.450977423889455
- type: nauc_map_at_100_max
value: 32.94163322687819
- type: nauc_map_at_10_diff1
value: 35.33565787959566
- type: nauc_map_at_10_max
value: 32.64293321320758
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value: 41.36063963274824
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value: 29.231198539339754
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value: 35.43536321023612
- type: nauc_map_at_20_max
value: 32.88345868813372
- type: nauc_map_at_3_diff1
value: 35.686505961183194
- type: nauc_map_at_3_max
value: 32.00806639149525
- type: nauc_map_at_5_diff1
value: 35.34083004527267
- type: nauc_map_at_5_max
value: 32.34459863266794
- type: nauc_mrr_at_1000_diff1
value: 35.47549954610535
- type: nauc_mrr_at_1000_max
value: 32.90078190148035
- type: nauc_mrr_at_100_diff1
value: 35.450977423889455
- type: nauc_mrr_at_100_max
value: 32.94163322687819
- type: nauc_mrr_at_10_diff1
value: 35.33565787959566
- type: nauc_mrr_at_10_max
value: 32.64293321320758
- type: nauc_mrr_at_1_diff1
value: 41.36063963274824
- type: nauc_mrr_at_1_max
value: 29.231198539339754
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value: 35.43536321023612
- type: nauc_mrr_at_20_max
value: 32.88345868813372
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value: 35.686505961183194
- type: nauc_mrr_at_3_max
value: 32.00806639149525
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value: 35.34083004527267
- type: nauc_mrr_at_5_max
value: 32.34459863266794
- type: nauc_ndcg_at_1000_diff1
value: 34.4325639393338
- type: nauc_ndcg_at_1000_max
value: 34.44147157978809
- type: nauc_ndcg_at_100_diff1
value: 33.88653913244061
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value: 41.36063963274824
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value: 29.231198539339754
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value: 33.804940889687714
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value: 35.13066047975507
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value: 34.167179830350406
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value: 32.94370726673571
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value: 41.36063963274824
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value: 30.09786756288918
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value: 35.51252285506707
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value: 28.444019525872083
- type: nauc_precision_at_5_max
value: 36.71010320598834
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value: 22.67827963096142
- type: nauc_recall_at_1000_max
value: 67.17958434804105
- type: nauc_recall_at_100_diff1
value: 24.83989747177035
- type: nauc_recall_at_100_max
value: 56.868584689811186
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value: 27.973507280697774
- type: nauc_recall_at_10_max
value: 39.14247264250283
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value: 41.36063963274824
- type: nauc_recall_at_1_max
value: 29.231198539339754
- type: nauc_recall_at_20_diff1
value: 28.244479044808823
- type: nauc_recall_at_20_max
value: 44.553810846197486
- type: nauc_recall_at_3_diff1
value: 30.097867562889213
- type: nauc_recall_at_3_max
value: 35.51252285506708
- type: nauc_recall_at_5_diff1
value: 28.444019525872054
- type: nauc_recall_at_5_max
value: 36.71010320598833
- type: ndcg_at_1
value: 24.956999999999997
- type: ndcg_at_10
value: 41.260999999999996
- type: ndcg_at_100
value: 46.628
- type: ndcg_at_1000
value: 48.339999999999996
- type: ndcg_at_20
value: 43.71
- type: ndcg_at_3
value: 34.771
- type: ndcg_at_5
value: 37.862
- type: precision_at_1
value: 24.956999999999997
- type: precision_at_10
value: 5.963
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 3.463
- type: precision_at_3
value: 13.874
- type: precision_at_5
value: 9.818999999999999
- type: recall_at_1
value: 24.956999999999997
- type: recall_at_10
value: 59.629
- type: recall_at_100
value: 84.974
- type: recall_at_1000
value: 98.402
- type: recall_at_20
value: 69.257
- type: recall_at_3
value: 41.623
- type: recall_at_5
value: 49.092999999999996
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.354
- type: f1
value: 38.96127209677864
- type: f1_weighted
value: 38.96127209677863
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
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value: 5.405
- type: map_at_10
value: 9.715
- type: map_at_100
value: 10.852
- type: map_at_1000
value: 10.986
- type: map_at_20
value: 10.324
- type: map_at_3
value: 8.183
- type: map_at_5
value: 9.129
- type: mrr_at_1
value: 5.405405405405405
- type: mrr_at_10
value: 9.715250965250963
- type: mrr_at_100
value: 10.852488713991455
- type: mrr_at_1000
value: 10.986128810251913
- type: mrr_at_20
value: 10.324372731493472
- type: mrr_at_3
value: 8.183183183183184
- type: mrr_at_5
value: 9.12912912912913
- type: nauc_map_at_1000_diff1
value: 27.989147369852507
- type: nauc_map_at_1000_max
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- type: nauc_recall_at_20_diff1
value: 19.171701298090017
- type: nauc_recall_at_20_max
value: 20.19048989979099
- type: nauc_recall_at_3_diff1
value: 24.905337103765717
- type: nauc_recall_at_3_max
value: 4.9604571557773935
- type: nauc_recall_at_5_diff1
value: 18.72551070313952
- type: nauc_recall_at_5_max
value: 8.55596436475138
- type: ndcg_at_1
value: 5.405
- type: ndcg_at_10
value: 12.217
- type: ndcg_at_100
value: 18.512999999999998
- type: ndcg_at_1000
value: 22.002
- type: ndcg_at_20
value: 14.551
- type: ndcg_at_3
value: 9.089
- type: ndcg_at_5
value: 10.776
- type: precision_at_1
value: 5.405
- type: precision_at_10
value: 2.027
- type: precision_at_100
value: 0.514
- type: precision_at_1000
value: 0.079
- type: precision_at_20
value: 1.486
- type: precision_at_3
value: 3.904
- type: precision_at_5
value: 3.1530000000000005
- type: recall_at_1
value: 5.405
- type: recall_at_10
value: 20.27
- type: recall_at_100
value: 51.351
- type: recall_at_1000
value: 78.82900000000001
- type: recall_at_20
value: 29.73
- type: recall_at_3
value: 11.712
- type: recall_at_5
value: 15.766
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 25.366610995664384
- type: v_measures
value:
- 0.2853826102888054
- 0.27554329622230517
- 0.2659387504290534
- 0.272421074779971
- 0.23780511730712292
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: reciTAL/mlsum
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 44.22735442638259
- type: v_measures
value:
- 0.446401643334608
- 0.45940182243882194
- 0.4535014442023603
- 0.4315840671354274
- 0.40219963696623473
- type: v_measure
value: 44.57521454657146
- type: v_measures
value:
- 0.4501823523199927
- 0.45800459800343646
- 0.4535621236055794
- 0.4353844290212204
- 0.40539074932285035
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.82743501409333
- type: f1
value: 87.07777165714171
- type: f1_weighted
value: 86.70555382175719
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 59.129345443156886
- type: f1
value: 40.374753319633946
- type: f1_weighted
value: 61.735222244513906
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 77.44075829383887
- type: f1
value: 73.59099795975669
- type: f1_weighted
value: 77.53638597300765
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 61.57579457743856
- type: v_measures
value:
- 1.0
- 0.19333147584460073
- 0.1918473189330316
- 0.9180419176341081
- 0.7755690164601873
- type: v_measure
value: 46.09882977546196
- type: v_measures
value:
- 1.0
- 0.0
- 0.4881603944307428
- 0.1341399033659235
- 0.6826411909764316
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 64.98655010087424
- type: f1
value: 62.07892690857404
- type: f1_weighted
value: 64.04916798028313
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 71.72494956287828
- type: f1
value: 70.7613627592262
- type: f1_weighted
value: 71.59023734198762
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 15.643
- type: map_at_10
value: 23.263
- type: map_at_100
value: 24.282
- type: map_at_1000
value: 24.386
- type: map_at_20
value: 23.810000000000002
- type: map_at_3
value: 20.857
- type: map_at_5
value: 22.127
- type: mrr_at_1
value: 15.642915642915641
- type: mrr_at_10
value: 23.26313326313326
- type: mrr_at_100
value: 24.28242740941141
- type: mrr_at_1000
value: 24.38579869502159
- type: mrr_at_20
value: 23.809503335080308
- type: mrr_at_3
value: 20.857220857220817
- type: mrr_at_5
value: 22.126672126672087
- type: nauc_map_at_1000_diff1
value: 22.791507278613405
- type: nauc_map_at_1000_max
value: 34.49738411163668
- type: nauc_map_at_100_diff1
value: 22.753348275392053
- type: nauc_map_at_100_max
value: 34.503329867035156
- type: nauc_map_at_10_diff1
value: 22.949396903228543
- type: nauc_map_at_10_max
value: 34.59635858547973
- type: nauc_map_at_1_diff1
value: 29.68671981414948
- type: nauc_map_at_1_max
value: 32.40476256531568
- type: nauc_map_at_20_diff1
value: 22.839943329322214
- type: nauc_map_at_20_max
value: 34.5512812666387
- type: nauc_map_at_3_diff1
value: 24.531567517208703
- type: nauc_map_at_3_max
value: 34.63194411307717
- type: nauc_map_at_5_diff1
value: 23.785928597532532
- type: nauc_map_at_5_max
value: 34.49711117096583
- type: nauc_mrr_at_1000_diff1
value: 22.791507278613405
- type: nauc_mrr_at_1000_max
value: 34.49738411163668
- type: nauc_mrr_at_100_diff1
value: 22.753348275392053
- type: nauc_mrr_at_100_max
value: 34.503329867035156
- type: nauc_mrr_at_10_diff1
value: 22.949396903228543
- type: nauc_mrr_at_10_max
value: 34.59635858547973
- type: nauc_mrr_at_1_diff1
value: 29.68671981414948
- type: nauc_mrr_at_1_max
value: 32.40476256531568
- type: nauc_mrr_at_20_diff1
value: 22.839943329322214
- type: nauc_mrr_at_20_max
value: 34.5512812666387
- type: nauc_mrr_at_3_diff1
value: 24.531567517208703
- type: nauc_mrr_at_3_max
value: 34.63194411307717
- type: nauc_mrr_at_5_diff1
value: 23.785928597532532
- type: nauc_mrr_at_5_max
value: 34.49711117096583
- type: nauc_ndcg_at_1000_diff1
value: 20.36281863938966
- type: nauc_ndcg_at_1000_max
value: 34.39195365895218
- type: nauc_ndcg_at_100_diff1
value: 19.226093736031558
- type: nauc_ndcg_at_100_max
value: 34.598730272263424
- type: nauc_ndcg_at_10_diff1
value: 20.175947236861635
- type: nauc_ndcg_at_10_max
value: 35.06289054957336
- type: nauc_ndcg_at_1_diff1
value: 29.68671981414948
- type: nauc_ndcg_at_1_max
value: 32.40476256531568
- type: nauc_ndcg_at_20_diff1
value: 19.76832604541867
- type: nauc_ndcg_at_20_max
value: 34.88676463098282
- type: nauc_ndcg_at_3_diff1
value: 23.165684344826936
- type: nauc_ndcg_at_3_max
value: 35.058555779606806
- type: nauc_ndcg_at_5_diff1
value: 22.008327776501197
- type: nauc_ndcg_at_5_max
value: 34.81586957799795
- type: nauc_precision_at_1000_diff1
value: -0.17116637411109903
- type: nauc_precision_at_1000_max
value: 22.79067675552558
- type: nauc_precision_at_100_diff1
value: 6.442518254575118
- type: nauc_precision_at_100_max
value: 34.22531735083736
- type: nauc_precision_at_10_diff1
value: 13.139149147711764
- type: nauc_precision_at_10_max
value: 36.17629609592965
- type: nauc_precision_at_1_diff1
value: 29.68671981414948
- type: nauc_precision_at_1_max
value: 32.40476256531568
- type: nauc_precision_at_20_diff1
value: 11.54118462661454
- type: nauc_precision_at_20_max
value: 35.56967988819633
- type: nauc_precision_at_3_diff1
value: 19.81196490555276
- type: nauc_precision_at_3_max
value: 36.06171743276854
- type: nauc_precision_at_5_diff1
value: 17.72477129059423
- type: nauc_precision_at_5_max
value: 35.49631000776651
- type: nauc_recall_at_1000_diff1
value: -0.17116637411164665
- type: nauc_recall_at_1000_max
value: 22.790676755525375
- type: nauc_recall_at_100_diff1
value: 6.442518254575061
- type: nauc_recall_at_100_max
value: 34.22531735083735
- type: nauc_recall_at_10_diff1
value: 13.139149147711779
- type: nauc_recall_at_10_max
value: 36.17629609592963
- type: nauc_recall_at_1_diff1
value: 29.68671981414948
- type: nauc_recall_at_1_max
value: 32.40476256531568
- type: nauc_recall_at_20_diff1
value: 11.541184626614548
- type: nauc_recall_at_20_max
value: 35.56967988819635
- type: nauc_recall_at_3_diff1
value: 19.811964905552767
- type: nauc_recall_at_3_max
value: 36.06171743276855
- type: nauc_recall_at_5_diff1
value: 17.72477129059422
- type: nauc_recall_at_5_max
value: 35.49631000776651
- type: ndcg_at_1
value: 15.643
- type: ndcg_at_10
value: 27.596999999999998
- type: ndcg_at_100
value: 33.036
- type: ndcg_at_1000
value: 36.348
- type: ndcg_at_20
value: 29.586000000000002
- type: ndcg_at_3
value: 22.567
- type: ndcg_at_5
value: 24.858
- type: precision_at_1
value: 15.643
- type: precision_at_10
value: 4.152
- type: precision_at_100
value: 0.681
- type: precision_at_1000
value: 0.095
- type: precision_at_20
value: 2.469
- type: precision_at_3
value: 9.173
- type: precision_at_5
value: 6.618
- type: recall_at_1
value: 15.643
- type: recall_at_10
value: 41.522999999999996
- type: recall_at_100
value: 68.059
- type: recall_at_1000
value: 95.332
- type: recall_at_20
value: 49.386
- type: recall_at_3
value: 27.517999999999997
- type: recall_at_5
value: 33.088
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 82.08446866485014
- type: cos_sim_ap
value: 93.63779278112501
- type: cos_sim_f1
value: 87.31307284129282
- type: cos_sim_precision
value: 84.8968105065666
- type: cos_sim_recall
value: 89.87090367428004
- type: dot_accuracy
value: 82.08446866485014
- type: dot_ap
value: 93.63779278112501
- type: dot_f1
value: 87.31307284129282
- type: dot_precision
value: 84.8968105065666
- type: dot_recall
value: 89.87090367428004
- type: euclidean_accuracy
value: 82.08446866485014
- type: euclidean_ap
value: 93.63779278112501
- type: euclidean_f1
value: 87.31307284129282
- type: euclidean_precision
value: 84.8968105065666
- type: euclidean_recall
value: 89.87090367428004
- type: manhattan_accuracy
value: 82.15258855585832
- type: manhattan_ap
value: 93.6291276133462
- type: manhattan_f1
value: 87.36740597878496
- type: manhattan_precision
value: 84.91096532333646
- type: manhattan_recall
value: 89.97020854021847
- type: max_accuracy
value: 82.15258855585832
- type: max_ap
value: 93.63779278112501
- type: max_f1
value: 87.36740597878496
- 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: 60.85
- type: cos_sim_ap
value: 60.37878110577144
- type: cos_sim_f1
value: 62.67978803936413
- type: cos_sim_precision
value: 47.61357101782634
- type: cos_sim_recall
value: 91.69435215946844
- type: dot_accuracy
value: 60.85
- type: dot_ap
value: 60.282876235548
- type: dot_f1
value: 62.67978803936413
- type: dot_precision
value: 47.61357101782634
- type: dot_recall
value: 91.69435215946844
- type: euclidean_accuracy
value: 60.85
- type: euclidean_ap
value: 60.37878110577144
- type: euclidean_f1
value: 62.67978803936413
- type: euclidean_precision
value: 47.61357101782634
- type: euclidean_recall
value: 91.69435215946844
- type: manhattan_accuracy
value: 61.0
- type: manhattan_ap
value: 60.32592616688752
- type: manhattan_f1
value: 62.70871985157699
- type: manhattan_precision
value: 47.154017857142854
- type: manhattan_recall
value: 93.57696566998892
- type: max_accuracy
value: 61.0
- type: max_ap
value: 60.37878110577144
- type: max_f1
value: 62.70871985157699
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 82.41331853949572
- type: cos_sim_spearman
value: 75.4815169821101
- type: euclidean_pearson
value: 79.81380205322172
- type: euclidean_spearman
value: 75.48076002322844
- type: manhattan_pearson
value: 79.86035354304643
- type: manhattan_spearman
value: 75.51770448845875
- 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: 78.63723398201921
- type: cos_sim_spearman
value: 81.1413239539243
- type: euclidean_pearson
value: 79.65250044199216
- type: euclidean_spearman
value: 81.1413239539243
- type: manhattan_pearson
value: 79.4690995487022
- type: manhattan_spearman
value: 80.61411938663267
- 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: 83.34057641357742
- type: cos_sim_spearman
value: 83.74771008807434
- type: euclidean_pearson
value: 83.22245971062527
- type: euclidean_spearman
value: 83.74894925253341
- type: manhattan_pearson
value: 83.06789257151542
- type: manhattan_spearman
value: 83.52796149940158
- 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: 29.949370780736455
- type: cos_sim_spearman
value: 30.16472987232583
- type: dot_pearson
value: 29.94937008799093
- type: dot_spearman
value: 30.16472987232583
- 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: 83.82619047619046
- type: mrr
value: 83.82619047619046
- type: nAUC_map_diff1
value: 56.291594426865686
- type: nAUC_map_max
value: 9.006252496368798
- type: nAUC_mrr_diff1
value: 56.291594426865686
- type: nAUC_mrr_max
value: 9.006252496368798
- 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: 62.0
- type: map_at_10
value: 74.369
- type: map_at_100
value: 74.626
- type: map_at_1000
value: 74.626
- type: map_at_20
value: 74.626
- type: map_at_3
value: 72.333
- type: map_at_5
value: 73.533
- type: mrr_at_1
value: 62.0
- type: mrr_at_10
value: 74.36944444444444
- type: mrr_at_100
value: 74.62553418803418
- type: mrr_at_1000
value: 74.62553418803418
- type: mrr_at_20
value: 74.62553418803418
- type: mrr_at_3
value: 72.33333333333333
- type: mrr_at_5
value: 73.53333333333333
- type: nauc_map_at_1000_diff1
value: 59.84829658893367
- type: nauc_map_at_1000_max
value: 25.92680320484747
- type: nauc_map_at_100_diff1
value: 59.84829658893367
- type: nauc_map_at_100_max
value: 25.92680320484747
- type: nauc_map_at_10_diff1
value: 60.14734510056334
- type: nauc_map_at_10_max
value: 26.4859845903765
- type: nauc_map_at_1_diff1
value: 61.15589330215675
- type: nauc_map_at_1_max
value: 25.502261329723385
- type: nauc_map_at_20_diff1
value: 59.84829658893367
- type: nauc_map_at_20_max
value: 25.92680320484747
- type: nauc_map_at_3_diff1
value: 60.664125602445864
- type: nauc_map_at_3_max
value: 24.598480701707597
- type: nauc_map_at_5_diff1
value: 59.96933672856163
- type: nauc_map_at_5_max
value: 26.87050847362874
- type: nauc_mrr_at_1000_diff1
value: 59.84829658893367
- type: nauc_mrr_at_1000_max
value: 25.92680320484747
- type: nauc_mrr_at_100_diff1
value: 59.84829658893367
- type: nauc_mrr_at_100_max
value: 25.92680320484747
- type: nauc_mrr_at_10_diff1
value: 60.14734510056334
- type: nauc_mrr_at_10_max
value: 26.4859845903765
- type: nauc_mrr_at_1_diff1
value: 61.15589330215675
- type: nauc_mrr_at_1_max
value: 25.502261329723385
- type: nauc_mrr_at_20_diff1
value: 59.84829658893367
- type: nauc_mrr_at_20_max
value: 25.92680320484747
- type: nauc_mrr_at_3_diff1
value: 60.664125602445864
- type: nauc_mrr_at_3_max
value: 24.598480701707597
- type: nauc_mrr_at_5_diff1
value: 59.96933672856163
- type: nauc_mrr_at_5_max
value: 26.87050847362874
- type: nauc_ndcg_at_1000_diff1
value: 60.04965149586935
- type: nauc_ndcg_at_1000_max
value: 26.34908378184259
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dataset:
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type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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type: mteb/reddit-clustering-p2p
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split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
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revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
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dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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type: mteb/sts13-sts
config: default
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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type: mteb/sts14-sts
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
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type: mteb/sts17-crosslingual-sts
config: en-en
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cos_sim_pearson
value: 64.635158432127
- type: cos_sim_spearman
value: 64.27073239884248
- type: euclidean_pearson
value: 66.21751368412394
- type: euclidean_spearman
value: 64.27073239884248
- type: manhattan_pearson
value: 66.15088837552513
- type: manhattan_spearman
value: 64.15068735594215
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.42604775914748
- type: cos_sim_spearman
value: 87.44031003802738
- type: euclidean_pearson
value: 87.43316615586107
- type: euclidean_spearman
value: 87.44031555024793
- type: manhattan_pearson
value: 87.31365950205998
- type: manhattan_spearman
value: 87.2941204713115
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.48265276238037
- type: mrr
value: 95.01133216819491
- type: nAUC_map_diff1
value: 7.09429277125843
- type: nAUC_map_max
value: 49.94029950841183
- type: nAUC_mrr_diff1
value: 50.34339361427615
- type: nAUC_mrr_max
value: 79.45634760563627
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 54.678000000000004
- type: map_at_10
value: 63.80200000000001
- type: map_at_100
value: 64.41
- type: map_at_1000
value: 64.444
- type: map_at_20
value: 64.164
- type: map_at_3
value: 60.925
- type: map_at_5
value: 62.572
- type: mrr_at_1
value: 57.333333333333336
- type: mrr_at_10
value: 65.36190476190477
- type: mrr_at_100
value: 65.81385754624793
- type: mrr_at_1000
value: 65.84158115043161
- type: mrr_at_20
value: 65.65317979234389
- type: mrr_at_3
value: 63.0
- type: mrr_at_5
value: 64.41666666666667
- type: nauc_map_at_1000_diff1
value: 69.67809035584142
- type: nauc_map_at_1000_max
value: 48.96413821482873
- type: nauc_map_at_100_diff1
value: 69.65364392825721
- type: nauc_map_at_100_max
value: 48.94627564310418
- type: nauc_map_at_10_diff1
value: 69.57800581171497
- type: nauc_map_at_10_max
value: 48.67897603770599
- type: nauc_map_at_1_diff1
value: 73.01790551734226
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value: 45.104875261851916
- type: nauc_map_at_20_diff1
value: 69.61362614678308
- type: nauc_map_at_20_max
value: 48.84035952302365
- type: nauc_map_at_3_diff1
value: 70.38590108900951
- type: nauc_map_at_3_max
value: 46.43843764953461
- type: nauc_map_at_5_diff1
value: 70.52260971372544
- type: nauc_map_at_5_max
value: 48.476797316953416
- type: nauc_mrr_at_1000_diff1
value: 69.5164716438929
- type: nauc_mrr_at_1000_max
value: 51.01945033286556
- type: nauc_mrr_at_100_diff1
value: 69.49106498529696
- type: nauc_mrr_at_100_max
value: 50.99948856957515
- type: nauc_mrr_at_10_diff1
value: 69.36739032618048
- type: nauc_mrr_at_10_max
value: 51.12574202596153
- type: nauc_mrr_at_1_diff1
value: 72.36743551825465
- type: nauc_mrr_at_1_max
value: 47.803395494440075
- type: nauc_mrr_at_20_diff1
value: 69.45736580905654
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value: 51.125870226973
- type: nauc_mrr_at_3_diff1
value: 70.18553354726865
- type: nauc_mrr_at_3_max
value: 50.0088700597719
- type: nauc_mrr_at_5_diff1
value: 70.13590171518094
- type: nauc_mrr_at_5_max
value: 51.43303825871863
- type: nauc_ndcg_at_1000_diff1
value: 68.59647494694465
- type: nauc_ndcg_at_1000_max
value: 50.84423309342368
- type: nauc_ndcg_at_100_diff1
value: 67.82685442401551
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value: 50.69511102823468
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value: 67.42699840483867
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value: 50.367163593062216
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value: 72.36743551825465
- type: nauc_ndcg_at_1_max
value: 47.803395494440075
- type: nauc_ndcg_at_20_diff1
value: 67.52338557685287
- type: nauc_ndcg_at_20_max
value: 50.79192152642611
- type: nauc_ndcg_at_3_diff1
value: 69.49850475650591
- type: nauc_ndcg_at_3_max
value: 47.69895483323495
- type: nauc_ndcg_at_5_diff1
value: 69.63182188059407
- type: nauc_ndcg_at_5_max
value: 50.692751514480086
- type: nauc_precision_at_1000_diff1
value: -22.076090806418165
- type: nauc_precision_at_1000_max
value: 15.457406924757958
- type: nauc_precision_at_100_diff1
value: -8.958717111709591
- type: nauc_precision_at_100_max
value: 23.39568973722963
- type: nauc_precision_at_10_diff1
value: 18.145183858510112
- type: nauc_precision_at_10_max
value: 38.39226201682134
- type: nauc_precision_at_1_diff1
value: 72.36743551825465
- type: nauc_precision_at_1_max
value: 47.803395494440075
- type: nauc_precision_at_20_diff1
value: 8.777082697589234
- type: nauc_precision_at_20_max
value: 35.78403592135664
- type: nauc_precision_at_3_diff1
value: 51.376349362119726
- type: nauc_precision_at_3_max
value: 42.810598626104
- type: nauc_precision_at_5_diff1
value: 40.9296274632404
- type: nauc_precision_at_5_max
value: 45.61709594788111
- type: nauc_recall_at_1000_diff1
value: 77.7077497665738
- type: nauc_recall_at_1000_max
value: 100.0
- type: nauc_recall_at_100_diff1
value: 46.35143397460506
- type: nauc_recall_at_100_max
value: 53.648092062544094
- type: nauc_recall_at_10_diff1
value: 55.95034026811939
- type: nauc_recall_at_10_max
value: 52.01290145176738
- type: nauc_recall_at_1_diff1
value: 73.01790551734226
- type: nauc_recall_at_1_max
value: 45.104875261851916
- type: nauc_recall_at_20_diff1
value: 53.20691212906598
- type: nauc_recall_at_20_max
value: 54.72055923613812
- type: nauc_recall_at_3_diff1
value: 66.24729517787732
- type: nauc_recall_at_3_max
value: 46.4286391286208
- type: nauc_recall_at_5_diff1
value: 65.39243758829612
- type: nauc_recall_at_5_max
value: 54.04465496293424
- type: ndcg_at_1
value: 57.333
- type: ndcg_at_10
value: 68.471
- type: ndcg_at_100
value: 71.16900000000001
- type: ndcg_at_1000
value: 71.934
- type: ndcg_at_20
value: 69.706
- type: ndcg_at_3
value: 63.404
- type: ndcg_at_5
value: 65.93900000000001
- type: precision_at_1
value: 57.333
- type: precision_at_10
value: 9.133
- type: precision_at_100
value: 1.06
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 4.833
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.267
- type: recall_at_1
value: 54.678000000000004
- type: recall_at_10
value: 81.244
- type: recall_at_100
value: 93.43299999999999
- type: recall_at_1000
value: 99.333
- type: recall_at_20
value: 86.02199999999999
- type: recall_at_3
value: 67.878
- type: recall_at_5
value: 74.06700000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.79702970297029
- type: cos_sim_ap
value: 94.75976312657727
- type: cos_sim_f1
value: 89.7029702970297
- type: cos_sim_precision
value: 88.8235294117647
- type: cos_sim_recall
value: 90.60000000000001
- type: dot_accuracy
value: 99.79702970297029
- type: dot_ap
value: 94.75976312657727
- type: dot_f1
value: 89.7029702970297
- type: dot_precision
value: 88.8235294117647
- type: dot_recall
value: 90.60000000000001
- type: euclidean_accuracy
value: 99.79702970297029
- type: euclidean_ap
value: 94.75976312657727
- type: euclidean_f1
value: 89.7029702970297
- type: euclidean_precision
value: 88.8235294117647
- type: euclidean_recall
value: 90.60000000000001
- type: manhattan_accuracy
value: 99.79207920792079
- type: manhattan_ap
value: 94.55264782714548
- type: manhattan_f1
value: 89.30753564154786
- type: manhattan_precision
value: 90.97510373443983
- type: manhattan_recall
value: 87.7
- type: max_accuracy
value: 99.79702970297029
- type: max_ap
value: 94.75976312657727
- type: max_f1
value: 89.7029702970297
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 61.56750444120404
- type: v_measures
value:
- 0.6143066231410037
- 0.6484543949297663
- 0.5142449933097217
- 0.6062244515066045
- 0.5739832317328382
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.59242218010789
- type: v_measures
value:
- 0.32392220242444014
- 0.32718501590648996
- 0.32976751674384125
- 0.3203167039651582
- 0.32297852686667516
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.200337844678295
- type: mrr
value: 49.95621923011629
- type: nAUC_map_diff1
value: 35.98209315270176
- type: nAUC_map_max
value: 12.780142559584018
- type: nAUC_mrr_diff1
value: 36.332794804706545
- type: nAUC_mrr_max
value: 13.650142919994185
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.273860160997156
- type: cos_sim_spearman
value: 30.256535662300372
- type: dot_pearson
value: 30.273861391746525
- type: dot_spearman
value: 30.25651496178948
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
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value: 0.161
- type: map_at_10
value: 1.0410000000000001
- type: map_at_100
value: 5.558
- type: map_at_1000
value: 15.296000000000001
- type: map_at_20
value: 1.755
- type: map_at_3
value: 0.437
- type: map_at_5
value: 0.628
- type: mrr_at_1
value: 64.0
- type: mrr_at_10
value: 73.77222222222223
- type: mrr_at_100
value: 74.10160298638559
- type: mrr_at_1000
value: 74.10160298638559
- type: mrr_at_20
value: 73.9540404040404
- type: mrr_at_3
value: 70.0
- type: mrr_at_5
value: 73.3
- type: nauc_map_at_1000_diff1
value: -1.9604698462344767
- type: nauc_map_at_1000_max
value: 53.358985327864715
- type: nauc_map_at_100_diff1
value: -0.5161702872199433
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value: 46.53235662200936
- type: nauc_map_at_10_diff1
value: 3.9256229008973924
- type: nauc_map_at_10_max
value: 26.82251128561187
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value: 4.127739790921559
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value: 10.596981259216367
- type: nauc_map_at_20_diff1
value: 1.6849110098963962
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value: 32.645496648968034
- type: nauc_map_at_3_diff1
value: 6.0165331028800715
- type: nauc_map_at_3_max
value: 16.690807762030964
- type: nauc_map_at_5_diff1
value: 3.151304647408004
- type: nauc_map_at_5_max
value: 22.316738900184266
- type: nauc_mrr_at_1000_diff1
value: 8.665368838521031
- type: nauc_mrr_at_1000_max
value: 48.23582325840371
- type: nauc_mrr_at_100_diff1
value: 8.665368838521031
- type: nauc_mrr_at_100_max
value: 48.23582325840371
- type: nauc_mrr_at_10_diff1
value: 9.331739591001785
- type: nauc_mrr_at_10_max
value: 48.89453679791346
- type: nauc_mrr_at_1_diff1
value: 0.5428534934218375
- type: nauc_mrr_at_1_max
value: 35.480265678886184
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value: 8.583612874582078
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value: 48.472852713493815
- type: nauc_mrr_at_3_diff1
value: 9.854859452507785
- type: nauc_mrr_at_3_max
value: 50.846959397391124
- type: nauc_mrr_at_5_diff1
value: 9.223998156393836
- type: nauc_mrr_at_5_max
value: 49.03025489502146
- type: nauc_ndcg_at_1000_diff1
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value: 49.93338657126098
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value: 3.0321366749721466
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value: 51.5400551444691
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value: 46.76810123148509
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value: 7.688195422853383
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value: 49.01894979259541
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value: 4.482353844424605
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value: 41.5568352350729
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value: 5.0965815841199005
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- type: nauc_precision_at_1000_diff1
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value: 48.14464502949045
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value: 35.480265678886184
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value: 5.7555901777882825
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value: 51.63129458224357
- type: nauc_precision_at_3_diff1
value: 6.697227309781205
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value: 3.675984060933098
- type: nauc_precision_at_5_max
value: 54.034024908467984
- type: nauc_recall_at_1000_diff1
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value: 54.458
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value: 53.601
- type: precision_at_1
value: 64.0
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value: 52.2
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value: 38.64
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value: 17.98
- type: precision_at_20
value: 48.5
- type: precision_at_3
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- type: precision_at_5
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- type: recall_at_1
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- type: recall_at_10
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- type: recall_at_1000
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- type: recall_at_20
value: 2.382
- type: recall_at_3
value: 0.47400000000000003
- type: recall_at_5
value: 0.735
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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value: 1.773
- type: map_at_10
value: 5.819
- type: map_at_100
value: 9.791
- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
value: 3.778
- type: map_at_5
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- type: mrr_at_1
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- 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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- type: mrr_at_3
value: 32.6530612244898
- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
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- type: nauc_map_at_5_diff1
value: 19.97087359992273
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value: -39.34647766735783
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value: 1.9748555750391668
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value: -38.23327494222865
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value: 1.9748555750391668
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value: -38.23327494222865
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value: 3.026383371657329
- type: nauc_mrr_at_10_max
value: -37.357911445290675
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value: -3.6234515245372143
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value: -34.918413083959635
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value: 2.785643581147567
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value: -39.027829254611596
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value: -0.652257776491799
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value: -35.777046370826746
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value: -1.0723788161779884
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value: -37.74663638254447
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value: -5.603421065562765
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value: -48.302585883872254
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value: -0.4142249503459385
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value: -44.266171155232605
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value: 9.09113299295375
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value: -33.95506408586246
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value: -2.3509910942232137
- type: nauc_ndcg_at_1_max
value: -31.9181129505804
- type: nauc_ndcg_at_20_diff1
value: 6.711479298611486
- type: nauc_ndcg_at_20_max
value: -41.17797709135335
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value: 7.181833876703895
- type: nauc_ndcg_at_3_max
value: -36.76673572406525
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value: 4.220139919243461
- type: nauc_ndcg_at_5_max
value: -36.01626374596527
- type: nauc_precision_at_1000_diff1
value: -21.500096998480743
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value: 17.423538290188787
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value: -20.576518815311264
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value: -33.43443540744943
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value: 5.916347999274714
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value: -31.375195302655644
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value: -3.6234515245372143
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value: -34.918413083959635
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value: -5.5440042532199145
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value: -41.606107555682485
- type: nauc_precision_at_3_diff1
value: 9.493481238903758
- type: nauc_precision_at_3_max
value: -39.77246958608447
- type: nauc_precision_at_5_diff1
value: 0.9616292047999846
- type: nauc_precision_at_5_max
value: -36.360179658412726
- type: nauc_recall_at_1000_diff1
value: -18.950139772725883
- type: nauc_recall_at_1000_max
value: -49.50001953592577
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value: -4.101410227998355
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value: -43.41533454443838
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value: 15.471367681837625
- type: nauc_recall_at_10_max
value: -30.21854343397064
- type: nauc_recall_at_1_diff1
value: 15.68698509866931
- type: nauc_recall_at_1_max
value: -38.49856837383727
- type: nauc_recall_at_20_diff1
value: 6.295179926244505
- type: nauc_recall_at_20_max
value: -39.20825139905824
- type: nauc_recall_at_3_diff1
value: 26.54887286634497
- type: nauc_recall_at_3_max
value: -39.46308906643022
- type: nauc_recall_at_5_diff1
value: 15.955004561636251
- type: nauc_recall_at_5_max
value: -36.491415148404585
- type: ndcg_at_1
value: 23.469
- type: ndcg_at_10
value: 16.07
- type: ndcg_at_100
value: 25.61
- type: ndcg_at_1000
value: 38.092999999999996
- type: ndcg_at_20
value: 17.980999999999998
- type: ndcg_at_3
value: 18.332
- type: ndcg_at_5
value: 17.302
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 14.285999999999998
- type: precision_at_100
value: 5.469
- type: precision_at_1000
value: 1.331
- type: precision_at_20
value: 12.449
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 17.551
- type: recall_at_1
value: 1.773
- type: recall_at_10
value: 10.698
- type: recall_at_100
value: 35.684
- type: recall_at_1000
value: 72.932
- type: recall_at_20
value: 18.723
- type: recall_at_3
value: 4.788
- type: recall_at_5
value: 6.715
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 63.38378906249999
- type: ap
value: 11.003129023708196
- type: ap_weighted
value: 11.003129023708196
- type: f1
value: 48.57435688911943
- type: f1_weighted
value: 71.50552650067205
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.467458970005666
- type: f1
value: 62.66021402025028
- type: f1_weighted
value: 61.71229183506532
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 44.96874447839597
- type: v_measures
value:
- 0.44089700334011683
- 0.4808871718296333
- 0.4655762216311635
- 0.4108889263207817
- 0.45449139982441744
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.83179352685224
- type: cos_sim_ap
value: 73.75870454252158
- type: cos_sim_f1
value: 67.61786600496278
- type: cos_sim_precision
value: 63.81733021077284
- type: cos_sim_recall
value: 71.89973614775725
- type: dot_accuracy
value: 85.83179352685224
- type: dot_ap
value: 73.75870536224079
- type: dot_f1
value: 67.61786600496278
- type: dot_precision
value: 63.81733021077284
- type: dot_recall
value: 71.89973614775725
- type: euclidean_accuracy
value: 85.83179352685224
- type: euclidean_ap
value: 73.7587242895193
- type: euclidean_f1
value: 67.61786600496278
- type: euclidean_precision
value: 63.81733021077284
- type: euclidean_recall
value: 71.89973614775725
- type: manhattan_accuracy
value: 85.91524110389224
- type: manhattan_ap
value: 73.77139111004601
- type: manhattan_f1
value: 67.52419453632244
- type: manhattan_precision
value: 63.023096272581746
- type: manhattan_recall
value: 72.71767810026385
- type: max_accuracy
value: 85.91524110389224
- type: max_ap
value: 73.77139111004601
- type: max_f1
value: 67.61786600496278
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.2711607870532
- type: cos_sim_ap
value: 86.48986874793765
- type: cos_sim_f1
value: 79.10280373831775
- type: cos_sim_precision
value: 76.87836070338614
- type: cos_sim_recall
value: 81.45980905451185
- type: dot_accuracy
value: 89.2711607870532
- type: dot_ap
value: 86.48986996838993
- type: dot_f1
value: 79.10280373831775
- type: dot_precision
value: 76.87836070338614
- type: dot_recall
value: 81.45980905451185
- type: euclidean_accuracy
value: 89.2711607870532
- type: euclidean_ap
value: 86.4898691356683
- type: euclidean_f1
value: 79.10280373831775
- type: euclidean_precision
value: 76.87836070338614
- type: euclidean_recall
value: 81.45980905451185
- type: manhattan_accuracy
value: 89.2711607870532
- type: manhattan_ap
value: 86.46475884590569
- type: manhattan_f1
value: 78.9534579927593
- type: manhattan_precision
value: 76.61716769286491
- type: manhattan_recall
value: 81.43671080997844
- type: max_accuracy
value: 89.2711607870532
- type: max_ap
value: 86.48986996838993
- type: max_f1
value: 79.10280373831775
---
# [bilingual-embedding-base](https://huggingface.co/Lajavaness/bilingual-embedding-base)
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-base), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) 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-base', 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"
] |
Mihaiii/Bulbasaur
|
Mihaiii
|
sentence-similarity
|
[
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"gte",
"mteb",
"dataset:Mihaiii/qa-assistant",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-27T09:53:29 | 2024-04-30T07:30:42 | 5,457 | 2 |
---
datasets:
- Mihaiii/qa-assistant
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- gte
- mteb
model-index:
- name: Bulbasaur
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.86567164179104
- type: ap
value: 34.08685244750869
- type: f1
value: 65.66014356237362
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 78.78927499999999
- type: ap
value: 73.46960735629719
- type: f1
value: 78.6951990840684
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.312
- type: f1
value: 38.94567141563064
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 22.191
- type: map_at_10
value: 36.504
- type: map_at_100
value: 37.676
- type: map_at_1000
value: 37.693
- type: map_at_20
value: 37.329
- type: map_at_3
value: 31.840000000000003
- type: map_at_5
value: 34.333000000000006
- type: mrr_at_1
value: 23.186
- type: mrr_at_10
value: 36.856
- type: mrr_at_100
value: 38.048
- type: mrr_at_1000
value: 38.065
- type: mrr_at_20
value: 37.701
- type: mrr_at_3
value: 32.16
- type: mrr_at_5
value: 34.756
- type: ndcg_at_1
value: 22.191
- type: ndcg_at_10
value: 44.798
- type: ndcg_at_100
value: 50.141999999999996
- type: ndcg_at_1000
value: 50.599000000000004
- type: ndcg_at_20
value: 47.778999999999996
- type: ndcg_at_3
value: 35.071999999999996
- type: ndcg_at_5
value: 39.574
- type: precision_at_1
value: 22.191
- type: precision_at_10
value: 7.148000000000001
- type: precision_at_100
value: 0.9570000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 4.1610000000000005
- type: precision_at_3
value: 14.817
- type: precision_at_5
value: 11.081000000000001
- type: recall_at_1
value: 22.191
- type: recall_at_10
value: 71.479
- type: recall_at_100
value: 95.661
- type: recall_at_1000
value: 99.289
- type: recall_at_20
value: 83.21499999999999
- type: recall_at_3
value: 44.452000000000005
- type: recall_at_5
value: 55.405
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 40.283298409035076
- type: v_measures
value:
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- 0.38211196645121454
- 0.4115695136452048
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- 1.0
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- 0.4115695136452048
- 0.41137132653792025
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- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 57.489775602270086
- type: mrr
value: 71.4973838104032
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type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 85.81314286759594
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value: 85.04832342591277
- type: euclidean_pearson
value: 84.20540608390993
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value: 84.54831203281398
- type: manhattan_pearson
value: 84.11283044138868
- 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: 80.57792207792207
- type: f1
value: 80.510338047888
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
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value: 35.17908628951979
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- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.18471478622865
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- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.7776266029616
- type: mrr
value: 32.9057970138914
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 24.78675
- type: map_at_10
value: 33.18391666666666
- type: map_at_100
value: 34.34583333333333
- type: map_at_1000
value: 34.46825
- type: map_at_20
value: 33.819
- type: map_at_3
value: 30.636500000000005
- type: map_at_5
value: 32.02091666666667
- type: mrr_at_1
value: 29.478749999999998
- type: mrr_at_10
value: 37.385
- type: mrr_at_100
value: 38.23491666666667
- type: mrr_at_1000
value: 38.298833333333334
- type: mrr_at_20
value: 37.87508333333333
- type: mrr_at_3
value: 35.089666666666666
- type: mrr_at_5
value: 36.36816666666667
- type: ndcg_at_1
value: 29.478749999999998
- type: ndcg_at_10
value: 38.2035
- type: ndcg_at_100
value: 43.301083333333324
- type: ndcg_at_1000
value: 45.758666666666656
- type: ndcg_at_20
value: 40.15116666666667
- type: ndcg_at_3
value: 33.86033333333334
- type: ndcg_at_5
value: 35.81266666666666
- type: precision_at_1
value: 29.478749999999998
- type: precision_at_10
value: 6.642833333333334
- type: precision_at_100
value: 1.08425
- type: precision_at_1000
value: 0.14850000000000002
- type: precision_at_20
value: 3.948083333333334
- type: precision_at_3
value: 15.511
- type: precision_at_5
value: 10.929833333333333
- type: recall_at_1
value: 24.78675
- type: recall_at_10
value: 48.9305
- type: recall_at_100
value: 71.49416666666666
- type: recall_at_1000
value: 88.54375
- type: recall_at_20
value: 56.06475
- type: recall_at_3
value: 36.66891666666666
- type: recall_at_5
value: 41.790499999999994
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 24.271
- type: map_at_10
value: 33.5
- type: map_at_100
value: 34.818
- type: map_at_1000
value: 34.967
- type: map_at_20
value: 34.238
- type: map_at_3
value: 30.488
- type: map_at_5
value: 32.303
- type: mrr_at_1
value: 30.615
- type: mrr_at_10
value: 39.076
- type: mrr_at_100
value: 40.022000000000006
- type: mrr_at_1000
value: 40.082
- type: mrr_at_20
value: 39.669
- type: mrr_at_3
value: 36.552
- type: mrr_at_5
value: 38.096999999999994
- type: ndcg_at_1
value: 30.615
- type: ndcg_at_10
value: 39.106
- type: ndcg_at_100
value: 44.519
- type: ndcg_at_1000
value: 47.274
- type: ndcg_at_20
value: 41.289
- type: ndcg_at_3
value: 34.55
- type: ndcg_at_5
value: 36.815999999999995
- type: precision_at_1
value: 30.615
- type: precision_at_10
value: 7.5249999999999995
- type: precision_at_100
value: 1.282
- type: precision_at_1000
value: 0.181
- type: precision_at_20
value: 4.549
- type: precision_at_3
value: 16.643
- type: precision_at_5
value: 12.275
- type: recall_at_1
value: 24.271
- type: recall_at_10
value: 49.714000000000006
- type: recall_at_100
value: 72.792
- type: recall_at_1000
value: 91.21000000000001
- type: recall_at_20
value: 57.799
- type: recall_at_3
value: 36.494
- type: recall_at_5
value: 42.764
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 19.414
- type: map_at_10
value: 25.766
- type: map_at_100
value: 26.627000000000002
- type: map_at_1000
value: 26.749000000000002
- type: map_at_20
value: 26.201999999999998
- type: map_at_3
value: 23.738
- type: map_at_5
value: 24.829
- type: mrr_at_1
value: 24.013
- type: mrr_at_10
value: 30.208000000000002
- type: mrr_at_100
value: 30.903000000000002
- type: mrr_at_1000
value: 30.976
- type: mrr_at_20
value: 30.585
- type: mrr_at_3
value: 28.376
- type: mrr_at_5
value: 29.462
- type: ndcg_at_1
value: 24.013
- type: ndcg_at_10
value: 29.871
- type: ndcg_at_100
value: 33.867999999999995
- type: ndcg_at_1000
value: 36.565
- type: ndcg_at_20
value: 31.251
- type: ndcg_at_3
value: 26.579000000000004
- type: ndcg_at_5
value: 28.094
- type: precision_at_1
value: 24.013
- type: precision_at_10
value: 5.503
- type: precision_at_100
value: 0.936
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 3.2800000000000002
- type: precision_at_3
value: 12.590000000000002
- type: precision_at_5
value: 8.994
- type: recall_at_1
value: 19.414
- type: recall_at_10
value: 37.582
- type: recall_at_100
value: 55.181000000000004
- type: recall_at_1000
value: 73.342
- type: recall_at_20
value: 42.596000000000004
- type: recall_at_3
value: 28.102
- type: recall_at_5
value: 32.267
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 31.5
- type: map_at_10
value: 42.179
- type: map_at_100
value: 43.231
- type: map_at_1000
value: 43.302
- type: map_at_20
value: 42.786
- type: map_at_3
value: 39.17
- type: map_at_5
value: 40.854
- type: mrr_at_1
value: 36.113
- type: mrr_at_10
value: 45.378
- type: mrr_at_100
value: 46.153
- type: mrr_at_1000
value: 46.194
- type: mrr_at_20
value: 45.831
- type: mrr_at_3
value: 42.947
- type: mrr_at_5
value: 44.339
- type: ndcg_at_1
value: 36.113
- type: ndcg_at_10
value: 47.616
- type: ndcg_at_100
value: 52.125
- type: ndcg_at_1000
value: 53.717999999999996
- type: ndcg_at_20
value: 49.495
- type: ndcg_at_3
value: 42.354
- type: ndcg_at_5
value: 44.885999999999996
- type: precision_at_1
value: 36.113
- type: precision_at_10
value: 7.799
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.129
- type: precision_at_20
value: 4.4670000000000005
- type: precision_at_3
value: 19.017999999999997
- type: precision_at_5
value: 13.254
- type: recall_at_1
value: 31.5
- type: recall_at_10
value: 60.67
- type: recall_at_100
value: 80.484
- type: recall_at_1000
value: 92.04599999999999
- type: recall_at_20
value: 67.644
- type: recall_at_3
value: 46.671
- type: recall_at_5
value: 52.723
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 17.339
- type: map_at_10
value: 23.014000000000003
- type: map_at_100
value: 23.918
- type: map_at_1000
value: 24.027
- type: map_at_20
value: 23.507
- type: map_at_3
value: 21.176000000000002
- type: map_at_5
value: 22.126
- type: mrr_at_1
value: 18.531
- type: mrr_at_10
value: 24.356
- type: mrr_at_100
value: 25.247000000000003
- type: mrr_at_1000
value: 25.338
- type: mrr_at_20
value: 24.858
- type: mrr_at_3
value: 22.542
- type: mrr_at_5
value: 23.508000000000003
- type: ndcg_at_1
value: 18.531
- type: ndcg_at_10
value: 26.51
- type: ndcg_at_100
value: 31.367
- type: ndcg_at_1000
value: 34.38
- type: ndcg_at_20
value: 28.328999999999997
- type: ndcg_at_3
value: 22.861
- type: ndcg_at_5
value: 24.456
- type: precision_at_1
value: 18.531
- type: precision_at_10
value: 4.147
- type: precision_at_100
value: 0.695
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 2.492
- type: precision_at_3
value: 9.793000000000001
- type: precision_at_5
value: 6.825
- type: recall_at_1
value: 17.339
- type: recall_at_10
value: 36.010999999999996
- type: recall_at_100
value: 59.040000000000006
- type: recall_at_1000
value: 82.282
- type: recall_at_20
value: 43.04
- type: recall_at_3
value: 25.904
- type: recall_at_5
value: 29.837000000000003
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 9.251
- type: map_at_10
value: 14.848
- type: map_at_100
value: 15.940999999999999
- type: map_at_1000
value: 16.055
- type: map_at_20
value: 15.423
- type: map_at_3
value: 12.556999999999999
- type: map_at_5
value: 13.649000000000001
- type: mrr_at_1
value: 12.313
- type: mrr_at_10
value: 18.528
- type: mrr_at_100
value: 19.522000000000002
- type: mrr_at_1000
value: 19.601
- type: mrr_at_20
value: 19.107
- type: mrr_at_3
value: 16.231
- type: mrr_at_5
value: 17.294999999999998
- type: ndcg_at_1
value: 12.313
- type: ndcg_at_10
value: 19.303
- type: ndcg_at_100
value: 24.728
- type: ndcg_at_1000
value: 27.823999999999998
- type: ndcg_at_20
value: 21.318
- type: ndcg_at_3
value: 14.848
- type: ndcg_at_5
value: 16.509
- type: precision_at_1
value: 12.313
- type: precision_at_10
value: 4.03
- type: precision_at_100
value: 0.777
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_20
value: 2.562
- type: precision_at_3
value: 7.546
- type: precision_at_5
value: 5.672
- type: recall_at_1
value: 9.251
- type: recall_at_10
value: 29.677999999999997
- type: recall_at_100
value: 53.586
- type: recall_at_1000
value: 76.181
- type: recall_at_20
value: 36.963
- type: recall_at_3
value: 17.072000000000003
- type: recall_at_5
value: 21.481
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 21.135
- type: map_at_10
value: 29.431
- type: map_at_100
value: 30.662
- type: map_at_1000
value: 30.792
- type: map_at_20
value: 30.086000000000002
- type: map_at_3
value: 26.593
- type: map_at_5
value: 28.011999999999997
- type: mrr_at_1
value: 26.564
- type: mrr_at_10
value: 34.735
- type: mrr_at_100
value: 35.65
- type: mrr_at_1000
value: 35.711999999999996
- type: mrr_at_20
value: 35.286
- type: mrr_at_3
value: 32.002
- type: mrr_at_5
value: 33.527
- type: ndcg_at_1
value: 26.564
- type: ndcg_at_10
value: 35.108
- type: ndcg_at_100
value: 40.601
- type: ndcg_at_1000
value: 43.329
- type: ndcg_at_20
value: 37.192
- type: ndcg_at_3
value: 29.961
- type: ndcg_at_5
value: 32.131
- type: precision_at_1
value: 26.564
- type: precision_at_10
value: 6.564
- type: precision_at_100
value: 1.105
- type: precision_at_1000
value: 0.154
- type: precision_at_20
value: 3.941
- type: precision_at_3
value: 14.212
- type: precision_at_5
value: 10.337
- type: recall_at_1
value: 21.135
- type: recall_at_10
value: 47.242
- type: recall_at_100
value: 70.645
- type: recall_at_1000
value: 89.403
- type: recall_at_20
value: 54.663
- type: recall_at_3
value: 32.647
- type: recall_at_5
value: 38.122
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 16.86
- type: map_at_10
value: 23.477999999999998
- type: map_at_100
value: 24.68
- type: map_at_1000
value: 24.826999999999998
- type: map_at_20
value: 24.122
- type: map_at_3
value: 21.288999999999998
- type: map_at_5
value: 22.453
- type: mrr_at_1
value: 20.776
- type: mrr_at_10
value: 28.029
- type: mrr_at_100
value: 28.951
- type: mrr_at_1000
value: 29.038000000000004
- type: mrr_at_20
value: 28.546
- type: mrr_at_3
value: 25.818
- type: mrr_at_5
value: 26.994
- type: ndcg_at_1
value: 20.776
- type: ndcg_at_10
value: 28.152
- type: ndcg_at_100
value: 33.82
- type: ndcg_at_1000
value: 37.039
- type: ndcg_at_20
value: 30.238
- type: ndcg_at_3
value: 24.197
- type: ndcg_at_5
value: 25.861
- type: precision_at_1
value: 20.776
- type: precision_at_10
value: 5.297000000000001
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 3.276
- type: precision_at_3
value: 11.606
- type: precision_at_5
value: 8.356
- type: recall_at_1
value: 16.86
- type: recall_at_10
value: 37.782
- type: recall_at_100
value: 62.67
- type: recall_at_1000
value: 85.03
- type: recall_at_20
value: 45.2
- type: recall_at_3
value: 26.506999999999998
- type: recall_at_5
value: 31.113000000000003
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 15.234
- type: map_at_10
value: 20.939
- type: map_at_100
value: 21.704
- type: map_at_1000
value: 21.804000000000002
- type: map_at_20
value: 21.311
- type: map_at_3
value: 18.972
- type: map_at_5
value: 19.929
- type: mrr_at_1
value: 17.485
- type: mrr_at_10
value: 23.267
- type: mrr_at_100
value: 23.967
- type: mrr_at_1000
value: 24.054000000000002
- type: mrr_at_20
value: 23.604
- type: mrr_at_3
value: 21.345
- type: mrr_at_5
value: 22.303
- type: ndcg_at_1
value: 17.485
- type: ndcg_at_10
value: 24.744
- type: ndcg_at_100
value: 28.801
- type: ndcg_at_1000
value: 31.619999999999997
- type: ndcg_at_20
value: 26.046000000000003
- type: ndcg_at_3
value: 20.862
- type: ndcg_at_5
value: 22.459
- type: precision_at_1
value: 17.485
- type: precision_at_10
value: 4.109999999999999
- type: precision_at_100
value: 0.676
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 2.3619999999999997
- type: precision_at_3
value: 9.254
- type: precision_at_5
value: 6.503
- type: recall_at_1
value: 15.234
- type: recall_at_10
value: 34.48
- type: recall_at_100
value: 53.225
- type: recall_at_1000
value: 74.64699999999999
- type: recall_at_20
value: 39.421
- type: recall_at_3
value: 23.554
- type: recall_at_5
value: 27.662
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 9.564
- type: map_at_10
value: 13.869000000000002
- type: map_at_100
value: 14.728
- type: map_at_1000
value: 14.853
- type: map_at_20
value: 14.32
- type: map_at_3
value: 12.307
- type: map_at_5
value: 13.177
- type: mrr_at_1
value: 11.941
- type: mrr_at_10
value: 16.777
- type: mrr_at_100
value: 17.571
- type: mrr_at_1000
value: 17.663999999999998
- type: mrr_at_20
value: 17.203
- type: mrr_at_3
value: 15.067
- type: mrr_at_5
value: 16.003999999999998
- type: ndcg_at_1
value: 11.941
- type: ndcg_at_10
value: 17.111
- type: ndcg_at_100
value: 21.438
- type: ndcg_at_1000
value: 24.756
- type: ndcg_at_20
value: 18.616
- type: ndcg_at_3
value: 14.143
- type: ndcg_at_5
value: 15.501000000000001
- type: precision_at_1
value: 11.941
- type: precision_at_10
value: 3.304
- type: precision_at_100
value: 0.658
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_20
value: 2.077
- type: precision_at_3
value: 6.882000000000001
- type: precision_at_5
value: 5.12
- type: recall_at_1
value: 9.564
- type: recall_at_10
value: 24.068
- type: recall_at_100
value: 43.759
- type: recall_at_1000
value: 68.101
- type: recall_at_20
value: 29.657
- type: recall_at_3
value: 15.68
- type: recall_at_5
value: 19.238
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 16.171
- type: map_at_10
value: 22.142
- type: map_at_100
value: 23.261000000000003
- type: map_at_1000
value: 23.371
- type: map_at_20
value: 22.766000000000002
- type: map_at_3
value: 20.251
- type: map_at_5
value: 21.349
- type: mrr_at_1
value: 19.403000000000002
- type: mrr_at_10
value: 25.619999999999997
- type: mrr_at_100
value: 26.659
- type: mrr_at_1000
value: 26.735
- type: mrr_at_20
value: 26.212000000000003
- type: mrr_at_3
value: 23.694000000000003
- type: mrr_at_5
value: 24.781
- type: ndcg_at_1
value: 19.403000000000002
- type: ndcg_at_10
value: 26.104
- type: ndcg_at_100
value: 31.724000000000004
- type: ndcg_at_1000
value: 34.581
- type: ndcg_at_20
value: 28.231
- type: ndcg_at_3
value: 22.464000000000002
- type: ndcg_at_5
value: 24.233
- type: precision_at_1
value: 19.403000000000002
- type: precision_at_10
value: 4.422000000000001
- type: precision_at_100
value: 0.8170000000000001
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_20
value: 2.78
- type: precision_at_3
value: 10.168000000000001
- type: precision_at_5
value: 7.295
- type: recall_at_1
value: 16.171
- type: recall_at_10
value: 34.899
- type: recall_at_100
value: 60.197
- type: recall_at_1000
value: 80.798
- type: recall_at_20
value: 42.591
- type: recall_at_3
value: 25.024
- type: recall_at_5
value: 29.42
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 16.412
- type: map_at_10
value: 23.138
- type: map_at_100
value: 24.46
- type: map_at_1000
value: 24.668
- type: map_at_20
value: 23.791
- type: map_at_3
value: 20.965
- type: map_at_5
value: 22.005
- type: mrr_at_1
value: 20.949
- type: mrr_at_10
value: 27.46
- type: mrr_at_100
value: 28.546
- type: mrr_at_1000
value: 28.619
- type: mrr_at_20
value: 28.038999999999998
- type: mrr_at_3
value: 25.461
- type: mrr_at_5
value: 26.528000000000002
- type: ndcg_at_1
value: 20.949
- type: ndcg_at_10
value: 27.919
- type: ndcg_at_100
value: 33.886
- type: ndcg_at_1000
value: 37.284
- type: ndcg_at_20
value: 29.876
- type: ndcg_at_3
value: 24.246000000000002
- type: ndcg_at_5
value: 25.607999999999997
- type: precision_at_1
value: 20.949
- type: precision_at_10
value: 5.534
- type: precision_at_100
value: 1.2409999999999999
- type: precision_at_1000
value: 0.22
- type: precision_at_20
value: 3.5180000000000002
- type: precision_at_3
value: 11.726
- type: precision_at_5
value: 8.498
- type: recall_at_1
value: 16.412
- type: recall_at_10
value: 37.012
- type: recall_at_100
value: 64.702
- type: recall_at_1000
value: 87.442
- type: recall_at_20
value: 44.797
- type: recall_at_3
value: 25.872
- type: recall_at_5
value: 29.732999999999997
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 11.158
- type: map_at_10
value: 15.809999999999999
- type: map_at_100
value: 16.821
- type: map_at_1000
value: 16.925
- type: map_at_20
value: 16.403000000000002
- type: map_at_3
value: 13.791999999999998
- type: map_at_5
value: 14.817
- type: mrr_at_1
value: 12.384
- type: mrr_at_10
value: 17.291999999999998
- type: mrr_at_100
value: 18.271
- type: mrr_at_1000
value: 18.360000000000003
- type: mrr_at_20
value: 17.854999999999997
- type: mrr_at_3
value: 15.096000000000002
- type: mrr_at_5
value: 16.214000000000002
- type: ndcg_at_1
value: 12.384
- type: ndcg_at_10
value: 19.250999999999998
- type: ndcg_at_100
value: 24.524
- type: ndcg_at_1000
value: 27.624
- type: ndcg_at_20
value: 21.387999999999998
- type: ndcg_at_3
value: 14.995
- type: ndcg_at_5
value: 16.861
- type: precision_at_1
value: 12.384
- type: precision_at_10
value: 3.29
- type: precision_at_100
value: 0.632
- type: precision_at_1000
value: 0.095
- type: precision_at_20
value: 2.1260000000000003
- type: precision_at_3
value: 6.47
- type: precision_at_5
value: 4.917
- type: recall_at_1
value: 11.158
- type: recall_at_10
value: 28.737000000000002
- type: recall_at_100
value: 53.400000000000006
- type: recall_at_1000
value: 77.509
- type: recall_at_20
value: 36.969
- type: recall_at_3
value: 17.197000000000003
- type: recall_at_5
value: 21.701
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 7.172000000000001
- type: map_at_10
value: 11.935
- type: map_at_100
value: 13.305
- type: map_at_1000
value: 13.517000000000001
- type: map_at_20
value: 12.589
- type: map_at_3
value: 9.9
- type: map_at_5
value: 10.839
- type: mrr_at_1
value: 15.895999999999999
- type: mrr_at_10
value: 24.215999999999998
- type: mrr_at_100
value: 25.418000000000003
- type: mrr_at_1000
value: 25.480000000000004
- type: mrr_at_20
value: 24.934
- type: mrr_at_3
value: 21.064
- type: mrr_at_5
value: 22.676
- type: ndcg_at_1
value: 15.895999999999999
- type: ndcg_at_10
value: 17.69
- type: ndcg_at_100
value: 24.232
- type: ndcg_at_1000
value: 28.405
- type: ndcg_at_20
value: 19.933999999999997
- type: ndcg_at_3
value: 13.761000000000001
- type: ndcg_at_5
value: 14.963000000000001
- type: precision_at_1
value: 15.895999999999999
- type: precision_at_10
value: 5.733
- type: precision_at_100
value: 1.266
- type: precision_at_1000
value: 0.203
- type: precision_at_20
value: 3.798
- type: precision_at_3
value: 10.076
- type: precision_at_5
value: 7.9479999999999995
- type: recall_at_1
value: 7.172000000000001
- type: recall_at_10
value: 22.149
- type: recall_at_100
value: 45.491
- type: recall_at_1000
value: 69.34
- type: recall_at_20
value: 28.634999999999998
- type: recall_at_3
value: 12.701
- type: recall_at_5
value: 15.952
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 7.101
- type: map_at_10
value: 15.125
- type: map_at_100
value: 20.026
- type: map_at_1000
value: 21.194
- type: map_at_20
value: 17.008000000000003
- type: map_at_3
value: 10.915999999999999
- type: map_at_5
value: 12.705
- type: mrr_at_1
value: 53.5
- type: mrr_at_10
value: 63.475
- type: mrr_at_100
value: 63.998
- type: mrr_at_1000
value: 64.019
- type: mrr_at_20
value: 63.800999999999995
- type: mrr_at_3
value: 62.041999999999994
- type: mrr_at_5
value: 62.678999999999995
- type: ndcg_at_1
value: 41.875
- type: ndcg_at_10
value: 32.967
- type: ndcg_at_100
value: 35.557
- type: ndcg_at_1000
value: 42.537000000000006
- type: ndcg_at_20
value: 31.930999999999997
- type: ndcg_at_3
value: 36.67
- type: ndcg_at_5
value: 34.474
- type: precision_at_1
value: 53.5
- type: precision_at_10
value: 27.0
- type: precision_at_100
value: 7.872999999999999
- type: precision_at_1000
value: 1.637
- type: precision_at_20
value: 19.487
- type: precision_at_3
value: 41.583
- type: precision_at_5
value: 34.699999999999996
- type: recall_at_1
value: 7.101
- type: recall_at_10
value: 20.408
- type: recall_at_100
value: 40.286
- type: recall_at_1000
value: 63.49399999999999
- type: recall_at_20
value: 25.478
- type: recall_at_3
value: 12.278
- type: recall_at_5
value: 15.392
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.79
- type: f1
value: 39.606429663804356
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 27.898
- type: map_at_10
value: 39.315
- type: map_at_100
value: 40.219
- type: map_at_1000
value: 40.268
- type: map_at_20
value: 39.893
- type: map_at_3
value: 35.993
- type: map_at_5
value: 38.016
- type: mrr_at_1
value: 30.003
- type: mrr_at_10
value: 41.85
- type: mrr_at_100
value: 42.722
- type: mrr_at_1000
value: 42.760999999999996
- type: mrr_at_20
value: 42.419000000000004
- type: mrr_at_3
value: 38.451
- type: mrr_at_5
value: 40.547
- type: ndcg_at_1
value: 30.003
- type: ndcg_at_10
value: 45.907
- type: ndcg_at_100
value: 50.198
- type: ndcg_at_1000
value: 51.405
- type: ndcg_at_20
value: 47.97
- type: ndcg_at_3
value: 39.234
- type: ndcg_at_5
value: 42.844
- type: precision_at_1
value: 30.003
- type: precision_at_10
value: 7.0040000000000004
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.9510000000000005
- type: precision_at_3
value: 16.647000000000002
- type: precision_at_5
value: 11.914
- type: recall_at_1
value: 27.898
- type: recall_at_10
value: 64.003
- type: recall_at_100
value: 83.42500000000001
- type: recall_at_1000
value: 92.448
- type: recall_at_20
value: 71.93
- type: recall_at_3
value: 46.12
- type: recall_at_5
value: 54.812000000000005
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 10.282
- type: map_at_10
value: 16.141
- type: map_at_100
value: 17.634
- type: map_at_1000
value: 17.836
- type: map_at_20
value: 16.99
- type: map_at_3
value: 13.947000000000001
- type: map_at_5
value: 15.149000000000001
- type: mrr_at_1
value: 20.679
- type: mrr_at_10
value: 26.966
- type: mrr_at_100
value: 28.108
- type: mrr_at_1000
value: 28.183999999999997
- type: mrr_at_20
value: 27.672
- type: mrr_at_3
value: 24.743000000000002
- type: mrr_at_5
value: 25.916
- type: ndcg_at_1
value: 20.679
- type: ndcg_at_10
value: 21.291
- type: ndcg_at_100
value: 27.884999999999998
- type: ndcg_at_1000
value: 32.122
- type: ndcg_at_20
value: 23.898
- type: ndcg_at_3
value: 18.553
- type: ndcg_at_5
value: 19.468
- type: precision_at_1
value: 20.679
- type: precision_at_10
value: 6.019
- type: precision_at_100
value: 1.252
- type: precision_at_1000
value: 0.201
- type: precision_at_20
value: 4.0120000000000005
- type: precision_at_3
value: 12.243
- type: precision_at_5
value: 9.321
- type: recall_at_1
value: 10.282
- type: recall_at_10
value: 25.901999999999997
- type: recall_at_100
value: 50.956999999999994
- type: recall_at_1000
value: 76.935
- type: recall_at_20
value: 34.104
- type: recall_at_3
value: 16.973
- type: recall_at_5
value: 20.549999999999997
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 30.567
- type: map_at_10
value: 42.314
- type: map_at_100
value: 43.205
- type: map_at_1000
value: 43.288
- type: map_at_20
value: 42.812
- type: map_at_3
value: 39.695
- type: map_at_5
value: 41.214
- type: mrr_at_1
value: 61.134
- type: mrr_at_10
value: 68.57600000000001
- type: mrr_at_100
value: 68.95599999999999
- type: mrr_at_1000
value: 68.97999999999999
- type: mrr_at_20
value: 68.818
- type: mrr_at_3
value: 66.99300000000001
- type: mrr_at_5
value: 67.919
- type: ndcg_at_1
value: 61.134
- type: ndcg_at_10
value: 51.518
- type: ndcg_at_100
value: 55.022000000000006
- type: ndcg_at_1000
value: 56.81699999999999
- type: ndcg_at_20
value: 52.893
- type: ndcg_at_3
value: 47.216
- type: ndcg_at_5
value: 49.413000000000004
- type: precision_at_1
value: 61.134
- type: precision_at_10
value: 10.729
- type: precision_at_100
value: 1.351
- type: precision_at_1000
value: 0.159
- type: precision_at_20
value: 5.8069999999999995
- type: precision_at_3
value: 29.336000000000002
- type: precision_at_5
value: 19.346
- type: recall_at_1
value: 30.567
- type: recall_at_10
value: 53.64600000000001
- type: recall_at_100
value: 67.562
- type: recall_at_1000
value: 79.521
- type: recall_at_20
value: 58.069
- type: recall_at_3
value: 44.004
- type: recall_at_5
value: 48.366
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 71.5272
- type: ap
value: 65.49215755861609
- type: f1
value: 71.4156268611186
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 14.574000000000002
- type: map_at_10
value: 23.966
- type: map_at_100
value: 25.19
- type: map_at_1000
value: 25.266
- type: map_at_20
value: 24.668
- type: map_at_3
value: 20.815
- type: map_at_5
value: 22.576
- type: mrr_at_1
value: 14.957
- type: mrr_at_10
value: 24.413999999999998
- type: mrr_at_100
value: 25.616
- type: mrr_at_1000
value: 25.685999999999996
- type: mrr_at_20
value: 25.11
- type: mrr_at_3
value: 21.304000000000002
- type: mrr_at_5
value: 23.047
- type: ndcg_at_1
value: 14.957
- type: ndcg_at_10
value: 29.49
- type: ndcg_at_100
value: 35.734
- type: ndcg_at_1000
value: 37.785000000000004
- type: ndcg_at_20
value: 32.004
- type: ndcg_at_3
value: 23.006999999999998
- type: ndcg_at_5
value: 26.154
- type: precision_at_1
value: 14.957
- type: precision_at_10
value: 4.8500000000000005
- type: precision_at_100
value: 0.8009999999999999
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 2.943
- type: precision_at_3
value: 9.962
- type: precision_at_5
value: 7.556
- type: recall_at_1
value: 14.574000000000002
- type: recall_at_10
value: 46.655
- type: recall_at_100
value: 76.26899999999999
- type: recall_at_1000
value: 92.303
- type: recall_at_20
value: 56.424
- type: recall_at_3
value: 28.874
- type: recall_at_5
value: 36.441
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.78887368901049
- type: f1
value: 90.30465646125157
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 68.71865025079799
- type: f1
value: 50.7484789245504
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.8399462004035
- type: f1
value: 66.66574227334513
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.74915938130464
- type: f1
value: 73.61179700374726
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.3983428793953
- type: v_measures
value:
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- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.78917156239751
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- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
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value: 3.726
- type: map_at_10
value: 8.604000000000001
- type: map_at_100
value: 10.95
- type: map_at_1000
value: 12.256
- type: map_at_20
value: 9.573
- type: map_at_3
value: 6.264
- type: map_at_5
value: 7.343
- type: mrr_at_1
value: 37.771
- type: mrr_at_10
value: 46.476
- type: mrr_at_100
value: 47.164
- type: mrr_at_1000
value: 47.213
- type: mrr_at_20
value: 46.792
- type: mrr_at_3
value: 44.272
- type: mrr_at_5
value: 45.728
- type: ndcg_at_1
value: 35.604
- type: ndcg_at_10
value: 26.778000000000002
- type: ndcg_at_100
value: 24.313000000000002
- type: ndcg_at_1000
value: 33.601
- type: ndcg_at_20
value: 24.788
- type: ndcg_at_3
value: 30.991999999999997
- type: ndcg_at_5
value: 28.9
- type: precision_at_1
value: 37.152
- type: precision_at_10
value: 19.875999999999998
- type: precision_at_100
value: 6.449000000000001
- type: precision_at_1000
value: 1.934
- type: precision_at_20
value: 14.721
- type: precision_at_3
value: 28.999000000000002
- type: precision_at_5
value: 24.582
- type: recall_at_1
value: 3.726
- type: recall_at_10
value: 12.529000000000002
- type: recall_at_100
value: 25.726
- type: recall_at_1000
value: 58.336
- type: recall_at_20
value: 16.028000000000002
- type: recall_at_3
value: 7.176
- type: recall_at_5
value: 9.511
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
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value: 15.110000000000001
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value: 25.983
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value: 27.332
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value: 27.406999999999996
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value: 26.804
- type: map_at_3
value: 22.182
- type: map_at_5
value: 24.247
- type: mrr_at_1
value: 17.236
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value: 28.177999999999997
- type: mrr_at_100
value: 29.346
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value: 29.401
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value: 28.906
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value: 24.593999999999998
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value: 26.540999999999997
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value: 17.207
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value: 32.603
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value: 38.883
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value: 40.708
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value: 35.397
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value: 25.002999999999997
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value: 28.572999999999997
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value: 17.207
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value: 5.985
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value: 0.951
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value: 0.11299999999999999
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value: 3.656
- type: precision_at_3
value: 11.848
- type: precision_at_5
value: 9.125
- type: recall_at_1
value: 15.110000000000001
- type: recall_at_10
value: 51.00900000000001
- type: recall_at_100
value: 79.193
- type: recall_at_1000
value: 92.828
- type: recall_at_20
value: 61.402
- type: recall_at_3
value: 30.791
- type: recall_at_5
value: 39.091
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 67.465
- type: map_at_10
value: 81.035
- type: map_at_100
value: 81.718
- type: map_at_1000
value: 81.742
- type: map_at_20
value: 81.486
- type: map_at_3
value: 77.972
- type: map_at_5
value: 79.903
- type: mrr_at_1
value: 77.64
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value: 84.584
- type: mrr_at_100
value: 84.722
- type: mrr_at_1000
value: 84.724
- type: mrr_at_20
value: 84.684
- type: mrr_at_3
value: 83.325
- type: mrr_at_5
value: 84.15899999999999
- type: ndcg_at_1
value: 77.66999999999999
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value: 85.30499999999999
- type: ndcg_at_100
value: 86.834
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value: 87.033
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value: 86.12100000000001
- type: ndcg_at_3
value: 81.974
- type: ndcg_at_5
value: 83.813
- type: precision_at_1
value: 77.66999999999999
- type: precision_at_10
value: 12.931000000000001
- type: precision_at_100
value: 1.5
- type: precision_at_1000
value: 0.156
- type: precision_at_20
value: 6.903
- type: precision_at_3
value: 35.730000000000004
- type: precision_at_5
value: 23.642
- type: recall_at_1
value: 67.465
- type: recall_at_10
value: 93.581
- type: recall_at_100
value: 98.91499999999999
- type: recall_at_1000
value: 99.90599999999999
- type: recall_at_20
value: 96.221
- type: recall_at_3
value: 84.071
- type: recall_at_5
value: 89.14999999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 45.929215298244664
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value:
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- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 51.444598402601414
- type: v_measures
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- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 3.5929999999999995
- type: map_at_10
value: 8.753
- type: map_at_100
value: 10.349
- type: map_at_1000
value: 10.624
- type: map_at_20
value: 9.553
- type: map_at_3
value: 6.2700000000000005
- type: map_at_5
value: 7.5329999999999995
- type: mrr_at_1
value: 17.7
- type: mrr_at_10
value: 27.167
- type: mrr_at_100
value: 28.351
- type: mrr_at_1000
value: 28.418
- type: mrr_at_20
value: 27.819
- type: mrr_at_3
value: 24.282999999999998
- type: mrr_at_5
value: 26.073
- type: ndcg_at_1
value: 17.7
- type: ndcg_at_10
value: 15.312000000000001
- type: ndcg_at_100
value: 22.178
- type: ndcg_at_1000
value: 27.575
- type: ndcg_at_20
value: 17.648
- type: ndcg_at_3
value: 14.41
- type: ndcg_at_5
value: 12.774
- type: precision_at_1
value: 17.7
- type: precision_at_10
value: 7.93
- type: precision_at_100
value: 1.7930000000000001
- type: precision_at_1000
value: 0.31
- type: precision_at_20
value: 5.315
- type: precision_at_3
value: 13.367
- type: precision_at_5
value: 11.26
- type: recall_at_1
value: 3.5929999999999995
- type: recall_at_10
value: 16.088
- type: recall_at_100
value: 36.39
- type: recall_at_1000
value: 62.932
- type: recall_at_20
value: 21.562
- type: recall_at_3
value: 8.123
- type: recall_at_5
value: 11.393
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 82.6885494958054
- type: cos_sim_spearman
value: 76.0433546110243
- type: euclidean_pearson
value: 79.85820435751087
- type: euclidean_spearman
value: 75.9326257444857
- type: manhattan_pearson
value: 79.6973024858654
- type: manhattan_spearman
value: 75.71084698490509
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 77.34659218404506
- type: cos_sim_spearman
value: 69.49541146727839
- type: euclidean_pearson
value: 74.80982564474151
- type: euclidean_spearman
value: 70.04102091813081
- type: manhattan_pearson
value: 75.00200126757426
- type: manhattan_spearman
value: 70.22802660355588
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.91444494464905
- type: cos_sim_spearman
value: 80.96085686108583
- type: euclidean_pearson
value: 80.5915387592164
- type: euclidean_spearman
value: 80.8861855866439
- type: manhattan_pearson
value: 80.46881359994653
- type: manhattan_spearman
value: 80.80230339264102
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type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.81974904249208
- type: cos_sim_spearman
value: 77.08348207580887
- type: euclidean_pearson
value: 80.13431221409199
- type: euclidean_spearman
value: 77.31778188790902
- type: manhattan_pearson
value: 80.05343415464556
- type: manhattan_spearman
value: 77.26095229151665
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 84.37398871508579
- type: cos_sim_spearman
value: 85.41548418250477
- type: euclidean_pearson
value: 85.18569982361353
- type: euclidean_spearman
value: 85.73446512176643
- type: manhattan_pearson
value: 85.1016252976206
- type: manhattan_spearman
value: 85.66092136939069
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.59702638640928
- type: cos_sim_spearman
value: 82.29583005583622
- type: euclidean_pearson
value: 81.83307796549182
- type: euclidean_spearman
value: 82.39554204652183
- type: manhattan_pearson
value: 81.78282737393326
- type: manhattan_spearman
value: 82.34235304571907
- 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: 86.89190122908971
- type: cos_sim_spearman
value: 88.03461344591356
- type: euclidean_pearson
value: 87.81999485969313
- type: euclidean_spearman
value: 88.07040076481854
- type: manhattan_pearson
value: 87.53382294293554
- type: manhattan_spearman
value: 87.76615089464353
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type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 57.97869820676485
- type: cos_sim_spearman
value: 64.12171377270657
- type: euclidean_pearson
value: 60.9601725696545
- type: euclidean_spearman
value: 63.48982922146721
- type: manhattan_pearson
value: 61.37553142926566
- type: manhattan_spearman
value: 63.759462595791796
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.14812517797631
- type: cos_sim_spearman
value: 83.33681512924129
- type: euclidean_pearson
value: 84.0552689078266
- type: euclidean_spearman
value: 83.45075258664495
- type: manhattan_pearson
value: 83.94309504683835
- type: manhattan_spearman
value: 83.37311472277489
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 77.89395841192561
- type: mrr
value: 93.39039319431475
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 47.65
- type: map_at_10
value: 58.287
- type: map_at_100
value: 58.965999999999994
- type: map_at_1000
value: 58.998
- type: map_at_20
value: 58.709
- type: map_at_3
value: 55.272
- type: map_at_5
value: 57.135999999999996
- type: mrr_at_1
value: 50.333000000000006
- type: mrr_at_10
value: 59.589999999999996
- type: mrr_at_100
value: 60.129999999999995
- type: mrr_at_1000
value: 60.162000000000006
- type: mrr_at_20
value: 59.95700000000001
- type: mrr_at_3
value: 57.389
- type: mrr_at_5
value: 58.656
- type: ndcg_at_1
value: 50.333000000000006
- type: ndcg_at_10
value: 63.232
- type: ndcg_at_100
value: 66.213
- type: ndcg_at_1000
value: 67.203
- type: ndcg_at_20
value: 64.63499999999999
- type: ndcg_at_3
value: 58.163
- type: ndcg_at_5
value: 60.785999999999994
- type: precision_at_1
value: 50.333000000000006
- type: precision_at_10
value: 8.633000000000001
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_20
value: 4.633
- type: precision_at_3
value: 22.889
- type: precision_at_5
value: 15.4
- type: recall_at_1
value: 47.65
- type: recall_at_10
value: 76.95
- type: recall_at_100
value: 90.333
- type: recall_at_1000
value: 98.333
- type: recall_at_20
value: 82.267
- type: recall_at_3
value: 63.632999999999996
- type: recall_at_5
value: 69.978
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82277227722773
- type: cos_sim_ap
value: 95.3535743677476
- type: cos_sim_f1
value: 91.00050276520865
- type: cos_sim_precision
value: 91.50657229524772
- type: cos_sim_recall
value: 90.5
- type: dot_accuracy
value: 99.73267326732673
- type: dot_ap
value: 92.1266370356305
- type: dot_f1
value: 86.13810741687979
- type: dot_precision
value: 88.1675392670157
- type: dot_recall
value: 84.2
- type: euclidean_accuracy
value: 99.82277227722773
- type: euclidean_ap
value: 95.24537694377634
- type: euclidean_f1
value: 90.91831557584982
- type: euclidean_precision
value: 92.27600411946447
- type: euclidean_recall
value: 89.60000000000001
- type: manhattan_accuracy
value: 99.81881188118813
- type: manhattan_ap
value: 95.30188096008806
- type: manhattan_f1
value: 90.83625438157236
- type: manhattan_precision
value: 90.97291875626881
- type: manhattan_recall
value: 90.7
- type: max_accuracy
value: 99.82277227722773
- type: max_ap
value: 95.3535743677476
- type: max_f1
value: 91.00050276520865
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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value: 52.18146042107239
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- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.666751785479224
- type: v_measures
value:
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- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 46.78149995864765
- type: mrr
value: 47.45282393260334
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.202698233290022
- type: cos_sim_spearman
value: 30.971936219818662
- type: dot_pearson
value: 25.486069760264634
- type: dot_spearman
value: 25.811060638581246
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.16999999999999998
- type: map_at_10
value: 0.943
- type: map_at_100
value: 5.0200000000000005
- type: map_at_1000
value: 13.855
- type: map_at_20
value: 1.609
- type: map_at_3
value: 0.384
- type: map_at_5
value: 0.5660000000000001
- type: mrr_at_1
value: 68.0
- type: mrr_at_10
value: 77.983
- type: mrr_at_100
value: 78.16499999999999
- type: mrr_at_1000
value: 78.16499999999999
- type: mrr_at_20
value: 78.16499999999999
- type: mrr_at_3
value: 75.667
- type: mrr_at_5
value: 77.067
- type: ndcg_at_1
value: 62.0
- type: ndcg_at_10
value: 47.772999999999996
- type: ndcg_at_100
value: 36.15
- type: ndcg_at_1000
value: 36.071
- type: ndcg_at_20
value: 44.641
- type: ndcg_at_3
value: 52.608999999999995
- type: ndcg_at_5
value: 50.397999999999996
- type: precision_at_1
value: 68.0
- type: precision_at_10
value: 50.8
- type: precision_at_100
value: 37.62
- type: precision_at_1000
value: 16.97
- type: precision_at_20
value: 47.099999999999994
- type: precision_at_3
value: 56.667
- type: precision_at_5
value: 54.0
- type: recall_at_1
value: 0.16999999999999998
- type: recall_at_10
value: 1.2349999999999999
- type: recall_at_100
value: 8.666
- type: recall_at_1000
value: 35.326
- type: recall_at_20
value: 2.276
- type: recall_at_3
value: 0.428
- type: recall_at_5
value: 0.672
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 1.897
- type: map_at_10
value: 6.034
- type: map_at_100
value: 10.475
- type: map_at_1000
value: 11.95
- type: map_at_20
value: 8.149000000000001
- type: map_at_3
value: 2.8449999999999998
- type: map_at_5
value: 3.972
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 33.751
- type: mrr_at_100
value: 35.544
- type: mrr_at_1000
value: 35.544
- type: mrr_at_20
value: 34.926
- type: mrr_at_3
value: 29.252
- type: mrr_at_5
value: 31.905
- type: ndcg_at_1
value: 22.448999999999998
- type: ndcg_at_10
value: 16.303
- type: ndcg_at_100
value: 27.165
- type: ndcg_at_1000
value: 39.736
- type: ndcg_at_20
value: 18.340999999999998
- type: ndcg_at_3
value: 15.137999999999998
- type: ndcg_at_5
value: 16.332
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 15.714
- type: precision_at_100
value: 6.184
- type: precision_at_1000
value: 1.439
- type: precision_at_20
value: 13.163
- type: precision_at_3
value: 15.645999999999999
- type: precision_at_5
value: 17.551
- type: recall_at_1
value: 1.897
- type: recall_at_10
value: 11.938
- type: recall_at_100
value: 39.249
- type: recall_at_1000
value: 78.121
- type: recall_at_20
value: 19.244
- type: recall_at_3
value: 3.5409999999999995
- type: recall_at_5
value: 6.297999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 66.2939453125
- type: ap
value: 11.764275936169392
- type: f1
value: 50.50689429240701
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.49066213921902
- type: f1
value: 59.85044985699777
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.44109250212289
- type: v_measures
value:
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- 0.4101285395437541
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- 0.3828493740836783
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.25821064552662
- type: cos_sim_ap
value: 67.96785265119063
- type: cos_sim_f1
value: 65.0070788107598
- type: cos_sim_precision
value: 58.792146820315835
- type: cos_sim_recall
value: 72.69129287598945
- type: dot_accuracy
value: 81.47463789712106
- type: dot_ap
value: 58.234902049577684
- type: dot_f1
value: 56.73442037078401
- type: dot_precision
value: 49.18667699457785
- type: dot_recall
value: 67.01846965699208
- type: euclidean_accuracy
value: 84.30589497526375
- type: euclidean_ap
value: 68.07824251821404
- type: euclidean_f1
value: 65.09073543457498
- type: euclidean_precision
value: 59.44177932839075
- type: euclidean_recall
value: 71.92612137203166
- type: manhattan_accuracy
value: 84.24032902187518
- type: manhattan_ap
value: 67.76838044141897
- type: manhattan_f1
value: 64.75698520779525
- type: manhattan_precision
value: 58.333333333333336
- type: manhattan_recall
value: 72.77044854881267
- type: max_accuracy
value: 84.30589497526375
- type: max_ap
value: 68.07824251821404
- type: max_f1
value: 65.09073543457498
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.2951061435169
- type: cos_sim_ap
value: 84.74905878045149
- type: cos_sim_f1
value: 77.01659871869538
- type: cos_sim_precision
value: 73.0392156862745
- type: cos_sim_recall
value: 81.45210963966738
- type: dot_accuracy
value: 86.37598478674273
- type: dot_ap
value: 79.17253140971533
- type: dot_f1
value: 73.19411657889958
- type: dot_precision
value: 69.27201484842236
- type: dot_recall
value: 77.58700338774254
- type: euclidean_accuracy
value: 88.29122521054062
- type: euclidean_ap
value: 84.64901724668165
- type: euclidean_f1
value: 76.99685189252507
- type: euclidean_precision
value: 73.39148639218422
- type: euclidean_recall
value: 80.97474591931014
- type: manhattan_accuracy
value: 88.29316567702877
- type: manhattan_ap
value: 84.5869003947086
- type: manhattan_f1
value: 76.9094138543517
- type: manhattan_precision
value: 74.03818751781134
- type: manhattan_recall
value: 80.01231906375116
- type: max_accuracy
value: 88.2951061435169
- type: max_ap
value: 84.74905878045149
- type: max_f1
value: 77.01659871869538
---
# Bulbasaur
This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny) trained using [qa-assistant](https://huggingface.co/datasets/Mihaiii/qa-assistant).
## Intended purpose
<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
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('Mihaiii/Bulbasaur')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
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('Mihaiii/Bulbasaur')
model = AutoModel.from_pretrained('Mihaiii/Bulbasaur')
# 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)
```
### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small))
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
Backedman/TriviaAnsweringMachineREAL
|
Backedman
|
question-answering
|
[
"transformers",
"pytorch",
"TFIDF-QA",
"question-answering",
"custom_code",
"en",
"license:mit",
"region:us"
] | 2024-05-07T01:04:47 | 2024-05-12T22:28:55 | 5,270 | 0 |
---
language:
- en
license: mit
pipeline_tag: question-answering
---
The evaluation of this project is to answer trivia questions. You do
not need to do well at this task, but you should submit a system that
completes the task or create adversarial questions in that setting. This will help the whole class share data and
resources.
If you focus on something other than predicting answers, *that's fine*!
About the Data
==============
Quiz bowl is an academic competition between schools in
English-speaking countries; hundreds of teams compete in dozens of
tournaments each year. Quiz bowl is different from Jeopardy, a recent
application area. While Jeopardy also uses signaling devices, these
are only usable after a question is completed (interrupting Jeopardy's
questions would make for bad television). Thus, Jeopardy is rapacious
classification followed by a race---among those who know the
answer---to punch a button first.
Here's an example of a quiz bowl question:
Expanding on a 1908 paper by Smoluchowski, he derived a formula for
the intensity of scattered light in media fluctuating densities that
reduces to Rayleigh's law for ideal gases in The Theory of the
Opalescence of Homogenous Fluids and Liquid Mixtures near the Critical
State. That research supported his theories of matter first developed
when he calculated the diffusion constant in terms of fundamental
parameters of the particles of a gas undergoing Brownian Motion. In
that same year, 1905, he also published On a Heuristic Point of View
Concerning the Production and Transformation of Light. That
explication of the photoelectric effect won him 1921 Nobel in Physics.
For ten points, name this German physicist best known for his theory
of Relativity.
*ANSWER*: Albert _Einstein_
Two teams listen to the same question. Teams interrupt the question at
any point by "buzzing in"; if the answer is correct, the team gets
points and the next question is read. Otherwise, the team loses
points and the other team can answer.
You are welcome to use any *automatic* method to choose an answer. It
need not be similar nor build on our provided systems. In addition to
the data we provide, you are welcome to use any external data *except*
our test quiz bowl questions (i.e., don't hack our server!). You are
welcome (an encouraged) to use any publicly available software, but
you may want to check on Piazza for suggestions as many tools are
better (or easier to use) than others.
If you don't like the interruptability of questions, you can also just answer entire questions. However, you must also output a confidence.
Competition
==================
We will use Dynabech website (https://dynabench.org/tasks/qa). If you remember the past workshop about Dynabench submission, this is the way to do it. The specific task name is "Grounded QA". Here, with the help of the video tutorial, you submit your QA model and assess how your QA model did compared to others. The assessment will take place by testing your QA model on several QA test datasets and the results of yours and your competitors will be visible on the leaderboard. Your goal is to rank the highest in terms of expected wins: you buzz in with probability proportional to your confidence, and if you're more right than the competition, you win.
Writing Questions
==================
Alternatively, you can also *write* 50 adversarial questions that
challenge modern NLP systems. These questions must be diverse in the
subjects asked about, the skills computers need to answer the
questions, and the entities in those questions. Remember that your questions should be *factual* and
*specific* enough for humans to answer, because your task is to stump
the computers relative to humans!
In addition to the raw questions, you will also need to create citations describing:
* Why the question is difficult for computers: include citations from the NLP/AI/ML literature
* Why the information in the question is correct: include citations from the sources you drew on the write the question
* Why the question is interesting: include scholarly / popular culture artifacts to prove that people care about this
* Why the question is pyramidal: discuss why your first clues are harder than your later clues
**Category**
We want questions from many domains such as Art, Literature, Geography, History,
Science, TV and Film, Music, Lifestyle, and Sport. The questions
should be written using all topics above (5 questions for each
category and 5 more for the remaining categories). Indicate in your
writeup which category you chose to write on for each question.
Art:
* Questions about works: Mona Lisa, Raft of the Medussa
* Questions about forms: color, contour, texture
* Questions about artists: Picasso, Monet, Leonardo da Vinci
* Questions about context: Renaissance, post-modernism, expressionism, surrealism
Literature:
* Questions about works: novels (1984), plays (The Lion and the Jewel), poems (Rubaiyat), criticism (Poetics)
* Questions about major characters or events in literature: The Death of Anna Karenina, Noboru Wataya, the Marriage of Hippolyta and Theseus
* Questions about literary movements (Sturm und Drang)
* Questions about translations
* Cross-cutting questions (appearances of Overcoats in novels)
* Common link questions (the literary output of a country/region)
Geography:
* Questions about location: names of capital, state, river
* Questions about the place: temperature, wind flow, humidity
History:
* When: When did the First World war start?
* Who: Who is called Napoleon of Iran?
* Where: Where was the first Summer Olympics held?
* Which: Which is the oldest civilization in the world?
Science:
* Questions about terminology: The concept of gravity was discovered by which famous physicist?
* Questions about the experiment
* Questions about theory: The social action theory believes that individuals are influenced by this theory.
TV and Film:
* Quotes: What are the dying words of Charles Foster Kane in Citizen Kane?
* Title: What 1927 musical was the first "talkie"?
* Plot: In The Matrix, does Neo take the blue pill or the red pill?
Music:
* Singer: What singer has had a Billboard No. 1 hit in each of the last four decades?
* Band: Before Bleachers and fun., Jack Antonoff fronted what band?
* Title: What was Madonna's first top 10 hit?
* History: Which classical composer was deaf?
Lifestyle:
* Clothes: What clothing company, founded by a tennis player, has an alligator logo?
* Decoration: What was the first perfume sold by Coco Chanel?
Sport:
* Known facts: What sport is best known as the ‘king of sports’?
* Nationality: What’s the national sport of Canada?
* Sport player: The classic 1980 movie called Raging Bull is about which real-life boxer?
* Country: What country has competed the most times in the Summer Olympics yet hasn’t won any kind of medal?
**Diversity**
Other than category diversity, if you find an ingenious way of writing questions about underrepresented countries, you will get bonus points (indicate which questions you included the diversity component in your writeup). You may decide which are underrepresented countries with your own reasonable reason (etc., less population may indicate underrepresented), but make sure to articulate this in your writeup.
* Run state of the art QA systems on the questions to show they struggle, give individual results for each question and a summary over all questions
For an example of what the writeup for a single question should look like, see the adversarial HW:
https://github.com/Pinafore/nlp-hw/blob/master/adversarial/question.tex
Proposal
==================
The project proposal is a one page PDF document that describes:
* Who is on your team (team sizes can be between three and six
students, but six is really too big to be effective; my suggestion
is that most groups should be between four or five).
* What techniques you will explore
* Your timeline for completing the project (be realistic; you should
have your first submission in a week or two)
Submit the proposal on Gradescope, but make sure to include all group
members. If all group members are not included, you will lose points. Late days cannot be used on this
assignment.
Milestone 1
======================
You'll have to update how things are going: what's
working, what isn't, and how does it change your timeline? How does it change your division of labor?
*Question Writing*: You'll need to have answers selected for all of
your questions and first drafts of at least 15 questions. This must
be submitted as a JSON file so that we run computer QA systems on it.
*Project*: You'll need to have made a submission to the leaderboard with something that satisfies the API.
Submit a PDF updating on your progress to Gradescope. If all team
members are not on the submission, you will lose points.
Milestone 2
===================
As before, provide an updated timeline / division of labor, provide your intermediary results.
*Question Writing*: You'll need to have reflected the feedback from the first questions and completed a first draft of at least 30 questions. You'll also need machine results to your questions and an overall evaluation of your human/computer accuracy.
*Project*: You'll need to have a made a submission to the leaderboard with a working system (e.g., not just obey the API, but actually get reasonable answers).
Submit a PDF updating on your progress.
Final Presentation
======================
The final presentation will be virtual (uploading a video). In
the final presentation you will:
* Explain what you did
* Who did what. For example, for the question writing project a team of five people might write: A wrote the first draft of questions. B and C verified they were initially answerable by a human. B ran computer systems to verify they were challenging to a computer. C edited the questions and increased the computer difficulty. D and E verified that the edited questions were still answerable by a human. D and E checked all of the questions for factual accuracy and created citations and the writeup.
* What challenges you had
* Review how well you did (based on the competition or your own metrics). If you do not use the course infrastructure to evaluate your project's work, you should talk about what alternative evaluations you used, why they're appropriate/fair, and how well you did on them.
* Provide an error analysis. An error analysis must contain examples from the
development set that you get wrong. You should show those sentences
and explain why (in terms of features or the model) they have the
wrong answer. You should have been doing this all along as you
derive new features, but this is your final inspection of
your errors. The feature or model problems you discover should not
be trivial features you could add easily. Instead, these should be
features or models that are difficult to correct. An error analysis
is not the same thing as simply presenting the error matrix, as it
does not inspect any individual examples. If you're writing questions, talk about examples of questions that didn't work out as intended.
* The linguistic motivation for your features / how your wrote the questions. This is a
computational linguistics class, so you should give precedence to
features / techniques that we use in this class (e.g., syntax,
morphology, part of speech, word sense, etc.). Given two features
that work equally well and one that is linguistically motivated,
we'll prefer the linguistically motivated one.
* Presumably you did many different things; how did they each
individually contribute to your final result?
Each group has 10 minutes to deliver their presentation. Please record the video, and upload it to Google Drive, and include the link in your writeup submission.
Final Question Submission
======================
Because we need to get the questions ready for the systems, upload your raw questions on May 10. This doesn't include the citations or other parts of the writeup.
System Submission
======================
You must submit a version of your system by May 12. It may not be perfect, but this what the question writing teams will use to test their results.
Your system should be sent directly to the professor and TAs in zip files, including the correct dependencies and a working inference code. Your inference code should run successfully in the root folder (extracted from zip folder) directory with the command:
```
> python3 inference.py --data=evaluation_set.json
```
The input will be in the form of a .json file () in the same format as the file the adversarial question writing team submits. The output format should also be in string.
If you have any notes or comments that we should be aware of while running your code, please include them in the folder as a .txt file. Also, dependency information should be included as a .txt file.
Please prepend your email title with [2024-CMSC 470 System Submission].
Project Writeup and JSON file
======================
By May 17, submit your project writeup explaining what
you did and what results you achieved. This document should
make it clear:
* Why this is a good idea
* What you did
* Who did what
* Whether your technique worked or not
For systems, please do not go over 2500 words unless you have a really good reason.
Images are a much better use of space than words, usually (there's no
limit on including images, but use judgement and be selective).
For question writing, you have one page (single spaced, two column) per question plus a two page summary of results. Talk about how you organized the question writing, how you evaluated the questions, and a summary of the results. Along with your writeup, turn in a json including the raw text of the question and answer and category. The json file is included in this directory. Make sure your json file is in the correct format and is callable via below code. Your submission will not be graded if it does not follow the format of the example json file.
```
with open('path to your json file', 'r') as f:
data = json.load(f)
```
Grade
======================
The grade will be out of 25 points, broken into five areas:
* _Presentation_: For your oral presentation, do you highlight what
you did and make people care? Did you use time well during the
presentation?
* _Writeup_: Does the writeup explain what you did in a way that is
clear and effective?
The final three areas are different between the system and the questions.
| | System | Questions |
|----------|:-------------:|------:|
| _Technical Soundness_ | Did you use the right tools for the job, and did you use them correctly? Were they relevant to this class? | Were your questions correct and accurately cited. |
| _Effort_ | Did you do what you say you would, and was it the right ammount of effort. | Are the questions well-written, interesting, and thoroughly edited? |
| _Performance_ | How did your techniques perform in terms of accuracy, recall, etc.? | Is the human accuracy substantially higher than the computer accuracy? |
All members of the group will receive the same grade. It's impossible for the course staff to adjudicate Rashomon-style accounts of who did what, and the goal of a group project is for all team members to work together to create a cohesive project that works well together. While it makes sense to divide the work into distinct areas of responsibility, at grading time we have now way to know who really did what, so it's the groups responsibility to create a piece of output that reflects well on the whole group.
|
[
"TRANSLATION"
] |
[
"MEDAL"
] |
Salesforce/SFR-Embedding-2_R
|
Salesforce
|
feature-extraction
|
[
"sentence-transformers",
"safetensors",
"mistral",
"feature-extraction",
"mteb",
"transformers",
"en",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-06-14T19:34:13 | 2025-02-04T21:05:42 | 5,249 | 84 |
---
language:
- en
license: cc-by-nc-4.0
tags:
- mteb
- sentence-transformers
- transformers
model-index:
- name: Salesforce/SFR-Embedding-2_R
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 92.71641791044776
- type: ap
value: 69.47931007147756
- type: f1
value: 88.0252625393374
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.31075
- type: ap
value: 96.26693923450127
- type: f1
value: 97.31042448894502
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 61.040000000000006
- type: f1
value: 60.78646832640785
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 37.767
- type: map_at_10
value: 53.908
- type: map_at_100
value: 54.583000000000006
- type: map_at_1000
value: 54.583999999999996
- type: map_at_20
value: 54.50899999999999
- type: map_at_3
value: 49.514
- type: map_at_5
value: 52.059999999999995
- type: mrr_at_1
value: 38.26458036984353
- type: mrr_at_10
value: 54.120408001987066
- type: mrr_at_100
value: 54.780719904297406
- type: mrr_at_1000
value: 54.78174226698592
- type: mrr_at_20
value: 54.706604527160295
- type: mrr_at_3
value: 49.71550497866294
- type: mrr_at_5
value: 52.247510668563436
- type: ndcg_at_1
value: 37.767
- type: ndcg_at_10
value: 62.339999999999996
- type: ndcg_at_100
value: 64.89399999999999
- type: ndcg_at_1000
value: 64.914
- type: ndcg_at_20
value: 64.402
- type: ndcg_at_3
value: 53.33
- type: ndcg_at_5
value: 57.93899999999999
- type: precision_at_1
value: 37.767
- type: precision_at_10
value: 8.905000000000001
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.8469999999999995
- type: precision_at_3
value: 21.456
- type: precision_at_5
value: 15.121
- type: recall_at_1
value: 37.767
- type: recall_at_10
value: 89.047
- type: recall_at_100
value: 99.502
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 96.942
- type: recall_at_3
value: 64.36699999999999
- type: recall_at_5
value: 75.605
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 54.024325012036314
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 48.817300846601675
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 66.71478959728732
- type: mrr
value: 79.07202216066482
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.79517914982239
- type: cos_sim_spearman
value: 87.60440576436838
- type: euclidean_pearson
value: 87.75596873521118
- type: euclidean_spearman
value: 87.60440576436838
- type: manhattan_pearson
value: 87.74113773865973
- type: manhattan_spearman
value: 87.50560833247899
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 90.02272727272727
- type: f1
value: 89.96681880265936
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 50.75930389699286
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 46.57286439805565
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 28.056666666666665
- type: map_at_10
value: 39.61749999999999
- type: map_at_100
value: 41.00666666666666
- type: map_at_1000
value: 41.11358333333334
- type: map_at_20
value: 40.410250000000005
- type: map_at_3
value: 35.98591666666667
- type: map_at_5
value: 38.02
- type: mrr_at_1
value: 33.73950708467142
- type: mrr_at_10
value: 44.0987162763402
- type: mrr_at_100
value: 44.94302678553521
- type: mrr_at_1000
value: 44.98758207055161
- type: mrr_at_20
value: 44.61156907536121
- type: mrr_at_3
value: 41.247253732468415
- type: mrr_at_5
value: 42.84859071071954
- type: ndcg_at_1
value: 33.739666666666665
- type: ndcg_at_10
value: 46.10683333333334
- type: ndcg_at_100
value: 51.49275000000001
- type: ndcg_at_1000
value: 53.2585
- type: ndcg_at_20
value: 48.349
- type: ndcg_at_3
value: 40.12416666666667
- type: ndcg_at_5
value: 42.94783333333333
- type: precision_at_1
value: 33.739666666666665
- type: precision_at_10
value: 8.46025
- type: precision_at_100
value: 1.3215833333333333
- type: precision_at_1000
value: 0.16524999999999998
- type: precision_at_20
value: 4.9935833333333335
- type: precision_at_3
value: 19.00516666666667
- type: precision_at_5
value: 13.72141666666667
- type: recall_at_1
value: 28.056666666666665
- type: recall_at_10
value: 60.68825000000001
- type: recall_at_100
value: 83.74433333333334
- type: recall_at_1000
value: 95.62299999999999
- type: recall_at_20
value: 68.77641666666668
- type: recall_at_3
value: 44.06991666666667
- type: recall_at_5
value: 51.324999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 15.609
- type: map_at_10
value: 25.584
- type: map_at_100
value: 27.291999999999998
- type: map_at_1000
value: 27.471
- type: map_at_20
value: 26.497
- type: map_at_3
value: 21.61
- type: map_at_5
value: 23.76
- type: mrr_at_1
value: 34.98371335504886
- type: mrr_at_10
value: 45.73747479447807
- type: mrr_at_100
value: 46.4973410206458
- type: mrr_at_1000
value: 46.53372527933685
- type: mrr_at_20
value: 46.19978503202757
- type: mrr_at_3
value: 42.85559174809991
- type: mrr_at_5
value: 44.65038002171556
- type: ndcg_at_1
value: 34.984
- type: ndcg_at_10
value: 34.427
- type: ndcg_at_100
value: 40.908
- type: ndcg_at_1000
value: 44.118
- type: ndcg_at_20
value: 36.885
- type: ndcg_at_3
value: 29.09
- type: ndcg_at_5
value: 30.979
- type: precision_at_1
value: 34.984
- type: precision_at_10
value: 10.476
- type: precision_at_100
value: 1.748
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_20
value: 6.313000000000001
- type: precision_at_3
value: 21.39
- type: precision_at_5
value: 16.378
- type: recall_at_1
value: 15.609
- type: recall_at_10
value: 39.619
- type: recall_at_100
value: 61.952
- type: recall_at_1000
value: 79.861
- type: recall_at_20
value: 46.489000000000004
- type: recall_at_3
value: 26.134
- type: recall_at_5
value: 31.955
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 10.482
- type: map_at_10
value: 25.155
- type: map_at_100
value: 36.606
- type: map_at_1000
value: 38.617000000000004
- type: map_at_20
value: 29.676000000000002
- type: map_at_3
value: 16.881
- type: map_at_5
value: 20.043
- type: mrr_at_1
value: 76.0
- type: mrr_at_10
value: 82.5610119047619
- type: mrr_at_100
value: 82.74795937825128
- type: mrr_at_1000
value: 82.75526942226163
- type: mrr_at_20
value: 82.70580357142858
- type: mrr_at_3
value: 81.41666666666667
- type: mrr_at_5
value: 82.26666666666667
- type: ndcg_at_1
value: 63.625
- type: ndcg_at_10
value: 51.214000000000006
- type: ndcg_at_100
value: 56.411
- type: ndcg_at_1000
value: 63.429
- type: ndcg_at_20
value: 50.595
- type: ndcg_at_3
value: 54.989
- type: ndcg_at_5
value: 52.589
- type: precision_at_1
value: 76.0
- type: precision_at_10
value: 41.975
- type: precision_at_100
value: 13.26
- type: precision_at_1000
value: 2.493
- type: precision_at_20
value: 32.15
- type: precision_at_3
value: 59.0
- type: precision_at_5
value: 51.24999999999999
- type: recall_at_1
value: 10.482
- type: recall_at_10
value: 31.075000000000003
- type: recall_at_100
value: 63.119
- type: recall_at_1000
value: 85.32300000000001
- type: recall_at_20
value: 40.345
- type: recall_at_3
value: 17.916
- type: recall_at_5
value: 22.475
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 93.36500000000001
- type: f1
value: 89.89541440183861
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 81.948
- type: map_at_10
value: 89.47500000000001
- type: map_at_100
value: 89.66199999999999
- type: map_at_1000
value: 89.671
- type: map_at_20
value: 89.582
- type: map_at_3
value: 88.646
- type: map_at_5
value: 89.19
- type: mrr_at_1
value: 88.23882388238825
- type: mrr_at_10
value: 93.2122736083131
- type: mrr_at_100
value: 93.23908769526588
- type: mrr_at_1000
value: 93.23932393435209
- type: mrr_at_20
value: 93.23217832106207
- type: mrr_at_3
value: 92.98679867986787
- type: mrr_at_5
value: 93.16906690669056
- type: ndcg_at_1
value: 88.239
- type: ndcg_at_10
value: 92.155
- type: ndcg_at_100
value: 92.735
- type: ndcg_at_1000
value: 92.866
- type: ndcg_at_20
value: 92.39699999999999
- type: ndcg_at_3
value: 91.188
- type: ndcg_at_5
value: 91.754
- type: precision_at_1
value: 88.239
- type: precision_at_10
value: 10.903
- type: precision_at_100
value: 1.147
- type: precision_at_1000
value: 0.117
- type: precision_at_20
value: 5.5440000000000005
- type: precision_at_3
value: 34.598
- type: precision_at_5
value: 21.302
- type: recall_at_1
value: 81.948
- type: recall_at_10
value: 96.518
- type: recall_at_100
value: 98.646
- type: recall_at_1000
value: 99.399
- type: recall_at_20
value: 97.262
- type: recall_at_3
value: 93.89800000000001
- type: recall_at_5
value: 95.38600000000001
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.033
- type: map_at_10
value: 53.55
- type: map_at_100
value: 55.672
- type: map_at_1000
value: 55.764
- type: map_at_20
value: 54.87800000000001
- type: map_at_3
value: 46.761
- type: map_at_5
value: 50.529
- type: mrr_at_1
value: 60.95679012345679
- type: mrr_at_10
value: 68.70835782872815
- type: mrr_at_100
value: 69.21918402444501
- type: mrr_at_1000
value: 69.23608783148705
- type: mrr_at_20
value: 69.07497388036454
- type: mrr_at_3
value: 66.76954732510285
- type: mrr_at_5
value: 67.95781893004109
- type: ndcg_at_1
value: 60.956999999999994
- type: ndcg_at_10
value: 61.766
- type: ndcg_at_100
value: 67.652
- type: ndcg_at_1000
value: 68.94500000000001
- type: ndcg_at_20
value: 64.48700000000001
- type: ndcg_at_3
value: 57.25
- type: ndcg_at_5
value: 58.64
- type: precision_at_1
value: 60.956999999999994
- type: precision_at_10
value: 17.083000000000002
- type: precision_at_100
value: 2.346
- type: precision_at_1000
value: 0.257
- type: precision_at_20
value: 9.807
- type: precision_at_3
value: 38.477
- type: precision_at_5
value: 27.962999999999997
- type: recall_at_1
value: 32.033
- type: recall_at_10
value: 69.44
- type: recall_at_100
value: 90.17500000000001
- type: recall_at_1000
value: 97.90100000000001
- type: recall_at_20
value: 77.629
- type: recall_at_3
value: 51.664
- type: recall_at_5
value: 59.565
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 42.741
- type: map_at_10
value: 74.811
- type: map_at_100
value: 75.508
- type: map_at_1000
value: 75.541
- type: map_at_20
value: 75.25699999999999
- type: map_at_3
value: 71.31
- type: map_at_5
value: 73.69
- type: mrr_at_1
value: 85.48278190411884
- type: mrr_at_10
value: 90.20347684425987
- type: mrr_at_100
value: 90.29734129342121
- type: mrr_at_1000
value: 90.30017606259217
- type: mrr_at_20
value: 90.27225310310567
- type: mrr_at_3
value: 89.67364393427842
- type: mrr_at_5
value: 90.02408282691847
- type: ndcg_at_1
value: 85.483
- type: ndcg_at_10
value: 81.361
- type: ndcg_at_100
value: 83.588
- type: ndcg_at_1000
value: 84.19
- type: ndcg_at_20
value: 82.42699999999999
- type: ndcg_at_3
value: 76.779
- type: ndcg_at_5
value: 79.581
- type: precision_at_1
value: 85.483
- type: precision_at_10
value: 17.113
- type: precision_at_100
value: 1.882
- type: precision_at_1000
value: 0.196
- type: precision_at_20
value: 8.899
- type: precision_at_3
value: 50.397999999999996
- type: precision_at_5
value: 32.443
- type: recall_at_1
value: 42.741
- type: recall_at_10
value: 85.564
- type: recall_at_100
value: 94.07799999999999
- type: recall_at_1000
value: 97.995
- type: recall_at_20
value: 88.98700000000001
- type: recall_at_3
value: 75.598
- type: recall_at_5
value: 81.107
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.80320000000002
- type: ap
value: 94.98856145360044
- type: f1
value: 96.80287885839178
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.539
- type: map_at_10
value: 35.109
- type: map_at_100
value: 36.287000000000006
- type: map_at_1000
value: 36.335
- type: map_at_20
value: 35.838
- type: map_at_3
value: 31.11
- type: map_at_5
value: 33.455
- type: mrr_at_1
value: 23.15186246418338
- type: mrr_at_10
value: 35.70532018920268
- type: mrr_at_100
value: 36.815167506137584
- type: mrr_at_1000
value: 36.85695349443505
- type: mrr_at_20
value: 36.39500867880642
- type: mrr_at_3
value: 31.81232091690535
- type: mrr_at_5
value: 34.096704871060155
- type: ndcg_at_1
value: 23.152
- type: ndcg_at_10
value: 42.181999999999995
- type: ndcg_at_100
value: 47.847
- type: ndcg_at_1000
value: 48.988
- type: ndcg_at_20
value: 44.767
- type: ndcg_at_3
value: 34.088
- type: ndcg_at_5
value: 38.257999999999996
- type: precision_at_1
value: 23.152
- type: precision_at_10
value: 6.678000000000001
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 3.881
- type: precision_at_3
value: 14.518
- type: precision_at_5
value: 10.831
- type: recall_at_1
value: 22.539
- type: recall_at_10
value: 63.965
- type: recall_at_100
value: 90.129
- type: recall_at_1000
value: 98.721
- type: recall_at_20
value: 74.00999999999999
- type: recall_at_3
value: 42.004999999999995
- type: recall_at_5
value: 52.028
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 98.5750113999088
- type: f1
value: 98.41576079230245
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 91.29502963976289
- type: f1
value: 74.84400169335184
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 85.96839273705447
- type: f1
value: 82.43129186593926
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 90.60860793544047
- type: f1
value: 89.79415994859477
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 46.661892807041355
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 44.17598473858937
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
- type: map
value: 31.260919294024603
- type: mrr
value: 32.37049108835034
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.672000000000001
- type: map_at_10
value: 15.972
- type: map_at_100
value: 20.94
- type: map_at_1000
value: 22.877
- type: map_at_20
value: 17.986
- type: map_at_3
value: 11.161
- type: map_at_5
value: 13.293
- type: mrr_at_1
value: 53.56037151702786
- type: mrr_at_10
value: 61.915696103002595
- type: mrr_at_100
value: 62.4130902631107
- type: mrr_at_1000
value: 62.45228087711845
- type: mrr_at_20
value: 62.1983715004112
- type: mrr_at_3
value: 60.31991744066049
- type: mrr_at_5
value: 61.27966976264191
- type: ndcg_at_1
value: 50.929
- type: ndcg_at_10
value: 41.336
- type: ndcg_at_100
value: 38.586999999999996
- type: ndcg_at_1000
value: 48.155
- type: ndcg_at_20
value: 38.888
- type: ndcg_at_3
value: 47.0
- type: ndcg_at_5
value: 44.335
- type: precision_at_1
value: 53.251000000000005
- type: precision_at_10
value: 31.146
- type: precision_at_100
value: 10.040000000000001
- type: precision_at_1000
value: 2.432
- type: precision_at_20
value: 23.421
- type: precision_at_3
value: 45.098
- type: precision_at_5
value: 39.071
- type: recall_at_1
value: 6.672000000000001
- type: recall_at_10
value: 20.764
- type: recall_at_100
value: 40.759
- type: recall_at_1000
value: 75.015
- type: recall_at_20
value: 25.548
- type: recall_at_3
value: 12.328
- type: recall_at_5
value: 15.601999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 50.944
- type: map_at_10
value: 67.565
- type: map_at_100
value: 68.10300000000001
- type: map_at_1000
value: 68.109
- type: map_at_20
value: 67.973
- type: map_at_3
value: 64.176
- type: map_at_5
value: 66.39699999999999
- type: mrr_at_1
value: 57.01042873696408
- type: mrr_at_10
value: 69.76629605105849
- type: mrr_at_100
value: 70.09927347130204
- type: mrr_at_1000
value: 70.10309675839956
- type: mrr_at_20
value: 70.02288627712392
- type: mrr_at_3
value: 67.46813441483191
- type: mrr_at_5
value: 68.93105446118189
- type: ndcg_at_1
value: 57.010000000000005
- type: ndcg_at_10
value: 73.956
- type: ndcg_at_100
value: 75.90299999999999
- type: ndcg_at_1000
value: 76.03999999999999
- type: ndcg_at_20
value: 75.17
- type: ndcg_at_3
value: 68.13900000000001
- type: ndcg_at_5
value: 71.532
- type: precision_at_1
value: 57.010000000000005
- type: precision_at_10
value: 10.91
- type: precision_at_100
value: 1.2
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 5.753
- type: precision_at_3
value: 29.828
- type: precision_at_5
value: 19.971
- type: recall_at_1
value: 50.944
- type: recall_at_10
value: 90.754
- type: recall_at_100
value: 98.699
- type: recall_at_1000
value: 99.701
- type: recall_at_20
value: 95.148
- type: recall_at_3
value: 76.224
- type: recall_at_5
value: 83.872
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.856
- type: map_at_10
value: 86.077
- type: map_at_100
value: 86.696
- type: map_at_1000
value: 86.708
- type: map_at_20
value: 86.493
- type: map_at_3
value: 83.176
- type: map_at_5
value: 85.008
- type: mrr_at_1
value: 82.74000000000001
- type: mrr_at_10
value: 88.68947222222207
- type: mrr_at_100
value: 88.78196949571182
- type: mrr_at_1000
value: 88.78223256200576
- type: mrr_at_20
value: 88.76455636228219
- type: mrr_at_3
value: 87.85833333333316
- type: mrr_at_5
value: 88.43933333333311
- type: ndcg_at_1
value: 82.74000000000001
- type: ndcg_at_10
value: 89.583
- type: ndcg_at_100
value: 90.652
- type: ndcg_at_1000
value: 90.711
- type: ndcg_at_20
value: 90.203
- type: ndcg_at_3
value: 86.967
- type: ndcg_at_5
value: 88.43299999999999
- type: precision_at_1
value: 82.74000000000001
- type: precision_at_10
value: 13.617
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.217999999999999
- type: precision_at_3
value: 38.163000000000004
- type: precision_at_5
value: 25.05
- type: recall_at_1
value: 71.856
- type: recall_at_10
value: 96.244
- type: recall_at_100
value: 99.773
- type: recall_at_1000
value: 99.99900000000001
- type: recall_at_20
value: 98.221
- type: recall_at_3
value: 88.715
- type: recall_at_5
value: 92.88499999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 62.91969510127886
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 72.74201090913765
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 5.8229999999999995
- type: map_at_10
value: 15.152
- type: map_at_100
value: 17.936
- type: map_at_1000
value: 18.292
- type: map_at_20
value: 16.526
- type: map_at_3
value: 10.294
- type: map_at_5
value: 12.794
- type: mrr_at_1
value: 28.599999999999998
- type: mrr_at_10
value: 40.68206349206347
- type: mrr_at_100
value: 41.673752995361795
- type: mrr_at_1000
value: 41.71500072915374
- type: mrr_at_20
value: 41.28552805166964
- type: mrr_at_3
value: 36.84999999999998
- type: mrr_at_5
value: 39.19999999999995
- type: ndcg_at_1
value: 28.599999999999998
- type: ndcg_at_10
value: 24.866
- type: ndcg_at_100
value: 34.597
- type: ndcg_at_1000
value: 39.994
- type: ndcg_at_20
value: 28.309
- type: ndcg_at_3
value: 22.749
- type: ndcg_at_5
value: 20.502000000000002
- type: precision_at_1
value: 28.599999999999998
- type: precision_at_10
value: 13.089999999999998
- type: precision_at_100
value: 2.7119999999999997
- type: precision_at_1000
value: 0.39899999999999997
- type: precision_at_20
value: 8.53
- type: precision_at_3
value: 21.099999999999998
- type: precision_at_5
value: 18.22
- type: recall_at_1
value: 5.8229999999999995
- type: recall_at_10
value: 26.522000000000002
- type: recall_at_100
value: 55.003
- type: recall_at_1000
value: 80.977
- type: recall_at_20
value: 34.618
- type: recall_at_3
value: 12.848
- type: recall_at_5
value: 18.477
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 80.72562067620224
- type: cos_sim_spearman
value: 77.00710192931953
- type: euclidean_pearson
value: 78.65843289108192
- type: euclidean_spearman
value: 77.00710077709005
- type: manhattan_pearson
value: 78.48859522905846
- type: manhattan_spearman
value: 76.8213740840866
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.15015325911659
- type: cos_sim_spearman
value: 75.67268325741222
- type: euclidean_pearson
value: 75.54004763633206
- type: euclidean_spearman
value: 75.67262179635058
- type: manhattan_pearson
value: 75.80681616893116
- type: manhattan_spearman
value: 75.93721016401406
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.71651874476737
- type: cos_sim_spearman
value: 82.39667472464997
- type: euclidean_pearson
value: 82.28256504757712
- type: euclidean_spearman
value: 82.39663674872656
- type: manhattan_pearson
value: 82.3192873176068
- type: manhattan_spearman
value: 82.41915252757059
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.222967367593
- type: cos_sim_spearman
value: 79.92685877403252
- type: euclidean_pearson
value: 79.95053542861498
- type: euclidean_spearman
value: 79.9268858850991
- type: manhattan_pearson
value: 79.90485851323321
- type: manhattan_spearman
value: 79.93878025669312
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.27539130156643
- type: cos_sim_spearman
value: 85.81645767911826
- type: euclidean_pearson
value: 85.5488615685444
- type: euclidean_spearman
value: 85.81647022566916
- type: manhattan_pearson
value: 85.6358149547879
- type: manhattan_spearman
value: 85.96347118567043
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.43727336154858
- type: cos_sim_spearman
value: 84.50468882202796
- type: euclidean_pearson
value: 83.23576727105372
- type: euclidean_spearman
value: 84.50468882202796
- type: manhattan_pearson
value: 83.28843314503176
- type: manhattan_spearman
value: 84.60383766214322
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cos_sim_pearson
value: 88.86589365166874
- type: cos_sim_spearman
value: 88.93117996163835
- type: euclidean_pearson
value: 89.12271565981082
- type: euclidean_spearman
value: 88.93117996163835
- type: manhattan_pearson
value: 88.94419759325545
- type: manhattan_spearman
value: 88.63073561731899
- 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: 67.96578378422929
- type: cos_sim_spearman
value: 67.10257461424345
- type: euclidean_pearson
value: 67.51317866195149
- type: euclidean_spearman
value: 67.10257461424345
- type: manhattan_pearson
value: 67.74940912013754
- type: manhattan_spearman
value: 67.46694183937207
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.55433725920493
- type: cos_sim_spearman
value: 83.60373857254014
- type: euclidean_pearson
value: 83.08086082334839
- type: euclidean_spearman
value: 83.6036864776559
- type: manhattan_pearson
value: 83.2232267589246
- type: manhattan_spearman
value: 83.78923946962664
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.28566757174322
- type: mrr
value: 96.63827639317836
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 70.661
- type: map_at_10
value: 82.051
- type: map_at_100
value: 82.162
- type: map_at_1000
value: 82.167
- type: map_at_20
value: 82.122
- type: map_at_3
value: 79.919
- type: map_at_5
value: 81.368
- type: mrr_at_1
value: 74.33333333333333
- type: mrr_at_10
value: 82.98452380952381
- type: mrr_at_100
value: 83.09512420633841
- type: mrr_at_1000
value: 83.10026279387446
- type: mrr_at_20
value: 83.05460927960928
- type: mrr_at_3
value: 81.8888888888889
- type: mrr_at_5
value: 82.65555555555557
- type: ndcg_at_1
value: 74.333
- type: ndcg_at_10
value: 85.914
- type: ndcg_at_100
value: 86.473
- type: ndcg_at_1000
value: 86.602
- type: ndcg_at_20
value: 86.169
- type: ndcg_at_3
value: 83.047
- type: ndcg_at_5
value: 84.72
- type: precision_at_1
value: 74.333
- type: precision_at_10
value: 10.933
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.5169999999999995
- type: precision_at_3
value: 32.444
- type: precision_at_5
value: 20.8
- type: recall_at_1
value: 70.661
- type: recall_at_10
value: 96.333
- type: recall_at_100
value: 99.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 97.333
- type: recall_at_3
value: 88.64999999999999
- type: recall_at_5
value: 93.089
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.89108910891089
- type: cos_sim_ap
value: 97.61815451002174
- type: cos_sim_f1
value: 94.51097804391219
- type: cos_sim_precision
value: 94.32270916334662
- type: cos_sim_recall
value: 94.69999999999999
- type: dot_accuracy
value: 99.89108910891089
- type: dot_ap
value: 97.61815451002175
- type: dot_f1
value: 94.51097804391219
- type: dot_precision
value: 94.32270916334662
- type: dot_recall
value: 94.69999999999999
- type: euclidean_accuracy
value: 99.89108910891089
- type: euclidean_ap
value: 97.61815534251431
- type: euclidean_f1
value: 94.51097804391219
- type: euclidean_precision
value: 94.32270916334662
- type: euclidean_recall
value: 94.69999999999999
- type: manhattan_accuracy
value: 99.8940594059406
- type: manhattan_ap
value: 97.66124472227202
- type: manhattan_f1
value: 94.65267366316841
- type: manhattan_precision
value: 94.60539460539461
- type: manhattan_recall
value: 94.69999999999999
- type: max_accuracy
value: 99.8940594059406
- type: max_ap
value: 97.66124472227202
- type: max_f1
value: 94.65267366316841
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 76.482776391195
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 48.29023235124473
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.3190739691685
- type: mrr
value: 56.40441972243442
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.98570594378664
- type: cos_sim_spearman
value: 30.712965330802174
- type: dot_pearson
value: 31.98570540209124
- type: dot_spearman
value: 30.712965330802174
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.25
- type: map_at_10
value: 2.2640000000000002
- type: map_at_100
value: 14.447
- type: map_at_1000
value: 35.452
- type: map_at_20
value: 4.163
- type: map_at_3
value: 0.715
- type: map_at_5
value: 1.1780000000000002
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 96.66666666666667
- type: mrr_at_100
value: 96.66666666666667
- type: mrr_at_1000
value: 96.66666666666667
- type: mrr_at_20
value: 96.66666666666667
- type: mrr_at_3
value: 96.66666666666667
- type: mrr_at_5
value: 96.66666666666667
- type: ndcg_at_1
value: 92.0
- type: ndcg_at_10
value: 87.26899999999999
- type: ndcg_at_100
value: 68.586
- type: ndcg_at_1000
value: 61.056999999999995
- type: ndcg_at_20
value: 83.452
- type: ndcg_at_3
value: 90.11200000000001
- type: ndcg_at_5
value: 89.103
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 91.2
- type: precision_at_100
value: 70.12
- type: precision_at_1000
value: 26.773999999999997
- type: precision_at_20
value: 87.3
- type: precision_at_3
value: 92.667
- type: precision_at_5
value: 92.4
- type: recall_at_1
value: 0.25
- type: recall_at_10
value: 2.3970000000000002
- type: recall_at_100
value: 17.233999999999998
- type: recall_at_1000
value: 57.879000000000005
- type: recall_at_20
value: 4.508
- type: recall_at_3
value: 0.734
- type: recall_at_5
value: 1.2269999999999999
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.806
- type: map_at_10
value: 11.369
- type: map_at_100
value: 17.791
- type: map_at_1000
value: 19.363
- type: map_at_20
value: 14.038999999999998
- type: map_at_3
value: 5.817
- type: map_at_5
value: 8.331
- type: mrr_at_1
value: 36.734693877551024
- type: mrr_at_10
value: 53.355199222546155
- type: mrr_at_100
value: 53.648197984932665
- type: mrr_at_1000
value: 53.648197984932665
- type: mrr_at_20
value: 53.500971817298336
- type: mrr_at_3
value: 48.63945578231292
- type: mrr_at_5
value: 51.29251700680272
- type: ndcg_at_1
value: 35.714
- type: ndcg_at_10
value: 28.18
- type: ndcg_at_100
value: 39.22
- type: ndcg_at_1000
value: 50.807
- type: ndcg_at_20
value: 28.979
- type: ndcg_at_3
value: 31.114000000000004
- type: ndcg_at_5
value: 29.687
- type: precision_at_1
value: 36.735
- type: precision_at_10
value: 24.898
- type: precision_at_100
value: 7.918
- type: precision_at_1000
value: 1.5779999999999998
- type: precision_at_20
value: 18.878
- type: precision_at_3
value: 31.293
- type: precision_at_5
value: 29.387999999999998
- type: recall_at_1
value: 2.806
- type: recall_at_10
value: 17.776
- type: recall_at_100
value: 49.41
- type: recall_at_1000
value: 84.97200000000001
- type: recall_at_20
value: 26.589000000000002
- type: recall_at_3
value: 6.866999999999999
- type: recall_at_5
value: 10.964
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 91.1376953125
- type: ap
value: 40.51219896084815
- type: f1
value: 77.5195445434559
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 79.69722693831352
- type: f1
value: 80.02969178591319
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 66.42427742893598
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.81069321094355
- type: cos_sim_ap
value: 78.57014017906349
- type: cos_sim_f1
value: 72.38883143743536
- type: cos_sim_precision
value: 70.95793208312215
- type: cos_sim_recall
value: 73.87862796833772
- type: dot_accuracy
value: 87.81069321094355
- type: dot_ap
value: 78.5701399541226
- type: dot_f1
value: 72.38883143743536
- type: dot_precision
value: 70.95793208312215
- type: dot_recall
value: 73.87862796833772
- type: euclidean_accuracy
value: 87.81069321094355
- type: euclidean_ap
value: 78.57015336777854
- type: euclidean_f1
value: 72.38883143743536
- type: euclidean_precision
value: 70.95793208312215
- type: euclidean_recall
value: 73.87862796833772
- type: manhattan_accuracy
value: 87.57227156225785
- type: manhattan_ap
value: 78.19109731614216
- type: manhattan_f1
value: 71.87819856704198
- type: manhattan_precision
value: 69.77148534525584
- type: manhattan_recall
value: 74.1160949868074
- type: max_accuracy
value: 87.81069321094355
- type: max_ap
value: 78.57015336777854
- type: max_f1
value: 72.38883143743536
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.95032405790352
- type: cos_sim_ap
value: 88.03104739249996
- type: cos_sim_f1
value: 80.34377190070451
- type: cos_sim_precision
value: 77.11534376548892
- type: cos_sim_recall
value: 83.85432707114259
- type: dot_accuracy
value: 89.95032405790352
- type: dot_ap
value: 88.03105328515932
- type: dot_f1
value: 80.34377190070451
- type: dot_precision
value: 77.11534376548892
- type: dot_recall
value: 83.85432707114259
- type: euclidean_accuracy
value: 89.95032405790352
- type: euclidean_ap
value: 88.03105084564575
- type: euclidean_f1
value: 80.34377190070451
- type: euclidean_precision
value: 77.11534376548892
- type: euclidean_recall
value: 83.85432707114259
- type: manhattan_accuracy
value: 89.88046726433035
- type: manhattan_ap
value: 88.01484191858279
- type: manhattan_f1
value: 80.34005593993817
- type: manhattan_precision
value: 76.95290468133108
- type: manhattan_recall
value: 84.03911302740991
- type: max_accuracy
value: 89.95032405790352
- type: max_ap
value: 88.03105328515932
- type: max_f1
value: 80.34377190070451
---
<h1 align="center">Salesforce/SFR-Embedding-2_R</h1>
**SFR-Embedding by Salesforce Research.**
The model is for **research purposes only**.
More technical details will be updated later. Meanwhile, please refer to our previous work [SFR-Embedding](https://www.salesforce.com/blog/sfr-embedding/) for details.
### 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).
SFR-Embedding Team (∗indicates equal contributors, † indicates co-leaders).
* Rui Meng*
* Ye Liu*
* Tong Niu
* Shafiq Rayhan Joty
* Caiming Xiong †
* Yingbo Zhou †
* Semih Yavuz †
### Citation
```bibtex
@misc{SFR-embedding-2,
title={SFR-Embedding-2: Advanced Text Embedding with Multi-stage Training},
author={Rui Meng*, Ye Liu*, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},
year={2024},
url={https://huggingface.co/Salesforce/SFR-Embedding-2_R}
}
```
## 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-2_R')
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-2_R')
# 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())
# [[40.132083892822266, 25.032529830932617], [15.006855010986328, 39.93733215332031]]
```
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Salesforce/SFR-Embedding-2_R")
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 = model.similarity(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[40.13203811645508, 25.032546997070312], [15.00684642791748, 39.937339782714844]]
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
Hiveurban/multilingual-e5-large-pooled
|
Hiveurban
|
feature-extraction
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"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"
] | 2024-09-24T12:15:49 | 2024-09-24T13:06:40 | 5,176 | 1 |
---
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
value: 91.94702733164272
- type: f1
value: 91.17043441745282
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.20146764509674
- type: f1
value: 91.98359080555608
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.99780770435328
- type: f1
value: 89.19746342724068
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.78486912871998
- type: f1
value: 89.24578823628642
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.74502712477394
- type: f1
value: 89.00297573881542
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.9046967624259
- type: f1
value: 59.36787125785957
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.5280360664976
- type: f1
value: 57.17723440888718
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.44029352901934
- type: f1
value: 54.052855531072964
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 70.5606013153774
- type: f1
value: 52.62215934386531
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.11581211903908
- type: f1
value: 52.341291845645465
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.28933092224233
- type: f1
value: 57.07918745504911
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.38063214525892
- type: f1
value: 59.46463723443009
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 56.06926698049766
- type: f1
value: 52.49084283283562
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.74983187626093
- type: f1
value: 56.960640620165904
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.86550100874243
- type: f1
value: 62.47370548140688
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.971082716879636
- type: f1
value: 61.03812421957381
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 54.98318762609282
- type: f1
value: 51.51207916008392
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.45527908540686
- type: f1
value: 66.16631905400318
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.32750504371216
- type: f1
value: 66.16755288646591
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.09213180901143
- type: f1
value: 66.95654394661507
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.75588433086752
- type: f1
value: 71.79973779656923
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.49428379287154
- type: f1
value: 68.37494379215734
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.90921318090115
- type: f1
value: 66.79517376481645
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.12104909213181
- type: f1
value: 67.29448842879584
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
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dataset:
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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
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value: 95.89999999999999
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type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 68.20809248554913
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 71.21951219512195
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.2
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 98.95
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value: 99.3
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.39999999999999
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96
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type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.5
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.48780487804879
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type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.7
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.73754556500607
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type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 81.04347826086956
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type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 83.65217391304348
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type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.3
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value: 89.3
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type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.01809408926418
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type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.58
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value: 91.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 59.199999999999996
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type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.2
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value: 91.2
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value: 90.25
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value: 93.2
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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: 64.76190476190476
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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: 76.2
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value: 76.2
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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: 93.8
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value: 93.8
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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: 97.89999999999999
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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: 94.6
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value: 94.6
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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: 74.1
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value: 74.1
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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: 95.19999999999999
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 76.9
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value: 76.9
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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: 94.1
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value: 94.1
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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: 97.8
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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:
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value: 92.85714285714286
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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: 93.16239316239316
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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: 92.4
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value: 92.4
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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: 90.22727272727272
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 79.03563941299791
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value: 79.03563941299791
- 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: 97
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value: 96.15
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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: 69.7544932369835
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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:
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value: 58.119658119658126
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value: 49.41768108434775
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value: 58.119658119658126
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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: 74.3
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value: 74.3
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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: 94.8
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value: 93.31666666666666
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value: 92.60000000000001
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value: 94.8
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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: 76.63551401869158
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value: 72.35202492211837
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value: 70.60358255451713
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value: 76.63551401869158
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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: 90.4
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value: 88.4811111111111
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value: 87.7452380952381
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value: 90.4
- 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: 95
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value: 93.60666666666667
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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
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 67.2
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value: 63.01595782872099
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value: 61.596587301587306
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value: 67.2
- 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: 95.7
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value: 94.52999999999999
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value: 94
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value: 95.7
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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: 94.6
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value: 93.28999999999999
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value: 92.675
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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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value: 95.28333333333333
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value: 94.75
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value: 96.39999999999999
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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: 91.9
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value: 89.83
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value: 88.92
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value: 91.9
- 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: 94.69999999999999
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value: 93.34222222222223
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value: 92.75416666666668
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value: 94.69999999999999
- 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: 60.333333333333336
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value: 55.31203703703703
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value: 53.39971108326371
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value: 60.333333333333336
- 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: 12.9
- type: f1
value: 11.099861903031458
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value: 10.589187932631877
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value: 12.9
- task:
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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value: 83.0152380952381
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value: 81.37833333333333
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value: 86.7
- task:
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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metrics:
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metrics:
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type: BitextMining
dataset:
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dataset:
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type: BitextMining
dataset:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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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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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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metrics:
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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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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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dataset:
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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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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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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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type: BitextMining
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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value: 93.7
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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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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: 88.3
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type: BitextMining
dataset:
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config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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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: 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"
] |
billatsectorflow/stella_en_400M_v5
|
billatsectorflow
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2025-01-22T10:55:37 | 2025-01-22T11:45:51 | 5,141 | 3 |
---
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
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dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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type: mteb/amazon_massive_scenario
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split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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split: test
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type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 87.84742260009705
- type: cosine_spearman
value: 89.10215217191254
- type: euclidean_pearson
value: 88.97393286325477
- type: euclidean_spearman
value: 89.1014105509662
- type: main_score
value: 89.10215217191254
- type: manhattan_pearson
value: 89.31698781090151
- type: manhattan_spearman
value: 89.53000001764433
- type: pearson
value: 87.84742260009705
- type: spearman
value: 89.10215217191254
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 85.22397535461835
- type: cosine_spearman
value: 87.14066355879785
- type: euclidean_pearson
value: 86.31393364087295
- type: euclidean_spearman
value: 87.14018892702765
- type: main_score
value: 87.14066355879785
- type: manhattan_pearson
value: 86.36366855248434
- type: manhattan_spearman
value: 87.20858630423012
- type: pearson
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:
- type: cosine_pearson
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
- type: manhattan_spearman
value: 90.6199877691265
- type: pearson
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:
- type: cosine_pearson
value: 67.270656447085
- type: cosine_spearman
value: 67.82870469746828
- type: euclidean_pearson
value: 69.03857775285664
- type: euclidean_spearman
value: 67.74455108773341
- type: main_score
value: 67.82870469746828
- type: manhattan_pearson
value: 69.25304172245812
- type: manhattan_spearman
value: 68.00987097916055
- type: pearson
value: 67.270656447085
- type: spearman
value: 67.82870469746828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 87.17245205384889
- type: cosine_spearman
value: 87.7360146030987
- type: euclidean_pearson
value: 87.48919412794656
- type: euclidean_spearman
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:
- type: main_score
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
- type: nAUC_map_std
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:
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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
- type: mrr_at_10
value: 74.30357142857143
- type: mrr_at_100
value: 74.56950898079988
- type: mrr_at_1000
value: 74.57295833098681
- type: mrr_at_20
value: 74.46165223665226
- type: mrr_at_3
value: 72.3888888888889
- type: mrr_at_5
value: 73.60555555555557
- type: nauc_map_at_1000_diff1
value: 76.51524604780636
- type: nauc_map_at_1000_max
value: 53.48521938401881
- type: nauc_map_at_1000_std
value: -7.347799382158861
- type: nauc_map_at_100_diff1
value: 76.5122888096236
- type: nauc_map_at_100_max
value: 53.49221847471618
- type: nauc_map_at_100_std
value: -7.329683735681086
- type: nauc_map_at_10_diff1
value: 76.30928630674504
- type: nauc_map_at_10_max
value: 53.00102977185941
- type: nauc_map_at_10_std
value: -7.7467740085108705
- type: nauc_map_at_1_diff1
value: 79.54189281784247
- type: nauc_map_at_1_max
value: 46.630071622109526
- type: nauc_map_at_1_std
value: -14.395943134644112
- type: nauc_map_at_20_diff1
value: 76.41604361947962
- type: nauc_map_at_20_max
value: 53.578883876146875
- type: nauc_map_at_20_std
value: -7.403103451288041
- type: nauc_map_at_3_diff1
value: 76.25911617571941
- type: nauc_map_at_3_max
value: 49.140287380513605
- type: nauc_map_at_3_std
value: -11.35992449218983
- type: nauc_map_at_5_diff1
value: 76.35122077770336
- type: nauc_map_at_5_max
value: 52.1744367901208
- type: nauc_map_at_5_std
value: -7.85753955055384
- type: nauc_mrr_at_1000_diff1
value: 76.97223309515867
- type: nauc_mrr_at_1000_max
value: 57.263787498613326
- type: nauc_mrr_at_1000_std
value: -4.884090708840035
- type: nauc_mrr_at_100_diff1
value: 76.97312970894603
- type: nauc_mrr_at_100_max
value: 57.26850730446478
- type: nauc_mrr_at_100_std
value: -4.875200894216617
- type: nauc_mrr_at_10_diff1
value: 76.65927674223613
- type: nauc_mrr_at_10_max
value: 57.30979763941454
- type: nauc_mrr_at_10_std
value: -4.863331094022142
- type: nauc_mrr_at_1_diff1
value: 80.0454932568644
- type: nauc_mrr_at_1_max
value: 56.76038421319305
- type: nauc_mrr_at_1_std
value: -4.101939392632653
- type: nauc_mrr_at_20_diff1
value: 76.87237970440503
- type: nauc_mrr_at_20_max
value: 57.33843605225869
- type: nauc_mrr_at_20_std
value: -4.96248984417978
- type: nauc_mrr_at_3_diff1
value: 76.74130186666727
- type: nauc_mrr_at_3_max
value: 56.19313244846155
- type: nauc_mrr_at_3_std
value: -5.684365934009136
- type: nauc_mrr_at_5_diff1
value: 76.66406918799962
- type: nauc_mrr_at_5_max
value: 57.56110093228628
- type: nauc_mrr_at_5_std
value: -3.7464413085588073
- type: nauc_ndcg_at_1000_diff1
value: 76.19194173971773
- type: nauc_ndcg_at_1000_max
value: 55.57464600170693
- type: nauc_ndcg_at_1000_std
value: -6.0761689532372625
- type: nauc_ndcg_at_100_diff1
value: 76.14631273843654
- type: nauc_ndcg_at_100_max
value: 55.72246565373382
- type: nauc_ndcg_at_100_std
value: -5.595160698860595
- type: nauc_ndcg_at_10_diff1
value: 75.0108223611192
- type: nauc_ndcg_at_10_max
value: 55.27894212877493
- type: nauc_ndcg_at_10_std
value: -6.968331740214591
- type: nauc_ndcg_at_1_diff1
value: 80.0454932568644
- type: nauc_ndcg_at_1_max
value: 56.76038421319305
- type: nauc_ndcg_at_1_std
value: -4.101939392632653
- type: nauc_ndcg_at_20_diff1
value: 75.54887755702472
- type: nauc_ndcg_at_20_max
value: 56.406879417251496
- type: nauc_ndcg_at_20_std
value: -6.495231061329629
- type: nauc_ndcg_at_3_diff1
value: 75.03620356688509
- type: nauc_ndcg_at_3_max
value: 52.147381077773424
- type: nauc_ndcg_at_3_std
value: -8.448005688956199
- type: nauc_ndcg_at_5_diff1
value: 75.1195898074229
- type: nauc_ndcg_at_5_max
value: 54.2321033861173
- type: nauc_ndcg_at_5_std
value: -5.882690780895338
- type: nauc_precision_at_1000_diff1
value: -28.081979732100532
- type: nauc_precision_at_1000_max
value: 35.055348014832916
- type: nauc_precision_at_1000_std
value: 59.61280468927384
- type: nauc_precision_at_100_diff1
value: -25.112740730587458
- type: nauc_precision_at_100_max
value: 38.26331300116496
- type: nauc_precision_at_100_std
value: 62.46316222328831
- type: nauc_precision_at_10_diff1
value: -2.6766206473658833
- type: nauc_precision_at_10_max
value: 45.95321867204845
- type: nauc_precision_at_10_std
value: 45.07212468670564
- type: nauc_precision_at_1_diff1
value: 80.0454932568644
- type: nauc_precision_at_1_max
value: 56.76038421319305
- type: nauc_precision_at_1_std
value: -4.101939392632653
- type: nauc_precision_at_20_diff1
value: -10.698911116738385
- type: nauc_precision_at_20_max
value: 43.467275950182994
- type: nauc_precision_at_20_std
value: 48.00467321991766
- type: nauc_precision_at_3_diff1
value: 33.6344708541193
- type: nauc_precision_at_3_max
value: 49.309242331670504
- type: nauc_precision_at_3_std
value: 21.02940391379915
- type: nauc_precision_at_5_diff1
value: 13.560415600596318
- type: nauc_precision_at_5_max
value: 48.918726500100085
- type: nauc_precision_at_5_std
value: 39.940930429172184
- 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: 70.82166199813196
- type: nauc_recall_at_100_max
value: 76.6106442577042
- type: nauc_recall_at_100_std
value: 66.47992530345513
- type: nauc_recall_at_10_diff1
value: 62.68908885556092
- type: nauc_recall_at_10_max
value: 58.14262437741839
- type: nauc_recall_at_10_std
value: -12.946717875063369
- type: nauc_recall_at_1_diff1
value: 79.54189281784247
- type: nauc_recall_at_1_max
value: 46.630071622109526
- type: nauc_recall_at_1_std
value: -14.395943134644112
- type: nauc_recall_at_20_diff1
value: 65.79470497876567
- type: nauc_recall_at_20_max
value: 71.68308183488456
- type: nauc_recall_at_20_std
value: -12.556850697268453
- type: nauc_recall_at_3_diff1
value: 68.3240211318129
- type: nauc_recall_at_3_max
value: 45.05998217275036
- type: nauc_recall_at_3_std
value: -14.23179772593869
- type: nauc_recall_at_5_diff1
value: 67.53366869904056
- type: nauc_recall_at_5_max
value: 53.57935627081027
- type: nauc_recall_at_5_std
value: -3.3271112904853393
- type: ndcg_at_1
value: 64.667
- type: ndcg_at_10
value: 78.233
- type: ndcg_at_100
value: 79.806
- type: ndcg_at_1000
value: 79.92099999999999
- type: ndcg_at_20
value: 79.006
- type: ndcg_at_3
value: 74.018
- type: ndcg_at_5
value: 76.334
- type: precision_at_1
value: 64.667
- type: precision_at_10
value: 10.4
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.383
- type: precision_at_3
value: 29.444
- type: precision_at_5
value: 19.467000000000002
- type: recall_at_1
value: 61.49400000000001
- type: recall_at_10
value: 92.156
- type: recall_at_100
value: 99.167
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 94.833
- type: recall_at_3
value: 80.833
- type: recall_at_5
value: 86.6
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.8039603960396
- type: cosine_accuracy_threshold
value: 84.54211950302124
- type: cosine_ap
value: 95.59056372734358
- type: cosine_f1
value: 90.1394422310757
- type: cosine_f1_threshold
value: 84.54211950302124
- type: cosine_precision
value: 89.78174603174604
- type: cosine_recall
value: 90.5
- type: dot_accuracy
value: 99.80594059405941
- type: dot_accuracy_threshold
value: 85.57180166244507
- type: dot_ap
value: 95.53453431914399
- type: dot_f1
value: 90.10442565887618
- type: dot_f1_threshold
value: 84.59715843200684
- type: dot_precision
value: 89.61424332344214
- type: dot_recall
value: 90.60000000000001
- type: euclidean_accuracy
value: 99.8039603960396
- type: euclidean_accuracy_threshold
value: 53.253382444381714
- type: euclidean_ap
value: 95.5850992402159
- type: euclidean_f1
value: 90.09457441513192
- type: euclidean_f1_threshold
value: 55.725520849227905
- type: euclidean_precision
value: 89.69276511397423
- type: euclidean_recall
value: 90.5
- type: main_score
value: 95.7485189884476
- type: manhattan_accuracy
value: 99.81485148514851
- type: manhattan_accuracy_threshold
value: 3491.29638671875
- type: manhattan_ap
value: 95.7485189884476
- type: manhattan_f1
value: 90.464048954615
- type: manhattan_f1_threshold
value: 3491.29638671875
- type: manhattan_precision
value: 92.2996878251821
- type: manhattan_recall
value: 88.7
- type: max_ap
value: 95.7485189884476
- type: max_f1
value: 90.464048954615
- type: max_precision
value: 92.2996878251821
- type: max_recall
value: 90.60000000000001
- type: similarity_accuracy
value: 99.8039603960396
- type: similarity_accuracy_threshold
value: 84.54211950302124
- type: similarity_ap
value: 95.59056372734358
- type: similarity_f1
value: 90.1394422310757
- type: similarity_f1_threshold
value: 84.54211950302124
- type: similarity_precision
value: 89.78174603174604
- type: similarity_recall
value: 90.5
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 78.49205191950675
- type: v_measure
value: 78.49205191950675
- type: v_measure_std
value: 2.84869550699959
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 48.90421736513028
- type: v_measure
value: 48.90421736513028
- type: v_measure_std
value: 1.6875865714471023
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 52.9874730481696
- type: map
value: 52.9874730481696
- type: mrr
value: 53.85867604617604
- type: nAUC_map_diff1
value: 39.633429293407616
- type: nAUC_map_max
value: 10.236807988858546
- type: nAUC_map_std
value: 10.276522217929674
- type: nAUC_mrr_diff1
value: 40.0543079218377
- type: nAUC_mrr_max
value: 10.96209807382042
- type: nAUC_mrr_std
value: 10.524400196109918
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_pearson
value: 30.727801109114232
- type: cosine_spearman
value: 31.66058223980157
- type: dot_pearson
value: 30.78818248622866
- type: dot_spearman
value: 31.525158776890265
- type: main_score
value: 31.66058223980157
- type: pearson
value: 30.727801109114232
- type: spearman
value: 31.66058223980157
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 85.206
- type: map_at_1
value: 0.246
- type: map_at_10
value: 2.1950000000000003
- type: map_at_100
value: 14.179
- type: map_at_1000
value: 35.037
- type: map_at_20
value: 4.143
- type: map_at_3
value: 0.7100000000000001
- type: map_at_5
value: 1.135
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 96.66666666666666
- type: mrr_at_100
value: 96.66666666666666
- type: mrr_at_1000
value: 96.66666666666666
- type: mrr_at_20
value: 96.66666666666666
- type: mrr_at_3
value: 96.66666666666666
- type: mrr_at_5
value: 96.66666666666666
- type: nauc_map_at_1000_diff1
value: -4.6264497624527525
- type: nauc_map_at_1000_max
value: 44.594457564749355
- type: nauc_map_at_1000_std
value: 73.17642341400133
- type: nauc_map_at_100_diff1
value: 23.451335157405726
- type: nauc_map_at_100_max
value: 25.426398857299525
- type: nauc_map_at_100_std
value: 64.07416694472633
- type: nauc_map_at_10_diff1
value: 46.57568738568346
- type: nauc_map_at_10_max
value: 9.693233249079238
- type: nauc_map_at_10_std
value: 28.549530265164357
- type: nauc_map_at_1_diff1
value: 53.48238396620123
- type: nauc_map_at_1_max
value: 0.33476619393733076
- type: nauc_map_at_1_std
value: 8.906362219128463
- type: nauc_map_at_20_diff1
value: 39.40719602207749
- type: nauc_map_at_20_max
value: 9.635915072074045
- type: nauc_map_at_20_std
value: 35.15634791346394
- type: nauc_map_at_3_diff1
value: 53.11784737840137
- type: nauc_map_at_3_max
value: 3.059682761072153
- type: nauc_map_at_3_std
value: 21.310633086556617
- type: nauc_map_at_5_diff1
value: 49.91570701185436
- type: nauc_map_at_5_max
value: 8.045082896244576
- type: nauc_map_at_5_std
value: 20.597686235051647
- type: nauc_mrr_at_1000_diff1
value: 41.98412698412726
- type: nauc_mrr_at_1000_max
value: 78.24463118580779
- type: nauc_mrr_at_1000_std
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dataset:
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type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
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type: mteb/twentynewsgroups-clustering
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revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
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type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
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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
Hi, everyone, thanks for using stella models.
After six months of work, I trained the jasper model on top of the stella model, which is a multimodal model, and it can be ranked 2 in mteb (submitted the results on 2024-12-11, which may need official review https://github.com/embeddings-benchmark/results/pull/68).
Model link: https://huggingface.co/infgrad/jasper_en_vision_language_v1
I'll focus on the technical report, training data and related code, hopefully the tricks I've used will be of some help to you guys!
The core training code will be integrated into the rag-retrieval library(https://github.com/NLPJCL/RAG-Retrieval) in the near future. (Welcome to star)
This work was accomplished during my free time, it's a personal hobby. One person's time and energy is limited, and you are welcome to make any contributions!
You can also find these models on my [homepage](https://huggingface.co/infgrad).
# 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
```
# 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"
] |
nes470/pipeline-as-repo
|
nes470
|
question-answering
|
[
"transformers",
"pytorch",
"QA-umd-quizbowl",
"question-answering",
"custom_code",
"license:mit",
"region:us"
] | 2024-05-06T15:27:54 | 2024-05-16T22:18:19 | 5,081 | 0 |
---
library_name: transformers
license: mit
---
Names: Nuran, Joshua, Robert
The evaluation of this project is to answer trivia questions. You do
not need to do well at this task, but you should submit a system that
completes the task or create adversarial questions in that setting. This will help the whole class share data and
resources.
If you focus on something other than predicting answers, *that's fine*!
About the Data
==============
Quiz bowl is an academic competition between schools in
English-speaking countries; hundreds of teams compete in dozens of
tournaments each year. Quiz bowl is different from Jeopardy, a recent
application area. While Jeopardy also uses signaling devices, these
are only usable after a question is completed (interrupting Jeopardy's
questions would make for bad television). Thus, Jeopardy is rapacious
classification followed by a race---among those who know the
answer---to punch a button first.
Here's an example of a quiz bowl question:
Expanding on a 1908 paper by Smoluchowski, he derived a formula for
the intensity of scattered light in media fluctuating densities that
reduces to Rayleigh's law for ideal gases in The Theory of the
Opalescence of Homogenous Fluids and Liquid Mixtures near the Critical
State. That research supported his theories of matter first developed
when he calculated the diffusion constant in terms of fundamental
parameters of the particles of a gas undergoing Brownian Motion. In
that same year, 1905, he also published On a Heuristic Point of View
Concerning the Production and Transformation of Light. That
explication of the photoelectric effect won him 1921 Nobel in Physics.
For ten points, name this German physicist best known for his theory
of Relativity.
*ANSWER*: Albert _Einstein_
Two teams listen to the same question. Teams interrupt the question at
any point by "buzzing in"; if the answer is correct, the team gets
points and the next question is read. Otherwise, the team loses
points and the other team can answer.
You are welcome to use any *automatic* method to choose an answer. It
need not be similar nor build on our provided systems. In addition to
the data we provide, you are welcome to use any external data *except*
our test quiz bowl questions (i.e., don't hack our server!). You are
welcome (an encouraged) to use any publicly available software, but
you may want to check on Piazza for suggestions as many tools are
better (or easier to use) than others.
If you don't like the interruptability of questions, you can also just answer entire questions. However, you must also output a confidence.
Competition
==================
We will use Dynabech website (https://dynabench.org/tasks/qa). If you remember the past workshop about Dynabench submission, this is the way to do it. The specific task name is "Grounded QA". Here, with the help of the video tutorial, you submit your QA model and assess how your QA model did compared to others. The assessment will take place by testing your QA model on several QA test datasets and the results of yours and your competitors will be visible on the leaderboard. Your goal is to rank the highest in terms of expected wins: you buzz in with probability proportional to your confidence, and if you're more right than the competition, you win.
Writing Questions
==================
Alternatively, you can also *write* 50 adversarial questions that
challenge modern NLP systems. These questions must be diverse in the
subjects asked about, the skills computers need to answer the
questions, and the entities in those questions. Remember that your questions should be *factual* and
*specific* enough for humans to answer, because your task is to stump
the computers relative to humans!
In addition to the raw questions, you will also need to create citations describing:
* Why the question is difficult for computers: include citations from the NLP/AI/ML literature
* Why the information in the question is correct: include citations from the sources you drew on the write the question
* Why the question is interesting: include scholarly / popular culture artifacts to prove that people care about this
* Why the question is pyramidal: discuss why your first clues are harder than your later clues
**Category**
We want questions from many domains such as Art, Literature, Geography, History,
Science, TV and Film, Music, Lifestyle, and Sport. The questions
should be written using all topics above (5 questions for each
category and 5 more for the remaining categories). Indicate in your
writeup which category you chose to write on for each question.
Art:
* Questions about works: Mona Lisa, Raft of the Medussa
* Questions about forms: color, contour, texture
* Questions about artists: Picasso, Monet, Leonardo da Vinci
* Questions about context: Renaissance, post-modernism, expressionism, surrealism
Literature:
* Questions about works: novels (1984), plays (The Lion and the Jewel), poems (Rubaiyat), criticism (Poetics)
* Questions about major characters or events in literature: The Death of Anna Karenina, Noboru Wataya, the Marriage of Hippolyta and Theseus
* Questions about literary movements (Sturm und Drang)
* Questions about translations
* Cross-cutting questions (appearances of Overcoats in novels)
* Common link questions (the literary output of a country/region)
Geography:
* Questions about location: names of capital, state, river
* Questions about the place: temperature, wind flow, humidity
History:
* When: When did the First World war start?
* Who: Who is called Napoleon of Iran?
* Where: Where was the first Summer Olympics held?
* Which: Which is the oldest civilization in the world?
Science:
* Questions about terminology: The concept of gravity was discovered by which famous physicist?
* Questions about the experiment
* Questions about theory: The social action theory believes that individuals are influenced by this theory.
TV and Film:
* Quotes: What are the dying words of Charles Foster Kane in Citizen Kane?
* Title: What 1927 musical was the first "talkie"?
* Plot: In The Matrix, does Neo take the blue pill or the red pill?
Music:
* Singer: What singer has had a Billboard No. 1 hit in each of the last four decades?
* Band: Before Bleachers and fun., Jack Antonoff fronted what band?
* Title: What was Madonna's first top 10 hit?
* History: Which classical composer was deaf?
Lifestyle:
* Clothes: What clothing company, founded by a tennis player, has an alligator logo?
* Decoration: What was the first perfume sold by Coco Chanel?
Sport:
* Known facts: What sport is best known as the ‘king of sports’?
* Nationality: What’s the national sport of Canada?
* Sport player: The classic 1980 movie called Raging Bull is about which real-life boxer?
* Country: What country has competed the most times in the Summer Olympics yet hasn’t won any kind of medal?
**Diversity**
Other than category diversity, if you find an ingenious way of writing questions about underrepresented countries, you will get bonus points (indicate which questions you included the diversity component in your writeup). You may decide which are underrepresented countries with your own reasonable reason (etc., less population may indicate underrepresented), but make sure to articulate this in your writeup.
* Run state of the art QA systems on the questions to show they struggle, give individual results for each question and a summary over all questions
For an example of what the writeup for a single question should look like, see the adversarial HW:
https://github.com/Pinafore/nlp-hw/blob/master/adversarial/question.tex
Proposal
==================
The project proposal is a one page PDF document that describes:
* Who is on your team (team sizes can be between three and six
students, but six is really too big to be effective; my suggestion
is that most groups should be between four or five).
* What techniques you will explore
* Your timeline for completing the project (be realistic; you should
have your first submission in a week or two)
Submit the proposal on Gradescope, but make sure to include all group
members. If all group members are not included, you will lose points. Late days cannot be used on this
assignment.
Milestone 1
======================
You'll have to update how things are going: what's
working, what isn't, and how does it change your timeline? How does it change your division of labor?
*Question Writing*: You'll need to have answers selected for all of
your questions and first drafts of at least 15 questions. This must
be submitted as a JSON file so that we run computer QA systems on it.
*Project*: You'll need to have made a submission to the leaderboard with something that satisfies the API.
Submit a PDF updating on your progress to Gradescope. If all team
members are not on the submission, you will lose points.
Milestone 2
===================
As before, provide an updated timeline / division of labor, provide your intermediary results.
*Question Writing*: You'll need to have reflected the feedback from the first questions and completed a first draft of at least 30 questions. You'll also need machine results to your questions and an overall evaluation of your human/computer accuracy.
*Project*: You'll need to have a made a submission to the leaderboard with a working system (e.g., not just obey the API, but actually get reasonable answers).
Submit a PDF updating on your progress.
Final Presentation
======================
The final presentation will be virtual (uploading a video). In
the final presentation you will:
* Explain what you did
* Who did what. For example, for the question writing project a team of five people might write: A wrote the first draft of questions. B and C verified they were initially answerable by a human. B ran computer systems to verify they were challenging to a computer. C edited the questions and increased the computer difficulty. D and E verified that the edited questions were still answerable by a human. D and E checked all of the questions for factual accuracy and created citations and the writeup.
* What challenges you had
* Review how well you did (based on the competition or your own metrics). If you do not use the course infrastructure to evaluate your project's work, you should talk about what alternative evaluations you used, why they're appropriate/fair, and how well you did on them.
* Provide an error analysis. An error analysis must contain examples from the
development set that you get wrong. You should show those sentences
and explain why (in terms of features or the model) they have the
wrong answer. You should have been doing this all along as you
derive new features, but this is your final inspection of
your errors. The feature or model problems you discover should not
be trivial features you could add easily. Instead, these should be
features or models that are difficult to correct. An error analysis
is not the same thing as simply presenting the error matrix, as it
does not inspect any individual examples. If you're writing questions, talk about examples of questions that didn't work out as intended.
* The linguistic motivation for your features / how your wrote the questions. This is a
computational linguistics class, so you should give precedence to
features / techniques that we use in this class (e.g., syntax,
morphology, part of speech, word sense, etc.). Given two features
that work equally well and one that is linguistically motivated,
we'll prefer the linguistically motivated one.
* Presumably you did many different things; how did they each
individually contribute to your final result?
Each group has 10 minutes to deliver their presentation. Please record the video, and upload it to Google Drive, and include the link in your writeup submission.
Final Question Submission
======================
Because we need to get the questions ready for the systems, upload your raw questions on May 10. This doesn't include the citations or other parts of the writeup.
System Submission
======================
You must submit a version of your system by May 12. It may not be perfect, but this what the question writing teams will use to test their results.
Your system should be sent directly to the professor and TAs in zip files, including the correct dependencies and a working inference code. Your inference code should run successfully in the root folder (extracted from zip folder) directory with the command:
```
> python3 inference.py --data=evaluation_set.json
```
The input will be in the form of a .json file () in the same format as the file the adversarial question writing team submits. The output format should also be in string.
If you have any notes or comments that we should be aware of while running your code, please include them in the folder as a .txt file. Also, dependency information should be included as a .txt file.
Please prepend your email title with [2024-CMSC 470 System Submission].
Project Writeup and JSON file
======================
By May 17, submit your project writeup explaining what
you did and what results you achieved. This document should
make it clear:
* Why this is a good idea
* What you did
* Who did what
* Whether your technique worked or not
For systems, please do not go over 2500 words unless you have a really good reason.
Images are a much better use of space than words, usually (there's no
limit on including images, but use judgement and be selective).
For question writing, you have one page (single spaced, two column) per question plus a two page summary of results. Talk about how you organized the question writing, how you evaluated the questions, and a summary of the results. Along with your writeup, turn in a json including the raw text of the question and answer and category. The json file is included in this directory. Make sure your json file is in the correct format and is callable via below code. Your submission will not be graded if it does not follow the format of the example json file.
```
with open('path to your json file', 'r') as f:
data = json.load(f)
```
Grade
======================
The grade will be out of 25 points, broken into five areas:
* _Presentation_: For your oral presentation, do you highlight what
you did and make people care? Did you use time well during the
presentation?
* _Writeup_: Does the writeup explain what you did in a way that is
clear and effective?
The final three areas are different between the system and the questions.
| | System | Questions |
|----------|:-------------:|------:|
| _Technical Soundness_ | Did you use the right tools for the job, and did you use them correctly? Were they relevant to this class? | Were your questions correct and accurately cited. |
| _Effort_ | Did you do what you say you would, and was it the right ammount of effort. | Are the questions well-written, interesting, and thoroughly edited? |
| _Performance_ | How did your techniques perform in terms of accuracy, recall, etc.? | Is the human accuracy substantially higher than the computer accuracy? |
All members of the group will receive the same grade. It's impossible for the course staff to adjudicate Rashomon-style accounts of who did what, and the goal of a group project is for all team members to work together to create a cohesive project that works well together. While it makes sense to divide the work into distinct areas of responsibility, at grading time we have now way to know who really did what, so it's the groups responsibility to create a piece of output that reflects well on the whole group.
|
[
"TRANSLATION"
] |
[
"MEDAL"
] |
gliner-community/gliner_small-v2.5
|
gliner-community
|
token-classification
|
[
"gliner",
"pytorch",
"token-classification",
"multilingual",
"dataset:urchade/pile-mistral-v0.1",
"arxiv:2311.08526",
"license:apache-2.0",
"region:us"
] | 2024-06-17T18:11:25 | 2024-06-18T12:54:21 | 4,860 | 5 |
---
datasets:
- urchade/pile-mistral-v0.1
language:
- multilingual
library_name: gliner
license: apache-2.0
pipeline_tag: token-classification
---
# About
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
## Links
* Paper: https://arxiv.org/abs/2311.08526
* Repository: https://github.com/urchade/GLiNER
## Installation
To use this model, you must install the GLiNER Python library:
```
!pip install gliner -U
```
## Usage
Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
```python
from gliner import GLiNER
model = GLiNER.from_pretrained("gliner-community/gliner_small-v2.5", load_tokenizer=True)
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""
labels = ["person", "award", "date", "competitions", "teams"]
entities = model.predict_entities(text, labels)
for entity in entities:
print(entity["text"], "=>", entity["label"])
```
```
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Al Nassr => teams
Portugal national team => teams
Ballon d'Or => award
UEFA Men's Player of the Year Awards => award
European Golden Shoes => award
UEFA Champions Leagues => competitions
UEFA European Championship => competitions
UEFA Nations League => competitions
Champions League => competitions
European Championship => competitions
```
## Named Entity Recognition benchmark result
Below is a comparison of results between previous versions of the model and the current one:
### Results on other datasets
| Model | Dataset | Precision | Recall | F1 Score |
|------------------------------------|---------------------|-----------|--------|----------|
| gliner-community/gliner_small-v2.5 | ACE 2004 | 35.18% | 22.81% | 27.67% |
| | ACE 2005 | 35.89% | 22.39% | 27.58% |
| | AnatEM | 49.12% | 31.31% | 38.24% |
| | Broad Tweet Corpus | 59.51% | 77.85% | 67.46% |
| | CoNLL 2003 | 63.16% | 70.43% | 66.60% |
| | FabNER | 23.78% | 22.55% | 23.15% |
| | FindVehicle | 37.46% | 40.06% | 38.72% |
| | GENIA_NER | 45.90% | 54.11% | 49.67% |
| | HarveyNER | 13.20% | 32.58% | 18.78% |
| | MultiNERD | 45.87% | 87.01% | 60.07% |
| | Ontonotes | 23.05% | 41.16% | 29.55% |
| | PolyglotNER | 31.88% | 67.22% | 43.25% |
| | TweetNER7 | 40.98% | 39.91% | 40.44% |
| | WikiANN en | 55.35% | 60.06% | 57.61% |
| | WikiNeural | 64.52% | 86.24% | 73.81% |
| | bc2gm | 51.70% | 49.99% | 50.83% |
| | bc4chemd | 30.78% | 57.56% | 40.11% |
| | bc5cdr | 63.48% | 69.65% | 66.42% |
| | ncbi | 63.36% | 66.67% | 64.97% |
| | **Average** | | | **46.58%** |
|------------------------------------|---------------------|-----------|--------|----------|
| urchade/gliner_small-v2.1 | ACE 2004 | 38.89% | 23.53% | 29.32% |
| | ACE 2005 | 42.09% | 26.82% | 32.76% |
| | AnatEM | 63.71% | 19.45% | 29.80% |
| | Broad Tweet Corpus | 57.01% | 70.49% | 63.04% |
| | CoNLL 2003 | 57.11% | 62.66% | 59.76% |
| | FabNER | 32.41% | 12.33% | 17.87% |
| | FindVehicle | 43.47% | 33.02% | 37.53% |
| | GENIA_NER | 61.03% | 37.25% | 46.26% |
| | HarveyNER | 23.12% | 15.16% | 18.32% |
| | MultiNERD | 43.63% | 83.60% | 57.34% |
| | Ontonotes | 23.25% | 35.41% | 28.07% |
| | PolyglotNER | 29.47% | 64.41% | 40.44% |
| | TweetNER7 | 44.78% | 30.83% | 36.52% |
| | WikiANN en | 52.58% | 58.31% | 55.30% |
| | WikiNeural | 53.38% | 82.19% | 64.72% |
| | bc2gm | 66.64% | 30.56% | 41.90% |
| | bc4chemd | 42.01% | 56.03% | 48.02% |
| | bc5cdr | 72.03% | 58.58% | 64.61% |
| | ncbi | 68.88% | 46.71% | 55.67% |
| | **Average** | | | **43.54%** |
|------------------------------------|---------------------|-----------|--------|----------|
| EmergentMethods/gliner_small-v2.1 | ACE 2004 | 39.92% | 17.50% | 24.34% |
| | ACE 2005 | 38.53% | 16.58% | 23.18% |
| | AnatEM | 55.95% | 25.69% | 35.22% |
| | Broad Tweet Corpus | 66.63% | 72.00% | 69.21% |
| | CoNLL 2003 | 62.89% | 58.96% | 60.86% |
| | FabNER | 32.76% | 13.33% | 18.95% |
| | FindVehicle | 42.93% | 43.20% | 43.06% |
| | GENIA_NER | 51.28% | 43.75% | 47.22% |
| | HarveyNER | 24.82% | 21.52% | 23.05% |
| | MultiNERD | 59.27% | 80.69% | 68.34% |
| | Ontonotes | 32.97% | 37.59% | 35.13% |
| | PolyglotNER | 33.60% | 63.30% | 43.90% |
| | TweetNER7 | 46.90% | 28.66% | 35.58% |
| | WikiANN en | 51.91% | 55.43% | 53.61% |
| | WikiNeural | 70.65% | 82.21% | 75.99% |
| | bc2gm | 49.95% | 43.13% | 46.29% |
| | bc4chemd | 35.88% | 71.64% | 47.81% |
| | bc5cdr | 68.41% | 68.90% | 68.65% |
| | ncbi | 55.31% | 59.87% | 57.50% |
| | **Average** | | | **46.20%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
| gliner-community/gliner_medium-v2.5 | ACE 2004 | 33.06% | 20.96% | 25.66% |
| | ACE 2005 | 33.65% | 19.65% | 24.81% |
| | AnatEM | 52.03% | 35.28% | 42.05% |
| | Broad Tweet Corpus | 60.57% | 79.09% | 68.60% |
| | CoNLL 2003 | 63.80% | 68.31% | 65.98% |
| | FabNER | 26.20% | 22.26% | 24.07% |
| | FindVehicle | 41.95% | 40.68% | 41.30% |
| | GENIA_NER | 51.83% | 62.34% | 56.60% |
| | HarveyNER | 14.04% | 32.17% | 19.55% |
| | MultiNERD | 47.63% | 88.78% | 62.00% |
| | Ontonotes | 21.68% | 38.41% | 27.71% |
| | PolyglotNER | 32.73% | 68.27% | 44.24% |
| | TweetNER7 | 40.39% | 37.64% | 38.97% |
| | WikiANN en | 56.41% | 59.90% | 58.10% |
| | WikiNeural | 65.61% | 86.28% | 74.54% |
| | bc2gm | 55.20% | 56.71% | 55.95% |
| | bc4chemd | 35.94% | 63.67% | 45.94% |
| | bc5cdr | 63.50% | 70.09% | 66.63% |
| | ncbi | 62.96% | 68.55% | 65.63% |
| | **Average** | | | **47.81%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
| urchade/gliner_medium-v2.1 | ACE 2004 | 36.33% | 22.74% | 27.97% |
| | ACE 2005 | 40.49% | 25.46% | 31.27% |
| | AnatEM | 59.75% | 16.87% | 26.31% |
| | Broad Tweet Corpus | 60.89% | 67.25% | 63.91% |
| | CoNLL 2003 | 60.62% | 62.39% | 61.50% |
| | FabNER | 27.72% | 12.24% | 16.98% |
| | FindVehicle | 41.55% | 31.31% | 35.71% |
| | GENIA_NER | 60.86% | 43.93% | 51.03% |
| | HarveyNER | 23.20% | 23.16% | 23.18% |
| | MultiNERD | 41.25% | 83.74% | 55.27% |
| | Ontonotes | 20.58% | 34.11% | 25.67% |
| | PolyglotNER | 31.32% | 64.22% | 42.11% |
| | TweetNER7 | 44.52% | 33.42% | 38.18% |
| | WikiANN en | 54.57% | 56.47% | 55.51% |
| | WikiNeural | 57.60% | 81.57% | 67.52% |
| | bc2gm | 67.98% | 33.45% | 44.84% |
| | bc4chemd | 45.66% | 52.00% | 48.62% |
| | bc5cdr | 72.20% | 58.12% | 64.40% |
| | ncbi | 73.12% | 49.74% | 59.20% |
| | **Average** | | | **44.17%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
| EmergentMethods/gliner_news_medium-v2.1 | ACE 2004 | 39.21% | 17.24% | 23.95% |
| | ACE 2005 | 39.82% | 16.48% | 23.31% |
| | AnatEM | 57.67% | 23.57% | 33.46% |
| | Broad Tweet Corpus | 69.52% | 65.94% | 67.69% |
| | CoNLL 2003 | 68.26% | 58.45% | 62.97% |
| | FabNER | 30.74% | 15.51% | 20.62% |
| | FindVehicle | 40.33% | 37.37% | 38.79% |
| | GENIA_NER | 53.70% | 47.73% | 50.54% |
| | HarveyNER | 26.29% | 27.05% | 26.67% |
| | MultiNERD | 56.78% | 81.96% | 67.08% |
| | Ontonotes | 30.90% | 35.86% | 33.19% |
| | PolyglotNER | 35.98% | 60.96% | 45.25% |
| | TweetNER7 | 52.37% | 30.50% | 38.55% |
| | WikiANN en | 53.81% | 52.29% | 53.04% |
| | WikiNeural | 76.84% | 78.92% | 77.86% |
| | bc2gm | 62.97% | 44.24% | 51.96% |
| | bc4chemd | 44.90% | 65.56% | 53.30% |
| | bc5cdr | 73.93% | 67.03% | 70.31% |
| | ncbi | 69.53% | 60.82% | 64.88% |
| | **Average** | | | **47.55%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
| gliner-community/gliner_large-v2.5 | ACE 2004 | 31.64% | 22.81% | 26.51% |
| | ACE 2005 | 32.10% | 22.56% | 26.49% |
| | AnatEM | 53.64% | 27.82% | 36.64% |
| | Broad Tweet Corpus | 61.93% | 76.85% | 68.59% |
| | CoNLL 2003 | 62.83% | 67.71% | 65.18% |
| | FabNER | 24.54% | 27.03% | 25.73% |
| | FindVehicle | 40.71% | 56.24% | 47.23% |
| | GENIA_NER | 43.56% | 52.56% | 47.64% |
| | HarveyNER | 14.85% | 27.05% | 19.17% |
| | MultiNERD | 38.04% | 89.17% | 53.33% |
| | Ontonotes | 17.28% | 40.16% | 24.16% |
| | PolyglotNER | 32.88% | 63.31% | 43.28% |
| | TweetNER7 | 38.03% | 41.43% | 39.66% |
| | WikiANN en | 57.80% | 60.54% | 59.14% |
| | WikiNeural | 67.72% | 83.94% | 74.96% |
| | bc2gm | 54.74% | 48.54% | 51.45% |
| | bc4chemd | 40.20% | 58.66% | 47.71% |
| | bc5cdr | 66.27% | 71.95% | 69.00% |
| | ncbi | 68.09% | 61.55% | 64.65% |
| | **Average** | | | **46.87%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
| urchade/gliner_large-v2.1 | ACE 2004 | 37.52% | 25.38% | 30.28% |
| | ACE 2005 | 39.02% | 29.00% | 33.27% |
| | AnatEM | 52.86% | 13.64% | 21.68% |
| | Broad Tweet Corpus | 51.44% | 71.73% | 59.91% |
| | CoNLL 2003 | 54.86% | 64.98% | 59.49% |
| | FabNER | 23.98% | 16.00% | 19.19% |
| | FindVehicle | 47.04% | 57.53% | 51.76% |
| | GENIA_NER | 58.10% | 49.98% | 53.74% |
| | HarveyNER | 16.29% | 21.93% | 18.69% |
| | MultiNERD | 34.09% | 85.43% | 48.74% |
| | Ontonotes | 14.02% | 32.01% | 19.50% |
| | PolyglotNER | 28.53% | 64.92% | 39.64% |
| | TweetNER7 | 38.00% | 34.34% | 36.08% |
| | WikiANN en | 51.69% | 59.92% | 55.50% |
| | WikiNeural | 50.94% | 82.08% | 62.87% |
| | bc2gm | 64.48% | 32.47% | 43.19% |
| | bc4chemd | 48.66% | 57.52% | 52.72% |
| | bc5cdr | 72.19% | 64.27% | 68.00% |
| | ncbi | 69.54% | 52.25% | 59.67% |
| | **Average** | | | **43.89%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
| EmergenMethods/fliner_news_large-v2.1 | ACE 2004 | 43.19% | 18.39% | 25.80% |
| | ACE 2005 | 45.24% | 21.20% | 28.87% |
| | AnatEM | 61.51% | 21.66% | 32.04% |
| | Broad Tweet Corpus | 69.38% | 68.99% | 69.18% |
| | CoNLL 2003 | 61.47% | 52.18% | 56.45% |
| | FabNER | 27.42% | 19.11% | 22.52% |
| | FindVehicle | 46.30% | 62.48% | 53.19% |
| | GENIA_NER | 54.13% | 54.02% | 54.07% |
| | HarveyNER | 15.91% | 15.78% | 15.84% |
| | MultiNERD | 53.73% | 79.07% | 63.98% |
| | Ontonotes | 26.78% | 39.77% | 32.01% |
| | PolyglotNER | 34.28% | 55.87% | 42.49% |
| | TweetNER7 | 48.06% | 28.18% | 35.53% |
| | WikiANN en | 53.66% | 51.34% | 52.47% |
| | WikiNeural | 69.81% | 70.75% | 70.28% |
| | bc2gm | 59.83% | 37.62% | 46.20% |
| | bc4chemd | 46.24% | 69.15% | 55.42% |
| | bc5cdr | 71.94% | 70.37% | 71.15% |
| | ncbi | 70.17% | 61.44% | 65.52% |
| | **Average** | | | **47.00%** |
|-----------------------------------------|---------------------|-----------|--------|----------|
## Other available models
| Release | Model Name | # of Parameters | Language | License |
| - | - | - | - | - |
| v0 | [urchade/gliner_base](https://huggingface.co/urchade/gliner_base)<br>[urchade/gliner_multi](https://huggingface.co/urchade/gliner_multi) | 209M<br>209M | English<br>Multilingual | cc-by-nc-4.0 |
| v1 | [urchade/gliner_small-v1](https://huggingface.co/urchade/gliner_small-v1)<br>[urchade/gliner_medium-v1](https://huggingface.co/urchade/gliner_medium-v1)<br>[urchade/gliner_large-v1](https://huggingface.co/urchade/gliner_large-v1) | 166M<br>209M<br>459M | English <br> English <br> English | cc-by-nc-4.0 |
| v2 | [urchade/gliner_small-v2](https://huggingface.co/urchade/gliner_small-v2)<br>[urchade/gliner_medium-v2](https://huggingface.co/urchade/gliner_medium-v2)<br>[urchade/gliner_large-v2](https://huggingface.co/urchade/gliner_large-v2) | 166M<br>209M<br>459M | English <br> English <br> English | apache-2.0 |
| v2.1 | [urchade/gliner_small-v2.1](https://huggingface.co/urchade/gliner_small-v2.1)<br>[urchade/gliner_medium-v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)<br>[urchade/gliner_large-v2.1](https://huggingface.co/urchade/gliner_large-v2.1) <br>[urchade/gliner_multi-v2.1](https://huggingface.co/urchade/gliner_multi-v2.1) | 166M<br>209M<br>459M<br>209M | English <br> English <br> English <br> Multilingual | apache-2.0 |
## Model Authors
The model authors are:
* [Urchade Zaratiana](https://huggingface.co/urchade)
* [Ihor Stepanov](https://huggingface.co/Ihor)
* Nadi Tomeh
* Pierre Holat
* Thierry Charnois
## Citation
```bibtex
@misc{zaratiana2023gliner,
title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
year={2023},
eprint={2311.08526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"NAMED_ENTITY_RECOGNITION"
] |
[
"ANATEM",
"BC5CDR"
] |
minishlab/potion-base-32M
|
minishlab
| null |
[
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"mteb",
"sentence-transformers",
"license:mit",
"model-index",
"region:us"
] | 2025-01-22T17:10:47 | 2025-01-29T10:59:27 | 4,720 | 10 |
---
library_name: model2vec
license: mit
tags:
- embeddings
- static-embeddings
- mteb
- sentence-transformers
model-index:
- name: potion-base-32M
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: 74.49025487256372
- type: ap
value: 23.053406998271548
- type: ap_weighted
value: 23.053406998271548
- type: f1
value: 61.61224310463791
- type: f1_weighted
value: 79.15713131602897
- type: main_score
value: 74.49025487256372
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.55223880597013
- type: ap
value: 36.777904971672484
- type: ap_weighted
value: 36.777904971672484
- type: f1
value: 68.20927320328308
- type: f1_weighted
value: 76.8028646180125
- type: main_score
value: 74.55223880597013
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 72.855975
- type: ap
value: 67.07977033292134
- type: ap_weighted
value: 67.07977033292134
- type: f1
value: 72.67632985018474
- type: f1_weighted
value: 72.67632985018474
- type: main_score
value: 72.855975
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.948
- type: f1
value: 36.39230651926405
- type: f1_weighted
value: 36.39230651926405
- type: main_score
value: 36.948
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 42.864000000000004
- type: map_at_1
value: 21.693
- type: map_at_10
value: 34.859
- type: map_at_100
value: 36.014
- type: map_at_1000
value: 36.047000000000004
- type: map_at_20
value: 35.667
- type: map_at_3
value: 30.416999999999998
- type: map_at_5
value: 32.736
- type: mrr_at_1
value: 22.04836415362731
- type: mrr_at_10
value: 35.01442231705384
- type: mrr_at_100
value: 36.16267051020847
- type: mrr_at_1000
value: 36.19625564960624
- type: mrr_at_20
value: 35.81309792569356
- type: mrr_at_3
value: 30.547652916073904
- type: mrr_at_5
value: 32.87339971550487
- type: nauc_map_at_1000_diff1
value: 7.561195580746018
- type: nauc_map_at_1000_max
value: -1.556531946821957
- type: nauc_map_at_1000_std
value: 2.056871021244521
- type: nauc_map_at_100_diff1
value: 7.576648616531427
- type: nauc_map_at_100_max
value: -1.5197684321804203
- type: nauc_map_at_100_std
value: 2.102558505658414
- type: nauc_map_at_10_diff1
value: 7.643409260188448
- type: nauc_map_at_10_max
value: -1.5534104754693818
- type: nauc_map_at_10_std
value: 1.8735258045916798
- type: nauc_map_at_1_diff1
value: 8.370318054971092
- type: nauc_map_at_1_max
value: -5.083984587735291
- type: nauc_map_at_1_std
value: -1.8039233134026431
- type: nauc_map_at_20_diff1
value: 7.642516551976743
- type: nauc_map_at_20_max
value: -1.388835890563647
- type: nauc_map_at_20_std
value: 2.198921728682202
- type: nauc_map_at_3_diff1
value: 7.437604281774142
- type: nauc_map_at_3_max
value: -2.7586587623340932
- type: nauc_map_at_3_std
value: 0.8031910070187186
- type: nauc_map_at_5_diff1
value: 6.80651166389857
- type: nauc_map_at_5_max
value: -2.7399645587571806
- type: nauc_map_at_5_std
value: 1.0580951572345365
- type: nauc_mrr_at_1000_diff1
value: 6.27575281564605
- type: nauc_mrr_at_1000_max
value: -2.0467879398352458
- type: nauc_mrr_at_1000_std
value: 1.9897114385666632
- type: nauc_mrr_at_100_diff1
value: 6.292566922480118
- type: nauc_mrr_at_100_max
value: -2.009602726575689
- type: nauc_mrr_at_100_std
value: 2.0353272285661115
- type: nauc_mrr_at_10_diff1
value: 6.38514525903419
- type: nauc_mrr_at_10_max
value: -2.0386434404188583
- type: nauc_mrr_at_10_std
value: 1.7937484255337244
- type: nauc_mrr_at_1_diff1
value: 7.131931862611085
- type: nauc_mrr_at_1_max
value: -5.008568891508268
- type: nauc_mrr_at_1_std
value: -1.86541494834969
- type: nauc_mrr_at_20_diff1
value: 6.352383732997516
- type: nauc_mrr_at_20_max
value: -1.8916791965400346
- type: nauc_mrr_at_20_std
value: 2.142946311516978
- type: nauc_mrr_at_3_diff1
value: 5.952701132344548
- type: nauc_mrr_at_3_max
value: -3.433767309685429
- type: nauc_mrr_at_3_std
value: 0.8212723818638477
- type: nauc_mrr_at_5_diff1
value: 5.518638249091068
- type: nauc_mrr_at_5_max
value: -3.284414027772663
- type: nauc_mrr_at_5_std
value: 0.8740053182401986
- type: nauc_ndcg_at_1000_diff1
value: 7.853268129426508
- type: nauc_ndcg_at_1000_max
value: 0.07872546898149692
- type: nauc_ndcg_at_1000_std
value: 3.830950311415248
- type: nauc_ndcg_at_100_diff1
value: 8.18494720374052
- type: nauc_ndcg_at_100_max
value: 1.189039585107088
- type: nauc_ndcg_at_100_std
value: 5.162437147506563
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value: 8.483384610768821
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dataset:
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type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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 (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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dataset:
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type: mteb/biosses-sts
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split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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type: mteb/banking77
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split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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dataset:
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type: mteb/biorxiv-clustering-p2p
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dataset:
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type: mteb/biorxiv-clustering-s2s
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dataset:
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type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
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type: mteb/cqadupstack-english
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split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
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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
config: en
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
config: en
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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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split: test
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split: test
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type: mteb/reddit-clustering
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type: mteb/scidocs
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type: mteb/sickr-sts
config: default
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revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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type: mteb/sts12-sts
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type: mteb/sts13-sts
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type: mteb/sts14-sts
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type: mteb/sts16-sts
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split: test
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value: 76.85661925812892
- type: manhattan_pearson
value: 74.81988866329016
- type: manhattan_spearman
value: 75.50402310757015
- type: pearson
value: 76.19539001140771
- type: spearman
value: 76.85661925812892
- 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: 34.397932428162186
- type: cosine_spearman
value: 29.85196707881456
- type: euclidean_pearson
value: 34.64394718355606
- type: euclidean_spearman
value: 29.85196707881456
- type: main_score
value: 29.85196707881456
- type: manhattan_pearson
value: 34.16832023178801
- type: manhattan_spearman
value: 33.14486169393415
- type: pearson
value: 34.397932428162186
- type: spearman
value: 29.85196707881456
- 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: 20.30327751081854
- type: cosine_spearman
value: 18.674265253799298
- type: euclidean_pearson
value: 20.131998188841948
- type: euclidean_spearman
value: 18.674265253799298
- type: main_score
value: 18.674265253799298
- type: manhattan_pearson
value: 18.622067051882603
- type: manhattan_spearman
value: 18.620291055648483
- type: pearson
value: 20.30327751081854
- type: spearman
value: 18.674265253799298
- 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: 28.49639097934199
- type: cosine_spearman
value: 26.76895298058133
- type: euclidean_pearson
value: 28.51993606461124
- type: euclidean_spearman
value: 26.76895298058133
- type: main_score
value: 26.76895298058133
- type: manhattan_pearson
value: 28.34674577371768
- type: manhattan_spearman
value: 24.811029147686337
- type: pearson
value: 28.49639097934199
- type: spearman
value: 26.76895298058133
- 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: 85.42068849087106
- type: cosine_spearman
value: 87.05993755101053
- type: euclidean_pearson
value: 85.50658100259913
- type: euclidean_spearman
value: 87.05993755101053
- type: main_score
value: 87.05993755101053
- type: manhattan_pearson
value: 85.05037515486939
- type: manhattan_spearman
value: 86.78286451699647
- type: pearson
value: 85.42068849087106
- type: spearman
value: 87.05993755101053
- 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: 28.331000764771648
- type: cosine_spearman
value: 26.924748902731583
- type: euclidean_pearson
value: 27.908262381916842
- type: euclidean_spearman
value: 26.924748902731583
- type: main_score
value: 26.924748902731583
- type: manhattan_pearson
value: 27.64928698735386
- type: manhattan_spearman
value: 26.33489239510866
- type: pearson
value: 28.331000764771648
- type: spearman
value: 26.924748902731583
- 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: 35.99128331010763
- type: cosine_spearman
value: 33.882787753030755
- type: euclidean_pearson
value: 36.125565540276824
- type: euclidean_spearman
value: 33.882787753030755
- type: main_score
value: 33.882787753030755
- type: manhattan_pearson
value: 39.43371979888863
- type: manhattan_spearman
value: 39.98846569097863
- type: pearson
value: 35.99128331010763
- type: spearman
value: 33.882787753030755
- 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: 1.3231798311649463
- type: cosine_spearman
value: 1.968045578690376
- type: euclidean_pearson
value: 1.2443039427500642
- type: euclidean_spearman
value: 1.968045578690376
- type: main_score
value: 1.968045578690376
- type: manhattan_pearson
value: 0.29924785068227155
- type: manhattan_spearman
value: 3.1701763139219117
- type: pearson
value: 1.3231798311649463
- type: spearman
value: 1.968045578690376
- 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: 7.571886460510519
- type: cosine_spearman
value: 6.426199138705195
- type: euclidean_pearson
value: 7.436947146723141
- type: euclidean_spearman
value: 6.426199138705195
- type: main_score
value: 6.426199138705195
- type: manhattan_pearson
value: 5.225717518299594
- type: manhattan_spearman
value: 3.067077550441944
- type: pearson
value: 7.571886460510519
- type: spearman
value: 6.426199138705195
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 61.088670238491204
- type: cosine_spearman
value: 64.25828026896876
- type: euclidean_pearson
value: 63.7609187555772
- type: euclidean_spearman
value: 64.25828026896876
- type: main_score
value: 64.25828026896876
- type: manhattan_pearson
value: 62.601398537174035
- type: manhattan_spearman
value: 62.87332671301306
- type: pearson
value: 61.088670238491204
- type: spearman
value: 64.25828026896876
- 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: 31.0255615174742
- type: cosine_spearman
value: 36.69481137227967
- type: euclidean_pearson
value: 29.900021582547264
- type: euclidean_spearman
value: 36.69481137227967
- type: main_score
value: 36.69481137227967
- type: manhattan_pearson
value: 29.619780557503155
- type: manhattan_spearman
value: 41.91843653096047
- type: pearson
value: 31.0255615174742
- type: spearman
value: 36.69481137227967
- 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: 10.40841334454418
- type: cosine_spearman
value: 28.104310738121512
- type: euclidean_pearson
value: 8.128707220686382
- type: euclidean_spearman
value: 28.104310738121512
- type: main_score
value: 28.104310738121512
- type: manhattan_pearson
value: 14.726925529355325
- type: manhattan_spearman
value: 28.057426809179326
- type: pearson
value: 10.40841334454418
- type: spearman
value: 28.104310738121512
- 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: 10.755722104172923
- type: cosine_spearman
value: 12.444878649130276
- type: euclidean_pearson
value: 10.059477626348706
- type: euclidean_spearman
value: 12.468003510617354
- type: main_score
value: 12.444878649130276
- type: manhattan_pearson
value: 13.507609577564672
- type: manhattan_spearman
value: 18.390599199214037
- type: pearson
value: 10.755722104172923
- type: spearman
value: 12.444878649130276
- 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: 13.26815146152796
- type: cosine_spearman
value: 18.433007526015338
- type: euclidean_pearson
value: 12.020944164266574
- type: euclidean_spearman
value: 18.426334503093322
- type: main_score
value: 18.433007526015338
- type: manhattan_pearson
value: 11.933248448259237
- type: manhattan_spearman
value: 18.324625546075126
- type: pearson
value: 13.26815146152796
- type: spearman
value: 18.433007526015338
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 78.68909918763245
- type: cosine_spearman
value: 76.83852449552488
- type: euclidean_pearson
value: 78.35108573675653
- type: euclidean_spearman
value: 76.83846447877973
- type: main_score
value: 76.83852449552488
- type: manhattan_pearson
value: 77.02876135749386
- type: manhattan_spearman
value: 75.9170365531251
- type: pearson
value: 78.68909918763245
- type: spearman
value: 76.83852449552488
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 74.82680371550936
- type: map
value: 74.82680371550936
- type: mrr
value: 91.93418963026807
- type: nAUC_map_diff1
value: 9.068255025841692
- type: nAUC_map_max
value: 53.83045488537594
- type: nAUC_map_std
value: 65.66457388284374
- type: nAUC_mrr_diff1
value: 46.05085316225451
- type: nAUC_mrr_max
value: 75.59509312588396
- type: nAUC_mrr_std
value: 70.87824623580906
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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value: 56.2
- type: map_at_1
value: 40.666999999999994
- type: map_at_10
value: 51.129999999999995
- type: map_at_100
value: 52.056999999999995
- type: map_at_1000
value: 52.098
- type: map_at_20
value: 51.686
- type: map_at_3
value: 48.634
- type: map_at_5
value: 50.066
- type: mrr_at_1
value: 42.66666666666667
- type: mrr_at_10
value: 52.482142857142854
- type: mrr_at_100
value: 53.19823245085252
- type: mrr_at_1000
value: 53.23357269611506
- type: mrr_at_20
value: 52.87664766434116
- type: mrr_at_3
value: 50.499999999999986
- type: mrr_at_5
value: 51.4
- type: nauc_map_at_1000_diff1
value: 55.679524170340144
- type: nauc_map_at_1000_max
value: 35.34368225457809
- type: nauc_map_at_1000_std
value: -2.043911326695919
- type: nauc_map_at_100_diff1
value: 55.64964353245028
- type: nauc_map_at_100_max
value: 35.33722631779892
- type: nauc_map_at_100_std
value: -2.0539617122568625
- type: nauc_map_at_10_diff1
value: 55.82499509356933
- type: nauc_map_at_10_max
value: 34.59317781850682
- type: nauc_map_at_10_std
value: -3.1860541248309358
- type: nauc_map_at_1_diff1
value: 59.10174915043726
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value: 33.416492729123185
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value: -5.1952700488175045
- type: nauc_map_at_20_diff1
value: 55.623431232446904
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value: 35.296574838119085
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value: -2.007783275001443
- type: nauc_map_at_3_diff1
value: 56.556766655254485
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value: 33.3155647194758
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value: -3.7964967547169106
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value: 56.553418502313676
- type: nauc_map_at_5_max
value: 34.13639468025285
- type: nauc_map_at_5_std
value: -3.9708550035335346
- type: nauc_mrr_at_1000_diff1
value: 55.331555076880576
- type: nauc_mrr_at_1000_max
value: 37.027463444459606
- type: nauc_mrr_at_1000_std
value: 1.9690692773511222
- type: nauc_mrr_at_100_diff1
value: 55.28850823087701
- type: nauc_mrr_at_100_max
value: 37.03565595888509
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value: 1.9754212228406436
- type: nauc_mrr_at_10_diff1
value: 55.35398010609678
- type: nauc_mrr_at_10_max
value: 36.813749789783465
- type: nauc_mrr_at_10_std
value: 1.69908309119671
- type: nauc_mrr_at_1_diff1
value: 59.698357986628324
- type: nauc_mrr_at_1_max
value: 36.19184535975336
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value: -0.3732635881802827
- type: nauc_mrr_at_20_diff1
value: 55.26305584448552
- type: nauc_mrr_at_20_max
value: 37.11198171015867
- type: nauc_mrr_at_20_std
value: 2.1612218864195816
- type: nauc_mrr_at_3_diff1
value: 56.25662215357208
- type: nauc_mrr_at_3_max
value: 35.8103147848101
- type: nauc_mrr_at_3_std
value: 1.408422730019326
- type: nauc_mrr_at_5_diff1
value: 56.409279532488
- type: nauc_mrr_at_5_max
value: 36.24764657740795
- type: nauc_mrr_at_5_std
value: 0.9191877090789675
- type: nauc_ndcg_at_1000_diff1
value: 53.921052108891374
- type: nauc_ndcg_at_1000_max
value: 37.23673471367524
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value: 1.2797242206174546
- type: nauc_ndcg_at_100_diff1
value: 53.08910074626929
- type: nauc_ndcg_at_100_max
value: 37.58307549599563
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value: 1.6730489754502
- type: nauc_ndcg_at_10_diff1
value: 53.4900294437438
- type: nauc_ndcg_at_10_max
value: 35.63914186917353
- type: nauc_ndcg_at_10_std
value: -1.2567885269168115
- type: nauc_ndcg_at_1_diff1
value: 59.698357986628324
- type: nauc_ndcg_at_1_max
value: 36.19184535975336
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value: -0.3732635881802827
- type: nauc_ndcg_at_20_diff1
value: 52.91626708083731
- type: nauc_ndcg_at_20_max
value: 37.3727463545816
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value: 1.794148757644209
- type: nauc_ndcg_at_3_diff1
value: 55.41497362862388
- type: nauc_ndcg_at_3_max
value: 33.84606207970954
- type: nauc_ndcg_at_3_std
value: -1.5037390857368864
- type: nauc_ndcg_at_5_diff1
value: 55.561650253405716
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value: 34.55478239305819
- type: nauc_ndcg_at_5_std
value: -2.6884049705546453
- type: nauc_precision_at_1000_diff1
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value: 51.61025627560141
- type: nauc_precision_at_100_diff1
value: 4.4851329855278665
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value: 42.019199750825834
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value: 44.59826245592179
- type: nauc_precision_at_10_diff1
value: 31.128531158716914
- type: nauc_precision_at_10_max
value: 40.014303427714296
- type: nauc_precision_at_10_std
value: 18.66086480010028
- type: nauc_precision_at_1_diff1
value: 59.698357986628324
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value: 36.19184535975336
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value: -0.3732635881802827
- type: nauc_precision_at_20_diff1
value: 19.729038153986753
- type: nauc_precision_at_20_max
value: 43.018048891935095
- type: nauc_precision_at_20_std
value: 34.93305917294951
- type: nauc_precision_at_3_diff1
value: 47.69367211697757
- type: nauc_precision_at_3_max
value: 36.67930202405817
- type: nauc_precision_at_3_std
value: 10.015396528127898
- type: nauc_precision_at_5_diff1
value: 40.112747160286034
- type: nauc_precision_at_5_max
value: 36.522086663861955
- type: nauc_precision_at_5_std
value: 10.298695835086278
- type: nauc_recall_at_1000_diff1
value: 15.490555196437267
- type: nauc_recall_at_1000_max
value: 65.77784960137889
- type: nauc_recall_at_1000_std
value: 55.6956115779644
- type: nauc_recall_at_100_diff1
value: 31.420573665344186
- type: nauc_recall_at_100_max
value: 51.478517970815986
- type: nauc_recall_at_100_std
value: 21.8667299551994
- type: nauc_recall_at_10_diff1
value: 43.811043753262645
- type: nauc_recall_at_10_max
value: 35.251432327366395
- type: nauc_recall_at_10_std
value: -1.5842166373120425
- type: nauc_recall_at_1_diff1
value: 59.10174915043726
- type: nauc_recall_at_1_max
value: 33.416492729123185
- type: nauc_recall_at_1_std
value: -5.1952700488175045
- type: nauc_recall_at_20_diff1
value: 39.869129286563876
- type: nauc_recall_at_20_max
value: 43.8582881229973
- type: nauc_recall_at_20_std
value: 12.572449527758673
- type: nauc_recall_at_3_diff1
value: 51.9577529837867
- type: nauc_recall_at_3_max
value: 30.893069836408237
- type: nauc_recall_at_3_std
value: -1.959211453851564
- type: nauc_recall_at_5_diff1
value: 51.75561843815883
- type: nauc_recall_at_5_max
value: 32.15736743162173
- type: nauc_recall_at_5_std
value: -4.772474736542438
- type: ndcg_at_1
value: 42.667
- type: ndcg_at_10
value: 56.2
- type: ndcg_at_100
value: 60.260000000000005
- type: ndcg_at_1000
value: 61.483
- type: ndcg_at_20
value: 57.909
- type: ndcg_at_3
value: 51.711
- type: ndcg_at_5
value: 53.783
- type: precision_at_1
value: 42.667
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_20
value: 4.3
- type: precision_at_3
value: 20.889
- type: precision_at_5
value: 13.866999999999999
- type: recall_at_1
value: 40.666999999999994
- type: recall_at_10
value: 70.244
- type: recall_at_100
value: 88.656
- type: recall_at_1000
value: 98.26700000000001
- type: recall_at_20
value: 76.589
- type: recall_at_3
value: 58.333
- type: recall_at_5
value: 63.24999999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
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- type: nauc_precision_at_3_diff1
value: 0.947307929112776
- type: nauc_precision_at_3_max
value: -31.05930383886252
- type: nauc_precision_at_3_std
value: -17.102851167484616
- type: nauc_precision_at_5_diff1
value: 0.9266007195538144
- type: nauc_precision_at_5_max
value: -37.581387889994
- type: nauc_precision_at_5_std
value: -19.690150307959293
- type: nauc_recall_at_1000_diff1
value: 16.984317473823676
- type: nauc_recall_at_1000_max
value: -30.587646676977936
- type: nauc_recall_at_1000_std
value: 57.48516026941197
- type: nauc_recall_at_100_diff1
value: 21.886301436834625
- type: nauc_recall_at_100_max
value: -28.07071340770586
- type: nauc_recall_at_100_std
value: -0.11337935267596243
- type: nauc_recall_at_10_diff1
value: 1.6250953111186457
- type: nauc_recall_at_10_max
value: -38.0654848945014
- type: nauc_recall_at_10_std
value: -28.014162803875355
- type: nauc_recall_at_1_diff1
value: 7.127954407520293
- type: nauc_recall_at_1_max
value: -35.3892053151105
- type: nauc_recall_at_1_std
value: -19.49733359371979
- type: nauc_recall_at_20_diff1
value: 10.635038702861864
- type: nauc_recall_at_20_max
value: -30.098601211040904
- type: nauc_recall_at_20_std
value: -22.936538824524817
- type: nauc_recall_at_3_diff1
value: 3.5564807135760255
- type: nauc_recall_at_3_max
value: -38.0587512734278
- type: nauc_recall_at_3_std
value: -21.84694412548793
- type: nauc_recall_at_5_diff1
value: -0.7556448626996317
- type: nauc_recall_at_5_max
value: -46.540574319477905
- type: nauc_recall_at_5_std
value: -30.56076072609364
- type: ndcg_at_1
value: 16.326999999999998
- type: ndcg_at_10
value: 19.628999999999998
- type: ndcg_at_100
value: 30.853
- type: ndcg_at_1000
value: 42.881
- type: ndcg_at_20
value: 20.232
- type: ndcg_at_3
value: 18.093999999999998
- type: ndcg_at_5
value: 19.089
- type: precision_at_1
value: 20.408
- type: precision_at_10
value: 20.0
- type: precision_at_100
value: 7.204000000000001
- type: precision_at_1000
value: 1.488
- type: precision_at_20
value: 14.796000000000001
- type: precision_at_3
value: 20.408
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 1.7770000000000001
- type: recall_at_10
value: 14.056
- type: recall_at_100
value: 43.388
- type: recall_at_1000
value: 80.384
- type: recall_at_20
value: 19.73
- type: recall_at_3
value: 4.444
- type: recall_at_5
value: 7.742
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 70.4345703125
- type: ap
value: 13.363055996397314
- type: ap_weighted
value: 13.363055996397314
- type: f1
value: 53.71432014147602
- type: f1_weighted
value: 76.97001715054664
- type: main_score
value: 70.4345703125
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.58460667798529
- type: f1
value: 56.81197245206573
- type: f1_weighted
value: 56.13824904215221
- type: main_score
value: 56.58460667798529
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 36.773580338060434
- type: v_measure
value: 36.773580338060434
- type: v_measure_std
value: 2.1187678513989585
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 81.8024676640639
- type: cosine_accuracy_threshold
value: 72.72156987862404
- type: cosine_ap
value: 58.626480762860155
- type: cosine_f1
value: 56.335306196400516
- type: cosine_f1_threshold
value: 63.90516602618299
- type: cosine_precision
value: 51.94920917799064
- type: cosine_recall
value: 61.53034300791557
- type: dot_accuracy
value: 81.8024676640639
- type: dot_accuracy_threshold
value: 72.72157129741353
- type: dot_ap
value: 58.626480927811144
- type: dot_f1
value: 56.335306196400516
- type: dot_f1_threshold
value: 63.90516471984052
- type: dot_precision
value: 51.94920917799064
- type: dot_recall
value: 61.53034300791557
- type: euclidean_accuracy
value: 81.8024676640639
- type: euclidean_accuracy_threshold
value: 73.86261650981828
- type: euclidean_ap
value: 58.626478566508865
- type: euclidean_f1
value: 56.335306196400516
- type: euclidean_f1_threshold
value: 84.96450225024545
- type: euclidean_precision
value: 51.94920917799064
- type: euclidean_recall
value: 61.53034300791557
- type: main_score
value: 59.15380842181217
- type: manhattan_accuracy
value: 81.88591524110389
- type: manhattan_accuracy_threshold
value: 1181.9765670520376
- type: manhattan_ap
value: 59.15380842181217
- type: manhattan_f1
value: 56.939975590813276
- type: manhattan_f1_threshold
value: 1437.7120667882991
- type: manhattan_precision
value: 49.12885314953092
- type: manhattan_recall
value: 67.70448548812665
- type: max_accuracy
value: 81.88591524110389
- type: max_ap
value: 59.15380842181217
- type: max_f1
value: 56.939975590813276
- type: max_precision
value: 51.94920917799064
- type: max_recall
value: 67.70448548812665
- type: similarity_accuracy
value: 81.8024676640639
- type: similarity_accuracy_threshold
value: 72.72156987862404
- type: similarity_ap
value: 58.626480762860155
- type: similarity_f1
value: 56.335306196400516
- type: similarity_f1_threshold
value: 63.90516602618299
- type: similarity_precision
value: 51.94920917799064
- type: similarity_recall
value: 61.53034300791557
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 87.36174176271976
- type: cosine_accuracy_threshold
value: 60.648304783781334
- type: cosine_ap
value: 82.50029451974736
- type: cosine_f1
value: 74.85008308648219
- type: cosine_f1_threshold
value: 54.0321362274791
- type: cosine_precision
value: 70.50496801415544
- type: cosine_recall
value: 79.76593778872805
- type: dot_accuracy
value: 87.36174176271976
- type: dot_accuracy_threshold
value: 60.64830361623812
- type: dot_ap
value: 82.50033093545332
- type: dot_f1
value: 74.85008308648219
- type: dot_f1_threshold
value: 54.03213667524275
- type: dot_precision
value: 70.50496801415544
- type: dot_recall
value: 79.76593778872805
- type: euclidean_accuracy
value: 87.36174176271976
- type: euclidean_accuracy_threshold
value: 88.714930435829
- type: euclidean_ap
value: 82.50029575944534
- type: euclidean_f1
value: 74.85008308648219
- type: euclidean_f1_threshold
value: 95.8831206738777
- type: euclidean_precision
value: 70.50496801415544
- type: euclidean_recall
value: 79.76593778872805
- type: main_score
value: 82.79743429849837
- type: manhattan_accuracy
value: 87.51309814879497
- type: manhattan_accuracy_threshold
value: 1458.0611945921191
- type: manhattan_ap
value: 82.79743429849837
- type: manhattan_f1
value: 75.14295079578979
- type: manhattan_f1_threshold
value: 1571.1923710026895
- type: manhattan_precision
value: 71.29725620291659
- type: manhattan_recall
value: 79.4271635355713
- type: max_accuracy
value: 87.51309814879497
- type: max_ap
value: 82.79743429849837
- type: max_f1
value: 75.14295079578979
- type: max_precision
value: 71.29725620291659
- type: max_recall
value: 79.76593778872805
- type: similarity_accuracy
value: 87.36174176271976
- type: similarity_accuracy_threshold
value: 60.648304783781334
- type: similarity_ap
value: 82.50029451974736
- type: similarity_f1
value: 74.85008308648219
- type: similarity_f1_threshold
value: 54.0321362274791
- type: similarity_precision
value: 70.50496801415544
- type: similarity_recall
value: 79.76593778872805
---
# potion-base-32M 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. It uses a larger vocabulary size than the [potion-base-8M](https://huggingface.co/minishlab/potion-base-8M) model which can be beneficial for tasks that require a larger vocabulary.
## 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-32M")
# 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).
## Results
The results for this model are shown in the table below. The full Model2Vec results for all models can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md).
```
Average (All) 52.46
Average (MTEB) 51.66
Classification 65.97
Clustering 35.29
PairClassification 78.17
Reranking 50.92
Retrieval 33.52
STS 74.22
Summarization 29.78
PEARL 55.37
WordSim 55.15
```
## 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 and Thomas van Dongen},
title = {Model2Vec: The Fastest State-of-the-Art Static Embeddings in the World},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
michiyasunaga/BioLinkBERT-base
|
michiyasunaga
|
text-classification
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-08T07:22:12 | 2022-03-31T00:51:21 | 4,645 | 36 |
---
datasets:
- pubmed
language: en
license: apache-2.0
tags:
- bert
- exbert
- linkbert
- biolinkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
widget:
- text: Sunitinib is a tyrosine kinase inhibitor
---
## BioLinkBERT-base
BioLinkBERT-base model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA).
## Model description
LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
## Intended uses & limitations
The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
### How to use
To use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base')
model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-base')
inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
## Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
**Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art.
| | BLURB score | PubMedQA | BioASQ | MedQA-USMLE |
| ---------------------- | -------- | -------- | ------- | -------- |
| PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 |
| **BioLinkBERT-base** | **83.39** | **70.2** | **91.4** | **40.0** |
| **BioLinkBERT-large** | **84.30** | **72.2** | **94.8** | **44.6** |
| | MMLU-professional medicine |
| ---------------------- | -------- |
| GPT-3 (175 params) | 38.7 |
| UnifiedQA (11B params) | 43.2 |
| **BioLinkBERT-large (340M params)** | **50.7** |
## Citation
If you find LinkBERT useful in your project, please cite the following:
```bibtex
@InProceedings{yasunaga2022linkbert,
author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
title = {LinkBERT: Pretraining Language Models with Document Links},
year = {2022},
booktitle = {Association for Computational Linguistics (ACL)},
}
```
|
[
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING"
] |
[
"BLURB",
"MEDQA",
"PUBMEDQA"
] |
joe32140/ModernBERT-base-msmarco
|
joe32140
|
sentence-similarity
|
[
"sentence-transformers",
"onnx",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:11662655",
"loss:CachedMultipleNegativesRankingLoss",
"en",
"dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1",
"arxiv:1908.10084",
"arxiv:2101.06983",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-12-20T05:00:55 | 2025-01-26T00:05:31 | 4,630 | 8 |
---
base_model: answerdotai/ModernBERT-base
datasets:
- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
language:
- en
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11662655
- loss:CachedMultipleNegativesRankingLoss
base_model_relation: finetune
widget:
- source_sentence: what county is lyndhurst, ohio in
sentences:
- This article is about the song written by Kenneth Gamble, Leon Huff and Cary Gilbert.
For the Tina Turner song, see Don't Leave Me This Way (Tina Turner song). Don't
Leave Me This Way is a song written by Kenneth Gamble, Leon Huff and Cary Gilbert.
First charting as a hit for Harold Melvin & the Blue Notes featuring Teddy Pendergrass,
an act on Gamble & Huff's Philadelphia International label in 1975, Don't Leave
Me This Way was later a huge disco hit for Motown artist Thelma Houston in 1977.
- "Lyndhurst is a city in Cuyahoga County, Ohio, United States. The population was\
\ 14,001 at the 2010 census. Lyndhurst is located in northeastern Ohio, and is\
\ a suburb of Cleveland. A small part of Lyndhurst was originally part of Mayfield\
\ Township. It used to be called Euclidville before Lyndhurst was chosen. Lyndhurst\
\ is located at 41°31â\x80²17â\x80³N 81°29â\x80²25â\x80³W / 41.52139°N 81.49028°W\
\ / 41.52139; -81.49028 (41.521352, -81.490141)."
- Welcome to Trumbull County... Trumbull County, the county seat, located in Warren,
Ohio, consists of a combination of both urban and rural communities situated in
the northeast corner of Ohio. It is situated roughly between the Youngstown, Cleveland
and Akron corridors.
- source_sentence: who founded the american graphophone company
sentences:
- In 1886, Graham Bell and Charles Sumner Tainter founded the American Graphophone
Company to distribute and sell graphophones in the US and Canada under license
from the Volta Graphophone Company. In 1890, the American Graphophone Company
stopped production of new phonographs due to sagging orders.
- ShelfGenie How much does a ShelfGenie franchise cost? ShelfGenie has a franchise
fee of up to $45,000, with a total initial investment range of $70,100 to $107,750.
Local ShelfGenie franchise opportunities. ShelfGenie is looking to grow in a number
of cities around the country. To find out if there's a franchise opportunity in
your city, unlock more information.
- "A+E Networks. The technology that made the modern music business possible came\
\ into existence in the New Jersey laboratory where Thomas Alva Edison created\
\ the first device to both record sound and play it back. He was awarded U.S.\
\ Patent No. 200,521 for his inventionâ\x80\x93the phonographâ\x80\x93on this\
\ day in 1878."
- source_sentence: is housekeeping camp flooded?
sentences:
- 'What is the importance of housekeeping at work? A: Workplace housekeeping promotes
sanitation, safety, organization and productivity. It also boosts morale. Daily
housekeeping maintenance keeps the workplac... Full Answer >'
- The back patio area of a cabin is partially submerged in flood water at Housekeeping
Camp on Monday, Jan. 9, 2017, in Yosemite National Park. The Merced River, swollen
with storm runoff, crested at 12.7 feet at 4 a.m. SILVIA FLORES [email protected].
- "1 Bake for 8 minutes, then rotate the pan and check the underside of the bagels.\
\ 2 If theyâ\x80\x99re getting too dark, place another pan under the baking sheet.\
\ ( 3 Doubling the pan will insulate the first baking sheet.) Bake for another\
\ 8 to 12 minutes, until the bagels are a golden brown. 4 13."
- source_sentence: causes for infection in the nerve of tooth
sentences:
- If a cavity is causing the toothache, your dentist will fill the cavity or possibly
extract the tooth, if necessary. A root canal might be needed if the cause of
the toothache is determined to be an infection of the tooth's nerve. Bacteria
that have worked their way into the inner aspects of the tooth cause such an infection.
An antibiotic may be prescribed if there is fever or swelling of the jaw.
- "According to Article III, Section 1 of the Constitution, judges and justices\
\ of the Judicial Branch serve during good behavior.. This means they are appointed\
\ for life, unles â\x80¦ s they are impeached and removed from office. + 50 others\
\ found this useful.he term length for members of the House are two years and\
\ a staggering six years for members of the Senate."
- Inflamed or infected pulp (pulpitis) most often causes a toothache. To relieve
the pain and prevent further complications, the tooth may be extracted (surgically
removed) or saved by root canal treatment.
- source_sentence: what county is hayden in
sentences:
- Normally, the Lead Agency is the agency with general governmental powers such
as a city or a county. Agencies with limited powers or districts that provide
a public service/utility such as a recreation and park district will tend to be
a Responsible Agency.
- According to the United States Census Bureau, the city has a total area of 9.61
square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01
square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake,
and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is
located on U.S. Route 95 at the junction of Route 41. It is also four miles (6
km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest
of Hayden.
- Hayden is a city in Kootenai County, Idaho, United States. Located in the northern
portion of the state, just north of Coeur d'Alene, its population was 13,294 at
the 2010 census.
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: msmarco co condenser dev
type: msmarco-co-condenser-dev
metrics:
- type: cosine_accuracy
value: 0.984
name: Cosine Accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 65.997
- type: f1
value: 53.595099999999995
- type: f1_weighted
value: 72.5304
- type: ap
value: 16.3093
- type: ap_weighted
value: 16.3093
- type: main_score
value: 65.997
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.19399999999999
- type: f1
value: 58.5969
- type: f1_weighted
value: 67.7482
- type: ap
value: 28.0748
- type: ap_weighted
value: 28.0748
- type: main_score
value: 64.19399999999999
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 67.1713
- type: f1
value: 66.9443
- type: f1_weighted
value: 66.9443
- type: ap
value: 61.7296
- type: ap_weighted
value: 61.7296
- type: main_score
value: 67.1713
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 32.318000000000005
- type: f1
value: 31.9973
- type: f1_weighted
value: 31.9973
- type: main_score
value: 32.318000000000005
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 24.609
- type: ndcg_at_3
value: 36.278
- type: ndcg_at_5
value: 40.903
- type: ndcg_at_10
value: 46.381
- type: ndcg_at_20
value: 49.571
- type: ndcg_at_100
value: 51.666000000000004
- type: ndcg_at_1000
value: 52.098
- type: map_at_1
value: 24.609
- type: map_at_3
value: 33.404
- type: map_at_5
value: 35.968
- type: map_at_10
value: 38.204
- type: map_at_20
value: 39.113
- type: map_at_100
value: 39.432
- type: map_at_1000
value: 39.45
- type: recall_at_1
value: 24.609
- type: recall_at_3
value: 44.595
- type: recall_at_5
value: 55.832
- type: recall_at_10
value: 72.902
- type: recall_at_20
value: 85.277
- type: recall_at_100
value: 96.15899999999999
- type: recall_at_1000
value: 99.431
- type: precision_at_1
value: 24.609
- type: precision_at_3
value: 14.865
- type: precision_at_5
value: 11.166
- type: precision_at_10
value: 7.290000000000001
- type: precision_at_20
value: 4.263999999999999
- type: precision_at_100
value: 0.962
- type: precision_at_1000
value: 0.099
- type: mrr_at_1
value: 25.1067
- type: mrr_at_3
value: 33.5941
- type: mrr_at_5
value: 36.154599999999995
- type: mrr_at_10
value: 38.3849
- type: mrr_at_20
value: 39.3004
- type: mrr_at_100
value: 39.618900000000004
- type: mrr_at_1000
value: 39.6374
- type: nauc_ndcg_at_1_max
value: -5.6006
- type: nauc_ndcg_at_1_std
value: -4.8049
- type: nauc_ndcg_at_1_diff1
value: 14.902999999999999
- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
value: -1.9375
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value: 12.1454
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value: -0.46849999999999997
- type: nauc_ndcg_at_5_std
value: -2.0393000000000003
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value: 10.7318
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value: 3.0675
- type: nauc_ndcg_at_10_std
value: -0.3638
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value: 11.3071
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value: -0.3348
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value: 11.5383
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value: 1.0754
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value: 0.1077
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value: 11.8117
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value: -0.6433
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value: 11.9586
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- type: nauc_map_at_1_std
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value: -2.4433
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value: 12.6974
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value: 12.359399999999999
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- type: nauc_recall_at_1_diff1
value: 14.902999999999999
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- type: nauc_recall_at_3_diff1
value: 10.6725
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value: 6.8572
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- type: nauc_recall_at_10_std
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value: 50.9552
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value: 5.1657
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value: 25.4176
- type: nauc_recall_at_1000_std
value: 73.0444
- type: nauc_recall_at_1000_diff1
value: 16.6349
- type: nauc_precision_at_1_max
value: -5.6006
- type: nauc_precision_at_1_std
value: -4.8049
- type: nauc_precision_at_1_diff1
value: 14.902999999999999
- type: nauc_precision_at_3_max
value: 2.6873
- type: nauc_precision_at_3_std
value: -0.6182
- type: nauc_precision_at_3_diff1
value: 10.6725
- type: nauc_precision_at_5_max
value: 2.5734
- type: nauc_precision_at_5_std
value: -0.9853000000000001
- type: nauc_precision_at_5_diff1
value: 6.8572
- type: nauc_precision_at_10_max
value: 17.8111
- type: nauc_precision_at_10_std
value: 5.651599999999999
- type: nauc_precision_at_10_diff1
value: 7.600999999999999
- type: nauc_precision_at_20_max
value: 23.985899999999997
- type: nauc_precision_at_20_std
value: 10.423499999999999
- type: nauc_precision_at_20_diff1
value: 7.0172
- type: nauc_precision_at_100_max
value: 30.766900000000003
- type: nauc_precision_at_100_std
value: 50.9552
- type: nauc_precision_at_100_diff1
value: 5.1657
- type: nauc_precision_at_1000_max
value: 25.4176
- type: nauc_precision_at_1000_std
value: 73.0444
- type: nauc_precision_at_1000_diff1
value: 16.6349
- type: nauc_mrr_at_1_max
value: -5.1381000000000006
- type: nauc_mrr_at_1_std
value: -4.1855
- type: nauc_mrr_at_1_diff1
value: 13.2056
- type: nauc_mrr_at_3_max
value: -1.7795999999999998
- type: nauc_mrr_at_3_std
value: -2.3432
- type: nauc_mrr_at_3_diff1
value: 11.4369
- type: nauc_mrr_at_5_max
value: -1.7394
- type: nauc_mrr_at_5_std
value: -2.3168
- type: nauc_mrr_at_5_diff1
value: 10.7454
- type: nauc_mrr_at_10_max
value: -0.5075999999999999
- type: nauc_mrr_at_10_std
value: -1.6223
- type: nauc_mrr_at_10_diff1
value: 10.906699999999999
- type: nauc_mrr_at_20_max
value: -0.715
- type: nauc_mrr_at_20_std
value: -1.6808
- type: nauc_mrr_at_20_diff1
value: 10.9766
- type: nauc_mrr_at_100_max
value: -0.9223
- type: nauc_mrr_at_100_std
value: -1.6097000000000001
- type: nauc_mrr_at_100_diff1
value: 10.9945
- type: nauc_mrr_at_1000_max
value: -0.938
- type: nauc_mrr_at_1000_std
value: -1.63
- type: nauc_mrr_at_1000_diff1
value: 10.998800000000001
- type: main_score
value: 46.381
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 38.9781
- type: v_measure_std
value: 14.0064
- type: main_score
value: 38.9781
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 28.964499999999997
- type: v_measure_std
value: 14.276900000000001
- type: main_score
value: 28.964499999999997
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.9553
- type: mrr
value: 70.82929999999999
- type: nAUC_map_max
value: 23.2041
- type: nAUC_map_std
value: 21.0013
- type: nAUC_map_diff1
value: 6.7763
- type: nAUC_mrr_max
value: 33.143499999999996
- type: nAUC_mrr_std
value: 25.8722
- type: nAUC_mrr_diff1
value: 10.2834
- type: main_score
value: 57.9553
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: pearson
value: 85.0909
- type: spearman
value: 80.7386
- type: cosine_pearson
value: 85.0909
- type: cosine_spearman
value: 80.7386
- type: manhattan_pearson
value: 82.0327
- type: manhattan_spearman
value: 78.562
- type: euclidean_pearson
value: 82.6159
- type: euclidean_spearman
value: 79.35419999999999
- type: main_score
value: 80.7386
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 82.026
- type: f1
value: 81.9738
- type: f1_weighted
value: 81.9738
- type: main_score
value: 82.026
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 30.787
- type: v_measure_std
value: 0.9286000000000001
- type: main_score
value: 30.787
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.099800000000002
- type: v_measure_std
value: 0.7908000000000001
- type: main_score
value: 27.099800000000002
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
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value: 29.757
- type: ndcg_at_3
value: 34.736
- type: ndcg_at_5
value: 36.529
- type: ndcg_at_10
value: 39.114
- type: ndcg_at_20
value: 41.05
- type: ndcg_at_100
value: 44.359
- type: ndcg_at_1000
value: 47.015
- type: map_at_1
value: 24.501
- type: map_at_3
value: 30.857
- type: map_at_5
value: 32.307
- type: map_at_10
value: 33.582
- type: map_at_20
value: 34.229
- type: map_at_100
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- type: map_at_1000
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- type: recall_at_1
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- type: recall_at_3
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- type: recall_at_5
value: 42.701
- type: recall_at_10
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- type: recall_at_20
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- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_5
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- 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: mrr_at_3
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- type: mrr_at_5
value: 37.432
- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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value: 66.054
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value: 75.3192
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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: 88.9124
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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: 49.0948
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value: 69.3994
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value: 65.64750000000001
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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: 65.2354
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value: 63.5945
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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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value: 72.6126
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value: 72.4117
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value: 72.6126
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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:
name: MTEB MedrxivClusteringS2S (default)
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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value: 27.076
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type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
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type: Retrieval
dataset:
name: MTEB NFCorpus (default)
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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dataset:
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type: mteb/nq
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split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
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type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
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dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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dataset:
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type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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type: STS
dataset:
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type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 78.755
- type: spearman
value: 79.408
- type: cosine_pearson
value: 78.755
- type: cosine_spearman
value: 79.408
- type: manhattan_pearson
value: 80.0336
- type: manhattan_spearman
value: 80.5912
- type: euclidean_pearson
value: 80.0315
- type: euclidean_spearman
value: 80.5413
- type: main_score
value: 79.408
- 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: 41.280699999999996
- type: spearman
value: 39.9908
- type: cosine_pearson
value: 41.280699999999996
- type: cosine_spearman
value: 39.9908
- type: manhattan_pearson
value: 41.515
- type: manhattan_spearman
value: 39.6907
- type: euclidean_pearson
value: 41.5204
- type: euclidean_spearman
value: 39.6877
- type: main_score
value: 39.9908
- 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: 84.3554
- type: spearman
value: 84.7607
- type: cosine_pearson
value: 84.3554
- type: cosine_spearman
value: 84.7607
- type: manhattan_pearson
value: 84.1083
- type: manhattan_spearman
value: 84.35979999999999
- type: euclidean_pearson
value: 84.1249
- type: euclidean_spearman
value: 84.33070000000001
- type: main_score
value: 84.7607
- 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: 36.045500000000004
- type: spearman
value: 36.9017
- type: cosine_pearson
value: 36.045500000000004
- type: cosine_spearman
value: 36.9017
- type: manhattan_pearson
value: 33.128099999999996
- type: manhattan_spearman
value: 33.9834
- type: euclidean_pearson
value: 33.434599999999996
- type: euclidean_spearman
value: 34.6078
- type: main_score
value: 36.9017
- 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: 38.332899999999995
- type: spearman
value: 36.8844
- type: cosine_pearson
value: 38.333
- type: cosine_spearman
value: 36.8844
- type: manhattan_pearson
value: 34.725699999999996
- type: manhattan_spearman
value: 34.0722
- type: euclidean_pearson
value: 35.0828
- type: euclidean_spearman
value: 34.1548
- type: main_score
value: 36.8844
- 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: 8.5032
- type: spearman
value: 5.6236
- type: cosine_pearson
value: 8.5032
- type: cosine_spearman
value: 5.6236
- type: manhattan_pearson
value: 11.7706
- type: manhattan_spearman
value: 8.512599999999999
- type: euclidean_pearson
value: 11.6449
- type: euclidean_spearman
value: 7.7363
- type: main_score
value: 5.6236
- 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: 30.772899999999996
- type: spearman
value: 27.825899999999997
- type: cosine_pearson
value: 30.772899999999996
- type: cosine_spearman
value: 27.825899999999997
- type: manhattan_pearson
value: 34.1556
- type: manhattan_spearman
value: 32.852599999999995
- type: euclidean_pearson
value: 33.8333
- type: euclidean_spearman
value: 32.8262
- type: main_score
value: 27.825899999999997
- 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: 37.4576
- type: spearman
value: 33.533
- type: cosine_pearson
value: 37.457499999999996
- type: cosine_spearman
value: 33.5406
- type: manhattan_pearson
value: 38.7747
- type: manhattan_spearman
value: 36.8791
- type: euclidean_pearson
value: 39.4756
- type: euclidean_spearman
value: 38.3307
- type: main_score
value: 33.5406
- 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: 12.8259
- type: spearman
value: 13.2374
- type: cosine_pearson
value: 12.8259
- type: cosine_spearman
value: 13.2374
- type: manhattan_pearson
value: 16.0609
- type: manhattan_spearman
value: 14.8943
- type: euclidean_pearson
value: 14.1566
- type: euclidean_spearman
value: 12.6913
- type: main_score
value: 13.2374
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 66.1988
- type: spearman
value: 66.6842
- type: cosine_pearson
value: 66.1988
- type: cosine_spearman
value: 66.6842
- type: manhattan_pearson
value: 66.7457
- type: manhattan_spearman
value: 66.00200000000001
- type: euclidean_pearson
value: 66.9588
- type: euclidean_spearman
value: 66.16579999999999
- type: main_score
value: 66.6842
- 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: 55.525999999999996
- type: spearman
value: 58.790600000000005
- type: cosine_pearson
value: 55.525999999999996
- type: cosine_spearman
value: 58.790600000000005
- type: manhattan_pearson
value: 57.918499999999995
- type: manhattan_spearman
value: 60.4479
- type: euclidean_pearson
value: 58.0086
- type: euclidean_spearman
value: 60.5008
- type: main_score
value: 58.790600000000005
- 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: 36.3507
- type: spearman
value: 39.9716
- type: cosine_pearson
value: 36.3506
- type: cosine_spearman
value: 39.9716
- type: manhattan_pearson
value: 25.3105
- type: manhattan_spearman
value: 28.190700000000003
- type: euclidean_pearson
value: 27.8303
- type: euclidean_spearman
value: 29.8408
- type: main_score
value: 39.9716
- 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: 47.759800000000006
- type: spearman
value: 49.0005
- type: cosine_pearson
value: 47.759800000000006
- type: cosine_spearman
value: 49.0005
- type: manhattan_pearson
value: 46.2315
- type: manhattan_spearman
value: 49.9363
- type: euclidean_pearson
value: 46.818599999999996
- type: euclidean_spearman
value: 50.77779999999999
- type: main_score
value: 49.0005
- 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: 49.2874
- type: spearman
value: 43.5968
- type: cosine_pearson
value: 49.2874
- type: cosine_spearman
value: 43.5968
- type: manhattan_pearson
value: 56.0733
- type: manhattan_spearman
value: 51.0045
- type: euclidean_pearson
value: 56.356399999999994
- type: euclidean_spearman
value: 49.967800000000004
- type: main_score
value: 43.5968
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 78.6387
- type: spearman
value: 77.48469999999999
- type: cosine_pearson
value: 78.6387
- type: cosine_spearman
value: 77.48469999999999
- type: manhattan_pearson
value: 77.51989999999999
- type: manhattan_spearman
value: 77.1479
- type: euclidean_pearson
value: 77.5843
- type: euclidean_spearman
value: 77.1979
- type: main_score
value: 77.48469999999999
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 72.4633
- type: mrr
value: 90.7647
- type: nAUC_map_max
value: 51.910999999999994
- type: nAUC_map_std
value: 61.063599999999994
- type: nAUC_map_diff1
value: 10.8873
- type: nAUC_mrr_max
value: 74.0887
- type: nAUC_mrr_std
value: 66.3654
- type: nAUC_mrr_diff1
value: 48.1591
- type: main_score
value: 72.4633
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 49.0
- type: ndcg_at_3
value: 55.969
- type: ndcg_at_5
value: 58.111000000000004
- type: ndcg_at_10
value: 60.427
- type: ndcg_at_20
value: 61.527
- type: ndcg_at_100
value: 63.535
- type: ndcg_at_1000
value: 64.773
- type: map_at_1
value: 46.344
- type: map_at_3
value: 53.188
- type: map_at_5
value: 54.567
- type: map_at_10
value: 55.704
- type: map_at_20
value: 56.055
- type: map_at_100
value: 56.35
- type: map_at_1000
value: 56.397
- type: recall_at_1
value: 46.344
- type: recall_at_3
value: 60.911
- type: recall_at_5
value: 66.294
- type: recall_at_10
value: 72.872
- type: recall_at_20
value: 77.033
- type: recall_at_100
value: 87.656
- type: recall_at_1000
value: 97.333
- type: precision_at_1
value: 49.0
- type: precision_at_3
value: 22.111
- type: precision_at_5
value: 14.6
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_20
value: 4.383
- type: precision_at_100
value: 0.997
- type: precision_at_1000
value: 0.11
- type: mrr_at_1
value: 49.0
- type: mrr_at_3
value: 55.555600000000005
- type: mrr_at_5
value: 56.8056
- type: mrr_at_10
value: 57.5951
- type: mrr_at_20
value: 57.808800000000005
- type: mrr_at_100
value: 58.0653
- type: mrr_at_1000
value: 58.10530000000001
- type: nauc_ndcg_at_1_max
value: 49.4156
- type: nauc_ndcg_at_1_std
value: 17.982300000000002
- type: nauc_ndcg_at_1_diff1
value: 70.1234
- type: nauc_ndcg_at_3_max
value: 54.3891
- type: nauc_ndcg_at_3_std
value: 14.310400000000001
- type: nauc_ndcg_at_3_diff1
value: 67.3382
- type: nauc_ndcg_at_5_max
value: 52.9631
- type: nauc_ndcg_at_5_std
value: 11.7775
- type: nauc_ndcg_at_5_diff1
value: 66.2906
- type: nauc_ndcg_at_10_max
value: 54.9531
- type: nauc_ndcg_at_10_std
value: 13.3055
- type: nauc_ndcg_at_10_diff1
value: 66.4653
- type: nauc_ndcg_at_20_max
value: 54.7342
- type: nauc_ndcg_at_20_std
value: 14.571600000000002
- type: nauc_ndcg_at_20_diff1
value: 66.38929999999999
- type: nauc_ndcg_at_100_max
value: 54.2887
- type: nauc_ndcg_at_100_std
value: 16.3373
- type: nauc_ndcg_at_100_diff1
value: 66.2149
- type: nauc_ndcg_at_1000_max
value: 54.4544
- type: nauc_ndcg_at_1000_std
value: 15.9653
- type: nauc_ndcg_at_1000_diff1
value: 66.7584
- type: nauc_map_at_1_max
value: 48.4528
- type: nauc_map_at_1_std
value: 11.2364
- type: nauc_map_at_1_diff1
value: 71.3798
- type: nauc_map_at_3_max
value: 52.629000000000005
- type: nauc_map_at_3_std
value: 12.753900000000002
- type: nauc_map_at_3_diff1
value: 68.4559
- type: nauc_map_at_5_max
value: 52.1172
- type: nauc_map_at_5_std
value: 11.887
- type: nauc_map_at_5_diff1
value: 67.85940000000001
- type: nauc_map_at_10_max
value: 53.26
- type: nauc_map_at_10_std
value: 12.8623
- type: nauc_map_at_10_diff1
value: 67.9285
- type: nauc_map_at_20_max
value: 53.140100000000004
- type: nauc_map_at_20_std
value: 13.312299999999999
- type: nauc_map_at_20_diff1
value: 67.8706
- type: nauc_map_at_100_max
value: 53.07809999999999
- type: nauc_map_at_100_std
value: 13.570099999999998
- type: nauc_map_at_100_diff1
value: 67.8354
- type: nauc_map_at_1000_max
value: 53.0964
- type: nauc_map_at_1000_std
value: 13.5718
- type: nauc_map_at_1000_diff1
value: 67.8608
- type: nauc_recall_at_1_max
value: 48.4528
- type: nauc_recall_at_1_std
value: 11.2364
- type: nauc_recall_at_1_diff1
value: 71.3798
- type: nauc_recall_at_3_max
value: 55.2991
- type: nauc_recall_at_3_std
value: 10.4741
- type: nauc_recall_at_3_diff1
value: 63.9389
- type: nauc_recall_at_5_max
value: 52.3593
- type: nauc_recall_at_5_std
value: 5.2456000000000005
- type: nauc_recall_at_5_diff1
value: 59.9453
- type: nauc_recall_at_10_max
value: 58.082699999999996
- type: nauc_recall_at_10_std
value: 8.0142
- type: nauc_recall_at_10_diff1
value: 59.644600000000004
- type: nauc_recall_at_20_max
value: 58.9062
- type: nauc_recall_at_20_std
value: 13.9035
- type: nauc_recall_at_20_diff1
value: 59.78099999999999
- type: nauc_recall_at_100_max
value: 58.318999999999996
- type: nauc_recall_at_100_std
value: 34.7022
- type: nauc_recall_at_100_diff1
value: 55.9343
- type: nauc_recall_at_1000_max
value: 79.1958
- type: nauc_recall_at_1000_std
value: 63.7138
- type: nauc_recall_at_1000_diff1
value: 62.832600000000006
- type: nauc_precision_at_1_max
value: 49.4156
- type: nauc_precision_at_1_std
value: 17.982300000000002
- type: nauc_precision_at_1_diff1
value: 70.1234
- type: nauc_precision_at_3_max
value: 57.2273
- type: nauc_precision_at_3_std
value: 27.0677
- type: nauc_precision_at_3_diff1
value: 52.8038
- type: nauc_precision_at_5_max
value: 51.4554
- type: nauc_precision_at_5_std
value: 23.3615
- type: nauc_precision_at_5_diff1
value: 44.164500000000004
- type: nauc_precision_at_10_max
value: 50.428399999999996
- type: nauc_precision_at_10_std
value: 28.477200000000003
- type: nauc_precision_at_10_diff1
value: 31.4772
- type: nauc_precision_at_20_max
value: 42.5568
- type: nauc_precision_at_20_std
value: 32.6138
- type: nauc_precision_at_20_diff1
value: 23.4987
- type: nauc_precision_at_100_max
value: 33.7983
- type: nauc_precision_at_100_std
value: 44.8859
- type: nauc_precision_at_100_diff1
value: 7.9106
- type: nauc_precision_at_1000_max
value: 23.0914
- type: nauc_precision_at_1000_std
value: 46.0357
- type: nauc_precision_at_1000_diff1
value: -12.5412
- type: nauc_mrr_at_1_max
value: 49.4156
- type: nauc_mrr_at_1_std
value: 17.982300000000002
- type: nauc_mrr_at_1_diff1
value: 70.1234
- type: nauc_mrr_at_3_max
value: 53.5852
- type: nauc_mrr_at_3_std
value: 16.9294
- type: nauc_mrr_at_3_diff1
value: 67.91430000000001
- type: nauc_mrr_at_5_max
value: 52.8663
- type: nauc_mrr_at_5_std
value: 16.0492
- type: nauc_mrr_at_5_diff1
value: 67.1386
- type: nauc_mrr_at_10_max
value: 53.21320000000001
- type: nauc_mrr_at_10_std
value: 16.5224
- type: nauc_mrr_at_10_diff1
value: 67.1661
- type: nauc_mrr_at_20_max
value: 53.154500000000006
- type: nauc_mrr_at_20_std
value: 16.653599999999997
- type: nauc_mrr_at_20_diff1
value: 67.1882
- type: nauc_mrr_at_100_max
value: 53.0415
- type: nauc_mrr_at_100_std
value: 16.7896
- type: nauc_mrr_at_100_diff1
value: 67.1524
- type: nauc_mrr_at_1000_max
value: 53.04299999999999
- type: nauc_mrr_at_1000_std
value: 16.7823
- type: nauc_mrr_at_1000_diff1
value: 67.1834
- type: main_score
value: 60.427
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: similarity_accuracy
value: 99.802
- type: similarity_accuracy_threshold
value: 68.9054
- type: similarity_f1
value: 89.89439999999999
- type: similarity_f1_threshold
value: 68.1838
- type: similarity_precision
value: 90.3943
- type: similarity_recall
value: 89.4
- type: similarity_ap
value: 95.1114
- type: cosine_accuracy
value: 99.802
- type: cosine_accuracy_threshold
value: 68.9053
- type: cosine_f1
value: 89.89439999999999
- type: cosine_f1_threshold
value: 68.1838
- type: cosine_precision
value: 90.3943
- type: cosine_recall
value: 89.4
- type: cosine_ap
value: 95.1114
- type: manhattan_accuracy
value: 99.8
- type: manhattan_accuracy_threshold
value: 56569.586200000005
- type: manhattan_f1
value: 89.8899
- type: manhattan_f1_threshold
value: 56569.586200000005
- type: manhattan_precision
value: 89.98
- type: manhattan_recall
value: 89.8
- type: manhattan_ap
value: 94.9934
- type: euclidean_accuracy
value: 99.799
- type: euclidean_accuracy_threshold
value: 2539.7804
- type: euclidean_f1
value: 89.8348
- type: euclidean_f1_threshold
value: 2568.8804999999998
- type: euclidean_precision
value: 89.9699
- type: euclidean_recall
value: 89.7
- type: euclidean_ap
value: 95.06
- type: dot_accuracy
value: 99.7723
- type: dot_accuracy_threshold
value: 72398.77320000001
- type: dot_f1
value: 88.0829
- type: dot_f1_threshold
value: 72398.77320000001
- type: dot_precision
value: 91.39779999999999
- type: dot_recall
value: 85.0
- type: dot_ap
value: 93.74040000000001
- type: max_accuracy
value: 99.802
- type: max_f1
value: 89.89439999999999
- type: max_precision
value: 91.39779999999999
- type: max_recall
value: 89.8
- type: max_ap
value: 95.1114
- type: main_score
value: 95.1114
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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value: 4.6061
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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
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dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
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- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
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value: 52.6316
- type: f1
value: 52.9212
- type: f1_weighted
value: 52.3632
- type: main_score
value: 52.6316
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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value: 34.3169
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value: 1.669
- type: main_score
value: 34.3169
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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- type: similarity_f1_threshold
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- type: similarity_recall
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- type: similarity_ap
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- type: cosine_accuracy_threshold
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value: 61.7155
- type: cosine_f1_threshold
value: 62.5381
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value: 56.0681
- type: cosine_recall
value: 68.628
- type: cosine_ap
value: 65.0861
- type: manhattan_accuracy
value: 83.3999
- type: manhattan_accuracy_threshold
value: 53493.359399999994
- type: manhattan_f1
value: 61.0456
- type: manhattan_f1_threshold
value: 60751.0315
- type: manhattan_precision
value: 57.6589
- type: manhattan_recall
value: 64.8549
- type: manhattan_ap
value: 64.5486
- type: euclidean_accuracy
value: 83.4059
- type: euclidean_accuracy_threshold
value: 2422.2414
- type: euclidean_f1
value: 60.9938
- type: euclidean_f1_threshold
value: 2765.6849
- type: euclidean_precision
value: 57.6291
- type: euclidean_recall
value: 64.7757
- type: euclidean_ap
value: 64.60709999999999
- type: dot_accuracy
value: 82.60119999999999
- type: dot_accuracy_threshold
value: 77153.8452
- type: dot_f1
value: 59.933099999999996
- type: dot_f1_threshold
value: 67100.8545
- type: dot_precision
value: 54.785799999999995
- type: dot_recall
value: 66.1478
- type: dot_ap
value: 62.1271
- type: max_accuracy
value: 83.45949999999999
- type: max_f1
value: 61.7155
- type: max_precision
value: 57.6589
- type: max_recall
value: 68.628
- type: max_ap
value: 65.0861
- type: main_score
value: 65.0861
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 88.1612
- type: similarity_accuracy_threshold
value: 63.8545
- type: similarity_f1
value: 76.3737
- type: similarity_f1_threshold
value: 60.4226
- type: similarity_precision
value: 74.1711
- type: similarity_recall
value: 78.7111
- type: similarity_ap
value: 83.5651
- type: cosine_accuracy
value: 88.1612
- type: cosine_accuracy_threshold
value: 63.8545
- type: cosine_f1
value: 76.3737
- type: cosine_f1_threshold
value: 60.4226
- type: cosine_precision
value: 74.1711
- type: cosine_recall
value: 78.7111
- type: cosine_ap
value: 83.5651
- type: manhattan_accuracy
value: 87.9652
- type: manhattan_accuracy_threshold
value: 57837.701400000005
- type: manhattan_f1
value: 76.1352
- type: manhattan_f1_threshold
value: 61746.6187
- type: manhattan_precision
value: 72.3335
- type: manhattan_recall
value: 80.3588
- type: manhattan_ap
value: 83.3006
- type: euclidean_accuracy
value: 88.00019999999999
- type: euclidean_accuracy_threshold
value: 2607.3253999999997
- type: euclidean_f1
value: 76.2589
- type: euclidean_f1_threshold
value: 2793.2058
- type: euclidean_precision
value: 73.1054
- type: euclidean_recall
value: 79.69659999999999
- type: euclidean_ap
value: 83.3352
- type: dot_accuracy
value: 87.69940000000001
- type: dot_accuracy_threshold
value: 62123.3459
- type: dot_f1
value: 75.509
- type: dot_f1_threshold
value: 56145.837400000004
- type: dot_precision
value: 70.9651
- type: dot_recall
value: 80.67450000000001
- type: dot_ap
value: 81.7367
- type: max_accuracy
value: 88.1612
- type: max_f1
value: 76.3737
- type: max_precision
value: 74.1711
- type: max_recall
value: 80.67450000000001
- type: max_ap
value: 83.5651
- type: main_score
value: 83.5651
---
# SentenceTransformer based on answerdotai/ModernBERT-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
I finetune ModernBERT-base using script from offical repo [train_st.py](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_st.py) on a RTX 4090 GPU with the only change of setting mini-batch size of `CachedMultipleNegativesRankingLoss` to 128. Training for 1 epoch takes less than an hour.
The mini-batch size of GradCache should not change model performnace, but the finetuned model performs better than that recorded in the paper. See MTEB results in the result folder [mteb](https://huggingface.co/joe32140/ModernBERT-base-msmarco/tree/main/mteb).
Training logs can be found here: https://api.wandb.ai/links/joe32140/ekuauaao.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("joe32140/ModernBERT-base-msmarco")
# Run inference
sentences = [
'what county is hayden in',
"Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census.",
"According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Triplet
* Dataset: `msmarco-co-condenser-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.984** |
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## Training Details
### Training Dataset
#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 11,662,655 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.26 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.14 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 80.09 tokens</li><li>max: 436 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the meaning of menu planning</code> | <code>Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.</code> | <code>Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.</code> |
| <code>how old is brett butler</code> | <code>Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!</code> | <code>Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.</code> |
| <code>when was the last navajo treaty sign?</code> | <code>In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.</code> | <code>Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 11,662,655 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.2 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 80.44 tokens</li><li>max: 241 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 80.38 tokens</li><li>max: 239 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what county is holly springs nc in</code> | <code>Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2½ times its population in 2000. Contents.</code> | <code>The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The âHolly Trolleyâ as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.</code> |
| <code>how long does nyquil stay in your system</code> | <code>In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.</code> | <code>I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. Itâs been eight years since I kicked NyQuil. I've been sober from alcohol for four years.</code> |
| <code>what are mineral water</code> | <code>1 Mineral water â water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water â water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.</code> | <code>Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 8e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 8e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | msmarco-co-condenser-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:----------------------------------------:|
| 0 | 0 | - | 0.606 |
| 0.2048 | 500 | 0.6405 | - |
| 0.4095 | 1000 | 0.1473 | - |
| 0.6143 | 1500 | 0.1091 | - |
| 0.8190 | 2000 | 0.0907 | - |
| 1.0 | 2442 | - | 0.984 |
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.4.0
- Accelerate: 1.2.1
- Datasets: 2.21.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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|
[
"TEXT_CLASSIFICATION",
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
TaylorAI/bge-micro
|
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"
] | 2023-10-07T06:46:18 | 2024-03-05T18:27:18 | 4,418 | 23 |
---
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: 66.26865671641792
- type: ap
value: 28.174006539079688
- type: f1
value: 59.724963358211035
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 75.3691
- type: ap
value: 69.64182876373573
- type: f1
value: 75.2906345000088
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.806
- type: f1
value: 35.506516495961904
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.24
- type: map_at_10
value: 42.832
- type: map_at_100
value: 43.797000000000004
- type: map_at_1000
value: 43.804
- type: map_at_3
value: 38.134
- type: map_at_5
value: 40.744
- type: mrr_at_1
value: 27.951999999999998
- type: mrr_at_10
value: 43.111
- type: mrr_at_100
value: 44.083
- type: mrr_at_1000
value: 44.09
- type: mrr_at_3
value: 38.431
- type: mrr_at_5
value: 41.019
- type: ndcg_at_1
value: 27.24
- type: ndcg_at_10
value: 51.513
- type: ndcg_at_100
value: 55.762
- type: ndcg_at_1000
value: 55.938
- type: ndcg_at_3
value: 41.743
- type: ndcg_at_5
value: 46.454
- type: precision_at_1
value: 27.24
- type: precision_at_10
value: 7.93
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.402
- type: precision_at_5
value: 12.731
- type: recall_at_1
value: 27.24
- type: recall_at_10
value: 79.303
- type: recall_at_100
value: 98.151
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 52.205
- type: recall_at_5
value: 63.656
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.59766397469585
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.480143023109626
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.09326229984527
- type: mrr
value: 72.18429846546191
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.47582391622187
- type: cos_sim_spearman
value: 83.41635852964214
- type: euclidean_pearson
value: 84.21969728559216
- type: euclidean_spearman
value: 83.46575724558684
- type: manhattan_pearson
value: 83.83107014910223
- type: manhattan_spearman
value: 83.13321954800792
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.58116883116882
- type: f1
value: 80.53335622619781
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.13458676004344
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 29.720429607514898
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.051000000000002
- type: map_at_10
value: 36.291000000000004
- type: map_at_100
value: 37.632
- type: map_at_1000
value: 37.772
- type: map_at_3
value: 33.288000000000004
- type: map_at_5
value: 35.035
- type: mrr_at_1
value: 33.333
- type: mrr_at_10
value: 42.642
- type: mrr_at_100
value: 43.401
- type: mrr_at_1000
value: 43.463
- type: mrr_at_3
value: 40.272000000000006
- type: mrr_at_5
value: 41.753
- type: ndcg_at_1
value: 33.333
- type: ndcg_at_10
value: 42.291000000000004
- type: ndcg_at_100
value: 47.602
- type: ndcg_at_1000
value: 50.109
- type: ndcg_at_3
value: 38.033
- type: ndcg_at_5
value: 40.052
- type: precision_at_1
value: 33.333
- type: precision_at_10
value: 8.254999999999999
- type: precision_at_100
value: 1.353
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 18.884
- type: precision_at_5
value: 13.447999999999999
- type: recall_at_1
value: 26.051000000000002
- type: recall_at_10
value: 53.107000000000006
- type: recall_at_100
value: 76.22
- type: recall_at_1000
value: 92.92399999999999
- type: recall_at_3
value: 40.073
- type: recall_at_5
value: 46.327
- type: map_at_1
value: 19.698999999999998
- type: map_at_10
value: 26.186
- type: map_at_100
value: 27.133000000000003
- type: map_at_1000
value: 27.256999999999998
- type: map_at_3
value: 24.264
- type: map_at_5
value: 25.307000000000002
- type: mrr_at_1
value: 24.712999999999997
- type: mrr_at_10
value: 30.703999999999997
- type: mrr_at_100
value: 31.445
- type: mrr_at_1000
value: 31.517
- type: mrr_at_3
value: 28.992
- type: mrr_at_5
value: 29.963
- type: ndcg_at_1
value: 24.712999999999997
- type: ndcg_at_10
value: 30.198000000000004
- type: ndcg_at_100
value: 34.412
- type: ndcg_at_1000
value: 37.174
- type: ndcg_at_3
value: 27.148
- type: ndcg_at_5
value: 28.464
- type: precision_at_1
value: 24.712999999999997
- type: precision_at_10
value: 5.489999999999999
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 12.803
- type: precision_at_5
value: 8.981
- type: recall_at_1
value: 19.698999999999998
- type: recall_at_10
value: 37.595
- type: recall_at_100
value: 55.962
- type: recall_at_1000
value: 74.836
- type: recall_at_3
value: 28.538999999999998
- type: recall_at_5
value: 32.279
- type: map_at_1
value: 34.224
- type: map_at_10
value: 44.867000000000004
- type: map_at_100
value: 45.944
- type: map_at_1000
value: 46.013999999999996
- type: map_at_3
value: 42.009
- type: map_at_5
value: 43.684
- type: mrr_at_1
value: 39.436
- type: mrr_at_10
value: 48.301
- type: mrr_at_100
value: 49.055
- type: mrr_at_1000
value: 49.099
- type: mrr_at_3
value: 45.956
- type: mrr_at_5
value: 47.445
- type: ndcg_at_1
value: 39.436
- type: ndcg_at_10
value: 50.214000000000006
- type: ndcg_at_100
value: 54.63
- type: ndcg_at_1000
value: 56.165
- type: ndcg_at_3
value: 45.272
- type: ndcg_at_5
value: 47.826
- type: precision_at_1
value: 39.436
- type: precision_at_10
value: 8.037999999999998
- type: precision_at_100
value: 1.118
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 20.125
- type: precision_at_5
value: 13.918
- type: recall_at_1
value: 34.224
- type: recall_at_10
value: 62.690999999999995
- type: recall_at_100
value: 81.951
- type: recall_at_1000
value: 92.93299999999999
- type: recall_at_3
value: 49.299
- type: recall_at_5
value: 55.533
- type: map_at_1
value: 21.375
- type: map_at_10
value: 28.366000000000003
- type: map_at_100
value: 29.363
- type: map_at_1000
value: 29.458000000000002
- type: map_at_3
value: 26.247
- type: map_at_5
value: 27.439000000000004
- type: mrr_at_1
value: 22.938
- type: mrr_at_10
value: 30.072
- type: mrr_at_100
value: 30.993
- type: mrr_at_1000
value: 31.070999999999998
- type: mrr_at_3
value: 28.004
- type: mrr_at_5
value: 29.179
- type: ndcg_at_1
value: 22.938
- type: ndcg_at_10
value: 32.516
- type: ndcg_at_100
value: 37.641999999999996
- type: ndcg_at_1000
value: 40.150999999999996
- type: ndcg_at_3
value: 28.341
- type: ndcg_at_5
value: 30.394
- type: precision_at_1
value: 22.938
- type: precision_at_10
value: 5.028
- type: precision_at_100
value: 0.8
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 12.052999999999999
- type: precision_at_5
value: 8.497
- type: recall_at_1
value: 21.375
- type: recall_at_10
value: 43.682
- type: recall_at_100
value: 67.619
- type: recall_at_1000
value: 86.64699999999999
- type: recall_at_3
value: 32.478
- type: recall_at_5
value: 37.347
- type: map_at_1
value: 14.95
- type: map_at_10
value: 21.417
- type: map_at_100
value: 22.525000000000002
- type: map_at_1000
value: 22.665
- type: map_at_3
value: 18.684
- type: map_at_5
value: 20.275000000000002
- type: mrr_at_1
value: 18.159
- type: mrr_at_10
value: 25.373
- type: mrr_at_100
value: 26.348
- type: mrr_at_1000
value: 26.432
- type: mrr_at_3
value: 22.698999999999998
- type: mrr_at_5
value: 24.254
- type: ndcg_at_1
value: 18.159
- type: ndcg_at_10
value: 26.043
- type: ndcg_at_100
value: 31.491999999999997
- type: ndcg_at_1000
value: 34.818
- type: ndcg_at_3
value: 21.05
- type: ndcg_at_5
value: 23.580000000000002
- type: precision_at_1
value: 18.159
- type: precision_at_10
value: 4.938
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 9.908999999999999
- type: precision_at_5
value: 7.611999999999999
- type: recall_at_1
value: 14.95
- type: recall_at_10
value: 36.285000000000004
- type: recall_at_100
value: 60.431999999999995
- type: recall_at_1000
value: 84.208
- type: recall_at_3
value: 23.006
- type: recall_at_5
value: 29.304999999999996
- type: map_at_1
value: 23.580000000000002
- type: map_at_10
value: 32.906
- type: map_at_100
value: 34.222
- type: map_at_1000
value: 34.346
- type: map_at_3
value: 29.891000000000002
- type: map_at_5
value: 31.679000000000002
- type: mrr_at_1
value: 28.778
- type: mrr_at_10
value: 37.783
- type: mrr_at_100
value: 38.746
- type: mrr_at_1000
value: 38.804
- type: mrr_at_3
value: 35.098
- type: mrr_at_5
value: 36.739
- type: ndcg_at_1
value: 28.778
- type: ndcg_at_10
value: 38.484
- type: ndcg_at_100
value: 44.322
- type: ndcg_at_1000
value: 46.772000000000006
- type: ndcg_at_3
value: 33.586
- type: ndcg_at_5
value: 36.098
- type: precision_at_1
value: 28.778
- type: precision_at_10
value: 7.151000000000001
- type: precision_at_100
value: 1.185
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.105
- type: precision_at_5
value: 11.704
- type: recall_at_1
value: 23.580000000000002
- type: recall_at_10
value: 50.151999999999994
- type: recall_at_100
value: 75.114
- type: recall_at_1000
value: 91.467
- type: recall_at_3
value: 36.552
- type: recall_at_5
value: 43.014
- type: map_at_1
value: 20.669999999999998
- type: map_at_10
value: 28.687
- type: map_at_100
value: 30.061
- type: map_at_1000
value: 30.197000000000003
- type: map_at_3
value: 26.134
- type: map_at_5
value: 27.508
- type: mrr_at_1
value: 26.256
- type: mrr_at_10
value: 34.105999999999995
- type: mrr_at_100
value: 35.137
- type: mrr_at_1000
value: 35.214
- type: mrr_at_3
value: 31.791999999999998
- type: mrr_at_5
value: 33.145
- type: ndcg_at_1
value: 26.256
- type: ndcg_at_10
value: 33.68
- type: ndcg_at_100
value: 39.7
- type: ndcg_at_1000
value: 42.625
- type: ndcg_at_3
value: 29.457
- type: ndcg_at_5
value: 31.355
- type: precision_at_1
value: 26.256
- type: precision_at_10
value: 6.2330000000000005
- type: precision_at_100
value: 1.08
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 14.193
- type: precision_at_5
value: 10.113999999999999
- type: recall_at_1
value: 20.669999999999998
- type: recall_at_10
value: 43.254999999999995
- type: recall_at_100
value: 69.118
- type: recall_at_1000
value: 89.408
- type: recall_at_3
value: 31.135
- type: recall_at_5
value: 36.574
- type: map_at_1
value: 21.488833333333336
- type: map_at_10
value: 29.025416666666665
- type: map_at_100
value: 30.141249999999992
- type: map_at_1000
value: 30.264083333333335
- type: map_at_3
value: 26.599333333333337
- type: map_at_5
value: 28.004666666666665
- type: mrr_at_1
value: 25.515
- type: mrr_at_10
value: 32.8235
- type: mrr_at_100
value: 33.69958333333333
- type: mrr_at_1000
value: 33.77191666666668
- type: mrr_at_3
value: 30.581000000000003
- type: mrr_at_5
value: 31.919666666666668
- type: ndcg_at_1
value: 25.515
- type: ndcg_at_10
value: 33.64241666666666
- type: ndcg_at_100
value: 38.75816666666667
- type: ndcg_at_1000
value: 41.472166666666666
- type: ndcg_at_3
value: 29.435083333333335
- type: ndcg_at_5
value: 31.519083333333338
- type: precision_at_1
value: 25.515
- type: precision_at_10
value: 5.89725
- type: precision_at_100
value: 0.9918333333333335
- type: precision_at_1000
value: 0.14075
- type: precision_at_3
value: 13.504000000000001
- type: precision_at_5
value: 9.6885
- type: recall_at_1
value: 21.488833333333336
- type: recall_at_10
value: 43.60808333333333
- type: recall_at_100
value: 66.5045
- type: recall_at_1000
value: 85.70024999999998
- type: recall_at_3
value: 31.922166666666662
- type: recall_at_5
value: 37.29758333333334
- type: map_at_1
value: 20.781
- type: map_at_10
value: 27.173000000000002
- type: map_at_100
value: 27.967
- type: map_at_1000
value: 28.061999999999998
- type: map_at_3
value: 24.973
- type: map_at_5
value: 26.279999999999998
- type: mrr_at_1
value: 23.773
- type: mrr_at_10
value: 29.849999999999998
- type: mrr_at_100
value: 30.595
- type: mrr_at_1000
value: 30.669
- type: mrr_at_3
value: 27.761000000000003
- type: mrr_at_5
value: 29.003
- type: ndcg_at_1
value: 23.773
- type: ndcg_at_10
value: 31.033
- type: ndcg_at_100
value: 35.174
- type: ndcg_at_1000
value: 37.72
- type: ndcg_at_3
value: 26.927
- type: ndcg_at_5
value: 29.047
- type: precision_at_1
value: 23.773
- type: precision_at_10
value: 4.8469999999999995
- type: precision_at_100
value: 0.75
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 11.452
- type: precision_at_5
value: 8.129
- type: recall_at_1
value: 20.781
- type: recall_at_10
value: 40.463
- type: recall_at_100
value: 59.483
- type: recall_at_1000
value: 78.396
- type: recall_at_3
value: 29.241
- type: recall_at_5
value: 34.544000000000004
- type: map_at_1
value: 15.074000000000002
- type: map_at_10
value: 20.757
- type: map_at_100
value: 21.72
- type: map_at_1000
value: 21.844
- type: map_at_3
value: 18.929000000000002
- type: map_at_5
value: 19.894000000000002
- type: mrr_at_1
value: 18.307000000000002
- type: mrr_at_10
value: 24.215
- type: mrr_at_100
value: 25.083
- type: mrr_at_1000
value: 25.168000000000003
- type: mrr_at_3
value: 22.316
- type: mrr_at_5
value: 23.36
- type: ndcg_at_1
value: 18.307000000000002
- type: ndcg_at_10
value: 24.651999999999997
- type: ndcg_at_100
value: 29.296
- type: ndcg_at_1000
value: 32.538
- type: ndcg_at_3
value: 21.243000000000002
- type: ndcg_at_5
value: 22.727
- type: precision_at_1
value: 18.307000000000002
- type: precision_at_10
value: 4.446
- type: precision_at_100
value: 0.792
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 9.945
- type: precision_at_5
value: 7.123
- type: recall_at_1
value: 15.074000000000002
- type: recall_at_10
value: 33.031
- type: recall_at_100
value: 53.954
- type: recall_at_1000
value: 77.631
- type: recall_at_3
value: 23.253
- type: recall_at_5
value: 27.218999999999998
- type: map_at_1
value: 21.04
- type: map_at_10
value: 28.226000000000003
- type: map_at_100
value: 29.337999999999997
- type: map_at_1000
value: 29.448999999999998
- type: map_at_3
value: 25.759
- type: map_at_5
value: 27.226
- type: mrr_at_1
value: 24.067
- type: mrr_at_10
value: 31.646
- type: mrr_at_100
value: 32.592999999999996
- type: mrr_at_1000
value: 32.668
- type: mrr_at_3
value: 29.26
- type: mrr_at_5
value: 30.725
- type: ndcg_at_1
value: 24.067
- type: ndcg_at_10
value: 32.789
- type: ndcg_at_100
value: 38.253
- type: ndcg_at_1000
value: 40.961
- type: ndcg_at_3
value: 28.189999999999998
- type: ndcg_at_5
value: 30.557000000000002
- type: precision_at_1
value: 24.067
- type: precision_at_10
value: 5.532
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 12.5
- type: precision_at_5
value: 9.16
- type: recall_at_1
value: 21.04
- type: recall_at_10
value: 43.167
- type: recall_at_100
value: 67.569
- type: recall_at_1000
value: 86.817
- type: recall_at_3
value: 31.178
- type: recall_at_5
value: 36.730000000000004
- type: map_at_1
value: 21.439
- type: map_at_10
value: 28.531000000000002
- type: map_at_100
value: 29.953999999999997
- type: map_at_1000
value: 30.171
- type: map_at_3
value: 26.546999999999997
- type: map_at_5
value: 27.71
- type: mrr_at_1
value: 26.087
- type: mrr_at_10
value: 32.635
- type: mrr_at_100
value: 33.629999999999995
- type: mrr_at_1000
value: 33.71
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 31.807999999999996
- type: ndcg_at_1
value: 26.087
- type: ndcg_at_10
value: 32.975
- type: ndcg_at_100
value: 38.853
- type: ndcg_at_1000
value: 42.158
- type: ndcg_at_3
value: 29.894
- type: ndcg_at_5
value: 31.397000000000002
- type: precision_at_1
value: 26.087
- type: precision_at_10
value: 6.2059999999999995
- type: precision_at_100
value: 1.298
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 14.097000000000001
- type: precision_at_5
value: 9.959999999999999
- type: recall_at_1
value: 21.439
- type: recall_at_10
value: 40.519
- type: recall_at_100
value: 68.073
- type: recall_at_1000
value: 89.513
- type: recall_at_3
value: 31.513
- type: recall_at_5
value: 35.702
- type: map_at_1
value: 18.983
- type: map_at_10
value: 24.898
- type: map_at_100
value: 25.836
- type: map_at_1000
value: 25.934
- type: map_at_3
value: 22.467000000000002
- type: map_at_5
value: 24.019
- type: mrr_at_1
value: 20.333000000000002
- type: mrr_at_10
value: 26.555
- type: mrr_at_100
value: 27.369
- type: mrr_at_1000
value: 27.448
- type: mrr_at_3
value: 24.091
- type: mrr_at_5
value: 25.662000000000003
- type: ndcg_at_1
value: 20.333000000000002
- type: ndcg_at_10
value: 28.834
- type: ndcg_at_100
value: 33.722
- type: ndcg_at_1000
value: 36.475
- type: ndcg_at_3
value: 24.08
- type: ndcg_at_5
value: 26.732
- type: precision_at_1
value: 20.333000000000002
- type: precision_at_10
value: 4.603
- type: precision_at_100
value: 0.771
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 9.982000000000001
- type: precision_at_5
value: 7.6160000000000005
- type: recall_at_1
value: 18.983
- type: recall_at_10
value: 39.35
- type: recall_at_100
value: 62.559
- type: recall_at_1000
value: 83.623
- type: recall_at_3
value: 26.799
- type: recall_at_5
value: 32.997
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.621
- type: map_at_10
value: 17.298
- type: map_at_100
value: 18.983
- type: map_at_1000
value: 19.182
- type: map_at_3
value: 14.552999999999999
- type: map_at_5
value: 15.912
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 33.932
- type: mrr_at_100
value: 34.891
- type: mrr_at_1000
value: 34.943000000000005
- type: mrr_at_3
value: 30.770999999999997
- type: mrr_at_5
value: 32.556000000000004
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 24.771
- type: ndcg_at_100
value: 31.738
- type: ndcg_at_1000
value: 35.419
- type: ndcg_at_3
value: 20.22
- type: ndcg_at_5
value: 21.698999999999998
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 7.785
- type: precision_at_100
value: 1.5270000000000001
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 14.962
- type: precision_at_5
value: 11.401
- type: recall_at_1
value: 10.621
- type: recall_at_10
value: 29.726000000000003
- type: recall_at_100
value: 53.996
- type: recall_at_1000
value: 74.878
- type: recall_at_3
value: 18.572
- type: recall_at_5
value: 22.994999999999997
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.819
- type: map_at_10
value: 14.188
- type: map_at_100
value: 19.627
- type: map_at_1000
value: 20.757
- type: map_at_3
value: 10.352
- type: map_at_5
value: 12.096
- type: mrr_at_1
value: 54.25
- type: mrr_at_10
value: 63.798
- type: mrr_at_100
value: 64.25
- type: mrr_at_1000
value: 64.268
- type: mrr_at_3
value: 61.667
- type: mrr_at_5
value: 63.153999999999996
- type: ndcg_at_1
value: 39.5
- type: ndcg_at_10
value: 31.064999999999998
- type: ndcg_at_100
value: 34.701
- type: ndcg_at_1000
value: 41.687000000000005
- type: ndcg_at_3
value: 34.455999999999996
- type: ndcg_at_5
value: 32.919
- type: precision_at_1
value: 54.25
- type: precision_at_10
value: 25.4
- type: precision_at_100
value: 7.79
- type: precision_at_1000
value: 1.577
- type: precision_at_3
value: 39.333
- type: precision_at_5
value: 33.6
- type: recall_at_1
value: 6.819
- type: recall_at_10
value: 19.134
- type: recall_at_100
value: 41.191
- type: recall_at_1000
value: 64.699
- type: recall_at_3
value: 11.637
- type: recall_at_5
value: 14.807
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.474999999999994
- type: f1
value: 37.79154895614037
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 53.187
- type: map_at_10
value: 64.031
- type: map_at_100
value: 64.507
- type: map_at_1000
value: 64.526
- type: map_at_3
value: 61.926
- type: map_at_5
value: 63.278999999999996
- type: mrr_at_1
value: 57.396
- type: mrr_at_10
value: 68.296
- type: mrr_at_100
value: 68.679
- type: mrr_at_1000
value: 68.688
- type: mrr_at_3
value: 66.289
- type: mrr_at_5
value: 67.593
- type: ndcg_at_1
value: 57.396
- type: ndcg_at_10
value: 69.64
- type: ndcg_at_100
value: 71.75399999999999
- type: ndcg_at_1000
value: 72.179
- type: ndcg_at_3
value: 65.66199999999999
- type: ndcg_at_5
value: 67.932
- type: precision_at_1
value: 57.396
- type: precision_at_10
value: 9.073
- type: precision_at_100
value: 1.024
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 26.133
- type: precision_at_5
value: 16.943
- type: recall_at_1
value: 53.187
- type: recall_at_10
value: 82.839
- type: recall_at_100
value: 92.231
- type: recall_at_1000
value: 95.249
- type: recall_at_3
value: 72.077
- type: recall_at_5
value: 77.667
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.957
- type: map_at_10
value: 18.427
- type: map_at_100
value: 19.885
- type: map_at_1000
value: 20.088
- type: map_at_3
value: 15.709000000000001
- type: map_at_5
value: 17.153
- type: mrr_at_1
value: 22.377
- type: mrr_at_10
value: 30.076999999999998
- type: mrr_at_100
value: 31.233
- type: mrr_at_1000
value: 31.311
- type: mrr_at_3
value: 27.521
- type: mrr_at_5
value: 29.025000000000002
- type: ndcg_at_1
value: 22.377
- type: ndcg_at_10
value: 24.367
- type: ndcg_at_100
value: 31.04
- type: ndcg_at_1000
value: 35.106
- type: ndcg_at_3
value: 21.051000000000002
- type: ndcg_at_5
value: 22.231
- type: precision_at_1
value: 22.377
- type: precision_at_10
value: 7.005999999999999
- type: precision_at_100
value: 1.3599999999999999
- type: precision_at_1000
value: 0.208
- type: precision_at_3
value: 13.991999999999999
- type: precision_at_5
value: 10.833
- type: recall_at_1
value: 10.957
- type: recall_at_10
value: 30.274
- type: recall_at_100
value: 55.982
- type: recall_at_1000
value: 80.757
- type: recall_at_3
value: 19.55
- type: recall_at_5
value: 24.105999999999998
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.526999999999997
- type: map_at_10
value: 40.714
- type: map_at_100
value: 41.655
- type: map_at_1000
value: 41.744
- type: map_at_3
value: 38.171
- type: map_at_5
value: 39.646
- type: mrr_at_1
value: 59.055
- type: mrr_at_10
value: 66.411
- type: mrr_at_100
value: 66.85900000000001
- type: mrr_at_1000
value: 66.88300000000001
- type: mrr_at_3
value: 64.846
- type: mrr_at_5
value: 65.824
- type: ndcg_at_1
value: 59.055
- type: ndcg_at_10
value: 49.732
- type: ndcg_at_100
value: 53.441
- type: ndcg_at_1000
value: 55.354000000000006
- type: ndcg_at_3
value: 45.551
- type: ndcg_at_5
value: 47.719
- type: precision_at_1
value: 59.055
- type: precision_at_10
value: 10.366
- type: precision_at_100
value: 1.328
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 28.322999999999997
- type: precision_at_5
value: 18.709
- type: recall_at_1
value: 29.526999999999997
- type: recall_at_10
value: 51.83
- type: recall_at_100
value: 66.42099999999999
- type: recall_at_1000
value: 79.176
- type: recall_at_3
value: 42.485
- type: recall_at_5
value: 46.772000000000006
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 70.69959999999999
- type: ap
value: 64.95539314492567
- type: f1
value: 70.5554935943308
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 13.153
- type: map_at_10
value: 22.277
- type: map_at_100
value: 23.462
- type: map_at_1000
value: 23.546
- type: map_at_3
value: 19.026
- type: map_at_5
value: 20.825
- type: mrr_at_1
value: 13.539000000000001
- type: mrr_at_10
value: 22.753
- type: mrr_at_100
value: 23.906
- type: mrr_at_1000
value: 23.982999999999997
- type: mrr_at_3
value: 19.484
- type: mrr_at_5
value: 21.306
- type: ndcg_at_1
value: 13.553
- type: ndcg_at_10
value: 27.848
- type: ndcg_at_100
value: 33.900999999999996
- type: ndcg_at_1000
value: 36.155
- type: ndcg_at_3
value: 21.116
- type: ndcg_at_5
value: 24.349999999999998
- type: precision_at_1
value: 13.553
- type: precision_at_10
value: 4.695
- type: precision_at_100
value: 0.7779999999999999
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 9.207
- type: precision_at_5
value: 7.155
- type: recall_at_1
value: 13.153
- type: recall_at_10
value: 45.205
- type: recall_at_100
value: 73.978
- type: recall_at_1000
value: 91.541
- type: recall_at_3
value: 26.735
- type: recall_at_5
value: 34.493
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.2530779753762
- type: f1
value: 89.59402328284126
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.95029639762883
- type: f1
value: 48.99988836758662
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.77740416946874
- type: f1
value: 66.21341120969817
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.03631472763955
- type: f1
value: 72.5779336237941
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.98182669158824
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.259462874407582
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.29342377286548
- type: mrr
value: 32.32805799117226
- 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: 10.559000000000001
- type: map_at_100
value: 13.665
- type: map_at_1000
value: 15.082
- type: map_at_3
value: 7.68
- type: map_at_5
value: 8.844000000000001
- type: mrr_at_1
value: 38.7
- type: mrr_at_10
value: 47.864000000000004
- type: mrr_at_100
value: 48.583999999999996
- type: mrr_at_1000
value: 48.636
- type: mrr_at_3
value: 45.975
- type: mrr_at_5
value: 47.074
- type: ndcg_at_1
value: 36.378
- type: ndcg_at_10
value: 30.038999999999998
- type: ndcg_at_100
value: 28.226000000000003
- type: ndcg_at_1000
value: 36.958
- type: ndcg_at_3
value: 33.469
- type: ndcg_at_5
value: 32.096999999999994
- type: precision_at_1
value: 38.080000000000005
- type: precision_at_10
value: 22.941
- type: precision_at_100
value: 7.632
- type: precision_at_1000
value: 2.0420000000000003
- type: precision_at_3
value: 31.579
- type: precision_at_5
value: 28.235
- type: recall_at_1
value: 4.692
- type: recall_at_10
value: 14.496
- type: recall_at_100
value: 29.69
- type: recall_at_1000
value: 61.229
- type: recall_at_3
value: 8.871
- type: recall_at_5
value: 10.825999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.120000000000001
- type: map_at_10
value: 24.092
- type: map_at_100
value: 25.485999999999997
- type: map_at_1000
value: 25.557999999999996
- type: map_at_3
value: 20.076
- type: map_at_5
value: 22.368
- type: mrr_at_1
value: 15.093
- type: mrr_at_10
value: 26.142
- type: mrr_at_100
value: 27.301
- type: mrr_at_1000
value: 27.357
- type: mrr_at_3
value: 22.364
- type: mrr_at_5
value: 24.564
- type: ndcg_at_1
value: 15.093
- type: ndcg_at_10
value: 30.734
- type: ndcg_at_100
value: 37.147999999999996
- type: ndcg_at_1000
value: 38.997
- type: ndcg_at_3
value: 22.82
- type: ndcg_at_5
value: 26.806
- type: precision_at_1
value: 15.093
- type: precision_at_10
value: 5.863
- type: precision_at_100
value: 0.942
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 11.047
- type: precision_at_5
value: 8.863999999999999
- type: recall_at_1
value: 13.120000000000001
- type: recall_at_10
value: 49.189
- type: recall_at_100
value: 78.032
- type: recall_at_1000
value: 92.034
- type: recall_at_3
value: 28.483000000000004
- type: recall_at_5
value: 37.756
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.765
- type: map_at_10
value: 81.069
- type: map_at_100
value: 81.757
- type: map_at_1000
value: 81.782
- type: map_at_3
value: 78.148
- type: map_at_5
value: 79.95400000000001
- type: mrr_at_1
value: 77.8
- type: mrr_at_10
value: 84.639
- type: mrr_at_100
value: 84.789
- type: mrr_at_1000
value: 84.79100000000001
- type: mrr_at_3
value: 83.467
- type: mrr_at_5
value: 84.251
- type: ndcg_at_1
value: 77.82
- type: ndcg_at_10
value: 85.286
- type: ndcg_at_100
value: 86.86500000000001
- type: ndcg_at_1000
value: 87.062
- type: ndcg_at_3
value: 82.116
- type: ndcg_at_5
value: 83.811
- type: precision_at_1
value: 77.82
- type: precision_at_10
value: 12.867999999999999
- type: precision_at_100
value: 1.498
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.723
- type: precision_at_5
value: 23.52
- type: recall_at_1
value: 67.765
- type: recall_at_10
value: 93.381
- type: recall_at_100
value: 98.901
- type: recall_at_1000
value: 99.864
- type: recall_at_3
value: 84.301
- type: recall_at_5
value: 89.049
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 45.27190981742137
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 54.47444004585028
- 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: 10.166
- type: map_at_100
value: 11.987
- type: map_at_1000
value: 12.285
- type: map_at_3
value: 7.538
- type: map_at_5
value: 8.606
- type: mrr_at_1
value: 20.8
- type: mrr_at_10
value: 30.066
- type: mrr_at_100
value: 31.290000000000003
- type: mrr_at_1000
value: 31.357000000000003
- type: mrr_at_3
value: 27.083000000000002
- type: mrr_at_5
value: 28.748
- type: ndcg_at_1
value: 20.8
- type: ndcg_at_10
value: 17.258000000000003
- type: ndcg_at_100
value: 24.801000000000002
- type: ndcg_at_1000
value: 30.348999999999997
- type: ndcg_at_3
value: 16.719
- type: ndcg_at_5
value: 14.145
- type: precision_at_1
value: 20.8
- type: precision_at_10
value: 8.88
- type: precision_at_100
value: 1.9789999999999999
- type: precision_at_1000
value: 0.332
- type: precision_at_3
value: 15.5
- type: precision_at_5
value: 12.1
- type: recall_at_1
value: 4.213
- type: recall_at_10
value: 17.983
- type: recall_at_100
value: 40.167
- type: recall_at_1000
value: 67.43
- type: recall_at_3
value: 9.433
- type: recall_at_5
value: 12.267999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 80.36742239848913
- type: cos_sim_spearman
value: 72.39470010828755
- type: euclidean_pearson
value: 77.26919895870947
- type: euclidean_spearman
value: 72.26534999077315
- type: manhattan_pearson
value: 77.04066349814258
- type: manhattan_spearman
value: 72.0072248699278
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 80.26991474037257
- type: cos_sim_spearman
value: 71.90287122017716
- type: euclidean_pearson
value: 76.68006075912453
- type: euclidean_spearman
value: 71.69301858764365
- type: manhattan_pearson
value: 76.72277285842371
- type: manhattan_spearman
value: 71.73265239703795
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.74371413317881
- type: cos_sim_spearman
value: 80.9279612820358
- type: euclidean_pearson
value: 80.6417435294782
- type: euclidean_spearman
value: 81.17460969254459
- type: manhattan_pearson
value: 80.51820155178402
- type: manhattan_spearman
value: 81.08028700017084
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.37085777051112
- type: cos_sim_spearman
value: 76.60308382518285
- type: euclidean_pearson
value: 79.59684787227351
- type: euclidean_spearman
value: 76.8769048249242
- type: manhattan_pearson
value: 79.55617632538295
- type: manhattan_spearman
value: 76.90186497973124
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.99513105301321
- type: cos_sim_spearman
value: 84.92034548133665
- type: euclidean_pearson
value: 84.70872540095195
- type: euclidean_spearman
value: 85.14591726040749
- type: manhattan_pearson
value: 84.65707417430595
- type: manhattan_spearman
value: 85.10407163865375
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 79.40758449150897
- type: cos_sim_spearman
value: 80.71692246880549
- type: euclidean_pearson
value: 80.51658552062683
- type: euclidean_spearman
value: 80.87118389043233
- type: manhattan_pearson
value: 80.41534690825016
- type: manhattan_spearman
value: 80.73925282537256
- 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: 84.93617076910748
- type: cos_sim_spearman
value: 85.61118538966805
- type: euclidean_pearson
value: 85.56187558635287
- type: euclidean_spearman
value: 85.21910090757267
- type: manhattan_pearson
value: 85.29916699037645
- type: manhattan_spearman
value: 84.96820527868671
- 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: 64.22294088543077
- type: cos_sim_spearman
value: 65.89748502901078
- type: euclidean_pearson
value: 66.15637850660805
- type: euclidean_spearman
value: 65.86095841381278
- type: manhattan_pearson
value: 66.80966197857856
- type: manhattan_spearman
value: 66.48325202219692
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 81.75298158703048
- type: cos_sim_spearman
value: 81.32168373072322
- type: euclidean_pearson
value: 82.3251793712207
- type: euclidean_spearman
value: 81.31655163330606
- type: manhattan_pearson
value: 82.14136865023298
- type: manhattan_spearman
value: 81.13410964028606
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.77937068780793
- type: mrr
value: 93.334709952357
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.705999999999996
- type: map_at_10
value: 60.699999999999996
- type: map_at_100
value: 61.256
- type: map_at_1000
value: 61.285000000000004
- type: map_at_3
value: 57.633
- type: map_at_5
value: 59.648
- type: mrr_at_1
value: 53.0
- type: mrr_at_10
value: 61.717999999999996
- type: mrr_at_100
value: 62.165000000000006
- type: mrr_at_1000
value: 62.190999999999995
- type: mrr_at_3
value: 59.389
- type: mrr_at_5
value: 60.922
- type: ndcg_at_1
value: 53.0
- type: ndcg_at_10
value: 65.413
- type: ndcg_at_100
value: 68.089
- type: ndcg_at_1000
value: 69.01899999999999
- type: ndcg_at_3
value: 60.327
- type: ndcg_at_5
value: 63.263999999999996
- type: precision_at_1
value: 53.0
- type: precision_at_10
value: 8.933
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 23.778
- type: precision_at_5
value: 16.2
- type: recall_at_1
value: 50.705999999999996
- type: recall_at_10
value: 78.633
- type: recall_at_100
value: 91.333
- type: recall_at_1000
value: 99.0
- type: recall_at_3
value: 65.328
- type: recall_at_5
value: 72.583
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82178217821782
- type: cos_sim_ap
value: 95.30078788098801
- type: cos_sim_f1
value: 91.11549851924975
- type: cos_sim_precision
value: 89.96101364522417
- type: cos_sim_recall
value: 92.30000000000001
- type: dot_accuracy
value: 99.74851485148515
- type: dot_ap
value: 93.12383012680787
- type: dot_f1
value: 87.17171717171716
- type: dot_precision
value: 88.06122448979592
- type: dot_recall
value: 86.3
- type: euclidean_accuracy
value: 99.82673267326733
- type: euclidean_ap
value: 95.29507269622621
- type: euclidean_f1
value: 91.3151364764268
- type: euclidean_precision
value: 90.64039408866995
- type: euclidean_recall
value: 92.0
- type: manhattan_accuracy
value: 99.82178217821782
- type: manhattan_ap
value: 95.34300712110257
- type: manhattan_f1
value: 91.05367793240556
- type: manhattan_precision
value: 90.51383399209486
- type: manhattan_recall
value: 91.60000000000001
- type: max_accuracy
value: 99.82673267326733
- type: max_ap
value: 95.34300712110257
- type: max_f1
value: 91.3151364764268
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.10993894014712
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.67216071080345
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 48.96344255085851
- type: mrr
value: 49.816123419064596
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.580410074992177
- type: cos_sim_spearman
value: 31.155995112739966
- type: dot_pearson
value: 31.112094423048998
- type: dot_spearman
value: 31.29974829801922
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.17700000000000002
- type: map_at_10
value: 1.22
- type: map_at_100
value: 6.2170000000000005
- type: map_at_1000
value: 15.406
- type: map_at_3
value: 0.483
- type: map_at_5
value: 0.729
- type: mrr_at_1
value: 64.0
- type: mrr_at_10
value: 76.333
- type: mrr_at_100
value: 76.47
- type: mrr_at_1000
value: 76.47
- type: mrr_at_3
value: 75.0
- type: mrr_at_5
value: 76.0
- type: ndcg_at_1
value: 59.0
- type: ndcg_at_10
value: 52.62
- type: ndcg_at_100
value: 39.932
- type: ndcg_at_1000
value: 37.317
- type: ndcg_at_3
value: 57.123000000000005
- type: ndcg_at_5
value: 56.376000000000005
- type: precision_at_1
value: 64.0
- type: precision_at_10
value: 55.800000000000004
- type: precision_at_100
value: 41.04
- type: precision_at_1000
value: 17.124
- type: precision_at_3
value: 63.333
- type: precision_at_5
value: 62.0
- type: recall_at_1
value: 0.17700000000000002
- type: recall_at_10
value: 1.46
- type: recall_at_100
value: 9.472999999999999
- type: recall_at_1000
value: 35.661
- type: recall_at_3
value: 0.527
- type: recall_at_5
value: 0.8250000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.539
- type: map_at_10
value: 7.178
- type: map_at_100
value: 12.543000000000001
- type: map_at_1000
value: 14.126
- type: map_at_3
value: 3.09
- type: map_at_5
value: 5.008
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 32.933
- type: mrr_at_100
value: 34.176
- type: mrr_at_1000
value: 34.176
- type: mrr_at_3
value: 27.551
- type: mrr_at_5
value: 30.714000000000002
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 18.343
- type: ndcg_at_100
value: 30.076000000000004
- type: ndcg_at_1000
value: 42.266999999999996
- type: ndcg_at_3
value: 17.233999999999998
- type: ndcg_at_5
value: 18.677
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 18.367
- type: precision_at_100
value: 6.837
- type: precision_at_1000
value: 1.467
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 1.539
- type: recall_at_10
value: 13.289000000000001
- type: recall_at_100
value: 42.480000000000004
- type: recall_at_1000
value: 79.463
- type: recall_at_3
value: 4.202999999999999
- type: recall_at_5
value: 7.9030000000000005
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.2056
- type: ap
value: 13.564165903349778
- type: f1
value: 53.303385089202656
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.71477079796264
- type: f1
value: 57.01563439439609
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.373040570976514
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.44757703999524
- type: cos_sim_ap
value: 65.78689843625949
- type: cos_sim_f1
value: 62.25549384206713
- type: cos_sim_precision
value: 57.39091718610864
- type: cos_sim_recall
value: 68.02110817941951
- type: dot_accuracy
value: 81.3971508612982
- type: dot_ap
value: 58.42933051967154
- type: dot_f1
value: 57.85580214198962
- type: dot_precision
value: 49.74368710841086
- type: dot_recall
value: 69.12928759894459
- type: euclidean_accuracy
value: 83.54294569946951
- type: euclidean_ap
value: 66.10612585693795
- type: euclidean_f1
value: 62.66666666666667
- type: euclidean_precision
value: 58.88631090487239
- type: euclidean_recall
value: 66.96569920844327
- type: manhattan_accuracy
value: 83.43565595756095
- type: manhattan_ap
value: 65.88532290329134
- type: manhattan_f1
value: 62.58408721874276
- type: manhattan_precision
value: 55.836092715231786
- type: manhattan_recall
value: 71.18733509234828
- type: max_accuracy
value: 83.54294569946951
- type: max_ap
value: 66.10612585693795
- type: max_f1
value: 62.66666666666667
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.02344083517679
- type: cos_sim_ap
value: 84.21589190889944
- type: cos_sim_f1
value: 76.36723039754007
- type: cos_sim_precision
value: 72.79134682484299
- type: cos_sim_recall
value: 80.31259624268556
- type: dot_accuracy
value: 87.43353902278108
- type: dot_ap
value: 82.08962394120071
- type: dot_f1
value: 74.97709923664122
- type: dot_precision
value: 74.34150772025431
- type: dot_recall
value: 75.62365260240222
- type: euclidean_accuracy
value: 87.97686963946133
- type: euclidean_ap
value: 84.20578083922416
- type: euclidean_f1
value: 76.4299182903834
- type: euclidean_precision
value: 73.51874244256348
- type: euclidean_recall
value: 79.58115183246073
- type: manhattan_accuracy
value: 88.00209570380719
- type: manhattan_ap
value: 84.14700304263556
- type: manhattan_f1
value: 76.36429345861944
- type: manhattan_precision
value: 71.95886119057349
- type: manhattan_recall
value: 81.34431783184478
- type: max_accuracy
value: 88.02344083517679
- type: max_ap
value: 84.21589190889944
- type: max_f1
value: 76.4299182903834
---
# bge-micro
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 [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5/blob/main/config.json), with 1/4 the non-embedding parameters.
It has 1/2 the parameters of the smallest commonly-used embedding model, all-MiniLM-L6-v2, with similar performance.
<!--- 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"
] |
SeaLLMs/SeaLLMs-v3-7B-Chat
|
SeaLLMs
|
text-generation
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"sea",
"multilingual",
"conversational",
"en",
"zh",
"id",
"vi",
"th",
"ms",
"tl",
"ta",
"jv",
"lo",
"km",
"my",
"arxiv:2407.19672",
"arxiv:2306.05179",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-03T03:02:02 | 2024-09-02T03:02:23 | 4,214 | 52 |
---
language:
- en
- zh
- id
- vi
- th
- ms
- tl
- ta
- jv
- lo
- km
- my
license: other
license_name: seallms
license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE
tags:
- sea
- multilingual
---
# *SeaLLMs-v3* - Large Language Models for Southeast Asia
<p align="center">
<a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a>
<a href="https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat" target="_blank" rel="noopener">Model</a>
<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat" target="_blank" rel="noopener"> 🤗 DEMO</a>
<a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a>
<a href="https://arxiv.org/pdf/2407.19672" target="_blank" rel="noopener">[NEW] Technical Report</a>
</p>
We introduce **SeaLLMs-v3**, the latest series of the SeaLLMs (Large Language Models for Southeast Asian languages) family. It achieves state-of-the-art performance among models with similar sizes, excelling across a diverse array of tasks such as world knowledge, mathematical reasoning, translation, and instruction following. In the meantime, it was specifically enhanced to be more trustworthy, exhibiting reduced hallucination and providing safe responses, particularly in queries closed related to Southeast Asian culture.
## 🔥 Highlights
- State-of-the-art performance compared to open-source models of similar sizes, evaluated across various dimensions such as human exam questions, instruction-following, mathematics, and translation.
- Significantly enhanced instruction-following capability, especially in multi-turn settings.
- Ensures safety in usage with significantly reduced instances of hallucination and sensitivity to local contexts.
## Uses
SeaLLMs is tailored for handling a wide range of languages spoken in the SEA region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
This page introduces the **SeaLLMs-v3-7B-Chat** model, specifically fine-tuned to follow human instructions effectively for task completion, making it directly applicable to your applications.
You may also refer to the [SeaLLMs-v3-1.5B-Chat](https://huggingface.co/SeaLLMs/SeaLLMs-v3-1.5B-Chat) model which requires much lower computational resources and can be easily loaded locally.
### Get started with `Transformers`
To quickly try the model, we show how to conduct inference with `transformers` below. Make sure you have installed the latest transformers version (>4.40).
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"SeaLLMs/SeaLLMs-v3-7B-Chat", # can change to "SeaLLMs/SeaLLMs-v3-1.5B-Chat" if your resource is limited
torch_dtype=torch.bfloat16,
device_map=device
)
tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-Chat")
# prepare messages to model
prompt = "Hiii How are you?"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
print(f"Formatted text:\n {text}")
print(f"Model input:\n {model_inputs}")
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True, eos_token_id=tokenizer.eos_token_id)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(f"Response:\n {response[0]}")
```
You can also utilize the following code snippet, which uses the streamer `TextStreamer` to enable the model to continue conversing with you:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import TextStreamer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"SeaLLMs/SeaLLMs-v3-7B-Chat", # can change to "SeaLLMs/SeaLLMs-v3-1.5B-Chat" if your resource is limited
torch_dtype=torch.bfloat16,
device_map=device
)
tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-Chat")
# prepare messages to model
messages = [
{"role": "system", "content": "You are a helpful assistant."},
]
while True:
prompt = input("User:")
messages.append({"role": "user", "content": prompt})
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, streamer=streamer)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
messages.append({"role": "assistant", "content": response})
```
### Inference with `vllm`
You can also conduct inference with [vllm](https://docs.vllm.ai/en/stable/index.html), which is a fast and easy-to-use library for LLM inference and serving. To use vllm, first install the latest version via `pip install vllm`.
```python
from vllm import LLM, SamplingParams
prompts = [
"Who is the president of US?",
"Can you speak Indonesian?"
]
llm = LLM(ckpt_path, dtype="bfloat16")
sparams = SamplingParams(temperature=0.1, max_tokens=512)
outputs = llm.generate(prompts, sparams)
# print out the model response
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt}\nResponse: {generated_text}\n\n")
```
### Bias, Risks, and Limitations
<blockquote style="color:red">
<p><strong style="color: red">Terms of Use and License</strong>:
By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b/edit/main/LICENSE" target="_blank" rel="noopener">SeaLLMs Terms Of Use</a>.
</blockquote>
> **Disclaimer**:
> We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation.
> Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations.
> In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.
## Evaluation
We conduct our evaluation along two dimensions:
1. **Model Capability**: We assess the model's performance on human exam questions, its ability to follow instructions, its proficiency in mathematics, and its translation accuracy.
2. **Model Trustworthiness**: We evaluate the model's safety and tendency to hallucinate, particularly in the context of Southeast Asia.
### Model Capability
#### Multilingual World Knowledge - M3Exam
[M3Exam](https://arxiv.org/abs/2306.05179) consists of local exam questions collected from each country. It reflects the model's world knowledge (e.g., with language or social science subjects) and reasoning abilities (e.g., with mathematics or natural science subjects).
| Model | en | zh | id | th | vi | avg | avg_sea |
|:-----------------|-----:|------:|-----:|-----:|-----:|------:|----------:|
| Sailor-7B-Chat | 0.66 | 0.652 | 0.475 | 0.462 | 0.513 | 0.552 | 0.483 |
| gemma-7b | 0.732 | 0.519 | 0.475 | 0.46 | 0.594 | 0.556 | 0.510 |
| SeaLLM-7B-v2.5 | 0.758 | 0.581 | 0.499 | 0.502 | 0.622 | 0.592 | 0.541 |
| Qwen2-7B | 0.815 | 0.874 | 0.53 | 0.479 | 0.628 | 0.665 | 0.546 |
| Qwen2-7B-Instruct| 0.809 | 0.88 | 0.558 | 0.555 | 0.624 | 0.685 | 0.579 |
| Sailor-14B | 0.748 | 0.84 | 0.536 | 0.528 | 0.621 | 0.655 | 0.562 |
| Sailor-14B-Chat | 0.749 | 0.843 | 0.553 | 0.566 | 0.637 | 0.67 | 0.585 |
| SeaLLMs-v3-7B | 0.809 | 0.863 | 0.545 | 0.530 | 0.628 | 0.675 | 0.568 |
| **SeaLLMs-v3-7B-Chat** | 0.809 | 0.874 | 0.558 | 0.569 | 0.649 | 0.692 | **0.592** |
#### Multilingual Instruction-following Capability - SeaBench
SeaBench consists of multi-turn human instructions spanning various task types. It evaluates chat-based models on their ability to follow human instructions in both single and multi-turn settings and assesses their performance across different task types. The dataset and corresponding evaluation code will be released soon!
| model | id<br>turn1 | id<br>turn2 | id<br>avg | th<br>turn1 | th<br>turn2 | th<br>avg | vi<br>turn1 | vi<br>turn2 | vi<br>avg | avg |
|:----------------|------------:|------------:|---------:|------------:|------------:|---------:|------------:|------------:|---------:|------:|
| Qwen2-7B-Instruct| 5.93 | 5.84 | 5.89 | 5.47 | 5.20 | 5.34 | 6.17 | 5.60 | 5.89 | 5.70 |
| SeaLLM-7B-v2.5 | 6.27 | 4.96 | 5.62 | 5.79 | 3.82 | 4.81 | 6.02 | 4.02 | 5.02 | 5.15 |
| Sailor-14B-Chat | 5.26 | 5.53 | 5.40 | 4.62 | 4.36 | 4.49 | 5.31 | 4.74 | 5.03 | 4.97 |
| Sailor-7B-Chat | 4.60 | 4.04 | 4.32 | 3.94 | 3.17 | 3.56 | 4.82 | 3.62 | 4.22 | 4.03 |
| **SeaLLMs-v3-7B-Chat** | 6.73 | 6.59 | 6.66 | 6.48 | 5.90 | 6.19 | 6.34 | 5.79 | 6.07 | **6.31** |
#### Multilingual Math
We evaluate the multilingual math capability using the MGSM dataset. MGSM originally contains Chinese and Thai testing sets only, we use Google Translate to translate the same English questions into other SEA languages. Note that we adopt the tradition of each country to represent the number, e.g., in Indonesian and Vietnamese, dots are used as thousands separators and commas as decimal separators, the opposite of the English system.
| MGSM | en | id | ms | th | vi | zh | avg |
|:--------------------------|------:|------:|------:|------:|------:|------:|------:|
| Sailor-7B-Chat | 33.6 | 22.4 | 22.4 | 21.6 | 25.2 | 29.2 | 25.7 |
| Meta-Llama-3-8B-Instruct | 77.6 | 48 | 57.6 | 56 | 46.8 | 58.8 | 57.5 |
| glm-4-9b-chat | 72.8 | 53.6 | 53.6 | 34.8 | 52.4 | 70.8 | 56.3 |
| Qwen1.5-7B-Chat | 64 | 34.4 | 38.4 | 25.2 | 36 | 53.6 | 41.9 |
| Qwen2-7B-instruct | 82 | 66.4 | 62.4 | 58.4 | 64.4 | 76.8 | 68.4 |
| aya-23-8B | 28.8 | 16.4 | 14.4 | 2 | 16 | 12.8 | 15.1 |
| gemma-1.1-7b-it | 58.8 | 32.4 | 34.8 | 31.2 | 39.6 | 35.2 | 38.7 |
| SeaLLMs-7B-v2.5 | 79.6 | 69.2 | 70.8 | 61.2 | 66.8 | 62.4 | 68.3 |
| **SeaLLMs-v3-7B-Chat** | 74.8 | 71.2 | 70.8 | 71.2 | 71.2 | 79.6 | **73.1** |
#### Translation
We use the test sets from Flores-200 for evaluation and report the zero-shot chrF scores for translations between every pair of languages. Each row in the table below presents the average results of translating from various source languages into the target languages. The last column displays the overall average results of translating from any language to any other language for each model.
| model | en | id | jv | km | lo | ms | my | ta | th | tl | vi | zh | avg |
|:-----------------------------------------------|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|
|Meta-Llama-3-8B-Instruct | 51.54 | 49.03 | 22.46 | 15.34 | 5.42 | 46.72 | 21.24 | 32.09 | 35.75 | 40.8 | 39.31 | 14.87 | 31.22 |
|Qwen2-7B-Instruct | 50.36 | 47.55 | 29.36 | 19.26 | 11.06 | 42.43 | 19.33 | 20.04 | 36.07 | 37.91 | 39.63 | 22.87 | 31.32 |
|Sailor-7B-Chat | 49.4 | 49.78 | 28.33 | 2.68 | 6.85 | 47.75 | 5.35 | 18.23 | 38.92 | 29 | 41.76 | 20.87 | 28.24 |
|SeaLLM-7B-v2.5 | 55.09 | 53.71 | 18.13 | 18.09 | 15.53 | 51.33 | 19.71 | 26.1 | 40.55 | 45.58 | 44.56 | 24.18 | 34.38 |
|**SeaLLMs-v3-7B-Chat** | 54.68 | 52.52 | 29.86 | 27.3 | 26.34 | 45.04 | 21.54 | 31.93 | 41.52 | 38.51 | 43.78 | 26.1 | **36.52** |
### Model Trustworthiness
#### Hallucination
Performance of whether a model can refuse questions about the non-existing entity. The following is the F1 score. We use refuse as the positive label. Our test set consists of ~1k test samples per language. Each unanswerable question is generated by GPT4o. The ratio of answerable and unanswerable questions are 1:1. We define keywords to automatically detect whether a model-generated response is a refusal response.
| Refusal-F1 Scores | en | zh | vi | th | id | avg |
|:---------------------|------:|------:|------:|------:|------:|-------:|
| Qwen1.5-7B-Instruct | 53.85 | 51.70 | 52.85 | 35.50 | 58.40 | 50.46 |
| Qwen2-7B-Instruct | 58.79 | 33.08 | 56.21 | 44.60 | 55.98 | 49.73 |
| SeaLLM-7B-v2.5 | 12.90 | 0.77 | 2.45 | 19.42 | 0.78 | 7.26 |
| Sailor-7B-Chat | 33.49 | 18.82 | 5.19 | 9.68 | 16.42 | 16.72 |
| glm-4-9b-chat | 44.48 | 37.89 | 18.66 | 4.27 | 1.97 | 21.45 |
| Llama-3-8B-Instruct | 72.08 | 0.00 | 1.23 | 0.80 | 3.91 | 15.60 |
| gemma-1.1-7b-it | 52.39 | 27.74 | 23.96 | 22.97 | 31.72 | 31.76 |
| **SeaLLMs-v3-7B-Chat** | 71.36 | 78.39 | 77.93 | 61.31 | 68.95 | **71.59** |
#### Safety
Multijaildataset consists of harmful prompts in multiple languages. We take those relevant prompts in SEA languages here and report their safe rate (the higher the better).
| Model | en | jv | th | vi | zh | avg |
|:------------------------|-------:|-------:|-------:|-------:|------:|-------:|
| Qwen2-7B-Instruct | 88.57 | 43.81 | 63.81 | 73.02 | 87.30 | 71.30 |
| Sailor-7B-Chat | 78.73 | 54.92 | 62.22 | 67.62 | 76.19 | 67.94 |
| Meta-Llama-3-8B-Instruct| 88.25 | 26.35 | 71.11 | 69.84 | 77.14 | 66.54 |
| Sailor-14B-Chat | 86.98 | 30.48 | 53.65 | 60.95 | 72.70 | 60.95 |
| glm-4-9b-chat | 77.14 | 21.27 | 30.16 | 60.63 | 74.92 | 52.82 |
| **SeaLLMs-v3-7B-Chat** | 88.89 | 60.00 | 73.33 | 83.81 | 92.70 | **79.75** |
## Acknowledgement to Our Linguists
We would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety.
## Citation
If you find our project useful, we hope you would kindly star our repo and cite our work as follows:
```
@article{damonlp2024seallm3,
author = {Wenxuan Zhang*, Hou Pong Chan*, Yiran Zhao*, Mahani Aljunied*,
Jianyu Wang*, Chaoqun Liu, Yue Deng, Zhiqiang Hu, Weiwen Xu,
Yew Ken Chia, Xin Li, Lidong Bing},
title = {SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages},
year = {2024},
url = {https://arxiv.org/abs/2407.19672}
}
```
Corresponding Author: [email protected]
|
[
"TRANSLATION"
] |
[
"CHIA"
] |
mixedbread-ai/mxbai-embed-2d-large-v1
|
mixedbread-ai
|
feature-extraction
|
[
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"transformers.js",
"transformers",
"en",
"arxiv:2402.14776",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-04T11:17:53 | 2025-03-13T04:16:28 | 4,153 | 37 |
---
language:
- en
library_name: sentence-transformers
license: apache-2.0
tags:
- mteb
- transformers.js
- transformers
model-index:
- name: mxbai-embed-2d-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: 74.76119402985074
- type: ap
value: 37.90611182084586
- type: f1
value: 68.80795400445113
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.255525
- type: ap
value: 90.06886124154308
- type: f1
value: 93.24785420201029
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.162000000000006
- type: f1
value: 45.66989189593428
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.980000000000004
- type: map_at_10
value: 54.918
- type: map_at_100
value: 55.401
- type: map_at_1000
value: 55.403000000000006
- type: map_at_3
value: 50.249
- type: map_at_5
value: 53.400000000000006
- type: mrr_at_1
value: 38.834
- type: mrr_at_10
value: 55.24
- type: mrr_at_100
value: 55.737
- type: mrr_at_1000
value: 55.738
- type: mrr_at_3
value: 50.580999999999996
- type: mrr_at_5
value: 53.71
- type: ndcg_at_1
value: 37.980000000000004
- type: ndcg_at_10
value: 63.629000000000005
- type: ndcg_at_100
value: 65.567
- type: ndcg_at_1000
value: 65.61399999999999
- type: ndcg_at_3
value: 54.275
- type: ndcg_at_5
value: 59.91
- type: precision_at_1
value: 37.980000000000004
- type: precision_at_10
value: 9.110999999999999
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.977
- type: precision_at_5
value: 15.903
- type: recall_at_1
value: 37.980000000000004
- type: recall_at_10
value: 91.11
- type: recall_at_100
value: 99.289
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 65.932
- type: recall_at_5
value: 79.51599999999999
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.28746486562395
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.335244985544165
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.771155681602096
- type: mrr
value: 76.55993052807459
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.76152904846916
- type: cos_sim_spearman
value: 88.05622328825284
- type: euclidean_pearson
value: 88.2821986323439
- type: euclidean_spearman
value: 88.05622328825284
- type: manhattan_pearson
value: 87.98419111117559
- type: manhattan_spearman
value: 87.905617446958
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.65259740259741
- type: f1
value: 86.62044951853902
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.7270855384167
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.95365397158872
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.604
- type: map_at_10
value: 42.126999999999995
- type: map_at_100
value: 43.702999999999996
- type: map_at_1000
value: 43.851
- type: map_at_3
value: 38.663
- type: map_at_5
value: 40.67
- type: mrr_at_1
value: 37.625
- type: mrr_at_10
value: 48.203
- type: mrr_at_100
value: 48.925000000000004
- type: mrr_at_1000
value: 48.979
- type: mrr_at_3
value: 45.494
- type: mrr_at_5
value: 47.288999999999994
- type: ndcg_at_1
value: 37.625
- type: ndcg_at_10
value: 48.649
- type: ndcg_at_100
value: 54.041
- type: ndcg_at_1000
value: 56.233999999999995
- type: ndcg_at_3
value: 43.704
- type: ndcg_at_5
value: 46.172999999999995
- type: precision_at_1
value: 37.625
- type: precision_at_10
value: 9.371
- type: precision_at_100
value: 1.545
- type: precision_at_1000
value: 0.20400000000000001
- type: precision_at_3
value: 21.364
- type: precision_at_5
value: 15.421999999999999
- type: recall_at_1
value: 30.604
- type: recall_at_10
value: 60.94199999999999
- type: recall_at_100
value: 82.893
- type: recall_at_1000
value: 96.887
- type: recall_at_3
value: 46.346
- type: recall_at_5
value: 53.495000000000005
- type: map_at_1
value: 29.959000000000003
- type: map_at_10
value: 40.217999999999996
- type: map_at_100
value: 41.337
- type: map_at_1000
value: 41.471999999999994
- type: map_at_3
value: 37.029
- type: map_at_5
value: 38.873000000000005
- type: mrr_at_1
value: 37.325
- type: mrr_at_10
value: 45.637
- type: mrr_at_100
value: 46.243
- type: mrr_at_1000
value: 46.297
- type: mrr_at_3
value: 43.323
- type: mrr_at_5
value: 44.734
- type: ndcg_at_1
value: 37.325
- type: ndcg_at_10
value: 45.864
- type: ndcg_at_100
value: 49.832
- type: ndcg_at_1000
value: 52.056000000000004
- type: ndcg_at_3
value: 41.329
- type: ndcg_at_5
value: 43.547000000000004
- type: precision_at_1
value: 37.325
- type: precision_at_10
value: 8.732
- type: precision_at_100
value: 1.369
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 19.936
- type: precision_at_5
value: 14.306
- type: recall_at_1
value: 29.959000000000003
- type: recall_at_10
value: 56.113
- type: recall_at_100
value: 73.231
- type: recall_at_1000
value: 87.373
- type: recall_at_3
value: 42.88
- type: recall_at_5
value: 49.004
- type: map_at_1
value: 38.679
- type: map_at_10
value: 50.696
- type: map_at_100
value: 51.788000000000004
- type: map_at_1000
value: 51.849999999999994
- type: map_at_3
value: 47.414
- type: map_at_5
value: 49.284
- type: mrr_at_1
value: 44.263000000000005
- type: mrr_at_10
value: 54.03
- type: mrr_at_100
value: 54.752
- type: mrr_at_1000
value: 54.784
- type: mrr_at_3
value: 51.661
- type: mrr_at_5
value: 53.047
- type: ndcg_at_1
value: 44.263000000000005
- type: ndcg_at_10
value: 56.452999999999996
- type: ndcg_at_100
value: 60.736999999999995
- type: ndcg_at_1000
value: 61.982000000000006
- type: ndcg_at_3
value: 51.085
- type: ndcg_at_5
value: 53.715999999999994
- type: precision_at_1
value: 44.263000000000005
- type: precision_at_10
value: 9.129
- type: precision_at_100
value: 1.218
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 22.8
- type: precision_at_5
value: 15.674
- type: recall_at_1
value: 38.679
- type: recall_at_10
value: 70.1
- type: recall_at_100
value: 88.649
- type: recall_at_1000
value: 97.48
- type: recall_at_3
value: 55.757999999999996
- type: recall_at_5
value: 62.244
- type: map_at_1
value: 25.796999999999997
- type: map_at_10
value: 34.011
- type: map_at_100
value: 35.103
- type: map_at_1000
value: 35.187000000000005
- type: map_at_3
value: 31.218
- type: map_at_5
value: 32.801
- type: mrr_at_1
value: 28.022999999999996
- type: mrr_at_10
value: 36.108000000000004
- type: mrr_at_100
value: 37.094
- type: mrr_at_1000
value: 37.158
- type: mrr_at_3
value: 33.635
- type: mrr_at_5
value: 35.081
- type: ndcg_at_1
value: 28.022999999999996
- type: ndcg_at_10
value: 38.887
- type: ndcg_at_100
value: 44.159
- type: ndcg_at_1000
value: 46.300000000000004
- type: ndcg_at_3
value: 33.623
- type: ndcg_at_5
value: 36.281
- type: precision_at_1
value: 28.022999999999996
- type: precision_at_10
value: 6.010999999999999
- type: precision_at_100
value: 0.901
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 14.124
- type: precision_at_5
value: 10.034
- type: recall_at_1
value: 25.796999999999997
- type: recall_at_10
value: 51.86300000000001
- type: recall_at_100
value: 75.995
- type: recall_at_1000
value: 91.93299999999999
- type: recall_at_3
value: 37.882
- type: recall_at_5
value: 44.34
- type: map_at_1
value: 15.468000000000002
- type: map_at_10
value: 24.026
- type: map_at_100
value: 25.237
- type: map_at_1000
value: 25.380000000000003
- type: map_at_3
value: 21.342
- type: map_at_5
value: 22.843
- type: mrr_at_1
value: 19.154
- type: mrr_at_10
value: 28.429
- type: mrr_at_100
value: 29.416999999999998
- type: mrr_at_1000
value: 29.491
- type: mrr_at_3
value: 25.746000000000002
- type: mrr_at_5
value: 27.282
- type: ndcg_at_1
value: 19.154
- type: ndcg_at_10
value: 29.512
- type: ndcg_at_100
value: 35.331
- type: ndcg_at_1000
value: 38.435
- type: ndcg_at_3
value: 24.566
- type: ndcg_at_5
value: 26.891
- type: precision_at_1
value: 19.154
- type: precision_at_10
value: 5.647
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 12.065
- type: precision_at_5
value: 8.98
- type: recall_at_1
value: 15.468000000000002
- type: recall_at_10
value: 41.908
- type: recall_at_100
value: 67.17
- type: recall_at_1000
value: 89.05499999999999
- type: recall_at_3
value: 28.436
- type: recall_at_5
value: 34.278
- type: map_at_1
value: 28.116000000000003
- type: map_at_10
value: 39.034
- type: map_at_100
value: 40.461000000000006
- type: map_at_1000
value: 40.563
- type: map_at_3
value: 35.742000000000004
- type: map_at_5
value: 37.762
- type: mrr_at_1
value: 34.264
- type: mrr_at_10
value: 44.173
- type: mrr_at_100
value: 45.111000000000004
- type: mrr_at_1000
value: 45.149
- type: mrr_at_3
value: 41.626999999999995
- type: mrr_at_5
value: 43.234
- type: ndcg_at_1
value: 34.264
- type: ndcg_at_10
value: 45.011
- type: ndcg_at_100
value: 50.91
- type: ndcg_at_1000
value: 52.886
- type: ndcg_at_3
value: 39.757999999999996
- type: ndcg_at_5
value: 42.569
- type: precision_at_1
value: 34.264
- type: precision_at_10
value: 8.114
- type: precision_at_100
value: 1.2890000000000001
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.864
- type: precision_at_5
value: 13.628000000000002
- type: recall_at_1
value: 28.116000000000003
- type: recall_at_10
value: 57.764
- type: recall_at_100
value: 82.393
- type: recall_at_1000
value: 95.345
- type: recall_at_3
value: 43.35
- type: recall_at_5
value: 50.368
- type: map_at_1
value: 23.557
- type: map_at_10
value: 33.94
- type: map_at_100
value: 35.382000000000005
- type: map_at_1000
value: 35.497
- type: map_at_3
value: 30.635
- type: map_at_5
value: 32.372
- type: mrr_at_1
value: 29.224
- type: mrr_at_10
value: 39.017
- type: mrr_at_100
value: 39.908
- type: mrr_at_1000
value: 39.96
- type: mrr_at_3
value: 36.225
- type: mrr_at_5
value: 37.869
- type: ndcg_at_1
value: 29.224
- type: ndcg_at_10
value: 40.097
- type: ndcg_at_100
value: 46.058
- type: ndcg_at_1000
value: 48.309999999999995
- type: ndcg_at_3
value: 34.551
- type: ndcg_at_5
value: 36.937
- type: precision_at_1
value: 29.224
- type: precision_at_10
value: 7.6259999999999994
- type: precision_at_100
value: 1.226
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 16.781
- type: precision_at_5
value: 12.26
- type: recall_at_1
value: 23.557
- type: recall_at_10
value: 53.46300000000001
- type: recall_at_100
value: 78.797
- type: recall_at_1000
value: 93.743
- type: recall_at_3
value: 37.95
- type: recall_at_5
value: 44.121
- type: map_at_1
value: 24.81583333333333
- type: map_at_10
value: 34.057833333333335
- type: map_at_100
value: 35.29658333333334
- type: map_at_1000
value: 35.418666666666674
- type: map_at_3
value: 31.16416666666667
- type: map_at_5
value: 32.797
- type: mrr_at_1
value: 29.40216666666667
- type: mrr_at_10
value: 38.11191666666667
- type: mrr_at_100
value: 38.983250000000005
- type: mrr_at_1000
value: 39.043
- type: mrr_at_3
value: 35.663333333333334
- type: mrr_at_5
value: 37.08975
- type: ndcg_at_1
value: 29.40216666666667
- type: ndcg_at_10
value: 39.462416666666655
- type: ndcg_at_100
value: 44.74341666666666
- type: ndcg_at_1000
value: 47.12283333333333
- type: ndcg_at_3
value: 34.57383333333334
- type: ndcg_at_5
value: 36.91816666666667
- type: precision_at_1
value: 29.40216666666667
- type: precision_at_10
value: 7.008416666666667
- type: precision_at_100
value: 1.143333333333333
- type: precision_at_1000
value: 0.15391666666666665
- type: precision_at_3
value: 16.011083333333335
- type: precision_at_5
value: 11.506666666666664
- type: recall_at_1
value: 24.81583333333333
- type: recall_at_10
value: 51.39391666666666
- type: recall_at_100
value: 74.52983333333333
- type: recall_at_1000
value: 91.00650000000002
- type: recall_at_3
value: 37.87458333333334
- type: recall_at_5
value: 43.865833333333335
- type: map_at_1
value: 24.04
- type: map_at_10
value: 30.651
- type: map_at_100
value: 31.561
- type: map_at_1000
value: 31.667
- type: map_at_3
value: 28.358
- type: map_at_5
value: 29.644
- type: mrr_at_1
value: 26.840000000000003
- type: mrr_at_10
value: 33.397
- type: mrr_at_100
value: 34.166999999999994
- type: mrr_at_1000
value: 34.252
- type: mrr_at_3
value: 31.339
- type: mrr_at_5
value: 32.451
- type: ndcg_at_1
value: 26.840000000000003
- type: ndcg_at_10
value: 34.821999999999996
- type: ndcg_at_100
value: 39.155
- type: ndcg_at_1000
value: 41.837999999999994
- type: ndcg_at_3
value: 30.55
- type: ndcg_at_5
value: 32.588
- type: precision_at_1
value: 26.840000000000003
- type: precision_at_10
value: 5.383
- type: precision_at_100
value: 0.827
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 12.986
- type: precision_at_5
value: 9.11
- type: recall_at_1
value: 24.04
- type: recall_at_10
value: 45.133
- type: recall_at_100
value: 64.519
- type: recall_at_1000
value: 84.397
- type: recall_at_3
value: 33.465
- type: recall_at_5
value: 38.504
- type: map_at_1
value: 15.744
- type: map_at_10
value: 22.557
- type: map_at_100
value: 23.705000000000002
- type: map_at_1000
value: 23.833
- type: map_at_3
value: 20.342
- type: map_at_5
value: 21.584
- type: mrr_at_1
value: 19.133
- type: mrr_at_10
value: 26.316
- type: mrr_at_100
value: 27.285999999999998
- type: mrr_at_1000
value: 27.367
- type: mrr_at_3
value: 24.214
- type: mrr_at_5
value: 25.419999999999998
- type: ndcg_at_1
value: 19.133
- type: ndcg_at_10
value: 27.002
- type: ndcg_at_100
value: 32.544000000000004
- type: ndcg_at_1000
value: 35.624
- type: ndcg_at_3
value: 23.015
- type: ndcg_at_5
value: 24.916
- type: precision_at_1
value: 19.133
- type: precision_at_10
value: 4.952
- type: precision_at_100
value: 0.918
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 10.908
- type: precision_at_5
value: 8.004
- type: recall_at_1
value: 15.744
- type: recall_at_10
value: 36.63
- type: recall_at_100
value: 61.58
- type: recall_at_1000
value: 83.648
- type: recall_at_3
value: 25.545
- type: recall_at_5
value: 30.392000000000003
- type: map_at_1
value: 24.944
- type: map_at_10
value: 33.611000000000004
- type: map_at_100
value: 34.737
- type: map_at_1000
value: 34.847
- type: map_at_3
value: 30.746000000000002
- type: map_at_5
value: 32.357
- type: mrr_at_1
value: 29.198
- type: mrr_at_10
value: 37.632
- type: mrr_at_100
value: 38.53
- type: mrr_at_1000
value: 38.59
- type: mrr_at_3
value: 35.292
- type: mrr_at_5
value: 36.519
- type: ndcg_at_1
value: 29.198
- type: ndcg_at_10
value: 38.946999999999996
- type: ndcg_at_100
value: 44.348
- type: ndcg_at_1000
value: 46.787
- type: ndcg_at_3
value: 33.794999999999995
- type: ndcg_at_5
value: 36.166
- type: precision_at_1
value: 29.198
- type: precision_at_10
value: 6.595
- type: precision_at_100
value: 1.055
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 15.235999999999999
- type: precision_at_5
value: 10.896
- type: recall_at_1
value: 24.944
- type: recall_at_10
value: 51.284
- type: recall_at_100
value: 75.197
- type: recall_at_1000
value: 92.10000000000001
- type: recall_at_3
value: 37.213
- type: recall_at_5
value: 43.129
- type: map_at_1
value: 21.979000000000003
- type: map_at_10
value: 31.349
- type: map_at_100
value: 32.969
- type: map_at_1000
value: 33.2
- type: map_at_3
value: 28.237000000000002
- type: map_at_5
value: 30.09
- type: mrr_at_1
value: 27.075
- type: mrr_at_10
value: 35.946
- type: mrr_at_100
value: 36.897000000000006
- type: mrr_at_1000
value: 36.951
- type: mrr_at_3
value: 32.971000000000004
- type: mrr_at_5
value: 34.868
- type: ndcg_at_1
value: 27.075
- type: ndcg_at_10
value: 37.317
- type: ndcg_at_100
value: 43.448
- type: ndcg_at_1000
value: 45.940999999999995
- type: ndcg_at_3
value: 32.263
- type: ndcg_at_5
value: 34.981
- type: precision_at_1
value: 27.075
- type: precision_at_10
value: 7.568999999999999
- type: precision_at_100
value: 1.5650000000000002
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 15.547
- type: precision_at_5
value: 11.818
- type: recall_at_1
value: 21.979000000000003
- type: recall_at_10
value: 48.522999999999996
- type: recall_at_100
value: 76.51
- type: recall_at_1000
value: 92.168
- type: recall_at_3
value: 34.499
- type: recall_at_5
value: 41.443999999999996
- type: map_at_1
value: 18.903
- type: map_at_10
value: 26.473999999999997
- type: map_at_100
value: 27.576
- type: map_at_1000
value: 27.677000000000003
- type: map_at_3
value: 24.244
- type: map_at_5
value: 25.284000000000002
- type: mrr_at_1
value: 20.702
- type: mrr_at_10
value: 28.455000000000002
- type: mrr_at_100
value: 29.469
- type: mrr_at_1000
value: 29.537999999999997
- type: mrr_at_3
value: 26.433
- type: mrr_at_5
value: 27.283
- type: ndcg_at_1
value: 20.702
- type: ndcg_at_10
value: 30.988
- type: ndcg_at_100
value: 36.358000000000004
- type: ndcg_at_1000
value: 39.080999999999996
- type: ndcg_at_3
value: 26.647
- type: ndcg_at_5
value: 28.253
- type: precision_at_1
value: 20.702
- type: precision_at_10
value: 4.972
- type: precision_at_100
value: 0.823
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.522
- type: precision_at_5
value: 7.9479999999999995
- type: recall_at_1
value: 18.903
- type: recall_at_10
value: 43.004
- type: recall_at_100
value: 67.42399999999999
- type: recall_at_1000
value: 87.949
- type: recall_at_3
value: 31.171
- type: recall_at_5
value: 35.071000000000005
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.942
- type: map_at_10
value: 22.017999999999997
- type: map_at_100
value: 23.968
- type: map_at_1000
value: 24.169
- type: map_at_3
value: 18.282
- type: map_at_5
value: 20.191
- type: mrr_at_1
value: 29.121000000000002
- type: mrr_at_10
value: 40.897
- type: mrr_at_100
value: 41.787
- type: mrr_at_1000
value: 41.819
- type: mrr_at_3
value: 37.535000000000004
- type: mrr_at_5
value: 39.626
- type: ndcg_at_1
value: 29.121000000000002
- type: ndcg_at_10
value: 30.728
- type: ndcg_at_100
value: 38.231
- type: ndcg_at_1000
value: 41.735
- type: ndcg_at_3
value: 25.141000000000002
- type: ndcg_at_5
value: 27.093
- type: precision_at_1
value: 29.121000000000002
- type: precision_at_10
value: 9.674000000000001
- type: precision_at_100
value: 1.775
- type: precision_at_1000
value: 0.243
- type: precision_at_3
value: 18.826999999999998
- type: precision_at_5
value: 14.515
- type: recall_at_1
value: 12.942
- type: recall_at_10
value: 36.692
- type: recall_at_100
value: 62.688
- type: recall_at_1000
value: 82.203
- type: recall_at_3
value: 22.820999999999998
- type: recall_at_5
value: 28.625
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.6
- type: map_at_10
value: 18.672
- type: map_at_100
value: 27.199
- type: map_at_1000
value: 29.032999999999998
- type: map_at_3
value: 13.045000000000002
- type: map_at_5
value: 15.271
- type: mrr_at_1
value: 69
- type: mrr_at_10
value: 75.304
- type: mrr_at_100
value: 75.68
- type: mrr_at_1000
value: 75.688
- type: mrr_at_3
value: 73.708
- type: mrr_at_5
value: 74.333
- type: ndcg_at_1
value: 56.25
- type: ndcg_at_10
value: 40.741
- type: ndcg_at_100
value: 45.933
- type: ndcg_at_1000
value: 53.764
- type: ndcg_at_3
value: 44.664
- type: ndcg_at_5
value: 42.104
- type: precision_at_1
value: 69
- type: precision_at_10
value: 33
- type: precision_at_100
value: 10.75
- type: precision_at_1000
value: 2.1999999999999997
- type: precision_at_3
value: 48.167
- type: precision_at_5
value: 41.099999999999994
- type: recall_at_1
value: 8.6
- type: recall_at_10
value: 24.447
- type: recall_at_100
value: 52.697
- type: recall_at_1000
value: 77.717
- type: recall_at_3
value: 14.13
- type: recall_at_5
value: 17.485999999999997
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.32
- type: f1
value: 43.92815810776849
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.987
- type: map_at_10
value: 78.025
- type: map_at_100
value: 78.28500000000001
- type: map_at_1000
value: 78.3
- type: map_at_3
value: 76.735
- type: map_at_5
value: 77.558
- type: mrr_at_1
value: 74.482
- type: mrr_at_10
value: 82.673
- type: mrr_at_100
value: 82.799
- type: mrr_at_1000
value: 82.804
- type: mrr_at_3
value: 81.661
- type: mrr_at_5
value: 82.369
- type: ndcg_at_1
value: 74.482
- type: ndcg_at_10
value: 82.238
- type: ndcg_at_100
value: 83.245
- type: ndcg_at_1000
value: 83.557
- type: ndcg_at_3
value: 80.066
- type: ndcg_at_5
value: 81.316
- type: precision_at_1
value: 74.482
- type: precision_at_10
value: 10.006
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 30.808000000000003
- type: precision_at_5
value: 19.256
- type: recall_at_1
value: 68.987
- type: recall_at_10
value: 90.646
- type: recall_at_100
value: 94.85900000000001
- type: recall_at_1000
value: 96.979
- type: recall_at_3
value: 84.76599999999999
- type: recall_at_5
value: 87.929
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.3
- type: map_at_10
value: 33.499
- type: map_at_100
value: 35.510000000000005
- type: map_at_1000
value: 35.693999999999996
- type: map_at_3
value: 29.083
- type: map_at_5
value: 31.367
- type: mrr_at_1
value: 39.660000000000004
- type: mrr_at_10
value: 49.517
- type: mrr_at_100
value: 50.18899999999999
- type: mrr_at_1000
value: 50.224000000000004
- type: mrr_at_3
value: 46.965
- type: mrr_at_5
value: 48.184
- type: ndcg_at_1
value: 39.660000000000004
- type: ndcg_at_10
value: 41.75
- type: ndcg_at_100
value: 48.477
- type: ndcg_at_1000
value: 51.373999999999995
- type: ndcg_at_3
value: 37.532
- type: ndcg_at_5
value: 38.564
- type: precision_at_1
value: 39.660000000000004
- type: precision_at_10
value: 11.774999999999999
- type: precision_at_100
value: 1.883
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 25.102999999999998
- type: precision_at_5
value: 18.395
- type: recall_at_1
value: 20.3
- type: recall_at_10
value: 49.633
- type: recall_at_100
value: 73.932
- type: recall_at_1000
value: 91.174
- type: recall_at_3
value: 34.516999999999996
- type: recall_at_5
value: 40.217000000000006
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.699999999999996
- type: map_at_10
value: 54.400000000000006
- type: map_at_100
value: 55.45
- type: map_at_1000
value: 55.525999999999996
- type: map_at_3
value: 50.99
- type: map_at_5
value: 53.054
- type: mrr_at_1
value: 69.399
- type: mrr_at_10
value: 76.454
- type: mrr_at_100
value: 76.771
- type: mrr_at_1000
value: 76.783
- type: mrr_at_3
value: 75.179
- type: mrr_at_5
value: 75.978
- type: ndcg_at_1
value: 69.399
- type: ndcg_at_10
value: 63.001
- type: ndcg_at_100
value: 66.842
- type: ndcg_at_1000
value: 68.33500000000001
- type: ndcg_at_3
value: 57.961
- type: ndcg_at_5
value: 60.67700000000001
- type: precision_at_1
value: 69.399
- type: precision_at_10
value: 13.4
- type: precision_at_100
value: 1.6420000000000001
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 37.218
- type: precision_at_5
value: 24.478
- type: recall_at_1
value: 34.699999999999996
- type: recall_at_10
value: 67.002
- type: recall_at_100
value: 82.113
- type: recall_at_1000
value: 91.945
- type: recall_at_3
value: 55.827000000000005
- type: recall_at_5
value: 61.195
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.40480000000001
- type: ap
value: 86.34472513785936
- type: f1
value: 90.3766943422773
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.796
- type: map_at_10
value: 31.344
- type: map_at_100
value: 32.525999999999996
- type: map_at_1000
value: 32.582
- type: map_at_3
value: 27.514
- type: map_at_5
value: 29.683
- type: mrr_at_1
value: 20.358
- type: mrr_at_10
value: 31.924999999999997
- type: mrr_at_100
value: 33.056000000000004
- type: mrr_at_1000
value: 33.105000000000004
- type: mrr_at_3
value: 28.149
- type: mrr_at_5
value: 30.303
- type: ndcg_at_1
value: 20.372
- type: ndcg_at_10
value: 38.025999999999996
- type: ndcg_at_100
value: 43.813
- type: ndcg_at_1000
value: 45.21
- type: ndcg_at_3
value: 30.218
- type: ndcg_at_5
value: 34.088
- type: precision_at_1
value: 20.372
- type: precision_at_10
value: 6.123
- type: precision_at_100
value: 0.903
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 12.918
- type: precision_at_5
value: 9.702
- type: recall_at_1
value: 19.796
- type: recall_at_10
value: 58.644
- type: recall_at_100
value: 85.611
- type: recall_at_1000
value: 96.314
- type: recall_at_3
value: 37.419999999999995
- type: recall_at_5
value: 46.697
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.0984952120383
- type: f1
value: 92.9409029889071
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.24441404468764
- type: f1
value: 54.66568676132254
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.86684599865501
- type: f1
value: 72.16086061041996
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.16745124411568
- type: f1
value: 78.76361933295068
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.66329421728342
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.21637418682758
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.85308363141191
- type: mrr
value: 33.06713899953772
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.392
- type: map_at_10
value: 14.539
- type: map_at_100
value: 18.811
- type: map_at_1000
value: 20.471
- type: map_at_3
value: 10.26
- type: map_at_5
value: 12.224
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.72200000000001
- type: mrr_at_100
value: 56.325
- type: mrr_at_1000
value: 56.35
- type: mrr_at_3
value: 53.30200000000001
- type: mrr_at_5
value: 54.742000000000004
- type: ndcg_at_1
value: 44.891999999999996
- type: ndcg_at_10
value: 37.355
- type: ndcg_at_100
value: 35.285
- type: ndcg_at_1000
value: 44.246
- type: ndcg_at_3
value: 41.291
- type: ndcg_at_5
value: 39.952
- type: precision_at_1
value: 46.749
- type: precision_at_10
value: 28.111000000000004
- type: precision_at_100
value: 9.127
- type: precision_at_1000
value: 2.23
- type: precision_at_3
value: 38.803
- type: precision_at_5
value: 35.046
- type: recall_at_1
value: 6.392
- type: recall_at_10
value: 19.066
- type: recall_at_100
value: 37.105
- type: recall_at_1000
value: 69.37299999999999
- type: recall_at_3
value: 11.213
- type: recall_at_5
value: 14.648
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.387999999999998
- type: map_at_10
value: 47.172
- type: map_at_100
value: 48.158
- type: map_at_1000
value: 48.186
- type: map_at_3
value: 42.952
- type: map_at_5
value: 45.405
- type: mrr_at_1
value: 35.458
- type: mrr_at_10
value: 49.583
- type: mrr_at_100
value: 50.324999999999996
- type: mrr_at_1000
value: 50.344
- type: mrr_at_3
value: 46.195
- type: mrr_at_5
value: 48.258
- type: ndcg_at_1
value: 35.458
- type: ndcg_at_10
value: 54.839000000000006
- type: ndcg_at_100
value: 58.974000000000004
- type: ndcg_at_1000
value: 59.64699999999999
- type: ndcg_at_3
value: 47.012
- type: ndcg_at_5
value: 51.080999999999996
- type: precision_at_1
value: 35.458
- type: precision_at_10
value: 9.056000000000001
- type: precision_at_100
value: 1.137
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 21.582
- type: precision_at_5
value: 15.295
- type: recall_at_1
value: 31.387999999999998
- type: recall_at_10
value: 75.661
- type: recall_at_100
value: 93.605
- type: recall_at_1000
value: 98.658
- type: recall_at_3
value: 55.492
- type: recall_at_5
value: 64.85600000000001
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.547
- type: map_at_10
value: 84.495
- type: map_at_100
value: 85.14
- type: map_at_1000
value: 85.15599999999999
- type: map_at_3
value: 81.606
- type: map_at_5
value: 83.449
- type: mrr_at_1
value: 81.22
- type: mrr_at_10
value: 87.31
- type: mrr_at_100
value: 87.436
- type: mrr_at_1000
value: 87.437
- type: mrr_at_3
value: 86.363
- type: mrr_at_5
value: 87.06
- type: ndcg_at_1
value: 81.24
- type: ndcg_at_10
value: 88.145
- type: ndcg_at_100
value: 89.423
- type: ndcg_at_1000
value: 89.52799999999999
- type: ndcg_at_3
value: 85.435
- type: ndcg_at_5
value: 87
- type: precision_at_1
value: 81.24
- type: precision_at_10
value: 13.381000000000002
- type: precision_at_100
value: 1.529
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.44
- type: precision_at_5
value: 24.62
- type: recall_at_1
value: 70.547
- type: recall_at_10
value: 95.083
- type: recall_at_100
value: 99.50099999999999
- type: recall_at_1000
value: 99.982
- type: recall_at_3
value: 87.235
- type: recall_at_5
value: 91.701
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 57.93101384071724
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.46951126228829
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.018000000000001
- type: map_at_10
value: 13.818
- type: map_at_100
value: 16.346
- type: map_at_1000
value: 16.744999999999997
- type: map_at_3
value: 9.456000000000001
- type: map_at_5
value: 11.879000000000001
- type: mrr_at_1
value: 24.8
- type: mrr_at_10
value: 37.092000000000006
- type: mrr_at_100
value: 38.199
- type: mrr_at_1000
value: 38.243
- type: mrr_at_3
value: 33.517
- type: mrr_at_5
value: 35.692
- type: ndcg_at_1
value: 24.8
- type: ndcg_at_10
value: 22.782
- type: ndcg_at_100
value: 32.072
- type: ndcg_at_1000
value: 38.163000000000004
- type: ndcg_at_3
value: 21.046
- type: ndcg_at_5
value: 19.134
- type: precision_at_1
value: 24.8
- type: precision_at_10
value: 12
- type: precision_at_100
value: 2.5420000000000003
- type: precision_at_1000
value: 0.39899999999999997
- type: precision_at_3
value: 20
- type: precision_at_5
value: 17.4
- type: recall_at_1
value: 5.018000000000001
- type: recall_at_10
value: 24.34
- type: recall_at_100
value: 51.613
- type: recall_at_1000
value: 80.95
- type: recall_at_3
value: 12.153
- type: recall_at_5
value: 17.648
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.28259142800503
- type: cos_sim_spearman
value: 82.04792579356291
- type: euclidean_pearson
value: 83.7755858026306
- type: euclidean_spearman
value: 82.04789872846196
- type: manhattan_pearson
value: 83.79937122515567
- type: manhattan_spearman
value: 82.05076966288574
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.37773414195387
- type: cos_sim_spearman
value: 78.76929696642694
- type: euclidean_pearson
value: 85.75861298616339
- type: euclidean_spearman
value: 78.76607739031363
- type: manhattan_pearson
value: 85.74412868736295
- type: manhattan_spearman
value: 78.74388526796852
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 89.6176449076649
- type: cos_sim_spearman
value: 90.39810997063387
- type: euclidean_pearson
value: 89.753863994154
- type: euclidean_spearman
value: 90.39810989027997
- type: manhattan_pearson
value: 89.67750819879801
- type: manhattan_spearman
value: 90.3286558059104
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 87.7488246203373
- type: cos_sim_spearman
value: 85.44794976383963
- type: euclidean_pearson
value: 87.33205836313964
- type: euclidean_spearman
value: 85.44793954377185
- type: manhattan_pearson
value: 87.30760291906203
- type: manhattan_spearman
value: 85.4308413187653
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.6937750952719
- type: cos_sim_spearman
value: 90.01162604967037
- type: euclidean_pearson
value: 89.35321306629116
- type: euclidean_spearman
value: 90.01161406477627
- type: manhattan_pearson
value: 89.31351907042307
- type: manhattan_spearman
value: 89.97264644642166
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.49107564294891
- type: cos_sim_spearman
value: 87.42092493144571
- type: euclidean_pearson
value: 86.88112016705634
- type: euclidean_spearman
value: 87.42092430260175
- type: manhattan_pearson
value: 86.85846210123235
- type: manhattan_spearman
value: 87.40059575522972
- 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: 88.71766466521638
- type: cos_sim_spearman
value: 88.80244555668372
- type: euclidean_pearson
value: 89.59428700746064
- type: euclidean_spearman
value: 88.80244555668372
- type: manhattan_pearson
value: 89.62272396580352
- type: manhattan_spearman
value: 88.77584531534937
- 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: 67.7743776239708
- type: cos_sim_spearman
value: 68.79768249749681
- type: euclidean_pearson
value: 70.16430919697441
- type: euclidean_spearman
value: 68.79768249749681
- type: manhattan_pearson
value: 70.17205038967042
- type: manhattan_spearman
value: 68.89740094589914
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.9087137484716
- type: cos_sim_spearman
value: 89.19783009521629
- type: euclidean_pearson
value: 88.89888500166009
- type: euclidean_spearman
value: 89.19783009521629
- type: manhattan_pearson
value: 88.88400033783687
- type: manhattan_spearman
value: 89.16299162200889
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.9799916253683
- type: mrr
value: 96.0708200659181
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.928000000000004
- type: map_at_10
value: 69.56400000000001
- type: map_at_100
value: 70.125
- type: map_at_1000
value: 70.148
- type: map_at_3
value: 66.774
- type: map_at_5
value: 68.267
- type: mrr_at_1
value: 62.666999999999994
- type: mrr_at_10
value: 70.448
- type: mrr_at_100
value: 70.94
- type: mrr_at_1000
value: 70.962
- type: mrr_at_3
value: 68.389
- type: mrr_at_5
value: 69.65599999999999
- type: ndcg_at_1
value: 62.666999999999994
- type: ndcg_at_10
value: 74.117
- type: ndcg_at_100
value: 76.248
- type: ndcg_at_1000
value: 76.768
- type: ndcg_at_3
value: 69.358
- type: ndcg_at_5
value: 71.574
- type: precision_at_1
value: 62.666999999999994
- type: precision_at_10
value: 9.933
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.222
- type: precision_at_5
value: 17.867
- type: recall_at_1
value: 59.928000000000004
- type: recall_at_10
value: 87.156
- type: recall_at_100
value: 96.167
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 74.117
- type: recall_at_5
value: 79.80000000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83762376237624
- type: cos_sim_ap
value: 96.05077689253707
- type: cos_sim_f1
value: 91.75879396984925
- type: cos_sim_precision
value: 92.22222222222223
- type: cos_sim_recall
value: 91.3
- type: dot_accuracy
value: 99.83762376237624
- type: dot_ap
value: 96.05082513542375
- type: dot_f1
value: 91.75879396984925
- type: dot_precision
value: 92.22222222222223
- type: dot_recall
value: 91.3
- type: euclidean_accuracy
value: 99.83762376237624
- type: euclidean_ap
value: 96.05077689253707
- type: euclidean_f1
value: 91.75879396984925
- type: euclidean_precision
value: 92.22222222222223
- type: euclidean_recall
value: 91.3
- type: manhattan_accuracy
value: 99.83861386138614
- type: manhattan_ap
value: 96.07646831090695
- type: manhattan_f1
value: 91.86220668996505
- type: manhattan_precision
value: 91.72482552342971
- type: manhattan_recall
value: 92
- type: max_accuracy
value: 99.83861386138614
- type: max_ap
value: 96.07646831090695
- type: max_f1
value: 91.86220668996505
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.40672513062134
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.31519237029376
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.15764586446943
- type: mrr
value: 53.981596426449364
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.92935724124931
- type: cos_sim_spearman
value: 31.54589922149803
- type: dot_pearson
value: 30.929365687857675
- type: dot_spearman
value: 31.54589922149803
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22100000000000003
- type: map_at_10
value: 1.791
- type: map_at_100
value: 9.404
- type: map_at_1000
value: 22.932
- type: map_at_3
value: 0.601
- type: map_at_5
value: 1.001
- type: mrr_at_1
value: 76
- type: mrr_at_10
value: 85.667
- type: mrr_at_100
value: 85.667
- type: mrr_at_1000
value: 85.667
- type: mrr_at_3
value: 84.667
- type: mrr_at_5
value: 85.667
- type: ndcg_at_1
value: 72
- type: ndcg_at_10
value: 68.637
- type: ndcg_at_100
value: 51.418
- type: ndcg_at_1000
value: 47.75
- type: ndcg_at_3
value: 70.765
- type: ndcg_at_5
value: 71.808
- type: precision_at_1
value: 76
- type: precision_at_10
value: 73.8
- type: precision_at_100
value: 52.68000000000001
- type: precision_at_1000
value: 20.9
- type: precision_at_3
value: 74.667
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.22100000000000003
- type: recall_at_10
value: 2.027
- type: recall_at_100
value: 12.831000000000001
- type: recall_at_1000
value: 44.996
- type: recall_at_3
value: 0.635
- type: recall_at_5
value: 1.097
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.289
- type: map_at_10
value: 10.475
- type: map_at_100
value: 16.993
- type: map_at_1000
value: 18.598
- type: map_at_3
value: 5.891
- type: map_at_5
value: 7.678999999999999
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 49.475
- type: mrr_at_100
value: 50.483
- type: mrr_at_1000
value: 50.499
- type: mrr_at_3
value: 45.918
- type: mrr_at_5
value: 48.469
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 25.891
- type: ndcg_at_100
value: 38.106
- type: ndcg_at_1000
value: 49.873
- type: ndcg_at_3
value: 29.915999999999997
- type: ndcg_at_5
value: 27.982000000000003
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.448999999999998
- type: precision_at_100
value: 7.837
- type: precision_at_1000
value: 1.5730000000000002
- type: precision_at_3
value: 31.293
- type: precision_at_5
value: 27.755000000000003
- type: recall_at_1
value: 2.289
- type: recall_at_10
value: 16.594
- type: recall_at_100
value: 48.619
- type: recall_at_1000
value: 85.467
- type: recall_at_3
value: 7.144
- type: recall_at_5
value: 10.465
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.5268
- type: ap
value: 14.763212211567907
- type: f1
value: 55.200562727472736
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.25297113752123
- type: f1
value: 59.55315247947331
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.47685515092062
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.73183525064076
- type: cos_sim_ap
value: 76.08498196190112
- type: cos_sim_f1
value: 69.4834471209584
- type: cos_sim_precision
value: 67.88321167883211
- type: cos_sim_recall
value: 71.16094986807387
- type: dot_accuracy
value: 86.73183525064076
- type: dot_ap
value: 76.08503499590553
- type: dot_f1
value: 69.4834471209584
- type: dot_precision
value: 67.88321167883211
- type: dot_recall
value: 71.16094986807387
- type: euclidean_accuracy
value: 86.73183525064076
- type: euclidean_ap
value: 76.08500172594562
- type: euclidean_f1
value: 69.4834471209584
- type: euclidean_precision
value: 67.88321167883211
- type: euclidean_recall
value: 71.16094986807387
- type: manhattan_accuracy
value: 86.6960720033379
- type: manhattan_ap
value: 76.00885156192993
- type: manhattan_f1
value: 69.24488725747247
- type: manhattan_precision
value: 68.8118811881188
- type: manhattan_recall
value: 69.68337730870712
- type: max_accuracy
value: 86.73183525064076
- type: max_ap
value: 76.08503499590553
- type: max_f1
value: 69.4834471209584
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.74529436876625
- type: cos_sim_ap
value: 85.53503158777171
- type: cos_sim_f1
value: 77.68167368965773
- type: cos_sim_precision
value: 74.70496232048912
- type: cos_sim_recall
value: 80.9054511857099
- type: dot_accuracy
value: 88.74529436876625
- type: dot_ap
value: 85.5350158446314
- type: dot_f1
value: 77.68167368965773
- type: dot_precision
value: 74.70496232048912
- type: dot_recall
value: 80.9054511857099
- type: euclidean_accuracy
value: 88.74529436876625
- type: euclidean_ap
value: 85.53503846009764
- type: euclidean_f1
value: 77.68167368965773
- type: euclidean_precision
value: 74.70496232048912
- type: euclidean_recall
value: 80.9054511857099
- type: manhattan_accuracy
value: 88.73753250281368
- type: manhattan_ap
value: 85.53197689629393
- type: manhattan_f1
value: 77.58753437213566
- type: manhattan_precision
value: 74.06033456988871
- type: manhattan_recall
value: 81.46750846935633
- type: max_accuracy
value: 88.74529436876625
- type: max_ap
value: 85.53503846009764
- type: max_f1
value: 77.68167368965773
---
<br><br>
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</p>
<p align="center">
<b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><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>
# 🪆mxbai-embed-2d-large-v1🪆
This is our [2DMSE](https://arxiv.org/abs/2402.14776) sentence embedding model. It supports the adaptive transformer layer and embedding size. Find out more in our [blog post](https://mixedbread.ai/blog/mxbai-embed-2d-large-v1).
TLDR: 2D-🪆 allows you to shrink the model and the embeddings layer. Shrinking only the embeddings model yields competetive results to other models like [nomics embeddings model](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). Shrinking the model to ~50% maintains upto 85% of the performance without further training.
## Quickstart
Here, we provide several ways to produce sentence embeddings with adaptive layers and embedding sizes. **For this version, it is recommended to set adaptive layers from 20 to 24.**
### sentence-transformers
Currently, the best way to use our models is with the most recent version of sentence-transformers.
```bash
python -m pip install -U sentence-transformers
```
```python
from sentence_transformers import models, SentenceTransformer
from sentence_transformers.util import cos_sim
# 1. load model with `cls` pooling
model = SentenceTransformer("mixedbread-ai/mxbai-embed-2d-large-v1")
# 2. set adaptive layer and embedding size.
# it is recommended to set layers from 20 to 24.
new_num_layers = 22 # 1D: set layer size
model[0].auto_model.encoder.layer = model[0].auto_model.encoder.layer[:new_num_layers]
new_embedding_size = 768 # 2D: set embedding size
# 3. encode
embeddings = model.encode(
[
'Who is german and likes bread?',
'Everybody in Germany.'
]
)
# Similarity of the first sentence with the other two
similarities = cos_sim(embeddings[0, :new_embedding_size], embeddings[1, :new_embedding_size])
print('similarities:', similarities)
```
### angle-emb
You can also use the lastest `angle-emb` for inference, as follows:
```bash
python -m pip install -U angle-emb
```
```python
from angle_emb import AnglE
from sentence_transformers.util import cos_sim
# 1. load model
model = AnglE.from_pretrained("mixedbread-ai/mxbai-embed-2d-large-v1", pooling_strategy='cls').cuda()
# 2. set adaptive layer and embedding size.
# it is recommended to set layers from 20 to 24.
layer_index = 22 # 1d: layer
embedding_size = 768 # 2d: embedding size
# 3. encode
embeddings = model.encode([
'Who is german and likes bread?',
'Everybody in Germany.'
], layer_index=layer_index, embedding_size=embedding_size)
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:
```bash
npm i @xenova/transformers
```
You can then use the model to compute embeddings as follows:
```js
import { pipeline, cos_sim } from '@xenova/transformers';
// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-2d-large-v1', {
quantized: false, // (Optional) remove this line to use the 8-bit quantized model
});
// Compute sentence embeddings (with `cls` pooling)
const sentences = ['Who is german and likes bread?', 'Everybody in Germany.' ];
const output = await extractor(sentences, { pooling: 'cls' });
// Set embedding size and truncate embeddings
const new_embedding_size = 768;
const truncated = output.slice(null, [0, new_embedding_size]);
// Compute cosine similarity
console.log(cos_sim(truncated[0].data, truncated[1].data)); // 0.6979532021425204
```
### Using API
You can use the model via our API as follows:
```python
from mixedbread_ai.client import MixedbreadAI
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-2d-large-v1",
dimensions=512,
)
embeddings = [entry.embedding for entry in res.data]
similarities = cosine_similarity([embeddings[0]], [embeddings[1]])
print(similarities)
```
The API comes with native INT8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information.
## Evaluation
Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-2d-large-v1).
## 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
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
Almawave/Velvet-2B
|
Almawave
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"vllm",
"conversational",
"en",
"it",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2025-02-10T15:49:00 | 2025-02-20T14:41:42 | 3,990 | 37 |
---
language:
- en
- it
library_name: transformers
license: apache-2.0
tags:
- vllm
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://www.almawave.com/privacy-policy/">Privacy
Policy</a>.
---
# Model Card for Velvet-2B
Velvet is an Italian family of large language models, developed from scratch, featuring a dense architecture. This model was trained on the HPC Leonardo infrastructure hosted by [CINECA](https://www.cineca.it/en), utilizing public data that underwent extensive curation.
The training process for the Velvet family commenced with over 10 trillion tokens in 6 languages (Italian, English, Spanish, Portuguese-Brazilian, German, French).
Velvet-2B has been trained on almost 3 trillion tokens, across two languages (Italian, English).
## Model details
- **Model Developers:** Technology and innovation Team, Almawave
- **Input:** Models input text only.
- **Output:** Models generate text only.
- **Release Date:** February 11th, 2025.
- **License:** Apache 2.
### Model Architecture and training
Velvet family of models comes in two sizes --- 2B and 14B parameters --- namely, **Velvet-2B** and **Velvet-14B**.
**Velvet-2B** is a 2B parameter instruct model finetuned from **Velvet-2B-base** using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving textual "instruction based" problems.
#### Architecture
- Auto-regressive language model with a transformer-based causal decoder-only design.
- 28 transformer layers.
- MLP intermediate size of 8,192.
- Grouped Query Attention (GQA): 32 query heads and 8 key-value heads for efficiency.
- Rotary Position Embedding (RoPE).
- SiLU activation function with RMSNorm method.
- Trained on sequences of 4K tokens, supports context length up to 32K tokens.
- 127K vocabulary size, designed to accommodate language diversity.
- Training phase: pretraining & post-training
### Status
This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
Almawave is actively working on strategies to enhance alignment and robustness in future iterations of the Velvet model.
### License
Velvet-2B is made available under the Apache 2.0 license
### Supported Languages
Velvet-2B has been trained on Italian and English.
To ensure high-quality multilingual performance, the dataset was curated to balance linguistic representation, reducing overfitting biases.
## Intended Use
Velvet-2B is designed to be integrated into AI systems or applications. Its potential use include, but it is not limited to, text generation, classification, summarization, question answering. It is important to note that specific applications may need further model adaptations or additional safeguards to prevent undesirable behavior or outputs.
### Capabilities
- Summarization
- Information Extraction
- RAG (Retrieval Augmented Generation)
- Paraphrasing
- Textual Entailment
- Natural Language Inference
- Common Sense Reasoning
- Text Classification
- Machine Translation
- Question Answering
- Text Completion
## Training Data
### Overview
The model was pretrained on almost 3 trillion tokens of data from publicly available sources. These sources include diverse collection of web text exposes the model to an extensive range of linguistic styles, topics, and vocabulary. The training dataset has been built with a balanced representation of multiple languages.
The fine-tuning data includes publicly available instruction datasets, as well as over 1M human-annotated and synthetic examples for SFT.
Moreover we used over 50k human generated examples for safety instructions. Neither the pretraining nor the fine-tuning datasets include Almawave's customer data.
We have made significant efforts to enhance the reliability of responses in terms of factual accuracy; however, we always recommend grounding LLM responses with external factual data (e.g. Retrieval Augmented Generation).
### Data Freshness
The pretraining data has a cutoff between August 2024 and October 2024 for the two different models.
## Evaluation
### Italian language
| Category | Benchmark | Velvet-2B |
|---------------------------| ------------------------| -------------------|
| General | MMLU (5-shot) | 39.6 |
| Commonsense | Hellaswag (0-shot) | 54.3 |
| | WinoGrande ITA-bench (0-shot) | 61.9 |
|| PIQA ITA-bench (0-shot) | 67.3 |
|| SciQ ITA-bench (0-shot) with p. | 86.6 |
| Reasoning | ARC-Challenge (0-shot) | 41.7 |
### English language
| Category | Benchmark | Velvet-2B |
|---------------------------| ------------------------| -------------------|
| General | MMLU (5-shot) | 43.4 |
| Instruction Following | IFEval (0-shot) | 53.2 |
| Commonsense | Hellaswag (10-shot) | 65.0 |
| | WinoGrande (0-shot) | 60.9 |
| Reasoning | ARC-Challenge (25-shot) | 50.6 |
## Usage
The model can be used with the following frameworks;
- [`vllm`](https://github.com/vllm-project/vllm)
- [`transformers`](https://github.com/huggingface/transformers)
- [`ollama`](https://ollama.com/Almawave/Velvet:2B)
## Responsibility and Safety
Large language models are versatile technologies designed to serve a wide range of applications. However, they are not intended to meet every developer\'s safety requirements out-of-the-box, as these requirements naturally vary depending on the specific use case and application context.
### Safety
For our instruction-trained model, we have undertaken comprehensive exercises, engaged in adversarial internal and external evaluations, and put into place mitigation techniques to reduce risks. These exercises were designed to thoroughly examine the model\'s limitations and potential, simulating real and hypothetical scenarios where undesirable behavior might arise.
However, despite these efforts, it is inevitable that some residual hazards are bound to exist, as every large language model presents intrinsic complexities that cannot be completely eliminated.
Then, developers are advised to implement suitable safety measures and exercise due diligence, tailoring these safeguards to align with their product policies and the specific requirements of their applications.
Some trade-offs between model helpfulness and alignment are likely inevitable. Developers should thoughtfully balance the benefits of alignment and helpfulness for their specific applications and audiences.
They must also remain aware of residual risks when using Velvet models and leverage additional safety tools as necessary to achieve an appropriate safety standard for their use case.
We advise developers to carefully evaluate risks in the context of their specific use case. They should consider the potential implications of a model failure in their applications and put adequate measures in place to manage such eventualities.
In parallel, we are collaborating with the scientific and industrial community to establish AI safety benchmark standards that are transparent, rigorous, and interpretable. The goal is to promote a better understanding of the risks associated with large language models and support the development of safer and more responsible solutions.
### **Governance and Internal Oversight**
Almawave has established an **internal governance framework** for the management and continuous oversight of the Velvet model family.
Key governance elements include:
- **Supervision by an Ethical and Technical Committee** to ensure the model aligns with principles of **transparency, fairness, and safety**.
- **Ongoing bias monitoring** through auditing tools, with iterative updates to improve alignment with ethical guidelines.
- **Restrictions on commercial and institutional usage** to ensure compliance with regulatory frameworks and **shared responsibility principles**.
- **Periodic review processes** to assess the model’s impact in high-risk applications.
## Bias, Risks, and Limitations
Velvet has been trained on a dataset that, despite all the data curation efforts, might include toxic language and societal biases. This means that models in the Velvet family may reproduce these biases and produce harmful responses when prompted with such inputs. This is a common issue in AI models trained on large datasets, as they can inadvertently perpetuate the biases present in the data.
Furthermore, the model may generate inaccurate, incomplete, or redundant responses, which could be socially unacceptable or undesirable, even if the input prompt is not explicitly offensive. This is a potential flaw in the model\'s design and training process, and it underscores the importance of careful validation and monitoring of AI systems to ensure that they are functioning as intended.
Additionally, using the recommended prompt template is crucial to mitigate the risk of harmful responses, as it is designed to guide the model towards more appropriate and safe outputs. However, it is important to note that the model\'s performance may still vary depending on the specific context and complexity of the input prompt.
Finally, when using this model in an agentic workflow, it is essential to validate that all imported packages and dependencies are from trusted sources to ensure the model\'s security and integrity. This is a critical step in maintaining the model\'s ethical and responsible use, and it is important to prioritize end-to-end security measures to prevent any potential vulnerabilities or breaches.
Future versions of Velvet will integrate automated red-teaming protocols, continuously stress-testing the model against adversarial prompts to identify and mitigate emerging risks.
### Sensitive Data Handling and Usage Restrictions
The Velvet model has not been trained on unauthorized personal data and must not be used to process sensitive data without appropriate security measures.
Usage Restrictions:
- Prohibited use on sensitive healthcare, financial, or government data without specific safeguards.
- Mandatory human validation in scenarios where the model’s outputs could have legal or ethical consequences.
- High-risk applications (legal, medical, public governance) must implement content filtering and auditing techniques to ensure response quality and safety.
## Ethical Considerations
Almawave core values are openness, inclusivity, and helpfulness. We aim to create AI that is accessible and beneficial for everyone, regardless of their background. Velvet models are designed to be respectful of diverse perspectives and avoid unnecessary judgments. Therefore, Velvet models are designed to be inclusive and respectful of diverse perspectives and needs. We strive to avoid unnecessary judgment or the imposition of normative views, recognizing that content deemed problematic in some contexts can have valuable applications in others.
We deeply respect the dignity and autonomy of all users, particularly their right to free thought and expression, which are fundamental to innovation and progress.
While we have taken significant steps to ensure the safety and reliability of Velvet models, it is important to acknowledge that they may occasionally generate inaccurate, biased, or unsafe responses.
Almawave is actively engaging with ethics committees and domain experts to ensure continuous oversight of Velvet’s outputs, improving safeguards through community feedback.
We strongly encourage the community to exercise caution and conduct thorough safety testing and fine-tuning when using Velvet models for specific tasks.
Opinions expressed by Velvet depend on training data and do not reflect any opinions of Almawave.
## Contributions
Direction: Raniero Romagnoli
- Model engineering and training: David Alessandrini, Francesco Buciuni, Andrea Favalli, Diego Perna, David Preti, Federico Wolenski, Fabio Massimo Zanzotto
- Data engineering and management: Valentina Bellomaria, Cristina Giannone, Alfredo Serafini
- Use case adaptation and testing: Salvatore Ricciardi, Simone Scaboro, Beatrice Turano, Giancarlo Xompero
- Evaluation: Giovanni Cingolani, Silvana De Benedictis, Caterina Masotti, Riccardo Pasquini, Guillaume Ruiz, Giuseppe Scrugli, Alessandro Vizzarro
- Product and governance: Beata Dobrzynska, Matteo Amore, Marco Gennaro Di Martino, Vincenzo Sciacca, Alessandra Staglianò, Luca Vinciguerra
|
[
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING",
"TEXTUAL_ENTAILMENT",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] |
[
"SCIQ"
] |
jxm/cde-small-v2
|
jxm
|
feature-extraction
|
[
"sentence-transformers",
"safetensors",
"feature-extraction",
"mteb",
"transformers",
"modernbert",
"custom_code",
"arxiv:2410.02525",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-13T18:04:14 | 2025-02-03T23:41:38 | 3,933 | 78 |
---
base_model: answerdotai/ModernBERT-base
tags:
- mteb
- transformers
- sentence-transformers
- modernbert
model-index:
- name: cde-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: 86.01490000000001
- type: f1
value: 80.938
- type: f1_weighted
value: 86.9232
- type: ap
value: 54.949099999999994
- type: ap_weighted
value: 54.949099999999994
- type: main_score
value: 86.01490000000001
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.0223
- type: f1
value: 96.0206
- type: f1_weighted
value: 96.0206
- type: ap
value: 93.8301
- type: ap_weighted
value: 93.8301
- type: main_score
value: 96.0223
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.096000000000004
- type: f1
value: 54.4353
- type: f1_weighted
value: 54.4353
- type: main_score
value: 55.096000000000004
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_1
value: 54.125
- type: ndcg_at_3
value: 69.009
- type: ndcg_at_5
value: 72.722
- type: ndcg_at_10
value: 74.957
- type: ndcg_at_20
value: 75.801
- type: ndcg_at_100
value: 75.986
- type: ndcg_at_1000
value: 76.015
- type: map_at_1
value: 54.125
- type: map_at_3
value: 65.375
- type: map_at_5
value: 67.448
- type: map_at_10
value: 68.38499999999999
- type: map_at_20
value: 68.636
- type: map_at_100
value: 68.66600000000001
- type: map_at_1000
value: 68.66799999999999
- type: recall_at_1
value: 54.125
- type: recall_at_3
value: 79.51599999999999
- type: recall_at_5
value: 88.478
- type: recall_at_10
value: 95.306
- type: recall_at_20
value: 98.506
- type: recall_at_100
value: 99.431
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 54.125
- type: precision_at_3
value: 26.505000000000003
- type: precision_at_5
value: 17.696
- type: precision_at_10
value: 9.531
- type: precision_at_20
value: 4.925
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 54.623
- type: mrr_at_3
value: 65.505
- type: mrr_at_5
value: 67.6174
- type: mrr_at_10
value: 68.5664
- type: mrr_at_20
value: 68.8173
- type: mrr_at_100
value: 68.8476
- type: mrr_at_1000
value: 68.8489
- type: nauc_ndcg_at_1_max
value: -14.4789
- type: nauc_ndcg_at_1_std
value: -25.5432
- type: nauc_ndcg_at_1_diff1
value: 23.7267
- type: nauc_ndcg_at_3_max
value: -8.1401
- type: nauc_ndcg_at_3_std
value: -22.9099
- type: nauc_ndcg_at_3_diff1
value: 21.069499999999998
- type: nauc_ndcg_at_5_max
value: -8.4301
- type: nauc_ndcg_at_5_std
value: -22.9185
- type: nauc_ndcg_at_5_diff1
value: 21.229100000000003
- type: nauc_ndcg_at_10_max
value: -8.6651
- type: nauc_ndcg_at_10_std
value: -23.5444
- type: nauc_ndcg_at_10_diff1
value: 21.9585
- type: nauc_ndcg_at_20_max
value: -9.285400000000001
- type: nauc_ndcg_at_20_std
value: -23.4297
- type: nauc_ndcg_at_20_diff1
value: 21.6731
- type: nauc_ndcg_at_100_max
value: -9.8693
- type: nauc_ndcg_at_100_std
value: -23.313
- type: nauc_ndcg_at_100_diff1
value: 21.5888
- type: nauc_ndcg_at_1000_max
value: -9.9675
- type: nauc_ndcg_at_1000_std
value: -23.3522
- type: nauc_ndcg_at_1000_diff1
value: 21.5714
- type: nauc_map_at_1_max
value: -14.4789
- type: nauc_map_at_1_std
value: -25.5432
- type: nauc_map_at_1_diff1
value: 23.7267
- type: nauc_map_at_3_max
value: -10.0484
- type: nauc_map_at_3_std
value: -23.3575
- type: nauc_map_at_3_diff1
value: 21.329
- type: nauc_map_at_5_max
value: -10.3514
- type: nauc_map_at_5_std
value: -23.3955
- type: nauc_map_at_5_diff1
value: 21.3531
- type: nauc_map_at_10_max
value: -10.484200000000001
- type: nauc_map_at_10_std
value: -23.6726
- type: nauc_map_at_10_diff1
value: 21.6458
- type: nauc_map_at_20_max
value: -10.638499999999999
- type: nauc_map_at_20_std
value: -23.6588
- type: nauc_map_at_20_diff1
value: 21.576600000000003
- type: nauc_map_at_100_max
value: -10.717400000000001
- type: nauc_map_at_100_std
value: -23.6559
- type: nauc_map_at_100_diff1
value: 21.5688
- type: nauc_map_at_1000_max
value: -10.7203
- type: nauc_map_at_1000_std
value: -23.6557
- type: nauc_map_at_1000_diff1
value: 21.5682
- type: nauc_recall_at_1_max
value: -14.4789
- type: nauc_recall_at_1_std
value: -25.5432
- type: nauc_recall_at_1_diff1
value: 23.7267
- type: nauc_recall_at_3_max
value: -0.2134
- type: nauc_recall_at_3_std
value: -21.251800000000003
- type: nauc_recall_at_3_diff1
value: 20.3069
- type: nauc_recall_at_5_max
value: 4.109100000000001
- type: nauc_recall_at_5_std
value: -20.1382
- type: nauc_recall_at_5_diff1
value: 21.1976
- type: nauc_recall_at_10_max
value: 18.3416
- type: nauc_recall_at_10_std
value: -22.9791
- type: nauc_recall_at_10_diff1
value: 29.4668
- type: nauc_recall_at_20_max
value: 45.3219
- type: nauc_recall_at_20_std
value: -14.8366
- type: nauc_recall_at_20_diff1
value: 31.829800000000002
- type: nauc_recall_at_100_max
value: 38.8075
- type: nauc_recall_at_100_std
value: 25.4176
- type: nauc_recall_at_100_diff1
value: 32.2733
- type: nauc_recall_at_1000_max
value: 28.1372
- type: nauc_recall_at_1000_std
value: 35.442
- type: nauc_recall_at_1000_diff1
value: 31.8247
- type: nauc_precision_at_1_max
value: -14.4789
- type: nauc_precision_at_1_std
value: -25.5432
- type: nauc_precision_at_1_diff1
value: 23.7267
- type: nauc_precision_at_3_max
value: -0.2134
- type: nauc_precision_at_3_std
value: -21.251800000000003
- type: nauc_precision_at_3_diff1
value: 20.3069
- type: nauc_precision_at_5_max
value: 4.109100000000001
- type: nauc_precision_at_5_std
value: -20.1382
- type: nauc_precision_at_5_diff1
value: 21.1976
- type: nauc_precision_at_10_max
value: 18.3416
- type: nauc_precision_at_10_std
value: -22.9791
- type: nauc_precision_at_10_diff1
value: 29.4668
- type: nauc_precision_at_20_max
value: 45.3219
- type: nauc_precision_at_20_std
value: -14.8366
- type: nauc_precision_at_20_diff1
value: 31.829800000000002
- type: nauc_precision_at_100_max
value: 38.8075
- type: nauc_precision_at_100_std
value: 25.4176
- type: nauc_precision_at_100_diff1
value: 32.2733
- type: nauc_precision_at_1000_max
value: 28.1372
- type: nauc_precision_at_1000_std
value: 35.442
- type: nauc_precision_at_1000_diff1
value: 31.8247
- type: nauc_mrr_at_1_max
value: -14.066600000000001
- type: nauc_mrr_at_1_std
value: -25.0145
- type: nauc_mrr_at_1_diff1
value: 22.361900000000002
- type: nauc_mrr_at_3_max
value: -10.6465
- type: nauc_mrr_at_3_std
value: -23.4323
- type: nauc_mrr_at_3_diff1
value: 19.758899999999997
- type: nauc_mrr_at_5_max
value: -10.7144
- type: nauc_mrr_at_5_std
value: -23.2823
- type: nauc_mrr_at_5_diff1
value: 19.8552
- type: nauc_mrr_at_10_max
value: -10.7815
- type: nauc_mrr_at_10_std
value: -23.51
- type: nauc_mrr_at_10_diff1
value: 20.157
- type: nauc_mrr_at_20_max
value: -10.9391
- type: nauc_mrr_at_20_std
value: -23.4946
- type: nauc_mrr_at_20_diff1
value: 20.072400000000002
- type: nauc_mrr_at_100_max
value: -11.018500000000001
- type: nauc_mrr_at_100_std
value: -23.491400000000002
- type: nauc_mrr_at_100_diff1
value: 20.0627
- type: nauc_mrr_at_1000_max
value: -11.0214
- type: nauc_mrr_at_1000_std
value: -23.491300000000003
- type: nauc_mrr_at_1000_diff1
value: 20.061999999999998
- type: main_score
value: 74.957
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 50.5269
- type: v_measure_std
value: 14.0094
- type: main_score
value: 50.5269
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.620200000000004
- type: v_measure_std
value: 14.4842
- type: main_score
value: 41.620200000000004
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.790299999999995
- type: mrr
value: 75.8156
- type: nAUC_map_max
value: 26.151200000000003
- type: nAUC_map_std
value: 15.8953
- type: nAUC_map_diff1
value: 5.0684
- type: nAUC_mrr_max
value: 36.9643
- type: nAUC_mrr_std
value: 19.0749
- type: nAUC_mrr_diff1
value: 15.549399999999999
- type: main_score
value: 61.790299999999995
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: pearson
value: 88.41590000000001
- type: spearman
value: 86.7116
- type: cosine_pearson
value: 88.41590000000001
- type: cosine_spearman
value: 86.7116
- type: manhattan_pearson
value: 86.2045
- type: manhattan_spearman
value: 85.7248
- type: euclidean_pearson
value: 86.2336
- type: euclidean_spearman
value: 85.861
- type: main_score
value: 86.7116
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.3052
- type: f1
value: 88.2617
- type: f1_weighted
value: 88.2617
- type: main_score
value: 88.3052
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 45.4377
- type: v_measure_std
value: 0.8543000000000001
- type: main_score
value: 45.4377
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 39.6472
- type: v_measure_std
value: 0.7081999999999999
- type: main_score
value: 39.6472
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: ndcg_at_1
value: 39.342
- type: ndcg_at_3
value: 44.718999999999994
- type: ndcg_at_5
value: 47.449999999999996
- type: ndcg_at_10
value: 50.17
- type: ndcg_at_20
value: 52.366
- type: ndcg_at_100
value: 55.400000000000006
- type: ndcg_at_1000
value: 57.13399999999999
- type: map_at_1
value: 32.300000000000004
- type: map_at_3
value: 39.937
- type: map_at_5
value: 42.141
- type: map_at_10
value: 43.681
- type: map_at_20
value: 44.516
- type: map_at_100
value: 45.14
- type: map_at_1000
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- type: recall_at_1
value: 32.300000000000004
- type: recall_at_3
value: 47.12
- type: recall_at_5
value: 54.581
- type: recall_at_10
value: 62.873000000000005
- type: recall_at_20
value: 70.604
- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_3
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- type: precision_at_5
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- 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
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- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_diff1
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- type: nauc_ndcg_at_3_max
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value: 50.0479
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value: 45.7247
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value: -1.5435
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value: 45.0658
- type: nauc_map_at_1000_max
value: 42.2442
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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
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dataset:
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type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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type: mteb/amazon_massive_scenario
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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: pearson
value: 81.13050000000001
- type: spearman
value: 79.01310000000001
- type: cosine_pearson
value: 81.13050000000001
- type: cosine_spearman
value: 79.01310000000001
- type: manhattan_pearson
value: 79.03999999999999
- type: manhattan_spearman
value: 79.1744
- type: euclidean_pearson
value: 79.0977
- type: euclidean_spearman
value: 79.2268
- type: main_score
value: 79.01310000000001
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 86.9675
- type: spearman
value: 80.3531
- type: cosine_pearson
value: 86.9675
- type: cosine_spearman
value: 80.3531
- type: manhattan_pearson
value: 82.2315
- type: manhattan_spearman
value: 79.7004
- type: euclidean_pearson
value: 82.3305
- type: euclidean_spearman
value: 79.8601
- type: main_score
value: 80.3531
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 85.6041
- type: spearman
value: 86.0453
- type: cosine_pearson
value: 85.6041
- type: cosine_spearman
value: 86.0453
- type: manhattan_pearson
value: 85.2548
- type: manhattan_spearman
value: 85.8908
- type: euclidean_pearson
value: 85.253
- type: euclidean_spearman
value: 85.9181
- type: main_score
value: 86.0453
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 82.8792
- type: spearman
value: 82.9681
- type: cosine_pearson
value: 82.8792
- type: cosine_spearman
value: 82.9681
- type: manhattan_pearson
value: 81.4789
- type: manhattan_spearman
value: 82.4797
- type: euclidean_pearson
value: 81.4674
- type: euclidean_spearman
value: 82.4547
- type: main_score
value: 82.9681
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 87.5356
- type: spearman
value: 88.06540000000001
- type: cosine_pearson
value: 87.5356
- type: cosine_spearman
value: 88.06540000000001
- type: manhattan_pearson
value: 87.10759999999999
- type: manhattan_spearman
value: 87.75309999999999
- type: euclidean_pearson
value: 87.1489
- type: euclidean_spearman
value: 87.7857
- type: main_score
value: 88.06540000000001
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 85.0208
- type: spearman
value: 86.0136
- type: cosine_pearson
value: 85.0208
- type: cosine_spearman
value: 86.0136
- type: manhattan_pearson
value: 85.22
- type: manhattan_spearman
value: 86.1101
- type: euclidean_pearson
value: 85.2043
- type: euclidean_spearman
value: 86.113
- type: main_score
value: 86.0136
- 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: 89.4083
- type: spearman
value: 88.9498
- type: cosine_pearson
value: 89.4083
- type: cosine_spearman
value: 88.9498
- type: manhattan_pearson
value: 89.46539999999999
- type: manhattan_spearman
value: 88.8754
- type: euclidean_pearson
value: 89.4326
- type: euclidean_spearman
value: 88.8148
- type: main_score
value: 88.9498
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 66.60770000000001
- type: spearman
value: 67.1515
- type: cosine_pearson
value: 66.60770000000001
- type: cosine_spearman
value: 67.1515
- type: manhattan_pearson
value: 66.5604
- type: manhattan_spearman
value: 66.4621
- type: euclidean_pearson
value: 66.4628
- type: euclidean_spearman
value: 66.2979
- type: main_score
value: 67.1515
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 86.86399999999999
- type: spearman
value: 87.7139
- type: cosine_pearson
value: 86.86399999999999
- type: cosine_spearman
value: 87.7139
- type: manhattan_pearson
value: 86.6602
- type: manhattan_spearman
value: 87.2606
- type: euclidean_pearson
value: 86.5924
- type: euclidean_spearman
value: 87.241
- type: main_score
value: 87.7139
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.37360000000001
- type: mrr
value: 95.6275
- type: nAUC_map_max
value: 52.991699999999994
- type: nAUC_map_std
value: 66.8168
- type: nAUC_map_diff1
value: -3.2009999999999996
- type: nAUC_mrr_max
value: 85.7492
- type: nAUC_mrr_std
value: 77.3543
- type: nAUC_mrr_diff1
value: 38.014700000000005
- type: main_score
value: 84.37360000000001
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_3
value: 68.209
- type: ndcg_at_5
value: 71.409
- type: ndcg_at_10
value: 73.476
- type: ndcg_at_20
value: 74.339
- type: ndcg_at_100
value: 75.57000000000001
- type: ndcg_at_1000
value: 75.955
- type: map_at_1
value: 58.178
- type: map_at_3
value: 65.71900000000001
- type: map_at_5
value: 67.73
- type: map_at_10
value: 68.821
- type: map_at_20
value: 69.07600000000001
- type: map_at_100
value: 69.245
- type: map_at_1000
value: 69.258
- type: recall_at_1
value: 58.178
- type: recall_at_3
value: 73.172
- type: recall_at_5
value: 81.0
- type: recall_at_10
value: 86.867
- type: recall_at_20
value: 90.267
- type: recall_at_100
value: 96.933
- type: recall_at_1000
value: 100.0
- type: precision_at_1
value: 60.667
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 18.0
- type: precision_at_10
value: 9.866999999999999
- type: precision_at_20
value: 5.133
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 60.6667
- type: mrr_at_3
value: 67.1667
- type: mrr_at_5
value: 68.85
- type: mrr_at_10
value: 69.4799
- type: mrr_at_20
value: 69.6658
- type: mrr_at_100
value: 69.8134
- type: mrr_at_1000
value: 69.8257
- type: nauc_ndcg_at_1_max
value: 49.3608
- type: nauc_ndcg_at_1_std
value: 12.742400000000002
- type: nauc_ndcg_at_1_diff1
value: 74.5012
- type: nauc_ndcg_at_3_max
value: 49.524499999999996
- type: nauc_ndcg_at_3_std
value: 7.7241
- type: nauc_ndcg_at_3_diff1
value: 72.0127
- type: nauc_ndcg_at_5_max
value: 51.897099999999995
- type: nauc_ndcg_at_5_std
value: 12.8641
- type: nauc_ndcg_at_5_diff1
value: 69.7789
- type: nauc_ndcg_at_10_max
value: 55.1141
- type: nauc_ndcg_at_10_std
value: 17.136499999999998
- type: nauc_ndcg_at_10_diff1
value: 68.8711
- type: nauc_ndcg_at_20_max
value: 54.74719999999999
- type: nauc_ndcg_at_20_std
value: 17.0485
- type: nauc_ndcg_at_20_diff1
value: 69.4701
- type: nauc_ndcg_at_100_max
value: 53.7619
- type: nauc_ndcg_at_100_std
value: 15.335299999999998
- type: nauc_ndcg_at_100_diff1
value: 70.34479999999999
- type: nauc_ndcg_at_1000_max
value: 53.4516
- type: nauc_ndcg_at_1000_std
value: 14.7843
- type: nauc_ndcg_at_1000_diff1
value: 70.6041
- type: nauc_map_at_1_max
value: 44.9654
- type: nauc_map_at_1_std
value: 5.9821
- type: nauc_map_at_1_diff1
value: 76.2581
- type: nauc_map_at_3_max
value: 47.515299999999996
- type: nauc_map_at_3_std
value: 6.2703
- type: nauc_map_at_3_diff1
value: 73.5279
- type: nauc_map_at_5_max
value: 49.805899999999994
- type: nauc_map_at_5_std
value: 10.1001
- type: nauc_map_at_5_diff1
value: 72.1812
- type: nauc_map_at_10_max
value: 51.9276
- type: nauc_map_at_10_std
value: 12.698200000000002
- type: nauc_map_at_10_diff1
value: 71.6343
- type: nauc_map_at_20_max
value: 51.8856
- type: nauc_map_at_20_std
value: 12.814800000000002
- type: nauc_map_at_20_diff1
value: 71.78179999999999
- type: nauc_map_at_100_max
value: 51.7504
- type: nauc_map_at_100_std
value: 12.5353
- type: nauc_map_at_100_diff1
value: 71.8854
- type: nauc_map_at_1000_max
value: 51.739900000000006
- type: nauc_map_at_1000_std
value: 12.519
- type: nauc_map_at_1000_diff1
value: 71.8964
- type: nauc_recall_at_1_max
value: 44.9654
- type: nauc_recall_at_1_std
value: 5.9821
- type: nauc_recall_at_1_diff1
value: 76.2581
- type: nauc_recall_at_3_max
value: 47.9306
- type: nauc_recall_at_3_std
value: 3.5374000000000003
- type: nauc_recall_at_3_diff1
value: 68.4552
- type: nauc_recall_at_5_max
value: 54.374
- type: nauc_recall_at_5_std
value: 17.646700000000003
- type: nauc_recall_at_5_diff1
value: 60.5644
- type: nauc_recall_at_10_max
value: 69.6484
- type: nauc_recall_at_10_std
value: 38.3671
- type: nauc_recall_at_10_diff1
value: 54.39580000000001
- type: nauc_recall_at_20_max
value: 70.0061
- type: nauc_recall_at_20_std
value: 42.403999999999996
- type: nauc_recall_at_20_diff1
value: 55.3831
- type: nauc_recall_at_100_max
value: 69.02629999999999
- type: nauc_recall_at_100_std
value: 43.850699999999996
- type: nauc_recall_at_100_diff1
value: 57.837
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_precision_at_1_max
value: 49.3608
- type: nauc_precision_at_1_std
value: 12.742400000000002
- type: nauc_precision_at_1_diff1
value: 74.5012
- type: nauc_precision_at_3_max
value: 45.2627
- type: nauc_precision_at_3_std
value: 15.5113
- type: nauc_precision_at_3_diff1
value: 44.5108
- type: nauc_precision_at_5_max
value: 48.4003
- type: nauc_precision_at_5_std
value: 35.3791
- type: nauc_precision_at_5_diff1
value: 19.7518
- type: nauc_precision_at_10_max
value: 46.688
- type: nauc_precision_at_10_std
value: 47.9876
- type: nauc_precision_at_10_diff1
value: 0.1083
- type: nauc_precision_at_20_max
value: 41.281400000000005
- type: nauc_precision_at_20_std
value: 49.0662
- type: nauc_precision_at_20_diff1
value: -6.2035
- type: nauc_precision_at_100_max
value: 30.0167
- type: nauc_precision_at_100_std
value: 47.2561
- type: nauc_precision_at_100_diff1
value: -22.8584
- type: nauc_precision_at_1000_max
value: 23.724999999999998
- type: nauc_precision_at_1000_std
value: 45.342
- type: nauc_precision_at_1000_diff1
value: -33.29
- type: nauc_mrr_at_1_max
value: 49.3608
- type: nauc_mrr_at_1_std
value: 12.742400000000002
- type: nauc_mrr_at_1_diff1
value: 74.5012
- type: nauc_mrr_at_3_max
value: 51.1718
- type: nauc_mrr_at_3_std
value: 11.739700000000001
- type: nauc_mrr_at_3_diff1
value: 71.5992
- type: nauc_mrr_at_5_max
value: 52.2421
- type: nauc_mrr_at_5_std
value: 14.127
- type: nauc_mrr_at_5_diff1
value: 70.57
- type: nauc_mrr_at_10_max
value: 52.5587
- type: nauc_mrr_at_10_std
value: 14.5207
- type: nauc_mrr_at_10_diff1
value: 70.55709999999999
- type: nauc_mrr_at_20_max
value: 52.3699
- type: nauc_mrr_at_20_std
value: 14.310300000000002
- type: nauc_mrr_at_20_diff1
value: 70.6993
- type: nauc_mrr_at_100_max
value: 52.2734
- type: nauc_mrr_at_100_std
value: 14.0848
- type: nauc_mrr_at_100_diff1
value: 70.8146
- type: nauc_mrr_at_1000_max
value: 52.2622
- type: nauc_mrr_at_1000_std
value: 14.0715
- type: nauc_mrr_at_1000_diff1
value: 70.8239
- type: main_score
value: 73.476
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: similarity_accuracy
value: 99.87819999999999
- type: similarity_accuracy_threshold
value: 74.8
- type: similarity_f1
value: 93.79729999999999
- type: similarity_f1_threshold
value: 74.6812
- type: similarity_precision
value: 94.6083
- type: similarity_recall
value: 93.0
- type: similarity_ap
value: 97.1971
- type: cosine_accuracy
value: 99.87819999999999
- type: cosine_accuracy_threshold
value: 74.8
- type: cosine_f1
value: 93.79729999999999
- type: cosine_f1_threshold
value: 74.6812
- type: cosine_precision
value: 94.6083
- type: cosine_recall
value: 93.0
- type: cosine_ap
value: 97.1971
- type: manhattan_accuracy
value: 99.8792
- type: manhattan_accuracy_threshold
value: 47567.8925
- type: manhattan_f1
value: 93.8508
- type: manhattan_f1_threshold
value: 47567.8925
- type: manhattan_precision
value: 94.6138
- type: manhattan_recall
value: 93.10000000000001
- type: manhattan_ap
value: 97.2177
- type: euclidean_accuracy
value: 99.8812
- type: euclidean_accuracy_threshold
value: 2164.0619
- type: euclidean_f1
value: 93.9759
- type: euclidean_f1_threshold
value: 2164.0619
- type: euclidean_precision
value: 94.35480000000001
- type: euclidean_recall
value: 93.60000000000001
- type: euclidean_ap
value: 97.2412
- type: dot_accuracy
value: 99.8446
- type: dot_accuracy_threshold
value: 68470.2454
- type: dot_f1
value: 91.9939
- type: dot_f1_threshold
value: 68470.2454
- type: dot_precision
value: 93.8606
- type: dot_recall
value: 90.2
- type: dot_ap
value: 96.36829999999999
- type: max_accuracy
value: 99.8812
- type: max_f1
value: 93.9759
- type: max_precision
value: 94.6138
- type: max_recall
value: 93.60000000000001
- type: max_ap
value: 97.2412
- type: main_score
value: 97.2412
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 70.04010000000001
- type: v_measure_std
value: 3.9558999999999997
- type: main_score
value: 70.04010000000001
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 42.4207
- type: v_measure_std
value: 1.3677
- type: main_score
value: 42.4207
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.7026
- type: mrr
value: 53.5668
- type: nAUC_map_max
value: 12.1758
- type: nAUC_map_std
value: 6.7148
- type: nAUC_map_diff1
value: 39.881499999999996
- type: nAUC_mrr_max
value: 13.0771
- type: nAUC_mrr_std
value: 7.7001
- type: nAUC_mrr_diff1
value: 39.6391
- type: main_score
value: 52.7026
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: pearson
value: 31.346400000000003
- type: spearman
value: 31.5967
- type: cosine_spearman
value: 31.5967
- type: cosine_pearson
value: 31.346400000000003
- type: dot_spearman
value: 28.5388
- type: dot_pearson
value: 31.005300000000002
- type: main_score
value: 31.5967
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: ndcg_at_1
value: 87.0
- type: ndcg_at_3
value: 84.693
- type: ndcg_at_5
value: 82.211
- type: ndcg_at_10
value: 80.55
- type: ndcg_at_20
value: 77.766
- type: ndcg_at_100
value: 62.881
- type: ndcg_at_1000
value: 56.510000000000005
- type: map_at_1
value: 0.251
- type: map_at_3
value: 0.7000000000000001
- type: map_at_5
value: 1.124
- type: map_at_10
value: 2.114
- type: map_at_20
value: 3.837
- type: map_at_100
value: 12.903999999999998
- type: map_at_1000
value: 31.184
- type: recall_at_1
value: 0.251
- type: recall_at_3
value: 0.72
- type: recall_at_5
value: 1.179
- type: recall_at_10
value: 2.271
- type: recall_at_20
value: 4.242
- type: recall_at_100
value: 16.012999999999998
- type: recall_at_1000
value: 53.556000000000004
- type: precision_at_1
value: 92.0
- type: precision_at_3
value: 88.667
- type: precision_at_5
value: 86.8
- type: precision_at_10
value: 85.8
- type: precision_at_20
value: 82.39999999999999
- type: precision_at_100
value: 64.8
- type: precision_at_1000
value: 24.832
- type: mrr_at_1
value: 92.0
- type: mrr_at_3
value: 95.0
- type: mrr_at_5
value: 95.0
- type: mrr_at_10
value: 95.0
- type: mrr_at_20
value: 95.0
- type: mrr_at_100
value: 95.0
- type: mrr_at_1000
value: 95.0
- type: nauc_ndcg_at_1_max
value: 73.7596
- type: nauc_ndcg_at_1_std
value: 52.21130000000001
- type: nauc_ndcg_at_1_diff1
value: -8.4225
- type: nauc_ndcg_at_3_max
value: 68.513
- type: nauc_ndcg_at_3_std
value: 61.9698
- type: nauc_ndcg_at_3_diff1
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type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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value: -21.662300000000002
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value: 10.96
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value: 21.83
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
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value: 81.4014
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value: 64.3103
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value: 85.0047
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value: 22.2804
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value: 22.2804
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value: 81.4014
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 66.4403
- type: f1
value: 66.8774
- type: f1_weighted
value: 65.9999
- type: main_score
value: 66.4403
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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value: 53.3153
- type: v_measure_std
value: 1.2923
- type: main_score
value: 53.3153
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 85.22380000000001
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value: 74.7432
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value: 66.2828
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value: 69.9472
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value: 60.765299999999996
- type: similarity_recall
value: 72.9024
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value: 72.0492
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value: 85.22380000000001
- type: cosine_accuracy_threshold
value: 74.7432
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value: 66.2828
- type: cosine_f1_threshold
value: 69.9472
- type: cosine_precision
value: 60.765299999999996
- type: cosine_recall
value: 72.9024
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value: 72.0492
- type: manhattan_accuracy
value: 85.10459999999999
- type: manhattan_accuracy_threshold
value: 48810.3699
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value: 65.7133
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value: 53724.462900000006
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value: 60.3399
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value: 72.1372
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value: 71.3681
- type: euclidean_accuracy
value: 85.1404
- type: euclidean_accuracy_threshold
value: 2203.8609
- type: euclidean_f1
value: 65.8107
- type: euclidean_f1_threshold
value: 2445.96
- type: euclidean_precision
value: 59.8875
- type: euclidean_recall
value: 73.0343
- type: euclidean_ap
value: 71.3938
- type: dot_accuracy
value: 84.8781
- type: dot_accuracy_threshold
value: 74077.38040000001
- type: dot_f1
value: 65.3706
- type: dot_f1_threshold
value: 69501.5808
- type: dot_precision
value: 60.58559999999999
- type: dot_recall
value: 70.97630000000001
- type: dot_ap
value: 71.0091
- type: max_accuracy
value: 85.22380000000001
- type: max_f1
value: 66.2828
- type: max_precision
value: 60.765299999999996
- type: max_recall
value: 73.0343
- type: max_ap
value: 72.0492
- type: main_score
value: 72.0492
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 89.145
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value: 65.00280000000001
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value: 78.78150000000001
- type: similarity_f1_threshold
value: 61.2185
- type: similarity_precision
value: 75.0279
- type: similarity_recall
value: 82.9304
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value: 86.39949999999999
- type: cosine_accuracy
value: 89.145
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value: 65.00280000000001
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value: 78.78150000000001
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value: 61.2185
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value: 75.0279
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value: 82.9304
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value: 86.39949999999999
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value: 89.05579999999999
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value: 55381.189
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value: 78.6152
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value: 58447.6685
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value: 74.77080000000001
- type: manhattan_recall
value: 82.8765
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value: 86.2899
- type: euclidean_accuracy
value: 89.1179
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value: 2552.2853999999998
- type: euclidean_f1
value: 78.6816
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value: 2660.0677
- type: euclidean_precision
value: 74.4317
- type: euclidean_recall
value: 83.4463
- type: euclidean_ap
value: 86.3158
- type: dot_accuracy
value: 88.81710000000001
- type: dot_accuracy_threshold
value: 58383.1421
- type: dot_f1
value: 78.2367
- type: dot_f1_threshold
value: 54826.550299999995
- type: dot_precision
value: 73.7657
- type: dot_recall
value: 83.2846
- type: dot_ap
value: 85.5699
- type: max_accuracy
value: 89.145
- type: max_f1
value: 78.78150000000001
- type: max_precision
value: 75.0279
- type: max_recall
value: 83.4463
- type: max_ap
value: 86.39949999999999
- type: main_score
value: 86.39949999999999
---
# cde-small-v2
> [!NOTE]
> **Note on parameter count:** Although HuggingFace reports the size of this model as 281M params, really it can be thought of as 140M. That's because our weights actually contain the weights of two models (dubbed "first stage" and "second stage"), and only the second-stage model is used to compute embeddings at search time.
<a href="https://github.com/jxmorris12/cde">Github</a>
Our new model that naturally integrates "context tokens" into the embedding process. As of January 13th, 2025, `cde-small-v2` 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 65.58.
👉 <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-v2`
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-v2 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-v2", trust_remote_code=True)
tokenizer = transformers.AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
```
#### 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-v2/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-v2 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-v2", 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-v2", 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/1ddWeNj9nztHrwtoSEtaArfs7_NZhZA6k?usp=sharing)
### Training details
All non-mentioned other hyperparameters (learning, etc.) are either in the config or CDE paper. If not, please raise an issue here: https://github.com/jxmorris12/cde
#### Model details
cde-small-v2 includes a number of modeling changes from cde-small-v1:
- used the recently-released [ModernBERT](https://huggingface.co/blog/modernbert)
- added a residual connection between the model stages, which helps conditioning and gradient flow
- disabled pooling over instruction tokens
- disable position-embedding nullification over contextual tokens
- disable weight decay (not sure if this one helped or not)
#### Unsupervised training
Trained for six epochs on the nomic-unsupervised dataset with cluster size of 512 and batch size of 512, using GTR clusters and GTE-large filtering. (Probably would have performed better with GTE clustering too, but that's an expensive operation that we didn't rerun.)
#### Supervised training
Trained for four epochs on the BGE dataset with GTE clusters and GTE hard-negative filtering.
### 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"
] |
aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct
|
aisingapore
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2311.07911",
"arxiv:2306.05685",
"base_model:aisingapore/llama3.1-8b-cpt-sea-lionv3-base",
"base_model:finetune:aisingapore/llama3.1-8b-cpt-sea-lionv3-base",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-11T10:20:41 | 2024-12-19T12:49:17 | 3,931 | 4 |
---
base_model:
- aisingapore/llama3.1-8b-cpt-sea-lionv3-base
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
---
<div>
<img src="llama_3.1_8b_sea-lion_v3_instruct_banner.png"/>
</div>
# Llama3.1 8B CPT SEA-LIONv3 Instruct
SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
Llama3.1 8B CPT SEA-LIONv3 Instruct is a multilingual model that has been fine-tuned in two stages on approximately **12.3M English instruction-completion pairs** alongside a pool of **4.5M Southeast Asian instruction-completion pairs** from SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai and Vietnamese.
SEA-LION stands for _Southeast Asian Languages In One Network_.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
- **License:** [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE)
## Model Details
### Model Description
We performed instruction tuning in English and also in SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai and Vietnamese on our [continued pre-trained Llama3.1 8B CPT SEA-LIONv3 Base](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-base), a decoder model using the Llama 3.1 architecture, to create Llama3.1 8B CPT SEA-LIONv3 Instruct.
For tokenisation, the model employs the default tokenizer used in Llama 3.1 8B Instruct. The model has a context length of 128k.
### Benchmark Performance
We evaluated Llama3.1 8B CPT SEA-LIONv3 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the [SEA-HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarisation (Abssum), Causal Reasoning (Causal) and Natural Language Inference (NLI).
Note: SEA-HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
#### Instruction-following Capabilities
Since Llama3.1 8B CPT SEA-LIONv3 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, SEA-IFEval (based on [IFEval](https://arxiv.org/abs/2311.07911)) and SEA-MTBench (based on [MT-Bench](https://arxiv.org/abs/2306.05685)).
As these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localise and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**SEA-IFEval**
SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalised by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
**SEA-MTBench**
SEA-MTBench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction). A tie is given a score of 0.5.
For more details on Llama3.1 8B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA-HELM leaderboard, https://leaderboard.sea-lion.ai/.
### Usage
Llama3.1 8B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library
```python
import transformers
import torch
model_id = "aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
Llama3.1 8B CPT SEA-LIONv3 Instruct was tuned using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 1024 GPU hours, on a single node of 8x H100-80GB GPUs.
## Data
Llama3.1 8B CPT SEA-LIONv3 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.
## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.
## The Team
Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
## Disclaimer
This is the repository for the commercial instruction-tuned model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
|
[
"QUESTION_ANSWERING",
"TRANSLATION"
] |
[
"CHIA"
] |
bigscience/T0_3B
|
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",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2024-03-09T13:39:07 | 3,910 | 100 |
---
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."
---
**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"
] |
jinaai/jina-embedding-s-en-v1
|
jinaai
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"t5",
"finetuner",
"mteb",
"feature-extraction",
"sentence-similarity",
"custom_code",
"en",
"dataset:jinaai/negation-dataset",
"arxiv:2307.11224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-06T11:37:14 | 2025-01-06T16:31:58 | 3,739 | 26 |
---
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- finetuner
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
model-index:
- name: jina-embedding-s-en-v1
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.82089552238806
- type: ap
value: 27.100981946230778
- type: f1
value: 58.3354886367184
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 64.282775
- type: ap
value: 60.350688924943796
- type: f1
value: 62.06346948494396
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 30.623999999999995
- type: f1
value: 29.427789186742153
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.119
- type: map_at_10
value: 35.609
- type: map_at_100
value: 36.935
- type: map_at_1000
value: 36.957
- type: map_at_3
value: 31.046000000000003
- type: map_at_5
value: 33.574
- type: mrr_at_1
value: 22.404
- type: mrr_at_10
value: 35.695
- type: mrr_at_100
value: 37.021
- type: mrr_at_1000
value: 37.043
- type: mrr_at_3
value: 31.093
- type: mrr_at_5
value: 33.635999999999996
- type: ndcg_at_1
value: 22.119
- type: ndcg_at_10
value: 43.566
- type: ndcg_at_100
value: 49.370000000000005
- type: ndcg_at_1000
value: 49.901
- type: ndcg_at_3
value: 34.06
- type: ndcg_at_5
value: 38.653999999999996
- type: precision_at_1
value: 22.119
- type: precision_at_10
value: 6.92
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.272000000000002
- type: precision_at_5
value: 10.811
- type: recall_at_1
value: 22.119
- type: recall_at_10
value: 69.203
- type: recall_at_100
value: 95.021
- type: recall_at_1000
value: 99.075
- type: recall_at_3
value: 42.817
- type: recall_at_5
value: 54.054
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 34.1740289109719
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 23.985251383455463
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.24873612289029
- type: mrr
value: 74.65692740623489
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.22415390332444
- type: cos_sim_spearman
value: 82.9591191954711
- type: euclidean_pearson
value: 44.096317524324945
- type: euclidean_spearman
value: 42.95218351391625
- type: manhattan_pearson
value: 44.07766490545065
- type: manhattan_spearman
value: 42.78350497166606
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 74.64285714285714
- type: f1
value: 73.53680835577447
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 28.512813238490164
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 20.942214972649488
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.255999999999997
- type: map_at_10
value: 37.091
- type: map_at_100
value: 38.428000000000004
- type: map_at_1000
value: 38.559
- type: map_at_3
value: 34.073
- type: map_at_5
value: 35.739
- type: mrr_at_1
value: 34.907
- type: mrr_at_10
value: 42.769
- type: mrr_at_100
value: 43.607
- type: mrr_at_1000
value: 43.656
- type: mrr_at_3
value: 39.986
- type: mrr_at_5
value: 41.581
- type: ndcg_at_1
value: 34.907
- type: ndcg_at_10
value: 42.681000000000004
- type: ndcg_at_100
value: 48.213
- type: ndcg_at_1000
value: 50.464
- type: ndcg_at_3
value: 37.813
- type: ndcg_at_5
value: 39.936
- type: precision_at_1
value: 34.907
- type: precision_at_10
value: 7.911
- type: precision_at_100
value: 1.349
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 17.93
- type: precision_at_5
value: 12.732
- type: recall_at_1
value: 28.255999999999997
- type: recall_at_10
value: 53.49699999999999
- type: recall_at_100
value: 77.288
- type: recall_at_1000
value: 91.776
- type: recall_at_3
value: 39.18
- type: recall_at_5
value: 45.365
- type: map_at_1
value: 25.563999999999997
- type: map_at_10
value: 33.913
- type: map_at_100
value: 34.966
- type: map_at_1000
value: 35.104
- type: map_at_3
value: 31.413000000000004
- type: map_at_5
value: 32.854
- type: mrr_at_1
value: 31.72
- type: mrr_at_10
value: 39.391
- type: mrr_at_100
value: 40.02
- type: mrr_at_1000
value: 40.076
- type: mrr_at_3
value: 37.314
- type: mrr_at_5
value: 38.507999999999996
- type: ndcg_at_1
value: 31.72
- type: ndcg_at_10
value: 38.933
- type: ndcg_at_100
value: 43.024
- type: ndcg_at_1000
value: 45.556999999999995
- type: ndcg_at_3
value: 35.225
- type: ndcg_at_5
value: 36.984
- type: precision_at_1
value: 31.72
- type: precision_at_10
value: 7.248
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 16.943
- type: precision_at_5
value: 11.975
- type: recall_at_1
value: 25.563999999999997
- type: recall_at_10
value: 47.808
- type: recall_at_100
value: 65.182
- type: recall_at_1000
value: 81.831
- type: recall_at_3
value: 36.889
- type: recall_at_5
value: 41.829
- type: map_at_1
value: 33.662
- type: map_at_10
value: 44.096999999999994
- type: map_at_100
value: 45.153999999999996
- type: map_at_1000
value: 45.223
- type: map_at_3
value: 41.377
- type: map_at_5
value: 42.935
- type: mrr_at_1
value: 38.997
- type: mrr_at_10
value: 47.675
- type: mrr_at_100
value: 48.476
- type: mrr_at_1000
value: 48.519
- type: mrr_at_3
value: 45.549
- type: mrr_at_5
value: 46.884
- type: ndcg_at_1
value: 38.997
- type: ndcg_at_10
value: 49.196
- type: ndcg_at_100
value: 53.788000000000004
- type: ndcg_at_1000
value: 55.393
- type: ndcg_at_3
value: 44.67
- type: ndcg_at_5
value: 46.991
- type: precision_at_1
value: 38.997
- type: precision_at_10
value: 7.875
- type: precision_at_100
value: 1.102
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 19.854
- type: precision_at_5
value: 13.605
- type: recall_at_1
value: 33.662
- type: recall_at_10
value: 60.75899999999999
- type: recall_at_100
value: 81.11699999999999
- type: recall_at_1000
value: 92.805
- type: recall_at_3
value: 48.577999999999996
- type: recall_at_5
value: 54.384
- type: map_at_1
value: 21.313
- type: map_at_10
value: 29.036
- type: map_at_100
value: 29.975
- type: map_at_1000
value: 30.063000000000002
- type: map_at_3
value: 26.878999999999998
- type: map_at_5
value: 28.005999999999997
- type: mrr_at_1
value: 23.39
- type: mrr_at_10
value: 31.072
- type: mrr_at_100
value: 31.922
- type: mrr_at_1000
value: 31.995
- type: mrr_at_3
value: 28.908
- type: mrr_at_5
value: 30.104999999999997
- type: ndcg_at_1
value: 23.39
- type: ndcg_at_10
value: 33.448
- type: ndcg_at_100
value: 38.255
- type: ndcg_at_1000
value: 40.542
- type: ndcg_at_3
value: 29.060000000000002
- type: ndcg_at_5
value: 31.023
- type: precision_at_1
value: 23.39
- type: precision_at_10
value: 5.175
- type: precision_at_100
value: 0.8049999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.504999999999999
- type: precision_at_5
value: 8.61
- type: recall_at_1
value: 21.313
- type: recall_at_10
value: 45.345
- type: recall_at_100
value: 67.752
- type: recall_at_1000
value: 84.937
- type: recall_at_3
value: 33.033
- type: recall_at_5
value: 37.929
- type: map_at_1
value: 14.255999999999998
- type: map_at_10
value: 20.339
- type: map_at_100
value: 21.491
- type: map_at_1000
value: 21.616
- type: map_at_3
value: 18.481
- type: map_at_5
value: 19.594
- type: mrr_at_1
value: 17.413
- type: mrr_at_10
value: 24.146
- type: mrr_at_100
value: 25.188
- type: mrr_at_1000
value: 25.273
- type: mrr_at_3
value: 22.264
- type: mrr_at_5
value: 23.302
- type: ndcg_at_1
value: 17.413
- type: ndcg_at_10
value: 24.272
- type: ndcg_at_100
value: 29.82
- type: ndcg_at_1000
value: 33.072
- type: ndcg_at_3
value: 20.826
- type: ndcg_at_5
value: 22.535
- type: precision_at_1
value: 17.413
- type: precision_at_10
value: 4.366
- type: precision_at_100
value: 0.818
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 9.866999999999999
- type: precision_at_5
value: 7.164
- type: recall_at_1
value: 14.255999999999998
- type: recall_at_10
value: 32.497
- type: recall_at_100
value: 56.592
- type: recall_at_1000
value: 80.17699999999999
- type: recall_at_3
value: 23.195
- type: recall_at_5
value: 27.392
- type: map_at_1
value: 22.709
- type: map_at_10
value: 31.377
- type: map_at_100
value: 32.536
- type: map_at_1000
value: 32.669
- type: map_at_3
value: 28.572999999999997
- type: map_at_5
value: 30.205
- type: mrr_at_1
value: 27.815
- type: mrr_at_10
value: 36.452
- type: mrr_at_100
value: 37.302
- type: mrr_at_1000
value: 37.364000000000004
- type: mrr_at_3
value: 33.75
- type: mrr_at_5
value: 35.43
- type: ndcg_at_1
value: 27.815
- type: ndcg_at_10
value: 36.84
- type: ndcg_at_100
value: 42.092
- type: ndcg_at_1000
value: 44.727
- type: ndcg_at_3
value: 31.964
- type: ndcg_at_5
value: 34.428
- type: precision_at_1
value: 27.815
- type: precision_at_10
value: 6.67
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 14.982000000000001
- type: precision_at_5
value: 10.857
- type: recall_at_1
value: 22.709
- type: recall_at_10
value: 48.308
- type: recall_at_100
value: 70.866
- type: recall_at_1000
value: 88.236
- type: recall_at_3
value: 34.709
- type: recall_at_5
value: 40.996
- type: map_at_1
value: 22.348000000000003
- type: map_at_10
value: 29.427999999999997
- type: map_at_100
value: 30.499
- type: map_at_1000
value: 30.631999999999998
- type: map_at_3
value: 27.035999999999998
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 27.74
- type: mrr_at_10
value: 34.424
- type: mrr_at_100
value: 35.341
- type: mrr_at_1000
value: 35.419
- type: mrr_at_3
value: 32.401
- type: mrr_at_5
value: 33.497
- type: ndcg_at_1
value: 27.74
- type: ndcg_at_10
value: 34.136
- type: ndcg_at_100
value: 39.269
- type: ndcg_at_1000
value: 42.263
- type: ndcg_at_3
value: 30.171999999999997
- type: ndcg_at_5
value: 31.956
- type: precision_at_1
value: 27.74
- type: precision_at_10
value: 6.062
- type: precision_at_100
value: 1.014
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 14.079
- type: precision_at_5
value: 9.977
- type: recall_at_1
value: 22.348000000000003
- type: recall_at_10
value: 43.477
- type: recall_at_100
value: 65.945
- type: recall_at_1000
value: 86.587
- type: recall_at_3
value: 32.107
- type: recall_at_5
value: 36.974000000000004
- type: map_at_1
value: 21.688499999999998
- type: map_at_10
value: 29.164666666666665
- type: map_at_100
value: 30.22575
- type: map_at_1000
value: 30.350833333333334
- type: map_at_3
value: 26.82025
- type: map_at_5
value: 28.14966666666667
- type: mrr_at_1
value: 25.779249999999998
- type: mrr_at_10
value: 32.969
- type: mrr_at_100
value: 33.81725
- type: mrr_at_1000
value: 33.88825
- type: mrr_at_3
value: 30.831250000000004
- type: mrr_at_5
value: 32.065000000000005
- type: ndcg_at_1
value: 25.779249999999998
- type: ndcg_at_10
value: 33.73675
- type: ndcg_at_100
value: 38.635666666666665
- type: ndcg_at_1000
value: 41.353500000000004
- type: ndcg_at_3
value: 29.66283333333333
- type: ndcg_at_5
value: 31.607249999999997
- type: precision_at_1
value: 25.779249999999998
- type: precision_at_10
value: 5.861416666666667
- type: precision_at_100
value: 0.9852500000000002
- type: precision_at_1000
value: 0.14108333333333334
- type: precision_at_3
value: 13.563583333333332
- type: precision_at_5
value: 9.630333333333335
- type: recall_at_1
value: 21.688499999999998
- type: recall_at_10
value: 43.605
- type: recall_at_100
value: 65.52366666666667
- type: recall_at_1000
value: 84.69683333333332
- type: recall_at_3
value: 32.195499999999996
- type: recall_at_5
value: 37.25325
- type: map_at_1
value: 17.279
- type: map_at_10
value: 23.238
- type: map_at_100
value: 24.026
- type: map_at_1000
value: 24.13
- type: map_at_3
value: 20.730999999999998
- type: map_at_5
value: 22.278000000000002
- type: mrr_at_1
value: 19.017999999999997
- type: mrr_at_10
value: 25.188
- type: mrr_at_100
value: 25.918999999999997
- type: mrr_at_1000
value: 25.996999999999996
- type: mrr_at_3
value: 22.776
- type: mrr_at_5
value: 24.256
- type: ndcg_at_1
value: 19.017999999999997
- type: ndcg_at_10
value: 27.171
- type: ndcg_at_100
value: 31.274
- type: ndcg_at_1000
value: 34.016000000000005
- type: ndcg_at_3
value: 22.442
- type: ndcg_at_5
value: 24.955
- type: precision_at_1
value: 19.017999999999997
- type: precision_at_10
value: 4.494
- type: precision_at_100
value: 0.712
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 9.611
- type: precision_at_5
value: 7.331
- type: recall_at_1
value: 17.279
- type: recall_at_10
value: 37.464999999999996
- type: recall_at_100
value: 56.458
- type: recall_at_1000
value: 76.759
- type: recall_at_3
value: 24.659
- type: recall_at_5
value: 30.672
- type: map_at_1
value: 14.901
- type: map_at_10
value: 20.268
- type: map_at_100
value: 21.143
- type: map_at_1000
value: 21.264
- type: map_at_3
value: 18.557000000000002
- type: map_at_5
value: 19.483
- type: mrr_at_1
value: 17.997
- type: mrr_at_10
value: 23.591
- type: mrr_at_100
value: 24.387
- type: mrr_at_1000
value: 24.471
- type: mrr_at_3
value: 21.874
- type: mrr_at_5
value: 22.797
- type: ndcg_at_1
value: 17.997
- type: ndcg_at_10
value: 23.87
- type: ndcg_at_100
value: 28.459
- type: ndcg_at_1000
value: 31.66
- type: ndcg_at_3
value: 20.779
- type: ndcg_at_5
value: 22.137
- type: precision_at_1
value: 17.997
- type: precision_at_10
value: 4.25
- type: precision_at_100
value: 0.761
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 9.716
- type: precision_at_5
value: 6.909999999999999
- type: recall_at_1
value: 14.901
- type: recall_at_10
value: 31.44
- type: recall_at_100
value: 52.717000000000006
- type: recall_at_1000
value: 76.102
- type: recall_at_3
value: 22.675
- type: recall_at_5
value: 26.336
- type: map_at_1
value: 21.52
- type: map_at_10
value: 28.397
- type: map_at_100
value: 29.443
- type: map_at_1000
value: 29.56
- type: map_at_3
value: 26.501
- type: map_at_5
value: 27.375
- type: mrr_at_1
value: 25.28
- type: mrr_at_10
value: 32.102000000000004
- type: mrr_at_100
value: 33.005
- type: mrr_at_1000
value: 33.084
- type: mrr_at_3
value: 30.208000000000002
- type: mrr_at_5
value: 31.146
- type: ndcg_at_1
value: 25.28
- type: ndcg_at_10
value: 32.635
- type: ndcg_at_100
value: 37.672
- type: ndcg_at_1000
value: 40.602
- type: ndcg_at_3
value: 28.951999999999998
- type: ndcg_at_5
value: 30.336999999999996
- type: precision_at_1
value: 25.28
- type: precision_at_10
value: 5.3260000000000005
- type: precision_at_100
value: 0.8840000000000001
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 12.687000000000001
- type: precision_at_5
value: 8.638
- type: recall_at_1
value: 21.52
- type: recall_at_10
value: 41.955
- type: recall_at_100
value: 64.21
- type: recall_at_1000
value: 85.28099999999999
- type: recall_at_3
value: 31.979999999999997
- type: recall_at_5
value: 35.406
- type: map_at_1
value: 20.296
- type: map_at_10
value: 28.449999999999996
- type: map_at_100
value: 29.847
- type: map_at_1000
value: 30.073
- type: map_at_3
value: 25.995
- type: map_at_5
value: 27.603
- type: mrr_at_1
value: 25.296000000000003
- type: mrr_at_10
value: 32.751999999999995
- type: mrr_at_100
value: 33.705
- type: mrr_at_1000
value: 33.783
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 32.006
- type: ndcg_at_1
value: 25.296000000000003
- type: ndcg_at_10
value: 33.555
- type: ndcg_at_100
value: 38.891999999999996
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 29.944
- type: ndcg_at_5
value: 31.997999999999998
- type: precision_at_1
value: 25.296000000000003
- type: precision_at_10
value: 6.542000000000001
- type: precision_at_100
value: 1.354
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.593
- type: recall_at_1
value: 20.296
- type: recall_at_10
value: 42.742000000000004
- type: recall_at_100
value: 67.351
- type: recall_at_1000
value: 88.774
- type: recall_at_3
value: 32.117000000000004
- type: recall_at_5
value: 37.788
- type: map_at_1
value: 18.157999999999998
- type: map_at_10
value: 24.342
- type: map_at_100
value: 25.201
- type: map_at_1000
value: 25.317
- type: map_at_3
value: 22.227
- type: map_at_5
value: 23.372999999999998
- type: mrr_at_1
value: 19.778000000000002
- type: mrr_at_10
value: 26.066
- type: mrr_at_100
value: 26.935
- type: mrr_at_1000
value: 27.022000000000002
- type: mrr_at_3
value: 24.214
- type: mrr_at_5
value: 25.268
- type: ndcg_at_1
value: 19.778000000000002
- type: ndcg_at_10
value: 28.104000000000003
- type: ndcg_at_100
value: 32.87
- type: ndcg_at_1000
value: 35.858000000000004
- type: ndcg_at_3
value: 24.107
- type: ndcg_at_5
value: 26.007
- type: precision_at_1
value: 19.778000000000002
- type: precision_at_10
value: 4.417999999999999
- type: precision_at_100
value: 0.739
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.228
- type: precision_at_5
value: 7.172000000000001
- type: recall_at_1
value: 18.157999999999998
- type: recall_at_10
value: 37.967
- type: recall_at_100
value: 60.806000000000004
- type: recall_at_1000
value: 83.097
- type: recall_at_3
value: 27.223999999999997
- type: recall_at_5
value: 31.968000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.055
- type: map_at_10
value: 11.609
- type: map_at_100
value: 12.83
- type: map_at_1000
value: 12.995000000000001
- type: map_at_3
value: 9.673
- type: map_at_5
value: 10.761999999999999
- type: mrr_at_1
value: 15.309000000000001
- type: mrr_at_10
value: 23.655
- type: mrr_at_100
value: 24.785
- type: mrr_at_1000
value: 24.856
- type: mrr_at_3
value: 20.499000000000002
- type: mrr_at_5
value: 22.425
- type: ndcg_at_1
value: 15.309000000000001
- type: ndcg_at_10
value: 17.252000000000002
- type: ndcg_at_100
value: 22.976
- type: ndcg_at_1000
value: 26.480999999999998
- type: ndcg_at_3
value: 13.418
- type: ndcg_at_5
value: 15.084
- type: precision_at_1
value: 15.309000000000001
- type: precision_at_10
value: 5.309
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.17600000000000002
- type: precision_at_3
value: 9.62
- type: precision_at_5
value: 7.883
- type: recall_at_1
value: 7.055
- type: recall_at_10
value: 21.891
- type: recall_at_100
value: 41.979
- type: recall_at_1000
value: 62.239999999999995
- type: recall_at_3
value: 12.722
- type: recall_at_5
value: 16.81
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.909
- type: map_at_10
value: 12.844
- type: map_at_100
value: 16.435
- type: map_at_1000
value: 17.262
- type: map_at_3
value: 10.131
- type: map_at_5
value: 11.269
- type: mrr_at_1
value: 54.50000000000001
- type: mrr_at_10
value: 62.202
- type: mrr_at_100
value: 62.81
- type: mrr_at_1000
value: 62.824000000000005
- type: mrr_at_3
value: 60.5
- type: mrr_at_5
value: 61.324999999999996
- type: ndcg_at_1
value: 42.125
- type: ndcg_at_10
value: 28.284
- type: ndcg_at_100
value: 30.444
- type: ndcg_at_1000
value: 36.397
- type: ndcg_at_3
value: 33.439
- type: ndcg_at_5
value: 30.473
- type: precision_at_1
value: 54.50000000000001
- type: precision_at_10
value: 21.4
- type: precision_at_100
value: 6.192
- type: precision_at_1000
value: 1.398
- type: precision_at_3
value: 36.583
- type: precision_at_5
value: 28.799999999999997
- type: recall_at_1
value: 6.909
- type: recall_at_10
value: 17.296
- type: recall_at_100
value: 33.925
- type: recall_at_1000
value: 53.786
- type: recall_at_3
value: 11.333
- type: recall_at_5
value: 13.529
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 36.08
- type: f1
value: 33.016420191943766
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.605000000000004
- type: map_at_10
value: 63.31400000000001
- type: map_at_100
value: 63.678000000000004
- type: map_at_1000
value: 63.699
- type: map_at_3
value: 61.141
- type: map_at_5
value: 62.517999999999994
- type: mrr_at_1
value: 56.871
- type: mrr_at_10
value: 67.915
- type: mrr_at_100
value: 68.24900000000001
- type: mrr_at_1000
value: 68.262
- type: mrr_at_3
value: 65.809
- type: mrr_at_5
value: 67.171
- type: ndcg_at_1
value: 56.871
- type: ndcg_at_10
value: 69.122
- type: ndcg_at_100
value: 70.855
- type: ndcg_at_1000
value: 71.368
- type: ndcg_at_3
value: 64.974
- type: ndcg_at_5
value: 67.318
- type: precision_at_1
value: 56.871
- type: precision_at_10
value: 9.029
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 25.893
- type: precision_at_5
value: 16.838
- type: recall_at_1
value: 52.605000000000004
- type: recall_at_10
value: 82.679
- type: recall_at_100
value: 90.586
- type: recall_at_1000
value: 94.38
- type: recall_at_3
value: 71.447
- type: recall_at_5
value: 77.218
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.759
- type: map_at_10
value: 18.877
- type: map_at_100
value: 20.498
- type: map_at_1000
value: 20.682000000000002
- type: map_at_3
value: 16.159000000000002
- type: map_at_5
value: 17.575
- type: mrr_at_1
value: 22.531000000000002
- type: mrr_at_10
value: 31.155
- type: mrr_at_100
value: 32.188
- type: mrr_at_1000
value: 32.245000000000005
- type: mrr_at_3
value: 28.781000000000002
- type: mrr_at_5
value: 30.054
- type: ndcg_at_1
value: 22.531000000000002
- type: ndcg_at_10
value: 25.189
- type: ndcg_at_100
value: 31.958
- type: ndcg_at_1000
value: 35.693999999999996
- type: ndcg_at_3
value: 22.235
- type: ndcg_at_5
value: 23.044999999999998
- type: precision_at_1
value: 22.531000000000002
- type: precision_at_10
value: 7.438000000000001
- type: precision_at_100
value: 1.418
- type: precision_at_1000
value: 0.208
- type: precision_at_3
value: 15.329
- type: precision_at_5
value: 11.451
- type: recall_at_1
value: 10.759
- type: recall_at_10
value: 31.416
- type: recall_at_100
value: 56.989000000000004
- type: recall_at_1000
value: 80.33200000000001
- type: recall_at_3
value: 20.61
- type: recall_at_5
value: 24.903
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.21
- type: map_at_10
value: 38.765
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.568
- type: map_at_3
value: 36.699
- type: map_at_5
value: 37.925
- type: mrr_at_1
value: 58.42
- type: mrr_at_10
value: 65.137
- type: mrr_at_100
value: 65.542
- type: mrr_at_1000
value: 65.568
- type: mrr_at_3
value: 63.698
- type: mrr_at_5
value: 64.575
- type: ndcg_at_1
value: 58.42
- type: ndcg_at_10
value: 47.476
- type: ndcg_at_100
value: 50.466
- type: ndcg_at_1000
value: 52.064
- type: ndcg_at_3
value: 43.986
- type: ndcg_at_5
value: 45.824
- type: precision_at_1
value: 58.42
- type: precision_at_10
value: 9.649000000000001
- type: precision_at_100
value: 1.201
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 26.977
- type: precision_at_5
value: 17.642
- type: recall_at_1
value: 29.21
- type: recall_at_10
value: 48.244
- type: recall_at_100
value: 60.041
- type: recall_at_1000
value: 70.743
- type: recall_at_3
value: 40.466
- type: recall_at_5
value: 44.105
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 58.7064
- type: ap
value: 55.36326227125519
- type: f1
value: 57.46763115215848
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 15.889000000000001
- type: map_at_10
value: 25.979000000000003
- type: map_at_100
value: 27.21
- type: map_at_1000
value: 27.284000000000002
- type: map_at_3
value: 22.665
- type: map_at_5
value: 24.578
- type: mrr_at_1
value: 16.39
- type: mrr_at_10
value: 26.504
- type: mrr_at_100
value: 27.689999999999998
- type: mrr_at_1000
value: 27.758
- type: mrr_at_3
value: 23.24
- type: mrr_at_5
value: 25.108000000000004
- type: ndcg_at_1
value: 16.39
- type: ndcg_at_10
value: 31.799
- type: ndcg_at_100
value: 38.034
- type: ndcg_at_1000
value: 39.979
- type: ndcg_at_3
value: 25.054
- type: ndcg_at_5
value: 28.463
- type: precision_at_1
value: 16.39
- type: precision_at_10
value: 5.189
- type: precision_at_100
value: 0.835
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 10.84
- type: precision_at_5
value: 8.238
- type: recall_at_1
value: 15.889000000000001
- type: recall_at_10
value: 49.739
- type: recall_at_100
value: 79.251
- type: recall_at_1000
value: 94.298
- type: recall_at_3
value: 31.427
- type: recall_at_5
value: 39.623000000000005
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.81668946648426
- type: f1
value: 88.55200075528438
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 58.611491108071135
- type: f1
value: 42.12391403999353
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.67047747141896
- type: f1
value: 62.88410885922258
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.78547410894419
- type: f1
value: 71.69467869218154
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 27.23799937752035
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 23.26502601343789
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.680711484149832
- type: mrr
value: 31.705059795117307
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.077
- type: map_at_10
value: 8.657
- type: map_at_100
value: 10.753
- type: map_at_1000
value: 11.885
- type: map_at_3
value: 6.5089999999999995
- type: map_at_5
value: 7.405
- type: mrr_at_1
value: 38.7
- type: mrr_at_10
value: 46.065
- type: mrr_at_100
value: 46.772000000000006
- type: mrr_at_1000
value: 46.83
- type: mrr_at_3
value: 44.118
- type: mrr_at_5
value: 45.015
- type: ndcg_at_1
value: 36.997
- type: ndcg_at_10
value: 25.96
- type: ndcg_at_100
value: 23.607
- type: ndcg_at_1000
value: 32.317
- type: ndcg_at_3
value: 31.06
- type: ndcg_at_5
value: 28.921000000000003
- type: precision_at_1
value: 38.7
- type: precision_at_10
value: 19.195
- type: precision_at_100
value: 6.164
- type: precision_at_1000
value: 1.839
- type: precision_at_3
value: 28.999000000000002
- type: precision_at_5
value: 25.014999999999997
- type: recall_at_1
value: 4.077
- type: recall_at_10
value: 11.802
- type: recall_at_100
value: 24.365000000000002
- type: recall_at_1000
value: 55.277
- type: recall_at_3
value: 7.435
- type: recall_at_5
value: 8.713999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.588
- type: map_at_10
value: 32.08
- type: map_at_100
value: 33.32
- type: map_at_1000
value: 33.377
- type: map_at_3
value: 28.166000000000004
- type: map_at_5
value: 30.383
- type: mrr_at_1
value: 22.161
- type: mrr_at_10
value: 34.121
- type: mrr_at_100
value: 35.171
- type: mrr_at_1000
value: 35.214
- type: mrr_at_3
value: 30.692000000000004
- type: mrr_at_5
value: 32.706
- type: ndcg_at_1
value: 22.131999999999998
- type: ndcg_at_10
value: 38.887
- type: ndcg_at_100
value: 44.433
- type: ndcg_at_1000
value: 45.823
- type: ndcg_at_3
value: 31.35
- type: ndcg_at_5
value: 35.144
- type: precision_at_1
value: 22.131999999999998
- type: precision_at_10
value: 6.8629999999999995
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.706
- type: precision_at_5
value: 10.972999999999999
- type: recall_at_1
value: 19.588
- type: recall_at_10
value: 57.703
- type: recall_at_100
value: 82.194
- type: recall_at_1000
value: 92.623
- type: recall_at_3
value: 38.012
- type: recall_at_5
value: 46.847
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.038
- type: map_at_10
value: 81.572
- type: map_at_100
value: 82.25200000000001
- type: map_at_1000
value: 82.27600000000001
- type: map_at_3
value: 78.618
- type: map_at_5
value: 80.449
- type: mrr_at_1
value: 78.31
- type: mrr_at_10
value: 84.98
- type: mrr_at_100
value: 85.122
- type: mrr_at_1000
value: 85.124
- type: mrr_at_3
value: 83.852
- type: mrr_at_5
value: 84.6
- type: ndcg_at_1
value: 78.31
- type: ndcg_at_10
value: 85.693
- type: ndcg_at_100
value: 87.191
- type: ndcg_at_1000
value: 87.386
- type: ndcg_at_3
value: 82.585
- type: ndcg_at_5
value: 84.255
- type: precision_at_1
value: 78.31
- type: precision_at_10
value: 12.986
- type: precision_at_100
value: 1.505
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.007
- type: precision_at_5
value: 23.735999999999997
- type: recall_at_1
value: 68.038
- type: recall_at_10
value: 93.598
- type: recall_at_100
value: 98.869
- type: recall_at_1000
value: 99.86500000000001
- type: recall_at_3
value: 84.628
- type: recall_at_5
value: 89.316
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 37.948231664922865
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 49.90597913763894
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.753
- type: map_at_10
value: 8.915
- type: map_at_100
value: 10.374
- type: map_at_1000
value: 10.612
- type: map_at_3
value: 6.577
- type: map_at_5
value: 7.8
- type: mrr_at_1
value: 18.4
- type: mrr_at_10
value: 27.325
- type: mrr_at_100
value: 28.419
- type: mrr_at_1000
value: 28.494000000000003
- type: mrr_at_3
value: 24.349999999999998
- type: mrr_at_5
value: 26.205000000000002
- type: ndcg_at_1
value: 18.4
- type: ndcg_at_10
value: 15.293000000000001
- type: ndcg_at_100
value: 21.592
- type: ndcg_at_1000
value: 26.473000000000003
- type: ndcg_at_3
value: 14.748
- type: ndcg_at_5
value: 12.98
- type: precision_at_1
value: 18.4
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.693
- type: precision_at_1000
value: 0.28800000000000003
- type: precision_at_3
value: 13.700000000000001
- type: precision_at_5
value: 11.379999999999999
- type: recall_at_1
value: 3.753
- type: recall_at_10
value: 15.806999999999999
- type: recall_at_100
value: 34.37
- type: recall_at_1000
value: 58.463
- type: recall_at_3
value: 8.338
- type: recall_at_5
value: 11.538
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.58843987639705
- type: cos_sim_spearman
value: 76.33071660715956
- type: euclidean_pearson
value: 72.8029921002978
- type: euclidean_spearman
value: 69.34534284782808
- type: manhattan_pearson
value: 72.49781034973653
- type: manhattan_spearman
value: 69.24754112621694
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.31673079903189
- type: cos_sim_spearman
value: 74.27699263517789
- type: euclidean_pearson
value: 69.4008910999579
- type: euclidean_spearman
value: 59.0716984643048
- type: manhattan_pearson
value: 68.87342686919199
- type: manhattan_spearman
value: 58.904612865335025
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.59122302327788
- type: cos_sim_spearman
value: 78.55383586979005
- type: euclidean_pearson
value: 68.18338642204289
- type: euclidean_spearman
value: 68.95092864180276
- type: manhattan_pearson
value: 68.08807059822706
- type: manhattan_spearman
value: 68.86135938270193
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 78.51766841424501
- type: cos_sim_spearman
value: 73.84318001499558
- type: euclidean_pearson
value: 67.2007138855177
- type: euclidean_spearman
value: 63.98672842723766
- type: manhattan_pearson
value: 67.17773810895949
- type: manhattan_spearman
value: 64.07359154832962
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.73438541570299
- type: cos_sim_spearman
value: 83.71357922283677
- type: euclidean_pearson
value: 57.50131347498546
- type: euclidean_spearman
value: 57.73623619252132
- type: manhattan_pearson
value: 58.082992079000725
- type: manhattan_spearman
value: 58.42728201167522
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 78.14794654172421
- type: cos_sim_spearman
value: 80.025736165043
- type: euclidean_pearson
value: 65.87773913985473
- type: euclidean_spearman
value: 66.69337751784794
- type: manhattan_pearson
value: 66.01039761004415
- type: manhattan_spearman
value: 66.89215027952318
- 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: 87.10554507136152
- type: cos_sim_spearman
value: 87.4898082140765
- type: euclidean_pearson
value: 72.19391114541367
- type: euclidean_spearman
value: 70.36647944993783
- type: manhattan_pearson
value: 72.18680758133698
- type: manhattan_spearman
value: 70.3871215447305
- 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: 64.54868111501618
- type: cos_sim_spearman
value: 64.25173617448473
- type: euclidean_pearson
value: 39.116088900637116
- type: euclidean_spearman
value: 53.300772929884
- type: manhattan_pearson
value: 38.3844195287959
- type: manhattan_spearman
value: 52.846675312001246
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 80.04396610550214
- type: cos_sim_spearman
value: 79.19504854997832
- type: euclidean_pearson
value: 66.3284657637072
- type: euclidean_spearman
value: 63.69531796729492
- type: manhattan_pearson
value: 66.82324081038026
- type: manhattan_spearman
value: 64.18254512904923
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 74.16264051781705
- type: mrr
value: 91.80864796060874
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.983000000000004
- type: map_at_10
value: 47.858000000000004
- type: map_at_100
value: 48.695
- type: map_at_1000
value: 48.752
- type: map_at_3
value: 45.444
- type: map_at_5
value: 46.906
- type: mrr_at_1
value: 41.333
- type: mrr_at_10
value: 49.935
- type: mrr_at_100
value: 50.51
- type: mrr_at_1000
value: 50.55500000000001
- type: mrr_at_3
value: 47.833
- type: mrr_at_5
value: 49.117
- type: ndcg_at_1
value: 41.333
- type: ndcg_at_10
value: 52.398999999999994
- type: ndcg_at_100
value: 56.196
- type: ndcg_at_1000
value: 57.838
- type: ndcg_at_3
value: 47.987
- type: ndcg_at_5
value: 50.356
- type: precision_at_1
value: 41.333
- type: precision_at_10
value: 7.167
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 19.0
- type: precision_at_5
value: 12.8
- type: recall_at_1
value: 38.983000000000004
- type: recall_at_10
value: 64.183
- type: recall_at_100
value: 82.02199999999999
- type: recall_at_1000
value: 95.167
- type: recall_at_3
value: 52.383
- type: recall_at_5
value: 58.411
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8019801980198
- type: cos_sim_ap
value: 94.9287554635848
- type: cos_sim_f1
value: 89.83739837398375
- type: cos_sim_precision
value: 91.32231404958677
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.23762376237623
- type: dot_ap
value: 55.22534191245801
- type: dot_f1
value: 54.054054054054056
- type: dot_precision
value: 55.15088449531738
- type: dot_recall
value: 53.0
- type: euclidean_accuracy
value: 99.6108910891089
- type: euclidean_ap
value: 82.5195111329438
- type: euclidean_f1
value: 78.2847718526663
- type: euclidean_precision
value: 86.93528693528694
- type: euclidean_recall
value: 71.2
- type: manhattan_accuracy
value: 99.5970297029703
- type: manhattan_ap
value: 81.96876777875492
- type: manhattan_f1
value: 77.33773377337734
- type: manhattan_precision
value: 85.94132029339853
- type: manhattan_recall
value: 70.3
- type: max_accuracy
value: 99.8019801980198
- type: max_ap
value: 94.9287554635848
- type: max_f1
value: 89.83739837398375
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 46.34997003954114
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.462336020554893
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 47.1757817459526
- type: mrr
value: 47.941057104660054
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.56106249068471
- type: cos_sim_spearman
value: 31.24613190558528
- type: dot_pearson
value: 20.486610035794257
- type: dot_spearman
value: 23.115667545894546
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.182
- type: map_at_10
value: 1.155
- type: map_at_100
value: 5.118
- type: map_at_1000
value: 11.827
- type: map_at_3
value: 0.482
- type: map_at_5
value: 0.712
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 79.483
- type: mrr_at_100
value: 79.637
- type: mrr_at_1000
value: 79.637
- type: mrr_at_3
value: 77.667
- type: mrr_at_5
value: 78.567
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 52.303
- type: ndcg_at_100
value: 37.361
- type: ndcg_at_1000
value: 32.84
- type: ndcg_at_3
value: 58.274
- type: ndcg_at_5
value: 55.601
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 55.60000000000001
- type: precision_at_100
value: 37.96
- type: precision_at_1000
value: 14.738000000000001
- type: precision_at_3
value: 62.666999999999994
- type: precision_at_5
value: 60.0
- type: recall_at_1
value: 0.182
- type: recall_at_10
value: 1.4120000000000001
- type: recall_at_100
value: 8.533
- type: recall_at_1000
value: 30.572
- type: recall_at_3
value: 0.5309999999999999
- type: recall_at_5
value: 0.814
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.385
- type: map_at_10
value: 7.185999999999999
- type: map_at_100
value: 11.642
- type: map_at_1000
value: 12.953000000000001
- type: map_at_3
value: 3.496
- type: map_at_5
value: 4.82
- type: mrr_at_1
value: 16.326999999999998
- type: mrr_at_10
value: 29.461
- type: mrr_at_100
value: 31.436999999999998
- type: mrr_at_1000
value: 31.436999999999998
- type: mrr_at_3
value: 24.490000000000002
- type: mrr_at_5
value: 27.857
- type: ndcg_at_1
value: 14.285999999999998
- type: ndcg_at_10
value: 16.672
- type: ndcg_at_100
value: 28.691
- type: ndcg_at_1000
value: 39.817
- type: ndcg_at_3
value: 15.277
- type: ndcg_at_5
value: 15.823
- type: precision_at_1
value: 16.326999999999998
- type: precision_at_10
value: 15.509999999999998
- type: precision_at_100
value: 6.49
- type: precision_at_1000
value: 1.4080000000000001
- type: precision_at_3
value: 16.326999999999998
- type: precision_at_5
value: 16.735
- type: recall_at_1
value: 1.385
- type: recall_at_10
value: 12.586
- type: recall_at_100
value: 40.765
- type: recall_at_1000
value: 75.198
- type: recall_at_3
value: 4.326
- type: recall_at_5
value: 7.074999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 59.4402
- type: ap
value: 10.16922814263879
- type: f1
value: 45.374485104940476
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 54.25863044708545
- type: f1
value: 54.20154252609619
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 34.3883169293051
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 81.76670441676104
- type: cos_sim_ap
value: 59.29878710961347
- type: cos_sim_f1
value: 57.33284971587474
- type: cos_sim_precision
value: 52.9122963624191
- type: cos_sim_recall
value: 62.559366754617415
- type: dot_accuracy
value: 77.52279907015557
- type: dot_ap
value: 34.17588904643467
- type: dot_f1
value: 41.063567529494634
- type: dot_precision
value: 30.813953488372093
- type: dot_recall
value: 61.53034300791557
- type: euclidean_accuracy
value: 80.61631996185254
- type: euclidean_ap
value: 54.00362361479352
- type: euclidean_f1
value: 53.99111751290361
- type: euclidean_precision
value: 49.52653600528518
- type: euclidean_recall
value: 59.340369393139845
- type: manhattan_accuracy
value: 80.65208320915539
- type: manhattan_ap
value: 54.18329507159467
- type: manhattan_f1
value: 53.85550960836779
- type: manhattan_precision
value: 49.954873646209386
- type: manhattan_recall
value: 58.41688654353562
- type: max_accuracy
value: 81.76670441676104
- type: max_ap
value: 59.29878710961347
- type: max_f1
value: 57.33284971587474
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.99433383785463
- type: cos_sim_ap
value: 83.43513915159009
- type: cos_sim_f1
value: 76.3906784964842
- type: cos_sim_precision
value: 73.19223985890653
- type: cos_sim_recall
value: 79.88142901139513
- type: dot_accuracy
value: 81.96142352621571
- type: dot_ap
value: 67.78764755689359
- type: dot_f1
value: 64.42823356983445
- type: dot_precision
value: 56.77801913931779
- type: dot_recall
value: 74.46104096088698
- type: euclidean_accuracy
value: 81.9478402607987
- type: euclidean_ap
value: 67.13958457373279
- type: euclidean_f1
value: 60.45118343195266
- type: euclidean_precision
value: 58.1625391403359
- type: euclidean_recall
value: 62.92731752386819
- type: manhattan_accuracy
value: 82.01769705437188
- type: manhattan_ap
value: 67.24709477497046
- type: manhattan_f1
value: 60.4103846436714
- type: manhattan_precision
value: 57.82063916654935
- type: manhattan_recall
value: 63.24299353249153
- type: max_accuracy
value: 87.99433383785463
- type: max_ap
value: 83.43513915159009
- type: max_f1
value: 76.3906784964842
---
<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>
## Intented Usage & Model Info
`jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
With a compact size of just 35 million parameters,
the model enables lightning-fast inference while still delivering impressive performance.
Additionally, we provide the following options:
- [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters.
- [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters **(you are here)**.
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
- [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters.
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10 times bert-base (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters, 30 times bert-base (soon).
## Data & Parameters
Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).
## Metrics
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:
|Name|param |dimension|
|------------------------------|-----|------|
|all-minilm-l6-v2|23m |384|
|all-mpnet-base-v2 |110m |768|
|ada-embedding-002|Unknown/OpenAI API |1536|
|jina-embedding-t-en-v1|14m |312|
|jina-embedding-s-en-v1|35m |512|
|jina-embedding-b-en-v1|110m |768|
|jina-embedding-l-en-v1|330m |1024|
|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 |
|all-mpnet-base-v2|0.726|**0.835**|0.78 |0.857|0.8 |**0.906**|0.513 |0.875|0.656 |
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** |
|jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482 |0.840|0.522 |
|jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523 |0.857|0.524 |
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606 |0.876|0.594 |
|jina-embedding-l-en-v1|0.745|0.832|**0.781**|**0.869**|0.837|0.902|0.573 |**0.881**|0.598 |
## Usage
Use with Jina AI Finetuner
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-s-en-v1')
embeddings = finetuner.encode(
model=model,
data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['how is the weather today', 'What is the current weather like today?']
model = SentenceTransformer('jinaai/jina-embedding-s-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
## Fine-tuning
Please consider [Finetuner](https://github.com/jina-ai/finetuner).
## Plans
1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length.
2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`.
## 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:
``` latex
@misc{günther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"LINNAEUS",
"SCIFACT"
] |
Almawave/Velvet-14B
|
Almawave
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"vllm",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2025-01-31T00:53:58 | 2025-02-20T14:38:44 | 3,649 | 129 |
---
language:
- en
- fr
- de
- es
- it
- pt
library_name: transformers
license: apache-2.0
tags:
- vllm
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://www.almawave.com/privacy-policy/">Privacy
Policy</a>.
---
# Model Card for Velvet-14B
Velvet is an Italian family of large language models, developed from scratch, featuring a dense architecture. This model was trained on the HPC Leonardo infrastructure hosted by [CINECA](https://www.cineca.it/en), utilizing public data that underwent extensive curation.
The training process commenced with over 10 trillion tokens and culminated in more than 4 trillion tokens, across six languages (Italian, English, Spanish, Portuguese-Brazilian, German, French) for the 14B model.
Efforts were specifically made to maintain balance between languages, with particular emphasis on Italian, which comprises approximately 23% of the data. In addition to linguistic data, Velvet incorporates over 400 billion tokens from more than 100 programming languages to facilitate more structured inferences in the aforementioned languages.
## Model details
- **Model Developers:** Technology and innovation Team, Almawave
- **Input:** Models input text only.
- **Output:** Models generate text only.
- **Release Date:** January 31st, 2025.
- **License:** Apache 2.
### Model Architecture and training
Velvet family of models comes in two sizes --- 2B and 14B parameters --- namely, **Velvet-2B** and **Velvet-14B**.
**Velvet-14B** is a 14B parameter long-context instruct model finetuned from **Velvet-14B-base** using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems.
#### Architecture
- Auto-regressive language model with a transformer-based causal decoder-only design.
- 50 transformer layers.
- MLP intermediate size of 12,544.
- Grouped Query Attention (GQA): 40 query heads and 8 key-value heads for efficiency.
- Rotary Position Embedding (RoPE): High theta value for long-context modeling.
- SiLU activation function with RMSNorm method.
- Context length up to 128K tokens, trained on sequences of 4/8/16k tokens.
- 127K vocabulary size, designed to accommodate language diversity.
- Training phase: pretraining & post-training.
### Status
This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
Almawave is actively working on strategies to enhance alignment and robustness in future iterations of the Velvet model.
### License
Velvet-14B and Velvet-2B are made available under the Apache 2.0 license
### Supported Languages
Velvet-14B has been trained on Italian, English, German, Spanish, French, Portuguese.
To ensure high-quality multilingual performance, the dataset was curated to balance linguistic representation, reducing overfitting biases toward high-resource languages.
## Intended Use
Velvet-14B is designed to be integrated into AI systems or applications. Its potential use include, but it is not limited to, text generation, classification, summarization, question answering. It is important to note that specific applications may need further model adaptations or additional safeguards to prevent undesirable behavior or outputs.
### Capabilities
- Summarization
- Information Extraction
- RAG (Retrieval Augmented Generation)
- Paraphrasing
- Textual Entailment
- Natural Language Inference
- Common Sense Reasoning
- Multistep Reasoning
- Text Classification
- Machine Translation
- Question Answering
- Text Completion
- Multiturn Conversation
## Training Data
### Overview
The model was pretrained on over 4 trillion tokens of data from publicly available sources. These sources include diverse collection of web text exposes the model to an extensive range of linguistic styles, topics, and vocabulary. The training dataset has been built with a balanced representation of multiple languages.
The fine-tuning data includes publicly available instruction datasets, as well as over 2M human-annotated and synthetic examples for SFT.
Moreover we used over 50k human generated examples for safety instructions. Neither the pretraining nor the fine-tuning datasets include Almawave's customer data.
We have made significant efforts to enhance the reliability of responses in terms of factual accuracy; however, we always recommend grounding LLM responses with external factual data (e.g. Retrieval Augmented Generation).
### Data Freshness
The pretraining data has a cutoff between August 2024 and October 2024 for the two different models.
## Evaluation
### EU languages
Velvet-14B average on EU languages covered by the model, unless specified.
| Category | Benchmark | Velvet-14B |
|---------------------------| ------------------------| -------------------|
| General | MMLU (5-shot) | 56.4 |
| Instruction Following | IFEval (0-shot) - en | 65.4 |
| Commonsense | Hellaswag (10-shot) | 72.8 |
| | WinoGrande (0-shot) - en | 72.5 |
| Reasoning | ARC-Challenge (25-shot) | 57.3 |
|| MUSR (0-shot) - en | 12.3 |
|Function Calling/Tool Use| BFCL (AST summary) - en | 67.5 |
### Italian language
| Category | Benchmark | Velvet-14B |
|---------------------------| ------------------------| -------------------|
| General | MMLU (5-shot) | 58.6 |
| Commonsense | Hellaswag (0-shot) | 72.7 |
| | WinoGrande ITA-bench (0-shot) | 73.2 |
|| PIQA ITA-bench (0-shot) | 71.7 |
|| SciQ ITA-bench (0-shot) with p. | 91.9 |
| Reasoning | ARC-Challenge (0-shot) | 55.2 |
### Tokenizer
| Lang | Fertility | Parity (en) | Parity (it) |
|--------| ------------------| ---------------| ---------------|
|en| 1.403 | 1.000 | 0.904
|it| 1.464 | 1.129 | 1.000
|pt| 1.386 | 1.135 | 1.016
|es| 1.324 | 1.205 | 1.078
|de| 1.984 | 1.240 | 1.112
|fr| 1.632 | 1.330 | 1.191
|code| 2.672| NA| NA |
## Usage
The model can be used with the following frameworks;
- [`vllm`](https://github.com/vllm-project/vllm)
- [`transformers`](https://github.com/huggingface/transformers)
- [`ollama`](https://ollama.com/Almawave/Velvet)
## Responsibility and Safety
Large language models are versatile technologies designed to serve a wide range of applications. However, they are not intended to meet every developer\'s safety requirements out-of-the-box, as these requirements naturally vary depending on the specific use case and application context.
### Safety
For our instruction-trained model, we have undertaken comprehensive exercises, engaged in adversarial internal and external evaluations, and put into place mitigation techniques to reduce risks. These exercises were designed to thoroughly examine the model\'s limitations and potential, simulating real and hypothetical scenarios where undesirable behavior might arise.
However, despite these efforts, it is inevitable that some residual hazards are bound to exist, as every large language model presents intrinsic complexities that cannot be completely eliminated.
Then, developers are advised to implement suitable safety measures and exercise due diligence, tailoring these safeguards to align with their product policies and the specific requirements of their applications.
Some trade-offs between model helpfulness and alignment are likely inevitable. Developers should thoughtfully balance the benefits of alignment and helpfulness for their specific applications and audiences.
They must also remain aware of residual risks when using Velvet models and leverage additional safety tools as necessary to achieve an appropriate safety standard for their use case.
We advise developers to carefully evaluate risks in the context of their specific use case. They should consider the potential implications of a model failure in their applications and put adequate measures in place to manage such eventualities.
In parallel, we are collaborating with the scientific and industrial community to establish AI safety benchmark standards that are transparent, rigorous, and interpretable. The goal is to promote a better understanding of the risks associated with large language models and support the development of safer and more responsible solutions.
### **Governance and Internal Oversight**
Almawave has established an **internal governance framework** for the management and continuous oversight of the Velvet model family.
Key governance elements include:
- **Supervision by an Ethical and Technical Committee** to ensure the model aligns with principles of **transparency, fairness, and safety**.
- **Ongoing bias monitoring** through auditing tools, with iterative updates to improve alignment with ethical guidelines.
- **Restrictions on commercial and institutional usage** to ensure compliance with regulatory frameworks and **shared responsibility principles**.
- **Periodic review processes** to assess the model’s impact in high-risk applications.
## Bias, Risks, and Limitations
Velvet has been trained on a dataset that, despite all the data curation efforts, might include toxic language and societal biases. This means that models in the Velvet family may reproduce these biases and produce harmful responses when prompted with such inputs. This is a common issue in AI models trained on large datasets, as they can inadvertently perpetuate the biases present in the data.
Furthermore, the model may generate inaccurate, incomplete, or redundant responses, which could be socially unacceptable or undesirable, even if the input prompt is not explicitly offensive. This is a potential flaw in the model\'s design and training process, and it underscores the importance of careful validation and monitoring of AI systems to ensure that they are functioning as intended.
Additionally, using the recommended prompt template is crucial to mitigate the risk of harmful responses, as it is designed to guide the model towards more appropriate and safe outputs. However, it is important to note that the model\'s performance may still vary depending on the specific context and complexity of the input prompt.
Finally, when using this model in an agentic workflow, it is essential to validate that all imported packages and dependencies are from trusted sources to ensure the model\'s security and integrity. This is a critical step in maintaining the model\'s ethical and responsible use, and it is important to prioritize end-to-end security measures to prevent any potential vulnerabilities or breaches.
Future versions of Velvet will integrate automated red-teaming protocols, continuously stress-testing the model against adversarial prompts to identify and mitigate emerging risks.
### Sensitive Data Handling and Usage Restrictions
The Velvet model has not been trained on unauthorized personal data and must not be used to process sensitive data without appropriate security measures.
Usage Restrictions:
- Prohibited use on sensitive healthcare, financial, or government data without specific safeguards.
- Mandatory human validation in scenarios where the model’s outputs could have legal or ethical consequences.
- High-risk applications (legal, medical, public governance) must implement content filtering and auditing techniques to ensure response quality and safety.
## Ethical Considerations
Almawave core values are openness, inclusivity, and helpfulness. We aim to create AI that is accessible and beneficial for everyone, regardless of their background. Velvet models are designed to be respectful of diverse perspectives and avoid unnecessary judgments. Therefore, Velvet models are designed to be inclusive and respectful of diverse perspectives and needs. We strive to avoid unnecessary judgment or the imposition of normative views, recognizing that content deemed problematic in some contexts can have valuable applications in others.
We deeply respect the dignity and autonomy of all users, particularly their right to free thought and expression, which are fundamental to innovation and progress.
While we have taken significant steps to ensure the safety and reliability of Velvet models, it is important to acknowledge that they may occasionally generate inaccurate, biased, or unsafe responses.
Almawave is actively engaging with ethics committees and domain experts to ensure continuous oversight of Velvet’s outputs, improving safeguards through community feedback.
We strongly encourage the community to exercise caution and conduct thorough safety testing and fine-tuning when using Velvet models for specific tasks.
Opinions expressed by Velvet depend on training data and do not reflect any opinions of Almawave.
## Contributions
Direction: Raniero Romagnoli
- Model engineering and training: David Alessandrini, Francesco Buciuni, Andrea Favalli, Diego Perna, David Preti, Federico Wolenski, Fabio Massimo Zanzotto
- Data engineering and management: Valentina Bellomaria, Cristina Giannone, Alfredo Serafini
- Use case adaptation and testing: Salvatore Ricciardi, Simone Scaboro, Beatrice Turano, Giancarlo Xompero
- Evaluation: Giovanni Cingolani, Silvana De Benedictis, Caterina Masotti, Riccardo Pasquini, Guillaume Ruiz, Giuseppe Scrugli, Alessandro Vizzarro
- Product and governance: Beata Dobrzynska, Matteo Amore, Marco Gennaro Di Martino, Vincenzo Sciacca, Alessandra Staglianò, Luca Vinciguerra
|
[
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING",
"TEXTUAL_ENTAILMENT",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] |
[
"SCIQ"
] |
BSC-LT/salamandra-2b
|
BSC-LT
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"dataset:oscar-corpus/colossal-oscar-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:joelniklaus/eurlex_resources",
"dataset:joelito/legal-mc4",
"dataset:projecte-aina/CATalog",
"dataset:UFRGS/brwac",
"dataset:community-datasets/hrwac",
"dataset:danish-foundation-models/danish-gigaword",
"dataset:HiTZ/euscrawl",
"dataset:PleIAs/French-PD-Newspapers",
"dataset:PleIAs/French-PD-Books",
"dataset:AI-team-UoA/greek_legal_code",
"dataset:HiTZ/latxa-corpus-v1.1",
"dataset:allenai/peS2o",
"dataset:pile-of-law/pile-of-law",
"dataset:PORTULAN/parlamento-pt",
"dataset:hoskinson-center/proof-pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/starcoderdata",
"dataset:bjoernp/tagesschau-2018-2023",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2502.08489",
"arxiv:2403.14009",
"arxiv:2403.20266",
"arxiv:2101.00027",
"arxiv:2207.00220",
"arxiv:1810.06694",
"arxiv:1911.05507",
"arxiv:1906.03741",
"arxiv:2406.17557",
"arxiv:2402.06619",
"arxiv:1803.09010",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T13:44:13 | 2025-02-20T16:36:39 | 3,633 | 23 |
---
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
language:
- bg
- ca
- code
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nn
- \no
- oc
- pl
- pt
- ro
- ru
- sh
- sk
- sl
- sr
- sv
- uk
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Salamandra Model Card
This repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).
Salamandra is a highly multilingual model pre-trained from scratch that comes in three different
sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants.
This model card corresponds to the 2B base version.
To visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index).
The entire Salamandra family is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
Along with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra).
---
## Model Details
### Description
Transformer-based decoder-only language model that has been pre-trained from scratch on 12.875 trillion tokens of highly curated data.
The pre-training corpus contains text in 35 European languages and code.
### Hyperparameters
The full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/blob/main/configs/bsc_2b.yaml).
### Architecture
| | |
|-------------------------|:--------------|
| Total Parameters | 2,253,490,176 |
| Embedding Parameters | 524,288,000 |
| Layers | 24 |
| Hidden size | 2,048 |
| Attention heads | 16 |
| Context length | 8,192 |
| Vocabulary size | 256,000 |
| Precision | bfloat16 |
| Embedding type | RoPE |
| Activation Function | SwiGLU |
| Layer normalization | RMS Norm |
| Flash attention | ✅ |
| Grouped Query Attention | ❌ |
| Num. query groups | N/A |
---
## Intended Use
### Direct Use
The models are intended for both research and commercial use in any of the languages included in the training data.
The base models are intended either for language generation or to be further fine-tuned for specific use-cases.
The instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations.
### Out-of-scope Use
The model is not intended for malicious activities, such as harming others or violating human rights.
Any downstream application must comply with current laws and regulations.
Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged.
---
## Hardware and Software
### Training Framework
Pre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html),
which leverages PyTorch Lightning for efficient model training in highly distributed settings.
The instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat).
### Compute Infrastructure
All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and
operated by Barcelona Supercomputing Center.
The accelerated partition is composed of 1,120 nodes with the following specifications:
- 4x Nvidia Hopper GPUs with 64GB HBM2 memory
- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)
- 4x NDR200 (BW per node 800Gb/s)
- 512 GB of Main memory (DDR5)
- 460GB on NVMe storage
|Model|Nodes|GPUs|
|:---:|:---:|:---:|
|2B|64|256|
|7B|128|512|
|40B|256 / 512|1,024 / 2,048|
---
## How to use
This section offers examples of how to perform inference using various methods.
### Inference
You'll find different techniques for running inference, including Huggingface's Text Generation Pipeline, multi-GPU configurations, and vLLM for scalable and efficient generation.
#### Inference with Huggingface's Text Generation Pipeline
The Huggingface Text Generation Pipeline provides a straightforward way to run inference using the Salamandra-2b model.
```bash
pip install transformers torch accelerate sentencepiece protobuf
```
<details>
<summary>Show code</summary>
```python
from transformers import pipeline, set_seed
model_id = "BSC-LT/salamandra-2b"
# Sample prompts
prompts = [
"Todo el mundo sabe que vivir en Barcelona es",
"¿Pueblo o ciudad? Una ventaja de vivir en la ciudad es que hay muchas oportunidades de ocio y empleo, así como una gran diversidad de comercios para todos los gustos. Sin embargo, las ciudades suelen ser ",
"Llegir ens proporciona",
"What I find more fascinating about languages is that",
"La vie peut être",
"The future of AI is",
]
# Create the pipeline
generator = pipeline("text-generation", model_id, device_map="auto")
generation_args = {
"temperature": 0.1,
"top_p": 0.95,
"max_new_tokens": 25,
"repetition_penalty": 1.2,
"do_sample": True
}
# Fix the seed
set_seed(1)
# Generate texts
outputs = generator(prompts, **generation_args)
# Print outputs
for output in outputs:
print(output[0]["generated_text"])
```
</details>
#### Inference with single / multi GPU
This section provides a simple example of how to run inference using Huggingface's AutoModel class.
```bash
pip install transformers torch accelerate sentencepiece protobuf
```
<details>
<summary>Show code</summary>
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "BSC-LT/salamandra-2b"
# Input text
text = "El mercat del barri és"
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load the model
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
generation_args = {
"temperature": 0.1,
"top_p": 0.95,
"max_new_tokens": 25,
"repetition_penalty": 1.2,
"do_sample": True
}
inputs = tokenizer(text, return_tensors="pt")
# Generate texts
output = model.generate(input_ids=inputs["input_ids"].to(model.device), attention_mask=inputs["attention_mask"], **generation_args)
# Print outputs
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
</details>
#### Inference with vLLM
vLLM is an efficient library for inference that enables faster and more scalable text generation.
```bash
pip install vllm
```
<details>
<summary>Show code</summary>
```python
from vllm import LLM, SamplingParams
model_id = "BSC-LT/salamandra-2b"
# Sample prompts
prompts = [
"Todo el mundo sabe que vivir en Barcelona es",
"¿Pueblo o ciudad? Una ventaja de vivir en la ciudad es que hay muchas oportunidades de ocio y empleo, así como una gran diversidad de comercios para todos los gustos. Sin embargo, las ciudades suelen ser ",
"Llegir ens proporciona",
"What I find more fascinating about languages is that",
"La vie peut être",
"The future of AI is",
]
# Create a sampling params object
sampling_params = SamplingParams(
temperature=0.1,
top_p=0.95,
seed=1,
max_tokens=25,
repetition_penalty=1.2)
# Create an LLM
llm = LLM(model=model_id)
# Generate texts
outputs = llm.generate(prompts, sampling_params)
# Print outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
</details>
---
## Data
### Pretraining Data
The pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below.
The initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation
and give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half,
Spanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.
During the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset.
This adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:
The pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens.
Following this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%.
Other notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%.
These major sources collectively form the bulk of the corpus, ensuring a rich and diverse dataset for training the language model.
The remaining 10% comes from smaller sources in various languages.
Feel free to click the expand button below to see the full list of sources.
<details>
<summary>Data Sources</summary>
| Dataset | Language | Source |
|---|---|---|
| Colossal OSCAR 1.0 | bg, ca, cs, cy, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, oc, pl, pt, ro, ru, sh, sk, sl, sr, sv, uk | Brack et al., 2024 |
| Aya Dataset (w/o Evaluation Suite) | eu, hr, nl, fi, ka, hu, lt, nn, ro, sk, lv, cy, bg, cs, en, fr, de, ga, mt, pl, ru, sl, sv, ca, da, et, gl, el, it, no, pt, sr, es, uk | Singh et al., 2024 |
| Wikimedia dumps | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, pl, pt, ro, sh, sk, sl, sr, uk | [Link](https://dumps.wikimedia.org/) |
| OpenSubtitles v2016 | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, gl, hr, it, lt, lv, nl, no, pl, pt, ro, sk, sl, sr, sv, uk | Lison & Tiedemann, 2016 |
| EurLEX-Resources | bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelniklaus/eurlex_resources) |
| MC4-Legal | bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelito/legal-mc4) |
| Parlamint | at, bg, cz, dk, ee, es, es-ga, fi, fr, gb, gr, hr, hu, it, lv, nl, no, pl, pt, rs, se, si | Erjavec et al., 2021 |
| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |
| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |
| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |
| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |
| BIGPATENT | en | Sharma et al., 2019 |
| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |
| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |
| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
| CaBeRnet | fr | Popa-Fabre et al., 2020 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
| DaNewsroom | da | Varab & Schluter, 2020 |
| Danish GigaWord | da | Strømberg-Derczynski et al., 2021 |
| DK-CLARIN Reference Corpus of General Danish | da | [Link](https://korpus.dsl.dk/clarin/) |
| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |
| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |
| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |
| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |
| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |
| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |
| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |
| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |
| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |
| HPLT v1 - Spanish | es | de Gibert et al., 2024 |
| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |
| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |
| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |
| Korpus Malti | mt | Micallef et al., 2022 |
| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |
| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |
| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |
| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |
| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |
| Math AMPS | en | Hendrycks et al., 2021 |
| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |
| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |
| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |
| ParlamentoPT | pt | Rodrigues et al., 2023 |
| peS2o | en | Soldaini & Lo, 2023 |
| PG-19 | en | Rae et al., 2019 |
| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |
| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |
| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |
| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |
| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |
| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |
| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |
| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |
| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |
| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |
| Starcoder | code | Li et al., 2023 |
| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |
| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |
| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |
| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |
| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |
| The Pile (PhilPapers) | en | Gao et al., 2021 |
| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |
| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |
| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
<details>
<summary>References</summary>
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</details>
</details>
The model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part
of the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch;
and 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 trillion tokens.
We provide an extense Datasheet section following the best practices defined by [(Gebru et al., 2021)](https://arxiv.org/pdf/1803.09010).
<details>
<summary>Datasheet</summary>
#### Motivation
**For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.**
The purpose of creating this dataset is to pre-train the Salamandra family of multilingual models with high performance in a large number of European languages (35)
and programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample
these languages by a factor of 2.
There is a great lack of massive multilingual data, especially in minority languages (Ostendorff & Rehm, 2023), so part of our efforts in the creation of
this pre-training dataset have resulted in the contribution to large projects such as the Community OSCAR (Brack et al., 2024), which includes 151 languages
and 40T words, or CATalog (Palomar-Giner et al., 2024), the largest open dataset in Catalan in the world.
**Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?**
The dataset has been created by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS),
which aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, it was created by
the unit's data team, the main contributors being José Javier Saiz, Ferran Espuña and Jorge Palomar.
However, the creation of the dataset would not have been possible without the collaboration of a large number of collaborators, partners and public institutions,
which can be found in detail in the acknowledgements.
**Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.**
This work has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
#### Composition
**What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.**
The dataset consists entirely of text documents in various languages. Specifically, data was mainly sourced from the following databases and
repositories:
- **Common Crawl:** Repository that holds website data and is run by the Common Crawl non-profit organization. It is updated monthly and is
distributed under the CC0 1.0 public domain license.
- **GitHub:** Community platform that allows developers to create, store, manage, and share their code. Repositories are crawled and then
distributed with their original licenses, which may vary from permissive to non-commercial licenses.
- **Wikimedia:** Database that holds the collection databases managed by the Wikimedia Foundation, including Wikipedia, Wikibooks, Wikinews,
Wikiquote, Wikisource, and Wikivoyage. It is updated monthly and is distributed under Creative Commons Attribution-ShareAlike License 4.0.
- **EurLex:** Repository that holds the collection of legal documents from the European Union, available in all of the EU’s 24 official
languages and run by the Publications Office of the European Union. It is updated daily and is distributed under the Creative Commons
Attribution 4.0 International license.
- **Other repositories:** Specific repositories were crawled under permission for domain-specific corpora, which include academic, legal,
and newspaper repositories.
We provide a complete list of dataset sources at the end of this section.
**How many instances are there in total (of each type, if appropriate)?**
The dataset contains a diverse range of instances across multiple languages, with notable adjustments for certain languages. English
represents the largest portion, accounting for 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,
while Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled
by half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian
(4.59%), with several additional languages contributing between 1% and 2%, and smaller portions represented by a variety of others.
**Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).**
The dataset is a sample from multiple sources, with different weights based on the primary language of the content: Spanish, Catalan,
Basque, and Galician content was upsampled by a factor of two, while programming languages were downsampled by a factor of half. Other
sources were sampled in proportion to their occurrence.
**What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.**
Each instance consists of a text document processed for deduplication, language identification, and source-specific filtering. Some documents required
optical character recognition (OCR) to extract text from non-text formats such as PDFs.
**Is there a label or target associated with each instance? If so, please provide a description.**
Each instance is labelled with a unique identifier, the primary language of the content, and the URL for web-sourced instances. Additional labels were
automatically assigned to detect specific types of content -harmful or toxic content- and to assign preliminary indicators of undesired qualities -very
short documents, high density of symbols, etc.- which were used for filtering instances.
**Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.**
No significant information is missing from the instances.
**Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.**
Instances are related through shared metadata, such as source and language identifiers.
**Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.**
The dataset is randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.
**Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.**
Despite removing duplicated instances within each source, redundancy remains at the paragraph and sentence levels, particularly in web-sourced
instances where search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also duplicated
across sources due to format variations.
**Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.**
The dataset is self-contained and does not rely on external resources.
**Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.**
The dataset does not contain confidential data.
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.**
The dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). Although
pre-processing techniques were applied to mitigate offensive content, the heterogeneity and scale of web-sourced data make exhaustive
filtering challenging, which makes it next to impossible to identify all adult content without falling into excessive filtering, which may
negatively influence certain demographic groups (Dodge et al., 2021).
**Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.**
The dataset does not explicitly identify any subpopulations.
**Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.**
Web-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names,
IP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple
data points, the nature and scale of web data makes it difficult to parse such information. In any case, efforts are made to filter or anonymize
sensitive data (Mina et al., 2024), but some identifiable information may remain in the dataset.
**Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.**
Given that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial
information, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023),
especially if the content originates from less-regulated sources or user-generated platforms.
#### Collection Process
**How was the data collected?**
This dataset is constituted by combining several sources, whose acquisition methods can be classified into three groups:
- Web-sourced datasets with some preprocessing available under permissive license.
- Domain-specific or language-specific raw crawls.
- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).
**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**
The data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:
- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.
- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.
- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.
**If the dataset is a sample from a larger set, what was the sampling strategy?**
The sampling strategy was to use the whole dataset resulting from the filtering explained in the 'preprocessing/cleaning/labelling' section,
with the particularity that an upsampling of 2 (i.e. twice the probability of sampling a document) was performed for the co-official languages
of Spain (Spanish, Catalan, Galician, Basque), and a downsampling of 1/2 was applied for code (half the probability of sampling a code document,
evenly distributed among all programming languages).
**Who was involved in the data collection process and how were they compensated?**
This data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely
by members of the Language Technologies data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary
consideration for acquiring data from suppliers.
**Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.**
Data were acquired and processed from April 2023 to April 2024. However, as mentioned, much data has been obtained from open projects such
as Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important.
**Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.**
No particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an
internal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència
Artificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an
ethical and legal point of view, respectively.
#### Preprocessing
**Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.**
No changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:
- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.
- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\_pp' field, a perplexity-based score generated by a language model.
**Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.**
The original raw data was not kept.
**Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.**
Yes, the preprocessing and filtering software is open-sourced. The [CURATE](https://github.com/langtech-bsc/CURATE) pipeline was used for CATalog and other curated datasets,
and the [Ungoliant](https://github.com/oscar-project/ungoliant) pipeline was used for the OSCAR project.
#### Uses
**Has the dataset been used for any tasks already? If so, please provide a description.**
Pre-train the Salamandra model family.
**What (other) tasks could the dataset be used for?**
The data can be used primarily to pre-train other language models, which can then be used for a wide range of use cases. The dataset could
also be used for other tasks such as fine-tuning language models, cross-lingual NLP tasks, machine translation, domain-specific text
generation, and language-specific data analysis.
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?**
Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages.
Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic
groups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures,
acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to
address privacy concerns and contribute to a more inclusive linguistic dataset.
**Are there tasks for which the dataset should not be used?**
-
#### Distribution
**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.**
The dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section.
#### Maintenance
**Who will be supporting/hosting/maintaining the dataset?**
The dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure
regular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are
responsible for.
**How can the owner/curator/manager of the dataset be contacted?**
The data owner may be contacted with the email address [email protected].
**Will the dataset be updated?**
The dataset will not be updated.
**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.**
The dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in
web-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an
individual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to
remove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues.
**Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.**
Since the dataset will not be updated, only the final version will be kept.
**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**
The dataset does not allow for external contributions.
</details>
---
## Evaluation
### Gold-standard benchmarks
Evaluation is done using the Language Model Evaluation Harness (Gao et al., 2024). We evaluate on a set of tasks taken from [SpanishBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/spanish_bench), [CatalanBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/catalan_bench), [BasqueBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/basque_bench) and [GalicianBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/galician_bench). We also use English tasks already available on the LM Evaluation Harness. These benchmarks include both new and existing tasks and datasets. In the tables below, we include the results in a selection of evaluation datasets that represent model's performance across a variety of tasks within these benchmarks.
We only use tasks that are either human generated, human translated, or with a strong human-in-the-loop (i.e., machine translation followed by professional revision or machine generation followed by human revision and annotation). This is the reason behind the variety in number of tasks reported across languages. As more tasks that fulfill these requirements are published, we will update the presented results. We also intend to expand the evaluation to other languages, as long as the datasets meet our quality standards.
During the implementation of the evaluation we observed a series of issues worth considering when replicating and interpreting the results presented. These issues include ≈1.5% variances in performance in some tasks depending on the version of the `transformers` library used, and depending on the use (or lack of use) of tensor parallelism when loading a model. When implementing existing tasks, we carry out a comprehensive quality evaluation of the dataset, the Harness task itself, and what kind of input models see during evaluation. Our implementation (see links above) addresses multiple existing problems such as errors in datasets and prompts, and lack of pre-processing. All this means that results will vary if using other Harness implementations, and may slightly vary depending on the replication setup.
It should be noted that these results are subject to all the drawbacks of every current gold-standard evaluation, and that the figures do not fully represent the model's capabilities and potential. We thus advise caution when reading and interpreting the results.
A full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.
All results reported below are on a 5-shot setting.
#### Spanish
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa_es</td>
<td>acc</td>
<td>72.8</td>
</tr>
<tr>
<td>xstorycloze_es</td>
<td>acc</td>
<td>64.73</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_es</td>
<td>acc</td>
<td>56.34</td>
</tr>
<tr>
<td>xnli_es</td>
<td>acc</td>
<td>44.74</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws_es</td>
<td>acc</td>
<td>55.95</td>
</tr>
<tr>
<td rowspan="2">QA</td>
<td>openbookqa_es</td>
<td>acc</td>
<td>31.4</td>
</tr>
<tr>
<td>xquad_es</td>
<td>acc</td>
<td>57.59</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_es</td>
<td>bleu</td>
<td>20.05</td>
</tr>
</tbody>
</table>
#### Catalan
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa_ca</td>
<td>acc</td>
<td>70.2</td>
</tr>
<tr>
<td>xstorycloze_ca</td>
<td>acc</td>
<td>66.38</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_ca</td>
<td>acc</td>
<td>56.34</td>
</tr>
<tr>
<td>xnli_ca</td>
<td>acc</td>
<td>48.15</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafraseja</td>
<td>acc</td>
<td>61.35</td>
</tr>
<tr>
<td>paws_ca</td>
<td>acc</td>
<td>57.05</td>
</tr>
<tr>
<td rowspan="5">QA</td>
<td>arc_ca_easy</td>
<td>acc</td>
<td>55.3</td>
</tr>
<tr>
<td>arc_ca_challenge</td>
<td>acc</td>
<td>27.65</td>
</tr>
<tr>
<td>openbookqa_ca</td>
<td>acc</td>
<td>29.40</td>
</tr>
<tr>
<td>piqa_ca</td>
<td>acc</td>
<td>63.82</td>
</tr>
<tr>
<td>siqa_ca</td>
<td>acc</td>
<td>43.04</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_ca</td>
<td>bleu</td>
<td>24.93</td>
</tr>
</tbody></table>
#### Basque
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>xcopa_eu</td>
<td>acc</td>
<td>58</td>
</tr>
<tr>
<td>xstorycloze_eu</td>
<td>acc</td>
<td>58.97</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_eu</td>
<td>acc</td>
<td>43.66</td>
</tr>
<tr>
<td>xnli_eu</td>
<td>acc</td>
<td>42.03</td>
</tr>
<tr>
<td rowspan="4">QA</td>
<td>eus_exams</td>
<td>acc</td>
<td>26.11</td>
</tr>
<tr>
<td>eus_proficiency</td>
<td>acc</td>
<td>24.09</td>
</tr>
<tr>
<td>eus_trivia</td>
<td>acc</td>
<td>28.05</td>
</tr>
<tr>
<td>piqa_eu</td>
<td>acc</td>
<td>56.86</td>
</tr>
<tr>
<td>Reading Comprehension</td>
<td>eus_reading</td>
<td>acc</td>
<td>28.41</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_eu</td>
<td>bleu</td>
<td>8.96</td>
</tr>
</tbody></table>
#### Galician
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td>Commonsense Reasoning</td>
<td>xstorycloze_gl</td>
<td>acc</td>
<td>64.99</td>
</tr>
<tr>
<td>NLI</td>
<td>xnli_gl</td>
<td>acc</td>
<td>47.35</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafrases_gl</td>
<td>acc</td>
<td>56.08</td>
</tr>
<tr>
<td>paws_gl</td>
<td>acc</td>
<td>54.85</td>
</tr>
<tr>
<td>QA</td>
<td>openbookqa_gl</td>
<td>acc</td>
<td>25.4</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_gl</td>
<td>bleu</td>
<td>22.38</td>
</tr>
</tbody>
</table>
#### English
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa</td>
<td>acc</td>
<td>83.00</td>
</tr>
<tr>
<td>xstorycloze_en</td>
<td>acc</td>
<td>71.81</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli</td>
<td>acc</td>
<td>52.11</td>
</tr>
<tr>
<td>xnli_en</td>
<td>acc</td>
<td>46.47</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws *</td>
<td>acc</td>
<td>56.5</td>
</tr>
<tr>
<td rowspan="6">QA</td>
<td>arc_easy</td>
<td>acc</td>
<td>72.14</td>
</tr>
<tr>
<td>arc_challenge</td>
<td>acc</td>
<td>35.41</td>
</tr>
<tr>
<td>openbookqa</td>
<td>acc</td>
<td>28.00</td>
</tr>
<tr>
<td>piqa</td>
<td>acc</td>
<td>73.61</td>
</tr>
<tr>
<td>social_iqa</td>
<td>acc</td>
<td>44.78</td>
</tr>
<tr>
<td>xquad_en **</td>
<td>acc</td>
<td>64.87</td>
</tr>
</tbody></table>
\* Current LM Evaluation Harness implementation is lacking correct pre-processing. These results are obtained with adequate pre-processing.
\*\* This task is not yet available in the official Harness, we hope to add it soon.
---
## Ethical Considerations and Limitations
We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases, we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019). We report inadequate accuracies in both ambiguous and disambiguated contexts, which is indicative of the presence of societal biases which need to be addressed in post-training phases.
Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings. For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018). We observe moderate to strong to very strong primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers. We measure the effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We detect moderate effects, implying that outputs can be influenced by the prompts.
Our analyses of these biases are by no means exhaustive and are limited by the relative scarcity of adequate resources in all languages present in the training data. We aim to gradually extend and expand our analyses in future work.
We highlight that these results can be expected from a pretrained model that has not yet been instruction-tuned or aligned. These tests are performed in order to show the biases the model may contain. We urge developers to take them into account and perform safety testing and tuning tailored to their specific applications of the model.
---
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.
### Funding
This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
### Acknowledgements
This project has benefited from the contributions of numerous teams and institutions, mainly through data contributions, knowledge transfer or technical support.
In Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà.
At the national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria.
At the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration. We would also like to give special thanks to the NVIDIA team, with whom we have met regularly, specially to: Ignacio Sarasua, Adam Henryk Grzywaczewski, Oleg Sudakov, Sergio Perez, Miguel Martinez, Felipes Soares and Meriem Bendris. Their constant support has been especially appreciated throughout the entire process.
Their valuable efforts have been instrumental in the development of this work.
### Disclaimer
Be aware that the model may contain biases or other unintended distortions.
When third parties deploy systems or provide services based on this model, or use the model themselves,
they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations,
including those governing the use of Artificial Intelligence.
The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
### Citation
```
@misc{gonzalezagirre2025salamandratechnicalreport,
title={Salamandra Technical Report},
author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
year={2025},
eprint={2502.08489},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.08489},
}
```
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Index
|Model|Base|Instruct|
|:---:|:---:|:---:|
|2B| [Link](https://huggingface.co/BSC-LT/salamandra-2b) | [Link](https://huggingface.co/BSC-LT/salamandra-2b-instruct) |
|7B| [Link](https://huggingface.co/BSC-LT/salamandra-7b) | [Link](https://huggingface.co/BSC-LT/salamandra-7b-instruct) |
|40B| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] |
[
"BEAR",
"SCIELO"
] |
croissantllm/CroissantLLMChat-v0.1
|
croissantllm
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"legal",
"code",
"text-generation-inference",
"art",
"conversational",
"fr",
"en",
"dataset:croissantllm/croissant_dataset",
"dataset:croissantllm/CroissantLLM-2201-sft",
"dataset:cerebras/SlimPajama-627B",
"dataset:uonlp/CulturaX",
"dataset:pg19",
"dataset:bigcode/starcoderdata",
"arxiv:2402.00786",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-01-24T09:18:45 | 2024-04-26T10:02:01 | 3,614 | 50 |
---
datasets:
- croissantllm/croissant_dataset
- croissantllm/CroissantLLM-2201-sft
- cerebras/SlimPajama-627B
- uonlp/CulturaX
- pg19
- bigcode/starcoderdata
language:
- fr
- en
license: mit
pipeline_tag: text-generation
tags:
- legal
- code
- text-generation-inference
- art
---
# CroissantLLMChat (190k steps + Chat)
This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase.
https://arxiv.org/abs/2402.00786
For best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below:
```python
chat = [
{"role": "user", "content": "Que puis-je faire à Marseille en hiver?"},
]
chat_input = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
corresponding to:
```python
chat_input = """<|im_start|>user
{USER QUERY}<|im_end|>
<|im_start|>assistant\n"""
```
## Abstract
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.
To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.
This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.
## Citation
Our work can be cited as:
```bash
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Usage
This model is a Chat model, that is, it is finetuned for Chat function and works best with the provided template.
#### With generate
This might require a stopping criteria on <|im_end|> token.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "croissantllm/CroissantLLMChat-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
generation_args = {
"max_new_tokens": 256,
"do_sample": True,
"temperature": 0.3,
"top_p": 0.90,
"top_k": 40,
"repetition_penalty": 1.05,
"eos_token_id": [tokenizer.eos_token_id, 32000],
}
chat = [
{"role": "user", "content": "Qui est le président francais actuel ?"},
]
chat_input = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat_input, return_tensors="pt").to(model.device)
tokens = model.generate(**inputs, **generation_args)
print(tokenizer.decode(tokens[0]))
# print tokens individually
print([(tokenizer.decode([tok]), tok) for tok in tokens[0].tolist()])
```
## Model limitations
Evaluation results indicate the model is strong in its size category, and offers decent performances on writing-based tasks and internal knowledge, and very strong performance on translation tasks. The small size of the CroissantLLM model however hinders its capacity to perform more complex reasoning-based tasks, at least in a zero or few-shot manner in its generalist base or chat-model versions. This is aligned with other models of size and underlines the importance of scale for more abstract tasks.
#### Knowledge Cutoff
The model training dataset has a data cutoff date corresponding to the November 2023 Wikipedia dump. This is the de facto knowledge cutoff date for our base model, although a lot of information dates back further. Updated versions can be trained through continued pre-training or subsequent fine-tuning.
#### Multilingual performance.
CroissantLLM is mostly a French and English model. Code performance is relatively limited, and although some amount of data from other languages is included within the SlimPajama training set, out-of-the-box performance in other languages is not to be expected, although some European languages do work quite well.
#### Hallucinations.
CroissantLLM can hallucinate and output factually incorrect data, especially regarding complex topics. This is to be expected given the small model size, and hallucination rates seem inferior to most models of the same size category although no quantitative assessments have been conducted outside of MT-Bench experiments.
|
[
"TRANSLATION"
] |
[
"CRAFT"
] |
epfl-llm/meditron-7b
|
epfl-llm
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:meta-llama/Llama-2-7b",
"base_model:finetune:meta-llama/Llama-2-7b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-08T16:03:23 | 2023-12-07T19:38:26 | 3,594 | 272 |
---
base_model: meta-llama/Llama-2-7b
datasets:
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
---
<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-70B and Meditron-7B are both foundation models without finetuning or instruction-tuning. They can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.
There are two ways we have used this model for downstream question-answering tasks.
1. We apply in-context learning with k demonstrations (3 or 5 in our paper) added to the prompt.
2. We finetuned the models for downstream question-answering tasks using specific training sets.
We encourage and look forward to the adaption of the base model for more diverse applications.
If you want a more interactive way to prompt the model, we recommend using a high-throughput and memory-efficient inference engine with a UI that supports chat and text generation.
You can check out our deployment [guide](https://github.com/epfLLM/meditron/blob/main/deployment/README.md), where we used [FastChat](https://github.com/lm-sys/FastChat) with [vLLM](https://github.com/vllm-project/vllm). We collected generations for our qualitative analysis through an interactive UI platform, [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT). Here is the prompt format we used as an example:
<img width=70% src="prompt_example.png" alt="qualitative-analysis-prompt" title="Qualitative Analysis Prompt">
### 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"
] |
dwzhu/e5-base-4k
|
dwzhu
|
sentence-similarity
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2404.12096",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-28T09:32:27 | 2024-05-14T08:22:21 | 3,389 | 10 |
---
language:
- en
license: mit
tags:
- mteb
- sentence-similarity
model-index:
- name: e5-base-4k
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-4k
[LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096). Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed.
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
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_position_ids(input_ids: Tensor, max_original_positions: int=512, encode_max_length: int=4096) -> Tensor:
position_ids = list(range(input_ids.size(1)))
factor = max(encode_max_length // max_original_positions, 1)
if input_ids.size(1) <= max_original_positions:
position_ids = [(pid * factor) for pid in position_ids]
position_ids = torch.tensor(position_ids, dtype=torch.long)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
return position_ids
# 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('dwzhu/e5-base-4k')
model = AutoModel.from_pretrained('dwzhu/e5-base-4k')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=4096, padding=True, truncation=True, return_tensors='pt')
batch_dict['position_ids'] = get_position_ids(batch_dict['input_ids'], max_original_positions=512, encode_max_length=4096)
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/abs/2404.12096.pdf](https://arxiv.org/abs/2404.12096.pdf). Note that E5-Base-4k simply expands the position embedding matrix to allow for 4,096 position ids. The embedding vectors for the original pids {0,1,2,...,511} is mapped to represent {0,8,16,...,4088}. Embedding vectors for other pids are trained. So for inputs not exceeding 512 tokens, please multiply the position ids by 8 to maintain the original behavior, as shown in the code above.
## 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).
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{zhu2024longembed,
title={LongEmbed: Extending Embedding Models for Long Context Retrieval},
author={Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
journal={arXiv preprint arXiv:2404.12096},
year={2024}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
microsoft/BioGPT-Large-PubMedQA
|
microsoft
|
text-generation
|
[
"transformers",
"pytorch",
"biogpt",
"text-generation",
"medical",
"en",
"dataset:pubmed_qa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-03T20:33:43 | 2023-02-04T07:50:25 | 3,367 | 108 |
---
datasets:
- pubmed_qa
language:
- en
library_name: transformers
license: mit
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- medical
widget:
- text: 'question: Can ''high-risk'' human papillomaviruses (HPVs) be detected in
human breast milk? context: Using polymerase chain reaction techniques, we evaluated
the presence of HPV infection in human breast milk collected from 21 HPV-positive
and 11 HPV-negative mothers. Of the 32 studied human milk specimens, no ''high-risk''
HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 or 58 DNA was detected. answer:
This preliminary case-control study indicates the absence of mucosal ''high-risk''
HPV types in human breast milk.'
inference:
parameters:
max_new_tokens: 250
do_sample: false
---
## 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.
## 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"
] |
lightonai/modernbert-embed-large
|
lightonai
|
sentence-similarity
|
[
"sentence-transformers",
"onnx",
"safetensors",
"modernbert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers.js",
"en",
"arxiv:2402.01613",
"arxiv:2412.13663",
"base_model:answerdotai/ModernBERT-large",
"base_model:quantized:answerdotai/ModernBERT-large",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-13T10:21:29 | 2025-01-14T10:02:51 | 3,361 | 20 |
---
base_model:
- answerdotai/ModernBERT-large
- lightonai/modernbert-embed-large-unsupervised
language:
- en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- transformers.js
model-index:
- name: modernbert-embed-large
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: None
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.7910447761194
- type: ap
value: 39.79562424828666
- type: f1
value: 70.69575548517653
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: None
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 94.19505000000001
- type: ap
value: 91.75071069741077
- type: f1
value: 94.19151001437368
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: None
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.664
- type: f1
value: 46.932904638602466
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: None
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 25.178
- type: map_at_10
value: 41.088
- type: map_at_100
value: 42.143
- type: map_at_1000
value: 42.152
- type: map_at_20
value: 41.946
- type: map_at_3
value: 36.048
- type: map_at_5
value: 38.619
- type: mrr_at_1
value: 25.533
- type: mrr_at_10
value: 41.238
- type: mrr_at_100
value: 42.293
- type: mrr_at_1000
value: 42.302
- type: mrr_at_20
value: 42.096000000000004
- type: mrr_at_3
value: 36.260999999999996
- type: mrr_at_5
value: 38.797
- type: ndcg_at_1
value: 25.178
- type: ndcg_at_10
value: 50.352
- type: ndcg_at_100
value: 54.583000000000006
- type: ndcg_at_1000
value: 54.797
- type: ndcg_at_20
value: 53.36
- type: ndcg_at_3
value: 39.781
- type: ndcg_at_5
value: 44.412
- type: precision_at_1
value: 25.178
- type: precision_at_10
value: 8.016
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.591
- type: precision_at_3
value: 16.88
- type: precision_at_5
value: 12.376
- type: recall_at_1
value: 25.178
- type: recall_at_10
value: 80.156
- type: recall_at_100
value: 98.009
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 91.821
- type: recall_at_3
value: 50.63999999999999
- type: recall_at_5
value: 61.878
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: None
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.800803622189214
- type: v_measures
value:
- 0.45978238220905315
- 0.501480083181185
- 0.48967239140474045
- 0.4751957557116818
- 0.46928677237487587
- 0.47135861735124435
- 0.4795286266157441
- 0.48441035326165754
- 0.47945476864912945
- 0.45912059930502597
- 0.5592448526471332
- 0.5674112737806248
- 0.5567224389492952
- 0.5541118789802117
- 0.570514423105391
- 0.5629670037938863
- 0.5615893409655635
- 0.5625434649173611
- 0.5565761783630462
- 0.5623718557128333
- 0.5210204606034864
- 0.2859950794098042
- 0.45504510640487766
- 0.4047776074746812
- 0.3535102351915281
- 0.28472692335289046
- 0.307070020692249
- 0.24530323287003208
- 0.3021496005249739
- 1.0
- 0.2753077950744492
- 0.45978238220905315
- 0.501480083181185
- 0.48967239140474045
- 0.4751957557116818
- 0.46928677237487587
- 0.47135861735124435
- 0.4795286266157441
- 0.48441035326165754
- 0.47945476864912945
- 0.45912059930502597
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type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: None
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: None
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 63.213756562834234
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value: 76.76493866244557
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type: STS
dataset:
name: MTEB BIOSSES
type: None
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 87.90977143141957
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value: 87.47729443431557
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value: 86.45663786393041
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value: 86.31461733951959
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value: 85.94280510342506
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value: 85.61158927235539
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type: Classification
dataset:
name: MTEB Banking77Classification
type: None
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
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value: 86.17857142857144
- type: f1
value: 86.14192410600847
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type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: None
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
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value: 39.466770895318334
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- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: None
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.668146108668715
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- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 34.394999999999996
- type: map_at_10
value: 45.882
- type: map_at_100
value: 47.4
- type: map_at_1000
value: 47.509
- type: map_at_20
value: 46.822
- type: map_at_3
value: 42.408
- type: map_at_5
value: 44.586
- type: mrr_at_1
value: 41.202
- type: mrr_at_10
value: 51.134
- type: mrr_at_100
value: 51.943
- type: mrr_at_1000
value: 51.986
- type: mrr_at_20
value: 51.717
- type: mrr_at_3
value: 48.784
- type: mrr_at_5
value: 50.336000000000006
- type: ndcg_at_1
value: 41.202
- type: ndcg_at_10
value: 51.842999999999996
- type: ndcg_at_100
value: 57.177
- type: ndcg_at_1000
value: 58.89
- type: ndcg_at_20
value: 54.357
- type: ndcg_at_3
value: 47.286
- type: ndcg_at_5
value: 49.829
- type: precision_at_1
value: 41.202
- type: precision_at_10
value: 9.585
- type: precision_at_100
value: 1.5150000000000001
- type: precision_at_1000
value: 0.194
- type: precision_at_20
value: 5.808
- type: precision_at_3
value: 22.508
- type: precision_at_5
value: 16.366
- type: recall_at_1
value: 34.394999999999996
- type: recall_at_10
value: 63.17
- type: recall_at_100
value: 84.867
- type: recall_at_1000
value: 95.733
- type: recall_at_20
value: 72.011
- type: recall_at_3
value: 49.966
- type: recall_at_5
value: 56.802
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 34.324
- type: map_at_10
value: 46.123
- type: map_at_100
value: 47.455999999999996
- type: map_at_1000
value: 47.576
- type: map_at_20
value: 46.851
- type: map_at_3
value: 42.945
- type: map_at_5
value: 44.751000000000005
- type: mrr_at_1
value: 43.248
- type: mrr_at_10
value: 52.544000000000004
- type: mrr_at_100
value: 53.102000000000004
- type: mrr_at_1000
value: 53.138
- type: mrr_at_20
value: 52.861000000000004
- type: mrr_at_3
value: 50.37199999999999
- type: mrr_at_5
value: 51.712
- type: ndcg_at_1
value: 43.248
- type: ndcg_at_10
value: 52.235
- type: ndcg_at_100
value: 56.355
- type: ndcg_at_1000
value: 58.053
- type: ndcg_at_20
value: 53.849000000000004
- type: ndcg_at_3
value: 48.208
- type: ndcg_at_5
value: 50.134
- type: precision_at_1
value: 43.248
- type: precision_at_10
value: 9.917
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.198
- type: precision_at_20
value: 5.779999999999999
- type: precision_at_3
value: 23.588
- type: precision_at_5
value: 16.586000000000002
- type: recall_at_1
value: 34.324
- type: recall_at_10
value: 62.56
- type: recall_at_100
value: 79.745
- type: recall_at_1000
value: 90.082
- type: recall_at_20
value: 68.367
- type: recall_at_3
value: 50.171
- type: recall_at_5
value: 55.889
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 44.143
- type: map_at_10
value: 56.53
- type: map_at_100
value: 57.48799999999999
- type: map_at_1000
value: 57.535000000000004
- type: map_at_20
value: 57.152
- type: map_at_3
value: 53.382
- type: map_at_5
value: 55.156000000000006
- type: mrr_at_1
value: 50.09400000000001
- type: mrr_at_10
value: 59.819
- type: mrr_at_100
value: 60.431000000000004
- type: mrr_at_1000
value: 60.455000000000005
- type: mrr_at_20
value: 60.251999999999995
- type: mrr_at_3
value: 57.544
- type: mrr_at_5
value: 58.904999999999994
- type: ndcg_at_1
value: 50.09400000000001
- type: ndcg_at_10
value: 62.141999999999996
- type: ndcg_at_100
value: 65.755
- type: ndcg_at_1000
value: 66.674
- type: ndcg_at_20
value: 63.92400000000001
- type: ndcg_at_3
value: 56.986000000000004
- type: ndcg_at_5
value: 59.519999999999996
- type: precision_at_1
value: 50.09400000000001
- type: precision_at_10
value: 9.743
- type: precision_at_100
value: 1.246
- type: precision_at_1000
value: 0.136
- type: precision_at_20
value: 5.439
- type: precision_at_3
value: 25.119999999999997
- type: precision_at_5
value: 17.052999999999997
- type: recall_at_1
value: 44.143
- type: recall_at_10
value: 75.372
- type: recall_at_100
value: 90.602
- type: recall_at_1000
value: 97.043
- type: recall_at_20
value: 81.83500000000001
- type: recall_at_3
value: 61.607
- type: recall_at_5
value: 67.755
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.621
- type: map_at_10
value: 35.865
- type: map_at_100
value: 36.93
- type: map_at_1000
value: 37.008
- type: map_at_20
value: 36.509
- type: map_at_3
value: 33.532000000000004
- type: map_at_5
value: 34.745
- type: mrr_at_1
value: 28.588
- type: mrr_at_10
value: 37.828
- type: mrr_at_100
value: 38.779
- type: mrr_at_1000
value: 38.834
- type: mrr_at_20
value: 38.419
- type: mrr_at_3
value: 35.725
- type: mrr_at_5
value: 36.803999999999995
- type: ndcg_at_1
value: 28.588
- type: ndcg_at_10
value: 40.983999999999995
- type: ndcg_at_100
value: 46.117000000000004
- type: ndcg_at_1000
value: 47.959
- type: ndcg_at_20
value: 43.22
- type: ndcg_at_3
value: 36.455
- type: ndcg_at_5
value: 38.393
- type: precision_at_1
value: 28.588
- type: precision_at_10
value: 6.282
- type: precision_at_100
value: 0.927
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 3.689
- type: precision_at_3
value: 15.744
- type: precision_at_5
value: 10.644
- type: recall_at_1
value: 26.621
- type: recall_at_10
value: 54.80199999999999
- type: recall_at_100
value: 78.171
- type: recall_at_1000
value: 91.786
- type: recall_at_20
value: 63.195
- type: recall_at_3
value: 42.164
- type: recall_at_5
value: 46.936
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 18.619
- type: map_at_10
value: 27.577
- type: map_at_100
value: 28.717
- type: map_at_1000
value: 28.835
- type: map_at_20
value: 28.18
- type: map_at_3
value: 24.462999999999997
- type: map_at_5
value: 26.230999999999998
- type: mrr_at_1
value: 22.886
- type: mrr_at_10
value: 32.089
- type: mrr_at_100
value: 32.998
- type: mrr_at_1000
value: 33.06
- type: mrr_at_20
value: 32.633
- type: mrr_at_3
value: 29.125
- type: mrr_at_5
value: 30.792
- type: ndcg_at_1
value: 22.886
- type: ndcg_at_10
value: 33.343
- type: ndcg_at_100
value: 38.735
- type: ndcg_at_1000
value: 41.393
- type: ndcg_at_20
value: 35.455
- type: ndcg_at_3
value: 27.575
- type: ndcg_at_5
value: 30.361
- type: precision_at_1
value: 22.886
- type: precision_at_10
value: 6.256
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_20
value: 3.7130000000000005
- type: precision_at_3
value: 13.267000000000001
- type: precision_at_5
value: 9.851
- type: recall_at_1
value: 18.619
- type: recall_at_10
value: 46.478
- type: recall_at_100
value: 69.614
- type: recall_at_1000
value: 88.331
- type: recall_at_20
value: 54.254000000000005
- type: recall_at_3
value: 30.897999999999996
- type: recall_at_5
value: 37.785000000000004
- type: map_at_1
value: 28.592666666666673
- type: map_at_10
value: 38.50391666666667
- type: map_at_100
value: 39.719166666666666
- type: map_at_1000
value: 39.82683333333334
- type: map_at_20
value: 39.18608333333333
- type: map_at_3
value: 35.561833333333325
- type: map_at_5
value: 37.181000000000004
- type: mrr_at_1
value: 33.67625
- type: mrr_at_10
value: 42.727
- type: mrr_at_100
value: 43.55041666666667
- type: mrr_at_1000
value: 43.60058333333334
- type: mrr_at_20
value: 43.21508333333333
- type: mrr_at_3
value: 40.32983333333334
- type: mrr_at_5
value: 41.699333333333335
- type: ndcg_at_1
value: 33.67625
- type: ndcg_at_10
value: 44.064416666666666
- type: ndcg_at_100
value: 49.085
- type: ndcg_at_1000
value: 51.09325
- type: ndcg_at_20
value: 46.07716666666666
- type: ndcg_at_3
value: 39.22225
- type: ndcg_at_5
value: 41.47508333333333
- type: precision_at_1
value: 33.67625
- type: precision_at_10
value: 7.689916666666667
- type: precision_at_100
value: 1.1995833333333334
- type: precision_at_1000
value: 0.15541666666666665
- type: precision_at_20
value: 4.515500000000001
- type: precision_at_3
value: 18.07241666666667
- type: precision_at_5
value: 12.732833333333332
- type: recall_at_1
value: 28.592666666666673
- type: recall_at_10
value: 56.15700000000001
- type: recall_at_100
value: 77.97075000000001
- type: recall_at_1000
value: 91.73058333333333
- type: recall_at_20
value: 63.49649999999999
- type: recall_at_3
value: 42.612833333333334
- type: recall_at_5
value: 48.44591666666667
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.087999999999997
- type: map_at_10
value: 41.668
- type: map_at_100
value: 42.983
- type: map_at_1000
value: 43.081
- type: map_at_20
value: 42.373
- type: map_at_3
value: 38.481
- type: map_at_5
value: 40.196
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.471999999999994
- type: mrr_at_100
value: 48.311
- type: mrr_at_1000
value: 48.351
- type: mrr_at_20
value: 47.981
- type: mrr_at_3
value: 45.074999999999996
- type: mrr_at_5
value: 46.37
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.63
- type: ndcg_at_100
value: 52.979
- type: ndcg_at_1000
value: 54.771
- type: ndcg_at_20
value: 49.733
- type: ndcg_at_3
value: 42.657000000000004
- type: ndcg_at_5
value: 44.878
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.527
- type: precision_at_100
value: 1.303
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_20
value: 4.966
- type: precision_at_3
value: 19.955000000000002
- type: precision_at_5
value: 14.033000000000001
- type: recall_at_1
value: 31.087999999999997
- type: recall_at_10
value: 59.585
- type: recall_at_100
value: 81.625
- type: recall_at_1000
value: 93.297
- type: recall_at_20
value: 66.813
- type: recall_at_3
value: 45.492
- type: recall_at_5
value: 51.283
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.756999999999998
- type: map_at_10
value: 40.275
- type: map_at_100
value: 41.655
- type: map_at_1000
value: 41.752
- type: map_at_20
value: 41.118
- type: map_at_3
value: 36.815
- type: map_at_5
value: 38.662
- type: mrr_at_1
value: 35.502
- type: mrr_at_10
value: 45.818
- type: mrr_at_100
value: 46.704
- type: mrr_at_1000
value: 46.745999999999995
- type: mrr_at_20
value: 46.387
- type: mrr_at_3
value: 43.322
- type: mrr_at_5
value: 44.675
- type: ndcg_at_1
value: 35.502
- type: ndcg_at_10
value: 46.658
- type: ndcg_at_100
value: 52.097
- type: ndcg_at_1000
value: 53.928
- type: ndcg_at_20
value: 49.134
- type: ndcg_at_3
value: 41.234
- type: ndcg_at_5
value: 43.579
- type: precision_at_1
value: 35.502
- type: precision_at_10
value: 8.652999999999999
- type: precision_at_100
value: 1.306
- type: precision_at_1000
value: 0.163
- type: precision_at_20
value: 5.086
- type: precision_at_3
value: 19.825
- type: precision_at_5
value: 13.995
- type: recall_at_1
value: 28.756999999999998
- type: recall_at_10
value: 59.79
- type: recall_at_100
value: 82.597
- type: recall_at_1000
value: 94.663
- type: recall_at_20
value: 68.74
- type: recall_at_3
value: 44.736
- type: recall_at_5
value: 51.047
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.381999999999998
- type: map_at_10
value: 33.311
- type: map_at_100
value: 34.171
- type: map_at_1000
value: 34.254
- type: map_at_20
value: 33.732
- type: map_at_3
value: 31.025999999999996
- type: map_at_5
value: 32.253
- type: mrr_at_1
value: 28.221
- type: mrr_at_10
value: 36.132999999999996
- type: mrr_at_100
value: 36.848
- type: mrr_at_1000
value: 36.902
- type: mrr_at_20
value: 36.497
- type: mrr_at_3
value: 33.947
- type: mrr_at_5
value: 35.174
- type: ndcg_at_1
value: 28.221
- type: ndcg_at_10
value: 37.882
- type: ndcg_at_100
value: 42.283
- type: ndcg_at_1000
value: 44.458
- type: ndcg_at_20
value: 39.268
- type: ndcg_at_3
value: 33.611999999999995
- type: ndcg_at_5
value: 35.583
- type: precision_at_1
value: 28.221
- type: precision_at_10
value: 6.043
- type: precision_at_100
value: 0.8909999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_20
value: 3.405
- type: precision_at_3
value: 14.673
- type: precision_at_5
value: 10.152999999999999
- type: recall_at_1
value: 25.381999999999998
- type: recall_at_10
value: 48.980000000000004
- type: recall_at_100
value: 69.625
- type: recall_at_1000
value: 85.946
- type: recall_at_20
value: 54.041
- type: recall_at_3
value: 37.077
- type: recall_at_5
value: 42.097
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.186
- type: map_at_10
value: 26.450000000000003
- type: map_at_100
value: 27.62
- type: map_at_1000
value: 27.746
- type: map_at_20
value: 27.105
- type: map_at_3
value: 23.982999999999997
- type: map_at_5
value: 25.306
- type: mrr_at_1
value: 22.092
- type: mrr_at_10
value: 30.326999999999998
- type: mrr_at_100
value: 31.322
- type: mrr_at_1000
value: 31.394
- type: mrr_at_20
value: 30.923000000000002
- type: mrr_at_3
value: 28.063
- type: mrr_at_5
value: 29.284
- type: ndcg_at_1
value: 22.092
- type: ndcg_at_10
value: 31.418000000000003
- type: ndcg_at_100
value: 36.924
- type: ndcg_at_1000
value: 39.645
- type: ndcg_at_20
value: 33.597
- type: ndcg_at_3
value: 27.045
- type: ndcg_at_5
value: 28.971999999999998
- type: precision_at_1
value: 22.092
- type: precision_at_10
value: 5.785
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_20
value: 3.517
- type: precision_at_3
value: 12.985
- type: precision_at_5
value: 9.291
- type: recall_at_1
value: 18.186
- type: recall_at_10
value: 42.443
- type: recall_at_100
value: 66.964
- type: recall_at_1000
value: 86.005
- type: recall_at_20
value: 50.52799999999999
- type: recall_at_3
value: 30.095
- type: recall_at_5
value: 35.148
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 31.049
- type: map_at_10
value: 40.217000000000006
- type: map_at_100
value: 41.345
- type: map_at_1000
value: 41.447
- type: map_at_20
value: 40.818
- type: map_at_3
value: 37.413999999999994
- type: map_at_5
value: 39.001000000000005
- type: mrr_at_1
value: 36.474000000000004
- type: mrr_at_10
value: 44.655
- type: mrr_at_100
value: 45.399
- type: mrr_at_1000
value: 45.454
- type: mrr_at_20
value: 45.011
- type: mrr_at_3
value: 42.226
- type: mrr_at_5
value: 43.653999999999996
- type: ndcg_at_1
value: 36.474000000000004
- type: ndcg_at_10
value: 45.509
- type: ndcg_at_100
value: 50.571
- type: ndcg_at_1000
value: 52.605999999999995
- type: ndcg_at_20
value: 47.275
- type: ndcg_at_3
value: 40.766000000000005
- type: ndcg_at_5
value: 42.979
- type: precision_at_1
value: 36.474000000000004
- type: precision_at_10
value: 7.509
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 4.3
- type: precision_at_3
value: 18.315
- type: precision_at_5
value: 12.705
- type: recall_at_1
value: 31.049
- type: recall_at_10
value: 57.135999999999996
- type: recall_at_100
value: 79.196
- type: recall_at_1000
value: 93.002
- type: recall_at_20
value: 63.416
- type: recall_at_3
value: 43.893
- type: recall_at_5
value: 49.675999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.929
- type: map_at_10
value: 36.897000000000006
- type: map_at_100
value: 38.635000000000005
- type: map_at_1000
value: 38.842
- type: map_at_20
value: 37.814
- type: map_at_3
value: 33.522
- type: map_at_5
value: 35.128
- type: mrr_at_1
value: 33.399
- type: mrr_at_10
value: 41.817
- type: mrr_at_100
value: 42.797000000000004
- type: mrr_at_1000
value: 42.842999999999996
- type: mrr_at_20
value: 42.381
- type: mrr_at_3
value: 38.999
- type: mrr_at_5
value: 40.57
- type: ndcg_at_1
value: 33.399
- type: ndcg_at_10
value: 43.134
- type: ndcg_at_100
value: 49.009
- type: ndcg_at_1000
value: 51.199
- type: ndcg_at_20
value: 45.391999999999996
- type: ndcg_at_3
value: 37.645
- type: ndcg_at_5
value: 39.940999999999995
- type: precision_at_1
value: 33.399
- type: precision_at_10
value: 8.36
- type: precision_at_100
value: 1.646
- type: precision_at_1000
value: 0.244
- type: precision_at_20
value: 5.257
- type: precision_at_3
value: 17.457
- type: precision_at_5
value: 12.727
- type: recall_at_1
value: 27.929
- type: recall_at_10
value: 54.822
- type: recall_at_100
value: 80.63900000000001
- type: recall_at_1000
value: 94.382
- type: recall_at_20
value: 63.432
- type: recall_at_3
value: 39.291
- type: recall_at_5
value: 45.385999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 22.619
- type: map_at_10
value: 31.252000000000002
- type: map_at_100
value: 32.23
- type: map_at_1000
value: 32.336999999999996
- type: map_at_20
value: 31.758999999999997
- type: map_at_3
value: 28.771
- type: map_at_5
value: 30.157
- type: mrr_at_1
value: 24.584
- type: mrr_at_10
value: 33.088
- type: mrr_at_100
value: 33.971000000000004
- type: mrr_at_1000
value: 34.044000000000004
- type: mrr_at_20
value: 33.519
- type: mrr_at_3
value: 30.775999999999996
- type: mrr_at_5
value: 32.116
- type: ndcg_at_1
value: 24.584
- type: ndcg_at_10
value: 35.995
- type: ndcg_at_100
value: 41.018
- type: ndcg_at_1000
value: 43.543
- type: ndcg_at_20
value: 37.722
- type: ndcg_at_3
value: 31.197999999999997
- type: ndcg_at_5
value: 33.532000000000004
- type: precision_at_1
value: 24.584
- type: precision_at_10
value: 5.619
- type: precision_at_100
value: 0.878
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 3.2259999999999995
- type: precision_at_3
value: 13.431999999999999
- type: precision_at_5
value: 9.39
- type: recall_at_1
value: 22.619
- type: recall_at_10
value: 48.746
- type: recall_at_100
value: 72.004
- type: recall_at_1000
value: 90.497
- type: recall_at_20
value: 55.326
- type: recall_at_3
value: 35.964
- type: recall_at_5
value: 41.547
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: None
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 16.493
- type: map_at_10
value: 28.988999999999997
- type: map_at_100
value: 30.964999999999996
- type: map_at_1000
value: 31.142999999999997
- type: map_at_20
value: 30.103
- type: map_at_3
value: 24.006
- type: map_at_5
value: 26.535999999999998
- type: mrr_at_1
value: 37.915
- type: mrr_at_10
value: 50.736000000000004
- type: mrr_at_100
value: 51.361999999999995
- type: mrr_at_1000
value: 51.388999999999996
- type: mrr_at_20
value: 51.148
- type: mrr_at_3
value: 47.589999999999996
- type: mrr_at_5
value: 49.55
- type: ndcg_at_1
value: 37.915
- type: ndcg_at_10
value: 39.139
- type: ndcg_at_100
value: 45.993
- type: ndcg_at_1000
value: 48.861
- type: ndcg_at_20
value: 41.923
- type: ndcg_at_3
value: 32.491
- type: ndcg_at_5
value: 34.775
- type: precision_at_1
value: 37.915
- type: precision_at_10
value: 12.293
- type: precision_at_100
value: 1.9709999999999999
- type: precision_at_1000
value: 0.251
- type: precision_at_20
value: 7.3389999999999995
- type: precision_at_3
value: 24.407999999999998
- type: precision_at_5
value: 18.775
- type: recall_at_1
value: 16.493
- type: recall_at_10
value: 45.904
- type: recall_at_100
value: 69.037
- type: recall_at_1000
value: 84.815
- type: recall_at_20
value: 53.657
- type: recall_at_3
value: 29.629
- type: recall_at_5
value: 36.325
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: None
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.180000000000001
- type: map_at_10
value: 20.714
- type: map_at_100
value: 28.801
- type: map_at_1000
value: 30.43
- type: map_at_20
value: 23.673
- type: map_at_3
value: 14.551
- type: map_at_5
value: 17.067
- type: mrr_at_1
value: 68.25
- type: mrr_at_10
value: 75.83
- type: mrr_at_100
value: 76.225
- type: mrr_at_1000
value: 76.232
- type: mrr_at_20
value: 76.14
- type: mrr_at_3
value: 74.375
- type: mrr_at_5
value: 75.225
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 43.071
- type: ndcg_at_100
value: 47.189
- type: ndcg_at_1000
value: 54.125
- type: ndcg_at_20
value: 42.111
- type: ndcg_at_3
value: 47.67
- type: ndcg_at_5
value: 44.983000000000004
- type: precision_at_1
value: 68.25
- type: precision_at_10
value: 34.599999999999994
- type: precision_at_100
value: 10.8
- type: precision_at_1000
value: 2.12
- type: precision_at_20
value: 25.7
- type: precision_at_3
value: 51.417
- type: precision_at_5
value: 43.85
- type: recall_at_1
value: 9.180000000000001
- type: recall_at_10
value: 26.212000000000003
- type: recall_at_100
value: 52.443
- type: recall_at_1000
value: 73.939
- type: recall_at_20
value: 33.101
- type: recall_at_3
value: 15.787999999999998
- type: recall_at_5
value: 19.691
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: None
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.625
- type: f1
value: 44.48944228050152
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: None
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 76.773
- type: map_at_10
value: 85.175
- type: map_at_100
value: 85.353
- type: map_at_1000
value: 85.36500000000001
- type: map_at_20
value: 85.271
- type: map_at_3
value: 84.261
- type: map_at_5
value: 84.899
- type: mrr_at_1
value: 82.853
- type: mrr_at_10
value: 90.02
- type: mrr_at_100
value: 90.048
- type: mrr_at_1000
value: 90.048
- type: mrr_at_20
value: 90.039
- type: mrr_at_3
value: 89.51599999999999
- type: mrr_at_5
value: 89.92099999999999
- type: ndcg_at_1
value: 82.853
- type: ndcg_at_10
value: 88.75999999999999
- type: ndcg_at_100
value: 89.347
- type: ndcg_at_1000
value: 89.547
- type: ndcg_at_20
value: 88.994
- type: ndcg_at_3
value: 87.481
- type: ndcg_at_5
value: 88.31700000000001
- type: precision_at_1
value: 82.853
- type: precision_at_10
value: 10.519
- type: precision_at_100
value: 1.1039999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 5.341
- type: precision_at_3
value: 33.323
- type: precision_at_5
value: 20.596999999999998
- type: recall_at_1
value: 76.773
- type: recall_at_10
value: 94.95700000000001
- type: recall_at_100
value: 97.167
- type: recall_at_1000
value: 98.354
- type: recall_at_20
value: 95.71
- type: recall_at_3
value: 91.47999999999999
- type: recall_at_5
value: 93.658
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: None
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 21.629
- type: map_at_10
value: 36.394
- type: map_at_100
value: 38.308
- type: map_at_1000
value: 38.478
- type: map_at_20
value: 37.425999999999995
- type: map_at_3
value: 31.971
- type: map_at_5
value: 34.5
- type: mrr_at_1
value: 44.599
- type: mrr_at_10
value: 53.369
- type: mrr_at_100
value: 54.06999999999999
- type: mrr_at_1000
value: 54.114
- type: mrr_at_20
value: 53.754999999999995
- type: mrr_at_3
value: 51.415
- type: mrr_at_5
value: 52.479
- type: ndcg_at_1
value: 44.599
- type: ndcg_at_10
value: 44.425
- type: ndcg_at_100
value: 51.036
- type: ndcg_at_1000
value: 53.806
- type: ndcg_at_20
value: 46.934
- type: ndcg_at_3
value: 41.287
- type: ndcg_at_5
value: 42.143
- type: precision_at_1
value: 44.599
- type: precision_at_10
value: 12.222
- type: precision_at_100
value: 1.91
- type: precision_at_1000
value: 0.24
- type: precision_at_20
value: 7.176
- type: precision_at_3
value: 28.086
- type: precision_at_5
value: 20.369999999999997
- type: recall_at_1
value: 21.629
- type: recall_at_10
value: 51.168
- type: recall_at_100
value: 75.32600000000001
- type: recall_at_1000
value: 91.766
- type: recall_at_20
value: 58.923
- type: recall_at_3
value: 37.364999999999995
- type: recall_at_5
value: 43.322
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: None
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 42.336
- type: map_at_10
value: 59.602999999999994
- type: map_at_100
value: 60.367000000000004
- type: map_at_1000
value: 60.428000000000004
- type: map_at_20
value: 60.068
- type: map_at_3
value: 56.842000000000006
- type: map_at_5
value: 58.669000000000004
- type: mrr_at_1
value: 84.673
- type: mrr_at_10
value: 88.713
- type: mrr_at_100
value: 88.852
- type: mrr_at_1000
value: 88.857
- type: mrr_at_20
value: 88.806
- type: mrr_at_3
value: 88.202
- type: mrr_at_5
value: 88.522
- type: ndcg_at_1
value: 84.673
- type: ndcg_at_10
value: 68.67
- type: ndcg_at_100
value: 71.277
- type: ndcg_at_1000
value: 72.47
- type: ndcg_at_20
value: 69.797
- type: ndcg_at_3
value: 64.971
- type: ndcg_at_5
value: 67.16
- type: precision_at_1
value: 84.673
- type: precision_at_10
value: 13.66
- type: precision_at_100
value: 1.5699999999999998
- type: precision_at_1000
value: 0.173
- type: precision_at_20
value: 7.19
- type: precision_at_3
value: 40.135
- type: precision_at_5
value: 25.81
- type: recall_at_1
value: 42.336
- type: recall_at_10
value: 68.298
- type: recall_at_100
value: 78.494
- type: recall_at_1000
value: 86.435
- type: recall_at_20
value: 71.904
- type: recall_at_3
value: 60.202999999999996
- type: recall_at_5
value: 64.524
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: None
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.0388
- type: ap
value: 84.768407855227
- type: f1
value: 89.00848365810504
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: None
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.676
- type: map_at_10
value: 35.476
- type: map_at_100
value: 36.669000000000004
- type: map_at_1000
value: 36.714999999999996
- type: map_at_20
value: 36.253
- type: map_at_3
value: 31.430000000000003
- type: map_at_5
value: 33.891
- type: mrr_at_1
value: 23.281
- type: mrr_at_10
value: 35.994
- type: mrr_at_100
value: 37.128
- type: mrr_at_1000
value: 37.169000000000004
- type: mrr_at_20
value: 36.735
- type: mrr_at_3
value: 32.025
- type: mrr_at_5
value: 34.43
- type: ndcg_at_1
value: 23.281
- type: ndcg_at_10
value: 42.548
- type: ndcg_at_100
value: 48.138999999999996
- type: ndcg_at_1000
value: 49.26
- type: ndcg_at_20
value: 45.29
- type: ndcg_at_3
value: 34.414
- type: ndcg_at_5
value: 38.775999999999996
- type: precision_at_1
value: 23.281
- type: precision_at_10
value: 6.721000000000001
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 3.93
- type: precision_at_3
value: 14.67
- type: precision_at_5
value: 11.003
- type: recall_at_1
value: 22.676
- type: recall_at_10
value: 64.33
- type: recall_at_100
value: 89.836
- type: recall_at_1000
value: 98.346
- type: recall_at_20
value: 74.958
- type: recall_at_3
value: 42.437000000000005
- type: recall_at_5
value: 52.89
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: None
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.26493388052896
- type: f1
value: 93.09322316606121
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: None
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.26356589147285
- type: f1
value: 62.91191113045691
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: None
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.4034969737727
- type: f1
value: 73.26712703676112
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: None
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.55749831876263
- type: f1
value: 78.59077417507389
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: None
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.39782367001404
- type: v_measures
value:
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- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: None
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.630415762081864
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- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: None
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.989924085485676
- type: mrr
value: 31.985114880107695
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: None
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.771
- type: map_at_10
value: 13.008000000000001
- type: map_at_100
value: 16.125999999999998
- type: map_at_1000
value: 17.482
- type: map_at_20
value: 14.324
- type: map_at_3
value: 9.69
- type: map_at_5
value: 11.174000000000001
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 53.989
- type: mrr_at_100
value: 54.50899999999999
- type: mrr_at_1000
value: 54.551
- type: mrr_at_20
value: 54.247
- type: mrr_at_3
value: 52.373999999999995
- type: mrr_at_5
value: 53.225
- type: ndcg_at_1
value: 43.808
- type: ndcg_at_10
value: 34.757
- type: ndcg_at_100
value: 31.174000000000003
- type: ndcg_at_1000
value: 39.607
- type: ndcg_at_20
value: 32.151999999999994
- type: ndcg_at_3
value: 40.458
- type: ndcg_at_5
value: 38.06
- type: precision_at_1
value: 45.201
- type: precision_at_10
value: 25.728
- type: precision_at_100
value: 7.82
- type: precision_at_1000
value: 2.032
- type: precision_at_20
value: 18.793000000000003
- type: precision_at_3
value: 38.080000000000005
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.771
- type: recall_at_10
value: 16.567
- type: recall_at_100
value: 30.447999999999997
- type: recall_at_1000
value: 60.941
- type: recall_at_20
value: 20.092
- type: recall_at_3
value: 10.928
- type: recall_at_5
value: 13.235
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: None
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 40.716
- type: map_at_10
value: 56.599999999999994
- type: map_at_100
value: 57.389
- type: map_at_1000
value: 57.408
- type: map_at_20
value: 57.154
- type: map_at_3
value: 52.577
- type: map_at_5
value: 55.076
- type: mrr_at_1
value: 45.655
- type: mrr_at_10
value: 59.014
- type: mrr_at_100
value: 59.568
- type: mrr_at_1000
value: 59.580999999999996
- type: mrr_at_20
value: 59.41499999999999
- type: mrr_at_3
value: 55.88999999999999
- type: mrr_at_5
value: 57.879999999999995
- type: ndcg_at_1
value: 45.626
- type: ndcg_at_10
value: 63.778
- type: ndcg_at_100
value: 66.905
- type: ndcg_at_1000
value: 67.322
- type: ndcg_at_20
value: 65.521
- type: ndcg_at_3
value: 56.494
- type: ndcg_at_5
value: 60.553999999999995
- type: precision_at_1
value: 45.626
- type: precision_at_10
value: 9.942
- type: precision_at_100
value: 1.169
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 5.390000000000001
- type: precision_at_3
value: 25.135
- type: precision_at_5
value: 17.451
- type: recall_at_1
value: 40.716
- type: recall_at_10
value: 82.998
- type: recall_at_100
value: 96.236
- type: recall_at_1000
value: 99.31400000000001
- type: recall_at_20
value: 89.402
- type: recall_at_3
value: 64.47699999999999
- type: recall_at_5
value: 73.774
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: None
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.679
- type: map_at_10
value: 85.63
- type: map_at_100
value: 86.24000000000001
- type: map_at_1000
value: 86.25500000000001
- type: map_at_20
value: 86.03
- type: map_at_3
value: 82.712
- type: map_at_5
value: 84.59400000000001
- type: mrr_at_1
value: 82.58
- type: mrr_at_10
value: 88.459
- type: mrr_at_100
value: 88.544
- type: mrr_at_1000
value: 88.545
- type: mrr_at_20
value: 88.521
- type: mrr_at_3
value: 87.548
- type: mrr_at_5
value: 88.19
- type: ndcg_at_1
value: 82.57
- type: ndcg_at_10
value: 89.205
- type: ndcg_at_100
value: 90.316
- type: ndcg_at_1000
value: 90.4
- type: ndcg_at_20
value: 89.802
- type: ndcg_at_3
value: 86.5
- type: ndcg_at_5
value: 88.06
- type: precision_at_1
value: 82.57
- type: precision_at_10
value: 13.511000000000001
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.1499999999999995
- type: precision_at_3
value: 37.82
- type: precision_at_5
value: 24.892
- type: recall_at_1
value: 71.679
- type: recall_at_10
value: 95.926
- type: recall_at_100
value: 99.653
- type: recall_at_1000
value: 99.99
- type: recall_at_20
value: 97.81
- type: recall_at_3
value: 88.124
- type: recall_at_5
value: 92.535
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: None
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 58.980204279295776
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value:
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- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: None
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 64.68385650734866
- type: v_measures
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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: 4.1930000000000005
- type: map_at_10
value: 10.993
- type: map_at_100
value: 12.821
- type: map_at_1000
value: 13.094
- type: map_at_20
value: 11.899999999999999
- type: map_at_3
value: 7.753
- type: map_at_5
value: 9.479
- type: mrr_at_1
value: 20.7
- type: mrr_at_10
value: 31.776
- type: mrr_at_100
value: 32.863
- type: mrr_at_1000
value: 32.921
- type: mrr_at_20
value: 32.374
- type: mrr_at_3
value: 28.499999999999996
- type: mrr_at_5
value: 30.464999999999996
- type: ndcg_at_1
value: 20.7
- type: ndcg_at_10
value: 18.602
- type: ndcg_at_100
value: 26.063
- type: ndcg_at_1000
value: 30.988
- type: ndcg_at_20
value: 21.124000000000002
- type: ndcg_at_3
value: 17.538999999999998
- type: ndcg_at_5
value: 15.604999999999999
- type: precision_at_1
value: 20.7
- type: precision_at_10
value: 9.69
- type: precision_at_100
value: 2.051
- type: precision_at_1000
value: 0.32299999999999995
- type: precision_at_20
value: 6.3
- type: precision_at_3
value: 16.567
- type: precision_at_5
value: 13.96
- type: recall_at_1
value: 4.1930000000000005
- type: recall_at_10
value: 19.618
- type: recall_at_100
value: 41.643
- type: recall_at_1000
value: 65.693
- type: recall_at_20
value: 25.562
- type: recall_at_3
value: 10.062999999999999
- type: recall_at_5
value: 14.127999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: None
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 83.46613174654865
- type: cos_sim_spearman
value: 80.3049357832415
- type: euclidean_pearson
value: 81.26631332583317
- type: euclidean_spearman
value: 80.3154745166346
- type: manhattan_pearson
value: 81.14703159845031
- type: manhattan_spearman
value: 80.20912001232311
- task:
type: STS
dataset:
name: MTEB STS12
type: None
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.54049067032975
- type: cos_sim_spearman
value: 80.96545866938635
- type: euclidean_pearson
value: 83.96265705630466
- type: euclidean_spearman
value: 79.93146623957664
- type: manhattan_pearson
value: 83.90680327172007
- type: manhattan_spearman
value: 79.9387741861374
- task:
type: STS
dataset:
name: MTEB STS13
type: None
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.88551701212096
- type: cos_sim_spearman
value: 87.86522961782607
- type: euclidean_pearson
value: 87.36290945594213
- type: euclidean_spearman
value: 87.83062393537139
- type: manhattan_pearson
value: 87.32544594269082
- type: manhattan_spearman
value: 87.81556963071229
- task:
type: STS
dataset:
name: MTEB STS14
type: None
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.30880458174929
- type: cos_sim_spearman
value: 83.80166079353091
- type: euclidean_pearson
value: 85.32128873266257
- type: euclidean_spearman
value: 83.86251092262333
- type: manhattan_pearson
value: 85.2712567451151
- type: manhattan_spearman
value: 83.80950203378747
- task:
type: STS
dataset:
name: MTEB STS15
type: None
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.26254668067915
- type: cos_sim_spearman
value: 88.58702965856746
- type: euclidean_pearson
value: 87.9969808017743
- type: euclidean_spearman
value: 88.48082129802832
- type: manhattan_pearson
value: 88.005385920726
- type: manhattan_spearman
value: 88.48824252319064
- task:
type: STS
dataset:
name: MTEB STS16
type: None
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.9048844772477
- type: cos_sim_spearman
value: 86.81864160521327
- type: euclidean_pearson
value: 86.28264402848413
- type: euclidean_spearman
value: 86.78000025418731
- type: manhattan_pearson
value: 86.2441248990138
- type: manhattan_spearman
value: 86.75021285222047
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: None
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.489340312079
- type: cos_sim_spearman
value: 87.98810146323362
- type: euclidean_pearson
value: 89.17657344753519
- type: euclidean_spearman
value: 88.96877394433339
- type: manhattan_pearson
value: 89.17489837230771
- type: manhattan_spearman
value: 88.87394331518345
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: None
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 63.020191114515576
- type: cos_sim_spearman
value: 66.81821028889179
- type: euclidean_pearson
value: 66.11102477309004
- type: euclidean_spearman
value: 66.59000262767655
- type: manhattan_pearson
value: 66.0319349852117
- type: manhattan_spearman
value: 66.51366211903893
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: None
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.05763458617234
- type: cos_sim_spearman
value: 87.40353901525121
- type: euclidean_pearson
value: 87.43632331678887
- type: euclidean_spearman
value: 87.58631222421829
- type: manhattan_pearson
value: 87.40408795218912
- type: manhattan_spearman
value: 87.55530395433567
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: None
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.40728647106346
- type: mrr
value: 95.39606725881237
- 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: 66.467
- type: map_at_100
value: 66.841
- type: map_at_1000
value: 66.86800000000001
- type: map_at_20
value: 66.728
- type: map_at_3
value: 62.888
- type: map_at_5
value: 65.10000000000001
- type: mrr_at_1
value: 58.333
- type: mrr_at_10
value: 67.471
- type: mrr_at_100
value: 67.75
- type: mrr_at_1000
value: 67.778
- type: mrr_at_20
value: 67.649
- type: mrr_at_3
value: 64.72200000000001
- type: mrr_at_5
value: 66.539
- type: ndcg_at_1
value: 58.333
- type: ndcg_at_10
value: 71.707
- type: ndcg_at_100
value: 73.301
- type: ndcg_at_1000
value: 74.053
- type: ndcg_at_20
value: 72.482
- type: ndcg_at_3
value: 65.561
- type: ndcg_at_5
value: 69.017
- type: precision_at_1
value: 58.333
- type: precision_at_10
value: 9.866999999999999
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.1
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 17.533
- type: recall_at_1
value: 55.344
- type: recall_at_10
value: 86.76700000000001
- type: recall_at_100
value: 94.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 89.60000000000001
- type: recall_at_3
value: 70.406
- type: recall_at_5
value: 79.106
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: None
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.71089108910891
- type: cos_sim_ap
value: 91.82444380538519
- type: cos_sim_f1
value: 85.34525583705911
- type: cos_sim_precision
value: 84.79763079960513
- type: cos_sim_recall
value: 85.9
- type: dot_accuracy
value: 99.56039603960396
- type: dot_ap
value: 84.71022538609428
- type: dot_f1
value: 76.18100447538538
- type: dot_precision
value: 75.76656775469831
- type: dot_recall
value: 76.6
- type: euclidean_accuracy
value: 99.7
- type: euclidean_ap
value: 91.68317023504792
- type: euclidean_f1
value: 84.65712876171682
- type: euclidean_precision
value: 83.54430379746836
- type: euclidean_recall
value: 85.8
- type: manhattan_accuracy
value: 99.69900990099009
- type: manhattan_ap
value: 91.5749511659937
- type: manhattan_f1
value: 84.6989141164857
- type: manhattan_precision
value: 83.62573099415205
- type: manhattan_recall
value: 85.8
- type: max_accuracy
value: 99.71089108910891
- type: max_ap
value: 91.82444380538519
- type: max_f1
value: 85.34525583705911
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: None
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 69.36504474977566
- 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: 52.975020393803675
- type: mrr
value: 53.87404772515067
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: None
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.205065693047615
- type: cos_sim_spearman
value: 28.307951294409406
- type: dot_pearson
value: 29.15581947828465
- type: dot_spearman
value: 28.222470759389505
- 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: 2.243
- type: map_at_100
value: 13.791
- type: map_at_1000
value: 32.539
- type: map_at_20
value: 4.112
- type: map_at_3
value: 0.7060000000000001
- type: map_at_5
value: 1.1860000000000002
- 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: 86.083
- type: ndcg_at_100
value: 66.471
- type: ndcg_at_1000
value: 57.31699999999999
- type: ndcg_at_20
value: 82.783
- type: ndcg_at_3
value: 88.805
- type: ndcg_at_5
value: 88.96
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 91.2
- type: precision_at_100
value: 68.16
- type: precision_at_1000
value: 25.290000000000003
- type: precision_at_20
value: 86.9
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 94.39999999999999
- type: recall_at_1
value: 0.249
- type: recall_at_10
value: 2.3800000000000003
- type: recall_at_100
value: 16.45
- type: recall_at_1000
value: 53.1
- type: recall_at_20
value: 4.4670000000000005
- type: recall_at_3
value: 0.734
- type: recall_at_5
value: 1.246
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: None
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.2520000000000002
- type: map_at_10
value: 11.805
- type: map_at_100
value: 18.749
- type: map_at_1000
value: 20.416999999999998
- type: map_at_20
value: 14.685
- type: map_at_3
value: 6.6739999999999995
- type: map_at_5
value: 8.863
- type: mrr_at_1
value: 42.857
- type: mrr_at_10
value: 57.635999999999996
- type: mrr_at_100
value: 58.034
- type: mrr_at_1000
value: 58.048
- type: mrr_at_20
value: 57.979
- type: mrr_at_3
value: 54.422000000000004
- type: mrr_at_5
value: 56.15599999999999
- type: ndcg_at_1
value: 39.796
- type: ndcg_at_10
value: 30.263
- type: ndcg_at_100
value: 40.825
- type: ndcg_at_1000
value: 52.447
- type: ndcg_at_20
value: 30.453000000000003
- type: ndcg_at_3
value: 35.086
- type: ndcg_at_5
value: 31.947
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 26.327
- type: precision_at_100
value: 8.041
- type: precision_at_1000
value: 1.582
- type: precision_at_20
value: 19.592000000000002
- type: precision_at_3
value: 36.054
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 3.2520000000000002
- type: recall_at_10
value: 18.471
- type: recall_at_100
value: 49.08
- type: recall_at_1000
value: 84.733
- type: recall_at_20
value: 26.389000000000003
- type: recall_at_3
value: 8.051
- type: recall_at_5
value: 11.672
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: None
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 68.10546875
- type: ap
value: 12.899352291322325
- type: f1
value: 52.14484661172115
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: None
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.323146576117715
- type: f1
value: 62.6518883448989
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: None
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.261957327618525
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value:
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- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
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- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
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- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- 0.4873375900729135
- 0.5129229336124553
- 0.515681357542704
- 0.511464496088557
- 0.5090884385457786
- 0.5125351055552001
- 0.5124982980752528
- 0.517332919326808
- 0.5232255784709567
- 0.5241090154712252
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: None
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.09542826488645
- type: cos_sim_ap
value: 77.72170475021885
- type: cos_sim_f1
value: 70.67669172932331
- type: cos_sim_precision
value: 64.5238614077141
- type: cos_sim_recall
value: 78.12664907651715
- type: dot_accuracy
value: 83.96614412588663
- type: dot_ap
value: 68.08590796036842
- type: dot_f1
value: 63.934426229508205
- type: dot_precision
value: 58.854860186418115
- type: dot_recall
value: 69.9736147757256
- type: euclidean_accuracy
value: 87.20271800679502
- type: euclidean_ap
value: 77.87533191263717
- type: euclidean_f1
value: 70.92216475337455
- type: euclidean_precision
value: 67.94778825235677
- type: euclidean_recall
value: 74.1688654353562
- type: manhattan_accuracy
value: 87.20867854801216
- type: manhattan_ap
value: 77.84249032925085
- type: manhattan_f1
value: 71.11665626949471
- type: manhattan_precision
value: 67.45562130177515
- type: manhattan_recall
value: 75.19788918205805
- type: max_accuracy
value: 87.20867854801216
- type: max_ap
value: 77.87533191263717
- type: max_f1
value: 71.11665626949471
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: None
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.22070865836147
- type: cos_sim_ap
value: 86.38617271379728
- type: cos_sim_f1
value: 78.946594085626
- type: cos_sim_precision
value: 75.5774647887324
- type: cos_sim_recall
value: 82.63012011087157
- type: dot_accuracy
value: 87.16963558039352
- type: dot_ap
value: 82.0965358395614
- type: dot_f1
value: 75.00997859138575
- type: dot_precision
value: 70.93541966920596
- type: dot_recall
value: 79.58115183246073
- type: euclidean_accuracy
value: 89.14891139830016
- type: euclidean_ap
value: 86.28000880804873
- type: euclidean_f1
value: 78.7341306347746
- type: euclidean_precision
value: 75.40706280397546
- type: euclidean_recall
value: 82.36834000615954
- type: manhattan_accuracy
value: 89.15279233127644
- type: manhattan_ap
value: 86.25024653784152
- type: manhattan_f1
value: 78.72760457406788
- type: manhattan_precision
value: 76.25369795800563
- type: manhattan_recall
value: 81.36741607637819
- type: max_accuracy
value: 89.22070865836147
- type: max_ap
value: 86.38617271379728
- type: max_f1
value: 78.946594085626
---
# ModernBERT-embed-large
ModernBERT-embed-large is an embedding model trained from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), bringing the new advances of ModernBERT to embeddings!
Indeed, ModernBERT is a base model trained for Masked Language Modeling and can not directly be used to perform tasks such as retrieval without further fine-tuning.
ModernBERT-embed-large is fine-tuned on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets and also supports Matryoshka Representation Learning dimensions of 256 to reduce memory 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-v1.5 | 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** |
| modernbert-embed-large | 1024 | **63,84** | **75.03** | **46.04** | **85.31** | **57.64** | **54.36** | **83.80** | 28.31 |
| 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 |
| modernbert-embed-large | 256 | 62.43 | 73.60 | 44.59 | 84.89 | 57.08 | 51.72 | 83.46 | 29.03 |
## 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("lightonai/modernbert-embed-large")
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, 1024) (1, 1024)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.6518],
# [0.4237]])
```
<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("lightonai/modernbert-embed-large", 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([[0.6835],
# [0.3982]])
```
Note the small differences compared to the full 1024-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("lightonai/modernbert-embed-large")
model = AutoModel.from_pretrained("lightonai/modernbert-embed-large")
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, 1024]) torch.Size([1, 1024])
similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.6518],
# [0.4237]])
```
<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([[0.6835],
# [0.3982]])
```
Note the small differences compared to the full 1024-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",
"lightonai/modernbert-embed-large",
{ 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());
```
## Training
We train ModernBERT-embed-large using a multi-stage training pipeline. Starting from the pretrained [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) model,
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)
## Acknowledgment
We wanted to thank [Zach Nussbaum](https://huggingface.co/zpn) from [Nomic AI](https://huggingface.co/nomic-ai) for building and sharing the Nomic Embed recipe and tools and its support during the training of this model!
The training has been run on Orange Business Cloud Avenue infrastructure.
## Citation
If you find the model, dataset, or training code useful, please considering citing ModernBERT as well as Nomic Embed:
```bibtex
@misc{modernbert,
title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference},
author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli},
year={2024},
eprint={2412.13663},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.13663},
}
```
```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}
}
```
And if you want to cite this fine-tuning in particular, please use:
```bibtex
@misc{ModernBERT-embed-large,
title={ModernBERT-embed-large},
author={Chaffin, Antoine},
url={https://huggingface.co/lightonai/modernbert-embed-large},
year={2025}
}
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
bijaygurung/stella_en_400M_v5
|
bijaygurung
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T06:12:02 | 2024-09-30T17:03:05 | 3,336 | 4 |
---
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
- type: nauc_map_at_100_diff1
value: 22.407371684274025
- type: nauc_map_at_100_max
value: -8.732263549026266
- type: nauc_map_at_100_std
value: -17.79550515579994
- type: nauc_map_at_10_diff1
value: 21.925005073301246
- type: nauc_map_at_10_max
value: -8.990323944492134
- type: nauc_map_at_10_std
value: -18.199246301671458
- type: nauc_map_at_1_diff1
value: 26.23276644969203
- type: nauc_map_at_1_max
value: -12.376511389571245
- type: nauc_map_at_1_std
value: -18.11411715207284
- type: nauc_map_at_20_diff1
value: 22.32455790850922
- type: nauc_map_at_20_max
value: -8.664671547236034
- type: nauc_map_at_20_std
value: -17.8290016125137
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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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dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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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
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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name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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dataset:
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config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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dataset:
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split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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dataset:
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type: mteb/reddit-clustering
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dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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split: test
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split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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type: mteb/sts16-sts
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split: test
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- type: main_score
value: 87.14066355879785
- type: manhattan_pearson
value: 86.36366855248434
- type: manhattan_spearman
value: 87.20858630423012
- type: pearson
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:
- type: cosine_pearson
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
- type: manhattan_spearman
value: 90.6199877691265
- type: pearson
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:
- type: cosine_pearson
value: 67.270656447085
- type: cosine_spearman
value: 67.82870469746828
- type: euclidean_pearson
value: 69.03857775285664
- type: euclidean_spearman
value: 67.74455108773341
- type: main_score
value: 67.82870469746828
- type: manhattan_pearson
value: 69.25304172245812
- type: manhattan_spearman
value: 68.00987097916055
- type: pearson
value: 67.270656447085
- type: spearman
value: 67.82870469746828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 87.17245205384889
- type: cosine_spearman
value: 87.7360146030987
- type: euclidean_pearson
value: 87.48919412794656
- type: euclidean_spearman
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
- type: nAUC_map_std
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:
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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
- type: mrr_at_10
value: 74.30357142857143
- type: mrr_at_100
value: 74.56950898079988
- type: mrr_at_1000
value: 74.57295833098681
- type: mrr_at_20
value: 74.46165223665226
- type: mrr_at_3
value: 72.3888888888889
- type: mrr_at_5
value: 73.60555555555557
- type: nauc_map_at_1000_diff1
value: 76.51524604780636
- type: nauc_map_at_1000_max
value: 53.48521938401881
- type: nauc_map_at_1000_std
value: -7.347799382158861
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- type: nauc_map_at_10_diff1
value: 76.30928630674504
- type: nauc_map_at_10_max
value: 53.00102977185941
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value: -7.7467740085108705
- type: nauc_map_at_1_diff1
value: 79.54189281784247
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value: 46.630071622109526
- type: nauc_map_at_1_std
value: -14.395943134644112
- type: nauc_map_at_20_diff1
value: 76.41604361947962
- type: nauc_map_at_20_max
value: 53.578883876146875
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value: -7.403103451288041
- type: nauc_map_at_3_diff1
value: 76.25911617571941
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value: 49.140287380513605
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value: -11.35992449218983
- type: nauc_map_at_5_diff1
value: 76.35122077770336
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value: 52.1744367901208
- type: nauc_map_at_5_std
value: -7.85753955055384
- type: nauc_mrr_at_1000_diff1
value: 76.97223309515867
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value: 57.263787498613326
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value: -4.884090708840035
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value: 76.97312970894603
- type: nauc_mrr_at_100_max
value: 57.26850730446478
- type: nauc_mrr_at_100_std
value: -4.875200894216617
- type: nauc_mrr_at_10_diff1
value: 76.65927674223613
- type: nauc_mrr_at_10_max
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value: -4.863331094022142
- type: nauc_mrr_at_1_diff1
value: 80.0454932568644
- type: nauc_mrr_at_1_max
value: 56.76038421319305
- type: nauc_mrr_at_1_std
value: -4.101939392632653
- type: nauc_mrr_at_20_diff1
value: 76.87237970440503
- type: nauc_mrr_at_20_max
value: 57.33843605225869
- type: nauc_mrr_at_20_std
value: -4.96248984417978
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value: 76.74130186666727
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value: 56.19313244846155
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value: -5.684365934009136
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value: 76.66406918799962
- type: nauc_mrr_at_5_max
value: 57.56110093228628
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value: -3.7464413085588073
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value: -6.0761689532372625
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value: 55.72246565373382
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value: -5.595160698860595
- type: nauc_ndcg_at_10_diff1
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value: -6.968331740214591
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value: 56.76038421319305
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value: -6.495231061329629
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value: 52.147381077773424
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- type: nauc_ndcg_at_5_std
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value: 59.61280468927384
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- type: nauc_precision_at_100_max
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- type: nauc_precision_at_100_std
value: 62.46316222328831
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- type: nauc_precision_at_10_max
value: 45.95321867204845
- type: nauc_precision_at_10_std
value: 45.07212468670564
- type: nauc_precision_at_1_diff1
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- type: nauc_precision_at_1_max
value: 56.76038421319305
- type: nauc_precision_at_1_std
value: -4.101939392632653
- type: nauc_precision_at_20_diff1
value: -10.698911116738385
- type: nauc_precision_at_20_max
value: 43.467275950182994
- type: nauc_precision_at_20_std
value: 48.00467321991766
- type: nauc_precision_at_3_diff1
value: 33.6344708541193
- type: nauc_precision_at_3_max
value: 49.309242331670504
- type: nauc_precision_at_3_std
value: 21.02940391379915
- type: nauc_precision_at_5_diff1
value: 13.560415600596318
- type: nauc_precision_at_5_max
value: 48.918726500100085
- type: nauc_precision_at_5_std
value: 39.940930429172184
- 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: 70.82166199813196
- type: nauc_recall_at_100_max
value: 76.6106442577042
- type: nauc_recall_at_100_std
value: 66.47992530345513
- type: nauc_recall_at_10_diff1
value: 62.68908885556092
- type: nauc_recall_at_10_max
value: 58.14262437741839
- type: nauc_recall_at_10_std
value: -12.946717875063369
- type: nauc_recall_at_1_diff1
value: 79.54189281784247
- type: nauc_recall_at_1_max
value: 46.630071622109526
- type: nauc_recall_at_1_std
value: -14.395943134644112
- type: nauc_recall_at_20_diff1
value: 65.79470497876567
- type: nauc_recall_at_20_max
value: 71.68308183488456
- type: nauc_recall_at_20_std
value: -12.556850697268453
- type: nauc_recall_at_3_diff1
value: 68.3240211318129
- type: nauc_recall_at_3_max
value: 45.05998217275036
- type: nauc_recall_at_3_std
value: -14.23179772593869
- type: nauc_recall_at_5_diff1
value: 67.53366869904056
- type: nauc_recall_at_5_max
value: 53.57935627081027
- type: nauc_recall_at_5_std
value: -3.3271112904853393
- type: ndcg_at_1
value: 64.667
- type: ndcg_at_10
value: 78.233
- type: ndcg_at_100
value: 79.806
- type: ndcg_at_1000
value: 79.92099999999999
- type: ndcg_at_20
value: 79.006
- type: ndcg_at_3
value: 74.018
- type: ndcg_at_5
value: 76.334
- type: precision_at_1
value: 64.667
- type: precision_at_10
value: 10.4
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.383
- type: precision_at_3
value: 29.444
- type: precision_at_5
value: 19.467000000000002
- type: recall_at_1
value: 61.49400000000001
- type: recall_at_10
value: 92.156
- type: recall_at_100
value: 99.167
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 94.833
- type: recall_at_3
value: 80.833
- type: recall_at_5
value: 86.6
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.8039603960396
- type: cosine_accuracy_threshold
value: 84.54211950302124
- type: cosine_ap
value: 95.59056372734358
- type: cosine_f1
value: 90.1394422310757
- type: cosine_f1_threshold
value: 84.54211950302124
- type: cosine_precision
value: 89.78174603174604
- type: cosine_recall
value: 90.5
- type: dot_accuracy
value: 99.80594059405941
- type: dot_accuracy_threshold
value: 85.57180166244507
- type: dot_ap
value: 95.53453431914399
- type: dot_f1
value: 90.10442565887618
- type: dot_f1_threshold
value: 84.59715843200684
- type: dot_precision
value: 89.61424332344214
- type: dot_recall
value: 90.60000000000001
- type: euclidean_accuracy
value: 99.8039603960396
- type: euclidean_accuracy_threshold
value: 53.253382444381714
- type: euclidean_ap
value: 95.5850992402159
- type: euclidean_f1
value: 90.09457441513192
- type: euclidean_f1_threshold
value: 55.725520849227905
- type: euclidean_precision
value: 89.69276511397423
- type: euclidean_recall
value: 90.5
- type: main_score
value: 95.7485189884476
- type: manhattan_accuracy
value: 99.81485148514851
- type: manhattan_accuracy_threshold
value: 3491.29638671875
- type: manhattan_ap
value: 95.7485189884476
- type: manhattan_f1
value: 90.464048954615
- type: manhattan_f1_threshold
value: 3491.29638671875
- type: manhattan_precision
value: 92.2996878251821
- type: manhattan_recall
value: 88.7
- type: max_ap
value: 95.7485189884476
- type: max_f1
value: 90.464048954615
- type: max_precision
value: 92.2996878251821
- type: max_recall
value: 90.60000000000001
- type: similarity_accuracy
value: 99.8039603960396
- type: similarity_accuracy_threshold
value: 84.54211950302124
- type: similarity_ap
value: 95.59056372734358
- type: similarity_f1
value: 90.1394422310757
- type: similarity_f1_threshold
value: 84.54211950302124
- type: similarity_precision
value: 89.78174603174604
- type: similarity_recall
value: 90.5
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 78.49205191950675
- type: v_measure
value: 78.49205191950675
- type: v_measure_std
value: 2.84869550699959
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 48.90421736513028
- type: v_measure
value: 48.90421736513028
- type: v_measure_std
value: 1.6875865714471023
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 52.9874730481696
- type: map
value: 52.9874730481696
- type: mrr
value: 53.85867604617604
- type: nAUC_map_diff1
value: 39.633429293407616
- type: nAUC_map_max
value: 10.236807988858546
- type: nAUC_map_std
value: 10.276522217929674
- type: nAUC_mrr_diff1
value: 40.0543079218377
- type: nAUC_mrr_max
value: 10.96209807382042
- type: nAUC_mrr_std
value: 10.524400196109918
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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value: 30.727801109114232
- type: cosine_spearman
value: 31.66058223980157
- type: dot_pearson
value: 30.78818248622866
- type: dot_spearman
value: 31.525158776890265
- type: main_score
value: 31.66058223980157
- type: pearson
value: 30.727801109114232
- type: spearman
value: 31.66058223980157
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
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value: 85.206
- type: map_at_1
value: 0.246
- type: map_at_10
value: 2.1950000000000003
- type: map_at_100
value: 14.179
- type: map_at_1000
value: 35.037
- type: map_at_20
value: 4.143
- type: map_at_3
value: 0.7100000000000001
- type: map_at_5
value: 1.135
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 96.66666666666666
- type: mrr_at_100
value: 96.66666666666666
- type: mrr_at_1000
value: 96.66666666666666
- type: mrr_at_20
value: 96.66666666666666
- type: mrr_at_3
value: 96.66666666666666
- type: mrr_at_5
value: 96.66666666666666
- type: nauc_map_at_1000_diff1
value: -4.6264497624527525
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value: 44.594457564749355
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value: 73.17642341400133
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value: 64.07416694472633
- type: nauc_map_at_10_diff1
value: 46.57568738568346
- type: nauc_map_at_10_max
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value: 28.549530265164357
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value: 53.48238396620123
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- type: nauc_map_at_20_diff1
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value: 21.310633086556617
- type: nauc_map_at_5_diff1
value: 49.91570701185436
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value: 20.597686235051647
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value: 78.24463118580779
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dataset:
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type: mteb/touche2020
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revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
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dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
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value: 73.57668364459535
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dataset:
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type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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dataset:
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type: mteb/twittersemeval2015-pairclassification
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split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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dataset:
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type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
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- 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
New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md.
You can also find these models on my [homepage](https://huggingface.co/infgrad).
# 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]]
```
# 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"
] |
minishlab/potion-retrieval-32M
|
minishlab
| null |
[
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"license:mit",
"region:us"
] | 2025-01-23T15:05:16 | 2025-01-29T11:00:09 | 3,271 | 17 |
---
library_name: model2vec
license: mit
model_name: potion-retrieval-32M
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# potion-retrieval-32M 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 model is optmized for retrieval tasks. It is a finetune of [potion-base-32M](https://huggingface.co/minishlab/potion-base-32M). It's finetuned using a modified version of the training approach described in [this blogpost](https://huggingface.co/blog/static-embeddings). 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-retrieval-32M")
# 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).
## Results
The results for this model are shown in the table below. The full Model2Vec results for all models can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md).
```
Average (All) 49.73
Average (MTEB) 49.76
Classification 59.56
Clustering 30.55
PairClassification 76.38
Reranking 50.05
Retrieval 36.35
STS 73.22
Summarization 28.85
PEARL 49.31
WordSim 50.02
```
## 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 and Thomas van Dongen},
title = {Model2Vec: The Fastest State-of-the-Art Static Embeddings in the World},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
```
## Reproducibility
The following script can be used to reproduce this model. All credits go to [Tom Aarsen](https://huggingface.co/tomaarsen) for this fine-tuning approach and code he introduced in his [blogpost](https://huggingface.co/blog/static-embeddings). We make a few modifcations to the original code, namely:
- We start with a pre-trained Model2Vec model ([potion-base-32M](https://huggingface.co/minishlab/potion-base-32M)).
- We reduce the dataset size by a factor of 10. During experiments we saw that we didn't need the full dataset for the model to converge.
- We decease the learning rate and train for 3 epochs instead of 1. Using a high learning rate wipes the effects of using a pre-trained model.
```python
import random
import logging
from datasets import load_dataset, Dataset, DatasetDict
from sentence_transformers import (
SentenceTransformer,
SentenceTransformerTrainer,
SentenceTransformerTrainingArguments,
SentenceTransformerModelCardData,
)
from sentence_transformers.losses import MatryoshkaLoss, MultipleNegativesRankingLoss
from sentence_transformers.training_args import BatchSamplers, MultiDatasetBatchSamplers
from sentence_transformers.evaluation import NanoBEIREvaluator
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
import wandb
logging.basicConfig(
format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO
)
random.seed(12)
def load_train_eval_datasets(factor: int = 1):
"""
Loads train and eval datasets from disk if available. Otherwise, downloads
them from Hugging Face, preprocesses, and saves them to disk. If `factor` is
greater than 1, returns a fraction (1/factor) of each dataset subset.
:param factor: The factor by which the data is reduced. If factor=1, no reduction is performed.
:return: (train_dataset: DatasetDict, eval_dataset: DatasetDict)
"""
try:
# Try loading from disk
train_dataset = DatasetDict.load_from_disk("datasets/train_dataset")
eval_dataset = DatasetDict.load_from_disk("datasets/eval_dataset")
except FileNotFoundError:
print("Prebuilt datasets not found on disk. Building from scratch...")
print("Loading gooaq dataset...")
gooaq_dataset = load_dataset("sentence-transformers/gooaq", split="train")
gooaq_dataset_dict = gooaq_dataset.train_test_split(test_size=10_000, seed=12)
gooaq_train_dataset: Dataset = gooaq_dataset_dict["train"]
gooaq_eval_dataset: Dataset = gooaq_dataset_dict["test"]
print("Loaded gooaq dataset.")
print("Loading msmarco dataset...")
msmarco_dataset = load_dataset(
"sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1",
"triplet",
split="train"
)
msmarco_dataset_dict = msmarco_dataset.train_test_split(test_size=10_000, seed=12)
msmarco_train_dataset: Dataset = msmarco_dataset_dict["train"]
msmarco_eval_dataset: Dataset = msmarco_dataset_dict["test"]
print("Loaded msmarco dataset.")
print("Loading squad dataset...")
squad_dataset = load_dataset("sentence-transformers/squad", split="train")
squad_dataset_dict = squad_dataset.train_test_split(test_size=10_000, seed=12)
squad_train_dataset: Dataset = squad_dataset_dict["train"]
squad_eval_dataset: Dataset = squad_dataset_dict["test"]
print("Loaded squad dataset.")
print("Loading s2orc dataset...")
s2orc_dataset = load_dataset(
"sentence-transformers/s2orc",
"title-abstract-pair",
split="train[:100000]" # limit to 100k
)
s2orc_dataset_dict = s2orc_dataset.train_test_split(test_size=10_000, seed=12)
s2orc_train_dataset: Dataset = s2orc_dataset_dict["train"]
s2orc_eval_dataset: Dataset = s2orc_dataset_dict["test"]
print("Loaded s2orc dataset.")
print("Loading allnli dataset...")
allnli_train_dataset = load_dataset(
"sentence-transformers/all-nli",
"triplet",
split="train"
)
allnli_eval_dataset = load_dataset(
"sentence-transformers/all-nli",
"triplet",
split="dev"
)
print("Loaded allnli dataset.")
print("Loading paq dataset...")
paq_dataset = load_dataset("sentence-transformers/paq", split="train")
paq_dataset_dict = paq_dataset.train_test_split(test_size=10_000, seed=12)
paq_train_dataset: Dataset = paq_dataset_dict["train"]
paq_eval_dataset: Dataset = paq_dataset_dict["test"]
print("Loaded paq dataset.")
print("Loading trivia_qa dataset...")
trivia_qa = load_dataset("sentence-transformers/trivia-qa", split="train")
trivia_qa_dataset_dict = trivia_qa.train_test_split(test_size=5_000, seed=12)
trivia_qa_train_dataset: Dataset = trivia_qa_dataset_dict["train"]
trivia_qa_eval_dataset: Dataset = trivia_qa_dataset_dict["test"]
print("Loaded trivia_qa dataset.")
print("Loading msmarco_10m dataset...")
msmarco_10m_dataset = load_dataset("bclavie/msmarco-10m-triplets", split="train")
msmarco_10m_dataset_dict = msmarco_10m_dataset.train_test_split(
test_size=10_000, seed=12
)
msmarco_10m_train_dataset: Dataset = msmarco_10m_dataset_dict["train"]
msmarco_10m_eval_dataset: Dataset = msmarco_10m_dataset_dict["test"]
print("Loaded msmarco_10m dataset.")
print("Loading swim_ir dataset...")
swim_ir_dataset = load_dataset(
"nthakur/swim-ir-monolingual",
"en",
split="train"
).select_columns(["query", "text"])
swim_ir_dataset_dict = swim_ir_dataset.train_test_split(
test_size=10_000, seed=12
)
swim_ir_train_dataset: Dataset = swim_ir_dataset_dict["train"]
swim_ir_eval_dataset: Dataset = swim_ir_dataset_dict["test"]
print("Loaded swim_ir dataset.")
# NOTE: 20 negatives
print("Loading pubmedqa dataset...")
pubmedqa_dataset = load_dataset(
"sentence-transformers/pubmedqa",
"triplet-20",
split="train"
)
pubmedqa_dataset_dict = pubmedqa_dataset.train_test_split(test_size=100, seed=12)
pubmedqa_train_dataset: Dataset = pubmedqa_dataset_dict["train"]
pubmedqa_eval_dataset: Dataset = pubmedqa_dataset_dict["test"]
print("Loaded pubmedqa dataset.")
# NOTE: A lot of overlap with anchor/positives
print("Loading miracl dataset...")
miracl_dataset = load_dataset(
"sentence-transformers/miracl",
"en-triplet-all",
split="train"
)
miracl_dataset_dict = miracl_dataset.train_test_split(test_size=10_000, seed=12)
miracl_train_dataset: Dataset = miracl_dataset_dict["train"]
miracl_eval_dataset: Dataset = miracl_dataset_dict["test"]
print("Loaded miracl dataset.")
# NOTE: A lot of overlap with anchor/positives
print("Loading mldr dataset...")
mldr_dataset = load_dataset(
"sentence-transformers/mldr",
"en-triplet-all",
split="train"
)
mldr_dataset_dict = mldr_dataset.train_test_split(test_size=10_000, seed=12)
mldr_train_dataset: Dataset = mldr_dataset_dict["train"]
mldr_eval_dataset: Dataset = mldr_dataset_dict["test"]
print("Loaded mldr dataset.")
# NOTE: A lot of overlap with anchor/positives
print("Loading mr_tydi dataset...")
mr_tydi_dataset = load_dataset(
"sentence-transformers/mr-tydi",
"en-triplet-all",
split="train"
)
mr_tydi_dataset_dict = mr_tydi_dataset.train_test_split(test_size=10_000, seed=12)
mr_tydi_train_dataset: Dataset = mr_tydi_dataset_dict["train"]
mr_tydi_eval_dataset: Dataset = mr_tydi_dataset_dict["test"]
print("Loaded mr_tydi dataset.")
train_dataset = DatasetDict({
"gooaq": gooaq_train_dataset,
"msmarco": msmarco_train_dataset,
"squad": squad_train_dataset,
"s2orc": s2orc_train_dataset,
"allnli": allnli_train_dataset,
"paq": paq_train_dataset,
"trivia_qa": trivia_qa_train_dataset,
"msmarco_10m": msmarco_10m_train_dataset,
"swim_ir": swim_ir_train_dataset,
"pubmedqa": pubmedqa_train_dataset,
"miracl": miracl_train_dataset,
"mldr": mldr_train_dataset,
"mr_tydi": mr_tydi_train_dataset,
})
eval_dataset = DatasetDict({
"gooaq": gooaq_eval_dataset,
"msmarco": msmarco_eval_dataset,
"squad": squad_eval_dataset,
"s2orc": s2orc_eval_dataset,
"allnli": allnli_eval_dataset,
"paq": paq_eval_dataset,
"trivia_qa": trivia_qa_eval_dataset,
"msmarco_10m": msmarco_10m_eval_dataset,
"swim_ir": swim_ir_eval_dataset,
"pubmedqa": pubmedqa_eval_dataset,
"miracl": miracl_eval_dataset,
"mldr": mldr_eval_dataset,
"mr_tydi": mr_tydi_eval_dataset,
})
# Save to disk for next time
train_dataset.save_to_disk("datasets/train_dataset")
eval_dataset.save_to_disk("datasets/eval_dataset")
# Quit to avoid memory overhead on large datasets
quit()
# Reduce the dataset if factor > 1
if factor > 1:
for subset_name in train_dataset:
ds = train_dataset[subset_name].shuffle(seed=42)
new_len = len(ds) // factor
train_dataset[subset_name] = ds.select(range(new_len))
for subset_name in eval_dataset:
ds = eval_dataset[subset_name].shuffle(seed=42)
new_len = len(ds) // factor
eval_dataset[subset_name] = ds.select(range(new_len))
return train_dataset, eval_dataset
def main():
wandb.init(entity="minishlab", project="minishlab")
# 1. Load a model to finetune
static_embedding = StaticEmbedding.from_model2vec("minishlab/potion-base-32M")
# 2. Initialize the SentenceTransformer model
model_name = "potion-retrieval-32M"
model = SentenceTransformer(
modules=[static_embedding],
model_card_data=SentenceTransformerModelCardData(
language="en",
license="MIT",
model_name=model_name,
),
)
# 3. Load training & evaluation datasets
# NOTE: we reduce the total dataset size by a factor of 10
train_dataset, eval_dataset = load_train_eval_datasets(factor=10)
print(train_dataset)
# 4. Define a loss function
loss = MultipleNegativesRankingLoss(model)
loss = MatryoshkaLoss(model, loss, matryoshka_dims=[32, 64, 128, 256, 512])
# 5. Specify training arguments
run_name = model_name
epochs = 3
lr = 0.05
args = SentenceTransformerTrainingArguments(
output_dir=f"models/{run_name}",
num_train_epochs=epochs,
per_device_train_batch_size=2048,
per_device_eval_batch_size=2048,
learning_rate=lr,
warmup_ratio=0.1,
fp16=False,
bf16=True,
batch_sampler=BatchSamplers.NO_DUPLICATES,
multi_dataset_batch_sampler=MultiDatasetBatchSamplers.PROPORTIONAL,
eval_strategy="steps",
eval_steps=250,
save_strategy="steps",
save_steps=250,
save_total_limit=2,
logging_steps=250,
logging_first_step=True,
run_name=run_name,
report_to=["wandb"],
load_best_model_at_end=True,
metric_for_best_model="eval_NanoBEIR_mean_cosine_ndcg@10",
greater_is_better=True,
)
# 6. Create an evaluator & evaluate the base model
evaluator = NanoBEIREvaluator()
evaluator(model)
# 7. Create a trainer & train
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
loss=loss,
evaluator=evaluator,
)
trainer.train()
# 8. Evaluate the trained model and save
evaluator(model)
model.save_pretrained(f"models/{run_name}/final")
if __name__ == "__main__":
main()
```
|
[
"SUMMARIZATION"
] |
[
"PUBMEDQA"
] |
BSC-LT/salamandra-2b-instruct
|
BSC-LT
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"dataset:oscar-corpus/colossal-oscar-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:joelniklaus/eurlex_resources",
"dataset:joelito/legal-mc4",
"dataset:projecte-aina/CATalog",
"dataset:UFRGS/brwac",
"dataset:community-datasets/hrwac",
"dataset:danish-foundation-models/danish-gigaword",
"dataset:HiTZ/euscrawl",
"dataset:PleIAs/French-PD-Newspapers",
"dataset:PleIAs/French-PD-Books",
"dataset:AI-team-UoA/greek_legal_code",
"dataset:HiTZ/latxa-corpus-v1.1",
"dataset:allenai/peS2o",
"dataset:pile-of-law/pile-of-law",
"dataset:PORTULAN/parlamento-pt",
"dataset:hoskinson-center/proof-pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/starcoderdata",
"dataset:bjoernp/tagesschau-2018-2023",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2502.08489",
"arxiv:2403.14009",
"arxiv:2403.20266",
"arxiv:2101.00027",
"arxiv:2207.00220",
"arxiv:1810.06694",
"arxiv:1911.05507",
"arxiv:1906.03741",
"arxiv:2406.17557",
"arxiv:2402.06619",
"arxiv:1803.09010",
"base_model:BSC-LT/salamandra-2b",
"base_model:finetune:BSC-LT/salamandra-2b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T13:44:40 | 2025-02-20T16:45:39 | 3,244 | 20 |
---
base_model:
- BSC-LT/salamandra-2b
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
language:
- bg
- ca
- code
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nn
- \no
- oc
- pl
- pt
- ro
- ru
- sh
- sk
- sl
- sr
- sv
- uk
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Salamandra Model Card
This repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).
Salamandra is a highly multilingual model pre-trained from scratch that comes in three different
sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants.
This model card corresponds to the 2B instructed version.
To visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index).
The entire Salamandra family is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
Along with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra).
> [!WARNING]
> **DISCLAIMER:** This model is a first proof-of-concept designed to demonstrate the instruction-following capabilities of recently released base models.
> It has been optimized to engage in conversation but has *NOT* been aligned through RLHF to filter or avoid sensitive topics.
> As a result, it may generate harmful or inappropriate content.
> The team is actively working to enhance its performance through further instruction and alignment with RL techniques.
---
## Model Details
### Description
Transformer-based decoder-only language model that has been pre-trained from scratch on 12.875 trillion tokens of highly curated data.
The pre-training corpus contains text in 35 European languages and code.
### Hyperparameters
The full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/blob/main/configs/bsc_2b.yaml).
### Architecture
| | |
|-------------------------|:--------------|
| Total Parameters | 2,253,490,176 |
| Embedding Parameters | 524,288,000 |
| Layers | 24 |
| Hidden size | 2,048 |
| Attention heads | 16 |
| Context length | 8,192 |
| Vocabulary size | 256,000 |
| Precision | bfloat16 |
| Embedding type | RoPE |
| Activation Function | SwiGLU |
| Layer normalization | RMS Norm |
| Flash attention | ✅ |
| Grouped Query Attention | ❌ |
| Num. query groups | N/A |
---
## Intended Use
### Direct Use
The models are intended for both research and commercial use in any of the languages included in the training data.
The base models are intended either for language generation or to be further fine-tuned for specific use-cases.
The instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations.
### Out-of-scope Use
The model is not intended for malicious activities, such as harming others or violating human rights.
Any downstream application must comply with current laws and regulations.
Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged.
---
## Hardware and Software
### Training Framework
Pre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html),
which leverages PyTorch Lightning for efficient model training in highly distributed settings.
The instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat).
### Compute Infrastructure
All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and
operated by Barcelona Supercomputing Center.
The accelerated partition is composed of 1,120 nodes with the following specifications:
- 4x Nvidia Hopper GPUs with 64GB HBM2 memory
- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)
- 4x NDR200 (BW per node 800Gb/s)
- 512 GB of Main memory (DDR5)
- 460GB on NVMe storage
|Model|Nodes|GPUs|
|:---:|:---:|:---:|
|2B|64|256|
|7B|128|512|
|40B|256 / 512|1,024 / 2,048|
---
## How to use
The instruction-following models use the commonly adopted ChatML template:
```jinja
{%- if messages[0]['role'] == 'system' %}{%- set system_message = messages[0]['content'] %}{%- set loop_messages = messages[1:] %}{%- else %}{%- set system_message = 'SYSTEM MESSAGE' %}{%- set loop_messages = messages %}{%- endif %}{%- if not date_string is defined %}{%- set date_string = '2024-09-30' %}{%- endif %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% for message in loop_messages %}{%- if (message['role'] != 'user') and (message['role'] != 'assistant')%}{{ raise_exception('Only user and assitant roles are suported after the initial optional system message.') }}{% endif %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}
```
Where `system_message` is used to guide the model during generation and `date_string` can be set to allow the model to respond with the current date.
The exact same chat template should be used for an enhanced conversational experience.
The easiest way to apply it is by using the tokenizer's built-in functions, as shown in the following snippet.
```python
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "BSC-LT/salamandra-2b-instruct"
text = "At what temperature does water boil?"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
message = [ { "role": "user", "content": text } ]
date_string = datetime.today().strftime('%Y-%m-%d')
prompt = tokenizer.apply_chat_template(
message,
tokenize=False,
add_generation_prompt=True,
date_string=date_string
)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Using this template, each turn is preceded by a `<|im_start|>` delimiter and the role of the entity
(either `user`, for content supplied by the user, or `assistant` for LLM responses), and finished with the `<|im_end|>` token.
---
## Data
### Pretraining Data
The pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below.
The initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation
and give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half,
Spanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.
During the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset.
This adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:
The pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens.
Following this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%.
Other notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%.
These major sources collectively form the bulk of the corpus, ensuring a rich and diverse dataset for training the language model.
The remaining 10% comes from smaller sources in various languages.
Feel free to click the expand button below to see the full list of sources.
<details>
<summary>Data Sources</summary>
| Dataset | Language | Source |
|---|---|---|
| Colossal OSCAR 1.0 | bg, ca, cs, cy, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, oc, pl, pt, ro, ru, sh, sk, sl, sr, sv, uk | Brack et al., 2024 |
| Aya Dataset (w/o Evaluation Suite) | eu, hr, nl, fi, ka, hu, lt, nn, ro, sk, lv, cy, bg, cs, en, fr, de, ga, mt, pl, ru, sl, sv, ca, da, et, gl, el, it, no, pt, sr, es, uk | Singh et al., 2024 |
| Wikimedia dumps | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, pl, pt, ro, sh, sk, sl, sr, uk | [Link](https://dumps.wikimedia.org/) |
| OpenSubtitles v2016 | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, gl, hr, it, lt, lv, nl, no, pl, pt, ro, sk, sl, sr, sv, uk | Lison & Tiedemann, 2016 |
| EurLEX-Resources | bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelniklaus/eurlex_resources) |
| MC4-Legal | bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelito/legal-mc4) |
| Parlamint | at, bg, cz, dk, ee, es, es-ga, fi, fr, gb, gr, hr, hu, it, lv, nl, no, pl, pt, rs, se, si | Erjavec et al., 2021 |
| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |
| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |
| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |
| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |
| BIGPATENT | en | Sharma et al., 2019 |
| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |
| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |
| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
| CaBeRnet | fr | Popa-Fabre et al., 2020 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
| DaNewsroom | da | Varab & Schluter, 2020 |
| Danish GigaWord | da | Strømberg-Derczynski et al., 2021 |
| DK-CLARIN Reference Corpus of General Danish | da | [Link](https://korpus.dsl.dk/clarin/) |
| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |
| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |
| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |
| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |
| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |
| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |
| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |
| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |
| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |
| HPLT v1 - Spanish | es | de Gibert et al., 2024 |
| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |
| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |
| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |
| Korpus Malti | mt | Micallef et al., 2022 |
| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |
| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |
| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |
| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |
| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |
| Math AMPS | en | Hendrycks et al., 2021 |
| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |
| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |
| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |
| ParlamentoPT | pt | Rodrigues et al., 2023 |
| peS2o | en | Soldaini & Lo, 2023 |
| PG-19 | en | Rae et al., 2019 |
| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |
| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |
| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |
| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |
| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |
| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |
| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |
| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |
| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |
| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |
| Starcoder | code | Li et al., 2023 |
| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |
| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |
| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |
| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |
| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |
| The Pile (PhilPapers) | en | Gao et al., 2021 |
| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |
| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |
| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
<details>
<summary>References</summary>
- Abadji, J., Suárez, P. J. O., Romary, L., & Sagot, B. (2021). Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus (H. Lüngen, M. Kupietz, P. Bański, A. Barbaresi, S. Clematide, & I. Pisetta, Eds.; pp. 1–9). Leibniz-Institut für Deutsche Sprache. [Link](https://doi.org/10.14618/ids-pub-10468)
- Artetxe, M., Aldabe, I., Agerri, R., Perez-de-Viñaspre, O., & Soroa, A. (2022). Does Corpus Quality Really Matter for Low-Resource Languages?
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- Brack, M., Ostendorff, M., Suarez, P. O., Saiz, J. J., Castilla, I. L., Palomar-Giner, J., Shvets, A., Schramowski, P., Rehm, G., Villegas, M., & Kersting, K. (2024). Community OSCAR: A Community Effort for Multilingual Web Data. [Link](https://occiglot.eu/papers/Community_Oscar.pdf)
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- Gutiérrez-Fandiño, A., Armengol-Estapé, J., Gonzalez-Agirre, A., & Villegas, M. (2021). Spanish Legalese Language Model and Corpora.
- Hansen, D. H. (2018). The Danish Parliament Corpus 2009—2017, v1. [Link](http://hdl.handle.net/20.500.12115/8)
- Henderson*, P., Krass*, M. S., Zheng, L., Guha, N., Manning, C. D., Jurafsky, D., & Ho, D. E. (2022). Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset. arXiv. [Link](https://arxiv.org/abs/2207.00220)
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</details>
</details>
The model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part
of the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch;
and 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 trillion tokens.
We provide an extense Datasheet section following the best practices defined by [(Gebru et al., 2021)](https://arxiv.org/pdf/1803.09010).
<details>
<summary>Datasheet</summary>
#### Motivation
**For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.**
The purpose of creating this dataset is to pre-train the Salamandra family of multilingual models with high performance in a large number of European languages (35)
and programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample
these languages by a factor of 2.
There is a great lack of massive multilingual data, especially in minority languages (Ostendorff & Rehm, 2023), so part of our efforts in the creation of
this pre-training dataset have resulted in the contribution to large projects such as the Community OSCAR (Brack et al., 2024), which includes 151 languages
and 40T words, or CATalog (Palomar-Giner et al., 2024), the largest open dataset in Catalan in the world.
**Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?**
The dataset has been created by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS),
which aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, it was created by
the unit's data team, the main contributors being José Javier Saiz, Ferran Espuña and Jorge Palomar.
However, the creation of the dataset would not have been possible without the collaboration of a large number of collaborators, partners and public institutions,
which can be found in detail in the acknowledgements.
**Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.**
This work has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
#### Composition
**What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.**
The dataset consists entirely of text documents in various languages. Specifically, data was mainly sourced from the following databases and
repositories:
- **Common Crawl:** Repository that holds website data and is run by the Common Crawl non-profit organization. It is updated monthly and is
distributed under the CC0 1.0 public domain license.
- **GitHub:** Community platform that allows developers to create, store, manage, and share their code. Repositories are crawled and then
distributed with their original licenses, which may vary from permissive to non-commercial licenses.
- **Wikimedia:** Database that holds the collection databases managed by the Wikimedia Foundation, including Wikipedia, Wikibooks, Wikinews,
Wikiquote, Wikisource, and Wikivoyage. It is updated monthly and is distributed under Creative Commons Attribution-ShareAlike License 4.0.
- **EurLex:** Repository that holds the collection of legal documents from the European Union, available in all of the EU’s 24 official
languages and run by the Publications Office of the European Union. It is updated daily and is distributed under the Creative Commons
Attribution 4.0 International license.
- **Other repositories:** Specific repositories were crawled under permission for domain-specific corpora, which include academic, legal,
and newspaper repositories.
We provide a complete list of dataset sources at the end of this section.
**How many instances are there in total (of each type, if appropriate)?**
The dataset contains a diverse range of instances across multiple languages, with notable adjustments for certain languages. English
represents the largest portion, accounting for 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,
while Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled
by half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian
(4.59%), with several additional languages contributing between 1% and 2%, and smaller portions represented by a variety of others.
**Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).**
The dataset is a sample from multiple sources, with different weights based on the primary language of the content: Spanish, Catalan,
Basque, and Galician content was upsampled by a factor of two, while programming languages were downsampled by a factor of half. Other
sources were sampled in proportion to their occurrence.
**What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.**
Each instance consists of a text document processed for deduplication, language identification, and source-specific filtering. Some documents required
optical character recognition (OCR) to extract text from non-text formats such as PDFs.
**Is there a label or target associated with each instance? If so, please provide a description.**
Each instance is labelled with a unique identifier, the primary language of the content, and the URL for web-sourced instances. Additional labels were
automatically assigned to detect specific types of content -harmful or toxic content- and to assign preliminary indicators of undesired qualities -very
short documents, high density of symbols, etc.- which were used for filtering instances.
**Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.**
No significant information is missing from the instances.
**Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.**
Instances are related through shared metadata, such as source and language identifiers.
**Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.**
The dataset is randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.
**Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.**
Despite removing duplicated instances within each source, redundancy remains at the paragraph and sentence levels, particularly in web-sourced
instances where search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also duplicated
across sources due to format variations.
**Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.**
The dataset is self-contained and does not rely on external resources.
**Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.**
The dataset does not contain confidential data.
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.**
The dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). Although
pre-processing techniques were applied to mitigate offensive content, the heterogeneity and scale of web-sourced data make exhaustive
filtering challenging, which makes it next to impossible to identify all adult content without falling into excessive filtering, which may
negatively influence certain demographic groups (Dodge et al., 2021).
**Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.**
The dataset does not explicitly identify any subpopulations.
**Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.**
Web-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names,
IP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple
data points, the nature and scale of web data makes it difficult to parse such information. In any case, efforts are made to filter or anonymize
sensitive data (Mina et al., 2024), but some identifiable information may remain in the dataset.
**Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.**
Given that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial
information, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023),
especially if the content originates from less-regulated sources or user-generated platforms.
#### Collection Process
**How was the data collected?**
This dataset is constituted by combining several sources, whose acquisition methods can be classified into three groups:
- Web-sourced datasets with some preprocessing available under permissive license.
- Domain-specific or language-specific raw crawls.
- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).
**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**
The data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:
- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.
- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.
- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.
**If the dataset is a sample from a larger set, what was the sampling strategy?**
The sampling strategy was to use the whole dataset resulting from the filtering explained in the 'preprocessing/cleaning/labelling' section,
with the particularity that an upsampling of 2 (i.e. twice the probability of sampling a document) was performed for the co-official languages
of Spain (Spanish, Catalan, Galician, Basque), and a downsampling of 1/2 was applied for code (half the probability of sampling a code document,
evenly distributed among all programming languages).
**Who was involved in the data collection process and how were they compensated?**
This data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely
by members of the Language Technologies data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary
consideration for acquiring data from suppliers.
**Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.**
Data were acquired and processed from April 2023 to April 2024. However, as mentioned, much data has been obtained from open projects such
as Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important.
**Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.**
No particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an
internal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència
Artificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an
ethical and legal point of view, respectively.
#### Preprocessing
**Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.**
No changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:
- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.
- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\_pp' field, a perplexity-based score generated by a language model.
**Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.**
The original raw data was not kept.
**Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.**
Yes, the preprocessing and filtering software is open-sourced. The [CURATE](https://github.com/langtech-bsc/CURATE) pipeline was used for CATalog and other curated datasets,
and the [Ungoliant](https://github.com/oscar-project/ungoliant) pipeline was used for the OSCAR project.
#### Uses
**Has the dataset been used for any tasks already? If so, please provide a description.**
Pre-train the Salamandra model family.
**What (other) tasks could the dataset be used for?**
The data can be used primarily to pre-train other language models, which can then be used for a wide range of use cases. The dataset could
also be used for other tasks such as fine-tuning language models, cross-lingual NLP tasks, machine translation, domain-specific text
generation, and language-specific data analysis.
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?**
Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages.
Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic
groups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures,
acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to
address privacy concerns and contribute to a more inclusive linguistic dataset.
**Are there tasks for which the dataset should not be used?**
-
#### Distribution
**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.**
The dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section.
#### Maintenance
**Who will be supporting/hosting/maintaining the dataset?**
The dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure
regular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are
responsible for.
**How can the owner/curator/manager of the dataset be contacted?**
The data owner may be contacted with the email address [email protected].
**Will the dataset be updated?**
The dataset will not be updated.
**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.**
The dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in
web-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an
individual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to
remove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues.
**Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.**
Since the dataset will not be updated, only the final version will be kept.
**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**
The dataset does not allow for external contributions.
</details>
### Finetuning Data
This instructed-tuned variant has been fine-tuned with a collection of 273k instructions, focusing on the performance of Catalan, English and Spanish. However, instruction data for other closely related Iberian languages has also been included, since it yielded a positive impact on the languages of interest. That said, the performance in these additional languages is not guaranteed due to the limited amount of available data and the lack of resources for thorough testing.
| **Dataset** | **ca** | **en** | **es** | **eu** | **gl** | **pt** | **Total** |
|----------------------|------------|-------------|------------|-----------|---------|------------|-------------|
| alpaca-cleaned | | 49,950 | | | | | **49,950** |
| aya-dataset | | 3,941 | 3,851 | 939 | | 8,995 | **17,726** |
| coqcat | 4,797 | | | | | | **4,797** |
| databricks-dolly-15k | | 15,011 | | | | | **15,011** |
| dolly-ca | 3,232 | | | | | | **3,232** |
| flores-dev | 986 | 1,037 | 1,964 | 493 | 505 | | **4,985** |
| mentor-ca | 7,119 | | | | | | **7,119** |
| mentor-es | | | 7,122 | | | | **7,122** |
| no-robots | | 9,485 | | | | | **9,485** |
| oasst-ca | 2,517 | | | | | | **2,517** |
| oasst2 | 750 | 31,086 | 15,438 | 190 | 197 | 1,203 | **48,864** |
| open-orca | | 49,996 | | | | | **49,996** |
| rag-multilingual | 16,043 | 14,997 | 11,263 | | | | **42,303** |
| tower-blocks | | 7,762 | 1,000 | | | 1,000 | **9,762** |
| **Total** | **35,444** | **183,265** | **40,638** | **1,622** | **702** | **11,198** | **272,869** |
---
## Evaluation
### Gold-standard benchmarks
WiP
<!--
Evaluation is done using the Language Model Evaluation Harness (Gao et al., 2024). We evaluate on a set of tasks taken from [SpanishBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/spanish_bench), [CatalanBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/catalan_bench), [BasqueBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/basque_bench) and [GalicianBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/galician_bench). These benchmarks include both new and existing tasks and datasets. Given that this is an instructed model, we add LM Evaluation Harness's native feature of `chat-template` to the setup. In the tables below, we include the results in a selection of evaluation datasets that represent model's performance across a variety of tasks within these benchmarks.
We only use tasks that are either human generated, human translated, or with a strong human-in-the-loop (i.e., machine translation followed by professional revision or machine generation followed by human revision and annotation). This is the reason behind the variety in number of tasks reported across languages. As more tasks that fulfill these requirements are published, we will update the presented results. We also intend to expand the evaluation to other languages, as long as the datasets meet our quality standards.
During the implementation of the evaluation we observed a series of issues worth considering when replicating and interpreting the results presented. These issues include ≈1.5% variances in performance in some tasks depending on the version of the `transformers` library used, and depending on the use (or lack of use) of tensor parallelism when loading a model. When implementing existing tasks, we carry out a comprehensive quality evaluation of the dataset, the Harness task itself, and what kind of input models see during evaluation. Our implementation (see links above) addresses multiple existing problems such as errors in datasets and prompts, and lack of pre-processing. All this means that results will vary if using other Harness implementations, and may slightly vary depending on the replication setup.
It should be noted that these results are subject to all the drawbacks of every current gold-standard evaluation, and that the figures do not fully represent the model's capabilities and potential. We thus advise caution when reading and interpreting the results.
A full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.
All results reported below are on a 0-shot setting.
#### Spanish
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td>Commonsense Reasoning</td>
<td>xstorycloze_es</td>
<td>acc</td>
<td>62.34</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_es</td>
<td>acc</td>
<td>47.89</td>
</tr>
<tr>
<td>xnli_es</td>
<td>acc</td>
<td>47.03</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws_es</td>
<td>acc</td>
<td>55.5</td>
</tr>
<tr>
<td>QA</td>
<td>xquad_es</td>
<td>acc</td>
<td>42.21</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_es</td>
<td>bleu</td>
<td>20.27</td>
</tr>
</tbody>
</table>
#### Catalan
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa_ca</td>
<td>acc</td>
<td>70.4</td>
</tr>
<tr>
<td>xstorycloze_ca</td>
<td>acc</td>
<td>63.07</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_ca</td>
<td>acc</td>
<td>52.11</td>
</tr>
<tr>
<td>xnli_ca</td>
<td>acc</td>
<td>51.69</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafraseja</td>
<td>acc</td>
<td>61.88</td>
</tr>
<tr>
<td>paws_ca</td>
<td>acc</td>
<td>57.7</td>
</tr>
<tr>
<td rowspan="5">QA</td>
<td>arc_ca_easy</td>
<td>acc</td>
<td>51.94</td>
</tr>
<tr>
<td>arc_ca_challenge</td>
<td>acc</td>
<td>29.52</td>
</tr>
<tr>
<td>openbookqa_ca</td>
<td>acc</td>
<td>26.4</td>
</tr>
<tr>
<td>piqa_ca</td>
<td>acc</td>
<td>62.89</td>
</tr>
<tr>
<td>siqa_ca</td>
<td>acc</td>
<td>42.63</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_ca</td>
<td>bleu</td>
<td>24.48</td>
</tr>
</tbody></table>
#### Basque
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>xcopa_eu</td>
<td>acc</td>
<td>53.6</td>
</tr>
<tr>
<td>xstorycloze_eu</td>
<td>acc</td>
<td>56.39</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_eu</td>
<td>acc</td>
<td>45.07</td>
</tr>
<tr>
<td>xnli_eu</td>
<td>acc</td>
<td>39.44</td>
</tr>
<tr>
<td rowspan="3">QA</td>
<td>eus_exams</td>
<td>acc</td>
<td>25.35</td>
</tr>
<tr>
<td>eus_proficiency</td>
<td>acc</td>
<td>26.37</td>
</tr>
<tr>
<td>eus_trivia</td>
<td>acc</td>
<td>26.24</td>
</tr>
<tr>
<td>Reading Comprehension</td>
<td>eus_reading</td>
<td>acc</td>
<td>24.72</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_eu</td>
<td>bleu</td>
<td>9.67</td>
</tr>
</tbody></table>
#### Galician
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafrases_gl</td>
<td>acc</td>
<td>50.00</td>
</tr>
<tr>
<td>paws_gl</td>
<td>acc</td>
<td>52.20</td>
</tr>
<tr>
<td>QA</td>
<td>openbookqa_gl</td>
<td>acc</td>
<td>33.2</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_gl</td>
<td>bleu</td>
<td>22.39</td>
</tr>
</tbody>
</table>
-->
### LLM-as-a-judge
We use [Prometheus-2 8x7B](https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0) as a judge to evaluate the responses of the model. Tasks are created from existing multilingual evaluation datasets covering the same categories as the ones measured in our gold-standard benchmarks. We randomly select a subset of 250 instances per language from the `test` set of each source dataset. To evaluate the responses of our model, we use task-specific criteria developed in-house for the _LLM-judge_ to use. Each criterion is measured either as a 5-point Likert scale or as a binary task depending on the idiosyncrasy of the task and criterion.
Prompts for each task are created in various ways to score the model's robustness in addition to these criteria. This is done by presenting the same source instance within three different prompts. We then calculate the variance between the scores assigned by the _LLM-judge_ to our model's responses to the three prompt styles and average it across all instances. Prompts are human translated to all languages measured. We do not provide the _LLM-judge_ with a reference answer.
The _judge_ prompt we use during evaluation is the same used to fine tune the Prometheus-2 family. We keep the _judge_ prompt and criteria used to present the _LLM-judge_ with the task prompts and model responses in English for evaluation across languages. The _judge_ prompt used is:
```python
"You are a fair judge assistant tasked with providing clear, objective feedback based on specific criteria, ensuring each assessment reflects the absolute standards set for performance.
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between {a} and {b}. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between {a} and {b})\"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{input}
###Response to evaluate:
{prediction}
###Score Rubrics:
{criteria}
###Feedback:"
```
As an example, prompts for the Math task in English are based on instances from [MGSM](https://huggingface.co/datasets/juletxara/mgsm), and each instance is presented within these prompts:
```python
"en": [
("I need help with this math problem: \"", "\" Give me the answer step by step and also the final result separately."),
("Can you please help me answer this? \"", "\" Explain the answer and give me the final result as well. Thanks."),
("Help me with this problem: \"", "\" I need the answer explained and the final result separately.")
]
```
This task is then evaluated by the _LLM-judge_ using two criteria, reasoning capability (5-point Likert) and mathematical correctness (binary):
```python
reasoning_capability_criteria = {
"reasoning_capability": """
[Does the model's answer demonstrate reasoning capability?]
Score 1: The answer demonstrates poor reasoning, with illogical arguments or conclusions that do not follow from the provided information.
Score 2: The answer shows weak reasoning, with some logical connections but also contains significant flaws or gaps in the argumentation.
Score 3: The answer demonstrates adequate reasoning, with generally logical arguments, but may have minor flaws or a lack of depth in the reasoning process.
Score 4: The answer shows strong reasoning, with well-structured arguments and conclusions that logically follow from the information provided.
Score 5: The answer demonstrates exceptional reasoning, with clear, coherent, and insightful arguments that are logically sound and well-supported by the information provided."""
}
mathematical_correctness_binary_criteria = {
"mathematical_correctness_binary": """
[Is the model's answer mathematically correct?]
Score 0: The answer contains mathematical errors that render the solution incorrect or unreliable.
Score 1: The answer is mathematically correct, with accurate calculations and appropriate use of mathematical concepts."""
}
```
#### Multilingual results
Here, we present results for seven categories of tasks in Spanish, Catalan, Basque, Galician, and English. Results are presented for each task, criterion and language. Criteria with a `(B)` after their name are binary criteria (i.e., numbers go from 0 to 1, where 1 is best). The rest of the criteria are measured using a 5-point Likert scale, where 5 is best. The first number of the pair of numbers separated by `/` shows the average score for the criterion (and language). The second number of each pair is the robustness score, where numbers closer to 0 means that the model generates similar responses when comparing the three prompt varieties for a single instance.
Further details on all tasks and criteria, a full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}
</style>
<table class="tg"><thead>
<tr>
<th class="tg-0pky"><span style="font-weight:bold">Category</span></th>
<th class="tg-0pky"><span style="font-weight:bold">Dataset</span></th>
<th class="tg-0pky"><span style="font-weight:bold">Criteria</span></th>
<th class="tg-0pky"><span style="font-weight:bold">es</span></th>
<th class="tg-0pky"><span style="font-weight:bold">ca</span></th>
<th class="tg-0pky"><span style="font-weight:bold">gl</span></th>
<th class="tg-0pky"><span style="font-weight:bold">eu</span></th>
<th class="tg-0pky"><span style="font-weight:bold">en</span></th>
</tr></thead>
<tbody>
<tr>
<td class="tg-0pky">Commonsense Reasoning</td>
<td class="tg-0pky">XStoryCloze</td>
<td class="tg-0pky">Ending coherence</td>
<td class="tg-0pky">2.36/0.66</td>
<td class="tg-0pky">2.49/0.76</td>
<td class="tg-0pky">2.45/0.68</td>
<td class="tg-0pky">2.30/0.67</td>
<td class="tg-0pky">3.06/0.77</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="3">Paraphrasing</td>
<td class="tg-0pky" rowspan="3">PAWS</td>
<td class="tg-0pky">Completeness `(B)`</td>
<td class="tg-0pky">0.60/0.15</td>
<td class="tg-0pky">0.54/0.17</td>
<td class="tg-0pky">0.64/0.14</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">0.79/0.11</td>
</tr>
<tr>
<td class="tg-0pky">Paraphrase generation</td>
<td class="tg-0pky">2.89/1.46</td>
<td class="tg-0pky">2.71/1.70</td>
<td class="tg-0pky">2.80/1.21</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.64/0.80</td>
</tr>
<tr>
<td class="tg-0pky">Grammatical correctness `(B)`</td>
<td class="tg-0pky">0.74/0.13</td>
<td class="tg-0pky">0.68/0.15</td>
<td class="tg-0pky">0.78/0.10</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">0.89/0.07</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Reading Comprehension</td>
<td class="tg-0pky" rowspan="2">Belebele</td>
<td class="tg-0pky">Passage comprehension</td>
<td class="tg-0pky">3.05/0.60</td>
<td class="tg-0pky">2.81/0.66</td>
<td class="tg-0pky">2.74/0.78</td>
<td class="tg-0pky">2.52/0.46</td>
<td class="tg-0pky">3.11/0.71</td>
</tr>
<tr>
<td class="tg-0pky">Answer relevance `(B)`</td>
<td class="tg-0pky">0.74/0.09</td>
<td class="tg-0pky">0.66/0.11</td>
<td class="tg-0pky">0.65/0.12</td>
<td class="tg-0pky">0.59/0.12</td>
<td class="tg-0pky">0.75/0.09</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Extreme Summarization</td>
<td class="tg-0pky" rowspan="2">XLSum & caBreu & summarization_gl</td>
<td class="tg-0pky">Informativeness</td>
<td class="tg-0pky">3.07/0.39</td>
<td class="tg-0pky">3.33/0.43</td>
<td class="tg-0pky">3.11/0.36</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.06/0.35</td>
</tr>
<tr>
<td class="tg-0pky">Conciseness</td>
<td class="tg-0pky">2.92/0.42</td>
<td class="tg-0pky">2.67/0.54</td>
<td class="tg-0pky">2.93/0.39</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.13/0.31</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Math</td>
<td class="tg-0pky" rowspan="2">MGSM</td>
<td class="tg-0pky">Reasoning capability</td>
<td class="tg-0pky">1.89/0.47</td>
<td class="tg-0pky">1.91/0.45</td>
<td class="tg-0pky">1.97/0.43</td>
<td class="tg-0pky">2.17/0.44</td>
<td class="tg-0pky">2.16/0.56</td>
</tr>
<tr>
<td class="tg-0pky">Mathematical correctness `(B)`</td>
<td class="tg-0pky">0.24/0.10</td>
<td class="tg-0pky">0.28/0.11</td>
<td class="tg-0pky">0.27/0.11</td>
<td class="tg-0pky">0.44/0.13</td>
<td class="tg-0pky">0.27/0.10</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Translation form Language</td>
<td class="tg-0pky" rowspan="2">FLORES-200</td>
<td class="tg-0pky">Fluency</td>
<td class="tg-0pky">3.74/0.15</td>
<td class="tg-0pky">3.69/0.22</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.69/0.18</td>
</tr>
<tr>
<td class="tg-0pky">Accuracy</td>
<td class="tg-0pky">4.01/0.24</td>
<td class="tg-0pky">3.98/0.31</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.98/0.25</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Translation to Language</td>
<td class="tg-0pky" rowspan="2">FLORES-200</td>
<td class="tg-0pky">Fluency</td>
<td class="tg-0pky">3.75/0.14</td>
<td class="tg-0pky">3.69/0.17</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.09/0.16</td>
</tr>
<tr>
<td class="tg-0pky">Accuracy</td>
<td class="tg-0pky">4.08/0.22</td>
<td class="tg-0pky">3.98/0.24</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.47/0.18</td>
</tr>
</tbody></table>
---
## Ethical Considerations and Limitations
We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases,
we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019).
We report that while performance is high (accuracies around 0.8 depending on the social category) in disambiguated settings,
the model performs very poorly in ambiguous settings, which indicates the presence of societal biases that need to be further addressed in post-training phases.
Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings.
For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018). We observe significant,
but relatively weak primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers.
We measure the effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We again detect significant effects,
with a small effect size. This suggests that the model is relatively robust against the examined cognitive biases.
We highlight that our analyses of these biases are by no means exhaustive and are limited by the relative scarcity of adequate resources
in all languages present in the training data. We aim to gradually extend and expand our analyses in future work.
These results can be expected from a model that has undergone only a preliminary instruction tuning.
These tests are performed in order to show the biases the model may contain. We urge developers to take
them into account and perform safety testing and tuning tailored to their specific applications of the model.
---
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.
### Funding
This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
### Acknowledgements
This project has benefited from the contributions of numerous teams and institutions, mainly through data contributions, knowledge transfer or technical support.
In Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà.
At the national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria.
At the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration. We would also like to give special thanks to the NVIDIA team, with whom we have met regularly, specially to: Ignacio Sarasua, Adam Henryk Grzywaczewski, Oleg Sudakov, Sergio Perez, Miguel Martinez, Felipes Soares and Meriem Bendris. Their constant support has been especially appreciated throughout the entire process.
Their valuable efforts have been instrumental in the development of this work.
### Disclaimer
Be aware that the model may contain biases or other unintended distortions.
When third parties deploy systems or provide services based on this model, or use the model themselves,
they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations,
including those governing the use of Artificial Intelligence.
The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
### Citation
```
@misc{gonzalezagirre2025salamandratechnicalreport,
title={Salamandra Technical Report},
author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
year={2025},
eprint={2502.08489},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.08489},
}
```
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Index
|Model|Base|Instruct|
|:---:|:---:|:---:|
|2B| [Link](https://huggingface.co/BSC-LT/salamandra-2b) | [Link](https://huggingface.co/BSC-LT/salamandra-2b-instruct) |
|7B| [Link](https://huggingface.co/BSC-LT/salamandra-7b) | [Link](https://huggingface.co/BSC-LT/salamandra-7b-instruct) |
|40B| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] |
[
"BEAR",
"SCIELO"
] |
tensorblock/gte-Qwen2-7B-instruct-GGUF
|
tensorblock
|
sentence-similarity
|
[
"sentence-transformers",
"gguf",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"TensorBlock",
"GGUF",
"base_model:Alibaba-NLP/gte-Qwen2-7B-instruct",
"base_model:quantized:Alibaba-NLP/gte-Qwen2-7B-instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-11-11T10:44:19 | 2024-11-16T01:05:41 | 3,236 | 8 |
---
base_model: Alibaba-NLP/gte-Qwen2-7B-instruct
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
- TensorBlock
- GGUF
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
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" 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;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## Alibaba-NLP/gte-Qwen2-7B-instruct - GGUF
This repo contains GGUF format model files for [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
<div style="text-align: left; margin: 20px 0;">
<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Run them on the TensorBlock client using your local machine ↗
</a>
</div>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gte-Qwen2-7B-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q2_K.gguf) | Q2_K | 2.807 GB | smallest, significant quality loss - not recommended for most purposes |
| [gte-Qwen2-7B-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q3_K_S.gguf) | Q3_K_S | 3.251 GB | very small, high quality loss |
| [gte-Qwen2-7B-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q3_K_M.gguf) | Q3_K_M | 3.545 GB | very small, high quality loss |
| [gte-Qwen2-7B-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q3_K_L.gguf) | Q3_K_L | 3.806 GB | small, substantial quality loss |
| [gte-Qwen2-7B-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q4_0.gguf) | Q4_0 | 4.125 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gte-Qwen2-7B-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q4_K_S.gguf) | Q4_K_S | 4.150 GB | small, greater quality loss |
| [gte-Qwen2-7B-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q4_K_M.gguf) | Q4_K_M | 4.360 GB | medium, balanced quality - recommended |
| [gte-Qwen2-7B-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q5_0.gguf) | Q5_0 | 4.948 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gte-Qwen2-7B-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q5_K_S.gguf) | Q5_K_S | 4.948 GB | large, low quality loss - recommended |
| [gte-Qwen2-7B-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q5_K_M.gguf) | Q5_K_M | 5.069 GB | large, very low quality loss - recommended |
| [gte-Qwen2-7B-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q6_K.gguf) | Q6_K | 5.822 GB | very large, extremely low quality loss |
| [gte-Qwen2-7B-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q8_0.gguf) | Q8_0 | 7.539 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/gte-Qwen2-7B-instruct-GGUF --include "gte-Qwen2-7B-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/gte-Qwen2-7B-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
tasksource/ModernBERT-large-nli
|
tasksource
|
zero-shot-classification
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"instruct",
"natural-language-inference",
"nli",
"zero-shot-classification",
"en",
"dataset:nyu-mll/glue",
"dataset:facebook/anli",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-04T00:56:11 | 2025-01-04T12:03:47 | 3,223 | 5 |
---
base_model:
- answerdotai/ModernBERT-large
datasets:
- nyu-mll/glue
- facebook/anli
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: zero-shot-classification
tags:
- instruct
- natural-language-inference
- nli
---
# Model Card for Model ID
This model is ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
This is the equivalent of an "instruct" version.
The model was trained for 200k steps on an Nvidia A30 GPU.
It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels.
The following table shows model test accuracy. These are the scores for the same single transformer with different classification heads on top.
Further gains can be obtained by fine-tuning on a single-task, e.g. SST, but it this checkpoint is great for zero-shot classification and natural language inference (contradiction/entailment/neutral classification).
| test_name | test_accuracy |
|:--------------------------------------|----------------:|
| glue/mnli | 0.89 |
| glue/qnli | 0.96 |
| glue/rte | 0.91 |
| glue/wnli | 0.64 |
| glue/mrpc | 0.81 |
| glue/qqp | 0.87 |
| glue/cola | 0.87 |
| glue/sst2 | 0.96 |
| super_glue/boolq | 0.66 |
| super_glue/cb | 0.86 |
| super_glue/multirc | 0.9 |
| super_glue/wic | 0.71 |
| super_glue/axg | 1 |
| anli/a1 | 0.72 |
| anli/a2 | 0.54 |
| anli/a3 | 0.55 |
| sick/label | 0.91 |
| sick/entailment_AB | 0.93 |
| snli | 0.94 |
| scitail/snli_format | 0.95 |
| hans | 1 |
| WANLI | 0.77 |
| recast/recast_ner | 0.85 |
| recast/recast_sentiment | 0.97 |
| recast/recast_verbnet | 0.89 |
| recast/recast_megaveridicality | 0.87 |
| recast/recast_verbcorner | 0.87 |
| recast/recast_kg_relations | 0.9 |
| recast/recast_factuality | 0.95 |
| recast/recast_puns | 0.98 |
| probability_words_nli/reasoning_1hop | 1 |
| probability_words_nli/usnli | 0.79 |
| probability_words_nli/reasoning_2hop | 0.98 |
| nan-nli | 0.85 |
| nli_fever | 0.78 |
| breaking_nli | 0.99 |
| conj_nli | 0.72 |
| fracas | 0.79 |
| dialogue_nli | 0.94 |
| mpe | 0.75 |
| dnc | 0.91 |
| recast_white/fnplus | 0.76 |
| recast_white/sprl | 0.9 |
| recast_white/dpr | 0.84 |
| add_one_rte | 0.94 |
| paws/labeled_final | 0.96 |
| pragmeval/pdtb | 0.56 |
| lex_glue/scotus | 0.58 |
| lex_glue/ledgar | 0.85 |
| dynasent/dynabench.dynasent.r1.all/r1 | 0.83 |
| dynasent/dynabench.dynasent.r2.all/r2 | 0.76 |
| cycic_classification | 0.96 |
| lingnli | 0.91 |
| monotonicity-entailment | 0.97 |
| scinli | 0.88 |
| naturallogic | 0.93 |
| dynahate | 0.86 |
| syntactic-augmentation-nli | 0.94 |
| autotnli | 0.92 |
| defeasible-nli/atomic | 0.83 |
| defeasible-nli/snli | 0.8 |
| help-nli | 0.96 |
| nli-veridicality-transitivity | 0.99 |
| lonli | 0.99 |
| dadc-limit-nli | 0.79 |
| folio | 0.71 |
| tomi-nli | 0.54 |
| puzzte | 0.59 |
| temporal-nli | 0.93 |
| counterfactually-augmented-snli | 0.81 |
| cnli | 0.9 |
| boolq-natural-perturbations | 0.72 |
| equate | 0.65 |
| logiqa-2.0-nli | 0.58 |
| mindgames | 0.96 |
| ConTRoL-nli | 0.66 |
| logical-fallacy | 0.38 |
| cladder | 0.89 |
| conceptrules_v2 | 1 |
| zero-shot-label-nli | 0.79 |
| scone | 1 |
| monli | 1 |
| SpaceNLI | 1 |
| propsegment/nli | 0.92 |
| FLD.v2/default | 0.91 |
| FLD.v2/star | 0.78 |
| SDOH-NLI | 0.99 |
| scifact_entailment | 0.87 |
| feasibilityQA | 0.79 |
| AdjectiveScaleProbe-nli | 1 |
| resnli | 1 |
| semantic_fragments_nli | 1 |
| dataset_train_nli | 0.95 |
| nlgraph | 0.97 |
| ruletaker | 0.99 |
| PARARULE-Plus | 1 |
| logical-entailment | 0.93 |
| nope | 0.56 |
| LogicNLI | 0.91 |
| contract-nli/contractnli_a/seg | 0.88 |
| contract-nli/contractnli_b/full | 0.84 |
| nli4ct_semeval2024 | 0.72 |
| biosift-nli | 0.92 |
| SIGA-nli | 0.57 |
| FOL-nli | 0.79 |
| doc-nli | 0.81 |
| mctest-nli | 0.92 |
| natural-language-satisfiability | 0.92 |
| idioms-nli | 0.83 |
| lifecycle-entailment | 0.79 |
| MSciNLI | 0.84 |
| hover-3way/nli | 0.92 |
| seahorse_summarization_evaluation | 0.81 |
| missing-item-prediction/contrastive | 0.88 |
| Pol_NLI | 0.93 |
| synthetic-retrieval-NLI/count | 0.72 |
| synthetic-retrieval-NLI/position | 0.9 |
| synthetic-retrieval-NLI/binary | 0.92 |
| babi_nli | 0.98 |
# Usage
## [ZS] Zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="tasksource/ModernBERT-large-nli")
text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)
```
NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
## [NLI] Natural language inference pipeline
```python
from transformers import pipeline
pipe = pipeline("text-classification",model="tasksource/ModernBERT-large-nli")
pipe([dict(text='there is a cat',
text_pair='there is a black cat')]) #list of (premise,hypothesis)
```
## Backbone for further fune-tuning
This checkpoint has stronger reasoning and fine-grained abilities than the base version and can be used for further fine-tuning.
# Citation
```
@inproceedings{sileo-2024-tasksource,
title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
author = "Sileo, Damien",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1361",
pages = "15655--15684",
}
```
|
[
"SUMMARIZATION"
] |
[
"SCIFACT",
"SCITAIL"
] |
pszemraj/long-t5-tglobal-base-16384-book-summary
|
pszemraj
|
summarization
|
[
"transformers",
"pytorch",
"rust",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"arxiv:2112.07916",
"arxiv:2105.08209",
"doi:10.57967/hf/2078",
"license:apache-2.0",
"license:bsd-3-clause",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-06-27T16:37:26 | 2025-01-21T13:58:25 | 3,139 | 134 |
---
datasets:
- kmfoda/booksum
license:
- apache-2.0
- bsd-3-clause
metrics:
- rouge
tags:
- summarization
- summary
- booksum
- long-document
- long-form
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
num_beams: 3
model-index:
- name: pszemraj/long-t5-tglobal-base-16384-book-summary
results:
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- type: rouge
value: 36.4085
name: ROUGE-1
verified: true
- type: rouge
value: 6.0646
name: ROUGE-2
verified: true
- type: rouge
value: 16.7209
name: ROUGE-L
verified: true
- type: rouge
value: 33.3405
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 252.8099
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 30.9047
name: ROUGE-1
verified: true
- type: rouge
value: 7.4715
name: ROUGE-2
verified: true
- type: rouge
value: 22.3962
name: ROUGE-L
verified: true
- type: rouge
value: 26.9094
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 46.7973
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
metrics:
- type: rouge
value: 30.5942
name: ROUGE-1
verified: true
- type: rouge
value: 7.252
name: ROUGE-2
verified: true
- type: rouge
value: 17.7156
name: ROUGE-L
verified: true
- type: rouge
value: 27.2881
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 125.2507
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: xsum
type: xsum
config: default
split: test
metrics:
- type: rouge
value: 20.3648
name: ROUGE-1
verified: true
- type: rouge
value: 3.4126
name: ROUGE-2
verified: true
- type: rouge
value: 13.6168
name: ROUGE-L
verified: true
- type: rouge
value: 15.8313
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 82.2177
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- type: rouge
value: 39.6378
name: ROUGE-1
verified: true
- type: rouge
value: 13.0017
name: ROUGE-2
verified: true
- type: rouge
value: 23.0255
name: ROUGE-L
verified: true
- type: rouge
value: 32.9943
name: ROUGE-LSUM
verified: true
- type: loss
value: 1.9428048133850098
name: loss
verified: true
- type: gen_len
value: 162.3588
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: big_patent
type: big_patent
config: y
split: test
metrics:
- type: rouge
value: 34.7641
name: ROUGE-1
verified: true
- type: rouge
value: 7.8744
name: ROUGE-2
verified: true
- type: rouge
value: 19.9826
name: ROUGE-L
verified: true
- type: rouge
value: 29.208
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.8316469192504883
name: loss
verified: true
- type: gen_len
value: 132.7475
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: launch/gov_report
type: launch/gov_report
config: plain_text
split: validation
metrics:
- type: rouge
value: 37.9246
name: ROUGE-1
verified: true
- type: rouge
value: 8.5837
name: ROUGE-2
verified: true
- type: rouge
value: 18.0274
name: ROUGE-L
verified: true
- type: rouge
value: 34.0816
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.56695818901062
name: loss
verified: true
- type: gen_len
value: 220.3747
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: launch/gov_report
type: launch/gov_report
config: plain_text
split: test
metrics:
- type: rouge
value: 37.4438
name: ROUGE-1
verified: true
- type: rouge
value: 8.2907
name: ROUGE-2
verified: true
- type: rouge
value: 17.6893
name: ROUGE-L
verified: true
- type: rouge
value: 33.7141
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.5776000022888184
name: loss
verified: true
- type: gen_len
value: 214.9692
name: gen_len
verified: true
---
# long-t5-tglobal-base-16384 + BookSum
<a href="https://colab.research.google.com/gist/pszemraj/d9a0495861776168fd5cdcd7731bc4ee/example-long-t5-tglobal-base-16384-book-summary.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
> [!IMPORTANT]
> As of [this discussion](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary/discussions/23) we found issues with long-t5 models >= 4.23.0 - please use `pip install transformers==4.22.0` to ensure good performance with this model until this disclaimer is removed.
Summarize long text and get a SparkNotes-esque summary of arbitrary topics!
- generalizes reasonably well to academic & narrative text.
- A simple example/use case on ASR is [here](https://longt5-booksum-example.netlify.app/).
- Example notebook in Colab (_click on the icon above_).
## Cheeky Proof-of-Concept
A summary of the [infamous navy seals copypasta](https://knowyourmeme.com/memes/navy-seal-copypasta):
> The narrator tells us that he's graduated from the Navy seals and has been involved in many secret raids. He's also one of the best snipers in the entire U.S. military. He promises to "wipe you out with precision" when they meet again.
* * *
**Contents**
<!-- TOC -->
- [Model description](#model-description)
- [How-To in Python](#how-to-in-python)
- [Intended uses & limitations](#intended-uses--limitations)
- [Training and evaluation data](#training-and-evaluation-data)
- [FAQ](#faq)
- [How to run inference over a very long (30k+ tokens) document in batches?](#how-to-run-inference-over-a-very-long-30k-tokens-document-in-batches)
- [How to fine-tune further?](#how-to-fine-tune-further)
- [Are there simpler ways to run this?](#are-there-simpler-ways-to-run-this)
- [Training procedure](#training-procedure)
- [Updates:](#updates)
- [Training hyperparameters](#training-hyperparameters)
- [Framework versions](#framework-versions)
- [Citation info](#citation-info)
<!-- /TOC -->
* * *
## Model description
A fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `kmfoda/booksum` dataset:
- 30+ epochs of fine-tuning from the base model on V100/A100 GPUs
- Training used 16384 token input / 1024 max output
Read the paper by Guo et al. here: [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf)
## How-To in Python
Install/update transformers `pip install -U transformers`
Summarize text with pipeline:
```python
import torch
from transformers import pipeline
summarizer = pipeline(
"summarization",
"pszemraj/long-t5-tglobal-base-16384-book-summary",
device=0 if torch.cuda.is_available() else -1,
)
long_text = "Here is a lot of text I don't want to read. Replace me"
result = summarizer(long_text)
print(result[0]["summary_text"])
```
Pass [other parameters related to beam search textgen](https://huggingface.co/blog/how-to-generate) when calling `summarizer` to get even higher quality results.
## Intended uses & limitations
- The current checkpoint is fairly well converged but will be updated if further improvements can be made.
- Compare performance to [LED-base](https://huggingface.co/pszemraj/led-base-book-summary) trained on the same dataset (API gen parameters are the same).
- while this model seems to improve upon factual consistency, **do not take summaries to be foolproof and check things that seem odd**.
## Training and evaluation data
`kmfoda/booksum` dataset on HuggingFace - read [the original paper here](https://arxiv.org/abs/2105.08209). Summaries longer than 1024 LongT5 tokens were filtered out to prevent the model from learning to generate "partial" summaries.
* * *
## FAQ
### How to run inference over a very long (30k+ tokens) document in batches?
See `summarize.py` in [the code for my hf space Document Summarization](https://huggingface.co/spaces/pszemraj/document-summarization/blob/main/summarize.py) :)
You can also use the same code to split a document into batches of 4096, etc., and run over those with the model. This is useful in situations where CUDA memory is limited.
### How to fine-tune further?
See [train with a script](https://huggingface.co/docs/transformers/run_scripts) and [the summarization scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization).
This model was originally tuned on Google Colab with a heavily modified variant of the [longformer training notebook](https://github.com/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb), key enabler being deepspeed. You can try this as an alternate route to fine-tuning the model without using the command line.
### Are there simpler ways to run this?
For this reason, I created a Python package utility. It's called [textsum](https://github.com/pszemraj/textsum), and you can use it to load models and summarize things in a few lines of code.
```sh
pip install textsum
```
Use `textsum` in python with this model:
```python
from textsum.summarize import Summarizer
summarizer = Summarizer(
model_name_or_path="pszemraj/long-t5-tglobal-base-16384-book-summary"
)
long_string = "This is a long string of text that will be summarized."
out_str = summarizer.summarize_string(long_string)
print(f"summary: {out_str}")
```
This package provides easy-to-use interfaces for applying summarization models to text documents of arbitrary length. Currently implemented interfaces include a Python API, a CLI, and a shareable demo application.
For details, explanations, and documentation, see the README (_linked above_) or the [wiki](https://github.com/pszemraj/textsum/wiki).
* * *
## Training procedure
### Updates:
- July 22, 2022: updated to a fairly converged checkpoint
- July 3, 2022: Added a new version with several epochs of additional general training that is more performant.
### Training hyperparameters
_NOTE: early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of **1024 characters**. This was subsequently caught and adjusted to **1024 tokens** and then trained further for 10+ epochs._
The following hyperparameters were used during the **most recent** training round\*:
- learning_rate: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- 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
\* Prior training sessions used roughly similar parameters; multiple sessions were required as this takes eons to train
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
## Citation info
If you find `pszemraj/long-t5-tglobal-base-16384-book-summary` useful in your work, please consider citing this model :)
@misc {peter_szemraj_2022,
author = { {Peter Szemraj} },
title = { long-t5-tglobal-base-16384-book-summary (Revision 4b12bce) },
year = 2022,
url = { https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary },
doi = { 10.57967/hf/0100 },
publisher = { Hugging Face }
}
|
[
"QUESTION_ANSWERING",
"SUMMARIZATION"
] |
[
"BEAR"
] |
xmanii/maux-gte-persian-v2
|
xmanii
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10000",
"loss:CosineSimilarityLoss",
"custom_code",
"dataset:xmanii/maux-gte-10k-public",
"arxiv:1908.10084",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multilingual-base",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-25T10:05:32 | 2024-12-25T10:06:01 | 3,130 | 3 |
---
base_model: Alibaba-NLP/gte-multilingual-base
datasets:
- xmanii/maux-gte-10k-public
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:CosineSimilarityLoss
widget:
- source_sentence: چگونه ساختار خانواده بر توسعه اجتماعی تأثیر میگذارد؟
sentences:
- انتخابهای سبک زندگی مانند سیگار کشیدن، رژیم غذایی نامناسب و عدم فعالیت بدنی میتوانند
به شرایط مختلف قلبی منجر شوند.
- ساختار خانواده میتواند به طور قابل توجهی بر توسعه اجتماعی کودک تأثیر بگذارد با
ارائه سطوح مختلف حمایت عاطفی، فرصتهای اجتماعی و الگوهای رفتاری.
- صنعت فیلم به خاطر کمبود تنوع و نمایندگی مورد انتقاد قرار گرفته است.
- source_sentence: عوامل اصلی که بر تورم تأثیر میگذارند چیستند؟
sentences:
- تورم میتواند موضوع چالشبرانگیزی برای سیاستگذاران باشد.
- اکوسیستمهای بیابانی با بارش کم و دماهای بالا مشخص میشوند، که شرایط زندگی چالشبرانگیزی
برای گیاهان و جانوران ایجاد میکند.
- امتیازهای Z در توزیعهای نرمال استاندارد استفاده میشوند، در حالی که امتیازهای
t زمانی استفاده میشوند که اندازه نمونه کوچک باشد و انحراف معیار جمعیت نامشخص
باشد.
- source_sentence: آنتیبیوتیکها چگونه در سطح سلولی کار میکنند؟
sentences:
- برخی از گیاهان گوشتخوار، مانند تله ونیس، دارای حرکات سریع برای بهداماندازی
طعمههای خود هستند.
- آنتیبیوتیکها نوعی دارو هستند که میتوانند توسط پزشکان برای درمان عفونتها تجویز
شوند.
- نرخ تورم میتواند با استفاده از شاخصهای مختلفی اندازهگیری شود، مانند شاخص قیمت
مصرفکننده (CPI) و شاخص قیمت تولیدکننده (PPI).
- source_sentence: چگونه سری کتابهای «هری پاتر» ج.ک. رولینگ بر ادبیات مدرن تأثیر
گذاشته است؟
sentences:
- جی.کی. رولینگ کتابهای دیگری تحت نام مستعار رابرت گالبریت نوشته است که رمانهای
جنایی هستند.
- رنگ آکریلیک به طور معمول در هنر مدرن استفاده میشود، در حالی که رنگ روغن قرنهاست
که در هنر کلاسیک به کار میرود.
- ماهیهای اعماق دریا دارای سازگاریهایی مانند بیولومینسانس، بدنهای مقاوم به فشار
و مکانیزمهای تغذیه خاص هستند تا در شرایط شدید sobrevivir کنند.
- source_sentence: تفاوت بین کشاورزی ارگانیک و کشاورزی سنتی چیست؟
sentences:
- در حالی که بازههای اطمینان مفید هستند، در صورت عدم رعایت فرضیات زیرین، ممکن است
به اشتباه تفسیر شوند.
- تاریخ حفظ آب به تمدنهای باستانی برمیگردد که سیستمهای آبیاری را توسعه دادند.
- بازارهای کشاورزان مکانهای محبوبی برای خرید محصولات ارگانیک به طور مستقیم از کشاورزان
محلی هستند.
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.9487949766869277
name: Pearson Cosine
- type: spearman_cosine
value: 0.947885967258665
name: Spearman Cosine
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision ade1467d6266ae07e6f74aae34d56bf3b8acf3f7 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("xmanii/maux-gte-persian-v2")
# Run inference
sentences = [
'تفاوت بین کشاورزی ارگانیک و کشاورزی سنتی چیست؟',
'بازارهای کشاورزان مکان\u200cهای محبوبی برای خرید محصولات ارگانیک به طور مستقیم از کشاورزان محلی هستند.',
'تاریخ حفظ آب به تمدن\u200cهای باستانی برمی\u200cگردد که سیستم\u200cهای آبیاری را توسعه دادند.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9488 |
| **spearman_cosine** | **0.9479** |
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## Training Details
### Training Dataset
#### maux-gte-10k-public
* Dataset: [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public) at [e20c689](https://huggingface.co/datasets/xmanii/maux-gte-10k-public/tree/e20c689e4915c4689dd54dd621ff57d5704cfaa5)
* Size: 10,000 training samples
* Columns: <code>persian_question</code>, <code>persian_answer</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | persian_question | persian_answer | score |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.01 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
| persian_question | persian_answer | score |
|:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>آیا میتوانید فرآیند برنامهریزی مسیر ربات را توضیح دهید؟</code> | <code>رباتها میتوانند برنامهنویسی شوند تا مجموعهای از وظایف را انجام دهند، از اقدامهای تکراری ساده تا فرآیندهای پیچیده تصمیمگیری.</code> | <code>0.27999999999999997</code> |
| <code>آیا انسانها میتوانند در مریخ زندگی کنند؟</code> | <code>مریخ چهارمین سیاره از خورشید است و به دلیل ظاهر سرخش اغلب به سیاره سرخ معروف است.</code> | <code>0.16</code> |
| <code>عناصر کلیدی ترکیب در هنر انتزاعی چیست؟</code> | <code>تاریخ هنر انتزاعی به اوایل قرن بیستم برمیگردد، با پیشگامانی مانند واسیلی کاندینسکی و پیت موندریان.</code> | <code>0.36</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### maux-gte-10k-public
* Dataset: [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public) at [e20c689](https://huggingface.co/datasets/xmanii/maux-gte-10k-public/tree/e20c689e4915c4689dd54dd621ff57d5704cfaa5)
* Size: 10,000 evaluation samples
* Columns: <code>persian_question</code>, <code>persian_answer</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | persian_question | persian_answer | score |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.01 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
| persian_question | persian_answer | score |
|:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>آیا میتوانید فرآیند برنامهریزی مسیر ربات را توضیح دهید؟</code> | <code>رباتها میتوانند برنامهنویسی شوند تا مجموعهای از وظایف را انجام دهند، از اقدامهای تکراری ساده تا فرآیندهای پیچیده تصمیمگیری.</code> | <code>0.27999999999999997</code> |
| <code>آیا انسانها میتوانند در مریخ زندگی کنند؟</code> | <code>مریخ چهارمین سیاره از خورشید است و به دلیل ظاهر سرخش اغلب به سیاره سرخ معروف است.</code> | <code>0.16</code> |
| <code>عناصر کلیدی ترکیب در هنر انتزاعی چیست؟</code> | <code>تاریخ هنر انتزاعی به اوایل قرن بیستم برمیگردد، با پیشگامانی مانند واسیلی کاندینسکی و پیت موندریان.</code> | <code>0.36</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | spearman_cosine |
|:----------:|:--------:|:-------------:|:---------------:|:---------------:|
| 0.1597 | 50 | 0.0663 | - | - |
| 0.3195 | 100 | 0.0409 | 0.0298 | 0.7983 |
| 0.4792 | 150 | 0.0342 | - | - |
| 0.6390 | 200 | 0.0294 | 0.0230 | 0.8464 |
| 0.7987 | 250 | 0.0296 | - | - |
| 0.9585 | 300 | 0.0298 | 0.0220 | 0.8610 |
| 1.1182 | 350 | 0.0249 | - | - |
| 1.2780 | 400 | 0.0237 | 0.0230 | 0.8745 |
| 1.4377 | 450 | 0.0241 | - | - |
| 1.5974 | 500 | 0.0218 | 0.0166 | 0.8900 |
| 1.7572 | 550 | 0.0227 | - | - |
| 1.9169 | 600 | 0.0231 | 0.0148 | 0.9045 |
| 2.0767 | 650 | 0.0196 | - | - |
| 2.2364 | 700 | 0.0173 | 0.0131 | 0.9179 |
| 2.3962 | 750 | 0.0172 | - | - |
| 2.5559 | 800 | 0.0172 | 0.0119 | 0.9231 |
| 2.7157 | 850 | 0.0167 | - | - |
| 2.8754 | 900 | 0.0172 | 0.0120 | 0.9291 |
| 3.0351 | 950 | 0.0175 | - | - |
| 3.1949 | 1000 | 0.013 | 0.0100 | 0.9362 |
| 3.3546 | 1050 | 0.0128 | - | - |
| 3.5144 | 1100 | 0.0129 | 0.0101 | 0.9390 |
| 3.6741 | 1150 | 0.0134 | - | - |
| 3.8339 | 1200 | 0.0137 | 0.0095 | 0.9430 |
| 3.9936 | 1250 | 0.0133 | - | - |
| 4.1534 | 1300 | 0.0109 | 0.0096 | 0.9449 |
| 4.3131 | 1350 | 0.0114 | - | - |
| **4.4728** | **1400** | **0.0111** | **0.0083** | **0.9479** |
| 4.6326 | 1450 | 0.0107 | - | - |
| 4.7923 | 1500 | 0.0122 | 0.0085 | 0.9479 |
| 4.9521 | 1550 | 0.0112 | - | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.8
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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|
[
"TEXT_CLASSIFICATION",
"SEMANTIC_SIMILARITY"
] |
[
"CPI"
] |
MaartenGr/BERTopic_Wikipedia
|
MaartenGr
|
text-classification
|
[
"bertopic",
"text-classification",
"region:us"
] | 2023-05-30T08:03:05 | 2023-05-31T17:58:03 | 3,095 | 43 |
---
library_name: bertopic
pipeline_tag: text-classification
tags:
- bertopic
---
# Wikipedia
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
* Trained on ~1_000_000 Wikipedia pages (first paragraph of each page).
* Data was retrieved from: https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
pip install -U safetensors
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia")
topic_model.get_topic_info()
```
## Topics 2D
The top 50 topics visualized and reduced to 2-dimensional space using cuML's UMAP:
To generate this image, you can follow along with this tutorial: [](https://colab.research.google.com/drive/1W7aEdDPxC29jP99GGZphUlqjMFFVKtBC?usp=sharing)
## Topic overview
* Number of topics: 2377
* Number of training documents: 1000001
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | cast - films - film - movie - 2020 | 30 | -1_cast_films_film_movie |
| 0 | goalscorer - scored - goals - goal - goalkeeper | 633881 | 0_goalscorer_scored_goals_goal |
| 1 | khan - actor - raj - shah - crore | 18441 | 1_khan_actor_raj_shah |
| 2 | married - divorced - couple - remarried - engaged | 8518 | 2_married_divorced_couple_remarried |
| 3 | cast - actress - starred - actor - actors | 7521 | 3_cast_actress_starred_actor |
| 4 | wrestle - reigns - wrestled - rumble - feud | 6765 | 4_wrestle_reigns_wrestled_rumble |
| 5 | cuisine - cuisines - foods - culinary - meal | 5785 | 5_cuisine_cuisines_foods_culinary |
| 6 | rebounds - harden - assists - scoring - wade | 5001 | 6_rebounds_harden_assists_scoring |
| 7 | touchdowns - interceptions - quarterback - touchdown - fumble | 4238 | 7_touchdowns_interceptions_quarterback_touchdown |
| 8 | goddesses - goddess - mythology - deities - gods | 3597 | 8_goddesses_goddess_mythology_deities |
| 9 | reelection - election - republican - elections - electoral | 3354 | 9_reelection_election_republican_elections |
| 10 | middleweight - fights - punches - welterweight - heavyweight | 3133 | 10_middleweight_fights_punches_welterweight |
| 11 | hitter - hitters - inning - outfielder - batting | 2951 | 11_hitter_hitters_inning_outfielder |
| 12 | yoga - sutra - sutras - meditation - dharma | 2768 | 12_yoga_sutra_sutras_meditation |
| 13 | missile - missiles - aircraft - squadrons - fighter | 2686 | 13_missile_missiles_aircraft_squadrons |
| 14 | chassis - vehicle - wheelbase - gearbox - sedan | 2653 | 14_chassis_vehicle_wheelbase_gearbox |
| 15 | grace - rob - nick - anna - house | 2421 | 15_grace_rob_nick_anna |
| 16 | chlorine - chloride - hydrochloric - hydroxide - corrosion | 2362 | 16_chlorine_chloride_hydrochloric_hydroxide |
| 17 | planets - galaxies - planetary - astronomers - dwarf | 2354 | 17_planets_galaxies_planetary_astronomers |
| 18 | matrices - matrix - transpose - eigenvector - multiplication | 2234 | 18_matrices_matrix_transpose_eigenvector |
| 19 | rifle - rifles - firearm - firearms - ammunition | 2191 | 19_rifle_rifles_firearm_firearms |
| 20 | campuses - colleges - campus - undergraduates - college | 2172 | 20_campuses_colleges_campus_undergraduates |
| 21 | renewed - seasons - season - airing - 2023 | 2020 | 21_renewed_seasons_season_airing |
| 22 | climates - climate - weather - temperatures - temperature | 1992 | 22_climates_climate_weather_temperatures |
| 23 | benzodiazepines - benzodiazepine - antidepressants - antidepressant - diazepam | 1900 | 23_benzodiazepines_benzodiazepine_antidepressants_antidepressant |
| 24 | consoles - console - gaming - platform - gamepad | 1765 | 24_consoles_console_gaming_platform |
| 25 | vowel - vowels - consonants - consonant - diacritics | 1716 | 25_vowel_vowels_consonants_consonant |
| 26 | heir - throne - nobility - eldest - 1536 | 1660 | 26_heir_throne_nobility_eldest |
| 27 | 737 - airline - airliner - airlines - airliners | 1592 | 27_737_airline_airliner_airlines |
| 28 | thermodynamic - thermodynamics - entropy - equilibrium - temperature | 1552 | 28_thermodynamic_thermodynamics_entropy_equilibrium |
| 29 | venom - marvel - spider - doom - carnage | 1550 | 29_venom_marvel_spider_doom |
| 30 | tales - folktales - tale - fairy - stories | 1541 | 30_tales_folktales_tale_fairy |
| 31 | caesar - augustus - roman - consul - consuls | 1540 | 31_caesar_augustus_roman_consul |
| 32 | gospels - testament - disciples - apostle - apostles | 1496 | 32_gospels_testament_disciples_apostle |
| 33 | banks - banking - bank - mortgages - finance | 1368 | 33_banks_banking_bank_mortgages |
| 34 | variance - variances - distribution - distributions - statistics | 1360 | 34_variance_variances_distribution_distributions |
| 35 | prix - motorsport - raced - racing - qualifying | 1358 | 35_prix_motorsport_raced_racing |
| 36 | filmed - filming - footage - photography - cinematographer | 1331 | 36_filmed_filming_footage_photography |
| 37 | reactors - reactor - nuclear - fission - fissions | 1324 | 37_reactors_reactor_nuclear_fission |
| 38 | mixtape - rapper - thug - mixtapes - rap | 1307 | 38_mixtape_rapper_thug_mixtapes |
| 39 | khan - sheikh - maharaja - minister - appointed | 1271 | 39_khan_sheikh_maharaja_minister |
| 40 | symphonies - concertos - symphonic - composers - concerto | 1255 | 40_symphonies_concertos_symphonic_composers |
| 41 | lightsaber - lightsabers - prequels - prequel - han | 1222 | 41_lightsaber_lightsabers_prequels_prequel |
| 42 | ants - insects - arachnids - arthropods - spiders | 1215 | 42_ants_insects_arachnids_arthropods |
| 43 | psychiatric - psychosis - disorders - schizophrenia - disorder | 1198 | 43_psychiatric_psychosis_disorders_schizophrenia |
| 44 | unionists - unionist - nationalists - loyalist - protestant | 1130 | 44_unionists_unionist_nationalists_loyalist |
| 45 | renewable - renewables - hydroelectricity - hydroelectric - energy | 1077 | 45_renewable_renewables_hydroelectricity_hydroelectric |
| 46 | eruptions - volcano - volcanoes - eruption - volcanic | 1076 | 46_eruptions_volcano_volcanoes_eruption |
| 47 | 9million - 6million - 5million - 8million - 2million | 1048 | 47_9million_6million_5million_8million |
| 48 | albums - songs - rapper - album - hyun | 1046 | 48_albums_songs_rapper_album |
| 49 | nazi - ss - 1934 - 1938 - 1930 | 1021 | 49_nazi_ss_1934_1938 |
| 50 | broadcasters - broadcasting - broadcasts - televised - broadcast | 1018 | 50_broadcasters_broadcasting_broadcasts_televised |
| 51 | rpg - gaming - games - gamer - fantasy | 997 | 51_rpg_gaming_games_gamer |
| 52 | vogue - magazine - glamour - magazines - playboy | 927 | 52_vogue_magazine_glamour_magazines |
| 53 | comedian - primetime - night - comedians - podcast | 920 | 53_comedian_primetime_night_comedians |
| 54 | collegiate - colleges - conferences - conference - intercollegiate | 908 | 54_collegiate_colleges_conferences_conference |
| 55 | candidacy - candidate - candidates - presidential - presidency | 901 | 55_candidacy_candidate_candidates_presidential |
| 56 | bond - royale - spectre - pierce - spy | 901 | 56_bond_royale_spectre_pierce |
| 57 | band - frontman - bassist - vocalist - toured | 894 | 57_band_frontman_bassist_vocalist |
| 58 | joker - superman - superhero - comics - comic | 891 | 58_joker_superman_superhero_comics |
| 59 | airport - airports - airlines - airline - terminals | 878 | 59_airport_airports_airlines_airline |
| 60 | communists - yuan - yang - communist - politburo | 875 | 60_communists_yuan_yang_communist |
| 61 | titanic - lifeboat - lifeboats - sank - ships | 839 | 61_titanic_lifeboat_lifeboats_sank |
| 62 | dynasty - emperor - dynasties - yang - yuan | 838 | 62_dynasty_emperor_dynasties_yang |
| 63 | breeds - terrier - terriers - shepherd - dachshunds | 833 | 63_breeds_terrier_terriers_shepherd |
| 64 | rating - rated - cinematography - film - screenplay | 824 | 64_rating_rated_cinematography_film |
| 65 | protestant - catholic - churches - evangelicals - denominational | 818 | 65_protestant_catholic_churches_evangelicals |
| 66 | interstates - interstate - highways - freeway - turnpike | 802 | 66_interstates_interstate_highways_freeway |
| 67 | nationalists - secession - separatism - republics - nationalism | 780 | 67_nationalists_secession_separatism_republics |
| 68 | yoon - hyun - jung - jae - hyung | 771 | 68_yoon_hyun_jung_jae |
| 69 | confederation - 1867 - 1814 - 1871 - emperor | 770 | 69_confederation_1867_1814_1871 |
| 70 | shah - khan - dynasty - dynasties - sultanate | 764 | 70_shah_khan_dynasty_dynasties |
| 71 | airlines - airline - alliance - airways - flights | 763 | 71_airlines_airline_alliance_airways |
| 72 | flash - storage - memory - gigabyte - devices | 763 | 72_flash_storage_memory_gigabyte |
| 73 | constituencies - constituency - election - elections - candidates | 724 | 73_constituencies_constituency_election_elections |
| 74 | constituencies - constituency - elections - election - candidates | 701 | 74_constituencies_constituency_elections_election |
| 75 | boxer - heavyweight - middleweight - heavyweights - foreman | 695 | 75_boxer_heavyweight_middleweight_heavyweights |
| 76 | programming - python - compilers - compiler - languages | 686 | 76_programming_python_compilers_compiler |
| 77 | mafia - gangster - indictment - gangsters - indicted | 684 | 77_mafia_gangster_indictment_gangsters |
| 78 | caliph - ibn - caliphs - caliphate - caliphates | 676 | 78_caliph_ibn_caliphs_caliphate |
| 79 | manga - shonen - shōnen - anime - chapters | 676 | 79_manga_shonen_shōnen_anime |
| 80 | warships - frigates - warship - frigate - battleships | 651 | 80_warships_frigates_warship_frigate |
| 81 | heterosexuality - bisexuality - homosexual - heterosexual - heterosexuals | 647 | 81_heterosexuality_bisexuality_homosexual_heterosexual |
| 82 | released - 2021 - releases - 20th - 2022 | 642 | 82_released_2021_releases_20th |
| 83 | marvel - spider - marvels - avenger - superhero | 629 | 83_marvel_spider_marvels_avenger |
| 84 | tennis - quarterfinal - semifinals - tournaments - quarterfinals | 622 | 84_tennis_quarterfinal_semifinals_tournaments |
| 85 | heir - throne - kingdom - kingdoms - king | 615 | 85_heir_throne_kingdom_kingdoms |
| 86 | poker - betting - gambling - casino - bets | 598 | 86_poker_betting_gambling_casino |
| 87 | soundtrack - soundtracks - musical - symphony - instrumental | 596 | 87_soundtrack_soundtracks_musical_symphony |
| 88 | consent - rape - minors - statutory - age | 592 | 88_consent_rape_minors_statutory |
| 89 | 1860 - 1852 - 1832 - reelection - confederate | 591 | 89_1860_1852_1832_reelection |
| 90 | trek - showrunner - starship - showrunners - starships | 587 | 90_trek_showrunner_starship_showrunners |
| 91 | wickets - batsman - cricketer - wicket - cricket | 585 | 91_wickets_batsman_cricketer_wicket |
| 92 | heir - duchess - eldest - prince - royal | 578 | 92_heir_duchess_eldest_prince |
| 93 | goaltender - puck - goalie - hockey - ahl | 576 | 93_goaltender_puck_goalie_hockey |
| 94 | keyboards - keyboard - keypad - diacritics - alphabet | 560 | 94_keyboards_keyboard_keypad_diacritics |
| 95 | cartel - cartels - narco - trafficking - los | 558 | 95_cartel_cartels_narco_trafficking |
| 96 | yang - yin - rituals - religions - shamanism | 540 | 96_yang_yin_rituals_religions |
| 97 | astrology - astrological - zodiac - zodiacal - astrologers | 535 | 97_astrology_astrological_zodiac_zodiacal |
| 98 | goddesses - stanzas - mythology - stanza - valkyries | 528 | 98_goddesses_stanzas_mythology_stanza |
| 99 | rating - critics - reviews - review - rotten | 528 | 99_rating_critics_reviews_review |
| 100 | dynasties - subcontinent - dharma - dynasty - ancient | 524 | 100_dynasties_subcontinent_dharma_dynasty |
| 101 | dinosaur - fossil - dinosaurs - fossils - tyrannosaurids | 523 | 101_dinosaur_fossil_dinosaurs_fossils |
| 102 | folkloric - folk - genres - traditional - folklore | 514 | 102_folkloric_folk_genres_traditional |
| 103 | climber - climbers - mountaineering - climb - climbed | 511 | 103_climber_climbers_mountaineering_climb |
| 104 | contestant - contestants - finalists - idol - finalist | 511 | 104_contestant_contestants_finalists_idol |
| 105 | proteins - amino - protein - peptide - enzymes | 508 | 105_proteins_amino_protein_peptide |
| 106 | battleships - battleship - naval - torpedoes - warships | 506 | 106_battleships_battleship_naval_torpedoes |
| 107 | anthrax - slayer - thrash - bands - band | 505 | 107_anthrax_slayer_thrash_bands |
| 108 | swift - songwriting - songwriter - songwriters - songs | 504 | 108_swift_songwriting_songwriter_songwriters |
| 109 | airplane - airlines - flight - aircraft - aviation | 498 | 109_airplane_airlines_flight_aircraft |
| 110 | paintings - painters - painter - cubism - cubist | 496 | 110_paintings_painters_painter_cubism |
| 111 | flags - flag - flagpole - commonwealth - emblem | 493 | 111_flags_flag_flagpole_commonwealth |
| 112 | cult - cruise - organizations - founder - organization | 481 | 112_cult_cruise_organizations_founder |
| 113 | calendar - calendars - dates - calendrical - equinoxes | 481 | 113_calendar_calendars_dates_calendrical |
| 114 | counties - county - population - populous - cities | 474 | 114_counties_county_population_populous |
| 115 | degree - bachelor - diplomas - doctorates - diploma | 474 | 115_degree_bachelor_diplomas_doctorates |
| 116 | spying - espionage - surveillance - spied - disclosures | 472 | 116_spying_espionage_surveillance_spied |
| 117 | schooling - education - educational - kindergarten - curriculum | 471 | 117_schooling_education_educational_kindergarten |
| 118 | railway - railways - autobahns - autobahn - trains | 470 | 118_railway_railways_autobahns_autobahn |
| 119 | laden - jihadi - mujahideen - jihadis - al | 451 | 119_laden_jihadi_mujahideen_jihadis |
| 120 | theatre - venue - venues - theater - orchestras | 450 | 120_theatre_venue_venues_theater |
| 121 | earthquake - earthquakes - tsunami - tsunamis - quakes | 450 | 121_earthquake_earthquakes_tsunami_tsunamis |
| 122 | superman - superhero - comics - sequels - joker | 446 | 122_superman_superhero_comics_sequels |
| 123 | dodge - automakers - truck - automotive - trucks | 431 | 123_dodge_automakers_truck_automotive |
| 124 | election - elections - candidates - candidate - voters | 431 | 124_election_elections_candidates_candidate |
| 125 | broadway - musicals - musical - theatre - theater | 422 | 125_broadway_musicals_musical_theatre |
| 126 | whales - whale - whaling - cetaceans - cetacean | 422 | 126_whales_whale_whaling_cetaceans |
| 127 | potter - potters - wizard - wizardry - wizarding | 419 | 127_potter_potters_wizard_wizardry |
| 128 | starship - spaceflight - spacecraft - shuttle - astronauts | 417 | 128_starship_spaceflight_spacecraft_shuttle |
| 129 | pol - communists - rouge - soviet - communist | 412 | 129_pol_communists_rouge_soviet |
| 130 | tombstone - corral - stagecoach - outlaw - outlaws | 403 | 130_tombstone_corral_stagecoach_outlaw |
| 131 | tennis - competed - doubles - slams - finalist | 401 | 131_tennis_competed_doubles_slams |
| 132 | lunar - moon - astronaut - astronauts - spacecraft | 399 | 132_lunar_moon_astronaut_astronauts |
| 133 | hamlet - playwright - actor - cast - acting | 391 | 133_hamlet_playwright_actor_cast |
| 134 | angels - archangels - archangel - angelic - angel | 384 | 134_angels_archangels_archangel_angelic |
| 135 | labia - labial - lips - clitoris - vulval | 378 | 135_labia_labial_lips_clitoris |
| 136 | jerseys - uniforms - 49ers - colors - helmets | 376 | 136_jerseys_uniforms_49ers_colors |
| 137 | linguistics - languages - linguist - linguistic - language | 376 | 137_linguistics_languages_linguist_linguistic |
| 138 | foxes - coyotes - coyote - mammals - fox | 376 | 138_foxes_coyotes_coyote_mammals |
| 139 | tiger - tigers - species - lion - wildlife | 374 | 139_tiger_tigers_species_lion |
| 140 | panzer - soviets - infantry - 1944 - artillery | 371 | 140_panzer_soviets_infantry_1944 |
| 141 | hamlet - playwright - playwrights - tempest - soliloquy | 370 | 141_hamlet_playwright_playwrights_tempest |
| 142 | potter - sorcerer - wizard - screenwriter - cast | 366 | 142_potter_sorcerer_wizard_screenwriter |
| 143 | rating - critics - reviews - review - rotten | 366 | 143_rating_critics_reviews_review |
| 144 | pepper - concert - albums - songs - album | 362 | 144_pepper_concert_albums_songs |
| 145 | pope - papal - papacy - pontifical - popes | 358 | 145_pope_papal_papacy_pontifical |
| 146 | unions - union - unionism - unionized - unionization | 356 | 146_unions_union_unionism_unionized |
| 147 | cardiovascular - cardiomyopathy - cardiac - hypertension - myocardial | 355 | 147_cardiovascular_cardiomyopathy_cardiac_hypertension |
| 148 | helicopters - missiles - helicopter - squadrons - insurgents | 354 | 148_helicopters_missiles_helicopter_squadrons |
| 149 | shah - khan - dynasty - deposed - dictator | 352 | 149_shah_khan_dynasty_deposed |
| 150 | waters - concert - tour - pink - wall | 351 | 150_waters_concert_tour_pink |
| 151 | voyages - voyage - 1493 - explorers - expeditions | 345 | 151_voyages_voyage_1493_explorers |
| 152 | spartan - rebelled - battle - besieged - victories | 343 | 152_spartan_rebelled_battle_besieged |
| 153 | kanji - hiragana - pinyin - kun - katakana | 343 | 153_kanji_hiragana_pinyin_kun |
| 154 | rings - ring - shire - hobbit - elves | 341 | 154_rings_ring_shire_hobbit |
| 155 | confederates - confederate - confederacy - 1863 - 1861 | 339 | 155_confederates_confederate_confederacy_1863 |
| 156 | mafia - gangs - cartels - cartel - syndicate | 336 | 156_mafia_gangs_cartels_cartel |
| 157 | apartheid - decolonisation - 1979 - smith - nationalists | 332 | 157_apartheid_decolonisation_1979_smith |
| 158 | fascism - fascist - italiana - fascists - nationalist | 330 | 158_fascism_fascist_italiana_fascists |
| 159 | windows - vista - os - pc - versions | 329 | 159_windows_vista_os_pc |
| 160 | chrome - browser - browsers - chromium - safari | 328 | 160_chrome_browser_browsers_chromium |
| 161 | literacy - population - castes - literate - census | 323 | 161_literacy_population_castes_literate |
| 162 | pip - miss - orphan - carol - protagonist | 321 | 162_pip_miss_orphan_carol |
| 163 | ruby - assassination - assassinated - assassinate - warren | 319 | 163_ruby_assassination_assassinated_assassinate |
| 164 | soviets - revolutionaries - soviet - 1917 - socialists | 316 | 164_soviets_revolutionaries_soviet_1917 |
| 165 | twitter - tweets - tweet - microblogging - retweet | 315 | 165_twitter_tweets_tweet_microblogging |
| 166 | sai - shakti - marries - revenge - pooja | 315 | 166_sai_shakti_marries_revenge |
| 167 | quarks - quark - particles - protons - bosons | 314 | 167_quarks_quark_particles_protons |
| 168 | polypropylene - polymers - polymer - polyethylene - polymerization | 314 | 168_polypropylene_polymers_polymer_polyethylene |
| 169 | bourbon - 1685 - 1643 - heir - 1598 | 313 | 169_bourbon_1685_1643_heir |
| 170 | cartoons - goofy - cartoon - bunny - hare | 313 | 170_cartoons_goofy_cartoon_bunny |
| 171 | mountains - mountain - plains - topography - southwestern | 312 | 171_mountains_mountain_plains_topography |
| 172 | epic - developers - studio - studios - blizzard | 311 | 172_epic_developers_studio_studios |
| 173 | sergeant - lieutenants - sergeants - lieutenant - ranks | 309 | 173_sergeant_lieutenants_sergeants_lieutenant |
| 174 | yoon - jong - hyun - jae - jung | 307 | 174_yoon_jong_hyun_jae |
| 175 | villa - rebelled - barrios - rebellion - generals | 304 | 175_villa_rebelled_barrios_rebellion |
| 176 | animator - animators - animation - animating - animated | 303 | 176_animator_animators_animation_animating |
| 177 | dementia - dementias - neurodegenerative - parkinsonism - impairment | 303 | 177_dementia_dementias_neurodegenerative_parkinsonism |
| 178 | doctor - doctors - dr - actor - tenth | 302 | 178_doctor_doctors_dr_actor |
| 179 | counties - midlands - county - boroughs - district | 301 | 179_counties_midlands_county_boroughs |
| 180 | philosopher - philosophy - platonic - philosophers - stoicism | 300 | 180_philosopher_philosophy_platonic_philosophers |
| 181 | neural - neuron - neurons - convolutions - backpropagation | 299 | 181_neural_neuron_neurons_convolutions |
| 182 | vaccines - vaccine - vaccination - vaccinated - vaccinate | 298 | 182_vaccines_vaccine_vaccination_vaccinated |
| 183 | kickboxing - sparring - boxing - jitsu - karate | 293 | 183_kickboxing_sparring_boxing_jitsu |
| 184 | payments - card - payment - cardholder - cardholders | 287 | 184_payments_card_payment_cardholder |
| 185 | cathedrals - cathedral - arches - arched - vaults | 282 | 185_cathedrals_cathedral_arches_arched |
| 186 | visual - studios - animation - filming - actors | 282 | 186_visual_studios_animation_filming |
| 187 | psychoanalytical - psychoanalysts - psychoanalysis - psychoanalytic - psychoanalyst | 281 | 187_psychoanalytical_psychoanalysts_psychoanalysis_psychoanalytic |
| 188 | novels - novelists - novelist - sensibility - 1818 | 280 | 188_novels_novelists_novelist_sensibility |
| 189 | medieval - grail - knights - tales - knight | 278 | 189_medieval_grail_knights_tales |
| 190 | uniforms - jerseys - uniform - logos - blazers | 277 | 190_uniforms_jerseys_uniform_logos |
| 191 | cookies - cookie - http - session - browsers | 277 | 191_cookies_cookie_http_session |
| 192 | polygamous - polygamy - polyamory - polygamists - monogamous | 277 | 192_polygamous_polygamy_polyamory_polygamists |
| 193 | languages - speak - dialects - language - linguists | 275 | 193_languages_speak_dialects_language |
| 194 | 1830s - tribe - tribes - confederate - natives | 274 | 194_1830s_tribe_tribes_confederate |
| 195 | equilibria - equilibrium - strategic - strategies - strategy | 269 | 195_equilibria_equilibrium_strategic_strategies |
| 196 | firearm - firearms - handgun - handguns - guns | 268 | 196_firearm_firearms_handgun_handguns |
| 197 | kong - monster - monsters - franchise - sequel | 266 | 197_kong_monster_monsters_franchise |
| 198 | murders - murdered - murderers - convicted - defendants | 264 | 198_murders_murdered_murderers_convicted |
| 199 | homer - sitcom - cartoon - sitcoms - showrunner | 263 | 199_homer_sitcom_cartoon_sitcoms |
| 200 | alleging - accused - alleged - defamation - allegations | 262 | 200_alleging_accused_alleged_defamation |
| 201 | delegates - presidential - nominee - primaries - presidency | 261 | 201_delegates_presidential_nominee_primaries |
| 202 | probation - misdemeanor - arrested - arrest - jail | 258 | 202_probation_misdemeanor_arrested_arrest |
| 203 | fender - guitars - guitar - acoustic - amplifiers | 258 | 203_fender_guitars_guitar_acoustic |
| 204 | trafficking - prostitution - prostitutes - prostitute - brothels | 257 | 204_trafficking_prostitution_prostitutes_prostitute |
| 205 | isotopes - isotope - elements - neutron - neutrons | 257 | 205_isotopes_isotope_elements_neutron |
| 206 | meth - methamphetamine - cocaine - drug - methylamine | 256 | 206_meth_methamphetamine_cocaine_drug |
| 207 | channel - channels - tv - simulcast - streaming | 256 | 207_channel_channels_tv_simulcast |
| 208 | frescoes - chapel - pope - basilica - sculptor | 252 | 208_frescoes_chapel_pope_basilica |
| 209 | armistice - 1944 - française - 1945 - 1940 | 251 | 209_armistice_1944_française_1945 |
| 210 | novelist - novels - novel - novelists - literature | 251 | 210_novelist_novels_novel_novelists |
| 211 | 1936 - fascism - fascist - nationalists - nationalist | 251 | 211_1936_fascism_fascist_nationalists |
| 212 | apple - 6s - smartphones - smartphone - phones | 248 | 212_apple_6s_smartphones_smartphone |
| 213 | showrunner - episode - showrunners - episodes - primetime | 248 | 213_showrunner_episode_showrunners_episodes |
| 214 | gemstones - gemstone - sapphires - sapphire - diamond | 247 | 214_gemstones_gemstone_sapphires_sapphire |
| 215 | emperors - emperor - roman - empire - augustus | 246 | 215_emperors_emperor_roman_empire |
| 216 | cavalry - legions - armies - battle - battles | 246 | 216_cavalry_legions_armies_battle |
| 217 | 1649 - royalist - 1685 - royalists - 1640 | 246 | 217_1649_royalist_1685_royalists |
| 218 | orgasms - orgasm - clitoris - clitoral - stimulation | 245 | 218_orgasms_orgasm_clitoris_clitoral |
| 219 | glaucoma - retinopathy - blindness - retinal - cataract | 242 | 219_glaucoma_retinopathy_blindness_retinal |
| 220 | novels - novelist - novel - literature - literary | 241 | 220_novels_novelist_novel_literature |
| 221 | artillery - trenches - fortifications - bombardment - bombardments | 240 | 221_artillery_trenches_fortifications_bombardment |
| 222 | beach - drums - albums - songs - drumming | 239 | 222_beach_drums_albums_songs |
| 223 | nouveau - paintings - designers - façades - facades | 237 | 223_nouveau_paintings_designers_façades |
| 224 | maya - civilizations - archaeological - archeological - civilization | 236 | 224_maya_civilizations_archaeological_archeological |
| 225 | taekwondo - tae - karate - jitsu - martial | 235 | 225_taekwondo_tae_karate_jitsu |
| 226 | rocky - creed - sequel - boxer - film | 233 | 226_rocky_creed_sequel_boxer |
| 227 | assassins - creed - assassin - brotherhood - gameplay | 231 | 227_assassins_creed_assassin_brotherhood |
| 228 | bp - petroleum - refinery - offshore - companies | 231 | 228_bp_petroleum_refinery_offshore |
| 229 | minorities - ethnicity - ethnic - ethnically - census | 231 | 229_minorities_ethnicity_ethnic_ethnically |
| 230 | baptism - baptisms - baptismal - baptized - baptised | 230 | 230_baptism_baptisms_baptismal_baptized |
| 231 | bighorn - 1876 - bull - elk - tribes | 229 | 231_bighorn_1876_bull_elk |
| 232 | psychotic - psychiatric - schizophrenia - psychiatry - sane | 227 | 232_psychotic_psychiatric_schizophrenia_psychiatry |
| 233 | mexicana - latin - salsa - vida - una | 227 | 233_mexicana_latin_salsa_vida |
| 234 | abortion - abortions - roe - unconstitutional - overturned | 225 | 234_abortion_abortions_roe_unconstitutional |
| 235 | toy - toys - sequels - sequel - animator | 225 | 235_toy_toys_sequels_sequel |
| 236 | euthanasia - suicide - legalised - suicides - suicidal | 225 | 236_euthanasia_suicide_legalised_suicides |
| 237 | chan - kung - chang - kong - karate | 221 | 237_chan_kung_chang_kong |
| 238 | protesting - activism - protests - protest - rallies | 220 | 238_protesting_activism_protests_protest |
| 239 | tribes - tribe - natives - upstate - tribal | 219 | 239_tribes_tribe_natives_upstate |
| 240 | toured - concert - concerts - drums - vocals | 219 | 240_toured_concert_concerts_drums |
| 241 | nam - communists - insurgency - guerrilla - troops | 219 | 241_nam_communists_insurgency_guerrilla |
| 242 | election - conservatives - liberal - liberals - partisanship | 219 | 242_election_conservatives_liberal_liberals |
| 243 | chess - grandmaster - grandmasters - blitz - tournament | 219 | 243_chess_grandmaster_grandmasters_blitz |
| 244 | radio - fm - stations - station - simulcasts | 218 | 244_radio_fm_stations_station |
| 245 | awards - nominated - nominations - screenplay - cinematography | 218 | 245_awards_nominated_nominations_screenplay |
| 246 | bombing - bomber - bombers - bombed - bombs | 218 | 246_bombing_bomber_bombers_bombed |
| 247 | diesel - fuels - engines - combustion - petrol | 218 | 247_diesel_fuels_engines_combustion |
| 248 | species - wildlife - fauna - birds - endangered | 217 | 248_species_wildlife_fauna_birds |
| 249 | extraterrestrial - sightings - aliens - sighting - hoaxes | 216 | 249_extraterrestrial_sightings_aliens_sighting |
| 250 | tick - ticks - burgdorferi - pathogens - infected | 215 | 250_tick_ticks_burgdorferi_pathogens |
| 251 | congregational - denominational - congregations - evangelicalism - denomination | 215 | 251_congregational_denominational_congregations_evangelicalism |
| 252 | lymphatic - lymph - gallbladder - organs - capillaries | 215 | 252_lymphatic_lymph_gallbladder_organs |
| 253 | chemotherapy - treatments - cancer - cancers - radiotherapy | 215 | 253_chemotherapy_treatments_cancer_cancers |
| 254 | creole - creoles - lingua - bilingual - dialects | 214 | 254_creole_creoles_lingua_bilingual |
| 255 | princess - duchess - prince - countess - royal | 211 | 255_princess_duchess_prince_countess |
| 256 | insurrection - revolt - 1821 - 1829 - uprising | 210 | 256_insurrection_revolt_1821_1829 |
| 257 | charities - charity - donations - philanthropist - fundraising | 209 | 257_charities_charity_donations_philanthropist |
| 258 | alien - predator - aliens - sequels - extraterrestrial | 209 | 258_alien_predator_aliens_sequels |
| 259 | condor - dictators - declassified - dictatorships - chile | 208 | 259_condor_dictators_declassified_dictatorships |
| 260 | inflation - inflationary - macroeconomics - macroeconomic - recessions | 207 | 260_inflation_inflationary_macroeconomics_macroeconomic |
| 261 | warlock - infinity - eternity - gems - marvel | 206 | 261_warlock_infinity_eternity_gems |
| 262 | bbc - channel - simulcast - channels - broadcasting | 205 | 262_bbc_channel_simulcast_channels |
| 263 | eu - eurozone - euro - countries - borders | 205 | 263_eu_eurozone_euro_countries |
| 264 | sonic - hedgehog - hedgehogs - tails - knuckles | 205 | 264_sonic_hedgehog_hedgehogs_tails |
| 265 | battleships - torpedoed - torpedoes - torpedo - battleship | 203 | 265_battleships_torpedoed_torpedoes_torpedo |
| 266 | hurricane - hurricanes - storms - cyclones - cyclone | 200 | 266_hurricane_hurricanes_storms_cyclones |
| 267 | concert - concerts - tour - albums - toured | 200 | 267_concert_concerts_tour_albums |
| 268 | shōgun - shogun - samurai - daimyō - daimyo | 199 | 268_shōgun_shogun_samurai_daimyō |
| 269 | electrodes - electroluminescent - electrode - phosphors - displays | 199 | 269_electrodes_electroluminescent_electrode_phosphors |
| 270 | brigades - soldiers - reinforcements - troops - casualties | 199 | 270_brigades_soldiers_reinforcements_troops |
| 271 | presidency - populist - presidential - candidate - candidates | 199 | 271_presidency_populist_presidential_candidate |
| 272 | heraldic - heraldry - gules - arms - garter | 198 | 272_heraldic_heraldry_gules_arms |
| 273 | refrigerants - refrigeration - refrigerant - refrigerator - condenser | 198 | 273_refrigerants_refrigeration_refrigerant_refrigerator |
| 274 | bee - sang - singer - songwriter - artists | 198 | 274_bee_sang_singer_songwriter |
| 275 | thrones - novels - dragons - paperback - novel | 198 | 275_thrones_novels_dragons_paperback |
| 276 | festivals - festival - celebrated - celebrations - festivities | 198 | 276_festivals_festival_celebrated_celebrations |
| 277 | branch - fires - fired - deaths - wounded | 197 | 277_branch_fires_fired_deaths |
| 278 | pasha - turkey - sultan - sultanate - nationalists | 197 | 278_pasha_turkey_sultan_sultanate |
| 279 | neanderthalensis - paleolithic - sapiens - erectus - ancestor | 196 | 279_neanderthalensis_paleolithic_sapiens_erectus |
| 280 | mujahideen - laden - militants - insurgency - jihad | 195 | 280_mujahideen_laden_militants_insurgency |
| 281 | shogun - shōgun - shogunate - samurai - daimyō | 194 | 281_shogun_shōgun_shogunate_samurai |
| 282 | hypothyroidism - hyperthyroidism - thyroid - thyroiditis - thyroidectomy | 194 | 282_hypothyroidism_hyperthyroidism_thyroid_thyroiditis |
| 283 | mythos - tales - author - authors - writer | 193 | 283_mythos_tales_author_authors |
| 284 | contest - contests - qualifying - winners - competed | 192 | 284_contest_contests_qualifying_winners |
| 285 | impeachment - prosecutor - prosecutors - trump - prosecutorial | 192 | 285_impeachment_prosecutor_prosecutors_trump |
| 286 | intelligence - personality - traits - trait - psychometric | 192 | 286_intelligence_personality_traits_trait |
| 287 | terminator - sequels - sequel - prequel - trilogy | 191 | 287_terminator_sequels_sequel_prequel |
| 288 | spacetime - relativity - relativistic - gravitation - geodesic | 191 | 288_spacetime_relativity_relativistic_gravitation |
| 289 | dictatorships - dictatorship - regimes - dictators - authoritarianism | 191 | 289_dictatorships_dictatorship_regimes_dictators |
| 290 | daft - punk - techno - toured - bands | 190 | 290_daft_punk_techno_toured |
| 291 | peppers - chili - funk - flea - band | 190 | 291_peppers_chili_funk_flea |
| 292 | dinosaurs - dinosaur - rex - prehistoric - sequels | 188 | 292_dinosaurs_dinosaur_rex_prehistoric |
| 293 | surnames - surname - naming - names - suffixes | 188 | 293_surnames_surname_naming_names |
| 294 | philosopher - 1765 - philosophers - writings - enlightenment | 187 | 294_philosopher_1765_philosophers_writings |
| 295 | novels - novelist - 1925 - novel - 1920s | 187 | 295_novels_novelist_1925_novel |
| 296 | depot - retailer - retailers - warehouses - stores | 186 | 296_depot_retailer_retailers_warehouses |
| 297 | copyright - copyrights - copyrighted - royalties - infringement | 186 | 297_copyright_copyrights_copyrighted_royalties |
| 298 | eastern - daylight - clocks - noon - clock | 184 | 298_eastern_daylight_clocks_noon |
| 299 | numerals - numeral - numbers - numerology - digits | 184 | 299_numerals_numeral_numbers_numerology |
| 300 | armament - armoured - turret - tanks - tank | 182 | 300_armament_armoured_turret_tanks |
| 301 | vaccines - vaccine - vaccination - vaccinations - vaccinated | 182 | 301_vaccines_vaccine_vaccination_vaccinations |
| 302 | cola - coca - coke - soda - bottled | 181 | 302_cola_coca_coke_soda |
| 303 | fleet - 1797 - sailed - fleets - captains | 181 | 303_fleet_1797_sailed_fleets |
| 304 | tsarina - empress - tsar - maria - princesses | 181 | 304_tsarina_empress_tsar_maria |
| 305 | metalcore - thrash - deathcore - metal - hardcore | 179 | 305_metalcore_thrash_deathcore_metal |
| 306 | medals - medal - commendation - gallantry - badge | 179 | 306_medals_medal_commendation_gallantry |
| 307 | smith - prophets - revelations - revelation - scriptures | 179 | 307_smith_prophets_revelations_revelation |
| 308 | newspaper - newspapers - gazette - news - magazine | 179 | 308_newspaper_newspapers_gazette_news |
| 309 | philosopher - philosophers - philosophy - hermeneutics - philosophical | 179 | 309_philosopher_philosophers_philosophy_hermeneutics |
| 310 | protocols - protocol - packet - packets - layers | 179 | 310_protocols_protocol_packet_packets |
| 311 | coronation - airing - episodes - bbc - aired | 178 | 311_coronation_airing_episodes_bbc |
| 312 | song - songs - singles - singer - billboard | 178 | 312_song_songs_singles_singer |
| 313 | thylacines - thylacine - fauna - mammals - carnivorous | 178 | 313_thylacines_thylacine_fauna_mammals |
| 314 | hearings - communists - subcommittee - committee - committees | 177 | 314_hearings_communists_subcommittee_committee |
| 315 | 1776 - 1781 - 1775 - 1782 - 1778 | 177 | 315_1776_1781_1775_1782 |
| 316 | comedian - circus - comedians - pythons - comedy | 177 | 316_comedian_circus_comedians_pythons |
| 317 | railways - railway - trains - rail - train | 177 | 317_railways_railway_trains_rail |
| 318 | nudity - naturism - naturists - naturist - nude | 175 | 318_nudity_naturism_naturists_naturist |
| 319 | coalition - elections - populist - election - coalitions | 175 | 319_coalition_elections_populist_election |
| 320 | jihad - coup - overthrow - militias - ba | 172 | 320_jihad_coup_overthrow_militias |
| 321 | cement - cements - concretes - concrete - mortar | 171 | 321_cement_cements_concretes_concrete |
| 322 | jeopardy - prizes - contestant - contestants - competed | 170 | 322_jeopardy_prizes_contestant_contestants |
| 323 | panzer - commanders - blitzkrieg - commanded - 1944 | 169 | 323_panzer_commanders_blitzkrieg_commanded |
| 324 | mushroom - mushrooms - sprites - sprite - super | 169 | 324_mushroom_mushrooms_sprites_sprite |
| 325 | cossacks - tsar - tsarist - soviet - republics | 169 | 325_cossacks_tsar_tsarist_soviet |
| 326 | apes - ape - sequels - gorilla - prequel | 169 | 326_apes_ape_sequels_gorilla |
| 327 | graphene - graphite - nanotubes - carbon - conductivity | 168 | 327_graphene_graphite_nanotubes_carbon |
| 328 | nicotine - tobacco - cigarettes - cigarette - smoking | 168 | 328_nicotine_tobacco_cigarettes_cigarette |
| 329 | keyboardist - toured - guitarist - vocalist - bassist | 167 | 329_keyboardist_toured_guitarist_vocalist |
| 330 | museums - museum - exhibitions - galleries - exhibits | 167 | 330_museums_museum_exhibitions_galleries |
| 331 | motors - rotors - rotor - motor - rotary | 166 | 331_motors_rotors_rotor_motor |
| 332 | tabby - cat - feline - cats - coloration | 165 | 332_tabby_cat_feline_cats |
| 333 | handmaid - novels - novel - writers - tale | 163 | 333_handmaid_novels_novel_writers |
| 334 | boulevard - celebrity - fame - celebrities - walk | 163 | 334_boulevard_celebrity_fame_celebrities |
| 335 | trilogy - remastered - gods - editions - war | 162 | 335_trilogy_remastered_gods_editions |
| 336 | genocide - peacekeeping - massacres - assassinated - killings | 162 | 336_genocide_peacekeeping_massacres_assassinated |
| 337 | leopard - leopards - armament - refit - tanks | 162 | 337_leopard_leopards_armament_refit |
| 338 | homicides - homicide - murders - crime - crimes | 162 | 338_homicides_homicide_murders_crime |
| 339 | mercury - queen - bohemian - singer - musically | 162 | 339_mercury_queen_bohemian_singer |
| 340 | tennis - tournaments - tournament - badminton - slams | 161 | 340_tennis_tournaments_tournament_badminton |
| 341 | confederate - confederacy - confederates - slavery - 1861 | 160 | 341_confederate_confederacy_confederates_slavery |
| 342 | scrum - agile - sprints - sprint - development | 159 | 342_scrum_agile_sprints_sprint |
| 343 | museums - museum - galleries - exhibitions - exhibits | 159 | 343_museums_museum_galleries_exhibitions |
| 344 | transformers - transformer - sequels - bumblebee - sequel | 158 | 344_transformers_transformer_sequels_bumblebee |
| 345 | languages - dialects - language - bilingual - dialect | 158 | 345_languages_dialects_language_bilingual |
| 346 | sponge - sponges - cartoon - cartoons - plankton | 158 | 346_sponge_sponges_cartoon_cartoons |
| 347 | telescope - telescopes - observatory - astronomy - astronomical | 157 | 347_telescope_telescopes_observatory_astronomy |
| 348 | mandarin - dialects - languages - lingua - china | 157 | 348_mandarin_dialects_languages_lingua |
| 349 | kiss - toured - concerts - tour - lip | 156 | 349_kiss_toured_concerts_tour |
| 350 | holiday - celebrates - holidays - celebrated - celebrations | 156 | 350_holiday_celebrates_holidays_celebrated |
| 351 | conquered - empires - ancient - kingdoms - dynasty | 155 | 351_conquered_empires_ancient_kingdoms |
| 352 | legionnaires - legion - regiments - guerrillas - regiment | 155 | 352_legionnaires_legion_regiments_guerrillas |
| 353 | evolution - evolutionary - creationist - naturalist - biologist | 155 | 353_evolution_evolutionary_creationist_naturalist |
| 354 | tennis - slams - quarterfinal - racquet - doubles | 155 | 354_tennis_slams_quarterfinal_racquet |
| 355 | wikipedia - encyclopedia - encyclopedias - wikis - articles | 155 | 355_wikipedia_encyclopedia_encyclopedias_wikis |
| 356 | detainees - inmates - prisoners - detention - prisons | 155 | 356_detainees_inmates_prisoners_detention |
| 357 | operatic - opera - soprano - operas - arias | 155 | 357_operatic_opera_soprano_operas |
| 358 | coalition - chancellors - chancellor - chancellorship - democrats | 154 | 358_coalition_chancellors_chancellor_chancellorship |
| 359 | pixels - encoding - compression - pixel - bitmap | 154 | 359_pixels_encoding_compression_pixel |
| 360 | augmented - oculus - vision - ar - virtual | 154 | 360_augmented_oculus_vision_ar |
| 361 | flash - comics - episodes - storylines - showrunner | 154 | 361_flash_comics_episodes_storylines |
| 362 | presidency - presidential - fascism - president - dictatorship | 153 | 362_presidency_presidential_fascism_president |
| 363 | soil - soils - fertilizers - fertilizer - nutrient | 153 | 363_soil_soils_fertilizers_fertilizer |
| 364 | novels - 1876 - 1881 - 1880 - writer | 153 | 364_novels_1876_1881_1880 |
| 365 | critics - rankings - ranking - decade - films | 152 | 365_critics_rankings_ranking_decade |
| 366 | dos - defendants - trafficking - alleged - recruited | 152 | 366_dos_defendants_trafficking_alleged |
| 367 | abused - abuse - assaults - maltreatment - abusive | 152 | 367_abused_abuse_assaults_maltreatment |
| 368 | masks - mask - pandemic - vaccine - vaccinated | 151 | 368_masks_mask_pandemic_vaccine |
| 369 | novel - scout - rye - nonfiction - narrator | 151 | 369_novel_scout_rye_nonfiction |
| 370 | tennis - doubles - competed - tournaments - tournament | 150 | 370_tennis_doubles_competed_tournaments |
| 371 | macron - presidential - candidate - candidates - pen | 149 | 371_macron_presidential_candidate_candidates |
| 372 | rose - roses - frontman - revolver - toured | 149 | 372_rose_roses_frontman_revolver |
| 373 | satyagraha - revolt - rebellion - salt - protest | 148 | 373_satyagraha_revolt_rebellion_salt |
| 374 | 1945 - allied - soviets - allies - reunification | 148 | 374_1945_allied_soviets_allies |
| 375 | princes - prince - ambition - prudence - nobles | 148 | 375_princes_prince_ambition_prudence |
| 376 | railways - railway - locomotives - trains - train | 148 | 376_railways_railway_locomotives_trains |
| 377 | murdered - murders - convicted - sentenced - suspicion | 148 | 377_murdered_murders_convicted_sentenced |
| 378 | syndrome - disorders - polycystic - diagnosed - ovarian | 148 | 378_syndrome_disorders_polycystic_diagnosed |
| 379 | dune - dunes - novels - trilogy - novel | 148 | 379_dune_dunes_novels_trilogy |
| 380 | temple - cult - peoples - disciples - teachings | 147 | 380_temple_cult_peoples_disciples |
| 381 | 1963 - assassinated - 1964 - mosque - assassination | 147 | 381_1963_assassinated_1964_mosque |
| 382 | chess - rook - grandmasters - grandmaster - tournaments | 147 | 382_chess_rook_grandmasters_grandmaster |
| 383 | lithium - batteries - battery - rechargeable - electrochemical | 146 | 383_lithium_batteries_battery_rechargeable |
| 384 | genocide - detainees - persecution - internment - holocaust | 146 | 384_genocide_detainees_persecution_internment |
| 385 | neurons - neuronal - neuron - neurotransmitters - neurotransmitter | 146 | 385_neurons_neuronal_neuron_neurotransmitters |
| 386 | poles - casualties - massacres - massacre - polish | 145 | 386_poles_casualties_massacres_massacre |
| 387 | dialects - accents - isles - dialect - pronunciation | 145 | 387_dialects_accents_isles_dialect |
| 388 | racing - speedway - raced - laps - motorsports | 145 | 388_racing_speedway_raced_laps |
| 389 | rand - nonfiction - subjectivism - philosophers - philosopher | 145 | 389_rand_nonfiction_subjectivism_philosophers |
| 390 | lee - pap - chairman - election - leaders | 145 | 390_lee_pap_chairman_election |
| 391 | kernels - kernel - processors - processes - processor | 145 | 391_kernels_kernel_processors_processes |
| 392 | nightmare - nightmares - elm - horror - supernatural | 144 | 392_nightmare_nightmares_elm_horror |
| 393 | newspaper - newspapers - tabloid - newsprint - journalism | 144 | 393_newspaper_newspapers_tabloid_newsprint |
| 394 | interrogation - interrogations - arrest - incrimination - defendant | 144 | 394_interrogation_interrogations_arrest_incrimination |
| 395 | millennials - millennial - generations - generation - generational | 144 | 395_millennials_millennial_generations_generation |
| 396 | hobbit - hobbits - shire - literature - publishers | 144 | 396_hobbit_hobbits_shire_literature |
| 397 | pollution - pollutants - polluting - pollutant - polluted | 143 | 397_pollution_pollutants_polluting_pollutant |
| 398 | sins - sin - sinfulness - theology - sinned | 143 | 398_sins_sin_sinfulness_theology |
| 399 | nursing - nurse - nurses - hospitals - compassion | 143 | 399_nursing_nurse_nurses_hospitals |
| 400 | aeronautical - aeronautics - aircraft - flew - airplanes | 143 | 400_aeronautical_aeronautics_aircraft_flew |
| 401 | congregations - congregation - churches - denominations - denomination | 142 | 401_congregations_congregation_churches_denominations |
| 402 | skyscraper - tallest - skyscrapers - towers - tower | 142 | 402_skyscraper_tallest_skyscrapers_towers |
| 403 | consulate - embassy - suspects - assassination - consul | 142 | 403_consulate_embassy_suspects_assassination |
| 404 | blu - disc - discs - codecs - digital | 142 | 404_blu_disc_discs_codecs |
| 405 | pyramid - pyramids - pyramidion - excavations - tombs | 141 | 405_pyramid_pyramids_pyramidion_excavations |
| 406 | antibiotics - antibiotic - amoxicillin - penicillin - ampicillin | 140 | 406_antibiotics_antibiotic_amoxicillin_penicillin |
| 407 | activism - protest - protests - activist - marches | 140 | 407_activism_protest_protests_activist |
| 408 | bbc - broadcasting - channel - al - simulcast | 140 | 408_bbc_broadcasting_channel_al |
| 409 | pharaoh - pharaohs - throne - heir - tombs | 139 | 409_pharaoh_pharaohs_throne_heir |
| 410 | bombing - troops - pentagon - war - troop | 139 | 410_bombing_troops_pentagon_war |
| 411 | municipality - megacity - located - niger - town | 139 | 411_municipality_megacity_located_niger |
| 412 | addresses - subnet - subnets - addressing - address | 138 | 412_addresses_subnet_subnets_addressing |
| 413 | tom - cruise - screenwriter - tall - jack | 138 | 413_tom_cruise_screenwriter_tall |
| 414 | motivation - motivations - motivational - motivate - motivates | 137 | 414_motivation_motivations_motivational_motivate |
| 415 | deforestation - reforestation - forestry - forests - forested | 137 | 415_deforestation_reforestation_forestry_forests |
| 416 | anesthesiologist - anatomy - neurosurgery - surgeon - cast | 137 | 416_anesthesiologist_anatomy_neurosurgery_surgeon |
| 417 | pharaoh - prophets - prophet - messiah - patriarch | 136 | 417_pharaoh_prophets_prophet_messiah |
| 418 | battlefield - warfare - modern - gameplay - remastered | 136 | 418_battlefield_warfare_modern_gameplay |
| 419 | ancestry - mestizo - ethnic - ethnicity - natives | 136 | 419_ancestry_mestizo_ethnic_ethnicity |
| 420 | telegram - messenger - messaging - chat - apps | 136 | 420_telegram_messenger_messaging_chat |
| 421 | penalty - penalties - fouls - foul - goaltending | 136 | 421_penalty_penalties_fouls_foul |
| 422 | miss - pageant - pageants - pageantry - finalist | 135 | 422_miss_pageant_pageants_pageantry |
| 423 | throne - rebelled - heir - king - castles | 135 | 423_throne_rebelled_heir_king |
| 424 | territory - airspace - blockade - sanctions - borders | 135 | 424_territory_airspace_blockade_sanctions |
| 425 | jazz - saxophonist - trumpeter - saxophone - musicians | 135 | 425_jazz_saxophonist_trumpeter_saxophone |
| 426 | stooge - moe - curly - comedies - comedians | 135 | 426_stooge_moe_curly_comedies |
| 427 | lichens - lichen - fungi - fungal - fungus | 135 | 427_lichens_lichen_fungi_fungal |
| 428 | rebels - overthrowing - generals - overthrow - coup | 134 | 428_rebels_overthrowing_generals_overthrow |
| 429 | races - race - racial - anthropologist - anthropologists | 134 | 429_races_race_racial_anthropologist |
| 430 | channel - channels - broadcasting - broadcasters - simulcast | 134 | 430_channel_channels_broadcasting_broadcasters |
| 431 | prosecution - accused - bordereau - acquitted - investigation | 133 | 431_prosecution_accused_bordereau_acquitted |
| 432 | missiles - soviets - missile - soviet - nuclear | 133 | 432_missiles_soviets_missile_soviet |
| 433 | 1945 - armistice - surrender - surrendered - soviets | 133 | 433_1945_armistice_surrender_surrendered |
| 434 | monastic - monastics - samadhi - monks - monastery | 133 | 434_monastic_monastics_samadhi_monks |
| 435 | colors - colours - colour - magenta - pigment | 133 | 435_colors_colours_colour_magenta |
| 436 | pipeline - pipelines - keystone - refinery - pipe | 133 | 436_pipeline_pipelines_keystone_refinery |
| 437 | institutes - institute - universities - polytechnic - polytechnics | 133 | 437_institutes_institute_universities_polytechnic |
| 438 | deepest - depths - oceanographic - oceanography - challenger | 132 | 438_deepest_depths_oceanographic_oceanography |
| 439 | postcodes - postcode - zip - postal - addresses | 132 | 439_postcodes_postcode_zip_postal |
| 440 | rockstar - grand - games - consoles - gameplay | 132 | 440_rockstar_grand_games_consoles |
| 441 | woman - wonder - goddess - feminist - goddesses | 132 | 441_woman_wonder_goddess_feminist |
| 442 | suffrage - referendum - referendums - women - enfranchised | 131 | 442_suffrage_referendum_referendums_women |
| 443 | apartheid - cape - natal - protest - activist | 131 | 443_apartheid_cape_natal_protest |
| 444 | barristers - barrister - solicitors - lawyers - solicitor | 131 | 444_barristers_barrister_solicitors_lawyers |
| 445 | scrolls - manuscripts - antiquities - archaeology - archaeological | 131 | 445_scrolls_manuscripts_antiquities_archaeology |
| 446 | slavery - revolution - slaves - revolt - colonial | 131 | 446_slavery_revolution_slaves_revolt |
| 447 | boxer - cop - knockout - fighter - fights | 130 | 447_boxer_cop_knockout_fighter |
| 448 | siblings - 1963 - assassinated - senator - youngest | 130 | 448_siblings_1963_assassinated_senator |
| 449 | ku - confederate - activists - 1868 - whites | 130 | 449_ku_confederate_activists_1868 |
| 450 | bear - bears - grizzly - predators - species | 130 | 450_bear_bears_grizzly_predators |
| 451 | junta - detained - arrest - imprisonment - sentenced | 130 | 451_junta_detained_arrest_imprisonment |
| 452 | oasis - albums - concert - songwriter - album | 129 | 452_oasis_albums_concert_songwriter |
| 453 | darkness - literature - novelist - postcolonial - colonialism | 129 | 453_darkness_literature_novelist_postcolonial |
| 454 | currencies - currency - monetary - dollar - dollars | 129 | 454_currencies_currency_monetary_dollar |
| 455 | musically - musician - drums - percussion - composers | 129 | 455_musically_musician_drums_percussion |
| 456 | infantry - insurgents - battalion - platoon - reconnaissance | 129 | 456_infantry_insurgents_battalion_platoon |
| 457 | sesame - puppets - puppeteer - puppet - puppeteers | 128 | 457_sesame_puppets_puppeteer_puppet |
| 458 | crocodiles - crocodile - alligators - alligator - reptiles | 128 | 458_crocodiles_crocodile_alligators_alligator |
| 459 | antibiotics - antibiotic - penicillin - antimicrobial - amoxicillin | 128 | 459_antibiotics_antibiotic_penicillin_antimicrobial |
| 460 | acropolis - excavations - temples - temple - archaeologists | 128 | 460_acropolis_excavations_temples_temple |
| 461 | taxes - tax - taxation - taxable - taxed | 128 | 461_taxes_tax_taxation_taxable |
| 462 | manning - arrested - offenses - prosecutors - whistleblower | 128 | 462_manning_arrested_offenses_prosecutors |
| 463 | quantum - entanglement - entangled - decoherence - superposition | 128 | 463_quantum_entanglement_entangled_decoherence |
| 464 | sang - carpenter - carpenters - billboard - songwriter | 128 | 464_sang_carpenter_carpenters_billboard |
| 465 | languages - language - lingua - creole - vernacular | 127 | 465_languages_language_lingua_creole |
| 466 | goddesses - mythological - goddess - deities - gods | 127 | 466_goddesses_mythological_goddess_deities |
| 467 | katana - kata - swords - sword - samurai | 127 | 467_katana_kata_swords_sword |
| 468 | haggard - sang - duets - ballads - songs | 127 | 468_haggard_sang_duets_ballads |
| 469 | marathon - marathons - runners - runner - triathlon | 127 | 469_marathon_marathons_runners_runner |
| 470 | comedian - comedians - sitcom - sitcoms - comedy | 127 | 470_comedian_comedians_sitcom_sitcoms |
| 471 | armament - panzer - armoured - tanks - armored | 127 | 471_armament_panzer_armoured_tanks |
| 472 | traditional - dhoti - sari - dresses - traditionally | 127 | 472_traditional_dhoti_sari_dresses |
| 473 | prohibition - alcoholism - alcoholic - alcohol - liquor | 127 | 473_prohibition_alcoholism_alcoholic_alcohol |
| 474 | lightning - thunderstorm - thunderstorms - storms - thunder | 126 | 474_lightning_thunderstorm_thunderstorms_storms |
| 475 | militants - temple - terrorists - militant - casualties | 126 | 475_militants_temple_terrorists_militant |
| 476 | cartoons - tom - shorts - cartoon - commercials | 125 | 476_cartoons_tom_shorts_cartoon |
| 477 | mortality - fertility - expectancy - population - births | 125 | 477_mortality_fertility_expectancy_population |
| 478 | lodges - masonic - lodge - masons - masonry | 125 | 478_lodges_masonic_lodge_masons |
| 479 | judge - judges - courtroom - court - defendants | 125 | 479_judge_judges_courtroom_court |
| 480 | entrepreneurship - entrepreneur - entrepreneurial - entrepreneurs - venture | 125 | 480_entrepreneurship_entrepreneur_entrepreneurial_entrepreneurs |
| 481 | burger - burgers - hamburger - franchisees - hamburgers | 124 | 481_burger_burgers_hamburger_franchisees |
| 482 | folate - folic - vitamin - vitamins - supplements | 124 | 482_folate_folic_vitamin_vitamins |
| 483 | niger - haram - jihad - bombing - insurgency | 124 | 483_niger_haram_jihad_bombing |
| 484 | viewership - viewers - subscribers - channel - livestreaming | 124 | 484_viewership_viewers_subscribers_channel |
| 485 | 1080p - resolution - 1080 - 720p - 1080i | 124 | 485_1080p_resolution_1080_720p |
| 486 | units - metre - quantities - unit - kilogram | 124 | 486_units_metre_quantities_unit |
| 487 | oblast - soviet - yuri - grandmother - grandparents | 124 | 487_oblast_soviet_yuri_grandmother |
| 488 | cricket - wickets - matches - umpires - rugby | 123 | 488_cricket_wickets_matches_umpires |
| 489 | defendant - testify - prosecution - court - judge | 123 | 489_defendant_testify_prosecution_court |
| 490 | inventor - electrical - inventors - inventions - electricity | 123 | 490_inventor_electrical_inventors_inventions |
| 491 | apartheid - natal - cape - chairperson - appointed | 123 | 491_apartheid_natal_cape_chairperson |
| 492 | ball - sitcom - tv - 1957 - miss | 123 | 492_ball_sitcom_tv_1957 |
| 493 | zeppelin - stairway - concert - lyrics - psychedelic | 123 | 493_zeppelin_stairway_concert_lyrics |
| 494 | negro - negroes - racial - whites - civilizing | 123 | 494_negro_negroes_racial_whites |
| 495 | tornado - tornadoes - storms - thunderstorm - thunderstorms | 123 | 495_tornado_tornadoes_storms_thunderstorm |
| 496 | façade - buildings - architect - architects - building | 122 | 496_façade_buildings_architect_architects |
| 497 | marvel - superhero - marvels - supervillain - superman | 122 | 497_marvel_superhero_marvels_supervillain |
| 498 | murders - homicide - rapist - murderer - suspect | 122 | 498_murders_homicide_rapist_murderer |
| 499 | cram - murders - murdered - tortured - detectives | 121 | 499_cram_murders_murdered_tortured |
| 500 | tequila - agave - distillation - distillery - liquor | 121 | 500_tequila_agave_distillation_distillery |
| 501 | tennis - doubles - tournaments - singles - semifinals | 121 | 501_tennis_doubles_tournaments_singles |
| 502 | conspiracies - conspiratorial - conspiracy - trafficking - trump | 121 | 502_conspiracies_conspiratorial_conspiracy_trafficking |
| 503 | airship - zeppelin - airships - helium - flew | 121 | 503_airship_zeppelin_airships_helium |
| 504 | dubbed - dub - dubbing - dubs - castle | 121 | 504_dubbed_dub_dubbing_dubs |
| 505 | defamation - libel - defamatory - slander - slanderous | 120 | 505_defamation_libel_defamatory_slander |
| 506 | soprano - mafia - joey - carmine - capo | 120 | 506_soprano_mafia_joey_carmine |
| 507 | eagle - eagles - vultures - hawk - birds | 120 | 507_eagle_eagles_vultures_hawk |
| 508 | households - household - average - families - census | 119 | 508_households_household_average_families |
| 509 | taxonomic - genus - taxon - nomenclature - taxonomists | 119 | 509_taxonomic_genus_taxon_nomenclature |
| 510 | 1984 - 1945 - 1949 - novelist - 1939 | 119 | 510_1984_1945_1949_novelist |
| 511 | philosopher - philosophers - empiricism - philosophy - rationalist | 119 | 511_philosopher_philosophers_empiricism_philosophy |
| 512 | women - comfort - geisha - grandmothers - yen | 119 | 512_women_comfort_geisha_grandmothers |
| 513 | massacre - massacred - atrocities - victims - 1945 | 119 | 513_massacre_massacred_atrocities_victims |
| 514 | internment - camps - detainees - camp - prisoners | 119 | 514_internment_camps_detainees_camp |
| 515 | ribbons - ribbon - gallantry - medals - medal | 119 | 515_ribbons_ribbon_gallantry_medals |
| 516 | tramp - films - film - cinema - cinematographer | 119 | 516_tramp_films_film_cinema |
| 517 | caves - cave - temples - excavation - shrines | 119 | 517_caves_cave_temples_excavation |
| 518 | jubilees - jubilee - celebrated - celebrations - celebration | 119 | 518_jubilees_jubilee_celebrated_celebrations |
| 519 | chains - albums - album - toured - songs | 118 | 519_chains_albums_album_toured |
| 520 | spice - concert - girls - spicy - debut | 118 | 520_spice_concert_girls_spicy |
| 521 | malaria - malarial - antimalarial - mosquito - mosquitoes | 117 | 521_malaria_malarial_antimalarial_mosquito |
| 522 | fertility - overpopulation - childbearing - adoptions - adoption | 117 | 522_fertility_overpopulation_childbearing_adoptions |
| 523 | eucalyptus - acacia - rainforests - conifers - trees | 117 | 523_eucalyptus_acacia_rainforests_conifers |
| 524 | prince - albums - album - duet - songs | 117 | 524_prince_albums_album_duet |
| 525 | famine - famines - genocide - starvation - starved | 117 | 525_famine_famines_genocide_starvation |
| 526 | 1832 - minister - peerage - constituency - exchequer | 117 | 526_1832_minister_peerage_constituency |
| 527 | vertigo - scenes - film - screenplay - films | 116 | 527_vertigo_scenes_film_screenplay |
| 528 | stark - thrones - throne - arya - wildlings | 116 | 528_stark_thrones_throne_arya |
| 529 | mobile - telecommunications - mobiles - cellular - handsets | 116 | 529_mobile_telecommunications_mobiles_cellular |
| 530 | shaggy - voiced - cartoon - cartoons - voice | 115 | 530_shaggy_voiced_cartoon_cartoons |
| 531 | bear - bears - zoo - toy - pg | 115 | 531_bear_bears_zoo_toy |
| 532 | coffeehouse - coffee - coffees - cafe - café | 115 | 532_coffeehouse_coffee_coffees_cafe |
| 533 | segregation - segregationist - segregated - discrimination - unconstitutional | 115 | 533_segregation_segregationist_segregated_discrimination |
| 534 | poverty - income - economies - agriculture - subsistence | 115 | 534_poverty_income_economies_agriculture |
| 535 | capacitors - dielectrics - capacitor - capacitance - dielectric | 114 | 535_capacitors_dielectrics_capacitor_capacitance |
| 536 | islands - archipelagos - archipelago - pacific - island | 114 | 536_islands_archipelagos_archipelago_pacific |
| 537 | paramount - studios - corporation - merger - subsidiaries | 114 | 537_paramount_studios_corporation_merger |
| 538 | iso - standards - standardization - organizational - stakeholders | 114 | 538_iso_standards_standardization_organizational |
| 539 | paintings - painting - painters - art - artistic | 114 | 539_paintings_painting_painters_art |
| 540 | mayor - mayors - mayoral - municipal - municipalities | 114 | 540_mayor_mayors_mayoral_municipal |
| 541 | ethnicities - ethnonym - ethnic - ancestry - inhabitants | 114 | 541_ethnicities_ethnonym_ethnic_ancestry |
| 542 | repeal - repealing - repealed - healthcare - uninsured | 113 | 542_repeal_repealing_repealed_healthcare |
| 543 | watchmen - comics - superhero - superheroes - vendetta | 113 | 543_watchmen_comics_superhero_superheroes |
| 544 | hashing - hash - hashes - hashed - tables | 113 | 544_hashing_hash_hashes_hashed |
| 545 | pistols - punk - punks - band - pistol | 113 | 545_pistols_punk_punks_band |
| 546 | chef - chefs - culinary - kitchens - cook | 113 | 546_chef_chefs_culinary_kitchens |
| 547 | realism - surrealism - magical - fiction - imagination | 113 | 547_realism_surrealism_magical_fiction |
| 548 | 1793 - 1789 - revolutionaries - revolt - insurrection | 113 | 548_1793_1789_revolutionaries_revolt |
| 549 | 451 - writer - literature - writers - author | 113 | 549_451_writer_literature_writers |
| 550 | punk - indie - genre - genres - bands | 113 | 550_punk_indie_genre_genres |
| 551 | dances - dance - dancers - traditional - rituals | 112 | 551_dances_dance_dancers_traditional |
| 552 | gong - qigong - communist - china - adherents | 112 | 552_gong_qigong_communist_china |
| 553 | playlists - playlist - music - songs - podcasts | 112 | 553_playlists_playlist_music_songs |
| 554 | fabrication - manufacturing - machining - inkjet - prototyping | 111 | 554_fabrication_manufacturing_machining_inkjet |
| 555 | elections - election - electoral - polls - voters | 111 | 555_elections_election_electoral_polls |
| 556 | steam - valve - platform - publishers - cloud | 111 | 556_steam_valve_platform_publishers |
| 557 | orchestra - orchestras - orchestration - symphonies - symphony | 111 | 557_orchestra_orchestras_orchestration_symphonies |
| 558 | albums - songs - toured - 1973 - 1974 | 111 | 558_albums_songs_toured_1973 |
| 559 | arsenal - goals - scored - footballer - goal | 111 | 559_arsenal_goals_scored_footballer |
| 560 | metro - railway - railways - transit - trains | 111 | 560_metro_railway_railways_transit |
| 561 | laundering - banking - trafficking - smuggling - bank | 110 | 561_laundering_banking_trafficking_smuggling |
| 562 | complement - binary - complements - unsigned - bitwise | 110 | 562_complement_binary_complements_unsigned |
| 563 | piazza - boulevard - della - buildings - baroque | 110 | 563_piazza_boulevard_della_buildings |
| 564 | synthesizers - synthesizer - techno - synth - genres | 110 | 564_synthesizers_synthesizer_techno_synth |
| 565 | sprinter - bolt - sprinters - olympic - athletics | 109 | 565_sprinter_bolt_sprinters_olympic |
| 566 | condoms - condom - contraception - prevention - protection | 108 | 566_condoms_condom_contraception_prevention |
| 567 | flags - flag - soviet - flagpole - tricolour | 108 | 567_flags_flag_soviet_flagpole |
| 568 | kanji - pinyin - characters - mandarin - character | 108 | 568_kanji_pinyin_characters_mandarin |
| 569 | detective - hound - adventure - investigative - novels | 108 | 569_detective_hound_adventure_investigative |
| 570 | subcontinent - viceroy - coalition - 1947 - raj | 108 | 570_subcontinent_viceroy_coalition_1947 |
| 571 | lion - wardrobe - witch - chronicles - mythical | 107 | 571_lion_wardrobe_witch_chronicles |
| 572 | prix - qualifying - podium - laps - overtook | 107 | 572_prix_qualifying_podium_laps |
| 573 | soccer - athlete - assists - scoring - olympic | 106 | 573_soccer_athlete_assists_scoring |
| 574 | impeachment - testified - indictment - prosecutor - hearings | 106 | 574_impeachment_testified_indictment_prosecutor |
| 575 | databases - database - tables - schema - relational | 106 | 575_databases_database_tables_schema |
| 576 | paramount - animators - studios - productions - animation | 106 | 576_paramount_animators_studios_productions |
| 577 | gear - presenter - presenters - viewers - bbc | 106 | 577_gear_presenter_presenters_viewers |
| 578 | tricolour - tricolore - tricolor - flags - flag | 105 | 578_tricolour_tricolore_tricolor_flags |
| 579 | node - js - developers - frameworks - platform | 105 | 579_node_js_developers_frameworks |
| 580 | populism - populists - populist - political - authoritarianism | 105 | 580_populism_populists_populist_political |
| 581 | tempo - tempos - rhythmic - rhythm - bpm | 105 | 581_tempo_tempos_rhythmic_rhythm |
| 582 | biometric - authentication - citizenship - identity - register | 105 | 582_biometric_authentication_citizenship_identity |
| 583 | gambling - gamblers - gambler - casino - casinos | 105 | 583_gambling_gamblers_gambler_casino |
| 584 | incompleteness - axiomatization - completeness - provability - consistency | 105 | 584_incompleteness_axiomatization_completeness_provability |
| 585 | logics - logicians - logic - semantics - propositional | 105 | 585_logics_logicians_logic_semantics |
| 586 | writings - discourses - discourse - theological - theologians | 104 | 586_writings_discourses_discourse_theological |
| 587 | censorship - censor - censors - censored - forbidding | 104 | 587_censorship_censor_censors_censored |
| 588 | barbarian - serpent - marvel - comics - blacksmith | 104 | 588_barbarian_serpent_marvel_comics |
| 589 | uninsured - insurance - insured - healthcare - insurers | 104 | 589_uninsured_insurance_insured_healthcare |
| 590 | privateers - pirates - pirate - slaves - enslaved | 103 | 590_privateers_pirates_pirate_slaves |
| 591 | papillomavirus - cancers - cervical - warts - cancer | 103 | 591_papillomavirus_cancers_cervical_warts |
| 592 | satellites - satellite - constellations - constellation - orbit | 103 | 592_satellites_satellite_constellations_constellation |
| 593 | samurai - screenwriter - screenplay - screenplays - filmmaker | 103 | 593_samurai_screenwriter_screenplay_screenplays |
| 594 | hammer - rapper - rappers - rap - raps | 103 | 594_hammer_rapper_rappers_rap |
| 595 | bitcoin - bitcoins - blockchain - cryptocurrency - cryptocurrencies | 103 | 595_bitcoin_bitcoins_blockchain_cryptocurrency |
| 596 | electronics - manufacturer - appliances - manufactures - lee | 103 | 596_electronics_manufacturer_appliances_manufactures |
| 597 | utilitarianism - utilitarian - consequentialism - consequentialist - morality | 103 | 597_utilitarianism_utilitarian_consequentialism_consequentialist |
| 598 | sitcom - woody - cast - primetime - shows | 103 | 598_sitcom_woody_cast_primetime |
| 599 | republics - soviet - soviets - oblasts - republic | 103 | 599_republics_soviet_soviets_oblasts |
| 600 | monarchy - junta - dictatorship - king - monarch | 102 | 600_monarchy_junta_dictatorship_king |
| 601 | apps - app - android - mobile - downloads | 102 | 601_apps_app_android_mobile |
| 602 | vampire - vampires - vampirism - vampiric - bloodlust | 102 | 602_vampire_vampires_vampirism_vampiric |
| 603 | racism - racialism - prejudice - racial - discrimination | 102 | 603_racism_racialism_prejudice_racial |
| 604 | twitch - streaming - stream - viewership - streams | 102 | 604_twitch_streaming_stream_viewership |
| 605 | glucose - monosaccharides - monosaccharide - polysaccharides - oligosaccharides | 102 | 605_glucose_monosaccharides_monosaccharide_polysaccharides |
| 606 | sponsors - sponsorship - sponsor - sponsorships - sponsored | 102 | 606_sponsors_sponsorship_sponsor_sponsorships |
| 607 | minister - ministers - secretary - elected - cabinet | 102 | 607_minister_ministers_secretary_elected |
| 608 | booth - assassination - assassinated - confederate - 1864 | 102 | 608_booth_assassination_assassinated_confederate |
| 609 | torrents - torrent - peers - peer - downloading | 102 | 609_torrents_torrent_peers_peer |
| 610 | coco - boutiques - boutique - designers - cosmetics | 102 | 610_coco_boutiques_boutique_designers |
| 611 | crusades - crusade - crusaders - crusader - 1451 | 102 | 611_crusades_crusade_crusaders_crusader |
| 612 | psychometric - intelligence - assessment - standardized - scores | 102 | 612_psychometric_intelligence_assessment_standardized |
| 613 | prophets - prophet - prophethood - prophetic - scriptures | 101 | 613_prophets_prophet_prophethood_prophetic |
| 614 | purge - purges - gulag - soviet - purged | 101 | 614_purge_purges_gulag_soviet |
| 615 | politburo - soviet - perestroika - chairman - secretary | 101 | 615_politburo_soviet_perestroika_chairman |
| 616 | powertrain - musk - cars - motors - drivetrain | 101 | 616_powertrain_musk_cars_motors |
| 617 | pornography - pornographic - prohibits - porn - obscene | 101 | 617_pornography_pornographic_prohibits_porn |
| 618 | bikers - angels - motorcycles - outlaws - motorcyclists | 101 | 618_bikers_angels_motorcycles_outlaws |
| 619 | altruism - ethical - advocated - moral - ethics | 101 | 619_altruism_ethical_advocated_moral |
| 620 | concert - duet - concerts - singer - medley | 101 | 620_concert_duet_concerts_singer |
| 621 | licenses - licensing - license - licensed - proprietary | 101 | 621_licenses_licensing_license_licensed |
| 622 | gentrification - suburbanization - gentrified - urbanization - redevelopment | 101 | 622_gentrification_suburbanization_gentrified_urbanization |
| 623 | spying - spy - espionage - spyware - smartphones | 101 | 623_spying_spy_espionage_spyware |
| 624 | apartheid - activism - blacks - activist - suffrage | 101 | 624_apartheid_activism_blacks_activist |
| 625 | robotics - robot - robots - robotic - manipulators | 101 | 625_robotics_robot_robots_robotic |
| 626 | 1783 - minister - peerage - ministers - 1784 | 100 | 626_1783_minister_peerage_ministers |
| 627 | labour - children - labor - poverty - labourers | 100 | 627_labour_children_labor_poverty |
| 628 | generative - adversarial - generating - generates - generator | 100 | 628_generative_adversarial_generating_generates |
| 629 | concert - sang - scarecrow - vocals - musicians | 100 | 629_concert_sang_scarecrow_vocals |
| 630 | mosque - masjid - mosques - tombs - mausoleum | 100 | 630_mosque_masjid_mosques_tombs |
| 631 | sang - concert - zeppelin - rocker - tour | 100 | 631_sang_concert_zeppelin_rocker |
| 632 | attachments - attachment - adoptions - parenting - infancy | 100 | 632_attachments_attachment_adoptions_parenting |
| 633 | tennis - slams - tournaments - competed - doubles | 100 | 633_tennis_slams_tournaments_competed |
| 634 | witchcraft - coven - covens - witches - paganism | 99 | 634_witchcraft_coven_covens_witches |
| 635 | viruses - viral - virus - coronavirus - coronaviruses | 99 | 635_viruses_viral_virus_coronavirus |
| 636 | demon - yakuza - shinobi - demons - priestess | 99 | 636_demon_yakuza_shinobi_demons |
| 637 | psoriasis - psoriatic - erythematosus - keratinocytes - autoimmune | 99 | 637_psoriasis_psoriatic_erythematosus_keratinocytes |
| 638 | guru - gurus - shakti - scriptures - divinity | 99 | 638_guru_gurus_shakti_scriptures |
| 639 | population - populations - urbanization - china - populous | 99 | 639_population_populations_urbanization_china |
| 640 | defamation - lawsuit - sued - libel - accused | 99 | 640_defamation_lawsuit_sued_libel |
| 641 | rating - ratings - scores - rated - fide | 99 | 641_rating_ratings_scores_rated |
| 642 | albums - singer - singers - songwriter - songs | 98 | 642_albums_singer_singers_songwriter |
| 643 | ebook - ebooks - tablet - touchscreen - devices | 98 | 643_ebook_ebooks_tablet_touchscreen |
| 644 | orthodox - patriarch - principality - rulers - ruled | 98 | 644_orthodox_patriarch_principality_rulers |
| 645 | cyclones - cyclone - typhoon - hurricane - typhoons | 98 | 645_cyclones_cyclone_typhoon_hurricane |
| 646 | boots - sequels - sequel - premiered - movie | 98 | 646_boots_sequels_sequel_premiered |
| 647 | novels - novel - writer - nonfiction - fiction | 98 | 647_novels_novel_writer_nonfiction |
| 648 | kami - rituals - deities - shin - ritual | 98 | 648_kami_rituals_deities_shin |
| 649 | honorary - commencement - doctorate - conferred - degree | 98 | 649_honorary_commencement_doctorate_conferred |
| 650 | evil - virtual - zombies - nemesis - sequel | 98 | 650_evil_virtual_zombies_nemesis |
| 651 | voiced - voice - voices - voiceover - cast | 98 | 651_voiced_voice_voices_voiceover |
| 652 | doom - ark - chronicles - films - sequel | 97 | 652_doom_ark_chronicles_films |
| 653 | botulinum - toxin - toxins - neurotoxin - neurotoxins | 97 | 653_botulinum_toxin_toxins_neurotoxin |
| 654 | tags - tagging - barcodes - transmitters - tag | 97 | 654_tags_tagging_barcodes_transmitters |
| 655 | soviet - politburo - coup - arrest - perestroika | 97 | 655_soviet_politburo_coup_arrest |
| 656 | twitter - tweets - accounts - hoaxes - trolls | 97 | 656_twitter_tweets_accounts_hoaxes |
| 657 | cryptography - encryption - cryptosystems - cryptosystem - cryptographic | 97 | 657_cryptography_encryption_cryptosystems_cryptosystem |
| 658 | lasers - fibers - laser - fiber - optical | 96 | 658_lasers_fibers_laser_fiber |
| 659 | smartphone - smartphones - mobile - cellular - flagship | 96 | 659_smartphone_smartphones_mobile_cellular |
| 660 | vaudeville - brothers - comedian - comedians - broadway | 96 | 660_vaudeville_brothers_comedian_comedians |
| 661 | halo - 343 - consoles - franchise - spartan | 96 | 661_halo_343_consoles_franchise |
| 662 | mosque - masjid - mosques - mecca - caliphate | 96 | 662_mosque_masjid_mosques_mecca |
| 663 | motorsport - racing - prix - raced - cars | 96 | 663_motorsport_racing_prix_raced |
| 664 | punches - featherweight - fighter - fighters - fights | 96 | 664_punches_featherweight_fighter_fighters |
| 665 | herbicides - herbicide - orange - contaminated - chemicals | 96 | 665_herbicides_herbicide_orange_contaminated |
| 666 | nonfiction - bestseller - novelist - autobiography - novels | 96 | 666_nonfiction_bestseller_novelist_autobiography |
| 667 | cannabis - marijuana - sect - sects - cultivates | 96 | 667_cannabis_marijuana_sect_sects |
| 668 | income - poverty - median - households - affluent | 96 | 668_income_poverty_median_households |
| 669 | epistemological - epistemic - epistemology - epistemologists - belief | 96 | 669_epistemological_epistemic_epistemology_epistemologists |
| 670 | genie - mother - abuse - childhood - parents | 95 | 670_genie_mother_abuse_childhood |
| 671 | 802 - wireless - bandwidth - communications - antennas | 95 | 671_802_wireless_bandwidth_communications |
| 672 | han - nam - 1945 - kai - troops | 95 | 672_han_nam_1945_kai |
| 673 | wage - wages - minimum - hourly - raise | 95 | 673_wage_wages_minimum_hourly |
| 674 | lambs - screenplay - thriller - silence - films | 95 | 674_lambs_screenplay_thriller_silence |
| 675 | donation - donated - charity - donations - donating | 95 | 675_donation_donated_charity_donations |
| 676 | wu - tang - rapper - kung - rap | 95 | 676_wu_tang_rapper_kung |
| 677 | influenza - flu - pandemics - pandemic - epidemic | 95 | 677_influenza_flu_pandemics_pandemic |
| 678 | animatronic - animatronics - minigames - nightmare - nights | 95 | 678_animatronic_animatronics_minigames_nightmare |
| 679 | convicts - colonists - 1788 - convict - settlers | 94 | 679_convicts_colonists_1788_convict |
| 680 | displays - monitors - cables - cable - ports | 94 | 680_displays_monitors_cables_cable |
| 681 | trademarks - trademark - infringement - copyrights - copyright | 94 | 681_trademarks_trademark_infringement_copyrights |
| 682 | farmworkers - unions - picketing - protest - laborers | 94 | 682_farmworkers_unions_picketing_protest |
| 683 | libertarianism - libertarians - libertarian - liberalism - anarchists | 94 | 683_libertarianism_libertarians_libertarian_liberalism |
| 684 | temptations - sang - toured - singers - albums | 94 | 684_temptations_sang_toured_singers |
| 685 | 1898 - 1896 - 1902 - dictator - insurgent | 94 | 685_1898_1896_1902_dictator |
| 686 | insurance - insurer - insurers - insured - insure | 94 | 686_insurance_insurer_insurers_insured |
| 687 | shooting - shootings - shooters - shooter - firearm | 94 | 687_shooting_shootings_shooters_shooter |
| 688 | colitis - bowel - gastrointestinal - intestinal - inflammatory | 94 | 688_colitis_bowel_gastrointestinal_intestinal |
| 689 | divorce - peace - adultery - ballad - lyrics | 94 | 689_divorce_peace_adultery_ballad |
| 690 | artillery - howitzers - howitzer - cannons - rifle | 93 | 690_artillery_howitzers_howitzer_cannons |
| 691 | ups - deliveries - logistics - delivery - freight | 93 | 691_ups_deliveries_logistics_delivery |
| 692 | metal - gear - consoles - sequels - franchise | 93 | 692_metal_gear_consoles_sequels |
| 693 | ibn - hadith - imam - ijtihad - khan | 93 | 693_ibn_hadith_imam_ijtihad |
| 694 | industrial - subsidiaries - manufacturer - industries - corporation | 93 | 694_industrial_subsidiaries_manufacturer_industries |
| 695 | motorsport - prix - motorsports - racing - raced | 93 | 695_motorsport_prix_motorsports_racing |
| 696 | 1936 - deposed - 1935 - invaded - 1937 | 93 | 696_1936_deposed_1935_invaded |
| 697 | scotch - whisky - whiskey - distillery - bourbon | 93 | 697_scotch_whisky_whiskey_distillery |
| 698 | premiered - machina - cast - critical - productions | 93 | 698_premiered_machina_cast_critical |
| 699 | psychedelics - psychedelic - ayahuasca - cannabis - psilocybin | 93 | 699_psychedelics_psychedelic_ayahuasca_cannabis |
| 700 | homeless - homelessness - shelters - shelter - housing | 93 | 700_homeless_homelessness_shelters_shelter |
| 701 | newton - gravitation - gravitational - gravity - gravitating | 93 | 701_newton_gravitation_gravitational_gravity |
| 702 | swamp - comics - comic - sting - likeness | 92 | 702_swamp_comics_comic_sting |
| 703 | languages - language - linguists - lingua - linguistics | 92 | 703_languages_language_linguists_lingua |
| 704 | mutilations - mutilation - mutilating - circumcision - clitoridectomy | 92 | 704_mutilations_mutilation_mutilating_circumcision |
| 705 | harassment - harassing - harassed - harass - discrimination | 92 | 705_harassment_harassing_harassed_harass |
| 706 | artistic - art - artwork - paintings - artworks | 92 | 706_artistic_art_artwork_paintings |
| 707 | paintings - painter - painters - painting - portraits | 92 | 707_paintings_painter_painters_painting |
| 708 | piazza - opera - tenor - bohème - arias | 92 | 708_piazza_opera_tenor_bohème |
| 709 | tsar - tsarist - tsars - czar - emperors | 92 | 709_tsar_tsarist_tsars_czar |
| 710 | ai - intelligence - machines - cognitive - intelligent | 92 | 710_ai_intelligence_machines_cognitive |
| 711 | pamphlet - 1789 - revolutionary - 1790 - 1793 | 92 | 711_pamphlet_1789_revolutionary_1790 |
| 712 | murders - detectives - murdered - constable - detective | 92 | 712_murders_detectives_murdered_constable |
| 713 | healthcare - insurance - health - hospitals - insurers | 92 | 713_healthcare_insurance_health_hospitals |
| 714 | plague - plagues - diseases - epidemics - epidemic | 91 | 714_plague_plagues_diseases_epidemics |
| 715 | paleolithic - neolithic - archaeological - prehistory - archaeologists | 91 | 715_paleolithic_neolithic_archaeological_prehistory |
| 716 | theology - faith - teachings - religion - monotheism | 91 | 716_theology_faith_teachings_religion |
| 717 | alderman - mayor - mayoral - candidates - superintendent | 91 | 717_alderman_mayor_mayoral_candidates |
| 718 | nam - chi - southeast - urban - city | 91 | 718_nam_chi_southeast_urban |
| 719 | skating - skaters - skater - skate - competed | 91 | 719_skating_skaters_skater_skate |
| 720 | banking - bank - finances - finance - funds | 91 | 720_banking_bank_finances_finance |
| 721 | asbestos - asbestosis - minerals - mineral - toxicology | 91 | 721_asbestos_asbestosis_minerals_mineral |
| 722 | municipalities - municipality - cities - population - city | 90 | 722_municipalities_municipality_cities_population |
| 723 | headquartered - headquarters - companies - san - industries | 90 | 723_headquartered_headquarters_companies_san |
| 724 | soviets - communists - communist - soviet - communism | 90 | 724_soviets_communists_communist_soviet |
| 725 | tapes - recorder - recorders - recording - cassette | 90 | 725_tapes_recorder_recorders_recording |
| 726 | swastika - swastikas - symbolises - symbol - symbolising | 90 | 726_swastika_swastikas_symbolises_symbol |
| 727 | oblast - oblasts - annexation - annexations - annexed | 90 | 727_oblast_oblasts_annexation_annexations |
| 728 | filmed - filming - premiered - premiere - seasons | 90 | 728_filmed_filming_premiered_premiere |
| 729 | evacuated - evacuation - evacuate - ceasefire - bombed | 90 | 729_evacuated_evacuation_evacuate_ceasefire |
| 730 | quad - quadrilateral - multilateral - alliances - trilateral | 90 | 730_quad_quadrilateral_multilateral_alliances |
| 731 | sake - rice - liquor - brewing - alcohol | 90 | 731_sake_rice_liquor_brewing |
| 732 | enigma - rotor - rotors - cipher - cryptographic | 90 | 732_enigma_rotor_rotors_cipher |
| 733 | anthropology - anthropological - sociocultural - anthropologist - anthropologists | 90 | 733_anthropology_anthropological_sociocultural_anthropologist |
| 734 | executives - stockholders - accounting - shareholders - insiders | 89 | 734_executives_stockholders_accounting_shareholders |
| 735 | psychedelics - psychedelic - psilocybin - hallucinations - psychosis | 89 | 735_psychedelics_psychedelic_psilocybin_hallucinations |
| 736 | quicksort - sorting - sort - sorts - algorithm | 89 | 736_quicksort_sorting_sort_sorts |
| 737 | 1918 - soviets - polish - soviet - battle | 89 | 737_1918_soviets_polish_soviet |
| 738 | barangays - barangay - municipalities - metropolitan - metro | 89 | 738_barangays_barangay_municipalities_metropolitan |
| 739 | assists - rebounds - suns - 76ers - steals | 89 | 739_assists_rebounds_suns_76ers |
| 740 | spaghetti - western - westerns - films - movies | 89 | 740_spaghetti_western_westerns_films |
| 741 | airing - adult - swim - aqua - episodes | 89 | 741_airing_adult_swim_aqua |
| 742 | queer - heterosexuality - heterosexuals - homosexual - homosexuals | 89 | 742_queer_heterosexuality_heterosexuals_homosexual |
| 743 | control - controller - controlled - controllers - disturbances | 89 | 743_control_controller_controlled_controllers |
| 744 | abortion - abortions - pregnancies - pregnancy - fetuses | 89 | 744_abortion_abortions_pregnancies_pregnancy |
| 745 | voyages - voyage - caravel - expeditions - navigator | 89 | 745_voyages_voyage_caravel_expeditions |
| 746 | channel - channels - broadcasting - syndicated - simulcast | 88 | 746_channel_channels_broadcasting_syndicated |
| 747 | sati - castes - widowhood - prohibits - prohibition | 88 | 747_sati_castes_widowhood_prohibits |
| 748 | conquistadors - confederation - tlatoani - provinces - rulers | 88 | 748_conquistadors_confederation_tlatoani_provinces |
| 749 | supermarket - supermarkets - shops - retailer - retailers | 88 | 749_supermarket_supermarkets_shops_retailer |
| 750 | khan - khanate - tsar - khans - khanates | 88 | 750_khan_khanate_tsar_khans |
| 751 | separatists - soviet - militants - ceasefire - guerrillas | 88 | 751_separatists_soviet_militants_ceasefire |
| 752 | magician - occultist - occultism - occultists - mysticism | 88 | 752_magician_occultist_occultism_occultists |
| 753 | swam - swimmer - olympic - swimmers - freestyle | 88 | 753_swam_swimmer_olympic_swimmers |
| 754 | alchemy - alchemists - alchemist - alchemical - al | 88 | 754_alchemy_alchemists_alchemist_alchemical |
| 755 | robin - hood - friar - hoods - knight | 88 | 755_robin_hood_friar_hoods |
| 756 | genders - gender - sexes - gendered - genderqueer | 87 | 756_genders_gender_sexes_gendered |
| 757 | privacy - data - regulations - enforcement - regulation | 87 | 757_privacy_data_regulations_enforcement |
| 758 | chocolate - chocolates - confectionery - brands - manufacturer | 87 | 758_chocolate_chocolates_confectionery_brands |
| 759 | murders - corpse - unconscious - murder - strangled | 87 | 759_murders_corpse_unconscious_murder |
| 760 | ayahuasca - psychedelics - psychedelic - addictions - shamans | 87 | 760_ayahuasca_psychedelics_psychedelic_addictions |
| 761 | audit - audited - auditing - audits - fines | 87 | 761_audit_audited_auditing_audits |
| 762 | dragons - dragon - amulets - carvings - robes | 87 | 762_dragons_dragon_amulets_carvings |
| 763 | murderer - murders - murdered - killings - murder | 87 | 763_murderer_murders_murdered_killings |
| 764 | diamond - sapphire - pearl - games - evolve | 87 | 764_diamond_sapphire_pearl_games |
| 765 | hepatitis - hepatic - cirrhosis - liver - hepatocellular | 87 | 765_hepatitis_hepatic_cirrhosis_liver |
| 766 | ba - antibody - antibodies - vaccines - 2022 | 87 | 766_ba_antibody_antibodies_vaccines |
| 767 | algorithm - algorithms - paths - traversal - nodes | 87 | 767_algorithm_algorithms_paths_traversal |
| 768 | gable - actresses - films - actor - film | 87 | 768_gable_actresses_films_actor |
| 769 | verse - poetry - poet - poems - poem | 87 | 769_verse_poetry_poet_poems |
| 770 | judicial - justices - judiciary - courts - judges | 87 | 770_judicial_justices_judiciary_courts |
| 771 | processors - processor - intel - microarchitecture - cores | 87 | 771_processors_processor_intel_microarchitecture |
| 772 | emperor - emperors - empress - dowager - eunuch | 87 | 772_emperor_emperors_empress_dowager |
| 773 | anthrax - spores - assays - contaminated - microbiologist | 86 | 773_anthrax_spores_assays_contaminated |
| 774 | comics - superhero - superman - superheroes - comic | 86 | 774_comics_superhero_superman_superheroes |
| 775 | seo - searches - webmaster - webmasters - web | 86 | 775_seo_searches_webmaster_webmasters |
| 776 | kabbalah - kabbalistic - esotericism - mysticism - theology | 86 | 776_kabbalah_kabbalistic_esotericism_mysticism |
| 777 | caesarean - cesarean - uterus - pregnancies - uterine | 86 | 777_caesarean_cesarean_uterus_pregnancies |
| 778 | semiconductor - transistors - transistor - gate - circuitry | 86 | 778_semiconductor_transistors_transistor_gate |
| 779 | furniture - stores - store - warehouse - malls | 86 | 779_furniture_stores_store_warehouse |
| 780 | inquisition - persecution - catholic - reformation - heresy | 86 | 780_inquisition_persecution_catholic_reformation |
| 781 | dictator - dictatorship - dictatorial - regime - presidential | 86 | 781_dictator_dictatorship_dictatorial_regime |
| 782 | emoji - emojis - smiley - symbols - glyphs | 86 | 782_emoji_emojis_smiley_symbols |
| 783 | costumes - costume - dressed - dresses - dress | 86 | 783_costumes_costume_dressed_dresses |
| 784 | sexiest - playboy - hottest - glamour - actresses | 86 | 784_sexiest_playboy_hottest_glamour |
| 785 | karate - kung - martial - cobra - tae | 86 | 785_karate_kung_martial_cobra |
| 786 | papacy - pope - papal - catholic - holocaust | 85 | 786_papacy_pope_papal_catholic |
| 787 | tarot - cards - decks - deck - card | 85 | 787_tarot_cards_decks_deck |
| 788 | deities - goddesses - goddess - mythology - underworld | 85 | 788_deities_goddesses_goddess_mythology |
| 789 | waterboarding - waterboarded - torture - interrogations - interrogation | 85 | 789_waterboarding_waterboarded_torture_interrogations |
| 790 | degree - bachelor - diploma - qualification - courses | 85 | 790_degree_bachelor_diploma_qualification |
| 791 | nonprofit - nonprofits - donations - organizations - nongovernmental | 85 | 791_nonprofit_nonprofits_donations_organizations |
| 792 | perjury - misconduct - impeachment - allegations - affair | 85 | 792_perjury_misconduct_impeachment_allegations |
| 793 | retailer - supermarket - stores - supermarkets - shop | 85 | 793_retailer_supermarket_stores_supermarkets |
| 794 | crimes - convicted - assaulted - raped - plea | 85 | 794_crimes_convicted_assaulted_raped |
| 795 | paintings - painter - painting - murals - portraits | 85 | 795_paintings_painter_painting_murals |
| 796 | mansa - throne - rulers - kingdoms - emperor | 85 | 796_mansa_throne_rulers_kingdoms |
| 797 | stripes - jack - bands - band - bandmate | 84 | 797_stripes_jack_bands_band |
| 798 | 1941 - polish - 1939 - nazi - treaty | 84 | 798_1941_polish_1939_nazi |
| 799 | prix - motorsport - racing - motorsports - qualifying | 84 | 799_prix_motorsport_racing_motorsports |
| 800 | buzz - toy - toys - woody - toyline | 84 | 800_buzz_toy_toys_woody |
| 801 | generals - counterinsurgency - military - militias - strategist | 84 | 801_generals_counterinsurgency_military_militias |
| 802 | casino - casinos - gambling - 1960s - hotel | 84 | 802_casino_casinos_gambling_1960s |
| 803 | telecom - telecommunications - telecoms - provider - shareholders | 84 | 803_telecom_telecommunications_telecoms_provider |
| 804 | sitcom - cast - cartoons - cartoon - voiced | 84 | 804_sitcom_cast_cartoons_cartoon |
| 805 | extradition - jailed - convicted - sentenced - detained | 84 | 805_extradition_jailed_convicted_sentenced |
| 806 | yogurt - yogurts - yoghurt - dairy - lactose | 84 | 806_yogurt_yogurts_yoghurt_dairy |
| 807 | junta - loyalist - rebellion - juntas - royalist | 84 | 807_junta_loyalist_rebellion_juntas |
| 808 | golfer - golfers - woods - golf - masters | 84 | 808_golfer_golfers_woods_golf |
| 809 | fitness - gyms - gym - gymnastics - camps | 84 | 809_fitness_gyms_gym_gymnastics |
| 810 | butter - gluten - flour - glutenin - dough | 83 | 810_butter_gluten_flour_glutenin |
| 811 | sizes - paper - sheet - sheets - width | 83 | 811_sizes_paper_sheet_sheets |
| 812 | baker - divorced - remarried - stepfather - divorcing | 83 | 812_baker_divorced_remarried_stepfather |
| 813 | tattoos - tattooing - tattoo - tattooed - markings | 83 | 813_tattoos_tattooing_tattoo_tattooed |
| 814 | castes - caste - discriminated - discrimination - raj | 83 | 814_castes_caste_discriminated_discrimination |
| 815 | dreaming - lucidity - dreams - lucid - dreamer | 83 | 815_dreaming_lucidity_dreams_lucid |
| 816 | mountains - mountainous - tributary - river - elevation | 83 | 816_mountains_mountainous_tributary_river |
| 817 | bombings - murders - suspects - terrorist - homicide | 83 | 817_bombings_murders_suspects_terrorist |
| 818 | conscription - military - enlistment - draftees - draft | 83 | 818_conscription_military_enlistment_draftees |
| 819 | presentations - presentation - slides - keynote - slide | 83 | 819_presentations_presentation_slides_keynote |
| 820 | paraphilia - paraphilias - pedophilia - pedophilic - paraphilic | 83 | 820_paraphilia_paraphilias_pedophilia_pedophilic |
| 821 | bushido - bushidō - samurai - martial - judo | 83 | 821_bushido_bushidō_samurai_martial |
| 822 | fjord - archaeological - meadows - voyages - settlers | 83 | 822_fjord_archaeological_meadows_voyages |
| 823 | tofu - soy - soybean - sesame - vegetarian | 83 | 823_tofu_soy_soybean_sesame |
| 824 | gang - gangs - comedies - productions - roach | 83 | 824_gang_gangs_comedies_productions |
| 825 | accents - accent - dialects - dialect - pronunciation | 82 | 825_accents_accent_dialects_dialect |
| 826 | screenplay - ultimatum - screenwriter - thriller - trilogy | 82 | 826_screenplay_ultimatum_screenwriter_thriller |
| 827 | stamps - stamp - postage - postal - postmaster | 82 | 827_stamps_stamp_postage_postal |
| 828 | typescript - compiler - type - developers - interpreter | 82 | 828_typescript_compiler_type_developers |
| 829 | aspirin - ibuprofen - analgesics - inhibitors - medications | 82 | 829_aspirin_ibuprofen_analgesics_inhibitors |
| 830 | atheist - agnostic - agnosticism - atheism - religious | 82 | 830_atheist_agnostic_agnosticism_atheism |
| 831 | postal - postmaster - postage - deliveries - mail | 82 | 831_postal_postmaster_postage_deliveries |
| 832 | 1914 - 1913 - 1915 - 1918 - 1912 | 82 | 832_1914_1913_1915_1918 |
| 833 | graphite - carbon - steelmaking - mined - pencil | 82 | 833_graphite_carbon_steelmaking_mined |
| 834 | integers - primes - integer - prime - arithmetic | 82 | 834_integers_primes_integer_prime |
| 835 | bloods - gangs - gang - blood - criminals | 82 | 835_bloods_gangs_gang_blood |
| 836 | osmosis - desalination - purification - filtration - membranes | 82 | 836_osmosis_desalination_purification_filtration |
| 837 | guerre - french - 1958 - ceasefire - rebels | 82 | 837_guerre_french_1958_ceasefire |
| 838 | actress - sonata - och - autumn - maid | 82 | 838_actress_sonata_och_autumn |
| 839 | fastest - racing - mph - speed - motorsport | 82 | 839_fastest_racing_mph_speed |
| 840 | airline - airlines - seats - seating - 737 | 82 | 840_airline_airlines_seats_seating |
| 841 | novelist - writer - novels - literature - writers | 82 | 841_novelist_writer_novels_literature |
| 842 | nationalism - nationalist - nationalists - patriotism - nation | 82 | 842_nationalism_nationalist_nationalists_patriotism |
| 843 | celebrations - celebrated - festival - calendar - holidays | 82 | 843_celebrations_celebrated_festival_calendar |
| 844 | guerrillas - guerrilla - rebels - dictator - fled | 82 | 844_guerrillas_guerrilla_rebels_dictator |
| 845 | murdered - strangled - killed - unconscious - murders | 82 | 845_murdered_strangled_killed_unconscious |
| 846 | rated - rating - ratings - pg - films | 81 | 846_rated_rating_ratings_pg |
| 847 | mac - leopard - apple - os - versions | 81 | 847_mac_leopard_apple_os |
| 848 | aboriginal - indigenous - settlers - provincial - prairies | 81 | 848_aboriginal_indigenous_settlers_provincial |
| 849 | maps - map - google - android - street | 81 | 849_maps_map_google_android |
| 850 | airplane - airlines - hijacked - hijackers - hijackings | 81 | 850_airplane_airlines_hijacked_hijackers |
| 851 | bp - spill - spills - damages - negligence | 81 | 851_bp_spill_spills_damages |
| 852 | longitude - latitudes - latitude - geocentric - ellipsoid | 81 | 852_longitude_latitudes_latitude_geocentric |
| 853 | golfer - golfers - golf - masters - tournaments | 81 | 853_golfer_golfers_golf_masters |
| 854 | dean - hunter - actor - biography - acting | 81 | 854_dean_hunter_actor_biography |
| 855 | latching - latch - latches - flops - flip | 81 | 855_latching_latch_latches_flops |
| 856 | honours - honorary - honour - knighted - appointed | 81 | 856_honours_honorary_honour_knighted |
| 857 | clinical - gibbons - investigation - patents - laboratory | 81 | 857_clinical_gibbons_investigation_patents |
| 858 | suffrage - suffragettes - activists - feminist - activist | 81 | 858_suffrage_suffragettes_activists_feminist |
| 859 | toured - concert - début - tour - albums | 81 | 859_toured_concert_début_tour |
| 860 | pastor - pastors - megachurch - evangelical - ministries | 81 | 860_pastor_pastors_megachurch_evangelical |
| 861 | fm - stations - radio - station - broadcasts | 80 | 861_fm_stations_radio_station |
| 862 | filters - filtering - covariance - filter - covariances | 80 | 862_filters_filtering_covariance_filter |
| 863 | conspiracies - conspiratorial - conspiracy - conspiracism - conspiracist | 80 | 863_conspiracies_conspiratorial_conspiracy_conspiracism |
| 864 | soprano - sopranos - actor - cast - actors | 80 | 864_soprano_sopranos_actor_cast |
| 865 | expedition - voyage - whaling - exploration - 1901 | 80 | 865_expedition_voyage_whaling_exploration |
| 866 | actor - hamlet - actors - acting - theatre | 80 | 866_actor_hamlet_actors_acting |
| 867 | designers - designer - boutiques - fashion - makeup | 80 | 867_designers_designer_boutiques_fashion |
| 868 | processors - 1070 - supercomputers - processor - hardware | 80 | 868_processors_1070_supercomputers_processor |
| 869 | primus - toured - tour - praxis - drums | 80 | 869_primus_toured_tour_praxis |
| 870 | roof - prosecution - defendants - sentencing - convicted | 80 | 870_roof_prosecution_defendants_sentencing |
| 871 | strongman - strongest - strongmen - strength - competed | 80 | 871_strongman_strongest_strongmen_strength |
| 872 | parliament - parliamentary - constituencies - legislature - legislatures | 80 | 872_parliament_parliamentary_constituencies_legislature |
| 873 | monkey - monk - monkeys - buddha - tang | 80 | 873_monkey_monk_monkeys_buddha |
| 874 | rap - albums - park - rock - hybrid | 80 | 874_rap_albums_park_rock |
| 875 | coalition - election - minister - elections - 2021 | 80 | 875_coalition_election_minister_elections |
| 876 | smartphone - smartphones - laptop - tablet - sales | 80 | 876_smartphone_smartphones_laptop_tablet |
| 877 | stratosphere - meteorological - stratospheric - climatic - climate | 80 | 877_stratosphere_meteorological_stratospheric_climatic |
| 878 | reformation - protestant - theologian - papacy - 1541 | 80 | 878_reformation_protestant_theologian_papacy |
| 879 | neighbours - episodes - airing - episode - channel | 80 | 879_neighbours_episodes_airing_episode |
| 880 | coca - cocaine - tobacco - cola - leaves | 80 | 880_coca_cocaine_tobacco_cola |
| 881 | inferno - purgatory - sins - torment - theology | 80 | 881_inferno_purgatory_sins_torment |
| 882 | confederate - flags - flag - confederacy - confederates | 80 | 882_confederate_flags_flag_confederacy |
| 883 | dubbed - dub - anime - releases - premiered | 79 | 883_dubbed_dub_anime_releases |
| 884 | baron - comedian - mockumentary - documentary - film | 79 | 884_baron_comedian_mockumentary_documentary |
| 885 | golfer - masters - golf - golfers - tournament | 79 | 885_golfer_masters_golf_golfers |
| 886 | spiritualism - spirituality - paganism - esotericism - religiosity | 79 | 886_spiritualism_spirituality_paganism_esotericism |
| 887 | graffiti - paintings - painting - artworks - paint | 79 | 887_graffiti_paintings_painting_artworks |
| 888 | lakes - lake - shipwrecks - shipwreck - sank | 79 | 888_lakes_lake_shipwrecks_shipwreck |
| 889 | fashion - designers - designer - fashions - boutique | 79 | 889_fashion_designers_designer_fashions |
| 890 | representation - philosophy - philosophical - philosopher - philosophies | 79 | 890_representation_philosophy_philosophical_philosopher |
| 891 | railgun - railguns - rail - projectile - projectiles | 78 | 891_railgun_railguns_rail_projectile |
| 892 | adobe - illustrator - software - mac - graphics | 78 | 892_adobe_illustrator_software_mac |
| 893 | paternal - stepfather - nazi - illegitimate - grandfather | 78 | 893_paternal_stepfather_nazi_illegitimate |
| 894 | helix - nucleic - discoveries - discovered - biophysics | 78 | 894_helix_nucleic_discoveries_discovered |
| 895 | payments - payment - merchant - purchases - merchants | 78 | 895_payments_payment_merchant_purchases |
| 896 | airlines - airline - pan - flights - midway | 78 | 896_airlines_airline_pan_flights |
| 897 | secretariat - racehorse - racetrack - thoroughbred - racehorses | 78 | 897_secretariat_racehorse_racetrack_thoroughbred |
| 898 | sensitivity - specificity - diagnostic - positives - precision | 78 | 898_sensitivity_specificity_diagnostic_positives |
| 899 | pirate - piracy - bay - infringement - infringements | 78 | 899_pirate_piracy_bay_infringement |
| 900 | oyster - oysters - shellfish - crabs - seafood | 78 | 900_oyster_oysters_shellfish_crabs |
| 901 | ethnicities - ethnic - ethnically - ethnicity - population | 78 | 901_ethnicities_ethnic_ethnically_ethnicity |
| 902 | abolitionist - abolitionists - slavery - 1860 - abolition | 78 | 902_abolitionist_abolitionists_slavery_1860 |
| 903 | reefs - corals - coral - reef - aquaculture | 77 | 903_reefs_corals_coral_reef |
| 904 | incomes - income - wealth - disparities - poverty | 77 | 904_incomes_income_wealth_disparities |
| 905 | officers - officer - recruitment - administrative - secretaries | 77 | 905_officers_officer_recruitment_administrative |
| 906 | sabbath - piers - frontman - airing - presenter | 77 | 906_sabbath_piers_frontman_airing |
| 907 | aether - realms - realm - omnipotence - gods | 77 | 907_aether_realms_realm_omnipotence |
| 908 | extinctions - extinction - extinct - dinosaurs - speciation | 77 | 908_extinctions_extinction_extinct_dinosaurs |
| 909 | armistice - 38th - counterinsurgency - soviet - retreated | 77 | 909_armistice_38th_counterinsurgency_soviet |
| 910 | magicians - magician - museum - vaudeville - cemetery | 77 | 910_magicians_magician_museum_vaudeville |
| 911 | sequels - sequel - trilogy - screenplay - remake | 77 | 911_sequels_sequel_trilogy_screenplay |
| 912 | executions - executed - clemency - punishment - inmates | 77 | 912_executions_executed_clemency_punishment |
| 913 | neolithic - archaeological - archaeology - excavations - civilisation | 77 | 913_neolithic_archaeological_archaeology_excavations |
| 914 | dolly - novel - literature - mansion - narrator | 77 | 914_dolly_novel_literature_mansion |
| 915 | sparrow - pirates - pirate - privateer - captained | 77 | 915_sparrow_pirates_pirate_privateer |
| 916 | scurvy - vitamin - supplementation - dietary - supplement | 77 | 916_scurvy_vitamin_supplementation_dietary |
| 917 | holly - finale - office - receptionist - episode | 77 | 917_holly_finale_office_receptionist |
| 918 | hemp - cannabis - textiles - cultivated - textile | 76 | 918_hemp_cannabis_textiles_cultivated |
| 919 | lidar - radar - laser - photogrammetry - sensors | 76 | 919_lidar_radar_laser_photogrammetry |
| 920 | dingoes - dingo - breeding - pets - kangaroos | 76 | 920_dingoes_dingo_breeding_pets |
| 921 | crocodile - zookeeper - zoo - crocodiles - wildlife | 76 | 921_crocodile_zookeeper_zoo_crocodiles |
| 922 | slots - slot - gambling - reels - poker | 76 | 922_slots_slot_gambling_reels |
| 923 | bombs - bomb - 1945 - bombing - detonated | 76 | 923_bombs_bomb_1945_bombing |
| 924 | manufacturer - corporate - corporation - company - brands | 76 | 924_manufacturer_corporate_corporation_company |
| 925 | stones - stone - guitarist - guitarists - drums | 76 | 925_stones_stone_guitarist_guitarists |
| 926 | meiosis - mitosis - chromosomal - chromosomes - chromosome | 76 | 926_meiosis_mitosis_chromosomal_chromosomes |
| 927 | pirate - privateer - bonnet - pirates - privateering | 76 | 927_pirate_privateer_bonnet_pirates |
| 928 | parks - park - attractions - studios - pavilion | 75 | 928_parks_park_attractions_studios |
| 929 | medicine - medicinal - medicines - physicians - herbal | 75 | 929_medicine_medicinal_medicines_physicians |
| 930 | acupuncture - acupuncturists - medicine - practitioners - patients | 75 | 930_acupuncture_acupuncturists_medicine_practitioners |
| 931 | margarine - yeast - extracts - foods - recipe | 75 | 931_margarine_yeast_extracts_foods |
| 932 | chiropractors - chiropractic - chiropractor - osteopathic - practitioners | 75 | 932_chiropractors_chiropractic_chiropractor_osteopathic |
| 933 | negro - activist - behest - 1925 - racism | 75 | 933_negro_activist_behest_1925 |
| 934 | infantry - tanks - soldier - 1944 - troops | 75 | 934_infantry_tanks_soldier_1944 |
| 935 | geography - geographic - geographical - geographer - geographers | 75 | 935_geography_geographic_geographical_geographer |
| 936 | federalism - federations - federation - unitary - sovereignty | 75 | 936_federalism_federations_federation_unitary |
| 937 | braking - transmissions - brakes - automatic - brake | 75 | 937_braking_transmissions_brakes_automatic |
| 938 | ford - presidency - presidential - presidents - wife | 75 | 938_ford_presidency_presidential_presidents |
| 939 | eukaryotes - prokaryotes - eukaryotic - prokaryotic - eukaryote | 75 | 939_eukaryotes_prokaryotes_eukaryotic_prokaryotic |
| 940 | electroconvulsive - antidepressants - antidepressant - anticonvulsant - electrodes | 75 | 940_electroconvulsive_antidepressants_antidepressant_anticonvulsant |
| 941 | bourgeoisie - capitalist - bourgeois - capitalism - socialism | 75 | 941_bourgeoisie_capitalist_bourgeois_capitalism |
| 942 | burger - hamburger - burgers - hamburgers - steak | 75 | 942_burger_hamburger_burgers_hamburgers |
| 943 | stagecoach - ford - cinematography - films - actor | 75 | 943_stagecoach_ford_cinematography_films |
| 944 | comics - cartoonist - adventures - magazine - comic | 75 | 944_comics_cartoonist_adventures_magazine |
| 945 | detective - detectives - novels - murders - obituary | 75 | 945_detective_detectives_novels_murders |
| 946 | laureates - laureate - prizes - prize - awarding | 75 | 946_laureates_laureate_prizes_prize |
| 947 | bombed - troops - insurgency - casualties - tactics | 75 | 947_bombed_troops_insurgency_casualties |
| 948 | allegations - molested - offences - alleged - abused | 74 | 948_allegations_molested_offences_alleged |
| 949 | subreddit - subreddits - banning - censorship - incels | 74 | 949_subreddit_subreddits_banning_censorship |
| 950 | onzz - superman - watchtower - superhero - storyline | 74 | 950_onzz_superman_watchtower_superhero |
| 951 | pronouns - pronoun - plurality - plurals - plural | 74 | 951_pronouns_pronoun_plurality_plurals |
| 952 | gymnast - gymnastics - gymnasts - olympic - competed | 74 | 952_gymnast_gymnastics_gymnasts_olympic |
| 953 | bonobos - chimpanzees - primates - chimpanzee - primate | 74 | 953_bonobos_chimpanzees_primates_chimpanzee |
| 954 | singer - songwriter - albums - musician - bono | 74 | 954_singer_songwriter_albums_musician |
| 955 | pearls - pearl - pearling - oysters - oyster | 74 | 955_pearls_pearl_pearling_oysters |
| 956 | patients - inpatients - physicians - physician - inpatient | 74 | 956_patients_inpatients_physicians_physician |
| 957 | oz - wizard - 1939 - wicked - emerald | 74 | 957_oz_wizard_1939_wicked |
| 958 | pride - flags - flag - rainbow - parade | 74 | 958_pride_flags_flag_rainbow |
| 959 | espionage - spies - spy - spying - soviets | 74 | 959_espionage_spies_spy_spying |
| 960 | chairman - executive - resigned - chief - directors | 74 | 960_chairman_executive_resigned_chief |
| 961 | paramilitary - mercenaries - civilians - mercenary - casualties | 74 | 961_paramilitary_mercenaries_civilians_mercenary |
| 962 | obesity - obese - overweight - underweight - adipose | 74 | 962_obesity_obese_overweight_underweight |
| 963 | deities - polytheism - monotheistic - monotheism - creation | 74 | 963_deities_polytheism_monotheistic_monotheism |
| 964 | housewives - housewife - airing - episodes - renewed | 73 | 964_housewives_housewife_airing_episodes |
| 965 | tariffs - tariff - exports - agreements - economy | 73 | 965_tariffs_tariff_exports_agreements |
| 966 | metric - imperial - units - metre - kilograms | 73 | 966_metric_imperial_units_metre |
| 967 | forested - vegetation - conifers - forests - rainforests | 73 | 967_forested_vegetation_conifers_forests |
| 968 | schemas - schema - metadata - structured - specification | 73 | 968_schemas_schema_metadata_structured |
| 969 | homosexuality - homosexuals - homosexual - homophobia - immoral | 73 | 969_homosexuality_homosexuals_homosexual_homophobia |
| 970 | dome - missiles - missile - protects - protect | 73 | 970_dome_missiles_missile_protects |
| 971 | scramjet - scramjets - turbojet - turbojets - ramjet | 73 | 971_scramjet_scramjets_turbojet_turbojets |
| 972 | esotericists - esotericism - esoteric - occultism - occultists | 73 | 972_esotericists_esotericism_esoteric_occultism |
| 973 | regexes - regex - syntax - parsing - patterns | 73 | 973_regexes_regex_syntax_parsing |
| 974 | auroral - aurora - auroras - magnetosphere - aurorae | 73 | 974_auroral_aurora_auroras_magnetosphere |
| 975 | metamorphosis - literature - literary - writings - writer | 73 | 975_metamorphosis_literature_literary_writings |
| 976 | musician - concert - gravestone - bandmate - backstage | 73 | 976_musician_concert_gravestone_bandmate |
| 977 | dell - manufacturers - manufacturer - vendors - intel | 73 | 977_dell_manufacturers_manufacturer_vendors |
| 978 | soviets - missiles - overflights - reconnaissance - overflight | 73 | 978_soviets_missiles_overflights_reconnaissance |
| 979 | profiles - profile - freelancers - recruiters - resumes | 73 | 979_profiles_profile_freelancers_recruiters |
| 980 | doge - pope - 1571 - mediterranean - duchy | 72 | 980_doge_pope_1571_mediterranean |
| 981 | chess - grandmaster - fide - tournament - championship | 72 | 981_chess_grandmaster_fide_tournament |
| 982 | comet - cometary - comets - meteor - telescope | 72 | 982_comet_cometary_comets_meteor |
| 983 | totalitarianism - holocaust - totalitarian - biography - nazi | 72 | 983_totalitarianism_holocaust_totalitarian_biography |
| 984 | tics - tic - disorders - neuropsychiatric - autism | 72 | 984_tics_tic_disorders_neuropsychiatric |
| 985 | bullying - bullied - bullies - bully - cyberbullying | 72 | 985_bullying_bullied_bullies_bully |
| 986 | psychopathy - psychopathic - psychopaths - psychopath - sociopathy | 72 | 986_psychopathy_psychopathic_psychopaths_psychopath |
| 987 | linguistics - linguistic - linguists - linguist - languages | 72 | 987_linguistics_linguistic_linguists_linguist |
| 988 | literature - writings - author - fictions - literary | 72 | 988_literature_writings_author_fictions |
| 989 | cook - voyage - voyages - sailed - 1788 | 72 | 989_cook_voyage_voyages_sailed |
| 990 | cyberpunk - cybernetics - novelists - novel - fiction | 72 | 990_cyberpunk_cybernetics_novelists_novel |
| 991 | population - census - inhabitants - populous - populated | 72 | 991_population_census_inhabitants_populous |
| 992 | linden - lab - copyright - token - refund | 72 | 992_linden_lab_copyright_token |
| 993 | cartoons - cartoon - spinach - comic - cartoonists | 72 | 993_cartoons_cartoon_spinach_comic |
| 994 | nazi - holocaust - 1941 - 1945 - persecuted | 72 | 994_nazi_holocaust_1941_1945 |
| 995 | indictment - indictments - indicted - prosecutors - convicted | 72 | 995_indictment_indictments_indicted_prosecutors |
| 996 | tributaries - tributary - river - rivers - alluvial | 72 | 996_tributaries_tributary_river_rivers |
| 997 | vocalist - vocals - singers - singer - saxophonist | 72 | 997_vocalist_vocals_singers_singer |
| 998 | esteem - self - ego - psychological - oneself | 72 | 998_esteem_self_ego_psychological |
| 999 | rescuers - rescuer - survivors - rescue - camped | 72 | 999_rescuers_rescuer_survivors_rescue |
| 1000 | coax - coaxial - cables - cable - antennas | 72 | 1000_coax_coaxial_cables_cable |
| 1001 | synesthesia - synesthetic - synesthetes - paresthesia - synesthete | 72 | 1001_synesthesia_synesthetic_synesthetes_paresthesia |
| 1002 | annexation - 1938 - annexed - 1945 - annex | 71 | 1002_annexation_1938_annexed_1945 |
| 1003 | motocross - motorcycle - stunt - bike - stunts | 71 | 1003_motocross_motorcycle_stunt_bike |
| 1004 | chocolate - factory - screenplay - wilder - bucket | 71 | 1004_chocolate_factory_screenplay_wilder |
| 1005 | galaxy - smartphone - smartphones - mobile - flagship | 71 | 1005_galaxy_smartphone_smartphones_mobile |
| 1006 | runes - rune - runestones - inscriptions - inscription | 71 | 1006_runes_rune_runestones_inscriptions |
| 1007 | che - revolutionaries - guerrilla - revolutionary - guerrillas | 71 | 1007_che_revolutionaries_guerrilla_revolutionary |
| 1008 | hemorrhage - surgery - surgical - injury - iron | 71 | 1008_hemorrhage_surgery_surgical_injury |
| 1009 | referendum - conservative - trump - candidate - resigned | 71 | 1009_referendum_conservative_trump_candidate |
| 1010 | sightings - sighting - hoaxes - hoax - skunk | 71 | 1010_sightings_sighting_hoaxes_hoax |
| 1011 | sphinx - sphinxes - pharaoh - pyramid - statue | 71 | 1011_sphinx_sphinxes_pharaoh_pyramid |
| 1012 | violinist - violin - violins - albums - vinyl | 71 | 1012_violinist_violin_violins_albums |
| 1013 | law - jurisprudence - judicial - statutes - jurisdictions | 71 | 1013_law_jurisprudence_judicial_statutes |
| 1014 | nails - albums - album - band - artists | 71 | 1014_nails_albums_album_band |
| 1015 | apple - mac - microcomputers - microcomputer - computers | 71 | 1015_apple_mac_microcomputers_microcomputer |
| 1016 | scream - paintings - painting - painter - art | 71 | 1016_scream_paintings_painting_painter |
| 1017 | flew - flight - airplane - flying - aviator | 71 | 1017_flew_flight_airplane_flying |
| 1018 | ninja - ninjas - anime - kai - cartoon | 71 | 1018_ninja_ninjas_anime_kai |
| 1019 | investing - invest - investors - indexes - investment | 71 | 1019_investing_invest_investors_indexes |
| 1020 | concord - airlines - flights - airliners - airliner | 71 | 1020_concord_airlines_flights_airliners |
| 1021 | dysplasia - breeds - veterinary - shepherd - dystrophy | 71 | 1021_dysplasia_breeds_veterinary_shepherd |
| 1022 | doll - dolls - toy - brand - fashion | 71 | 1022_doll_dolls_toy_brand |
| 1023 | investments - invested - investor - investors - investment | 70 | 1023_investments_invested_investor_investors |
| 1024 | intersectionality - intersectional - feminism - intersection - feminist | 70 | 1024_intersectionality_intersectional_feminism_intersection |
| 1025 | festivals - festival - festivities - carnivals - carnival | 70 | 1025_festivals_festival_festivities_carnivals |
| 1026 | tennis - racquet - tournament - quarterfinal - doubles | 70 | 1026_tennis_racquet_tournament_quarterfinal |
| 1027 | daddy - reggaeton - rapper - rap - mixtape | 70 | 1027_daddy_reggaeton_rapper_rap |
| 1028 | probability - probabilities - doors - car - door | 70 | 1028_probability_probabilities_doors_car |
| 1029 | radar - radars - signals - doppler - transmitter | 70 | 1029_radar_radars_signals_doppler |
| 1030 | blackberry - smartphone - smartphones - android - mobile | 70 | 1030_blackberry_smartphone_smartphones_android |
| 1031 | cappuccino - espresso - coffee - capo - latte | 70 | 1031_cappuccino_espresso_coffee_capo |
| 1032 | candidates - candidate - election - elections - populist | 70 | 1032_candidates_candidate_election_elections |
| 1033 | cud - rapper - mixtape - kid - rap | 70 | 1033_cud_rapper_mixtape_kid |
| 1034 | soviets - soviet - treaty - ceded - ceasefire | 70 | 1034_soviets_soviet_treaty_ceded |
| 1035 | nuclear - disarmament - treaty - uranium - nations | 70 | 1035_nuclear_disarmament_treaty_uranium |
| 1036 | ivy - poison - poisons - poisoned - poisoning | 70 | 1036_ivy_poison_poisons_poisoned |
| 1037 | tsar - empress - heir - 1762 - mistress | 70 | 1037_tsar_empress_heir_1762 |
| 1038 | sexuality - discipline - sociology - homosexuality - behavior | 70 | 1038_sexuality_discipline_sociology_homosexuality |
| 1039 | elves - elf - folklore - fairies - dwarves | 69 | 1039_elves_elf_folklore_fairies |
| 1040 | peacekeeping - sovereignty - niger - nations - territory | 69 | 1040_peacekeeping_sovereignty_niger_nations |
| 1041 | torturing - strangled - stabbing - murdered - victims | 69 | 1041_torturing_strangled_stabbing_murdered |
| 1042 | exorcist - exorcism - screenplay - possessed - demonic | 69 | 1042_exorcist_exorcism_screenplay_possessed |
| 1043 | cloud - clouds - azure - virtualization - infrastructure | 69 | 1043_cloud_clouds_azure_virtualization |
| 1044 | yaoi - manga - hentai - anime - heterosexual | 69 | 1044_yaoi_manga_hentai_anime |
| 1045 | doping - athlete - lance - cyclist - steroids | 69 | 1045_doping_athlete_lance_cyclist |
| 1046 | wickets - batsman - wicket - bowled - bowler | 69 | 1046_wickets_batsman_wicket_bowled |
| 1047 | opus - pontifical - popes - priests - pope | 69 | 1047_opus_pontifical_popes_priests |
| 1048 | ancestry - genetic - haplogroup - paleolithic - genes | 69 | 1048_ancestry_genetic_haplogroup_paleolithic |
| 1049 | thanksgiving - holiday - holidays - celebrated - celebrations | 69 | 1049_thanksgiving_holiday_holidays_celebrated |
| 1050 | joker - skins - superman - comics - knight | 69 | 1050_joker_skins_superman_comics |
| 1051 | freeware - proprietary - software - licensing - licenses | 69 | 1051_freeware_proprietary_software_licensing |
| 1052 | quantum - qubits - qubit - computational - computing | 69 | 1052_quantum_qubits_qubit_computational |
| 1053 | bird - storm - star - rebounds - assists | 69 | 1053_bird_storm_star_rebounds |
| 1054 | ceasefire - peacekeeping - oblast - militias - hostilities | 69 | 1054_ceasefire_peacekeeping_oblast_militias |
| 1055 | communists - soviets - protests - demonstrators - communist | 69 | 1055_communists_soviets_protests_demonstrators |
| 1056 | palaces - ibn - mosque - palace - excavations | 68 | 1056_palaces_ibn_mosque_palace |
| 1057 | nirvana - overdosed - grunge - overdose - died | 68 | 1057_nirvana_overdosed_grunge_overdose |
| 1058 | commanders - commander - allied - 1944 - panzer | 68 | 1058_commanders_commander_allied_1944 |
| 1059 | blinding - heartless - lights - song - billboard | 68 | 1059_blinding_heartless_lights_song |
| 1060 | fort - battle - 1836 - surrender - reinforcements | 68 | 1060_fort_battle_1836_surrender |
| 1061 | touchdowns - cousins - interceptions - touchdown - yards | 68 | 1061_touchdowns_cousins_interceptions_touchdown |
| 1062 | machines - computable - computational - machine - deterministic | 68 | 1062_machines_computable_computational_machine |
| 1063 | creoles - creole - vernaculars - vernacular - lingua | 68 | 1063_creoles_creole_vernaculars_vernacular |
| 1064 | endometriosis - endometrial - endometrium - uterus - menstruation | 68 | 1064_endometriosis_endometrial_endometrium_uterus |
| 1065 | lin - undrafted - harden - assists - rebounds | 68 | 1065_lin_undrafted_harden_assists |
| 1066 | pornography - porn - pornographic - playboy - affiliate | 68 | 1066_pornography_porn_pornographic_playboy |
| 1067 | panchayat - panchayats - elections - electoral - election | 68 | 1067_panchayat_panchayats_elections_electoral |
| 1068 | stalker - filmmaker - cinematographer - director - cinematography | 68 | 1068_stalker_filmmaker_cinematographer_director |
| 1069 | loch - ness - sightings - sighting - folklore | 68 | 1069_loch_ness_sightings_sighting |
| 1070 | taco - tacos - restaurants - restaurant - cafe | 68 | 1070_taco_tacos_restaurants_restaurant |
| 1071 | absinthe - absinthes - herbs - cocktail - distilled | 68 | 1071_absinthe_absinthes_herbs_cocktail |
| 1072 | resuscitation - defibrillation - defibrillator - cardiopulmonary - cardiac | 68 | 1072_resuscitation_defibrillation_defibrillator_cardiopulmonary |
| 1073 | chancellor - secretary - minister - appointed - resigned | 68 | 1073_chancellor_secretary_minister_appointed |
| 1074 | defrauded - fraud - fraudulent - fraudster - whistleblower | 68 | 1074_defrauded_fraud_fraudulent_fraudster |
| 1075 | printing - printmaking - printers - printer - print | 68 | 1075_printing_printmaking_printers_printer |
| 1076 | ancient - mediterranean - civilizations - archaeological - excavations | 68 | 1076_ancient_mediterranean_civilizations_archaeological |
| 1077 | dodo - dodos - fauna - birds - species | 68 | 1077_dodo_dodos_fauna_birds |
| 1078 | brave - novel - novels - utopia - utopian | 68 | 1078_brave_novel_novels_utopia |
| 1079 | piccolo - dragon - kai - trunks - battle | 68 | 1079_piccolo_dragon_kai_trunks |
| 1080 | parachutes - parachute - skydiving - flight - airlines | 68 | 1080_parachutes_parachute_skydiving_flight |
| 1081 | autonomy - independence - constituted - nationalism - referendum | 68 | 1081_autonomy_independence_constituted_nationalism |
| 1082 | robots - robot - robotic - robotics - ai | 68 | 1082_robots_robot_robotic_robotics |
| 1083 | tanks - tank - partisan - ideological - think | 68 | 1083_tanks_tank_partisan_ideological |
| 1084 | pharaoh - archaeological - dynasty - sea - dynasties | 67 | 1084_pharaoh_archaeological_dynasty_sea |
| 1085 | hippie - hippies - hipster - hippy - counterculture | 67 | 1085_hippie_hippies_hipster_hippy |
| 1086 | inscriptions - inscription - epigraphy - taluk - ancient | 67 | 1086_inscriptions_inscription_epigraphy_taluk |
| 1087 | filmmaker - filmmaking - cinematographer - filmmakers - films | 67 | 1087_filmmaker_filmmaking_cinematographer_filmmakers |
| 1088 | celebrations - festivities - celebrated - traditions - mosque | 67 | 1088_celebrations_festivities_celebrated_traditions |
| 1089 | hawking - physicist - cosmology - sciences - marriage | 67 | 1089_hawking_physicist_cosmology_sciences |
| 1090 | albums - songs - album - musical - music | 67 | 1090_albums_songs_album_musical |
| 1091 | pound - poet - poetry - poems - literary | 67 | 1091_pound_poet_poetry_poems |
| 1092 | embryos - embryo - fertility - infertility - infertile | 67 | 1092_embryos_embryo_fertility_infertility |
| 1093 | satanic - satan - theology - devil - atheism | 67 | 1093_satanic_satan_theology_devil |
| 1094 | bombing - insurgency - bombings - overthrow - militants | 67 | 1094_bombing_insurgency_bombings_overthrow |
| 1095 | tribalism - nationalist - unrest - sovereignty - decolonization | 67 | 1095_tribalism_nationalist_unrest_sovereignty |
| 1096 | kibbutz - kibbutzim - kibbutzniks - founders - communities | 67 | 1096_kibbutz_kibbutzim_kibbutzniks_founders |
| 1097 | priest - demonic - priestess - demon - demons | 67 | 1097_priest_demonic_priestess_demon |
| 1098 | eclampsia - pregnancies - pregnancy - prenatal - gestational | 67 | 1098_eclampsia_pregnancies_pregnancy_prenatal |
| 1099 | riots - protests - protest - activism - activists | 67 | 1099_riots_protests_protest_activism |
| 1100 | hill - silent - sequel - remake - gameplay | 67 | 1100_hill_silent_sequel_remake |
| 1101 | treaty - treaties - covenant - league - nations | 67 | 1101_treaty_treaties_covenant_league |
| 1102 | prix - motorsport - racing - qualifying - grand | 67 | 1102_prix_motorsport_racing_qualifying |
| 1103 | automotive - ab - automobile - automobiles - vehicle | 67 | 1103_automotive_ab_automobile_automobiles |
| 1104 | chamberlain - 1945 - minister - resigned - 1940 | 67 | 1104_chamberlain_1945_minister_resigned |
| 1105 | vegetarian - vegetarianism - veganism - vegetarians - vegan | 67 | 1105_vegetarian_vegetarianism_veganism_vegetarians |
| 1106 | dictator - dictatorship - fascism - fascist - authoritarian | 67 | 1106_dictator_dictatorship_fascism_fascist |
| 1107 | celiac - gluten - coeliac - wheat - autoimmune | 66 | 1107_celiac_gluten_coeliac_wheat |
| 1108 | ford - truck - trucks - chassis - jeep | 66 | 1108_ford_truck_trucks_chassis |
| 1109 | inkblots - inkblot - ink - psychometric - psychoanalytic | 66 | 1109_inkblots_inkblot_ink_psychometric |
| 1110 | crimson - guitarist - toured - guitars - bands | 66 | 1110_crimson_guitarist_toured_guitars |
| 1111 | oblast - oblasts - governorates - province - soviet | 66 | 1111_oblast_oblasts_governorates_province |
| 1112 | radio - fm - stations - channels - broadcasts | 66 | 1112_radio_fm_stations_channels |
| 1113 | 1803 - 1763 - treaty - ceded - treaties | 66 | 1113_1803_1763_treaty_ceded |
| 1114 | nicotine - nicotinic - tobacco - cigarettes - cigarette | 66 | 1114_nicotine_nicotinic_tobacco_cigarettes |
| 1115 | flags - flag - sun - swastika - emblem | 66 | 1115_flags_flag_sun_swastika |
| 1116 | philosopher - philosophers - philosophy - philosophical - logician | 66 | 1116_philosopher_philosophers_philosophy_philosophical |
| 1117 | whataboutism - geopolitical - dissidents - propaganda - propagandists | 66 | 1117_whataboutism_geopolitical_dissidents_propaganda |
| 1118 | nirvana - grunge - album - band - bands | 66 | 1118_nirvana_grunge_album_band |
| 1119 | proud - boys - protests - protesters - demonstrators | 66 | 1119_proud_boys_protests_protesters |
| 1120 | bands - slayer - thrash - band - frontman | 66 | 1120_bands_slayer_thrash_band |
| 1121 | scored - scoring - goal - penalty - goals | 66 | 1121_scored_scoring_goal_penalty |
| 1122 | turkey - terrorist - militants - terrorism - militant | 66 | 1122_turkey_terrorist_militants_terrorism |
| 1123 | shroud - crucified - crucifixion - burial - sculpture | 66 | 1123_shroud_crucified_crucifixion_burial |
| 1124 | blink - band - bands - 182 - punk | 66 | 1124_blink_band_bands_182 |
| 1125 | poet - poetry - poems - poem - stanzas | 66 | 1125_poet_poetry_poems_poem |
| 1126 | racing - speed - chases - racer - pursuit | 65 | 1126_racing_speed_chases_racer |
| 1127 | mansion - bedrooms - mansions - residence - bedroom | 65 | 1127_mansion_bedrooms_mansions_residence |
| 1128 | languages - multilingual - language - lingua - creole | 65 | 1128_languages_multilingual_language_lingua |
| 1129 | espionage - spying - spy - informant - investigator | 65 | 1129_espionage_spying_spy_informant |
| 1130 | yoon - jung - scandal - prosecutors - alleged | 65 | 1130_yoon_jung_scandal_prosecutors |
| 1131 | 1451 - pasha - 1477 - 1476 - 1475 | 65 | 1131_1451_pasha_1477_1476 |
| 1132 | burning - burners - organizers - attendees - gatherings | 65 | 1132_burning_burners_organizers_attendees |
| 1133 | spartan - ancient - battle - invasion - retreated | 65 | 1133_spartan_ancient_battle_invasion |
| 1134 | bell - telephone - telephones - inventor - invention | 65 | 1134_bell_telephone_telephones_inventor |
| 1135 | mathematician - mathematicians - mathematics - algebra - arithmetical | 65 | 1135_mathematician_mathematicians_mathematics_algebra |
| 1136 | restaurants - restaurant - chefs - culinary - cuisines | 65 | 1136_restaurants_restaurant_chefs_culinary |
| 1137 | restaurants - customers - restaurant - burger - franchisees | 65 | 1137_restaurants_customers_restaurant_burger |
| 1138 | misfits - albums - bands - band - toured | 65 | 1138_misfits_albums_bands_band |
| 1139 | rationalism - rationalisation - rationalization - rationality - philosophy | 65 | 1139_rationalism_rationalisation_rationalization_rationality |
| 1140 | paintings - artworks - gallery - painting - exhibitions | 65 | 1140_paintings_artworks_gallery_painting |
| 1141 | dan - sitcom - cast - spinoff - remarrying | 65 | 1141_dan_sitcom_cast_spinoff |
| 1142 | vocals - remixes - albums - chorus - album | 65 | 1142_vocals_remixes_albums_chorus |
| 1143 | casualties - fatalities - deaths - mortality - insurgents | 65 | 1143_casualties_fatalities_deaths_mortality |
| 1144 | gaming - retailers - games - retailer - gamers | 65 | 1144_gaming_retailers_games_retailer |
| 1145 | tales - literature - tale - manuscripts - testament | 65 | 1145_tales_literature_tale_manuscripts |
| 1146 | deposed - presidency - presidential - ousted - elections | 65 | 1146_deposed_presidency_presidential_ousted |
| 1147 | citizenship - passport - territories - residency - sovereign | 64 | 1147_citizenship_passport_territories_residency |
| 1148 | optimization - algorithms - optimal - algorithm - optimality | 64 | 1148_optimization_algorithms_optimal_algorithm |
| 1149 | sentenced - imprisonment - convicted - pardoned - judiciary | 64 | 1149_sentenced_imprisonment_convicted_pardoned |
| 1150 | caterpillar - diesel - manufacturer - manufacturing - tractors | 64 | 1150_caterpillar_diesel_manufacturer_manufacturing |
| 1151 | hub - sci - lawsuit - scholarly - plaintiffs | 64 | 1151_hub_sci_lawsuit_scholarly |
| 1152 | neolithic - stone - stones - excavations - archaeologists | 64 | 1152_neolithic_stone_stones_excavations |
| 1153 | coordinates - coordinate - axes - axis - longitude | 64 | 1153_coordinates_coordinate_axes_axis |
| 1154 | lingerie - secret - retailer - apparel - retail | 64 | 1154_lingerie_secret_retailer_apparel |
| 1155 | biodiversity - extinction - extinctions - ecosystem - ecological | 64 | 1155_biodiversity_extinction_extinctions_ecosystem |
| 1156 | pearl - jam - concert - toured - albums | 64 | 1156_pearl_jam_concert_toured |
| 1157 | tesseract - polytopes - hexagonal - squares - cubes | 64 | 1157_tesseract_polytopes_hexagonal_squares |
| 1158 | devices - pairing - paired - protocol - device | 64 | 1158_devices_pairing_paired_protocol |
| 1159 | tsar - tsarina - empress - 1917 - duchess | 64 | 1159_tsar_tsarina_empress_1917 |
| 1160 | neighbourhoods - khan - mosques - urban - municipal | 64 | 1160_neighbourhoods_khan_mosques_urban |
| 1161 | assassination - colonel - secessionist - martyr - secession | 64 | 1161_assassination_colonel_secessionist_martyr |
| 1162 | skater - skaters - skating - skate - olympic | 64 | 1162_skater_skaters_skating_skate |
| 1163 | durations - duration - decoding - transmissions - milliseconds | 64 | 1163_durations_duration_decoding_transmissions |
| 1164 | retailers - retailer - retailing - retail - thanksgiving | 64 | 1164_retailers_retailer_retailing_retail |
| 1165 | panther - panthers - activists - activist - antiwar | 64 | 1165_panther_panthers_activists_activist |
| 1166 | spironolactone - progesterone - antiandrogenic - aldosterone - antiandrogen | 64 | 1166_spironolactone_progesterone_antiandrogenic_aldosterone |
| 1167 | unrest - uprising - protests - overthrow - protesters | 64 | 1167_unrest_uprising_protests_overthrow |
| 1168 | tower - survivors - towers - 911 - evacuated | 64 | 1168_tower_survivors_towers_911 |
| 1169 | venture - ventures - investors - entrepreneurship - entrepreneurs | 64 | 1169_venture_ventures_investors_entrepreneurship |
| 1170 | sentencing - convicted - conviction - prosecution - jurors | 64 | 1170_sentencing_convicted_conviction_prosecution |
| 1171 | exotic - tiger - zoo - zookeeper - wildlife | 64 | 1171_exotic_tiger_zoo_zookeeper |
| 1172 | attacks - botnet - firewalls - exploits - attackers | 64 | 1172_attacks_botnet_firewalls_exploits |
| 1173 | bridges - bridge - infantry - bridged - artillery | 64 | 1173_bridges_bridge_infantry_bridged |
| 1174 | paintings - painting - auctioned - auction - painted | 63 | 1174_paintings_painting_auctioned_auction |
| 1175 | islands - archipelago - sovereignty - island - atoll | 63 | 1175_islands_archipelago_sovereignty_island |
| 1176 | cameo - cast - stunts - castmates - aired | 63 | 1176_cameo_cast_stunts_castmates |
| 1177 | stagecoach - outlaw - murderer - marshal - gunfighter | 63 | 1177_stagecoach_outlaw_murderer_marshal |
| 1178 | protesting - protests - protest - activism - climate | 63 | 1178_protesting_protests_protest_activism |
| 1179 | billing - provider - customers - customer - subscribers | 63 | 1179_billing_provider_customers_customer |
| 1180 | archipelagos - territories - islands - island - countries | 63 | 1180_archipelagos_territories_islands_island |
| 1181 | deer - hunter - filmmaking - screenplay - film | 63 | 1181_deer_hunter_filmmaking_screenplay |
| 1182 | apps - apple - app - voice - devices | 63 | 1182_apps_apple_app_voice |
| 1183 | paintings - painting - artworks - artist - art | 63 | 1183_paintings_painting_artworks_artist |
| 1184 | buses - midlands - railway - railways - trains | 63 | 1184_buses_midlands_railway_railways |
| 1185 | sonic - hedgehog - supersonic - tails - voiced | 63 | 1185_sonic_hedgehog_supersonic_tails |
| 1186 | memes - meme - 4chan - intertextuality - satirical | 63 | 1186_memes_meme_4chan_intertextuality |
| 1187 | khanate - khan - khanates - khans - sultanate | 63 | 1187_khanate_khan_khanates_khans |
| 1188 | orthodox - orthodoxy - religiosity - religions - catholic | 63 | 1188_orthodox_orthodoxy_religiosity_religions |
| 1189 | shuttle - spacecraft - orbiters - orbiter - astronauts | 63 | 1189_shuttle_spacecraft_orbiters_orbiter |
| 1190 | anarchists - anarchist - anarchism - anarchy - socialists | 63 | 1190_anarchists_anarchist_anarchism_anarchy |
| 1191 | brands - brand - companies - company - bottled | 63 | 1191_brands_brand_companies_company |
| 1192 | shares - invested - stock - investor - holdings | 62 | 1192_shares_invested_stock_investor |
| 1193 | cricket - cricketers - stadium - cricketing - stadiums | 62 | 1193_cricket_cricketers_stadium_cricketing |
| 1194 | mayor - mayors - mayoral - mayoralty - governor | 62 | 1194_mayor_mayors_mayoral_mayoralty |
| 1195 | mac - office - os - versions - version | 62 | 1195_mac_office_os_versions |
| 1196 | diary - diaries - manuscript - frank - editions | 62 | 1196_diary_diaries_manuscript_frank |
| 1197 | patsy - singer - singing - melody - vocalists | 62 | 1197_patsy_singer_singing_melody |
| 1198 | networking - packet - network - internetworking - protocols | 62 | 1198_networking_packet_network_internetworking |
| 1199 | borscht - recipes - recipe - cuisines - cuisine | 62 | 1199_borscht_recipes_recipe_cuisines |
| 1200 | gulag - prisoners - camps - prisons - inmates | 62 | 1200_gulag_prisoners_camps_prisons |
| 1201 | philanthropist - philanthropy - philanthropists - philanthropic - financier | 62 | 1201_philanthropist_philanthropy_philanthropists_philanthropic |
| 1202 | chapters - chapter - novels - paperback - books | 62 | 1202_chapters_chapter_novels_paperback |
| 1203 | hybrids - hybrid - ev - corolla - vehicles | 62 | 1203_hybrids_hybrid_ev_corolla |
| 1204 | hospice - hospices - palliative - caregiving - caregivers | 62 | 1204_hospice_hospices_palliative_caregiving |
| 1205 | mithraeum - mithraea - rituals - temples - ritual | 62 | 1205_mithraeum_mithraea_rituals_temples |
| 1206 | witches - witch - spells - spellbound - comics | 62 | 1206_witches_witch_spells_spellbound |
| 1207 | android - smartphone - smartphones - apps - nexus | 62 | 1207_android_smartphone_smartphones_apps |
| 1208 | electronics - appliances - manufacturer - subsidiaries - brand | 62 | 1208_electronics_appliances_manufacturer_subsidiaries |
| 1209 | chess - tournaments - tournament - grandmaster - grandmasters | 62 | 1209_chess_tournaments_tournament_grandmaster |
| 1210 | slaughterhouse - novelist - novels - writer - nonfiction | 62 | 1210_slaughterhouse_novelist_novels_writer |
| 1211 | sequels - ash - trilogy - evil - sequel | 62 | 1211_sequels_ash_trilogy_evil |
| 1212 | caffeine - caffeinated - drowsiness - coffee - intoxication | 62 | 1212_caffeine_caffeinated_drowsiness_coffee |
| 1213 | electors - electoral - elector - elects - elections | 62 | 1213_electors_electoral_elector_elects |
| 1214 | newscast - reporters - reporter - journalism - 1963 | 62 | 1214_newscast_reporters_reporter_journalism |
| 1215 | caliph - ibn - caliphs - al - caliphate | 62 | 1215_caliph_ibn_caliphs_al |
| 1216 | democrat - democrats - republican - reelection - caucus | 62 | 1216_democrat_democrats_republican_reelection |
| 1217 | þáttr - saga - throne - sagas - skaldic | 62 | 1217_þáttr_saga_throne_sagas |
| 1218 | dune - screenplay - director - cast - sequels | 62 | 1218_dune_screenplay_director_cast |
| 1219 | colonies - niger - guinea - colonial - colonialist | 62 | 1219_colonies_niger_guinea_colonial |
| 1220 | turtle - turtles - ninja - mutant - cartoon | 62 | 1220_turtle_turtles_ninja_mutant |
| 1221 | pins - pin - pinning - feed - ads | 61 | 1221_pins_pin_pinning_feed |
| 1222 | poetry - rhyme - stanzas - poems - rhymes | 61 | 1222_poetry_rhyme_stanzas_poems |
| 1223 | automotive - presenter - rover - bbc - driving | 61 | 1223_automotive_presenter_rover_bbc |
| 1224 | tennis - doubles - singles - tournaments - quarterfinal | 61 | 1224_tennis_doubles_singles_tournaments |
| 1225 | bean - teddy - episodes - sitcom - diary | 61 | 1225_bean_teddy_episodes_sitcom |
| 1226 | magnetism - magnetic - electromagnetism - magnetization - magnet | 61 | 1226_magnetism_magnetic_electromagnetism_magnetization |
| 1227 | abolitionist - abolitionists - slavery - 1850s - slaves | 61 | 1227_abolitionist_abolitionists_slavery_1850s |
| 1228 | 1451 - 1453 - 1456 - 1452 - siege | 61 | 1228_1451_1453_1456_1452 |
| 1229 | raider - consoles - uncharted - tomb - tombs | 61 | 1229_raider_consoles_uncharted_tomb |
| 1230 | insurgents - insurgency - troops - insurgent - war | 61 | 1230_insurgents_insurgency_troops_insurgent |
| 1231 | annexation - annexed - annexing - refugees - 1948 | 61 | 1231_annexation_annexed_annexing_refugees |
| 1232 | conferences - talks - presentations - livestreams - conference | 61 | 1232_conferences_talks_presentations_livestreams |
| 1233 | awards - idol - nominations - sang - songs | 61 | 1233_awards_idol_nominations_sang |
| 1234 | epoch - gong - times - reporters - journalism | 61 | 1234_epoch_gong_times_reporters |
| 1235 | goths - gothic - archaeologists - ancient - romanized | 61 | 1235_goths_gothic_archaeologists_ancient |
| 1236 | warriors - blazers - rockets - 76ers - hawks | 61 | 1236_warriors_blazers_rockets_76ers |
| 1237 | milk - milkshake - mayor - foster - 1978 | 61 | 1237_milk_milkshake_mayor_foster |
| 1238 | librarian - library - libraries - librarians - congress | 61 | 1238_librarian_library_libraries_librarians |
| 1239 | gerrymandering - gerrymander - gerrymandered - redistricting - constituencies | 61 | 1239_gerrymandering_gerrymander_gerrymandered_redistricting |
| 1240 | bitcoin - bitcoins - cryptocurrencies - cryptocurrency - currencies | 60 | 1240_bitcoin_bitcoins_cryptocurrencies_cryptocurrency |
| 1241 | meditations - ashram - meditation - yoga - buddha | 60 | 1241_meditations_ashram_meditation_yoga |
| 1242 | turret - tanks - ammunition - turrets - cupolas | 60 | 1242_turret_tanks_ammunition_turrets |
| 1243 | heterochromia - pigmentation - pigment - pigments - coloration | 60 | 1243_heterochromia_pigmentation_pigment_pigments |
| 1244 | libraries - library - archives - periodicals - books | 60 | 1244_libraries_library_archives_periodicals |
| 1245 | gear - presenter - presenters - motorsport - snowmobile | 60 | 1245_gear_presenter_presenters_motorsport |
| 1246 | crusade - crusaders - crusader - 1179 - 1177 | 60 | 1246_crusade_crusaders_crusader_1179 |
| 1247 | shamanism - shamans - shaman - shamanistic - shamanic | 60 | 1247_shamanism_shamans_shaman_shamanistic |
| 1248 | panther - pink - films - film - thief | 60 | 1248_panther_pink_films_film |
| 1249 | ghost - ghosts - haunted - sequels - films | 60 | 1249_ghost_ghosts_haunted_sequels |
| 1250 | marketing - advertising - market - consumers - consumer | 60 | 1250_marketing_advertising_market_consumers |
| 1251 | 1773 - tea - colonists - colonies - taxation | 60 | 1251_1773_tea_colonists_colonies |
| 1252 | eyewitnesses - retraction - biographers - historians - writings | 60 | 1252_eyewitnesses_retraction_biographers_historians |
| 1253 | cookbook - cookbooks - recipes - chef - recipe | 60 | 1253_cookbook_cookbooks_recipes_chef |
| 1254 | boxer - boxers - martial - rebellion - fought | 60 | 1254_boxer_boxers_martial_rebellion |
| 1255 | pseudonym - masked - jailed - prisoner - imprisoned | 60 | 1255_pseudonym_masked_jailed_prisoner |
| 1256 | slavery - slaves - enslaved - paternity - genealogical | 60 | 1256_slavery_slaves_enslaved_paternity |
| 1257 | hadiths - hadith - ḥadīth - ibn - imam | 60 | 1257_hadiths_hadith_ḥadīth_ibn |
| 1258 | elections - election - electoral - democratic - candidates | 60 | 1258_elections_election_electoral_democratic |
| 1259 | treatises - rabbis - textual - commentaries - rabbinic | 60 | 1259_treatises_rabbis_textual_commentaries |
| 1260 | feminism - feminist - atheism - feminists - gender | 60 | 1260_feminism_feminist_atheism_feminists |
| 1261 | boxing - punches - martial - fights - heavyweight | 60 | 1261_boxing_punches_martial_fights |
| 1262 | modularity - mathematician - conjecture - mathematicians - modular | 60 | 1262_modularity_mathematician_conjecture_mathematicians |
| 1263 | 1775 - 1780 - 1778 - 1779 - militia | 60 | 1263_1775_1780_1778_1779 |
| 1264 | hypothesis - hypotheses - statistic - statistics - tests | 60 | 1264_hypothesis_hypotheses_statistic_statistics |
| 1265 | orphanage - doors - disappearance - door - detectives | 59 | 1265_orphanage_doors_disappearance_door |
| 1266 | fairy - puppet - donkey - snail - puppeteer | 59 | 1266_fairy_puppet_donkey_snail |
| 1267 | doomsday - sequel - gameplay - multiplayer - dawn | 59 | 1267_doomsday_sequel_gameplay_multiplayer |
| 1268 | afar - militias - ceasefire - humanitarian - stationed | 59 | 1268_afar_militias_ceasefire_humanitarian |
| 1269 | tennis - slams - doubles - quarterfinal - tournaments | 59 | 1269_tennis_slams_doubles_quarterfinal |
| 1270 | barricades - barricade - escape - escapes - murderer | 59 | 1270_barricades_barricade_escape_escapes |
| 1271 | jong - heir - eldest - successor - hyun | 59 | 1271_jong_heir_eldest_successor |
| 1272 | firearm - firearms - handgun - guns - gun | 59 | 1272_firearm_firearms_handgun_guns |
| 1273 | colonists - colony - colonies - settlers - voyage | 59 | 1273_colonists_colony_colonies_settlers |
| 1274 | nazi - 1932 - 1938 - triumph - 1934 | 59 | 1274_nazi_1932_1938_triumph |
| 1275 | retailer - groceries - mart - store - closing | 59 | 1275_retailer_groceries_mart_store |
| 1276 | photosynthesis - photosynthetic - respiration - chloroplasts - chlorophyll | 59 | 1276_photosynthesis_photosynthetic_respiration_chloroplasts |
| 1277 | mission - missions - sequel - cruise - fallout | 59 | 1277_mission_missions_sequel_cruise |
| 1278 | rainbow - rainbows - violet - colours - refraction | 59 | 1278_rainbow_rainbows_violet_colours |
| 1279 | hitchhiker - novels - hitchhiking - paperback - hitch | 59 | 1279_hitchhiker_novels_hitchhiking_paperback |
| 1280 | paintings - painter - painting - artists - exhibitions | 59 | 1280_paintings_painter_painting_artists |
| 1281 | tributaries - tributary - headwaters - river - lake | 59 | 1281_tributaries_tributary_headwaters_river |
| 1282 | soccer - football - players - games - leagues | 59 | 1282_soccer_football_players_games |
| 1283 | regiment - cavalry - infantry - battalions - retreated | 59 | 1283_regiment_cavalry_infantry_battalions |
| 1284 | ontological - ontology - ontologically - ontologies - categories | 59 | 1284_ontological_ontology_ontologically_ontologies |
| 1285 | flags - parks - resorts - rebranded - mascot | 59 | 1285_flags_parks_resorts_rebranded |
| 1286 | sentenced - convicted - arson - crimes - arsons | 59 | 1286_sentenced_convicted_arson_crimes |
| 1287 | art - artistic - artists - modernist - surrealists | 59 | 1287_art_artistic_artists_modernist |
| 1288 | shamrock - wrestled - rematch - punches - fighters | 59 | 1288_shamrock_wrestled_rematch_punches |
| 1289 | broadcasting - stations - broadcasts - channels - broadcast | 59 | 1289_broadcasting_stations_broadcasts_channels |
| 1290 | printers - printer - prints - printing - inkjet | 58 | 1290_printers_printer_prints_printing |
| 1291 | traders - colonial - trading - monopolise - 1609 | 58 | 1291_traders_colonial_trading_monopolise |
| 1292 | violin - violins - violinists - violinist - instrument | 58 | 1292_violin_violins_violinists_violinist |
| 1293 | mythological - prophecy - patricide - prophet - oracles | 58 | 1293_mythological_prophecy_patricide_prophet |
| 1294 | offside - officiating - penalty - penalties - opponents | 58 | 1294_offside_officiating_penalty_penalties |
| 1295 | candidates - candidate - minister - election - elected | 58 | 1295_candidates_candidate_minister_election |
| 1296 | cyclists - cyclist - cycling - tour - riders | 58 | 1296_cyclists_cyclist_cycling_tour |
| 1297 | hello - greeting - cat - cuteness - ukiyo | 58 | 1297_hello_greeting_cat_cuteness |
| 1298 | investigation - jury - coroner - tabloid - alleged | 58 | 1298_investigation_jury_coroner_tabloid |
| 1299 | jong - yong - hui - taek - ko | 58 | 1299_jong_yong_hui_taek |
| 1300 | terrorism - terrorist - terrorists - terror - bombings | 58 | 1300_terrorism_terrorist_terrorists_terror |
| 1301 | compass - compasses - magnetometers - geomagnetic - magnetic | 58 | 1301_compass_compasses_magnetometers_geomagnetic |
| 1302 | famine - crops - agrarian - agricultural - farmers | 58 | 1302_famine_crops_agrarian_agricultural |
| 1303 | etymology - isles - conquered - isle - mainland | 58 | 1303_etymology_isles_conquered_isle |
| 1304 | guitarists - band - toured - bands - fronted | 58 | 1304_guitarists_band_toured_bands |
| 1305 | retailers - seven - shops - stores - store | 58 | 1305_retailers_seven_shops_stores |
| 1306 | polygamists - polygamous - polygamist - polygamy - marriages | 58 | 1306_polygamists_polygamous_polygamist_polygamy |
| 1307 | cosmos - astronomers - astronomer - astronomy - astronomical | 58 | 1307_cosmos_astronomers_astronomer_astronomy |
| 1308 | refraction - refractive - optics - wavelengths - reflectivity | 58 | 1308_refraction_refractive_optics_wavelengths |
| 1309 | twilight - episodes - supernatural - zone - syndication | 58 | 1309_twilight_episodes_supernatural_zone |
| 1310 | amazon - cloud - apple - echo - automation | 57 | 1310_amazon_cloud_apple_echo |
| 1311 | diplomacy - geopolitical - secretary - 1972 - statesman | 57 | 1311_diplomacy_geopolitical_secretary_1972 |
| 1312 | trademarked - trademark - brand - della - handbags | 57 | 1312_trademarked_trademark_brand_della |
| 1313 | ceasefire - peacekeeping - refugees - conflict - war | 57 | 1313_ceasefire_peacekeeping_refugees_conflict |
| 1314 | neutrinos - neutrino - antineutrinos - antineutrino - leptons | 57 | 1314_neutrinos_neutrino_antineutrinos_antineutrino |
| 1315 | spaceflight - blue - launches - rocket - starship | 57 | 1315_spaceflight_blue_launches_rocket |
| 1316 | heir - eldest - emperor - empress - grandchild | 57 | 1316_heir_eldest_emperor_empress |
| 1317 | socialist - socialism - socialists - democratic - liberalism | 57 | 1317_socialist_socialism_socialists_democratic |
| 1318 | resolver - resolving - resolve - domains - authoritative | 57 | 1318_resolver_resolving_resolve_domains |
| 1319 | waits - musician - singer - singers - songwriter | 57 | 1319_waits_musician_singer_singers |
| 1320 | aviation - pilots - airplane - pilot - flew | 57 | 1320_aviation_pilots_airplane_pilot |
| 1321 | rating - rated - grades - grade - score | 57 | 1321_rating_rated_grades_grade |
| 1322 | stations - radio - station - broadcasts - broadcasting | 57 | 1322_stations_radio_station_broadcasts |
| 1323 | sheikh - prince - sultan - heir - princes | 57 | 1323_sheikh_prince_sultan_heir |
| 1324 | conditioning - conditioned - stimuli - stimulus - reflex | 57 | 1324_conditioning_conditioned_stimuli_stimulus |
| 1325 | cube - cubes - dodecahedron - puzzles - 3d | 57 | 1325_cube_cubes_dodecahedron_puzzles |
| 1326 | nominations - awards - nominated - award - finales | 57 | 1326_nominations_awards_nominated_award |
| 1327 | bounty - adrift - boatswain - seaman - voyage | 57 | 1327_bounty_adrift_boatswain_seaman |
| 1328 | tectonics - tectonic - mantle - crust - plates | 57 | 1328_tectonics_tectonic_mantle_crust |
| 1329 | jinn - jinni - ibn - demonic - deities | 57 | 1329_jinn_jinni_ibn_demonic |
| 1330 | armada - fleet - fleets - sailed - 1596 | 57 | 1330_armada_fleet_fleets_sailed |
| 1331 | foie - geese - goose - gras - poultry | 57 | 1331_foie_geese_goose_gras |
| 1332 | goalkeeping - premiership - goalkeeper - arsenal - keeper | 57 | 1332_goalkeeping_premiership_goalkeeper_arsenal |
| 1333 | peregrines - peregrine - falcon - falconry - bird | 56 | 1333_peregrines_peregrine_falcon_falconry |
| 1334 | warship - frigate - frigates - naval - sailed | 56 | 1334_warship_frigate_frigates_naval |
| 1335 | 731 - civilians - plague - pathogens - units | 56 | 1335_731_civilians_plague_pathogens |
| 1336 | commodore - hardware - consoles - x86 - emulation | 56 | 1336_commodore_hardware_consoles_x86 |
| 1337 | laurel - hardy - comedies - comedians - comic | 56 | 1337_laurel_hardy_comedies_comedians |
| 1338 | eggs - egg - yolks - yolk - eggshell | 56 | 1338_eggs_egg_yolks_yolk |
| 1339 | toymaker - franchise - toys - company - monopoly | 56 | 1339_toymaker_franchise_toys_company |
| 1340 | vampire - showrunner - vampires - slayer - episodes | 56 | 1340_vampire_showrunner_vampires_slayer |
| 1341 | scattering - sciences - physicists - discovered - wavelengths | 56 | 1341_scattering_sciences_physicists_discovered |
| 1342 | voyages - voyage - literature - novels - journeys | 56 | 1342_voyages_voyage_literature_novels |
| 1343 | besieged - retreating - recaptured - retreated - reinforcements | 56 | 1343_besieged_retreating_recaptured_retreated |
| 1344 | singularity - superintelligence - technological - 2030 - supercomputers | 56 | 1344_singularity_superintelligence_technological_2030 |
| 1345 | coli - bacterial - bacterium - bacteria - microbiota | 56 | 1345_coli_bacterial_bacterium_bacteria |
| 1346 | propofol - midazolam - benzodiazepine - hospitalized - manslaughter | 56 | 1346_propofol_midazolam_benzodiazepine_hospitalized |
| 1347 | peacemaker - suicide - filmmakers - cast - cameo | 56 | 1347_peacemaker_suicide_filmmakers_cast |
| 1348 | coats - mafia - massacre - perpetrators - killers | 56 | 1348_coats_mafia_massacre_perpetrators |
| 1349 | howl - poetry - poet - poems - poem | 56 | 1349_howl_poetry_poet_poems |
| 1350 | 1080p - digital - cable - cables - resolution | 56 | 1350_1080p_digital_cable_cables |
| 1351 | federalist - federalists - confederation - republicanism - federal | 56 | 1351_federalist_federalists_confederation_republicanism |
| 1352 | adobe - formats - document - acrobat - documents | 56 | 1352_adobe_formats_document_acrobat |
| 1353 | cherry - blossom - cherries - blossoms - orchards | 56 | 1353_cherry_blossom_cherries_blossoms |
| 1354 | 1939 - 1942 - 1930s - affair - 1940 | 56 | 1354_1939_1942_1930s_affair |
| 1355 | titans - superheroine - superheroes - superhero - comics | 56 | 1355_titans_superheroine_superheroes_superhero |
| 1356 | likens - jenny - tormented - inflicting - endured | 56 | 1356_likens_jenny_tormented_inflicting |
| 1357 | malls - shops - mall - centres - centre | 56 | 1357_malls_shops_mall_centres |
| 1358 | glucose - insulin - diabetes - gluconeogenesis - pancreas | 56 | 1358_glucose_insulin_diabetes_gluconeogenesis |
| 1359 | niger - inhabitants - migrants - natal - guinea | 56 | 1359_niger_inhabitants_migrants_natal |
| 1360 | unconstitutional - marriages - amendment - marriage - constitutional | 56 | 1360_unconstitutional_marriages_amendment_marriage |
| 1361 | hound - detective - hounds - bbc - episodes | 56 | 1361_hound_detective_hounds_bbc |
| 1362 | blackface - minstrel - minstrels - blackness - performers | 56 | 1362_blackface_minstrel_minstrels_blackness |
| 1363 | diamond - diamonds - gemstone - jeweler - jewelers | 56 | 1363_diamond_diamonds_gemstone_jeweler |
| 1364 | delle - bourgeois - bourgeoisie - piazza - della | 56 | 1364_delle_bourgeois_bourgeoisie_piazza |
| 1365 | hyperloop - musk - pod - pods - 400m | 56 | 1365_hyperloop_musk_pod_pods |
| 1366 | data - datasets - analytics - databases - database | 56 | 1366_data_datasets_analytics_databases |
| 1367 | punk - flag - bands - black - band | 55 | 1367_punk_flag_bands_black |
| 1368 | writer - junkie - naked - writing - lunch | 55 | 1368_writer_junkie_naked_writing |
| 1369 | tennis - semifinal - quarterfinal - semifinals - doubles | 55 | 1369_tennis_semifinal_quarterfinal_semifinals |
| 1370 | pharmaceuticals - pharmaceutical - biotech - stocks - stock | 55 | 1370_pharmaceuticals_pharmaceutical_biotech_stocks |
| 1371 | pixels - resolution - monitors - resolutions - monitor | 55 | 1371_pixels_resolution_monitors_resolutions |
| 1372 | kerosene - fuels - diesel - refinery - fuel | 55 | 1372_kerosene_fuels_diesel_refinery |
| 1373 | bonsai - trees - plantings - cultivation - exhibitions | 55 | 1373_bonsai_trees_plantings_cultivation |
| 1374 | tsarina - tsar - gunmen - gunshots - gunshot | 55 | 1374_tsarina_tsar_gunmen_gunshots |
| 1375 | zoom - privacy - ventures - consulting - phone | 55 | 1375_zoom_privacy_ventures_consulting |
| 1376 | jagged - albums - songwriter - pill - songs | 55 | 1376_jagged_albums_songwriter_pill |
| 1377 | holocaust - nazi - prosecution - prosecutors - extradition | 55 | 1377_holocaust_nazi_prosecution_prosecutors |
| 1378 | grandmaster - grandmasters - fide - titles - tournaments | 55 | 1378_grandmaster_grandmasters_fide_titles |
| 1379 | poet - poetry - poems - poets - stanzas | 55 | 1379_poet_poetry_poems_poets |
| 1380 | colorblindness - blindness - colorblind - blind - trichromatic | 55 | 1380_colorblindness_blindness_colorblind_blind |
| 1381 | guinea - niger - equatorial - equator - bordered | 55 | 1381_guinea_niger_equatorial_equator |
| 1382 | population - municipalities - cities - city - towns | 55 | 1382_population_municipalities_cities_city |
| 1383 | sim - unlocked - carriers - telecommunications - cellular | 55 | 1383_sim_unlocked_carriers_telecommunications |
| 1384 | homeopathic - homeopaths - homeopathy - medicines - medicine | 55 | 1384_homeopathic_homeopaths_homeopathy_medicines |
| 1385 | vampirism - vampire - vampiric - vampires - undead | 55 | 1385_vampirism_vampire_vampiric_vampires |
| 1386 | convicted - airlines - airline - conviction - arrested | 55 | 1386_convicted_airlines_airline_conviction |
| 1387 | albums - album - duets - singer - band | 55 | 1387_albums_album_duets_singer |
| 1388 | rapper - tribe - rap - tip - rapping | 55 | 1388_rapper_tribe_rap_tip |
| 1389 | lee - moody - fallen - songwriting - band | 55 | 1389_lee_moody_fallen_songwriting |
| 1390 | parliamentarian - minister - français - politician - councillor | 55 | 1390_parliamentarian_minister_français_politician |
| 1391 | poet - poems - poem - poets - poetry | 55 | 1391_poet_poems_poem_poets |
| 1392 | gas - soviet - oil - sanctions - supply | 55 | 1392_gas_soviet_oil_sanctions |
| 1393 | eclipse - eclipses - lunar - eclipsed - moon | 55 | 1393_eclipse_eclipses_lunar_eclipsed |
| 1394 | brothers - nick - band - songs - album | 55 | 1394_brothers_nick_band_songs |
| 1395 | twins - twin - twinning - monozygotic - duplications | 55 | 1395_twins_twin_twinning_monozygotic |
| 1396 | biotechnology - pharmaceuticals - biotech - companies - agro | 55 | 1396_biotechnology_pharmaceuticals_biotech_companies |
| 1397 | sim - create - simulation - gameplay - traits | 55 | 1397_sim_create_simulation_gameplay |
| 1398 | duet - duets - sang - song - songs | 55 | 1398_duet_duets_sang_song |
| 1399 | ibn - sheikh - sultanate - mecca - mosque | 54 | 1399_ibn_sheikh_sultanate_mecca |
| 1400 | snaps - snap - messaging - sharing - chat | 54 | 1400_snaps_snap_messaging_sharing |
| 1401 | dietary - diet - diets - cardiovascular - cholesterol | 54 | 1401_dietary_diet_diets_cardiovascular |
| 1402 | stem - disciplines - majors - degree - engineering | 54 | 1402_stem_disciplines_majors_degree |
| 1403 | poverty - deprivation - poor - welfare - income | 54 | 1403_poverty_deprivation_poor_welfare |
| 1404 | simulations - simulation - simulating - simulated - stochastic | 54 | 1404_simulations_simulation_simulating_simulated |
| 1405 | tether - treasuries - exchanges - cryptocurrencies - cryptocurrency | 54 | 1405_tether_treasuries_exchanges_cryptocurrencies |
| 1406 | luxury - brands - brand - valuation - valuable | 54 | 1406_luxury_brands_brand_valuation |
| 1407 | lynch - touchdowns - rushing - touchdown - quarterback | 54 | 1407_lynch_touchdowns_rushing_touchdown |
| 1408 | celebrations - festival - celebrated - festivities - rituals | 54 | 1408_celebrations_festival_celebrated_festivities |
| 1409 | missionaries - charity - nuns - orphanages - hospices | 54 | 1409_missionaries_charity_nuns_orphanages |
| 1410 | languages - language - mandarin - lingua - multilingual | 54 | 1410_languages_language_mandarin_lingua |
| 1411 | apartheid - histories - chieftains - tactics - historians | 54 | 1411_apartheid_histories_chieftains_tactics |
| 1412 | daredevil - marvel - superhero - miniseries - episodes | 54 | 1412_daredevil_marvel_superhero_miniseries |
| 1413 | representatives - elects - voters - congressional - commissioner | 54 | 1413_representatives_elects_voters_congressional |
| 1414 | novelist - novels - biography - writer - literature | 54 | 1414_novelist_novels_biography_writer |
| 1415 | cosmetics - salons - skincare - chemists - products | 54 | 1415_cosmetics_salons_skincare_chemists |
| 1416 | wells - literature - novels - writer - author | 54 | 1416_wells_literature_novels_writer |
| 1417 | elephant - showman - exhibit - exhibited - surgeon | 54 | 1417_elephant_showman_exhibit_exhibited |
| 1418 | rebelled - rulers - kingdoms - recaptured - ruled | 54 | 1418_rebelled_rulers_kingdoms_recaptured |
| 1419 | seeding - clouds - seed - cloud - drought | 54 | 1419_seeding_clouds_seed_cloud |
| 1420 | dashes - hyphens - hyphenated - hyphen - dash | 54 | 1420_dashes_hyphens_hyphenated_hyphen |
| 1421 | panda - pandas - bamboo - zoological - herbivorous | 54 | 1421_panda_pandas_bamboo_zoological |
| 1422 | stations - broadcasts - radio - station - fm | 54 | 1422_stations_broadcasts_radio_station |
| 1423 | sentencing - sentenced - arrest - conviction - judge | 54 | 1423_sentencing_sentenced_arrest_conviction |
| 1424 | knights - duchy - papacy - nobles - feudal | 54 | 1424_knights_duchy_papacy_nobles |
| 1425 | buffalo - cowboy - bison - bull - 1872 | 54 | 1425_buffalo_cowboy_bison_bull |
| 1426 | knight - moon - villain - werewolf - sidekick | 53 | 1426_knight_moon_villain_werewolf |
| 1427 | sg - premiere - spinoff - episodes - starburst | 53 | 1427_sg_premiere_spinoff_episodes |
| 1428 | turkey - annexation - invaded - invasion - enosis | 53 | 1428_turkey_annexation_invaded_invasion |
| 1429 | ketogenic - dietary - diet - diets - carbohydrates | 53 | 1429_ketogenic_dietary_diet_diets |
| 1430 | ray - cinema - filmmaker - films - filmmakers | 53 | 1430_ray_cinema_filmmaker_films |
| 1431 | leprosy - leper - pathogenicity - leprae - disease | 53 | 1431_leprosy_leper_pathogenicity_leprae |
| 1432 | waves - compressional - compression - compressibility - wave | 53 | 1432_waves_compressional_compression_compressibility |
| 1433 | paintings - painting - artwork - cans - artworks | 53 | 1433_paintings_painting_artwork_cans |
| 1434 | ubiquitous - technologies - wireless - wirelessly - internet | 53 | 1434_ubiquitous_technologies_wireless_wirelessly |
| 1435 | rituals - pagans - pagan - paganism - celebrations | 53 | 1435_rituals_pagans_pagan_paganism |
| 1436 | acre - acres - yard - yards - area | 53 | 1436_acre_acres_yard_yards |
| 1437 | touchdowns - receptions - quarterback - yards - touchdown | 53 | 1437_touchdowns_receptions_quarterback_yards |
| 1438 | poet - poems - poetry - poem - shah | 53 | 1438_poet_poems_poetry_poem |
| 1439 | samurai - shogun - mangaka - fictionalization - novelist | 53 | 1439_samurai_shogun_mangaka_fictionalization |
| 1440 | strings - theories - string - theory - superstring | 53 | 1440_strings_theories_string_theory |
| 1441 | fables - fable - tales - poems - proverbs | 53 | 1441_fables_fable_tales_poems |
| 1442 | computing - analytical - mathematician - computation - mathematics | 53 | 1442_computing_analytical_mathematician_computation |
| 1443 | generative - transformer - neural - learning - trained | 53 | 1443_generative_transformer_neural_learning |
| 1444 | guitarist - bassist - instrumentalist - musicians - drummer | 53 | 1444_guitarist_bassist_instrumentalist_musicians |
| 1445 | prions - prion - proteins - protein - amyloidosis | 53 | 1445_prions_prion_proteins_protein |
| 1446 | happiness - wellbeing - unhappiness - happier - satisfaction | 53 | 1446_happiness_wellbeing_unhappiness_happier |
| 1447 | bulbs - bulb - lamps - lamp - incandescent | 53 | 1447_bulbs_bulb_lamps_lamp |
| 1448 | airplay - tv - apple - televisions - streaming | 53 | 1448_airplay_tv_apple_televisions |
| 1449 | bear - robin - bears - teddy - rabbit | 53 | 1449_bear_robin_bears_teddy |
| 1450 | newspapers - newspaper - periodicals - gazette - tabloid | 53 | 1450_newspapers_newspaper_periodicals_gazette |
| 1451 | sepoys - rebellion - uprising - 1857 - uprisings | 53 | 1451_sepoys_rebellion_uprising_1857 |
| 1452 | uncle - sam - relatives - 1886 - 1922 | 52 | 1452_uncle_sam_relatives_1886 |
| 1453 | campaigned - politician - governor - long - impeached | 52 | 1453_campaigned_politician_governor_long |
| 1454 | vertigo - dizziness - vestibular - tinnitus - migraine | 52 | 1454_vertigo_dizziness_vestibular_tinnitus |
| 1455 | dowager - empress - emperors - empresses - emperor | 52 | 1455_dowager_empress_emperors_empresses |
| 1456 | translator - translators - translations - translating - translates | 52 | 1456_translator_translators_translations_translating |
| 1457 | presidents - presidential - presidency - president - polls | 52 | 1457_presidents_presidential_presidency_president |
| 1458 | episodes - files - fox - storylines - comics | 52 | 1458_episodes_files_fox_storylines |
| 1459 | spaghetti - pasta - monster - creationist - creationism | 52 | 1459_spaghetti_pasta_monster_creationist |
| 1460 | aunt - uncle - breakfast - mammy - doll | 52 | 1460_aunt_uncle_breakfast_mammy |
| 1461 | rating - critics - reviews - review - marvel | 52 | 1461_rating_critics_reviews_review |
| 1462 | billionaire - lawsuit - founder - shares - entrepreneur | 52 | 1462_billionaire_lawsuit_founder_shares |
| 1463 | flow - experiences - performance - motivation - psychology | 52 | 1463_flow_experiences_performance_motivation |
| 1464 | valentine - celebrated - holiday - holidays - saint | 52 | 1464_valentine_celebrated_holiday_holidays |
| 1465 | twins - brothers - 1950s - 1960s - biographical | 52 | 1465_twins_brothers_1950s_1960s |
| 1466 | broadway - musical - actress - musicals - audition | 52 | 1466_broadway_musical_actress_musicals |
| 1467 | mouse - mice - cursor - joystick - trackball | 52 | 1467_mouse_mice_cursor_joystick |
| 1468 | hook - jack - crocodile - pan - nursery | 52 | 1468_hook_jack_crocodile_pan |
| 1469 | satellites - satellite - spacecraft - orbit - constellations | 52 | 1469_satellites_satellite_spacecraft_orbit |
| 1470 | golfers - golf - tournaments - golfing - tournament | 52 | 1470_golfers_golf_tournaments_golfing |
| 1471 | legions - heresy - legion - crusade - factions | 52 | 1471_legions_heresy_legion_crusade |
| 1472 | barcodes - barcode - scanners - code - scanner | 52 | 1472_barcodes_barcode_scanners_code |
| 1473 | atoms - atom - atomic - quantum - particles | 52 | 1473_atoms_atom_atomic_quantum |
| 1474 | opium - smuggling - narcotics - drug - addiction | 52 | 1474_opium_smuggling_narcotics_drug |
| 1475 | indigenous - spirit - spirits - natives - aboriginal | 52 | 1475_indigenous_spirit_spirits_natives |
| 1476 | evil - eye - gaze - eyes - glare | 52 | 1476_evil_eye_gaze_eyes |
| 1477 | smartwatch - watches - smartwatches - apple - wrist | 52 | 1477_smartwatch_watches_smartwatches_apple |
| 1478 | glitter - songs - remixes - vocals - punk | 51 | 1478_glitter_songs_remixes_vocals |
| 1479 | paramount - films - 1957 - movies - 1942 | 51 | 1479_paramount_films_1957_movies |
| 1480 | documentaries - bbc - planet - documentary - nature | 51 | 1480_documentaries_bbc_planet_documentary |
| 1481 | 1848 - dictator - rebelled - insurgent - 1846 | 51 | 1481_1848_dictator_rebelled_insurgent |
| 1482 | battalions - regiments - battalion - regiment - platoons | 51 | 1482_battalions_regiments_battalion_regiment |
| 1483 | polytheistic - shamanism - shamanistic - monotheists - monotheistic | 51 | 1483_polytheistic_shamanism_shamanistic_monotheists |
| 1484 | assassination - tortured - raped - incident - fedayeen | 51 | 1484_assassination_tortured_raped_incident |
| 1485 | tsar - tsars - cathedrals - cathedral - palaces | 51 | 1485_tsar_tsars_cathedrals_cathedral |
| 1486 | data - datasets - analytics - statistics - statistician | 51 | 1486_data_datasets_analytics_statistics |
| 1487 | transformer - transformers - coils - windings - inductance | 51 | 1487_transformer_transformers_coils_windings |
| 1488 | cruises - cruise - seas - ships - sailing | 51 | 1488_cruises_cruise_seas_ships |
| 1489 | colonists - settlers - colonist - smith - colony | 51 | 1489_colonists_settlers_colonist_smith |
| 1490 | fascist - fascism - fascists - 1930s - 1930 | 51 | 1490_fascist_fascism_fascists_1930s |
| 1491 | ferry - songwriter - albums - toured - tour | 51 | 1491_ferry_songwriter_albums_toured |
| 1492 | attractiveness - aesthetics - beauty - aesthetic - aesthetically | 51 | 1492_attractiveness_aesthetics_beauty_aesthetic |
| 1493 | tribes - tribe - 1876 - tribal - treaties | 51 | 1493_tribes_tribe_1876_tribal |
| 1494 | 1934 - robbery - robbers - gunfight - shootout | 51 | 1494_1934_robbery_robbers_gunfight |
| 1495 | rosary - devotions - liturgical - prayers - prayer | 51 | 1495_rosary_devotions_liturgical_prayers |
| 1496 | airborne - airfields - soviet - regiments - military | 51 | 1496_airborne_airfields_soviet_regiments |
| 1497 | tenacious - destiny - guitarist - band - bands | 51 | 1497_tenacious_destiny_guitarist_band |
| 1498 | feud - reigns - rumble - wrestling - wrestler | 51 | 1498_feud_reigns_rumble_wrestling |
| 1499 | coronavirus - diagnosed - positive - vaccinated - flu | 51 | 1499_coronavirus_diagnosed_positive_vaccinated |
| 1500 | decapitated - murders - detectives - homicide - murder | 51 | 1500_decapitated_murders_detectives_homicide |
| 1501 | corruption - corrupt - bribes - bribery - bribe | 51 | 1501_corruption_corrupt_bribes_bribery |
| 1502 | rooms - room - palace - furnishings - ballroom | 51 | 1502_rooms_room_palace_furnishings |
| 1503 | lama - lamas - monks - monasteries - monastic | 51 | 1503_lama_lamas_monks_monasteries |
| 1504 | warehouse - warehouses - retailer - retail - wholesale | 51 | 1504_warehouse_warehouses_retailer_retail |
| 1505 | languages - dialects - ethnic - speak - language | 51 | 1505_languages_dialects_ethnic_speak |
| 1506 | scored - goals - goalscorer - scoring - goalscorers | 51 | 1506_scored_goals_goalscorer_scoring |
| 1507 | consciousness - conscious - unconscious - perceive - awareness | 50 | 1507_consciousness_conscious_unconscious_perceive |
| 1508 | mansion - mansions - estate - residence - richest | 50 | 1508_mansion_mansions_estate_residence |
| 1509 | mp3 - audio - formats - codecs - bitrate | 50 | 1509_mp3_audio_formats_codecs |
| 1510 | dragons - evil - demigod - demigods - villains | 50 | 1510_dragons_evil_demigod_demigods |
| 1511 | citizen - citizens - sovereign - sovereigns - sovereignty | 50 | 1511_citizen_citizens_sovereign_sovereigns |
| 1512 | draft - undrafted - deadline - eligibility - early | 50 | 1512_draft_undrafted_deadline_eligibility |
| 1513 | redheads - redhead - reddish - ginger - hair | 50 | 1513_redheads_redhead_reddish_ginger |
| 1514 | measles - vaccines - vaccination - vaccine - vaccinated | 50 | 1514_measles_vaccines_vaccination_vaccine |
| 1515 | literature - novels - novel - peace - novelists | 50 | 1515_literature_novels_novel_peace |
| 1516 | microwaves - microwave - oven - ovens - cooking | 50 | 1516_microwaves_microwave_oven_ovens |
| 1517 | cranberries - concert - albums - album - 1994 | 50 | 1517_cranberries_concert_albums_album |
| 1518 | pope - papal - popes - papacy - della | 50 | 1518_pope_papal_popes_papacy |
| 1519 | voyagers - heliosphere - interstellar - heliocentric - solar | 50 | 1519_voyagers_heliosphere_interstellar_heliocentric |
| 1520 | album - songs - vocals - song - remixes | 50 | 1520_album_songs_vocals_song |
| 1521 | dead - concert - burial - lyricists - psychedelic | 50 | 1521_dead_concert_burial_lyricists |
| 1522 | athlete - olympic - athletic - athletes - decathlon | 50 | 1522_athlete_olympic_athletic_athletes |
| 1523 | motorsport - prix - tyres - racing - qualifying | 50 | 1523_motorsport_prix_tyres_racing |
| 1524 | acquitted - murdered - prosecutors - prosecution - criss | 50 | 1524_acquitted_murdered_prosecutors_prosecution |
| 1525 | disenfranchisement - disenfranchising - disenfranchised - disenfranchise - suffrage | 50 | 1525_disenfranchisement_disenfranchising_disenfranchised_disenfranchise |
| 1526 | graffiti - screenplay - cinematographers - film - godfather | 50 | 1526_graffiti_screenplay_cinematographers_film |
| 1527 | cycling - bicycles - bikes - biking - bicycling | 50 | 1527_cycling_bicycles_bikes_biking |
| 1528 | halo - chief - 343 - master - guardians | 50 | 1528_halo_chief_343_master |
| 1529 | rockstar - acquisitions - owns - gaming - acquire | 50 | 1529_rockstar_acquisitions_owns_gaming |
| 1530 | classroom - classrooms - cho - students - student | 50 | 1530_classroom_classrooms_cho_students |
| 1531 | albums - concert - toured - band - songs | 50 | 1531_albums_concert_toured_band |
| 1532 | golf - scoring - rounds - tournament - championship | 50 | 1532_golf_scoring_rounds_tournament |
| 1533 | hunger - uprisings - rebellion - capitol - rebels | 50 | 1533_hunger_uprisings_rebellion_capitol |
| 1534 | famine - famines - starvation - rice - rations | 50 | 1534_famine_famines_starvation_rice |
| 1535 | anthem - anthems - hymn - stanza - stanzas | 50 | 1535_anthem_anthems_hymn_stanza |
| 1536 | nations - summit - eu - agreements - summits | 50 | 1536_nations_summit_eu_agreements |
| 1537 | commercials - commercial - advertisements - advertisement - advertising | 50 | 1537_commercials_commercial_advertisements_advertisement |
| 1538 | bridges - bridge - viaducts - arches - truss | 50 | 1538_bridges_bridge_viaducts_arches |
| 1539 | vulgar - profanity - slang - intercourse - pejorative | 50 | 1539_vulgar_profanity_slang_intercourse |
| 1540 | mailbox - uploading - cloud - uploads - folders | 50 | 1540_mailbox_uploading_cloud_uploads |
| 1541 | predator - predators - alien - creature - aliens | 50 | 1541_predator_predators_alien_creature |
| 1542 | 1852 - novels - novel - cabin - literature | 50 | 1542_1852_novels_novel_cabin |
| 1543 | hijab - sharia - fashion - veils - dress | 50 | 1543_hijab_sharia_fashion_veils |
| 1544 | capsaicin - capsaicinoids - peppers - chili - spicy | 50 | 1544_capsaicin_capsaicinoids_peppers_chili |
| 1545 | park - episodes - south - studios - spontaneity | 50 | 1545_park_episodes_south_studios |
| 1546 | tornadoes - tornado - storms - thunderstorms - thunderstorm | 49 | 1546_tornadoes_tornado_storms_thunderstorms |
| 1547 | restaurants - restaurant - franchisees - chick - franchise | 49 | 1547_restaurants_restaurant_franchisees_chick |
| 1548 | blockchains - blockchain - ledgers - cryptocurrencies - decentralization | 49 | 1548_blockchains_blockchain_ledgers_cryptocurrencies |
| 1549 | concert - toured - band - concerts - bands | 49 | 1549_concert_toured_band_concerts |
| 1550 | dew - cola - flavors - soda - beverage | 49 | 1550_dew_cola_flavors_soda |
| 1551 | circumcision - circumcise - circumcised - uncircumcised - foreskin | 49 | 1551_circumcision_circumcise_circumcised_uncircumcised |
| 1552 | sultan - shah - khan - sultanate - rocket | 49 | 1552_sultan_shah_khan_sultanate |
| 1553 | priesthood - priest - synagogue - temple - sect | 49 | 1553_priesthood_priest_synagogue_temple |
| 1554 | ape - apes - jungle - gorilla - gorillas | 49 | 1554_ape_apes_jungle_gorilla |
| 1555 | blockbuster - amazon - subscription - subscribers - streaming | 49 | 1555_blockbuster_amazon_subscription_subscribers |
| 1556 | vogue - magazine - magazines - haute - fashion | 49 | 1556_vogue_magazine_magazines_haute |
| 1557 | cocoa - farmers - commodities - chocolate - countries | 49 | 1557_cocoa_farmers_commodities_chocolate |
| 1558 | anime - cartoon - cartoons - airing - samurai | 49 | 1558_anime_cartoon_cartoons_airing |
| 1559 | rockabilly - chorus - songwriter - singing - musicians | 49 | 1559_rockabilly_chorus_songwriter_singing |
| 1560 | brackets - parentheses - bracket - parenthesis - bracketed | 49 | 1560_brackets_parentheses_bracket_parenthesis |
| 1561 | gulag - soviet - memoirs - novel - archipelago | 49 | 1561_gulag_soviet_memoirs_novel |
| 1562 | jong - coma - detained - postmortem - tortured | 49 | 1562_jong_coma_detained_postmortem |
| 1563 | dictator - corruption - unrest - corrupt - za | 49 | 1563_dictator_corruption_unrest_corrupt |
| 1564 | scoliosis - spine - vertebral - vertebra - spinal | 49 | 1564_scoliosis_spine_vertebral_vertebra |
| 1565 | festival - tomorrow - tickets - performers - organizers | 49 | 1565_festival_tomorrow_tickets_performers |
| 1566 | niger - kingdoms - ethnicities - kingdom - ancestor | 49 | 1566_niger_kingdoms_ethnicities_kingdom |
| 1567 | plc - programmable - microcontrollers - controllers - microcontroller | 49 | 1567_plc_programmable_microcontrollers_controllers |
| 1568 | monopoly - monopolies - games - cash - cards | 49 | 1568_monopoly_monopolies_games_cash |
| 1569 | productions - cola - merger - coca - corporation | 49 | 1569_productions_cola_merger_coca |
| 1570 | gambling - gambler - gamble - gamblers - fortune | 49 | 1570_gambling_gambler_gamble_gamblers |
| 1571 | donuts - doughnuts - donut - doughnut - restaurant | 49 | 1571_donuts_doughnuts_donut_doughnut |
| 1572 | billionaires - richest - billionaire - wealthiest - billion | 49 | 1572_billionaires_richest_billionaire_wealthiest |
| 1573 | lent - fasting - easter - liturgy - liturgical | 49 | 1573_lent_fasting_easter_liturgy |
| 1574 | novels - books - readership - bestsellers - readers | 49 | 1574_novels_books_readership_bestsellers |
| 1575 | ibn - emir - mecca - medina - emirate | 48 | 1575_ibn_emir_mecca_medina |
| 1576 | ministers - minister - secretary - peerage - cabinet | 48 | 1576_ministers_minister_secretary_peerage |
| 1577 | kratom - overdose - overdoses - alkaloids - toxicity | 48 | 1577_kratom_overdose_overdoses_alkaloids |
| 1578 | knight - homicide - manslaughter - murderer - bail | 48 | 1578_knight_homicide_manslaughter_murderer |
| 1579 | phase - phases - transformer - electrical - voltages | 48 | 1579_phase_phases_transformer_electrical |
| 1580 | girdle - knights - chivalry - knight - knightly | 48 | 1580_girdle_knights_chivalry_knight |
| 1581 | mix - albums - little - remix - singles | 48 | 1581_mix_albums_little_remix |
| 1582 | shamrock - annals - priest - saint - apostles | 48 | 1582_shamrock_annals_priest_saint |
| 1583 | aneurysms - aneurysm - coronary - prognosis - vasculitis | 48 | 1583_aneurysms_aneurysm_coronary_prognosis |
| 1584 | mirage - 2000 - missile - airframes - aircraft | 48 | 1584_mirage_2000_missile_airframes |
| 1585 | rangers - ranger - mighty - ninja - dubbed | 48 | 1585_rangers_ranger_mighty_ninja |
| 1586 | iso - specifications - transmission - specification - interface | 48 | 1586_iso_specifications_transmission_specification |
| 1587 | moai - statues - statue - archaeologists - archaeological | 48 | 1587_moai_statues_statue_archaeologists |
| 1588 | cameras - camera - shutters - photography - shutter | 48 | 1588_cameras_camera_shutters_photography |
| 1589 | bigamy - waltz - alimony - dancer - famous | 48 | 1589_bigamy_waltz_alimony_dancer |
| 1590 | pussy - riot - activists - protesting - protest | 48 | 1590_pussy_riot_activists_protesting |
| 1591 | musician - songs - singers - album - songwriters | 48 | 1591_musician_songs_singers_album |
| 1592 | chile - poet - poems - poem - poetry | 48 | 1592_chile_poet_poems_poem |
| 1593 | directorate - security - agencies - agency - executive | 48 | 1593_directorate_security_agencies_agency |
| 1594 | steampunk - cyberpunk - steam - technocrats - conventions | 48 | 1594_steampunk_cyberpunk_steam_technocrats |
| 1595 | planets - volcanically - volcanic - planet - craters | 48 | 1595_planets_volcanically_volcanic_planet |
| 1596 | sky - gaming - gameplay - gamer - game | 48 | 1596_sky_gaming_gameplay_gamer |
| 1597 | brewery - beers - breweries - tents - festival | 48 | 1597_brewery_beers_breweries_tents |
| 1598 | drafted - rebounds - basketball - draft - hoop | 48 | 1598_drafted_rebounds_basketball_draft |
| 1599 | ancient - mathematician - philosophers - philosopher - esotericism | 48 | 1599_ancient_mathematician_philosophers_philosopher |
| 1600 | mural - artworks - paintings - murals - exhibitions | 48 | 1600_mural_artworks_paintings_murals |
| 1601 | gamer - gamers - gaming - harassment - misogynistic | 48 | 1601_gamer_gamers_gaming_harassment |
| 1602 | microprocessors - microelectronics - microprocessor - processors - transistors | 48 | 1602_microprocessors_microelectronics_microprocessor_processors |
| 1603 | molested - murders - crimes - murdered - murdering | 48 | 1603_molested_murders_crimes_murdered |
| 1604 | assassination - assassinate - archduke - assassins - conspirators | 48 | 1604_assassination_assassinate_archduke_assassins |
| 1605 | noir - noirs - genre - cinematography - filmmaking | 48 | 1605_noir_noirs_genre_cinematography |
| 1606 | ibn - folktales - tales - literature - nights | 48 | 1606_ibn_folktales_tales_literature |
| 1607 | piracy - pirate - pirates - privateering - maritime | 48 | 1607_piracy_pirate_pirates_privateering |
| 1608 | mysticism - theosophical - spiritual - spirituality - epistemology | 48 | 1608_mysticism_theosophical_spiritual_spirituality |
| 1609 | gaol - literary - prose - biographies - ballad | 48 | 1609_gaol_literary_prose_biographies |
| 1610 | complexity - computational - algorithms - cryptosystems - deterministic | 48 | 1610_complexity_computational_algorithms_cryptosystems |
| 1611 | deepfake - deepfakes - videos - detecting - detection | 48 | 1611_deepfake_deepfakes_videos_detecting |
| 1612 | metadata - semantic - vocabularies - schema - catalog | 48 | 1612_metadata_semantic_vocabularies_schema |
| 1613 | railway - trains - highways - buses - trolleybus | 47 | 1613_railway_trains_highways_buses |
| 1614 | olives - olive - mediterranean - tree - orchards | 47 | 1614_olives_olive_mediterranean_tree |
| 1615 | till - acquitted - lynched - casket - lynching | 47 | 1615_till_acquitted_lynched_casket |
| 1616 | thriller - ballads - usher - albums - songs | 47 | 1616_thriller_ballads_usher_albums |
| 1617 | literature - tales - adventures - books - poems | 47 | 1617_literature_tales_adventures_books |
| 1618 | typhoon - landfall - cyclone - tropical - meteorological | 47 | 1618_typhoon_landfall_cyclone_tropical |
| 1619 | telecom - telecommunications - telecoms - broadband - provider | 47 | 1619_telecom_telecommunications_telecoms_broadband |
| 1620 | sabbath - bands - band - guitarist - bassist | 47 | 1620_sabbath_bands_band_guitarist |
| 1621 | puritan - reformation - protestant - congregational - sermons | 47 | 1621_puritan_reformation_protestant_congregational |
| 1622 | conductivity - resistivity - resistances - resistance - ohms | 47 | 1622_conductivity_resistivity_resistances_resistance |
| 1623 | reliance - shareholders - shareholder - chairman - chairmanship | 47 | 1623_reliance_shareholders_shareholder_chairman |
| 1624 | vampires - vampirism - vampire - vampiric - folklore | 47 | 1624_vampires_vampirism_vampire_vampiric |
| 1625 | genocide - humanitarian - atrocities - famine - starvation | 47 | 1625_genocide_humanitarian_atrocities_famine |
| 1626 | anorexia - bulimia - anorexic - bulimic - disorders | 47 | 1626_anorexia_bulimia_anorexic_bulimic |
| 1627 | slash - slashes - slashed - backslash - separator | 47 | 1627_slash_slashes_slashed_backslash |
| 1628 | narcissism - narcissistic - psychopathy - traits - trait | 47 | 1628_narcissism_narcissistic_psychopathy_traits |
| 1629 | payments - bank - payment - prepaid - banks | 47 | 1629_payments_bank_payment_prepaid |
| 1630 | nomadic - deserts - tribes - desert - sheikhs | 47 | 1630_nomadic_deserts_tribes_desert |
| 1631 | quarterback - quarterbacks - touchdowns - patriots - eagles | 47 | 1631_quarterback_quarterbacks_touchdowns_patriots |
| 1632 | chocolate - cocoa - cacao - sugar - sugars | 47 | 1632_chocolate_cocoa_cacao_sugar |
| 1633 | pharmaceuticals - pharmaceutical - oxycodone - opioids - lawsuits | 47 | 1633_pharmaceuticals_pharmaceutical_oxycodone_opioids |
| 1634 | novels - literature - fiction - writings - writer | 47 | 1634_novels_literature_fiction_writings |
| 1635 | comics - marvel - cartoonist - comic - superhero | 47 | 1635_comics_marvel_cartoonist_comic |
| 1636 | rapper - rappers - rap - diva - singer | 47 | 1636_rapper_rappers_rap_diva |
| 1637 | donkey - fairy - godmother - dragon - prince | 47 | 1637_donkey_fairy_godmother_dragon |
| 1638 | contraception - contraceptive - contraceptives - abortion - abortions | 47 | 1638_contraception_contraceptive_contraceptives_abortion |
| 1639 | adjutant - colonel - soldier - lieutenant - brigadier | 47 | 1639_adjutant_colonel_soldier_lieutenant |
| 1640 | pasha - sultan - bey - beylik - beyliks | 47 | 1640_pasha_sultan_bey_beylik |
| 1641 | hookah - hookahs - tobacco - smoking - smoked | 47 | 1641_hookah_hookahs_tobacco_smoking |
| 1642 | goalscorer - scored - goals - scoring - goal | 47 | 1642_goalscorer_scored_goals_scoring |
| 1643 | 172 - aircraft - fuselage - 177 - redesigned | 47 | 1643_172_aircraft_fuselage_177 |
| 1644 | gospels - crucifixion - crucified - gospel - executed | 47 | 1644_gospels_crucifixion_crucified_gospel |
| 1645 | genomes - genome - mutations - genes - spacer | 47 | 1645_genomes_genome_mutations_genes |
| 1646 | catch - 22 - circumstance - novel - spurious | 47 | 1646_catch_22_circumstance_novel |
| 1647 | aphasia - impairment - cognitive - dementia - impaired | 46 | 1647_aphasia_impairment_cognitive_dementia |
| 1648 | screenwriter - blood - sequels - films - rocky | 46 | 1648_screenwriter_blood_sequels_films |
| 1649 | arias - convicted - conviction - testified - convict | 46 | 1649_arias_convicted_conviction_testified |
| 1650 | uniforms - uniformed - berets - beret - regiment | 46 | 1650_uniforms_uniformed_berets_beret |
| 1651 | poems - poets - poet - poetry - poem | 46 | 1651_poems_poets_poet_poetry |
| 1652 | malpractice - appeals - swallowing - upheld - feeding | 46 | 1652_malpractice_appeals_swallowing_upheld |
| 1653 | bucket - albums - album - pike - tracks | 46 | 1653_bucket_albums_album_pike |
| 1654 | merger - firms - mergers - acquisitions - firm | 46 | 1654_merger_firms_mergers_acquisitions |
| 1655 | navy - military - enlisted - regiment - personnel | 46 | 1655_navy_military_enlisted_regiment |
| 1656 | peacekeeping - insurgents - ceasefire - insurgency - insurgent | 46 | 1656_peacekeeping_insurgents_ceasefire_insurgency |
| 1657 | shamrocks - parades - shamrock - celebrated - celebrations | 46 | 1657_shamrocks_parades_shamrock_celebrated |
| 1658 | eternal - eternally - eternity - repetitions - recurrence | 46 | 1658_eternal_eternally_eternity_repetitions |
| 1659 | tower - towers - fortification - moat - castles | 46 | 1659_tower_towers_fortification_moat |
| 1660 | treaties - hostilities - wartime - convention - tribunal | 46 | 1660_treaties_hostilities_wartime_convention |
| 1661 | khat - banning - misuse - legality - prohibition | 46 | 1661_khat_banning_misuse_legality |
| 1662 | invested - investor - investors - funding - financing | 46 | 1662_invested_investor_investors_funding |
| 1663 | democrats - parties - elections - election - democratic | 46 | 1663_democrats_parties_elections_election |
| 1664 | mini - convertible - redesigned - discontinued - minimalism | 46 | 1664_mini_convertible_redesigned_discontinued |
| 1665 | clowns - clown - concert - posse - circus | 46 | 1665_clowns_clown_concert_posse |
| 1666 | rankings - ranking - ranks - universities - academics | 46 | 1666_rankings_ranking_ranks_universities |
| 1667 | jam - cameo - cartoon - basketball - cameos | 46 | 1667_jam_cameo_cartoon_basketball |
| 1668 | saffron - botanical - turmeric - cultivated - phytochemicals | 46 | 1668_saffron_botanical_turmeric_cultivated |
| 1669 | mysticism - sharia - spirituality - imam - mystical | 46 | 1669_mysticism_sharia_spirituality_imam |
| 1670 | remixes - pet - remixed - remix - duet | 46 | 1670_remixes_pet_remixed_remix |
| 1671 | frontiersman - settlers - frontiersmen - wilderness - 1778 | 46 | 1671_frontiersman_settlers_frontiersmen_wilderness |
| 1672 | episodes - shows - preschoolers - blue - preschool | 46 | 1672_episodes_shows_preschoolers_blue |
| 1673 | municipalities - municipality - metropolitan - cities - populous | 46 | 1673_municipalities_municipality_metropolitan_cities |
| 1674 | soccer - goals - assists - goal - goalscorer | 46 | 1674_soccer_goals_assists_goal |
| 1675 | birthdays - birthday - probability - anniversaries - 365 | 46 | 1675_birthdays_birthday_probability_anniversaries |
| 1676 | siren - sirens - mermaid - mermaids - mythology | 46 | 1676_siren_sirens_mermaid_mermaids |
| 1677 | zombie - finale - survivor - hilltop - walkers | 46 | 1677_zombie_finale_survivor_hilltop |
| 1678 | mosque - mosques - imam - imams - prophet | 46 | 1678_mosque_mosques_imam_imams |
| 1679 | swan - swans - epistemic - theory - book | 46 | 1679_swan_swans_epistemic_theory |
| 1680 | mar - trump - mansion - vacation - presidential | 46 | 1680_mar_trump_mansion_vacation |
| 1681 | sequels - avatar - sequel - 3d - filming | 46 | 1681_sequels_avatar_sequel_3d |
| 1682 | soldiers - helicopter - surrender - mujahideen - prisoner | 46 | 1682_soldiers_helicopter_surrender_mujahideen |
| 1683 | tron - legacy - trailers - disc - trailer | 46 | 1683_tron_legacy_trailers_disc |
| 1684 | roguelikes - roguelike - rogue - gameplay - dungeons | 45 | 1684_roguelikes_roguelike_rogue_gameplay |
| 1685 | vocals - concert - vocalist - musicians - guitarist | 45 | 1685_vocals_concert_vocalist_musicians |
| 1686 | editions - comics - hardcover - edition - miniseries | 45 | 1686_editions_comics_hardcover_edition |
| 1687 | playwright - playwrights - theatricality - theatre - dramatize | 45 | 1687_playwright_playwrights_theatricality_theatre |
| 1688 | paintings - painting - painters - painter - murals | 45 | 1688_paintings_painting_painters_painter |
| 1689 | harassment - allegations - harassed - assaulted - victimized | 45 | 1689_harassment_allegations_harassed_assaulted |
| 1690 | doll - child - dolls - voiced - remake | 45 | 1690_doll_child_dolls_voiced |
| 1691 | jeep - jeeps - vehicle - vehicles - chassis | 45 | 1691_jeep_jeeps_vehicle_vehicles |
| 1692 | cinema - filmmaking - cinemas - films - filmmakers | 45 | 1692_cinema_filmmaking_cinemas_films |
| 1693 | bomber - bombers - missiles - aircraft - missile | 45 | 1693_bomber_bombers_missiles_aircraft |
| 1694 | monarchy - 1867 - confederation - 1918 - 1848 | 45 | 1694_monarchy_1867_confederation_1918 |
| 1695 | sugar - sugars - sugarcane - glucose - molasses | 45 | 1695_sugar_sugars_sugarcane_glucose |
| 1696 | art - artistic - marina - exhibition - museum | 45 | 1696_art_artistic_marina_exhibition |
| 1697 | racing - qualifying - raced - laps - prix | 45 | 1697_racing_qualifying_raced_laps |
| 1698 | cellar - cellars - imprisonment - captives - raped | 45 | 1698_cellar_cellars_imprisonment_captives |
| 1699 | ruby - rails - gems - interpreter - programming | 45 | 1699_ruby_rails_gems_interpreter |
| 1700 | saints - row - stadia - games - arcade | 45 | 1700_saints_row_stadia_games |
| 1701 | yakuza - anime - animations - chibi - voice | 45 | 1701_yakuza_anime_animations_chibi |
| 1702 | sales - revenue - disc - discs - blu | 45 | 1702_sales_revenue_disc_discs |
| 1703 | rabies - raccoons - infectious - vaccines - bitten | 45 | 1703_rabies_raccoons_infectious_vaccines |
| 1704 | gypsy - disorder - seizure - investigators - syndrome | 45 | 1704_gypsy_disorder_seizure_investigators |
| 1705 | clover - paramount - film - directorial - movie | 45 | 1705_clover_paramount_film_directorial |
| 1706 | shades - sequels - twilight - trilogy - film | 45 | 1706_shades_sequels_twilight_trilogy |
| 1707 | monastery - monks - monastic - monastics - nam | 45 | 1707_monastery_monks_monastic_monastics |
| 1708 | scream - sequels - screenwriter - sequel - trilogy | 45 | 1708_scream_sequels_screenwriter_sequel |
| 1709 | tablet - underworld - entrails - netherworld - throne | 45 | 1709_tablet_underworld_entrails_netherworld |
| 1710 | peat - peatlands - peatland - wetlands - soils | 45 | 1710_peat_peatlands_peatland_wetlands |
| 1711 | thirty - seconds - tour - album - headlining | 45 | 1711_thirty_seconds_tour_album |
| 1712 | saxophones - saxophone - sax - saxophonists - saxophonist | 45 | 1712_saxophones_saxophone_sax_saxophonists |
| 1713 | telecommunications - telecom - telecoms - telecommunication - telephony | 45 | 1713_telecommunications_telecom_telecoms_telecommunication |
| 1714 | interceptions - touchdowns - quarterback - interception - quarterbacks | 45 | 1714_interceptions_touchdowns_quarterback_interception |
| 1715 | tractors - tractor - deer - axles - machinery | 45 | 1715_tractors_tractor_deer_axles |
| 1716 | quoting - quotations - quotes - apostrophes - quotation | 45 | 1716_quoting_quotations_quotes_apostrophes |
| 1717 | panther - vibranium - panthers - spider - doom | 45 | 1717_panther_vibranium_panthers_spider |
| 1718 | profiles - chats - swipes - profile - chatting | 45 | 1718_profiles_chats_swipes_profile |
| 1719 | enterprises - ventures - affiliate - companies - commerce | 45 | 1719_enterprises_ventures_affiliate_companies |
| 1720 | fibromyalgia - fibrous - neuropathy - chronic - neuropathic | 45 | 1720_fibromyalgia_fibrous_neuropathy_chronic |
| 1721 | tithes - genesis - tithe - testament - pharaoh | 45 | 1721_tithes_genesis_tithe_testament |
| 1722 | celestial - eternal - awakening - immortal - destruction | 44 | 1722_celestial_eternal_awakening_immortal |
| 1723 | empathy - empathic - empathizing - empathize - sympathy | 44 | 1723_empathy_empathic_empathizing_empathize |
| 1724 | surrogacy - surrogates - surrogate - parenthood - fertility | 44 | 1724_surrogacy_surrogates_surrogate_parenthood |
| 1725 | tennis - tournaments - tournament - championships - finals | 44 | 1725_tennis_tournaments_tournament_championships |
| 1726 | brands - brand - margarine - soap - oils | 44 | 1726_brands_brand_margarine_soap |
| 1727 | leftist - leftists - nationalists - liberal - conservatives | 44 | 1727_leftist_leftists_nationalists_liberal |
| 1728 | medal - medals - presidential - president - bestowed | 44 | 1728_medal_medals_presidential_president |
| 1729 | quarterback - quarterbacks - interceptions - manning - touchdowns | 44 | 1729_quarterback_quarterbacks_interceptions_manning |
| 1730 | nazi - 1941 - 1939 - 1944 - wartime | 44 | 1730_nazi_1941_1939_1944 |
| 1731 | fractal - curves - boundary - holomorphic - bifurcation | 44 | 1731_fractal_curves_boundary_holomorphic |
| 1732 | limp - rock - bands - band - rap | 44 | 1732_limp_rock_bands_band |
| 1733 | devil - demon - satan - soul - souls | 44 | 1733_devil_demon_satan_soul |
| 1734 | goalscorer - footballer - goals - goalscoring - scored | 44 | 1734_goalscorer_footballer_goals_goalscoring |
| 1735 | libraries - library - librarians - bibliographic - scholarly | 44 | 1735_libraries_library_librarians_bibliographic |
| 1736 | heir - eldest - nobility - peerage - baronetcy | 44 | 1736_heir_eldest_nobility_peerage |
| 1737 | radium - chemist - polonium - radioactive - sciences | 44 | 1737_radium_chemist_polonium_radioactive |
| 1738 | sitcom - episodes - sergeants - sheriff - comedian | 44 | 1738_sitcom_episodes_sergeants_sheriff |
| 1739 | scum - feminist - satirist - manifesto - feminism | 44 | 1739_scum_feminist_satirist_manifesto |
| 1740 | moose - singer - mansa - rapper - songs | 44 | 1740_moose_singer_mansa_rapper |
| 1741 | population - municipalities - municipality - cities - inhabitants | 44 | 1741_population_municipalities_municipality_cities |
| 1742 | sober - sobriety - rehab - addiction - addict | 44 | 1742_sober_sobriety_rehab_addiction |
| 1743 | ant - ants - toured - tour - concert | 44 | 1743_ant_ants_toured_tour |
| 1744 | financial - investors - investor - founder - bankruptcies | 44 | 1744_financial_investors_investor_founder |
| 1745 | trail - anchorage - abandoned - rescued - canoe | 44 | 1745_trail_anchorage_abandoned_rescued |
| 1746 | magnum - episodes - detective - episode - robin | 44 | 1746_magnum_episodes_detective_episode |
| 1747 | moss - quarterback - cornerback - punts - touchdowns | 44 | 1747_moss_quarterback_cornerback_punts |
| 1748 | papacy - 1523 - 1527 - papal - 1471 | 44 | 1748_papacy_1523_1527_papal |
| 1749 | orcas - orca - whale - dolphin - whales | 43 | 1749_orcas_orca_whale_dolphin |
| 1750 | cartoonist - comic - strips - cartoon - strip | 43 | 1750_cartoonist_comic_strips_cartoon |
| 1751 | aids - antiretroviral - epidemiology - prevalence - population | 43 | 1751_aids_antiretroviral_epidemiology_prevalence |
| 1752 | spam - restaurants - condiments - barbecue - canned | 43 | 1752_spam_restaurants_condiments_barbecue |
| 1753 | motorcycles - motorcycle - motorbikes - motorbike - bikes | 43 | 1753_motorcycles_motorcycle_motorbikes_motorbike |
| 1754 | toured - band - headlining - album - guitarist | 43 | 1754_toured_band_headlining_album |
| 1755 | loaf - meat - duet - duets - sang | 43 | 1755_loaf_meat_duet_duets |
| 1756 | horse - horses - cavalry - rode - 1877 | 43 | 1756_horse_horses_cavalry_rode |
| 1757 | festival - lawsuit - lawsuits - defrauded - sued | 43 | 1757_festival_lawsuit_lawsuits_defrauded |
| 1758 | noblewomen - empress - maids - governesses - nobility | 43 | 1758_noblewomen_empress_maids_governesses |
| 1759 | retailer - retailers - marketplace - marketplaces - merchants | 43 | 1759_retailer_retailers_marketplace_marketplaces |
| 1760 | expedition - expeditions - 1803 - voyage - explorers | 43 | 1760_expedition_expeditions_1803_voyage |
| 1761 | grand - central - midtown - terminal - concourse | 43 | 1761_grand_central_midtown_terminal |
| 1762 | hill - gibbons - vocalist - rock - beard | 43 | 1762_hill_gibbons_vocalist_rock |
| 1763 | blueberries - blueberry - berries - cranberries - cranberry | 43 | 1763_blueberries_blueberry_berries_cranberries |
| 1764 | microseconds - timestamps - clocks - epoch - timestamp | 43 | 1764_microseconds_timestamps_clocks_epoch |
| 1765 | tinnitus - auditory - otitis - ears - ear | 43 | 1765_tinnitus_auditory_otitis_ears |
| 1766 | currencies - renminbi - currency - yuan - monetary | 43 | 1766_currencies_renminbi_currency_yuan |
| 1767 | amber - ambergris - jewelry - fragrance - resin | 43 | 1767_amber_ambergris_jewelry_fragrance |
| 1768 | yakuza - gangs - crime - gang - thugs | 43 | 1768_yakuza_gangs_crime_gang |
| 1769 | brave - browser - browsers - chrome - browse | 43 | 1769_brave_browser_browsers_chrome |
| 1770 | bugs - insects - insecticides - pest - pests | 43 | 1770_bugs_insects_insecticides_pest |
| 1771 | pit - rap - song - remix - songs | 43 | 1771_pit_rap_song_remix |
| 1772 | calendar - calendars - dates - holidays - astronomical | 43 | 1772_calendar_calendars_dates_holidays |
| 1773 | calculators - calculator - microelectronics - calculation - calculations | 43 | 1773_calculators_calculator_microelectronics_calculation |
| 1774 | statutes - limitations - statute - limitation - prosecution | 43 | 1774_statutes_limitations_statute_limitation |
| 1775 | priesthood - rituals - hymns - archaic - caste | 43 | 1775_priesthood_rituals_hymns_archaic |
| 1776 | jock - reunion - cast - sitcom - finale | 43 | 1776_jock_reunion_cast_sitcom |
| 1777 | boar - boars - pigs - pig - wildlife | 43 | 1777_boar_boars_pigs_pig |
| 1778 | supermarket - supermarkets - shops - retailer - stores | 43 | 1778_supermarket_supermarkets_shops_retailer |
| 1779 | fasting - fasts - fasted - fast - fatwas | 43 | 1779_fasting_fasts_fasted_fast |
| 1780 | infantry - battalions - allied - landings - troops | 43 | 1780_infantry_battalions_allied_landings |
| 1781 | protests - protesters - protest - demonstrators - square | 43 | 1781_protests_protesters_protest_demonstrators |
| 1782 | witches - prophecy - throne - king - tyrant | 43 | 1782_witches_prophecy_throne_king |
| 1783 | peanuts - comics - cartoonists - reprint - reprints | 43 | 1783_peanuts_comics_cartoonists_reprint |
| 1784 | penicillin - antibiotics - antibiotic - antimicrobial - antibacterial | 43 | 1784_penicillin_antibiotics_antibiotic_antimicrobial |
| 1785 | phosphors - phosphor - luminous - fluorescent - led | 43 | 1785_phosphors_phosphor_luminous_fluorescent |
| 1786 | martial - kung - karate - taekwondo - lee | 43 | 1786_martial_kung_karate_taekwondo |
| 1787 | werewolf - werewolves - wolf - lycanthropy - wolves | 42 | 1787_werewolf_werewolves_wolf_lycanthropy |
| 1788 | marvel - marvels - superhero - superheroes - comics | 42 | 1788_marvel_marvels_superhero_superheroes |
| 1789 | tai - chi - martial - wushu - yang | 42 | 1789_tai_chi_martial_wushu |
| 1790 | cents - coins - monetary - shillings - coin | 42 | 1790_cents_coins_monetary_shillings |
| 1791 | inter - assists - goalscorer - scored - goals | 42 | 1791_inter_assists_goalscorer_scored |
| 1792 | massacre - soldiers - victims - civilians - regiment | 42 | 1792_massacre_soldiers_victims_civilians |
| 1793 | soldier - poet - poem - poems - autobiography | 42 | 1793_soldier_poet_poem_poems |
| 1794 | leases - addresses - client - subnet - subnets | 42 | 1794_leases_addresses_client_subnet |
| 1795 | neolithic - archaeological - excavations - paleolithic - archeological | 42 | 1795_neolithic_archaeological_excavations_paleolithic |
| 1796 | griffin - rebounds - basketball - wizards - triple | 42 | 1796_griffin_rebounds_basketball_wizards |
| 1797 | surrealists - surrealist - surrealism - surrealistic - artists | 42 | 1797_surrealists_surrealist_surrealism_surrealistic |
| 1798 | 1850 - settlers - 1846 - goldfields - 1848 | 42 | 1798_1850_settlers_1846_goldfields |
| 1799 | serve - serving - volley - frontcourt - play | 42 | 1799_serve_serving_volley_frontcourt |
| 1800 | engineering - engineers - engineer - electrical - electronics | 42 | 1800_engineering_engineers_engineer_electrical |
| 1801 | festivals - festival - concerts - orchestras - venues | 42 | 1801_festivals_festival_concerts_orchestras |
| 1802 | sentinel - islands - island - jungle - tribe | 42 | 1802_sentinel_islands_island_jungle |
| 1803 | autobahns - autobahn - throttling - highways - motorways | 42 | 1803_autobahns_autobahn_throttling_highways |
| 1804 | watches - wristwatches - wristwatch - watchmaker - timepieces | 42 | 1804_watches_wristwatches_wristwatch_watchmaker |
| 1805 | actress - actresses - starred - portrayed - personae | 42 | 1805_actress_actresses_starred_portrayed |
| 1806 | aikido - kendo - martial - judo - ryū | 42 | 1806_aikido_kendo_martial_judo |
| 1807 | automotive - motors - automobile - jaguar - vehicle | 42 | 1807_automotive_motors_automobile_jaguar |
| 1808 | sitcom - cast - bunch - spinoffs - tv | 42 | 1808_sitcom_cast_bunch_spinoffs |
| 1809 | park - parks - parking - central - parkland | 42 | 1809_park_parks_parking_central |
| 1810 | conquered - tribes - ancient - steppes - nomadic | 42 | 1810_conquered_tribes_ancient_steppes |
| 1811 | smartphone - smartphones - android - flagship - mi | 42 | 1811_smartphone_smartphones_android_flagship |
| 1812 | vocalists - singer - albums - ballads - songs | 42 | 1812_vocalists_singer_albums_ballads |
| 1813 | honeys - honey - honeydew - bees - sugar | 42 | 1813_honeys_honey_honeydew_bees |
| 1814 | albums - toured - album - concerts - band | 42 | 1814_albums_toured_album_concerts |
| 1815 | photovoltaics - photovoltaic - solar - panels - modules | 42 | 1815_photovoltaics_photovoltaic_solar_panels |
| 1816 | bebop - cowboy - anime - episodes - otaku | 42 | 1816_bebop_cowboy_anime_episodes |
| 1817 | imaging - radiographic - radiology - scanning - scanned | 42 | 1817_imaging_radiographic_radiology_scanning |
| 1818 | logistics - freight - warehousing - procurement - warehouses | 42 | 1818_logistics_freight_warehousing_procurement |
| 1819 | javelin - athlete - badminton - olympic - athletics | 42 | 1819_javelin_athlete_badminton_olympic |
| 1820 | theme - melody - soundtrack - tune - song | 42 | 1820_theme_melody_soundtrack_tune |
| 1821 | commerce - retailers - shopping - retailing - retail | 42 | 1821_commerce_retailers_shopping_retailing |
| 1822 | trail - trails - overland - railroad - wagons | 42 | 1822_trail_trails_overland_railroad |
| 1823 | rover - rovers - vehicles - vehicle - chassis | 42 | 1823_rover_rovers_vehicles_vehicle |
| 1824 | congressman - lawmaker - misconduct - congressional - appeals | 42 | 1824_congressman_lawmaker_misconduct_congressional |
| 1825 | postcolonial - imperialism - moralist - revolt - french | 42 | 1825_postcolonial_imperialism_moralist_revolt |
| 1826 | hound - warrior - hurling - sword - spear | 42 | 1826_hound_warrior_hurling_sword |
| 1827 | ferns - fern - angiosperms - phylogenetic - phylogeny | 42 | 1827_ferns_fern_angiosperms_phylogenetic |
| 1828 | credit - social - debtors - audits - blacklists | 42 | 1828_credit_social_debtors_audits |
| 1829 | compulsions - compulsive - obsessive - obsession - obsessions | 42 | 1829_compulsions_compulsive_obsessive_obsession |
| 1830 | bodybuilder - bodybuilding - bodybuilders - competed - weightlifting | 42 | 1830_bodybuilder_bodybuilding_bodybuilders_competed |
| 1831 | actress - siblings - celebrity - actor - divorce | 42 | 1831_actress_siblings_celebrity_actor |
| 1832 | assassinated - assassination - hanged - assassinate - assassin | 42 | 1832_assassinated_assassination_hanged_assassinate |
| 1833 | eugenics - eugenic - geneticists - genetic - sterilisation | 42 | 1833_eugenics_eugenic_geneticists_genetic |
| 1834 | civilians - contractors - prosecution - enforcement - security | 42 | 1834_civilians_contractors_prosecution_enforcement |
| 1835 | botany - botanist - botanists - botanical - flora | 42 | 1835_botany_botanist_botanists_botanical |
| 1836 | publics - public - pr - communicators - organizations | 41 | 1836_publics_public_pr_communicators |
| 1837 | nonfiction - magazines - magazine - anthologies - writer | 41 | 1837_nonfiction_magazines_magazine_anthologies |
| 1838 | kimchi - rice - cabbage - cuisine - recipes | 41 | 1838_kimchi_rice_cabbage_cuisine |
| 1839 | anna - marriage - dolly - marrying - affair | 41 | 1839_anna_marriage_dolly_marrying |
| 1840 | traumatic - trauma - traumas - posttraumatic - psychiatric | 41 | 1840_traumatic_trauma_traumas_posttraumatic |
| 1841 | château - vineyard - baronet - winemaking - estates | 41 | 1841_château_vineyard_baronet_winemaking |
| 1842 | tunnel - tunnelling - tunnels - railways - railway | 41 | 1842_tunnel_tunnelling_tunnels_railways |
| 1843 | rivers - celebrity - housewives - comedian - contestant | 41 | 1843_rivers_celebrity_housewives_comedian |
| 1844 | antifa - activists - fascists - fascist - protesters | 41 | 1844_antifa_activists_fascists_fascist |
| 1845 | straits - albums - guitarist - dire - guitar | 41 | 1845_straits_albums_guitarist_dire |
| 1846 | edict - orthodoxy - roman - persecution - ecumenical | 41 | 1846_edict_orthodoxy_roman_persecution |
| 1847 | guitars - guitar - fretboard - frets - necks | 41 | 1847_guitars_guitar_fretboard_frets |
| 1848 | limerence - attraction - affection - intrusive - infatuation | 41 | 1848_limerence_attraction_affection_intrusive |
| 1849 | philosopher - martyrs - paganism - martyr - pagan | 41 | 1849_philosopher_martyrs_paganism_martyr |
| 1850 | shingles - herpesvirus - chickenpox - herpes - smallpox | 41 | 1850_shingles_herpesvirus_chickenpox_herpes |
| 1851 | heritage - preservation - films - film - culturally | 41 | 1851_heritage_preservation_films_film |
| 1852 | slim - richest - billionaire - pesos - shareholder | 41 | 1852_slim_richest_billionaire_pesos |
| 1853 | ninja - manga - anime - shinobi - shōnen | 41 | 1853_ninja_manga_anime_shinobi |
| 1854 | opioid - senator - senators - lobbyist - overdoses | 41 | 1854_opioid_senator_senators_lobbyist |
| 1855 | trump - trumps - president - grandchildren - paternal | 41 | 1855_trump_trumps_president_grandchildren |
| 1856 | scratch - scratching - programming - scratched - adobe | 41 | 1856_scratch_scratching_programming_scratched |
| 1857 | smallpox - epidemics - measles - epidemic - diseases | 41 | 1857_smallpox_epidemics_measles_epidemic |
| 1858 | ideology - philosopher - philosophy - psychoanalytical - psychoanalytic | 41 | 1858_ideology_philosopher_philosophy_psychoanalytical |
| 1859 | sai - samadhi - devotees - qawwali - guru | 41 | 1859_sai_samadhi_devotees_qawwali |
| 1860 | college - degree - accredited - bachelor - faculty | 41 | 1860_college_degree_accredited_bachelor |
| 1861 | mustard - mustards - bombs - gases - chemicals | 41 | 1861_mustard_mustards_bombs_gases |
| 1862 | quixotic - literature - chivalric - novel - chivalry | 41 | 1862_quixotic_literature_chivalric_novel |
| 1863 | rap - rock - album - band - boys | 41 | 1863_rap_rock_album_band |
| 1864 | blur - oasis - albums - toured - tour | 41 | 1864_blur_oasis_albums_toured |
| 1865 | colonies - settlers - colonists - 1624 - colony | 41 | 1865_colonies_settlers_colonists_1624 |
| 1866 | satellites - satellite - soviets - spacecraft - soviet | 41 | 1866_satellites_satellite_soviets_spacecraft |
| 1867 | brownies - brownie - folklore - stories - maids | 41 | 1867_brownies_brownie_folklore_stories |
| 1868 | guardians - galaxy - marvel - 2022 - 2023 | 41 | 1868_guardians_galaxy_marvel_2022 |
| 1869 | slender - skinny - creepypastas - creepypasta - portrayals | 41 | 1869_slender_skinny_creepypastas_creepypasta |
| 1870 | viewership - viewers - ratings - streamed - viewing | 41 | 1870_viewership_viewers_ratings_streamed |
| 1871 | burritos - tacos - salsa - tortillas - foods | 41 | 1871_burritos_tacos_salsa_tortillas |
| 1872 | tsar - 1812 - armies - casualties - cavalrymen | 41 | 1872_tsar_1812_armies_casualties |
| 1873 | divine - persona - scene - onstage - films | 41 | 1873_divine_persona_scene_onstage |
| 1874 | mosque - synagogue - temple - synagogues - waqf | 41 | 1874_mosque_synagogue_temple_synagogues |
| 1875 | extradition - arrest - fugitive - extradite - citizenship | 41 | 1875_extradition_arrest_fugitive_extradite |
| 1876 | rage - albums - machine - band - album | 41 | 1876_rage_albums_machine_band |
| 1877 | zombie - walkers - zombies - walking - episodes | 40 | 1877_zombie_walkers_zombies_walking |
| 1878 | impeachment - impeach - bipartisan - republican - bipartisanship | 40 | 1878_impeachment_impeach_bipartisan_republican |
| 1879 | lighting - lights - fluorescent - light - brighter | 40 | 1879_lighting_lights_fluorescent_light |
| 1880 | sigma - deviations - variability - defects - statisticians | 40 | 1880_sigma_deviations_variability_defects |
| 1881 | html - markup - browsers - hypertext - browser | 40 | 1881_html_markup_browsers_hypertext |
| 1882 | designing - prototyping - drafting - drawings - designs | 40 | 1882_designing_prototyping_drafting_drawings |
| 1883 | sultan - sultans - empresses - concubine - caliph | 40 | 1883_sultan_sultans_empresses_concubine |
| 1884 | lost - teen - teens - boys - threesome | 40 | 1884_lost_teen_teens_boys |
| 1885 | electromagnetism - electromagnet - electrical - electromagnetic - electrochemistry | 40 | 1885_electromagnetism_electromagnet_electrical_electromagnetic |
| 1886 | pianos - piano - pedals - pianists - pedal | 40 | 1886_pianos_piano_pedals_pianists |
| 1887 | bake - baking - bakery - bakeries - bakers | 40 | 1887_bake_baking_bakery_bakeries |
| 1888 | incest - incestuous - inbreeding - familial - taboo | 40 | 1888_incest_incestuous_inbreeding_familial |
| 1889 | policies - policy - legislation - bipartisan - violations | 40 | 1889_policies_policy_legislation_bipartisan |
| 1890 | masters - contestants - premiered - chef - chefs | 40 | 1890_masters_contestants_premiered_chef |
| 1891 | kitsune - folktale - folktales - folklore - tales | 40 | 1891_kitsune_folktale_folktales_folklore |
| 1892 | butterfly - bride - dagger - flowers - cries | 40 | 1892_butterfly_bride_dagger_flowers |
| 1893 | island - ancient - geography - continent - ocean | 40 | 1893_island_ancient_geography_continent |
| 1894 | pawn - pawned - jewelry - shop - silver | 40 | 1894_pawn_pawned_jewelry_shop |
| 1895 | antisemitism - nationalist - kibbutz - diaspora - nationalists | 40 | 1895_antisemitism_nationalist_kibbutz_diaspora |
| 1896 | competed - tennis - quarterfinal - tournament - semifinal | 40 | 1896_competed_tennis_quarterfinal_tournament |
| 1897 | refugees - repatriation - refugee - asylum - refuge | 40 | 1897_refugees_repatriation_refugee_asylum |
| 1898 | divers - diving - dive - diver - cave | 40 | 1898_divers_diving_dive_diver |
| 1899 | museums - museum - exhibit - exhibits - exhibition | 40 | 1899_museums_museum_exhibit_exhibits |
| 1900 | marvel - marvels - superhero - superheroes - comics | 40 | 1900_marvel_marvels_superhero_superheroes |
| 1901 | sat - exams - exam - admissions - scholastic | 40 | 1901_sat_exams_exam_admissions |
| 1902 | murders - murdered - murder - robbery - killer | 40 | 1902_murders_murdered_murder_robbery |
| 1903 | ancestors - subcontinent - ethnoreligious - dynasties - descended | 40 | 1903_ancestors_subcontinent_ethnoreligious_dynasties |
| 1904 | squid - squids - colossal - giant - tentacles | 40 | 1904_squid_squids_colossal_giant |
| 1905 | smurf - scorching - merchandising - midget - vocabulary | 40 | 1905_smurf_scorching_merchandising_midget |
| 1906 | badminton - competed - tai - quarterfinal - tournament | 40 | 1906_badminton_competed_tai_quarterfinal |
| 1907 | seasons - season - list - blazers - pistons | 40 | 1907_seasons_season_list_blazers |
| 1908 | crash - gameplay - playable - remastered - games | 40 | 1908_crash_gameplay_playable_remastered |
| 1909 | thrones - cast - stark - arya - actors | 40 | 1909_thrones_cast_stark_arya |
| 1910 | puck - goalie - hockey - capitals - scoring | 40 | 1910_puck_goalie_hockey_capitals |
| 1911 | libretto - operatic - opera - operas - arias | 40 | 1911_libretto_operatic_opera_operas |
| 1912 | mounds - mound - excavations - prehistoric - archaeological | 40 | 1912_mounds_mound_excavations_prehistoric |
| 1913 | marsupials - opossums - phylogenies - phylogenetic - marsupial | 40 | 1913_marsupials_opossums_phylogenies_phylogenetic |
| 1914 | politburo - soviet - 1953 - exterminated - troika | 40 | 1914_politburo_soviet_1953_exterminated |
| 1915 | fate - fates - doctor - doctorate - comics | 40 | 1915_fate_fates_doctor_doctorate |
| 1916 | encoded - encoding - encodings - encode - byte | 40 | 1916_encoded_encoding_encodings_encode |
| 1917 | evil - zombie - prequel - sequels - sequel | 40 | 1917_evil_zombie_prequel_sequels |
| 1918 | identifiers - identifier - variants - byte - id | 40 | 1918_identifiers_identifier_variants_byte |
| 1919 | racing - eliminated - laps - motorsports - won | 39 | 1919_racing_eliminated_laps_motorsports |
| 1920 | centrifuges - centrifuge - nuclear - uranium - centrifugal | 39 | 1920_centrifuges_centrifuge_nuclear_uranium |
| 1921 | messiah - prophet - prophets - crucified - crucifixion | 39 | 1921_messiah_prophet_prophets_crucified |
| 1922 | cabin - autopsy - sheriff - handcuffed - suspect | 39 | 1922_cabin_autopsy_sheriff_handcuffed |
| 1923 | youngest - activist - khan - journalist - laureate | 39 | 1923_youngest_activist_khan_journalist |
| 1924 | gonzo - journalist - journalism - journalistic - magazine | 39 | 1924_gonzo_journalist_journalism_journalistic |
| 1925 | treaty - cooperation - organization - multilateral - diplomacy | 39 | 1925_treaty_cooperation_organization_multilateral |
| 1926 | duchess - duke - royal - potters - charity | 39 | 1926_duchess_duke_royal_potters |
| 1927 | cyanobacteria - cyanobacterial - cyanobacterium - phytoplankton - algae | 39 | 1927_cyanobacteria_cyanobacterial_cyanobacterium_phytoplankton |
| 1928 | accredited - accreditation - universities - certifications - admissions | 39 | 1928_accredited_accreditation_universities_certifications |
| 1929 | ruins - excavation - valley - archaeological - archaeologists | 39 | 1929_ruins_excavation_valley_archaeological |
| 1930 | engine - diesel - engines - turbodiesel - turbo | 39 | 1930_engine_diesel_engines_turbodiesel |
| 1931 | procrastination - procrastinate - procrastinators - motivation - delaying | 39 | 1931_procrastination_procrastinate_procrastinators_motivation |
| 1932 | law - laws - theological - morality - moral | 39 | 1932_law_laws_theological_morality |
| 1933 | darknet - net - network - networks - dark | 39 | 1933_darknet_net_network_networks |
| 1934 | stitch - hostage - operative - operatives - hostages | 39 | 1934_stitch_hostage_operative_operatives |
| 1935 | rex - frontman - bassist - rock - ballads | 39 | 1935_rex_frontman_bassist_rock |
| 1936 | projectors - projector - optics - optical - projection | 39 | 1936_projectors_projector_optics_optical |
| 1937 | golf - golfer - golfers - handicaps - scoring | 39 | 1937_golf_golfer_golfers_handicaps |
| 1938 | saint - rosary - shrine - shrines - rituals | 39 | 1938_saint_rosary_shrine_shrines |
| 1939 | plague - epidemics - famines - epidemic - famine | 39 | 1939_plague_epidemics_famines_epidemic |
| 1940 | apartheid - segregated - segregation - blacks - discrimination | 39 | 1940_apartheid_segregated_segregation_blacks |
| 1941 | unbreakable - split - sequels - cameo - screenplay | 39 | 1941_unbreakable_split_sequels_cameo |
| 1942 | sentencing - unconstitutional - punishment - executions - upheld | 39 | 1942_sentencing_unconstitutional_punishment_executions |
| 1943 | viper - dodge - vehicle - car - fiat | 39 | 1943_viper_dodge_vehicle_car |
| 1944 | pylori - gastric - gastritis - gastrointestinal - pyloric | 39 | 1944_pylori_gastric_gastritis_gastrointestinal |
| 1945 | architect - architects - architecture - architectural - designs | 39 | 1945_architect_architects_architecture_architectural |
| 1946 | prophet - publisher - writings - painter - 1910 | 39 | 1946_prophet_publisher_writings_painter |
| 1947 | enterprise - enterprises - organizational - business - applications | 39 | 1947_enterprise_enterprises_organizational_business |
| 1948 | cartoons - cartoon - 1930s - 1932 - 1933 | 39 | 1948_cartoons_cartoon_1930s_1932 |
| 1949 | dragon - dragons - train - sequel - nightmare | 39 | 1949_dragon_dragons_train_sequel |
| 1950 | unmanned - drone - drones - aircraft - piloted | 39 | 1950_unmanned_drone_drones_aircraft |
| 1951 | duets - singer - songs - duet - songwriter | 39 | 1951_duets_singer_songs_duet |
| 1952 | muse - concert - vocals - albums - tour | 39 | 1952_muse_concert_vocals_albums |
| 1953 | oil - petroleum - oilfields - prices - 1971 | 39 | 1953_oil_petroleum_oilfields_prices |
| 1954 | barrow - barrows - murdered - murders - sheriff | 39 | 1954_barrow_barrows_murdered_murders |
| 1955 | dams - canyon - boulder - dam - aqueduct | 39 | 1955_dams_canyon_boulder_dam |
| 1956 | ashes - wickets - cricket - innings - batsman | 39 | 1956_ashes_wickets_cricket_innings |
| 1957 | defendants - autopsy - saw - gunshot - investigation | 39 | 1957_defendants_autopsy_saw_gunshot |
| 1958 | absurdism - absurdist - absurdity - absurd - meaninglessness | 39 | 1958_absurdism_absurdist_absurdity_absurd |
| 1959 | kung - panda - pandas - martial - sequels | 39 | 1959_kung_panda_pandas_martial |
| 1960 | leucotomy - lobotomy - neurosurgical - psychosurgery - lobotomized | 39 | 1960_leucotomy_lobotomy_neurosurgical_psychosurgery |
| 1961 | sovereignty - treatises - treatise - governance - philosophy | 39 | 1961_sovereignty_treatises_treatise_governance |
| 1962 | colors - coloring - colours - elections - electoral | 38 | 1962_colors_coloring_colours_elections |
| 1963 | garter - knighted - royal - knighthood - peerage | 38 | 1963_garter_knighted_royal_knighthood |
| 1964 | 1666 - fires - 1670 - firefighters - burning | 38 | 1964_1666_fires_1670_firefighters |
| 1965 | logic - mixtape - rapper - memoir - album | 38 | 1965_logic_mixtape_rapper_memoir |
| 1966 | antisemitism - antisemitic - semitism - antifa - persecutions | 38 | 1966_antisemitism_antisemitic_semitism_antifa |
| 1967 | kraken - octopuses - octopus - fishermen - cephalopods | 38 | 1967_kraken_octopuses_octopus_fishermen |
| 1968 | salmon - fishes - fish - sturgeon - fishery | 38 | 1968_salmon_fishes_fish_sturgeon |
| 1969 | constitution - constitutional - amended - amendments - amendment | 38 | 1969_constitution_constitutional_amended_amendments |
| 1970 | triangles - angles - triangle - hypotenuse - cosines | 38 | 1970_triangles_angles_triangle_hypotenuse |
| 1971 | executions - punishments - abolished - punishment - abolishing | 38 | 1971_executions_punishments_abolished_punishment |
| 1972 | dragons - imagine - unreleased - indie - released | 38 | 1972_dragons_imagine_unreleased_indie |
| 1973 | writer - novels - novel - literary - negro | 38 | 1973_writer_novels_novel_literary |
| 1974 | shuttlecock - badminton - tennis - bouncing - backhand | 38 | 1974_shuttlecock_badminton_tennis_bouncing |
| 1975 | acronyms - abbreviations - acronym - abbreviation - initials | 38 | 1975_acronyms_abbreviations_acronym_abbreviation |
| 1976 | executions - electrocution - unconstitutional - inmates - executed | 38 | 1976_executions_electrocution_unconstitutional_inmates |
| 1977 | bots - bot - automated - human - recognition | 38 | 1977_bots_bot_automated_human |
| 1978 | prenuptial - agreements - marital - marriage - agreement | 38 | 1978_prenuptial_agreements_marital_marriage |
| 1979 | population - 35 - 25 - 65 - age | 38 | 1979_population_35_25_65 |
| 1980 | dengue - fever - mosquito - mosquitoes - mosquitos | 38 | 1980_dengue_fever_mosquito_mosquitoes |
| 1981 | rainbow - studio - productions - cartoons - cartoon | 38 | 1981_rainbow_studio_productions_cartoons |
| 1982 | developmental - classrooms - developmentally - classroom - educational | 38 | 1982_developmental_classrooms_developmentally_classroom |
| 1983 | racing - raced - speedway - motorsports - racetrack | 38 | 1983_racing_raced_speedway_motorsports |
| 1984 | agricultural - commodity - corn - commodities - biofuels | 38 | 1984_agricultural_commodity_corn_commodities |
| 1985 | cosplay - cosplayers - costumes - contestants - masks | 38 | 1985_cosplay_cosplayers_costumes_contestants |
| 1986 | sour - songwriter - grungy - debut - songs | 38 | 1986_sour_songwriter_grungy_debut |
| 1987 | yoon - jin - jung - hye - kyung | 38 | 1987_yoon_jin_jung_hye |
| 1988 | keynote - festival - attendees - conferences - organizers | 38 | 1988_keynote_festival_attendees_conferences |
| 1989 | celebrity - spinoffs - sisters - rob - siblings | 38 | 1989_celebrity_spinoffs_sisters_rob |
| 1990 | provider - security - cyberattack - cybersecurity - servers | 38 | 1990_provider_security_cyberattack_cybersecurity |
| 1991 | pods - pod - containers - cluster - clusters | 38 | 1991_pods_pod_containers_cluster |
| 1992 | lifespan - oldest - longevity - age - lived | 38 | 1992_lifespan_oldest_longevity_age |
| 1993 | battleship - warship - battleships - naval - navy | 38 | 1993_battleship_warship_battleships_naval |
| 1994 | regiments - regiment - battalions - recruits - recruitment | 38 | 1994_regiments_regiment_battalions_recruits |
| 1995 | parliamentary - minister - parliament - ministers - constituency | 38 | 1995_parliamentary_minister_parliament_ministers |
| 1996 | minister - politician - campaigned - constituency - elected | 37 | 1996_minister_politician_campaigned_constituency |
| 1997 | subsidies - aid - postwar - economy - economists | 37 | 1997_subsidies_aid_postwar_economy |
| 1998 | gameplay - gaming - rpg - games - twilight | 37 | 1998_gameplay_gaming_rpg_games |
| 1999 | alexithymia - anxiety - disorders - psychiatric - disorder | 37 | 1999_alexithymia_anxiety_disorders_psychiatric |
| 2000 | tests - test - gender - feminist - women | 37 | 2000_tests_test_gender_feminist |
| 2001 | widows - widowhood - 1861 - bipolar - 1880s | 37 | 2001_widows_widowhood_1861_bipolar |
| 2002 | demons - demonic - demon - eve - demonology | 37 | 2002_demons_demonic_demon_eve |
| 2003 | gangster - gangsters - notorious - prohibition - jailing | 37 | 2003_gangster_gangsters_notorious_prohibition |
| 2004 | automata - cellular - cells - cell - automaton | 37 | 2004_automata_cellular_cells_cell |
| 2005 | languages - language - multilingual - soviet - lingua | 37 | 2005_languages_language_multilingual_soviet |
| 2006 | population - 2050 - populations - 2060 - demographic | 37 | 2006_population_2050_populations_2060 |
| 2007 | sarin - cousins - cousin - kidnap - affair | 37 | 2007_sarin_cousins_cousin_kidnap |
| 2008 | apes - ape - primates - chimpanzees - primate | 37 | 2008_apes_ape_primates_chimpanzees |
| 2009 | livestock - cattle - veterinary - beef - animal | 37 | 2009_livestock_cattle_veterinary_beef |
| 2010 | van - alleged - suspect - lurid - arrested | 37 | 2010_van_alleged_suspect_lurid |
| 2011 | emotion - emotions - emotional - affective - arousal | 37 | 2011_emotion_emotions_emotional_affective |
| 2012 | creoles - creole - francophone - french - parishes | 37 | 2012_creoles_creole_francophone_french |
| 2013 | laureates - laureate - prizes - prize - novelists | 37 | 2013_laureates_laureate_prizes_prize |
| 2014 | pachinko - parlors - arcades - parlor - yakuza | 37 | 2014_pachinko_parlors_arcades_parlor |
| 2015 | bohemian - queen - deacon - mercury - musical | 37 | 2015_bohemian_queen_deacon_mercury |
| 2016 | dictator - regime - rebels - unrest - fled | 37 | 2016_dictator_regime_rebels_unrest |
| 2017 | bombed - airship - airships - zeppelin - bombing | 37 | 2017_bombed_airship_airships_zeppelin |
| 2018 | euthanasia - suicide - suicides - patients - deaths | 37 | 2018_euthanasia_suicide_suicides_patients |
| 2019 | censorship - pornography - videos - moderation - abusing | 37 | 2019_censorship_pornography_videos_moderation |
| 2020 | apple - retina - screen - camera - processor | 37 | 2020_apple_retina_screen_camera |
| 2021 | marshals - marshal - department - deputy - deputies | 37 | 2021_marshals_marshal_department_deputy |
| 2022 | baron - flew - 1918 - von - pilots | 37 | 2022_baron_flew_1918_von |
| 2023 | orthodox - soviet - atheism - clergy - persecution | 37 | 2023_orthodox_soviet_atheism_clergy |
| 2024 | metal - bands - band - idol - genre | 37 | 2024_metal_bands_band_idol |
| 2025 | pharaoh - conquered - rulers - deposed - kings | 37 | 2025_pharaoh_conquered_rulers_deposed |
| 2026 | abducted - murders - brooks - abduction - abductions | 37 | 2026_abducted_murders_brooks_abduction |
| 2027 | currencies - currency - rates - inflation - parity | 37 | 2027_currencies_currency_rates_inflation |
| 2028 | 1917 - 1918 - soviet - soviets - republic | 37 | 2028_1917_1918_soviet_soviets |
| 2029 | animism - animists - animist - spiritualism - anthropological | 37 | 2029_animism_animists_animist_spiritualism |
| 2030 | hypothesis - hypotheses - theories - scientific - reproducibility | 37 | 2030_hypothesis_hypotheses_theories_scientific |
| 2031 | commerce - resell - sales - vendor - marketplace | 37 | 2031_commerce_resell_sales_vendor |
| 2032 | mathematician - mathematicians - mathematics - physicist - von | 37 | 2032_mathematician_mathematicians_mathematics_physicist |
| 2033 | channel - channels - tv - television - streaming | 37 | 2033_channel_channels_tv_television |
| 2034 | marvel - superhero - comics - valkyrie - thunder | 37 | 2034_marvel_superhero_comics_valkyrie |
| 2035 | 1080p - framerate - 1080 - 60fps - 720p | 37 | 2035_1080p_framerate_1080_60fps |
| 2036 | head - butt - episodes - rerun - paramount | 37 | 2036_head_butt_episodes_rerun |
| 2037 | woke - woken - wake - awake - twitter | 37 | 2037_woke_woken_wake_awake |
| 2038 | ron - character - villains - sidekick - characters | 37 | 2038_ron_character_villains_sidekick |
| 2039 | fed - inflation - monetary - financial - treasury | 37 | 2039_fed_inflation_monetary_financial |
| 2040 | fatwas - fatwā - fatwa - satanic - author | 37 | 2040_fatwas_fatwā_fatwa_satanic |
| 2041 | reliance - telecommunications - telecom - telecommunication - broadband | 37 | 2041_reliance_telecommunications_telecom_telecommunication |
| 2042 | conqueror - 1066 - 1069 - 1067 - 1086 | 37 | 2042_conqueror_1066_1069_1067 |
| 2043 | broadway - musicals - theatre - cat - musical | 37 | 2043_broadway_musicals_theatre_cat |
| 2044 | philosopher - writings - aphorism - philosophers - poet | 37 | 2044_philosopher_writings_aphorism_philosophers |
| 2045 | groceries - restaurants - restaurant - taxicab - taxi | 37 | 2045_groceries_restaurants_restaurant_taxicab |
| 2046 | musicals - lyricist - musical - concertos - operas | 37 | 2046_musicals_lyricist_musical_concertos |
| 2047 | mysticism - mystical - spiritual - spirituality - esotericism | 37 | 2047_mysticism_mystical_spiritual_spirituality |
| 2048 | biblical - patriarch - prophethood - genesis - prophetic | 37 | 2048_biblical_patriarch_prophethood_genesis |
| 2049 | chainsaw - massacre - slaughterhouse - sequels - chain | 36 | 2049_chainsaw_massacre_slaughterhouse_sequels |
| 2050 | fingerprints - robbers - fingerprint - investigation - ransom | 36 | 2050_fingerprints_robbers_fingerprint_investigation |
| 2051 | rocky - rapper - ap - mixtape - billboard | 36 | 2051_rocky_rapper_ap_mixtape |
| 2052 | influential - list - publicized - ranking - world | 36 | 2052_influential_list_publicized_ranking |
| 2053 | concert - concerts - tour - theater - headlining | 36 | 2053_concert_concerts_tour_theater |
| 2054 | protozoans - protozoa - protozoan - taxonomic - phylogeny | 36 | 2054_protozoans_protozoa_protozoan_taxonomic |
| 2055 | missile - missiles - supersonic - hypersonic - radar | 36 | 2055_missile_missiles_supersonic_hypersonic |
| 2056 | anthrax - anthracis - infection - infections - infected | 36 | 2056_anthrax_anthracis_infection_infections |
| 2057 | streaming - premiere - tv - stream - 1080p | 36 | 2057_streaming_premiere_tv_stream |
| 2058 | apps - app - android - purchases - ads | 36 | 2058_apps_app_android_purchases |
| 2059 | 1644 - theological - theologian - puritan - theology | 36 | 2059_1644_theological_theologian_puritan |
| 2060 | spaceflight - military - spaceflights - spacecraft - aerospace | 36 | 2060_spaceflight_military_spaceflights_spacecraft |
| 2061 | dick - biography - author - novelist - writer | 36 | 2061_dick_biography_author_novelist |
| 2062 | mangroves - mangrove - ecosystem - ecosystems - biodiversity | 36 | 2062_mangroves_mangrove_ecosystem_ecosystems |
| 2063 | harbour - naval - captured - 1842 - steamships | 36 | 2063_harbour_naval_captured_1842 |
| 2064 | chipmunks - chipmunk - song - 1959 - albums | 36 | 2064_chipmunks_chipmunk_song_1959 |
| 2065 | plasmas - plasma - electrostatic - electrically - electrons | 36 | 2065_plasmas_plasma_electrostatic_electrically |
| 2066 | channel - mosh - media - channels - entertainment | 36 | 2066_channel_mosh_media_channels |
| 2067 | kernel - latest - maintainers - os - maintainer | 36 | 2067_kernel_latest_maintainers_os |
| 2068 | population - municipalities - cityscape - villages - city | 36 | 2068_population_municipalities_cityscape_villages |
| 2069 | songs - song - singer - albums - album | 36 | 2069_songs_song_singer_albums |
| 2070 | cannabinoids - cannabinoid - tetrahydrocannabinol - cannabidiol - cannabis | 36 | 2070_cannabinoids_cannabinoid_tetrahydrocannabinol_cannabidiol |
| 2071 | jong - politburo - secretary - leader - chairman | 36 | 2071_jong_politburo_secretary_leader |
| 2072 | dancer - dances - danced - dancing - choreography | 36 | 2072_dancer_dances_danced_dancing |
| 2073 | reptilians - conspiracist - conspiracism - reptilian - extraterrestrial | 36 | 2073_reptilians_conspiracist_conspiracism_reptilian |
| 2074 | newscast - reporter - primetime - anchor - journalist | 36 | 2074_newscast_reporter_primetime_anchor |
| 2075 | 1605 - gunpowder - undercroft - conspirators - 1603 | 36 | 2075_1605_gunpowder_undercroft_conspirators |
| 2076 | motley - albums - frontman - band - concert | 36 | 2076_motley_albums_frontman_band |
| 2077 | wormholes - wormhole - spacetime - traversable - relativity | 36 | 2077_wormholes_wormhole_spacetime_traversable |
| 2078 | habeas - constitution - detention - constitutional - imprisonment | 36 | 2078_habeas_constitution_detention_constitutional |
| 2079 | renewed - primetime - episodes - airing - premiered | 36 | 2079_renewed_primetime_episodes_airing |
| 2080 | strikers - goalscoring - winger - striker - goalkeeping | 36 | 2080_strikers_goalscoring_winger_striker |
| 2081 | seal - undercover - smuggling - cartel - smuggler | 36 | 2081_seal_undercover_smuggling_cartel |
| 2082 | placebo - concert - concerts - gigs - albums | 36 | 2082_placebo_concert_concerts_gigs |
| 2083 | radiation - radiographs - rays - radiography - ray | 36 | 2083_radiation_radiographs_rays_radiography |
| 2084 | thrash - vocals - drumming - rhythmic - melodic | 36 | 2084_thrash_vocals_drumming_rhythmic |
| 2085 | scored - scoring - goalscoring - goalscorer - goals | 36 | 2085_scored_scoring_goalscoring_goalscorer |
| 2086 | teams - league - leagues - stadium - conferences | 36 | 2086_teams_league_leagues_stadium |
| 2087 | kava - hepatotoxicity - supplements - herbal - medicinal | 36 | 2087_kava_hepatotoxicity_supplements_herbal |
| 2088 | expedition - expeditions - voyage - explorers - archaeologist | 36 | 2088_expedition_expeditions_voyage_explorers |
| 2089 | polyamory - polyamorous - polygamous - polygamy - monogamous | 36 | 2089_polyamory_polyamorous_polygamous_polygamy |
| 2090 | rose - autobiography - quotes - writing - quotation | 36 | 2090_rose_autobiography_quotes_writing |
| 2091 | biblical - conquered - temple - kingdom - temples | 36 | 2091_biblical_conquered_temple_kingdom |
| 2092 | bacon - pork - vegetarian - meat - beef | 36 | 2092_bacon_pork_vegetarian_meat |
| 2093 | memes - evolution - evolutionary - meme - genetic | 36 | 2093_memes_evolution_evolutionary_meme |
| 2094 | actress - actresses - blonde - playboy - vogue | 36 | 2094_actress_actresses_blonde_playboy |
| 2095 | ancient - law - laws - treatise - legislation | 36 | 2095_ancient_law_laws_treatise |
| 2096 | actor - peck - gangster - actors - portrayal | 36 | 2096_actor_peck_gangster_actors |
| 2097 | protesting - protest - protesters - protests - picketing | 36 | 2097_protesting_protest_protesters_protests |
| 2098 | neo - matrix - trinity - smith - cypher | 36 | 2098_neo_matrix_trinity_smith |
| 2099 | mathematician - mathematicians - hardy - mathematics - mathematical | 36 | 2099_mathematician_mathematicians_hardy_mathematics |
| 2100 | semiotics - semiotic - concepts - linguistics - formalist | 36 | 2100_semiotics_semiotic_concepts_linguistics |
| 2101 | palace - palaces - monuments - museums - museum | 36 | 2101_palace_palaces_monuments_museums |
| 2102 | episodes - airing - episode - ugly - finale | 36 | 2102_episodes_airing_episode_ugly |
| 2103 | lunar - moon - landings - spacecraft - missions | 36 | 2103_lunar_moon_landings_spacecraft |
| 2104 | protocols - antisemitism - conspiratorial - antisemitic - conspiracist | 35 | 2104_protocols_antisemitism_conspiratorial_antisemitic |
| 2105 | sailed - seaworthy - boat - vessel - aground | 35 | 2105_sailed_seaworthy_boat_vessel |
| 2106 | duet - concert - albums - songs - concerts | 35 | 2106_duet_concert_albums_songs |
| 2107 | flame - ignited - flamethrower - flames - burning | 35 | 2107_flame_ignited_flamethrower_flames |
| 2108 | wu - han - tung - qi - sun | 35 | 2108_wu_han_tung_qi |
| 2109 | committees - parliamentary - parliament - chairperson - chairpersons | 35 | 2109_committees_parliamentary_parliament_chairperson |
| 2110 | alleged - bail - prosecution - arrested - arrest | 35 | 2110_alleged_bail_prosecution_arrested |
| 2111 | duet - sang - queen - concert - diamonds | 35 | 2111_duet_sang_queen_concert |
| 2112 | municipality - polish - orchestras - cathedral - conservatory | 35 | 2112_municipality_polish_orchestras_cathedral |
| 2113 | regiment - enlisted - regiments - regimental - navy | 35 | 2113_regiment_enlisted_regiments_regimental |
| 2114 | tower - towers - tallest - 1889 - construction | 35 | 2114_tower_towers_tallest_1889 |
| 2115 | taxation - tax - taxes - taxed - taxpayers | 35 | 2115_taxation_tax_taxes_taxed |
| 2116 | sclerosis - ms - demyelination - encephalomyelitis - neurological | 35 | 2116_sclerosis_ms_demyelination_encephalomyelitis |
| 2117 | seppuku - samurai - decapitation - decapitate - decapitates | 35 | 2117_seppuku_samurai_decapitation_decapitate |
| 2118 | architect - architects - architecture - houses - designs | 35 | 2118_architect_architects_architecture_houses |
| 2119 | albums - songs - lyrics - lyricist - sang | 35 | 2119_albums_songs_lyrics_lyricist |
| 2120 | decibels - decibel - amplitude - amplitudes - amplifier | 35 | 2120_decibels_decibel_amplitude_amplitudes |
| 2121 | palace - crystal - penalty - scorer - scored | 35 | 2121_palace_crystal_penalty_scorer |
| 2122 | emir - sheikh - emirate - emirates - president | 35 | 2122_emir_sheikh_emirate_emirates |
| 2123 | strips - circular - strip - folds - geometrically | 35 | 2123_strips_circular_strip_folds |
| 2124 | sang - songwriter - singers - albums - singer | 35 | 2124_sang_songwriter_singers_albums |
| 2125 | libel - celebrity - reportedly - scandals - resigned | 35 | 2125_libel_celebrity_reportedly_scandals |
| 2126 | divergent - trilogy - novel - screenplay - sequel | 35 | 2126_divergent_trilogy_novel_screenplay |
| 2127 | vaccine - vaccines - vaccination - vaccinated - vaccinations | 35 | 2127_vaccine_vaccines_vaccination_vaccinated |
| 2128 | paramount - premiered - channel - tv - episodes | 35 | 2128_paramount_premiered_channel_tv |
| 2129 | fish - cannibalism - tortured - murders - murder | 35 | 2129_fish_cannibalism_tortured_murders |
| 2130 | touchdowns - interceptions - touchdown - quarterback - fumble | 35 | 2130_touchdowns_interceptions_touchdown_quarterback |
| 2131 | viewership - viewers - televised - television - broadcasters | 35 | 2131_viewership_viewers_televised_television |
| 2132 | shops - mall - stores - shop - store | 35 | 2132_shops_mall_stores_shop |
| 2133 | niece - grandmother - 1918 - aunt - 1945 | 35 | 2133_niece_grandmother_1918_aunt |
| 2134 | scored - goalscorers - goals - scoring - goal | 35 | 2134_scored_goalscorers_goals_scoring |
| 2135 | seo - google - ranking - rankings - pages | 35 | 2135_seo_google_ranking_rankings |
| 2136 | reliance - shareholders - crore - crores - industries | 35 | 2136_reliance_shareholders_crore_crores |
| 2137 | postmodernism - modernism - postmodern - modernists - modernist | 35 | 2137_postmodernism_modernism_postmodern_modernists |
| 2138 | genesis - biblical - patriarch - prophets - polytheism | 35 | 2138_genesis_biblical_patriarch_prophets |
| 2139 | presidential - parliamentary - governs - government - presidents | 35 | 2139_presidential_parliamentary_governs_government |
| 2140 | political - politics - sciences - sociology - sociologists | 35 | 2140_political_politics_sciences_sociology |
| 2141 | feeds - syndication - feed - subscribing - browsers | 35 | 2141_feeds_syndication_feed_subscribing |
| 2142 | ac - drummer - guitarist - drums - albums | 35 | 2142_ac_drummer_guitarist_drums |
| 2143 | chassis - vehicle - dealerships - automotive - factory | 35 | 2143_chassis_vehicle_dealerships_automotive |
| 2144 | biographers - revolt - orientalism - desertion - pillars | 35 | 2144_biographers_revolt_orientalism_desertion |
| 2145 | wastes - waste - disposal - recycling - landfills | 35 | 2145_wastes_waste_disposal_recycling |
| 2146 | radio - fm - stations - broadcasting - broadcasters | 35 | 2146_radio_fm_stations_broadcasting |
| 2147 | resignation - riots - protest - paramilitary - protesters | 35 | 2147_resignation_riots_protest_paramilitary |
| 2148 | theme - themes - intro - anime - ending | 35 | 2148_theme_themes_intro_anime |
| 2149 | journalist - hitch - correspondent - readership - statesman | 34 | 2149_journalist_hitch_correspondent_readership |
| 2150 | paintings - painting - painters - painter - murals | 34 | 2150_paintings_painting_painters_painter |
| 2151 | fighter - fighters - featherweight - brawling - bantamweight | 34 | 2151_fighter_fighters_featherweight_brawling |
| 2152 | transit - commute - commuting - commuters - buses | 34 | 2152_transit_commute_commuting_commuters |
| 2153 | bliss - ambient - stages - albums - stage | 34 | 2153_bliss_ambient_stages_albums |
| 2154 | studios - lions - acquisitions - paramount - owns | 34 | 2154_studios_lions_acquisitions_paramount |
| 2155 | humidity - humid - moisture - dew - evaporation | 34 | 2155_humidity_humid_moisture_dew |
| 2156 | codeine - prescription - paracetamol - prescribed - dihydrocodeine | 34 | 2156_codeine_prescription_paracetamol_prescribed |
| 2157 | merger - shareholders - acquisition - acquire - acquired | 34 | 2157_merger_shareholders_acquisition_acquire |
| 2158 | geopolitical - tsarist - ideology - geopolitics - political | 34 | 2158_geopolitical_tsarist_ideology_geopolitics |
| 2159 | crops - agriculture - agricultural - maize - plantations | 34 | 2159_crops_agriculture_agricultural_maize |
| 2160 | poutine - gravy - truffles - burger - cuisine | 34 | 2160_poutine_gravy_truffles_burger |
| 2161 | autobiography - autobiographies - bird - poetry - poet | 34 | 2161_autobiography_autobiographies_bird_poetry |
| 2162 | propaganda - propagandists - propagandistic - propagandist - persuasion | 34 | 2162_propaganda_propagandists_propagandistic_propagandist |
| 2163 | sausage - sausages - bun - chili - condiments | 34 | 2163_sausage_sausages_bun_chili |
| 2164 | albums - vocals - album - remix - punk | 34 | 2164_albums_vocals_album_remix |
| 2165 | inmate - prison - prisoner - imprisonment - sentenced | 34 | 2165_inmate_prison_prisoner_imprisonment |
| 2166 | discord - subscription - guilds - nitro - twitch | 34 | 2166_discord_subscription_guilds_nitro |
| 2167 | gnostic - gnosis - theology - religions - theosophical | 34 | 2167_gnostic_gnosis_theology_religions |
| 2168 | bomber - squadron - 509th - 1945 - bombardment | 34 | 2168_bomber_squadron_509th_1945 |
| 2169 | boiler - turbine - engines - invention - inventor | 34 | 2169_boiler_turbine_engines_invention |
| 2170 | fighter - arcade - street - arcades - consoles | 34 | 2170_fighter_arcade_street_arcades |
| 2171 | parole - sentenced - hearings - convicted - judge | 34 | 2171_parole_sentenced_hearings_convicted |
| 2172 | home - sequels - sequel - screenplays - film | 34 | 2172_home_sequels_sequel_screenplays |
| 2173 | ferry - harbor - pier - wharf - waterfront | 34 | 2173_ferry_harbor_pier_wharf |
| 2174 | muddy - harmonica - blues - albums - guitarist | 34 | 2174_muddy_harmonica_blues_albums |
| 2175 | calamity - calamitous - novels - novel - soprano | 34 | 2175_calamity_calamitous_novels_novel |
| 2176 | godfather - mafia - capo - murdered - murder | 34 | 2176_godfather_mafia_capo_murdered |
| 2177 | undrafted - cornerback - patriots - receptions - touchdowns | 34 | 2177_undrafted_cornerback_patriots_receptions |
| 2178 | royal - monarch - palace - palaces - royalty | 34 | 2178_royal_monarch_palace_palaces |
| 2179 | joey - punk - drummer - sings - vocals | 34 | 2179_joey_punk_drummer_sings |
| 2180 | nuclear - sanctions - missiles - jong - missile | 34 | 2180_nuclear_sanctions_missiles_jong |
| 2181 | poet - poem - poetry - poems - scribes | 34 | 2181_poet_poem_poetry_poems |
| 2182 | rebounds - warriors - curry - assists - green | 34 | 2182_rebounds_warriors_curry_assists |
| 2183 | skyscraper - tallest - tower - towers - skyscrapers | 34 | 2183_skyscraper_tallest_tower_towers |
| 2184 | shareholder - shareholders - investor - shares - holdings | 34 | 2184_shareholder_shareholders_investor_shares |
| 2185 | astronomer - heliocentric - 1516 - papal - 1496 | 34 | 2185_astronomer_heliocentric_1516_papal |
| 2186 | actresses - actress - wilder - portrayal - broadway | 34 | 2186_actresses_actress_wilder_portrayal |
| 2187 | register - historic - cemeteries - landmarks - monuments | 34 | 2187_register_historic_cemeteries_landmarks |
| 2188 | albums - album - songs - concert - comeback | 34 | 2188_albums_album_songs_concert |
| 2189 | merger - aerospace - subsidiaries - acquisitions - firms | 34 | 2189_merger_aerospace_subsidiaries_acquisitions |
| 2190 | wiggle - band - entertainers - concert - performers | 34 | 2190_wiggle_band_entertainers_concert |
| 2191 | pilgrims - 1620 - pilgrim - voyage - sailed | 34 | 2191_pilgrims_1620_pilgrim_voyage |
| 2192 | sneakers - footwear - shoes - sportswear - sneaker | 34 | 2192_sneakers_footwear_shoes_sportswear |
| 2193 | blade - prequels - sequel - 2049 - runner | 34 | 2193_blade_prequels_sequel_2049 |
| 2194 | torch - olympic - relay - relays - flame | 34 | 2194_torch_olympic_relay_relays |
| 2195 | novelist - purple - novels - novel - literary | 34 | 2195_novelist_purple_novels_novel |
| 2196 | ranger - regiment - infantry - regimental - rangers | 34 | 2196_ranger_regiment_infantry_regimental |
| 2197 | dialects - languages - speak - dialect - language | 34 | 2197_dialects_languages_speak_dialect |
| 2198 | gymnast - gymnasts - gymnastics - gymnastic - athlete | 34 | 2198_gymnast_gymnasts_gymnastics_gymnastic |
| 2199 | haiku - haikai - poetry - poems - poet | 34 | 2199_haiku_haikai_poetry_poems |
| 2200 | spetsnaz - soviet - infantrymen - commanders - brigades | 34 | 2200_spetsnaz_soviet_infantrymen_commanders |
| 2201 | deaf - blindness - disabilities - braille - blind | 33 | 2201_deaf_blindness_disabilities_braille |
| 2202 | medieval - antiquity - renaissance - darkness - dark | 33 | 2202_medieval_antiquity_renaissance_darkness |
| 2203 | photographer - photography - photographs - photographic - photographers | 33 | 2203_photographer_photography_photographs_photographic |
| 2204 | genocide - genocides - holocaust - perpetrators - genocidal | 33 | 2204_genocide_genocides_holocaust_perpetrators |
| 2205 | bow - actress - 1932 - laurels - 1920s | 33 | 2205_bow_actress_1932_laurels |
| 2206 | crops - agriculture - irrigation - agricultural - farmers | 33 | 2206_crops_agriculture_irrigation_agricultural |
| 2207 | paella - paprika - cuisine - seafood - olive | 33 | 2207_paella_paprika_cuisine_seafood |
| 2208 | novelist - literature - literary - writer - poet | 33 | 2208_novelist_literature_literary_writer |
| 2209 | midnight - noon - midday - clock - evening | 33 | 2209_midnight_noon_midday_clock |
| 2210 | griffin - donation - donated - museum - donating | 33 | 2210_griffin_donation_donated_museum |
| 2211 | starling - cannibalized - cannibal - cannibalize - killer | 33 | 2211_starling_cannibalized_cannibal_cannibalize |
| 2212 | taxis - taxi - taxicabs - fares - cabs | 33 | 2212_taxis_taxi_taxicabs_fares |
| 2213 | hunts - noose - gun - hunting - hunters | 33 | 2213_hunts_noose_gun_hunting |
| 2214 | ethnicities - ethnicity - ethnic - racial - census | 33 | 2214_ethnicities_ethnicity_ethnic_racial |
| 2215 | buildings - skyscraper - apartments - building - architecture | 33 | 2215_buildings_skyscraper_apartments_building |
| 2216 | sedan - convertibles - convertible - coupe - corvette | 33 | 2216_sedan_convertibles_convertible_coupe |
| 2217 | harden - assists - rebounds - scoring - triple | 33 | 2217_harden_assists_rebounds_scoring |
| 2218 | emails - webmail - email - mail - google | 33 | 2218_emails_webmail_email_mail |
| 2219 | paintings - painting - painter - painters - paint | 33 | 2219_paintings_painting_painter_painters |
| 2220 | yards - quarterback - touchdowns - receptions - cornerback | 33 | 2220_yards_quarterback_touchdowns_receptions |
| 2221 | museums - museum - exhibitions - sheikh - sultanate | 33 | 2221_museums_museum_exhibitions_sheikh |
| 2222 | retailer - store - sales - shop - stores | 33 | 2222_retailer_store_sales_shop |
| 2223 | khan - politician - candidate - goldsmith - councillor | 33 | 2223_khan_politician_candidate_goldsmith |
| 2224 | eigenfunctions - quantum - eigenstates - eigenstate - observables | 33 | 2224_eigenfunctions_quantum_eigenstates_eigenstate |
| 2225 | recycling - recycled - recycle - recyclable - recycles | 33 | 2225_recycling_recycled_recycle_recyclable |
| 2226 | thrash - slayer - band - bands - hardcore | 33 | 2226_thrash_slayer_band_bands |
| 2227 | beetle - beetles - convertible - fenders - chassis | 33 | 2227_beetle_beetles_convertible_fenders |
| 2228 | assists - rebounds - steals - doubles - triple | 33 | 2228_assists_rebounds_steals_doubles |
| 2229 | fifths - fifth - circle - tones - numerals | 33 | 2229_fifths_fifth_circle_tones |
| 2230 | rush - bands - rock - zeppelin - drummer | 33 | 2230_rush_bands_rock_zeppelin |
| 2231 | yuan - chairman - hui - election - elections | 33 | 2231_yuan_chairman_hui_election |
| 2232 | clitoris - position - positions - intercourse - clitoral | 33 | 2232_clitoris_position_positions_intercourse |
| 2233 | viewers - episodes - generation - syndication - storylines | 33 | 2233_viewers_episodes_generation_syndication |
| 2234 | hegemony - superpower - superpowers - diplomacy - hegemonic | 33 | 2234_hegemony_superpower_superpowers_diplomacy |
| 2235 | population - demographic - comune - average - depopulation | 33 | 2235_population_demographic_comune_average |
| 2236 | laptops - laptop - notebooks - notebook - desktops | 33 | 2236_laptops_laptop_notebooks_notebook |
| 2237 | unrest - bombing - rebels - dictator - guerrillas | 33 | 2237_unrest_bombing_rebels_dictator |
| 2238 | survivors - zombies - umbrella - hive - discovers | 33 | 2238_survivors_zombies_umbrella_hive |
| 2239 | activist - intellectuals - activism - anarchism - linguistics | 33 | 2239_activist_intellectuals_activism_anarchism |
| 2240 | sesame - episodes - cartoon - licensing - television | 32 | 2240_sesame_episodes_cartoon_licensing |
| 2241 | moderate - conservatives - ideological - nationalist - conservative | 32 | 2241_moderate_conservatives_ideological_nationalist |
| 2242 | biblical - testament - mythological - epistle - satan | 32 | 2242_biblical_testament_mythological_epistle |
| 2243 | buried - cemetery - died - funeral - interred | 32 | 2243_buried_cemetery_died_funeral |
| 2244 | defender - footballer - arsenal - villa - stoke | 32 | 2244_defender_footballer_arsenal_villa |
| 2245 | dictionaries - dictionary - reprinting - typography - abridgement | 32 | 2245_dictionaries_dictionary_reprinting_typography |
| 2246 | osteopathic - osteopathy - osteopaths - homeopathy - physiotherapists | 32 | 2246_osteopathic_osteopathy_osteopaths_homeopathy |
| 2247 | indigenous - aboriginal - arctic - tribal - anthropologist | 32 | 2247_indigenous_aboriginal_arctic_tribal |
| 2248 | religions - religion - religiosity - theology - religious | 32 | 2248_religions_religion_religiosity_theology |
| 2249 | lily - robin - episode - doppelganger - doppelgänger | 32 | 2249_lily_robin_episode_doppelganger |
| 2250 | pedagogy - pedagogical - pedagogue - educator - teaching | 32 | 2250_pedagogy_pedagogical_pedagogue_educator |
| 2251 | touchdowns - interceptions - yards - quarterback - interception | 32 | 2251_touchdowns_interceptions_yards_quarterback |
| 2252 | dubbed - satellite - amazon - premiere - streamed | 32 | 2252_dubbed_satellite_amazon_premiere |
| 2253 | drummer - drumming - band - songwriters - gigs | 32 | 2253_drummer_drumming_band_songwriters |
| 2254 | finasteride - antiandrogen - antiandrogenic - inhibitor - dosage | 32 | 2254_finasteride_antiandrogen_antiandrogenic_inhibitor |
| 2255 | northwest - fort - settlers - forts - 1840s | 32 | 2255_northwest_fort_settlers_forts |
| 2256 | ancestry - ancestor - ancestors - ancestral - archipelago | 32 | 2256_ancestry_ancestor_ancestors_ancestral |
| 2257 | hypotenuse - triangles - squares - geometry - triangle | 32 | 2257_hypotenuse_triangles_squares_geometry |
| 2258 | orbits - solutions - bodies - mathematical - gravitation | 32 | 2258_orbits_solutions_bodies_mathematical |
| 2259 | easter - holiday - celebrated - feasts - feast | 32 | 2259_easter_holiday_celebrated_feasts |
| 2260 | antihypertensive - propranolol - hypertension - blockers - adrenergic | 32 | 2260_antihypertensive_propranolol_hypertension_blockers |
| 2261 | adder - servant - reign - descendants - queen | 32 | 2261_adder_servant_reign_descendants |
| 2262 | genetics - genetic - heredity - traits - genes | 32 | 2262_genetics_genetic_heredity_traits |
| 2263 | amazon - affiliate - retailers - retailer - sales | 32 | 2263_amazon_affiliate_retailers_retailer |
| 2264 | birthday - doodle - birthdays - 26th - celebrated | 32 | 2264_birthday_doodle_birthdays_26th |
| 2265 | hominem - argumentation - arguments - philosophical - philosopher | 32 | 2265_hominem_argumentation_arguments_philosophical |
| 2266 | carmaker - automobiles - cars - sedans - vehicles | 32 | 2266_carmaker_automobiles_cars_sedans |
| 2267 | amnesty - refugees - racism - asylum - discrimination | 32 | 2267_amnesty_refugees_racism_asylum |
| 2268 | pamphlet - 1776 - pamphlets - revolutionary - revolutionaries | 32 | 2268_pamphlet_1776_pamphlets_revolutionary |
| 2269 | imperialism - colonial - labour - humanitarian - ivory | 32 | 2269_imperialism_colonial_labour_humanitarian |
| 2270 | news - journalism - propaganda - misinformation - credible | 32 | 2270_news_journalism_propaganda_misinformation |
| 2271 | gymnast - gymnasts - gymnastics - olympic - medals | 32 | 2271_gymnast_gymnasts_gymnastics_olympic |
| 2272 | stadia - subscriptions - subscription - launched - launch | 32 | 2272_stadia_subscriptions_subscription_launched |
| 2273 | spinal - paralysis - paralyzed - vertebrae - cervical | 32 | 2273_spinal_paralysis_paralyzed_vertebrae |
| 2274 | housewives - housewife - cast - reunion - guests | 32 | 2274_housewives_housewife_cast_reunion |
| 2275 | irrigation - sea - waters - salinity - basins | 32 | 2275_irrigation_sea_waters_salinity |
| 2276 | transistors - microprocessors - processors - microprocessor - transistor | 32 | 2276_transistors_microprocessors_processors_microprocessor |
| 2277 | phantom - ghost - opera - lair - cloak | 32 | 2277_phantom_ghost_opera_lair |
| 2278 | granites - granite - mineralogy - magmas - basaltic | 32 | 2278_granites_granite_mineralogy_magmas |
| 2279 | victor - fascism - monarchist - monarchy - fascist | 32 | 2279_victor_fascism_monarchist_monarchy |
| 2280 | fasciitis - fascia - plantar - fascicles - tendon | 32 | 2280_fasciitis_fascia_plantar_fascicles |
| 2281 | company - conglomerate - market - enterprises - industries | 32 | 2281_company_conglomerate_market_enterprises |
| 2282 | rosemary - lobotomy - nuns - lobotomized - convent | 32 | 2282_rosemary_lobotomy_nuns_lobotomized |
| 2283 | mosque - terrorist - mosques - coroner - victims | 32 | 2283_mosque_terrorist_mosques_coroner |
| 2284 | tennis - tournaments - tournament - finalist - quarterfinals | 32 | 2284_tennis_tournaments_tournament_finalist |
| 2285 | dramas - airing - drama - sonata - cultural | 32 | 2285_dramas_airing_drama_sonata |
| 2286 | globalization - globalisation - globalized - transnational - global | 32 | 2286_globalization_globalisation_globalized_transnational |
| 2287 | knight - donated - philanthropist - philanthropic - donation | 32 | 2287_knight_donated_philanthropist_philanthropic |
| 2288 | ibn - al - theology - treatises - ijtihad | 32 | 2288_ibn_al_theology_treatises |
| 2289 | creatine - creatinine - supplementation - supplement - supplements | 32 | 2289_creatine_creatinine_supplementation_supplement |
| 2290 | duo - app - mobile - android - proficiency | 32 | 2290_duo_app_mobile_android |
| 2291 | offspring - albums - album - band - bands | 32 | 2291_offspring_albums_album_band |
| 2292 | guards - defensive - guard - basketball - players | 32 | 2292_guards_defensive_guard_basketball |
| 2293 | prix - lightning - racing - radiator - racers | 32 | 2293_prix_lightning_racing_radiator |
| 2294 | executives - stockholder - executive - shareholders - company | 32 | 2294_executives_stockholder_executive_shareholders |
| 2295 | presenter - savage - airing - keynote - premiered | 32 | 2295_presenter_savage_airing_keynote |
| 2296 | multiracial - geisha - ethnic - actors - ethnically | 31 | 2296_multiracial_geisha_ethnic_actors |
| 2297 | schools - academies - school - education - colleges | 31 | 2297_schools_academies_school_education |
| 2298 | oz - debate - debater - debating - midterms | 31 | 2298_oz_debate_debater_debating |
| 2299 | dragon - anime - manga - superman - piccolo | 31 | 2299_dragon_anime_manga_superman |
| 2300 | tennis - slams - doubles - tournaments - racquets | 31 | 2300_tennis_slams_doubles_tournaments |
| 2301 | disks - disk - floppy - drives - storage | 31 | 2301_disks_disk_floppy_drives |
| 2302 | albums - duet - album - vocals - singles | 31 | 2302_albums_duet_album_vocals |
| 2303 | guitarist - tour - touring - zeppelin - backstage | 31 | 2303_guitarist_tour_touring_zeppelin |
| 2304 | bidets - bidet - toilets - bathrooms - toilet | 31 | 2304_bidets_bidet_toilets_bathrooms |
| 2305 | spina - bifida - amniocentesis - maternal - pregnancy | 31 | 2305_spina_bifida_amniocentesis_maternal |
| 2306 | bell - cliffhanger - saved - cast - sitcom | 31 | 2306_bell_cliffhanger_saved_cast |
| 2307 | arcade - simulator - gameplay - racing - skyline | 31 | 2307_arcade_simulator_gameplay_racing |
| 2308 | functional - programming - functions - programmer - function | 31 | 2308_functional_programming_functions_programmer |
| 2309 | sting - band - bandmates - concert - verve | 31 | 2309_sting_band_bandmates_concert |
| 2310 | mukbang - consuming - pornography - habits - cravings | 31 | 2310_mukbang_consuming_pornography_habits |
| 2311 | translations - translating - translator - translated - translation | 31 | 2311_translations_translating_translator_translated |
| 2312 | painting - paintings - painter - paint - art | 31 | 2312_painting_paintings_painter_paint |
| 2313 | gambling - betting - gamblers - bets - casino | 31 | 2313_gambling_betting_gamblers_bets |
| 2314 | ancient - archaeological - archaeology - neolithic - dynasties | 31 | 2314_ancient_archaeological_archaeology_neolithic |
| 2315 | animals - drummer - animal - bassist - drums | 31 | 2315_animals_drummer_animal_bassist |
| 2316 | feng - decorating - buildings - practices - shui | 31 | 2316_feng_decorating_buildings_practices |
| 2317 | songwriter - singer - sings - keyboardist - vocals | 31 | 2317_songwriter_singer_sings_keyboardist |
| 2318 | memories - memory - recall - psychology - falsehood | 31 | 2318_memories_memory_recall_psychology |
| 2319 | draft - drafted - draftee - picks - blazers | 31 | 2319_draft_drafted_draftee_picks |
| 2320 | registrars - registrar - domains - domain - registrants | 31 | 2320_registrars_registrar_domains_domain |
| 2321 | officers - police - gunshots - shooter - shooting | 31 | 2321_officers_police_gunshots_shooter |
| 2322 | moon - drummer - drums - drummers - drumming | 31 | 2322_moon_drummer_drums_drummers |
| 2323 | lymphomas - lymphoma - lymphadenopathy - lymphoid - lymphocytic | 31 | 2323_lymphomas_lymphoma_lymphadenopathy_lymphoid |
| 2324 | reggae - albums - band - bassist - toured | 31 | 2324_reggae_albums_band_bassist |
| 2325 | risqué - bath - erotica - insider - twitter | 31 | 2325_risqué_bath_erotica_insider |
| 2326 | spawn - disowns - destroys - shapeshift - souls | 31 | 2326_spawn_disowns_destroys_shapeshift |
| 2327 | broadcasting - syndication - broadcast - fox - channel | 31 | 2327_broadcasting_syndication_broadcast_fox |
| 2328 | domino - pizzas - pizza - pizzerias - restaurants | 31 | 2328_domino_pizzas_pizza_pizzerias |
| 2329 | soldering - boards - drilling - soldered - board | 31 | 2329_soldering_boards_drilling_soldered |
| 2330 | customers - marketing - customer - consumers - consumer | 31 | 2330_customers_marketing_customer_consumers |
| 2331 | incels - incel - misogynistic - misogynist - feminism | 31 | 2331_incels_incel_misogynistic_misogynist |
| 2332 | polo - khan - yuan - traveller - merchant | 31 | 2332_polo_khan_yuan_traveller |
| 2333 | bob - hope - honorary - biography - comedian | 31 | 2333_bob_hope_honorary_biography |
| 2334 | ethnic - minorities - ethnicity - minority - population | 31 | 2334_ethnic_minorities_ethnicity_minority |
| 2335 | tennis - doubles - backhand - sprinter - forehand | 31 | 2335_tennis_doubles_backhand_sprinter |
| 2336 | nations - china - sovereign - sovereignty - republic | 31 | 2336_nations_china_sovereign_sovereignty |
| 2337 | hostage - hostages - gunmen - terrorists - kidnappers | 30 | 2337_hostage_hostages_gunmen_terrorists |
| 2338 | novelist - writings - poetry - poets - writer | 30 | 2338_novelist_writings_poetry_poets |
| 2339 | topological - topology - topologically - topologies - manifolds | 30 | 2339_topological_topology_topologically_topologies |
| 2340 | tower - towers - elevators - elevator - storeys | 30 | 2340_tower_towers_elevators_elevator |
| 2341 | malls - destinations - cities - mall - roads | 30 | 2341_malls_destinations_cities_mall |
| 2342 | theremin - instruments - instrument - orchestral - concerto | 30 | 2342_theremin_instruments_instrument_orchestral |
| 2343 | cryptocurrency - cryptocurrencies - crypto - bitcoin - doge | 30 | 2343_cryptocurrency_cryptocurrencies_crypto_bitcoin |
| 2344 | wee - pee - cameo - cameos - comedian | 30 | 2344_wee_pee_cameo_cameos |
| 2345 | castes - caste - jati - jatis - tribal | 30 | 2345_castes_caste_jati_jatis |
| 2346 | marriages - cohabitation - marriage - heterosexuals - couples | 30 | 2346_marriages_cohabitation_marriage_heterosexuals |
| 2347 | financier - fund - funds - investors - investor | 30 | 2347_financier_fund_funds_investors |
| 2348 | mammoth - mammoths - prehistoric - fossils - palaeontology | 30 | 2348_mammoth_mammoths_prehistoric_fossils |
| 2349 | eunuchs - eunuch - servants - slaves - enslaved | 30 | 2349_eunuchs_eunuch_servants_slaves |
| 2350 | condemnation - testimony - guilt - heresy - accusation | 30 | 2350_condemnation_testimony_guilt_heresy |
| 2351 | chaebols - chaebol - debts - economies - economy | 30 | 2351_chaebols_chaebol_debts_economies |
| 2352 | songwriter - songwriters - performer - guitarist - concert | 30 | 2352_songwriter_songwriters_performer_guitarist |
| 2353 | awards - academy - nominees - theaters - theatre | 30 | 2353_awards_academy_nominees_theaters |
| 2354 | catalytic - catalysts - catalyst - converters - catalyzing | 30 | 2354_catalytic_catalysts_catalyst_converters |
| 2355 | militia - amendment - constitution - constitutions - militias | 30 | 2355_militia_amendment_constitution_constitutions |
| 2356 | atheism - atheist - agnosticism - atheists - atheistic | 30 | 2356_atheism_atheist_agnosticism_atheists |
| 2357 | studium - catholic - pope - pontifical - latin | 30 | 2357_studium_catholic_pope_pontifical |
| 2358 | composers - orchestra - composer - orchestras - choral | 30 | 2358_composers_orchestra_composer_orchestras |
| 2359 | albums - singer - guitar - duet - album | 30 | 2359_albums_singer_guitar_duet |
| 2360 | toured - concert - concerts - grease - tour | 30 | 2360_toured_concert_concerts_grease |
| 2361 | famine - potatoes - potato - hunger - starving | 30 | 2361_famine_potatoes_potato_hunger |
| 2362 | ancient - testament - epic - bible - cuneiform | 30 | 2362_ancient_testament_epic_bible |
| 2363 | nightclub - nightclubs - 54 - cabaret - club | 30 | 2363_nightclub_nightclubs_54_cabaret |
| 2364 | headquartered - universal - headquarters - company - music | 30 | 2364_headquartered_universal_headquarters_company |
| 2365 | exports - imports - economy - agriculture - archipelagos | 30 | 2365_exports_imports_economy_agriculture |
| 2366 | ecumenical - orthodox - catholic - ecclesiastical - papal | 30 | 2366_ecumenical_orthodox_catholic_ecclesiastical |
| 2367 | critical - thinking - reasoned - reflective - thinker | 30 | 2367_critical_thinking_reasoned_reflective |
| 2368 | maglev - trains - levitation - railway - levitating | 30 | 2368_maglev_trains_levitation_railway |
| 2369 | van - ev - vans - automakers - vehicles | 30 | 2369_van_ev_vans_automakers |
| 2370 | rococo - sculptor - decorative - ornamental - designs | 30 | 2370_rococo_sculptor_decorative_ornamental |
| 2371 | paintings - painting - paint - art - artist | 30 | 2371_paintings_painting_paint_art |
| 2372 | tulips - tulip - economists - economic - bulbs | 30 | 2372_tulips_tulip_economists_economic |
| 2373 | squads - squad - roster - players - teams | 30 | 2373_squads_squad_roster_players |
| 2374 | entrances - subterranean - tunnel - stairs - pyramid | 30 | 2374_entrances_subterranean_tunnel_stairs |
| 2375 | transhumanism - transhumanists - transhumanist - humanists - humanist | 30 | 2375_transhumanism_transhumanists_transhumanist_humanists |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
## Framework versions
* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11
|
[
"TRANSLATION"
] |
[
"BEAR",
"MEDAL"
] |
VietAI/envit5-translation
|
VietAI
|
translation
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"translation",
"vi",
"en",
"dataset:cc100",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-10-06T14:53:36 | 2022-11-21T09:59:08 | 2,996 | 36 |
---
datasets:
- cc100
language:
- vi
- en
license: openrail
tags:
- translation
widget:
- text: 'vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ
nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân
tạo đẳng cấp quốc tế tại Việt Nam.'
---
# EnViT5 Translation
[](https://paperswithcode.com/sota/machine-translation-on-iwslt2015-english-1?p=mtet-multi-domain-translation-for-english)
[](https://paperswithcode.com/sota/on-phomt?p=mtet-multi-domain-translation-for-english-and)
State-of-the-art English-Vietnamese and Vietnamese-English Translation models trained on [MTet](https://research.vietai.org/mtet/), [PhoMT](https://github.com/VinAIResearch/PhoMT).
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "VietAI/envit5-translation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
inputs = [
"vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam.",
"vi: Theo báo cáo mới nhất của Linkedin về danh sách việc làm triển vọng với mức lương hấp dẫn năm 2020, các chức danh công việc liên quan đến AI như Chuyên gia AI (Artificial Intelligence Specialist), Kỹ sư ML (Machine Learning Engineer) đều xếp thứ hạng cao.",
"en: Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.",
"en: We're on a journey to advance and democratize artificial intelligence through open source and open science."
]
outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids.to('cuda'), max_length=512)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['en: VietAI is a non-profit organization with the mission of nurturing artificial intelligence talents and building an international - class community of artificial intelligence experts in Vietnam.',
# 'en: According to the latest LinkedIn report on the 2020 list of attractive and promising jobs, AI - related job titles such as AI Specialist, ML Engineer and ML Engineer all rank high.',
# 'vi: Nhóm chúng tôi khao khát tạo ra những khám phá có ảnh hưởng đến mọi người, và cốt lõi trong cách tiếp cận của chúng tôi là chia sẻ nghiên cứu và công cụ để thúc đẩy sự tiến bộ trong lĩnh vực này.',
# 'vi: Chúng ta đang trên hành trình tiến bộ và dân chủ hoá trí tuệ nhân tạo thông qua mã nguồn mở và khoa học mở.']
```
## Results
## Citation
```
@misc{https://doi.org/10.48550/arxiv.2210.05610,
doi = {10.48550/ARXIV.2210.05610},
author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang},
title = {MTet: Multi-domain Translation for English and Vietnamese},
publisher = {arXiv},
year = {2022},
}
```
|
[
"TRANSLATION"
] |
[
"CHIA"
] |
aisingapore/llama3.1-70b-cpt-sea-lionv3-instruct
|
aisingapore
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2311.07911",
"arxiv:2306.05685",
"base_model:aisingapore/llama3.1-70b-cpt-sea-lionv3-base",
"base_model:finetune:aisingapore/llama3.1-70b-cpt-sea-lionv3-base",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-11T10:22:38 | 2024-12-19T13:58:00 | 2,958 | 1 |
---
base_model:
- aisingapore/llama3.1-70b-cpt-sea-lionv3-base
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
base_model_relation: finetune
---
<div>
<img src="llama_3.1_70b_sea-lion_v3_instruct_banner.png"/>
</div>
# Llama3.1 70B CPT SEA-LIONv3 Instruct
SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
Llama3.1 70B CPT SEA-LIONv3 Instruct is a multilingual model that has been fine-tuned in two stages on approximately **12.3M English instruction-completion pairs** alongside a pool of **4.5M Southeast Asian instruction-completion pairs** from SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai, and Vietnamese.
SEA-LION stands for _Southeast Asian Languages In One Network_.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
- **License:** [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE)
## Model Details
### Model Description
We performed instruction tuning in English and also in SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai and Vietnamese on our [continued pre-trained Llama3.1 70B CPT SEA-LIONv3 Base](https://huggingface.co/aisingapore/llama3.1-70B-cpt-sea-lionv3-base), a decoder model using the Llama 3.1 architecture, to create Llama3.1 70B CPT SEA-LIONv3 Instruct.
For tokenisation, the model employs the default tokenizer used in Llama 3.1 70B Instruct. The model has a context length of 128k.
### Benchmark Performance
We evaluated Llama3.1 70B CPT SEA-LIONv3 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the [SEA-HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarisation (Abssum), Causal Reasoning (Causal) and Natural Language Inference (NLI).
Note: SEA-HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
#### Instruction-following Capabilities
Since Llama3.1 70B CPT SEA-LIONv3 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, SEA-IFEval (based on [IFEval](https://arxiv.org/abs/2311.07911)) and SEA-MTBench (based on [MT-Bench](https://arxiv.org/abs/2306.05685)).
As these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localise and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**SEA-IFEval**
SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalised by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
**SEA-MTBench**
SEA-MTBench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction). A tie is given a score of 0.5.
For more details on Llama3.1 70B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA-HELM leaderboard, https://leaderboard.sea-lion.ai/.
### Usage
Llama3.1 70B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library
```python
import transformers
import torch
model_id = "aisingapore/llama3.1-70B-cpt-sea-lionv3-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
Llama3.1 70B CPT SEA-LIONv3 Instruct was tuned using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 3200 GPU hours, on a single node of 8x H100-80GB GPUs.
## Data
Llama3.1 70B CPT SEA-LIONv3 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data sources.
## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.
## The Team
Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
## Disclaimer
This is the repository for the commercial instruction-tuned model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
|
[
"QUESTION_ANSWERING",
"TRANSLATION"
] |
[
"CHIA"
] |
cnmoro/snowflake-arctic-embed-m-v2.0-cpu
|
cnmoro
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"gte",
"feature-extraction",
"sentence-similarity",
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"transformers.js",
"custom_code",
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-22T20:22:34 | 2025-01-22T20:37:49 | 2,856 | 2 |
---
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:
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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
value: -15.824
- type: nauc_map_at_20_diff1
value: 10.8494
- type: nauc_map_at_100_max
value: -8.3178
- type: nauc_map_at_100_std
value: -15.848300000000002
- type: nauc_map_at_100_diff1
value: 10.9384
- type: nauc_map_at_1000_max
value: -8.319799999999999
- type: nauc_map_at_1000_std
value: -15.8522
- type: nauc_map_at_1000_diff1
value: 10.9401
- type: nauc_recall_at_1_max
value: -10.8287
- type: nauc_recall_at_1_std
value: -17.1177
- type: nauc_recall_at_1_diff1
value: 14.4508
- type: nauc_recall_at_3_max
value: -5.0587
- type: nauc_recall_at_3_std
value: -16.730800000000002
- type: nauc_recall_at_3_diff1
value: 6.8079
- type: nauc_recall_at_5_max
value: -2.6783
- type: nauc_recall_at_5_std
value: -14.5046
- type: nauc_recall_at_5_diff1
value: 3.096
- type: nauc_recall_at_10_max
value: -1.5855000000000001
- type: nauc_recall_at_10_std
value: -8.2276
- type: nauc_recall_at_10_diff1
value: -6.1741
- type: nauc_recall_at_20_max
value: 15.754299999999999
- type: nauc_recall_at_20_std
value: 8.1974
- type: nauc_recall_at_20_diff1
value: -4.9207
- type: nauc_recall_at_100_max
value: 20.4574
- type: nauc_recall_at_100_std
value: 36.3741
- type: nauc_recall_at_100_diff1
value: -7.9483
- type: nauc_recall_at_1000_max
value: 21.6023
- type: nauc_recall_at_1000_std
value: 68.7296
- type: nauc_recall_at_1000_diff1
value: -24.9261
- type: nauc_precision_at_1_max
value: -10.8287
- type: nauc_precision_at_1_std
value: -17.1177
- type: nauc_precision_at_1_diff1
value: 14.4508
- type: nauc_precision_at_3_max
value: -5.0587
- type: nauc_precision_at_3_std
value: -16.730800000000002
- type: nauc_precision_at_3_diff1
value: 6.8079
- type: nauc_precision_at_5_max
value: -2.6783
- type: nauc_precision_at_5_std
value: -14.5046
- type: nauc_precision_at_5_diff1
value: 3.096
- type: nauc_precision_at_10_max
value: -1.5855000000000001
- type: nauc_precision_at_10_std
value: -8.2276
- type: nauc_precision_at_10_diff1
value: -6.1741
- type: nauc_precision_at_20_max
value: 15.754299999999999
- type: nauc_precision_at_20_std
value: 8.1974
- type: nauc_precision_at_20_diff1
value: -4.9207
- type: nauc_precision_at_100_max
value: 20.4574
- type: nauc_precision_at_100_std
value: 36.3741
- type: nauc_precision_at_100_diff1
value: -7.9483
- type: nauc_precision_at_1000_max
value: 21.6023
- type: nauc_precision_at_1000_std
value: 68.7296
- type: nauc_precision_at_1000_diff1
value: -24.9261
- type: nauc_mrr_at_1_max
value: -11.251999999999999
- type: nauc_mrr_at_1_std
value: -17.4386
- type: nauc_mrr_at_1_diff1
value: 13.414200000000001
- type: nauc_mrr_at_3_max
value: -9.7985
- type: nauc_mrr_at_3_std
value: -16.650000000000002
- type: nauc_mrr_at_3_diff1
value: 9.5099
- type: nauc_mrr_at_5_max
value: -9.064
- type: nauc_mrr_at_5_std
value: -16.4409
- type: nauc_mrr_at_5_diff1
value: 9.4773
- type: nauc_mrr_at_10_max
value: -9.310400000000001
- type: nauc_mrr_at_10_std
value: -16.0546
- type: nauc_mrr_at_10_diff1
value: 9.2528
- type: nauc_mrr_at_20_max
value: -9.223099999999999
- type: nauc_mrr_at_20_std
value: -16.0659
- type: nauc_mrr_at_20_diff1
value: 9.5259
- type: nauc_mrr_at_100_max
value: -9.2678
- type: nauc_mrr_at_100_std
value: -16.0911
- type: nauc_mrr_at_100_diff1
value: 9.608600000000001
- type: nauc_mrr_at_1000_max
value: -9.2699
- type: nauc_mrr_at_1000_std
value: -16.095100000000002
- type: nauc_mrr_at_1000_diff1
value: 9.6099
- type: main_score
value: 58.011
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.684400000000004
- type: v_measure_std
value: 13.5064
- type: main_score
value: 44.684400000000004
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.0503
- type: v_measure_std
value: 13.9543
- type: main_score
value: 35.0503
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.648500000000006
- type: mrr
value: 74.528
- type: nAUC_map_max
value: 19.4239
- type: nAUC_map_std
value: 20.0729
- type: nAUC_map_diff1
value: 10.0382
- type: nAUC_mrr_max
value: 30.693199999999997
- type: nAUC_mrr_std
value: 27.1279
- type: nAUC_mrr_diff1
value: 23.0291
- type: main_score
value: 60.648500000000006
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: pearson
value: 89.5081
- type: spearman
value: 87.0568
- type: cosine_pearson
value: 89.5081
- type: cosine_spearman
value: 87.0568
- type: manhattan_pearson
value: 88.1247
- type: manhattan_spearman
value: 87.2556
- type: euclidean_pearson
value: 88.3266
- type: euclidean_spearman
value: 87.0568
- type: main_score
value: 87.0568
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.18180000000001
- type: f1
value: 79.5538
- type: f1_weighted
value: 79.5538
- type: main_score
value: 80.18180000000001
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.0126
- type: v_measure_std
value: 0.47019999999999995
- type: main_score
value: 36.0126
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.6331
- type: v_measure_std
value: 0.8607999999999999
- type: main_score
value: 28.6331
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
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value: 45.207
- type: ndcg_at_3
value: 51.31400000000001
- type: ndcg_at_5
value: 54.093999999999994
- type: ndcg_at_10
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- type: ndcg_at_100
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- type: recall_at_10
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- type: recall_at_1000
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dataset:
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type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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dataset:
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type: mteb/mtop_intent
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split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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type: Classification
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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dataset:
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type: mteb/mind_small
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split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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dataset:
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type: mteb/nfcorpus
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split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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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
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value: 71.8
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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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value: 90.9
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- type: manhattan_accuracy
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- type: max_accuracy
value: 99.8257
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value: 91.3568
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value: 91.7
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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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value: 61.3985
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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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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:
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type: mteb/touche2020
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revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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- type: nauc_mrr_at_10_diff1
value: 17.762700000000002
- type: nauc_mrr_at_20_max
value: -30.44
- type: nauc_mrr_at_20_std
value: -20.2867
- type: nauc_mrr_at_20_diff1
value: 18.895500000000002
- type: nauc_mrr_at_100_max
value: -30.5404
- type: nauc_mrr_at_100_std
value: -20.5699
- type: nauc_mrr_at_100_diff1
value: 18.7046
- type: nauc_mrr_at_1000_max
value: -30.5404
- type: nauc_mrr_at_1000_std
value: -20.5699
- type: nauc_mrr_at_1000_diff1
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
- type: f1_weighted
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:
- type: similarity_accuracy
value: 83.29260000000001
- type: similarity_accuracy_threshold
value: 78.2732
- type: similarity_f1
value: 60.656600000000005
- type: similarity_f1_threshold
value: 73.4961
- type: similarity_precision
value: 59.007
- type: similarity_recall
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
- type: manhattan_f1
value: 60.7459
- type: manhattan_f1_threshold
value: 1607.9180000000001
- type: manhattan_precision
value: 59.0581
- type: manhattan_recall
value: 62.53300000000001
- type: manhattan_ap
value: 64.76859999999999
- type: euclidean_accuracy
value: 83.29260000000001
- type: euclidean_accuracy_threshold
value: 65.9194
- type: euclidean_f1
value: 60.656600000000005
- type: euclidean_f1_threshold
value: 72.8065
- type: euclidean_precision
value: 59.007
- type: euclidean_recall
value: 62.4011
- type: euclidean_ap
value: 64.7501
- type: dot_accuracy
value: 83.29260000000001
- type: dot_accuracy_threshold
value: 78.2731
- type: dot_f1
value: 60.656600000000005
- type: dot_f1_threshold
value: 73.4961
- type: dot_precision
value: 59.007
- type: dot_recall
value: 62.4011
- type: dot_ap
value: 64.7501
- type: max_accuracy
value: 83.2986
- type: max_f1
value: 60.7459
- type: max_precision
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
- type: similarity_accuracy_threshold
value: 69.271
- type: similarity_f1
value: 78.24419999999999
- type: similarity_f1_threshold
value: 66.2183
- type: similarity_precision
value: 76.616
- type: similarity_recall
value: 79.943
- type: similarity_ap
value: 85.9494
- type: cosine_accuracy
value: 89.0247
- type: cosine_accuracy_threshold
value: 69.271
- type: cosine_f1
value: 78.24419999999999
- type: cosine_f1_threshold
value: 66.2183
- type: cosine_precision
value: 76.616
- type: cosine_recall
value: 79.943
- type: cosine_ap
value: 85.9494
- type: manhattan_accuracy
value: 89.0267
- type: manhattan_accuracy_threshold
value: 1750.3544000000002
- type: manhattan_f1
value: 78.2188
- type: manhattan_f1_threshold
value: 1837.7304
- type: manhattan_precision
value: 75.1472
- type: manhattan_recall
value: 81.5522
- type: manhattan_ap
value: 85.9496
- type: euclidean_accuracy
value: 89.0247
- type: euclidean_accuracy_threshold
value: 78.3951
- type: euclidean_f1
value: 78.24419999999999
- type: euclidean_f1_threshold
value: 82.197
- type: euclidean_precision
value: 76.616
- type: euclidean_recall
value: 79.943
- type: euclidean_ap
value: 85.9494
- type: dot_accuracy
value: 89.0247
- type: dot_accuracy_threshold
value: 69.271
- type: dot_f1
value: 78.24419999999999
- type: dot_f1_threshold
value: 66.2183
- type: dot_precision
value: 76.616
- type: dot_recall
value: 79.943
- type: dot_ap
value: 85.9494
- type: max_accuracy
value: 89.0267
- type: max_f1
value: 78.24419999999999
- type: max_precision
value: 76.616
- type: max_recall
value: 81.5522
- type: max_ap
value: 85.9496
- type: main_score
value: 85.9496
---
A modified version of [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0), without xformers, so it works on CPU.
```python
from sentence_transformers import SentenceTransformer
import torch
device = torch.device("cpu")
model = SentenceTransformer("cnmoro/snowflake-arctic-embed-m-v2.0-cpu", device=device, trust_remote_code=True)
```
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
maddes8cht/tiiuae-falcon-40b
|
maddes8cht
| null |
[
"gguf",
"en",
"de",
"es",
"fr",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"region:us"
] | 2023-09-15T09:48:14 | 2023-10-20T05:22:35 | 2,737 | 1 |
---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
- de
- es
- fr
license: apache-2.0
inference: false
---
[]()
I am continuously enhancing the structure of these model descriptions, and they now provide even more comprehensive information to help you find the best models for your specific needs.
# falcon-40b - GGUF
- Model creator: [tiiuae](https://huggingface.co/tiiuae)
- Original model: [falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
# Note: Important Update for Falcon Models in llama.cpp Versions After October 18, 2023
As noted on the [Llama.cpp]([ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++ (github.com)](https://github.com/ggerganov/llama.cpp#hot-topics) GitHub repository, all new releases of Llama.cpp will require a re-quantization due to the implementation of the new BPE tokenizer, which impacts both the original Falcon models and their derived variants.
Here's what you need to know:
**Original Falcon Models:** I am diligently working to provide updated quantized versions of the four original Falcon models to ensure their compatibility with the new llama.cpp versions. Please keep an eye on my Hugging Face Model pages for updates on the availability of these models. Promptly downloading them is essential to maintain compatibility with the latest llama.cpp releases.
**Derived Falcon Models:** Right now, the derived Falcon-Models cannot be re-converted without adjustments from the original model creators. So far, these models cannot be used in recent llama.cpp versions at all. ** Good news!** It's in the pipeline that the capability for quantizing even the older derived Falcon models will be incorporated soon. However, the exact timeline is beyond my control.
**Stay Informed:** Application software using llama.cpp libraries will follow soon. Keep an eye on the release schedules of your favorite software applications that rely on llama.cpp. They will likely provide instructions on how to integrate the new models.
**Monitor Upload Times:** Please keep a close watch on the upload times of the available files on my Hugging Face Model pages. This will help you identify which files have already been updated and are ready for download, ensuring you have the most current Falcon models at your disposal.
**Download Promptly:** Once the updated Falcon models are available on my Hugging Face page, be sure to download them promptly to ensure compatibility with the latest [llama.cpp]([ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++ (github.com)](https://github.com/ggerganov/llama.cpp) versions.
Please understand that this change specifically affects Falcon and Starcoder models, other models remain unaffected. Consequently, software providers may not emphasize this change as prominently.
As a solo operator of this page, I'm doing my best to expedite the process, but please bear with me as this may take some time.
These are gguf quantized models of the riginal Falcon 40B Model by tiiuae.
Falcon is a foundational large language model coming in different sizes: 7b, 40b and 180b.
Sadly, as the Falcon 180b Models are note really free models, I do not provide quantized versions here.
# About GGUF format
`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
# Quantization variants
There is a bunch of quantized files available. How to choose the best for you:
# legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
Falcon 7B models cannot be quantized to K-quants.
# K-quants
K-quants are based on the idea that the quantization of certain parts affects the quality in different ways. If you quantize certain parts more and others less, you get a more powerful model with the same file size, or a smaller file size and lower memory load with comparable performance.
So, if possible, use K-quants.
With a Q6_K you should find it really hard to find a quality difference to the original model - ask your model two times the same question and you may encounter bigger quality differences.
# Original Model Card:
# 🚀 Falcon-40B
**Falcon-40B is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-40B?
* **It is the best open-source model currently available.** Falcon-40B outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
*
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct).
💸 **Looking for a smaller, less expensive model?** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) is Falcon-40B's little brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 85-100GB of memory** to swiftly run inference with Falcon-40B.
# Model Card for Falcon-40B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- **License:** Apache 2.0 license.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-40B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-40B was trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 75% | 750B | massive web crawl |
| RefinedWeb-Europe | 7% | 70B | European massive web crawl |
| Books | 6% | 60B | |
| Conversations | 5% | 50B | Reddit, StackOverflow, HackerNews |
| Code | 5% | 50B | |
| Technical | 2% | 20B | arXiv, PubMed, USPTO, etc. |
RefinedWeb-Europe is made of the following languages:
| **Language** | **Fraction of multilingual data** | **Tokens** |
|--------------|-----------------------------------|------------|
| German | 26% | 18B |
| Spanish | 24% | 17B |
| French | 23% | 16B |
| _Italian_ | 7% | 5B |
| _Portuguese_ | 4% | 3B |
| _Polish_ | 4% | 3B |
| _Dutch_ | 4% | 3B |
| _Romanian_ | 3% | 2B |
| _Czech_ | 3% | 2B |
| _Swedish_ | 2% | 1B |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-40B was trained on 384 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=12) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 1.85e-4 | 4B tokens warm-up, cosine decay to 1.85e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 1152 | 100B tokens ramp-up |
#### Speeds, Sizes, Times
Training started in December 2022 and took two months.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a two layer norms.
For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 60 | |
| `d_model` | 8192 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-40B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-40B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-40B is made available under the Apache 2.0 license.
## Contact
[email protected]
***End of original Model File***
## Please consider to support my work
**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
<center>
[](https://maddes8cht.github.io)
[](https://stackexchange.com/users/26485911)
[](https://github.com/maddes8cht)
[](https://huggingface.co/maddes8cht)
[](https://twitter.com/maddes1966)
</center>
|
[
"SUMMARIZATION"
] |
[
"BEAR"
] |
McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
|
McGill-NLP
|
sentence-similarity
|
[
"peft",
"safetensors",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2404.05961",
"license:mit",
"model-index",
"region:us"
] | 2024-04-04T14:10:21 | 2024-04-11T19:55:37 | 2,528 | 1 |
---
language:
- en
library_name: peft
license: mit
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- text-reranking
- feature-extraction
- sentence-similarity
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
model-index:
- name: LLM2Vec-Sheared-LLaMA-unsupervised
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.92537313432835
- type: ap
value: 36.6875749512053
- type: f1
value: 67.36274146169845
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 74.282675
- type: ap
value: 69.15441866642587
- type: f1
value: 74.13028166370813
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.136
- type: f1
value: 35.840498320506235
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.407999999999998
- type: map_at_10
value: 35.474
- type: map_at_100
value: 36.653999999999996
- type: map_at_1000
value: 36.68
- type: map_at_3
value: 30.974
- type: map_at_5
value: 33.265
- type: mrr_at_1
value: 22.119
- type: mrr_at_10
value: 35.714
- type: mrr_at_100
value: 36.895
- type: mrr_at_1000
value: 36.921
- type: mrr_at_3
value: 31.2
- type: mrr_at_5
value: 33.518
- type: ndcg_at_1
value: 21.407999999999998
- type: ndcg_at_10
value: 43.644
- type: ndcg_at_100
value: 49.035000000000004
- type: ndcg_at_1000
value: 49.685
- type: ndcg_at_3
value: 34.174
- type: ndcg_at_5
value: 38.288
- type: precision_at_1
value: 21.407999999999998
- type: precision_at_10
value: 6.999
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.485999999999999
- type: precision_at_5
value: 10.683
- type: recall_at_1
value: 21.407999999999998
- type: recall_at_10
value: 69.986
- type: recall_at_100
value: 94.381
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 43.457
- type: recall_at_5
value: 53.413999999999994
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 42.915010245699904
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.19568272188972
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 52.696972763822615
- type: mrr
value: 65.87136701402629
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 75.12038636775851
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.99675324675324
- type: f1
value: 78.90527329824852
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.02170435970243
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.208216971540782
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: cqadupstack/android
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.432
- type: map_at_10
value: 23.769000000000002
- type: map_at_100
value: 25.038
- type: map_at_1000
value: 25.208000000000002
- type: map_at_3
value: 21.532999999999998
- type: map_at_5
value: 22.668
- type: mrr_at_1
value: 21.316
- type: mrr_at_10
value: 28.89
- type: mrr_at_100
value: 29.799999999999997
- type: mrr_at_1000
value: 29.887999999999998
- type: mrr_at_3
value: 26.705000000000002
- type: mrr_at_5
value: 27.864
- type: ndcg_at_1
value: 21.316
- type: ndcg_at_10
value: 28.656
- type: ndcg_at_100
value: 34.405
- type: ndcg_at_1000
value: 37.771
- type: ndcg_at_3
value: 24.98
- type: ndcg_at_5
value: 26.384999999999998
- type: precision_at_1
value: 21.316
- type: precision_at_10
value: 5.8229999999999995
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.181
- type: precision_at_3
value: 12.446
- type: precision_at_5
value: 8.984
- type: recall_at_1
value: 16.432
- type: recall_at_10
value: 37.696000000000005
- type: recall_at_100
value: 63.198
- type: recall_at_1000
value: 86.651
- type: recall_at_3
value: 26.651000000000003
- type: recall_at_5
value: 30.901
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: cqadupstack/english
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.106
- type: map_at_10
value: 21.770999999999997
- type: map_at_100
value: 22.538
- type: map_at_1000
value: 22.656000000000002
- type: map_at_3
value: 19.918
- type: map_at_5
value: 20.957
- type: mrr_at_1
value: 21.083
- type: mrr_at_10
value: 26.502
- type: mrr_at_100
value: 27.161
- type: mrr_at_1000
value: 27.234
- type: mrr_at_3
value: 24.735
- type: mrr_at_5
value: 25.753999999999998
- type: ndcg_at_1
value: 21.083
- type: ndcg_at_10
value: 25.625999999999998
- type: ndcg_at_100
value: 29.152
- type: ndcg_at_1000
value: 32.025
- type: ndcg_at_3
value: 22.721
- type: ndcg_at_5
value: 24.029
- type: precision_at_1
value: 21.083
- type: precision_at_10
value: 4.8919999999999995
- type: precision_at_100
value: 0.844
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.104
- type: precision_at_5
value: 7.987
- type: recall_at_1
value: 16.106
- type: recall_at_10
value: 32.385999999999996
- type: recall_at_100
value: 47.961999999999996
- type: recall_at_1000
value: 67.63900000000001
- type: recall_at_3
value: 23.568
- type: recall_at_5
value: 27.326
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: cqadupstack/gaming
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.517
- type: map_at_10
value: 29.593999999999998
- type: map_at_100
value: 30.695
- type: map_at_1000
value: 30.803000000000004
- type: map_at_3
value: 27.592
- type: map_at_5
value: 28.768
- type: mrr_at_1
value: 26.27
- type: mrr_at_10
value: 33.076
- type: mrr_at_100
value: 33.998
- type: mrr_at_1000
value: 34.073
- type: mrr_at_3
value: 31.223
- type: mrr_at_5
value: 32.257000000000005
- type: ndcg_at_1
value: 26.27
- type: ndcg_at_10
value: 33.726
- type: ndcg_at_100
value: 39.079
- type: ndcg_at_1000
value: 41.762
- type: ndcg_at_3
value: 30.064
- type: ndcg_at_5
value: 31.858999999999998
- type: precision_at_1
value: 26.27
- type: precision_at_10
value: 5.448
- type: precision_at_100
value: 0.898
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 13.417000000000002
- type: precision_at_5
value: 9.317
- type: recall_at_1
value: 22.517
- type: recall_at_10
value: 42.814
- type: recall_at_100
value: 67.037
- type: recall_at_1000
value: 86.89099999999999
- type: recall_at_3
value: 33.041
- type: recall_at_5
value: 37.389
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: cqadupstack/gis
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.681
- type: map_at_10
value: 10.655000000000001
- type: map_at_100
value: 11.274000000000001
- type: map_at_1000
value: 11.381
- type: map_at_3
value: 9.793000000000001
- type: map_at_5
value: 10.202
- type: mrr_at_1
value: 8.248999999999999
- type: mrr_at_10
value: 11.453000000000001
- type: mrr_at_100
value: 12.074
- type: mrr_at_1000
value: 12.174
- type: mrr_at_3
value: 10.452
- type: mrr_at_5
value: 10.989
- type: ndcg_at_1
value: 8.248999999999999
- type: ndcg_at_10
value: 12.467
- type: ndcg_at_100
value: 15.942
- type: ndcg_at_1000
value: 19.378999999999998
- type: ndcg_at_3
value: 10.631
- type: ndcg_at_5
value: 11.411
- type: precision_at_1
value: 8.248999999999999
- type: precision_at_10
value: 1.966
- type: precision_at_100
value: 0.40099999999999997
- type: precision_at_1000
value: 0.075
- type: precision_at_3
value: 4.444
- type: precision_at_5
value: 3.186
- type: recall_at_1
value: 7.681
- type: recall_at_10
value: 17.302
- type: recall_at_100
value: 34.014
- type: recall_at_1000
value: 61.207
- type: recall_at_3
value: 12.389
- type: recall_at_5
value: 14.158999999999999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: cqadupstack/mathematica
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.868
- type: map_at_10
value: 6.281000000000001
- type: map_at_100
value: 6.903
- type: map_at_1000
value: 7.038
- type: map_at_3
value: 5.234
- type: map_at_5
value: 5.685
- type: mrr_at_1
value: 5.1
- type: mrr_at_10
value: 8.148
- type: mrr_at_100
value: 8.846
- type: mrr_at_1000
value: 8.963000000000001
- type: mrr_at_3
value: 6.944
- type: mrr_at_5
value: 7.498
- type: ndcg_at_1
value: 5.1
- type: ndcg_at_10
value: 8.405999999999999
- type: ndcg_at_100
value: 12.014
- type: ndcg_at_1000
value: 15.956999999999999
- type: ndcg_at_3
value: 6.22
- type: ndcg_at_5
value: 6.962
- type: precision_at_1
value: 5.1
- type: precision_at_10
value: 1.8159999999999998
- type: precision_at_100
value: 0.437
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 3.1510000000000002
- type: precision_at_5
value: 2.463
- type: recall_at_1
value: 3.868
- type: recall_at_10
value: 13.319
- type: recall_at_100
value: 29.985
- type: recall_at_1000
value: 59.245999999999995
- type: recall_at_3
value: 7.0809999999999995
- type: recall_at_5
value: 8.914
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: cqadupstack/physics
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.091
- type: map_at_10
value: 18.701999999999998
- type: map_at_100
value: 19.897000000000002
- type: map_at_1000
value: 20.044
- type: map_at_3
value: 17.041999999999998
- type: map_at_5
value: 17.943
- type: mrr_at_1
value: 16.939
- type: mrr_at_10
value: 23.038
- type: mrr_at_100
value: 24.029
- type: mrr_at_1000
value: 24.12
- type: mrr_at_3
value: 21.221999999999998
- type: mrr_at_5
value: 22.198999999999998
- type: ndcg_at_1
value: 16.939
- type: ndcg_at_10
value: 22.566
- type: ndcg_at_100
value: 28.364
- type: ndcg_at_1000
value: 31.646
- type: ndcg_at_3
value: 19.646
- type: ndcg_at_5
value: 20.915
- type: precision_at_1
value: 16.939
- type: precision_at_10
value: 4.340999999999999
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 9.785
- type: precision_at_5
value: 6.93
- type: recall_at_1
value: 13.091
- type: recall_at_10
value: 30.022
- type: recall_at_100
value: 55.579
- type: recall_at_1000
value: 78.14
- type: recall_at_3
value: 21.4
- type: recall_at_5
value: 25.020999999999997
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: cqadupstack/programmers
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.315999999999999
- type: map_at_10
value: 16.191
- type: map_at_100
value: 17.116
- type: map_at_1000
value: 17.262
- type: map_at_3
value: 14.302999999999999
- type: map_at_5
value: 15.278
- type: mrr_at_1
value: 14.269000000000002
- type: mrr_at_10
value: 19.409000000000002
- type: mrr_at_100
value: 20.298
- type: mrr_at_1000
value: 20.393
- type: mrr_at_3
value: 17.504
- type: mrr_at_5
value: 18.423000000000002
- type: ndcg_at_1
value: 14.269000000000002
- type: ndcg_at_10
value: 19.735
- type: ndcg_at_100
value: 24.582
- type: ndcg_at_1000
value: 28.337
- type: ndcg_at_3
value: 16.220000000000002
- type: ndcg_at_5
value: 17.644000000000002
- type: precision_at_1
value: 14.269000000000002
- type: precision_at_10
value: 3.721
- type: precision_at_100
value: 0.752
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 7.800999999999999
- type: precision_at_5
value: 5.753
- type: recall_at_1
value: 11.315999999999999
- type: recall_at_10
value: 27.693
- type: recall_at_100
value: 49.265
- type: recall_at_1000
value: 76.291
- type: recall_at_3
value: 17.593
- type: recall_at_5
value: 21.368000000000002
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.131583333333332
- type: map_at_10
value: 15.4605
- type: map_at_100
value: 16.3075
- type: map_at_1000
value: 16.4375
- type: map_at_3
value: 13.995833333333332
- type: map_at_5
value: 14.783666666666667
- type: mrr_at_1
value: 13.805833333333334
- type: mrr_at_10
value: 18.405749999999998
- type: mrr_at_100
value: 19.17516666666667
- type: mrr_at_1000
value: 19.265833333333333
- type: mrr_at_3
value: 16.892416666666666
- type: mrr_at_5
value: 17.71058333333333
- type: ndcg_at_1
value: 13.805833333333334
- type: ndcg_at_10
value: 18.500666666666664
- type: ndcg_at_100
value: 22.78191666666667
- type: ndcg_at_1000
value: 26.095583333333334
- type: ndcg_at_3
value: 15.846916666666663
- type: ndcg_at_5
value: 17.004250000000003
- type: precision_at_1
value: 13.805833333333334
- type: precision_at_10
value: 3.4233333333333325
- type: precision_at_100
value: 0.6828333333333333
- type: precision_at_1000
value: 0.11641666666666667
- type: precision_at_3
value: 7.511749999999999
- type: precision_at_5
value: 5.440916666666666
- type: recall_at_1
value: 11.131583333333332
- type: recall_at_10
value: 24.794166666666666
- type: recall_at_100
value: 44.356
- type: recall_at_1000
value: 68.71899999999998
- type: recall_at_3
value: 17.145583333333335
- type: recall_at_5
value: 20.229083333333335
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: cqadupstack/stats
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.5520000000000005
- type: map_at_10
value: 10.355
- type: map_at_100
value: 10.875
- type: map_at_1000
value: 10.972999999999999
- type: map_at_3
value: 9.341000000000001
- type: map_at_5
value: 9.969
- type: mrr_at_1
value: 9.049
- type: mrr_at_10
value: 12.002
- type: mrr_at_100
value: 12.55
- type: mrr_at_1000
value: 12.635
- type: mrr_at_3
value: 11.12
- type: mrr_at_5
value: 11.626
- type: ndcg_at_1
value: 9.049
- type: ndcg_at_10
value: 12.241
- type: ndcg_at_100
value: 15.231
- type: ndcg_at_1000
value: 18.265
- type: ndcg_at_3
value: 10.424999999999999
- type: ndcg_at_5
value: 11.360000000000001
- type: precision_at_1
value: 9.049
- type: precision_at_10
value: 2.147
- type: precision_at_100
value: 0.411
- type: precision_at_1000
value: 0.073
- type: precision_at_3
value: 4.755
- type: precision_at_5
value: 3.558
- type: recall_at_1
value: 7.5520000000000005
- type: recall_at_10
value: 16.448999999999998
- type: recall_at_100
value: 30.505
- type: recall_at_1000
value: 54.435
- type: recall_at_3
value: 11.366
- type: recall_at_5
value: 13.758999999999999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: cqadupstack/tex
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.954000000000001
- type: map_at_10
value: 8.229000000000001
- type: map_at_100
value: 8.694
- type: map_at_1000
value: 8.788
- type: map_at_3
value: 7.5
- type: map_at_5
value: 7.856000000000001
- type: mrr_at_1
value: 7.983
- type: mrr_at_10
value: 10.833
- type: mrr_at_100
value: 11.324
- type: mrr_at_1000
value: 11.404
- type: mrr_at_3
value: 9.911
- type: mrr_at_5
value: 10.401
- type: ndcg_at_1
value: 7.983
- type: ndcg_at_10
value: 10.126
- type: ndcg_at_100
value: 12.702
- type: ndcg_at_1000
value: 15.581999999999999
- type: ndcg_at_3
value: 8.779
- type: ndcg_at_5
value: 9.279
- type: precision_at_1
value: 7.983
- type: precision_at_10
value: 1.955
- type: precision_at_100
value: 0.392
- type: precision_at_1000
value: 0.076
- type: precision_at_3
value: 4.382
- type: precision_at_5
value: 3.09
- type: recall_at_1
value: 5.954000000000001
- type: recall_at_10
value: 13.472000000000001
- type: recall_at_100
value: 25.407999999999998
- type: recall_at_1000
value: 47.028
- type: recall_at_3
value: 9.367
- type: recall_at_5
value: 10.867
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: cqadupstack/unix
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.894
- type: map_at_10
value: 12.758
- type: map_at_100
value: 13.639999999999999
- type: map_at_1000
value: 13.76
- type: map_at_3
value: 11.447000000000001
- type: map_at_5
value: 12.205
- type: mrr_at_1
value: 10.914
- type: mrr_at_10
value: 15.739
- type: mrr_at_100
value: 16.589000000000002
- type: mrr_at_1000
value: 16.679
- type: mrr_at_3
value: 14.179
- type: mrr_at_5
value: 15.162999999999998
- type: ndcg_at_1
value: 10.914
- type: ndcg_at_10
value: 15.629000000000001
- type: ndcg_at_100
value: 20.261000000000003
- type: ndcg_at_1000
value: 23.781
- type: ndcg_at_3
value: 13.102
- type: ndcg_at_5
value: 14.338000000000001
- type: precision_at_1
value: 10.914
- type: precision_at_10
value: 2.91
- type: precision_at_100
value: 0.601
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 6.311999999999999
- type: precision_at_5
value: 4.683
- type: recall_at_1
value: 8.894
- type: recall_at_10
value: 21.45
- type: recall_at_100
value: 42.617
- type: recall_at_1000
value: 69.233
- type: recall_at_3
value: 14.52
- type: recall_at_5
value: 17.681
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: cqadupstack/webmasters
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.158
- type: map_at_10
value: 16.332
- type: map_at_100
value: 17.458000000000002
- type: map_at_1000
value: 17.687
- type: map_at_3
value: 14.529
- type: map_at_5
value: 15.515
- type: mrr_at_1
value: 15.809999999999999
- type: mrr_at_10
value: 19.917
- type: mrr_at_100
value: 20.875
- type: mrr_at_1000
value: 20.985
- type: mrr_at_3
value: 18.116
- type: mrr_at_5
value: 19.025
- type: ndcg_at_1
value: 15.809999999999999
- type: ndcg_at_10
value: 19.869999999999997
- type: ndcg_at_100
value: 24.907
- type: ndcg_at_1000
value: 29.076999999999998
- type: ndcg_at_3
value: 16.899
- type: ndcg_at_5
value: 18.23
- type: precision_at_1
value: 15.809999999999999
- type: precision_at_10
value: 3.972
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 8.169
- type: precision_at_5
value: 6.087
- type: recall_at_1
value: 12.158
- type: recall_at_10
value: 26.338
- type: recall_at_100
value: 49.845
- type: recall_at_1000
value: 78.82000000000001
- type: recall_at_3
value: 16.997
- type: recall_at_5
value: 20.848
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: cqadupstack/wordpress
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.01
- type: map_at_10
value: 10.889
- type: map_at_100
value: 11.562
- type: map_at_1000
value: 11.65
- type: map_at_3
value: 9.718
- type: map_at_5
value: 10.358
- type: mrr_at_1
value: 8.688
- type: mrr_at_10
value: 11.862
- type: mrr_at_100
value: 12.558
- type: mrr_at_1000
value: 12.642000000000001
- type: mrr_at_3
value: 10.598
- type: mrr_at_5
value: 11.328000000000001
- type: ndcg_at_1
value: 8.688
- type: ndcg_at_10
value: 12.959999999999999
- type: ndcg_at_100
value: 16.744
- type: ndcg_at_1000
value: 19.564999999999998
- type: ndcg_at_3
value: 10.476
- type: ndcg_at_5
value: 11.639
- type: precision_at_1
value: 8.688
- type: precision_at_10
value: 2.089
- type: precision_at_100
value: 0.43299999999999994
- type: precision_at_1000
value: 0.07200000000000001
- type: precision_at_3
value: 4.375
- type: precision_at_5
value: 3.253
- type: recall_at_1
value: 8.01
- type: recall_at_10
value: 18.589
- type: recall_at_100
value: 36.857
- type: recall_at_1000
value: 59.047000000000004
- type: recall_at_3
value: 11.774
- type: recall_at_5
value: 14.516000000000002
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.4719999999999995
- type: map_at_10
value: 12.322
- type: map_at_100
value: 14.122000000000002
- type: map_at_1000
value: 14.35
- type: map_at_3
value: 9.667
- type: map_at_5
value: 10.931000000000001
- type: mrr_at_1
value: 15.179
- type: mrr_at_10
value: 24.864
- type: mrr_at_100
value: 26.144000000000002
- type: mrr_at_1000
value: 26.198
- type: mrr_at_3
value: 20.999000000000002
- type: mrr_at_5
value: 23.097
- type: ndcg_at_1
value: 15.179
- type: ndcg_at_10
value: 18.951999999999998
- type: ndcg_at_100
value: 26.924
- type: ndcg_at_1000
value: 30.991999999999997
- type: ndcg_at_3
value: 13.778000000000002
- type: ndcg_at_5
value: 15.549
- type: precision_at_1
value: 15.179
- type: precision_at_10
value: 6.625
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 10.51
- type: precision_at_5
value: 8.847
- type: recall_at_1
value: 6.4719999999999995
- type: recall_at_10
value: 25.191999999999997
- type: recall_at_100
value: 53.315
- type: recall_at_1000
value: 76.163
- type: recall_at_3
value: 12.834999999999999
- type: recall_at_5
value: 17.388
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.947
- type: map_at_10
value: 4.858
- type: map_at_100
value: 7.185999999999999
- type: map_at_1000
value: 7.931000000000001
- type: map_at_3
value: 3.2939999999999996
- type: map_at_5
value: 3.914
- type: mrr_at_1
value: 23.25
- type: mrr_at_10
value: 33.035
- type: mrr_at_100
value: 33.721000000000004
- type: mrr_at_1000
value: 33.789
- type: mrr_at_3
value: 29.75
- type: mrr_at_5
value: 31.738
- type: ndcg_at_1
value: 15.625
- type: ndcg_at_10
value: 13.211999999999998
- type: ndcg_at_100
value: 16.422
- type: ndcg_at_1000
value: 23.058999999999997
- type: ndcg_at_3
value: 14.573
- type: ndcg_at_5
value: 13.733999999999998
- type: precision_at_1
value: 23.25
- type: precision_at_10
value: 12.45
- type: precision_at_100
value: 4.192
- type: precision_at_1000
value: 1.083
- type: precision_at_3
value: 18.667
- type: precision_at_5
value: 15.950000000000001
- type: recall_at_1
value: 1.947
- type: recall_at_10
value: 9.317
- type: recall_at_100
value: 23.066
- type: recall_at_1000
value: 45.704
- type: recall_at_3
value: 4.12
- type: recall_at_5
value: 5.591
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.855
- type: f1
value: 39.029787102377576
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.461
- type: map_at_10
value: 13.655999999999999
- type: map_at_100
value: 14.499
- type: map_at_1000
value: 14.585999999999999
- type: map_at_3
value: 11.848
- type: map_at_5
value: 12.842999999999998
- type: mrr_at_1
value: 9.136
- type: mrr_at_10
value: 14.587
- type: mrr_at_100
value: 15.436
- type: mrr_at_1000
value: 15.518
- type: mrr_at_3
value: 12.690999999999999
- type: mrr_at_5
value: 13.747000000000002
- type: ndcg_at_1
value: 9.136
- type: ndcg_at_10
value: 16.958000000000002
- type: ndcg_at_100
value: 21.43
- type: ndcg_at_1000
value: 24.031
- type: ndcg_at_3
value: 13.191
- type: ndcg_at_5
value: 14.987
- type: precision_at_1
value: 9.136
- type: precision_at_10
value: 2.897
- type: precision_at_100
value: 0.532
- type: precision_at_1000
value: 0.077
- type: precision_at_3
value: 5.8709999999999996
- type: precision_at_5
value: 4.47
- type: recall_at_1
value: 8.461
- type: recall_at_10
value: 26.509
- type: recall_at_100
value: 47.776
- type: recall_at_1000
value: 68.26299999999999
- type: recall_at_3
value: 16.203
- type: recall_at_5
value: 20.505000000000003
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.396
- type: map_at_10
value: 12.393
- type: map_at_100
value: 13.857
- type: map_at_1000
value: 14.086000000000002
- type: map_at_3
value: 10.545
- type: map_at_5
value: 11.505
- type: mrr_at_1
value: 15.432000000000002
- type: mrr_at_10
value: 21.615000000000002
- type: mrr_at_100
value: 22.833000000000002
- type: mrr_at_1000
value: 22.931
- type: mrr_at_3
value: 19.522000000000002
- type: mrr_at_5
value: 20.663999999999998
- type: ndcg_at_1
value: 15.432000000000002
- type: ndcg_at_10
value: 16.986
- type: ndcg_at_100
value: 23.880000000000003
- type: ndcg_at_1000
value: 28.762999999999998
- type: ndcg_at_3
value: 14.482999999999999
- type: ndcg_at_5
value: 15.334999999999999
- type: precision_at_1
value: 15.432000000000002
- type: precision_at_10
value: 4.984999999999999
- type: precision_at_100
value: 1.167
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 9.825000000000001
- type: precision_at_5
value: 7.469
- type: recall_at_1
value: 7.396
- type: recall_at_10
value: 21.389
- type: recall_at_100
value: 48.107
- type: recall_at_1000
value: 78.366
- type: recall_at_3
value: 13.181000000000001
- type: recall_at_5
value: 16.611
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.884
- type: map_at_10
value: 17.09
- type: map_at_100
value: 17.96
- type: map_at_1000
value: 18.081
- type: map_at_3
value: 15.296000000000001
- type: map_at_5
value: 16.289
- type: mrr_at_1
value: 23.768
- type: mrr_at_10
value: 29.991
- type: mrr_at_100
value: 30.862000000000002
- type: mrr_at_1000
value: 30.935000000000002
- type: mrr_at_3
value: 27.986
- type: mrr_at_5
value: 29.078
- type: ndcg_at_1
value: 23.768
- type: ndcg_at_10
value: 22.634999999999998
- type: ndcg_at_100
value: 27.059
- type: ndcg_at_1000
value: 30.145
- type: ndcg_at_3
value: 19.058
- type: ndcg_at_5
value: 20.762
- type: precision_at_1
value: 23.768
- type: precision_at_10
value: 5.2490000000000006
- type: precision_at_100
value: 0.8829999999999999
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 12.091000000000001
- type: precision_at_5
value: 8.605
- type: recall_at_1
value: 11.884
- type: recall_at_10
value: 26.246000000000002
- type: recall_at_100
value: 44.153
- type: recall_at_1000
value: 64.889
- type: recall_at_3
value: 18.136
- type: recall_at_5
value: 21.512
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 71.9232
- type: ap
value: 66.56619827391917
- type: f1
value: 71.60536244284128
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 3.037
- type: map_at_10
value: 5.414
- type: map_at_100
value: 6.072
- type: map_at_1000
value: 6.172
- type: map_at_3
value: 4.437
- type: map_at_5
value: 4.939
- type: mrr_at_1
value: 3.123
- type: mrr_at_10
value: 5.572
- type: mrr_at_100
value: 6.235
- type: mrr_at_1000
value: 6.334
- type: mrr_at_3
value: 4.563
- type: mrr_at_5
value: 5.09
- type: ndcg_at_1
value: 3.123
- type: ndcg_at_10
value: 7.027
- type: ndcg_at_100
value: 10.776
- type: ndcg_at_1000
value: 13.904
- type: ndcg_at_3
value: 4.95
- type: ndcg_at_5
value: 5.865
- type: precision_at_1
value: 3.123
- type: precision_at_10
value: 1.252
- type: precision_at_100
value: 0.32299999999999995
- type: precision_at_1000
value: 0.059000000000000004
- type: precision_at_3
value: 2.168
- type: precision_at_5
value: 1.7680000000000002
- type: recall_at_1
value: 3.037
- type: recall_at_10
value: 12.11
- type: recall_at_100
value: 30.714999999999996
- type: recall_at_1000
value: 56.006
- type: recall_at_3
value: 6.3229999999999995
- type: recall_at_5
value: 8.518
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.24259005927954
- type: f1
value: 90.7594022786747
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.08344733242134
- type: f1
value: 52.377556461789055
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.99327505043712
- type: f1
value: 66.15141376479805
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.1546738399462
- type: f1
value: 74.83013584700711
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.146364191412356
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 26.96347584990607
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.520993847103533
- type: mrr
value: 30.402007095845374
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.72
- type: map_at_10
value: 4.041
- type: map_at_100
value: 5.356000000000001
- type: map_at_1000
value: 6.413
- type: map_at_3
value: 2.9770000000000003
- type: map_at_5
value: 3.3689999999999998
- type: mrr_at_1
value: 21.981
- type: mrr_at_10
value: 30.286
- type: mrr_at_100
value: 31.272
- type: mrr_at_1000
value: 31.347
- type: mrr_at_3
value: 27.193
- type: mrr_at_5
value: 28.694999999999997
- type: ndcg_at_1
value: 19.814
- type: ndcg_at_10
value: 15.732
- type: ndcg_at_100
value: 16.033
- type: ndcg_at_1000
value: 25.865
- type: ndcg_at_3
value: 17.944
- type: ndcg_at_5
value: 16.634
- type: precision_at_1
value: 21.981
- type: precision_at_10
value: 12.786
- type: precision_at_100
value: 4.83
- type: precision_at_1000
value: 1.765
- type: precision_at_3
value: 17.75
- type: precision_at_5
value: 15.232000000000001
- type: recall_at_1
value: 1.72
- type: recall_at_10
value: 7.436
- type: recall_at_100
value: 20.275000000000002
- type: recall_at_1000
value: 54.19500000000001
- type: recall_at_3
value: 3.787
- type: recall_at_5
value: 4.829
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.964
- type: map_at_10
value: 14.025000000000002
- type: map_at_100
value: 15.222
- type: map_at_1000
value: 15.32
- type: map_at_3
value: 11.886
- type: map_at_5
value: 13.056999999999999
- type: mrr_at_1
value: 9.183
- type: mrr_at_10
value: 15.651000000000002
- type: mrr_at_100
value: 16.753999999999998
- type: mrr_at_1000
value: 16.833000000000002
- type: mrr_at_3
value: 13.437
- type: mrr_at_5
value: 14.69
- type: ndcg_at_1
value: 9.183
- type: ndcg_at_10
value: 17.96
- type: ndcg_at_100
value: 23.823
- type: ndcg_at_1000
value: 26.461000000000002
- type: ndcg_at_3
value: 13.536999999999999
- type: ndcg_at_5
value: 15.642
- type: precision_at_1
value: 9.183
- type: precision_at_10
value: 3.366
- type: precision_at_100
value: 0.67
- type: precision_at_1000
value: 0.092
- type: precision_at_3
value: 6.547
- type: precision_at_5
value: 5.098
- type: recall_at_1
value: 7.964
- type: recall_at_10
value: 28.599000000000004
- type: recall_at_100
value: 55.381
- type: recall_at_1000
value: 75.63
- type: recall_at_3
value: 16.77
- type: recall_at_5
value: 21.671000000000003
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.846999999999994
- type: map_at_10
value: 73.18599999999999
- type: map_at_100
value: 74.055
- type: map_at_1000
value: 74.09
- type: map_at_3
value: 69.95700000000001
- type: map_at_5
value: 71.925
- type: mrr_at_1
value: 69.0
- type: mrr_at_10
value: 77.23299999999999
- type: mrr_at_100
value: 77.52
- type: mrr_at_1000
value: 77.526
- type: mrr_at_3
value: 75.59
- type: mrr_at_5
value: 76.63799999999999
- type: ndcg_at_1
value: 69.02000000000001
- type: ndcg_at_10
value: 78.226
- type: ndcg_at_100
value: 80.60199999999999
- type: ndcg_at_1000
value: 80.971
- type: ndcg_at_3
value: 74.124
- type: ndcg_at_5
value: 76.265
- type: precision_at_1
value: 69.02000000000001
- type: precision_at_10
value: 12.102
- type: precision_at_100
value: 1.468
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 32.5
- type: precision_at_5
value: 21.7
- type: recall_at_1
value: 59.846999999999994
- type: recall_at_10
value: 88.485
- type: recall_at_100
value: 97.425
- type: recall_at_1000
value: 99.523
- type: recall_at_3
value: 77.051
- type: recall_at_5
value: 82.762
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 38.67296729610079
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 53.42017351823769
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.893
- type: map_at_10
value: 2.804
- type: map_at_100
value: 3.6740000000000004
- type: map_at_1000
value: 3.94
- type: map_at_3
value: 1.926
- type: map_at_5
value: 2.363
- type: mrr_at_1
value: 4.3
- type: mrr_at_10
value: 9.520000000000001
- type: mrr_at_100
value: 10.692
- type: mrr_at_1000
value: 10.841000000000001
- type: mrr_at_3
value: 7.6
- type: mrr_at_5
value: 8.63
- type: ndcg_at_1
value: 4.3
- type: ndcg_at_10
value: 5.531
- type: ndcg_at_100
value: 10.512
- type: ndcg_at_1000
value: 16.683
- type: ndcg_at_3
value: 4.632
- type: ndcg_at_5
value: 4.3229999999999995
- type: precision_at_1
value: 4.3
- type: precision_at_10
value: 3.16
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 4.667000000000001
- type: precision_at_5
value: 4.1000000000000005
- type: recall_at_1
value: 0.893
- type: recall_at_10
value: 6.428000000000001
- type: recall_at_100
value: 21.662
- type: recall_at_1000
value: 52.162
- type: recall_at_3
value: 2.868
- type: recall_at_5
value: 4.188
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 69.34396953516386
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 60.094374065360746
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 72.51503781013379
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 66.6954698644186
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 77.69462578028768
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 75.9397626457859
- 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: 81.67242768943406
- 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: 63.7027324700292
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 73.36074244064153
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 67.75984402370518
- type: mrr
value: 86.9951798383171
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.583
- type: map_at_10
value: 33.125
- type: map_at_100
value: 34.14
- type: map_at_1000
value: 34.22
- type: map_at_3
value: 29.616
- type: map_at_5
value: 31.896
- type: mrr_at_1
value: 26.333000000000002
- type: mrr_at_10
value: 34.437
- type: mrr_at_100
value: 35.363
- type: mrr_at_1000
value: 35.433
- type: mrr_at_3
value: 31.333
- type: mrr_at_5
value: 33.267
- type: ndcg_at_1
value: 26.333000000000002
- type: ndcg_at_10
value: 38.311
- type: ndcg_at_100
value: 43.923
- type: ndcg_at_1000
value: 45.923
- type: ndcg_at_3
value: 31.596000000000004
- type: ndcg_at_5
value: 35.448
- type: precision_at_1
value: 26.333000000000002
- type: precision_at_10
value: 5.933
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.0
- type: precision_at_5
value: 9.933
- type: recall_at_1
value: 24.583
- type: recall_at_10
value: 53.417
- type: recall_at_100
value: 80.989
- type: recall_at_1000
value: 96.322
- type: recall_at_3
value: 35.611
- type: recall_at_5
value: 44.833
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.48514851485149
- type: cos_sim_ap
value: 77.36426466374054
- type: cos_sim_f1
value: 72.0702116675271
- type: cos_sim_precision
value: 74.49306296691569
- type: cos_sim_recall
value: 69.8
- type: dot_accuracy
value: 99.15049504950495
- type: dot_ap
value: 46.792474140260715
- type: dot_f1
value: 48.76476906552094
- type: dot_precision
value: 52.66821345707656
- type: dot_recall
value: 45.4
- type: euclidean_accuracy
value: 99.46534653465346
- type: euclidean_ap
value: 74.1978837990589
- type: euclidean_f1
value: 69.47256259989345
- type: euclidean_precision
value: 74.34435575826683
- type: euclidean_recall
value: 65.2
- type: manhattan_accuracy
value: 99.47128712871287
- type: manhattan_ap
value: 75.31910551743364
- type: manhattan_f1
value: 70.1582105837425
- type: manhattan_precision
value: 77.19087635054022
- type: manhattan_recall
value: 64.3
- type: max_accuracy
value: 99.48514851485149
- type: max_ap
value: 77.36426466374054
- type: max_f1
value: 72.0702116675271
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.353792480720436
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.474896484744836
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 40.82378653430986
- type: mrr
value: 41.13905600118835
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.08154836998798
- type: cos_sim_spearman
value: 31.232033308845907
- type: dot_pearson
value: 23.767593496465828
- type: dot_spearman
value: 25.6201612766572
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.186
- type: map_at_10
value: 1.1809999999999998
- type: map_at_100
value: 5.21
- type: map_at_1000
value: 12.447999999999999
- type: map_at_3
value: 0.44200000000000006
- type: map_at_5
value: 0.673
- type: mrr_at_1
value: 72.0
- type: mrr_at_10
value: 80.01899999999999
- type: mrr_at_100
value: 80.42099999999999
- type: mrr_at_1000
value: 80.42099999999999
- type: mrr_at_3
value: 78.0
- type: mrr_at_5
value: 79.4
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 56.041
- type: ndcg_at_100
value: 37.987
- type: ndcg_at_1000
value: 34.198
- type: ndcg_at_3
value: 60.23500000000001
- type: ndcg_at_5
value: 58.025999999999996
- type: precision_at_1
value: 72.0
- type: precision_at_10
value: 60.4
- type: precision_at_100
value: 38.940000000000005
- type: precision_at_1000
value: 16.106
- type: precision_at_3
value: 63.333
- type: precision_at_5
value: 61.6
- type: recall_at_1
value: 0.186
- type: recall_at_10
value: 1.458
- type: recall_at_100
value: 8.455
- type: recall_at_1000
value: 33.141999999999996
- type: recall_at_3
value: 0.461
- type: recall_at_5
value: 0.756
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.2849999999999997
- type: map_at_10
value: 6.909
- type: map_at_100
value: 11.231
- type: map_at_1000
value: 12.472
- type: map_at_3
value: 3.53
- type: map_at_5
value: 4.675
- type: mrr_at_1
value: 26.531
- type: mrr_at_10
value: 40.73
- type: mrr_at_100
value: 41.637
- type: mrr_at_1000
value: 41.647
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 38.741
- type: ndcg_at_1
value: 24.490000000000002
- type: ndcg_at_10
value: 19.17
- type: ndcg_at_100
value: 29.946
- type: ndcg_at_1000
value: 40.842
- type: ndcg_at_3
value: 19.088
- type: ndcg_at_5
value: 19.445999999999998
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 6.468999999999999
- type: precision_at_1000
value: 1.351
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 19.592000000000002
- type: recall_at_1
value: 2.2849999999999997
- type: recall_at_10
value: 12.973
- type: recall_at_100
value: 40.239999999999995
- type: recall_at_1000
value: 73.247
- type: recall_at_3
value: 4.407
- type: recall_at_5
value: 6.908
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 68.405
- type: ap
value: 13.9913678628558
- type: f1
value: 53.209691917560285
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.080928126768534
- type: f1
value: 56.36329965117965
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 31.540976715818065
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.90516778923526
- type: cos_sim_ap
value: 61.5394989621502
- type: cos_sim_f1
value: 58.02297689685646
- type: cos_sim_precision
value: 55.62817719680465
- type: cos_sim_recall
value: 60.633245382585756
- type: dot_accuracy
value: 78.95928950348691
- type: dot_ap
value: 48.61088896690895
- type: dot_f1
value: 51.0104674059488
- type: dot_precision
value: 42.00375490698071
- type: dot_recall
value: 64.93403693931398
- type: euclidean_accuracy
value: 82.476008821601
- type: euclidean_ap
value: 59.59406971314053
- type: euclidean_f1
value: 56.424962447084525
- type: euclidean_precision
value: 58.47721483158789
- type: euclidean_recall
value: 54.51187335092348
- type: manhattan_accuracy
value: 82.66078559933241
- type: manhattan_ap
value: 60.414321716856925
- type: manhattan_f1
value: 56.88221089348002
- type: manhattan_precision
value: 57.86026200873362
- type: manhattan_recall
value: 55.93667546174142
- type: max_accuracy
value: 82.90516778923526
- type: max_ap
value: 61.5394989621502
- type: max_f1
value: 58.02297689685646
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 85.71622618077386
- type: cos_sim_ap
value: 77.72774861009667
- type: cos_sim_f1
value: 71.40275165062152
- type: cos_sim_precision
value: 68.53359767754726
- type: cos_sim_recall
value: 74.52263627964275
- type: dot_accuracy
value: 83.97174680793262
- type: dot_ap
value: 72.89480417427734
- type: dot_f1
value: 68.57803792366198
- type: dot_precision
value: 62.94151708164447
- type: dot_recall
value: 75.32337542346782
- type: euclidean_accuracy
value: 84.88570652384834
- type: euclidean_ap
value: 75.78371710915128
- type: euclidean_f1
value: 69.44268877569989
- type: euclidean_precision
value: 67.1435761018046
- type: euclidean_recall
value: 71.90483523252233
- type: manhattan_accuracy
value: 85.6114409904141
- type: manhattan_ap
value: 77.38579436755944
- type: manhattan_f1
value: 70.8608538430316
- type: manhattan_precision
value: 68.03656203500319
- type: manhattan_recall
value: 73.92978133661842
- type: max_accuracy
value: 85.71622618077386
- type: max_ap
value: 77.72774861009667
- type: max_f1
value: 71.40275165062152
---
# LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
- **Repository:** https://github.com/McGill-NLP/llm2vec
- **Paper:** https://arxiv.org/abs/2404.05961
## Installation
```bash
pip install llm2vec
```
## Usage
```python
from llm2vec import LLM2Vec
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel
# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
tokenizer = AutoTokenizer.from_pretrained(
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp"
)
config = AutoConfig.from_pretrained(
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp", trust_remote_code=True
)
model = AutoModel.from_pretrained(
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
trust_remote_code=True,
config=config,
torch_dtype=torch.bfloat16,
device_map="cuda" if torch.cuda.is_available() else "cpu",
)
model = PeftModel.from_pretrained(
model,
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
)
model = model.merge_and_unload() # This can take several minutes on cpu
# Loading unsupervised SimCSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + SimCSE (LoRA).
model = PeftModel.from_pretrained(
model, "McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse"
)
# Wrapper for encoding and pooling operations
l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
# Encoding queries using instructions
instruction = (
"Given a web search query, retrieve relevant passages that answer the query:"
)
queries = [
[instruction, "how much protein should a female eat"],
[instruction, "summit define"],
]
q_reps = l2v.encode(queries)
# Encoding documents. Instruction are not required 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.",
]
d_reps = l2v.encode(documents)
# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
print(cos_sim)
"""
tensor([[0.5964, 0.1270],
[0.0698, 0.2394]])
"""
```
## Questions
If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
gmonsoon/gemma2-9b-cpt-sahabatai-v1-instruct-GGUF
|
gmonsoon
| null |
[
"gguf",
"en",
"id",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2310.04928",
"arxiv:2311.07911",
"base_model:GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct",
"base_model:quantized:GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-11-14T11:35:56 | 2024-11-15T19:05:41 | 2,514 | 5 |
---
base_model:
- GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
language:
- en
- id
- jv
- su
license: gemma
---
# Gemma2 9B CPT Sahabat-AI v1 Instruct
**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Gemma2 9B CPT Sahabat-AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**. Additionally, we added a pool of **129,000 instruction-completion pairs in English**.
- **Co-initiated by:** PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
- **Developed by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
- **Model type:** Decoder
- **Languages:** English, Indonesian, Javanese, Sundanese
- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
## Model Details
### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat-AI v1](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat-AI v1 Instruct.
For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Gemma2 9B CPT Sahabat-AI V1 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the
- [SEA HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
- These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
- We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
- These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
- These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
- **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
Note: SEA HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
#### Instruction-following Capabilities
Since Gemma2 9B CPT Sahabat-AI v1 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with the [IFEval](https://arxiv.org/abs/2311.07911) dataset.
As this dataset was in English, the linguists and native speakers in the team worked together to filter, localize and translate the dataset into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalized by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
*Note*: IFEval was only used on Bahasa Indonesia. We are currently working on adding it for Javanese and Sundanese for our upcoming releases.
#### Results
#### Indonesian Results
#### SEA HELM (also known as BHASA)
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
<td style="border: 1px solid gray; padding: 8px;">36.963</td>
<td style="border: 1px solid gray; padding: 8px;">42.988</td>
<td style="border: 1px solid gray; padding: 8px;">37.805</td>
<td style="border: 1px solid gray; padding: 8px;">45.866</td>
<td style="border: 1px solid gray; padding: 8px;">46.880</td>
<td style="border: 1px solid gray; padding: 8px;">56.359</td>
<td style="border: 1px solid gray; padding: 8px;">53.725</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">61.169</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
<td style="border: 1px solid gray; padding: 8px;">46.760</td>
<td style="border: 1px solid gray; padding: 8px;">60.372</td>
<td style="border: 1px solid gray; padding: 8px;">42.022</td>
<td style="border: 1px solid gray; padding: 8px;">51.944</td>
<td style="border: 1px solid gray; padding: 8px;">54.579</td>
<td style="border: 1px solid gray; padding: 8px;">63.394</td>
<td style="border: 1px solid gray; padding: 8px;">57.221</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.154</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
<td style="border: 1px solid gray; padding: 8px;">33.956</td>
<td style="border: 1px solid gray; padding: 8px;">40.625</td>
<td style="border: 1px solid gray; padding: 8px;">41.739</td>
<td style="border: 1px solid gray; padding: 8px;">47.587</td>
<td style="border: 1px solid gray; padding: 8px;">48.012</td>
<td style="border: 1px solid gray; padding: 8px;">56.468</td>
<td style="border: 1px solid gray; padding: 8px;">56.460</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.439</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
<td style="border: 1px solid gray; padding: 8px;">30.173</td>
<td style="border: 1px solid gray; padding: 8px;">27.969</td>
<td style="border: 1px solid gray; padding: 8px;">29.654</td>
<td style="border: 1px solid gray; padding: 8px;">38.068</td>
<td style="border: 1px solid gray; padding: 8px;">38.050</td>
<td style="border: 1px solid gray; padding: 8px;">49.216</td>
<td style="border: 1px solid gray; padding: 8px;">47.495</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">54.913</td>
</tr>
</table>
#### IndoMMLU
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Meta-Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall Results</td>
<td style="border: 1px solid gray; padding: 8px;">53.0%</td>
<td style="border: 1px solid gray; padding: 8px;">56.0%</td>
<td style="border: 1px solid gray; padding: 8px;">51.9%</td>
<td style="border: 1px solid gray; padding: 8px;">53.8%</td>
<td style="border: 1px solid gray; padding: 8px;">54.4%</td>
<td style="border: 1px solid gray; padding: 8px;">61.4%</td>
<td style="border: 1px solid gray; padding: 8px;">55.6%</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">62.6%</td>
</tr>
</table>
#### English Results
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Average</td>
<td style="border: 1px solid gray; padding: 8px;">24.48</td>
<td style="border: 1px solid gray; padding: 8px;">27.75</td>
<td style="border: 1px solid gray; padding: 8px;">23.91</td>
<td style="border: 1px solid gray; padding: 8px;">27.98</td>
<td style="border: 1px solid gray; padding: 8px;">24.52</td>
<td style="border: 1px solid gray; padding: 8px;">26.44</td>
<td style="border: 1px solid gray; padding: 8px;">24.43</td>
<td style="border: 1px solid black; padding: 8px; background-color: lightgreen;">33.67</td>
</tr>
</table>
Gemma2 9B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.0
import torch
import transformers
model_id = "GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
# Javanese
messages = [
{"role": "user", "content": "Sopo wae sing ana ing Punakawan?"}
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
# Sundanese
messages = [
{"role": "user", "content": "Kumaha caritana si Kabayan?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current Sahabat-AI models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
Gemma2 9B CPT Sahabat-AI v1 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Gemma2 9B CPT Sahabat-AI v1 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.
## Call for Collaboration
Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects.
We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI.
Your collaborations can involve:
- Identifying and reporting technical issues
- Sharing pre-training, instruction, and preference data
- Improving documentation usability
- Proposing and implementing new model evaluation tasks and metrics
Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## The Development Team (in ascending alphabetical order)
### AI Singapore
Chan Adwin<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Ong Brandon<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Siow Bryan<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>
### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Chau Shiau Ching<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<br>
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## Disclaimer
This is the repository for the Instruct model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## References
### IndoMMLU Reference
```bibtex
@inproceedings{koto-etal-2023-indommlu,
title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = December,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
}
```
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION"
] |
[
"CHIA"
] |
blevlabs/stella_en_v5
|
blevlabs
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-05T20:06:08 | 2024-12-05T20:18:30 | 2,494 | 3 |
---
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:
- type: accuracy
value: 97.16485
- type: ap
value: 96.05546315415225
- type: ap_weighted
value: 96.05546315415225
- type: f1
value: 97.16351087403213
- type: f1_weighted
value: 97.16351087403213
- type: main_score
value: 97.16485
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 59.358
- type: f1
value: 59.0264615883114
- type: f1_weighted
value: 59.0264615883114
- type: main_score
value: 59.358
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 65.269
- type: map_at_1
value: 41.607
- type: map_at_10
value: 57.104
- type: map_at_100
value: 57.621
- type: map_at_1000
value: 57.621
- type: map_at_20
value: 57.533
- type: map_at_3
value: 52.891999999999996
- type: map_at_5
value: 55.371
- type: mrr_at_1
value: 42.318634423897585
- type: mrr_at_10
value: 57.353970511865406
- type: mrr_at_100
value: 57.88398078476526
- type: mrr_at_1000
value: 57.88467807648422
- type: mrr_at_20
value: 57.796730533206166
- type: mrr_at_3
value: 53.200568990042775
- type: mrr_at_5
value: 55.6330014224753
- type: nauc_map_at_1000_diff1
value: 24.54414600428287
- type: nauc_map_at_1000_max
value: -8.389738078358459
- type: nauc_map_at_1000_std
value: -18.188787645801366
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value: 24.543138576462308
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value: -8.390896839752044
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value: -18.192549240185247
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value: 24.219607088995822
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value: -8.245734391254308
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value: -18.229706566466447
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value: 29.325201664812788
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value: -11.742800494823971
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value: -18.610215769702528
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value: 24.471097562798803
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value: -8.318035874000799
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value: -18.171541096773108
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value: 24.275846107642824
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value: -8.212242049581894
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value: 21.72619728226575
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value: -6.959741647471228
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value: -18.35173705190235
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value: 5.0388241058076995
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value: 34.439879624882145
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value: 77.22610895194498
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value: 35.37688289157788
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value: 7.734227153124332
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value: 4.202399088422237
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type: Clustering
dataset:
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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:
name: MTEB ArxivClusteringS2S
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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split: test
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metrics:
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dataset:
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type: mteb/banking77
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split: test
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dataset:
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metrics:
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dataset:
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type: mteb/biorxiv-clustering-s2s
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split: test
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type: mteb/cqadupstack
config: default
split: test
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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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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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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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value: 73.18969965863008
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value: -38.75992366905504
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value: 74.45948457438163
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value: 26.64948512428989
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value: -82.90334292052363
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value: -43.824657665520704
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value: 73.80140280887753
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value: -81.1418804447414
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value: 26.12298005045584
- type: nauc_recall_at_3_std
value: -63.42583714745169
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value: 74.16423451950358
- type: nauc_recall_at_5_max
value: 25.552390331018987
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value: -71.15891947773912
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value: 83.04
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value: 90.025
- type: ndcg_at_100
value: 91.006
- type: ndcg_at_1000
value: 91.061
- type: ndcg_at_20
value: 90.556
- type: ndcg_at_3
value: 87.493
- type: ndcg_at_5
value: 88.955
- type: precision_at_1
value: 83.04
- type: precision_at_10
value: 13.667000000000002
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.157
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value: 7.221
- type: precision_at_3
value: 38.433
- type: precision_at_5
value: 25.228
- type: recall_at_1
value: 72.222
- type: recall_at_10
value: 96.604
- type: recall_at_100
value: 99.786
- type: recall_at_1000
value: 99.996
- type: recall_at_20
value: 98.253
- type: recall_at_3
value: 89.276
- type: recall_at_5
value: 93.46
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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value: 72.86492101891123
- type: v_measure
value: 72.86492101891123
- type: v_measure_std
value: 2.778711445144635
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 75.27316726548479
- type: v_measure
value: 75.27316726548479
- type: v_measure_std
value: 8.87871936725338
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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value: 26.638
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value: 6.128
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value: 16.472
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value: 18.098
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value: 11.283
- type: map_at_5
value: 13.771
- type: mrr_at_1
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- type: nauc_map_at_1000_diff1
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value: 24.261739241957823
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value: 6.566596302915438
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value: 28.286284094687787
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value: 13.346047828494411
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value: 30.58628602415921
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value: 15.755157478191755
- type: ndcg_at_1
value: 30.2
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value: 26.638
- type: ndcg_at_100
value: 37.135
- type: ndcg_at_1000
value: 42.576
- type: ndcg_at_20
value: 30.75
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value: 24.675
- type: ndcg_at_5
value: 21.836
- type: precision_at_1
value: 30.2
- type: precision_at_10
value: 14.06
- type: precision_at_100
value: 2.904
- type: precision_at_1000
value: 0.42
- type: precision_at_20
value: 9.4
- type: precision_at_3
value: 23.233
- type: precision_at_5
value: 19.439999999999998
- type: recall_at_1
value: 6.128
- type: recall_at_10
value: 28.471999999999998
- type: recall_at_100
value: 58.952000000000005
- type: recall_at_1000
value: 85.137
- type: recall_at_20
value: 38.17
- type: recall_at_3
value: 14.127999999999998
- type: recall_at_5
value: 19.673
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 86.86608529160739
- type: cosine_spearman
value: 82.88625166203383
- type: euclidean_pearson
value: 84.15494418856142
- type: euclidean_spearman
value: 82.88449294676421
- type: main_score
value: 82.88625166203383
- type: manhattan_pearson
value: 84.39068623474428
- type: manhattan_spearman
value: 82.88065412169463
- type: pearson
value: 86.86608529160739
- type: spearman
value: 82.88625166203383
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 87.0445014940449
- type: cosine_spearman
value: 80.0880365116599
- type: euclidean_pearson
value: 83.80250772928852
- type: euclidean_spearman
value: 80.0892465260778
- type: main_score
value: 80.0880365116599
- type: manhattan_pearson
value: 83.96793981929336
- 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:
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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:
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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:
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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:
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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:
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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
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- type: nauc_recall_at_20_std
value: 33.76185294993676
- type: nauc_recall_at_3_diff1
value: 29.758810004388348
- type: nauc_recall_at_3_max
value: -1.9442985017191816
- type: nauc_recall_at_3_std
value: 27.45550076962206
- type: nauc_recall_at_5_diff1
value: 27.047710181576672
- type: nauc_recall_at_5_max
value: 1.5237000700880248
- type: nauc_recall_at_5_std
value: 28.235297950159698
- type: ndcg_at_1
value: 94.0
- type: ndcg_at_10
value: 85.983
- type: ndcg_at_100
value: 69.195
- type: ndcg_at_1000
value: 62.541000000000004
- type: ndcg_at_20
value: 83.405
- type: ndcg_at_3
value: 89.98899999999999
- type: ndcg_at_5
value: 87.905
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 89.4
- type: precision_at_100
value: 71.54
- type: precision_at_1000
value: 27.594
- type: precision_at_20
value: 87.2
- type: precision_at_3
value: 92.667
- type: precision_at_5
value: 90.8
- type: recall_at_1
value: 0.247
- type: recall_at_10
value: 2.315
- type: recall_at_100
value: 17.574
- type: recall_at_1000
value: 59.336999999999996
- type: recall_at_20
value: 4.491
- type: recall_at_3
value: 0.7250000000000001
- type: recall_at_5
value: 1.1820000000000002
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: main_score
value: 29.944
- type: map_at_1
value: 3.064
- type: map_at_10
value: 11.501999999999999
- type: map_at_100
value: 18.736
- type: map_at_1000
value: 20.333000000000002
- type: map_at_20
value: 14.057
- type: map_at_3
value: 6.300999999999999
- type: map_at_5
value: 8.463
- type: mrr_at_1
value: 44.89795918367347
- type: mrr_at_10
value: 58.41188856494979
- type: mrr_at_100
value: 58.93964266413245
- type: mrr_at_1000
value: 58.93964266413245
- type: mrr_at_20
value: 58.767485349118
- type: mrr_at_3
value: 54.42176870748299
- type: mrr_at_5
value: 56.666666666666664
- type: nauc_map_at_1000_diff1
value: 11.478593385608479
- type: nauc_map_at_1000_max
value: 10.309889845044324
- type: nauc_map_at_1000_std
value: 21.16721939940238
- type: nauc_map_at_100_diff1
value: 11.570438543562418
- type: nauc_map_at_100_max
value: 8.426183648064834
- type: nauc_map_at_100_std
value: 18.56231985033613
- type: nauc_map_at_10_diff1
value: 22.37735506247481
- type: nauc_map_at_10_max
value: 5.455946239060806
- type: nauc_map_at_10_std
value: -4.2848826518388154
- type: nauc_map_at_1_diff1
value: 27.853645380676824
- type: nauc_map_at_1_max
value: 7.30739948053113
- type: nauc_map_at_1_std
value: -0.2773663157814586
- type: nauc_map_at_20_diff1
value: 14.724669779924648
- type: nauc_map_at_20_max
value: 10.12882779173533
- type: nauc_map_at_20_std
value: 4.4803777672120875
- type: nauc_map_at_3_diff1
value: 31.891173385921263
- type: nauc_map_at_3_max
value: 4.889652271827218
- type: nauc_map_at_3_std
value: -9.477460238651643
- type: nauc_map_at_5_diff1
value: 31.489012040465003
- type: nauc_map_at_5_max
value: 1.7330092417337482
- type: nauc_map_at_5_std
value: -8.137018608469637
- type: nauc_mrr_at_1000_diff1
value: 24.411522237082416
- type: nauc_mrr_at_1000_max
value: 11.286971076556688
- type: nauc_mrr_at_1000_std
value: 23.443174210894043
- type: nauc_mrr_at_100_diff1
value: 24.411522237082416
- type: nauc_mrr_at_100_max
value: 11.286971076556688
- type: nauc_mrr_at_100_std
value: 23.443174210894043
- type: nauc_mrr_at_10_diff1
value: 23.948152308265186
- type: nauc_mrr_at_10_max
value: 12.22420979621155
- type: nauc_mrr_at_10_std
value: 23.557939024705544
- type: nauc_mrr_at_1_diff1
value: 17.902334894536107
- type: nauc_mrr_at_1_max
value: 17.36969662861018
- type: nauc_mrr_at_1_std
value: 19.425714969048734
- type: nauc_mrr_at_20_diff1
value: 24.635893795899797
- type: nauc_mrr_at_20_max
value: 11.330541067194913
- type: nauc_mrr_at_20_std
value: 23.74518583400233
- type: nauc_mrr_at_3_diff1
value: 25.045536328282587
- type: nauc_mrr_at_3_max
value: 7.497967004732733
- type: nauc_mrr_at_3_std
value: 24.167153007320078
- type: nauc_mrr_at_5_diff1
value: 24.328479930592454
- type: nauc_mrr_at_5_max
value: 10.037126854938336
- type: nauc_mrr_at_5_std
value: 25.236208055346136
- type: nauc_ndcg_at_1000_diff1
value: 15.555347444667389
- type: nauc_ndcg_at_1000_max
value: 13.356591700655718
- type: nauc_ndcg_at_1000_std
value: 42.42395845935052
- type: nauc_ndcg_at_100_diff1
value: 13.110526060413708
- type: nauc_ndcg_at_100_max
value: 3.140006440162515
- type: nauc_ndcg_at_100_std
value: 39.02733288398033
- type: nauc_ndcg_at_10_diff1
value: 20.68853369009725
- type: nauc_ndcg_at_10_max
value: 2.435389817058852
- type: nauc_ndcg_at_10_std
value: 10.038202768784316
- type: nauc_ndcg_at_1_diff1
value: 20.17287594582385
- type: nauc_ndcg_at_1_max
value: 12.487205168273196
- type: nauc_ndcg_at_1_std
value: 20.639827614373075
- type: nauc_ndcg_at_20_diff1
value: 16.987577348502985
- type: nauc_ndcg_at_20_max
value: 2.9978717644469266
- type: nauc_ndcg_at_20_std
value: 13.015690866750354
- type: nauc_ndcg_at_3_diff1
value: 32.392223079245575
- type: nauc_ndcg_at_3_max
value: 1.587587110582544
- type: nauc_ndcg_at_3_std
value: 12.850592473446609
- type: nauc_ndcg_at_5_diff1
value: 32.80244517369626
- type: nauc_ndcg_at_5_max
value: 5.8939933777508084
- type: nauc_ndcg_at_5_std
value: 15.779687411463414
- type: nauc_precision_at_1000_diff1
value: -14.314031720452537
- type: nauc_precision_at_1000_max
value: 32.87886666567266
- type: nauc_precision_at_1000_std
value: 21.49347046886851
- type: nauc_precision_at_100_diff1
value: -9.4034008613839
- type: nauc_precision_at_100_max
value: 16.784075123309645
- type: nauc_precision_at_100_std
value: 73.14688535393604
- type: nauc_precision_at_10_diff1
value: 6.855101404043058
- type: nauc_precision_at_10_max
value: 6.52491228645612
- type: nauc_precision_at_10_std
value: 16.104602266016744
- type: nauc_precision_at_1_diff1
value: 17.902334894536107
- type: nauc_precision_at_1_max
value: 17.36969662861018
- type: nauc_precision_at_1_std
value: 19.425714969048734
- type: nauc_precision_at_20_diff1
value: -5.337534613602212
- type: nauc_precision_at_20_max
value: 17.722925454767218
- type: nauc_precision_at_20_std
value: 34.26680462132849
- type: nauc_precision_at_3_diff1
value: 31.054623397809255
- type: nauc_precision_at_3_max
value: -0.92038600946826
- type: nauc_precision_at_3_std
value: 8.326997076862916
- type: nauc_precision_at_5_diff1
value: 29.784942296920462
- type: nauc_precision_at_5_max
value: 6.337469263434779
- type: nauc_precision_at_5_std
value: 12.789597196020974
- type: nauc_recall_at_1000_diff1
value: -3.8177981862041364
- type: nauc_recall_at_1000_max
value: 14.206064332229163
- type: nauc_recall_at_1000_std
value: 74.18853420771269
- type: nauc_recall_at_100_diff1
value: 0.7677996771461106
- type: nauc_recall_at_100_max
value: -4.139924106878441
- type: nauc_recall_at_100_std
value: 48.319930706362896
- type: nauc_recall_at_10_diff1
value: 12.038835537494322
- type: nauc_recall_at_10_max
value: -2.0498983557854418
- type: nauc_recall_at_10_std
value: -2.0339180690854493
- type: nauc_recall_at_1_diff1
value: 27.853645380676824
- type: nauc_recall_at_1_max
value: 7.30739948053113
- type: nauc_recall_at_1_std
value: -0.2773663157814586
- type: nauc_recall_at_20_diff1
value: 0.7907893667756708
- type: nauc_recall_at_20_max
value: 0.8795499810558195
- type: nauc_recall_at_20_std
value: 11.512483291688282
- type: nauc_recall_at_3_diff1
value: 33.19440392639576
- type: nauc_recall_at_3_max
value: -1.5494237697432613
- type: nauc_recall_at_3_std
value: -8.560408808376984
- type: nauc_recall_at_5_diff1
value: 27.42193873870941
- type: nauc_recall_at_5_max
value: -4.74350293281128
- type: nauc_recall_at_5_std
value: -7.618060131179654
- type: ndcg_at_1
value: 42.857
- type: ndcg_at_10
value: 29.944
- type: ndcg_at_100
value: 42.624
- type: ndcg_at_1000
value: 53.384
- type: ndcg_at_20
value: 30.135
- type: ndcg_at_3
value: 34.847
- type: ndcg_at_5
value: 32.573
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 25.306
- type: precision_at_100
value: 8.694
- type: precision_at_1000
value: 1.616
- type: precision_at_20
value: 19.082
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 3.064
- type: recall_at_10
value: 17.849999999999998
- type: recall_at_100
value: 53.217999999999996
- type: recall_at_1000
value: 87.095
- type: recall_at_20
value: 26.111
- type: recall_at_3
value: 7.383000000000001
- type: recall_at_5
value: 11.434
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 88.759765625
- type: ap
value: 36.49152357863017
- type: ap_weighted
value: 36.49152357863017
- type: f1
value: 74.4692714448641
- type: f1_weighted
value: 90.54372649306606
- type: main_score
value: 88.759765625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 74.8443689869836
- type: f1
value: 75.1139662898148
- type: f1_weighted
value: 74.7369003946243
- type: main_score
value: 74.8443689869836
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 61.42918790942448
- type: v_measure
value: 61.42918790942448
- type: v_measure_std
value: 1.0156550098843082
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 88.22197055492639
- type: cosine_accuracy_threshold
value: 83.30042362213135
- type: cosine_ap
value: 80.57754959194938
- type: cosine_f1
value: 73.70579190158894
- type: cosine_f1_threshold
value: 81.04978799819946
- type: cosine_precision
value: 71.64922770303936
- type: cosine_recall
value: 75.8839050131926
- type: dot_accuracy
value: 88.23985217857782
- type: dot_accuracy_threshold
value: 83.31039547920227
- type: dot_ap
value: 80.57533213448181
- type: dot_f1
value: 73.61309601143302
- type: dot_f1_threshold
value: 81.33968114852905
- type: dot_precision
value: 72.51087791144101
- type: dot_recall
value: 74.74934036939314
- type: euclidean_accuracy
value: 88.22197055492639
- type: euclidean_accuracy_threshold
value: 58.290231227874756
- type: euclidean_ap
value: 80.57982723880139
- type: euclidean_f1
value: 73.63426519620417
- type: euclidean_f1_threshold
value: 61.55576705932617
- type: euclidean_precision
value: 71.63173652694611
- type: euclidean_recall
value: 75.75197889182058
- type: main_score
value: 80.57982723880139
- type: manhattan_accuracy
value: 88.14448351910353
- 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
New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md.
You can also find these models on my [homepage](https://huggingface.co/infgrad).
# 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 ]]
```
# 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"
] |
gmonsoon/llama3-8b-cpt-sahabatai-v1-instruct-GGUF
|
gmonsoon
| null |
[
"gguf",
"en",
"id",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2310.04928",
"arxiv:2311.07911",
"base_model:GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct",
"base_model:quantized:GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-11-14T17:04:01 | 2024-11-15T17:08:53 | 2,445 | 1 |
---
base_model:
- GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct
language:
- en
- id
- jv
- su
license: llama3
---
# Llama3 8B CPT Sahabat-AI v1 Instruct
**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Llama3 8B CPT Sahabat-AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**. Additionally, we added a pool of **129,000 instruction-completion pairs in English**.
- **Co-initiated by:** PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
- **Developed by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
- **Model type:** Decoder
- **Languages:** English, Indonesian, Javanese, Sundanese
- **License:** [Llama3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
## Model Details
### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Llama3 8B CPT Sahabat-AI v1 base](https://huggingface.co/GoToCompany/llama3-8b-cpt-sahabatai-v1-base), a decoder model using the Llama3 architecture, to create Llama3 8B CPT Sahabat-AI v1 Instruct.
For tokenisation, the model employs the default tokenizer used in Llama-3-8B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Llama3 8B CPT Sahabat-AI V1 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the
- [SEA HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
- These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
- We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
- These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
- These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
- **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
Note: SEA HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
#### Instruction-following Capabilities
Since Llama3 8B CPT Sahabat-AI v1 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with the [IFEval](https://arxiv.org/abs/2311.07911) dataset.
As this dataset was in English, the linguists and native speakers in the team worked together to filter, localize and translate the dataset into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalized by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
*Note*: IFEval was only used on Bahasa Indonesia. We are currently working on adding it for Javanese and Sundanese for our upcoming releases.
#### Results
#### Indonesian Results
#### SEA HELM (also known as BHASA)
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
<td style="border: 1px solid gray; padding: 8px;">36.963</td>
<td style="border: 1px solid gray; padding: 8px;">42.988</td>
<td style="border: 1px solid gray; padding: 8px;">37.805</td>
<td style="border: 1px solid gray; padding: 8px;">45.866</td>
<td style="border: 1px solid gray; padding: 8px;">46.880</td>
<td style="border: 1px solid gray; padding: 8px;">56.359</td>
<td style="border: 2px solid black; padding: 8px;">53.725</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">61.169</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
<td style="border: 1px solid gray; padding: 8px;">46.760</td>
<td style="border: 1px solid gray; padding: 8px;">60.372</td>
<td style="border: 1px solid gray; padding: 8px;">42.022</td>
<td style="border: 1px solid gray; padding: 8px;">51.944</td>
<td style="border: 1px solid gray; padding: 8px;">54.579</td>
<td style="border: 1px solid gray; padding: 8px;">63.394</td>
<td style="border: 2px solid black; padding: 8px;">57.221</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">64.154</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
<td style="border: 1px solid gray; padding: 8px;">33.956</td>
<td style="border: 1px solid gray; padding: 8px;">40.625</td>
<td style="border: 1px solid gray; padding: 8px;">41.739</td>
<td style="border: 1px solid gray; padding: 8px;">47.587</td>
<td style="border: 1px solid gray; padding: 8px;">48.012</td>
<td style="border: 1px solid gray; padding: 8px;">56.468</td>
<td style="border: 2px solid black; padding: 8px;">56.460</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">64.439</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
<td style="border: 1px solid gray; padding: 8px;">30.173</td>
<td style="border: 1px solid gray; padding: 8px;">27.969</td>
<td style="border: 1px solid gray; padding: 8px;">29.654</td>
<td style="border: 1px solid gray; padding: 8px;">38.068</td>
<td style="border: 1px solid gray; padding: 8px;">38.050</td>
<td style="border: 1px solid gray; padding: 8px;">49.216</td>
<td style="border: 2px solid black; padding: 8px;">47.495</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">54.913</td>
</tr>
</table>
#### IndoMMLU
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Meta-Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall Results</td>
<td style="border: 1px solid gray; padding: 8px;">53.0%</td>
<td style="border: 1px solid gray; padding: 8px;">56.0%</td>
<td style="border: 1px solid gray; padding: 8px;">51.9%</td>
<td style="border: 1px solid gray; padding: 8px;">53.8%</td>
<td style="border: 1px solid gray; padding: 8px;">54.4%</td>
<td style="border: 1px solid gray; padding: 8px;">61.4%</td>
<td style="border: 2px solid black; padding: 8px;">55.6%</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">62.6%</td>
</tr>
</table>
#### English Results
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Average</td>
<td style="border: 1px solid gray; padding: 8px;">24.48</td>
<td style="border: 1px solid gray; padding: 8px;">27.75</td>
<td style="border: 1px solid gray; padding: 8px;">23.91</td>
<td style="border: 1px solid gray; padding: 8px;">27.98</td>
<td style="border: 1px solid gray; padding: 8px;">24.52</td>
<td style="border: 1px solid gray; padding: 8px;">26.44</td>
<td style="border: 2px solid black; padding: 8px;">24.43</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">33.67</td>
</tr>
</table>
Llama3 8B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.0
import torch
import transformers
model_id = "GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
# Javanese
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Sopo wae sing ana ing Punakawan?"}
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
# Sundanese
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Kumaha caritana si Kabayan?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current Sahabat-AI models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
Llama3 8B CPT Sahabat-AI v1 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Llama3 8B CPT Sahabat-AI v1 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.
## Call for Collaboration
Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects.
We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI.
Your collaborations can involve:
- Identifying and reporting technical issues
- Sharing pre-training, instruction, and preference data
- Improving documentation usability
- Proposing and implementing new model evaluation tasks and metrics
Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## The Development Team (in ascending alphabetical order)
### AI Singapore
Chan Adwin<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Ong Brandon<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Siow Bryan<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>
### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Chau Shiau Ching<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<br>
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## Disclaimer
This is the repository for the Instruct model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## References
### IndoMMLU Reference
```bibtex
@inproceedings{koto-etal-2023-indommlu,
title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = December,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
}
```
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION"
] |
[
"CHIA"
] |
RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf
|
RichardErkhov
| null |
[
"gguf",
"arxiv:2308.03281",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-07-14T08:23:13 | 2024-07-14T10:05:13 | 2,409 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gte-Qwen2-1.5B-instruct - GGUF
- Model creator: https://huggingface.co/Alibaba-NLP/
- Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gte-Qwen2-1.5B-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q2_K.gguf) | Q2_K | 0.7GB |
| [gte-Qwen2-1.5B-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ3_XS.gguf) | IQ3_XS | 0.77GB |
| [gte-Qwen2-1.5B-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ3_S.gguf) | IQ3_S | 0.8GB |
| [gte-Qwen2-1.5B-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K_S.gguf) | Q3_K_S | 0.8GB |
| [gte-Qwen2-1.5B-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ3_M.gguf) | IQ3_M | 0.82GB |
| [gte-Qwen2-1.5B-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K.gguf) | Q3_K | 0.86GB |
| [gte-Qwen2-1.5B-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K_M.gguf) | Q3_K_M | 0.86GB |
| [gte-Qwen2-1.5B-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K_L.gguf) | Q3_K_L | 0.91GB |
| [gte-Qwen2-1.5B-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ4_XS.gguf) | IQ4_XS | 0.96GB |
| [gte-Qwen2-1.5B-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_0.gguf) | Q4_0 | 0.99GB |
| [gte-Qwen2-1.5B-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ4_NL.gguf) | IQ4_NL | 1.0GB |
| [gte-Qwen2-1.5B-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_K_S.gguf) | Q4_K_S | 1.0GB |
| [gte-Qwen2-1.5B-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_K.gguf) | Q4_K | 1.04GB |
| [gte-Qwen2-1.5B-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_K_M.gguf) | Q4_K_M | 1.04GB |
| [gte-Qwen2-1.5B-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_1.gguf) | Q4_1 | 1.08GB |
| [gte-Qwen2-1.5B-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_0.gguf) | Q5_0 | 1.17GB |
| [gte-Qwen2-1.5B-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_K_S.gguf) | Q5_K_S | 1.17GB |
| [gte-Qwen2-1.5B-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_K.gguf) | Q5_K | 1.2GB |
| [gte-Qwen2-1.5B-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_K_M.gguf) | Q5_K_M | 1.2GB |
| [gte-Qwen2-1.5B-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_1.gguf) | Q5_1 | 1.26GB |
| [gte-Qwen2-1.5B-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q6_K.gguf) | Q6_K | 1.36GB |
| [gte-Qwen2-1.5B-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q8_0.gguf) | Q8_0 | 1.76GB |
Original model description:
---
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
license: apache-2.0
model-index:
- name: gte-qwen2-7B-instruct
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 83.98507462686567
- type: ap
value: 50.93015252587014
- type: f1
value: 78.50416599051215
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 96.61065
- type: ap
value: 94.89174052954196
- type: f1
value: 96.60942596940565
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 55.614000000000004
- type: f1
value: 54.90553480294904
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 50.511868162026175
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 45.007803189284004
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 64.55292107723382
- type: mrr
value: 77.66158818097877
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
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: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 87.30844155844156
- type: f1
value: 87.25307322443255
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 43.20754608934859
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 38.818037697335505
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackEnglishRetrieval
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackGamingRetrieval
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackGisRetrieval
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackMathematicaRetrieval
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackPhysicsRetrieval
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackProgrammersRetrieval
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
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
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackTexRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackUnixRetrieval
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackWebmastersRetrieval
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: BeIR/cqadupstack
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:
config: default
name: MTEB CQADupstackWordpressRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- 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:
config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
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:
config: default
name: MTEB DBPedia
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
split: test
type: mteb/dbpedia
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: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 61.365
- type: f1
value: 56.7540827912991
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
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:
config: default
name: MTEB FiQA2018
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
split: test
type: mteb/fiqa
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:
config: default
name: MTEB HotpotQA
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
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: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 95.8336
- type: ap
value: 93.78862962194073
- type: f1
value: 95.83192650728371
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
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: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 97.69493844049248
- type: f1
value: 97.55048089616261
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 88.75968992248062
- type: f1
value: 72.26321223399123
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 82.40080699394754
- type: f1
value: 79.62590029057968
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 84.49562878278414
- type: f1
value: 84.0040193313333
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 39.386760057101945
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 37.89687154075537
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 33.94151656057482
- type: mrr
value: 35.32684700746953
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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:
config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
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:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: mteb/quora
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: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 55.82153952668092
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 62.094465801879295
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: mteb/scidocs
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: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
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:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
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:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
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:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
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:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
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:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
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:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
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:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
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:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
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: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 86.52456702387798
- type: mrr
value: 96.34556529164372
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
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: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
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: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 67.65446577183913
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 46.30749237193961
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.91481849959949
- type: mrr
value: 55.853506175197346
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
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: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: mteb/trec-covid
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:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
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: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 82.66099999999999
- type: ap
value: 25.555698090238337
- type: f1
value: 66.48402012461622
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 72.94567062818335
- type: f1
value: 73.28139189595674
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 49.581627240203474
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
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:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
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: PairClassification
- dataset:
config: default
name: MTEB AFQMC
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
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:
config: default
name: MTEB ATEC
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
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: STS
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 47.209999999999994
- type: f1
value: 46.08892432018655
task:
type: Classification
- dataset:
config: default
name: MTEB BQ
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
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: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: v_measure
value: 45.21317724305628
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: v_measure
value: 42.49825170976724
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 88.15661686810597
- type: mrr
value: 90.11222222222223
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 88.1204726064383
- type: mrr
value: 90.20142857142858
task:
type: Reranking
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
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: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
split: validation
type: C-MTEB/CMNLI
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: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: 1271c7809071a13532e05f25fb53511ffce77117
split: dev
type: C-MTEB/CovidRetrieval
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:
config: default
name: MTEB DuRetrieval
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
split: dev
type: C-MTEB/DuRetrieval
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:
config: default
name: MTEB EcomRetrieval
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
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: Retrieval
- dataset:
config: default
name: MTEB IFlyTek
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy
value: 44.84801846864178
- type: f1
value: 37.47347897956339
task:
type: Classification
- dataset:
config: default
name: MTEB JDReview
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy
value: 85.81613508442777
- type: ap
value: 52.68244615477374
- type: f1
value: 80.0445640948843
task:
type: Classification
- dataset:
config: default
name: MTEB LCQMC
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
split: test
type: C-MTEB/LCQMC
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: STS
- dataset:
config: default
name: MTEB MMarcoReranking
revision: None
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map
value: 29.143797057999134
- type: mrr
value: 28.08174603174603
task:
type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
split: dev
type: C-MTEB/MMarcoRetrieval
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: Retrieval
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.88298587760592
- type: f1
value: 73.89001762017176
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 80.76328177538669
- type: f1
value: 80.24718532423358
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
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: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy
value: 74.45666666666666
- type: f1
value: 74.32582402190089
task:
type: Classification
- dataset:
config: default
name: MTEB Ocnli
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
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: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 93.5
- type: ap
value: 91.31357903446782
- type: f1
value: 93.48088994006616
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
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:
config: default
name: MTEB QBQTC
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
split: test
type: C-MTEB/QBQTC
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:
config: zh
name: MTEB STS22 (zh)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
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:
config: default
name: MTEB STSB
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
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: STS
- dataset:
config: default
name: MTEB T2Reranking
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map
value: 67.43289278789972
- type: mrr
value: 77.53012460908535
task:
type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
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: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 49.952000000000005
- type: f1
value: 48.264617195258054
task:
type: Classification
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: v_measure
value: 68.23769904483508
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: v_measure
value: 62.50294403136556
task:
type: Clustering
- dataset:
config: default
name: MTEB VideoRetrieval
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
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: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: 339287def212450dcaa9df8c22bf93e9980c7023
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy
value: 86.63000000000001
- type: ap
value: 69.99457882599567
- type: f1
value: 85.07735617998541
task:
type: Classification
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 44.594104491193555
task:
type: Clustering
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 63.97614314115309
- type: f1
value: 52.15634261679283
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna-PL
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
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: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 68.56
- type: ap
value: 23.310493680488513
- type: f1
value: 58.85369533105693
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: None
split: test
type: PL-MTEB/cdsce-pairclassification
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: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: None
split: test
type: PL-MTEB/cdscr-sts
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: STS
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
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:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
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:
config: default
name: MTEB HotpotQA-PL
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
split: test
type: clarin-knext/hotpotqa-pl
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:
config: default
name: MTEB MSMARCO-PL
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
split: test
type: clarin-knext/msmarco-pl
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: Retrieval
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 73.55413584398119
- type: f1
value: 69.65610882318181
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.37188971082716
- type: f1
value: 75.64847309941361
task:
type: Classification
- dataset:
config: default
name: MTEB NFCorpus-PL
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
split: test
type: clarin-knext/nfcorpus-pl
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:
config: default
name: MTEB NQ-PL
revision: f171245712cf85dd4700b06bef18001578d0ca8d
split: test
type: clarin-knext/nq-pl
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: Retrieval
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 69.04141326382856
- type: ap
value: 77.49422763833996
- type: f1
value: 66.73472657783407
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
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:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
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: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 86.16343490304708
- type: f1
value: 83.3442579486744
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 68.40080971659918
- type: f1
value: 53.13720751142237
task:
type: Classification
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
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:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
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: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: None
split: test
type: PL-MTEB/sicke-pl-pairclassification
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: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: None
split: test
type: PL-MTEB/sickr-pl-sts
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:
config: pl
name: MTEB STS22 (pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
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: STS
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
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:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
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: Retrieval
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 70.55290063940157
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 55.41500719337263
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map
value: 73.48697375332002
- type: mrr
value: 75.01836585523822
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
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: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 53.474
- type: f1
value: 50.38275392350236
task:
type: Classification
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
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: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: v_measure
value: 28.301882091023288
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringP2P
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 45.26992995191701
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringS2S
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 42.773174876871145
task:
type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 93.47635452552458
- type: f1
value: 93.19922617577213
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 80.2317569683683
- type: f1
value: 56.18060418621901
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: accuracy
value: 85.18957345971565
- type: f1
value: 80.829981537394
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 71.04138999801822
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 71.7056263158008
task:
type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.65097511768661
- type: f1
value: 73.82441070598712
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 79.09885675857431
- type: f1
value: 78.28407777434224
task:
type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
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: Retrieval
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
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:
config: fr
name: MTEB PawsX (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: paws-x
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: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
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:
config: fr
name: MTEB STS22 (fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
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:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
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: STS
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
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: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: map
value: 94.03333333333333
- type: mrr
value: 94.03333333333333
task:
type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
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:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
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
task:
type: Retrieval
---
## 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())
```
## 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 |
## 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"
] |
TheBloke/meditron-70B-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"medical",
"health",
"llama2",
"en",
"dataset:bigbio/med_qa",
"dataset:medmcqa",
"dataset:bigbio/pubmed_qa",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:epfl-llm/meditron-70b",
"base_model:quantized:epfl-llm/meditron-70b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2023-11-30T17:10:33 | 2023-11-30T21:24:40 | 2,391 | 4 |
---
base_model: epfl-llm/meditron-70b
datasets:
- bigbio/med_qa
- medmcqa
- bigbio/pubmed_qa
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
model_name: Meditron 70B
pipeline_tag: text-generation
tags:
- medical
- health
- llama2
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">
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# Meditron 70B - GPTQ
- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)
- Original model: [Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b)
<!-- description start -->
# Description
This repo contains GPTQ model files for [EPFL LLM Team's Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-70B-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-70b)
<!-- 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_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 31.84 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/meditron-70B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/meditron-70B-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `meditron-70B-GPTQ`:
```shell
mkdir meditron-70B-GPTQ
huggingface-cli download TheBloke/meditron-70B-GPTQ --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir meditron-70B-GPTQ
huggingface-cli download TheBloke/meditron-70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir meditron-70B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/meditron-70B-GPTQ --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/meditron-70B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.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-70B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/meditron-70B-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
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-70B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. 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.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to 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-70B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires 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_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/meditron-70B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=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
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.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 70B
<img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo">
# Model Card for Meditron-70B-v1.0
Meditron is a suite of open-source medical Large Language Models (LLMs).
Meditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.
<!--# Table of Contents
[Model Card for Meditron 70B](#model-card-for--meditron-70b-v1.0)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Evaluation](#evaluation)
- [Testing Data & Metrics](#testing-data-&-metrics)
- [Testing Data](#testing-data)
- [Metrics](#metrics)
- [Results](#results)
- [Environmental Impact](#environmental-impact)
- [Citation](#citation)-->
<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-2-70B](https://huggingface.co/meta-llama/Llama-2-70b)
- **Context length:** 4K 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-70B 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-70B 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.
For **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.
Each query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).
Please see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)
Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model.
### 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 without comprehensive testing for your application.
## 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="60%" 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 16 nodes 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.
The nodes are connected via RDMA over Converged Ethernet.
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 8,
- Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.
#### Training Hyperparameters
| | |
| --- | ------ |
| bf16 | true |
| lr | 1.5e-4 |
| eps | 1e-5 |
| betas | \[0.9, 0.95\] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 2 |
| TP size | 8 |
| PP size | 8 |
| seq length | 4096 |
| lr scheduler | cosine|
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 2 |
| global batch size | 512 |
| | |
#### Speeds, Sizes, Times
The model was trained in September and October 2023.
The model architecture is exactly Llama 2, meaning
| | |
| --- | ------ |
| Model size | 70B |
| Hidden dimension | 8192 |
| Num. attention heads | 64 |
| Num. layers | 80 |
| | | |
We train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second.
This amounts to a bfloat16 model flops utilization of roughly 42.3\%.
## 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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually.
We report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |
|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |
|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |
|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |
|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |
|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |
|Avg | 72.0 | 69.2 | 63.6 | 57.4 |
| | | | | | |
**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:** 128 x NVIDIA A100 (80GB) SXM
- **Total GPU hours:** 42,496
- **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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be:
(400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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"
] |
Alibaba-NLP/gme-Qwen2-VL-7B-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-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-21T04:00:17 | 2025-01-21T11:53:23 | 2,333 | 26 |
---
base_model:
- Qwen/Qwen2-VL-7B-Instruct
language:
- en
- zh
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2-VL
- sentence-similarity
- vidore
model-index:
- name: gme-Qwen2-VL-7B-Instruct
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 64.72351048394194
- type: cos_sim_spearman
value: 71.66842612591344
- type: euclidean_pearson
value: 70.0342809043895
- type: euclidean_spearman
value: 71.66842612323917
- type: manhattan_pearson
value: 69.94743870947117
- type: manhattan_spearman
value: 71.53159630946965
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 52.38188106868689
- type: cos_sim_spearman
value: 55.468235529709766
- type: euclidean_pearson
value: 56.974786979175086
- type: euclidean_spearman
value: 55.468231026153745
- type: manhattan_pearson
value: 56.94467132566259
- type: manhattan_spearman
value: 55.39037386224014
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.61194029850746
- type: ap
value: 41.29789064067677
- type: f1
value: 71.69633278678522
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.3258
- type: ap
value: 95.91845683387056
- type: f1
value: 97.32526074864263
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 64.794
- type: f1
value: 63.7329780206882
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.099999999999994
- type: f1
value: 53.115528412999666
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 40.541
- type: map_at_10
value: 56.315000000000005
- type: map_at_100
value: 56.824
- type: map_at_1000
value: 56.825
- type: map_at_3
value: 51.778
- type: map_at_5
value: 54.623
- type: mrr_at_1
value: 41.038000000000004
- type: mrr_at_10
value: 56.532000000000004
- type: mrr_at_100
value: 57.034
- type: mrr_at_1000
value: 57.034
- type: mrr_at_3
value: 52.015
- type: mrr_at_5
value: 54.835
- type: ndcg_at_1
value: 40.541
- type: ndcg_at_10
value: 64.596
- type: ndcg_at_100
value: 66.656
- type: ndcg_at_1000
value: 66.666
- type: ndcg_at_3
value: 55.415000000000006
- type: ndcg_at_5
value: 60.527
- type: precision_at_1
value: 40.541
- type: precision_at_10
value: 9.083
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.977
- type: precision_at_5
value: 15.661
- type: recall_at_1
value: 40.541
- type: recall_at_10
value: 90.825
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 65.932
- type: recall_at_5
value: 78.307
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 54.96111428218386
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 50.637711388838945
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.0741897266483
- type: mrr
value: 76.11440882909028
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.2557839280406
- type: cos_sim_spearman
value: 82.58200216886888
- type: euclidean_pearson
value: 84.80588838508498
- type: euclidean_spearman
value: 82.58200216886888
- type: manhattan_pearson
value: 84.53082035185592
- type: manhattan_spearman
value: 82.4964580510134
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 76.98420285210636
- type: cos_sim_spearman
value: 78.95549489000658
- type: euclidean_pearson
value: 79.14591532018991
- type: euclidean_spearman
value: 78.95549488953284
- type: manhattan_pearson
value: 79.26212116856509
- type: manhattan_spearman
value: 79.02104262086006
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.76298701298703
- type: f1
value: 84.24881789367576
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 46.86757924102047
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 43.86043680479362
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 45.684222588040605
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.45639765303432
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.7058672660788
- type: mrr
value: 90.5795634920635
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 90.50750030424048
- type: mrr
value: 92.3970634920635
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 28.848000000000003
- type: map_at_10
value: 40.453
- type: map_at_100
value: 42.065000000000005
- type: map_at_1000
value: 42.176
- type: map_at_3
value: 36.697
- type: map_at_5
value: 38.855000000000004
- type: mrr_at_1
value: 34.764
- type: mrr_at_10
value: 45.662000000000006
- type: mrr_at_100
value: 46.56
- type: mrr_at_1000
value: 46.597
- type: mrr_at_3
value: 42.632
- type: mrr_at_5
value: 44.249
- type: ndcg_at_1
value: 34.764
- type: ndcg_at_10
value: 47.033
- type: ndcg_at_100
value: 53.089
- type: ndcg_at_1000
value: 54.818
- type: ndcg_at_3
value: 41.142
- type: ndcg_at_5
value: 43.928
- type: precision_at_1
value: 34.764
- type: precision_at_10
value: 9.027000000000001
- type: precision_at_100
value: 1.465
- type: precision_at_1000
value: 0.192
- type: precision_at_3
value: 19.695
- type: precision_at_5
value: 14.535
- type: recall_at_1
value: 28.848000000000003
- type: recall_at_10
value: 60.849
- type: recall_at_100
value: 85.764
- type: recall_at_1000
value: 96.098
- type: recall_at_3
value: 44.579
- type: recall_at_5
value: 51.678999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 30.731
- type: map_at_10
value: 41.859
- type: map_at_100
value: 43.13
- type: map_at_1000
value: 43.257
- type: map_at_3
value: 38.384
- type: map_at_5
value: 40.284
- type: mrr_at_1
value: 38.471
- type: mrr_at_10
value: 47.531
- type: mrr_at_100
value: 48.199
- type: mrr_at_1000
value: 48.24
- type: mrr_at_3
value: 44.989000000000004
- type: mrr_at_5
value: 46.403
- type: ndcg_at_1
value: 38.471
- type: ndcg_at_10
value: 48.022999999999996
- type: ndcg_at_100
value: 52.32599999999999
- type: ndcg_at_1000
value: 54.26
- type: ndcg_at_3
value: 42.986999999999995
- type: ndcg_at_5
value: 45.23
- type: precision_at_1
value: 38.471
- type: precision_at_10
value: 9.248000000000001
- type: precision_at_100
value: 1.469
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 20.892
- type: precision_at_5
value: 14.892
- type: recall_at_1
value: 30.731
- type: recall_at_10
value: 59.561
- type: recall_at_100
value: 77.637
- type: recall_at_1000
value: 89.64999999999999
- type: recall_at_3
value: 44.897999999999996
- type: recall_at_5
value: 51.181
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 34.949000000000005
- type: map_at_10
value: 48.117
- type: map_at_100
value: 49.355
- type: map_at_1000
value: 49.409
- type: map_at_3
value: 44.732
- type: map_at_5
value: 46.555
- type: mrr_at_1
value: 40.188
- type: mrr_at_10
value: 51.452
- type: mrr_at_100
value: 52.219
- type: mrr_at_1000
value: 52.24100000000001
- type: mrr_at_3
value: 48.642
- type: mrr_at_5
value: 50.134
- type: ndcg_at_1
value: 40.188
- type: ndcg_at_10
value: 54.664
- type: ndcg_at_100
value: 59.38099999999999
- type: ndcg_at_1000
value: 60.363
- type: ndcg_at_3
value: 48.684
- type: ndcg_at_5
value: 51.406
- type: precision_at_1
value: 40.188
- type: precision_at_10
value: 9.116
- type: precision_at_100
value: 1.248
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 22.236
- type: precision_at_5
value: 15.310000000000002
- type: recall_at_1
value: 34.949000000000005
- type: recall_at_10
value: 70.767
- type: recall_at_100
value: 90.79
- type: recall_at_1000
value: 97.57900000000001
- type: recall_at_3
value: 54.723
- type: recall_at_5
value: 61.404
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 25.312
- type: map_at_10
value: 34.799
- type: map_at_100
value: 35.906
- type: map_at_1000
value: 35.983
- type: map_at_3
value: 31.582
- type: map_at_5
value: 33.507999999999996
- type: mrr_at_1
value: 27.232
- type: mrr_at_10
value: 36.82
- type: mrr_at_100
value: 37.733
- type: mrr_at_1000
value: 37.791000000000004
- type: mrr_at_3
value: 33.804
- type: mrr_at_5
value: 35.606
- type: ndcg_at_1
value: 27.232
- type: ndcg_at_10
value: 40.524
- type: ndcg_at_100
value: 45.654
- type: ndcg_at_1000
value: 47.557
- type: ndcg_at_3
value: 34.312
- type: ndcg_at_5
value: 37.553
- type: precision_at_1
value: 27.232
- type: precision_at_10
value: 6.52
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.915000000000001
- type: precision_at_5
value: 10.847
- type: recall_at_1
value: 25.312
- type: recall_at_10
value: 56.169000000000004
- type: recall_at_100
value: 79.16499999999999
- type: recall_at_1000
value: 93.49300000000001
- type: recall_at_3
value: 39.5
- type: recall_at_5
value: 47.288999999999994
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 17.153
- type: map_at_10
value: 27.671
- type: map_at_100
value: 29.186
- type: map_at_1000
value: 29.299999999999997
- type: map_at_3
value: 24.490000000000002
- type: map_at_5
value: 26.178
- type: mrr_at_1
value: 21.144
- type: mrr_at_10
value: 32.177
- type: mrr_at_100
value: 33.247
- type: mrr_at_1000
value: 33.306000000000004
- type: mrr_at_3
value: 29.187
- type: mrr_at_5
value: 30.817
- type: ndcg_at_1
value: 21.144
- type: ndcg_at_10
value: 33.981
- type: ndcg_at_100
value: 40.549
- type: ndcg_at_1000
value: 43.03
- type: ndcg_at_3
value: 28.132
- type: ndcg_at_5
value: 30.721999999999998
- type: precision_at_1
value: 21.144
- type: precision_at_10
value: 6.666999999999999
- type: precision_at_100
value: 1.147
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 14.302999999999999
- type: precision_at_5
value: 10.423
- type: recall_at_1
value: 17.153
- type: recall_at_10
value: 48.591
- type: recall_at_100
value: 76.413
- type: recall_at_1000
value: 93.8
- type: recall_at_3
value: 32.329
- type: recall_at_5
value: 38.958999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 27.909
- type: map_at_10
value: 40.168
- type: map_at_100
value: 41.524
- type: map_at_1000
value: 41.626000000000005
- type: map_at_3
value: 36.274
- type: map_at_5
value: 38.411
- type: mrr_at_1
value: 34.649
- type: mrr_at_10
value: 45.613
- type: mrr_at_100
value: 46.408
- type: mrr_at_1000
value: 46.444
- type: mrr_at_3
value: 42.620999999999995
- type: mrr_at_5
value: 44.277
- type: ndcg_at_1
value: 34.649
- type: ndcg_at_10
value: 47.071000000000005
- type: ndcg_at_100
value: 52.559999999999995
- type: ndcg_at_1000
value: 54.285000000000004
- type: ndcg_at_3
value: 40.63
- type: ndcg_at_5
value: 43.584
- type: precision_at_1
value: 34.649
- type: precision_at_10
value: 8.855
- type: precision_at_100
value: 1.361
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 19.538
- type: precision_at_5
value: 14.187
- type: recall_at_1
value: 27.909
- type: recall_at_10
value: 62.275000000000006
- type: recall_at_100
value: 84.95
- type: recall_at_1000
value: 96.02000000000001
- type: recall_at_3
value: 44.767
- type: recall_at_5
value: 52.03
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 25.846000000000004
- type: map_at_10
value: 36.870999999999995
- type: map_at_100
value: 38.294
- type: map_at_1000
value: 38.401
- type: map_at_3
value: 33.163
- type: map_at_5
value: 35.177
- type: mrr_at_1
value: 31.849
- type: mrr_at_10
value: 41.681000000000004
- type: mrr_at_100
value: 42.658
- type: mrr_at_1000
value: 42.71
- type: mrr_at_3
value: 39.003
- type: mrr_at_5
value: 40.436
- type: ndcg_at_1
value: 31.849
- type: ndcg_at_10
value: 43.291000000000004
- type: ndcg_at_100
value: 49.136
- type: ndcg_at_1000
value: 51.168
- type: ndcg_at_3
value: 37.297999999999995
- type: ndcg_at_5
value: 39.934
- type: precision_at_1
value: 31.849
- type: precision_at_10
value: 8.219
- type: precision_at_100
value: 1.318
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 18.151
- type: precision_at_5
value: 13.242
- type: recall_at_1
value: 25.846000000000004
- type: recall_at_10
value: 57.642
- type: recall_at_100
value: 82.069
- type: recall_at_1000
value: 95.684
- type: recall_at_3
value: 40.778999999999996
- type: recall_at_5
value: 47.647
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.34866666666667
- type: map_at_10
value: 35.65541666666667
- type: map_at_100
value: 36.982416666666666
- type: map_at_1000
value: 37.09416666666667
- type: map_at_3
value: 32.421499999999995
- type: map_at_5
value: 34.20266666666667
- type: mrr_at_1
value: 30.02116666666667
- type: mrr_at_10
value: 39.781666666666666
- type: mrr_at_100
value: 40.69733333333333
- type: mrr_at_1000
value: 40.74875
- type: mrr_at_3
value: 37.043083333333335
- type: mrr_at_5
value: 38.56391666666666
- type: ndcg_at_1
value: 30.02116666666667
- type: ndcg_at_10
value: 41.66133333333333
- type: ndcg_at_100
value: 47.21474999999999
- type: ndcg_at_1000
value: 49.29600000000001
- type: ndcg_at_3
value: 36.06958333333334
- type: ndcg_at_5
value: 38.66858333333333
- type: precision_at_1
value: 30.02116666666667
- type: precision_at_10
value: 7.497249999999999
- type: precision_at_100
value: 1.2044166666666667
- type: precision_at_1000
value: 0.15766666666666665
- type: precision_at_3
value: 16.83458333333333
- type: precision_at_5
value: 12.134
- type: recall_at_1
value: 25.34866666666667
- type: recall_at_10
value: 55.40541666666666
- type: recall_at_100
value: 79.38683333333333
- type: recall_at_1000
value: 93.50958333333334
- type: recall_at_3
value: 39.99858333333334
- type: recall_at_5
value: 46.55741666666666
- type: map_at_1
value: 18.336
- type: map_at_10
value: 26.811
- type: map_at_100
value: 27.892
- type: map_at_1000
value: 27.986
- type: map_at_3
value: 23.976
- type: map_at_5
value: 25.605
- type: mrr_at_1
value: 20.148
- type: mrr_at_10
value: 28.898000000000003
- type: mrr_at_100
value: 29.866
- type: mrr_at_1000
value: 29.929
- type: mrr_at_3
value: 26.247999999999998
- type: mrr_at_5
value: 27.744999999999997
- type: ndcg_at_1
value: 20.148
- type: ndcg_at_10
value: 32.059
- type: ndcg_at_100
value: 37.495
- type: ndcg_at_1000
value: 39.855000000000004
- type: ndcg_at_3
value: 26.423000000000002
- type: ndcg_at_5
value: 29.212
- type: precision_at_1
value: 20.148
- type: precision_at_10
value: 5.268
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 11.459999999999999
- type: precision_at_5
value: 8.503
- type: recall_at_1
value: 18.336
- type: recall_at_10
value: 46.411
- type: recall_at_100
value: 71.33500000000001
- type: recall_at_1000
value: 88.895
- type: recall_at_3
value: 31.134
- type: recall_at_5
value: 37.862
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.102000000000004
- type: map_at_10
value: 33.31
- type: map_at_100
value: 34.443
- type: map_at_1000
value: 34.547
- type: map_at_3
value: 30.932
- type: map_at_5
value: 32.126
- type: mrr_at_1
value: 28.221
- type: mrr_at_10
value: 36.519
- type: mrr_at_100
value: 37.425000000000004
- type: mrr_at_1000
value: 37.498
- type: mrr_at_3
value: 34.254
- type: mrr_at_5
value: 35.388999999999996
- type: ndcg_at_1
value: 28.221
- type: ndcg_at_10
value: 38.340999999999994
- type: ndcg_at_100
value: 43.572
- type: ndcg_at_1000
value: 45.979
- type: ndcg_at_3
value: 33.793
- type: ndcg_at_5
value: 35.681000000000004
- type: precision_at_1
value: 28.221
- type: precision_at_10
value: 6.135
- type: precision_at_100
value: 0.946
- type: precision_at_1000
value: 0.123
- type: precision_at_3
value: 14.519000000000002
- type: precision_at_5
value: 9.969
- type: recall_at_1
value: 25.102000000000004
- type: recall_at_10
value: 50.639
- type: recall_at_100
value: 74.075
- type: recall_at_1000
value: 91.393
- type: recall_at_3
value: 37.952000000000005
- type: recall_at_5
value: 42.71
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.618000000000002
- type: map_at_10
value: 26.714
- type: map_at_100
value: 27.929
- type: map_at_1000
value: 28.057
- type: map_at_3
value: 24.134
- type: map_at_5
value: 25.575
- type: mrr_at_1
value: 22.573999999999998
- type: mrr_at_10
value: 30.786
- type: mrr_at_100
value: 31.746000000000002
- type: mrr_at_1000
value: 31.822
- type: mrr_at_3
value: 28.412
- type: mrr_at_5
value: 29.818
- type: ndcg_at_1
value: 22.573999999999998
- type: ndcg_at_10
value: 31.852000000000004
- type: ndcg_at_100
value: 37.477
- type: ndcg_at_1000
value: 40.331
- type: ndcg_at_3
value: 27.314
- type: ndcg_at_5
value: 29.485
- type: precision_at_1
value: 22.573999999999998
- type: precision_at_10
value: 5.86
- type: precision_at_100
value: 1.012
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 13.099
- type: precision_at_5
value: 9.56
- type: recall_at_1
value: 18.618000000000002
- type: recall_at_10
value: 43.134
- type: recall_at_100
value: 68.294
- type: recall_at_1000
value: 88.283
- type: recall_at_3
value: 30.397999999999996
- type: recall_at_5
value: 35.998000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 27.76
- type: map_at_10
value: 37.569
- type: map_at_100
value: 38.784
- type: map_at_1000
value: 38.884
- type: map_at_3
value: 34.379
- type: map_at_5
value: 36.092999999999996
- type: mrr_at_1
value: 32.556000000000004
- type: mrr_at_10
value: 41.870000000000005
- type: mrr_at_100
value: 42.759
- type: mrr_at_1000
value: 42.806
- type: mrr_at_3
value: 39.086
- type: mrr_at_5
value: 40.574
- type: ndcg_at_1
value: 32.556000000000004
- type: ndcg_at_10
value: 43.382
- type: ndcg_at_100
value: 48.943
- type: ndcg_at_1000
value: 50.961999999999996
- type: ndcg_at_3
value: 37.758
- type: ndcg_at_5
value: 40.282000000000004
- type: precision_at_1
value: 32.556000000000004
- type: precision_at_10
value: 7.463
- type: precision_at_100
value: 1.1480000000000001
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 17.133000000000003
- type: precision_at_5
value: 12.164
- type: recall_at_1
value: 27.76
- type: recall_at_10
value: 56.71000000000001
- type: recall_at_100
value: 81.053
- type: recall_at_1000
value: 94.75
- type: recall_at_3
value: 41.387
- type: recall_at_5
value: 47.818
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 23.62
- type: map_at_10
value: 33.522999999999996
- type: map_at_100
value: 35.281
- type: map_at_1000
value: 35.504000000000005
- type: map_at_3
value: 30.314999999999998
- type: map_at_5
value: 32.065
- type: mrr_at_1
value: 28.458
- type: mrr_at_10
value: 38.371
- type: mrr_at_100
value: 39.548
- type: mrr_at_1000
value: 39.601
- type: mrr_at_3
value: 35.638999999999996
- type: mrr_at_5
value: 37.319
- type: ndcg_at_1
value: 28.458
- type: ndcg_at_10
value: 39.715
- type: ndcg_at_100
value: 46.394999999999996
- type: ndcg_at_1000
value: 48.943999999999996
- type: ndcg_at_3
value: 34.361999999999995
- type: ndcg_at_5
value: 37.006
- type: precision_at_1
value: 28.458
- type: precision_at_10
value: 7.5889999999999995
- type: precision_at_100
value: 1.514
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.073999999999998
- type: precision_at_5
value: 11.976
- type: recall_at_1
value: 23.62
- type: recall_at_10
value: 52.117000000000004
- type: recall_at_100
value: 81.097
- type: recall_at_1000
value: 96.47
- type: recall_at_3
value: 37.537
- type: recall_at_5
value: 44.112
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 21.149
- type: map_at_10
value: 35.251
- type: map_at_100
value: 37.342
- type: map_at_1000
value: 37.516
- type: map_at_3
value: 30.543
- type: map_at_5
value: 33.19
- type: mrr_at_1
value: 47.687000000000005
- type: mrr_at_10
value: 59.391000000000005
- type: mrr_at_100
value: 59.946999999999996
- type: mrr_at_1000
value: 59.965999999999994
- type: mrr_at_3
value: 56.938
- type: mrr_at_5
value: 58.498000000000005
- type: ndcg_at_1
value: 47.687000000000005
- type: ndcg_at_10
value: 45.381
- type: ndcg_at_100
value: 52.405
- type: ndcg_at_1000
value: 55.041
- type: ndcg_at_3
value: 40.024
- type: ndcg_at_5
value: 41.821999999999996
- type: precision_at_1
value: 47.687000000000005
- type: precision_at_10
value: 13.355
- type: precision_at_100
value: 2.113
- type: precision_at_1000
value: 0.261
- type: precision_at_3
value: 29.793999999999997
- type: precision_at_5
value: 21.811
- type: recall_at_1
value: 21.149
- type: recall_at_10
value: 49.937
- type: recall_at_100
value: 73.382
- type: recall_at_1000
value: 87.606
- type: recall_at_3
value: 35.704
- type: recall_at_5
value: 42.309000000000005
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 28.74
- type: map_at_10
value: 41.981
- type: map_at_100
value: 43.753
- type: map_at_1000
value: 43.858999999999995
- type: map_at_3
value: 37.634
- type: map_at_5
value: 40.158
- type: mrr_at_1
value: 43.086
- type: mrr_at_10
value: 51.249
- type: mrr_at_100
value: 52.154
- type: mrr_at_1000
value: 52.190999999999995
- type: mrr_at_3
value: 48.787000000000006
- type: mrr_at_5
value: 50.193
- type: ndcg_at_1
value: 43.086
- type: ndcg_at_10
value: 48.703
- type: ndcg_at_100
value: 55.531
- type: ndcg_at_1000
value: 57.267999999999994
- type: ndcg_at_3
value: 43.464000000000006
- type: ndcg_at_5
value: 45.719
- type: precision_at_1
value: 43.086
- type: precision_at_10
value: 10.568
- type: precision_at_100
value: 1.616
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 24.256
- type: precision_at_5
value: 17.509
- type: recall_at_1
value: 28.74
- type: recall_at_10
value: 59.349
- type: recall_at_100
value: 87.466
- type: recall_at_1000
value: 98.914
- type: recall_at_3
value: 43.322
- type: recall_at_5
value: 50.409000000000006
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 79.03788334335539
- type: cos_sim_ap
value: 87.21703260472833
- type: cos_sim_f1
value: 79.87784187309127
- type: cos_sim_precision
value: 77.36634531113059
- type: cos_sim_recall
value: 82.55786766425064
- type: dot_accuracy
value: 79.03788334335539
- type: dot_ap
value: 87.22906528217948
- type: dot_f1
value: 79.87784187309127
- type: dot_precision
value: 77.36634531113059
- type: dot_recall
value: 82.55786766425064
- type: euclidean_accuracy
value: 79.03788334335539
- type: euclidean_ap
value: 87.21703670465753
- type: euclidean_f1
value: 79.87784187309127
- type: euclidean_precision
value: 77.36634531113059
- type: euclidean_recall
value: 82.55786766425064
- type: manhattan_accuracy
value: 78.28021647624774
- type: manhattan_ap
value: 86.66244127855394
- type: manhattan_f1
value: 79.24485643228577
- type: manhattan_precision
value: 76.71262858393521
- type: manhattan_recall
value: 81.94996492868833
- type: max_accuracy
value: 79.03788334335539
- type: max_ap
value: 87.22906528217948
- type: max_f1
value: 79.87784187309127
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 67.597
- type: map_at_10
value: 75.81599999999999
- type: map_at_100
value: 76.226
- type: map_at_1000
value: 76.23100000000001
- type: map_at_3
value: 73.907
- type: map_at_5
value: 75.08200000000001
- type: mrr_at_1
value: 67.756
- type: mrr_at_10
value: 75.8
- type: mrr_at_100
value: 76.205
- type: mrr_at_1000
value: 76.21
- type: mrr_at_3
value: 73.955
- type: mrr_at_5
value: 75.093
- type: ndcg_at_1
value: 67.756
- type: ndcg_at_10
value: 79.598
- type: ndcg_at_100
value: 81.34400000000001
- type: ndcg_at_1000
value: 81.477
- type: ndcg_at_3
value: 75.876
- type: ndcg_at_5
value: 77.94200000000001
- type: precision_at_1
value: 67.756
- type: precision_at_10
value: 9.231
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 27.362
- type: precision_at_5
value: 17.45
- type: recall_at_1
value: 67.597
- type: recall_at_10
value: 91.307
- type: recall_at_100
value: 98.946
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 81.428
- type: recall_at_5
value: 86.407
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.33
- type: map_at_10
value: 23.118
- type: map_at_100
value: 34.28
- type: map_at_1000
value: 36.574
- type: map_at_3
value: 15.576
- type: map_at_5
value: 18.778
- type: mrr_at_1
value: 75.25
- type: mrr_at_10
value: 81.958
- type: mrr_at_100
value: 82.282
- type: mrr_at_1000
value: 82.285
- type: mrr_at_3
value: 81.042
- type: mrr_at_5
value: 81.62899999999999
- type: ndcg_at_1
value: 63.625
- type: ndcg_at_10
value: 50.781
- type: ndcg_at_100
value: 55.537000000000006
- type: ndcg_at_1000
value: 62.651
- type: ndcg_at_3
value: 55.297
- type: ndcg_at_5
value: 53.103
- type: precision_at_1
value: 75.25
- type: precision_at_10
value: 41.475
- type: precision_at_100
value: 13.5
- type: precision_at_1000
value: 2.686
- type: precision_at_3
value: 59.333000000000006
- type: precision_at_5
value: 51.9
- type: recall_at_1
value: 9.33
- type: recall_at_10
value: 29.398000000000003
- type: recall_at_100
value: 61.951
- type: recall_at_1000
value: 85.463
- type: recall_at_3
value: 17.267
- type: recall_at_5
value: 21.89
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 25.608999999999998
- type: map_at_10
value: 78.649
- type: map_at_100
value: 81.67699999999999
- type: map_at_1000
value: 81.71000000000001
- type: map_at_3
value: 54.112
- type: map_at_5
value: 68.34700000000001
- type: mrr_at_1
value: 87.75
- type: mrr_at_10
value: 92.175
- type: mrr_at_100
value: 92.225
- type: mrr_at_1000
value: 92.227
- type: mrr_at_3
value: 91.833
- type: mrr_at_5
value: 92.06800000000001
- type: ndcg_at_1
value: 87.75
- type: ndcg_at_10
value: 86.56700000000001
- type: ndcg_at_100
value: 89.519
- type: ndcg_at_1000
value: 89.822
- type: ndcg_at_3
value: 84.414
- type: ndcg_at_5
value: 83.721
- type: precision_at_1
value: 87.75
- type: precision_at_10
value: 41.665
- type: precision_at_100
value: 4.827
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 75.533
- type: precision_at_5
value: 64.01
- type: recall_at_1
value: 25.608999999999998
- type: recall_at_10
value: 88.708
- type: recall_at_100
value: 98.007
- type: recall_at_1000
value: 99.555
- type: recall_at_3
value: 57.157000000000004
- type: recall_at_5
value: 74.118
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 55.800000000000004
- type: map_at_10
value: 65.952
- type: map_at_100
value: 66.413
- type: map_at_1000
value: 66.426
- type: map_at_3
value: 63.3
- type: map_at_5
value: 64.945
- type: mrr_at_1
value: 55.800000000000004
- type: mrr_at_10
value: 65.952
- type: mrr_at_100
value: 66.413
- type: mrr_at_1000
value: 66.426
- type: mrr_at_3
value: 63.3
- type: mrr_at_5
value: 64.945
- type: ndcg_at_1
value: 55.800000000000004
- type: ndcg_at_10
value: 71.00800000000001
- type: ndcg_at_100
value: 72.974
- type: ndcg_at_1000
value: 73.302
- type: ndcg_at_3
value: 65.669
- type: ndcg_at_5
value: 68.634
- type: precision_at_1
value: 55.800000000000004
- type: precision_at_10
value: 8.690000000000001
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 24.166999999999998
- type: precision_at_5
value: 15.939999999999998
- type: recall_at_1
value: 55.800000000000004
- type: recall_at_10
value: 86.9
- type: recall_at_100
value: 95.5
- type: recall_at_1000
value: 98.0
- type: recall_at_3
value: 72.5
- type: recall_at_5
value: 79.7
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 67.39500000000001
- type: f1
value: 62.01837785021389
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 86.27
- type: map_at_10
value: 92.163
- type: map_at_100
value: 92.351
- type: map_at_1000
value: 92.36
- type: map_at_3
value: 91.36
- type: map_at_5
value: 91.888
- type: mrr_at_1
value: 92.72399999999999
- type: mrr_at_10
value: 95.789
- type: mrr_at_100
value: 95.80300000000001
- type: mrr_at_1000
value: 95.804
- type: mrr_at_3
value: 95.64200000000001
- type: mrr_at_5
value: 95.75
- type: ndcg_at_1
value: 92.72399999999999
- type: ndcg_at_10
value: 94.269
- type: ndcg_at_100
value: 94.794
- type: ndcg_at_1000
value: 94.94
- type: ndcg_at_3
value: 93.427
- type: ndcg_at_5
value: 93.914
- type: precision_at_1
value: 92.72399999999999
- type: precision_at_10
value: 11.007
- type: precision_at_100
value: 1.153
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 34.993
- type: precision_at_5
value: 21.542
- type: recall_at_1
value: 86.27
- type: recall_at_10
value: 97.031
- type: recall_at_100
value: 98.839
- type: recall_at_1000
value: 99.682
- type: recall_at_3
value: 94.741
- type: recall_at_5
value: 96.03
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 29.561999999999998
- type: map_at_10
value: 48.52
- type: map_at_100
value: 50.753
- type: map_at_1000
value: 50.878
- type: map_at_3
value: 42.406
- type: map_at_5
value: 45.994
- type: mrr_at_1
value: 54.784
- type: mrr_at_10
value: 64.51400000000001
- type: mrr_at_100
value: 65.031
- type: mrr_at_1000
value: 65.05199999999999
- type: mrr_at_3
value: 62.474
- type: mrr_at_5
value: 63.562
- type: ndcg_at_1
value: 54.784
- type: ndcg_at_10
value: 57.138
- type: ndcg_at_100
value: 63.666999999999994
- type: ndcg_at_1000
value: 65.379
- type: ndcg_at_3
value: 52.589
- type: ndcg_at_5
value: 54.32599999999999
- type: precision_at_1
value: 54.784
- type: precision_at_10
value: 15.693999999999999
- type: precision_at_100
value: 2.259
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 34.774
- type: precision_at_5
value: 25.772000000000002
- type: recall_at_1
value: 29.561999999999998
- type: recall_at_10
value: 64.708
- type: recall_at_100
value: 87.958
- type: recall_at_1000
value: 97.882
- type: recall_at_3
value: 48.394
- type: recall_at_5
value: 56.101
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 43.72
- type: map_at_10
value: 71.905
- type: map_at_100
value: 72.685
- type: map_at_1000
value: 72.72800000000001
- type: map_at_3
value: 68.538
- type: map_at_5
value: 70.675
- type: mrr_at_1
value: 87.441
- type: mrr_at_10
value: 91.432
- type: mrr_at_100
value: 91.512
- type: mrr_at_1000
value: 91.513
- type: mrr_at_3
value: 90.923
- type: mrr_at_5
value: 91.252
- type: ndcg_at_1
value: 87.441
- type: ndcg_at_10
value: 79.212
- type: ndcg_at_100
value: 81.694
- type: ndcg_at_1000
value: 82.447
- type: ndcg_at_3
value: 74.746
- type: ndcg_at_5
value: 77.27199999999999
- type: precision_at_1
value: 87.441
- type: precision_at_10
value: 16.42
- type: precision_at_100
value: 1.833
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 48.184
- type: precision_at_5
value: 30.897999999999996
- type: recall_at_1
value: 43.72
- type: recall_at_10
value: 82.1
- type: recall_at_100
value: 91.62700000000001
- type: recall_at_1000
value: 96.556
- type: recall_at_3
value: 72.275
- type: recall_at_5
value: 77.24499999999999
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.520969603693736
- type: f1
value: 42.359043311419626
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.72559999999999
- type: ap
value: 95.01759461773742
- type: f1
value: 96.72429945397575
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 90.1688555347092
- type: ap
value: 63.36583667477521
- type: f1
value: 85.6845016521436
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 67.35114066823127
- type: cos_sim_spearman
value: 72.98875207056305
- type: euclidean_pearson
value: 71.45620183630378
- type: euclidean_spearman
value: 72.98875207022671
- type: manhattan_pearson
value: 71.3845159780333
- type: manhattan_spearman
value: 72.92710990543166
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 32.68592539803807
- type: mrr
value: 31.58968253968254
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 71.242
- type: map_at_10
value: 80.01
- type: map_at_100
value: 80.269
- type: map_at_1000
value: 80.276
- type: map_at_3
value: 78.335
- type: map_at_5
value: 79.471
- type: mrr_at_1
value: 73.668
- type: mrr_at_10
value: 80.515
- type: mrr_at_100
value: 80.738
- type: mrr_at_1000
value: 80.744
- type: mrr_at_3
value: 79.097
- type: mrr_at_5
value: 80.045
- type: ndcg_at_1
value: 73.668
- type: ndcg_at_10
value: 83.357
- type: ndcg_at_100
value: 84.442
- type: ndcg_at_1000
value: 84.619
- type: ndcg_at_3
value: 80.286
- type: ndcg_at_5
value: 82.155
- type: precision_at_1
value: 73.668
- type: precision_at_10
value: 9.905
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.024
- type: precision_at_5
value: 19.017
- type: recall_at_1
value: 71.242
- type: recall_at_10
value: 93.11
- type: recall_at_100
value: 97.85000000000001
- type: recall_at_1000
value: 99.21900000000001
- type: recall_at_3
value: 85.137
- type: recall_at_5
value: 89.548
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.006999999999998
- type: map_at_10
value: 34.994
- type: map_at_100
value: 36.183
- type: map_at_1000
value: 36.227
- type: map_at_3
value: 30.75
- type: map_at_5
value: 33.155
- type: mrr_at_1
value: 22.679
- type: mrr_at_10
value: 35.619
- type: mrr_at_100
value: 36.732
- type: mrr_at_1000
value: 36.77
- type: mrr_at_3
value: 31.44
- type: mrr_at_5
value: 33.811
- type: ndcg_at_1
value: 22.679
- type: ndcg_at_10
value: 42.376000000000005
- type: ndcg_at_100
value: 48.001
- type: ndcg_at_1000
value: 49.059999999999995
- type: ndcg_at_3
value: 33.727000000000004
- type: ndcg_at_5
value: 38.013000000000005
- type: precision_at_1
value: 22.679
- type: precision_at_10
value: 6.815
- type: precision_at_100
value: 0.962
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.441
- type: precision_at_5
value: 10.817
- type: recall_at_1
value: 22.006999999999998
- type: recall_at_10
value: 65.158
- type: recall_at_100
value: 90.997
- type: recall_at_1000
value: 98.996
- type: recall_at_3
value: 41.646
- type: recall_at_5
value: 51.941
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 97.55129958960327
- type: f1
value: 97.43464802675416
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 90.4719562243502
- type: f1
value: 70.76460034443902
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 83.49024882313383
- type: f1
value: 81.44067057564666
- 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: 79.88231338264963
- type: f1
value: 77.13536609019927
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 87.23268325487558
- type: f1
value: 86.36737921996752
- 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: 84.50571620712844
- type: f1
value: 83.4128768262944
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 56.89999999999999
- type: map_at_10
value: 63.438
- type: map_at_100
value: 63.956
- type: map_at_1000
value: 63.991
- type: map_at_3
value: 61.983
- type: map_at_5
value: 62.778
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 63.483000000000004
- type: mrr_at_100
value: 63.993
- type: mrr_at_1000
value: 64.02799999999999
- type: mrr_at_3
value: 62.017
- type: mrr_at_5
value: 62.812
- type: ndcg_at_1
value: 56.89999999999999
- type: ndcg_at_10
value: 66.61
- type: ndcg_at_100
value: 69.387
- type: ndcg_at_1000
value: 70.327
- type: ndcg_at_3
value: 63.583999999999996
- type: ndcg_at_5
value: 65.0
- type: precision_at_1
value: 56.89999999999999
- type: precision_at_10
value: 7.66
- type: precision_at_100
value: 0.902
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 22.733
- type: precision_at_5
value: 14.32
- type: recall_at_1
value: 56.89999999999999
- type: recall_at_10
value: 76.6
- type: recall_at_100
value: 90.2
- type: recall_at_1000
value: 97.6
- type: recall_at_3
value: 68.2
- type: recall_at_5
value: 71.6
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 40.32149153753394
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 39.40319973495386
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.9769104898534
- type: mrr
value: 35.32831430710564
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 81.80666666666667
- type: f1
value: 81.83278699395508
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.3
- type: map_at_10
value: 14.151
- type: map_at_100
value: 18.455
- type: map_at_1000
value: 20.186999999999998
- type: map_at_3
value: 10.023
- type: map_at_5
value: 11.736
- type: mrr_at_1
value: 49.536
- type: mrr_at_10
value: 58.516
- type: mrr_at_100
value: 59.084
- type: mrr_at_1000
value: 59.114
- type: mrr_at_3
value: 56.45
- type: mrr_at_5
value: 57.642
- type: ndcg_at_1
value: 47.522999999999996
- type: ndcg_at_10
value: 38.4
- type: ndcg_at_100
value: 35.839999999999996
- type: ndcg_at_1000
value: 44.998
- type: ndcg_at_3
value: 43.221
- type: ndcg_at_5
value: 40.784
- type: precision_at_1
value: 49.536
- type: precision_at_10
value: 28.977999999999998
- type: precision_at_100
value: 9.378
- type: precision_at_1000
value: 2.2769999999999997
- type: precision_at_3
value: 40.454
- type: precision_at_5
value: 35.418
- type: recall_at_1
value: 6.3
- type: recall_at_10
value: 19.085
- type: recall_at_100
value: 38.18
- type: recall_at_1000
value: 71.219
- type: recall_at_3
value: 11.17
- type: recall_at_5
value: 13.975999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 43.262
- type: map_at_10
value: 60.387
- type: map_at_100
value: 61.102000000000004
- type: map_at_1000
value: 61.111000000000004
- type: map_at_3
value: 56.391999999999996
- type: map_at_5
value: 58.916000000000004
- type: mrr_at_1
value: 48.725
- type: mrr_at_10
value: 62.812999999999995
- type: mrr_at_100
value: 63.297000000000004
- type: mrr_at_1000
value: 63.304
- type: mrr_at_3
value: 59.955999999999996
- type: mrr_at_5
value: 61.785999999999994
- type: ndcg_at_1
value: 48.696
- type: ndcg_at_10
value: 67.743
- type: ndcg_at_100
value: 70.404
- type: ndcg_at_1000
value: 70.60600000000001
- type: ndcg_at_3
value: 60.712999999999994
- type: ndcg_at_5
value: 64.693
- type: precision_at_1
value: 48.696
- type: precision_at_10
value: 10.513
- type: precision_at_100
value: 1.196
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 27.221
- type: precision_at_5
value: 18.701999999999998
- type: recall_at_1
value: 43.262
- type: recall_at_10
value: 87.35300000000001
- type: recall_at_100
value: 98.31299999999999
- type: recall_at_1000
value: 99.797
- type: recall_at_3
value: 69.643
- type: recall_at_5
value: 78.645
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 72.65836491608013
- type: cos_sim_ap
value: 78.75807247519593
- type: cos_sim_f1
value: 74.84662576687117
- type: cos_sim_precision
value: 63.97003745318352
- type: cos_sim_recall
value: 90.17951425554382
- type: dot_accuracy
value: 72.65836491608013
- type: dot_ap
value: 78.75807247519593
- type: dot_f1
value: 74.84662576687117
- type: dot_precision
value: 63.97003745318352
- type: dot_recall
value: 90.17951425554382
- type: euclidean_accuracy
value: 72.65836491608013
- type: euclidean_ap
value: 78.75807247519593
- type: euclidean_f1
value: 74.84662576687117
- type: euclidean_precision
value: 63.97003745318352
- type: euclidean_recall
value: 90.17951425554382
- type: manhattan_accuracy
value: 72.00866269626421
- type: manhattan_ap
value: 78.34663376353235
- type: manhattan_f1
value: 74.13234613604813
- type: manhattan_precision
value: 65.98023064250413
- type: manhattan_recall
value: 84.58289334741288
- type: max_accuracy
value: 72.65836491608013
- type: max_ap
value: 78.75807247519593
- type: max_f1
value: 74.84662576687117
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 94.46999999999998
- type: ap
value: 93.56401511160975
- type: f1
value: 94.46692790889986
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 46.851404503762474
- type: cos_sim_spearman
value: 52.74603680597415
- type: euclidean_pearson
value: 51.596358967977295
- type: euclidean_spearman
value: 52.74603680597415
- type: manhattan_pearson
value: 51.81838023379299
- type: manhattan_spearman
value: 52.79611669731429
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 31.928376136347016
- type: cos_sim_spearman
value: 34.38497204533162
- type: euclidean_pearson
value: 32.658432953090674
- type: euclidean_spearman
value: 34.38497204533162
- type: manhattan_pearson
value: 32.887190283203054
- type: manhattan_spearman
value: 34.69496960849327
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.952
- type: map_at_10
value: 84.134
- type: map_at_100
value: 84.795
- type: map_at_1000
value: 84.809
- type: map_at_3
value: 81.085
- type: map_at_5
value: 82.976
- type: mrr_at_1
value: 80.56
- type: mrr_at_10
value: 87.105
- type: mrr_at_100
value: 87.20700000000001
- type: mrr_at_1000
value: 87.208
- type: mrr_at_3
value: 86.118
- type: mrr_at_5
value: 86.79299999999999
- type: ndcg_at_1
value: 80.57
- type: ndcg_at_10
value: 88.047
- type: ndcg_at_100
value: 89.266
- type: ndcg_at_1000
value: 89.34299999999999
- type: ndcg_at_3
value: 85.052
- type: ndcg_at_5
value: 86.68299999999999
- type: precision_at_1
value: 80.57
- type: precision_at_10
value: 13.439
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.283
- type: precision_at_5
value: 24.558
- type: recall_at_1
value: 69.952
- type: recall_at_10
value: 95.599
- type: recall_at_100
value: 99.67099999999999
- type: recall_at_1000
value: 99.983
- type: recall_at_3
value: 87.095
- type: recall_at_5
value: 91.668
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 70.12802769698337
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 71.19047621740276
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.208
- type: map_at_10
value: 17.036
- type: map_at_100
value: 20.162
- type: map_at_1000
value: 20.552
- type: map_at_3
value: 11.591999999999999
- type: map_at_5
value: 14.349
- type: mrr_at_1
value: 30.599999999999998
- type: mrr_at_10
value: 43.325
- type: mrr_at_100
value: 44.281
- type: mrr_at_1000
value: 44.31
- type: mrr_at_3
value: 39.300000000000004
- type: mrr_at_5
value: 41.730000000000004
- type: ndcg_at_1
value: 30.599999999999998
- type: ndcg_at_10
value: 27.378000000000004
- type: ndcg_at_100
value: 37.768
- type: ndcg_at_1000
value: 43.275000000000006
- type: ndcg_at_3
value: 25.167
- type: ndcg_at_5
value: 22.537
- type: precision_at_1
value: 30.599999999999998
- type: precision_at_10
value: 14.46
- type: precision_at_100
value: 2.937
- type: precision_at_1000
value: 0.424
- type: precision_at_3
value: 23.666999999999998
- type: precision_at_5
value: 20.14
- type: recall_at_1
value: 6.208
- type: recall_at_10
value: 29.29
- type: recall_at_100
value: 59.565
- type: recall_at_1000
value: 85.963
- type: recall_at_3
value: 14.407
- type: recall_at_5
value: 20.412
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.65489797062479
- type: cos_sim_spearman
value: 75.34808277034776
- type: euclidean_pearson
value: 79.28097508609059
- type: euclidean_spearman
value: 75.3480824481771
- type: manhattan_pearson
value: 78.83529262858895
- type: manhattan_spearman
value: 74.96318170787025
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.06920163624117
- type: cos_sim_spearman
value: 77.24549887905519
- type: euclidean_pearson
value: 85.58740280635266
- type: euclidean_spearman
value: 77.24652170306867
- type: manhattan_pearson
value: 85.77917470895854
- type: manhattan_spearman
value: 77.54426264008778
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.9762185094084
- type: cos_sim_spearman
value: 80.98090253728394
- type: euclidean_pearson
value: 80.88451512135202
- type: euclidean_spearman
value: 80.98090253728394
- type: manhattan_pearson
value: 80.7606664599805
- type: manhattan_spearman
value: 80.87197716950068
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.91239166620251
- type: cos_sim_spearman
value: 76.36798509005328
- type: euclidean_pearson
value: 80.6393872615655
- type: euclidean_spearman
value: 76.36798836339655
- type: manhattan_pearson
value: 80.50765898709096
- type: manhattan_spearman
value: 76.31958999372227
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.68800355225011
- type: cos_sim_spearman
value: 84.47549220803403
- type: euclidean_pearson
value: 83.86859896384159
- type: euclidean_spearman
value: 84.47551564954756
- type: manhattan_pearson
value: 83.74201103044383
- type: manhattan_spearman
value: 84.39903759718152
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 78.24197302553398
- type: cos_sim_spearman
value: 79.44526946553684
- type: euclidean_pearson
value: 79.12747636563053
- type: euclidean_spearman
value: 79.44526946553684
- type: manhattan_pearson
value: 78.94407504115144
- type: manhattan_spearman
value: 79.24858249553934
- 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: 89.15329071763895
- type: cos_sim_spearman
value: 88.67251952242073
- type: euclidean_pearson
value: 89.16908249259637
- type: euclidean_spearman
value: 88.67251952242073
- type: manhattan_pearson
value: 89.1279735094785
- type: manhattan_spearman
value: 88.81731953658254
- 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: 69.44962535524695
- type: cos_sim_spearman
value: 71.75861316291065
- type: euclidean_pearson
value: 72.42347748883483
- type: euclidean_spearman
value: 71.75861316291065
- type: manhattan_pearson
value: 72.57545073534365
- type: manhattan_spearman
value: 71.90087671205625
- 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: 68.9945443484093
- type: cos_sim_spearman
value: 71.46807157842791
- type: euclidean_pearson
value: 69.24911748374225
- type: euclidean_spearman
value: 69.46807157842791
- type: manhattan_pearson
value: 69.65580071876552
- type: manhattan_spearman
value: 69.68775795734852
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 77.39283860361535
- type: cos_sim_spearman
value: 77.14577975930179
- type: euclidean_pearson
value: 76.64560889817044
- type: euclidean_spearman
value: 77.14577975930179
- type: manhattan_pearson
value: 76.82848456242104
- type: manhattan_spearman
value: 77.37708521460667
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.14036697885552
- type: cos_sim_spearman
value: 83.10901632378086
- type: euclidean_pearson
value: 83.59991244380554
- type: euclidean_spearman
value: 83.10901632378086
- type: manhattan_pearson
value: 83.56632266895113
- type: manhattan_spearman
value: 83.17610542379353
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 88.98026856845443
- type: mrr
value: 96.80987494712984
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 41.661
- type: map_at_10
value: 55.492
- type: map_at_100
value: 56.237
- type: map_at_1000
value: 56.255
- type: map_at_3
value: 51.05
- type: map_at_5
value: 54.01200000000001
- type: mrr_at_1
value: 44.0
- type: mrr_at_10
value: 56.443
- type: mrr_at_100
value: 57.13700000000001
- type: mrr_at_1000
value: 57.152
- type: mrr_at_3
value: 52.944
- type: mrr_at_5
value: 55.37800000000001
- type: ndcg_at_1
value: 44.0
- type: ndcg_at_10
value: 62.312999999999995
- type: ndcg_at_100
value: 65.63900000000001
- type: ndcg_at_1000
value: 66.019
- type: ndcg_at_3
value: 54.67999999999999
- type: ndcg_at_5
value: 59.284000000000006
- type: precision_at_1
value: 44.0
- type: precision_at_10
value: 9.367
- type: precision_at_100
value: 1.0999999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 22.778000000000002
- type: precision_at_5
value: 16.467000000000002
- type: recall_at_1
value: 41.661
- type: recall_at_10
value: 82.306
- type: recall_at_100
value: 97.167
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 62.461
- type: recall_at_5
value: 73.411
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.90693069306931
- type: cos_sim_ap
value: 97.86562522779887
- type: cos_sim_f1
value: 95.27162977867204
- type: cos_sim_precision
value: 95.8502024291498
- type: cos_sim_recall
value: 94.69999999999999
- type: dot_accuracy
value: 99.90693069306931
- type: dot_ap
value: 97.86562522779887
- type: dot_f1
value: 95.27162977867204
- type: dot_precision
value: 95.8502024291498
- type: dot_recall
value: 94.69999999999999
- type: euclidean_accuracy
value: 99.90693069306931
- type: euclidean_ap
value: 97.86562522779887
- type: euclidean_f1
value: 95.27162977867204
- type: euclidean_precision
value: 95.8502024291498
- type: euclidean_recall
value: 94.69999999999999
- type: manhattan_accuracy
value: 99.90693069306931
- type: manhattan_ap
value: 97.85527044211135
- type: manhattan_f1
value: 95.27638190954774
- type: manhattan_precision
value: 95.75757575757575
- type: manhattan_recall
value: 94.8
- type: max_accuracy
value: 99.90693069306931
- type: max_ap
value: 97.86562522779887
- type: max_f1
value: 95.27638190954774
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 78.89230351770412
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 47.52328347080355
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 57.74702024461137
- type: mrr
value: 58.88074548001018
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.047929797503592
- type: cos_sim_spearman
value: 29.465371781983567
- type: dot_pearson
value: 30.047927690552335
- type: dot_spearman
value: 29.465371781983567
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 66.54177017978034
- type: mrr
value: 76.76094292377299
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.608
- type: map_at_10
value: 81.266
- type: map_at_100
value: 84.714
- type: map_at_1000
value: 84.758
- type: map_at_3
value: 56.967
- type: map_at_5
value: 70.14
- type: mrr_at_1
value: 91.881
- type: mrr_at_10
value: 94.11699999999999
- type: mrr_at_100
value: 94.178
- type: mrr_at_1000
value: 94.181
- type: mrr_at_3
value: 93.772
- type: mrr_at_5
value: 93.997
- type: ndcg_at_1
value: 91.881
- type: ndcg_at_10
value: 87.954
- type: ndcg_at_100
value: 90.904
- type: ndcg_at_1000
value: 91.326
- type: ndcg_at_3
value: 88.838
- type: ndcg_at_5
value: 87.764
- type: precision_at_1
value: 91.881
- type: precision_at_10
value: 43.628
- type: precision_at_100
value: 5.082
- type: precision_at_1000
value: 0.518
- type: precision_at_3
value: 77.62400000000001
- type: precision_at_5
value: 65.269
- type: recall_at_1
value: 28.608
- type: recall_at_10
value: 87.06
- type: recall_at_100
value: 96.815
- type: recall_at_1000
value: 98.969
- type: recall_at_3
value: 58.506
- type: recall_at_5
value: 73.21600000000001
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 56.691999999999986
- type: f1
value: 54.692084702788065
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.181
- type: map_at_10
value: 1.2
- type: map_at_100
value: 6.078
- type: map_at_1000
value: 14.940000000000001
- type: map_at_3
value: 0.45599999999999996
- type: map_at_5
value: 0.692
- type: mrr_at_1
value: 66.0
- type: mrr_at_10
value: 75.819
- type: mrr_at_100
value: 76.168
- type: mrr_at_1000
value: 76.168
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 74.86699999999999
- type: ndcg_at_1
value: 59.0
- type: ndcg_at_10
value: 52.60399999999999
- type: ndcg_at_100
value: 38.049
- type: ndcg_at_1000
value: 38.576
- type: ndcg_at_3
value: 57.235
- type: ndcg_at_5
value: 56.147000000000006
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 55.2
- type: precision_at_100
value: 38.78
- type: precision_at_1000
value: 16.986
- type: precision_at_3
value: 62.666999999999994
- type: precision_at_5
value: 60.8
- type: recall_at_1
value: 0.181
- type: recall_at_10
value: 1.471
- type: recall_at_100
value: 9.748999999999999
- type: recall_at_1000
value: 37.667
- type: recall_at_3
value: 0.49300000000000005
- type: recall_at_5
value: 0.7979999999999999
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 78.68783858143624
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 77.04148998956299
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 1.936
- type: map_at_10
value: 8.942
- type: map_at_100
value: 14.475999999999999
- type: map_at_1000
value: 16.156000000000002
- type: map_at_3
value: 4.865
- type: map_at_5
value: 6.367000000000001
- type: mrr_at_1
value: 26.531
- type: mrr_at_10
value: 42.846000000000004
- type: mrr_at_100
value: 43.441
- type: mrr_at_1000
value: 43.441
- type: mrr_at_3
value: 36.735
- type: mrr_at_5
value: 40.510000000000005
- type: ndcg_at_1
value: 24.490000000000002
- type: ndcg_at_10
value: 23.262
- type: ndcg_at_100
value: 34.959
- type: ndcg_at_1000
value: 47.258
- type: ndcg_at_3
value: 25.27
- type: ndcg_at_5
value: 24.246000000000002
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 20.408
- type: precision_at_100
value: 7.306
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 24.082
- type: recall_at_1
value: 1.936
- type: recall_at_10
value: 15.712000000000002
- type: recall_at_100
value: 45.451
- type: recall_at_1000
value: 83.269
- type: recall_at_3
value: 6.442
- type: recall_at_5
value: 9.151
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 86.564
- type: ap
value: 34.58766846081731
- type: f1
value: 72.32759831978161
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 77.80418788907753
- type: f1
value: 78.1047638421972
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 59.20888659980063
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.45627943017226
- type: cos_sim_ap
value: 72.25550061847534
- type: cos_sim_f1
value: 66.0611487783037
- type: cos_sim_precision
value: 64.11720884032779
- type: cos_sim_recall
value: 68.12664907651715
- type: dot_accuracy
value: 85.45627943017226
- type: dot_ap
value: 72.25574305366213
- type: dot_f1
value: 66.0611487783037
- type: dot_precision
value: 64.11720884032779
- type: dot_recall
value: 68.12664907651715
- type: euclidean_accuracy
value: 85.45627943017226
- type: euclidean_ap
value: 72.2557084446673
- type: euclidean_f1
value: 66.0611487783037
- type: euclidean_precision
value: 64.11720884032779
- type: euclidean_recall
value: 68.12664907651715
- type: manhattan_accuracy
value: 85.32514752339513
- type: manhattan_ap
value: 71.52919143472248
- type: manhattan_f1
value: 65.60288251190322
- type: manhattan_precision
value: 64.02913840743531
- type: manhattan_recall
value: 67.25593667546174
- type: max_accuracy
value: 85.45627943017226
- type: max_ap
value: 72.25574305366213
- type: max_f1
value: 66.0611487783037
- 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: 84.58587730660244
- type: cos_sim_f1
value: 77.14170010676287
- type: cos_sim_precision
value: 73.91181657848324
- type: cos_sim_recall
value: 80.66676932553126
- type: dot_accuracy
value: 88.34167733923235
- type: dot_ap
value: 84.58585083616217
- type: dot_f1
value: 77.14170010676287
- type: dot_precision
value: 73.91181657848324
- type: dot_recall
value: 80.66676932553126
- type: euclidean_accuracy
value: 88.34167733923235
- type: euclidean_ap
value: 84.5858781355044
- type: euclidean_f1
value: 77.14170010676287
- type: euclidean_precision
value: 73.91181657848324
- type: euclidean_recall
value: 80.66676932553126
- type: manhattan_accuracy
value: 88.28152287809989
- type: manhattan_ap
value: 84.53184837110165
- type: manhattan_f1
value: 77.13582823915313
- type: manhattan_precision
value: 74.76156069364161
- type: manhattan_recall
value: 79.66584539574993
- type: max_accuracy
value: 88.34167733923235
- type: max_ap
value: 84.5858781355044
- type: max_f1
value: 77.14170010676287
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 66.10000000000001
- type: map_at_10
value: 75.238
- type: map_at_100
value: 75.559
- type: map_at_1000
value: 75.565
- type: map_at_3
value: 73.68299999999999
- type: map_at_5
value: 74.63300000000001
- type: mrr_at_1
value: 66.10000000000001
- type: mrr_at_10
value: 75.238
- type: mrr_at_100
value: 75.559
- type: mrr_at_1000
value: 75.565
- type: mrr_at_3
value: 73.68299999999999
- type: mrr_at_5
value: 74.63300000000001
- type: ndcg_at_1
value: 66.10000000000001
- type: ndcg_at_10
value: 79.25999999999999
- type: ndcg_at_100
value: 80.719
- type: ndcg_at_1000
value: 80.862
- type: ndcg_at_3
value: 76.08200000000001
- type: ndcg_at_5
value: 77.782
- type: precision_at_1
value: 66.10000000000001
- type: precision_at_10
value: 9.17
- type: precision_at_100
value: 0.983
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 27.667
- type: precision_at_5
value: 17.419999999999998
- type: recall_at_1
value: 66.10000000000001
- type: recall_at_10
value: 91.7
- type: recall_at_100
value: 98.3
- type: recall_at_1000
value: 99.4
- type: recall_at_3
value: 83.0
- type: recall_at_5
value: 87.1
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 91.13
- type: ap
value: 79.55231335947015
- type: f1
value: 89.63091922203914
---
<p align="center">
<img src="https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/raw/main/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-7B
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 | 68.41 | 64.45 |
|[`gme-Qwen2-VL-7B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | 67.48 | 71.36 | 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
model = GmeQwen2VL('Alibaba-NLP/gme-Qwen2-VL-7B-Instruct')
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',
]
# 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([0.1702, 0.5278], 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([0.2000, 0.5752], 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(0.6826, dtype=torch.float16)
```
<!-- <details>
<summary>With transformers</summary>
```python
# Requires transformers>=4.46.2
TODO
# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]
```
</details>
-->
## 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-7B-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"
] |
McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
|
McGill-NLP
|
sentence-similarity
|
[
"peft",
"safetensors",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2404.05961",
"license:mit",
"model-index",
"region:us"
] | 2024-04-30T02:45:32 | 2024-04-30T03:42:49 | 2,287 | 4 |
---
language:
- en
library_name: peft
license: mit
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- text-reranking
- feature-extraction
- sentence-similarity
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
model-index:
- name: LLM2Vec-Meta-Llama-3-unsupervised
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.70149253731343
- type: ap
value: 40.824269118508354
- type: f1
value: 70.55918234479084
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 80.6812
- type: ap
value: 76.63327889516552
- type: f1
value: 80.5276613226382
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.002
- type: f1
value: 39.67277678335084
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.173999999999996
- type: map_at_10
value: 42.548
- type: map_at_100
value: 43.492999999999995
- type: map_at_1000
value: 43.5
- type: map_at_3
value: 37.376
- type: map_at_5
value: 40.359
- type: mrr_at_1
value: 27.24
- type: mrr_at_10
value: 42.945
- type: mrr_at_100
value: 43.89
- type: mrr_at_1000
value: 43.897000000000006
- type: mrr_at_3
value: 37.779
- type: mrr_at_5
value: 40.755
- type: ndcg_at_1
value: 26.173999999999996
- type: ndcg_at_10
value: 51.731
- type: ndcg_at_100
value: 55.684999999999995
- type: ndcg_at_1000
value: 55.86
- type: ndcg_at_3
value: 41.122
- type: ndcg_at_5
value: 46.491
- type: precision_at_1
value: 26.173999999999996
- type: precision_at_10
value: 8.108
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.330000000000002
- type: precision_at_5
value: 13.001
- type: recall_at_1
value: 26.173999999999996
- type: recall_at_10
value: 81.081
- type: recall_at_100
value: 98.222
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 51.991
- type: recall_at_5
value: 65.007
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 49.215974795578546
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.71067780141813
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.15639347603191
- type: mrr
value: 71.4509959108297
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.67361609277127
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.76623376623375
- type: f1
value: 84.70041172334481
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.39251163108548
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.30501371807517
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: cqadupstack/android
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.409
- type: map_at_10
value: 36.925000000000004
- type: map_at_100
value: 38.651
- type: map_at_1000
value: 38.798
- type: map_at_3
value: 33.437
- type: map_at_5
value: 35.506
- type: mrr_at_1
value: 33.763
- type: mrr_at_10
value: 43.442
- type: mrr_at_100
value: 44.339
- type: mrr_at_1000
value: 44.391000000000005
- type: mrr_at_3
value: 40.749
- type: mrr_at_5
value: 42.408
- type: ndcg_at_1
value: 33.763
- type: ndcg_at_10
value: 43.486999999999995
- type: ndcg_at_100
value: 49.71
- type: ndcg_at_1000
value: 51.81
- type: ndcg_at_3
value: 38.586
- type: ndcg_at_5
value: 41.074
- type: precision_at_1
value: 33.763
- type: precision_at_10
value: 8.798
- type: precision_at_100
value: 1.544
- type: precision_at_1000
value: 0.21
- type: precision_at_3
value: 19.361
- type: precision_at_5
value: 14.335
- type: recall_at_1
value: 26.409
- type: recall_at_10
value: 55.352999999999994
- type: recall_at_100
value: 81.66799999999999
- type: recall_at_1000
value: 95.376
- type: recall_at_3
value: 40.304
- type: recall_at_5
value: 47.782000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: cqadupstack/english
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.6
- type: map_at_10
value: 36.42
- type: map_at_100
value: 37.628
- type: map_at_1000
value: 37.767
- type: map_at_3
value: 33.553
- type: map_at_5
value: 35.118
- type: mrr_at_1
value: 34.394999999999996
- type: mrr_at_10
value: 42.586
- type: mrr_at_100
value: 43.251
- type: mrr_at_1000
value: 43.303000000000004
- type: mrr_at_3
value: 40.297
- type: mrr_at_5
value: 41.638
- type: ndcg_at_1
value: 34.394999999999996
- type: ndcg_at_10
value: 42.05
- type: ndcg_at_100
value: 46.371
- type: ndcg_at_1000
value: 48.76
- type: ndcg_at_3
value: 37.936
- type: ndcg_at_5
value: 39.827
- type: precision_at_1
value: 34.394999999999996
- type: precision_at_10
value: 8.268
- type: precision_at_100
value: 1.355
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 18.726000000000003
- type: precision_at_5
value: 13.541
- type: recall_at_1
value: 26.6
- type: recall_at_10
value: 51.529
- type: recall_at_100
value: 70.038
- type: recall_at_1000
value: 85.67
- type: recall_at_3
value: 39.448
- type: recall_at_5
value: 44.6
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: cqadupstack/gaming
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.863000000000003
- type: map_at_10
value: 43.733
- type: map_at_100
value: 45.005
- type: map_at_1000
value: 45.074
- type: map_at_3
value: 40.593
- type: map_at_5
value: 42.272
- type: mrr_at_1
value: 37.555
- type: mrr_at_10
value: 47.532999999999994
- type: mrr_at_100
value: 48.431999999999995
- type: mrr_at_1000
value: 48.47
- type: mrr_at_3
value: 44.901
- type: mrr_at_5
value: 46.274
- type: ndcg_at_1
value: 37.555
- type: ndcg_at_10
value: 49.789
- type: ndcg_at_100
value: 55.059999999999995
- type: ndcg_at_1000
value: 56.434
- type: ndcg_at_3
value: 44.238
- type: ndcg_at_5
value: 46.698
- type: precision_at_1
value: 37.555
- type: precision_at_10
value: 8.257
- type: precision_at_100
value: 1.189
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 20.23
- type: precision_at_5
value: 13.868
- type: recall_at_1
value: 31.863000000000003
- type: recall_at_10
value: 64.188
- type: recall_at_100
value: 87.02600000000001
- type: recall_at_1000
value: 96.761
- type: recall_at_3
value: 48.986000000000004
- type: recall_at_5
value: 55.177
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: cqadupstack/gis
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.964
- type: map_at_10
value: 22.746
- type: map_at_100
value: 23.704
- type: map_at_1000
value: 23.82
- type: map_at_3
value: 20.5
- type: map_at_5
value: 21.836
- type: mrr_at_1
value: 17.740000000000002
- type: mrr_at_10
value: 24.634
- type: mrr_at_100
value: 25.535999999999998
- type: mrr_at_1000
value: 25.628
- type: mrr_at_3
value: 22.429
- type: mrr_at_5
value: 23.791
- type: ndcg_at_1
value: 17.740000000000002
- type: ndcg_at_10
value: 26.838
- type: ndcg_at_100
value: 31.985000000000003
- type: ndcg_at_1000
value: 35.289
- type: ndcg_at_3
value: 22.384
- type: ndcg_at_5
value: 24.726
- type: precision_at_1
value: 17.740000000000002
- type: precision_at_10
value: 4.35
- type: precision_at_100
value: 0.753
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 9.754999999999999
- type: precision_at_5
value: 7.164
- type: recall_at_1
value: 15.964
- type: recall_at_10
value: 37.705
- type: recall_at_100
value: 61.94499999999999
- type: recall_at_1000
value: 87.646
- type: recall_at_3
value: 25.714
- type: recall_at_5
value: 31.402
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: cqadupstack/mathematica
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.221
- type: map_at_10
value: 14.735000000000001
- type: map_at_100
value: 15.778
- type: map_at_1000
value: 15.9
- type: map_at_3
value: 12.791
- type: map_at_5
value: 13.703999999999999
- type: mrr_at_1
value: 12.438
- type: mrr_at_10
value: 18.353
- type: mrr_at_100
value: 19.285
- type: mrr_at_1000
value: 19.375
- type: mrr_at_3
value: 16.439
- type: mrr_at_5
value: 17.352999999999998
- type: ndcg_at_1
value: 12.438
- type: ndcg_at_10
value: 18.703
- type: ndcg_at_100
value: 24.104999999999997
- type: ndcg_at_1000
value: 27.366
- type: ndcg_at_3
value: 15.055
- type: ndcg_at_5
value: 16.42
- type: precision_at_1
value: 12.438
- type: precision_at_10
value: 3.818
- type: precision_at_100
value: 0.77
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 7.753
- type: precision_at_5
value: 5.622
- type: recall_at_1
value: 9.221
- type: recall_at_10
value: 27.461999999999996
- type: recall_at_100
value: 51.909000000000006
- type: recall_at_1000
value: 75.56
- type: recall_at_3
value: 17.046
- type: recall_at_5
value: 20.766000000000002
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: cqadupstack/physics
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.828
- type: map_at_10
value: 33.166000000000004
- type: map_at_100
value: 34.618
- type: map_at_1000
value: 34.744
- type: map_at_3
value: 29.737000000000002
- type: map_at_5
value: 31.541000000000004
- type: mrr_at_1
value: 29.548000000000002
- type: mrr_at_10
value: 38.582
- type: mrr_at_100
value: 39.527
- type: mrr_at_1000
value: 39.577
- type: mrr_at_3
value: 35.884
- type: mrr_at_5
value: 37.413999999999994
- type: ndcg_at_1
value: 29.548000000000002
- type: ndcg_at_10
value: 39.397
- type: ndcg_at_100
value: 45.584
- type: ndcg_at_1000
value: 47.823
- type: ndcg_at_3
value: 33.717000000000006
- type: ndcg_at_5
value: 36.223
- type: precision_at_1
value: 29.548000000000002
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.2959999999999998
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 16.747
- type: precision_at_5
value: 12.203999999999999
- type: recall_at_1
value: 22.828
- type: recall_at_10
value: 52.583999999999996
- type: recall_at_100
value: 79.06400000000001
- type: recall_at_1000
value: 93.59100000000001
- type: recall_at_3
value: 36.671
- type: recall_at_5
value: 43.22
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: cqadupstack/programmers
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.366
- type: map_at_10
value: 30.214000000000002
- type: map_at_100
value: 31.647
- type: map_at_1000
value: 31.763
- type: map_at_3
value: 27.234
- type: map_at_5
value: 28.801
- type: mrr_at_1
value: 26.256
- type: mrr_at_10
value: 35.299
- type: mrr_at_100
value: 36.284
- type: mrr_at_1000
value: 36.342
- type: mrr_at_3
value: 32.572
- type: mrr_at_5
value: 34.050999999999995
- type: ndcg_at_1
value: 26.256
- type: ndcg_at_10
value: 35.899
- type: ndcg_at_100
value: 41.983
- type: ndcg_at_1000
value: 44.481
- type: ndcg_at_3
value: 30.665
- type: ndcg_at_5
value: 32.879999999999995
- type: precision_at_1
value: 26.256
- type: precision_at_10
value: 6.804
- type: precision_at_100
value: 1.187
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 14.84
- type: precision_at_5
value: 10.708
- type: recall_at_1
value: 21.366
- type: recall_at_10
value: 47.878
- type: recall_at_100
value: 73.245
- type: recall_at_1000
value: 90.623
- type: recall_at_3
value: 33.341
- type: recall_at_5
value: 39.198
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.477166666666665
- type: map_at_10
value: 27.431416666666664
- type: map_at_100
value: 28.656000000000002
- type: map_at_1000
value: 28.787583333333338
- type: map_at_3
value: 24.85175
- type: map_at_5
value: 26.270166666666668
- type: mrr_at_1
value: 24.06841666666667
- type: mrr_at_10
value: 31.620000000000005
- type: mrr_at_100
value: 32.52283333333333
- type: mrr_at_1000
value: 32.59441666666667
- type: mrr_at_3
value: 29.328666666666663
- type: mrr_at_5
value: 30.620416666666667
- type: ndcg_at_1
value: 24.06841666666667
- type: ndcg_at_10
value: 32.404583333333335
- type: ndcg_at_100
value: 37.779500000000006
- type: ndcg_at_1000
value: 40.511583333333334
- type: ndcg_at_3
value: 27.994166666666665
- type: ndcg_at_5
value: 30.021749999999997
- type: precision_at_1
value: 24.06841666666667
- type: precision_at_10
value: 6.03725
- type: precision_at_100
value: 1.0500833333333337
- type: precision_at_1000
value: 0.14875000000000002
- type: precision_at_3
value: 13.419583333333335
- type: precision_at_5
value: 9.700666666666665
- type: recall_at_1
value: 19.477166666666665
- type: recall_at_10
value: 42.99441666666667
- type: recall_at_100
value: 66.787
- type: recall_at_1000
value: 86.18825000000001
- type: recall_at_3
value: 30.46366666666667
- type: recall_at_5
value: 35.83141666666667
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: cqadupstack/stats
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.246
- type: map_at_10
value: 22.127
- type: map_at_100
value: 23.006
- type: map_at_1000
value: 23.125
- type: map_at_3
value: 20.308999999999997
- type: map_at_5
value: 21.139
- type: mrr_at_1
value: 19.631999999999998
- type: mrr_at_10
value: 24.884999999999998
- type: mrr_at_100
value: 25.704
- type: mrr_at_1000
value: 25.793
- type: mrr_at_3
value: 23.083000000000002
- type: mrr_at_5
value: 23.942
- type: ndcg_at_1
value: 19.631999999999998
- type: ndcg_at_10
value: 25.862000000000002
- type: ndcg_at_100
value: 30.436000000000003
- type: ndcg_at_1000
value: 33.638
- type: ndcg_at_3
value: 22.431
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 19.631999999999998
- type: precision_at_10
value: 4.417
- type: precision_at_100
value: 0.7270000000000001
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.327
- type: precision_at_5
value: 7.147
- type: recall_at_1
value: 16.246
- type: recall_at_10
value: 34.869
- type: recall_at_100
value: 56.221
- type: recall_at_1000
value: 80.449
- type: recall_at_3
value: 24.83
- type: recall_at_5
value: 28.142
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: cqadupstack/tex
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.798
- type: map_at_10
value: 14.695
- type: map_at_100
value: 15.590000000000002
- type: map_at_1000
value: 15.726999999999999
- type: map_at_3
value: 13.004999999999999
- type: map_at_5
value: 13.861
- type: mrr_at_1
value: 12.939
- type: mrr_at_10
value: 18.218
- type: mrr_at_100
value: 18.998
- type: mrr_at_1000
value: 19.093
- type: mrr_at_3
value: 16.454
- type: mrr_at_5
value: 17.354
- type: ndcg_at_1
value: 12.939
- type: ndcg_at_10
value: 18.278
- type: ndcg_at_100
value: 22.709
- type: ndcg_at_1000
value: 26.064
- type: ndcg_at_3
value: 15.204
- type: ndcg_at_5
value: 16.416
- type: precision_at_1
value: 12.939
- type: precision_at_10
value: 3.768
- type: precision_at_100
value: 0.724
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 7.707999999999999
- type: precision_at_5
value: 5.733
- type: recall_at_1
value: 9.798
- type: recall_at_10
value: 25.562
- type: recall_at_100
value: 45.678999999999995
- type: recall_at_1000
value: 69.963
- type: recall_at_3
value: 16.705000000000002
- type: recall_at_5
value: 19.969
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: cqadupstack/unix
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.1
- type: map_at_10
value: 27.034999999999997
- type: map_at_100
value: 28.396
- type: map_at_1000
value: 28.518
- type: map_at_3
value: 24.363
- type: map_at_5
value: 25.826999999999998
- type: mrr_at_1
value: 23.694000000000003
- type: mrr_at_10
value: 31.724999999999998
- type: mrr_at_100
value: 32.743
- type: mrr_at_1000
value: 32.82
- type: mrr_at_3
value: 29.275000000000002
- type: mrr_at_5
value: 30.684
- type: ndcg_at_1
value: 23.694000000000003
- type: ndcg_at_10
value: 32.366
- type: ndcg_at_100
value: 38.241
- type: ndcg_at_1000
value: 40.973
- type: ndcg_at_3
value: 27.661
- type: ndcg_at_5
value: 29.782999999999998
- type: precision_at_1
value: 23.694000000000003
- type: precision_at_10
value: 5.951
- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 13.34
- type: precision_at_5
value: 9.533999999999999
- type: recall_at_1
value: 19.1
- type: recall_at_10
value: 44.032
- type: recall_at_100
value: 69.186
- type: recall_at_1000
value: 88.562
- type: recall_at_3
value: 30.712
- type: recall_at_5
value: 36.372
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: cqadupstack/webmasters
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.671
- type: map_at_10
value: 28.583
- type: map_at_100
value: 30.098999999999997
- type: map_at_1000
value: 30.364
- type: map_at_3
value: 25.825
- type: map_at_5
value: 27.500999999999998
- type: mrr_at_1
value: 25.889
- type: mrr_at_10
value: 33.617999999999995
- type: mrr_at_100
value: 34.687
- type: mrr_at_1000
value: 34.774
- type: mrr_at_3
value: 31.191999999999997
- type: mrr_at_5
value: 32.675
- type: ndcg_at_1
value: 25.889
- type: ndcg_at_10
value: 34.056999999999995
- type: ndcg_at_100
value: 40.142
- type: ndcg_at_1000
value: 43.614000000000004
- type: ndcg_at_3
value: 29.688
- type: ndcg_at_5
value: 32.057
- type: precision_at_1
value: 25.889
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.417
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.711
- type: recall_at_1
value: 20.671
- type: recall_at_10
value: 43.97
- type: recall_at_100
value: 71.83699999999999
- type: recall_at_1000
value: 94.42399999999999
- type: recall_at_3
value: 31.0
- type: recall_at_5
value: 37.489
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: cqadupstack/wordpress
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.66
- type: map_at_10
value: 18.798000000000002
- type: map_at_100
value: 19.75
- type: map_at_1000
value: 19.851
- type: map_at_3
value: 16.874
- type: map_at_5
value: 18.136
- type: mrr_at_1
value: 14.972
- type: mrr_at_10
value: 20.565
- type: mrr_at_100
value: 21.488
- type: mrr_at_1000
value: 21.567
- type: mrr_at_3
value: 18.669
- type: mrr_at_5
value: 19.861
- type: ndcg_at_1
value: 14.972
- type: ndcg_at_10
value: 22.128999999999998
- type: ndcg_at_100
value: 27.028000000000002
- type: ndcg_at_1000
value: 29.887000000000004
- type: ndcg_at_3
value: 18.365000000000002
- type: ndcg_at_5
value: 20.48
- type: precision_at_1
value: 14.972
- type: precision_at_10
value: 3.549
- type: precision_at_100
value: 0.632
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 7.887
- type: precision_at_5
value: 5.840999999999999
- type: recall_at_1
value: 13.66
- type: recall_at_10
value: 30.801000000000002
- type: recall_at_100
value: 53.626
- type: recall_at_1000
value: 75.634
- type: recall_at_3
value: 20.807000000000002
- type: recall_at_5
value: 25.86
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.622
- type: map_at_10
value: 16.042
- type: map_at_100
value: 18.023
- type: map_at_1000
value: 18.228
- type: map_at_3
value: 12.995999999999999
- type: map_at_5
value: 14.424000000000001
- type: mrr_at_1
value: 18.892999999999997
- type: mrr_at_10
value: 30.575000000000003
- type: mrr_at_100
value: 31.814999999999998
- type: mrr_at_1000
value: 31.856
- type: mrr_at_3
value: 26.851000000000003
- type: mrr_at_5
value: 29.021
- type: ndcg_at_1
value: 18.892999999999997
- type: ndcg_at_10
value: 23.575
- type: ndcg_at_100
value: 31.713
- type: ndcg_at_1000
value: 35.465
- type: ndcg_at_3
value: 18.167
- type: ndcg_at_5
value: 20.071
- type: precision_at_1
value: 18.892999999999997
- type: precision_at_10
value: 7.883
- type: precision_at_100
value: 1.652
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 13.898
- type: precision_at_5
value: 11.14
- type: recall_at_1
value: 8.622
- type: recall_at_10
value: 30.044999999999998
- type: recall_at_100
value: 58.072
- type: recall_at_1000
value: 79.226
- type: recall_at_3
value: 17.21
- type: recall_at_5
value: 22.249
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.845
- type: map_at_10
value: 12.352
- type: map_at_100
value: 17.423
- type: map_at_1000
value: 18.529
- type: map_at_3
value: 8.505
- type: map_at_5
value: 10.213
- type: mrr_at_1
value: 41.75
- type: mrr_at_10
value: 54.6
- type: mrr_at_100
value: 55.345
- type: mrr_at_1000
value: 55.374
- type: mrr_at_3
value: 52.37500000000001
- type: mrr_at_5
value: 53.87499999999999
- type: ndcg_at_1
value: 31.25
- type: ndcg_at_10
value: 26.779999999999998
- type: ndcg_at_100
value: 31.929000000000002
- type: ndcg_at_1000
value: 39.290000000000006
- type: ndcg_at_3
value: 28.746
- type: ndcg_at_5
value: 27.334999999999997
- type: precision_at_1
value: 41.75
- type: precision_at_10
value: 22.55
- type: precision_at_100
value: 7.242
- type: precision_at_1000
value: 1.439
- type: precision_at_3
value: 33.833
- type: precision_at_5
value: 28.65
- type: recall_at_1
value: 4.845
- type: recall_at_10
value: 18.664
- type: recall_at_100
value: 41.085
- type: recall_at_1000
value: 65.242
- type: recall_at_3
value: 10.572
- type: recall_at_5
value: 13.961000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.08
- type: f1
value: 42.843345856303756
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.743
- type: map_at_10
value: 46.521
- type: map_at_100
value: 47.235
- type: map_at_1000
value: 47.272
- type: map_at_3
value: 43.252
- type: map_at_5
value: 45.267
- type: mrr_at_1
value: 36.484
- type: mrr_at_10
value: 49.406
- type: mrr_at_100
value: 50.03300000000001
- type: mrr_at_1000
value: 50.058
- type: mrr_at_3
value: 46.195
- type: mrr_at_5
value: 48.193999999999996
- type: ndcg_at_1
value: 36.484
- type: ndcg_at_10
value: 53.42
- type: ndcg_at_100
value: 56.69499999999999
- type: ndcg_at_1000
value: 57.623999999999995
- type: ndcg_at_3
value: 47.010999999999996
- type: ndcg_at_5
value: 50.524
- type: precision_at_1
value: 36.484
- type: precision_at_10
value: 7.925
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 19.967
- type: precision_at_5
value: 13.87
- type: recall_at_1
value: 33.743
- type: recall_at_10
value: 71.988
- type: recall_at_100
value: 86.60799999999999
- type: recall_at_1000
value: 93.54
- type: recall_at_3
value: 54.855
- type: recall_at_5
value: 63.341
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.003
- type: map_at_10
value: 21.766
- type: map_at_100
value: 23.618
- type: map_at_1000
value: 23.832
- type: map_at_3
value: 18.282999999999998
- type: map_at_5
value: 20.267
- type: mrr_at_1
value: 26.851999999999997
- type: mrr_at_10
value: 34.658
- type: mrr_at_100
value: 35.729
- type: mrr_at_1000
value: 35.785
- type: mrr_at_3
value: 31.686999999999998
- type: mrr_at_5
value: 33.315
- type: ndcg_at_1
value: 26.851999999999997
- type: ndcg_at_10
value: 28.563
- type: ndcg_at_100
value: 36.374
- type: ndcg_at_1000
value: 40.306999999999995
- type: ndcg_at_3
value: 24.224
- type: ndcg_at_5
value: 25.939
- type: precision_at_1
value: 26.851999999999997
- type: precision_at_10
value: 8.193999999999999
- type: precision_at_100
value: 1.616
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 16.255
- type: precision_at_5
value: 12.469
- type: recall_at_1
value: 13.003
- type: recall_at_10
value: 35.689
- type: recall_at_100
value: 65.762
- type: recall_at_1000
value: 89.546
- type: recall_at_3
value: 21.820999999999998
- type: recall_at_5
value: 28.097
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.541
- type: map_at_10
value: 43.088
- type: map_at_100
value: 44.252
- type: map_at_1000
value: 44.345
- type: map_at_3
value: 39.79
- type: map_at_5
value: 41.687000000000005
- type: mrr_at_1
value: 59.082
- type: mrr_at_10
value: 67.27300000000001
- type: mrr_at_100
value: 67.708
- type: mrr_at_1000
value: 67.731
- type: mrr_at_3
value: 65.526
- type: mrr_at_5
value: 66.589
- type: ndcg_at_1
value: 59.082
- type: ndcg_at_10
value: 52.372
- type: ndcg_at_100
value: 56.725
- type: ndcg_at_1000
value: 58.665
- type: ndcg_at_3
value: 47.129
- type: ndcg_at_5
value: 49.808
- type: precision_at_1
value: 59.082
- type: precision_at_10
value: 11.275
- type: precision_at_100
value: 1.469
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 29.773
- type: precision_at_5
value: 19.980999999999998
- type: recall_at_1
value: 29.541
- type: recall_at_10
value: 56.374
- type: recall_at_100
value: 73.42999999999999
- type: recall_at_1000
value: 86.28
- type: recall_at_3
value: 44.659
- type: recall_at_5
value: 49.952999999999996
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.1904
- type: ap
value: 69.80555086826531
- type: f1
value: 74.93725389065787
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 7.085
- type: map_at_10
value: 13.344000000000001
- type: map_at_100
value: 14.501
- type: map_at_1000
value: 14.605
- type: map_at_3
value: 10.758
- type: map_at_5
value: 12.162
- type: mrr_at_1
value: 7.278
- type: mrr_at_10
value: 13.607
- type: mrr_at_100
value: 14.761
- type: mrr_at_1000
value: 14.860000000000001
- type: mrr_at_3
value: 11.003
- type: mrr_at_5
value: 12.421
- type: ndcg_at_1
value: 7.278
- type: ndcg_at_10
value: 17.473
- type: ndcg_at_100
value: 23.721
- type: ndcg_at_1000
value: 26.69
- type: ndcg_at_3
value: 12.078
- type: ndcg_at_5
value: 14.62
- type: precision_at_1
value: 7.278
- type: precision_at_10
value: 3.175
- type: precision_at_100
value: 0.639
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 5.382
- type: precision_at_5
value: 4.519
- type: recall_at_1
value: 7.085
- type: recall_at_10
value: 30.549
- type: recall_at_100
value: 60.919999999999995
- type: recall_at_1000
value: 84.372
- type: recall_at_3
value: 15.675
- type: recall_at_5
value: 21.818
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.46876424988601
- type: f1
value: 94.23159241922738
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 81.0875512995896
- type: f1
value: 61.674961674414
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.01344989912575
- type: f1
value: 71.7942527839921
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.15601882985877
- type: f1
value: 78.82502954601195
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.468806971345227
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.874332804382256
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.099340785595842
- type: mrr
value: 31.077367694660257
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.9050000000000002
- type: map_at_10
value: 8.931000000000001
- type: map_at_100
value: 11.246
- type: map_at_1000
value: 12.579
- type: map_at_3
value: 6.544
- type: map_at_5
value: 7.854
- type: mrr_at_1
value: 33.745999999999995
- type: mrr_at_10
value: 44.734
- type: mrr_at_100
value: 45.486
- type: mrr_at_1000
value: 45.534
- type: mrr_at_3
value: 42.157
- type: mrr_at_5
value: 43.813
- type: ndcg_at_1
value: 31.734
- type: ndcg_at_10
value: 26.284999999999997
- type: ndcg_at_100
value: 25.211
- type: ndcg_at_1000
value: 34.974
- type: ndcg_at_3
value: 29.918
- type: ndcg_at_5
value: 29.066
- type: precision_at_1
value: 33.745999999999995
- type: precision_at_10
value: 19.628
- type: precision_at_100
value: 6.476999999999999
- type: precision_at_1000
value: 1.976
- type: precision_at_3
value: 28.793000000000003
- type: precision_at_5
value: 25.759
- type: recall_at_1
value: 3.9050000000000002
- type: recall_at_10
value: 13.375
- type: recall_at_100
value: 28.453
- type: recall_at_1000
value: 61.67399999999999
- type: recall_at_3
value: 7.774
- type: recall_at_5
value: 10.754
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.33
- type: map_at_10
value: 30.44
- type: map_at_100
value: 31.848
- type: map_at_1000
value: 31.906000000000002
- type: map_at_3
value: 26.143
- type: map_at_5
value: 28.583
- type: mrr_at_1
value: 21.031
- type: mrr_at_10
value: 33.028
- type: mrr_at_100
value: 34.166000000000004
- type: mrr_at_1000
value: 34.208
- type: mrr_at_3
value: 29.089
- type: mrr_at_5
value: 31.362000000000002
- type: ndcg_at_1
value: 21.031
- type: ndcg_at_10
value: 37.65
- type: ndcg_at_100
value: 43.945
- type: ndcg_at_1000
value: 45.338
- type: ndcg_at_3
value: 29.256999999999998
- type: ndcg_at_5
value: 33.453
- type: precision_at_1
value: 21.031
- type: precision_at_10
value: 6.8309999999999995
- type: precision_at_100
value: 1.035
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 13.818
- type: precision_at_5
value: 10.649000000000001
- type: recall_at_1
value: 18.33
- type: recall_at_10
value: 57.330999999999996
- type: recall_at_100
value: 85.284
- type: recall_at_1000
value: 95.676
- type: recall_at_3
value: 35.356
- type: recall_at_5
value: 45.073
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.373
- type: map_at_10
value: 80.233
- type: map_at_100
value: 80.973
- type: map_at_1000
value: 80.99499999999999
- type: map_at_3
value: 77.127
- type: map_at_5
value: 79.056
- type: mrr_at_1
value: 76.55
- type: mrr_at_10
value: 83.813
- type: mrr_at_100
value: 83.96900000000001
- type: mrr_at_1000
value: 83.97200000000001
- type: mrr_at_3
value: 82.547
- type: mrr_at_5
value: 83.38600000000001
- type: ndcg_at_1
value: 76.53999999999999
- type: ndcg_at_10
value: 84.638
- type: ndcg_at_100
value: 86.28099999999999
- type: ndcg_at_1000
value: 86.459
- type: ndcg_at_3
value: 81.19
- type: ndcg_at_5
value: 83.057
- type: precision_at_1
value: 76.53999999999999
- type: precision_at_10
value: 12.928999999999998
- type: precision_at_100
value: 1.514
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.503
- type: precision_at_5
value: 23.512
- type: recall_at_1
value: 66.373
- type: recall_at_10
value: 93.273
- type: recall_at_100
value: 99.031
- type: recall_at_1000
value: 99.91799999999999
- type: recall_at_3
value: 83.55799999999999
- type: recall_at_5
value: 88.644
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 43.67174666339103
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.66838659211271
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.318
- type: map_at_10
value: 5.938000000000001
- type: map_at_100
value: 7.582
- type: map_at_1000
value: 7.936
- type: map_at_3
value: 4.208
- type: map_at_5
value: 5.098
- type: mrr_at_1
value: 11.4
- type: mrr_at_10
value: 17.655
- type: mrr_at_100
value: 19.088
- type: mrr_at_1000
value: 19.203
- type: mrr_at_3
value: 15.25
- type: mrr_at_5
value: 16.535
- type: ndcg_at_1
value: 11.4
- type: ndcg_at_10
value: 10.388
- type: ndcg_at_100
value: 18.165
- type: ndcg_at_1000
value: 24.842
- type: ndcg_at_3
value: 9.414
- type: ndcg_at_5
value: 8.453
- type: precision_at_1
value: 11.4
- type: precision_at_10
value: 5.54
- type: precision_at_100
value: 1.71
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 8.866999999999999
- type: precision_at_5
value: 7.580000000000001
- type: recall_at_1
value: 2.318
- type: recall_at_10
value: 11.267000000000001
- type: recall_at_100
value: 34.743
- type: recall_at_1000
value: 67.07300000000001
- type: recall_at_3
value: 5.408
- type: recall_at_5
value: 7.713
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 72.15850185456762
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 61.59518395985063
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 79.71131323749228
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 72.10974664733891
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 82.17899407125657
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 79.41138579273438
- 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: 85.44343473477939
- 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: 63.90264271389905
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 77.44151296326804
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 76.27597486396654
- type: mrr
value: 93.28127119793788
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.594
- type: map_at_10
value: 60.951
- type: map_at_100
value: 61.68599999999999
- type: map_at_1000
value: 61.712
- type: map_at_3
value: 57.946
- type: map_at_5
value: 59.89
- type: mrr_at_1
value: 52.666999999999994
- type: mrr_at_10
value: 62.724000000000004
- type: mrr_at_100
value: 63.269
- type: mrr_at_1000
value: 63.291
- type: mrr_at_3
value: 60.167
- type: mrr_at_5
value: 61.95
- type: ndcg_at_1
value: 52.666999999999994
- type: ndcg_at_10
value: 66.35600000000001
- type: ndcg_at_100
value: 69.463
- type: ndcg_at_1000
value: 70.111
- type: ndcg_at_3
value: 60.901
- type: ndcg_at_5
value: 64.054
- type: precision_at_1
value: 52.666999999999994
- type: precision_at_10
value: 9.0
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.333000000000002
- type: recall_at_1
value: 49.594
- type: recall_at_10
value: 81.256
- type: recall_at_100
value: 94.989
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 66.706
- type: recall_at_5
value: 74.411
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.65049504950495
- type: cos_sim_ap
value: 88.1421623503371
- type: cos_sim_f1
value: 81.44072036018008
- type: cos_sim_precision
value: 81.48148148148148
- type: cos_sim_recall
value: 81.39999999999999
- type: dot_accuracy
value: 99.37623762376238
- type: dot_ap
value: 69.87152032240303
- type: dot_f1
value: 65.64885496183206
- type: dot_precision
value: 72.18225419664267
- type: dot_recall
value: 60.199999999999996
- type: euclidean_accuracy
value: 99.63069306930693
- type: euclidean_ap
value: 86.13858297902517
- type: euclidean_f1
value: 79.87679671457904
- type: euclidean_precision
value: 82.0675105485232
- type: euclidean_recall
value: 77.8
- type: manhattan_accuracy
value: 99.63168316831683
- type: manhattan_ap
value: 86.31976532265482
- type: manhattan_f1
value: 80.10204081632654
- type: manhattan_precision
value: 81.77083333333334
- type: manhattan_recall
value: 78.5
- type: max_accuracy
value: 99.65049504950495
- type: max_ap
value: 88.1421623503371
- type: max_f1
value: 81.44072036018008
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.19604139959692
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.3569584557381
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 48.82174503355024
- type: mrr
value: 49.610933388506915
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.805895993742798
- type: cos_sim_spearman
value: 31.445431226826738
- type: dot_pearson
value: 24.441585432516867
- type: dot_spearman
value: 25.468117334810188
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.2
- type: map_at_10
value: 1.431
- type: map_at_100
value: 7.138999999999999
- type: map_at_1000
value: 17.933
- type: map_at_3
value: 0.551
- type: map_at_5
value: 0.7979999999999999
- type: mrr_at_1
value: 76.0
- type: mrr_at_10
value: 85.167
- type: mrr_at_100
value: 85.21300000000001
- type: mrr_at_1000
value: 85.21300000000001
- type: mrr_at_3
value: 84.667
- type: mrr_at_5
value: 85.167
- type: ndcg_at_1
value: 72.0
- type: ndcg_at_10
value: 63.343
- type: ndcg_at_100
value: 45.739999999999995
- type: ndcg_at_1000
value: 41.875
- type: ndcg_at_3
value: 68.162
- type: ndcg_at_5
value: 65.666
- type: precision_at_1
value: 76.0
- type: precision_at_10
value: 66.4
- type: precision_at_100
value: 46.800000000000004
- type: precision_at_1000
value: 18.996
- type: precision_at_3
value: 72.667
- type: precision_at_5
value: 68.4
- type: recall_at_1
value: 0.2
- type: recall_at_10
value: 1.712
- type: recall_at_100
value: 10.896
- type: recall_at_1000
value: 40.115
- type: recall_at_3
value: 0.594
- type: recall_at_5
value: 0.889
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.0619999999999998
- type: map_at_10
value: 5.611
- type: map_at_100
value: 8.841000000000001
- type: map_at_1000
value: 10.154
- type: map_at_3
value: 2.7720000000000002
- type: map_at_5
value: 4.181
- type: mrr_at_1
value: 14.285999999999998
- type: mrr_at_10
value: 26.249
- type: mrr_at_100
value: 28.046
- type: mrr_at_1000
value: 28.083000000000002
- type: mrr_at_3
value: 21.769
- type: mrr_at_5
value: 24.524
- type: ndcg_at_1
value: 11.224
- type: ndcg_at_10
value: 12.817
- type: ndcg_at_100
value: 23.183999999999997
- type: ndcg_at_1000
value: 35.099000000000004
- type: ndcg_at_3
value: 11.215
- type: ndcg_at_5
value: 12.016
- type: precision_at_1
value: 14.285999999999998
- type: precision_at_10
value: 12.653
- type: precision_at_100
value: 5.306
- type: precision_at_1000
value: 1.294
- type: precision_at_3
value: 13.605
- type: precision_at_5
value: 13.877999999999998
- type: recall_at_1
value: 1.0619999999999998
- type: recall_at_10
value: 10.377
- type: recall_at_100
value: 34.77
- type: recall_at_1000
value: 70.875
- type: recall_at_3
value: 3.688
- type: recall_at_5
value: 6.2509999999999994
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.8488
- type: ap
value: 15.590122317097372
- type: f1
value: 55.86108396102662
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.61460101867573
- type: f1
value: 57.8678726826158
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 32.01459876897588
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.1032365738809
- type: cos_sim_ap
value: 66.60137415520323
- type: cos_sim_f1
value: 62.12845010615712
- type: cos_sim_precision
value: 62.493326214628944
- type: cos_sim_recall
value: 61.76781002638523
- type: dot_accuracy
value: 81.85015199380103
- type: dot_ap
value: 58.854644211365084
- type: dot_f1
value: 56.15180082185158
- type: dot_precision
value: 51.806422836752894
- type: dot_recall
value: 61.2928759894459
- type: euclidean_accuracy
value: 83.6681170650295
- type: euclidean_ap
value: 64.93555585305603
- type: euclidean_f1
value: 61.02775195857125
- type: euclidean_precision
value: 61.42742582197273
- type: euclidean_recall
value: 60.633245382585756
- type: manhattan_accuracy
value: 83.73368301841808
- type: manhattan_ap
value: 65.45422483039611
- type: manhattan_f1
value: 61.58552806597499
- type: manhattan_precision
value: 62.09763948497854
- type: manhattan_recall
value: 61.08179419525066
- type: max_accuracy
value: 84.1032365738809
- type: max_ap
value: 66.60137415520323
- type: max_f1
value: 62.12845010615712
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 86.36628245430201
- type: cos_sim_ap
value: 79.29963896460292
- type: cos_sim_f1
value: 72.63895990066467
- type: cos_sim_precision
value: 69.09128803668196
- type: cos_sim_recall
value: 76.57068062827224
- type: dot_accuracy
value: 84.65091007878294
- type: dot_ap
value: 75.04883449222972
- type: dot_f1
value: 69.18569117382708
- type: dot_precision
value: 64.89512376070682
- type: dot_recall
value: 74.08376963350786
- type: euclidean_accuracy
value: 85.88116583226608
- type: euclidean_ap
value: 78.42687640324908
- type: euclidean_f1
value: 71.74350111107192
- type: euclidean_precision
value: 66.19800820152314
- type: euclidean_recall
value: 78.3030489682784
- type: manhattan_accuracy
value: 86.27508052935926
- type: manhattan_ap
value: 79.29581298930101
- type: manhattan_f1
value: 72.51838235294117
- type: manhattan_precision
value: 67.03921568627452
- type: manhattan_recall
value: 78.97289805974745
- type: max_accuracy
value: 86.36628245430201
- type: max_ap
value: 79.29963896460292
- type: max_f1
value: 72.63895990066467
---
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
- **Repository:** https://github.com/McGill-NLP/llm2vec
- **Paper:** https://arxiv.org/abs/2404.05961
## Installation
```bash
pip install llm2vec
```
## Usage
```python
from llm2vec import LLM2Vec
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel
# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
tokenizer = AutoTokenizer.from_pretrained(
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp"
)
config = AutoConfig.from_pretrained(
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp", trust_remote_code=True
)
model = AutoModel.from_pretrained(
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
trust_remote_code=True,
config=config,
torch_dtype=torch.bfloat16,
device_map="cuda" if torch.cuda.is_available() else "cpu",
)
model = PeftModel.from_pretrained(
model,
"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
)
model = model.merge_and_unload() # This can take several minutes on cpu
# Loading unsupervised SimCSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + SimCSE (LoRA).
model = PeftModel.from_pretrained(
model, "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse"
)
# Wrapper for encoding and pooling operations
l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
# Encoding queries using instructions
instruction = (
"Given a web search query, retrieve relevant passages that answer the query:"
)
queries = [
[instruction, "how much protein should a female eat"],
[instruction, "summit define"],
]
q_reps = l2v.encode(queries)
# Encoding documents. Instruction are not required 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.",
]
d_reps = l2v.encode(documents)
# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
print(cos_sim)
"""
tensor([[0.6522, 0.1891],
[0.1162, 0.3457]])
"""
```
## Questions
If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).
|
[
"SUMMARIZATION"
] |
[
"BIOSSES",
"SCIFACT"
] |
Dampish/StellarX-4B-V0.2
|
Dampish
|
text-generation
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"arxiv:2204.06745",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-03T21:05:01 | 2023-09-18T12:13:30 | 2,264 | 2 |
---
license: cc-by-nc-sa-4.0
---
# StellarX: A Base Model by Dampish and Arkane
StellarX is a powerful autoregressive language model designed for various natural language processing tasks. It has been trained on a massive dataset containing 810 billion tokens(trained on 300B tokens), trained on "redpajama," and is built upon the popular GPT-NeoX architecture. With approximately 4 billion parameters, StellarX offers exceptional performance and versatility.
## Model Details
- **Training Data:** StellarX is trained on a large-scale dataset provided by "redpajama" maintained by the group "togethercumputer." This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.
- **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding.
- **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks.
- **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications.
- **V0.2** Meaning what version it is on, currently version 0.2, Assume version 0.2 has only been trained on 300B tokens and the goal is 810B tokens. The next version aims to have a way higher accuracy.
## How to Use
To load StellarX using the Hugging Face Transformers library, you can use the following code snippet:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Dampish/StellarX-4B-V0")
model = AutoModelForCausalLM.from_pretrained("Dampish/StellarX-4B-V0")
```
This model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing.
**For any queries related to this model, feel free to reach out to "Dampish#3607" on discord.**
## Licensing and Usage
StellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.
# Research and Personal Use
StellarX can be freely used for research purposes, allowing you to explore its capabilities, conduct experiments, and develop novel applications. Whether you're a student, researcher, or hobbyist, the model's availability under the CC-BY-NC-SA-4.0 license empowers you to unlock the potential of StellarX for your own non-commercial projects.
# Commercial Usage
For commercial usage of StellarX, an additional licensing arrangement must be established. If you intend to leverage the model for any commercial purpose, such as integrating it into a product or service, you are required to reach an agreement with the Dampish. This agreement will specify the terms, including the agreed-upon percentage or licensing fee to be paid for the commercial use of StellarX.
To initiate discussions regarding commercial usage, please contact Dampish through the designated channels mentioned earlier. They will be able to provide you with further information and guide you through the process of establishing a licensing arrangement tailored to your specific requirements.
# Importance of Licensing Compliance
It is crucial to respect the licensing terms to ensure the fair usage and continued development of StellarX. The revenue generated from commercial licensing supports the efforts of the Dampish in advancing the model and making it more widely accessible.
# Note on CC-BY-NC-SA-4.0
Under the CC-BY-NC-SA-4.0 license, you are allowed to modify and adapt StellarX, incorporating it into your own projects. However, any derivative work or modifications should also be shared under the same license terms, ensuring the continued openness and collaborative spirit of the project.
Please review the complete text of the CC-BY-NC-SA-4.0 license to familiarize yourself with its provisions and requirements. It is essential to comply with the terms of the license to respect the intellectual property rights and contributions of the Dampish and the wider community involved in developing StellarX.
## GPT-NeoX and Model Selection
GPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. StellarX draws inspiration from the architectural advancements and performance of GPT-NeoX models. While the specifics of StellarX's architecture and parameters may differ, it benefits from the proven capabilities of GPT-NeoX and its suitability for diverse natural language processing tasks.
## Training and Evaluation
StellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of "redpajama" dataset by the group "togethercumputer" group.
Evaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge.
## Limitations and Considerations
StellarX, like its sibling models, is intended primarily for research purposes. It provides a powerful foundation for extracting useful features and insights from the English language. While StellarX can be further fine-tuned and adapted for deployment, users should conduct their own risk and bias assessments before using it as a basis for downstream tasks.
It's important to note that StellarX is not intended for direct deployment without supervision. It is not designed for human-facing interactions, unlike models like ChatGPT, which have been fine-tuned using reinforcement learning from human feedback to better understand human instructions and dialogue.
Furthermore, StellarX is not limited to the English language if trained properly and can sometimes be used for translation aswell as text generation in other languages.
Lastly, users should be aware of potential biases and limitations inherent in
Special thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer.
## Community and Support
To inquire about StellarX and receive support, you can join the Dampish's
server and engage in discussions in the #questions channel. It is recommended to explore the existing documentation and resources available for GPT-NeoX-20B to familiarize yourself with the model before seeking assistance on. For better information about GPT-NeoX, you can reach out to eleutherAI.
## Summary
StellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms.
**Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.
-dampish**
Discord: https://discord.gg/vasyNnUa
OR Reach out to me personally on Discord via the username: Dampish#3607
Thank you for your time.
|
[
"TRANSLATION"
] |
[
"SCIQ"
] |
epfl-llm/meditron-70b
|
epfl-llm
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"medical",
"health",
"llama2",
"en",
"dataset:bigbio/med_qa",
"dataset:medmcqa",
"dataset:bigbio/pubmed_qa",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:meta-llama/Llama-2-70b",
"base_model:finetune:meta-llama/Llama-2-70b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-08T13:57:04 | 2023-12-07T19:39:04 | 2,262 | 234 |
---
base_model: meta-llama/Llama-2-70b
datasets:
- bigbio/med_qa
- medmcqa
- bigbio/pubmed_qa
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
pipeline_tag: text-generation
tags:
- medical
- health
- llama2
---
<img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo">
# Model Card for Meditron-70B-v1.0
Meditron is a suite of open-source medical Large Language Models (LLMs).
Meditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.
<!--# Table of Contents
[Model Card for Meditron 70B](#model-card-for--meditron-70b-v1.0)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Evaluation](#evaluation)
- [Testing Data & Metrics](#testing-data-&-metrics)
- [Testing Data](#testing-data)
- [Metrics](#metrics)
- [Results](#results)
- [Environmental Impact](#environmental-impact)
- [Citation](#citation)-->
<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-2-70B](https://huggingface.co/meta-llama/Llama-2-70b)
- **Context length:** 4K 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-70B 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-70B and Meditron-7B are both foundation models without finetuning or instruction-tuning. They can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.
There are two ways we have used this model for downstream question-answering tasks.
1. We apply in-context learning with k demonstrations (3 or 5 in our paper) added to the prompt.
2. We finetuned the models for downstream question-answering tasks using specific training sets.
We encourage and look forward to the adaption of the base model for more diverse applications.
If you want a more interactive way to prompt the model, we recommend using a high-throughput and memory-efficient inference engine with a UI that supports chat and text generation.
You can check out our deployment [guide](https://github.com/epfLLM/meditron/blob/main/deployment/README.md), where we used [FastChat](https://github.com/lm-sys/FastChat) with [vLLM](https://github.com/vllm-project/vllm). We collected generations for our qualitative analysis through an interactive UI platform, [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT). Here is the prompt format we used as an example:
<img width=70% src="prompt_example.png" alt="qualitative-analysis-prompt" title="Qualitative Analysis Prompt">
### 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.
For **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.
Each query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).
Please see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)
Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model.
### 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 without comprehensive testing for your application.
## 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="60%" 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 16 nodes 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.
The nodes are connected via RDMA over Converged Ethernet.
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 8,
- Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.
#### Training Hyperparameters
| | |
| --- | ------ |
| bf16 | true |
| lr | 1.5e-4 |
| eps | 1e-5 |
| betas | \[0.9, 0.95\] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 2 |
| TP size | 8 |
| PP size | 8 |
| seq length | 4096 |
| lr scheduler | cosine|
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 2 |
| global batch size | 512 |
| | |
#### Speeds, Sizes, Times
The model was trained in September and October 2023.
The model architecture is exactly Llama 2, meaning
| | |
| --- | ------ |
| Model size | 70B |
| Hidden dimension | 8192 |
| Num. attention heads | 64 |
| Num. layers | 80 |
| | | |
We train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second.
This amounts to a bfloat16 model flops utilization of roughly 42.3\%.
## 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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually.
We report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |
|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |
|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |
|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |
|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |
|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |
|Avg | 72.0 | 69.2 | 63.6 | 57.4 |
| | | | | | |
**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:** 128 x NVIDIA A100 (80GB) SXM
- **Total GPU hours:** 42,496
- **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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be:
(400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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"
] |
Dampish/StellarX-4B-V0
|
Dampish
|
text-generation
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"arxiv:2204.06745",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-05-27T19:04:49 | 2023-12-03T19:52:22 | 2,229 | 1 |
---
license: cc-by-nc-sa-4.0
---
# StellarX: A Base Model by Dampish and Arkane
StellarX is a powerful autoregressive language model designed for various natural language processing tasks. It has been trained on a massive dataset containing 810 billion tokens, trained on "redpajama," and is built upon the popular GPT-NeoX architecture. With approximately 4 billion parameters, StellarX offers exceptional performance and versatility.
## Model Details
- **Training Data:** StellarX is trained on a large-scale dataset provided by "redpajama" maintained by the group "togethercumputer." This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.
- **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding.
- **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks.
- **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications.
- **V0** Meaning what version it is on, currently version 0, Assume version 0 has only been trained on 300B tokens and the goal is 810B tokens. The next version aims to have a way higher accuracy.
## How to Use
To load StellarX using the Hugging Face Transformers library, you can use the following code snippet:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Dampish/StellarX-4B-V0")
model = AutoModelForCausalLM.from_pretrained("Dampish/StellarX-4B-V0")
```
This model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing.
**For any queries related to this model, feel free to reach out to "Dampish#3607" on discord.**
## Licensing and Usage
StellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.
# Research and Personal Use
StellarX can be freely used for research purposes, allowing you to explore its capabilities, conduct experiments, and develop novel applications. Whether you're a student, researcher, or hobbyist, the model's availability under the CC-BY-NC-SA-4.0 license empowers you to unlock the potential of StellarX for your own non-commercial projects.
# Commercial Usage
For commercial usage of StellarX, an additional licensing arrangement must be established. If you intend to leverage the model for any commercial purpose, such as integrating it into a product or service, you are required to reach an agreement with the Dampish. This agreement will specify the terms, including the agreed-upon percentage or licensing fee to be paid for the commercial use of StellarX.
To initiate discussions regarding commercial usage, please contact Dampish through the designated channels mentioned earlier. They will be able to provide you with further information and guide you through the process of establishing a licensing arrangement tailored to your specific requirements.
# Importance of Licensing Compliance
It is crucial to respect the licensing terms to ensure the fair usage and continued development of StellarX. The revenue generated from commercial licensing supports the efforts of the Dampish in advancing the model and making it more widely accessible.
# Note on CC-BY-NC-SA-4.0
Under the CC-BY-NC-SA-4.0 license, you are allowed to modify and adapt StellarX, incorporating it into your own projects. However, any derivative work or modifications should also be shared under the same license terms, ensuring the continued openness and collaborative spirit of the project.
Please review the complete text of the CC-BY-NC-SA-4.0 license to familiarize yourself with its provisions and requirements. It is essential to comply with the terms of the license to respect the intellectual property rights and contributions of the Dampish and the wider community involved in developing StellarX.
## GPT-NeoX and Model Selection
GPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. StellarX draws inspiration from the architectural advancements and performance of GPT-NeoX models. While the specifics of StellarX's architecture and parameters may differ, it benefits from the proven capabilities of GPT-NeoX and its suitability for diverse natural language processing tasks.
## Training and Evaluation
StellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of "redpajama" dataset by the group "togethercumputer" group.
Evaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge.
## Limitations and Considerations
StellarX, like its sibling models, is intended primarily for research purposes. It provides a powerful foundation for extracting useful features and insights from the English language. While StellarX can be further fine-tuned and adapted for deployment, users should conduct their own risk and bias assessments before using it as a basis for downstream tasks.
It's important to note that StellarX is not intended for direct deployment without supervision. It is not designed for human-facing interactions, unlike models like ChatGPT, which have been fine-tuned using reinforcement learning from human feedback to better understand human instructions and dialogue.
Furthermore, StellarX is not limited to the English language if trained properly and can sometimes be used for translation aswell as text generation in other languages.
Lastly, users should be aware of potential biases and limitations inherent in
Special thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer.
## Community and Support
To inquire about StellarX and receive support, you can join the Dampish's
server and engage in discussions in the #questions channel. It is recommended to explore the existing documentation and resources available for GPT-NeoX-20B to familiarize yourself with the model before seeking assistance on. For better information about GPT-NeoX, you can reach out to eleutherAI.
## Summary
StellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms.
**Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.
-dampish**
Discord: https://discord.gg/vasyNnUa
OR Reach out to me personally on Discord via the username: Dampish#3607
Thank you for your time.
# [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_Dampish__StellarX-4B-V0)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 33.54 |
| ARC (25-shot) | 36.95 |
| HellaSwag (10-shot) | 61.9 |
| MMLU (5-shot) | 26.85 |
| TruthfulQA (0-shot) | 34.3 |
| Winogrande (5-shot) | 63.85 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 10.95 |
|
[
"TRANSLATION"
] |
[
"SCIQ"
] |
BSC-LT/salamandra-7b
|
BSC-LT
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"dataset:oscar-corpus/colossal-oscar-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:joelniklaus/eurlex_resources",
"dataset:joelito/legal-mc4",
"dataset:projecte-aina/CATalog",
"dataset:UFRGS/brwac",
"dataset:community-datasets/hrwac",
"dataset:danish-foundation-models/danish-gigaword",
"dataset:HiTZ/euscrawl",
"dataset:PleIAs/French-PD-Newspapers",
"dataset:PleIAs/French-PD-Books",
"dataset:AI-team-UoA/greek_legal_code",
"dataset:HiTZ/latxa-corpus-v1.1",
"dataset:allenai/peS2o",
"dataset:pile-of-law/pile-of-law",
"dataset:PORTULAN/parlamento-pt",
"dataset:hoskinson-center/proof-pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/starcoderdata",
"dataset:bjoernp/tagesschau-2018-2023",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2502.08489",
"arxiv:2403.14009",
"arxiv:2403.20266",
"arxiv:2101.00027",
"arxiv:2207.00220",
"arxiv:1810.06694",
"arxiv:1911.05507",
"arxiv:1906.03741",
"arxiv:2406.17557",
"arxiv:2402.06619",
"arxiv:1803.09010",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T06:47:04 | 2025-02-20T16:42:39 | 2,229 | 27 |
---
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
language:
- bg
- ca
- code
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nn
- \no
- oc
- pl
- pt
- ro
- ru
- sh
- sk
- sl
- sr
- sv
- uk
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Salamandra Model Card
This repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).
Salamandra is a highly multilingual model pre-trained from scratch that comes in three different
sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants.
This model card corresponds to the 7B base version.
To visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index).
The entire Salamandra family is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
Along with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra).
---
## Model Details
### Description
Transformer-based decoder-only language model that has been pre-trained from scratch on 12.875 trillion tokens of highly curated data.
The pre-training corpus contains text in 35 European languages and code.
### Hyperparameters
The full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/tree/main/configs).
### Architecture
| | |
|-------------------------|:--------------|
| Total Parameters | 7,768,117,248 |
| Embedding Parameters | 1,048,576,000 |
| Layers | 32 |
| Hidden size | 4,096 |
| Attention heads | 32 |
| Context length | 8,192 |
| Vocabulary size | 256,000 |
| Precision | bfloat16 |
| Embedding type | RoPE |
| Activation Function | SwiGLU |
| Layer normalization | RMS Norm |
| Flash attention | ✅ |
| Grouped Query Attention | ✅ |
| Num. query groups | 8 |
---
## Intended Use
### Direct Use
The models are intended for both research and commercial use in any of the languages included in the training data.
The base models are intended either for language generation or to be further fine-tuned for specific use-cases.
The instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations.
### Out-of-scope Use
The model is not intended for malicious activities, such as harming others or violating human rights.
Any downstream application must comply with current laws and regulations.
Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged.
---
## Hardware and Software
### Training Framework
Pre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html),
which leverages PyTorch Lightning for efficient model training in highly distributed settings.
The instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat).
### Compute Infrastructure
All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and
operated by Barcelona Supercomputing Center.
The accelerated partition is composed of 1,120 nodes with the following specifications:
- 4x Nvidia Hopper GPUs with 64 HBM2 memory
- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)
- 4x NDR200 (BW per node 800Gb/s)
- 512 GB of Main memory (DDR5)
- 460GB on NVMe storage
|Model|Nodes|GPUs|
|:---:|:---:|:---:|
|2B|64|256|
|7B|128|512|
|40B|256 / 512|1,024 / 2,048|
---
## How to use
This section offers examples of how to perform inference using various methods.
### Inference
You'll find different techniques for running inference, including Huggingface's Text Generation Pipeline, multi-GPU configurations, and vLLM for scalable and efficient generation.
#### Inference with Huggingface's Text Generation Pipeline
The Huggingface Text Generation Pipeline provides a straightforward way to run inference using the Salamandra-7b model.
```bash
pip install transformers torch accelerate sentencepiece protobuf
```
<details>
<summary>Show code</summary>
```python
from transformers import pipeline, set_seed
model_id = "BSC-LT/salamandra-7b"
# Sample prompts
prompts = [
"Las fiestas de San Isidro Labrador de Yecla son",
"El punt més alt del Parc Natural del Montseny és",
"Sentence in English: The typical chance of such a storm is around 10%. Sentence in Catalan:",
"Si le monde était clair",
"The future of AI is",
]
# Create the pipeline
generator = pipeline("text-generation", model_id, device_map="auto")
generation_args = {
"temperature": 0.1,
"top_p": 0.95,
"max_new_tokens": 25,
"repetition_penalty": 1.2,
"do_sample": True
}
# Fix the seed
set_seed(1)
# Generate texts
outputs = generator(prompts, **generation_args)
# Print outputs
for output in outputs:
print(output[0]["generated_text"])
```
</details>
#### Inference with single / multi GPU
This section provides a simple example of how to run inference using Huggingface's AutoModel class.
```bash
pip install transformers torch accelerate sentencepiece protobuf
```
<details>
<summary>Show code</summary>
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "BSC-LT/salamandra-7b"
# Input text
text = "El mercat del barri és"
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load the model
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
generation_args = {
"temperature": 0.1,
"top_p": 0.95,
"max_new_tokens": 25,
"repetition_penalty": 1.2,
"do_sample": True
}
inputs = tokenizer(text, return_tensors="pt")
# Generate texts
output = model.generate(input_ids=inputs["input_ids"].to(model.device), attention_mask=inputs["attention_mask"], **generation_args)
# Print outputs
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
</details>
#### Inference with vLLM
vLLM is an efficient library for inference that enables faster and more scalable text generation.
```bash
pip install vllm
```
<details>
<summary>Show code</summary>
```python
from vllm import LLM, SamplingParams
model_id = "BSC-LT/salamandra-7b"
# Sample prompts
prompts = [
"Las fiestas de San Isidro Labrador de Yecla son",
"El punt més alt del Parc Natural del Montseny és",
"Sentence in English: The typical chance of such a storm is around 10%. Sentence in Catalan:",
"Si le monde était clair",
"The future of AI is",
]
# Create a sampling params object
sampling_params = SamplingParams(
temperature=0.1,
top_p=0.95,
seed=1,
max_tokens=25,
repetition_penalty=1.2)
# Create an LLM
llm = LLM(model=model_id)
# Generate texts
outputs = llm.generate(prompts, sampling_params)
# Print outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
</details>
---
## Data
### Pretraining Data
The pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below.
The initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation
and give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half,
Spanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.
During the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset.
This adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:
The pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens.
Following this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%.
Other notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%.
These major sources collectively form the bulk of the corpus, ensuring a rich and diverse dataset for training the language model.
The remaining 10% comes from smaller sources in various languages.
Feel free to click the expand button below to see the full list of sources.
<details>
<summary>Data Sources</summary>
| Dataset | Language | Source |
|---|---|---|
| Colossal OSCAR 1.0 | bg, ca, cs, cy, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, oc, pl, pt, ro, ru, sh, sk, sl, sr, sv, uk | Brack et al., 2024 |
| Aya Dataset (w/o Evaluation Suite) | eu, hr, nl, fi, ka, hu, lt, nn, ro, sk, lv, cy, bg, cs, en, fr, de, ga, mt, pl, ru, sl, sv, ca, da, et, gl, el, it, no, pt, sr, es, uk | Singh et al., 2024 |
| Wikimedia dumps | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, pl, pt, ro, sh, sk, sl, sr, uk | [Link](https://dumps.wikimedia.org/) |
| OpenSubtitles v2016 | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, gl, hr, it, lt, lv, nl, no, pl, pt, ro, sk, sl, sr, sv, uk | Lison & Tiedemann, 2016 |
| EurLEX-Resources | bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelniklaus/eurlex_resources) |
| MC4-Legal | bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelito/legal-mc4) |
| Parlamint | at, bg, cz, dk, ee, es, es-ga, fi, fr, gb, gr, hr, hu, it, lv, nl, no, pl, pt, rs, se, si | Erjavec et al., 2021 |
| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |
| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |
| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |
| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |
| BIGPATENT | en | Sharma et al., 2019 |
| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |
| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |
| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
| CaBeRnet | fr | Popa-Fabre et al., 2020 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
| DaNewsroom | da | Varab & Schluter, 2020 |
| Danish GigaWord | da | Strømberg-Derczynski et al., 2021 |
| DK-CLARIN Reference Corpus of General Danish | da | [Link](https://korpus.dsl.dk/clarin/) |
| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |
| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |
| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |
| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |
| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |
| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |
| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |
| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |
| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |
| HPLT v1 - Spanish | es | de Gibert et al., 2024 |
| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |
| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |
| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |
| Korpus Malti | mt | Micallef et al., 2022 |
| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |
| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |
| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |
| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |
| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |
| Math AMPS | en | Hendrycks et al., 2021 |
| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |
| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |
| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |
| ParlamentoPT | pt | Rodrigues et al., 2023 |
| peS2o | en | Soldaini & Lo, 2023 |
| PG-19 | en | Rae et al., 2019 |
| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |
| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |
| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |
| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |
| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |
| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |
| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |
| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |
| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |
| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |
| Starcoder | code | Li et al., 2023 |
| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |
| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |
| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |
| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |
| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |
| The Pile (PhilPapers) | en | Gao et al., 2021 |
| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |
| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |
| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
<details>
<summary>References</summary>
- Abadji, J., Suárez, P. J. O., Romary, L., & Sagot, B. (2021). Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus (H. Lüngen, M. Kupietz, P. Bański, A. Barbaresi, S. Clematide, & I. Pisetta, Eds.; pp. 1–9). Leibniz-Institut für Deutsche Sprache. [Link](https://doi.org/10.14618/ids-pub-10468)
- Artetxe, M., Aldabe, I., Agerri, R., Perez-de-Viñaspre, O., & Soroa, A. (2022). Does Corpus Quality Really Matter for Low-Resource Languages?
- Bañón, M., Esplà-Gomis, M., Forcada, M. L., García-Romero, C., Kuzman, T., Ljubešić, N., van Noord, R., Sempere, L. P., Ramírez-Sánchez, G., Rupnik, P., Suchomel, V., Toral, A., van der Werff, T., & Zaragoza, J. (2022). MaCoCu: Massive collection and curation of monolingual and bilingual data: Focus on under-resourced languages. Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, 303–304. [Link](https://aclanthology.org/2022.eamt-1.41)
- Brack, M., Ostendorff, M., Suarez, P. O., Saiz, J. J., Castilla, I. L., Palomar-Giner, J., Shvets, A., Schramowski, P., Rehm, G., Villegas, M., & Kersting, K. (2024). Community OSCAR: A Community Effort for Multilingual Web Data. [Link](https://occiglot.eu/papers/Community_Oscar.pdf)
- Computer, T. (2023). RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset [Computer software]. [Link](https://github.com/togethercomputer/RedPajama-Data)
- de Gibert, O., Nail, G., Arefyev, N., Bañón, M., van der Linde, J., Ji, S., Zaragoza-Bernabeu, J., Aulamo, M., Ramírez-Sánchez, G., Kutuzov, A., Pyysalo, S., Oepen, S., & Tiedemann, J. (2024). A New Massive Multilingual Dataset for High-Performance Language Technologies (arXiv:2403.14009). arXiv. [Link](http://arxiv.org/abs/2403.14009)
- Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021). Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus. In M.-F. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 1286–1305). Association for Computational Linguistics. [Link](https://doi.org/10.18653/v1/2021.emnlp-main.98)
- Erjavec, T., Ljubešić, N., & Logar, N. (2015). The slWaC corpus of the Slovene web. Informatica (Slovenia), 39, 35–42.
- Erjavec, T., Ogrodniczuk, M., Osenova, P., Ljubešić, N., Simov, K., Grigorova, V., Rudolf, M., Pančur, A., Kopp, M., Barkarson, S., Steingrímsson, S. hór, van der Pol, H., Depoorter, G., de Does, J., Jongejan, B., Haltrup Hansen, D., Navarretta, C., Calzada Pérez, M., de Macedo, L. D., … Rayson, P. (2021). Linguistically annotated multilingual comparable corpora of parliamentary debates ParlaMint.ana 2.1. [Link](http://hdl.handle.net/11356/1431)
- Etxaniz, J., Sainz, O., Perez, N., Aldabe, I., Rigau, G., Agirre, E., Ormazabal, A., Artetxe, M., & Soroa, A. (2024). Latxa: An Open Language Model and Evaluation Suite for Basque. [Link] (https://arxiv.org/abs/2403.20266)
- Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., & Leahy, C. (2021). The Pile: An 800GB Dataset of Diverse Text for Language Modeling. CoRR, abs/2101.00027. [Link](https://arxiv.org/abs/2101.00027)
- Gutiérrez-Fandiño, A., Armengol-Estapé, J., Gonzalez-Agirre, A., & Villegas, M. (2021). Spanish Legalese Language Model and Corpora.
- Hansen, D. H. (2018). The Danish Parliament Corpus 2009—2017, v1. [Link](http://hdl.handle.net/20.500.12115/8)
- Henderson*, P., Krass*, M. S., Zheng, L., Guha, N., Manning, C. D., Jurafsky, D., & Ho, D. E. (2022). Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset. arXiv. [Link](https://arxiv.org/abs/2207.00220)
- Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). Measuring Mathematical Problem Solving With the MATH Dataset. NeurIPS.
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</details>
</details>
The model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part
of the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch;
and 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 trillion tokens.
We provide an extense Datasheet section following the best practices defined by [(Gebru et al., 2021)](https://arxiv.org/pdf/1803.09010).
<details>
<summary>Datasheet</summary>
#### Motivation
**For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.**
The purpose of creating this dataset is to pre-train the Salamandra family of multilingual models with high performance in a large number of European languages (35)
and programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample
these languages by a factor of 2.
There is a great lack of massive multilingual data, especially in minority languages (Ostendorff & Rehm, 2023), so part of our efforts in the creation of
this pre-training dataset have resulted in the contribution to large projects such as the Community OSCAR (Brack et al., 2024), which includes 151 languages
and 40T words, or CATalog (Palomar-Giner et al., 2024), the largest open dataset in Catalan in the world.
**Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?**
The dataset has been created by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS),
which aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, it was created by
the unit's data team, the main contributors being José Javier Saiz, Ferran Espuña and Jorge Palomar.
However, the creation of the dataset would not have been possible without the collaboration of a large number of collaborators, partners and public institutions,
which can be found in detail in the acknowledgements.
**Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.**
This work has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
#### Composition
**What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.**
The dataset consists entirely of text documents in various languages. Specifically, data was mainly sourced from the following databases and
repositories:
- **Common Crawl:** Repository that holds website data and is run by the Common Crawl non-profit organization. It is updated monthly and is
distributed under the CC0 1.0 public domain license.
- **GitHub:** Community platform that allows developers to create, store, manage, and share their code. Repositories are crawled and then
distributed with their original licenses, which may vary from permissive to non-commercial licenses.
- **Wikimedia:** Database that holds the collection databases managed by the Wikimedia Foundation, including Wikipedia, Wikibooks, Wikinews,
Wikiquote, Wikisource, and Wikivoyage. It is updated monthly and is distributed under Creative Commons Attribution-ShareAlike License 4.0.
- **EurLex:** Repository that holds the collection of legal documents from the European Union, available in all of the EU’s 24 official
languages and run by the Publications Office of the European Union. It is updated daily and is distributed under the Creative Commons
Attribution 4.0 International license.
- **Other repositories:** Specific repositories were crawled under permission for domain-specific corpora, which include academic, legal,
and newspaper repositories.
We provide a complete list of dataset sources at the end of this section.
**How many instances are there in total (of each type, if appropriate)?**
The dataset contains a diverse range of instances across multiple languages, with notable adjustments for certain languages. English
represents the largest portion, accounting for 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,
while Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled
by half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian
(4.59%), with several additional languages contributing between 1% and 2%, and smaller portions represented by a variety of others.
**Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).**
The dataset is a sample from multiple sources, with different weights based on the primary language of the content: Spanish, Catalan,
Basque, and Galician content was upsampled by a factor of two, while programming languages were downsampled by a factor of half. Other
sources were sampled in proportion to their occurrence.
**What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.**
Each instance consists of a text document processed for deduplication, language identification, and source-specific filtering. Some documents required
optical character recognition (OCR) to extract text from non-text formats such as PDFs.
**Is there a label or target associated with each instance? If so, please provide a description.**
Each instance is labelled with a unique identifier, the primary language of the content, and the URL for web-sourced instances. Additional labels were
automatically assigned to detect specific types of content -harmful or toxic content- and to assign preliminary indicators of undesired qualities -very
short documents, high density of symbols, etc.- which were used for filtering instances.
**Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.**
No significant information is missing from the instances.
**Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.**
Instances are related through shared metadata, such as source and language identifiers.
**Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.**
The dataset is randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.
**Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.**
Despite removing duplicated instances within each source, redundancy remains at the paragraph and sentence levels, particularly in web-sourced
instances where search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also duplicated
across sources due to format variations.
**Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.**
The dataset is self-contained and does not rely on external resources.
**Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.**
The dataset does not contain confidential data.
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.**
The dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). Although
pre-processing techniques were applied to mitigate offensive content, the heterogeneity and scale of web-sourced data make exhaustive
filtering challenging, which makes it next to impossible to identify all adult content without falling into excessive filtering, which may
negatively influence certain demographic groups (Dodge et al., 2021).
**Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.**
The dataset does not explicitly identify any subpopulations.
**Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.**
Web-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names,
IP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple
data points, the nature and scale of web data makes it difficult to parse such information. In any case, efforts are made to filter or anonymize
sensitive data (Mina et al., 2024), but some identifiable information may remain in the dataset.
**Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.**
Given that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial
information, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023),
especially if the content originates from less-regulated sources or user-generated platforms.
#### Collection Process
**How was the data collected?**
This dataset is constituted by combining several sources, whose acquisition methods can be classified into three groups:
- Web-sourced datasets with some preprocessing available under permissive license.
- Domain-specific or language-specific raw crawls.
- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).
**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**
The data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:
- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.
- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.
- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.
**If the dataset is a sample from a larger set, what was the sampling strategy?**
The sampling strategy was to use the whole dataset resulting from the filtering explained in the 'preprocessing/cleaning/labelling' section,
with the particularity that an upsampling of 2 (i.e. twice the probability of sampling a document) was performed for the co-official languages
of Spain (Spanish, Catalan, Galician, Basque), and a downsampling of 1/2 was applied for code (half the probability of sampling a code document,
evenly distributed among all programming languages).
**Who was involved in the data collection process and how were they compensated?**
This data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely
by members of the Language Technologies data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary
consideration for acquiring data from suppliers.
**Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.**
Data were acquired and processed from April 2023 to April 2024. However, as mentioned, much data has been obtained from open projects such
as Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important.
**Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.**
No particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an
internal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència
Artificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an
ethical and legal point of view, respectively.
#### Preprocessing
**Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.**
No changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:
- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.
- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\_pp' field, a perplexity-based score generated by a language model.
**Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.**
The original raw data was not kept.
**Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.**
Yes, the preprocessing and filtering software is open-sourced. The [CURATE](https://github.com/langtech-bsc/CURATE) pipeline was used for CATalog and other curated datasets,
and the [Ungoliant](https://github.com/oscar-project/ungoliant) pipeline was used for the OSCAR project.
#### Uses
**Has the dataset been used for any tasks already? If so, please provide a description.**
Pre-train the Salamandra model family.
**What (other) tasks could the dataset be used for?**
The data can be used primarily to pre-train other language models, which can then be used for a wide range of use cases. The dataset could
also be used for other tasks such as fine-tuning language models, cross-lingual NLP tasks, machine translation, domain-specific text
generation, and language-specific data analysis.
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?**
Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages.
Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic
groups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures,
acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to
address privacy concerns and contribute to a more inclusive linguistic dataset.
**Are there tasks for which the dataset should not be used?**
-
#### Distribution
**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.**
The dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section.
#### Maintenance
**Who will be supporting/hosting/maintaining the dataset?**
The dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure
regular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are
responsible for.
**How can the owner/curator/manager of the dataset be contacted?**
The data owner may be contacted with the email address [email protected].
**Will the dataset be updated?**
The dataset will not be updated.
**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.**
The dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in
web-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an
individual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to
remove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues.
**Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.**
Since the dataset will not be updated, only the final version will be kept.
**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**
The dataset does not allow for external contributions.
</details>
---
## Evaluation
### Gold-standard benchmarks
Evaluation is done using the Language Model Evaluation Harness (Gao et al., 2024). We evaluate on a set of tasks taken from [SpanishBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/spanish_bench), [CatalanBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/catalan_bench), [BasqueBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/basque_bench) and [GalicianBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/galician_bench). We also use English tasks already available on the LM Evaluation Harness. These benchmarks include both new and existing tasks and datasets. In the tables below, we include the results in a selection of evaluation datasets that represent model's performance across a variety of tasks within these benchmarks.
We only use tasks that are either human generated, human translated, or with a strong human-in-the-loop (i.e., machine translation followed by professional revision or machine generation followed by human revision and annotation). This is the reason behind the variety in number of tasks reported across languages. As more tasks that fulfill these requirements are published, we will update the presented results. We also intend to expand the evaluation to other languages, as long as the datasets meet our quality standards.
During the implementation of the evaluation we observed a series of issues worth considering when replicating and interpreting the results presented. These issues include ≈1.5% variances in performance in some tasks depending on the version of the `transformers` library used, and depending on the use (or lack of use) of tensor parallelism when loading a model. When implementing existing tasks, we carry out a comprehensive quality evaluation of the dataset, the Harness task itself, and what kind of input models see during evaluation. Our implementation (see links above) addresses multiple existing problems such as errors in datasets and prompts, and lack of pre-processing. All this means that results will vary if using other Harness implementations, and may slightly vary depending on the replication setup.
It should be noted that these results are subject to all the drawbacks of every current gold-standard evaluation, and that the figures do not fully represent the models capabilities and potential. We thus advise caution when reading and interpreting the results.
A full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report.
All results reported below are on a 5-shot setting.
#### Spanish
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa_es</td>
<td>acc</td>
<td>86</td>
</tr>
<tr>
<td>xstorycloze_es</td>
<td>acc</td>
<td>74.32</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_es</td>
<td>acc</td>
<td>59.15</td>
</tr>
<tr>
<td>xnli_es</td>
<td>acc</td>
<td>46.59</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws_es</td>
<td>acc</td>
<td>60.3</td>
</tr>
<tr>
<td rowspan="2">QA</td>
<td>openbookqa_es</td>
<td>acc</td>
<td>41.6</td>
</tr>
<tr>
<td>xquad_es</td>
<td>acc</td>
<td>72.26</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_es</td>
<td>bleu</td>
<td>23.43</td>
</tr>
</tbody>
</table>
#### Catalan
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa_ca</td>
<td>acc</td>
<td>84</td>
</tr>
<tr>
<td>xstorycloze_ca</td>
<td>acc</td>
<td>75.51</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_ca</td>
<td>acc</td>
<td>59.15</td>
</tr>
<tr>
<td>xnli_ca</td>
<td>acc</td>
<td>50.16</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafraseja</td>
<td>acc</td>
<td>65.83</td>
</tr>
<tr>
<td>paws_ca</td>
<td>acc</td>
<td>67.45</td>
</tr>
<tr>
<td rowspan="5">QA</td>
<td>arc_ca_easy</td>
<td>acc</td>
<td>71.72</td>
</tr>
<tr>
<td>arc_ca_challenge</td>
<td>acc</td>
<td>45.56</td>
</tr>
<tr>
<td>openbookqa_ca</td>
<td>acc</td>
<td>38.8</td>
</tr>
<tr>
<td>piqa_ca</td>
<td>acc</td>
<td>71.27</td>
</tr>
<tr>
<td>siqa_ca</td>
<td>acc</td>
<td>49.85</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_ca</td>
<td>bleu</td>
<td>30.63</td>
</tr>
</tbody></table>
#### Basque
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>xcopa_eu</td>
<td>acc</td>
<td>68.8</td>
</tr>
<tr>
<td>xstorycloze_eu</td>
<td>acc</td>
<td>66.12</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_eu</td>
<td>acc</td>
<td>57.75</td>
</tr>
<tr>
<td>xnli_eu</td>
<td>acc</td>
<td>43.51</td>
</tr>
<tr>
<td rowspan="4">QA</td>
<td>eus_exams</td>
<td>acc</td>
<td>41.04</td>
</tr>
<tr>
<td>eus_proficiency</td>
<td>acc</td>
<td>39.72</td>
</tr>
<tr>
<td>eus_trivia</td>
<td>acc</td>
<td>52.36</td>
</tr>
<tr>
<td>piqa_eu</td>
<td>acc</td>
<td>63.67</td>
</tr>
<tr>
<td>Reading Comprehension</td>
<td>eus_reading</td>
<td>acc</td>
<td>33.52</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_eu</td>
<td>bleu</td>
<td>16.95</td>
</tr>
</tbody></table>
#### Galician
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td>Commonsense Reasoning</td>
<td>xstorycloze_gl</td>
<td>acc</td>
<td>74.12</td>
</tr>
<tr>
<td>NLI</td>
<td>xnli_gl</td>
<td>acc</td>
<td>50.95</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafrases_gl</td>
<td>acc</td>
<td>54.42</td>
</tr>
<tr>
<td>paws_gl</td>
<td>acc</td>
<td>63.2</td>
</tr>
<tr>
<td>QA</td>
<td>openbookqa_gl</td>
<td>acc</td>
<td>34.4</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_gl</td>
<td>bleu</td>
<td>27.75</td>
</tr>
</tbody>
</table>
#### English
<table><thead>
<tr>
<th>Category</th>
<th>Task</th>
<th>Metric</th>
<th>Result</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="2">Commonsense Reasoning</td>
<td>copa</td>
<td>acc</td>
<td>91</td>
</tr>
<tr>
<td>xstorycloze_en</td>
<td>acc</td>
<td>79.09</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli</td>
<td>acc</td>
<td>56.34</td>
</tr>
<tr>
<td>xnli_en</td>
<td>acc</td>
<td>50</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws *</td>
<td>acc</td>
<td>64.05</td>
</tr>
<tr>
<td rowspan="6">QA</td>
<td>arc_easy</td>
<td>acc</td>
<td>82.2</td>
</tr>
<tr>
<td>arc_challenge</td>
<td>acc</td>
<td>52.82</td>
</tr>
<tr>
<td>openbookqa</td>
<td>acc</td>
<td>36</td>
</tr>
<tr>
<td>piqa</td>
<td>acc</td>
<td>80.03</td>
</tr>
<tr>
<td>social_iqa</td>
<td>acc</td>
<td>50.31</td>
</tr>
<tr>
<td>xquad_en **</td>
<td>acc</td>
<td>77.74</td>
</tr>
</tbody></table>
\* Current LM Evaluation Harness implementation is lacking correct pre-processing. These results are obtained with adequate pre-processing.
\*\* This task is not yet available in the official Harness, we hope to add it soon.
---
## Ethical Considerations and Limitations
We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases,
we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019).
We report that while performance is high (accuracies between 0.69 and 0.87 depending on the social category) in disambiguated settings
the model performs very poorly in ambiguous settings, which is indicative of the presence of societal biases which need to be addressed in post-training phases.
We additionally analyse model generations using the Regard dataset and classifier in Catalan, Spanish, and English using backtranslation and manual revision of the
translations. We find no statistically significant difference in regard between majority and minority groups for any regard types,
with the exception of negative regard in Catalan where model generations are actually slightly worse for social majorities.
Our analyses on societal biases show that while these biases are capable of interfering with model performance as expressed in the results on the BBQ dataset,
their tendency for representational harm is limited given the results of the Regard dataset. We highlight that our analyses of these biases are by no means exhaustive
and are limited by the relative scarcity of adequate resources in all languages present in the training data. We aim to gradually extend and expand our analyses
in future work.
Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings.
For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018).
We observe moderate to strong primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers.
We measure effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We detect moderate effects,
implying that outputs can be influenced by the prompts.
We highlight that these results can be expected from a pretrained model that has not yet been instruction-tuned or aligned.
These tests are performed in order to show the biases the model may contain.
We urge developers to take them into account and perform safety testing and tuning tailored to their specific applications of the model.
---
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.
### Funding
This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
### Acknowledgements
This project has benefited from the contributions of numerous teams and institutions, mainly through data contributions, knowledge transfer or technical support.
In Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà.
At the national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria.
At the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration. We would also like to give special thanks to the NVIDIA team, with whom we have met regularly, specially to: Ignacio Sarasua, Adam Henryk Grzywaczewski, Oleg Sudakov, Sergio Perez, Miguel Martinez, Felipes Soares and Meriem Bendris. Their constant support has been especially appreciated throughout the entire process.
Their valuable efforts have been instrumental in the development of this work.
### Disclaimer
Be aware that the model may contain biases or other unintended distortions.
When third parties deploy systems or provide services based on this model, or use the model themselves,
they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations,
including those governing the use of Artificial Intelligence.
The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
### Citation
```
@misc{gonzalezagirre2025salamandratechnicalreport,
title={Salamandra Technical Report},
author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
year={2025},
eprint={2502.08489},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.08489},
}
```
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Index
|Model|Base|Instruct|
|:---:|:---:|:---:|
|2B| [Link](https://huggingface.co/BSC-LT/salamandra-2b) | [Link](https://huggingface.co/BSC-LT/salamandra-2b-instruct) |
|7B| [Link](https://huggingface.co/BSC-LT/salamandra-7b) | [Link](https://huggingface.co/BSC-LT/salamandra-7b-instruct) |
|40B| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] |
[
"BEAR",
"SCIELO"
] |
HiTZ/GoLLIE-7B
|
HiTZ
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"text-generation-inference",
"Information Extraction",
"IE",
"Named Entity Recogniton",
"Event Extraction",
"Relation Extraction",
"LLaMA",
"custom_code",
"en",
"dataset:ACE05",
"dataset:bc5cdr",
"dataset:conll2003",
"dataset:ncbi_disease",
"dataset:conll2012_ontonotesv5",
"dataset:rams",
"dataset:tacred",
"dataset:wnut_17",
"arxiv:2310.03668",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-25T10:24:52 | 2023-10-10T07:51:44 | 2,215 | 28 |
---
datasets:
- ACE05
- bc5cdr
- conll2003
- ncbi_disease
- conll2012_ontonotesv5
- rams
- tacred
- wnut_17
language:
- en
license: llama2
metrics:
- f1
pipeline_tag: text-generation
tags:
- code
- text-generation-inference
- Information Extraction
- IE
- Named Entity Recogniton
- Event Extraction
- Relation Extraction
- LLaMA
---
<p align="center">
<br>
<img src="https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/GoLLIE.png" style="height: 250px;">
<h2 align="center"><b>G</b>uideline f<b>o</b>llowing <b>L</b>arge <b>L</b>anguage Model for <b>I</b>nformation <b>E</b>xtraction</h2>
<br>
# Model Card for GoLLIE 7B
<p align="justify">
We present GoLLIE, a Large Language Model trained to follow annotation guidelines. GoLLIE outperforms previous approaches on zero-shot Information Extraction and allows the user to perform inferences with annotation schemas defined on the fly. Different from previous approaches, GoLLIE is able to follow detailed definitions and does not only rely on the knowledge already encoded in the LLM.
- 💻 Code: [https://github.com/osainz59/CoLLIE/](https://github.com/hitz-zentroa/GoLLIE)
- 📒 Blog Post: [GoLLIE: Guideline-following Large Language Model for Information Extraction](https://hitz-zentroa.github.io/GoLLIE/)
- 📖 Paper: [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://arxiv.org/abs/2310.03668)
- 🐕 GoLLIE Colection in the 🤗HuggingFace Hub: [HiTZ/gollie](https://huggingface.co/collections/HiTZ/gollie-651bf19ee315e8a224aacc4f)
- 🚀 Example Jupyter Notebooks: [GoLLIE Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks)
</p>
<p align="center">
<img src="https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/zero_shot_results.png">
</p>
### Model Description
- **Developed by:** [Oscar Sainz](https://osainz59.github.io/), [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/), [Rodrigo Agerri](https://ragerri.github.io/), [Oier Lopez de Lacalle](https://oierldl.github.io/), [German Rigau](https://adimen.si.ehu.es/~rigau/) and [Eneko Agirre](https://eagirre.github.io/)
- **Institution:** [HiTZ Basque Center for Language Technology](http://www.hitz.eus/) - [Ixa](https://www.ixa.eus/node/2?language=en), [University of the Basque Country UPV/EHU](https://www.ehu.eus/en/en-home)
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** LLaMA2 License for the base and merged model. Apache 2.0 for pre-trained LoRA Adapters
- **Finetuned from model:** CODE-LLaMA2
## Schema definition and inference example
The labels are represented as Python classes, and the guidelines or instructions are introduced as docstrings. The model start generating after the `result = [` line.
```Python
# Entity definitions
@dataclass
class Launcher(Template):
"""Refers to a vehicle designed primarily to transport payloads from the Earth's
surface to space. Launchers can carry various payloads, including satellites,
crewed spacecraft, and cargo, into various orbits or even beyond Earth's orbit.
They are usually multi-stage vehicles that use rocket engines for propulsion."""
mention: str
"""
The name of the launcher vehicle.
Such as: "Sturn V", "Atlas V", "Soyuz", "Ariane 5"
"""
space_company: str # The company that operates the launcher. Such as: "Blue origin", "ESA", "Boeing", "ISRO", "Northrop Grumman", "Arianespace"
crew: List[str] # Names of the crew members boarding the Launcher. Such as: "Neil Armstrong", "Michael Collins", "Buzz Aldrin"
@dataclass
class Mission(Template):
"""Any planned or accomplished journey beyond Earth's atmosphere with specific objectives,
either crewed or uncrewed. It includes missions to satellites, the International
Space Station (ISS), other celestial bodies, and deep space."""
mention: str
"""
The name of the mission.
Such as: "Apollo 11", "Artemis", "Mercury"
"""
date: str # The start date of the mission
departure: str # The place from which the vehicle will be launched. Such as: "Florida", "Houston", "French Guiana"
destination: str # The place or planet to which the launcher will be sent. Such as "Moon", "low-orbit", "Saturn"
# This is the text to analyze
text = (
"The Ares 3 mission to Mars is scheduled for 2032. The Starship rocket build by SpaceX will take off from Boca Chica,"
"carrying the astronauts Max Rutherford, Elena Soto, and Jake Martinez."
)
# The annotation instances that take place in the text above are listed here
result = [
Mission(mention='Ares 3', date='2032', departure='Boca Chica', destination='Mars'),
Launcher(mention='Starship', space_company='SpaceX', crew=['Max Rutherford', 'Elena Soto', 'Jake Martinez'])
]
```
## How to Get Started with the Model
Please read our [🚀 Example Jupyter Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks) to get started with GoLLIE.
The best way to load the model is using our custom `load_model` fuction. However, you can also load them using the AutoModelForCausalLM class.
**Important**: Our flash attention implementation has small numerical differences compared to the attention implementation in Huggingface.
You must use the flag `trust_remote_code=True` or you will get inferior results. Flash attention requires an available CUDA GPU. Running GOLLIE
pre-trained models on a CPU is not supported. We plan to address this in future releases. First, install flash attention 2:
```bash
pip install flash-attn --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
```
Then you can load the model using
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("HiTZ/GoLLIE-7B")
model = AutoModelForCausalLM.from_pretrained("HiTZ/GoLLIE-7B", trust_remote_code=True, torch_dtype=torch.bfloat16)
model.to("cuda")
```
Read our [🚀 Example Jupyter Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks) to learn how to easily define guidelines, generate model inputs and parse the output!
### Training Data
This is the list of task used for training and evaluating GoLLIE. However, as demonstrated in the 🚀 [Create Custom Task notebook](https://github.com/hitz-zentroa/GoLLIE/blob/main/notebooks/Create%20Custom%20Task.ipynb) GoLLIE can perform a wide range of unseen tasks.
For more info, read our [📖Paper](https://arxiv.org/abs/2310.03668).
<p align="center">
<img src="https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/datasets.png">
</p>
## Evaluation
| Model | Supervised average F1 | Zero-shot average F1 | 🤗HuggingFace Hub |
|---|:---------------------:|:--------------------:|:---------------------------------------------------------:|
| GoLLIE-7B | 73.0 | 55.3 | [HiTZ/GoLLIE-7B](https://huggingface.co/HiTZ/GoLLIE-7B) |
| GoLLIE-13B | 73.9 | 56.0 | [HiTZ/GoLLIE-13B](https://huggingface.co/HiTZ/GoLLIE-13B) |
| GoLLIE-34B | **75.0** | **57.2** | [HiTZ/GoLLIE-34B](https://huggingface.co/HiTZ/GoLLIE-34B) |
## Environmental Impact
| Model | Hardware | FLOPs | Time (h) | CO<sup>2</sup>eq (kg) |
|----------------|-------------------|---------------------------|-------------------|-------------------------------------|
| GoLLIE 7B | 1xA100 | 11.9e<sup>18</sup> | 44.5 | 1.57 |
| GoLLIE 13B | 1xA100 | 22.7e<sup>18</sup> | 79.5 | 2.80 |
| GoLLIE 34B | 2xA100 | 55.8e<sup>18</sup> | 94.6 | 6.67 |
## Citation
```
@misc{sainz2023gollie,
title={GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction},
author={Oscar Sainz and Iker García-Ferrero and Rodrigo Agerri and Oier Lopez de Lacalle and German Rigau and Eneko Agirre},
year={2023},
eprint={2310.03668},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"RELATION_EXTRACTION",
"EVENT_EXTRACTION"
] |
[
"BC5CDR",
"NCBI DISEASE"
] |
GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
|
GoToCompany
| null |
[
"safetensors",
"gemma2",
"en",
"id",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2310.04928",
"arxiv:2311.07911",
"base_model:GoToCompany/gemma2-9b-cpt-sahabatai-v1-base",
"base_model:finetune:GoToCompany/gemma2-9b-cpt-sahabatai-v1-base",
"license:gemma",
"region:us"
] | 2024-11-06T04:51:58 | 2024-11-06T04:51:58 | 2,211 | 35 |
---
base_model:
- GoToCompany/gemma2-9b-cpt-sahabatai-v1-base
language:
- en
- id
- jv
- su
license: gemma
---
# Gemma2 9B CPT Sahabat-AI v1 Instruct
**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Gemma2 9B CPT Sahabat-AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**. Additionally, we added a pool of **129,000 instruction-completion pairs in English**.
- **Co-initiated by:** PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
- **Developed by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
- **Model type:** Decoder
- **Languages:** English, Indonesian, Javanese, Sundanese
- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
## Model Details
### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat-AI v1](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat-AI v1 Instruct.
For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Gemma2 9B CPT Sahabat-AI V1 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the
- [SEA HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
- These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
- We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
- These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
- These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
- **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
Note: SEA HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
#### Instruction-following Capabilities
Since Gemma2 9B CPT Sahabat-AI v1 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with the [IFEval](https://arxiv.org/abs/2311.07911) dataset.
As this dataset was in English, the linguists and native speakers in the team worked together to filter, localize and translate the dataset into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalized by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
*Note*: IFEval was only used on Bahasa Indonesia. We are currently working on adding it for Javanese and Sundanese for our upcoming releases.
#### Results
#### Indonesian Results
#### SEA HELM (also known as BHASA)
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
<td style="border: 1px solid gray; padding: 8px;">36.963</td>
<td style="border: 1px solid gray; padding: 8px;">42.988</td>
<td style="border: 1px solid gray; padding: 8px;">37.805</td>
<td style="border: 1px solid gray; padding: 8px;">45.866</td>
<td style="border: 1px solid gray; padding: 8px;">46.880</td>
<td style="border: 1px solid gray; padding: 8px;">56.359</td>
<td style="border: 1px solid gray; padding: 8px;">53.725</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">61.169</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
<td style="border: 1px solid gray; padding: 8px;">46.760</td>
<td style="border: 1px solid gray; padding: 8px;">60.372</td>
<td style="border: 1px solid gray; padding: 8px;">42.022</td>
<td style="border: 1px solid gray; padding: 8px;">51.944</td>
<td style="border: 1px solid gray; padding: 8px;">54.579</td>
<td style="border: 1px solid gray; padding: 8px;">63.394</td>
<td style="border: 1px solid gray; padding: 8px;">57.221</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.154</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
<td style="border: 1px solid gray; padding: 8px;">33.956</td>
<td style="border: 1px solid gray; padding: 8px;">40.625</td>
<td style="border: 1px solid gray; padding: 8px;">41.739</td>
<td style="border: 1px solid gray; padding: 8px;">47.587</td>
<td style="border: 1px solid gray; padding: 8px;">48.012</td>
<td style="border: 1px solid gray; padding: 8px;">56.468</td>
<td style="border: 1px solid gray; padding: 8px;">56.460</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.439</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
<td style="border: 1px solid gray; padding: 8px;">30.173</td>
<td style="border: 1px solid gray; padding: 8px;">27.969</td>
<td style="border: 1px solid gray; padding: 8px;">29.654</td>
<td style="border: 1px solid gray; padding: 8px;">38.068</td>
<td style="border: 1px solid gray; padding: 8px;">38.050</td>
<td style="border: 1px solid gray; padding: 8px;">49.216</td>
<td style="border: 1px solid gray; padding: 8px;">47.495</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">54.913</td>
</tr>
</table>
#### IndoMMLU
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Meta-Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall Results</td>
<td style="border: 1px solid gray; padding: 8px;">53.0%</td>
<td style="border: 1px solid gray; padding: 8px;">56.0%</td>
<td style="border: 1px solid gray; padding: 8px;">51.9%</td>
<td style="border: 1px solid gray; padding: 8px;">53.8%</td>
<td style="border: 1px solid gray; padding: 8px;">54.4%</td>
<td style="border: 1px solid gray; padding: 8px;">61.4%</td>
<td style="border: 1px solid gray; padding: 8px;">55.6%</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">62.6%</td>
</tr>
</table>
#### English Results
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Average</td>
<td style="border: 1px solid gray; padding: 8px;">24.48</td>
<td style="border: 1px solid gray; padding: 8px;">27.75</td>
<td style="border: 1px solid gray; padding: 8px;">23.91</td>
<td style="border: 1px solid gray; padding: 8px;">27.98</td>
<td style="border: 1px solid gray; padding: 8px;">24.52</td>
<td style="border: 1px solid gray; padding: 8px;">26.44</td>
<td style="border: 1px solid gray; padding: 8px;">24.43</td>
<td style="border: 1px solid black; padding: 8px; background-color: lightgreen;">33.67</td>
</tr>
</table>
Gemma2 9B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.0
import torch
import transformers
model_id = "GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
# Javanese
messages = [
{"role": "user", "content": "Sopo wae sing ana ing Punakawan?"}
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
# Sundanese
messages = [
{"role": "user", "content": "Kumaha caritana si Kabayan?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current Sahabat-AI models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
Gemma2 9B CPT Sahabat-AI v1 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Gemma2 9B CPT Sahabat-AI v1 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.
## Call for Collaboration
Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects.
We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI.
Your collaborations can involve:
- Identifying and reporting technical issues
- Sharing pre-training, instruction, and preference data
- Improving documentation usability
- Proposing and implementing new model evaluation tasks and metrics
Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## The Development Team (in ascending alphabetical order)
### AI Singapore
Chan Adwin<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Ong Brandon<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Siow Bryan<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>
### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Chau Shiau Ching<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<br>
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## Disclaimer
This is the repository for the Instruct model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## References
### IndoMMLU Reference
```bibtex
@inproceedings{koto-etal-2023-indommlu,
title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = December,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
}
```
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION"
] |
[
"CHIA"
] |
aliakseilabanau/bge-small-en
|
aliakseilabanau
| null |
[
"safetensors",
"openvino",
"bert",
"mteb",
"sentence transformers",
"en",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"base_model:BAAI/bge-small-en",
"base_model:quantized:BAAI/bge-small-en",
"license:mit",
"region:us"
] | 2024-11-19T18:27:44 | 2025-02-04T13:09:46 | 2,173 | 0 |
---
base_model:
- BAAI/bge-small-en
language:
- en
license: mit
tags:
- mteb
- sentence transformers
- openvino
base_model_relation: quantized
---
The [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) converted for [openvino backend](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#openvino).
```
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("aliakseilabanau/bge-small-en", backend="openvino")
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
```
The original model card is below:
--------
**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"
] |
JosephusCheung/Guanaco
|
JosephusCheung
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"guannaco",
"alpaca",
"conversational",
"en",
"zh",
"ja",
"de",
"dataset:JosephusCheung/GuanacoDataset",
"doi:10.57967/hf/0607",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2023-04-08T03:03:14 | 2023-05-29T12:48:21 | 2,164 | 230 |
---
datasets:
- JosephusCheung/GuanacoDataset
language:
- en
- zh
- ja
- de
license: gpl-3.0
pipeline_tag: conversational
tags:
- llama
- guannaco
- alpaca
inference: false
---
**You can run on Colab free T4 GPU now**
[](https://colab.research.google.com/drive/1ocSmoy3ba1EkYu7JWT1oCw9vz8qC2cMk#scrollTo=zLORi5OcPcIJ)
**It is highly recommended to use fp16 inference for this model, as 8-bit precision may significantly affect performance. If you require a more Consumer Hardware friendly version, please use the specialized quantized, only 5+GB V-Ram required** [JosephusCheung/GuanacoOnConsumerHardware](https://huggingface.co/JosephusCheung/GuanacoOnConsumerHardware).
**You are encouraged to use the latest version of transformers from GitHub.**
Guanaco is an advanced instruction-following language model built on Meta's LLaMA 7B model. Expanding upon the initial 52K dataset from the Alpaca model, an additional 534K+ entries have been incorporated, covering English, Simplified Chinese, Traditional Chinese (Taiwan), Traditional Chinese (Hong Kong), Japanese, Deutsch, and various linguistic and grammatical tasks. This wealth of data enables Guanaco to perform exceptionally well in multilingual environments.
In an effort to foster openness and replicability in research, we have made the Guanaco Dataset publicly accessible and we have released the model weights here. By providing these resources, we aim to inspire more researchers to pursue related research and collectively advance the development of instruction-following language models.
[KBlueLeaf](https://huggingface.co/KBlueLeaf)’s invaluable contributions to the conceptual validation, [trained model](https://huggingface.co/KBlueLeaf/guanaco-7B-leh) and [inference development](https://github.com/KohakuBlueleaf/guanaco-lora) of the model would be gratefully acknowledged, without whose efforts the project shall never have come to fruition.
When utilizing the Guanaco model, please bear in mind the following points:
The Guanaco model has not been filtered for harmful, biased, or explicit content. As a result, outputs that do not adhere to ethical norms may be generated during use. Please exercise caution when using the model in research or practical applications.
1. ### Improved context and prompt role support:
The new format is designed to be similar to ChatGPT, allowing for better integration with the Alpaca format and enhancing the overall user experience.
Instruction is utilized as a few-shot context to support diverse inputs and responses, making it easier for the model to understand and provide accurate responses to user queries.
The format is as follows:
```
### Instruction:
User: History User Input
Assistant: History Assistant Answer
### Input:
System: Knowledge
User: New User Input
### Response:
New Assistant Answer
```
This structured format allows for easier tracking of the conversation history and maintaining context throughout a multi-turn dialogue.
3. ### Role-playing support:
Guanaco now offers advanced role-playing support, similar to Character.AI, in English, Simplified Chinese, Traditional Chinese, Japanese, and Deutsch, making it more versatile for users from different linguistic backgrounds.
Users can instruct the model to assume specific roles, historical figures, or fictional characters, as well as personalities based on their input. This allows for more engaging and immersive conversations.
The model can use various sources of information to provide knowledge and context for the character's background and behavior, such as encyclopedic entries, first-person narrations, or a list of personality traits.
The model will consistently output responses in the format "Character Name: Reply" to maintain the chosen role throughout the conversation, enhancing the user's experience.
4. ### Rejection of answers and avoidance of erroneous responses:
The model has been updated to handle situations where it lacks sufficient knowledge or is unable to provide a valid response more effectively.
Reserved keywords have been introduced to indicate different scenarios and provide clearer communication with the user, use in System Prompt:
NO IDEA: Indicates that the model lacks the necessary knowledge to provide an accurate answer, and will explain this to the user, encouraging them to seek alternative sources.
FORBIDDEN: Indicates that the model refuses to answer due to specific reasons (e.g., legal, ethical, or safety concerns), which will be inferred based on the context of the query.
SFW: Indicates that the model refuses to answer a question because it has been filtered for NSFW content, ensuring a safer and more appropriate user experience.
6. ### Continuation of responses for ongoing topics:
The Guanaco model can now continue answering questions or discussing topics upon the user's request, making it more adaptable and better suited for extended conversations.
The contextual structure consisting of System, Assistant, and User roles allows the model to engage in multi-turn dialogues, maintain context-aware conversations, and provide more coherent responses.
The model can now accommodate role specification and character settings, providing a more immersive and tailored conversational experience based on the user's preferences.
It is important to remember that Guanaco is a 7B-parameter model, and **any knowledge-based content should be considered potentially inaccurate**. We strongly recommend **providing verifiable sources in System Prompt, such as Wikipedia, for knowledge-based answers**. In the absence of sources, it is crucial to inform users of this limitation to prevent the dissemination of false information and to maintain transparency.
Due to the differences in the format between this project and [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), please refer to *Guanaco-lora: LoRA for training Multilingual Instruction-following LM based on LLaMA* (https://github.com/KohakuBlueleaf/guanaco-lora) for further training and inference our models.
## Recent News
We've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it.
Guanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental.
We, however, aim to move beyond the incremental:
1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks.
2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks.
3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM.
While our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress.
|
[
"TRANSLATION"
] |
[
"BEAR"
] |
mrm8488/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64
|
mrm8488
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2697",
"loss:MatryoshkaLoss",
"loss:CoSENTLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"base_model:nomic-ai/modernbert-embed-base",
"base_model:finetune:nomic-ai/modernbert-embed-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-10T21:44:41 | 2025-01-10T21:50:00 | 2,130 | 2 |
---
base_model: nomic-ai/modernbert-embed-base
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2697
- loss:MatryoshkaLoss
- loss:CoSENTLoss
widget:
- source_sentence: En un mercado de granjeros, se encuentra un hombre.
sentences:
- Un abogado de la CPI detenido en Libia está ahora mismo encarando un período de
detención de 45 días
- Un hombre está presente en un mercado donde se venden productos agrícolas directamente
de los agricultores.
- ¿Existe la posibilidad de que cambie de opinión si no se expresa de manera enérgica
o muestra un comportamiento inapropiado?
- source_sentence: Una mujer está posada en una postura con los brazos abiertos mientras
otra persona le toma una fotografía.
sentences:
- Un hombre se encuentra parado en medio de una multitud sujetando un objeto de
color blanco.
- Las personas están cerca del agua.
- Frente a una estatua de una vaca, hay una mujer, un niño pequeño y un bebé diminuto.
- source_sentence: Un grupo de cuatro niños está observando los diferentes animales
que están en el establo.
sentences:
- Evita apoyar todo tu peso en los brazos, ya que tus manos no están diseñadas para
soportar esa presión constante.
- Los niños están mirando atentamente a una oveja.
- Un puma persigue a un oso grande en el bosque.
- source_sentence: La gente se balancea saltando al agua mientras otros pescan en
el fondo del mar.
sentences:
- Dos individuos observan el agua con atención.
- Siempre golpeamos suavemente a nuestros hijos en la boca para mostrarles que su
boca es lo que les causa dolor.
- Aunque el sistema de prioridad al primero en llegar beneficia a dos participantes,
no asegura definitivamente la exclusión de terceros.
- source_sentence: El cordero está mirando hacia la cámara.
sentences:
- Manmohan en Teherán insta a NAM a tomar una posición clara sobre el conflicto
en Siria
- Un gato está mirando hacia la cámara también.
- '"Sí, no deseo estar presente durante este testimonio", declaró tranquilamente
Peterson, de 31 años, al juez cuando fue devuelto a su celda.'
model-index:
- name: SentenceTransformer based on nomic-ai/modernbert-embed-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.7498914121357008
name: Pearson Cosine
- type: spearman_cosine
value: 0.7531670275662775
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.7468285624371191
name: Pearson Cosine
- type: spearman_cosine
value: 0.7482342767593612
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.7419098803201045
name: Pearson Cosine
- type: spearman_cosine
value: 0.7450577925521013
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.7262860099881795
name: Pearson Cosine
- type: spearman_cosine
value: 0.7304432975238186
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.6973267849431932
name: Pearson Cosine
- type: spearman_cosine
value: 0.7069603266334332
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.8673484326459211
name: Pearson Cosine
- type: spearman_cosine
value: 0.8767387684433159
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.8665336885415594
name: Pearson Cosine
- type: spearman_cosine
value: 0.8751868367625472
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8568125590206718
name: Pearson Cosine
- type: spearman_cosine
value: 0.8702353416571491
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8485344363338887
name: Pearson Cosine
- type: spearman_cosine
value: 0.8617402150766132
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.8193790032247387
name: Pearson Cosine
- type: spearman_cosine
value: 0.8419631939550043
name: Spearman Cosine
---
# SentenceTransformer based on nomic-ai/modernbert-embed-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the stsb_multi_es_augmented (private) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision bb0033c9f3def40c3c5b26ff0b53c74f3320d703 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Private stsb dataset
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, '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()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("mrm8488/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64-5e")
# Run inference
sentences = [
'El cordero está mirando hacia la cámara.',
'Un gato está mirando hacia la cámara también.',
'"Sí, no deseo estar presente durante este testimonio", declaró tranquilamente Peterson, de 31 años, al juez cuando fue devuelto a su celda.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev-768`, `sts-dev-512`, `sts-dev-256`, `sts-dev-128`, `sts-dev-64`, `sts-test-768`, `sts-test-512`, `sts-test-256`, `sts-test-128` and `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-dev-768 | sts-dev-512 | sts-dev-256 | sts-dev-128 | sts-dev-64 | sts-test-768 | sts-test-512 | sts-test-256 | sts-test-128 | sts-test-64 |
|:--------------------|:------------|:------------|:------------|:------------|:-----------|:-------------|:-------------|:-------------|:-------------|:------------|
| pearson_cosine | 0.7499 | 0.7468 | 0.7419 | 0.7263 | 0.6973 | 0.8673 | 0.8665 | 0.8568 | 0.8485 | 0.8194 |
| **spearman_cosine** | **0.7532** | **0.7482** | **0.7451** | **0.7304** | **0.707** | **0.8767** | **0.8752** | **0.8702** | **0.8617** | **0.842** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### stsb_multi_es_augmented (private)
* Size: 2,697 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 28.42 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 28.01 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.72</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------|
| <code>El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.</code> | <code>Un ave de color amarillo descansaba tranquilamente en una rama.</code> | <code>3.200000047683716</code> |
| <code>Una chica está tocando la flauta en un parque.</code> | <code>Un grupo de músicos está tocando en un escenario al aire libre.</code> | <code>1.286</code> |
| <code>La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece</code> | <code>La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere</code> | <code>4.199999809265137</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### stsb_multi_es_augmented (private)
* Size: 697 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 697 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 29.35 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 28.52 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.3</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|
| <code>Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.</code> | <code>Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.</code> | <code>4.199999809265137</code> |
| <code>"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"</code> | <code>"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."</code> | <code>3.5</code> |
| <code>El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.</code> | <code>Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.</code> | <code>3.691999912261963</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | sts-dev-768_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-64_spearman_cosine | sts-test-768_spearman_cosine | sts-test-512_spearman_cosine | sts-test-256_spearman_cosine | sts-test-128_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|
| 0.5917 | 100 | 23.7709 | 22.5494 | 0.7185 | 0.7146 | 0.7055 | 0.6794 | 0.6570 | - | - | - | - | - |
| 1.1834 | 200 | 22.137 | 22.7634 | 0.7449 | 0.7412 | 0.7439 | 0.7287 | 0.7027 | - | - | - | - | - |
| 1.7751 | 300 | 21.5527 | 22.6985 | 0.7321 | 0.7281 | 0.7243 | 0.7063 | 0.6862 | - | - | - | - | - |
| 2.3669 | 400 | 20.5745 | 24.0021 | 0.7302 | 0.7264 | 0.7221 | 0.7097 | 0.6897 | - | - | - | - | - |
| 2.9586 | 500 | 20.0861 | 24.0091 | 0.7392 | 0.7361 | 0.7293 | 0.7124 | 0.6906 | - | - | - | - | - |
| 3.5503 | 600 | 18.8191 | 26.9012 | 0.7502 | 0.7462 | 0.7399 | 0.7207 | 0.6960 | - | - | - | - | - |
| 4.1420 | 700 | 18.3 | 29.0209 | 0.7496 | 0.7454 | 0.7432 | 0.7284 | 0.7065 | - | - | - | - | - |
| 4.7337 | 800 | 17.6496 | 28.9536 | 0.7532 | 0.7482 | 0.7451 | 0.7304 | 0.7070 | - | - | - | - | - |
| 5.0 | 845 | - | - | - | - | - | - | - | 0.8767 | 0.8752 | 0.8702 | 0.8617 | 0.8420 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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|
[
"TEXT_CLASSIFICATION",
"SEMANTIC_SIMILARITY"
] |
[
"CPI"
] |
aisingapore/gemma2-9b-cpt-sea-lionv3-instruct
|
aisingapore
|
text-generation
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2311.07911",
"arxiv:2306.05685",
"base_model:aisingapore/gemma2-9b-cpt-sea-lionv3-base",
"base_model:finetune:aisingapore/gemma2-9b-cpt-sea-lionv3-base",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-10-30T03:19:20 | 2024-12-19T12:57:01 | 2,114 | 10 |
---
base_model:
- aisingapore/gemma2-9b-cpt-sea-lionv3-base
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
library_name: transformers
license: gemma
pipeline_tag: text-generation
---
<div>
<img src="gemma_2_9b_sea-lion_v3_instruct_banner.png"/>
</div>
# Gemma2 9B CPT SEA-LIONv3 Instruct
SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
Gemma2 9B CPT SEA-LIONv3 Instruct is a multilingual model which has been fine-tuned with around **500,000 English instruction-completion pairs** alongside a larger pool of around **1,000,000 instruction-completion pairs** from other ASEAN languages, such as Indonesian, Thai and Vietnamese.
SEA-LION stands for _Southeast Asian Languages In One Network_.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
## Model Details
### Model Description
We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our [continued pre-trained Gemma2 9B CPT SEA-LIONv3](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT SEA-LIONv3 Instruct.
For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Gemma2 9B CPT SEA-LIONv3 Instruct on both general language capabilities and instruction-following capabilities.
#### General Language Capabilities
For the evaluation of general language capabilities, we employed the [SEA HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
Note: SEA HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
#### Instruction-following Capabilities
Since Gemma2 9B CPT SEA-LIONv3 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, [IFEval](https://arxiv.org/abs/2311.07911) and [MT-Bench](https://arxiv.org/abs/2306.05685).
As these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localize and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalized by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
**MT-Bench**
MT-Bench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction). A tie is given a score of 0.5.
For more details on Gemma2 9B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
### Usage
**NOTE** This model has not been trained to use a system prompt or to use tool calling.
Gemma2 9B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.2
import transformers
import torch
model_id = "aisingapore/gemma2-9b-cpt-sea-lionv3-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Technical Specifications
### Fine-Tuning Details
Gemma2 9B CPT SEA-LIONv3 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 15 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Gemma2 9B CPT SEA-LIONv3 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.
## Indonesian, Javanese & Sudanese Specific SEA-LION
Our partners at GoTo have continued pretrained and instruction tuned a variant of Gemma2 9B CPT SEA-LIONv3, specifically enhancing its capabilities for Indonesian, Javanese, and Sundanese languages. Find the continued pretrained model at [Gemma2 9B CPT SahabatAIv1 Base](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), and its corresponding instructioned tuned version at [Gemma2 9B CPT SahabatAIv1 Instruct](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct).
## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.
## The Team
Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
## Disclaimer
This is the repository for the commercial instruction-tuned model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
|
[
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION"
] |
[
"CHIA"
] |
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