Datasets:
Euanyu
/
bigbio_dataset_models_BioNLP_results

Dataset card Data Studio Files Community
id
stringlengths
9
104
author
stringlengths
3
36
task_category
stringclasses
32 values
tags
sequencelengths
1
4.05k
created_time
int64
1,646B
1,742B
last_modified
timestamp[s]date
2021-02-13 00:06:56
2025-03-18 09:30:19
downloads
int64
0
15.6M
likes
int64
0
4.86k
README
stringlengths
44
1.01M
matched_bigbio_names
sequencelengths
1
8
is_bionlp
stringclasses
3 values
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" ]
1,675,978,924,000
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>
[ "SCIQ" ]
Non_BioNLP
LoneStriker/BioMistral-7B-TIES-6.0bpw-h6-exl2
LoneStriker
text-generation
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "ties", "medical", "biology", "conversational", "fr", "en", "pl", "es", "it", "ro", "de", "nl", "dataset:pubmed", "arxiv:2306.01708", "arxiv:2402.10373", "base_model:BioMistral/BioMistral-7B", "base_model:merge:BioMistral/BioMistral-7B", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:merge:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,708,358,824,000
2024-02-19T16:10:06
10
0
--- base_model: - mistralai/Mistral-7B-Instruct-v0.1 - BioMistral/BioMistral-7B datasets: - pubmed language: - fr - en - pl - es - it - ro - de - nl library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - mergekit - merge - ties - medical - biology --- # BioMistral-7B-mistral7instruct-ties This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as a base. ### Models Merged The following models were included in the merge: * [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-Instruct-v0.1 - model: BioMistral/BioMistral-7B parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: normalize: true dtype: bfloat16 ``` <p align="center"> <img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/> </p> # BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains **Abstract:** Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. **Advisory Notice!** Although BioMistral is intended to encapsulate medical knowledge sourced from high-quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real-world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real-world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generation or any professional health and medical purposes. # 1. BioMistral models **BioMistral** is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC. | Model Name | Base Model | Model Type | Sequence Length | Download | |:-------------------:|:----------------------------------:|:-------------------:|:---------------:|:-----------------------------------------------------:| | BioMistral-7B | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Further Pre-trained | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) | | BioMistral-7B-DARE | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge DARE | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) | | BioMistral-7B-TIES | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge TIES | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) | | BioMistral-7B-SLERP | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge SLERP | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) | # 2. Quantized Models | Base Model | Method | q_group_size | w_bit | version | VRAM GB | Time | Download | |:-------------------:|:------:|:------------:|:-----:|:-------:|:-------:|:------:|:--------:| | BioMistral-7B | FP16/BF16 | | | | 15.02 | x1.00 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) | | BioMistral-7B | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) | | BioMistral-7B | AWQ | 128 | 4 | GEMV | 4.68 | x10.30 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) | | BioMistral-7B | BnB.4 | | 4 | | 5.03 | x3.25 | [HuggingFace](blank) | | BioMistral-7B | BnB.8 | | 8 | | 8.04 | x4.34 | [HuggingFace](blank) | | BioMistral-7B-DARE | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) | | BioMistral-7B-TIES | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) | | BioMistral-7B-SLERP | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) | # 2. Using BioMistral You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow. Loading the model and tokenizer : ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B") model = AutoModel.from_pretrained("BioMistral/BioMistral-7B") ``` # 3. Supervised Fine-tuning Benchmark | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA | MedQA 5 opts | PubMedQA | MedMCQA | Avg. | |-------------------------------------------|:---------------------------------------------:|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|------------------| | **BioMistral 7B** | 59.9 | 64.0 | 56.5 | 60.4 | 59.0 | 54.7 | 50.6 | 42.8 | 77.5 | 48.1 | 57.3 | | **Mistral 7B Instruct** | **62.9** | 57.0 | 55.6 | 59.4 | 62.5 | <u>57.2</u> | 42.0 | 40.9 | 75.7 | 46.1 | 55.9 | | | | | | | | | | | | | | | **BioMistral 7B Ensemble** | <u>62.8</u> | 62.7 | <u>57.5</u> | **63.5** | 64.3 | 55.7 | 50.6 | 43.6 | 77.5 | **48.8** | 58.7 | | **BioMistral 7B DARE** | 62.3 | **67.0** | 55.8 | 61.4 | **66.9** | **58.0** | **51.1** | **45.2** | <u>77.7</u> | <u>48.7</u> | **59.4** | | **BioMistral 7B TIES** | 60.1 | <u>65.0</u> | **58.5** | 60.5 | 60.4 | 56.5 | 49.5 | 43.2 | 77.5 | 48.1 | 57.9 | | **BioMistral 7B SLERP** | 62.5 | 64.7 | 55.8 | <u>62.7</u> | <u>64.8</u> | 56.3 | <u>50.8</u> | <u>44.3</u> | **77.8** | 48.6 | <u>58.8</u> | | | | | | | | | | | | | | | **MedAlpaca 7B** | 53.1 | 58.0 | 54.1 | 58.8 | 58.1 | 48.6 | 40.1 | 33.7 | 73.6 | 37.0 | 51.5 | | **PMC-LLaMA 7B** | 24.5 | 27.7 | 35.3 | 17.4 | 30.3 | 23.3 | 25.5 | 20.2 | 72.9 | 26.6 | 30.4 | | **MediTron-7B** | 41.6 | 50.3 | 46.4 | 27.9 | 44.4 | 30.8 | 41.6 | 28.1 | 74.9 | 41.3 | 42.7 | | **BioMedGPT-LM-7B** | 51.4 | 52.0 | 49.4 | 53.3 | 50.7 | 49.1 | 42.5 | 33.9 | 76.8 | 37.6 | 49.7 | | | | | | | | | | | | | | | **GPT-3.5 Turbo 1106*** | 74.71 | 74.00 | 65.92 | 72.79 | 72.91 | 64.73 | 57.71 | 50.82 | 72.66 | 53.79 | 66.0 | Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT. # Citation BibTeX Arxiv : [https://arxiv.org/abs/2402.10373](https://arxiv.org/abs/2402.10373) ```bibtex @misc{labrak2024biomistral, title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour}, year={2024}, eprint={2402.10373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` **CAUTION!** Both direct and downstream users need to be informed about the risks, biases, and constraints inherent in the model. While the model can produce natural language text, our exploration of its capabilities and limitations is just beginning. In fields such as medicine, comprehending these limitations is crucial. Hence, we strongly advise against deploying this model for natural language generation in production or for professional tasks in the realm of health and medicine.
[ "MEDQA", "PUBMEDQA" ]
BioNLP
binbin83/setfit-MiniLM-dialog-act-fr
binbin83
text-classification
[ "sentence-transformers", "safetensors", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
1,706,696,844,000
2024-01-31T10:39:25
5
0
--- license: apache-2.0 pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification --- # binbin83/setfit-MiniLM-dialog-act-13nov The model is a multi-class multi-label text classifier to distinguish the different dialog act in semi-structured interview. The data used fot fine-tuning were in French. This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("binbin83/setfit-MiniLM-dialog-act-13nov") label_dict = {'Introductory': 0, 'FollowUp': 1, 'Probing': 2, 'Specifying': 3, 'Structuring': 4, 'DirectQuestion': 5, 'Interpreting': 6, 'Ending': 7} # Run inference preds = model(["Vous pouvez continuer", "Pouvez-vous me dire précisément quel a été l'odre chronologique des événements ?"]) labels = [[[f for f, p in zip(labels_dict, ps) if p] for ps in [pred]] for pred in preds ] ``` ## Labels and training data Brinkmann, S., & Kvale, S (1), define classification of dialog act in interview: * Introductory: Can you tell me about ... (something specific)?, * Follow-up verbal cues: repeat back keywords to participants, ask for reflection or unpacking of point just made, * Probing: Can you say a little more about X? Why do you think X...? (for example, Why do you think X is that way? Why do you think X is important?), * Specifying: Can you give me an example of X?, * Indirect: How do you think other people view X?, * Structuring: Thank you for that. I’d like to move to another topic... * Direct (later stages): When you mention X, are you thinking like Y or Z?, * Interpreting: So, what I have gathered is that..., * Ending: I have asked all the questions I had, but I wanted to check whether there is something else about your experience/understanding we haven’t covered? Do you have any questions for me?, On our corpus of interviews, we humanly label 500 turn of speech using this classification. We use 0.7 to train and evaluate on 0.3. The entire corpus is composed of the following examples: ('DirectQuestion', 23), ('Probing', 15), ('Interpreting', 15), ('Specifying', 14), ('Structuring', 7), ('FollowUp', 6), ('Introductory', 5), ('Ending', 5) (1) Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the Craft of Qualitative Research Interviewing. (3. ed.) SAGE Publications. ## Training and Performances We finetune: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" using SetFit with CosineLossSimilarity and this parapeters: epochs = 20, batch_size=32, num_iterations = 50 On the test dataset : ('Probing', 146), ('Specifying', 135), ('FollowUp', 134), ('DirectQuestion', 125), ('Interpreting', 44), ('Structuring', 27), ('Introductory', 12), ('Ending', 12) On our test dataset, we get this results: {'f1': 0.35005547563028, 'f1_micro': 0.3686131386861314, 'f1_sample': 0.3120075046904315, 'accuracy': 0.19887429643527205} ## BibTeX entry and citation info To cite the current study: ```bibtex @article{ doi = {conference paper}, url = {https://arxiv.org/abs/2209.11055}, author = {Quillivic Robin, Charles Payet}, keywords = {NLP, JADT}, title = {Semi-Structured Interview Analysis: A French NLP Toolbox for Social Sciences}, publisher = {JADT}, year = {2024}, copyright = {Creative Commons Attribution 4.0 International} } ``` To cite the setFit paper: ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
[ "CRAFT" ]
Non_BioNLP
microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
microsoft
fill-mask
[ "transformers", "pytorch", "jax", "bert", "fill-mask", "exbert", "en", "arxiv:2007.15779", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,646,263,745,000
2023-11-06T18:03:43
124,839
227
--- language: en license: mit tags: - exbert widget: - text: '[MASK] is a tumor suppressor gene.' --- ## MSR BiomedBERT (abstracts + full text) <div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;"> * This model was previously named **"PubMedBERT (abstracts + full text)"**. * You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" or update your `transformers` library to version 4.22+ if you need to refer to the old name. </div> Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/) and _full-text_ articles from [PubMedCentral](https://www.ncbi.nlm.nih.gov/pmc/). This model achieves state-of-the-art performance on many biomedical NLP tasks, and currently holds the top score on the [Biomedical Language Understanding and Reasoning Benchmark](https://aka.ms/BLURB). ## Citation If you find BiomedBERT useful in your research, please cite the following paper: ```latex @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, year = {2020}, eprint = {arXiv:2007.15779}, } ``` <a href="https://huggingface.co/exbert/?model=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=3&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
[ "BLURB" ]
BioNLP
dzanbek/fd3b6340-e602-4cbf-86a8-773f84a73015
dzanbek
null
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "base_model:adapter:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "region:us" ]
1,736,776,518,000
2025-01-13T15:30:14
1
0
--- base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 library_name: peft tags: - axolotl - generated_from_trainer model-index: - name: fd3b6340-e602-4cbf-86a8-773f84a73015 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6d6f61a5e2d2e90a_train_data.json ds_type: json format: custom path: /workspace/input_data/6d6f61a5e2d2e90a_train_data.json type: field_input: context field_instruction: question field_output: final_decision format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: dzanbek/fd3b6340-e602-4cbf-86a8-773f84a73015 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/6d6f61a5e2d2e90a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 25dafc7c-5eb3-4bf6-b3fd-a340821007ea wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 25dafc7c-5eb3-4bf6-b3fd-a340821007ea warmup_steps: 10 weight_decay: 0.01 xformers_attention: true ``` </details><br> # fd3b6340-e602-4cbf-86a8-773f84a73015 This model is a fine-tuned version of [rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28](https://huggingface.co/rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 2.0510 | | 1.8879 | 0.0003 | 8 | 1.6765 | | 1.4689 | 0.0006 | 16 | 1.4930 | | 1.4127 | 0.0010 | 24 | 1.4638 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
[ "PUBMEDQA" ]
BioNLP
exafluence/EXF-Medistral-Nemo-12B-4bit
exafluence
null
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "Mistral-Nemo-Base-2407", "Medical", "Healthcare", "Open-MedQA-Nexus", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
1,729,272,895,000
2024-10-19T13:42:56
0
0
--- base_model: unsloth/mistral-nemo-instruct-2407-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - Mistral-Nemo-Base-2407 - Medical - Healthcare - Open-MedQA-Nexus --- # EXF-Medistral-Nemo-12B ## Model Description **EXF-Medistral-Nemo-12B** is a fine-tuned version of the **Mistral-Nemo-12B** model, optimized for tasks in the medical domain. It has been trained on the **Open-Nexus-MedQA** dataset, which integrates a wide range of medical knowledge from public datasets like **ChatDoctor**, **icliniq**, and others, to enhance the model’s ability to answer medical questions accurately and reliably. This model is designed to assist in clinical decision support, medical coding, and patient care by generating responses based on comprehensive medical knowledge. ## Model Architecture - **Base Model**: Mistral-Nemo-12B - **Parameters**: 12 billion - **Fine-tuning Dataset**: Open-Nexus-MedQA - **Task**: Medical question-answering (QA), medical coding, and healthcare information retrieval. ## Training Data The model was fine-tuned on the **Open-Nexus-MedQA** dataset, which aggregates data from multiple medical QA sources such as: - **ChatDoctor** - **icliniq.com** - **HealthCareMagic** - **CareQA** - **MedInstruct** The dataset contains medical queries ranging from simple conditions to complex diagnoses, accompanied by accurate, domain-specific responses, making it a robust training source for real-world medical applications. ## Intended Use **EXF-Medistral-Nemo-12B** is ideal for: - **Medical Question-Answering**: It can be used for generating responses to patient queries or supporting healthcare professionals with clinical information. - **Medical Coding**: The model supports tasks related to **CMS**, **OASIS**, **ICD-10**, and other coding systems. - **Clinical Decision Support**: Assisting doctors and healthcare providers by offering evidence-based suggestions or answers. - **Patient Care Tools**: Powering medical chatbots or virtual assistants for patients seeking health information. ## Performance The model has been fine-tuned for precision in the medical domain, demonstrating high accuracy in understanding and generating responses to complex medical queries. It excels in: - **Medical terminology comprehension** - **Providing accurate ICD-10 and CMS codes** - **Generating medically relevant and safe answers** ## Limitations - **Not a Diagnostic Tool**: This model is not intended to replace medical professionals or provide definitive medical diagnoses. Always consult with a licensed healthcare provider for medical advice. - **Training Data Bias**: The dataset is based on publicly available medical QA data, which might not cover all edge cases or international healthcare systems. ## How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("exafluence/EXF-Medistral-Nemo-12B-4bit") model = AutoModelForCausalLM.from_pretrained("exafluence/EXF-Medistral-Nemo-12B-4bit") input_text = "What are the symptoms of type 2 diabetes?" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs, skip_special_tokens=True)) ``` ## License This model is provided under a proprietary license. Usage is restricted to non-commercial purposes unless explicit permission is granted. Citation If you use this model, please cite: ```bibtex @inproceedings{exafluence2024EXFMedistralNemo12B, title={EXF-Medistral-Nemo-12B: A Fine-Tuned Medical Language Model for Healthcare Applications}, author={Exafluence Inc.}, year={2024}, url={https://huggingface.co/exafluence/EXF-Medistral-Nemo-12B} doi={https://doi.org/10.57967/hf/3284} } ``` ## Contact For any questions or inquiries regarding usage, licensing, or access, please contact Exafluence Inc. # Uploaded model - **Developed by:** exafluence - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-nemo-instruct-2407-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[ "MEDQA" ]
BioNLP
lhong4759/6926fa73-9c18-487b-ade4-a5bdca5efd9f
lhong4759
null
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "base_model:adapter:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "8-bit", "bitsandbytes", "region:us" ]
1,736,776,310,000
2025-01-13T15:07:25
1
0
--- base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 library_name: peft tags: - axolotl - generated_from_trainer model-index: - name: 6926fa73-9c18-487b-ade4-a5bdca5efd9f results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9687e4b10aa5235f_train_data.json ds_type: json format: custom path: /workspace/input_data/9687e4b10aa5235f_train_data.json type: field_input: context field_instruction: question field_output: final_decision format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: lhong4759/6926fa73-9c18-487b-ade4-a5bdca5efd9f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/9687e4b10aa5235f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 7dc6f577-ea4d-4a37-a58b-fdcc42f9a448 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 7dc6f577-ea4d-4a37-a58b-fdcc42f9a448 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 6926fa73-9c18-487b-ade4-a5bdca5efd9f This model is a fine-tuned version of [rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28](https://huggingface.co/rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0001 | 0.0080 | 200 | 0.0447 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
[ "PUBMEDQA" ]
BioNLP
RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf
RichardErkhov
null
[ "gguf", "endpoints_compatible", "region:us" ]
1,719,303,190,000
2024-06-25T12:58:47
47
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) JSL-Med-Sft-Llama-3-8B - GGUF - Model creator: https://huggingface.co/johnsnowlabs/ - Original model: https://huggingface.co/johnsnowlabs/JSL-Med-Sft-Llama-3-8B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [JSL-Med-Sft-Llama-3-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q2_K.gguf) | Q2_K | 2.96GB | | [JSL-Med-Sft-Llama-3-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.IQ3_XS.gguf) | IQ3_XS | 3.28GB | | [JSL-Med-Sft-Llama-3-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.IQ3_S.gguf) | IQ3_S | 3.43GB | | [JSL-Med-Sft-Llama-3-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q3_K_S.gguf) | Q3_K_S | 3.41GB | | [JSL-Med-Sft-Llama-3-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.IQ3_M.gguf) | IQ3_M | 3.52GB | | [JSL-Med-Sft-Llama-3-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q3_K.gguf) | Q3_K | 3.74GB | | [JSL-Med-Sft-Llama-3-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q3_K_M.gguf) | Q3_K_M | 3.74GB | | [JSL-Med-Sft-Llama-3-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q3_K_L.gguf) | Q3_K_L | 4.03GB | | [JSL-Med-Sft-Llama-3-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.IQ4_XS.gguf) | IQ4_XS | 4.18GB | | [JSL-Med-Sft-Llama-3-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q4_0.gguf) | Q4_0 | 4.34GB | | [JSL-Med-Sft-Llama-3-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.IQ4_NL.gguf) | IQ4_NL | 4.38GB | | [JSL-Med-Sft-Llama-3-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q4_K_S.gguf) | Q4_K_S | 4.37GB | | [JSL-Med-Sft-Llama-3-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q4_K.gguf) | Q4_K | 4.58GB | | [JSL-Med-Sft-Llama-3-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q4_K_M.gguf) | Q4_K_M | 4.58GB | | [JSL-Med-Sft-Llama-3-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q4_1.gguf) | Q4_1 | 4.78GB | | [JSL-Med-Sft-Llama-3-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q5_0.gguf) | Q5_0 | 5.21GB | | [JSL-Med-Sft-Llama-3-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q5_K_S.gguf) | Q5_K_S | 5.21GB | | [JSL-Med-Sft-Llama-3-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q5_K.gguf) | Q5_K | 5.34GB | | [JSL-Med-Sft-Llama-3-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q5_K_M.gguf) | Q5_K_M | 5.34GB | | [JSL-Med-Sft-Llama-3-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q5_1.gguf) | Q5_1 | 5.65GB | | [JSL-Med-Sft-Llama-3-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q6_K.gguf) | Q6_K | 6.14GB | | [JSL-Med-Sft-Llama-3-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/johnsnowlabs_-_JSL-Med-Sft-Llama-3-8B-gguf/blob/main/JSL-Med-Sft-Llama-3-8B.Q8_0.gguf) | Q8_0 | 7.95GB | Original model description: --- tags: - llama-3-8b - sft - medical base_model: - meta-llama/Meta-Llama-3-8B license: cc-by-nc-nd-4.0 --- # JSL-Med-Sft-Llama-3-8B [<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com) This model is developed by [John Snow Labs](https://www.johnsnowlabs.com/). This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at [email protected]. ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "johnsnowlabs/JSL-Med-Sft-Llama-3-8B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## 🏆 Evaluation | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |-------------------------------|-------|------|-----:|--------|-----:|---|-----:| |stem |N/A |none | 0|acc_norm|0.5803|± |0.0067| | | |none | 0|acc |0.6141|± |0.0057| | - medmcqa |Yaml |none | 0|acc |0.5752|± |0.0076| | | |none | 0|acc_norm|0.5752|± |0.0076| | - medqa_4options |Yaml |none | 0|acc |0.5970|± |0.0138| | | |none | 0|acc_norm|0.5970|± |0.0138| | - anatomy (mmlu) | 0|none | 0|acc |0.6963|± |0.0397| | - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7472|± |0.0267| | - college_biology (mmlu) | 0|none | 0|acc |0.7847|± |0.0344| | - college_medicine (mmlu) | 0|none | 0|acc |0.6185|± |0.0370| | - medical_genetics (mmlu) | 0|none | 0|acc |0.8300|± |0.0378| | - professional_medicine (mmlu)| 0|none | 0|acc |0.7022|± |0.0278| | - pubmedqa | 1|none | 0|acc |0.7480|± |0.0194| |Groups|Version|Filter|n-shot| Metric |Value | |Stderr| |------|-------|------|-----:|--------|-----:|---|-----:| |stem |N/A |none | 0|acc_norm|0.5803|± |0.0067| | | |none | 0|acc |0.6141|± |0.0057|
[ "MEDQA", "PUBMEDQA" ]
BioNLP
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443
fine-tuned
feature-extraction
[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,716,912,319,000
2024-05-28T16:05:51
6
0
--- datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443 - allenai/c4 language: - en - en license: apache-2.0 pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [**BAAI/bge-large-en-v1.5**](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
[ "SCIFACT" ]
Non_BioNLP
Salesforce/xgen-mm-phi3-mini-instruct-r-v1
Salesforce
image-text-to-text
[ "transformers", "safetensors", "xgenmm", "feature-extraction", "image-text-to-text", "conversational", "custom_code", "en", "arxiv:2408.08872", "license:cc-by-nc-4.0", "region:us" ]
1,714,972,746,000
2025-02-03T06:26:42
843
185
--- language: - en license: cc-by-nc-4.0 pipeline_tag: image-text-to-text --- # 📣 News 📌 [08/19/2024] xGen-MM-v1.5 released: - [🤗 xgen-mm-phi3-mini-instruct-interleave-r-v1.5](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-interleave-r-v1.5) - [🤗 xgen-mm-phi3-mini-base-r-v1.5](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-base-r-v1.5) - [🤗 xgen-mm-phi3-mini-instruct-singleimg-r-v1.5](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-singleimg-r-v1.5) - [🤗 xgen-mm-phi3-mini-instruct-dpo-r-v1.5](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-dpo-r-v1.5) # Model description We are excited to announce the continuation and rebranding of our **BLIP series** into **XGen-MM**, to be better aligned with Salesforce's unified XGen initiative for large foundation models! This rebranding marks a significant step in our ongoing development of cutting-edge multimodal technologies. `XGen-MM` is a series of the latest foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This series advances upon the successful designs of the `BLIP` series, incorporating fundamental enhancements that ensure a more robust and superior foundation. \ These models have been trained at scale on high-quality image caption datasets and interleaved image-text data. XGen-MM highlights a few features below, * The **pretrained** foundation model, `xgen-mm-phi3-mini-base-r-v1`, achieves state-of-the-art performance under 5b parameters and demonstrates strong in-context learning capabilities. * The **instruct** fine-tuned model, `xgen-mm-phi3-mini-instruct-r-v1`, achieves state-of-the-art performance among open-source and closed-source VLMs under 5b parameters. * `xgen-mm-phi3-mini-instruct-r-v1` supports flexible high-resolution image encoding with efficient visual token sampling. More technical details will come with a technical report soon. # Results ### Pretrain (base model without instruction tuning) | Model | Shot | COCO (val) | NoCaps (val) | TextCaps (val) | OKVQA (val) | TextVQA (val) | VizWiz (testdev) | VQAv2 (testdev) | |-------------|------|------------|--------------|----------------|--------------|---------------|------------------|-----------------| | Flamingo-3B | 4 | 85.0 | - | - | 43.3 | 32.7 | 34 | 53.2 | | | 8 | 90.6 | - | - | 44.6 | 32.4 | 38.4 | 55.4 | | MM1-3B | 0 | 73.5 | 55.6 | 63.3 | 26.1 | 29.4 | 15.6 | 46.2 | | | 4 | 112.3 | 99.7 | 84.1 | 48.6 | 45.3 | 38.0 | 57.9 | | | 8 | 114.6 | 104.7 | 88.8 | 48.4 | 44.6 | 46.4 | 63.6 | | **xgen-mm-phi3-mini-base-r-v1 (Ours)**| 0 | **81.7** | **80.2** | 60.7 | **26.5** | **36.0** | **21.2** | **48.1** | | | 4 | 110.5 | **101.7** | **84.6** | **49.2** | **46.1** | **38.4** | **63.9** | | | 8 | 112.1 | 104.4 | 87.7 | **49.1** | **46.4** | 44.3 | **63.8** | ### Instruct (after instruction tuning) | Model | SEED-IMG | MMBench(dev) | MME-total | MME-P | MME-C | MMStar | MMMU (val) | MMVet | MathVista (mini) | ScienceQA (test) | POPE | AI2D | | |----------------------------|----------|--------------|-----------|----------|---------|----------|------------|----------|------------------|------------------|----------|----------|---| | MM1-3B-Chat | 68.8 | 67.8 | 1761 | **1482** | 279 | - | 33.9 | 43.7 | - | - | **87.4** | - | | | openbmb/MiniCPM-V-2 | 67.1 | 69.6 | 1808 | - | - | - | 38.2 | - | 38.7 | - | - | - | | | VILA1.5-3B | 67.9 | 63.4 | - | 1442 | - | - | 33.3 | 35.4 | - | 69.0 | 85.9 | - | | | xtuner/llava-phi-3-mini-hf | 70.0 | 69.2 | 1790 | 1477 | 313 | 43.7 | **41.4** | - | - | 73.7 | 87.3 | 69.3 | | | **xgen-mm-phi3-mini-instruct-r-v1 (Ours)** | **72.1** | **74.1** | **1827** | 1467 | **360** | **44.6** | 39.8 | **45.1** | **39.3** | **74.2** | 87.2 | **75.8** | | # How to use ~~> We require the use of the development version (`"4.41.0.dev0"`) of the `transformers` library. To get it, as of 05/07/2024, one can use `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers.`~~ ```python from transformers import AutoModelForVision2Seq, AutoTokenizer, AutoImageProcessor, StoppingCriteria import torch import requests from PIL import Image # define the prompt template def apply_prompt_template(prompt): s = ( '<|system|>\nA chat between a curious user and an artificial intelligence assistant. ' "The assistant gives helpful, detailed, and polite answers to the user's questions.<|end|>\n" f'<|user|>\n<image>\n{prompt}<|end|>\n<|assistant|>\n' ) return s class EosListStoppingCriteria(StoppingCriteria): def __init__(self, eos_sequence = [32007]): self.eos_sequence = eos_sequence def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: last_ids = input_ids[:,-len(self.eos_sequence):].tolist() return self.eos_sequence in last_ids # load models model_name_or_path = "Salesforce/xgen-mm-phi3-mini-instruct-r-v1" model = AutoModelForVision2Seq.from_pretrained(model_name_or_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, use_fast=False, legacy=False) image_processor = AutoImageProcessor.from_pretrained(model_name_or_path, trust_remote_code=True) tokenizer = model.update_special_tokens(tokenizer) # craft a test sample img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') query = "how many dogs are in the picture?" model = model.cuda() inputs = image_processor([raw_image], return_tensors="pt", image_aspect_ratio='anyres') prompt = apply_prompt_template(query) language_inputs = tokenizer([prompt], return_tensors="pt") inputs.update(language_inputs) inputs = {name: tensor.cuda() for name, tensor in inputs.items()} generated_text = model.generate(**inputs, image_size=[raw_image.size], pad_token_id=tokenizer.pad_token_id, do_sample=False, max_new_tokens=768, top_p=None, num_beams=1, stopping_criteria = [EosListStoppingCriteria()], ) prediction = tokenizer.decode(generated_text[0], skip_special_tokens=True).split("<|end|>")[0] print("==> prediction: ", prediction) # output: ==> prediction: There is one dog in the picture. ``` More comprehensive examples can be found in the [notebook](demo.ipynb). # Reproducibility: Our SFT evaluation is based on the VLMEvalKit, in which we fixed some inconsistencies with the official benchmarks (e.g., LLM judge API). During our development, we noticed that the raw resolution of the input image would noticeably affect the model output in some cases. # Bias, Risks, Limitations, and Ethical Considerations The main data sources are from the internet, including webpages, image stock sites, and curated datasets released by the research community. We have excluded certain data, such as LAION, due to known CSAM concerns. The model may be subject to bias from the original data source, as well as bias from LLMs and commercial APIs. We strongly recommend users assess safety and fairness before applying to downstream applications. # 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 and AI AUP. # License Our code and weights are released under the Creative Commons Attribution Non Commercial 4.0 [LICENSE](LICENSE.txt). Please fill out a form at [here](https://forms.gle/ffPc9oZC2ZGeJ1N68) to consult the commercial use of model weights. # Code acknowledgment [LAVIS](https://github.com/salesforce/LAVIS) \ [openflamingo](https://github.com/mlfoundations/open_flamingo) \ [VLMEvalKit](https://github.com/open-compass/VLMEvalKit/tree/main) # Citation ``` @misc{xue2024xgenmmblip3familyopen, title={xGen-MM (BLIP-3): A Family of Open Large Multimodal Models}, author={Le Xue and Manli Shu and Anas Awadalla and Jun Wang and An Yan and Senthil Purushwalkam and Honglu Zhou and Viraj Prabhu and Yutong Dai and Michael S Ryoo and Shrikant Kendre and Jieyu Zhang and Can Qin and Shu Zhang and Chia-Chih Chen and Ning Yu and Juntao Tan and Tulika Manoj Awalgaonkar and Shelby Heinecke and Huan Wang and Yejin Choi and Ludwig Schmidt and Zeyuan Chen and Silvio Savarese and Juan Carlos Niebles and Caiming Xiong and Ran Xu}, year={2024}, eprint={2408.08872}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2408.08872}, } ``` # Troubleshoot 1. If you missed any packages, please consider the following ``` pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121 pip install open_clip_torch==2.24.0 pip install einops pip install einops-exts pip install transformers==4.41.1 ``` # Changelog * 05/24/2024 * update codebase to be compatible with `transformers==4.41.1`.
[ "CHIA", "CRAFT" ]
Non_BioNLP
minishlab/M2V_base_glove_subword
minishlab
null
[ "model2vec", "onnx", "safetensors", "embeddings", "static-embeddings", "mteb", "sentence-transformers", "en", "base_model:BAAI/bge-base-en-v1.5", "base_model:quantized:BAAI/bge-base-en-v1.5", "license:mit", "model-index", "region:us" ]
1,727,893,116,000
2025-01-21T19:18:20
44
2
--- base_model: BAAI/bge-base-en-v1.5 language: - en library_name: model2vec license: mit tags: - embeddings - static-embeddings - mteb - sentence-transformers model-index: - name: M2V_base_glove_subword 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: 66.4167916041979 - type: ap value: 18.202949885376736 - type: ap_weighted value: 18.202949885376736 - type: f1 value: 54.98453722214898 - type: f1_weighted value: 72.84623161234782 - type: main_score value: 66.4167916041979 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 68.044776119403 - type: ap value: 31.604323176091363 - type: ap_weighted value: 31.604323176091363 - type: f1 value: 62.53323789238326 - type: f1_weighted value: 71.2243167389672 - type: main_score value: 68.044776119403 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification (default) type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 67.21602499999999 - type: ap value: 62.24635378305934 - type: ap_weighted value: 62.24635378305934 - type: f1 value: 66.68107362746888 - type: f1_weighted value: 66.68107362746888 - type: main_score value: 67.21602499999999 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 32.384 - type: f1 value: 32.05276706247388 - type: f1_weighted value: 32.05276706247388 - type: main_score value: 32.384 - task: type: Retrieval dataset: name: MTEB ArguAna (default) type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: main_score value: 29.599999999999998 - type: map_at_1 value: 14.438 - type: map_at_10 value: 23.803 - type: map_at_100 value: 24.85 - type: map_at_1000 value: 24.925 - type: map_at_20 value: 24.395 - type: map_at_3 value: 20.519000000000002 - type: map_at_5 value: 22.183 - type: mrr_at_1 value: 14.65149359886202 - type: mrr_at_10 value: 23.8787847998374 - type: mrr_at_100 value: 24.945306088918446 - type: mrr_at_1000 value: 25.019829460538446 - type: mrr_at_20 value: 24.48722055512828 - type: mrr_at_3 value: 20.661450924608815 - type: mrr_at_5 value: 22.254623044096704 - type: nauc_map_at_1000_diff1 value: 11.677995826704251 - type: nauc_map_at_1000_max value: -1.7036225489906935 - type: nauc_map_at_1000_std value: 13.608156164552337 - type: nauc_map_at_100_diff1 value: 11.69898827728831 - type: nauc_map_at_100_max value: -1.6896771319000576 - type: nauc_map_at_100_std value: 13.657417732243642 - type: nauc_map_at_10_diff1 value: 11.381029737026354 - type: nauc_map_at_10_max value: -1.7701185174946374 - type: nauc_map_at_10_std value: 12.878108250073275 - type: nauc_map_at_1_diff1 value: 13.270492079181698 - type: nauc_map_at_1_max value: -5.320050131923338 - type: nauc_map_at_1_std value: 9.145476528935111 - type: nauc_map_at_20_diff1 value: 11.636255256667027 - type: nauc_map_at_20_max value: -1.5972839976414983 - type: nauc_map_at_20_std value: 13.42888801202754 - type: nauc_map_at_3_diff1 value: 10.870897941570064 - type: nauc_map_at_3_max value: -3.2129671196535785 - type: nauc_map_at_3_std value: 11.017585726260462 - type: nauc_map_at_5_diff1 value: 11.323413777040606 - type: nauc_map_at_5_max value: -2.4760041260478904 - type: nauc_map_at_5_std value: 12.029899752157688 - type: nauc_mrr_at_1000_diff1 value: 10.742715816971687 - type: nauc_mrr_at_1000_max value: -1.7753021168425986 - type: nauc_mrr_at_1000_std value: 13.427125200171295 - type: nauc_mrr_at_100_diff1 value: 10.765635069630173 - type: nauc_mrr_at_100_max value: -1.7612670077500088 - type: nauc_mrr_at_100_std value: 13.47656838026296 - type: nauc_mrr_at_10_diff1 value: 10.35632278742462 - type: nauc_mrr_at_10_max value: -1.9593749415315034 - type: nauc_mrr_at_10_std value: 12.726659151321748 - type: nauc_mrr_at_1_diff1 value: 12.18980309927674 - type: nauc_mrr_at_1_max value: -4.630938342229097 - type: nauc_mrr_at_1_std value: 8.958732319219887 - type: nauc_mrr_at_20_diff1 value: 10.689736739154682 - type: nauc_mrr_at_20_max value: -1.689535123826222 - type: nauc_mrr_at_20_std value: 13.251612129414687 - type: nauc_mrr_at_3_diff1 value: 9.852214578314367 - type: nauc_mrr_at_3_max value: -3.33487013011876 - type: nauc_mrr_at_3_std value: 10.877855458667428 - type: nauc_mrr_at_5_diff1 value: 10.270810271458073 - type: nauc_mrr_at_5_max value: -2.677309074821081 - type: nauc_mrr_at_5_std value: 11.882706514806639 - type: nauc_ndcg_at_1000_diff1 value: 12.681360792690615 - type: nauc_ndcg_at_1000_max value: 0.30517667512214525 - type: nauc_ndcg_at_1000_std value: 17.50402456957222 - type: nauc_ndcg_at_100_diff1 value: 13.169226394338585 - type: nauc_ndcg_at_100_max value: 0.7398525127020716 - type: nauc_ndcg_at_100_std value: 18.85172563798729 - type: nauc_ndcg_at_10_diff1 value: 11.874278269234175 - type: nauc_ndcg_at_10_max value: 0.742178692340471 - type: nauc_ndcg_at_10_std value: 15.317281484021455 - type: nauc_ndcg_at_1_diff1 value: 13.270492079181698 - type: nauc_ndcg_at_1_max value: -5.320050131923338 - type: nauc_ndcg_at_1_std value: 9.145476528935111 - type: nauc_ndcg_at_20_diff1 value: 12.77788972412781 - type: nauc_ndcg_at_20_max value: 1.3509880113588073 - type: nauc_ndcg_at_20_std value: 17.20165293396484 - type: nauc_ndcg_at_3_diff1 value: 10.59415387301215 - type: nauc_ndcg_at_3_max value: -2.5275550083941534 - type: nauc_ndcg_at_3_std value: 11.765849158403212 - type: nauc_ndcg_at_5_diff1 value: 11.479181039452788 - type: nauc_ndcg_at_5_max value: -1.1695551867031702 - type: nauc_ndcg_at_5_std value: 13.366137540722084 - type: nauc_precision_at_1000_diff1 value: 24.13842177102596 - type: nauc_precision_at_1000_max value: 15.778091220725535 - type: nauc_precision_at_1000_std value: 57.991198111902065 - type: nauc_precision_at_100_diff1 value: 21.17988197332234 - type: nauc_precision_at_100_max value: 10.072329200503201 - type: nauc_precision_at_100_std value: 44.359368185927 - type: nauc_precision_at_10_diff1 value: 13.619970980685995 - type: nauc_precision_at_10_max value: 7.683020411909876 - type: nauc_precision_at_10_std value: 21.79402262800611 - type: nauc_precision_at_1_diff1 value: 13.270492079181698 - type: nauc_precision_at_1_max value: -5.320050131923338 - type: nauc_precision_at_1_std value: 9.145476528935111 - type: nauc_precision_at_20_diff1 value: 16.97319915821357 - type: nauc_precision_at_20_max value: 10.315905315799096 - type: nauc_precision_at_20_std value: 28.82688927043146 - type: nauc_precision_at_3_diff1 value: 10.02754671342287 - type: nauc_precision_at_3_max value: -0.8699973044493069 - type: nauc_precision_at_3_std value: 13.603782123513389 - type: nauc_precision_at_5_diff1 value: 12.084329744277978 - type: nauc_precision_at_5_max value: 2.074626490481966 - type: nauc_precision_at_5_std value: 16.608205795807304 - type: nauc_recall_at_1000_diff1 value: 24.138421771026135 - type: nauc_recall_at_1000_max value: 15.778091220725404 - type: nauc_recall_at_1000_std value: 57.99119811190208 - type: nauc_recall_at_100_diff1 value: 21.179881973322274 - type: nauc_recall_at_100_max value: 10.072329200503164 - type: nauc_recall_at_100_std value: 44.359368185926975 - type: nauc_recall_at_10_diff1 value: 13.619970980685975 - type: nauc_recall_at_10_max value: 7.683020411909859 - type: nauc_recall_at_10_std value: 21.794022628006108 - type: nauc_recall_at_1_diff1 value: 13.270492079181698 - type: nauc_recall_at_1_max value: -5.320050131923338 - type: nauc_recall_at_1_std value: 9.145476528935111 - type: nauc_recall_at_20_diff1 value: 16.973199158213596 - type: nauc_recall_at_20_max value: 10.315905315799101 - type: nauc_recall_at_20_std value: 28.82688927043146 - type: nauc_recall_at_3_diff1 value: 10.02754671342289 - type: nauc_recall_at_3_max value: -0.869997304449278 - type: nauc_recall_at_3_std value: 13.603782123513424 - type: nauc_recall_at_5_diff1 value: 12.084329744277952 - type: nauc_recall_at_5_max value: 2.074626490481952 - type: nauc_recall_at_5_std value: 16.60820579580728 - type: ndcg_at_1 value: 14.438 - type: ndcg_at_10 value: 29.599999999999998 - type: ndcg_at_100 value: 35.062 - type: ndcg_at_1000 value: 37.266 - type: ndcg_at_20 value: 31.734 - type: ndcg_at_3 value: 22.62 - type: ndcg_at_5 value: 25.643 - type: precision_at_1 value: 14.438 - type: precision_at_10 value: 4.843999999999999 - type: precision_at_100 value: 0.748 - type: precision_at_1000 value: 0.093 - type: precision_at_20 value: 2.841 - type: precision_at_3 value: 9.578000000000001 - type: precision_at_5 value: 7.226000000000001 - type: recall_at_1 value: 14.438 - type: recall_at_10 value: 48.435 - type: recall_at_100 value: 74.822 - type: recall_at_1000 value: 92.60300000000001 - type: recall_at_20 value: 56.828 - type: recall_at_3 value: 28.733999999999998 - type: recall_at_5 value: 36.131 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P (default) type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: main_score value: 35.46255145204994 - type: v_measure value: 35.46255145204994 - type: v_measure_std value: 14.146815377034603 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S (default) type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: main_score value: 26.34189987196252 - type: v_measure value: 26.34189987196252 - type: v_measure_std value: 14.798697652139317 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions (default) type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: main_score value: 52.85912447389551 - type: map value: 52.85912447389551 - type: mrr value: 66.7957173635844 - type: nAUC_map_diff1 value: 11.291158204891948 - type: nAUC_map_max value: 14.0571982637716 - type: nAUC_map_std value: 7.658903761935503 - type: nAUC_mrr_diff1 value: 13.851083215099605 - type: nAUC_mrr_max value: 19.44964881732576 - type: nAUC_mrr_std value: 9.313450884539453 - task: type: STS dataset: name: MTEB BIOSSES (default) type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cosine_pearson value: 73.38282679412139 - type: cosine_spearman value: 75.59389113278942 - type: euclidean_pearson value: 46.852724684799625 - type: euclidean_spearman value: 55.00125324086669 - type: main_score value: 75.59389113278942 - type: manhattan_pearson value: 45.7988833997748 - type: manhattan_spearman value: 53.28856361366204 - type: pearson value: 73.38282679412139 - type: spearman value: 75.59389113278942 - task: type: Classification dataset: name: MTEB Banking77Classification (default) type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 71.38636363636363 - type: f1 value: 71.55994805461263 - type: f1_weighted value: 71.55994805461263 - type: main_score value: 71.38636363636363 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P (default) type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: main_score value: 31.47309865069476 - type: v_measure value: 31.47309865069476 - type: v_measure_std value: 0.6360736715097297 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S (default) type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: main_score value: 22.58199120148109 - type: v_measure value: 22.58199120148109 - type: v_measure_std value: 1.1055877138914942 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval (default) type: mteb/cqadupstack-android config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: main_score value: 28.518 - type: map_at_1 value: 17.355999999999998 - type: map_at_10 value: 24.007 - type: map_at_100 value: 25.016 - type: map_at_1000 value: 25.176 - type: map_at_20 value: 24.457 - type: map_at_3 value: 21.794 - type: map_at_5 value: 23.04 - type: mrr_at_1 value: 22.603719599427755 - type: mrr_at_10 value: 29.108760814769386 - type: mrr_at_100 value: 29.908376499291993 - type: mrr_at_1000 value: 29.994015228435632 - type: mrr_at_20 value: 29.504080407211593 - type: mrr_at_3 value: 27.25321888412018 - type: mrr_at_5 value: 28.233190271816884 - type: nauc_map_at_1000_diff1 value: 47.869786003745816 - type: nauc_map_at_1000_max value: 27.54096137497838 - type: nauc_map_at_1000_std value: -7.400161145378304 - type: nauc_map_at_100_diff1 value: 47.84118234991334 - type: nauc_map_at_100_max value: 27.54904954135266 - type: nauc_map_at_100_std value: -7.477944025206194 - type: nauc_map_at_10_diff1 value: 47.9735876072791 - type: nauc_map_at_10_max value: 27.391055282545462 - type: nauc_map_at_10_std value: -7.809853508011509 - type: nauc_map_at_1_diff1 value: 58.07291238335911 - type: nauc_map_at_1_max value: 29.491926251716666 - type: nauc_map_at_1_std value: -7.759388303825668 - type: nauc_map_at_20_diff1 value: 47.98612480482489 - type: nauc_map_at_20_max value: 27.475036492625026 - type: nauc_map_at_20_std value: -7.516599563783101 - type: nauc_map_at_3_diff1 value: 49.45201738384499 - type: nauc_map_at_3_max value: 27.178788486813954 - type: nauc_map_at_3_std value: -8.675581883315793 - type: nauc_map_at_5_diff1 value: 48.54428206844137 - type: nauc_map_at_5_max value: 27.04154567160208 - type: nauc_map_at_5_std value: -7.985715295487552 - type: nauc_mrr_at_1000_diff1 value: 46.574864956985365 - type: nauc_mrr_at_1000_max value: 28.087519043166832 - type: nauc_mrr_at_1000_std value: -6.451015366036509 - type: nauc_mrr_at_100_diff1 value: 46.56229597151685 - type: nauc_mrr_at_100_max value: 28.097330034559143 - type: nauc_mrr_at_100_std value: -6.475319386029993 - type: nauc_mrr_at_10_diff1 value: 46.72161155094325 - type: nauc_mrr_at_10_max value: 28.136796558719162 - type: nauc_mrr_at_10_std value: -6.804592873002316 - type: nauc_mrr_at_1_diff1 value: 55.89633445168951 - type: nauc_mrr_at_1_max value: 30.47937590769701 - type: nauc_mrr_at_1_std value: -7.1323488254717935 - type: nauc_mrr_at_20_diff1 value: 46.693169452232546 - type: nauc_mrr_at_20_max value: 28.140872936089373 - type: nauc_mrr_at_20_std value: -6.484331458969132 - type: nauc_mrr_at_3_diff1 value: 47.808872121231374 - type: nauc_mrr_at_3_max value: 28.510278015059086 - type: nauc_mrr_at_3_std value: -7.418313420962369 - type: nauc_mrr_at_5_diff1 value: 47.00163108991785 - type: nauc_mrr_at_5_max value: 28.03825046154691 - type: nauc_mrr_at_5_std value: -7.007540109114421 - type: nauc_ndcg_at_1000_diff1 value: 44.04808574593522 - type: nauc_ndcg_at_1000_max value: 26.938526842644773 - type: nauc_ndcg_at_1000_std value: -4.429274627595189 - type: nauc_ndcg_at_100_diff1 value: 43.556532019049136 - type: nauc_ndcg_at_100_max value: 27.236734895647253 - type: nauc_ndcg_at_100_std value: -5.869942528569457 - type: nauc_ndcg_at_10_diff1 value: 44.125042380771696 - type: nauc_ndcg_at_10_max value: 27.283104729889622 - type: nauc_ndcg_at_10_std value: -7.250075385018749 - type: nauc_ndcg_at_1_diff1 value: 55.89633445168951 - type: nauc_ndcg_at_1_max value: 30.47937590769701 - type: nauc_ndcg_at_1_std value: -7.1323488254717935 - type: nauc_ndcg_at_20_diff1 value: 44.41899784089651 - type: nauc_ndcg_at_20_max value: 27.132007799782926 - type: nauc_ndcg_at_20_std value: -6.018341603261965 - type: nauc_ndcg_at_3_diff1 value: 46.43333330203715 - type: nauc_ndcg_at_3_max value: 26.867159196890523 - type: nauc_ndcg_at_3_std value: -7.989033187697878 - type: nauc_ndcg_at_5_diff1 value: 44.97708505801694 - type: nauc_ndcg_at_5_max value: 26.53850652652143 - type: nauc_ndcg_at_5_std value: -7.429040061351512 - type: nauc_precision_at_1000_diff1 value: 10.90587664149544 - type: nauc_precision_at_1000_max value: 0.7573834415907932 - type: nauc_precision_at_1000_std value: 4.187233421717695 - type: nauc_precision_at_100_diff1 value: 16.70162637068987 - type: nauc_precision_at_100_max value: 15.017760634485006 - type: nauc_precision_at_100_std value: -1.4401234272452257 - type: nauc_precision_at_10_diff1 value: 27.11447978714884 - type: nauc_precision_at_10_max value: 25.239563326602838 - type: nauc_precision_at_10_std value: -5.113529015570373 - type: nauc_precision_at_1_diff1 value: 55.89633445168951 - type: nauc_precision_at_1_max value: 30.47937590769701 - type: nauc_precision_at_1_std value: -7.1323488254717935 - type: nauc_precision_at_20_diff1 value: 24.467549645043032 - type: nauc_precision_at_20_max value: 23.51675958880599 - type: nauc_precision_at_20_std value: -2.2460962355932654 - type: nauc_precision_at_3_diff1 value: 36.99310143703273 - type: nauc_precision_at_3_max value: 24.28484429048304 - type: nauc_precision_at_3_std value: -8.294205947711662 - type: nauc_precision_at_5_diff1 value: 32.53111998357926 - type: nauc_precision_at_5_max value: 23.890361705484153 - type: nauc_precision_at_5_std value: -6.119004280837306 - type: nauc_recall_at_1000_diff1 value: 26.372327810550182 - type: nauc_recall_at_1000_max value: 17.386452637452958 - type: nauc_recall_at_1000_std value: 17.18893134942721 - type: nauc_recall_at_100_diff1 value: 27.138092417145288 - type: nauc_recall_at_100_max value: 22.704436530088913 - type: nauc_recall_at_100_std value: -1.0716953053918568 - type: nauc_recall_at_10_diff1 value: 32.41154313152003 - type: nauc_recall_at_10_max value: 23.2359443305839 - type: nauc_recall_at_10_std value: -5.002290149250385 - type: nauc_recall_at_1_diff1 value: 58.07291238335911 - type: nauc_recall_at_1_max value: 29.491926251716666 - type: nauc_recall_at_1_std value: -7.759388303825668 - type: nauc_recall_at_20_diff1 value: 33.00899946361021 - type: nauc_recall_at_20_max value: 22.82808333164438 - type: nauc_recall_at_20_std value: -1.4141291649557204 - type: nauc_recall_at_3_diff1 value: 38.920601224546644 - type: nauc_recall_at_3_max value: 23.89232056113095 - type: nauc_recall_at_3_std value: -7.8481952205795995 - type: nauc_recall_at_5_diff1 value: 35.257535866907 - type: nauc_recall_at_5_max value: 22.164920959223334 - type: nauc_recall_at_5_std value: -5.9961105131656725 - type: ndcg_at_1 value: 22.604 - type: ndcg_at_10 value: 28.518 - type: ndcg_at_100 value: 33.442 - type: ndcg_at_1000 value: 36.691 - type: ndcg_at_20 value: 29.918 - type: ndcg_at_3 value: 25.278 - type: ndcg_at_5 value: 26.647 - type: precision_at_1 value: 22.604 - type: precision_at_10 value: 5.608 - type: precision_at_100 value: 1.0210000000000001 - type: precision_at_1000 value: 0.163 - type: precision_at_20 value: 3.319 - type: precision_at_3 value: 12.589 - type: precision_at_5 value: 8.984 - type: recall_at_1 value: 17.355999999999998 - type: recall_at_10 value: 36.59 - type: recall_at_100 value: 59.38099999999999 - type: recall_at_1000 value: 81.382 - type: recall_at_20 value: 41.972 - type: recall_at_3 value: 26.183 - type: recall_at_5 value: 30.653000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval (default) type: mteb/cqadupstack-english config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: main_score value: 24.698999999999998 - type: map_at_1 value: 16.182 - type: map_at_10 value: 21.187 - type: map_at_100 value: 22.028 - type: map_at_1000 value: 22.147 - type: map_at_20 value: 21.603 - type: map_at_3 value: 19.689999999999998 - type: map_at_5 value: 20.402 - type: mrr_at_1 value: 20.573248407643312 - type: mrr_at_10 value: 25.743301991709615 - type: mrr_at_100 value: 26.466582692758493 - type: mrr_at_1000 value: 26.54213235591294 - type: mrr_at_20 value: 26.116902322631823 - type: mrr_at_3 value: 24.32059447983014 - type: mrr_at_5 value: 24.960721868365162 - type: nauc_map_at_1000_diff1 value: 43.80371326276162 - type: nauc_map_at_1000_max value: 10.307189223525215 - type: nauc_map_at_1000_std value: 1.1410206622059031 - type: nauc_map_at_100_diff1 value: 43.80398291664643 - type: nauc_map_at_100_max value: 10.294039476698776 - type: nauc_map_at_100_std value: 1.0838400387773035 - type: nauc_map_at_10_diff1 value: 43.987106322737205 - type: nauc_map_at_10_max value: 10.44970205412866 - type: nauc_map_at_10_std value: 0.4638949254801207 - type: nauc_map_at_1_diff1 value: 50.262982039499725 - type: nauc_map_at_1_max value: 11.253389960693605 - type: nauc_map_at_1_std value: -1.1369036906864514 - type: nauc_map_at_20_diff1 value: 43.86541706002641 - type: nauc_map_at_20_max value: 10.333426229095483 - type: nauc_map_at_20_std value: 0.7704746445769103 - type: nauc_map_at_3_diff1 value: 44.96796698986098 - type: nauc_map_at_3_max value: 10.573187295958576 - type: nauc_map_at_3_std value: 0.01433549559929614 - type: nauc_map_at_5_diff1 value: 44.245307311061204 - type: nauc_map_at_5_max value: 10.644568381319045 - type: nauc_map_at_5_std value: -0.029700274583380155 - type: nauc_mrr_at_1000_diff1 value: 42.327672613522914 - type: nauc_mrr_at_1000_max value: 11.6999240554554 - type: nauc_mrr_at_1000_std value: 2.112897885106764 - type: nauc_mrr_at_100_diff1 value: 42.31642286015079 - type: nauc_mrr_at_100_max value: 11.68787957194085 - type: nauc_mrr_at_100_std value: 2.105610688222343 - type: nauc_mrr_at_10_diff1 value: 42.467973855007116 - type: nauc_mrr_at_10_max value: 11.797064798974974 - type: nauc_mrr_at_10_std value: 1.9779659522730684 - type: nauc_mrr_at_1_diff1 value: 47.71737815016663 - type: nauc_mrr_at_1_max value: 14.383095652386146 - type: nauc_mrr_at_1_std value: -0.07474670021285572 - type: nauc_mrr_at_20_diff1 value: 42.3995701621796 - type: nauc_mrr_at_20_max value: 11.701616710562975 - type: nauc_mrr_at_20_std value: 2.085148056092746 - type: nauc_mrr_at_3_diff1 value: 42.95240734385427 - type: nauc_mrr_at_3_max value: 12.039509345325337 - type: nauc_mrr_at_3_std value: 1.7687962861822382 - type: nauc_mrr_at_5_diff1 value: 42.694804355468115 - type: nauc_mrr_at_5_max value: 11.929565017206377 - type: nauc_mrr_at_5_std value: 1.694875246947431 - type: nauc_ndcg_at_1000_diff1 value: 41.00761525475331 - type: nauc_ndcg_at_1000_max value: 9.858142865194182 - type: nauc_ndcg_at_1000_std value: 3.670728963648605 - type: nauc_ndcg_at_100_diff1 value: 40.95449329238105 - type: nauc_ndcg_at_100_max value: 9.326306956218327 - type: nauc_ndcg_at_100_std value: 2.8868853641438506 - type: nauc_ndcg_at_10_diff1 value: 41.53254984337585 - type: nauc_ndcg_at_10_max value: 10.057078591477252 - type: nauc_ndcg_at_10_std value: 1.604308043004992 - type: nauc_ndcg_at_1_diff1 value: 47.71737815016663 - type: nauc_ndcg_at_1_max value: 14.383095652386146 - type: nauc_ndcg_at_1_std value: -0.07474670021285572 - type: nauc_ndcg_at_20_diff1 value: 41.440675477881086 - type: nauc_ndcg_at_20_max value: 9.630011024652227 - type: nauc_ndcg_at_20_std value: 2.2157732372759256 - type: nauc_ndcg_at_3_diff1 value: 42.46487256960971 - type: nauc_ndcg_at_3_max value: 11.038048797533829 - type: nauc_ndcg_at_3_std value: 1.2243654696200774 - type: nauc_ndcg_at_5_diff1 value: 41.83878536100888 - type: nauc_ndcg_at_5_max value: 10.720801901432624 - type: nauc_ndcg_at_5_std value: 0.8712149388513847 - type: nauc_precision_at_1000_diff1 value: 1.5865611853545292 - type: nauc_precision_at_1000_max value: 6.681393322922304 - type: nauc_precision_at_1000_std value: 14.974673269542507 - type: nauc_precision_at_100_diff1 value: 13.555729326347315 - type: nauc_precision_at_100_max value: 7.545824391218551 - type: nauc_precision_at_100_std value: 13.934044415661273 - type: nauc_precision_at_10_diff1 value: 25.53208157998575 - type: nauc_precision_at_10_max value: 10.861163675534936 - type: nauc_precision_at_10_std value: 4.879245837329693 - type: nauc_precision_at_1_diff1 value: 47.71737815016663 - type: nauc_precision_at_1_max value: 14.383095652386146 - type: nauc_precision_at_1_std value: -0.07474670021285572 - type: nauc_precision_at_20_diff1 value: 22.554580803838196 - type: nauc_precision_at_20_max value: 9.173222510159171 - type: nauc_precision_at_20_std value: 8.91005482914735 - type: nauc_precision_at_3_diff1 value: 33.10508327009392 - type: nauc_precision_at_3_max value: 12.86002329562499 - type: nauc_precision_at_3_std value: 2.974310102418383 - type: nauc_precision_at_5_diff1 value: 29.21043001216549 - type: nauc_precision_at_5_max value: 11.911630406472423 - type: nauc_precision_at_5_std value: 3.0525160145985994 - type: nauc_recall_at_1000_diff1 value: 30.47927917267733 - type: nauc_recall_at_1000_max value: 7.6799659504807245 - type: nauc_recall_at_1000_std value: 12.501272715675682 - type: nauc_recall_at_100_diff1 value: 31.37456182815277 - type: nauc_recall_at_100_max value: 4.3121178276146 - type: nauc_recall_at_100_std value: 6.610653786295896 - type: nauc_recall_at_10_diff1 value: 35.70919804366768 - type: nauc_recall_at_10_max value: 7.164595283036483 - type: nauc_recall_at_10_std value: 2.511197530002145 - type: nauc_recall_at_1_diff1 value: 50.262982039499725 - type: nauc_recall_at_1_max value: 11.253389960693605 - type: nauc_recall_at_1_std value: -1.1369036906864514 - type: nauc_recall_at_20_diff1 value: 34.61353209754079 - type: nauc_recall_at_20_max value: 5.959396627193594 - type: nauc_recall_at_20_std value: 4.38802472107702 - type: nauc_recall_at_3_diff1 value: 38.54587550067196 - type: nauc_recall_at_3_max value: 8.303476446370226 - type: nauc_recall_at_3_std value: 0.918233189682653 - type: nauc_recall_at_5_diff1 value: 36.97453761390672 - type: nauc_recall_at_5_max value: 8.452744877863443 - type: nauc_recall_at_5_std value: 0.31182896781455743 - type: ndcg_at_1 value: 20.573 - type: ndcg_at_10 value: 24.698999999999998 - type: ndcg_at_100 value: 28.626 - type: ndcg_at_1000 value: 31.535999999999998 - type: ndcg_at_20 value: 25.971 - type: ndcg_at_3 value: 22.400000000000002 - type: ndcg_at_5 value: 23.153000000000002 - type: precision_at_1 value: 20.573 - type: precision_at_10 value: 4.682 - type: precision_at_100 value: 0.835 - type: precision_at_1000 value: 0.132 - type: precision_at_20 value: 2.806 - type: precision_at_3 value: 10.955 - type: precision_at_5 value: 7.580000000000001 - type: recall_at_1 value: 16.182 - type: recall_at_10 value: 30.410999999999998 - type: recall_at_100 value: 47.94 - type: recall_at_1000 value: 68.073 - type: recall_at_20 value: 35.241 - type: recall_at_3 value: 23.247999999999998 - type: recall_at_5 value: 25.611 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval (default) type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: main_score value: 34.837 - type: map_at_1 value: 21.804000000000002 - type: map_at_10 value: 30.117 - type: map_at_100 value: 31.022 - type: map_at_1000 value: 31.123 - type: map_at_20 value: 30.592999999999996 - type: map_at_3 value: 27.485 - type: map_at_5 value: 29.015 - type: mrr_at_1 value: 25.391849529780565 - type: mrr_at_10 value: 33.06018311190724 - type: mrr_at_100 value: 33.86542467064614 - type: mrr_at_1000 value: 33.93133191694629 - type: mrr_at_20 value: 33.48454644646544 - type: mrr_at_3 value: 30.700104493207924 - type: mrr_at_5 value: 32.12016718913267 - type: nauc_map_at_1000_diff1 value: 45.5807513160407 - type: nauc_map_at_1000_max value: 21.915072082554456 - type: nauc_map_at_1000_std value: -7.325013122158723 - type: nauc_map_at_100_diff1 value: 45.54127845733458 - type: nauc_map_at_100_max value: 21.90856139725234 - type: nauc_map_at_100_std value: -7.378234997163831 - type: nauc_map_at_10_diff1 value: 45.56616787985884 - type: nauc_map_at_10_max value: 21.977377645141427 - type: nauc_map_at_10_std value: -7.953791461768689 - type: nauc_map_at_1_diff1 value: 50.13523755859727 - type: nauc_map_at_1_max value: 22.079872106357826 - type: nauc_map_at_1_std value: -10.517989063520115 - type: nauc_map_at_20_diff1 value: 45.47328572468456 - type: nauc_map_at_20_max value: 21.907938618532206 - type: nauc_map_at_20_std value: -7.654370878334637 - type: nauc_map_at_3_diff1 value: 46.64296035971972 - type: nauc_map_at_3_max value: 21.55745539420763 - type: nauc_map_at_3_std value: -9.322387704640397 - type: nauc_map_at_5_diff1 value: 45.87814328869891 - type: nauc_map_at_5_max value: 21.97551177369846 - type: nauc_map_at_5_std value: -8.442300800960686 - type: nauc_mrr_at_1000_diff1 value: 46.21214184609282 - type: nauc_mrr_at_1000_max value: 24.121552423232732 - type: nauc_mrr_at_1000_std value: -5.197081534530456 - type: nauc_mrr_at_100_diff1 value: 46.192209374562324 - type: nauc_mrr_at_100_max value: 24.117295080133403 - type: nauc_mrr_at_100_std value: -5.20106321371411 - type: nauc_mrr_at_10_diff1 value: 46.214433219910426 - type: nauc_mrr_at_10_max value: 24.337609381566494 - type: nauc_mrr_at_10_std value: -5.539128286307364 - type: nauc_mrr_at_1_diff1 value: 52.2527723494356 - type: nauc_mrr_at_1_max value: 25.421197106410293 - type: nauc_mrr_at_1_std value: -7.805349072851469 - type: nauc_mrr_at_20_diff1 value: 46.10135736013422 - type: nauc_mrr_at_20_max value: 24.17582977429519 - type: nauc_mrr_at_20_std value: -5.3844233771043255 - type: nauc_mrr_at_3_diff1 value: 47.089100932315574 - type: nauc_mrr_at_3_max value: 24.589442349183855 - type: nauc_mrr_at_3_std value: -6.861652459272909 - type: nauc_mrr_at_5_diff1 value: 46.50908152902759 - type: nauc_mrr_at_5_max value: 24.44902343275474 - type: nauc_mrr_at_5_std value: -5.90486733129187 - type: nauc_ndcg_at_1000_diff1 value: 44.01232290993056 - type: nauc_ndcg_at_1000_max value: 21.7547520856293 - type: nauc_ndcg_at_1000_std value: -2.8320334767530118 - type: nauc_ndcg_at_100_diff1 value: 43.333079641772805 - type: nauc_ndcg_at_100_max value: 21.696558885860842 - type: nauc_ndcg_at_100_std value: -3.8168722593708466 - type: nauc_ndcg_at_10_diff1 value: 43.55004080963945 - type: nauc_ndcg_at_10_max value: 22.437821635174988 - type: nauc_ndcg_at_10_std value: -6.156552890106106 - type: nauc_ndcg_at_1_diff1 value: 52.2527723494356 - type: nauc_ndcg_at_1_max value: 25.421197106410293 - type: nauc_ndcg_at_1_std value: -7.805349072851469 - type: nauc_ndcg_at_20_diff1 value: 43.09035864009835 - type: nauc_ndcg_at_20_max value: 21.94863122459976 - type: nauc_ndcg_at_20_std value: -5.4130728717458965 - type: nauc_ndcg_at_3_diff1 value: 45.44710289580689 - type: nauc_ndcg_at_3_max value: 22.400341906939868 - type: nauc_ndcg_at_3_std value: -8.619757656107849 - type: nauc_ndcg_at_5_diff1 value: 44.1896655275832 - type: nauc_ndcg_at_5_max value: 22.587591758610802 - type: nauc_ndcg_at_5_std value: -7.2269233073063575 - type: nauc_precision_at_1000_diff1 value: 10.365353118490535 - type: nauc_precision_at_1000_max value: 7.8252547949888545 - type: nauc_precision_at_1000_std value: 26.55091491372318 - type: nauc_precision_at_100_diff1 value: 21.049854477557055 - type: nauc_precision_at_100_max value: 16.20485886511922 - type: nauc_precision_at_100_std value: 15.969890079702717 - type: nauc_precision_at_10_diff1 value: 32.52426180873231 - type: nauc_precision_at_10_max value: 22.685662047893707 - type: nauc_precision_at_10_std value: 1.4729404419557324 - type: nauc_precision_at_1_diff1 value: 52.2527723494356 - type: nauc_precision_at_1_max value: 25.421197106410293 - type: nauc_precision_at_1_std value: -7.805349072851469 - type: nauc_precision_at_20_diff1 value: 28.090691152210972 - type: nauc_precision_at_20_max value: 20.90743423717082 - type: nauc_precision_at_20_std value: 4.817506381512236 - type: nauc_precision_at_3_diff1 value: 40.80538406829336 - type: nauc_precision_at_3_max value: 23.323105131070363 - type: nauc_precision_at_3_std value: -5.540716529624683 - type: nauc_precision_at_5_diff1 value: 36.58280618039231 - type: nauc_precision_at_5_max value: 23.634816479662742 - type: nauc_precision_at_5_std value: -1.7820384730109589 - type: nauc_recall_at_1000_diff1 value: 34.29190280951983 - type: nauc_recall_at_1000_max value: 13.798111582798564 - type: nauc_recall_at_1000_std value: 28.5351988388723 - type: nauc_recall_at_100_diff1 value: 32.064087882086476 - type: nauc_recall_at_100_max value: 16.090743768333688 - type: nauc_recall_at_100_std value: 8.307894883910041 - type: nauc_recall_at_10_diff1 value: 35.79378711197085 - type: nauc_recall_at_10_max value: 20.68575839918982 - type: nauc_recall_at_10_std value: -2.946830801840792 - type: nauc_recall_at_1_diff1 value: 50.13523755859727 - type: nauc_recall_at_1_max value: 22.079872106357826 - type: nauc_recall_at_1_std value: -10.517989063520115 - type: nauc_recall_at_20_diff1 value: 33.44790152149905 - type: nauc_recall_at_20_max value: 18.594618679781895 - type: nauc_recall_at_20_std value: -0.31826446038001266 - type: nauc_recall_at_3_diff1 value: 40.94878372307589 - type: nauc_recall_at_3_max value: 20.42680666854128 - type: nauc_recall_at_3_std value: -8.903430047857414 - type: nauc_recall_at_5_diff1 value: 37.927274464064844 - type: nauc_recall_at_5_max value: 21.06930934356292 - type: nauc_recall_at_5_std value: -5.831090950499156 - type: ndcg_at_1 value: 25.392 - type: ndcg_at_10 value: 34.837 - type: ndcg_at_100 value: 39.291 - type: ndcg_at_1000 value: 41.676 - type: ndcg_at_20 value: 36.416 - type: ndcg_at_3 value: 29.958000000000002 - type: ndcg_at_5 value: 32.435 - type: precision_at_1 value: 25.392 - type: precision_at_10 value: 5.806 - type: precision_at_100 value: 0.8789999999999999 - type: precision_at_1000 value: 0.117 - type: precision_at_20 value: 3.3320000000000003 - type: precision_at_3 value: 13.501 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 21.804000000000002 - type: recall_at_10 value: 46.367999999999995 - type: recall_at_100 value: 66.526 - type: recall_at_1000 value: 83.795 - type: recall_at_20 value: 52.201 - type: recall_at_3 value: 33.351 - type: recall_at_5 value: 39.345 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval (default) type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: main_score value: 15.889000000000001 - type: map_at_1 value: 9.472999999999999 - type: map_at_10 value: 13.439 - type: map_at_100 value: 14.165 - type: map_at_1000 value: 14.267 - type: map_at_20 value: 13.778000000000002 - type: map_at_3 value: 12.136 - type: map_at_5 value: 12.803 - type: mrr_at_1 value: 10.056497175141244 - type: mrr_at_10 value: 14.27383194332347 - type: mrr_at_100 value: 15.012089041940587 - type: mrr_at_1000 value: 15.104068046441926 - type: mrr_at_20 value: 14.623929801790952 - type: mrr_at_3 value: 12.86252354048964 - type: mrr_at_5 value: 13.55743879472693 - type: nauc_map_at_1000_diff1 value: 30.334633457872854 - type: nauc_map_at_1000_max value: 16.879524053860088 - type: nauc_map_at_1000_std value: -11.608379714877143 - type: nauc_map_at_100_diff1 value: 30.315313717026044 - type: nauc_map_at_100_max value: 16.85237939531867 - type: nauc_map_at_100_std value: -11.622151859571831 - type: nauc_map_at_10_diff1 value: 30.914146463660085 - type: nauc_map_at_10_max value: 16.957132658303777 - type: nauc_map_at_10_std value: -11.731838090023269 - type: nauc_map_at_1_diff1 value: 38.059077642105095 - type: nauc_map_at_1_max value: 17.258898457644563 - type: nauc_map_at_1_std value: -15.1141417910556 - type: nauc_map_at_20_diff1 value: 30.657379748220464 - type: nauc_map_at_20_max value: 16.728415773059652 - type: nauc_map_at_20_std value: -11.58808790930077 - type: nauc_map_at_3_diff1 value: 33.46033892507575 - type: nauc_map_at_3_max value: 17.063496859962274 - type: nauc_map_at_3_std value: -12.540868416387656 - type: nauc_map_at_5_diff1 value: 31.833328131003665 - type: nauc_map_at_5_max value: 16.85136559752421 - type: nauc_map_at_5_std value: -12.482629966798948 - type: nauc_mrr_at_1000_diff1 value: 29.41507065744396 - type: nauc_mrr_at_1000_max value: 18.49824554052624 - type: nauc_mrr_at_1000_std value: -10.326025120569037 - type: nauc_mrr_at_100_diff1 value: 29.379801930215717 - type: nauc_mrr_at_100_max value: 18.488234248143247 - type: nauc_mrr_at_100_std value: -10.335639545339422 - type: nauc_mrr_at_10_diff1 value: 29.91432794618661 - type: nauc_mrr_at_10_max value: 18.724879448569546 - type: nauc_mrr_at_10_std value: -10.404101745775053 - type: nauc_mrr_at_1_diff1 value: 37.90615317749033 - type: nauc_mrr_at_1_max value: 18.93535243576158 - type: nauc_mrr_at_1_std value: -13.352192729903559 - type: nauc_mrr_at_20_diff1 value: 29.578605690031328 - type: nauc_mrr_at_20_max value: 18.407726379219987 - type: nauc_mrr_at_20_std value: -10.298490989990624 - type: nauc_mrr_at_3_diff1 value: 32.02343883506372 - type: nauc_mrr_at_3_max value: 18.633783635235847 - type: nauc_mrr_at_3_std value: -11.228435347275935 - type: nauc_mrr_at_5_diff1 value: 30.69962523728713 - type: nauc_mrr_at_5_max value: 18.72446829188985 - type: nauc_mrr_at_5_std value: -11.138830180701982 - type: nauc_ndcg_at_1000_diff1 value: 25.382297853226866 - type: nauc_ndcg_at_1000_max value: 17.43716304218148 - type: nauc_ndcg_at_1000_std value: -10.190696887337486 - type: nauc_ndcg_at_100_diff1 value: 24.735480242752285 - type: nauc_ndcg_at_100_max value: 16.71943454741711 - type: nauc_ndcg_at_100_std value: -9.924909206899162 - type: nauc_ndcg_at_10_diff1 value: 27.358228148721842 - type: nauc_ndcg_at_10_max value: 16.922883804711265 - type: nauc_ndcg_at_10_std value: -10.016699536056024 - type: nauc_ndcg_at_1_diff1 value: 37.90615317749033 - type: nauc_ndcg_at_1_max value: 18.93535243576158 - type: nauc_ndcg_at_1_std value: -13.352192729903559 - type: nauc_ndcg_at_20_diff1 value: 26.463382227572517 - type: nauc_ndcg_at_20_max value: 16.22031339406569 - type: nauc_ndcg_at_20_std value: -9.66724467521929 - type: nauc_ndcg_at_3_diff1 value: 31.53806923827287 - type: nauc_ndcg_at_3_max value: 17.049495750298107 - type: nauc_ndcg_at_3_std value: -11.58504512374531 - type: nauc_ndcg_at_5_diff1 value: 29.10131680215961 - type: nauc_ndcg_at_5_max value: 16.786497467751296 - type: nauc_ndcg_at_5_std value: -11.594059282963107 - type: nauc_precision_at_1000_diff1 value: 5.724183211042247 - type: nauc_precision_at_1000_max value: 22.481314169026508 - type: nauc_precision_at_1000_std value: -2.4780053135041844 - type: nauc_precision_at_100_diff1 value: 8.982535905232872 - type: nauc_precision_at_100_max value: 19.23627381958997 - type: nauc_precision_at_100_std value: -6.469375758025859 - type: nauc_precision_at_10_diff1 value: 18.446003934213422 - type: nauc_precision_at_10_max value: 18.317564090743698 - type: nauc_precision_at_10_std value: -5.258776187738409 - type: nauc_precision_at_1_diff1 value: 37.90615317749033 - type: nauc_precision_at_1_max value: 18.93535243576158 - type: nauc_precision_at_1_std value: -13.352192729903559 - type: nauc_precision_at_20_diff1 value: 16.32313052813914 - type: nauc_precision_at_20_max value: 16.623118796672443 - type: nauc_precision_at_20_std value: -5.178876021009233 - type: nauc_precision_at_3_diff1 value: 28.153843298140956 - type: nauc_precision_at_3_max value: 18.261053599119773 - type: nauc_precision_at_3_std value: -8.633656740784398 - type: nauc_precision_at_5_diff1 value: 22.30147327973116 - type: nauc_precision_at_5_max value: 17.724668119940276 - type: nauc_precision_at_5_std value: -9.147827083942738 - type: nauc_recall_at_1000_diff1 value: 12.936742845571006 - type: nauc_recall_at_1000_max value: 17.728147389670845 - type: nauc_recall_at_1000_std value: -10.026543773605697 - type: nauc_recall_at_100_diff1 value: 12.196046010910255 - type: nauc_recall_at_100_max value: 14.320146451643033 - type: nauc_recall_at_100_std value: -7.059868030131276 - type: nauc_recall_at_10_diff1 value: 19.81974166368456 - type: nauc_recall_at_10_max value: 15.137717469839288 - type: nauc_recall_at_10_std value: -6.894031649742936 - type: nauc_recall_at_1_diff1 value: 38.059077642105095 - type: nauc_recall_at_1_max value: 17.258898457644563 - type: nauc_recall_at_1_std value: -15.1141417910556 - type: nauc_recall_at_20_diff1 value: 17.87014099435801 - type: nauc_recall_at_20_max value: 13.410148544576403 - type: nauc_recall_at_20_std value: -6.139892629545985 - type: nauc_recall_at_3_diff1 value: 27.941355405054267 - type: nauc_recall_at_3_max value: 15.300277815129304 - type: nauc_recall_at_3_std value: -10.440312722587832 - type: nauc_recall_at_5_diff1 value: 23.715987229368274 - type: nauc_recall_at_5_max value: 15.063760707410282 - type: nauc_recall_at_5_std value: -10.521011536014003 - type: ndcg_at_1 value: 10.056 - type: ndcg_at_10 value: 15.889000000000001 - type: ndcg_at_100 value: 20.007 - type: ndcg_at_1000 value: 23.324 - type: ndcg_at_20 value: 17.127 - type: ndcg_at_3 value: 13.171 - type: ndcg_at_5 value: 14.358 - type: precision_at_1 value: 10.056 - type: precision_at_10 value: 2.588 - type: precision_at_100 value: 0.49300000000000005 - type: precision_at_1000 value: 0.083 - type: precision_at_20 value: 1.559 - type: precision_at_3 value: 5.612 - type: precision_at_5 value: 4.0680000000000005 - type: recall_at_1 value: 9.472999999999999 - type: recall_at_10 value: 22.676 - type: recall_at_100 value: 42.672 - type: recall_at_1000 value: 68.939 - type: recall_at_20 value: 27.462999999999997 - type: recall_at_3 value: 15.383 - type: recall_at_5 value: 18.174 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval (default) type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: main_score value: 11.0 - type: map_at_1 value: 5.148 - type: map_at_10 value: 8.469999999999999 - type: map_at_100 value: 9.212 - type: map_at_1000 value: 9.322 - type: map_at_20 value: 8.808 - type: map_at_3 value: 7.131 - type: map_at_5 value: 7.815999999999999 - type: mrr_at_1 value: 6.343283582089552 - type: mrr_at_10 value: 10.370913290689412 - type: mrr_at_100 value: 11.152489765865017 - type: mrr_at_1000 value: 11.240647288895591 - type: mrr_at_20 value: 10.741514212977526 - type: mrr_at_3 value: 8.872305140961858 - type: mrr_at_5 value: 9.631011608623549 - type: nauc_map_at_1000_diff1 value: 23.766626012326586 - type: nauc_map_at_1000_max value: 12.653376257429583 - type: nauc_map_at_1000_std value: 8.616529960924888 - type: nauc_map_at_100_diff1 value: 23.738827084996768 - type: nauc_map_at_100_max value: 12.649650411660854 - type: nauc_map_at_100_std value: 8.541383664809612 - type: nauc_map_at_10_diff1 value: 23.999578907568026 - type: nauc_map_at_10_max value: 12.71263636252209 - type: nauc_map_at_10_std value: 7.591195966672301 - type: nauc_map_at_1_diff1 value: 35.57446018071185 - type: nauc_map_at_1_max value: 14.079653770667337 - type: nauc_map_at_1_std value: 11.69336879118923 - type: nauc_map_at_20_diff1 value: 24.160966681198037 - type: nauc_map_at_20_max value: 12.874042661878926 - type: nauc_map_at_20_std value: 8.47225999927236 - type: nauc_map_at_3_diff1 value: 26.388037294578943 - type: nauc_map_at_3_max value: 12.836707260430186 - type: nauc_map_at_3_std value: 6.661759987628506 - type: nauc_map_at_5_diff1 value: 24.670961314269608 - type: nauc_map_at_5_max value: 12.93683340709218 - type: nauc_map_at_5_std value: 6.6199426801021435 - type: nauc_mrr_at_1000_diff1 value: 23.216930411387928 - type: nauc_mrr_at_1000_max value: 15.19292342533299 - type: nauc_mrr_at_1000_std value: 8.443837847880454 - type: nauc_mrr_at_100_diff1 value: 23.191640457286802 - type: nauc_mrr_at_100_max value: 15.176060930237956 - type: nauc_mrr_at_100_std value: 8.438353759551372 - type: nauc_mrr_at_10_diff1 value: 23.641665699722576 - type: nauc_mrr_at_10_max value: 15.363771027025361 - type: nauc_mrr_at_10_std value: 7.6943977364817675 - type: nauc_mrr_at_1_diff1 value: 34.13967231695169 - type: nauc_mrr_at_1_max value: 18.217995055452356 - type: nauc_mrr_at_1_std value: 11.691078655411745 - type: nauc_mrr_at_20_diff1 value: 23.584124655747633 - type: nauc_mrr_at_20_max value: 15.504561511128212 - type: nauc_mrr_at_20_std value: 8.487309205927613 - type: nauc_mrr_at_3_diff1 value: 26.239880657367205 - type: nauc_mrr_at_3_max value: 15.653548540177347 - type: nauc_mrr_at_3_std value: 6.349852805707984 - type: nauc_mrr_at_5_diff1 value: 23.976240360223915 - type: nauc_mrr_at_5_max value: 15.744338647107542 - type: nauc_mrr_at_5_std value: 6.487124576469712 - type: nauc_ndcg_at_1000_diff1 value: 19.496197697682945 - type: nauc_ndcg_at_1000_max value: 12.101852407794244 - type: nauc_ndcg_at_1000_std value: 12.016860314478954 - type: nauc_ndcg_at_100_diff1 value: 18.9745151618046 - type: nauc_ndcg_at_100_max value: 11.815079877327287 - type: nauc_ndcg_at_100_std value: 10.61036714041141 - type: nauc_ndcg_at_10_diff1 value: 20.49507024120394 - type: nauc_ndcg_at_10_max value: 13.081108599437465 - type: nauc_ndcg_at_10_std value: 7.930411944011889 - type: nauc_ndcg_at_1_diff1 value: 34.13967231695169 - type: nauc_ndcg_at_1_max value: 18.217995055452356 - type: nauc_ndcg_at_1_std value: 11.691078655411745 - type: nauc_ndcg_at_20_diff1 value: 20.839258395401707 - type: nauc_ndcg_at_20_max value: 13.485012044482616 - type: nauc_ndcg_at_20_std value: 10.423314754071841 - type: nauc_ndcg_at_3_diff1 value: 24.534248413854158 - type: nauc_ndcg_at_3_max value: 13.612373481617901 - type: nauc_ndcg_at_3_std value: 5.122655306518725 - type: nauc_ndcg_at_5_diff1 value: 21.45736115604528 - type: nauc_ndcg_at_5_max value: 13.50049057414957 - type: nauc_ndcg_at_5_std value: 5.5599020003710375 - type: nauc_precision_at_1000_diff1 value: 5.214729837045339 - type: nauc_precision_at_1000_max value: 7.049726610933547 - type: nauc_precision_at_1000_std value: 10.217710184510343 - type: nauc_precision_at_100_diff1 value: 10.428281377918521 - type: nauc_precision_at_100_max value: 9.592496174158226 - type: nauc_precision_at_100_std value: 11.524579687966593 - type: nauc_precision_at_10_diff1 value: 13.144126104006663 - type: nauc_precision_at_10_max value: 12.791519232802509 - type: nauc_precision_at_10_std value: 7.117254065134753 - type: nauc_precision_at_1_diff1 value: 34.13967231695169 - type: nauc_precision_at_1_max value: 18.217995055452356 - type: nauc_precision_at_1_std value: 11.691078655411745 - type: nauc_precision_at_20_diff1 value: 14.534665391717477 - type: nauc_precision_at_20_max value: 13.373720011165052 - type: nauc_precision_at_20_std value: 12.735872233304013 - type: nauc_precision_at_3_diff1 value: 20.050332454808 - type: nauc_precision_at_3_max value: 14.287141036751699 - type: nauc_precision_at_3_std value: 2.1412848715847774 - type: nauc_precision_at_5_diff1 value: 16.547335020939435 - type: nauc_precision_at_5_max value: 14.007790386514285 - type: nauc_precision_at_5_std value: 2.0821824154130835 - type: nauc_recall_at_1000_diff1 value: 12.811540518810224 - type: nauc_recall_at_1000_max value: 8.292364898702107 - type: nauc_recall_at_1000_std value: 21.172583907189164 - type: nauc_recall_at_100_diff1 value: 10.763207100689536 - type: nauc_recall_at_100_max value: 7.433707421662763 - type: nauc_recall_at_100_std value: 13.860488374098953 - type: nauc_recall_at_10_diff1 value: 14.171919964914773 - type: nauc_recall_at_10_max value: 12.3310517183378 - type: nauc_recall_at_10_std value: 8.627373443421941 - type: nauc_recall_at_1_diff1 value: 35.57446018071185 - type: nauc_recall_at_1_max value: 14.079653770667337 - type: nauc_recall_at_1_std value: 11.69336879118923 - type: nauc_recall_at_20_diff1 value: 15.254229786832758 - type: nauc_recall_at_20_max value: 12.944155764013084 - type: nauc_recall_at_20_std value: 13.947428525952118 - type: nauc_recall_at_3_diff1 value: 19.723050472865584 - type: nauc_recall_at_3_max value: 12.208432070640235 - type: nauc_recall_at_3_std value: 3.2560341221626357 - type: nauc_recall_at_5_diff1 value: 14.200616898717133 - type: nauc_recall_at_5_max value: 12.262563917077088 - type: nauc_recall_at_5_std value: 4.115380825048154 - type: ndcg_at_1 value: 6.343 - type: ndcg_at_10 value: 11.0 - type: ndcg_at_100 value: 15.332 - type: ndcg_at_1000 value: 18.505 - type: ndcg_at_20 value: 12.280000000000001 - type: ndcg_at_3 value: 8.297 - type: ndcg_at_5 value: 9.482 - type: precision_at_1 value: 6.343 - type: precision_at_10 value: 2.251 - type: precision_at_100 value: 0.516 - type: precision_at_1000 value: 0.091 - type: precision_at_20 value: 1.437 - type: precision_at_3 value: 4.104 - type: precision_at_5 value: 3.234 - type: recall_at_1 value: 5.148 - type: recall_at_10 value: 16.955000000000002 - type: recall_at_100 value: 37.295 - type: recall_at_1000 value: 60.681 - type: recall_at_20 value: 21.847 - type: recall_at_3 value: 9.735000000000001 - type: recall_at_5 value: 12.595999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval (default) type: mteb/cqadupstack-physics config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: main_score value: 22.671 - type: map_at_1 value: 13.99 - type: map_at_10 value: 19.16 - type: map_at_100 value: 20.247999999999998 - type: map_at_1000 value: 20.392 - type: map_at_20 value: 19.741 - type: map_at_3 value: 17.527 - type: map_at_5 value: 18.431 - type: mrr_at_1 value: 17.035611164581326 - type: mrr_at_10 value: 22.920886994515485 - type: mrr_at_100 value: 23.890327247971815 - type: mrr_at_1000 value: 23.98416758924587 - type: mrr_at_20 value: 23.478953217825296 - type: mrr_at_3 value: 21.158164902149515 - type: mrr_at_5 value: 22.154315046519095 - type: nauc_map_at_1000_diff1 value: 40.20942586785694 - type: nauc_map_at_1000_max value: 19.62019855432636 - type: nauc_map_at_1000_std value: -6.491186533676609 - type: nauc_map_at_100_diff1 value: 40.20129829669095 - type: nauc_map_at_100_max value: 19.550525879706164 - type: nauc_map_at_100_std value: -6.557075399749154 - type: nauc_map_at_10_diff1 value: 40.467281905527244 - type: nauc_map_at_10_max value: 19.43593214249552 - type: nauc_map_at_10_std value: -7.194947764095804 - type: nauc_map_at_1_diff1 value: 49.99688096548819 - type: nauc_map_at_1_max value: 22.94216810488251 - type: nauc_map_at_1_std value: -8.778905956805103 - type: nauc_map_at_20_diff1 value: 40.23228770570461 - type: nauc_map_at_20_max value: 19.53074463716011 - type: nauc_map_at_20_std value: -6.93310286275384 - type: nauc_map_at_3_diff1 value: 42.462368040248364 - type: nauc_map_at_3_max value: 20.15932725435944 - type: nauc_map_at_3_std value: -7.524246324724258 - type: nauc_map_at_5_diff1 value: 40.874264936734775 - type: nauc_map_at_5_max value: 19.741200249921643 - type: nauc_map_at_5_std value: -7.301832585861893 - type: nauc_mrr_at_1000_diff1 value: 36.93104632204301 - type: nauc_mrr_at_1000_max value: 22.851961632870285 - type: nauc_mrr_at_1000_std value: -6.050824088401521 - type: nauc_mrr_at_100_diff1 value: 36.90287005748533 - type: nauc_mrr_at_100_max value: 22.838209556819866 - type: nauc_mrr_at_100_std value: -6.064342814003103 - type: nauc_mrr_at_10_diff1 value: 36.93428786395009 - type: nauc_mrr_at_10_max value: 22.89500409199853 - type: nauc_mrr_at_10_std value: -6.581360935957288 - type: nauc_mrr_at_1_diff1 value: 46.11618926628157 - type: nauc_mrr_at_1_max value: 27.154042077346617 - type: nauc_mrr_at_1_std value: -7.408231463170914 - type: nauc_mrr_at_20_diff1 value: 36.964474819881275 - type: nauc_mrr_at_20_max value: 22.9072805988528 - type: nauc_mrr_at_20_std value: -6.306124053032698 - type: nauc_mrr_at_3_diff1 value: 38.9506895551962 - type: nauc_mrr_at_3_max value: 24.218011709989156 - type: nauc_mrr_at_3_std value: -6.7973818662665995 - type: nauc_mrr_at_5_diff1 value: 37.42273475691658 - type: nauc_mrr_at_5_max value: 23.270403975249025 - type: nauc_mrr_at_5_std value: -6.745230968723559 - type: nauc_ndcg_at_1000_diff1 value: 35.79628671266452 - type: nauc_ndcg_at_1000_max value: 19.26627785321929 - type: nauc_ndcg_at_1000_std value: -2.569388520550047 - type: nauc_ndcg_at_100_diff1 value: 35.768798848849585 - type: nauc_ndcg_at_100_max value: 18.377203611905518 - type: nauc_ndcg_at_100_std value: -3.3799540521604636 - type: nauc_ndcg_at_10_diff1 value: 36.510770710845314 - type: nauc_ndcg_at_10_max value: 18.461708026439457 - type: nauc_ndcg_at_10_std value: -6.491226580238661 - type: nauc_ndcg_at_1_diff1 value: 46.11618926628157 - type: nauc_ndcg_at_1_max value: 27.154042077346617 - type: nauc_ndcg_at_1_std value: -7.408231463170914 - type: nauc_ndcg_at_20_diff1 value: 36.070548441535124 - type: nauc_ndcg_at_20_max value: 18.42396263230167 - type: nauc_ndcg_at_20_std value: -5.61879907431204 - type: nauc_ndcg_at_3_diff1 value: 39.41782933627965 - type: nauc_ndcg_at_3_max value: 21.047162846620946 - type: nauc_ndcg_at_3_std value: -6.840755018811107 - type: nauc_ndcg_at_5_diff1 value: 37.17959347569529 - type: nauc_ndcg_at_5_max value: 19.680732729842823 - type: nauc_ndcg_at_5_std value: -6.707637987639474 - type: nauc_precision_at_1000_diff1 value: 0.49247246717968796 - type: nauc_precision_at_1000_max value: 14.62495465729825 - type: nauc_precision_at_1000_std value: 9.669209534147573 - type: nauc_precision_at_100_diff1 value: 11.5414175528365 - type: nauc_precision_at_100_max value: 18.504188333036936 - type: nauc_precision_at_100_std value: 6.194157348432716 - type: nauc_precision_at_10_diff1 value: 23.453163613392075 - type: nauc_precision_at_10_max value: 20.06043852181855 - type: nauc_precision_at_10_std value: -3.1717316064536836 - type: nauc_precision_at_1_diff1 value: 46.11618926628157 - type: nauc_precision_at_1_max value: 27.154042077346617 - type: nauc_precision_at_1_std value: -7.408231463170914 - type: nauc_precision_at_20_diff1 value: 20.708737669355788 - type: nauc_precision_at_20_max value: 20.584185448256555 - type: nauc_precision_at_20_std value: -0.7112923884678451 - type: nauc_precision_at_3_diff1 value: 31.594155528934703 - type: nauc_precision_at_3_max value: 21.789282355041912 - type: nauc_precision_at_3_std value: -3.9339318840163666 - type: nauc_precision_at_5_diff1 value: 26.10899513884069 - type: nauc_precision_at_5_max value: 21.193775642825518 - type: nauc_precision_at_5_std value: -4.04371021464142 - type: nauc_recall_at_1000_diff1 value: 19.475747590569128 - type: nauc_recall_at_1000_max value: 10.531569131631349 - type: nauc_recall_at_1000_std value: 20.376238758750535 - type: nauc_recall_at_100_diff1 value: 24.539661771959622 - type: nauc_recall_at_100_max value: 8.849671325401761 - type: nauc_recall_at_100_std value: 8.155353459396068 - type: nauc_recall_at_10_diff1 value: 27.94562559317398 - type: nauc_recall_at_10_max value: 12.341122611885497 - type: nauc_recall_at_10_std value: -4.945672050235199 - type: nauc_recall_at_1_diff1 value: 49.99688096548819 - type: nauc_recall_at_1_max value: 22.94216810488251 - type: nauc_recall_at_1_std value: -8.778905956805103 - type: nauc_recall_at_20_diff1 value: 26.721295492823483 - type: nauc_recall_at_20_max value: 11.354327070591353 - type: nauc_recall_at_20_std value: -2.0775832506536145 - type: nauc_recall_at_3_diff1 value: 35.18424498331245 - type: nauc_recall_at_3_max value: 16.737206820951112 - type: nauc_recall_at_3_std value: -6.362047908804104 - type: nauc_recall_at_5_diff1 value: 30.146390141726233 - type: nauc_recall_at_5_max value: 14.718619551703243 - type: nauc_recall_at_5_std value: -5.7544278604675165 - type: ndcg_at_1 value: 17.036 - type: ndcg_at_10 value: 22.671 - type: ndcg_at_100 value: 28.105999999999998 - type: ndcg_at_1000 value: 31.432 - type: ndcg_at_20 value: 24.617 - type: ndcg_at_3 value: 19.787 - type: ndcg_at_5 value: 21.122 - type: precision_at_1 value: 17.036 - type: precision_at_10 value: 4.09 - type: precision_at_100 value: 0.836 - type: precision_at_1000 value: 0.131 - type: precision_at_20 value: 2.6470000000000002 - type: precision_at_3 value: 9.208 - type: precision_at_5 value: 6.660000000000001 - type: recall_at_1 value: 13.99 - type: recall_at_10 value: 29.743000000000002 - type: recall_at_100 value: 53.735 - type: recall_at_1000 value: 76.785 - type: recall_at_20 value: 36.624 - type: recall_at_3 value: 21.583 - type: recall_at_5 value: 24.937 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval (default) type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: main_score value: 16.306 - type: map_at_1 value: 8.802999999999999 - type: map_at_10 value: 13.148000000000001 - type: map_at_100 value: 13.971 - type: map_at_1000 value: 14.105 - type: map_at_20 value: 13.529 - type: map_at_3 value: 11.638 - type: map_at_5 value: 12.356 - type: mrr_at_1 value: 11.073059360730593 - type: mrr_at_10 value: 15.919583967529165 - type: mrr_at_100 value: 16.709279732986573 - type: mrr_at_1000 value: 16.815285605955996 - type: mrr_at_20 value: 16.30432527215681 - type: mrr_at_3 value: 14.23135464231354 - type: mrr_at_5 value: 15.041856925418564 - type: nauc_map_at_1000_diff1 value: 30.659955136068056 - type: nauc_map_at_1000_max value: 18.44163576415389 - type: nauc_map_at_1000_std value: -3.8367034295883577 - type: nauc_map_at_100_diff1 value: 30.67476361799846 - type: nauc_map_at_100_max value: 18.428682857132582 - type: nauc_map_at_100_std value: -3.8897179777637882 - type: nauc_map_at_10_diff1 value: 30.59247711976844 - type: nauc_map_at_10_max value: 18.705778597272683 - type: nauc_map_at_10_std value: -5.022221490794733 - type: nauc_map_at_1_diff1 value: 40.141433107510736 - type: nauc_map_at_1_max value: 23.026643526851306 - type: nauc_map_at_1_std value: -5.749563342494851 - type: nauc_map_at_20_diff1 value: 30.68509526178602 - type: nauc_map_at_20_max value: 18.45627985639005 - type: nauc_map_at_20_std value: -4.406952661617948 - type: nauc_map_at_3_diff1 value: 31.73558283054405 - type: nauc_map_at_3_max value: 18.205161864303328 - type: nauc_map_at_3_std value: -5.435667326361934 - type: nauc_map_at_5_diff1 value: 30.794538196458472 - type: nauc_map_at_5_max value: 18.500170217691768 - type: nauc_map_at_5_std value: -5.684418245921586 - type: nauc_mrr_at_1000_diff1 value: 29.43077651539303 - type: nauc_mrr_at_1000_max value: 20.25130465933273 - type: nauc_mrr_at_1000_std value: -4.403299701181712 - type: nauc_mrr_at_100_diff1 value: 29.42440095545253 - type: nauc_mrr_at_100_max value: 20.262024168775454 - type: nauc_mrr_at_100_std value: -4.46104833589502 - type: nauc_mrr_at_10_diff1 value: 29.557535725132624 - type: nauc_mrr_at_10_max value: 20.517669578964018 - type: nauc_mrr_at_10_std value: -4.768947635082991 - type: nauc_mrr_at_1_diff1 value: 37.4774948212758 - type: nauc_mrr_at_1_max value: 23.439278749784055 - type: nauc_mrr_at_1_std value: -5.157088191908156 - type: nauc_mrr_at_20_diff1 value: 29.48470932914118 - type: nauc_mrr_at_20_max value: 20.278594953830762 - type: nauc_mrr_at_20_std value: -4.705845733248912 - type: nauc_mrr_at_3_diff1 value: 30.77059795240642 - type: nauc_mrr_at_3_max value: 20.391982151070895 - type: nauc_mrr_at_3_std value: -5.0478682718453385 - type: nauc_mrr_at_5_diff1 value: 30.028856765971984 - type: nauc_mrr_at_5_max value: 20.557553687197167 - type: nauc_mrr_at_5_std value: -5.24319954121192 - type: nauc_ndcg_at_1000_diff1 value: 27.40711483349399 - type: nauc_ndcg_at_1000_max value: 17.126369493537826 - type: nauc_ndcg_at_1000_std value: 0.5342836524997823 - type: nauc_ndcg_at_100_diff1 value: 27.711441526870356 - type: nauc_ndcg_at_100_max value: 17.276247470704032 - type: nauc_ndcg_at_100_std value: -0.8750376980385484 - type: nauc_ndcg_at_10_diff1 value: 27.720574369240204 - type: nauc_ndcg_at_10_max value: 18.456829787593097 - type: nauc_ndcg_at_10_std value: -4.216473937357797 - type: nauc_ndcg_at_1_diff1 value: 37.4774948212758 - type: nauc_ndcg_at_1_max value: 23.439278749784055 - type: nauc_ndcg_at_1_std value: -5.157088191908156 - type: nauc_ndcg_at_20_diff1 value: 27.746972988773933 - type: nauc_ndcg_at_20_max value: 17.52494953980253 - type: nauc_ndcg_at_20_std value: -2.9781030890977322 - type: nauc_ndcg_at_3_diff1 value: 29.522350537696717 - type: nauc_ndcg_at_3_max value: 18.011604144671008 - type: nauc_ndcg_at_3_std value: -4.725546369301677 - type: nauc_ndcg_at_5_diff1 value: 28.15851614794711 - type: nauc_ndcg_at_5_max value: 18.317965726201184 - type: nauc_ndcg_at_5_std value: -5.54058686011457 - type: nauc_precision_at_1000_diff1 value: 4.343913518236252 - type: nauc_precision_at_1000_max value: 7.949664745091711 - type: nauc_precision_at_1000_std value: 2.986855849342956 - type: nauc_precision_at_100_diff1 value: 15.435700494268618 - type: nauc_precision_at_100_max value: 15.530490741404742 - type: nauc_precision_at_100_std value: 4.089210125048146 - type: nauc_precision_at_10_diff1 value: 19.57474708128042 - type: nauc_precision_at_10_max value: 19.632161038711597 - type: nauc_precision_at_10_std value: -1.7830580435403458 - type: nauc_precision_at_1_diff1 value: 37.4774948212758 - type: nauc_precision_at_1_max value: 23.439278749784055 - type: nauc_precision_at_1_std value: -5.157088191908156 - type: nauc_precision_at_20_diff1 value: 20.568797026407644 - type: nauc_precision_at_20_max value: 17.15052399771233 - type: nauc_precision_at_20_std value: 0.6381100303472123 - type: nauc_precision_at_3_diff1 value: 23.53527003948809 - type: nauc_precision_at_3_max value: 18.260774860471376 - type: nauc_precision_at_3_std value: -4.277699429606214 - type: nauc_precision_at_5_diff1 value: 20.957492799575085 - type: nauc_precision_at_5_max value: 20.041536239699173 - type: nauc_precision_at_5_std value: -5.250189398148323 - type: nauc_recall_at_1000_diff1 value: 19.56836100145482 - type: nauc_recall_at_1000_max value: 7.776560050916105 - type: nauc_recall_at_1000_std value: 20.13708584784103 - type: nauc_recall_at_100_diff1 value: 22.16510567224014 - type: nauc_recall_at_100_max value: 11.397641876417932 - type: nauc_recall_at_100_std value: 7.58221141431797 - type: nauc_recall_at_10_diff1 value: 21.305911125564595 - type: nauc_recall_at_10_max value: 15.61442350884527 - type: nauc_recall_at_10_std value: -2.264275057856056 - type: nauc_recall_at_1_diff1 value: 40.141433107510736 - type: nauc_recall_at_1_max value: 23.026643526851306 - type: nauc_recall_at_1_std value: -5.749563342494851 - type: nauc_recall_at_20_diff1 value: 21.33360178111777 - type: nauc_recall_at_20_max value: 13.007427262980725 - type: nauc_recall_at_20_std value: 0.8315450930852684 - type: nauc_recall_at_3_diff1 value: 24.26871252397936 - type: nauc_recall_at_3_max value: 13.78009182310998 - type: nauc_recall_at_3_std value: -4.427807391785745 - type: nauc_recall_at_5_diff1 value: 22.146386144738443 - type: nauc_recall_at_5_max value: 14.558261310921718 - type: nauc_recall_at_5_std value: -5.453171833787222 - type: ndcg_at_1 value: 11.073 - type: ndcg_at_10 value: 16.306 - type: ndcg_at_100 value: 20.605 - type: ndcg_at_1000 value: 24.321 - type: ndcg_at_20 value: 17.605999999999998 - type: ndcg_at_3 value: 13.242 - type: ndcg_at_5 value: 14.424000000000001 - type: precision_at_1 value: 11.073 - type: precision_at_10 value: 3.174 - type: precision_at_100 value: 0.632 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_20 value: 1.981 - type: precision_at_3 value: 6.317 - type: precision_at_5 value: 4.658 - type: recall_at_1 value: 8.802999999999999 - type: recall_at_10 value: 23.294999999999998 - type: recall_at_100 value: 42.543 - type: recall_at_1000 value: 69.501 - type: recall_at_20 value: 27.788 - type: recall_at_3 value: 14.935 - type: recall_at_5 value: 17.862000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval (default) type: CQADupstackRetrieval_is_a_combined_dataset config: default split: test revision: CQADupstackRetrieval_is_a_combined_dataset metrics: - type: main_score value: 19.211500000000004 - type: ndcg_at_10 value: 19.211500000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval (default) type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: main_score value: 13.274 - type: map_at_1 value: 7.514 - type: map_at_10 value: 10.763 - type: map_at_100 value: 11.466 - type: map_at_1000 value: 11.565 - type: map_at_20 value: 11.153 - type: map_at_3 value: 9.489 - type: map_at_5 value: 10.05 - type: mrr_at_1 value: 9.049079754601227 - type: mrr_at_10 value: 12.66140812153082 - type: mrr_at_100 value: 13.34440731558096 - type: mrr_at_1000 value: 13.431250805407094 - type: mrr_at_20 value: 13.015821938908093 - type: mrr_at_3 value: 11.349693251533745 - type: mrr_at_5 value: 11.955521472392643 - type: nauc_map_at_1000_diff1 value: 22.974932209110474 - type: nauc_map_at_1000_max value: 19.2179493418811 - type: nauc_map_at_1000_std value: -4.027224925667458 - type: nauc_map_at_100_diff1 value: 23.00306330611636 - type: nauc_map_at_100_max value: 19.279597737188887 - type: nauc_map_at_100_std value: -4.054272921846715 - type: nauc_map_at_10_diff1 value: 23.185643422536508 - type: nauc_map_at_10_max value: 19.620815876636478 - type: nauc_map_at_10_std value: -4.67640325592363 - type: nauc_map_at_1_diff1 value: 29.800345069729406 - type: nauc_map_at_1_max value: 23.87910907490326 - type: nauc_map_at_1_std value: -6.320599828399073 - type: nauc_map_at_20_diff1 value: 23.142569498191413 - type: nauc_map_at_20_max value: 19.48779289778967 - type: nauc_map_at_20_std value: -4.111902735804231 - type: nauc_map_at_3_diff1 value: 25.743034910929975 - type: nauc_map_at_3_max value: 20.90755349054651 - type: nauc_map_at_3_std value: -5.380592645823912 - type: nauc_map_at_5_diff1 value: 23.42137416675548 - type: nauc_map_at_5_max value: 19.329228837468158 - type: nauc_map_at_5_std value: -5.563525004474619 - type: nauc_mrr_at_1000_diff1 value: 24.10086479687415 - type: nauc_mrr_at_1000_max value: 20.398011792778824 - type: nauc_mrr_at_1000_std value: -2.1446120511727957 - type: nauc_mrr_at_100_diff1 value: 24.115697677435794 - type: nauc_mrr_at_100_max value: 20.458646264375886 - type: nauc_mrr_at_100_std value: -2.151550159504517 - type: nauc_mrr_at_10_diff1 value: 24.293579862933555 - type: nauc_mrr_at_10_max value: 20.839345603643498 - type: nauc_mrr_at_10_std value: -2.480503488415708 - type: nauc_mrr_at_1_diff1 value: 31.141124432852486 - type: nauc_mrr_at_1_max value: 25.3974393459875 - type: nauc_mrr_at_1_std value: -4.603112328474119 - type: nauc_mrr_at_20_diff1 value: 24.199943135873237 - type: nauc_mrr_at_20_max value: 20.685578492011537 - type: nauc_mrr_at_20_std value: -2.216739386860867 - type: nauc_mrr_at_3_diff1 value: 27.18978712305054 - type: nauc_mrr_at_3_max value: 21.95145492661433 - type: nauc_mrr_at_3_std value: -3.3010871727045004 - type: nauc_mrr_at_5_diff1 value: 24.55785813047769 - type: nauc_mrr_at_5_max value: 20.630334122680697 - type: nauc_mrr_at_5_std value: -3.4751492733475713 - type: nauc_ndcg_at_1000_diff1 value: 18.214182224000904 - type: nauc_ndcg_at_1000_max value: 15.022677670245125 - type: nauc_ndcg_at_1000_std value: -1.2757783952996276 - type: nauc_ndcg_at_100_diff1 value: 19.45648169337917 - type: nauc_ndcg_at_100_max value: 16.160731902664246 - type: nauc_ndcg_at_100_std value: -1.2021617745185982 - type: nauc_ndcg_at_10_diff1 value: 20.78032928549088 - type: nauc_ndcg_at_10_max value: 18.37701966895512 - type: nauc_ndcg_at_10_std value: -2.859756963061105 - type: nauc_ndcg_at_1_diff1 value: 31.141124432852486 - type: nauc_ndcg_at_1_max value: 25.3974393459875 - type: nauc_ndcg_at_1_std value: -4.603112328474119 - type: nauc_ndcg_at_20_diff1 value: 20.568804870494365 - type: nauc_ndcg_at_20_max value: 17.688797629532804 - type: nauc_ndcg_at_20_std value: -1.601270033947706 - type: nauc_ndcg_at_3_diff1 value: 25.352168775398777 - type: nauc_ndcg_at_3_max value: 20.42319619108203 - type: nauc_ndcg_at_3_std value: -4.2521134409577845 - type: nauc_ndcg_at_5_diff1 value: 21.18713014585295 - type: nauc_ndcg_at_5_max value: 17.939191093215953 - type: nauc_ndcg_at_5_std value: -4.743032229404275 - type: nauc_precision_at_1000_diff1 value: 4.892829090188313 - type: nauc_precision_at_1000_max value: 7.933069592889083 - type: nauc_precision_at_1000_std value: 4.24278581923629 - type: nauc_precision_at_100_diff1 value: 13.066398116495034 - type: nauc_precision_at_100_max value: 14.384247527346716 - type: nauc_precision_at_100_std value: 6.056873634302884 - type: nauc_precision_at_10_diff1 value: 16.616656372852148 - type: nauc_precision_at_10_max value: 18.665616620054436 - type: nauc_precision_at_10_std value: 1.1124326621912484 - type: nauc_precision_at_1_diff1 value: 31.141124432852486 - type: nauc_precision_at_1_max value: 25.3974393459875 - type: nauc_precision_at_1_std value: -4.603112328474119 - type: nauc_precision_at_20_diff1 value: 17.294215780840165 - type: nauc_precision_at_20_max value: 18.09538722850449 - type: nauc_precision_at_20_std value: 5.524315844370954 - type: nauc_precision_at_3_diff1 value: 25.1866897673422 - type: nauc_precision_at_3_max value: 19.72076391537079 - type: nauc_precision_at_3_std value: -1.6649392928833502 - type: nauc_precision_at_5_diff1 value: 17.254095768389526 - type: nauc_precision_at_5_max value: 16.94859363403111 - type: nauc_precision_at_5_std value: -1.9187213027734356 - type: nauc_recall_at_1000_diff1 value: 2.1491291924120404 - type: nauc_recall_at_1000_max value: -0.6564763388554173 - type: nauc_recall_at_1000_std value: 2.480520716627822 - type: nauc_recall_at_100_diff1 value: 10.764856128055248 - type: nauc_recall_at_100_max value: 6.734689971662489 - type: nauc_recall_at_100_std value: 3.0407690200004334 - type: nauc_recall_at_10_diff1 value: 14.979718773625542 - type: nauc_recall_at_10_max value: 14.109838347838258 - type: nauc_recall_at_10_std value: -0.5378433013187329 - type: nauc_recall_at_1_diff1 value: 29.800345069729406 - type: nauc_recall_at_1_max value: 23.87910907490326 - type: nauc_recall_at_1_std value: -6.320599828399073 - type: nauc_recall_at_20_diff1 value: 14.511882633459333 - type: nauc_recall_at_20_max value: 12.011480653201415 - type: nauc_recall_at_20_std value: 2.0767690218465877 - type: nauc_recall_at_3_diff1 value: 20.6626126323687 - type: nauc_recall_at_3_max value: 17.25857728630443 - type: nauc_recall_at_3_std value: -3.7939883071411717 - type: nauc_recall_at_5_diff1 value: 14.1235036082108 - type: nauc_recall_at_5_max value: 12.727411826064857 - type: nauc_recall_at_5_std value: -4.60850604165874 - type: ndcg_at_1 value: 9.049 - type: ndcg_at_10 value: 13.274 - type: ndcg_at_100 value: 17.086000000000002 - type: ndcg_at_1000 value: 19.936999999999998 - type: ndcg_at_20 value: 14.582999999999998 - type: ndcg_at_3 value: 10.725999999999999 - type: ndcg_at_5 value: 11.623 - type: precision_at_1 value: 9.049 - type: precision_at_10 value: 2.423 - type: precision_at_100 value: 0.479 - type: precision_at_1000 value: 0.079 - type: precision_at_20 value: 1.526 - type: precision_at_3 value: 4.9590000000000005 - type: precision_at_5 value: 3.62 - type: recall_at_1 value: 7.514 - type: recall_at_10 value: 19.31 - type: recall_at_100 value: 37.413999999999994 - type: recall_at_1000 value: 59.021 - type: recall_at_20 value: 24.21 - type: recall_at_3 value: 12.113999999999999 - type: recall_at_5 value: 14.371 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval (default) type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: main_score value: 10.994 - type: map_at_1 value: 6.225 - type: map_at_10 value: 8.953999999999999 - type: map_at_100 value: 9.603 - type: map_at_1000 value: 9.712 - type: map_at_20 value: 9.278 - type: map_at_3 value: 8.074 - type: map_at_5 value: 8.547 - type: mrr_at_1 value: 7.708189951823813 - type: mrr_at_10 value: 11.010238805317954 - type: mrr_at_100 value: 11.697852969394127 - type: mrr_at_1000 value: 11.788096222755389 - type: mrr_at_20 value: 11.36125747114887 - type: mrr_at_3 value: 9.967882541867406 - type: mrr_at_5 value: 10.53223216334021 - type: nauc_map_at_1000_diff1 value: 28.62895539988389 - type: nauc_map_at_1000_max value: 16.242894414293037 - type: nauc_map_at_1000_std value: -4.569604418870727 - type: nauc_map_at_100_diff1 value: 28.61807781605406 - type: nauc_map_at_100_max value: 16.21900205663456 - type: nauc_map_at_100_std value: -4.742228052779668 - type: nauc_map_at_10_diff1 value: 29.55698899178743 - type: nauc_map_at_10_max value: 16.619065435982105 - type: nauc_map_at_10_std value: -5.272914850396907 - type: nauc_map_at_1_diff1 value: 38.11099020611636 - type: nauc_map_at_1_max value: 19.754663729177466 - type: nauc_map_at_1_std value: -7.100435784719483 - type: nauc_map_at_20_diff1 value: 28.96213016918891 - type: nauc_map_at_20_max value: 16.40536013245705 - type: nauc_map_at_20_std value: -5.152060847207817 - type: nauc_map_at_3_diff1 value: 31.518330681088514 - type: nauc_map_at_3_max value: 17.648594434363673 - type: nauc_map_at_3_std value: -5.013522244046003 - type: nauc_map_at_5_diff1 value: 30.53555288667588 - type: nauc_map_at_5_max value: 17.552873944829003 - type: nauc_map_at_5_std value: -5.459559007946099 - type: nauc_mrr_at_1000_diff1 value: 28.56870451139856 - type: nauc_mrr_at_1000_max value: 18.199477946334998 - type: nauc_mrr_at_1000_std value: -3.83210753499382 - type: nauc_mrr_at_100_diff1 value: 28.55289316686771 - type: nauc_mrr_at_100_max value: 18.190933266659705 - type: nauc_mrr_at_100_std value: -3.910114024174217 - type: nauc_mrr_at_10_diff1 value: 29.44010525180224 - type: nauc_mrr_at_10_max value: 18.5618742276953 - type: nauc_mrr_at_10_std value: -4.318500155132472 - type: nauc_mrr_at_1_diff1 value: 37.756041398612425 - type: nauc_mrr_at_1_max value: 22.180382124822522 - type: nauc_mrr_at_1_std value: -6.881985725496932 - type: nauc_mrr_at_20_diff1 value: 28.862633708506863 - type: nauc_mrr_at_20_max value: 18.368745544312883 - type: nauc_mrr_at_20_std value: -4.231869471717514 - type: nauc_mrr_at_3_diff1 value: 31.67790485910417 - type: nauc_mrr_at_3_max value: 20.067426011874694 - type: nauc_mrr_at_3_std value: -4.35750935851484 - type: nauc_mrr_at_5_diff1 value: 30.3892346503623 - type: nauc_mrr_at_5_max value: 19.427471974651258 - type: nauc_mrr_at_5_std value: -4.501090877808792 - type: nauc_ndcg_at_1000_diff1 value: 23.124264919835152 - type: nauc_ndcg_at_1000_max value: 13.725127541654583 - type: nauc_ndcg_at_1000_std value: 0.8488267118015322 - type: nauc_ndcg_at_100_diff1 value: 22.931912676541813 - type: nauc_ndcg_at_100_max value: 13.573133160305714 - type: nauc_ndcg_at_100_std value: -1.9712575029716004 - type: nauc_ndcg_at_10_diff1 value: 26.49225179330549 - type: nauc_ndcg_at_10_max value: 15.334589645844614 - type: nauc_ndcg_at_10_std value: -4.732200420388755 - type: nauc_ndcg_at_1_diff1 value: 37.756041398612425 - type: nauc_ndcg_at_1_max value: 22.180382124822522 - type: nauc_ndcg_at_1_std value: -6.881985725496932 - type: nauc_ndcg_at_20_diff1 value: 24.758487984247115 - type: nauc_ndcg_at_20_max value: 14.685319575357777 - type: nauc_ndcg_at_20_std value: -4.432729957713687 - type: nauc_ndcg_at_3_diff1 value: 30.04172743163936 - type: nauc_ndcg_at_3_max value: 17.942422342704166 - type: nauc_ndcg_at_3_std value: -4.371869609553122 - type: nauc_ndcg_at_5_diff1 value: 28.394597447013364 - type: nauc_ndcg_at_5_max value: 17.337563726465902 - type: nauc_ndcg_at_5_std value: -4.979815289974346 - type: nauc_precision_at_1000_diff1 value: 13.358015963281982 - type: nauc_precision_at_1000_max value: 13.588027398642533 - type: nauc_precision_at_1000_std value: 16.038391304073617 - type: nauc_precision_at_100_diff1 value: 14.048154067920237 - type: nauc_precision_at_100_max value: 13.442039272771812 - type: nauc_precision_at_100_std value: 6.293550136432713 - type: nauc_precision_at_10_diff1 value: 19.7938197345429 - type: nauc_precision_at_10_max value: 15.498999930693053 - type: nauc_precision_at_10_std value: -2.820921985501471 - type: nauc_precision_at_1_diff1 value: 37.756041398612425 - type: nauc_precision_at_1_max value: 22.180382124822522 - type: nauc_precision_at_1_std value: -6.881985725496932 - type: nauc_precision_at_20_diff1 value: 16.86330177780297 - type: nauc_precision_at_20_max value: 14.757498925286052 - type: nauc_precision_at_20_std value: -1.4878113085077458 - type: nauc_precision_at_3_diff1 value: 26.22068335923554 - type: nauc_precision_at_3_max value: 19.552244504819107 - type: nauc_precision_at_3_std value: -2.903836612504541 - type: nauc_precision_at_5_diff1 value: 23.01543740291806 - type: nauc_precision_at_5_max value: 18.976238791156298 - type: nauc_precision_at_5_std value: -3.772870601995056 - type: nauc_recall_at_1000_diff1 value: 11.344856628291772 - type: nauc_recall_at_1000_max value: 5.496064714954898 - type: nauc_recall_at_1000_std value: 14.552915745152944 - type: nauc_recall_at_100_diff1 value: 11.37183345326816 - type: nauc_recall_at_100_max value: 6.152609534633153 - type: nauc_recall_at_100_std value: 3.3240506595168617 - type: nauc_recall_at_10_diff1 value: 19.414706457137537 - type: nauc_recall_at_10_max value: 10.013408222848447 - type: nauc_recall_at_10_std value: -4.469998335412016 - type: nauc_recall_at_1_diff1 value: 38.11099020611636 - type: nauc_recall_at_1_max value: 19.754663729177466 - type: nauc_recall_at_1_std value: -7.100435784719483 - type: nauc_recall_at_20_diff1 value: 15.570619584248163 - type: nauc_recall_at_20_max value: 8.816676896160281 - type: nauc_recall_at_20_std value: -3.7706693105174836 - type: nauc_recall_at_3_diff1 value: 25.664091285326485 - type: nauc_recall_at_3_max value: 14.868700645447488 - type: nauc_recall_at_3_std value: -3.5813114627791736 - type: nauc_recall_at_5_diff1 value: 22.650699032516435 - type: nauc_recall_at_5_max value: 14.046776424466485 - type: nauc_recall_at_5_std value: -5.072422590207594 - type: ndcg_at_1 value: 7.707999999999999 - type: ndcg_at_10 value: 10.994 - type: ndcg_at_100 value: 14.562 - type: ndcg_at_1000 value: 17.738 - type: ndcg_at_20 value: 12.152000000000001 - type: ndcg_at_3 value: 9.286999999999999 - type: ndcg_at_5 value: 10.057 - type: precision_at_1 value: 7.707999999999999 - type: precision_at_10 value: 2.068 - type: precision_at_100 value: 0.466 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_20 value: 1.352 - type: precision_at_3 value: 4.508 - type: precision_at_5 value: 3.3169999999999997 - type: recall_at_1 value: 6.225 - type: recall_at_10 value: 15.177999999999999 - type: recall_at_100 value: 31.726 - type: recall_at_1000 value: 55.286 - type: recall_at_20 value: 19.516 - type: recall_at_3 value: 10.381 - type: recall_at_5 value: 12.354999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval (default) type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: main_score value: 17.415 - type: map_at_1 value: 11.61 - type: map_at_10 value: 14.879000000000001 - type: map_at_100 value: 15.64 - type: map_at_1000 value: 15.744 - type: map_at_20 value: 15.222 - type: map_at_3 value: 13.818 - type: map_at_5 value: 14.221 - type: mrr_at_1 value: 14.085820895522389 - type: mrr_at_10 value: 17.784144752428336 - type: mrr_at_100 value: 18.59055632302295 - type: mrr_at_1000 value: 18.680733729013262 - type: mrr_at_20 value: 18.159102701666594 - type: mrr_at_3 value: 16.68221393034826 - type: mrr_at_5 value: 17.10665422885572 - type: nauc_map_at_1000_diff1 value: 39.56056915227938 - type: nauc_map_at_1000_max value: 27.13397943596498 - type: nauc_map_at_1000_std value: -7.0908382945611175 - type: nauc_map_at_100_diff1 value: 39.54030188989168 - type: nauc_map_at_100_max value: 27.13281562979474 - type: nauc_map_at_100_std value: -7.165159503138965 - type: nauc_map_at_10_diff1 value: 40.318171341397765 - type: nauc_map_at_10_max value: 27.535451283580016 - type: nauc_map_at_10_std value: -7.689737441073707 - type: nauc_map_at_1_diff1 value: 47.05601088674895 - type: nauc_map_at_1_max value: 30.576608334052853 - type: nauc_map_at_1_std value: -9.67702524348975 - type: nauc_map_at_20_diff1 value: 39.80136558735939 - type: nauc_map_at_20_max value: 27.051853945437948 - type: nauc_map_at_20_std value: -7.409144616339466 - type: nauc_map_at_3_diff1 value: 42.15633029927089 - type: nauc_map_at_3_max value: 28.386143076096086 - type: nauc_map_at_3_std value: -9.106105164113686 - type: nauc_map_at_5_diff1 value: 41.46860741828094 - type: nauc_map_at_5_max value: 28.202178480215373 - type: nauc_map_at_5_std value: -8.399626801433124 - type: nauc_mrr_at_1000_diff1 value: 37.78472411053756 - type: nauc_mrr_at_1000_max value: 28.338277069066432 - type: nauc_mrr_at_1000_std value: -7.391912169514899 - type: nauc_mrr_at_100_diff1 value: 37.74697100045658 - type: nauc_mrr_at_100_max value: 28.35832528792151 - type: nauc_mrr_at_100_std value: -7.4298805804754995 - type: nauc_mrr_at_10_diff1 value: 38.428674914285196 - type: nauc_mrr_at_10_max value: 28.708508212507105 - type: nauc_mrr_at_10_std value: -7.884064754659524 - type: nauc_mrr_at_1_diff1 value: 45.69997352898185 - type: nauc_mrr_at_1_max value: 32.47880480030532 - type: nauc_mrr_at_1_std value: -9.337266605729418 - type: nauc_mrr_at_20_diff1 value: 37.99989625388078 - type: nauc_mrr_at_20_max value: 28.255616608253824 - type: nauc_mrr_at_20_std value: -7.614369324242356 - type: nauc_mrr_at_3_diff1 value: 40.126736669268766 - type: nauc_mrr_at_3_max value: 29.616770044400464 - type: nauc_mrr_at_3_std value: -9.336882852739908 - type: nauc_mrr_at_5_diff1 value: 39.41517859913304 - type: nauc_mrr_at_5_max value: 29.312224024493094 - type: nauc_mrr_at_5_std value: -8.792379282413792 - type: nauc_ndcg_at_1000_diff1 value: 34.318717429678735 - type: nauc_ndcg_at_1000_max value: 24.57185685965525 - type: nauc_ndcg_at_1000_std value: -2.367526484055821 - type: nauc_ndcg_at_100_diff1 value: 33.59453283807552 - type: nauc_ndcg_at_100_max value: 24.73858681825266 - type: nauc_ndcg_at_100_std value: -4.087141295771279 - type: nauc_ndcg_at_10_diff1 value: 36.635105955522235 - type: nauc_ndcg_at_10_max value: 25.975386842872318 - type: nauc_ndcg_at_10_std value: -6.3751364798979315 - type: nauc_ndcg_at_1_diff1 value: 45.69997352898185 - type: nauc_ndcg_at_1_max value: 32.47880480030532 - type: nauc_ndcg_at_1_std value: -9.337266605729418 - type: nauc_ndcg_at_20_diff1 value: 35.16876791291799 - type: nauc_ndcg_at_20_max value: 24.477658044207647 - type: nauc_ndcg_at_20_std value: -5.555064208738701 - type: nauc_ndcg_at_3_diff1 value: 39.82534185570945 - type: nauc_ndcg_at_3_max value: 28.139721552476963 - type: nauc_ndcg_at_3_std value: -9.160710946542384 - type: nauc_ndcg_at_5_diff1 value: 38.98115351105197 - type: nauc_ndcg_at_5_max value: 27.515452028134202 - type: nauc_ndcg_at_5_std value: -8.025551102160557 - type: nauc_precision_at_1000_diff1 value: 12.303392079476001 - type: nauc_precision_at_1000_max value: 15.521101561430214 - type: nauc_precision_at_1000_std value: 13.875729823362349 - type: nauc_precision_at_100_diff1 value: 15.718813920537666 - type: nauc_precision_at_100_max value: 20.036566730817615 - type: nauc_precision_at_100_std value: 5.068608226979542 - type: nauc_precision_at_10_diff1 value: 25.3121404066982 - type: nauc_precision_at_10_max value: 24.190797754465372 - type: nauc_precision_at_10_std value: -3.28815407741081 - type: nauc_precision_at_1_diff1 value: 45.69997352898185 - type: nauc_precision_at_1_max value: 32.47880480030532 - type: nauc_precision_at_1_std value: -9.337266605729418 - type: nauc_precision_at_20_diff1 value: 21.370193752136633 - type: nauc_precision_at_20_max value: 19.74829392747058 - type: nauc_precision_at_20_std value: -1.1434647531180093 - type: nauc_precision_at_3_diff1 value: 33.27263719269652 - type: nauc_precision_at_3_max value: 27.28958835327579 - type: nauc_precision_at_3_std value: -9.03699952848916 - type: nauc_precision_at_5_diff1 value: 31.109130426292463 - type: nauc_precision_at_5_max value: 26.959336149040137 - type: nauc_precision_at_5_std value: -6.946474296738139 - type: nauc_recall_at_1000_diff1 value: 17.923508430691957 - type: nauc_recall_at_1000_max value: 10.80984639138324 - type: nauc_recall_at_1000_std value: 17.38699739341662 - type: nauc_recall_at_100_diff1 value: 17.188512794168755 - type: nauc_recall_at_100_max value: 15.470956979815659 - type: nauc_recall_at_100_std value: 4.263468796063786 - type: nauc_recall_at_10_diff1 value: 27.628371666732892 - type: nauc_recall_at_10_max value: 19.847290125705662 - type: nauc_recall_at_10_std value: -2.718782096589473 - type: nauc_recall_at_1_diff1 value: 47.05601088674895 - type: nauc_recall_at_1_max value: 30.576608334052853 - type: nauc_recall_at_1_std value: -9.67702524348975 - type: nauc_recall_at_20_diff1 value: 23.787114240920214 - type: nauc_recall_at_20_max value: 15.65621275614017 - type: nauc_recall_at_20_std value: -0.6996887505536454 - type: nauc_recall_at_3_diff1 value: 37.16605995449111 - type: nauc_recall_at_3_max value: 24.971735910807293 - type: nauc_recall_at_3_std value: -8.874845333377282 - type: nauc_recall_at_5_diff1 value: 34.15194539098878 - type: nauc_recall_at_5_max value: 23.788685123818407 - type: nauc_recall_at_5_std value: -6.520745742182325 - type: ndcg_at_1 value: 14.086000000000002 - type: ndcg_at_10 value: 17.415 - type: ndcg_at_100 value: 21.705 - type: ndcg_at_1000 value: 24.851 - type: ndcg_at_20 value: 18.674 - type: ndcg_at_3 value: 15.369 - type: ndcg_at_5 value: 15.903 - type: precision_at_1 value: 14.086000000000002 - type: precision_at_10 value: 2.9010000000000002 - type: precision_at_100 value: 0.567 - type: precision_at_1000 value: 0.093 - type: precision_at_20 value: 1.754 - type: precision_at_3 value: 6.903 - type: precision_at_5 value: 4.571 - type: recall_at_1 value: 11.61 - type: recall_at_10 value: 22.543 - type: recall_at_100 value: 42.586 - type: recall_at_1000 value: 66.3 - type: recall_at_20 value: 27.296 - type: recall_at_3 value: 16.458000000000002 - type: recall_at_5 value: 18.087 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval (default) type: mteb/cqadupstack-webmasters config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: main_score value: 21.398 - type: map_at_1 value: 12.418 - type: map_at_10 value: 17.634 - type: map_at_100 value: 18.427 - type: map_at_1000 value: 18.601 - type: map_at_20 value: 17.949 - type: map_at_3 value: 16.070999999999998 - type: map_at_5 value: 16.909 - type: mrr_at_1 value: 16.007905138339922 - type: mrr_at_10 value: 21.244275048622875 - type: mrr_at_100 value: 21.913675154893422 - type: mrr_at_1000 value: 22.00394675539023 - type: mrr_at_20 value: 21.484105638892164 - type: mrr_at_3 value: 19.729907773386028 - type: mrr_at_5 value: 20.579710144927535 - type: nauc_map_at_1000_diff1 value: 33.276058954347164 - type: nauc_map_at_1000_max value: 22.686785676254438 - type: nauc_map_at_1000_std value: -15.623983007245663 - type: nauc_map_at_100_diff1 value: 33.277163035857754 - type: nauc_map_at_100_max value: 22.79483533389435 - type: nauc_map_at_100_std value: -15.806523169464585 - type: nauc_map_at_10_diff1 value: 33.31349011893446 - type: nauc_map_at_10_max value: 23.16070733276047 - type: nauc_map_at_10_std value: -16.557456309767332 - type: nauc_map_at_1_diff1 value: 43.560854870215444 - type: nauc_map_at_1_max value: 22.785972852704127 - type: nauc_map_at_1_std value: -17.629946377144794 - type: nauc_map_at_20_diff1 value: 33.570999449061176 - type: nauc_map_at_20_max value: 22.993901876226587 - type: nauc_map_at_20_std value: -16.272939675166977 - type: nauc_map_at_3_diff1 value: 35.03763295449743 - type: nauc_map_at_3_max value: 22.445582103531297 - type: nauc_map_at_3_std value: -16.560038144492275 - type: nauc_map_at_5_diff1 value: 34.27964006257987 - type: nauc_map_at_5_max value: 23.332248714244795 - type: nauc_map_at_5_std value: -16.57243447707981 - type: nauc_mrr_at_1000_diff1 value: 32.944240054080296 - type: nauc_mrr_at_1000_max value: 21.812793329305745 - type: nauc_mrr_at_1000_std value: -13.642145832181225 - type: nauc_mrr_at_100_diff1 value: 32.92776460042595 - type: nauc_mrr_at_100_max value: 21.791203022888052 - type: nauc_mrr_at_100_std value: -13.640560468524749 - type: nauc_mrr_at_10_diff1 value: 32.9752685024834 - type: nauc_mrr_at_10_max value: 22.104988021339146 - type: nauc_mrr_at_10_std value: -14.271356854639786 - type: nauc_mrr_at_1_diff1 value: 42.51316330983356 - type: nauc_mrr_at_1_max value: 23.297138888078976 - type: nauc_mrr_at_1_std value: -14.903606813837882 - type: nauc_mrr_at_20_diff1 value: 33.22223363073958 - type: nauc_mrr_at_20_max value: 21.974295331873055 - type: nauc_mrr_at_20_std value: -13.88205443342369 - type: nauc_mrr_at_3_diff1 value: 33.993832814261395 - type: nauc_mrr_at_3_max value: 21.556945052605887 - type: nauc_mrr_at_3_std value: -13.797171517214505 - type: nauc_mrr_at_5_diff1 value: 33.35409476101201 - type: nauc_mrr_at_5_max value: 21.981426511175837 - type: nauc_mrr_at_5_std value: -14.09531063812787 - type: nauc_ndcg_at_1000_diff1 value: 29.438860831545004 - type: nauc_ndcg_at_1000_max value: 21.25973393436945 - type: nauc_ndcg_at_1000_std value: -11.16393916502227 - type: nauc_ndcg_at_100_diff1 value: 28.444184419510172 - type: nauc_ndcg_at_100_max value: 21.18616561891909 - type: nauc_ndcg_at_100_std value: -12.037980607459001 - type: nauc_ndcg_at_10_diff1 value: 29.271087139678205 - type: nauc_ndcg_at_10_max value: 22.032768110468098 - type: nauc_ndcg_at_10_std value: -15.467782849927971 - type: nauc_ndcg_at_1_diff1 value: 42.51316330983356 - type: nauc_ndcg_at_1_max value: 23.297138888078976 - type: nauc_ndcg_at_1_std value: -14.903606813837882 - type: nauc_ndcg_at_20_diff1 value: 30.46132048728029 - type: nauc_ndcg_at_20_max value: 21.81477297472493 - type: nauc_ndcg_at_20_std value: -14.218418166481491 - type: nauc_ndcg_at_3_diff1 value: 32.0153358591922 - type: nauc_ndcg_at_3_max value: 20.770546204709458 - type: nauc_ndcg_at_3_std value: -14.747432002736549 - type: nauc_ndcg_at_5_diff1 value: 30.981699893250898 - type: nauc_ndcg_at_5_max value: 22.090548813686304 - type: nauc_ndcg_at_5_std value: -15.09612387707668 - type: nauc_precision_at_1000_diff1 value: 7.2014592078746125 - type: nauc_precision_at_1000_max value: -5.678465880888778 - type: nauc_precision_at_1000_std value: 22.430084503019 - type: nauc_precision_at_100_diff1 value: 7.47376139946301 - type: nauc_precision_at_100_max value: 2.300260757829557 - type: nauc_precision_at_100_std value: 13.810673946221709 - type: nauc_precision_at_10_diff1 value: 15.542740121996912 - type: nauc_precision_at_10_max value: 15.807667200751279 - type: nauc_precision_at_10_std value: -9.58878382311598 - type: nauc_precision_at_1_diff1 value: 42.51316330983356 - type: nauc_precision_at_1_max value: 23.297138888078976 - type: nauc_precision_at_1_std value: -14.903606813837882 - type: nauc_precision_at_20_diff1 value: 17.44141625096109 - type: nauc_precision_at_20_max value: 12.987380515646793 - type: nauc_precision_at_20_std value: -3.3241327401895018 - type: nauc_precision_at_3_diff1 value: 24.31306633873876 - type: nauc_precision_at_3_max value: 20.59991114197874 - type: nauc_precision_at_3_std value: -12.702555430555881 - type: nauc_precision_at_5_diff1 value: 21.113937977245538 - type: nauc_precision_at_5_max value: 19.40330569402618 - type: nauc_precision_at_5_std value: -11.001297546039366 - type: nauc_recall_at_1000_diff1 value: 14.316639289353503 - type: nauc_recall_at_1000_max value: 14.663280590084184 - type: nauc_recall_at_1000_std value: 10.373834237194783 - type: nauc_recall_at_100_diff1 value: 14.159748016577145 - type: nauc_recall_at_100_max value: 15.266942159548291 - type: nauc_recall_at_100_std value: 0.09898266158022606 - type: nauc_recall_at_10_diff1 value: 19.311511962157848 - type: nauc_recall_at_10_max value: 21.086642659351444 - type: nauc_recall_at_10_std value: -15.03280805118371 - type: nauc_recall_at_1_diff1 value: 43.560854870215444 - type: nauc_recall_at_1_max value: 22.785972852704127 - type: nauc_recall_at_1_std value: -17.629946377144794 - type: nauc_recall_at_20_diff1 value: 22.84188696362324 - type: nauc_recall_at_20_max value: 19.255833980651115 - type: nauc_recall_at_20_std value: -10.769401250685878 - type: nauc_recall_at_3_diff1 value: 25.289776971942963 - type: nauc_recall_at_3_max value: 19.495340268606647 - type: nauc_recall_at_3_std value: -14.682485696338162 - type: nauc_recall_at_5_diff1 value: 23.28267489764339 - type: nauc_recall_at_5_max value: 21.90368937976734 - type: nauc_recall_at_5_std value: -15.19826645274188 - type: ndcg_at_1 value: 16.008 - type: ndcg_at_10 value: 21.398 - type: ndcg_at_100 value: 25.241999999999997 - type: ndcg_at_1000 value: 28.833 - type: ndcg_at_20 value: 22.234 - type: ndcg_at_3 value: 18.86 - type: ndcg_at_5 value: 20.037 - type: precision_at_1 value: 16.008 - type: precision_at_10 value: 4.328 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_20 value: 2.579 - type: precision_at_3 value: 9.157 - type: precision_at_5 value: 6.837999999999999 - type: recall_at_1 value: 12.418 - type: recall_at_10 value: 27.935 - type: recall_at_100 value: 47.525 - type: recall_at_1000 value: 72.146 - type: recall_at_20 value: 31.861 - type: recall_at_3 value: 20.148 - type: recall_at_5 value: 23.296 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval (default) type: mteb/cqadupstack-wordpress config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: main_score value: 13.536999999999999 - type: map_at_1 value: 7.468 - type: map_at_10 value: 10.972999999999999 - type: map_at_100 value: 11.744 - type: map_at_1000 value: 11.854000000000001 - type: map_at_20 value: 11.336 - type: map_at_3 value: 9.618 - type: map_at_5 value: 10.205 - type: mrr_at_1 value: 8.317929759704251 - type: mrr_at_10 value: 12.179752369216331 - type: mrr_at_100 value: 12.980085498763907 - type: mrr_at_1000 value: 13.075701345231755 - type: mrr_at_20 value: 12.550195110376356 - type: mrr_at_3 value: 10.659272951324708 - type: mrr_at_5 value: 11.30622304374615 - type: nauc_map_at_1000_diff1 value: 25.499689183541758 - type: nauc_map_at_1000_max value: 26.492088085006486 - type: nauc_map_at_1000_std value: -10.29049248054652 - type: nauc_map_at_100_diff1 value: 25.573124155292685 - type: nauc_map_at_100_max value: 26.56159077339433 - type: nauc_map_at_100_std value: -10.400824123310946 - type: nauc_map_at_10_diff1 value: 25.485224554587006 - type: nauc_map_at_10_max value: 26.83491339438951 - type: nauc_map_at_10_std value: -11.212653836584204 - type: nauc_map_at_1_diff1 value: 33.63991109177576 - type: nauc_map_at_1_max value: 34.23354700535017 - type: nauc_map_at_1_std value: -13.602316051776613 - type: nauc_map_at_20_diff1 value: 25.401091624302076 - type: nauc_map_at_20_max value: 26.619190203647534 - type: nauc_map_at_20_std value: -10.956292541627727 - type: nauc_map_at_3_diff1 value: 26.825203283397762 - type: nauc_map_at_3_max value: 27.86659163589406 - type: nauc_map_at_3_std value: -11.12760272108276 - type: nauc_map_at_5_diff1 value: 25.95917424438333 - type: nauc_map_at_5_max value: 26.96719585977185 - type: nauc_map_at_5_std value: -12.304191598798255 - type: nauc_mrr_at_1000_diff1 value: 26.058089211778814 - type: nauc_mrr_at_1000_max value: 25.715522107102462 - type: nauc_mrr_at_1000_std value: -9.26865979619022 - type: nauc_mrr_at_100_diff1 value: 26.098211857983944 - type: nauc_mrr_at_100_max value: 25.751358106929445 - type: nauc_mrr_at_100_std value: -9.348646640329418 - type: nauc_mrr_at_10_diff1 value: 26.245525532384857 - type: nauc_mrr_at_10_max value: 25.751651308654733 - type: nauc_mrr_at_10_std value: -10.162612510927444 - type: nauc_mrr_at_1_diff1 value: 33.74283305857714 - type: nauc_mrr_at_1_max value: 33.58837545702206 - type: nauc_mrr_at_1_std value: -11.623065310526266 - type: nauc_mrr_at_20_diff1 value: 25.889783688319756 - type: nauc_mrr_at_20_max value: 25.752118615901914 - type: nauc_mrr_at_20_std value: -9.822357050457521 - type: nauc_mrr_at_3_diff1 value: 27.564445527656073 - type: nauc_mrr_at_3_max value: 27.360005995543013 - type: nauc_mrr_at_3_std value: -9.833890331593217 - type: nauc_mrr_at_5_diff1 value: 26.822524992606787 - type: nauc_mrr_at_5_max value: 26.284478920424583 - type: nauc_mrr_at_5_std value: -11.036920037435278 - type: nauc_ndcg_at_1000_diff1 value: 22.865864500824603 - type: nauc_ndcg_at_1000_max value: 22.771334973757252 - type: nauc_ndcg_at_1000_std value: -4.391248945624055 - type: nauc_ndcg_at_100_diff1 value: 24.137939988386144 - type: nauc_ndcg_at_100_max value: 23.87513301750976 - type: nauc_ndcg_at_100_std value: -6.566673889142541 - type: nauc_ndcg_at_10_diff1 value: 23.28670973899235 - type: nauc_ndcg_at_10_max value: 24.466850763499494 - type: nauc_ndcg_at_10_std value: -10.258177551014816 - type: nauc_ndcg_at_1_diff1 value: 33.74283305857714 - type: nauc_ndcg_at_1_max value: 33.58837545702206 - type: nauc_ndcg_at_1_std value: -11.623065310526266 - type: nauc_ndcg_at_20_diff1 value: 22.989442500386524 - type: nauc_ndcg_at_20_max value: 24.104082915814125 - type: nauc_ndcg_at_20_std value: -9.45785928337488 - type: nauc_ndcg_at_3_diff1 value: 25.178014460273445 - type: nauc_ndcg_at_3_max value: 25.942767533173754 - type: nauc_ndcg_at_3_std value: -9.91363038933204 - type: nauc_ndcg_at_5_diff1 value: 23.991757042799776 - type: nauc_ndcg_at_5_max value: 24.67696954394957 - type: nauc_ndcg_at_5_std value: -12.31985800626722 - type: nauc_precision_at_1000_diff1 value: 8.73756056198236 - type: nauc_precision_at_1000_max value: -2.2039393198217896 - type: nauc_precision_at_1000_std value: 11.030221537933079 - type: nauc_precision_at_100_diff1 value: 20.215172391403144 - type: nauc_precision_at_100_max value: 17.018645260191438 - type: nauc_precision_at_100_std value: 3.767328710045164 - type: nauc_precision_at_10_diff1 value: 17.587454651591 - type: nauc_precision_at_10_max value: 18.519756223864587 - type: nauc_precision_at_10_std value: -7.57980264597448 - type: nauc_precision_at_1_diff1 value: 33.74283305857714 - type: nauc_precision_at_1_max value: 33.58837545702206 - type: nauc_precision_at_1_std value: -11.623065310526266 - type: nauc_precision_at_20_diff1 value: 16.8264764027673 - type: nauc_precision_at_20_max value: 17.684383034724306 - type: nauc_precision_at_20_std value: -4.715192266545397 - type: nauc_precision_at_3_diff1 value: 21.074816828033445 - type: nauc_precision_at_3_max value: 21.203608983260384 - type: nauc_precision_at_3_std value: -7.0598567996303165 - type: nauc_precision_at_5_diff1 value: 19.232226617012476 - type: nauc_precision_at_5_max value: 18.21464537199811 - type: nauc_precision_at_5_std value: -11.192063817701081 - type: nauc_recall_at_1000_diff1 value: 13.682126336330219 - type: nauc_recall_at_1000_max value: 11.290148994929623 - type: nauc_recall_at_1000_std value: 15.234970859087472 - type: nauc_recall_at_100_diff1 value: 21.54257810474028 - type: nauc_recall_at_100_max value: 18.319728481896473 - type: nauc_recall_at_100_std value: 1.8896944275133083 - type: nauc_recall_at_10_diff1 value: 18.303586564099813 - type: nauc_recall_at_10_max value: 20.31707691425135 - type: nauc_recall_at_10_std value: -8.56717254223721 - type: nauc_recall_at_1_diff1 value: 33.63991109177576 - type: nauc_recall_at_1_max value: 34.23354700535017 - type: nauc_recall_at_1_std value: -13.602316051776613 - type: nauc_recall_at_20_diff1 value: 18.133732998590617 - type: nauc_recall_at_20_max value: 19.491824859679376 - type: nauc_recall_at_20_std value: -6.958404447908455 - type: nauc_recall_at_3_diff1 value: 20.923379689287973 - type: nauc_recall_at_3_max value: 22.305262469725605 - type: nauc_recall_at_3_std value: -9.33545310798814 - type: nauc_recall_at_5_diff1 value: 18.697534927162877 - type: nauc_recall_at_5_max value: 19.872464448638226 - type: nauc_recall_at_5_std value: -13.201942499761413 - type: ndcg_at_1 value: 8.318 - type: ndcg_at_10 value: 13.536999999999999 - type: ndcg_at_100 value: 17.814 - type: ndcg_at_1000 value: 21.037 - type: ndcg_at_20 value: 14.795 - type: ndcg_at_3 value: 10.584 - type: ndcg_at_5 value: 11.631 - type: precision_at_1 value: 8.318 - type: precision_at_10 value: 2.348 - type: precision_at_100 value: 0.488 - type: precision_at_1000 value: 0.084 - type: precision_at_20 value: 1.4789999999999999 - type: precision_at_3 value: 4.559 - type: precision_at_5 value: 3.327 - type: recall_at_1 value: 7.468 - type: recall_at_10 value: 20.508000000000003 - type: recall_at_100 value: 40.969 - type: recall_at_1000 value: 66.01 - type: recall_at_20 value: 25.151 - type: recall_at_3 value: 12.187000000000001 - type: recall_at_5 value: 14.868 - task: type: Retrieval dataset: name: MTEB ClimateFEVER (default) type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: main_score value: 14.015 - type: map_at_1 value: 5.794 - type: map_at_10 value: 9.467 - type: map_at_100 value: 10.583 - type: map_at_1000 value: 10.738 - type: map_at_20 value: 10.019 - type: map_at_3 value: 7.800999999999999 - type: map_at_5 value: 8.530999999999999 - type: mrr_at_1 value: 12.37785016286645 - type: mrr_at_10 value: 19.195232924874603 - type: mrr_at_100 value: 20.36171753911915 - type: mrr_at_1000 value: 20.43422170175313 - type: mrr_at_20 value: 19.925433949052078 - type: mrr_at_3 value: 16.612377850162883 - type: mrr_at_5 value: 17.928338762214977 - type: nauc_map_at_1000_diff1 value: 30.77100530113992 - type: nauc_map_at_1000_max value: 3.930399825338355 - type: nauc_map_at_1000_std value: 19.339256296860647 - type: nauc_map_at_100_diff1 value: 30.731834293026033 - type: nauc_map_at_100_max value: 3.9391965871824577 - type: nauc_map_at_100_std value: 18.994224188430934 - type: nauc_map_at_10_diff1 value: 30.52002817023447 - type: nauc_map_at_10_max value: 4.047355652304053 - type: nauc_map_at_10_std value: 16.271456948493867 - type: nauc_map_at_1_diff1 value: 40.78221783055125 - type: nauc_map_at_1_max value: 6.03643489529247 - type: nauc_map_at_1_std value: 10.164994264153364 - type: nauc_map_at_20_diff1 value: 30.667265850525062 - type: nauc_map_at_20_max value: 3.808011497380771 - type: nauc_map_at_20_std value: 17.64597024700993 - type: nauc_map_at_3_diff1 value: 32.9882945525325 - type: nauc_map_at_3_max value: 4.81442279492956 - type: nauc_map_at_3_std value: 11.72899701083213 - type: nauc_map_at_5_diff1 value: 31.319747944398486 - type: nauc_map_at_5_max value: 4.789346536725522 - type: nauc_map_at_5_std value: 13.280932876910251 - type: nauc_mrr_at_1000_diff1 value: 28.72974681423866 - type: nauc_mrr_at_1000_max value: 5.334428633833756 - type: nauc_mrr_at_1000_std value: 21.94603472046183 - type: nauc_mrr_at_100_diff1 value: 28.71022403484308 - type: nauc_mrr_at_100_max value: 5.333420382518744 - type: nauc_mrr_at_100_std value: 21.95720361127466 - type: nauc_mrr_at_10_diff1 value: 28.123142846152966 - type: nauc_mrr_at_10_max value: 5.476579464822251 - type: nauc_mrr_at_10_std value: 20.85306394069719 - type: nauc_mrr_at_1_diff1 value: 34.81794628491484 - type: nauc_mrr_at_1_max value: 6.5806430588232905 - type: nauc_mrr_at_1_std value: 14.459527094653325 - type: nauc_mrr_at_20_diff1 value: 28.439259242098213 - type: nauc_mrr_at_20_max value: 5.357148444191085 - type: nauc_mrr_at_20_std value: 21.61419717452997 - type: nauc_mrr_at_3_diff1 value: 29.687849776616204 - type: nauc_mrr_at_3_max value: 5.740633779727121 - type: nauc_mrr_at_3_std value: 17.8879483888456 - type: nauc_mrr_at_5_diff1 value: 28.47430129361797 - type: nauc_mrr_at_5_max value: 5.630703322113187 - type: nauc_mrr_at_5_std value: 19.229576158387964 - type: nauc_ndcg_at_1000_diff1 value: 29.601902706390376 - type: nauc_ndcg_at_1000_max value: 2.953924251677932 - type: nauc_ndcg_at_1000_std value: 33.43699716309924 - type: nauc_ndcg_at_100_diff1 value: 28.61050534370323 - type: nauc_ndcg_at_100_max value: 3.4205261114094623 - type: nauc_ndcg_at_100_std value: 29.71705615290654 - type: nauc_ndcg_at_10_diff1 value: 27.08320442286844 - type: nauc_ndcg_at_10_max value: 3.7887194412304863 - type: nauc_ndcg_at_10_std value: 21.676623605562256 - type: nauc_ndcg_at_1_diff1 value: 34.81794628491484 - type: nauc_ndcg_at_1_max value: 6.5806430588232905 - type: nauc_ndcg_at_1_std value: 14.459527094653325 - type: nauc_ndcg_at_20_diff1 value: 27.787198576453758 - type: nauc_ndcg_at_20_max value: 3.1540397427527713 - type: nauc_ndcg_at_20_std value: 24.886749384694483 - type: nauc_ndcg_at_3_diff1 value: 29.951818040541088 - type: nauc_ndcg_at_3_max value: 5.01579970046346 - type: nauc_ndcg_at_3_std value: 15.279492475081327 - type: nauc_ndcg_at_5_diff1 value: 28.06492691727927 - type: nauc_ndcg_at_5_max value: 4.89933436886099 - type: nauc_ndcg_at_5_std value: 16.918642834035854 - type: nauc_precision_at_1000_diff1 value: 15.771733257364474 - type: nauc_precision_at_1000_max value: 1.823845951487625 - type: nauc_precision_at_1000_std value: 49.1852294234272 - type: nauc_precision_at_100_diff1 value: 18.265609570523985 - type: nauc_precision_at_100_max value: 4.2756221878446885 - type: nauc_precision_at_100_std value: 44.777126764828196 - type: nauc_precision_at_10_diff1 value: 17.001368989158973 - type: nauc_precision_at_10_max value: 3.567699919296151 - type: nauc_precision_at_10_std value: 32.23622509514423 - type: nauc_precision_at_1_diff1 value: 34.81794628491484 - type: nauc_precision_at_1_max value: 6.5806430588232905 - type: nauc_precision_at_1_std value: 14.459527094653325 - type: nauc_precision_at_20_diff1 value: 17.635731357627552 - type: nauc_precision_at_20_max value: 3.034597543962715 - type: nauc_precision_at_20_std value: 37.444737258116376 - type: nauc_precision_at_3_diff1 value: 22.582871559622486 - type: nauc_precision_at_3_max value: 6.018578205165446 - type: nauc_precision_at_3_std value: 19.760719025296815 - type: nauc_precision_at_5_diff1 value: 18.665624106588705 - type: nauc_precision_at_5_max value: 5.618829486159042 - type: nauc_precision_at_5_std value: 24.487192977269594 - type: nauc_recall_at_1000_diff1 value: 26.313094272841823 - type: nauc_recall_at_1000_max value: -3.0358409209748767 - type: nauc_recall_at_1000_std value: 52.23483909347241 - type: nauc_recall_at_100_diff1 value: 22.619825448361848 - type: nauc_recall_at_100_max value: -0.48782855898636057 - type: nauc_recall_at_100_std value: 39.456946722540245 - type: nauc_recall_at_10_diff1 value: 21.248191636390427 - type: nauc_recall_at_10_max value: 1.057162598023577 - type: nauc_recall_at_10_std value: 26.28529915222162 - type: nauc_recall_at_1_diff1 value: 40.78221783055125 - type: nauc_recall_at_1_max value: 6.03643489529247 - type: nauc_recall_at_1_std value: 10.164994264153364 - type: nauc_recall_at_20_diff1 value: 22.329681015763143 - type: nauc_recall_at_20_max value: -0.9021963926705002 - type: nauc_recall_at_20_std value: 31.423263430139137 - type: nauc_recall_at_3_diff1 value: 27.367759082174025 - type: nauc_recall_at_3_max value: 3.9289202004328527 - type: nauc_recall_at_3_std value: 13.622863131134919 - type: nauc_recall_at_5_diff1 value: 22.76288213235621 - type: nauc_recall_at_5_max value: 3.471221773429057 - type: nauc_recall_at_5_std value: 17.585600220417064 - type: ndcg_at_1 value: 12.378 - type: ndcg_at_10 value: 14.015 - type: ndcg_at_100 value: 19.555 - type: ndcg_at_1000 value: 22.979 - type: ndcg_at_20 value: 16.019 - type: ndcg_at_3 value: 10.780000000000001 - type: ndcg_at_5 value: 11.773 - type: precision_at_1 value: 12.378 - type: precision_at_10 value: 4.567 - type: precision_at_100 value: 1.035 - type: precision_at_1000 value: 0.166 - type: precision_at_20 value: 3.114 - type: precision_at_3 value: 7.926 - type: precision_at_5 value: 6.215 - type: recall_at_1 value: 5.794 - type: recall_at_10 value: 17.407 - type: recall_at_100 value: 37.191 - type: recall_at_1000 value: 56.851 - type: recall_at_20 value: 23.165 - type: recall_at_3 value: 9.713 - type: recall_at_5 value: 12.415 - task: type: Retrieval dataset: name: MTEB DBPedia (default) type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: main_score value: 19.899 - type: map_at_1 value: 3.465 - type: map_at_10 value: 7.794 - type: map_at_100 value: 10.933 - type: map_at_1000 value: 11.752 - type: map_at_20 value: 9.016 - type: map_at_3 value: 5.427 - type: map_at_5 value: 6.502 - type: mrr_at_1 value: 34.75 - type: mrr_at_10 value: 45.200793650793656 - type: mrr_at_100 value: 46.05239344037991 - type: mrr_at_1000 value: 46.0856684337964 - type: mrr_at_20 value: 45.710684362077565 - type: mrr_at_3 value: 42.208333333333336 - type: mrr_at_5 value: 43.808333333333344 - type: nauc_map_at_1000_diff1 value: 18.86972613270399 - type: nauc_map_at_1000_max value: 20.274156189253244 - type: nauc_map_at_1000_std value: 22.191040122589133 - type: nauc_map_at_100_diff1 value: 18.788504382797093 - type: nauc_map_at_100_max value: 18.991259275904696 - type: nauc_map_at_100_std value: 19.224470200905856 - type: nauc_map_at_10_diff1 value: 18.750083550817912 - type: nauc_map_at_10_max value: 10.317804767409177 - type: nauc_map_at_10_std value: 4.146780937716071 - type: nauc_map_at_1_diff1 value: 24.593368387483753 - type: nauc_map_at_1_max value: 4.589639725353537 - type: nauc_map_at_1_std value: -8.92237341364795 - type: nauc_map_at_20_diff1 value: 18.991788660584362 - type: nauc_map_at_20_max value: 13.525701435829877 - type: nauc_map_at_20_std value: 10.505788067068151 - type: nauc_map_at_3_diff1 value: 18.3208401615434 - type: nauc_map_at_3_max value: 9.337037518676164 - type: nauc_map_at_3_std value: -3.652233530159517 - type: nauc_map_at_5_diff1 value: 18.092639410476284 - type: nauc_map_at_5_max value: 10.092917720641017 - type: nauc_map_at_5_std value: 0.17001723577182712 - type: nauc_mrr_at_1000_diff1 value: 29.78358698105705 - type: nauc_mrr_at_1000_max value: 28.715621788566008 - type: nauc_mrr_at_1000_std value: 22.028656730472925 - type: nauc_mrr_at_100_diff1 value: 29.790252324106998 - type: nauc_mrr_at_100_max value: 28.742783310038494 - type: nauc_mrr_at_100_std value: 22.03968708083945 - type: nauc_mrr_at_10_diff1 value: 29.438930345540236 - type: nauc_mrr_at_10_max value: 28.65369065827219 - type: nauc_mrr_at_10_std value: 21.78750467411176 - type: nauc_mrr_at_1_diff1 value: 35.330827390243996 - type: nauc_mrr_at_1_max value: 26.56882708002626 - type: nauc_mrr_at_1_std value: 21.623824720391546 - type: nauc_mrr_at_20_diff1 value: 29.738885034343433 - type: nauc_mrr_at_20_max value: 28.757633233697227 - type: nauc_mrr_at_20_std value: 21.94206110931751 - type: nauc_mrr_at_3_diff1 value: 30.084883512926936 - type: nauc_mrr_at_3_max value: 28.504733195949854 - type: nauc_mrr_at_3_std value: 21.343105616755405 - type: nauc_mrr_at_5_diff1 value: 29.162370505723974 - type: nauc_mrr_at_5_max value: 28.302134300102317 - type: nauc_mrr_at_5_std value: 21.967069891186686 - type: nauc_ndcg_at_1000_diff1 value: 21.5599701482179 - type: nauc_ndcg_at_1000_max value: 19.60442562497246 - type: nauc_ndcg_at_1000_std value: 38.57803059971978 - type: nauc_ndcg_at_100_diff1 value: 20.869754081262034 - type: nauc_ndcg_at_100_max value: 17.061854693160267 - type: nauc_ndcg_at_100_std value: 28.495912815567348 - type: nauc_ndcg_at_10_diff1 value: 21.68424149188379 - type: nauc_ndcg_at_10_max value: 17.7957499268384 - type: nauc_ndcg_at_10_std value: 20.329697185043177 - type: nauc_ndcg_at_1_diff1 value: 33.15797652004303 - type: nauc_ndcg_at_1_max value: 19.169777835934728 - type: nauc_ndcg_at_1_std value: 16.460300389696954 - type: nauc_ndcg_at_20_diff1 value: 20.980003079381408 - type: nauc_ndcg_at_20_max value: 16.31240132872873 - type: nauc_ndcg_at_20_std value: 21.336530494236147 - type: nauc_ndcg_at_3_diff1 value: 23.747010783899103 - type: nauc_ndcg_at_3_max value: 20.514543159699503 - type: nauc_ndcg_at_3_std value: 19.913679184651535 - type: nauc_ndcg_at_5_diff1 value: 21.811506356457578 - type: nauc_ndcg_at_5_max value: 19.600228375339086 - type: nauc_ndcg_at_5_std value: 20.80223119600392 - type: nauc_precision_at_1000_diff1 value: 7.616167380395875 - type: nauc_precision_at_1000_max value: 24.36987688613695 - type: nauc_precision_at_1000_std value: 28.517709442088883 - type: nauc_precision_at_100_diff1 value: 10.899372478558005 - type: nauc_precision_at_100_max value: 32.52543047557354 - type: nauc_precision_at_100_std value: 40.418143841067725 - type: nauc_precision_at_10_diff1 value: 12.454659530883022 - type: nauc_precision_at_10_max value: 26.633347275996822 - type: nauc_precision_at_10_std value: 31.766535462628333 - type: nauc_precision_at_1_diff1 value: 35.330827390243996 - type: nauc_precision_at_1_max value: 26.56882708002626 - type: nauc_precision_at_1_std value: 21.623824720391546 - type: nauc_precision_at_20_diff1 value: 13.710148345557894 - type: nauc_precision_at_20_max value: 30.06641352798287 - type: nauc_precision_at_20_std value: 37.51642649937503 - type: nauc_precision_at_3_diff1 value: 19.379905126167277 - type: nauc_precision_at_3_max value: 29.474064921517996 - type: nauc_precision_at_3_std value: 24.324769024438673 - type: nauc_precision_at_5_diff1 value: 14.983583546795229 - type: nauc_precision_at_5_max value: 29.377923800204137 - type: nauc_precision_at_5_std value: 28.792665620205433 - type: nauc_recall_at_1000_diff1 value: 9.420323994147108 - type: nauc_recall_at_1000_max value: 1.716458858147155 - type: nauc_recall_at_1000_std value: 42.675208969537806 - type: nauc_recall_at_100_diff1 value: 10.524089820623148 - type: nauc_recall_at_100_max value: 4.847393922578022 - type: nauc_recall_at_100_std value: 25.881256479477425 - type: nauc_recall_at_10_diff1 value: 10.405559854705523 - type: nauc_recall_at_10_max value: -0.7229949712397538 - type: nauc_recall_at_10_std value: 1.2453684953323285 - type: nauc_recall_at_1_diff1 value: 24.593368387483753 - type: nauc_recall_at_1_max value: 4.589639725353537 - type: nauc_recall_at_1_std value: -8.92237341364795 - type: nauc_recall_at_20_diff1 value: 9.153545675349667 - type: nauc_recall_at_20_max value: 1.0523663509920702 - type: nauc_recall_at_20_std value: 9.617722656364721 - type: nauc_recall_at_3_diff1 value: 11.453608857041628 - type: nauc_recall_at_3_max value: 6.541125581241787 - type: nauc_recall_at_3_std value: -6.374588849217941 - type: nauc_recall_at_5_diff1 value: 10.747977942968255 - type: nauc_recall_at_5_max value: 3.2154611210290445 - type: nauc_recall_at_5_std value: -1.2652013924076986 - type: ndcg_at_1 value: 24.25 - type: ndcg_at_10 value: 19.899 - type: ndcg_at_100 value: 23.204 - type: ndcg_at_1000 value: 29.658 - type: ndcg_at_20 value: 19.583000000000002 - type: ndcg_at_3 value: 21.335 - type: ndcg_at_5 value: 20.413999999999998 - type: precision_at_1 value: 34.75 - type: precision_at_10 value: 18.075 - type: precision_at_100 value: 5.897 - type: precision_at_1000 value: 1.22 - type: precision_at_20 value: 13.55 - type: precision_at_3 value: 26.833000000000002 - type: precision_at_5 value: 22.6 - type: recall_at_1 value: 3.465 - type: recall_at_10 value: 12.606 - type: recall_at_100 value: 29.843999999999998 - type: recall_at_1000 value: 52.242999999999995 - type: recall_at_20 value: 16.930999999999997 - type: recall_at_3 value: 6.425 - type: recall_at_5 value: 8.818 - task: type: Classification dataset: name: MTEB EmotionClassification (default) type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 38.339999999999996 - type: f1 value: 34.598741976118816 - type: f1_weighted value: 40.51989104726522 - type: main_score value: 38.339999999999996 - task: type: Retrieval dataset: name: MTEB FEVER (default) type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: main_score value: 25.006 - type: map_at_1 value: 13.943 - type: map_at_10 value: 20.706 - type: map_at_100 value: 21.740000000000002 - type: map_at_1000 value: 21.822 - type: map_at_20 value: 21.267 - type: map_at_3 value: 18.35 - type: map_at_5 value: 19.636 - type: mrr_at_1 value: 14.79147914791479 - type: mrr_at_10 value: 21.939967806304423 - type: mrr_at_100 value: 22.991772526136195 - type: mrr_at_1000 value: 23.068306121221312 - type: mrr_at_20 value: 22.521146379622163 - type: mrr_at_3 value: 19.484448444844478 - type: mrr_at_5 value: 20.817331733173358 - type: nauc_map_at_1000_diff1 value: 19.35822964414219 - type: nauc_map_at_1000_max value: 8.897124191699918 - type: nauc_map_at_1000_std value: -14.004128494439424 - type: nauc_map_at_100_diff1 value: 19.34567869663468 - type: nauc_map_at_100_max value: 8.8745190516295 - type: nauc_map_at_100_std value: -14.025946762212236 - type: nauc_map_at_10_diff1 value: 19.478894508723158 - type: nauc_map_at_10_max value: 8.614136366133858 - type: nauc_map_at_10_std value: -14.636265322683597 - type: nauc_map_at_1_diff1 value: 23.688109743445253 - type: nauc_map_at_1_max value: 10.721419669570178 - type: nauc_map_at_1_std value: -17.00198995751755 - type: nauc_map_at_20_diff1 value: 19.40994853288039 - type: nauc_map_at_20_max value: 8.788561538894676 - type: nauc_map_at_20_std value: -14.287595480928521 - type: nauc_map_at_3_diff1 value: 20.019246737479236 - type: nauc_map_at_3_max value: 8.530000749651693 - type: nauc_map_at_3_std value: -16.31053852110094 - type: nauc_map_at_5_diff1 value: 19.574801722611753 - type: nauc_map_at_5_max value: 8.431256040109632 - type: nauc_map_at_5_std value: -15.42991927435635 - type: nauc_mrr_at_1000_diff1 value: 19.199456594864415 - type: nauc_mrr_at_1000_max value: 9.053366261880821 - type: nauc_mrr_at_1000_std value: -14.325311358790312 - type: nauc_mrr_at_100_diff1 value: 19.183968461336264 - type: nauc_mrr_at_100_max value: 9.0406708211084 - type: nauc_mrr_at_100_std value: -14.333168371749 - type: nauc_mrr_at_10_diff1 value: 19.286280952658004 - type: nauc_mrr_at_10_max value: 8.786679451075301 - type: nauc_mrr_at_10_std value: -14.85433165190137 - type: nauc_mrr_at_1_diff1 value: 23.372945217632637 - type: nauc_mrr_at_1_max value: 10.757009456320713 - type: nauc_mrr_at_1_std value: -17.37470573558239 - type: nauc_mrr_at_20_diff1 value: 19.204260097760162 - type: nauc_mrr_at_20_max value: 8.967269936629057 - type: nauc_mrr_at_20_std value: -14.556203577633491 - type: nauc_mrr_at_3_diff1 value: 19.802237510569196 - type: nauc_mrr_at_3_max value: 8.660412322072549 - type: nauc_mrr_at_3_std value: -16.483667365878983 - type: nauc_mrr_at_5_diff1 value: 19.417190218500963 - type: nauc_mrr_at_5_max value: 8.592050482160923 - type: nauc_mrr_at_5_std value: -15.666970940052721 - type: nauc_ndcg_at_1000_diff1 value: 17.770326257033936 - type: nauc_ndcg_at_1000_max value: 9.986868282212038 - type: nauc_ndcg_at_1000_std value: -9.378246687942493 - type: nauc_ndcg_at_100_diff1 value: 17.57851695979306 - type: nauc_ndcg_at_100_max value: 9.516456101829059 - type: nauc_ndcg_at_100_std value: -9.92852108588332 - type: nauc_ndcg_at_10_diff1 value: 18.211042534939516 - type: nauc_ndcg_at_10_max value: 8.263500593038305 - type: nauc_ndcg_at_10_std value: -12.860334730832001 - type: nauc_ndcg_at_1_diff1 value: 23.372945217632637 - type: nauc_ndcg_at_1_max value: 10.757009456320713 - type: nauc_ndcg_at_1_std value: -17.37470573558239 - type: nauc_ndcg_at_20_diff1 value: 17.910709608958474 - type: nauc_ndcg_at_20_max value: 8.893940446709529 - type: nauc_ndcg_at_20_std value: -11.689263799945813 - type: nauc_ndcg_at_3_diff1 value: 19.09880112910806 - type: nauc_ndcg_at_3_max value: 8.023263463318175 - type: nauc_ndcg_at_3_std value: -16.092374418892373 - type: nauc_ndcg_at_5_diff1 value: 18.42900402442049 - type: nauc_ndcg_at_5_max value: 7.8858287226066235 - type: nauc_ndcg_at_5_std value: -14.661280178399608 - type: nauc_precision_at_1000_diff1 value: 3.642347466781283 - type: nauc_precision_at_1000_max value: 16.952404316587614 - type: nauc_precision_at_1000_std value: 21.40131424089912 - type: nauc_precision_at_100_diff1 value: 9.750805732461842 - type: nauc_precision_at_100_max value: 13.757879488937125 - type: nauc_precision_at_100_std value: 8.039378982280406 - type: nauc_precision_at_10_diff1 value: 14.7918457440186 - type: nauc_precision_at_10_max value: 8.123251440844076 - type: nauc_precision_at_10_std value: -7.766522118292242 - type: nauc_precision_at_1_diff1 value: 23.372945217632637 - type: nauc_precision_at_1_max value: 10.757009456320713 - type: nauc_precision_at_1_std value: -17.37470573558239 - type: nauc_precision_at_20_diff1 value: 13.317651277911787 - type: nauc_precision_at_20_max value: 10.204911801413331 - type: nauc_precision_at_20_std value: -3.322012947463638 - type: nauc_precision_at_3_diff1 value: 16.938989829945534 - type: nauc_precision_at_3_max value: 7.007727368306191 - type: nauc_precision_at_3_std value: -15.264146253300096 - type: nauc_precision_at_5_diff1 value: 15.595830777905029 - type: nauc_precision_at_5_max value: 6.87438645405223 - type: nauc_precision_at_5_std value: -12.548740115098678 - type: nauc_recall_at_1000_diff1 value: 9.009543867034727 - type: nauc_recall_at_1000_max value: 18.305044258577915 - type: nauc_recall_at_1000_std value: 23.009148418514425 - type: nauc_recall_at_100_diff1 value: 11.15850015080056 - type: nauc_recall_at_100_max value: 11.780408791390519 - type: nauc_recall_at_100_std value: 6.246652097817795 - type: nauc_recall_at_10_diff1 value: 15.099829144415247 - type: nauc_recall_at_10_max value: 7.075068492864811 - type: nauc_recall_at_10_std value: -7.878092251138417 - type: nauc_recall_at_1_diff1 value: 23.688109743445253 - type: nauc_recall_at_1_max value: 10.721419669570178 - type: nauc_recall_at_1_std value: -17.00198995751755 - type: nauc_recall_at_20_diff1 value: 13.85704310580134 - type: nauc_recall_at_20_max value: 9.007426388276338 - type: nauc_recall_at_20_std value: -3.9997271157444843 - type: nauc_recall_at_3_diff1 value: 16.851129797737183 - type: nauc_recall_at_3_max value: 6.616028659229676 - type: nauc_recall_at_3_std value: -15.286301162412613 - type: nauc_recall_at_5_diff1 value: 15.671635716227339 - type: nauc_recall_at_5_max value: 6.342388043913686 - type: nauc_recall_at_5_std value: -12.39987752967968 - type: ndcg_at_1 value: 14.791000000000002 - type: ndcg_at_10 value: 25.006 - type: ndcg_at_100 value: 30.471999999999998 - type: ndcg_at_1000 value: 32.806000000000004 - type: ndcg_at_20 value: 27.058 - type: ndcg_at_3 value: 20.112 - type: ndcg_at_5 value: 22.413 - type: precision_at_1 value: 14.791000000000002 - type: precision_at_10 value: 4.055000000000001 - type: precision_at_100 value: 0.697 - type: precision_at_1000 value: 0.092 - type: precision_at_20 value: 2.465 - type: precision_at_3 value: 8.626000000000001 - type: precision_at_5 value: 6.382000000000001 - type: recall_at_1 value: 13.943 - type: recall_at_10 value: 37.397000000000006 - type: recall_at_100 value: 63.334999999999994 - type: recall_at_1000 value: 81.428 - type: recall_at_20 value: 45.358 - type: recall_at_3 value: 24.082 - type: recall_at_5 value: 29.563 - task: type: Retrieval dataset: name: MTEB FiQA2018 (default) type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: main_score value: 11.167 - type: map_at_1 value: 5.055 - type: map_at_10 value: 7.974 - type: map_at_100 value: 8.738 - type: map_at_1000 value: 8.916 - type: map_at_20 value: 8.341 - type: map_at_3 value: 6.857 - type: map_at_5 value: 7.5009999999999994 - type: mrr_at_1 value: 10.030864197530864 - type: mrr_at_10 value: 14.756087105624141 - type: mrr_at_100 value: 15.562190249516133 - type: mrr_at_1000 value: 15.69044643307793 - type: mrr_at_20 value: 15.164252290155286 - type: mrr_at_3 value: 13.297325102880658 - type: mrr_at_5 value: 14.130658436213992 - type: nauc_map_at_1000_diff1 value: 21.581584639641356 - type: nauc_map_at_1000_max value: -3.591350057991658 - type: nauc_map_at_1000_std value: 2.2450733180258466 - type: nauc_map_at_100_diff1 value: 21.678068750484663 - type: nauc_map_at_100_max value: -3.754793884673454 - type: nauc_map_at_100_std value: 2.1134125512643034 - type: nauc_map_at_10_diff1 value: 22.267707890250872 - type: nauc_map_at_10_max value: -4.109027667129512 - type: nauc_map_at_10_std value: 1.7397026170215282 - type: nauc_map_at_1_diff1 value: 24.393602819317127 - type: nauc_map_at_1_max value: -5.463161484041758 - type: nauc_map_at_1_std value: 3.4527844717330898 - type: nauc_map_at_20_diff1 value: 22.16603827194384 - type: nauc_map_at_20_max value: -3.829133240985351 - type: nauc_map_at_20_std value: 2.273305218017184 - type: nauc_map_at_3_diff1 value: 25.550971234557217 - type: nauc_map_at_3_max value: -5.912131631375139 - type: nauc_map_at_3_std value: 2.6270431833752226 - type: nauc_map_at_5_diff1 value: 23.693227817850918 - type: nauc_map_at_5_max value: -4.430117256044587 - type: nauc_map_at_5_std value: 1.90476330618582 - type: nauc_mrr_at_1000_diff1 value: 18.407848757651383 - type: nauc_mrr_at_1000_max value: 1.4692643101259266 - type: nauc_mrr_at_1000_std value: -1.4737021198395484 - type: nauc_mrr_at_100_diff1 value: 18.373936364611946 - type: nauc_mrr_at_100_max value: 1.4600491055347338 - type: nauc_mrr_at_100_std value: -1.5315816773226647 - type: nauc_mrr_at_10_diff1 value: 18.812075225359994 - type: nauc_mrr_at_10_max value: 1.1423422260007967 - type: nauc_mrr_at_10_std value: -1.4331421942145333 - type: nauc_mrr_at_1_diff1 value: 21.042020105537055 - type: nauc_mrr_at_1_max value: -1.8286330117738627 - type: nauc_mrr_at_1_std value: 0.6107108684145417 - type: nauc_mrr_at_20_diff1 value: 18.67480478225173 - type: nauc_mrr_at_20_max value: 1.262037517477333 - type: nauc_mrr_at_20_std value: -1.3030974525400356 - type: nauc_mrr_at_3_diff1 value: 20.263359986054837 - type: nauc_mrr_at_3_max value: -0.3775317483949404 - type: nauc_mrr_at_3_std value: -1.365236958935102 - type: nauc_mrr_at_5_diff1 value: 19.555216165143772 - type: nauc_mrr_at_5_max value: 0.364621169263337 - type: nauc_mrr_at_5_std value: -1.0513020604553038 - type: nauc_ndcg_at_1000_diff1 value: 15.768274611971735 - type: nauc_ndcg_at_1000_max value: 2.0520976478520327 - type: nauc_ndcg_at_1000_std value: 2.877627036243521 - type: nauc_ndcg_at_100_diff1 value: 16.128663871942763 - type: nauc_ndcg_at_100_max value: -0.34227560585178396 - type: nauc_ndcg_at_100_std value: 0.8164780238765409 - type: nauc_ndcg_at_10_diff1 value: 19.282198569420846 - type: nauc_ndcg_at_10_max value: -1.3250908207898342 - type: nauc_ndcg_at_10_std value: 0.28825143098016265 - type: nauc_ndcg_at_1_diff1 value: 21.042020105537055 - type: nauc_ndcg_at_1_max value: -1.8286330117738627 - type: nauc_ndcg_at_1_std value: 0.6107108684145417 - type: nauc_ndcg_at_20_diff1 value: 19.028654575882847 - type: nauc_ndcg_at_20_max value: -0.9325610304848784 - type: nauc_ndcg_at_20_std value: 1.5749962746078057 - type: nauc_ndcg_at_3_diff1 value: 21.864688221213875 - type: nauc_ndcg_at_3_max value: -2.6883486751081693 - type: nauc_ndcg_at_3_std value: 0.17632918486246743 - type: nauc_ndcg_at_5_diff1 value: 21.280319590515656 - type: nauc_ndcg_at_5_max value: -1.7628672417522795 - type: nauc_ndcg_at_5_std value: 0.35504411508050127 - type: nauc_precision_at_1000_diff1 value: -5.134118935123325 - type: nauc_precision_at_1000_max value: 22.854317653101646 - type: nauc_precision_at_1000_std value: -5.519945670535999 - type: nauc_precision_at_100_diff1 value: 2.410623305126647 - type: nauc_precision_at_100_max value: 11.323949150994391 - type: nauc_precision_at_100_std value: -4.4400164174748395 - type: nauc_precision_at_10_diff1 value: 11.14562925123435 - type: nauc_precision_at_10_max value: 6.701684471603129 - type: nauc_precision_at_10_std value: -3.507090397196342 - type: nauc_precision_at_1_diff1 value: 21.042020105537055 - type: nauc_precision_at_1_max value: -1.8286330117738627 - type: nauc_precision_at_1_std value: 0.6107108684145417 - type: nauc_precision_at_20_diff1 value: 10.58098788224169 - type: nauc_precision_at_20_max value: 7.5107799297769935 - type: nauc_precision_at_20_std value: -1.5100106529478114 - type: nauc_precision_at_3_diff1 value: 19.795198818057667 - type: nauc_precision_at_3_max value: 0.4713854827815967 - type: nauc_precision_at_3_std value: -3.125924766538086 - type: nauc_precision_at_5_diff1 value: 16.907379789095696 - type: nauc_precision_at_5_max value: 4.140243156305644 - type: nauc_precision_at_5_std value: -1.8178346354290582 - type: nauc_recall_at_1000_diff1 value: 4.711761259530349 - type: nauc_recall_at_1000_max value: 3.897303116005553 - type: nauc_recall_at_1000_std value: 14.259168849028104 - type: nauc_recall_at_100_diff1 value: 4.811342813866857 - type: nauc_recall_at_100_max value: -0.46422331209391143 - type: nauc_recall_at_100_std value: 1.702190380676355 - type: nauc_recall_at_10_diff1 value: 14.112982578958079 - type: nauc_recall_at_10_max value: -0.6934250965951679 - type: nauc_recall_at_10_std value: -0.19882683954238423 - type: nauc_recall_at_1_diff1 value: 24.393602819317127 - type: nauc_recall_at_1_max value: -5.463161484041758 - type: nauc_recall_at_1_std value: 3.4527844717330898 - type: nauc_recall_at_20_diff1 value: 13.19557557901834 - type: nauc_recall_at_20_max value: 0.1538644708778628 - type: nauc_recall_at_20_std value: 3.0492797001932974 - type: nauc_recall_at_3_diff1 value: 24.182210704492558 - type: nauc_recall_at_3_max value: -6.034324229051654 - type: nauc_recall_at_3_std value: 2.8490090980023637 - type: nauc_recall_at_5_diff1 value: 19.011063131073744 - type: nauc_recall_at_5_max value: -2.119359618883548 - type: nauc_recall_at_5_std value: 0.8198903805407032 - type: ndcg_at_1 value: 10.030999999999999 - type: ndcg_at_10 value: 11.167 - type: ndcg_at_100 value: 15.409 - type: ndcg_at_1000 value: 19.947 - type: ndcg_at_20 value: 12.483 - type: ndcg_at_3 value: 9.532 - type: ndcg_at_5 value: 10.184 - type: precision_at_1 value: 10.030999999999999 - type: precision_at_10 value: 3.1329999999999996 - type: precision_at_100 value: 0.7270000000000001 - type: precision_at_1000 value: 0.15 - type: precision_at_20 value: 2.06 - type: precision_at_3 value: 6.481000000000001 - type: precision_at_5 value: 4.877 - type: recall_at_1 value: 5.055 - type: recall_at_10 value: 14.193 - type: recall_at_100 value: 31.47 - type: recall_at_1000 value: 60.007 - type: recall_at_20 value: 18.532 - type: recall_at_3 value: 8.863999999999999 - type: recall_at_5 value: 11.354000000000001 - task: type: Retrieval dataset: name: MTEB HotpotQA (default) type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: main_score value: 30.837999999999997 - type: map_at_1 value: 17.535 - type: map_at_10 value: 24.127000000000002 - type: map_at_100 value: 24.897 - type: map_at_1000 value: 24.991 - type: map_at_20 value: 24.537 - type: map_at_3 value: 22.314 - type: map_at_5 value: 23.369 - type: mrr_at_1 value: 35.07089804186361 - type: mrr_at_10 value: 41.84109835696607 - type: mrr_at_100 value: 42.50312939357189 - type: mrr_at_1000 value: 42.557192847100204 - type: mrr_at_20 value: 42.23392771922393 - type: mrr_at_3 value: 40.0540175557057 - type: mrr_at_5 value: 41.09723160027011 - type: nauc_map_at_1000_diff1 value: 53.405765033756104 - type: nauc_map_at_1000_max value: 7.122736293690594 - type: nauc_map_at_1000_std value: 25.154222353909706 - type: nauc_map_at_100_diff1 value: 53.424105025391235 - type: nauc_map_at_100_max value: 7.127661247301736 - type: nauc_map_at_100_std value: 25.080306702030054 - type: nauc_map_at_10_diff1 value: 53.83507469889932 - type: nauc_map_at_10_max value: 7.239978390454264 - type: nauc_map_at_10_std value: 24.216110502987867 - type: nauc_map_at_1_diff1 value: 64.45610830977103 - type: nauc_map_at_1_max value: 10.831236114417758 - type: nauc_map_at_1_std value: 18.282463736681766 - type: nauc_map_at_20_diff1 value: 53.50246555744542 - type: nauc_map_at_20_max value: 7.1666672586766085 - type: nauc_map_at_20_std value: 24.648695320801803 - type: nauc_map_at_3_diff1 value: 55.467529631560474 - type: nauc_map_at_3_max value: 8.281275214726968 - type: nauc_map_at_3_std value: 22.436972833181386 - type: nauc_map_at_5_diff1 value: 54.2596974292177 - type: nauc_map_at_5_max value: 7.5791705198322585 - type: nauc_map_at_5_std value: 23.272036332669295 - type: nauc_mrr_at_1000_diff1 value: 60.01986079158693 - type: nauc_mrr_at_1000_max value: 9.046571417308733 - type: nauc_mrr_at_1000_std value: 22.078576232724707 - type: nauc_mrr_at_100_diff1 value: 60.01145860886984 - type: nauc_mrr_at_100_max value: 9.036448042324515 - type: nauc_mrr_at_100_std value: 22.073613864801413 - type: nauc_mrr_at_10_diff1 value: 60.138490480821595 - type: nauc_mrr_at_10_max value: 9.09851806151594 - type: nauc_mrr_at_10_std value: 21.871816692853095 - type: nauc_mrr_at_1_diff1 value: 64.45610830977103 - type: nauc_mrr_at_1_max value: 10.831236114417758 - type: nauc_mrr_at_1_std value: 18.282463736681766 - type: nauc_mrr_at_20_diff1 value: 60.020756965348596 - type: nauc_mrr_at_20_max value: 9.067384772615947 - type: nauc_mrr_at_20_std value: 22.007284296200602 - type: nauc_mrr_at_3_diff1 value: 60.848848858927965 - type: nauc_mrr_at_3_max value: 9.77819590832476 - type: nauc_mrr_at_3_std value: 20.7857772481929 - type: nauc_mrr_at_5_diff1 value: 60.23023654313581 - type: nauc_mrr_at_5_max value: 9.297697720996952 - type: nauc_mrr_at_5_std value: 21.305246554366864 - type: nauc_ndcg_at_1000_diff1 value: 51.9050817941371 - type: nauc_ndcg_at_1000_max value: 6.253060051785559 - type: nauc_ndcg_at_1000_std value: 29.724428357103015 - type: nauc_ndcg_at_100_diff1 value: 52.197825295468256 - type: nauc_ndcg_at_100_max value: 6.212784383093877 - type: nauc_ndcg_at_100_std value: 28.65006820758606 - type: nauc_ndcg_at_10_diff1 value: 53.6117173506942 - type: nauc_ndcg_at_10_max value: 6.6792682572264646 - type: nauc_ndcg_at_10_std value: 25.56356291488488 - type: nauc_ndcg_at_1_diff1 value: 64.45610830977103 - type: nauc_ndcg_at_1_max value: 10.831236114417758 - type: nauc_ndcg_at_1_std value: 18.282463736681766 - type: nauc_ndcg_at_20_diff1 value: 52.725481130189465 - type: nauc_ndcg_at_20_max value: 6.443880761918098 - type: nauc_ndcg_at_20_std value: 26.623544659694815 - type: nauc_ndcg_at_3_diff1 value: 56.087927881432066 - type: nauc_ndcg_at_3_max value: 8.38309550543212 - type: nauc_ndcg_at_3_std value: 22.573762514655623 - type: nauc_ndcg_at_5_diff1 value: 54.351073912334144 - type: nauc_ndcg_at_5_max value: 7.325834612406898 - type: nauc_ndcg_at_5_std value: 23.7625099537027 - type: nauc_precision_at_1000_diff1 value: 24.555760070632065 - type: nauc_precision_at_1000_max value: -0.030378364610462727 - type: nauc_precision_at_1000_std value: 43.44197980424529 - type: nauc_precision_at_100_diff1 value: 31.89263750680818 - type: nauc_precision_at_100_max value: 0.5967214311073074 - type: nauc_precision_at_100_std value: 38.028330866223165 - type: nauc_precision_at_10_diff1 value: 42.72001946616996 - type: nauc_precision_at_10_max value: 2.759405409849438 - type: nauc_precision_at_10_std value: 29.948179807406504 - type: nauc_precision_at_1_diff1 value: 64.45610830977103 - type: nauc_precision_at_1_max value: 10.831236114417758 - type: nauc_precision_at_1_std value: 18.282463736681766 - type: nauc_precision_at_20_diff1 value: 38.77807631886789 - type: nauc_precision_at_20_max value: 1.8720818516278552 - type: nauc_precision_at_20_std value: 32.59464097769524 - type: nauc_precision_at_3_diff1 value: 50.84352281110305 - type: nauc_precision_at_3_max value: 6.8098905022703455 - type: nauc_precision_at_3_std value: 24.54656806570455 - type: nauc_precision_at_5_diff1 value: 46.09980845642094 - type: nauc_precision_at_5_max value: 4.489864393832119 - type: nauc_precision_at_5_std value: 26.34146412719015 - type: nauc_recall_at_1000_diff1 value: 24.55576007063215 - type: nauc_recall_at_1000_max value: -0.030378364610333563 - type: nauc_recall_at_1000_std value: 43.441979804245264 - type: nauc_recall_at_100_diff1 value: 31.892637506808146 - type: nauc_recall_at_100_max value: 0.5967214311073054 - type: nauc_recall_at_100_std value: 38.02833086622307 - type: nauc_recall_at_10_diff1 value: 42.72001946616998 - type: nauc_recall_at_10_max value: 2.7594054098494403 - type: nauc_recall_at_10_std value: 29.94817980740652 - type: nauc_recall_at_1_diff1 value: 64.45610830977103 - type: nauc_recall_at_1_max value: 10.831236114417758 - type: nauc_recall_at_1_std value: 18.282463736681766 - type: nauc_recall_at_20_diff1 value: 38.77807631886782 - type: nauc_recall_at_20_max value: 1.872081851627872 - type: nauc_recall_at_20_std value: 32.594640977695256 - type: nauc_recall_at_3_diff1 value: 50.843522811103036 - type: nauc_recall_at_3_max value: 6.809890502270356 - type: nauc_recall_at_3_std value: 24.546568065704555 - type: nauc_recall_at_5_diff1 value: 46.09980845642094 - type: nauc_recall_at_5_max value: 4.48986439383211 - type: nauc_recall_at_5_std value: 26.341464127190157 - type: ndcg_at_1 value: 35.071000000000005 - type: ndcg_at_10 value: 30.837999999999997 - type: ndcg_at_100 value: 34.473 - type: ndcg_at_1000 value: 36.788 - type: ndcg_at_20 value: 32.193 - type: ndcg_at_3 value: 27.412999999999997 - type: ndcg_at_5 value: 29.160999999999998 - type: precision_at_1 value: 35.071000000000005 - type: precision_at_10 value: 6.694999999999999 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.127 - type: precision_at_20 value: 3.785 - type: precision_at_3 value: 17.187 - type: precision_at_5 value: 11.700000000000001 - type: recall_at_1 value: 17.535 - type: recall_at_10 value: 33.477000000000004 - type: recall_at_100 value: 48.015 - type: recall_at_1000 value: 63.483999999999995 - type: recall_at_20 value: 37.846000000000004 - type: recall_at_3 value: 25.779999999999998 - type: recall_at_5 value: 29.250999999999998 - task: type: Classification dataset: name: MTEB ImdbClassification (default) type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 66.5616 - type: ap value: 61.38581579080602 - type: ap_weighted value: 61.38581579080602 - type: f1 value: 66.15361405073979 - type: f1_weighted value: 66.15361405073978 - type: main_score value: 66.5616 - task: type: Retrieval dataset: name: MTEB MSMARCO (default) type: mteb/msmarco config: default split: test revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: main_score value: 28.034 - type: map_at_1 value: 0.66 - type: map_at_10 value: 4.3709999999999996 - type: map_at_100 value: 12.02 - type: map_at_1000 value: 15.081 - type: map_at_20 value: 6.718 - type: map_at_3 value: 1.7389999999999999 - type: map_at_5 value: 2.5919999999999996 - type: mrr_at_1 value: 41.86046511627907 - type: mrr_at_10 value: 54.15651531930602 - type: mrr_at_100 value: 54.68712248786739 - type: mrr_at_1000 value: 54.68712248786739 - type: mrr_at_20 value: 54.272794389073454 - type: mrr_at_3 value: 51.937984496124024 - type: mrr_at_5 value: 52.40310077519379 - type: nauc_map_at_1000_diff1 value: 8.067177552562086 - type: nauc_map_at_1000_max value: 50.80997888655191 - type: nauc_map_at_1000_std value: 55.48450092063327 - type: nauc_map_at_100_diff1 value: 11.852088152898117 - type: nauc_map_at_100_max value: 48.192262801076275 - type: nauc_map_at_100_std value: 46.99716861803027 - type: nauc_map_at_10_diff1 value: 12.440097979884552 - type: nauc_map_at_10_max value: 29.873253516213786 - type: nauc_map_at_10_std value: 30.42960299808594 - type: nauc_map_at_1_diff1 value: 34.552395254431445 - type: nauc_map_at_1_max value: 38.69572501766299 - type: nauc_map_at_1_std value: 23.493916737503017 - type: nauc_map_at_20_diff1 value: 13.785974512045621 - type: nauc_map_at_20_max value: 34.54060954861762 - type: nauc_map_at_20_std value: 36.78361062739522 - type: nauc_map_at_3_diff1 value: 25.396598443628488 - type: nauc_map_at_3_max value: 40.38715214284343 - type: nauc_map_at_3_std value: 25.366480567034372 - type: nauc_map_at_5_diff1 value: 21.758905499107037 - type: nauc_map_at_5_max value: 35.664518863717646 - type: nauc_map_at_5_std value: 27.149202253810024 - type: nauc_mrr_at_1000_diff1 value: 17.603886573367394 - type: nauc_mrr_at_1000_max value: 58.66874119428572 - type: nauc_mrr_at_1000_std value: 42.279175325006555 - type: nauc_mrr_at_100_diff1 value: 17.603886573367394 - type: nauc_mrr_at_100_max value: 58.66874119428572 - type: nauc_mrr_at_100_std value: 42.279175325006555 - type: nauc_mrr_at_10_diff1 value: 17.323803643197643 - type: nauc_mrr_at_10_max value: 58.762972566248315 - type: nauc_mrr_at_10_std value: 42.56956515834332 - type: nauc_mrr_at_1_diff1 value: 27.861672627434668 - type: nauc_mrr_at_1_max value: 62.257123563504756 - type: nauc_mrr_at_1_std value: 44.379176486800986 - type: nauc_mrr_at_20_diff1 value: 17.44644565955209 - type: nauc_mrr_at_20_max value: 58.58190663195971 - type: nauc_mrr_at_20_std value: 42.33627290946193 - type: nauc_mrr_at_3_diff1 value: 17.262663278109798 - type: nauc_mrr_at_3_max value: 56.454793834736094 - type: nauc_mrr_at_3_std value: 41.08451346276091 - type: nauc_mrr_at_5_diff1 value: 16.613650570034434 - type: nauc_mrr_at_5_max value: 55.66285623344173 - type: nauc_mrr_at_5_std value: 40.38311275408144 - type: nauc_ndcg_at_1000_diff1 value: 10.174068866047635 - type: nauc_ndcg_at_1000_max value: 51.73192889106936 - type: nauc_ndcg_at_1000_std value: 59.65401111712334 - type: nauc_ndcg_at_100_diff1 value: 7.828653579924433 - type: nauc_ndcg_at_100_max value: 54.36206806281852 - type: nauc_ndcg_at_100_std value: 44.08756682730974 - type: nauc_ndcg_at_10_diff1 value: 3.1020204706672807 - type: nauc_ndcg_at_10_max value: 49.25209127878138 - type: nauc_ndcg_at_10_std value: 39.03800796651823 - type: nauc_ndcg_at_1_diff1 value: 31.384674368521292 - type: nauc_ndcg_at_1_max value: 46.68691593258891 - type: nauc_ndcg_at_1_std value: 23.497422044367447 - type: nauc_ndcg_at_20_diff1 value: 2.1223938698830445 - type: nauc_ndcg_at_20_max value: 52.82778912003725 - type: nauc_ndcg_at_20_std value: 40.85957147213028 - type: nauc_ndcg_at_3_diff1 value: 15.620541244360142 - type: nauc_ndcg_at_3_max value: 53.11313758866487 - type: nauc_ndcg_at_3_std value: 30.214636563641196 - type: nauc_ndcg_at_5_diff1 value: 11.094092047013888 - type: nauc_ndcg_at_5_max value: 50.15717166769855 - type: nauc_ndcg_at_5_std value: 32.63549193285381 - type: nauc_precision_at_1000_diff1 value: -18.87788252321529 - type: nauc_precision_at_1000_max value: 47.752842936932964 - type: nauc_precision_at_1000_std value: 46.53172081645067 - type: nauc_precision_at_100_diff1 value: -11.675608943686981 - type: nauc_precision_at_100_max value: 57.37789290450161 - type: nauc_precision_at_100_std value: 45.99043825302317 - type: nauc_precision_at_10_diff1 value: -5.316480906785367 - type: nauc_precision_at_10_max value: 50.9022661670284 - type: nauc_precision_at_10_std value: 41.249198804648444 - type: nauc_precision_at_1_diff1 value: 27.861672627434668 - type: nauc_precision_at_1_max value: 62.257123563504756 - type: nauc_precision_at_1_std value: 44.379176486800986 - type: nauc_precision_at_20_diff1 value: -4.546893782120849 - type: nauc_precision_at_20_max value: 54.59631672833982 - type: nauc_precision_at_20_std value: 42.784497023294186 - type: nauc_precision_at_3_diff1 value: 9.61605571022061 - type: nauc_precision_at_3_max value: 58.49382945748053 - type: nauc_precision_at_3_std value: 36.589164698407316 - type: nauc_precision_at_5_diff1 value: 4.337255192132767 - type: nauc_precision_at_5_max value: 51.9951147484678 - type: nauc_precision_at_5_std value: 34.468467294436486 - type: nauc_recall_at_1000_diff1 value: 12.99503296673786 - type: nauc_recall_at_1000_max value: 40.71962531328987 - type: nauc_recall_at_1000_std value: 61.64030151991186 - type: nauc_recall_at_100_diff1 value: 10.859337421704575 - type: nauc_recall_at_100_max value: 38.842397587549044 - type: nauc_recall_at_100_std value: 44.123802055364514 - type: nauc_recall_at_10_diff1 value: 5.054631656084283 - type: nauc_recall_at_10_max value: 16.616637058750165 - type: nauc_recall_at_10_std value: 23.85056756316223 - type: nauc_recall_at_1_diff1 value: 34.552395254431445 - type: nauc_recall_at_1_max value: 38.69572501766299 - type: nauc_recall_at_1_std value: 23.493916737503017 - type: nauc_recall_at_20_diff1 value: 11.266581564744333 - type: nauc_recall_at_20_max value: 20.205268245387963 - type: nauc_recall_at_20_std value: 25.000674179475464 - type: nauc_recall_at_3_diff1 value: 23.716522929925635 - type: nauc_recall_at_3_max value: 33.675409791018915 - type: nauc_recall_at_3_std value: 23.659590089606255 - type: nauc_recall_at_5_diff1 value: 13.826629690116377 - type: nauc_recall_at_5_max value: 21.450396058089545 - type: nauc_recall_at_5_std value: 21.053365906790678 - type: ndcg_at_1 value: 27.907 - type: ndcg_at_10 value: 28.034 - type: ndcg_at_100 value: 28.166000000000004 - type: ndcg_at_1000 value: 36.361 - type: ndcg_at_20 value: 28.047 - type: ndcg_at_3 value: 28.388999999999996 - type: ndcg_at_5 value: 28.307 - type: precision_at_1 value: 41.86 - type: precision_at_10 value: 37.208999999999996 - type: precision_at_100 value: 18.093 - type: precision_at_1000 value: 3.995 - type: precision_at_20 value: 33.372 - type: precision_at_3 value: 42.636 - type: precision_at_5 value: 40.0 - type: recall_at_1 value: 0.66 - type: recall_at_10 value: 6.287 - type: recall_at_100 value: 24.134 - type: recall_at_1000 value: 48.431999999999995 - type: recall_at_20 value: 10.897 - type: recall_at_3 value: 2.138 - type: recall_at_5 value: 3.3770000000000002 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.81988144094848 - type: f1 value: 84.06333895718355 - type: f1_weighted value: 84.95181538630469 - type: main_score value: 84.81988144094848 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 62.41222070223438 - type: f1 value: 46.156097858146175 - type: f1_weighted value: 66.23266420473301 - type: main_score value: 62.41222070223438 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 62.50168123739073 - type: f1 value: 60.72805496384179 - type: f1_weighted value: 62.787680759907204 - type: main_score value: 62.50168123739073 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 66.09280430396772 - type: f1 value: 65.36448769357172 - type: f1_weighted value: 66.15203456480924 - type: main_score value: 66.09280430396772 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P (default) type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: main_score value: 26.932942933622616 - type: v_measure value: 26.932942933622616 - type: v_measure_std value: 1.593124055965666 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S (default) type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: main_score value: 22.9594415386389 - type: v_measure value: 22.9594415386389 - type: v_measure_std value: 1.2719806552652395 - task: type: Reranking dataset: name: MTEB MindSmallReranking (default) type: mteb/mind_small config: default split: test revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 metrics: - type: main_score value: 28.527234738258063 - type: map value: 28.527234738258063 - type: mrr value: 29.001137590751057 - type: nAUC_map_diff1 value: 17.894640005397015 - type: nAUC_map_max value: -32.33772009018379 - type: nAUC_map_std value: -13.932018270818118 - type: nAUC_mrr_diff1 value: 16.6645956799536 - type: nAUC_mrr_max value: -26.591327847291947 - type: nAUC_mrr_std value: -11.52072949105865 - task: type: Retrieval dataset: name: MTEB NFCorpus (default) type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: main_score value: 23.318 - type: map_at_1 value: 3.9739999999999998 - type: map_at_10 value: 7.636 - type: map_at_100 value: 9.565999999999999 - type: map_at_1000 value: 10.731 - type: map_at_20 value: 8.389000000000001 - type: map_at_3 value: 5.836 - type: map_at_5 value: 6.6339999999999995 - type: mrr_at_1 value: 31.57894736842105 - type: mrr_at_10 value: 41.40436876504987 - type: mrr_at_100 value: 42.171381521810616 - type: mrr_at_1000 value: 42.21952740910268 - type: mrr_at_20 value: 41.75160733542153 - type: mrr_at_3 value: 38.544891640866865 - type: mrr_at_5 value: 40.495356037151694 - type: nauc_map_at_1000_diff1 value: 36.856779722587405 - type: nauc_map_at_1000_max value: 1.0732856849015824 - type: nauc_map_at_1000_std value: 9.651983758926798 - type: nauc_map_at_100_diff1 value: 37.7388774830525 - type: nauc_map_at_100_max value: 0.5350831297890865 - type: nauc_map_at_100_std value: 5.572219889903966 - type: nauc_map_at_10_diff1 value: 41.10439950831827 - type: nauc_map_at_10_max value: -1.9365518645162703 - type: nauc_map_at_10_std value: -0.14823142437775177 - type: nauc_map_at_1_diff1 value: 45.5844553027814 - type: nauc_map_at_1_max value: -8.272551322248038 - type: nauc_map_at_1_std value: -5.988582518897944 - type: nauc_map_at_20_diff1 value: 38.99926603388708 - type: nauc_map_at_20_max value: -0.8765984795564569 - type: nauc_map_at_20_std value: 1.8427808317285952 - type: nauc_map_at_3_diff1 value: 44.541009820342296 - type: nauc_map_at_3_max value: -5.314865046137034 - type: nauc_map_at_3_std value: -4.401240111896542 - type: nauc_map_at_5_diff1 value: 43.93142627220787 - type: nauc_map_at_5_max value: -4.452186699937273 - type: nauc_map_at_5_std value: -1.926768039888005 - type: nauc_mrr_at_1000_diff1 value: 31.753283629515227 - type: nauc_mrr_at_1000_max value: 9.689948388217696 - type: nauc_mrr_at_1000_std value: 22.70267321039036 - type: nauc_mrr_at_100_diff1 value: 31.729775359589773 - type: nauc_mrr_at_100_max value: 9.729637548794349 - type: nauc_mrr_at_100_std value: 22.680656825829267 - type: nauc_mrr_at_10_diff1 value: 31.725910736285666 - type: nauc_mrr_at_10_max value: 9.676299619743284 - type: nauc_mrr_at_10_std value: 22.987975982720496 - type: nauc_mrr_at_1_diff1 value: 33.222931085618626 - type: nauc_mrr_at_1_max value: 3.484453564278958 - type: nauc_mrr_at_1_std value: 14.566253883401012 - type: nauc_mrr_at_20_diff1 value: 31.70316773246007 - type: nauc_mrr_at_20_max value: 9.857726052213023 - type: nauc_mrr_at_20_std value: 22.691706596582133 - type: nauc_mrr_at_3_diff1 value: 33.123605268114545 - type: nauc_mrr_at_3_max value: 7.595554226164336 - type: nauc_mrr_at_3_std value: 22.833951307229185 - type: nauc_mrr_at_5_diff1 value: 32.33356989096538 - type: nauc_mrr_at_5_max value: 8.78887950599465 - type: nauc_mrr_at_5_std value: 23.75577044154664 - type: nauc_ndcg_at_1000_diff1 value: 29.06381153030341 - type: nauc_ndcg_at_1000_max value: 12.496787837448844 - type: nauc_ndcg_at_1000_std value: 21.957810402478064 - type: nauc_ndcg_at_100_diff1 value: 30.705847017840128 - type: nauc_ndcg_at_100_max value: 7.14809714223451 - type: nauc_ndcg_at_100_std value: 17.218742555337656 - type: nauc_ndcg_at_10_diff1 value: 28.03996243029464 - type: nauc_ndcg_at_10_max value: 4.699374701730214 - type: nauc_ndcg_at_10_std value: 24.227816808454218 - type: nauc_ndcg_at_1_diff1 value: 33.51847942809358 - type: nauc_ndcg_at_1_max value: -0.15139755316818274 - type: nauc_ndcg_at_1_std value: 17.16967561523347 - type: nauc_ndcg_at_20_diff1 value: 28.20952557682163 - type: nauc_ndcg_at_20_max value: 4.145398659710493 - type: nauc_ndcg_at_20_std value: 22.993088607717066 - type: nauc_ndcg_at_3_diff1 value: 27.613082038987592 - type: nauc_ndcg_at_3_max value: 1.4593269064387369 - type: nauc_ndcg_at_3_std value: 23.50820643331994 - type: nauc_ndcg_at_5_diff1 value: 28.240414065564686 - type: nauc_ndcg_at_5_max value: 3.5129825777351504 - type: nauc_ndcg_at_5_std value: 25.518429908335165 - type: nauc_precision_at_1000_diff1 value: 3.744031922083433 - type: nauc_precision_at_1000_max value: -0.5091331293991512 - type: nauc_precision_at_1000_std value: 44.81402869309276 - type: nauc_precision_at_100_diff1 value: 6.830797386827996 - type: nauc_precision_at_100_max value: 4.0810548509653755 - type: nauc_precision_at_100_std value: 42.7474662572479 - type: nauc_precision_at_10_diff1 value: 12.394335511926892 - type: nauc_precision_at_10_max value: 10.49971612535947 - type: nauc_precision_at_10_std value: 34.03347850666832 - type: nauc_precision_at_1_diff1 value: 33.222931085618626 - type: nauc_precision_at_1_max value: 3.484453564278958 - type: nauc_precision_at_1_std value: 14.566253883401012 - type: nauc_precision_at_20_diff1 value: 9.64344422081397 - type: nauc_precision_at_20_max value: 6.621958244946981 - type: nauc_precision_at_20_std value: 37.86581516903579 - type: nauc_precision_at_3_diff1 value: 20.278708738039267 - type: nauc_precision_at_3_max value: 7.392289389157268 - type: nauc_precision_at_3_std value: 27.036426818980896 - type: nauc_precision_at_5_diff1 value: 18.449282750023514 - type: nauc_precision_at_5_max value: 9.979980772916283 - type: nauc_precision_at_5_std value: 33.01802732071948 - type: nauc_recall_at_1000_diff1 value: 16.342561945689592 - type: nauc_recall_at_1000_max value: 5.937671266428497 - type: nauc_recall_at_1000_std value: 10.42918010425554 - type: nauc_recall_at_100_diff1 value: 19.13895811746396 - type: nauc_recall_at_100_max value: 3.153899391811738 - type: nauc_recall_at_100_std value: 1.04689826072118 - type: nauc_recall_at_10_diff1 value: 30.635745816653586 - type: nauc_recall_at_10_max value: 1.5673249988390006 - type: nauc_recall_at_10_std value: -3.6633108112395276 - type: nauc_recall_at_1_diff1 value: 45.5844553027814 - type: nauc_recall_at_1_max value: -8.272551322248038 - type: nauc_recall_at_1_std value: -5.988582518897944 - type: nauc_recall_at_20_diff1 value: 24.449469640898666 - type: nauc_recall_at_20_max value: 3.6319822015373404 - type: nauc_recall_at_20_std value: -3.460880541269202 - type: nauc_recall_at_3_diff1 value: 40.57120118352399 - type: nauc_recall_at_3_max value: -6.4276251434173135 - type: nauc_recall_at_3_std value: -5.987479062691147 - type: nauc_recall_at_5_diff1 value: 36.21768314516704 - type: nauc_recall_at_5_max value: -4.847092890211095 - type: nauc_recall_at_5_std value: -3.0514943484880144 - type: ndcg_at_1 value: 29.876 - type: ndcg_at_10 value: 23.318 - type: ndcg_at_100 value: 22.178 - type: ndcg_at_1000 value: 31.543 - type: ndcg_at_20 value: 21.718 - type: ndcg_at_3 value: 26.625 - type: ndcg_at_5 value: 25.412000000000003 - type: precision_at_1 value: 31.579 - type: precision_at_10 value: 17.244999999999997 - type: precision_at_100 value: 5.82 - type: precision_at_1000 value: 1.857 - type: precision_at_20 value: 12.709000000000001 - type: precision_at_3 value: 24.974 - type: precision_at_5 value: 21.981 - type: recall_at_1 value: 3.9739999999999998 - type: recall_at_10 value: 11.433 - type: recall_at_100 value: 24.861 - type: recall_at_1000 value: 57.75900000000001 - type: recall_at_20 value: 14.167 - type: recall_at_3 value: 6.773999999999999 - type: recall_at_5 value: 8.713 - task: type: Retrieval dataset: name: MTEB NQ (default) type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: main_score value: 17.682000000000002 - type: map_at_1 value: 7.968999999999999 - type: map_at_10 value: 13.828 - type: map_at_100 value: 14.881 - type: map_at_1000 value: 14.979999999999999 - type: map_at_20 value: 14.421999999999999 - type: map_at_3 value: 11.681999999999999 - type: map_at_5 value: 12.837000000000002 - type: mrr_at_1 value: 9.096176129779836 - type: mrr_at_10 value: 15.333772462248707 - type: mrr_at_100 value: 16.309634922879194 - type: mrr_at_1000 value: 16.39475249150789 - type: mrr_at_20 value: 15.891392914358688 - type: mrr_at_3 value: 13.064889918887577 - type: mrr_at_5 value: 14.311993047508642 - type: nauc_map_at_1000_diff1 value: 19.775928600522615 - type: nauc_map_at_1000_max value: 6.286282728873767 - type: nauc_map_at_1000_std value: 10.433091988799701 - type: nauc_map_at_100_diff1 value: 19.76472010726201 - type: nauc_map_at_100_max value: 6.3000520043276245 - type: nauc_map_at_100_std value: 10.369742430725108 - type: nauc_map_at_10_diff1 value: 19.717104003612306 - type: nauc_map_at_10_max value: 5.9416407746652915 - type: nauc_map_at_10_std value: 9.269462518525886 - type: nauc_map_at_1_diff1 value: 22.577309259900126 - type: nauc_map_at_1_max value: 4.4722142164380605 - type: nauc_map_at_1_std value: 3.7899645702785345 - type: nauc_map_at_20_diff1 value: 19.71861462412693 - type: nauc_map_at_20_max value: 6.104405666589615 - type: nauc_map_at_20_std value: 9.774250304834347 - type: nauc_map_at_3_diff1 value: 20.745180167104174 - type: nauc_map_at_3_max value: 4.726336508000744 - type: nauc_map_at_3_std value: 7.012706580698335 - type: nauc_map_at_5_diff1 value: 20.401667911889596 - type: nauc_map_at_5_max value: 5.021580992513943 - type: nauc_map_at_5_std value: 8.232301301005908 - type: nauc_mrr_at_1000_diff1 value: 19.876105574468276 - type: nauc_mrr_at_1000_max value: 5.92950987632599 - type: nauc_mrr_at_1000_std value: 10.422385358307675 - type: nauc_mrr_at_100_diff1 value: 19.864601593092164 - type: nauc_mrr_at_100_max value: 5.937364432461887 - type: nauc_mrr_at_100_std value: 10.372545373358479 - type: nauc_mrr_at_10_diff1 value: 19.8074129108612 - type: nauc_mrr_at_10_max value: 5.583608572112338 - type: nauc_mrr_at_10_std value: 9.660933453553797 - type: nauc_mrr_at_1_diff1 value: 22.771833118893053 - type: nauc_mrr_at_1_max value: 4.270593166778219 - type: nauc_mrr_at_1_std value: 4.72067370933128 - type: nauc_mrr_at_20_diff1 value: 19.816299723557 - type: nauc_mrr_at_20_max value: 5.803282270363233 - type: nauc_mrr_at_20_std value: 9.982388740482714 - type: nauc_mrr_at_3_diff1 value: 20.764352672106014 - type: nauc_mrr_at_3_max value: 4.308188794966225 - type: nauc_mrr_at_3_std value: 7.424575450681196 - type: nauc_mrr_at_5_diff1 value: 20.468124439169884 - type: nauc_mrr_at_5_max value: 4.717164145352797 - type: nauc_mrr_at_5_std value: 8.75784949698527 - type: nauc_ndcg_at_1000_diff1 value: 18.988627444499162 - type: nauc_ndcg_at_1000_max value: 8.336437983015612 - type: nauc_ndcg_at_1000_std value: 17.785235937443314 - type: nauc_ndcg_at_100_diff1 value: 18.72435211905066 - type: nauc_ndcg_at_100_max value: 8.509559844610813 - type: nauc_ndcg_at_100_std value: 16.272027197158785 - type: nauc_ndcg_at_10_diff1 value: 18.50083720860625 - type: nauc_ndcg_at_10_max value: 6.816989264362351 - type: nauc_ndcg_at_10_std value: 11.70379688056292 - type: nauc_ndcg_at_1_diff1 value: 23.028151500845926 - type: nauc_ndcg_at_1_max value: 4.252790790979486 - type: nauc_ndcg_at_1_std value: 4.919320655470863 - type: nauc_ndcg_at_20_diff1 value: 18.61317480699593 - type: nauc_ndcg_at_20_max value: 7.400038137531198 - type: nauc_ndcg_at_20_std value: 12.975329660907905 - type: nauc_ndcg_at_3_diff1 value: 20.331305466487297 - type: nauc_ndcg_at_3_max value: 4.451813547010051 - type: nauc_ndcg_at_3_std value: 7.835866814473613 - type: nauc_ndcg_at_5_diff1 value: 19.933475062151903 - type: nauc_ndcg_at_5_max value: 5.0523614629035 - type: nauc_ndcg_at_5_std value: 9.763459907678518 - type: nauc_precision_at_1000_diff1 value: 10.24793761705778 - type: nauc_precision_at_1000_max value: 10.459646580367272 - type: nauc_precision_at_1000_std value: 35.19560755022326 - type: nauc_precision_at_100_diff1 value: 14.032733274764734 - type: nauc_precision_at_100_max value: 12.582877921585014 - type: nauc_precision_at_100_std value: 30.56446230218432 - type: nauc_precision_at_10_diff1 value: 15.46863641183508 - type: nauc_precision_at_10_max value: 8.026206096826051 - type: nauc_precision_at_10_std value: 17.580067448009732 - type: nauc_precision_at_1_diff1 value: 23.028151500845926 - type: nauc_precision_at_1_max value: 4.252790790979486 - type: nauc_precision_at_1_std value: 4.919320655470863 - type: nauc_precision_at_20_diff1 value: 15.577209585349616 - type: nauc_precision_at_20_max value: 9.37176988371138 - type: nauc_precision_at_20_std value: 20.825242862847972 - type: nauc_precision_at_3_diff1 value: 19.697434012748303 - type: nauc_precision_at_3_max value: 3.817741628018302 - type: nauc_precision_at_3_std value: 9.855204198464552 - type: nauc_precision_at_5_diff1 value: 18.757352510786994 - type: nauc_precision_at_5_max value: 4.78932962761337 - type: nauc_precision_at_5_std value: 13.485110478478058 - type: nauc_recall_at_1000_diff1 value: 16.784291464246394 - type: nauc_recall_at_1000_max value: 15.357886220356304 - type: nauc_recall_at_1000_std value: 47.3266711354422 - type: nauc_recall_at_100_diff1 value: 15.651366556591528 - type: nauc_recall_at_100_max value: 14.108369717831499 - type: nauc_recall_at_100_std value: 30.26307437972032 - type: nauc_recall_at_10_diff1 value: 15.332913342892315 - type: nauc_recall_at_10_max value: 8.769293510819189 - type: nauc_recall_at_10_std value: 15.625436932641975 - type: nauc_recall_at_1_diff1 value: 22.577309259900126 - type: nauc_recall_at_1_max value: 4.4722142164380605 - type: nauc_recall_at_1_std value: 3.7899645702785345 - type: nauc_recall_at_20_diff1 value: 15.760837708226655 - type: nauc_recall_at_20_max value: 10.11729976512556 - type: nauc_recall_at_20_std value: 18.300935029131725 - type: nauc_recall_at_3_diff1 value: 19.039476605698372 - type: nauc_recall_at_3_max value: 4.107922037298003 - type: nauc_recall_at_3_std value: 9.115412171303978 - type: nauc_recall_at_5_diff1 value: 18.363415603635758 - type: nauc_recall_at_5_max value: 5.241253574533175 - type: nauc_recall_at_5_std value: 12.124948884672802 - type: ndcg_at_1 value: 9.067 - type: ndcg_at_10 value: 17.682000000000002 - type: ndcg_at_100 value: 22.982 - type: ndcg_at_1000 value: 25.692999999999998 - type: ndcg_at_20 value: 19.747 - type: ndcg_at_3 value: 13.219 - type: ndcg_at_5 value: 15.312999999999999 - type: precision_at_1 value: 9.067 - type: precision_at_10 value: 3.3000000000000003 - type: precision_at_100 value: 0.631 - type: precision_at_1000 value: 0.089 - type: precision_at_20 value: 2.136 - type: precision_at_3 value: 6.228 - type: precision_at_5 value: 4.925 - type: recall_at_1 value: 7.968999999999999 - type: recall_at_10 value: 28.208 - type: recall_at_100 value: 52.776 - type: recall_at_1000 value: 73.571 - type: recall_at_20 value: 35.941 - type: recall_at_3 value: 16.338 - type: recall_at_5 value: 21.217 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval (default) type: mteb/quora config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics: - type: main_score value: 74.323 - type: map_at_1 value: 57.30800000000001 - type: map_at_10 value: 69.32000000000001 - type: map_at_100 value: 70.106 - type: map_at_1000 value: 70.149 - type: map_at_20 value: 69.807 - type: map_at_3 value: 66.418 - type: map_at_5 value: 68.184 - type: mrr_at_1 value: 66.0 - type: mrr_at_10 value: 73.97885714285673 - type: mrr_at_100 value: 74.29274218615109 - type: mrr_at_1000 value: 74.3051429938558 - type: mrr_at_20 value: 74.18544015014858 - type: mrr_at_3 value: 72.26666666666631 - type: mrr_at_5 value: 73.37966666666605 - type: nauc_map_at_1000_diff1 value: 69.18960163699573 - type: nauc_map_at_1000_max value: 37.38136640005 - type: nauc_map_at_1000_std value: -2.570923100785111 - type: nauc_map_at_100_diff1 value: 69.18751629878942 - type: nauc_map_at_100_max value: 37.36952143443813 - type: nauc_map_at_100_std value: -2.5886077139396027 - type: nauc_map_at_10_diff1 value: 69.09406013156409 - type: nauc_map_at_10_max value: 36.877436974500775 - type: nauc_map_at_10_std value: -3.3540620889292203 - type: nauc_map_at_1_diff1 value: 70.93951368121674 - type: nauc_map_at_1_max value: 32.233487451612305 - type: nauc_map_at_1_std value: -7.055750788201864 - type: nauc_map_at_20_diff1 value: 69.14097261555858 - type: nauc_map_at_20_max value: 37.18308654380657 - type: nauc_map_at_20_std value: -2.912685185426714 - type: nauc_map_at_3_diff1 value: 69.01140661964882 - type: nauc_map_at_3_max value: 35.56708493366717 - type: nauc_map_at_3_std value: -5.47958763916843 - type: nauc_map_at_5_diff1 value: 68.97841901572657 - type: nauc_map_at_5_max value: 36.356674331191265 - type: nauc_map_at_5_std value: -4.271166648670905 - type: nauc_mrr_at_1000_diff1 value: 70.61597700848178 - type: nauc_mrr_at_1000_max value: 40.41208966087904 - type: nauc_mrr_at_1000_std value: -0.15890737609620642 - type: nauc_mrr_at_100_diff1 value: 70.61360632996228 - type: nauc_mrr_at_100_max value: 40.41568433400612 - type: nauc_mrr_at_100_std value: -0.1448505595676874 - type: nauc_mrr_at_10_diff1 value: 70.5233993892019 - type: nauc_mrr_at_10_max value: 40.36230785474746 - type: nauc_mrr_at_10_std value: -0.22757815568658987 - type: nauc_mrr_at_1_diff1 value: 72.6747651764081 - type: nauc_mrr_at_1_max value: 40.02178963789037 - type: nauc_mrr_at_1_std value: -2.575126954097418 - type: nauc_mrr_at_20_diff1 value: 70.58326373490296 - type: nauc_mrr_at_20_max value: 40.41333734338905 - type: nauc_mrr_at_20_std value: -0.1345473571856357 - type: nauc_mrr_at_3_diff1 value: 70.37817581234762 - type: nauc_mrr_at_3_max value: 40.203366387087705 - type: nauc_mrr_at_3_std value: -1.2261489082901087 - type: nauc_mrr_at_5_diff1 value: 70.45626657672184 - type: nauc_mrr_at_5_max value: 40.3234615411654 - type: nauc_mrr_at_5_std value: -0.3805672716488398 - type: nauc_ndcg_at_1000_diff1 value: 69.21984468258341 - type: nauc_ndcg_at_1000_max value: 39.0253925541956 - type: nauc_ndcg_at_1000_std value: 0.8160264523775477 - type: nauc_ndcg_at_100_diff1 value: 69.15328478391302 - type: nauc_ndcg_at_100_max value: 38.96655324359319 - type: nauc_ndcg_at_100_std value: 1.1256651981311283 - type: nauc_ndcg_at_10_diff1 value: 68.53510190998198 - type: nauc_ndcg_at_10_max value: 37.91208417950795 - type: nauc_ndcg_at_10_std value: -0.7377655073302805 - type: nauc_ndcg_at_1_diff1 value: 72.63228601131651 - type: nauc_ndcg_at_1_max value: 40.16828628757125 - type: nauc_ndcg_at_1_std value: -2.528909627178983 - type: nauc_ndcg_at_20_diff1 value: 68.822583729052 - type: nauc_ndcg_at_20_max value: 38.41592366520079 - type: nauc_ndcg_at_20_std value: 0.06798311113755548 - type: nauc_ndcg_at_3_diff1 value: 68.1481692592636 - type: nauc_ndcg_at_3_max value: 37.31206796055115 - type: nauc_ndcg_at_3_std value: -3.254883595992796 - type: nauc_ndcg_at_5_diff1 value: 68.24715917081343 - type: nauc_ndcg_at_5_max value: 37.56264948769021 - type: nauc_ndcg_at_5_std value: -1.8709773297999994 - type: nauc_precision_at_1000_diff1 value: -27.810948267157137 - type: nauc_precision_at_1000_max value: -0.24668486328059996 - type: nauc_precision_at_1000_std value: 20.580820056804715 - type: nauc_precision_at_100_diff1 value: -22.061161829256797 - type: nauc_precision_at_100_max value: 4.679165403717356 - type: nauc_precision_at_100_std value: 21.989059211475855 - type: nauc_precision_at_10_diff1 value: -3.9320543024872556 - type: nauc_precision_at_10_max value: 14.010070678201766 - type: nauc_precision_at_10_std value: 16.669492507338155 - type: nauc_precision_at_1_diff1 value: 72.63228601131651 - type: nauc_precision_at_1_max value: 40.16828628757125 - type: nauc_precision_at_1_std value: -2.528909627178983 - type: nauc_precision_at_20_diff1 value: -12.164765481707331 - type: nauc_precision_at_20_max value: 10.511899418907312 - type: nauc_precision_at_20_std value: 19.320026937145183 - type: nauc_precision_at_3_diff1 value: 22.621554858906986 - type: nauc_precision_at_3_max value: 24.326914902507287 - type: nauc_precision_at_3_std value: 6.099411862597304 - type: nauc_precision_at_5_diff1 value: 8.981227790660293 - type: nauc_precision_at_5_max value: 19.916827592062745 - type: nauc_precision_at_5_std value: 11.93677912655441 - type: nauc_recall_at_1000_diff1 value: 60.79128240819883 - type: nauc_recall_at_1000_max value: 44.80906309211301 - type: nauc_recall_at_1000_std value: 56.54768589270181 - type: nauc_recall_at_100_diff1 value: 61.18835279218082 - type: nauc_recall_at_100_max value: 39.61329094249297 - type: nauc_recall_at_100_std value: 31.736658564346342 - type: nauc_recall_at_10_diff1 value: 61.3639032751697 - type: nauc_recall_at_10_max value: 34.510711243051375 - type: nauc_recall_at_10_std value: 4.855117542870995 - type: nauc_recall_at_1_diff1 value: 70.93951368121674 - type: nauc_recall_at_1_max value: 32.233487451612305 - type: nauc_recall_at_1_std value: -7.055750788201864 - type: nauc_recall_at_20_diff1 value: 61.27124485304799 - type: nauc_recall_at_20_max value: 36.11805010411244 - type: nauc_recall_at_20_std value: 11.38763207684191 - type: nauc_recall_at_3_diff1 value: 63.91101210841338 - type: nauc_recall_at_3_max value: 33.23862328274836 - type: nauc_recall_at_3_std value: -4.857791490570391 - type: nauc_recall_at_5_diff1 value: 62.37552817951354 - type: nauc_recall_at_5_max value: 33.86753069930419 - type: nauc_recall_at_5_std value: -0.4857746420435554 - type: ndcg_at_1 value: 66.02 - type: ndcg_at_10 value: 74.323 - type: ndcg_at_100 value: 76.806 - type: ndcg_at_1000 value: 77.436 - type: ndcg_at_20 value: 75.47500000000001 - type: ndcg_at_3 value: 70.44500000000001 - type: ndcg_at_5 value: 72.48 - type: precision_at_1 value: 66.02 - type: precision_at_10 value: 11.273 - type: precision_at_100 value: 1.373 - type: precision_at_1000 value: 0.149 - type: precision_at_20 value: 6.101 - type: precision_at_3 value: 30.5 - type: precision_at_5 value: 20.31 - type: recall_at_1 value: 57.30800000000001 - type: recall_at_10 value: 84.152 - type: recall_at_100 value: 93.989 - type: recall_at_1000 value: 97.89999999999999 - type: recall_at_20 value: 88.138 - type: recall_at_3 value: 73.137 - type: recall_at_5 value: 78.655 - task: type: Clustering dataset: name: MTEB RedditClustering (default) type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: main_score value: 28.89014544508522 - type: v_measure value: 28.89014544508522 - type: v_measure_std value: 4.477854992673074 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P (default) type: mteb/reddit-clustering-p2p config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: main_score value: 41.588064041506414 - type: v_measure value: 41.588064041506414 - type: v_measure_std value: 12.234957713539355 - task: type: Retrieval dataset: name: MTEB SCIDOCS (default) type: mteb/scidocs config: default split: test revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 metrics: - type: main_score value: 9.923 - type: map_at_1 value: 2.15 - type: map_at_10 value: 5.379 - type: map_at_100 value: 6.487 - type: map_at_1000 value: 6.726999999999999 - type: map_at_20 value: 5.845000000000001 - type: map_at_3 value: 3.943 - type: map_at_5 value: 4.642 - type: mrr_at_1 value: 10.6 - type: mrr_at_10 value: 17.65234126984126 - type: mrr_at_100 value: 18.72231260720679 - type: mrr_at_1000 value: 18.83457574677834 - type: mrr_at_20 value: 18.178004510968904 - type: mrr_at_3 value: 14.96666666666667 - type: mrr_at_5 value: 16.426666666666666 - type: nauc_map_at_1000_diff1 value: 11.904585832905996 - type: nauc_map_at_1000_max value: 13.966912689458244 - type: nauc_map_at_1000_std value: 14.274562318051975 - type: nauc_map_at_100_diff1 value: 11.914962635425084 - type: nauc_map_at_100_max value: 13.792005445505046 - type: nauc_map_at_100_std value: 13.688572560422358 - type: nauc_map_at_10_diff1 value: 12.924485348386265 - type: nauc_map_at_10_max value: 12.924904365030008 - type: nauc_map_at_10_std value: 11.028226417787405 - type: nauc_map_at_1_diff1 value: 17.278503151293908 - type: nauc_map_at_1_max value: 7.878679954463645 - type: nauc_map_at_1_std value: 5.787632681875146 - type: nauc_map_at_20_diff1 value: 12.361611976516448 - type: nauc_map_at_20_max value: 13.430602876791497 - type: nauc_map_at_20_std value: 11.626342360129135 - type: nauc_map_at_3_diff1 value: 13.25103680109857 - type: nauc_map_at_3_max value: 11.851782553996365 - type: nauc_map_at_3_std value: 7.429469629304992 - type: nauc_map_at_5_diff1 value: 13.800025735259355 - type: nauc_map_at_5_max value: 12.565449305066048 - type: nauc_map_at_5_std value: 9.75302950224773 - type: nauc_mrr_at_1000_diff1 value: 12.268595456055587 - type: nauc_mrr_at_1000_max value: 9.25353359860505 - type: nauc_mrr_at_1000_std value: 9.108487924061626 - type: nauc_mrr_at_100_diff1 value: 12.221030310338321 - type: nauc_mrr_at_100_max value: 9.25521408834954 - type: nauc_mrr_at_100_std value: 9.138330201368367 - type: nauc_mrr_at_10_diff1 value: 12.574921954053705 - type: nauc_mrr_at_10_max value: 9.022771164246922 - type: nauc_mrr_at_10_std value: 8.72904050693386 - type: nauc_mrr_at_1_diff1 value: 17.46158729503331 - type: nauc_mrr_at_1_max value: 7.638928315208697 - type: nauc_mrr_at_1_std value: 6.095710473752395 - type: nauc_mrr_at_20_diff1 value: 12.138920051010647 - type: nauc_mrr_at_20_max value: 9.276258507402064 - type: nauc_mrr_at_20_std value: 8.886687014526801 - type: nauc_mrr_at_3_diff1 value: 14.193338999133834 - type: nauc_mrr_at_3_max value: 8.299120353947483 - type: nauc_mrr_at_3_std value: 7.8035097667232005 - type: nauc_mrr_at_5_diff1 value: 13.111703855187907 - type: nauc_mrr_at_5_max value: 9.120679964295672 - type: nauc_mrr_at_5_std value: 8.32132668626495 - type: nauc_ndcg_at_1000_diff1 value: 8.86999972791066 - type: nauc_ndcg_at_1000_max value: 15.310859480575436 - type: nauc_ndcg_at_1000_std value: 21.250542726021116 - type: nauc_ndcg_at_100_diff1 value: 8.721788996698756 - type: nauc_ndcg_at_100_max value: 13.753927264089416 - type: nauc_ndcg_at_100_std value: 17.83014109593192 - type: nauc_ndcg_at_10_diff1 value: 10.851214040795984 - type: nauc_ndcg_at_10_max value: 11.754038261909226 - type: nauc_ndcg_at_10_std value: 11.732493442071242 - type: nauc_ndcg_at_1_diff1 value: 17.46158729503331 - type: nauc_ndcg_at_1_max value: 7.638928315208697 - type: nauc_ndcg_at_1_std value: 6.095710473752395 - type: nauc_ndcg_at_20_diff1 value: 9.76180043441647 - type: nauc_ndcg_at_20_max value: 12.820709997321758 - type: nauc_ndcg_at_20_std value: 12.721916889128632 - type: nauc_ndcg_at_3_diff1 value: 12.839313795789275 - type: nauc_ndcg_at_3_max value: 10.610706825785767 - type: nauc_ndcg_at_3_std value: 8.204558555180421 - type: nauc_ndcg_at_5_diff1 value: 12.406813811698386 - type: nauc_ndcg_at_5_max value: 11.878799458897053 - type: nauc_ndcg_at_5_std value: 10.186784386212949 - type: nauc_precision_at_1000_diff1 value: 2.8398170540614176 - type: nauc_precision_at_1000_max value: 16.99931587707156 - type: nauc_precision_at_1000_std value: 31.86724716316765 - type: nauc_precision_at_100_diff1 value: 3.4160417262207297 - type: nauc_precision_at_100_max value: 14.437629378775577 - type: nauc_precision_at_100_std value: 24.60677482735814 - type: nauc_precision_at_10_diff1 value: 7.433603751797789 - type: nauc_precision_at_10_max value: 12.127707014834115 - type: nauc_precision_at_10_std value: 14.347141705378737 - type: nauc_precision_at_1_diff1 value: 17.46158729503331 - type: nauc_precision_at_1_max value: 7.638928315208697 - type: nauc_precision_at_1_std value: 6.095710473752395 - type: nauc_precision_at_20_diff1 value: 5.555321803900292 - type: nauc_precision_at_20_max value: 13.975730968140612 - type: nauc_precision_at_20_std value: 15.701599582613069 - type: nauc_precision_at_3_diff1 value: 10.570021043882896 - type: nauc_precision_at_3_max value: 11.640698048065092 - type: nauc_precision_at_3_std value: 8.880832670930209 - type: nauc_precision_at_5_diff1 value: 10.192070602011636 - type: nauc_precision_at_5_max value: 12.979688593338693 - type: nauc_precision_at_5_std value: 12.116013499683467 - type: nauc_recall_at_1000_diff1 value: 2.883533640208864 - type: nauc_recall_at_1000_max value: 18.09724738913881 - type: nauc_recall_at_1000_std value: 32.15747757955521 - type: nauc_recall_at_100_diff1 value: 3.6040687535563998 - type: nauc_recall_at_100_max value: 14.732664182141772 - type: nauc_recall_at_100_std value: 24.427986607748 - type: nauc_recall_at_10_diff1 value: 7.587316953732061 - type: nauc_recall_at_10_max value: 12.334929718954289 - type: nauc_recall_at_10_std value: 14.094286673978088 - type: nauc_recall_at_1_diff1 value: 17.278503151293908 - type: nauc_recall_at_1_max value: 7.878679954463645 - type: nauc_recall_at_1_std value: 5.787632681875146 - type: nauc_recall_at_20_diff1 value: 5.706170516654628 - type: nauc_recall_at_20_max value: 14.095625029855203 - type: nauc_recall_at_20_std value: 15.241931131705527 - type: nauc_recall_at_3_diff1 value: 10.574961375800127 - type: nauc_recall_at_3_max value: 11.733105660119586 - type: nauc_recall_at_3_std value: 8.540340847563677 - type: nauc_recall_at_5_diff1 value: 10.158076693596577 - type: nauc_recall_at_5_max value: 13.152816873926534 - type: nauc_recall_at_5_std value: 11.843127888328391 - type: ndcg_at_1 value: 10.6 - type: ndcg_at_10 value: 9.923 - type: ndcg_at_100 value: 15.463 - type: ndcg_at_1000 value: 20.673 - type: ndcg_at_20 value: 11.468 - type: ndcg_at_3 value: 9.120000000000001 - type: ndcg_at_5 value: 8.08 - type: precision_at_1 value: 10.6 - type: precision_at_10 value: 5.319999999999999 - type: precision_at_100 value: 1.357 - type: precision_at_1000 value: 0.262 - type: precision_at_20 value: 3.56 - type: precision_at_3 value: 8.733 - type: precision_at_5 value: 7.3 - type: recall_at_1 value: 2.15 - type: recall_at_10 value: 10.745000000000001 - type: recall_at_100 value: 27.478 - type: recall_at_1000 value: 53.067 - type: recall_at_20 value: 14.432 - type: recall_at_3 value: 5.295 - type: recall_at_5 value: 7.37 - task: type: STS dataset: name: MTEB SICK-R (default) type: mteb/sickr-sts config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cosine_pearson value: 75.0950047498747 - type: cosine_spearman value: 66.17240782538595 - type: euclidean_pearson value: 67.00770252295281 - type: euclidean_spearman value: 60.910363132843514 - type: main_score value: 66.17240782538595 - type: manhattan_pearson value: 67.05219198532856 - type: manhattan_spearman value: 61.09670227979067 - type: pearson value: 75.0950047498747 - type: spearman value: 66.17240782538595 - task: type: STS dataset: name: MTEB STS12 (default) type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cosine_pearson value: 70.27191745166907 - type: cosine_spearman value: 61.89139464648924 - type: euclidean_pearson value: 54.34524146536028 - type: euclidean_spearman value: 50.72726514543895 - type: main_score value: 61.89139464648924 - type: manhattan_pearson value: 54.0517351204108 - type: manhattan_spearman value: 50.62237885284486 - type: pearson value: 70.27191745166907 - type: spearman value: 61.89139464648924 - task: type: STS dataset: name: MTEB STS13 (default) type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cosine_pearson value: 70.19582039979868 - type: cosine_spearman value: 71.66792475528088 - type: euclidean_pearson value: 55.582203822685486 - type: euclidean_spearman value: 56.20322977297382 - type: main_score value: 71.66792475528088 - type: manhattan_pearson value: 55.95799094895162 - type: manhattan_spearman value: 56.588522991206325 - type: pearson value: 70.19582039979868 - type: spearman value: 71.66792475528088 - task: type: STS dataset: name: MTEB STS14 (default) type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cosine_pearson value: 69.52140108419252 - type: cosine_spearman value: 67.82634222687376 - type: euclidean_pearson value: 56.45640217254015 - type: euclidean_spearman value: 56.232462674683994 - type: main_score value: 67.82634222687376 - type: manhattan_pearson value: 56.71095067060834 - type: manhattan_spearman value: 56.419654300835596 - type: pearson value: 69.52140108419252 - type: spearman value: 67.82634222687376 - task: type: STS dataset: name: MTEB STS15 (default) type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cosine_pearson value: 73.66221619412464 - type: cosine_spearman value: 75.48765072240437 - type: euclidean_pearson value: 56.971989853952046 - type: euclidean_spearman value: 59.57242983168428 - type: main_score value: 75.48765072240437 - type: manhattan_pearson value: 57.292670731862025 - type: manhattan_spearman value: 59.64547291104911 - type: pearson value: 73.66221619412464 - type: spearman value: 75.48765072240437 - task: type: STS dataset: name: MTEB STS16 (default) type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cosine_pearson value: 62.328630460915925 - type: cosine_spearman value: 66.48155706668948 - type: euclidean_pearson value: 48.85087938485013 - type: euclidean_spearman value: 51.58756922385477 - type: main_score value: 66.48155706668948 - type: manhattan_pearson value: 49.02650798849104 - type: manhattan_spearman value: 51.597849334470936 - type: pearson value: 62.328630460915925 - type: spearman value: 66.48155706668948 - 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: 21.344883409729785 - type: cosine_spearman value: 19.492480027372526 - type: euclidean_pearson value: -8.605176891549817 - type: euclidean_spearman value: -7.528098935541785 - type: main_score value: 19.492480027372526 - type: manhattan_pearson value: -10.120526712428015 - type: manhattan_spearman value: -8.968202174485103 - type: pearson value: 21.344883409729785 - type: spearman value: 19.492480027372526 - 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: 14.966581838953037 - type: cosine_spearman value: 13.24509138766898 - type: euclidean_pearson value: -6.690226814122847 - type: euclidean_spearman value: -11.282875560023765 - type: main_score value: 13.24509138766898 - type: manhattan_pearson value: -7.476797502897139 - type: manhattan_spearman value: -11.92841312081328 - type: pearson value: 14.966581838953037 - type: spearman value: 13.24509138766898 - 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: 18.309414985775234 - type: cosine_spearman value: 14.341489363671842 - type: euclidean_pearson value: -12.122888971186411 - type: euclidean_spearman value: -16.469354911796607 - type: main_score value: 14.341489363671842 - type: manhattan_pearson value: -10.903411096507561 - type: manhattan_spearman value: -13.076094357191614 - type: pearson value: 18.309414985775234 - type: spearman value: 14.341489363671842 - task: type: STS dataset: name: MTEB STS17 (en-de) type: mteb/sts17-crosslingual-sts config: en-de split: test revision: faeb762787bd10488a50c8b5be4a3b82e411949c metrics: - type: cosine_pearson value: 21.301586456013037 - type: cosine_spearman value: 22.571419522164376 - type: euclidean_pearson value: -6.367176828477704 - type: euclidean_spearman value: -9.877915052256634 - type: main_score value: 22.571419522164376 - type: manhattan_pearson value: -4.676449796672262 - type: manhattan_spearman value: -7.3330561255268805 - type: pearson value: 21.301586456013037 - type: spearman value: 22.571419522164376 - 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: 16.140292893693204 - type: cosine_spearman value: 10.216376215477217 - type: euclidean_pearson value: -15.27866395332899 - type: euclidean_spearman value: -14.09405330374556 - type: main_score value: 10.216376215477217 - type: manhattan_pearson value: -14.968016143069224 - type: manhattan_spearman value: -12.871979788571364 - type: pearson value: 16.140292893693204 - type: spearman value: 10.216376215477217 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: faeb762787bd10488a50c8b5be4a3b82e411949c metrics: - type: cosine_pearson value: 78.42242639560023 - type: cosine_spearman value: 80.2472005970173 - type: euclidean_pearson value: 66.28797094299918 - type: euclidean_spearman value: 67.13581863643712 - type: main_score value: 80.2472005970173 - type: manhattan_pearson value: 66.02431023839748 - type: manhattan_spearman value: 67.15538442088678 - type: pearson value: 78.42242639560023 - type: spearman value: 80.2472005970173 - 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: -5.762967943082491 - type: cosine_spearman value: -6.184248227377756 - type: euclidean_pearson value: -12.170911062337659 - type: euclidean_spearman value: -9.846378276134612 - type: main_score value: -6.184248227377756 - type: manhattan_pearson value: -13.126030597269658 - type: manhattan_spearman value: -11.320163726484019 - type: pearson value: -5.762967943082491 - type: spearman value: -6.184248227377756 - 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: -8.666319610669559 - type: cosine_spearman value: -10.0877070299522 - type: euclidean_pearson value: -21.16722886445997 - type: euclidean_spearman value: -25.725365743898504 - type: main_score value: -10.0877070299522 - type: manhattan_pearson value: -22.03289222804741 - type: manhattan_spearman value: -26.785390252425533 - type: pearson value: -8.666319610669559 - type: spearman value: -10.0877070299522 - 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: 16.880423266497427 - type: cosine_spearman value: 18.497107178067477 - type: euclidean_pearson value: 14.33062698609246 - type: euclidean_spearman value: 16.623349996837863 - type: main_score value: 18.497107178067477 - type: manhattan_pearson value: 21.024602299309286 - type: manhattan_spearman value: 24.281840448539402 - type: pearson value: 16.880423266497427 - type: spearman value: 18.497107178067477 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 metrics: - type: cosine_pearson value: 44.98861387948161 - type: cosine_spearman value: 59.04270974068145 - type: euclidean_pearson value: 49.574894395857484 - type: euclidean_spearman value: 58.827686687567805 - type: main_score value: 59.04270974068145 - type: manhattan_pearson value: 48.65094961023066 - type: manhattan_spearman value: 58.3204048215355 - type: pearson value: 44.98861387948161 - type: spearman value: 59.04270974068145 - 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: 26.505168004689462 - type: cosine_spearman value: 28.591720613248732 - type: euclidean_pearson value: 24.74526273753091 - type: euclidean_spearman value: 28.416241187559642 - type: main_score value: 28.591720613248732 - type: manhattan_pearson value: 23.527990703124505 - type: manhattan_spearman value: 33.434031878984136 - type: pearson value: 26.505168004689462 - type: spearman value: 28.591720613248732 - 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: 11.552622364692777 - type: cosine_spearman value: 10.973019756392695 - type: euclidean_pearson value: 2.373117729670719 - type: euclidean_spearman value: 1.961823192174414 - type: main_score value: 10.973019756392695 - type: manhattan_pearson value: 2.4552310228655108 - type: manhattan_spearman value: 2.9778196586898273 - type: pearson value: 11.552622364692777 - type: spearman value: 10.973019756392695 - 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: 10.466988163502029 - type: cosine_spearman value: -0.21879166839686814 - type: euclidean_pearson value: 22.096342233944544 - type: euclidean_spearman value: 3.010990103175947 - type: main_score value: -0.21879166839686814 - type: manhattan_pearson value: 27.847325418935775 - type: manhattan_spearman value: 4.74569547403683 - type: pearson value: 10.466988163502029 - type: spearman value: -0.21879166839686814 - task: type: STS dataset: name: MTEB STSBenchmark (default) type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cosine_pearson value: 66.80057012864974 - type: cosine_spearman value: 66.52235871936412 - type: euclidean_pearson value: 55.372109895942536 - type: euclidean_spearman value: 56.04078716357898 - type: main_score value: 66.52235871936412 - type: manhattan_pearson value: 55.58797025494765 - type: manhattan_spearman value: 56.179959581772266 - type: pearson value: 66.80057012864974 - type: spearman value: 66.52235871936412 - task: type: Reranking dataset: name: MTEB SciDocsRR (default) type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: main_score value: 71.11074203128574 - type: map value: 71.11074203128574 - type: mrr value: 89.77809499868323 - type: nAUC_map_diff1 value: 11.228330835325687 - type: nAUC_map_max value: 54.45812469406701 - type: nAUC_map_std value: 63.051723849534525 - type: nAUC_mrr_diff1 value: 47.94323704040123 - type: nAUC_mrr_max value: 72.52180244204617 - type: nAUC_mrr_std value: 64.6185657337566 - task: type: Retrieval dataset: name: MTEB SciFact (default) type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: main_score value: 50.663000000000004 - type: map_at_1 value: 34.9 - type: map_at_10 value: 45.591 - type: map_at_100 value: 46.478 - type: map_at_1000 value: 46.544000000000004 - type: map_at_20 value: 45.999 - type: map_at_3 value: 43.354 - type: map_at_5 value: 44.733000000000004 - type: mrr_at_1 value: 37.0 - type: mrr_at_10 value: 47.36547619047619 - type: mrr_at_100 value: 48.09705728333796 - type: mrr_at_1000 value: 48.152949244883104 - type: mrr_at_20 value: 47.69512736718619 - type: mrr_at_3 value: 45.388888888888886 - type: mrr_at_5 value: 46.605555555555554 - type: nauc_map_at_1000_diff1 value: 52.100145151741394 - type: nauc_map_at_1000_max value: 27.410237212009648 - type: nauc_map_at_1000_std value: 2.9904718168509814 - type: nauc_map_at_100_diff1 value: 52.078009501467115 - type: nauc_map_at_100_max value: 27.388902536377337 - type: nauc_map_at_100_std value: 2.9956426758632553 - type: nauc_map_at_10_diff1 value: 52.22446655004901 - type: nauc_map_at_10_max value: 27.537880755428052 - type: nauc_map_at_10_std value: 2.5329635707923672 - type: nauc_map_at_1_diff1 value: 56.87947977552147 - type: nauc_map_at_1_max value: 26.992163127256497 - type: nauc_map_at_1_std value: -0.9440039327267877 - type: nauc_map_at_20_diff1 value: 52.106371246476826 - type: nauc_map_at_20_max value: 27.32862929056924 - type: nauc_map_at_20_std value: 2.7349113689801996 - type: nauc_map_at_3_diff1 value: 53.35317860724047 - type: nauc_map_at_3_max value: 26.25510463708658 - type: nauc_map_at_3_std value: 2.289593280073433 - type: nauc_map_at_5_diff1 value: 51.678047431193974 - type: nauc_map_at_5_max value: 27.418395689002818 - type: nauc_map_at_5_std value: 2.1245361198440267 - type: nauc_mrr_at_1000_diff1 value: 49.98301669091194 - type: nauc_mrr_at_1000_max value: 29.333209267321198 - type: nauc_mrr_at_1000_std value: 5.252782451549811 - type: nauc_mrr_at_100_diff1 value: 49.967980336744034 - type: nauc_mrr_at_100_max value: 29.331397088810657 - type: nauc_mrr_at_100_std value: 5.261178047875302 - type: nauc_mrr_at_10_diff1 value: 50.02865512004594 - type: nauc_mrr_at_10_max value: 29.665247088988096 - type: nauc_mrr_at_10_std value: 5.105677188444364 - type: nauc_mrr_at_1_diff1 value: 55.219664224743944 - type: nauc_mrr_at_1_max value: 29.369235255966586 - type: nauc_mrr_at_1_std value: 1.294523738013475 - type: nauc_mrr_at_20_diff1 value: 49.98301552378738 - type: nauc_mrr_at_20_max value: 29.388470718856922 - type: nauc_mrr_at_20_std value: 5.178678395201041 - type: nauc_mrr_at_3_diff1 value: 51.00229122885918 - type: nauc_mrr_at_3_max value: 28.064602643242907 - type: nauc_mrr_at_3_std value: 4.744718855685464 - type: nauc_mrr_at_5_diff1 value: 49.20787956974137 - type: nauc_mrr_at_5_max value: 29.663856377950655 - type: nauc_mrr_at_5_std value: 4.889452630825029 - type: nauc_ndcg_at_1000_diff1 value: 50.26524611758448 - type: nauc_ndcg_at_1000_max value: 28.816092638532105 - type: nauc_ndcg_at_1000_std value: 5.777693934805941 - type: nauc_ndcg_at_100_diff1 value: 49.810321964883876 - type: nauc_ndcg_at_100_max value: 28.85200497094049 - type: nauc_ndcg_at_100_std value: 6.4161665223690445 - type: nauc_ndcg_at_10_diff1 value: 50.31987402674788 - type: nauc_ndcg_at_10_max value: 29.1957589259604 - type: nauc_ndcg_at_10_std value: 4.249172262339034 - type: nauc_ndcg_at_1_diff1 value: 55.219664224743944 - type: nauc_ndcg_at_1_max value: 29.369235255966586 - type: nauc_ndcg_at_1_std value: 1.294523738013475 - type: nauc_ndcg_at_20_diff1 value: 49.95117201846568 - type: nauc_ndcg_at_20_max value: 28.252381258706883 - type: nauc_ndcg_at_20_std value: 4.799900939787535 - type: nauc_ndcg_at_3_diff1 value: 51.81554260088138 - type: nauc_ndcg_at_3_max value: 27.121304990834222 - type: nauc_ndcg_at_3_std value: 3.720528057690934 - type: nauc_ndcg_at_5_diff1 value: 48.77973374919412 - type: nauc_ndcg_at_5_max value: 29.131535344710002 - type: nauc_ndcg_at_5_std value: 3.565095958368389 - type: nauc_precision_at_1000_diff1 value: -7.462742973759457 - type: nauc_precision_at_1000_max value: 21.45790554414784 - type: nauc_precision_at_1000_std value: 24.38429850971904 - type: nauc_precision_at_100_diff1 value: 10.210409634704046 - type: nauc_precision_at_100_max value: 27.700772933352024 - type: nauc_precision_at_100_std value: 27.80962272064547 - type: nauc_precision_at_10_diff1 value: 34.576585797430766 - type: nauc_precision_at_10_max value: 33.364848337655786 - type: nauc_precision_at_10_std value: 14.448906660652794 - type: nauc_precision_at_1_diff1 value: 55.219664224743944 - type: nauc_precision_at_1_max value: 29.369235255966586 - type: nauc_precision_at_1_std value: 1.294523738013475 - type: nauc_precision_at_20_diff1 value: 28.759871255957847 - type: nauc_precision_at_20_max value: 28.756353659179982 - type: nauc_precision_at_20_std value: 17.539177234113616 - type: nauc_precision_at_3_diff1 value: 44.99876896761731 - type: nauc_precision_at_3_max value: 28.597098219106442 - type: nauc_precision_at_3_std value: 9.21762492818973 - type: nauc_precision_at_5_diff1 value: 34.186850914452485 - type: nauc_precision_at_5_max value: 33.954540973558686 - type: nauc_precision_at_5_std value: 10.546528423678431 - type: nauc_recall_at_1000_diff1 value: 23.83001981280335 - type: nauc_recall_at_1000_max value: 43.846644348796225 - type: nauc_recall_at_1000_std value: 60.408553665368835 - type: nauc_recall_at_100_diff1 value: 38.4746907480832 - type: nauc_recall_at_100_max value: 33.882306484150135 - type: nauc_recall_at_100_std value: 27.750836673176565 - type: nauc_recall_at_10_diff1 value: 44.98978983013661 - type: nauc_recall_at_10_max value: 31.241708340662296 - type: nauc_recall_at_10_std value: 6.026684637828198 - type: nauc_recall_at_1_diff1 value: 56.87947977552147 - type: nauc_recall_at_1_max value: 26.992163127256497 - type: nauc_recall_at_1_std value: -0.9440039327267877 - type: nauc_recall_at_20_diff1 value: 43.253384002784074 - type: nauc_recall_at_20_max value: 26.89815696422301 - type: nauc_recall_at_20_std value: 8.446980210355042 - type: nauc_recall_at_3_diff1 value: 48.89792955260931 - type: nauc_recall_at_3_max value: 26.765492965973237 - type: nauc_recall_at_3_std value: 5.600856860068723 - type: nauc_recall_at_5_diff1 value: 40.79334879234603 - type: nauc_recall_at_5_max value: 31.676509416439163 - type: nauc_recall_at_5_std value: 4.7055724522242 - type: ndcg_at_1 value: 37.0 - type: ndcg_at_10 value: 50.663000000000004 - type: ndcg_at_100 value: 55.022999999999996 - type: ndcg_at_1000 value: 56.643 - type: ndcg_at_20 value: 52.001 - type: ndcg_at_3 value: 46.424 - type: ndcg_at_5 value: 48.653999999999996 - type: precision_at_1 value: 37.0 - type: precision_at_10 value: 7.133000000000001 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.11 - type: precision_at_20 value: 3.8670000000000004 - type: precision_at_3 value: 19.0 - type: precision_at_5 value: 12.733 - type: recall_at_1 value: 34.9 - type: recall_at_10 value: 64.372 - type: recall_at_100 value: 84.806 - type: recall_at_1000 value: 97.26700000000001 - type: recall_at_20 value: 69.428 - type: recall_at_3 value: 52.983000000000004 - type: recall_at_5 value: 58.428000000000004 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions (default) type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cosine_accuracy value: 99.6029702970297 - type: cosine_accuracy_threshold value: 78.96339297294617 - type: cosine_ap value: 85.09945680365945 - type: cosine_f1 value: 79.00249376558605 - type: cosine_f1_threshold value: 77.54697799682617 - type: cosine_precision value: 78.80597014925374 - type: cosine_recall value: 79.2 - type: dot_accuracy value: 99.07128712871287 - type: dot_accuracy_threshold value: 113537.78076171875 - type: dot_ap value: 32.974014883183614 - type: dot_f1 value: 38.70665417057169 - type: dot_f1_threshold value: 82395.60546875 - type: dot_precision value: 36.41975308641975 - type: dot_recall value: 41.3 - type: euclidean_accuracy value: 99.35742574257425 - type: euclidean_accuracy_threshold value: 1716.6461944580078 - type: euclidean_ap value: 60.79241641393818 - type: euclidean_f1 value: 61.254199328107504 - type: euclidean_f1_threshold value: 1787.368392944336 - type: euclidean_precision value: 69.59287531806616 - type: euclidean_recall value: 54.7 - type: main_score value: 85.09945680365945 - type: manhattan_accuracy value: 99.35544554455446 - type: manhattan_accuracy_threshold value: 21216.224670410156 - type: manhattan_ap value: 60.67247165482485 - type: manhattan_f1 value: 61.16876024030584 - type: manhattan_f1_threshold value: 22668.411254882812 - type: manhattan_precision value: 67.38868832731649 - type: manhattan_recall value: 56.00000000000001 - type: max_accuracy value: 99.6029702970297 - type: max_ap value: 85.09945680365945 - type: max_f1 value: 79.00249376558605 - type: max_precision value: 78.80597014925374 - type: max_recall value: 79.2 - type: similarity_accuracy value: 99.6029702970297 - type: similarity_accuracy_threshold value: 78.96339297294617 - type: similarity_ap value: 85.09945680365945 - type: similarity_f1 value: 79.00249376558605 - type: similarity_f1_threshold value: 77.54697799682617 - type: similarity_precision value: 78.80597014925374 - type: similarity_recall value: 79.2 - task: type: Clustering dataset: name: MTEB StackExchangeClustering (default) type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: main_score value: 40.01875953666112 - type: v_measure value: 40.01875953666112 - type: v_measure_std value: 4.519991014119391 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P (default) type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: main_score value: 28.81354037080584 - type: v_measure value: 28.81354037080584 - type: v_measure_std value: 1.4144350664362755 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions (default) type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: main_score value: 44.09716409649705 - type: map value: 44.09716409649705 - type: mrr value: 44.662380103556565 - type: nAUC_map_diff1 value: 35.29255607823797 - type: nAUC_map_max value: 16.421837723462147 - type: nAUC_map_std value: 6.1302069782322315 - type: nAUC_mrr_diff1 value: 34.559928528154806 - type: nAUC_mrr_max value: 17.207604918830953 - type: nAUC_mrr_std value: 6.664790258906265 - task: type: Summarization dataset: name: MTEB SummEval (default) type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cosine_pearson value: 29.294245469087553 - type: cosine_spearman value: 30.080488918284974 - type: dot_pearson value: 18.322393003009722 - type: dot_spearman value: 20.941469677129597 - type: main_score value: 30.080488918284974 - type: pearson value: 29.294245469087553 - type: spearman value: 30.080488918284974 - task: type: Retrieval dataset: name: MTEB TRECCOVID (default) type: mteb/trec-covid config: default split: test revision: bb9466bac8153a0349341eb1b22e06409e78ef4e metrics: - type: main_score value: 39.983999999999995 - type: map_at_1 value: 0.106 - type: map_at_10 value: 0.644 - type: map_at_100 value: 3.021 - type: map_at_1000 value: 7.86 - type: map_at_20 value: 1.0959999999999999 - type: map_at_3 value: 0.26 - type: map_at_5 value: 0.383 - type: mrr_at_1 value: 52.0 - type: mrr_at_10 value: 63.62142857142856 - type: mrr_at_100 value: 64.14120879120878 - type: mrr_at_1000 value: 64.15196147938082 - type: mrr_at_20 value: 64.06428571428572 - type: mrr_at_3 value: 60.33333333333333 - type: mrr_at_5 value: 62.133333333333326 - type: nauc_map_at_1000_diff1 value: 24.416863084123577 - type: nauc_map_at_1000_max value: 38.56500518410879 - type: nauc_map_at_1000_std value: 57.28416632982124 - type: nauc_map_at_100_diff1 value: 7.320029678013508 - type: nauc_map_at_100_max value: 31.67441200824679 - type: nauc_map_at_100_std value: 46.99676723594155 - type: nauc_map_at_10_diff1 value: 2.1592330331050635 - type: nauc_map_at_10_max value: 26.48308930412215 - type: nauc_map_at_10_std value: 32.1215432254444 - type: nauc_map_at_1_diff1 value: 19.602070971946954 - type: nauc_map_at_1_max value: 8.20575258643758 - type: nauc_map_at_1_std value: 17.150126202821102 - type: nauc_map_at_20_diff1 value: 1.4525678948841099 - type: nauc_map_at_20_max value: 25.398372034894923 - type: nauc_map_at_20_std value: 37.98656048425611 - type: nauc_map_at_3_diff1 value: 14.189476148666769 - type: nauc_map_at_3_max value: 13.645814074115348 - type: nauc_map_at_3_std value: 24.193562926020505 - type: nauc_map_at_5_diff1 value: 6.385516140164152 - type: nauc_map_at_5_max value: 19.028014747196977 - type: nauc_map_at_5_std value: 27.2670171970273 - type: nauc_mrr_at_1000_diff1 value: 29.927939844415192 - type: nauc_mrr_at_1000_max value: 19.139062731303653 - type: nauc_mrr_at_1000_std value: 30.750244889158466 - type: nauc_mrr_at_100_diff1 value: 29.955577537768708 - type: nauc_mrr_at_100_max value: 19.15999969363906 - type: nauc_mrr_at_100_std value: 30.777558250465532 - type: nauc_mrr_at_10_diff1 value: 29.75190425697829 - type: nauc_mrr_at_10_max value: 19.247901214296146 - type: nauc_mrr_at_10_std value: 30.12495769940457 - type: nauc_mrr_at_1_diff1 value: 25.319658305674935 - type: nauc_mrr_at_1_max value: 19.408020022852174 - type: nauc_mrr_at_1_std value: 30.518526579248036 - type: nauc_mrr_at_20_diff1 value: 29.381724804135523 - type: nauc_mrr_at_20_max value: 18.78203200071421 - type: nauc_mrr_at_20_std value: 30.201392736164536 - type: nauc_mrr_at_3_diff1 value: 33.49197973287976 - type: nauc_mrr_at_3_max value: 16.821299944157854 - type: nauc_mrr_at_3_std value: 32.95866142740776 - type: nauc_mrr_at_5_diff1 value: 30.519933718405962 - type: nauc_mrr_at_5_max value: 20.873028786250366 - type: nauc_mrr_at_5_std value: 31.53952703715278 - type: nauc_ndcg_at_1000_diff1 value: 19.56599546833078 - type: nauc_ndcg_at_1000_max value: 31.55417192496882 - type: nauc_ndcg_at_1000_std value: 46.03469380933216 - type: nauc_ndcg_at_100_diff1 value: 17.03409656600608 - type: nauc_ndcg_at_100_max value: 30.018921010755896 - type: nauc_ndcg_at_100_std value: 42.083969481235535 - type: nauc_ndcg_at_10_diff1 value: 9.622601053598032 - type: nauc_ndcg_at_10_max value: 24.036876646465473 - type: nauc_ndcg_at_10_std value: 29.264022469658542 - type: nauc_ndcg_at_1_diff1 value: 10.162034267788544 - type: nauc_ndcg_at_1_max value: 14.902101527295905 - type: nauc_ndcg_at_1_std value: 22.89481729606148 - type: nauc_ndcg_at_20_diff1 value: 11.827596896516578 - type: nauc_ndcg_at_20_max value: 21.89722632493682 - type: nauc_ndcg_at_20_std value: 34.10813108354046 - type: nauc_ndcg_at_3_diff1 value: 9.885830514681343 - type: nauc_ndcg_at_3_max value: 18.645371242229174 - type: nauc_ndcg_at_3_std value: 27.61014855490183 - type: nauc_ndcg_at_5_diff1 value: 7.016021785588281 - type: nauc_ndcg_at_5_max value: 21.223071359768444 - type: nauc_ndcg_at_5_std value: 26.398061449644693 - type: nauc_precision_at_1000_diff1 value: 21.951465290665013 - type: nauc_precision_at_1000_max value: 29.28795349580752 - type: nauc_precision_at_1000_std value: 43.851885410437404 - type: nauc_precision_at_100_diff1 value: 20.103205413776266 - type: nauc_precision_at_100_max value: 29.53467404908886 - type: nauc_precision_at_100_std value: 43.41214281168461 - type: nauc_precision_at_10_diff1 value: 9.327632341614823 - type: nauc_precision_at_10_max value: 27.739929968318993 - type: nauc_precision_at_10_std value: 30.029060765584443 - type: nauc_precision_at_1_diff1 value: 25.319658305674935 - type: nauc_precision_at_1_max value: 19.408020022852174 - type: nauc_precision_at_1_std value: 30.518526579248036 - type: nauc_precision_at_20_diff1 value: 12.507551705078598 - type: nauc_precision_at_20_max value: 25.437784661790673 - type: nauc_precision_at_20_std value: 37.6038493343788 - type: nauc_precision_at_3_diff1 value: 17.302840903240426 - type: nauc_precision_at_3_max value: 18.240884706076184 - type: nauc_precision_at_3_std value: 32.34758075311221 - type: nauc_precision_at_5_diff1 value: 10.643711764387417 - type: nauc_precision_at_5_max value: 24.411239239889554 - type: nauc_precision_at_5_std value: 28.767392128200953 - type: nauc_recall_at_1000_diff1 value: 18.932208342315853 - type: nauc_recall_at_1000_max value: 28.482052015706234 - type: nauc_recall_at_1000_std value: 44.983993721189705 - type: nauc_recall_at_100_diff1 value: 12.30127094174658 - type: nauc_recall_at_100_max value: 25.614395729836016 - type: nauc_recall_at_100_std value: 40.04868566707452 - type: nauc_recall_at_10_diff1 value: -4.63806503951543 - type: nauc_recall_at_10_max value: 25.05145496553497 - type: nauc_recall_at_10_std value: 24.09893875274637 - type: nauc_recall_at_1_diff1 value: 19.602070971946954 - type: nauc_recall_at_1_max value: 8.20575258643758 - type: nauc_recall_at_1_std value: 17.150126202821102 - type: nauc_recall_at_20_diff1 value: 3.229932027028801 - type: nauc_recall_at_20_max value: 18.794275827349168 - type: nauc_recall_at_20_std value: 30.248974156728046 - type: nauc_recall_at_3_diff1 value: 15.00878750843053 - type: nauc_recall_at_3_max value: 9.046387583277276 - type: nauc_recall_at_3_std value: 22.79927256744018 - type: nauc_recall_at_5_diff1 value: 1.9090462818828973 - type: nauc_recall_at_5_max value: 17.416622454402713 - type: nauc_recall_at_5_std value: 21.915265437836833 - type: ndcg_at_1 value: 45.0 - type: ndcg_at_10 value: 39.983999999999995 - type: ndcg_at_100 value: 27.095999999999997 - type: ndcg_at_1000 value: 24.454 - type: ndcg_at_20 value: 37.319 - type: ndcg_at_3 value: 43.704 - type: ndcg_at_5 value: 41.568 - type: precision_at_1 value: 52.0 - type: precision_at_10 value: 42.6 - type: precision_at_100 value: 27.72 - type: precision_at_1000 value: 11.844000000000001 - type: precision_at_20 value: 39.6 - type: precision_at_3 value: 48.667 - type: precision_at_5 value: 45.6 - type: recall_at_1 value: 0.106 - type: recall_at_10 value: 0.9159999999999999 - type: recall_at_100 value: 5.715 - type: recall_at_1000 value: 23.662 - type: recall_at_20 value: 1.7160000000000002 - type: recall_at_3 value: 0.302 - type: recall_at_5 value: 0.482 - task: type: Retrieval dataset: name: MTEB Touche2020 (default) type: mteb/touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: main_score value: 13.753000000000002 - type: map_at_1 value: 1.5970000000000002 - type: map_at_10 value: 4.601 - type: map_at_100 value: 7.7700000000000005 - type: map_at_1000 value: 9.096 - type: map_at_20 value: 5.817 - type: map_at_3 value: 2.377 - type: map_at_5 value: 2.98 - type: mrr_at_1 value: 22.448979591836736 - type: mrr_at_10 value: 33.38030450275348 - type: mrr_at_100 value: 35.01828931874863 - type: mrr_at_1000 value: 35.037725664715595 - type: mrr_at_20 value: 34.6865889212828 - type: mrr_at_3 value: 28.231292517006807 - type: mrr_at_5 value: 31.394557823129254 - type: nauc_map_at_1000_diff1 value: -11.252417383140266 - type: nauc_map_at_1000_max value: -37.24375623641661 - type: nauc_map_at_1000_std value: -38.122086330314595 - type: nauc_map_at_100_diff1 value: -13.970621196322664 - type: nauc_map_at_100_max value: -39.871220844684366 - type: nauc_map_at_100_std value: -41.05324590181932 - type: nauc_map_at_10_diff1 value: -12.163263778180402 - type: nauc_map_at_10_max value: -36.76984556993433 - type: nauc_map_at_10_std value: -37.53503392844242 - type: nauc_map_at_1_diff1 value: -21.481769300580112 - type: nauc_map_at_1_max value: -34.78475326600437 - type: nauc_map_at_1_std value: -31.34442054238037 - type: nauc_map_at_20_diff1 value: -14.607331295503842 - type: nauc_map_at_20_max value: -40.507883730110066 - type: nauc_map_at_20_std value: -42.25172210956502 - type: nauc_map_at_3_diff1 value: -16.11765086583003 - type: nauc_map_at_3_max value: -39.875149479128375 - type: nauc_map_at_3_std value: -36.495342441290575 - type: nauc_map_at_5_diff1 value: -12.762015642768567 - type: nauc_map_at_5_max value: -35.84513643191068 - type: nauc_map_at_5_std value: -34.507874404019105 - type: nauc_mrr_at_1000_diff1 value: -14.380678398651431 - type: nauc_mrr_at_1000_max value: -34.916144132151764 - type: nauc_mrr_at_1000_std value: -37.97719898398948 - type: nauc_mrr_at_100_diff1 value: -14.315571331226579 - type: nauc_mrr_at_100_max value: -34.82941353583672 - type: nauc_mrr_at_100_std value: -37.88850059416566 - type: nauc_mrr_at_10_diff1 value: -15.357854232460392 - type: nauc_mrr_at_10_max value: -35.50556512154432 - type: nauc_mrr_at_10_std value: -39.177327110088726 - type: nauc_mrr_at_1_diff1 value: -20.81375579297355 - type: nauc_mrr_at_1_max value: -29.68218990777337 - type: nauc_mrr_at_1_std value: -32.340167902766225 - type: nauc_mrr_at_20_diff1 value: -14.007415589033556 - type: nauc_mrr_at_20_max value: -35.07243301300378 - type: nauc_mrr_at_20_std value: -38.4083789449898 - type: nauc_mrr_at_3_diff1 value: -18.09416617081835 - type: nauc_mrr_at_3_max value: -36.95185320631812 - type: nauc_mrr_at_3_std value: -35.64342684468998 - type: nauc_mrr_at_5_diff1 value: -15.183051674277138 - type: nauc_mrr_at_5_max value: -34.67724348034976 - type: nauc_mrr_at_5_std value: -35.5955991849333 - type: nauc_ndcg_at_1000_diff1 value: 0.8638249190254136 - type: nauc_ndcg_at_1000_max value: -27.240531292789573 - type: nauc_ndcg_at_1000_std value: -26.34406627094641 - type: nauc_ndcg_at_100_diff1 value: -10.272509858747428 - type: nauc_ndcg_at_100_max value: -40.27645670071093 - type: nauc_ndcg_at_100_std value: -40.20324905617718 - type: nauc_ndcg_at_10_diff1 value: -10.251898880214641 - type: nauc_ndcg_at_10_max value: -31.66063506955603 - type: nauc_ndcg_at_10_std value: -35.18245248110904 - type: nauc_ndcg_at_1_diff1 value: -22.15796091381088 - type: nauc_ndcg_at_1_max value: -28.012386493294734 - type: nauc_ndcg_at_1_std value: -28.75534254770048 - type: nauc_ndcg_at_20_diff1 value: -13.257359699197114 - type: nauc_ndcg_at_20_max value: -39.25007814100781 - type: nauc_ndcg_at_20_std value: -41.74617039563512 - type: nauc_ndcg_at_3_diff1 value: -14.633327352889419 - type: nauc_ndcg_at_3_max value: -35.76970667496168 - type: nauc_ndcg_at_3_std value: -34.78512355124301 - type: nauc_ndcg_at_5_diff1 value: -9.008702427186012 - type: nauc_ndcg_at_5_max value: -27.057510395795788 - type: nauc_ndcg_at_5_std value: -31.06336991460067 - type: nauc_precision_at_1000_diff1 value: 24.915422567175415 - type: nauc_precision_at_1000_max value: 47.53560015584683 - type: nauc_precision_at_1000_std value: 38.21701614763806 - type: nauc_precision_at_100_diff1 value: 6.645491992850349 - type: nauc_precision_at_100_max value: -14.578256280924878 - type: nauc_precision_at_100_std value: -23.049085659678926 - type: nauc_precision_at_10_diff1 value: -0.9667619260601806 - type: nauc_precision_at_10_max value: -25.529150834147217 - type: nauc_precision_at_10_std value: -35.81209624358855 - type: nauc_precision_at_1_diff1 value: -20.81375579297355 - type: nauc_precision_at_1_max value: -29.68218990777337 - type: nauc_precision_at_1_std value: -32.340167902766225 - type: nauc_precision_at_20_diff1 value: -5.664913271170427 - type: nauc_precision_at_20_max value: -31.789766954167682 - type: nauc_precision_at_20_std value: -43.24957806575219 - type: nauc_precision_at_3_diff1 value: -8.78321692449596 - type: nauc_precision_at_3_max value: -40.94190027571407 - type: nauc_precision_at_3_std value: -40.42051526602616 - type: nauc_precision_at_5_diff1 value: -0.6700857649701735 - type: nauc_precision_at_5_max value: -25.396527239026117 - type: nauc_precision_at_5_std value: -31.60992759387055 - type: nauc_recall_at_1000_diff1 value: 6.608885618295343 - type: nauc_recall_at_1000_max value: -17.90157348658524 - type: nauc_recall_at_1000_std value: 1.4128128959708763 - type: nauc_recall_at_100_diff1 value: -10.790017345080633 - type: nauc_recall_at_100_max value: -42.67969932770011 - type: nauc_recall_at_100_std value: -36.57531070739207 - type: nauc_recall_at_10_diff1 value: -9.632249853815987 - type: nauc_recall_at_10_max value: -35.775869145222444 - type: nauc_recall_at_10_std value: -38.6290217611413 - type: nauc_recall_at_1_diff1 value: -21.481769300580112 - type: nauc_recall_at_1_max value: -34.78475326600437 - type: nauc_recall_at_1_std value: -31.34442054238037 - type: nauc_recall_at_20_diff1 value: -16.584366120363462 - type: nauc_recall_at_20_max value: -45.0011419751979 - type: nauc_recall_at_20_std value: -46.22137916249736 - type: nauc_recall_at_3_diff1 value: -16.227776403050605 - type: nauc_recall_at_3_max value: -46.19831636902846 - type: nauc_recall_at_3_std value: -39.31769096438802 - type: nauc_recall_at_5_diff1 value: -8.463083898122722 - type: nauc_recall_at_5_max value: -34.1285878720165 - type: nauc_recall_at_5_std value: -33.56523176213727 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 13.753000000000002 - type: ndcg_at_100 value: 23.552 - type: ndcg_at_1000 value: 36.061 - type: ndcg_at_20 value: 15.113999999999999 - type: ndcg_at_3 value: 14.994 - type: ndcg_at_5 value: 13.927 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 13.469000000000001 - type: precision_at_100 value: 5.531 - type: precision_at_1000 value: 1.333 - type: precision_at_20 value: 11.224 - type: precision_at_3 value: 15.645999999999999 - type: precision_at_5 value: 14.693999999999999 - type: recall_at_1 value: 1.5970000000000002 - type: recall_at_10 value: 9.428 - type: recall_at_100 value: 34.227000000000004 - type: recall_at_1000 value: 72.233 - type: recall_at_20 value: 15.456 - type: recall_at_3 value: 3.024 - type: recall_at_5 value: 4.776 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification (default) type: mteb/toxic_conversations_50k config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 65.6884765625 - type: ap value: 11.395400787741414 - type: ap_weighted value: 11.395400787741414 - type: f1 value: 49.997667284332806 - type: f1_weighted value: 73.34420433686675 - type: main_score value: 65.6884765625 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification (default) type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 49.83305036785513 - type: f1 value: 49.97910620163813 - type: f1_weighted value: 49.32130156716104 - type: main_score value: 49.83305036785513 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering (default) type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: main_score value: 25.27920179659098 - type: v_measure value: 25.27920179659098 - type: v_measure_std value: 2.092324622279832 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 (default) type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cosine_accuracy value: 82.19586338439531 - type: cosine_accuracy_threshold value: 75.0169038772583 - type: cosine_ap value: 60.22081236487149 - type: cosine_f1 value: 57.192894671003245 - type: cosine_f1_threshold value: 69.5034384727478 - type: cosine_precision value: 54.3767840152236 - type: cosine_recall value: 60.31662269129288 - type: dot_accuracy value: 77.92215533170412 - type: dot_accuracy_threshold value: 106759.60693359375 - type: dot_ap value: 40.49772647740827 - type: dot_f1 value: 46.14293314417449 - type: dot_f1_threshold value: 67732.36083984375 - type: dot_precision value: 34.748931623931625 - type: dot_recall value: 68.65435356200528 - type: euclidean_accuracy value: 80.45538534898968 - type: euclidean_accuracy_threshold value: 2147.9385375976562 - type: euclidean_ap value: 52.814058086493475 - type: euclidean_f1 value: 50.80232161147149 - type: euclidean_f1_threshold value: 2624.5105743408203 - type: euclidean_precision value: 44.66680008004803 - type: euclidean_recall value: 58.89182058047493 - type: main_score value: 60.22081236487149 - type: manhattan_accuracy value: 80.53883292602968 - type: manhattan_accuracy_threshold value: 27107.672119140625 - type: manhattan_ap value: 53.53662771884282 - type: manhattan_f1 value: 51.65052816901407 - type: manhattan_f1_threshold value: 33232.24792480469 - type: manhattan_precision value: 44.299735749339376 - type: manhattan_recall value: 61.92612137203166 - type: max_accuracy value: 82.19586338439531 - type: max_ap value: 60.22081236487149 - type: max_f1 value: 57.192894671003245 - type: max_precision value: 54.3767840152236 - type: max_recall value: 68.65435356200528 - type: similarity_accuracy value: 82.19586338439531 - type: similarity_accuracy_threshold value: 75.0169038772583 - type: similarity_ap value: 60.22081236487149 - type: similarity_f1 value: 57.192894671003245 - type: similarity_f1_threshold value: 69.5034384727478 - type: similarity_precision value: 54.3767840152236 - type: similarity_recall value: 60.31662269129288 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus (default) type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cosine_accuracy value: 85.86758256684907 - type: cosine_accuracy_threshold value: 73.03299903869629 - type: cosine_ap value: 78.79896751132692 - type: cosine_f1 value: 70.93762938984453 - type: cosine_f1_threshold value: 69.51396465301514 - type: cosine_precision value: 69.39391707784078 - type: cosine_recall value: 72.55158607945796 - type: dot_accuracy value: 81.69169868436373 - type: dot_accuracy_threshold value: 51796.2890625 - type: dot_ap value: 66.49022700054283 - type: dot_f1 value: 62.167484157387854 - type: dot_f1_threshold value: 42622.021484375 - type: dot_precision value: 58.10078297530617 - type: dot_recall value: 66.84631967970435 - type: euclidean_accuracy value: 83.17809601428183 - type: euclidean_accuracy_threshold value: 1687.9749298095703 - type: euclidean_ap value: 70.39367677734302 - type: euclidean_f1 value: 62.79221027661935 - type: euclidean_f1_threshold value: 1905.8393478393555 - type: euclidean_precision value: 62.40778766446118 - type: euclidean_recall value: 63.181398213735754 - type: main_score value: 78.79896751132692 - type: manhattan_accuracy value: 83.23631000892615 - type: manhattan_accuracy_threshold value: 21191.021728515625 - type: manhattan_ap value: 70.60408795606112 - type: manhattan_f1 value: 62.99311208515969 - type: manhattan_f1_threshold value: 23671.893310546875 - type: manhattan_precision value: 64.05603311047437 - type: manhattan_recall value: 61.964890668309216 - type: max_accuracy value: 85.86758256684907 - type: max_ap value: 78.79896751132692 - type: max_f1 value: 70.93762938984453 - type: max_precision value: 69.39391707784078 - type: max_recall value: 72.55158607945796 - type: similarity_accuracy value: 85.86758256684907 - type: similarity_accuracy_threshold value: 73.03299903869629 - type: similarity_ap value: 78.79896751132692 - type: similarity_f1 value: 70.93762938984453 - type: similarity_f1_threshold value: 69.51396465301514 - type: similarity_precision value: 69.39391707784078 - type: similarity_recall value: 72.55158607945796 --- # M2V_base_glove_subword Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("minishlab/M2V_base_glove_subword") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` Alternatively, you can distill your own model using the `distill` method: ```python from model2vec.distill import distill # Choose a Sentence Transformer model model_name = "BAAI/bge-base-en-v1.5" # Distill the model m2v_model = distill(model_name=model_name, pca_dims=256) # Save the model m2v_model.save_pretrained("m2v_model") ``` ## How it works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Additional Resources - [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec) - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @software{minishlab2024model2vec, authors = {Stephan Tulkens, Thomas van Dongen}, title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model}, year = {2024}, url = {https://github.com/MinishLab/model2vec}, } ```
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
KeyurRamoliya/multilingual-e5-large-GGUF
KeyurRamoliya
feature-extraction
[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "feature-extraction", "llama-cpp", "gguf-my-repo", "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", "base_model:intfloat/multilingual-e5-large", "base_model:quantized:intfloat/multilingual-e5-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,724,391,037,000
2024-08-23T05:30:43
14
1
--- base_model: intfloat/multilingual-e5-large 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 - llama-cpp - gguf-my-repo 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 revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.34095494283793 - type: f1 value: 67.01134288992947 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (he) type: mteb/amazon_massive_intent config: he split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.61264290517822 - type: f1 value: 64.68730512660757 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hi) type: mteb/amazon_massive_intent config: hi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.79757901815738 - type: f1 value: 65.24938539425598 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hu) type: mteb/amazon_massive_intent config: hu split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.68728984532616 - type: f1 value: 67.0487169762553 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hy) type: mteb/amazon_massive_intent config: hy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.07464694014795 - type: f1 value: 59.183532276789286 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (id) type: mteb/amazon_massive_intent config: id split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.04707464694015 - type: f1 value: 67.66829629003848 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (is) type: mteb/amazon_massive_intent config: is split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.42434431741762 - type: f1 value: 59.01617226544757 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (it) type: mteb/amazon_massive_intent config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.53127101546738 - type: f1 value: 68.10033760906255 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ja) type: mteb/amazon_massive_intent config: ja split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.50504371217215 - type: f1 value: 69.74931103158923 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (jv) type: mteb/amazon_massive_intent config: jv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.91190316072628 - type: f1 value: 54.05551136648796 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ka) type: mteb/amazon_massive_intent config: ka split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.78211163416275 - type: f1 value: 49.874888544058535 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (km) type: mteb/amazon_massive_intent config: km split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 47.017484868863484 - type: f1 value: 44.53364263352014 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (kn) type: mteb/amazon_massive_intent config: kn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.16207128446537 - type: f1 value: 59.01185692320829 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ko) type: mteb/amazon_massive_intent config: ko split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.42501681237391 - type: f1 value: 67.13169450166086 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (lv) type: mteb/amazon_massive_intent config: lv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.0780094149294 - type: f1 value: 64.41720167850707 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ml) type: mteb/amazon_massive_intent config: ml split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.57162071284466 - type: f1 value: 62.414138683804424 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (mn) type: mteb/amazon_massive_intent config: mn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.71149966375252 - type: f1 value: 58.594805125087234 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ms) type: mteb/amazon_massive_intent config: ms split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.03900470746471 - type: f1 value: 63.87937257883887 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (my) type: mteb/amazon_massive_intent config: my split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.8776059179556 - type: f1 value: 57.48587618059131 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (nb) type: mteb/amazon_massive_intent config: nb split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.87895090786819 - type: f1 value: 66.8141299430347 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (nl) type: mteb/amazon_massive_intent config: nl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.45057162071285 - type: f1 value: 67.46444039673516 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.546738399462 - type: f1 value: 68.63640876702655 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pt) type: mteb/amazon_massive_intent config: pt split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.72965702757229 - type: f1 value: 68.54119560379115 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ro) type: mteb/amazon_massive_intent config: ro split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.35574983187625 - type: f1 value: 65.88844917691927 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ru) type: mteb/amazon_massive_intent config: ru split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.70477471418964 - type: f1 value: 69.19665697061978 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sl) type: mteb/amazon_massive_intent config: sl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.0880968392737 - type: f1 value: 64.76962317666086 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sq) type: mteb/amazon_massive_intent config: sq split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.18493611297916 - type: f1 value: 62.49984559035371 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sv) type: mteb/amazon_massive_intent config: sv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.75857431069265 - type: f1 value: 69.20053687623418 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sw) type: mteb/amazon_massive_intent config: sw split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.500336247478145 - type: f1 value: 55.2972398687929 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ta) type: mteb/amazon_massive_intent config: ta split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.68997982515132 - type: f1 value: 59.36848202755348 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (te) type: mteb/amazon_massive_intent config: te split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.01950235373235 - type: f1 value: 60.09351954625423 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (th) type: mteb/amazon_massive_intent config: th split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.29186281102892 - type: f1 value: 67.57860496703447 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (tl) type: mteb/amazon_massive_intent config: tl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.77471418964357 - type: f1 value: 61.913983147713836 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (tr) type: mteb/amazon_massive_intent config: tr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.87222595830532 - type: f1 value: 66.03679033708141 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ur) type: mteb/amazon_massive_intent config: ur split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.04505716207127 - type: f1 value: 61.28569169817908 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (vi) type: mteb/amazon_massive_intent config: vi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.38466711499663 - type: f1 value: 67.20532357036844 - 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: 71.12306657700067 - type: f1 value: 68.91251226588182 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-TW) type: mteb/amazon_massive_intent config: zh-TW split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.20040349697378 - type: f1 value: 66.02657347714175 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (af) type: mteb/amazon_massive_scenario config: af split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.73907195696032 - type: f1 value: 66.98484521791418 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (am) type: mteb/amazon_massive_scenario config: am split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.58843308675185 - type: f1 value: 58.95591723092005 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ar) type: mteb/amazon_massive_scenario config: ar split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.22730329522528 - type: f1 value: 66.0894499712115 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (az) type: mteb/amazon_massive_scenario config: az split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.48285137861465 - type: f1 value: 65.21963176785157 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (bn) type: mteb/amazon_massive_scenario config: bn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.74714189643578 - type: f1 value: 66.8212192745412 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (cy) type: mteb/amazon_massive_scenario config: cy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.09213180901143 - type: f1 value: 56.70735546356339 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (da) type: mteb/amazon_massive_scenario config: da split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.05716207128448 - type: f1 value: 74.8413712365364 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (de) type: mteb/amazon_massive_scenario config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.69737726967047 - type: f1 value: 74.7664341963 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (el) type: mteb/amazon_massive_scenario config: el split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.90383322125084 - type: f1 value: 73.59201554448323 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.51176866173503 - type: f1 value: 77.46104434577758 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (es) type: mteb/amazon_massive_scenario config: es split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.31069266980496 - type: f1 value: 74.61048660675635 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fa) type: mteb/amazon_massive_scenario config: fa split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.95225285810356 - type: f1 value: 72.33160006574627 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fi) type: mteb/amazon_massive_scenario config: fi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.12373907195696 - type: f1 value: 73.20921012557481 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.86684599865501 - type: f1 value: 73.82348774610831 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (he) type: mteb/amazon_massive_scenario config: he split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.40215198386012 - type: f1 value: 71.11945183971858 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hi) type: mteb/amazon_massive_scenario config: hi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.12844653665098 - type: f1 value: 71.34450495911766 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hu) type: mteb/amazon_massive_scenario config: hu split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.52252858103566 - type: f1 value: 73.98878711342999 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hy) type: mteb/amazon_massive_scenario config: hy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.93611297915265 - type: f1 value: 63.723200467653385 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (id) type: mteb/amazon_massive_scenario config: id split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.11903160726295 - type: f1 value: 73.82138439467096 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (is) type: mteb/amazon_massive_scenario config: is split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.15198386012105 - type: f1 value: 66.02172193802167 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (it) type: mteb/amazon_massive_scenario config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.32414256893072 - type: f1 value: 74.30943421170574 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ja) type: mteb/amazon_massive_scenario config: ja split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.46805648957633 - type: f1 value: 77.62808409298209 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (jv) type: mteb/amazon_massive_scenario config: jv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.318762609280434 - type: f1 value: 62.094284066075076 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ka) type: mteb/amazon_massive_scenario config: ka split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 58.34902488231338 - type: f1 value: 57.12893860987984 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (km) type: mteb/amazon_massive_scenario config: km split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 50.88433086751849 - type: f1 value: 48.2272350802058 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (kn) type: mteb/amazon_massive_scenario config: kn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.4425016812374 - type: f1 value: 64.61463095996173 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ko) type: mteb/amazon_massive_scenario config: ko split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.04707464694015 - type: f1 value: 75.05099199098998 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (lv) type: mteb/amazon_massive_scenario config: lv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.50437121721586 - type: f1 value: 69.83397721096314 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ml) type: mteb/amazon_massive_scenario config: ml split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.94283792871553 - type: f1 value: 68.8704663703913 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (mn) type: mteb/amazon_massive_scenario config: mn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.79488903833222 - type: f1 value: 63.615424063345436 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ms) type: mteb/amazon_massive_scenario config: ms split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.88231338264963 - type: f1 value: 68.57892302593237 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (my) type: mteb/amazon_massive_scenario config: my split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.248150638870214 - type: f1 value: 61.06680605338809 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (nb) type: mteb/amazon_massive_scenario config: nb split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.84196368527236 - type: f1 value: 74.52566464968763 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (nl) type: mteb/amazon_massive_scenario config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.8285137861466 - type: f1 value: 74.8853197608802 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.13248150638869 - type: f1 value: 74.3982040999179 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pt) type: mteb/amazon_massive_scenario config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.49024882313383 - type: f1 value: 73.82153848368573 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ro) type: mteb/amazon_massive_scenario config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.72158708809684 - type: f1 value: 71.85049433180541 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ru) type: mteb/amazon_massive_scenario config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.137861466039 - type: f1 value: 75.37628348188467 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sl) type: mteb/amazon_massive_scenario config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.86953597848016 - type: f1 value: 71.87537624521661 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sq) type: mteb/amazon_massive_scenario config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.27572293207801 - type: f1 value: 68.80017302344231 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sv) type: mteb/amazon_massive_scenario config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.09952925353059 - type: f1 value: 76.07992707688408 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sw) type: mteb/amazon_massive_scenario config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.140551445864155 - type: f1 value: 61.73855010331415 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ta) type: mteb/amazon_massive_scenario config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.27774041694687 - type: f1 value: 64.83664868894539 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (te) type: mteb/amazon_massive_scenario config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.69468728984533 - type: f1 value: 64.76239666920868 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (th) type: mteb/amazon_massive_scenario config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.44653665097512 - type: f1 value: 73.14646052013873 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (tl) type: mteb/amazon_massive_scenario config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.71351714862139 - type: f1 value: 66.67212180163382 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (tr) type: mteb/amazon_massive_scenario config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.9946200403497 - type: f1 value: 73.87348793725525 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ur) type: mteb/amazon_massive_scenario config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.15400134498992 - type: f1 value: 67.09433241421094 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (vi) type: mteb/amazon_massive_scenario config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.11365164761264 - type: f1 value: 73.59502539433753 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.82582380632145 - type: f1 value: 76.89992945316313 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-TW) type: mteb/amazon_massive_scenario config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.81237390719569 - type: f1 value: 72.36499770986265 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.480506569594695 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 29.71252128004552 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.421396787056548 - type: mrr value: 32.48155274872267 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.595 - type: map_at_10 value: 12.642000000000001 - type: map_at_100 value: 15.726 - type: map_at_1000 value: 17.061999999999998 - type: map_at_3 value: 9.125 - type: map_at_5 value: 10.866000000000001 - type: mrr_at_1 value: 43.344 - type: mrr_at_10 value: 52.227999999999994 - type: mrr_at_100 value: 52.898999999999994 - type: mrr_at_1000 value: 52.944 - type: mrr_at_3 value: 49.845 - type: mrr_at_5 value: 51.115 - type: ndcg_at_1 value: 41.949999999999996 - type: ndcg_at_10 value: 33.995 - type: ndcg_at_100 value: 30.869999999999997 - type: ndcg_at_1000 value: 39.487 - type: ndcg_at_3 value: 38.903999999999996 - type: ndcg_at_5 value: 37.236999999999995 - type: precision_at_1 value: 43.344 - type: precision_at_10 value: 25.480000000000004 - type: precision_at_100 value: 7.672 - type: precision_at_1000 value: 2.028 - type: precision_at_3 value: 36.636 - type: precision_at_5 value: 32.632 - type: recall_at_1 value: 5.595 - type: recall_at_10 value: 16.466 - type: recall_at_100 value: 31.226 - type: recall_at_1000 value: 62.778999999999996 - type: recall_at_3 value: 9.931 - type: recall_at_5 value: 12.884 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 40.414 - type: map_at_10 value: 56.754000000000005 - type: map_at_100 value: 57.457 - type: map_at_1000 value: 57.477999999999994 - type: map_at_3 value: 52.873999999999995 - type: map_at_5 value: 55.175 - type: mrr_at_1 value: 45.278 - type: mrr_at_10 value: 59.192 - type: mrr_at_100 value: 59.650000000000006 - type: mrr_at_1000 value: 59.665 - type: mrr_at_3 value: 56.141 - type: mrr_at_5 value: 57.998000000000005 - type: ndcg_at_1 value: 45.278 - type: ndcg_at_10 value: 64.056 - type: ndcg_at_100 value: 66.89 - type: ndcg_at_1000 value: 67.364 - type: ndcg_at_3 value: 56.97 - type: ndcg_at_5 value: 60.719 - type: precision_at_1 value: 45.278 - type: precision_at_10 value: 9.994 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.512 - type: precision_at_5 value: 17.509 - type: recall_at_1 value: 40.414 - type: recall_at_10 value: 83.596 - type: recall_at_100 value: 95.72 - type: recall_at_1000 value: 99.24 - type: recall_at_3 value: 65.472 - type: recall_at_5 value: 74.039 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.352 - type: map_at_10 value: 84.369 - type: map_at_100 value: 85.02499999999999 - type: map_at_1000 value: 85.04 - type: map_at_3 value: 81.42399999999999 - type: map_at_5 value: 83.279 - type: mrr_at_1 value: 81.05 - type: mrr_at_10 value: 87.401 - type: mrr_at_100 value: 87.504 - type: mrr_at_1000 value: 87.505 - type: mrr_at_3 value: 86.443 - type: mrr_at_5 value: 87.10799999999999 - type: ndcg_at_1 value: 81.04 - type: ndcg_at_10 value: 88.181 - type: ndcg_at_100 value: 89.411 - type: ndcg_at_1000 value: 89.507 - type: ndcg_at_3 value: 85.28099999999999 - type: ndcg_at_5 value: 86.888 - type: precision_at_1 value: 81.04 - type: precision_at_10 value: 13.406 - type: precision_at_100 value: 1.5350000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.31 - type: precision_at_5 value: 24.54 - type: recall_at_1 value: 70.352 - type: recall_at_10 value: 95.358 - type: recall_at_100 value: 99.541 - type: recall_at_1000 value: 99.984 - type: recall_at_3 value: 87.111 - type: recall_at_5 value: 91.643 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 46.54068723291946 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.216287629895994 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.023000000000001 - type: map_at_10 value: 10.071 - type: map_at_100 value: 11.892 - type: map_at_1000 value: 12.196 - type: map_at_3 value: 7.234 - type: map_at_5 value: 8.613999999999999 - type: mrr_at_1 value: 19.900000000000002 - type: mrr_at_10 value: 30.516 - type: mrr_at_100 value: 31.656000000000002 - type: mrr_at_1000 value: 31.723000000000003 - type: mrr_at_3 value: 27.400000000000002 - type: mrr_at_5 value: 29.270000000000003 - type: ndcg_at_1 value: 19.900000000000002 - type: ndcg_at_10 value: 17.474 - type: ndcg_at_100 value: 25.020999999999997 - type: ndcg_at_1000 value: 30.728 - type: ndcg_at_3 value: 16.588 - type: ndcg_at_5 value: 14.498 - type: precision_at_1 value: 19.900000000000002 - type: precision_at_10 value: 9.139999999999999 - type: precision_at_100 value: 2.011 - type: precision_at_1000 value: 0.33899999999999997 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 12.839999999999998 - type: recall_at_1 value: 4.023000000000001 - type: recall_at_10 value: 18.497 - type: recall_at_100 value: 40.8 - type: recall_at_1000 value: 68.812 - type: recall_at_3 value: 9.508 - type: recall_at_5 value: 12.983 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.967008785134 - type: cos_sim_spearman value: 80.23142141101837 - type: euclidean_pearson value: 81.20166064704539 - type: euclidean_spearman value: 80.18961335654585 - type: manhattan_pearson value: 81.13925443187625 - type: manhattan_spearman value: 80.07948723044424 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.94262461316023 - type: cos_sim_spearman value: 80.01596278563865 - type: euclidean_pearson value: 83.80799622922581 - type: euclidean_spearman value: 79.94984954947103 - type: manhattan_pearson value: 83.68473841756281 - type: manhattan_spearman value: 79.84990707951822 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 80.57346443146068 - type: cos_sim_spearman value: 81.54689837570866 - type: euclidean_pearson value: 81.10909881516007 - type: euclidean_spearman value: 81.56746243261762 - type: manhattan_pearson value: 80.87076036186582 - type: manhattan_spearman value: 81.33074987964402 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.54733787179849 - type: cos_sim_spearman value: 77.72202105610411 - type: euclidean_pearson value: 78.9043595478849 - type: euclidean_spearman value: 77.93422804309435 - type: manhattan_pearson value: 78.58115121621368 - type: manhattan_spearman value: 77.62508135122033 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.59880017237558 - type: cos_sim_spearman value: 89.31088630824758 - type: euclidean_pearson value: 88.47069261564656 - type: euclidean_spearman value: 89.33581971465233 - type: manhattan_pearson value: 88.40774264100956 - type: manhattan_spearman value: 89.28657485627835 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.08055117917084 - type: cos_sim_spearman value: 85.78491813080304 - type: euclidean_pearson value: 84.99329155500392 - type: euclidean_spearman value: 85.76728064677287 - type: manhattan_pearson value: 84.87947428989587 - type: manhattan_spearman value: 85.62429454917464 - task: type: STS dataset: name: MTEB STS17 (ko-ko) type: mteb/sts17-crosslingual-sts config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.14190939287384 - type: cos_sim_spearman value: 82.27331573306041 - type: euclidean_pearson value: 81.891896953716 - type: euclidean_spearman value: 82.37695542955998 - type: manhattan_pearson value: 81.73123869460504 - type: manhattan_spearman value: 82.19989168441421 - task: type: STS dataset: name: MTEB STS17 (ar-ar) type: mteb/sts17-crosslingual-sts config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 76.84695301843362 - type: cos_sim_spearman value: 77.87790986014461 - type: euclidean_pearson value: 76.91981583106315 - type: euclidean_spearman value: 77.88154772749589 - type: manhattan_pearson value: 76.94953277451093 - type: manhattan_spearman value: 77.80499230728604 - task: type: STS dataset: name: MTEB STS17 (en-ar) type: mteb/sts17-crosslingual-sts config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 75.44657840482016 - type: cos_sim_spearman value: 75.05531095119674 - type: euclidean_pearson value: 75.88161755829299 - type: euclidean_spearman value: 74.73176238219332 - type: manhattan_pearson value: 75.63984765635362 - type: manhattan_spearman value: 74.86476440770737 - task: type: STS dataset: name: MTEB STS17 (en-de) type: mteb/sts17-crosslingual-sts config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.64700140524133 - type: cos_sim_spearman value: 86.16014210425672 - type: euclidean_pearson value: 86.49086860843221 - type: euclidean_spearman value: 86.09729326815614 - type: manhattan_pearson value: 86.43406265125513 - type: manhattan_spearman value: 86.17740150939994 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.91170098764921 - type: cos_sim_spearman value: 88.12437004058931 - type: euclidean_pearson value: 88.81828254494437 - type: euclidean_spearman value: 88.14831794572122 - type: manhattan_pearson value: 88.93442183448961 - type: manhattan_spearman value: 88.15254630778304 - task: type: STS dataset: name: MTEB STS17 (en-tr) type: mteb/sts17-crosslingual-sts config: en-tr split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 72.91390577997292 - type: cos_sim_spearman value: 71.22979457536074 - type: euclidean_pearson value: 74.40314008106749 - type: euclidean_spearman value: 72.54972136083246 - type: manhattan_pearson value: 73.85687539530218 - type: manhattan_spearman value: 72.09500771742637 - task: type: STS dataset: name: MTEB STS17 (es-en) type: mteb/sts17-crosslingual-sts config: es-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 80.9301067983089 - type: cos_sim_spearman value: 80.74989828346473 - type: euclidean_pearson value: 81.36781301814257 - type: euclidean_spearman value: 80.9448819964426 - type: manhattan_pearson value: 81.0351322685609 - type: manhattan_spearman value: 80.70192121844177 - task: type: STS dataset: name: MTEB STS17 (es-es) type: mteb/sts17-crosslingual-sts config: es-es split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.13820465980005 - type: cos_sim_spearman value: 86.73532498758757 - type: euclidean_pearson value: 87.21329451846637 - type: euclidean_spearman value: 86.57863198601002 - type: manhattan_pearson value: 87.06973713818554 - type: manhattan_spearman value: 86.47534918791499 - task: type: STS dataset: name: MTEB STS17 (fr-en) type: mteb/sts17-crosslingual-sts config: fr-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.48720108904415 - type: cos_sim_spearman value: 85.62221757068387 - type: euclidean_pearson value: 86.1010129512749 - type: euclidean_spearman value: 85.86580966509942 - type: manhattan_pearson value: 86.26800938808971 - type: manhattan_spearman value: 85.88902721678429 - task: type: STS dataset: name: MTEB STS17 (it-en) type: mteb/sts17-crosslingual-sts config: it-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 83.98021347333516 - type: cos_sim_spearman value: 84.53806553803501 - type: euclidean_pearson value: 84.61483347248364 - type: euclidean_spearman value: 85.14191408011702 - type: manhattan_pearson value: 84.75297588825967 - type: manhattan_spearman value: 85.33176753669242 - task: type: STS dataset: name: MTEB STS17 (nl-en) type: mteb/sts17-crosslingual-sts config: nl-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.51856644893233 - type: cos_sim_spearman value: 85.27510748506413 - type: euclidean_pearson value: 85.09886861540977 - type: euclidean_spearman value: 85.62579245860887 - type: manhattan_pearson value: 84.93017860464607 - type: manhattan_spearman value: 85.5063988898453 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.581573200584195 - type: cos_sim_spearman value: 63.05503590247928 - type: euclidean_pearson value: 63.652564812602094 - type: euclidean_spearman value: 62.64811520876156 - type: manhattan_pearson value: 63.506842893061076 - type: manhattan_spearman value: 62.51289573046917 - task: type: STS dataset: name: MTEB STS22 (de) type: mteb/sts22-crosslingual-sts config: de split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 48.2248801729127 - type: cos_sim_spearman value: 56.5936604678561 - type: euclidean_pearson value: 43.98149464089 - type: euclidean_spearman value: 56.108561882423615 - type: manhattan_pearson value: 43.86880305903564 - type: manhattan_spearman value: 56.04671150510166 - task: type: STS dataset: name: MTEB STS22 (es) type: mteb/sts22-crosslingual-sts config: es split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 55.17564527009831 - type: cos_sim_spearman value: 64.57978560979488 - type: euclidean_pearson value: 58.8818330154583 - type: euclidean_spearman value: 64.99214839071281 - type: manhattan_pearson value: 58.72671436121381 - type: manhattan_spearman value: 65.10713416616109 - task: type: STS dataset: name: MTEB STS22 (pl) type: mteb/sts22-crosslingual-sts config: pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 26.772131864023297 - type: cos_sim_spearman value: 34.68200792408681 - type: euclidean_pearson value: 16.68082419005441 - type: euclidean_spearman value: 34.83099932652166 - type: manhattan_pearson value: 16.52605949659529 - type: manhattan_spearman value: 34.82075801399475 - task: type: STS dataset: name: MTEB STS22 (tr) type: mteb/sts22-crosslingual-sts config: tr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 54.42415189043831 - type: cos_sim_spearman value: 63.54594264576758 - type: euclidean_pearson value: 57.36577498297745 - type: euclidean_spearman value: 63.111466379158074 - type: manhattan_pearson value: 57.584543715873885 - type: manhattan_spearman value: 63.22361054139183 - task: type: STS dataset: name: MTEB STS22 (ar) type: mteb/sts22-crosslingual-sts config: ar split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 47.55216762405518 - type: cos_sim_spearman value: 56.98670142896412 - type: euclidean_pearson value: 50.15318757562699 - type: euclidean_spearman value: 56.524941926541906 - type: manhattan_pearson value: 49.955618528674904 - type: manhattan_spearman value: 56.37102209240117 - task: type: STS dataset: name: MTEB STS22 (ru) type: mteb/sts22-crosslingual-sts config: ru split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 49.20540980338571 - type: cos_sim_spearman value: 59.9009453504406 - type: euclidean_pearson value: 49.557749853620535 - type: euclidean_spearman value: 59.76631621172456 - type: manhattan_pearson value: 49.62340591181147 - type: manhattan_spearman value: 59.94224880322436 - task: type: STS dataset: name: MTEB STS22 (zh) type: mteb/sts22-crosslingual-sts config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 51.508169956576985 - type: cos_sim_spearman value: 66.82461565306046 - type: euclidean_pearson value: 56.2274426480083 - type: euclidean_spearman value: 66.6775323848333 - type: manhattan_pearson value: 55.98277796300661 - type: manhattan_spearman value: 66.63669848497175 - task: type: STS dataset: name: MTEB STS22 (fr) type: mteb/sts22-crosslingual-sts config: fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 72.86478788045507 - type: cos_sim_spearman value: 76.7946552053193 - type: euclidean_pearson value: 75.01598530490269 - type: euclidean_spearman value: 76.83618917858281 - type: manhattan_pearson value: 74.68337628304332 - type: manhattan_spearman value: 76.57480204017773 - task: type: STS dataset: name: MTEB STS22 (de-en) type: mteb/sts22-crosslingual-sts config: de-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 55.922619099401984 - type: cos_sim_spearman value: 56.599362477240774 - type: euclidean_pearson value: 56.68307052369783 - type: euclidean_spearman value: 54.28760436777401 - type: manhattan_pearson value: 56.67763566500681 - type: manhattan_spearman value: 53.94619541711359 - task: type: STS dataset: name: MTEB STS22 (es-en) type: mteb/sts22-crosslingual-sts config: es-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.74357206710913 - type: cos_sim_spearman value: 72.5208244925311 - type: euclidean_pearson value: 67.49254562186032 - type: euclidean_spearman value: 72.02469076238683 - type: manhattan_pearson value: 67.45251772238085 - type: manhattan_spearman value: 72.05538819984538 - task: type: STS dataset: name: MTEB STS22 (it) type: mteb/sts22-crosslingual-sts config: it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 71.25734330033191 - type: cos_sim_spearman value: 76.98349083946823 - type: euclidean_pearson value: 73.71642838667736 - type: euclidean_spearman value: 77.01715504651384 - type: manhattan_pearson value: 73.61712711868105 - type: manhattan_spearman value: 77.01392571153896 - task: type: STS dataset: name: MTEB STS22 (pl-en) type: mteb/sts22-crosslingual-sts config: pl-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.18215462781212 - type: cos_sim_spearman value: 65.54373266117607 - type: euclidean_pearson value: 64.54126095439005 - type: euclidean_spearman value: 65.30410369102711 - type: manhattan_pearson value: 63.50332221148234 - type: manhattan_spearman value: 64.3455878104313 - task: type: STS dataset: name: MTEB STS22 (zh-en) type: mteb/sts22-crosslingual-sts config: zh-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.30509221440029 - type: cos_sim_spearman value: 65.99582704642478 - type: euclidean_pearson value: 63.43818859884195 - type: euclidean_spearman value: 66.83172582815764 - type: manhattan_pearson value: 63.055779168508764 - type: manhattan_spearman value: 65.49585020501449 - task: type: STS dataset: name: MTEB STS22 (es-it) type: mteb/sts22-crosslingual-sts config: es-it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 59.587830825340404 - type: cos_sim_spearman value: 68.93467614588089 - type: euclidean_pearson value: 62.3073527367404 - type: euclidean_spearman value: 69.69758171553175 - type: manhattan_pearson value: 61.9074580815789 - type: manhattan_spearman value: 69.57696375597865 - task: type: STS dataset: name: MTEB STS22 (de-fr) type: mteb/sts22-crosslingual-sts config: de-fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 57.143220125577066 - type: cos_sim_spearman value: 67.78857859159226 - type: euclidean_pearson value: 55.58225107923733 - type: euclidean_spearman value: 67.80662907184563 - type: manhattan_pearson value: 56.24953502726514 - type: manhattan_spearman value: 67.98262125431616 - task: type: STS dataset: name: MTEB STS22 (de-pl) type: mteb/sts22-crosslingual-sts config: de-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 21.826928900322066 - type: cos_sim_spearman value: 49.578506634400405 - type: euclidean_pearson value: 27.939890138843214 - type: euclidean_spearman value: 52.71950519136242 - type: manhattan_pearson value: 26.39878683847546 - type: manhattan_spearman value: 47.54609580342499 - task: type: STS dataset: name: MTEB STS22 (fr-pl) type: mteb/sts22-crosslingual-sts config: fr-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 57.27603854632001 - type: cos_sim_spearman value: 50.709255283710995 - type: euclidean_pearson value: 59.5419024445929 - type: euclidean_spearman value: 50.709255283710995 - type: manhattan_pearson value: 59.03256832438492 - type: manhattan_spearman value: 61.97797868009122 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.00757054859712 - type: cos_sim_spearman value: 87.29283629622222 - type: euclidean_pearson value: 86.54824171775536 - type: euclidean_spearman value: 87.24364730491402 - type: manhattan_pearson value: 86.5062156915074 - type: manhattan_spearman value: 87.15052170378574 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.03549357197389 - type: mrr value: 95.05437645143527 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.260999999999996 - type: map_at_10 value: 66.259 - type: map_at_100 value: 66.884 - type: map_at_1000 value: 66.912 - type: map_at_3 value: 63.685 - type: map_at_5 value: 65.35499999999999 - type: mrr_at_1 value: 60.333000000000006 - type: mrr_at_10 value: 67.5 - type: mrr_at_100 value: 68.013 - type: mrr_at_1000 value: 68.038 - type: mrr_at_3 value: 65.61099999999999 - type: mrr_at_5 value: 66.861 - type: ndcg_at_1 value: 60.333000000000006 - type: ndcg_at_10 value: 70.41 - type: ndcg_at_100 value: 73.10600000000001 - type: ndcg_at_1000 value: 73.846 - type: ndcg_at_3 value: 66.133 - type: ndcg_at_5 value: 68.499 - type: precision_at_1 value: 60.333000000000006 - type: precision_at_10 value: 9.232999999999999 - type: precision_at_100 value: 1.0630000000000002 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.667 - type: precision_at_5 value: 17.067 - type: recall_at_1 value: 57.260999999999996 - type: recall_at_10 value: 81.94399999999999 - type: recall_at_100 value: 93.867 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.339 - type: recall_at_5 value: 76.25 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.74356435643564 - type: cos_sim_ap value: 93.13411948212683 - type: cos_sim_f1 value: 86.80521991300147 - type: cos_sim_precision value: 84.00374181478017 - type: cos_sim_recall value: 89.8 - type: dot_accuracy value: 99.67920792079208 - type: dot_ap value: 89.27277565444479 - type: dot_f1 value: 83.9276990718124 - type: dot_precision value: 82.04393505253104 - type: dot_recall value: 85.9 - type: euclidean_accuracy value: 99.74257425742574 - type: euclidean_ap value: 93.17993008259062 - type: euclidean_f1 value: 86.69396110542476 - type: euclidean_precision value: 88.78406708595388 - type: euclidean_recall value: 84.7 - type: manhattan_accuracy value: 99.74257425742574 - type: manhattan_ap value: 93.14413755550099 - type: manhattan_f1 value: 86.82483594144371 - type: manhattan_precision value: 87.66564729867483 - type: manhattan_recall value: 86 - type: max_accuracy value: 99.74356435643564 - type: max_ap value: 93.17993008259062 - type: max_f1 value: 86.82483594144371 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 57.525863806168566 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.68850574423839 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.71580650644033 - type: mrr value: 50.50971903913081 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.152190498799484 - type: cos_sim_spearman value: 29.686180371952727 - type: dot_pearson value: 27.248664793816342 - type: dot_spearman value: 28.37748983721745 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.20400000000000001 - type: map_at_10 value: 1.6209999999999998 - type: map_at_100 value: 9.690999999999999 - type: map_at_1000 value: 23.733 - type: map_at_3 value: 0.575 - type: map_at_5 value: 0.885 - type: mrr_at_1 value: 78 - type: mrr_at_10 value: 86.56700000000001 - type: mrr_at_100 value: 86.56700000000001 - type: mrr_at_1000 value: 86.56700000000001 - type: mrr_at_3 value: 85.667 - type: mrr_at_5 value: 86.56700000000001 - type: ndcg_at_1 value: 76 - type: ndcg_at_10 value: 71.326 - type: ndcg_at_100 value: 54.208999999999996 - type: ndcg_at_1000 value: 49.252 - type: ndcg_at_3 value: 74.235 - type: ndcg_at_5 value: 73.833 - type: precision_at_1 value: 78 - type: precision_at_10 value: 74.8 - type: precision_at_100 value: 55.50000000000001 - type: precision_at_1000 value: 21.836 - type: precision_at_3 value: 78 - type: precision_at_5 value: 78 - type: recall_at_1 value: 0.20400000000000001 - type: recall_at_10 value: 1.894 - type: recall_at_100 value: 13.245999999999999 - type: recall_at_1000 value: 46.373 - type: recall_at_3 value: 0.613 - type: recall_at_5 value: 0.991 - task: type: BitextMining dataset: name: MTEB Tatoeba (sqi-eng) type: mteb/tatoeba-bitext-mining config: sqi-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.89999999999999 - type: f1 value: 94.69999999999999 - type: precision value: 94.11666666666667 - type: recall value: 95.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (fry-eng) type: mteb/tatoeba-bitext-mining config: fry-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 68.20809248554913 - type: f1 value: 63.431048720066066 - type: precision value: 61.69143958161298 - type: recall value: 68.20809248554913 - task: type: BitextMining dataset: name: MTEB Tatoeba (kur-eng) type: mteb/tatoeba-bitext-mining config: kur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 71.21951219512195 - type: f1 value: 66.82926829268293 - type: precision value: 65.1260162601626 - type: recall value: 71.21951219512195 - task: type: BitextMining dataset: name: MTEB Tatoeba (tur-eng) type: mteb/tatoeba-bitext-mining config: tur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.2 - type: f1 value: 96.26666666666667 - type: precision value: 95.8 - type: recall value: 97.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (deu-eng) type: mteb/tatoeba-bitext-mining config: deu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 99.3 - type: f1 value: 99.06666666666666 - type: precision value: 98.95 - type: recall value: 99.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (nld-eng) type: mteb/tatoeba-bitext-mining config: nld-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.63333333333333 - type: precision value: 96.26666666666668 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (ron-eng) type: mteb/tatoeba-bitext-mining config: ron-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96 - type: f1 value: 94.86666666666666 - type: precision value: 94.31666666666668 - type: recall value: 96 - task: type: BitextMining dataset: name: MTEB Tatoeba (ang-eng) type: mteb/tatoeba-bitext-mining config: ang-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 47.01492537313433 - type: f1 value: 40.178867566927266 - type: precision value: 38.179295828549556 - type: recall value: 47.01492537313433 - task: type: BitextMining dataset: name: MTEB Tatoeba (ido-eng) type: mteb/tatoeba-bitext-mining config: ido-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 86.5 - type: f1 value: 83.62537480063796 - type: precision value: 82.44555555555554 - type: recall value: 86.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (jav-eng) type: mteb/tatoeba-bitext-mining config: jav-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 80.48780487804879 - type: f1 value: 75.45644599303138 - type: precision value: 73.37398373983739 - type: recall value: 80.48780487804879 - task: type: BitextMining dataset: name: MTEB Tatoeba (isl-eng) type: mteb/tatoeba-bitext-mining config: isl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.7 - type: f1 value: 91.95666666666666 - type: precision value: 91.125 - type: recall value: 93.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (slv-eng) type: mteb/tatoeba-bitext-mining config: slv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.73754556500607 - type: f1 value: 89.65168084244632 - type: precision value: 88.73025516403402 - type: recall value: 91.73754556500607 - task: type: BitextMining dataset: name: MTEB Tatoeba (cym-eng) type: mteb/tatoeba-bitext-mining config: cym-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81.04347826086956 - type: f1 value: 76.2128364389234 - type: precision value: 74.2 - type: recall value: 81.04347826086956 - task: type: BitextMining dataset: name: MTEB Tatoeba (kaz-eng) type: mteb/tatoeba-bitext-mining config: kaz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.65217391304348 - type: f1 value: 79.4376811594203 - type: precision value: 77.65797101449274 - type: recall value: 83.65217391304348 - task: type: BitextMining dataset: name: MTEB Tatoeba (est-eng) type: mteb/tatoeba-bitext-mining config: est-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.5 - type: f1 value: 85.02690476190476 - type: precision value: 83.96261904761904 - type: recall value: 87.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (heb-eng) type: mteb/tatoeba-bitext-mining config: heb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.3 - type: f1 value: 86.52333333333333 - type: precision value: 85.22833333333332 - type: recall value: 89.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (gla-eng) type: mteb/tatoeba-bitext-mining config: gla-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.01809408926418 - type: f1 value: 59.00594446432805 - type: precision value: 56.827215807915444 - type: recall value: 65.01809408926418 - task: type: BitextMining dataset: name: MTEB Tatoeba (mar-eng) type: mteb/tatoeba-bitext-mining config: mar-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.2 - type: f1 value: 88.58 - type: precision value: 87.33333333333334 - type: recall value: 91.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (lat-eng) type: mteb/tatoeba-bitext-mining config: lat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 59.199999999999996 - type: f1 value: 53.299166276284915 - type: precision value: 51.3383908045977 - type: recall value: 59.199999999999996 - task: type: BitextMining dataset: name: MTEB Tatoeba (bel-eng) type: mteb/tatoeba-bitext-mining config: bel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.2 - type: f1 value: 91.2 - type: precision value: 90.25 - type: recall value: 93.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (pms-eng) type: mteb/tatoeba-bitext-mining config: pms-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 64.76190476190476 - type: f1 value: 59.867110667110666 - type: precision value: 58.07390192653351 - type: recall value: 64.76190476190476 - task: type: BitextMining dataset: name: MTEB Tatoeba (gle-eng) type: mteb/tatoeba-bitext-mining config: gle-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.2 - type: f1 value: 71.48147546897547 - type: precision value: 69.65409090909091 - type: recall value: 76.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (pes-eng) type: mteb/tatoeba-bitext-mining config: pes-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.8 - type: f1 value: 92.14 - type: precision value: 91.35833333333333 - type: recall value: 93.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (nob-eng) type: mteb/tatoeba-bitext-mining config: nob-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.89999999999999 - type: f1 value: 97.2 - type: precision value: 96.85000000000001 - type: recall value: 97.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (bul-eng) type: mteb/tatoeba-bitext-mining config: bul-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.6 - type: f1 value: 92.93333333333334 - type: precision value: 92.13333333333333 - type: recall value: 94.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cbk-eng) type: mteb/tatoeba-bitext-mining config: cbk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.1 - type: f1 value: 69.14817460317461 - type: precision value: 67.2515873015873 - type: recall value: 74.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (hun-eng) type: mteb/tatoeba-bitext-mining config: hun-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.19999999999999 - type: f1 value: 94.01333333333335 - type: precision value: 93.46666666666667 - type: recall value: 95.19999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (uig-eng) type: mteb/tatoeba-bitext-mining config: uig-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.9 - type: f1 value: 72.07523809523809 - type: precision value: 70.19777777777779 - type: recall value: 76.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (rus-eng) type: mteb/tatoeba-bitext-mining config: rus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.1 - type: f1 value: 92.31666666666666 - type: precision value: 91.43333333333332 - type: recall value: 94.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (spa-eng) type: mteb/tatoeba-bitext-mining config: spa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.8 - type: f1 value: 97.1 - type: precision value: 96.76666666666668 - type: recall value: 97.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (hye-eng) type: mteb/tatoeba-bitext-mining config: hye-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.85714285714286 - type: f1 value: 90.92093441150045 - type: precision value: 90.00449236298293 - type: recall value: 92.85714285714286 - task: type: BitextMining dataset: name: MTEB Tatoeba (tel-eng) type: mteb/tatoeba-bitext-mining config: tel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.16239316239316 - type: f1 value: 91.33903133903132 - type: precision value: 90.56267806267806 - type: recall value: 93.16239316239316 - task: type: BitextMining dataset: name: MTEB Tatoeba (afr-eng) type: mteb/tatoeba-bitext-mining config: afr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.4 - type: f1 value: 90.25666666666666 - type: precision value: 89.25833333333334 - type: recall value: 92.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (mon-eng) type: mteb/tatoeba-bitext-mining config: mon-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.22727272727272 - type: f1 value: 87.53030303030303 - type: precision value: 86.37121212121211 - type: recall value: 90.22727272727272 - task: type: BitextMining dataset: name: MTEB Tatoeba (arz-eng) type: mteb/tatoeba-bitext-mining config: arz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 79.03563941299791 - type: f1 value: 74.7349505840072 - type: precision value: 72.9035639412998 - type: recall 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: - type: accuracy value: 97 - type: f1 value: 96.15 - type: precision value: 95.76666666666668 - type: recall value: 97 - task: type: BitextMining dataset: name: MTEB Tatoeba (nov-eng) type: mteb/tatoeba-bitext-mining config: nov-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.26459143968872 - type: f1 value: 71.55642023346303 - type: precision value: 69.7544932369835 - type: recall value: 76.26459143968872 - task: type: BitextMining dataset: name: MTEB Tatoeba (gsw-eng) type: mteb/tatoeba-bitext-mining config: gsw-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 58.119658119658126 - type: f1 value: 51.65242165242165 - type: precision value: 49.41768108434775 - type: recall value: 58.119658119658126 - task: type: BitextMining dataset: name: MTEB Tatoeba (nds-eng) type: mteb/tatoeba-bitext-mining config: nds-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.3 - type: f1 value: 69.52055555555555 - type: precision value: 67.7574938949939 - type: recall value: 74.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (ukr-eng) type: mteb/tatoeba-bitext-mining config: ukr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.8 - type: f1 value: 93.31666666666666 - type: precision value: 92.60000000000001 - type: recall value: 94.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (uzb-eng) type: mteb/tatoeba-bitext-mining config: uzb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.63551401869158 - type: f1 value: 72.35202492211837 - type: precision value: 70.60358255451713 - type: recall value: 76.63551401869158 - task: type: BitextMining dataset: name: MTEB Tatoeba (lit-eng) type: mteb/tatoeba-bitext-mining config: lit-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.4 - type: f1 value: 88.4811111111111 - type: precision value: 87.7452380952381 - type: recall 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: - type: accuracy value: 95 - type: f1 value: 93.60666666666667 - type: precision value: 92.975 - type: recall value: 95 - task: type: BitextMining dataset: name: MTEB Tatoeba (lfn-eng) type: mteb/tatoeba-bitext-mining config: lfn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 67.2 - type: f1 value: 63.01595782872099 - type: precision value: 61.596587301587306 - type: recall 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: - type: accuracy value: 95.7 - type: f1 value: 94.52999999999999 - type: precision value: 94 - type: recall value: 95.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (ita-eng) type: mteb/tatoeba-bitext-mining config: ita-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.6 - type: f1 value: 93.28999999999999 - type: precision value: 92.675 - type: recall value: 94.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cmn-eng) type: mteb/tatoeba-bitext-mining config: cmn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.28333333333333 - type: precision value: 94.75 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (lvs-eng) type: mteb/tatoeba-bitext-mining config: lvs-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.9 - type: f1 value: 89.83 - type: precision value: 88.92 - type: recall 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: - type: accuracy value: 94.69999999999999 - type: f1 value: 93.34222222222223 - type: precision value: 92.75416666666668 - type: recall 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: - type: accuracy value: 60.333333333333336 - type: f1 value: 55.31203703703703 - type: precision value: 53.39971108326371 - type: recall 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 - type: precision value: 10.589187932631877 - type: recall 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: - type: accuracy value: 86.7 - type: f1 value: 83.0152380952381 - type: precision value: 81.37833333333333 - type: recall 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: - type: accuracy value: 63.39285714285714 - type: f1 value: 56.832482993197274 - type: precision value: 54.56845238095237 - type: recall value: 63.39285714285714 - task: type: BitextMining dataset: name: MTEB Tatoeba (arq-eng) type: mteb/tatoeba-bitext-mining config: arq-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 48.73765093304062 - type: f1 value: 41.555736920720456 - type: precision value: 39.06874531737319 - type: recall value: 48.73765093304062 - task: type: BitextMining dataset: name: MTEB Tatoeba (kab-eng) type: mteb/tatoeba-bitext-mining config: kab-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 41.099999999999994 - type: f1 value: 36.540165945165946 - type: precision value: 35.05175685425686 - type: recall value: 41.099999999999994 - task: type: BitextMining dataset: name: MTEB Tatoeba (fra-eng) type: mteb/tatoeba-bitext-mining config: fra-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.89999999999999 - type: f1 value: 93.42333333333333 - type: precision value: 92.75833333333333 - type: recall value: 94.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (por-eng) type: mteb/tatoeba-bitext-mining config: por-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.89999999999999 - type: f1 value: 93.63333333333334 - type: precision value: 93.01666666666665 - type: recall value: 94.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (tat-eng) type: mteb/tatoeba-bitext-mining config: tat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.9 - type: f1 value: 73.64833333333334 - type: precision value: 71.90282106782105 - type: recall value: 77.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (oci-eng) type: mteb/tatoeba-bitext-mining config: oci-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 59.4 - type: f1 value: 54.90521367521367 - type: precision value: 53.432840025471606 - type: recall value: 59.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (pol-eng) type: mteb/tatoeba-bitext-mining config: pol-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.6 - type: precision value: 96.2 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (war-eng) type: mteb/tatoeba-bitext-mining config: war-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 67.2 - type: f1 value: 62.25926129426129 - type: precision value: 60.408376623376626 - type: recall value: 67.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (aze-eng) type: mteb/tatoeba-bitext-mining config: aze-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.2 - type: f1 value: 87.60666666666667 - type: precision value: 86.45277777777778 - type: recall value: 90.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (vie-eng) type: mteb/tatoeba-bitext-mining config: vie-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.7 - type: f1 value: 97 - type: precision value: 96.65 - type: recall value: 97.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (nno-eng) type: mteb/tatoeba-bitext-mining config: nno-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.2 - type: f1 value: 91.39746031746031 - type: precision value: 90.6125 - type: recall value: 93.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (cha-eng) type: mteb/tatoeba-bitext-mining config: cha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 32.11678832116788 - type: f1 value: 27.210415386260234 - type: precision value: 26.20408990846947 - type: recall value: 32.11678832116788 - task: type: BitextMining dataset: name: MTEB Tatoeba (mhr-eng) type: mteb/tatoeba-bitext-mining config: mhr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.5 - type: f1 value: 6.787319277832475 - type: precision value: 6.3452094433344435 - type: recall value: 8.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (dan-eng) type: mteb/tatoeba-bitext-mining config: dan-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.1 - type: f1 value: 95.08 - type: precision value: 94.61666666666667 - type: recall value: 96.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (ell-eng) type: mteb/tatoeba-bitext-mining config: ell-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.3 - type: f1 value: 93.88333333333333 - type: precision value: 93.18333333333332 - type: recall value: 95.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (amh-eng) type: mteb/tatoeba-bitext-mining config: amh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.11904761904762 - type: f1 value: 80.69444444444444 - type: precision value: 78.72023809523809 - type: recall value: 85.11904761904762 - task: type: BitextMining dataset: name: MTEB Tatoeba (pam-eng) type: mteb/tatoeba-bitext-mining config: pam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 11.1 - type: f1 value: 9.276381801735853 - type: precision value: 8.798174603174601 - type: recall value: 11.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (hsb-eng) type: mteb/tatoeba-bitext-mining config: hsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 63.56107660455487 - type: f1 value: 58.70433569191332 - type: precision value: 56.896926581464015 - type: recall value: 63.56107660455487 - task: type: BitextMining dataset: name: MTEB Tatoeba (srp-eng) type: mteb/tatoeba-bitext-mining config: srp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.69999999999999 - type: f1 value: 93.10000000000001 - type: precision value: 92.35 - type: recall value: 94.69999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (epo-eng) type: mteb/tatoeba-bitext-mining config: epo-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.8 - type: f1 value: 96.01222222222222 - type: precision value: 95.67083333333332 - type: recall value: 96.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (kzj-eng) type: mteb/tatoeba-bitext-mining config: kzj-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 9.2 - type: f1 value: 7.911555250305249 - type: precision value: 7.631246556216846 - type: recall value: 9.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (awa-eng) type: mteb/tatoeba-bitext-mining config: awa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.48917748917748 - type: f1 value: 72.27375798804371 - type: precision value: 70.14430014430013 - type: recall value: 77.48917748917748 - task: type: BitextMining dataset: name: MTEB Tatoeba (fao-eng) type: mteb/tatoeba-bitext-mining config: fao-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.09923664122137 - type: f1 value: 72.61541257724463 - type: precision value: 70.8998380754106 - type: recall value: 77.09923664122137 - task: type: BitextMining dataset: name: MTEB Tatoeba (mal-eng) type: mteb/tatoeba-bitext-mining config: mal-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.2532751091703 - type: f1 value: 97.69529354682193 - type: precision value: 97.42843279961184 - type: recall value: 98.2532751091703 - task: type: BitextMining dataset: name: MTEB Tatoeba (ile-eng) type: mteb/tatoeba-bitext-mining config: ile-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.8 - type: f1 value: 79.14672619047619 - type: precision value: 77.59489247311828 - type: recall value: 82.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (bos-eng) type: mteb/tatoeba-bitext-mining config: bos-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.35028248587571 - type: f1 value: 92.86252354048965 - type: precision value: 92.2080979284369 - type: recall value: 94.35028248587571 - task: type: BitextMining dataset: name: MTEB Tatoeba (cor-eng) type: mteb/tatoeba-bitext-mining config: cor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.5 - type: f1 value: 6.282429263935621 - type: precision value: 5.783274240739785 - type: recall value: 8.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (cat-eng) type: mteb/tatoeba-bitext-mining config: cat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.7 - type: f1 value: 91.025 - type: precision value: 90.30428571428571 - type: recall value: 92.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (eus-eng) type: mteb/tatoeba-bitext-mining config: eus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81 - type: f1 value: 77.8232380952381 - type: precision value: 76.60194444444444 - type: recall value: 81 - task: type: BitextMining dataset: name: MTEB Tatoeba (yue-eng) type: mteb/tatoeba-bitext-mining config: yue-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91 - type: f1 value: 88.70857142857142 - type: precision value: 87.7 - type: recall value: 91 - task: type: BitextMining dataset: name: MTEB Tatoeba (swe-eng) type: mteb/tatoeba-bitext-mining config: swe-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.3 - type: precision value: 94.76666666666667 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (dtp-eng) type: mteb/tatoeba-bitext-mining config: dtp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.1 - type: f1 value: 7.001008218834307 - type: precision value: 6.708329562594269 - type: recall value: 8.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (kat-eng) type: mteb/tatoeba-bitext-mining config: kat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.1313672922252 - type: f1 value: 84.09070598748882 - type: precision value: 82.79171454104429 - type: recall value: 87.1313672922252 - task: type: BitextMining dataset: name: MTEB Tatoeba (jpn-eng) type: mteb/tatoeba-bitext-mining config: jpn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.28333333333333 - type: precision value: 94.73333333333332 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (csb-eng) type: mteb/tatoeba-bitext-mining config: csb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 42.29249011857708 - type: f1 value: 36.981018542283365 - type: precision value: 35.415877813576024 - type: recall value: 42.29249011857708 - task: type: BitextMining dataset: name: MTEB Tatoeba (xho-eng) type: mteb/tatoeba-bitext-mining config: xho-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.80281690140845 - type: f1 value: 80.86854460093896 - type: precision value: 79.60093896713614 - type: recall value: 83.80281690140845 - task: type: BitextMining dataset: name: MTEB Tatoeba (orv-eng) type: mteb/tatoeba-bitext-mining config: orv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 45.26946107784431 - type: f1 value: 39.80235464678088 - type: precision value: 38.14342660001342 - type: recall value: 45.26946107784431 - task: type: BitextMining dataset: name: MTEB Tatoeba (ind-eng) type: mteb/tatoeba-bitext-mining config: ind-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.3 - type: f1 value: 92.9 - type: precision value: 92.26666666666668 - type: recall value: 94.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (tuk-eng) type: mteb/tatoeba-bitext-mining config: tuk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 37.93103448275862 - type: f1 value: 33.15192743764172 - type: precision value: 31.57456528146183 - type: recall value: 37.93103448275862 - task: type: BitextMining dataset: name: MTEB Tatoeba (max-eng) type: mteb/tatoeba-bitext-mining config: max-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 69.01408450704226 - type: f1 value: 63.41549295774648 - type: precision value: 61.342778895595806 - type: recall value: 69.01408450704226 - task: type: BitextMining dataset: name: MTEB Tatoeba (swh-eng) type: mteb/tatoeba-bitext-mining config: swh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.66666666666667 - type: f1 value: 71.60705960705961 - type: precision value: 69.60683760683762 - type: recall value: 76.66666666666667 - task: type: BitextMining dataset: name: MTEB Tatoeba (hin-eng) type: mteb/tatoeba-bitext-mining config: hin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.8 - type: f1 value: 94.48333333333333 - type: precision value: 93.83333333333333 - type: recall value: 95.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (dsb-eng) type: mteb/tatoeba-bitext-mining config: dsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 52.81837160751566 - type: f1 value: 48.435977731384824 - type: precision value: 47.11291973845539 - type: recall value: 52.81837160751566 - task: type: BitextMining dataset: name: MTEB Tatoeba (ber-eng) type: mteb/tatoeba-bitext-mining config: ber-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 44.9 - type: f1 value: 38.88962621607783 - type: precision value: 36.95936507936508 - type: recall value: 44.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (tam-eng) type: mteb/tatoeba-bitext-mining config: tam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.55374592833876 - type: f1 value: 88.22553125484721 - type: precision value: 87.26927252985884 - type: recall value: 90.55374592833876 - task: type: BitextMining dataset: name: MTEB Tatoeba (slk-eng) type: mteb/tatoeba-bitext-mining config: slk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.6 - type: f1 value: 93.13333333333333 - type: precision value: 92.45333333333333 - type: recall value: 94.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (tgl-eng) type: mteb/tatoeba-bitext-mining config: tgl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.7 - type: f1 value: 91.99666666666667 - type: precision value: 91.26666666666668 - type: recall value: 93.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (ast-eng) type: mteb/tatoeba-bitext-mining config: ast-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.03937007874016 - type: f1 value: 81.75853018372703 - type: precision value: 80.34120734908137 - type: recall value: 85.03937007874016 - task: type: BitextMining dataset: name: MTEB Tatoeba (mkd-eng) type: mteb/tatoeba-bitext-mining config: mkd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.3 - type: f1 value: 85.5 - type: precision value: 84.25833333333334 - type: recall value: 88.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (khm-eng) type: mteb/tatoeba-bitext-mining config: khm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.51246537396122 - type: f1 value: 60.02297410192148 - type: precision value: 58.133467727289236 - type: recall value: 65.51246537396122 - task: type: BitextMining dataset: name: MTEB Tatoeba (ces-eng) type: mteb/tatoeba-bitext-mining config: ces-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96 - type: f1 value: 94.89 - type: precision value: 94.39166666666667 - type: recall value: 96 - task: type: BitextMining dataset: name: MTEB Tatoeba (tzl-eng) type: mteb/tatoeba-bitext-mining config: tzl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 57.692307692307686 - type: f1 value: 53.162393162393165 - type: precision value: 51.70673076923077 - type: recall value: 57.692307692307686 - task: type: BitextMining dataset: name: MTEB Tatoeba (urd-eng) type: mteb/tatoeba-bitext-mining config: urd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.60000000000001 - type: f1 value: 89.21190476190475 - type: precision value: 88.08666666666667 - type: recall value: 91.60000000000001 - task: type: BitextMining dataset: name: MTEB Tatoeba (ara-eng) type: mteb/tatoeba-bitext-mining config: ara-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88 - type: f1 value: 85.47 - type: precision value: 84.43266233766234 - type: recall value: 88 - task: type: BitextMining dataset: name: MTEB Tatoeba (kor-eng) type: mteb/tatoeba-bitext-mining config: kor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.7 - type: f1 value: 90.64999999999999 - type: precision value: 89.68333333333332 - type: recall value: 92.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (yid-eng) type: mteb/tatoeba-bitext-mining config: yid-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 80.30660377358491 - type: f1 value: 76.33044137466307 - type: precision value: 74.78970125786164 - type: recall value: 80.30660377358491 - task: type: BitextMining dataset: name: MTEB Tatoeba (fin-eng) type: mteb/tatoeba-bitext-mining config: fin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.44 - type: precision value: 94.99166666666666 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (tha-eng) type: mteb/tatoeba-bitext-mining config: tha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.53284671532847 - type: f1 value: 95.37712895377129 - type: precision value: 94.7992700729927 - type: recall value: 96.53284671532847 - task: type: BitextMining dataset: name: MTEB Tatoeba (wuu-eng) type: mteb/tatoeba-bitext-mining config: wuu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89 - type: f1 value: 86.23190476190476 - type: precision value: 85.035 - type: recall value: 89 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.585 - type: map_at_10 value: 9.012 - type: map_at_100 value: 14.027000000000001 - type: map_at_1000 value: 15.565000000000001 - type: map_at_3 value: 5.032 - type: map_at_5 value: 6.657 - type: mrr_at_1 value: 28.571 - type: mrr_at_10 value: 45.377 - type: mrr_at_100 value: 46.119 - type: mrr_at_1000 value: 46.127 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 42.585 - type: ndcg_at_1 value: 27.551 - type: ndcg_at_10 value: 23.395 - type: ndcg_at_100 value: 33.342 - type: ndcg_at_1000 value: 45.523 - type: ndcg_at_3 value: 25.158 - type: ndcg_at_5 value: 23.427 - type: precision_at_1 value: 28.571 - type: precision_at_10 value: 21.429000000000002 - type: precision_at_100 value: 6.714 - type: precision_at_1000 value: 1.473 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.585 - type: recall_at_10 value: 15.418999999999999 - type: recall_at_100 value: 42.485 - type: recall_at_1000 value: 79.536 - type: recall_at_3 value: 6.239999999999999 - type: recall_at_5 value: 8.996 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.3234 - type: ap value: 14.361688653847423 - type: f1 value: 54.819068624319044 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.97792869269949 - type: f1 value: 62.28965628513728 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 38.90540145385218 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.53513739047506 - type: cos_sim_ap value: 75.27741586677557 - type: cos_sim_f1 value: 69.18792902473774 - type: cos_sim_precision value: 67.94708725515136 - type: cos_sim_recall value: 70.47493403693932 - type: dot_accuracy value: 84.7052512368123 - type: dot_ap value: 69.36075482849378 - type: dot_f1 value: 64.44688376631296 - type: dot_precision value: 59.92288500793831 - type: dot_recall value: 69.70976253298153 - type: euclidean_accuracy value: 86.60666388508076 - type: euclidean_ap value: 75.47512772621097 - type: euclidean_f1 value: 69.413872536473 - type: euclidean_precision value: 67.39562624254472 - type: euclidean_recall value: 71.55672823218997 - type: manhattan_accuracy value: 86.52917684925792 - type: manhattan_ap value: 75.34000110496703 - type: manhattan_f1 value: 69.28489190226429 - type: manhattan_precision value: 67.24608889992551 - type: manhattan_recall value: 71.45118733509234 - type: max_accuracy value: 86.60666388508076 - type: max_ap value: 75.47512772621097 - type: max_f1 value: 69.413872536473 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.01695967710637 - type: cos_sim_ap value: 85.8298270742901 - type: cos_sim_f1 value: 78.46988128389272 - type: cos_sim_precision value: 74.86017897091722 - type: cos_sim_recall value: 82.44533415460425 - type: dot_accuracy value: 88.19420188613343 - type: dot_ap value: 83.82679165901324 - type: dot_f1 value: 76.55833777304208 - type: dot_precision value: 75.6884875846501 - type: dot_recall value: 77.44841392054204 - type: euclidean_accuracy value: 89.03054294252338 - type: euclidean_ap value: 85.89089555185325 - type: euclidean_f1 value: 78.62997658079624 - type: euclidean_precision value: 74.92329149232914 - type: euclidean_recall value: 82.72251308900523 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.86458997929147 - type: manhattan_f1 value: 78.50685331000291 - type: manhattan_precision value: 74.5499861534201 - type: manhattan_recall value: 82.90729904527257 - type: max_accuracy value: 89.03054294252338 - type: max_ap value: 85.89089555185325 - type: max_f1 value: 78.62997658079624 --- # KeyurRamoliya/multilingual-e5-large-Q8_0-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-large) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo KeyurRamoliya/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo KeyurRamoliya/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo KeyurRamoliya/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo KeyurRamoliya/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ```
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
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" ]
1,733,912,441,000
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.
[ "CHIA" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.celestial
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,704,441,954,000
2024-01-05T08:05:57
33
1
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/evaluate/celestial.../celestial_17_3.0.png widget: - text: celestial output: url: images/celestial_17_3.0.png - text: celestial output: url: images/celestial_19_3.0.png - text: celestial output: url: images/celestial_20_3.0.png - text: celestial output: url: images/celestial_21_3.0.png - text: celestial output: url: images/celestial_22_3.0.png inference: false instance_prompt: celestial --- # ntcai.xyz slider - celestial (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/celestial_17_-3.0.png" width=256 height=256 /> | <img src="images/celestial_17_0.0.png" width=256 height=256 /> | <img src="images/celestial_17_3.0.png" width=256 height=256 /> | | <img src="images/celestial_19_-3.0.png" width=256 height=256 /> | <img src="images/celestial_19_0.0.png" width=256 height=256 /> | <img src="images/celestial_19_3.0.png" width=256 height=256 /> | | <img src="images/celestial_20_-3.0.png" width=256 height=256 /> | <img src="images/celestial_20_0.0.png" width=256 height=256 /> | <img src="images/celestial_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` celestial ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.celestial', weight_name='celestial.safetensors', adapter_name="celestial") # Activate the LoRA pipe.set_adapters(["celestial"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, celestial" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 880+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.magicalenchanted
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,704,020,261,000
2023-12-31T10:57:44
7
2
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/evaluate/magical,enchanted.../magical,enchanted_17_3.0.png widget: - text: magical,enchanted output: url: images/magical,enchanted_17_3.0.png - text: magical,enchanted output: url: images/magical,enchanted_19_3.0.png - text: magical,enchanted output: url: images/magical,enchanted_20_3.0.png - text: magical,enchanted output: url: images/magical,enchanted_21_3.0.png - text: magical,enchanted output: url: images/magical,enchanted_22_3.0.png inference: false instance_prompt: magical,enchanted --- # ntcai.xyz slider - magical,enchanted (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/magical,enchanted_17_-3.0.png" width=256 height=256 /> | <img src="images/magical,enchanted_17_0.0.png" width=256 height=256 /> | <img src="images/magical,enchanted_17_3.0.png" width=256 height=256 /> | | <img src="images/magical,enchanted_19_-3.0.png" width=256 height=256 /> | <img src="images/magical,enchanted_19_0.0.png" width=256 height=256 /> | <img src="images/magical,enchanted_19_3.0.png" width=256 height=256 /> | | <img src="images/magical,enchanted_20_-3.0.png" width=256 height=256 /> | <img src="images/magical,enchanted_20_0.0.png" width=256 height=256 /> | <img src="images/magical,enchanted_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` magical,enchanted ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.magicalenchanted', weight_name='magical,enchanted.safetensors', adapter_name="magical,enchanted") # Activate the LoRA pipe.set_adapters(["magical,enchanted"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, magical,enchanted" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 760+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
helpmefindaname/flair-eml-sapbert-bc5cdr-chemical
helpmefindaname
null
[ "flair", "pytorch", "entity-mention-linker", "region:us" ]
1,703,380,990,000
2023-12-24T02:23:43
7
0
--- tags: - flair - entity-mention-linker --- ## sapbert-bc5cdr-chemical Biomedical Entity Mention Linking for chemical ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)>=0.14.0** (`pip install flair` or `pip install git+https://github.com/flairNLP/flair.git`) ```python from flair.data import Sentence from flair.models import Classifier, EntityMentionLinker sentence = Sentence("Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome") # load hunflair to detect the entity mentions we want to link. tagger = Classifier.load("hunflair") tagger.predict(sentence) # load the linker and dictionary linker = EntityMentionLinker.load("helpmefindaname/flair-eml-sapbert-bc5cdr-chemical") dictionary = linker.dictionary # find then candidates for the mentions linker.predict(sentence) # print the results for each entity mention: for span in sentence.get_spans(linker.entity_label_type): print(f"Span: {span.text}") for candidate_label in span.get_labels(linker.label_type): candidate = dictionary[candidate_label.value] print(f"Candidate: {candidate.concept_name}") ``` As an alternative to downloading the already precomputed model (much storage). You can also build the model and compute the embeddings for the dataset using: ```python linker = EntityMentionLinker.build("dmis-lab/biosyn-sapbert-bc5cdr-chemical", "chemical", dictionary_name_or_path="ctd-chemicals", hybrid_search=False, entity_type="chemical-eml") ``` This will reduce the download requirements, at the cost of computation. This EntityMentionLinker uses [https://huggingface.co/dmis-lab/biosyn-sapbert-bc5cdr-chemical](dmis-lab/biosyn-sapbert-bc5cdr-chemical) as embeddings for linking mentions to candidates.
[ "BC5CDR" ]
BioNLP
mradermacher/Einstein-v7-Qwen2-7B-GGUF
mradermacher
null
[ "transformers", "gguf", "axolotl", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "science", "physics", "chemistry", "biology", "math", "qwen", "qwen2", "en", "dataset:allenai/ai2_arc", "dataset:camel-ai/physics", "dataset:camel-ai/chemistry", "dataset:camel-ai/biology", "dataset:camel-ai/math", "dataset:metaeval/reclor", "dataset:openbookqa", "dataset:mandyyyyii/scibench", "dataset:derek-thomas/ScienceQA", "dataset:TIGER-Lab/ScienceEval", "dataset:jondurbin/airoboros-3.2", "dataset:LDJnr/Capybara", "dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5", "dataset:STEM-AI-mtl/Electrical-engineering", "dataset:knowrohit07/saraswati-stem", "dataset:sablo/oasst2_curated", "dataset:lmsys/lmsys-chat-1m", "dataset:TIGER-Lab/MathInstruct", "dataset:bigbio/med_qa", "dataset:meta-math/MetaMathQA-40K", "dataset:piqa", "dataset:scibench", "dataset:sciq", "dataset:Open-Orca/SlimOrca", "dataset:migtissera/Synthia-v1.3", "dataset:allenai/WildChat", "dataset:microsoft/orca-math-word-problems-200k", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:teknium/GPTeacher-General-Instruct", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:totally-not-an-llm/EverythingLM-data-V3", "dataset:HuggingFaceH4/no_robots", "dataset:OpenAssistant/oasst_top1_2023-08-25", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:abacusai/SystemChat-1.1", "dataset:H-D-T/Buzz-V1.2", "base_model:Weyaxi/Einstein-v7-Qwen2-7B", "base_model:quantized:Weyaxi/Einstein-v7-Qwen2-7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]
1,719,376,166,000
2024-06-26T13:43:00
552
0
--- base_model: Weyaxi/Einstein-v7-Qwen2-7B datasets: - allenai/ai2_arc - camel-ai/physics - camel-ai/chemistry - camel-ai/biology - camel-ai/math - metaeval/reclor - openbookqa - mandyyyyii/scibench - derek-thomas/ScienceQA - TIGER-Lab/ScienceEval - jondurbin/airoboros-3.2 - LDJnr/Capybara - Cot-Alpaca-GPT4-From-OpenHermes-2.5 - STEM-AI-mtl/Electrical-engineering - knowrohit07/saraswati-stem - sablo/oasst2_curated - lmsys/lmsys-chat-1m - TIGER-Lab/MathInstruct - bigbio/med_qa - meta-math/MetaMathQA-40K - openbookqa - piqa - metaeval/reclor - derek-thomas/ScienceQA - scibench - sciq - Open-Orca/SlimOrca - migtissera/Synthia-v1.3 - TIGER-Lab/ScienceEval - allenai/WildChat - microsoft/orca-math-word-problems-200k - openchat/openchat_sharegpt4_dataset - teknium/GPTeacher-General-Instruct - m-a-p/CodeFeedback-Filtered-Instruction - totally-not-an-llm/EverythingLM-data-V3 - HuggingFaceH4/no_robots - OpenAssistant/oasst_top1_2023-08-25 - WizardLM/WizardLM_evol_instruct_70k - abacusai/SystemChat-1.1 - H-D-T/Buzz-V1.2 language: - en library_name: transformers license: other tags: - axolotl - instruct - finetune - chatml - gpt4 - synthetic data - science - physics - chemistry - biology - math - qwen - qwen2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q2_K.gguf) | Q2_K | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ3_XS.gguf) | IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ3_S.gguf) | IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ3_M.gguf) | IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
[ "SCIQ" ]
Non_BioNLP
mlx-community/1.5-Pints-16K-v0.1
mlx-community
text-generation
[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:pints-ai/Expository-Prose-V1", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:meta-math/MetaMathQA", "dataset:HuggingFaceH4/deita-10k-v0-sft", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:togethercomputer/llama-instruct", "dataset:LDJnr/Capybara", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:pints-ai/1.5-Pints-16K-v0.1", "base_model:finetune:pints-ai/1.5-Pints-16K-v0.1", "license:mit", "model-index", "region:us" ]
1,730,136,199,000
2024-10-28T17:25:32
7
1
--- base_model: pints-ai/1.5-Pints-16K-v0.1 datasets: - pints-ai/Expository-Prose-V1 - HuggingFaceH4/ultrachat_200k - Open-Orca/SlimOrca-Dedup - meta-math/MetaMathQA - HuggingFaceH4/deita-10k-v0-sft - WizardLM/WizardLM_evol_instruct_V2_196k - togethercomputer/llama-instruct - LDJnr/Capybara - HuggingFaceH4/ultrafeedback_binarized language: - en license: mit pipeline_tag: text-generation tags: - mlx extra_gated_prompt: Though best efforts has been made to ensure, as much as possible, that all texts in the training corpora are royalty free, this does not constitute a legal guarantee that such is the case. **By using any of the models, corpora or part thereof, the user agrees to bear full responsibility to do the necessary due diligence to ensure that he / she is in compliance with their local copyright laws. Additionally, the user agrees to bear any damages arising as a direct cause (or otherwise) of using any artifacts released by the pints research team, as well as full responsibility for the consequences of his / her usage (or implementation) of any such released artifacts. The user also indemnifies Pints Research Team (and any of its members or agents) of any damage, related or unrelated, to the release or subsequent usage of any findings, artifacts or code by the team. For the avoidance of doubt, any artifacts released by the Pints Research team are done so in accordance with the 'fair use' clause of Copyright Law, in hopes that this will aid the research community in bringing LLMs to the next frontier. extra_gated_fields: Company: text Country: country Specific date: date_picker I want to use this model for: type: select options: - Research - Education - label: Other value: other I agree to use this model for in accordance to the afore-mentioned Terms of Use: checkbox model-index: - name: 1.5-Pints results: - task: type: text-generation dataset: name: MTBench type: ai2_arc metrics: - type: LLM-as-a-Judge value: 3.4 name: MTBench source: url: https://huggingface.co/spaces/lmsys/mt-bench name: MTBench --- # mlx-community/1.5-Pints-16K-v0.1 The Model [mlx-community/1.5-Pints-16K-v0.1](https://huggingface.co/mlx-community/1.5-Pints-16K-v0.1) was converted to MLX format from [pints-ai/1.5-Pints-16K-v0.1](https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1) using mlx-lm version **0.19.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/1.5-Pints-16K-v0.1") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
[ "BEAR" ]
Non_BioNLP
LoneStriker/SeaLLM-7B-v2-6.0bpw-h6-exl2
LoneStriker
text-generation
[ "transformers", "mistral", "text-generation", "multilingual", "sea", "conversational", "en", "zh", "vi", "id", "th", "ms", "km", "lo", "my", "tl", "arxiv:2312.00738", "arxiv:2205.11916", "arxiv:2306.05179", "arxiv:2306.05685", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,706,976,692,000
2024-02-03T16:14:09
8
0
--- language: - en - zh - vi - id - th - ms - km - lo - my - tl license: other license_name: seallms license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE tags: - multilingual - sea --- <p align="center"> <img src="seal_logo.png" width="200" /> </p> # *SeaLLM-7B-v2* - Large Language Models for Southeast Asia <p align="center"> <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2" target="_blank" rel="noopener"> 🤗 Tech Memo</a> &nbsp;&nbsp; <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B" target="_blank" rel="noopener"> 🤗 DEMO</a> &nbsp;&nbsp; <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a> &nbsp;&nbsp; <a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a> </p> We introduce [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc. ### Highlights * [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves the **7B-SOTA** on the **GSM8K** task with **78.2** score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH for Thai 🇹🇭. * It scores competitively against GPT-3.5 in many zero-shot commonsense benchmark, with **82.5, 68.3, 80.9** scores on Arc-C, Winogrande, and Hellaswag. * It achieves **7.54** score on the 🇬🇧 **MT-bench**, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model. * It scores **45.46** on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models ([Vistral-7B](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)) of similar sizes. ### Release and DEMO - DEMO: [SeaLLMs/SeaLLM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B). - Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf). - Model weights: [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2). <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. > The logo was generated by DALL-E 3. ### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1? * SeaLLM-7B-v2 is continue-pretrained from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) and underwent carefully designed tuning with focus in reasoning. ## Evaluation ### Zero-shot Multilingual Math Reasoning [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.2** score on the GSM8K, making it the **state of the art** in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with **22.4** vs 18.1 scores. ![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png) <details> <summary>See details on English and translated GSM8K and MATH</summary> <br> | Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1 | Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6 | Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | | | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4 </details> #### Zero-shot MGSM [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th. | Model | MGSM-Zh | MGSM-Th |-----| ----- | --- | ChatGPT (reported) | 61.2* | 47.2* | Qwen-14B-chat | 59.6 | 28 | SeaLLM-7B-v2 | **64.8** | **62.4** ### Zero-shot Commonsense Reasoning We compare [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in [(Kojima et al., 2023)](https://arxiv.org/pdf/2205.11916.pdf) to grab the answer. Note that we **DID NOT** use "Let's think step-by-step" to invoke explicit CoT. | Model | Arc-Challenge | Winogrande | Hellaswag |-----| ----- | --- | -- | | ChatGPT (reported) | 84.6* | 66.8* | 72.0* | ChatGPT (reproduced) | 84.1 | 63.1 | 79.5 | Mistral-7B-Instruct | 68.1 | 56.4 | 45.6 | SeaLLM-7B-v2 | 82.5 | 68.3 | 80.9 ### Multilingual World Knowledge We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi. | Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- | | ChatGPT | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41 |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- | | SeaLLM-13B | Multi | 52.78 | 62.69 | 44.50 | 46.45 | | 39.28 | 36.39 | Vistral-7B | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27 | SeaLLM-7B-v2 | Multi | 60.72 | 70.91 | 55.43 | 51.15 | 45.46 | 42.25 | 35.52 ### MT-Bench On the English [MT-bench](https://arxiv.org/abs/2306.05685) metric, SeaLLM-7B-v2 achieves **7.54** score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages. Refer to [mt_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/mt_bench/seallm_7b_v2.jsonl) for the MT-bench predictions of SeaLLM-7B-v2. | Model | Access | Langs | MT-Bench | --- | --- | --- | --- | | GPT-4-turbo | closed | multi | 9.32 | GPT-4-0613 | closed | multi | 9.18 | Mixtral-8x7b (46B) | open | multi | 8.3 | Starling-LM-7B-alpha | open | mono (en) | 8.0 | OpenChat-3.5-7B | open | mono (en) | 7.81 | **SeaLLM-7B-v2** | **open** | **multi (10+)** | **7.54** | [Qwen-14B](https://huggingface.co/Qwen/Qwen-14B-Chat) | open | multi | 6.96 | [Llama-2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | open | mono (en) | 6.86 | Mistral-7B-instuct | open | mono (en) | 6.84 ### Sea-Bench Similar to MT-Bench, [Sea-bench](https://huggingface.co/datasets/SeaLLMs/Sea-bench) is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages. As shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance. ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) Refer to [sea_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/sea_bench/seallm_7b_v2.jsonl) for the Sea-bench predictions of SeaLLM-7B-v2. ### Usage #### Instruction format ```python prompt = """<|im_start|>system You are a helpful assistant.</s> <|im_start|>user Hello world</s> <|im_start|>assistant Hi there, how can I help?</s> # ! ENSURE 1 and only 1 bos `<s>` at the beginning of sequence print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))) ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁', '<0x0A>', '<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁', '<0x0A>', '<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>', '▁', '<0x0A>'] """ ``` #### Using transformers's chat_template ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2", torch_dtype=torch.bfloat16, device_map=device) tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2") messages = [ {"role": "user", "content": "Hello world"}, {"role": "assistant", "content": "Hi there, how can I help you today?"}, {"role": "user", "content": "Explain general relativity in details."} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) print(tokenizer.convert_ids_to_tokens(encodeds[0])) # ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁', '<0x0A>', '<', '|', 'im .... model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` #### Using vLLM ```python from vllm import LLM, SamplingParams TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>" TURN_PREFIX = "<|im_start|>{role}\n" def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None): # conversations: list of dict with key `role` and `content` (openai format) if conversations[0]['role'] != 'system' and system_prompt is not None: conversations = [{"role": "system", "content": system_prompt}] + conversations text = '' for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) text += prompt if add_assistant_prefix: prompt = TURN_PREFIX.format(role='assistant') text += prompt return text sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['</s>', '<|im_start|>']) llm = LLM("SeaLLMs/SeaLLM-7B-v2", dtype="bfloat16") message = "Explain general relativity in details." prompt = seallm_chat_convo_format(message, True) gen = llm.generate(prompt, sampling_params) print(gen[0].outputs[0].text) ``` ## 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: Corresponding Author: [[email protected]](mailto:[email protected]) **Author list and order will change!** * `*` and `^` are equal contributions. ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```
[ "CHIA" ]
Non_BioNLP
medicalai/ClinicalGPT-base-zh
medicalai
text-generation
[ "transformers", "pytorch", "bloom", "text-generation", "medical", "arxiv:2306.09968", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,687,350,710,000
2025-01-07T16:06:21
3,473
44
--- license: afl-3.0 tags: - medical --- # ClinicalGPT This model card introduces ClinicalGPT model, a large language model designed and optimized for clinical scenarios. ClinicalGPT is fine-tuned on extensive and diverse medical datasets, including medical records, domain-specific knowledge, and multi-round dialogue consultations. The model is undergoing ongoing and continuous updates. ## Model Fine-tuning We set the learning rate to 5e-5, with a batch size of 128 and a maximum length of 1,024, training across 3 epochs. ## How to use the model Load the model via the transformers library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("medicalai/ClinicalGPT-base-zh") model = AutoModelForCausalLM.from_pretrained("medicalai/ClinicalGPT-base-zh") ``` ## Limitations The project is intended for research purposes only and restricted from commercial or clinical use. The generated content by the model is subject to factors such as model computations, randomness, misinterpretation, and biases, and this project cannot guarantee its accuracy. This project assumes no legal liability for any content produced by the model. Users are advised to exercise caution and independently verify the generated results. ## Citation Please cite these articles: 1.Wang, G., Liu, X., Liu, H., Yang, G. et al. A Generalist Medical Language Model for Disease Diagnosis Assistance. Nat Med (2025). https://doi.org/10.1038/s41591-024-03416-6 2.Wang, G., Yang, G., Du, Z., Fan, L., & Li, X. (2023). ClinicalGPT: large language models finetuned with diverse medical data and comprehensive evaluation. arXiv preprint arXiv:2306.09968.
[ "MEDICAL DATA" ]
BioNLP
pritamdeka/PubMedBERT-MNLI-MedNLI
pritamdeka
text-classification
[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,685,399,049,000
2024-03-01T02:58:46
270
3
--- base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext metrics: - accuracy tags: - generated_from_trainer model-index: - name: PubMedBERT-MNLI-MedNLI results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PubMedBERT-MNLI-MedNLI This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [MNLI](https://huggingface.co/datasets/multi_nli) dataset first and then on the [MedNLI](https://physionet.org/content/mednli/1.0.0/) dataset. It achieves the following results on the evaluation set: - Loss: 0.9501 - Accuracy: 0.8667 ## Model description More information needed ## Intended uses & limitations The model can be used for NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows: ```python from transformers import TextClassificationPipeline, AutoModel, AutoTokenizer, AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI", num_labels=3, id2label = {1: 'entailment', 0: 'contradiction',2:'neutral'}) tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI") pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device=0, batch_size=128) pipe(['ALDH1 expression is associated with better breast cancer outcomes', 'In a series of 577 breast carcinomas, expression of ALDH1 detected by immunostaining correlated with poor prognosis.']) ``` The output for the above will be: ```python [[{'label': 'contradiction', 'score': 0.10193759202957153}, {'label': 'entailment', 'score': 0.2933262586593628}, {'label': 'neutral', 'score': 0.6047361493110657}], [{'label': 'contradiction', 'score': 0.21726925671100616}, {'label': 'entailment', 'score': 0.24485822021961212}, {'label': 'neutral', 'score': 0.5378724932670593}]] ``` ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5673 | 1.42 | 500 | 0.4358 | 0.8437 | | 0.2898 | 2.85 | 1000 | 0.4845 | 0.8523 | | 0.1669 | 4.27 | 1500 | 0.6233 | 0.8573 | | 0.1087 | 5.7 | 2000 | 0.7263 | 0.8573 | | 0.0728 | 7.12 | 2500 | 0.8841 | 0.8638 | | 0.0512 | 8.55 | 3000 | 0.9501 | 0.8667 | | 0.0372 | 9.97 | 3500 | 1.0440 | 0.8566 | | 0.0262 | 11.4 | 4000 | 1.0770 | 0.8609 | | 0.0243 | 12.82 | 4500 | 1.0931 | 0.8616 | | 0.023 | 14.25 | 5000 | 1.1088 | 0.8631 | | 0.0163 | 15.67 | 5500 | 1.1264 | 0.8581 | | 0.0111 | 17.09 | 6000 | 1.1541 | 0.8616 | | 0.0098 | 18.52 | 6500 | 1.1542 | 0.8631 | | 0.0074 | 19.94 | 7000 | 1.1653 | 0.8638 | ### Framework versions - Transformers 4.22.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1 ## Citing & Authors <!--- Describe where people can find more information --> If you use the model kindly cite the following work ``` @inproceedings{deka-etal-2023-multiple, title = "Multiple Evidence Combination for Fact-Checking of Health-Related Information", author = "Deka, Pritam and Jurek-Loughrey, Anna and P, Deepak", booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.bionlp-1.20", pages = "237--247", abstract = "Fact-checking of health-related claims has become necessary in this digital age, where any information posted online is easily available to everyone. The most effective way to verify such claims is by using evidences obtained from reliable sources of medical knowledge, such as PubMed. Recent advances in the field of NLP have helped automate such fact-checking tasks. In this work, we propose a domain-specific BERT-based model using a transfer learning approach for the task of predicting the veracity of claim-evidence pairs for the verification of health-related facts. We also improvise on a method to combine multiple evidences retrieved for a single claim, taking into consideration conflicting evidences as well. We also show how our model can be exploited when labelled data is available and how back-translation can be used to augment data when there is data scarcity.", } ```
[ "MEDNLI" ]
BioNLP
Locutusque/TinyMistral-248M-Instruct
Locutusque
text-generation
[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "en", "dataset:Locutusque/InstructMixCleaned", "dataset:berkeley-nest/Nectar", "base_model:Locutusque/TinyMistral-248M", "base_model:finetune:Locutusque/TinyMistral-248M", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,700,844,286,000
2023-12-17T21:02:42
156
11
--- base_model: Locutusque/TinyMistral-248M datasets: - Locutusque/InstructMixCleaned - berkeley-nest/Nectar language: - en license: apache-2.0 pipeline_tag: text-generation widget: - text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else or switch/case statements to handle different conditions related to the Consent. Provide detailed comments explaining your control flow and the reasoning behind each decision. <|ASSISTANT|> ' - text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> ' - text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> ' - text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> ' - text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> ' - text: '<|USER|> How to manage a lazy employee: Address the employee verbally. Don''t allow an employee''s laziness or lack of enthusiasm to become a recurring issue. Tell the employee you''re hoping to speak with them about workplace expectations and performance, and schedule a time to sit down together. Question: To manage a lazy employee, it is suggested to talk to the employee. True, False, or Neither? <|ASSISTANT|> ' inference: parameters: temperature: 0.5 do_sample: true top_p: 0.5 top_k: 30 max_new_tokens: 250 repetition_penalty: 1.15 --- Base model Locutusque/TinyMistral-248M fully fine-tuned on Locutusque/InstructMix. During validation, this model achieved an average perplexity of 3.23 on Locutusque/InstructMix dataset. It has so far been trained on approximately 608,000 examples. More epochs are planned for this model.
[ "CRAFT" ]
Non_BioNLP
IIC/xlm-roberta-large-cantemist
IIC
text-classification
[ "transformers", "pytorch", "xlm-roberta", "text-classification", "biomedical", "clinical", "eHR", "spanish", "xlm-roberta-large", "es", "dataset:PlanTL-GOB-ES/cantemist-ner", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,687,188,421,000
2024-11-25T10:40:59
61
0
--- datasets: - PlanTL-GOB-ES/cantemist-ner language: es license: mit metrics: - f1 tags: - biomedical - clinical - eHR - spanish - xlm-roberta-large widget: - text: El diagnóstico definitivo de nuestro paciente fue de un Adenocarcinoma de pulmón cT2a cN3 cM1a Estadio IV (por una única lesión pulmonar contralateral) PD-L1 90%, EGFR negativo, ALK negativo y ROS-1 negativo. - text: Durante el ingreso se realiza una TC, observándose un nódulo pulmonar en el LII y una masa renal derecha indeterminada. Se realiza punción biopsia del nódulo pulmonar, con hallazgos altamente sospechosos de carcinoma. - text: Trombosis paraneoplásica con sospecha de hepatocarcinoma por imagen, sobre hígado cirrótico, en paciente con índice Child-Pugh B. model-index: - name: IIC/xlm-roberta-large-cantemist results: - task: type: token-classification dataset: name: cantemist-ner type: PlanTL-GOB-ES/cantemist-ner metrics: - type: f1 value: 0.904 name: f1 --- # xlm-roberta-large-cantemist This model is a finetuned version of xlm-roberta-large for the cantemist dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.904 Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information. ## Parameters used | parameter | Value | |-------------------------|:-----:| | batch size | 16 | | learning rate | 2e05 | | classifier dropout | 0.1 | | warmup ratio | 0 | | warmup steps | 0 | | weight decay | 0 | | optimizer | AdamW | | epochs | 10 | | early stopping patience | 3 | ## BibTeX entry and citation info ```bibtext @article{10.1093/jamia/ocae054, author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma}, title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks}, journal = {Journal of the American Medical Informatics Association}, volume = {31}, number = {9}, pages = {2137-2146}, year = {2024}, month = {03}, issn = {1527-974X}, doi = {10.1093/jamia/ocae054}, url = {https://doi.org/10.1093/jamia/ocae054}, } ```
[ "CANTEMIST" ]
BioNLP
espnet/slurp_slu_2pass_gt
espnet
automatic-speech-recognition
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:slurp", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,663,098,551,000
2022-09-13T19:49:59
1
0
--- datasets: - slurp language: en license: cc-by-4.0 tags: - espnet - audio - automatic-speech-recognition --- ## ESPnet2 ASR model ### `espnet/slurp_slu_2pass_gt` This model was trained by Siddhant using slurp recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 3b54bfe52a294cdfce668c20d777bfa65f413745 pip install -e . cd egs2/slurp/slu1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/slurp_slu_2pass_gt ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sat Aug 20 15:34:30 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `45e2b13071f3cc4abbc3a7b2484bd6cffedd4d1c` - Commit date: `Mon Aug 15 09:13:31 2022 -0400` ## slu_train_asr_bert_conformer_deliberation_raw_en_word ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_slu_model_valid.acc.ave_10best/devel|8690|108484|90.9|6.2|2.9|2.7|11.8|39.9| |inference_slu_model_valid.acc.ave_10best/test|13078|159666|90.7|6.2|3.1|2.6|11.9|38.7| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_slu_model_valid.acc.ave_10best/devel|8690|512732|95.5|2.3|2.2|2.5|7.0|39.9| |inference_slu_model_valid.acc.ave_10best/test|13078|757056|95.3|2.3|2.3|2.5|7.1|38.7| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_bert_conformer_deliberation.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/slu_train_asr_bert_conformer_deliberation_raw_en_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - encoder - postdecoder.model num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/slu_stats_raw_en_word/train/speech_shape - exp/slu_stats_raw_en_word/train/text_shape.word - exp/slu_stats_raw_en_word/train/transcript_shape.word valid_shape_file: - exp/slu_stats_raw_en_word/valid/speech_shape - exp/slu_stats_raw_en_word/valid/text_shape.word - exp/slu_stats_raw_en_word/valid/transcript_shape.word batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - sound - - dump/raw/train/text - text - text - - dump/raw/train/transcript - transcript - text valid_data_path_and_name_and_type: - - dump/raw/devel/wav.scp - speech - sound - - dump/raw/devel/text - text - text - - dump/raw/devel/transcript - transcript - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0002 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁the - s - ▁to - ▁i - ▁me - ▁you - ▁what - ▁a - ▁is - ▁my - ▁please - a - '''' - y - ▁in - ing - ▁s - e - ▁for - i - ▁on - d - t - o - u - er - p - ▁of - es - re - l - ▁it - ▁p - le - ▁f - ▁m - ▁email - ▁d - m - ▁c - st - r - n - ar - ▁h - b - ▁that - c - ▁this - h - an - email_query - ▁play - ▁re - ▁b - ▁do - ▁can - at - ▁have - g - ▁from - ▁and - en - email_sendemail - ▁olly - 'on' - ▁new - it - qa_factoid - calendar_set - ▁any - or - ▁g - ▁how - ▁t - ▁tell - ch - ▁not - ▁about - ▁at - ate - general_negate - f - ▁today - ▁e - ed - ▁list - ▁r - in - k - ic - social_post - ▁are - play_music - general_quirky - ▁l - al - v - ent - ▁n - ▁be - ▁an - ▁st - et - ▁am - general_praise - ▁time - weather_query - ▁up - ▁check - calendar_query - ▁w - om - ur - ▁send - ▁with - ly - w - general_explain - ad - ▁th - news_query - ▁one - ▁emails - day - ▁sh - ce - ▁last - ve - ▁he - z - ▁ch - ▁will - ▁set - ▁would - ▁was - x - general_repeat - ▁add - ou - ▁again - ▁ex - is - ct - general_affirm - general_confirm - ▁song - ▁next - ▁j - ▁meeting - um - ation - ▁turn - ▁did - if - ▁alarm - am - ▁like - datetime_query - ter - ▁remind - ▁o - qa_definition - ▁said - ▁calendar - ll - se - ers - th - ▁get - our - ▁need - ▁all - ot - ▁want - ▁off - and - ▁right - ▁de - ▁tr - ut - general_dontcare - ▁ - ▁week - as - ▁tweet - ight - ir - ▁your - ▁event - ▁news - ▁se - ay - ion - ▁com - ▁there - ▁ye - ▁weather - un - ▁confirm - ld - calendar_remove - ▁y - ▁lights - ▁more - ▁v - play_radio - ▁does - ▁po - ▁now - id - email_querycontact - ▁show - ▁could - ery - op - ▁day - ▁pm - ▁music - ▁tomorrow - ▁train - ▁u - ine - ▁or - ange - qa_currency - ice - ▁contact - ▁just - ▁jo - ▁think - qa_stock - end - ss - ber - ▁tw - ▁command - ▁make - ▁no - ▁mo - pe - ▁find - general_commandstop - ▁when - social_query - ▁so - ong - ▁co - ant - ow - ▁much - ▁where - ul - ue - ri - ap - ▁start - ▁mar - ▁by - one - ▁know - ▁wor - oo - ▁give - ▁let - ▁events - der - ▁ro - ▁pr - ▁pl - play_podcasts - art - us - ▁work - ▁current - ol - cooking_recipe - nt - ▁correct - transport_query - ia - ▁stock - ▁br - ive - ▁app - ▁two - ▁latest - lists_query - ▁some - recommendation_events - ab - ▁go - ▁but - ook - ke - alarm_set - play_audiobook - ▁k - ▁response - ▁wr - cast - ▁open - ▁cle - ▁done - ▁got - ▁ca - ite - ase - ▁thank - iv - ah - ag - ▁answer - ie - ▁five - ▁book - ist - ▁rec - ore - ▁john - ment - ▁appreci - ▁fri - ack - ▁remove - ated - ock - ree - j - ▁good - ▁many - orn - fe - ▁radio - ▁we - int - ▁facebook - ▁cl - ▁sev - ▁schedule - ard - ▁per - ▁li - ▁going - nd - ain - recommendation_locations - ▁post - lists_createoradd - ff - ▁su - red - iot_hue_lightoff - lists_remove - ▁ar - een - ▁say - ro - ▁volume - ▁le - ▁reply - ▁complaint - ▁out - ▁delete - ▁ne - ame - ▁detail - ▁if - im - ▁happ - orr - ich - em - ▁ev - ction - ▁dollar - ▁as - alarm_query - audio_volume_mute - ac - music_query - ▁mon - ther - ▁thanks - cel - ▁who - ave - ▁service - ▁mail - ty - ▁hear - de - ▁si - ▁wh - ood - ell - ▁con - ▁once - ound - ▁don - ▁loc - ▁light - ▁birthday - ▁inf - ort - ffe - ▁playlist - el - ening - ▁us - ▁un - ▁has - own - ▁inc - ai - ▁speak - age - ▁mess - ast - ci - ver - ▁ten - ▁underst - ▁pro - ▁q - enty - ▁ticket - gh - audio_volume_up - ▁take - ▁bo - ally - ome - transport_ticket - ind - iot_hue_lightchange - pp - iot_coffee - ▁res - plain - io - lar - takeaway_query - ge - takeaway_order - email_addcontact - play_game - ak - ▁fa - transport_traffic - music_likeness - ▁rep - act - ust - transport_taxi - iot_hue_lightdim - ▁mu - ▁ti - ick - ▁ha - ould - general_joke - '1' - qa_maths - ▁lo - iot_cleaning - q - ake - ill - her - iot_hue_lightup - pl - '2' - alarm_remove - orrect - ▁cont - mail - out - audio_volume_down - book - ail - recommendation_movies - ck - ▁man - ▁mus - ▁che - me - ume - ▁answ - datetime_convert - ▁late - iot_wemo_on - ▁twe - music_settings - iot_wemo_off - orre - ith - ▁tom - ▁fr - ere - ▁ad - xt - ▁ab - ank - general_greet - now - ▁meet - ▁curre - ▁respon - ▁ag - ght - audio_volume_other - ink - ▁spe - iot_hue_lighton - ▁rem - lly - '?' - urn - ▁op - ▁complain - ▁comm - let - music_dislikeness - ove - ▁sch - ather - ▁rad - edule - ▁under - icket - lease - ▁bir - erv - ▁birth - ▁face - ▁cur - sw - ▁serv - ek - aid - '9' - ▁vol - edu - '5' - cooking_query - lete - ▁joh - ▁det - firm - nder - '0' - irm - '8' - '&' - _ - list - pon - qa_query - '7' - '3' - '-' - reci - ▁doll - <sos/eos> transcript_token_list: - <blank> - <unk> - the - to - i - me - you - is - what - please - my - a - for - 'on' - in - of - email - this - it - have - from - and - play - olly - that - new - can - do - how - tell - about - at - any - today - not - time - are - check - list - send - with - an - one - emails - last - will - am - again - set - next - would - was - up - like - turn - said - calendar - meeting - get - what's - right - all - did - be - need - want - song - tweet - add - event - your - news - 'off' - weather - there - lights - more - now - alarm - pm - music - show - confirm - train - could - think - does - make - command - just - find - when - tomorrow - much - where - week - by - give - events - know - day - start - two - latest - response - that's - remind - done - but - thank - stock - some - you've - answer - five - open - current - many - remove - radio - good - book - 'no' - facebook - going - it's - volume - reply - work - delete - go - complaint - contact - if - service - let - thanks - so - hear - once - correct - john - playlist - birthday - got - post - ten - order - sorry - has - date - hey - coffee - who - rate - three - exchange - further - light - twenty - price - mail - reminder - explain - podcast - ticket - down - really - clear - seven - schedule - alarms - say - morning - change - twitter - cancel - number - dollar - stop - out - appreciated - hundred - wrong - don't - information - address - contacts - read - york - us - which - should - 'yes' - details - songs - between - nine - anything - s1 - received - playing - shut - dot - mind - com - google - most - put - job - traffic - four - best - six - create - recent - yeah - happening - friday - name - very - area - mom - or - take - appointment - yeap - room - world - home - hour - message - eight - clarify - s2 - party - episode - here - elaborate - alexa - appreciate - customer - i'd - sent - thing - march - look - tonight - place - try - after - definition - call - well - times - rock - phone - speak - today's - whats - food - thirty - see - joke - every - pizza - write - lists - game - shopping - weekend - rephrase - month - matter - s - update - station - vacuum - great - detail - long - gmail - old - repeat - city - audiobook - perfectly - status - inbox - mute - local - near - restaurant - thousand - tuesday - year - we - media - before - around - resume - musch - her - house - taxi - hours - didn't - describe - answers - understand - incorrect - word - listen - first - item - d - trump - save - days - socket - recipe - nice - u - reminders - social - search - as - monday - subject - location - movie - saturday - euro - dinner - them - ask - let's - scheduled - plug - i'm - gotten - question - minutes - friend - favorite - meetings - define - instructions - exactly - cook - understood - sentence - thursday - grocery - correcly - their - words - temperature - person - amazon - catch - company - mean - something - correctly - living - fantastic - help - following - dollars - rain - speakers - instruction - helpful - increase - consumer - evening - family - upcoming - jazz - saying - way - switch - forecast - task - cleaner - love - late - boss - wednesday - yesterday - updates - lower - people - cool - wonderful - twelve - afternoon - color - wake - oh - lunch - perfect - back - understanding - useful - amazing - his - dim - movies - chicago - things - takeaway - fifty - unread - happy - available - noon - wouldn't - night - had - appointments - idea - michael - doing - over - doesn't - select - hi - shit - may - they - delivery - nearest - buy - apple - car - left - confirmed - report - worth - robot - uber - wemo - sunday - excellent - outside - blue - looking - messages - top - wear - point - too - i've - country - prices - bring - store - awesome - unclear - ok - mark - speaker - app - sound - hot - live - jackson - bad - recently - currently - smith - pull - whatever - india - messed - kitchen - ninety - percent - him - use - office - brightness - care - gave - description - tom - regarding - meaning - meet - siri - bob - joe - hmm - leave - sarah - smart - come - chicken - seventeen - walmart - bill - enough - choose - louder - our - trending - born - london - zone - account - cnn - audio - president - isn't - compose - coming - second - manner - pick - album - uhh - plus - provide - erase - notification - played - channel - donald - pound - instagram - made - bbc - recommend - happened - united - replay - shop - free - dammit - nope - b - nearby - pop - shops - california - highest - notifications - shuffle - fm - chinese - currency - uh - restaurants - jack - april - robert - only - been - why - states - friends - skip - important - he - samsung - later - notify - bedroom - john's - mails - eleven - red - exact - cold - cup - rates - incorrectly - fifth - money - boston - spoke - tomorrow's - forward - respond - funny - wait - business - market - star - headlines - third - favorites - bother - retry - stocks - high - g - favourite - george - umbrella - directions - wedding - content - m - close - spoken - concert - run - alert - searching - mary - into - artist - located - mike - anyone - snow - tickets - then - reset - garden - route - hello - tall - likes - talk - forty - share - feed - were - indian - washington - difference - remember - convert - receive - tune - level - asking - capital - life - dad - yen - street - raining - mistake - correctly? - quite - pandora - jane - town - yet - player - park - san - american - far - sports - raise - popular - display - these - couldn't - mountain - dentist - importance - unimportant - complain - clean - continue - euros - los - ready - yahoo - can't - classical - politics - newest - lighting - miami - trip - horrible - info - added - prepare - iphone - machine - mother - miles - via - chris - tv - since - bathroom - state - cheese - request - items - oops - ah - closest - warm - microsoft - settings - value - keep - brighter - note - everything - wife - decrease - okay - using - rap - election - sunny - eat - usa - eighty - fifteen - until - wanted - wrongly - dog - obama - years - coat - week's - japan - quiet - paris - angeles - comcast - target - emailed - airport - interesting - mcdonalds - mr - married - green - product - past - little - other - t - listening - cooking - activate - earth - dance - title - florida - rupee - travel - kids - takeout - pending - america - making - its - than - doctor - population - bar - plans - power - fourth - silent - ride - milk - how's - seventy - sure - fine - jennifer - july - sister - brighten - picture - deliver - singer - clock - inform - brad - burger - never - pesos - object - hero - arrive - classic - olive - games - group - watch - line - justin - cost - project - called - lets - track - still - starbucks - form - repeating - christmas - breaking - due - cheapest - forget - posted - james - posts - central - lot - stories - whole - small - ever - steak - review - requested - wish - david - workout - alex - seems - given - gym - largest - la - average - compare - china - fifteenth - having - rupees - band - background - meal - online - reserve - file - lamp - laugh - sun - anniversary - eastern - busy - mobile - bit - jokes - places - geographic - else - chess - meant - working - p - planned - program - seconds - rated - large - issues - road - pay - big - holiday - daily - 'true' - celebrity - better - hut - being - sixty - away - helped - peter - god - cab - someone - internet - page - anna - feel - video - steve - opening - lately - sandy - bank - weeks - id - sam - pitt - river - february - i'll - saved - soup - phrase - distance - economy - hits - sony - eggs - low - water - text - topic - co - begin - attend - groceries - adele - reach - within - pause - half - yourself - kind - dark - replied - enter - must - asked - beatles - fun - ingredients - against - invite - soon - colour - different - jacket - updated - seattle - denver - canada - vegas - mode - pasta - january - doe - listed - refresh - listened - team - longest - spotify - remainder - telling - mumbai - you're - orlando - card - rice - during - reduce - locate - future - starting - boil - genre - class - slow - famous - named - allen - youtube - works - olly's - dc - brew - through - pounds - football - pacific - white - sings - egg - oil - festival - clothes - moment - die - orange - school - kim - las - divided - whether - photo - everyday - ryan - bills - headline - fix - square - npr - jake - brother - todays - terrible - weekly - type - topics - months - chat - yoga - reading - products - extra - cut - adjust - king - personal - client - jan - data - doctor's - computer - rohit - johns - o'clock - canadian - mistakes - rid - names - control - sunscreen - per - lady - head - taylor - always - budget - pink - bought - x - side - ahead - articles - english - ny - able - reschedule - fast - hashtag - tweets - countries - numbers - running - alabama - blank - madonna - bright - yellow - west - went - options - story - october - russia - together - n - basketball - joe's - dominos - tomorrows - less - situation - colors - mom's - end - payment - drop - downtown - provider - joes - means - helping - mexican - friday's - cricket - return - needed - death - tech - charlotte - heavy - draft - sea - paul - r - condition - seventh - dallas - hip - related - article - heard - war - elvis - everest - problem - stating - bieber - system - sales - shoes - hard - become - based - kevin - age - she - quality - mile - hair - gas - biggest - inr - climate - hate - twentieth - sucks - dean - angelina - turkey - harry - cake - national - record - longer - dave - subjects - brown - supposed - ocean - church - drive - gandhi - needs - above - theatre - cookies - abraham - gone - map - television - such - face - sale - jim - francisco - sean - june - romantic - compared - curry - ball - jeff - subway - lincoln - bed - lagos - turned - south - won - trains - girlfriend - mahatma - nsa - hop - amy - commute - solve - came - created - dont - history - math - telephone - says - laptop - pawel - offer - fox - single - sixth - midnight - missed - potter - loud - richard - chuck - looks - practice - body - dan - husband - waiting - birth - stuff - adam - sender - gaga - truck - france - texas - restart - intel - colours - statue - liberty - intensity - previous - problems - outlook - visit - wine - peso - continent - utterance - helps - asssistance - each - north - grand - patrick - match - opinion - plan - trump's - papa - instead - martin - root - purchase - perry - richards - closing - cloudy - eddie - senders - move - susan - tesco - size - shows - folder - spaghetti - doctors - stores - presidential - dates - theater - menu - agenda - ann - code - animal - frequency - kansas - roomba - technology - tasks - without - flight - who's - beach - empty - tired - driving - entire - carry - british - dr - asia - rccg - uncle - vacation - pepperoni - programme - standard - reminding - maximum - starts - tallest - gonna - fourteenth - playback - medium - nike - cruise - changed - diego - arrange - bowie - learn - mount - particular - costumer - sundays - fire - calls - silence - podcasts - spain - dominoes - website - italy - strongly - agree - agreed - suggest - mood - fourteen - result - metallica - thinking - session - profile - england - active - ohio - grid - fall - pot - marriage - queue - told - narendra - jerry - mt - frank - tenth - wishes - recording - finished - international - calculate - hit - towers - ninth - site - feeling - macy's - tag - actually - black - birthdays - hottest - mary's - expect - snapchat - jay - smith's - mountains - building - setting - cleaning - height - initiate - hall - breakfast - martha - conference - aol - win - steps - fancy - smartphone - led - zeppelin - houses - holy - currencies - club - children - atlanta - einstein - happen - cell - landline - coworker - objects - negative - modi - soft - haven't - mention - radius - books - daughter - results - earlier - bruce - butter - stars - remaining - delivers - device - domino's - unmute - joy - twelfth - voice - taking - snowing - sick - boots - cleveland - journey - destination - worker - poker - lee - katy - australia - incoming - least - lisa - experience - million - recurring - scenario - sacramento - geography - library - brief - jolie - monthly - elton - sirius - alaska - lyrics - oven - log - random - moscow - barack - disney - alive - measurements - maker - poor - error - stone - versus - hotmail - interpret - sarah's - memorial - goes - stay - delhi - health - special - speed - thirteen - test - edinburgh - credit - facts - cat - neighborhood - sometime - empire - entry - financial - comment - link - hockey - circuit - holidays - singh - jodhpur - rockville - ones - features - bread - eye - mall - directv - contain - seacrest - chance - under - table - few - hotel - rude - services - yesterday's - certain - fb - abc - netflix - linda - notes - length - reminded - shoe - wild - employees - beef - sushi - fastest - thirteenth - recommendations - fish - tennis - main - jersey - jones - break - concerts - gomez - angry - uk - replies - emily - kickball - released - upload - effects - quickest - italian - caroline - emma - real - human - minute - took - activity - jeff's - staff - handler - touch - hold - joanne - range - moon - submit - ends - tomato - lost - prime - twelveth - phones - amd - hectic - bobburgers - screwed - porch - reviews - vegan - rihanna - houston - ham - mondays - general - engaged - walk - melody - electronic - held - selected - equal - getting - tata - wall - clothing - round - leaving - nasdaq - total - pressure - expensive - border - exhibition - trash - november - handle - halloween - attachment - kardashian - shoot - rewind - rating - toronto - department - procedure - member - ray - chelsea - rohan - arrow - checked - modify - wasn't - chances - protest - lottery - prince - include - jo - net - pie - sleep - enjoy - nineties - taco - banana - source - quieter - bored - desert - guys - gary - activities - already - contract - st - minister - disable - woman - europe - arijit - audible - presentation - cad - records - trips - booking - tacos - sally - non - centre - direct - advance - selena - policy - orders - stefan - arrival - divide - chocolate - dish - teeth - hdfc - silvia - stove - coast - defined - digest - snafu - manager - pinterest - tim - conversation - bulldog - titanic - brunch - heat - canyon - dial - earliest - region - stopped - foreign - folk - watching - brexit - albert - joejoe - early - cities - manchester - december - biloxi - often - questions - garage - tunes - possible - ms - ar - kiss - shares - bangalore - heading - derek's - desk - cheers - tomasz - terms - companyname - sara - asap - super - meryl - streep - rent - dress - cinema - usually - trend - conversion - friendly - ties - ordered - electricity - marked - migration - choice - journal - norris - aniston - mailbox - minus - fried - miley - cyrus - newly - theory - rest - swift - windy - dan's - mass - comes - selfie - wings - julie - masti - celine - plays - pack - including - responded - jason's - ale - apples - dolly - oranges - lg - washer - substitute - global - feedback - grandma - ben - drainage - invoice - sunset - takeaways - man - art - universe - suitable - antonio - full - delivered - laundry - wrote - min - register - snap - nixon - bird - spend - rome - jesse - calories - cappuccino - quickly - buying - britney - spears - spacey - jobs - arriving - jean - potholes - janet - pictures - ashwin - morgan - freeman - baby - microwave - yellowstone - francis - dubai - invitation - hope - melbourne - rocky - kroger - rivers - charles - jim's - rectify - statement - carpet - baked - jessica - meatballs - mushrooms - amount - switzerland - relating - zero - front - phonebook - hows - cheesecake - carryout - magic - ola - replace - recorded - access - land - where's - elephant - removed - liz - load - metal - package - diner - goog - bob's - k - year's - mars - guy - assistant - rahman - eagle - part - burn - aran - stevens - daughter's - eighteen - chemistry - action - selling - thats - koc - lines - sugar - major - chair - easter - departing - africa - nigeria - requests - conditions - you'll - manhattan - roll - cracow - candy - crush - bell - massive - gold - happens - usual - andrew - equals - dead - plane - graduation - warned - shaun - triangle - wyatt's - pass - function - max - space - programmes - awful - parton - exciting - battery - hwu - recipes - dirham - rushmore - johndoe - button - express - pontificate - easiest - magda - selection - reservations - guess - copy - classes - supplies - schedules - winning - berkeley - notice - headed - outgoing - mi - rainy - wikipedia - entertainment - dow - everyone - aunt - furniture - oceans - softer - heart - newmail - while - baseball - easy - stations - philadelphia - alice - swat - yearly - poem - soccer - president's - milan - paper - kardashian's - loop - shown - sandals - yo - scan - nevada - apahelp - coldplay - french - bay - higher - rumplestiltskin - airlines - fresh - standing - cream - hamburger - broadway - oscars - tokyo - cable - shipment - formula - teacher - sweet - golden - newsfeed - confirmation - shirt - austin - own - canon - wanna - gods - spanish - count - seat - ideas - study - tara - mutual - jennifer's - because - edit - denmark - direction - timer - growth - luther - marketing - cd - mine - public - peter's - bolshoi - flat - crazy - others - dry - pub - theatres - bro - fashion - teams - cycle - pickup - dion - teach - series - checkout - male - noise - solitaire - pf - cassie - travelling - davis - naty - income - disco - dropping - donna - follow - shelly - accidents - plot - irene - download - circle - law - tea - organize - principal - weekends - camera - solution - bombay - wuthering - heights - charged - colorado - kong - keys - race - mona - entries - j - nyc - potatoes - gospel - raju - trivia - bike - dating - oregon - event's - prefers - rush - percentages - peking - cooker - husbands - won't - tower - heaven - hugh - june's - fake - figure - purple - takes - l - howard - stern - nineteen - percentage - motorola - doe's - outstanding - tesla - laura - dale - warning - eighteenth - golf - island - career - bieber's - vacuuming - pizzas - refund - weekday - s's - derek - thanksgiving - delayed - query - buffet - rachel - pants - wash - survey - photos - except - topography - door - jen - queen - depart - cheap - theaters - web - jesse's - multiply - workhouse - press - click - loss - recipient - verizon - volcano - rolls - royce - pixel - affirmative - completing - thai - walking - bananas - hollywood - equation - dirty - scores - katrina - exam - creating - letter - sing - construction - broadcast - tom's - rupies - management - permanently - converting - ist - iron - religion - kings - tucson - standup - tic - tac - toe - headset - sex - diapers - purpose - seventeenth - eighth - dylan - temple - refer - gift - fact - drink - inches - air - carpets - newcastle - clients - private - tasting - sams - nj - chili - cultural - swimming - they're - iowa - jordan - period - accept - cincinnati - college - rainbow - myself - deep - deepest - warming - sky - vp - seeing - indianapolis - kmart - nikesupport - image - suck - broiler - timeline - dell - parisa - brandon - example - y - filter - sad - shine - sixteen - christian - pic - pdr - fry - another - network - omelette - kilometers - municipality - giving - leo - cups - earthquake - susan's - application - cross - across - carl - pawel's - sauce - relativity - rail - sisters - letting - shorts - vs - rajesh - swift's - starving - discussing - block - written - n9ne - women - celebrities - bake - cookie - continents - workers - leonardo - mel - gibson - shall - beauty - sum - fair - deli - middle - same - nile - sell - role - boat - sandwich - parts - hearing - knows - sand - manoj - delivering - rahul - neil - australian - kindly - properly - assist - esurance - emilia - breach - loudly - harvard - marc - nintendo - scrabble - farm - lie - patio - greg - screen - degrees - yesterdays - carrots - receipt - lasagna - clooney - there's - degree - preferences - hallway - latin - nicest - lauren - worst - also - checkers - input - boyfriend - masala - tournament - monet's - burmuda - section - eric - japanese - supervisor - junk - performance - effective - urgent - oldest - tone - sweater - goa - bag - lowest - aus - peace - julia - summer - fan - hurricane - colder - steven - sachin - tendulkar - watson - exorbitant - bags - macs - yulia - matthew - pole - toby - pennsylvania - carmen - tiffany - complete - electric - wallet - albums - maths - distribution - eminem - familiar - regard - upwards - ron - couple - acme - angel - zoo - nineteenth - shazam - inflation - offers - devotional - jackie - tony - artificial - intelligence - grill - father - predictions - repeats - manila - cooked - reason - learning - nowadays - cheer - jingle - bells - anxiety - hoizer - girl - pondichery - position - teachers - dictionary - nap - cafe - m's - meting - crime - eve - horn - bristol - pubs - companies - johnson - resolve - waterfall - female - biriyani - drama - nothappy - haircut - remote - colleagues - bones - saturdays - cambridge - jam - maine - category - invented - chang's - boy - planning - chen - assignment - publish - hunt - alerts - dad's - deal - leading - trail - follows - young - jay's - summary - ko - beyonce - vergara - mexico - whishes - arrived - placid - specific - depot - tikka - expire - markets - problematic - highly - blues - thirtieth - brooklyn - tatum - argentinian - redso - des - moines - women's - richard's - cellphone - division - hong - political - charley's - steakhouse - accident - normal - wakeup - satellite - freezing - forex - jimmy - chores - snooze - design - museum - guide - speech - ran - shift - inferior - mashed - jcpenney - environment - raw - disturbed - sia - chips - anybody - present - reynolds - limbaugh - weekdays - islands - viral - asian - streets - inception - meatloaf - alternative - compliant - sensex - phil - est - hand - switched - recap - ferrari - nandy - promotion - kate - brothers - ma - followers - closer - deleted - gloves - bands - platter - boland - corner - strong - chipotle - eu - amtrak - son - charges - version - rajdhani - chart - manage - musical - hat - den - tonight's - syria - stronger - homelessness - nails - support - ally - sentences - penn - ago - turning - center - hungry - actress - keywords - usain - bolt - ongoing - cancelled - idol - julia's - wells - fargo - ri - sarahs - computers - devices - toms - regards - quote - production - brother's - inch - shell - marathon - directory - dictate - huey - lewis - elections - alone - marry - apart - danielle - jane's - mankind - singularity - nye - feynman - whom - inventory - makes - dept - apple's - education - bugs - settle - when's - geographical - jason - exchanges - mcdonald's - tgi - ship - hershey - facing - faulty - zita - jeremy - irons - wallmart - sphere - hp - gottten - pardon - engagement - showing - format - absolute - interest - messenger - gate - enable - columbus - hips - tour - sterling - thumbs - priced - tablet - amc - bible - safeway - organism - undertake - freedom - charger - documents - jars - clay - members - o - vegetables - delicious - beaumont - tx - finance - exhibitions - trumps - month's - v - applebee - dakota - bus - brighton - pa - darken - promoted - liverpool - utah - suggestions - micheal - complaints - pencil - keith - fridays - temperatures - hardware - exercise - jpearsonjessica - release - hoover - goshen - chester - wood - woodchuck - healthcare - borges - calculator - dune - reality - jobe - gossip - piece - convenient - titled - pork - belongs - hongbin - wreck - tool - started - gather - bruno - costa - patel - daniel - corporate - controversy - wendy's - texans - biography - flowers - investing - arrives - finish - spot - crop - culture - enjoying - fetch - kill - auto - washing - buffalo - he's - titles - ross - whose - types - pleasant - erin - madison - tuesday's - lif - khan - affordable - season - policies - c - expected - hypothesis - seth - kicked - unhappy - gallery - xorg - used - monali - thakur - noodles - cher - sally's - tracks - mid - launch - glasgow - bridge - releases - pitt's - server - clarity - yens - motivational - scratch - blanket - aib - reads - singing - monas - tuesdays - winter - rocket - lands - chan - economic - sister's - aa - film - pb - indiana - departure - pipeline - stitch - sleeved - hail - logan - style - quantum - physics - labeled - delia - began - rrcg - shape - awards - improve - pertaining - trance - lives - weight - met - brian - sinatra - sunglasses - attending - falls - requesting - sunday's - overhead - greg's - rom - historic - georgia - guest - jaipur - iroomba - alfredo - pride - prejudice - fill - interview - daddy - wangs - manchow - university - locally - lowes - tiring - east - medical - metro - bach - schubert - rooster - czk - channing - pad's - identify - yelp - scandal - affect - suffering - enabled - arby's - saw - mango - itunes - highlights - brings - sixteenth - tourist - wendys - presley - sold - intern - affairs - fries - buttermilk - panda - wants - floor - clint - eastwood - moe's - planets - equivalent - morrocco - gravity - uploaded - someplace - availability - issue - fly - jpy - natural - delta - disappointed - files - q - cindy - shortest - simple - ring - lotion - maroon - fort - died - bonus - repetitive - icecream - statistics - rebel - lawn - leith - measure - daytime - september - pilots - pda's - shade - sil - cap - punjab - gwalior - ashley - juice - nagar - ellen - programs - fairs - invest - suits - ingredient - launches - leaves - bjork - crater - elevation - stewart - hotels - spices - bubbles - grass - broccoli - capricious - philosophy - anthony's - apply - pings - gps - thomas - koontz - acdc - beijing - ratings - union - prayer - todo - angles - scissors - stashable - cinch - bacon - passive - que - occurred - lakeland - tulsa - advise - singapore - risotto - invested - model - helmsworth - bench - julian - buddy - rogers - brains - chap - badminton - dick - lopez - apartment - points - germany - unknown - thugs - healthy - rash - casey - oriam - ps - plants - mailed - ikoyi - grassmarket - marleen's - locations - bush - mac - reaching - allan - till - cheering - guitar - oxford - densely - populated - son's - hubby - comparison - putin - barcelona - gss - energy - pan - nyack - worked - unavailable - bryan - adams - miss - checkbook - jared's - enrique - iglesias - forms - jeans - voices - alan - tudek - animals - olx - mts - freed - jenn's - coordinates - humid - demographic - otherwise - tiffany's - outdoor - sheila - lincon - dust - serve - conduct - estimated - gaana - funds - downloaded - indignation - meijer - necessary - grubhub - pancakes - mario - bars - birmingham - sites - donuts - chopra - textual - rapids - cant - prefix - sounds - provides - amy's - benton - leeds - dsw - returning - defective - digital - bhaji - carlos - linux - upgrade - shark - attacks - screening - exposure - souffle - tracking - od - progress - paused - gilmore - hour's - imdb - orleans - european - gdp - surfers - theme - ash - ikea - klm - marilia - cars - robin - williams - surfin - ottawa - trade - contains - field - someone's - prague - brno - rene - interests - radiolab - harris - strive - accommodating - fell - relationship - pharmacy - memo - nancy - paid - expressing - disapproval - yard - royale - hide - amber - cheeseburger - coca - cola - al - matrimony - scott - potato - funniest - polling - mother's - chase - xmtune - matt - murphy - detroit - taiwan - organic - secrets - domino - ac - assistants - z - fred - owner - required - saga - hanks - trading - erosser - rosser - vikki - dhaka - notepad - oldies - alison - recur - w - mentioning - languages - lavender - toned - videos - stein - chennai - resuming - moms - foke - beep - discussion - woodland - lowry - meetups - powerball - toyota - focus - concentrate - nbc - roosendaal - deactivate - shrimp - parmigiana - bumper - spouses - lucknow - paying - hurry - served - rhythm - enquiry - hartford - plaza - hyundai - wishing - websites - briefing - complex - calculations - jarvis - highway - fired - dissatisfied - sandra - bullock - ratio - haskell - sharon - horse - mum's - dillinger - sunblock - sub - tab - crude - software - stadium - step - short - reddit - appoints - agra - sheet - keyboard - kfi - district - connery - carnival - wok - shutting - phoenix - cloth - rehan - lego - alphabetical - mexco - charles's - foodpoisoning - ultra - madonna's - harley - davidson - daylight - afi - infy - launched - inboxes - secretary - increased - resolving - fuel - injector - multiple - interval - mike's - espresso - sasha - susie - salesperson - country's - cylinder - specifications - ivory - pst - zoella's - jackman - reacting - potential - frying - boise - wendy - divisible - automated - katherine - pre - gaming - containing - decade - industry - foot - chemical - cause - taste - bra - julianne - hough - addresses - vonstaragrabber - lion - restroom - kohl's - mentioned - hz - royal - bloodline - relationships - billings - levin - quarter - lori's - lori - exclamation - definitions - birds - raj - priya - allows - worlds - kelly - clarkson - garam - scarlet - found - cub - dmv - excessively - lake - dried - reporting - smile - changes - charmin - eternal - smoked - meat - beanos - processing - chip - logic - insightbb - highland - terrace - child - peck - midwest - cardinal - anthony - barrack - jancy - thompson - cassy - gulls - alternate - sin - dragons - msnbc - residential - leader - siblings - pedro - serendipitous - bestbuy - targets - wawa - mentions - engagements - hawaii - jr - applied - halifax - ahmedabad - monty - python - stronomy - blahblah - blah - arrivals - subtract - payoneer - formal - connors - indranagar - transform - marcia - perpetual - arranging - cvs - callum - steffi - attention - kanye - mommy - chucky - forest - polarized - proposal - conrad - coldest - hue - dictator - clancy - geranium - delays - build - lense - rai - transistor - dildo - warren - exercises - forman - kinley - bottle - retail - yan - regal - unprofessional - annual - payday - tricep - arts - ripped - vietnam - trends - chaise - preparation - nestle - paula - deen's - bmw - microsoft's - bookstore - below - moving - pretty - lock - administrator - edition - airways - marvel - garner's - rubix - cube - kfc - milwaukee - pager - alexander - gilchrist - goods - performing - unopened - security - chain - probiotic - colleague - knowing - novel - fiesta - comcasts - acer - farmers - fraud - weighing - india's - gotse - grapefruit - similar - tmobile - nifty - sessions - recital - greatest - openings - zip - demento - fatigued - disease - prevention - overcharged - unquote - cotton - tweeter - railways - flipkart - fist - renee - nutritional - starred - calculated - mattress - hillstead - paul's - jill's - disregard - pesto - stinks - nobody - behind - kid - nature - ounces - ted - boiled - dancom - wars - fmod - span - along - malls - joining - frequently - realdonaldtrump - bobby - mcgee - pwd - obamacare - clicked - falling - pampers - virgin - hayden - pat - amie - infosys - technologies - roads - aerosmith - airtel - dairy - sends - dues - tobytoday - ileana - d'cruz - rended - taj - ashok - typhoon - rama - final - missouri - virginia - announce - haughty - salmon - joking - goodnight - rebecca - believe - vowels - ban - haze - insight - cable's - fellow - tweeters - canoe - warriors - assassinated - acceleration - detailed - wife's - robert's - angus - interested - jen's - sjobs - cdn - ruth - simran - aapa - kadai - armor - sms - indefatigable - indicate - fra - floors - modcloth - honor - weigh - priority - hiking - smoky - judawa - expense - deals - plethora - sam's - august - elain - bbq - leap - congressional - representatives - voting - reproductive - ge - bbb - contacted - assigned - jill - drafts - scoring - touches - relevance - goggins - medvesek - philippiness - booked - board - locality - beth - katey - fans - approximately - charitable - rae - darker - anymore - printing - significance - fondle - mate - larry's - larrylarry - faripir - gurpur - seasons - softball - refreshments - jamie - carrie - underwood - abdul - kalam - subterranean - colombo - sri - lanka - quit - dollar's - award - among - spouse - forgot - ass - millionaire - indians - americas - julie's - transcribe - garbage - geographics - tree - criticize - tanzania - heather's - answering - spam - phishing - reseda - axel - kailey - prettiest - century - mattel - toys - grateful - fixing - maidan - sophia - betty - reasons - russian - applicable - loving - claire - crashed - batteries - philips - person's - compile - ali - matthews - apologize - comcastcom - luke - jean's - carefully - beg - trying - flooringco - seams - baking - skiing - calming - continuously - tale - roraima - innova - bowling - beginning - identifier - diverse - santa - continuous - hangman - vegetarian - roast - rewards - allow - immediately - shelley - hennessey - waking - dicaprio - ways - immigration - raised - lose - digger - cosmetic - perth - feet - chick - tornadoes - upstairs - badly - timings - lobster - runner - forum - thunderstorms - powered - plugged - rod - mgccc - bleed - ga - pune - mixed - dishes - radisson - cheetah - what'sapp - cm - father's - skill - graham - eggless - collect - favorited - flag - ssmith - virtual - bryant - spots - scapingyards - washed - springfield - draw - insurance - quantity - brightener - cuba - stream - raincoat - maiden - soundtracks - deliveroo - humidity - crowded - built - mesa - rosenstock - workpdf - occurring - environmental - dbell - converse - radia - logged - scabble - loads - jacob - hasbro - aldi - piramid - completely - method - hems - loose - connect - snapchats - arizona - festivals - hospital - peppers - bowl - korn - lupe - eurostar - umf - unchecked - berlin - lane - synonyms - hampshire - shakira - brads - keanu - reeves - johns's - increasing - burgers - stan - falklands - valley - maria - hangin - glow - we're - newsource - clark - carrey - jams - crashing - outback - sugars - defines - joel - venue - huffington - images - elizabeth - case - agnes - randomly - mecky - incredible - even - decreased - vacations - honey - akon - barbara - handsome - forensic - spielberg - korea - coding - achievements - albert's - clerk - hopes - zimbabwe - buble - research - excel - gun - rogen - resin - tooth - filling - mody - marinara - vicki's - mardi - gras - monika - relatives - chillin - lol - levis - tricounty - messy - disgusted - emoteck - foroogh - quick - decline - emailstudy - atdfd - giant - trey - kalka - mcdo - timestamp - operate - watched - infinity - tactics - upbeat - synonym - racing - towards - fog - muted - coke - eighties - tvs - theresa - brent - kamycka - dejvicka - tap - peanut - circumference - saskatoon - sync - sofa - mcdonald - silenced - catalogue - algorithm - sanctimonious - talked - realize - reveca - paok - wipe - bisque - br - rather - silly - stat - tar - vitamins - gain - xm - fongs - anywhere - zanes - se - chronicles - weber - commence - causes - sangli - german - hedges - truthdig - coffees - commuter - plain - mimo's - oscar - restrictions - treasure - louis - stevenson - fifa - beast - pav - prambors - hannah - ringcast - vegetable - episodes - overnight - apps - nathan - dismiss - karl - hourly - eyes - breeds - inside - tribune - join - crabmeat - shakira's - yankee - greenwich - gala - jump - recall - johnny - cash - pod - cast - rare - suppose - enjoyment - emo - nayagara - passion - pit - marckel - bohemian - emma's - arijit's - pet - prize - receptionist's - beat - freds - probles - patagonia - quart - '?' - zach - duration - jlo - alphabetic - phohouse - badpho - daybreak - biryani - battle - divergent - moby - jungle - jaiho - casserole - shooter - columbine - wednesdays - soul - accumulation - squash - calm - debate - schools - amd's - lee's - managers - myspace - relaxing - bahar - antarctica - atmosphere - pinpoint - payments - illinois - louisiana - cfo - pool - vyas - morel - mysore - rise - sdfa - newspaper - calorie - dangerous - sunrise - mostly - dining - shake - flood - prescription - mix - view - jana - spa - comments - pear - factor - clearance - northern - language - arnold - exxon - mobil - dragon - fruit - differences - seashells - seashore - velocity - motorolla - haggis - fiji - irwin - similarities - hypertrophy - sharukh - implement - kazakhstan - mediterranean - roman - grigorean - hardword - quead - amphibious - roberts - climatic - tornado - prone - rising - declining - megatel - denzel - washington's - citizens - arm - persos - belarus - gyllenhal - geology - helicopter - iphone's - drained - manger - navy - daikin - jerk - nexus - interaction - platform - tweeting - at&t - mahaboobsayyad - kellogg - ashmit - ismail - listing - enalen - projects - clara - clinic - exams - ammunition - mark's - divya - jjnzt - activation - andy - terry's - brenden - jeffrey - burnette - protests - joshua - pianist - whiz - schadenfraude - rials - storage - bot - provided - massachusetts - channin - store's - rump - prior - re - intelligent - recognise - irobot - areas - lighter - yell - uses - cn - gadgets - skynet - marie - lamb - balcony - nyt - bennett - ralph - pda - balloon - maps - degeneres - character - evans - actor - fitbit - malika - shivaji - attitude - lily's - concerned - upon - startup - stuffs - tawa - relative - legacy - cst - leah - remini - mortgage - amed - cleaners - seal - abita - grammar - backdoor - minimize - leisure - billie - spicy - training - comfortably - sunburn - minneapolis - habits - braking - notifier - swan - thoughts - pleasure - those - kashmirstart - sells - i'dl - kettle - 'false' - rta - valia's - visiting - techno - mornings - mow - cbs - slightly - francine - vice - postpone - mins - xyz - hwood - kept - spider - reopen - billy - connery's - eiffel - itinerary - crash - valentine's - likexchange - divorce - danville - il - government - menus - capabara - origin - assistance - vicinity - chit - drinks - flabbergasted - xy - self - double - castle - refrigerator - bakery - spray - pyramids - bio - basic - humans - schwarzenegger - inchoate - rules - caftan - raleigh - hobby - ajay - devgn - corden - aud - prevailing - kenny's - crew - aww - spying - employer - thier - juanpedro - craig - leon's - looked - players - costs - providers - sydney - documentary - hyphen - represent - strings - pianos - acoustical - celeb - pong - linear - turn_down - reaches - strength - routine - billboard - piano - ed - sheeran - diet - vietnamese - yams - grandmother's - rihana - require - stressed - option - affected - acquire - retrieve - clarion - congress - turiellos - mates - solar - dice - jalapenos - wished - painting - therapy - warehouse - mop - neighbor - flappy - returns - someones - spring - wonton - moves - jagger - fishing - hiphop - dunkin - donut - atlantic - daughters - hula - hoop - lessons - scrote's - indie - grief - lebron - naughty - preprogrammed - alt - needy - sharpen - butcher - knife - pulled - starbuck's - backward - terrorist - invaders - parent - crescent - brewhouse - prado - science - playlists - debbie's - sleeping - searched - lindsey - lohan - competitions - subtracting - challenge - beer - gainers - chili's - frubs - police - softly - practical - assessment - bonefish - rotating - placed - lakers - barenaked - ladies - lord - rings - mar - sneakers - artists - sanantha - shuffles - shuffled - bardonia - county - analyze - pattern - girls - league - fjords - nothing - brewing - smurfs - tommy's - lovin - cottage - ming - photosynthesis - danny's - repeated - peaceful - migrations - zydeco - inkheart - seller - occurence - telegraph - invited - wifi - levels - willie - nelson - dolores - alter - retirement - professional - development - sainsburys - byron's - floyd - raingear - notorious - bone - explanation - database - likely - lucky - irish - sshow - ramsey - aired - sprint - preparing - academy - yeshudas - angels - dancing - aretha - franklin's - layers - glass - kuch - hai - wakey - knitting - mujhe - feb - king's - malinda - parents - mirchi - gallon - seen - parks - safest - evacuation - beautiful - sofia - francs - consequences - various - dicaprio's - networth - phelps - disk - constructed - concern - effectively - lawrence - zac - galifrankas - wheat - prediction - schemes - mega - capricorns - dinky - lanegan's - princess - pregnant - smallest - americans - retweet - insta - sonys - bk - alzacz - kohls - cleanliness - pizzahut - delay - lpg - satisfied - choke - suqcom - repairs - killing - miller - budgets - iamironman - gbaby - gma - loves - kate's - margaret - ben's - brady - palmer - homework - tax - regional - archive - fitness - vault - footloose - child's - damage - petco - canceled - passing - pikes - peak - avatar - diverge - maron - fault - sword - eventual - contest - dangal - mauritania - abs - wondering - southampton - resources - soy - lexmark's - hilly - lyon - beirut - tribute - madrid - ate - sweat - charlize - theron - atif - aslam - capture - actual - shane - dawson - zedd - snooker - loquaciousness - sholay - tofu - nightmare - avenged - sevenfold - matters - prompt - panic - brilliant - boston's - mckinleyville - astrology - strait - countdown - cats - fruits - embassy - pita - gyros - negotiations - hairdresser - courteous - enthusiastic - funk - sense - heathens - cabinet - irctc - stored - shutoff - glasses - ella - fitzgerald - rover's - vet - polar - bears - oceanside - medicine - anita - barrow - burrito - oliver - covering - ground - zucchini - textile - antebellum - chimes - covington - species - bees - cranston - kilometer - behaved - rudely - jimi - hendrix - calms - outwards - califonia - composed - hint - shipping - frosting - sport - napoleon - hill - athens - middletown - shirts - sample - politician - investigated - rapper - con - cuisine - wizard - brick - conroe - iterate - architect - salon - babaji - passed - maryland - surya - monopoly - avenue - considering - celebration - brewed - galoshes - tutorials - workouts - millenium - toward - neighbourhood - bannon - storming - reoccurring - longtime - sweetheart - memos - starfish - centaur - philippines - oar - departs - preferably - latte - sides - pentagon - fashioned - rescheduled - transportation - twins - duker - deadline - samurai - obaba - bp - ambiance - automatically - object's - boost - morale - jogging - spell - firefly - mura - masa - checklist - biographies - sucked - congested - avinash - commando - jolie's - instrumentals - clarksville - tablespoons - surveys - flour - acela - calone - bucket - fulls - valid - references - critical - perpetuate - luncheon - ohm's - values - plying - expectations - musician - mindsweper - throughout - noontime - included - tour's - voted - walgreens - chickens - monday's - crankshaft - surfer - lunchtime - skramz - compounds - diabetes - might - reservation - homosapien - engadget - boeing - brisbane - ear - headphones - minimum - worry - snowplows - burying - driveway - adapt - destroy - impanema - equipment - turnt - attractive - conducted - cinnamon - freshener - watsapp - bean - awfully - entitled - murderer - ford - forties - scenery - morocco - sf - blokus - preacher - taken - stormy - centers - ethics - popup - mysterious - puts - stage - considerations - lourie - artic - scoop - carion - merced - bypass - passwords - quantico - grade - examples - cuisines - hibernate - bear - published - authors - tempo - keidis - tidal - cookoff - zones - probable - summerfest - dogs - aren't - necessarily - carolina - eleventh - chilling - sleeve - invoking - term - herald - maria's - poltergeist - imagine - uv - index - johncena - instruct - oscillate - liter - nelly - shawarma - baster - pali - vilnius - tabs - debates - singers - activated - ozzy - osbourne - danish - happypeoplecom - accounting - backpack - im - puttanesca - keeps - worse - wrigley - braise - loin - carnatic - bases - nick - swisher - stolen - clouds - cleared - bola's - norman - reedus - screwdriver - window - volcanoes - rowan - atkinson - minneapoliscity - delicacies - monitor - overall - gymnastics - channels - kxly - botswana - enjoyable - spectre - chane - decentralized - men's - freeze - postal - becomes - ccn - berth - michigan - composition - shahi - panner - dakar - jakarta - equalizer - weird - barely - rodriguez - oklahoma - giraffes - margarita - difficult - crabs - firework - probability - tools - emigration - legislation - pdf - cheeseburgers - applications - adopters - priest - walks - mechanic - h - showers - signs - contrast - recollect - gm's - duck - beavers - tail - lucking - horkersd - wo - myrtle - hr - steam - entirety - anirudh - colored - tropical - bedrooms - yellowish - elephants - expenses - contents - warmer - royksopp - etc - progressives - peoples - cultures - unset - iceland - mp - mangalore - tanya - quad - particulars - insert - tvf - formidable - origins - eden - depressed - mc - donalds - rub - regrets - judgments - scope - intellectual - capacity - ahmadabad - stethoscope - superstitions - rl - stine - quinoa - martial - smooth - damn - speeding - stephen - halley - barry - jealous - siri's - java - scenarios - pc - transfer - tw - agent - nightime - creamy - mirch - dil - cannon - cameras - process - merriam - webster - dubstep - rangoon - wines - older - navigate - chandelier - egs - recognize - subscriptions - mileage - studies - microphone - immigrant - electronics - careful - paint - fund - success - resolved - bola - eva's - roller - augusta - midtown - surprise - children's - dongle - seashell - bots - fallen - centimeters - poisoning - sci - fi - outcome - reform - sleepy - moderate - chrome - ultraviolet - george's - geek - courses - rundown - legend - equipments - usher - manor - advertisers - clue - depending - strongest - outstation - fallout - shoal - lastfm - relocate - pollution - awareness - bryce - jessie - carol - nsnbc - vacuumed - chives - splits - arbor - receiving - toast - futures - brokers - routes - fixed - additional - switches - church's - governor - enacted - grams - guitarists - android - babe - sonny - sear - eliminate - remain - uc - polk - pakistani - bedside - reshuffle - frida - devil's - rusk - actors - pakistan - happenings - sit - montauk - beethoven - legends - sunshine - mothers - smoke - feels - rockies - miamy - operations - addition - subtraction - incite - annoying - cristiano - ronaldo - spin - cows - jenny - spread - wallstreet - selections - nashik - ipl - oswald - chambers - horoscope - mgk - dog's - residing - cricketer - dhoni - byron - fluctuations - talks - palermo - shallowest - bbcnews - nsdl - flights - lineup - stick - ribs - jeopardy - timetables - emi - maya - mackensie - osteen - jimmie's - adjustments - precocious - fork - husband's - audi - hibachi - disputed - crack - visible - boiling - rogan - karachi - babysitter - kidnapping - hamburgers - madonnas - lessen - ipo - greenville - carries - creamed - pickled - herring - tackle - brush - geyser - savings - torey - hurt - subscribe - picks - birthdate - goals - cairo - projected - patrick's - capita - honda - intended - hurriedly - activates - it'll - wsj - spy - broods - grommet - steven's - underground - seahawks - participants - workday - ammi - nightlife - donner - summit - ukraine's - ended - arrangements - altucher's - writer - fortune - brisket - grant - audiobooks - twilight - bass - hunger - roses - barbecue - tuna - deadly - killers - finally - trilogy - grisham - goblet - roadblocks - birthday's - biscuits - lawyers - steve's - kari - labyrinth - commonwealth - sharma - gulf - petrol - earthly - ultimate - ending - allison - canberra - honolulu - flash - salman - gresham - hindustani - stroganoff - sock - creates - geo - traits - moral - rein - blood - slayer - pro - bono - succinct - dalls - somethings - sharp - izzo - whiny - bitch - macaroni - nights - jumper - blind - cure - cancer - vibrant - sloth - transition - recycling - bbc's - columbia - kentucky - hire - opera - prefer - avoid - sort - comedy - compassionate - nc - va - riddles - segment - youth - charity - surrounding - punjabi - sharply - lovett - barber - label - hypocrisy - subscriber - captain - disillusion - hyderabad - dashboard - storm - barrel - panasonic - clinton - canasta - mittens - badra - amit - trivedi - crystal - lewis's - everywhere - rue - evaporated - mma - offered - tutoring - peas - dream - cafes - lauderdale - deletion - precise - parliamentary - remotely - connection - calendars - stupidest - shovel - western - cutting - ll - rapping - spelling - mama - tatum's - fulton - universal - garner - chill - icebo - college's - rehman - soundcloud - scorecards - ketchup - jimmy's - crate - lexmark - preference - females - federal - andreas - sportsnet - favourites - janice - bins - pamela - covered - rhapsody - italian's - ke - panera - remainders - tandoori - sukhwinder - sunidhi - etymology - googleplex - slide - wearing - trivial - pursuit - cancels - martina - mcbride - finances - vocab - zipcode - compaq - composer - margarine - jonathan - entrepreneur - extended - combo - memories - tupac - affects - drunks - ford's - liked - dealership - olky - realtor - thighs - ourselves - economics - medication - gross - domestic - donaldson - prostate - wicker - rooms - instrumental - savannah - outing - affleck - quotes - tire - montana - exhausted - acoustic - commercials - convenience - consciousness - serge - gainsbourg - windows - turks - generate - pedicures - btaxes - departures - frasier - amazon's - bluetooth - verus - neat - forecasted - bing's - dropped - recurrent - candidate - aware - blackeyed - pees - prince's - perimeter - rectangle - aaron - carter - involve - drugs - lighten - slicker - rains - cloud - carrot - popcorn - carmike - cinemas - greater - minestart - frog - lenon - unique - hanging - hung - sporty - seldom - jocko's - kid's - viewers - cantonese - usage - specs - bugatti - veyron - chief - blockbuster - krishnarajpuram - interstate - hammers - obligatory - wonder - southeast - marlon - brando - ferrel - tal - obidallah - manoeuvres - merita - rotate - changs - pepsi - shanghai - branden - wind - landmarks - dvr - congestion - valentines - eastwind - lomaine - geneva - officially - hopkins - takjistan - dimmer - karo - apne - aur - karna - chahta - hu - purchased - otherplace - giraffe - ute - requirement - watts - powerful - bulb - oclock - nba - hulu - composing - melissas - millilitres - spoons - goulash - thor - harischand - mg - i95 - sb - kilo - diana - llyod - webber - wool - penultimate - bang - philosophers - nietzche - focault - profession - kilograms - turkeys - bibulous - angeline - atm - narwhal - kilamanjaro - captia - volkswagen - onkyo - av - receiver - ipad - aniston's - summarize - ice - jindel - pump - nikki - minaj - nationality - snoodle - yemen - sudan - unprompted - organization - megan - fares - engage - functioning - dinar - conservative - korean - sahara - kingdom - antartica - telugu - tamil - tsunami - rajani - khanth - venture - goalkeeper - dushambe - abrupt - hbo - sopranos - parana - cave - anime - posters - johny - depp - invisible - graphical - joli - pricing - beech - nuclear - triad - hilton - borders - lucille - redhead - geraldine - ferraro - bde - lowered - phrases - nicole - mcgoat's - manipulate - roip - nasa - google's - davy - crockett - springsteen's - richest - costliest - easily - gm - psso - kroner - maple - trees - christie - brinkley - libraries - gmb - key - mongolia - anastasia - telekenesis - promise - stray - cruise's - starring - odyssey - polish - zloty - hook - ups - integral - exponential - berkshire - hathaway - tables - pink's - alligator - porto - tommy - hilfiger - print - networks - snaps - celebrate - bina - yay - smiley - emoticon - commented - folgers - hathway - huge - lfi - tagged - treated - hersheys - aircel - nastyburger - linkedin - tracy - waiter - drain - charge - neptunal - poorly - waited - inappropriate - potus - accounts - vodafone - complaining - spoiled - positive - tumblr - unpleasant - overpricing - cheating - connected - else's - greetings - thought - waste - excess - micro - lodge - snapdeal - sonic - hole - sole - patel's - insect - packet - elsewhere - moan - easyjet - snotty - expired - xl - sizes - filing - applebee's - angela - merkel - swagging - moto - sluggish - flavia - mum - jacob's - existing - cannot - pleas - mahmoud - ebay - smsayyad1985 - kishore17051985 - fedex - truette - petey's - tessa - gaurav - karen - mongomery - llc - joseph - turnpike - accumulated - deadlines - fees - ppt - emergency - missing - carl's - attach - physical - drill - marilyn - jugal - here's - bug - sarasigmon123 - lindafancy55 - markpolomm - gary's - mailing - bill's - erins - beth's - wont - stacy - cadwell - tori - aloud - brenda - thisome - smurfette - smithjoe - hwacuk - chong - giselle - bosses - havent - frieda's - jjjindia - exists - batch - samuelwaters - joose - hellen - builders - accepted - victor - taxi's - terry - macdonald - yahoocom - metion - rodger - christy's - otp - jayesh - tried - morgan's - office's - rob - qerwerq - secured - gerry - raj's - junable - shopyourway - reference - jhonny's - marissa - rosa - bert - ana - goddammit - pronounce - serious - recheck - slowly - failed - fuck - executed - clearly - errors - showed - races - thursdays - funky - handmaid's - beam - scotty - debit - wiki - editor's - automobiles - promo - discount - director - act - bejeweled - aside - snakes - ladders - marsala - influx - bayou - reasonably - tapas - az - ddlj - meatball - newscast - bibber - tmz - devon - applebees - hihop - doggie - feelings - radios - litle - tsos - congratulate - links - treble - flame - eta - encourage - students - choices - lobby - vf - chore - butterfly - clips - urban - regular - bi-weekly - baltimore - sport's - breakups - dale's - brea - douglasville - fundraiser - dolphines - maradona - pe - becky - appointed - deputy - utar - pradesh - anniston - handy - sainsbury's - attenuate - parcel - jakes - bristo - stressful - deposit - mathematical - superstar - survivor - destiny's - westcombe - facility - oboe - mcnamara - abolish - swim - repair - grub - hub - ill - dec - dreams - wyatts - obstacle - poach - dental - rose - davinci - trevor - noah - ncaa - entrapreneur - sanam - differs - ave - hopsin - enya - wbc - accordingly - remarks - sufi - beibers - arrested - sensor - music's - author - antwerp - cnn's - foodnetworkcom - customize - preferred - unable - duct - tape - gooseto - apig - ringer - secure - passage - tomatoes - wan - senelena - americano - makeup - robotics - teleconference - robotic - poughkeepsie - steel - day's - soundtrack - tobymac - transit - gloria - furious - nazi - hunting - effect - marvin - gaye - pasadena - ca - constrain - singles - outer - nowhereville - comfortable - erica - grebe - wooly - trigonametry - obsessed - graphics - undone - tough - treasury - toledo - munich - obtain - nutritionally - balanced - internal - locks - exit - mocking - lyft - transaction - tasty - mixture - according - hands - supports - canceling - congressman's - lenin - spagetti - controversial - statements - walker - humor - nkotb - jon - snow's - possibility - wellington - nz - advantages - disadvantages - driver - towels - stretch - gear - joey - crimson - chose - pineapple - asparagus - teaspoons - bling - medieval - engines - foods - hurts - cannibal - tonic - bitcoin - collection - hidden - figures - brasil - politic - superb - dalida - capuccino - analysts - thankama - kodaikanal - vote - burritto - chipolte - abut - sedaka - chamber - rfi - knock - cnncom - remchi - fl - ortcars - flip - wire - thriller - fiasco - breaks - dam - paradise - presidency - sigur - ros - socks - van - halen - wayne - spare - lightness - appropriately - both - musics - coastal - cry - friend's - wore - veganism - picnic - regent - visited - therapist - inauguration - swatishs - dorothy - known - supervision - superbowl - eric's - bday - kar - abhi - achche - ache - rahe - honge - mhz - sponge - bistros - brownies - tenderloin - enchiladas - gluten - hotdog - row - bing - notebook - pulldown - clearer - medford - drivers - waverley - canal - connecting - summers - gibraltar - monoprice - mxblue - mechanical - turbulence - carey - blunder - factorial - depends - commands - stand - draymond - susumu - hirasawa - yosemite - '200' - baguette - stonehenge - douriff - ivf - ivr - litt - runs - hesitant - crock - guetta - malaysia - whelers - sadness - william - coral - daft - punk - sandle - santha - ingerman - calc - shibaru - alcohols - nano - gina - desta - mgmt - bana - talking - garvin - trilly - nytimes - chhana - mereya - favor - strained - cooler - films - einstein's - aroma - ska - raphsody - trebuchet - forth - relate - qualifications - kirk - franklin - arithmetic - skyfall - bathrooms - raghu - dixit - reports - availables - haddock - odd - cape - cod - noisy - dull - hackernews - porn - pad - fight - fighter - nzd - melodious - burton - helena - campaign - mcclanahan - mummy's - motown - rasgulla - janta - pvt - ltd - heartthrob - justin's - velociraptor - hippo - senatra - giggle - peru - nirvana - anirudh's - retro - mf - doom - summarise - ariana - grande - predicted - creed - user - desire - kenny - roger - sia's - thrills - wapo - stockholm - okinawa - occasionally - shuffling - veggie - mukkala - mukkabilla - guardian - anytime - themes - horror - ennema - eatha - homestead - forever - mayor's - stance - council - master - louies - keane's - fears - noe - reggae - largo - swiftm - afi's - xinhua - dedicated - bottom - franks - yelawolf - ucl - flop - grammys - espn - joni - mitchell - shot - tequila - sleepyhead - aces - redder - edms - lamp's - loudest - brolly - thao - nguyen - interior - dine - dogwalking - nytimescom - overcast - deactive - foo - disasters - opacity - dea - guam - drug - abuse - itzhak - perlman - drawing - sweden - bombing - ireland - poll - hotha - defrosting - salt - toggle - spb - weatherit - either - forecasts - intellicast - weathercom - orevena - recorder - pizzahouse - reorganize - sticky - umbrellas - opened - cleaned - shakin - bakey - tips - hypoallergenic - sarcastic - cheat - ii - developers - edg - yaad - dilana - kahin - samantha's - rita's - adding - bro's - attendees - maggie - valet - groomer - timeframe - pete - faculty - parade - greens - jack's - walter - gemma - nail - arora's - namkeen - tonights - ggg - tie - iheartradio - rov - javan - wfrn - kicks - osteen's - wgrr - lite - prairie - companion - palhunik - pudding - tutorial - welsh - rarebit - oatmeal - pathia - achieve - veg - pulav - crockpot - prepared - keno - pinball - fishdom - nfs - harvest - crops - farmvile - millionaires - vodka - depend - pon - stationary - mad - errands - paav - queried - pepper - rowling - shadi - viewed - mlb - heavyweight - citadel - scene - circus - trolls - grab - kung - fu - bowery - railway - coach - fare - metrolink - navigation - westwood - layfayette - inconvenience - emotions - arrahman - cosmos - multiplied - abouts - hitting - eliot's - el - ribbons - sperm - whale - eaten - lbs - pinhead - timeliness - defining - thesaurus - penalty - approval - poetry - ambulance - jello - shots - ferrell - stassi - schroedder's - tacobell - hierophant - zealand - stockton - emissions - blowing - kennedy - ziggurat - gagas - gretszky - hemingway - pages - earn - nobel - actions - sloths - parton's - madagascar - acting - tiangle - trebuchets - googs - gandhiji - amal - brazil - adviser - rich - acted - rihanas - stamp - mugy - msn - busdriver - fergie - flick - ribons - nakumuka - postmates - complaintum - glinder - gta - rcg - outlet - hadock - mclanahan - coal - mumy's - piza - wheelers - guarante - debugging - debuging - proper - sung - bilando - terrorism - cover - dimmed - vanilli - marauthr - wooo - michael's - shutdown - pittsburgh - precipitation - riff - portland - muggy - giants - banks - steelz - ensure - ricky - matin - tyres - plant - chased - advice - gossiping - society - mitushree - hairdresser's - biology - fsu - reflect - yashas - vinay - vally - closed - shoutcast - pilkington - soda - powder - sambar - cookingforu - thermonuclear - battleship - cereal - wishlist - wrist - hipsterhood - duncan - trussel's - simmons - wide - cisco - crafts - sporting - presently - sheffield - septa - lead - fransisco - washingdon - evolution - mariah - kya - tum - mere - karne - karoge - acts - assembly - idle - brand - meridian - terranova - guarantee - marian - fields - farthest - philippine - cambodia - situated - foruget - monopricechanical - peenth - moroco - piz - tre - supplwn - viki - shivle - loged - applebe - acess - madagar - anp - socer - subcribe - pluged - imigration - audiowan - debie's - imediately - f - locar - duark - rebeca - talle - banas - ragh - acordingly - wakely - en - bress - acording - stefanan - puding - vegie - vius - edie - domizza - eg - cheeseiza - ocurred - brightnes - alaba - memory - fransico - sunderland - boogie - butt - leviathan - shinning - premier - cleanup - wacky - aman - cherry - bomb - solstice - silently - closet - nakumukka - shed - responses - yankees - investigation - dooa - pieces - imogen - heap - stole - dynamite - cease - operating - rained - uptown - suggestion - finlee's - bedtime - sockets - sanfranscio - abbas - cn's - vibrate - cooling - sheriffs - hike - ilayaraja - speaking - un - storms - roof - tube - jackpot - classmates - extremely - somewhere - drenched - sentient - budy - heating - apt - parenting - concerning - seo - searches - sticking - patterns - numbered - impression - reunion - presents - mehta - willing - discuss - evan - parker - violin - lesson - musicworkz - registration - opens - evening's - thursday's - nineteenth's - hayathis - shower - corresponding - showcase - famosa - kamp - neal - brenan - gx - nonstop - rm - giver - traveller - knowledge - crispy - supper - broil - noodle - stuffed - maccoroni - almond - clash - clans - ping - keeper - enemy - coc - detergent - corn - dill - pickles - ranch - dressing - lentils - translate - toothpaste - rearrange - groups - santana - pritzker - winners - libertarian - mc's - vitaly - nfl - mythical - oriented - provisional - experiences - safely - themselves - mia - reducing - learly - court - vin - diesel - netbooks - chinatown - aberdeen - queens - luni - purchasing - timing - bagmati - narrow - egypt - represented - revelation - britain - aamir - priyanka - middleton - base - original - nhl - goal - scorers - osteoperosis - laws - correlation - motivation - ncaaa - tense - touring - framework - adel - diamond - schwarzenegger's - stomachs - cow - chairs - steph - subjegant - pategonia - michelle - todlers - stakes - tinder - matches - fjord - equator - triumph - hell - moldova - presley's - wa - rajinikanth - basalt - bali - airplane - hash - lit - <sos/eos> two_pass: false pre_postencoder_norm: false init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false transcript_token_list: - <blank> - <unk> - the - to - i - me - you - is - what - please - my - a - for - 'on' - in - of - email - this - it - have - from - and - play - olly - that - new - can - do - how - tell - about - at - any - today - not - time - are - check - list - send - with - an - one - emails - last - will - am - again - set - next - would - was - up - like - turn - said - calendar - meeting - get - what's - right - all - did - be - need - want - song - tweet - add - event - your - news - 'off' - weather - there - lights - more - now - alarm - pm - music - show - confirm - train - could - think - does - make - command - just - find - when - tomorrow - much - where - week - by - give - events - know - day - start - two - latest - response - that's - remind - done - but - thank - stock - some - you've - answer - five - open - current - many - remove - radio - good - book - 'no' - facebook - going - it's - volume - reply - work - delete - go - complaint - contact - if - service - let - thanks - so - hear - once - correct - john - playlist - birthday - got - post - ten - order - sorry - has - date - hey - coffee - who - rate - three - exchange - further - light - twenty - price - mail - reminder - explain - podcast - ticket - down - really - clear - seven - schedule - alarms - say - morning - change - twitter - cancel - number - dollar - stop - out - appreciated - hundred - wrong - don't - information - address - contacts - read - york - us - which - should - 'yes' - details - songs - between - nine - anything - s1 - received - playing - shut - dot - mind - com - google - most - put - job - traffic - four - best - six - create - recent - yeah - happening - friday - name - very - area - mom - or - take - appointment - yeap - room - world - home - hour - message - eight - clarify - s2 - party - episode - here - elaborate - alexa - appreciate - customer - i'd - sent - thing - march - look - tonight - place - try - after - definition - call - well - times - rock - phone - speak - today's - whats - food - thirty - see - joke - every - pizza - write - lists - game - shopping - weekend - rephrase - month - matter - s - update - station - vacuum - great - detail - long - gmail - old - repeat - city - audiobook - perfectly - status - inbox - mute - local - near - restaurant - thousand - tuesday - year - we - media - before - around - resume - musch - her - house - taxi - hours - didn't - describe - answers - understand - incorrect - word - listen - first - item - d - trump - save - days - socket - recipe - nice - u - reminders - social - search - as - monday - subject - location - movie - saturday - euro - dinner - them - ask - let's - scheduled - plug - i'm - gotten - question - minutes - friend - favorite - meetings - define - instructions - exactly - cook - understood - sentence - thursday - grocery - correcly - their - words - temperature - person - amazon - catch - company - mean - something - correctly - living - fantastic - help - following - dollars - rain - speakers - instruction - helpful - increase - consumer - evening - family - upcoming - jazz - saying - way - switch - forecast - task - cleaner - love - late - boss - wednesday - yesterday - updates - lower - people - cool - wonderful - twelve - afternoon - color - wake - oh - lunch - perfect - back - understanding - useful - amazing - his - dim - movies - chicago - things - takeaway - fifty - unread - happy - available - noon - wouldn't - night - had - appointments - idea - michael - doing - over - doesn't - select - hi - shit - may - they - delivery - nearest - buy - apple - car - left - confirmed - report - worth - robot - uber - wemo - sunday - excellent - outside - blue - looking - messages - top - wear - point - too - i've - country - prices - bring - store - awesome - unclear - ok - mark - speaker - app - sound - hot - live - jackson - bad - recently - currently - smith - pull - whatever - india - messed - kitchen - ninety - percent - him - use - office - brightness - care - gave - description - tom - regarding - meaning - meet - siri - bob - joe - hmm - leave - sarah - smart - come - chicken - seventeen - walmart - bill - enough - choose - louder - our - trending - born - london - zone - account - cnn - audio - president - isn't - compose - coming - second - manner - pick - album - uhh - plus - provide - erase - notification - played - channel - donald - pound - instagram - made - bbc - recommend - happened - united - replay - shop - free - dammit - nope - b - nearby - pop - shops - california - highest - notifications - shuffle - fm - chinese - currency - uh - restaurants - jack - april - robert - only - been - why - states - friends - skip - important - he - samsung - later - notify - bedroom - john's - mails - eleven - red - exact - cold - cup - rates - incorrectly - fifth - money - boston - spoke - tomorrow's - forward - respond - funny - wait - business - market - star - headlines - third - favorites - bother - retry - stocks - high - g - favourite - george - umbrella - directions - wedding - content - m - close - spoken - concert - run - alert - searching - mary - into - artist - located - mike - anyone - snow - tickets - then - reset - garden - route - hello - tall - likes - talk - forty - share - feed - were - indian - washington - difference - remember - convert - receive - tune - level - asking - capital - life - dad - yen - street - raining - mistake - correctly? - quite - pandora - jane - town - yet - player - park - san - american - far - sports - raise - popular - display - these - couldn't - mountain - dentist - importance - unimportant - complain - clean - continue - euros - los - ready - yahoo - can't - classical - politics - newest - lighting - miami - trip - horrible - info - added - prepare - iphone - machine - mother - miles - via - chris - tv - since - bathroom - state - cheese - request - items - oops - ah - closest - warm - microsoft - settings - value - keep - brighter - note - everything - wife - decrease - okay - using - rap - election - sunny - eat - usa - eighty - fifteen - until - wanted - wrongly - dog - obama - years - coat - week's - japan - quiet - paris - angeles - comcast - target - emailed - airport - interesting - mcdonalds - mr - married - green - product - past - little - other - t - listening - cooking - activate - earth - dance - title - florida - rupee - travel - kids - takeout - pending - america - making - its - than - doctor - population - bar - plans - power - fourth - silent - ride - milk - how's - seventy - sure - fine - jennifer - july - sister - brighten - picture - deliver - singer - clock - inform - brad - burger - never - pesos - object - hero - arrive - classic - olive - games - group - watch - line - justin - cost - project - called - lets - track - still - starbucks - form - repeating - christmas - breaking - due - cheapest - forget - posted - james - posts - central - lot - stories - whole - small - ever - steak - review - requested - wish - david - workout - alex - seems - given - gym - largest - la - average - compare - china - fifteenth - having - rupees - band - background - meal - online - reserve - file - lamp - laugh - sun - anniversary - eastern - busy - mobile - bit - jokes - places - geographic - else - chess - meant - working - p - planned - program - seconds - rated - large - issues - road - pay - big - holiday - daily - 'true' - celebrity - better - hut - being - sixty - away - helped - peter - god - cab - someone - internet - page - anna - feel - video - steve - opening - lately - sandy - bank - weeks - id - sam - pitt - river - february - i'll - saved - soup - phrase - distance - economy - hits - sony - eggs - low - water - text - topic - co - begin - attend - groceries - adele - reach - within - pause - half - yourself - kind - dark - replied - enter - must - asked - beatles - fun - ingredients - against - invite - soon - colour - different - jacket - updated - seattle - denver - canada - vegas - mode - pasta - january - doe - listed - refresh - listened - team - longest - spotify - remainder - telling - mumbai - you're - orlando - card - rice - during - reduce - locate - future - starting - boil - genre - class - slow - famous - named - allen - youtube - works - olly's - dc - brew - through - pounds - football - pacific - white - sings - egg - oil - festival - clothes - moment - die - orange - school - kim - las - divided - whether - photo - everyday - ryan - bills - headline - fix - square - npr - jake - brother - todays - terrible - weekly - type - topics - months - chat - yoga - reading - products - extra - cut - adjust - king - personal - client - jan - data - doctor's - computer - rohit - johns - o'clock - canadian - mistakes - rid - names - control - sunscreen - per - lady - head - taylor - always - budget - pink - bought - x - side - ahead - articles - english - ny - able - reschedule - fast - hashtag - tweets - countries - numbers - running - alabama - blank - madonna - bright - yellow - west - went - options - story - october - russia - together - n - basketball - joe's - dominos - tomorrows - less - situation - colors - mom's - end - payment - drop - downtown - provider - joes - means - helping - mexican - friday's - cricket - return - needed - death - tech - charlotte - heavy - draft - sea - paul - r - condition - seventh - dallas - hip - related - article - heard - war - elvis - everest - problem - stating - bieber - system - sales - shoes - hard - become - based - kevin - age - she - quality - mile - hair - gas - biggest - inr - climate - hate - twentieth - sucks - dean - angelina - turkey - harry - cake - national - record - longer - dave - subjects - brown - supposed - ocean - church - drive - gandhi - needs - above - theatre - cookies - abraham - gone - map - television - such - face - sale - jim - francisco - sean - june - romantic - compared - curry - ball - jeff - subway - lincoln - bed - lagos - turned - south - won - trains - girlfriend - mahatma - nsa - hop - amy - commute - solve - came - created - dont - history - math - telephone - says - laptop - pawel - offer - fox - single - sixth - midnight - missed - potter - loud - richard - chuck - looks - practice - body - dan - husband - waiting - birth - stuff - adam - sender - gaga - truck - france - texas - restart - intel - colours - statue - liberty - intensity - previous - problems - outlook - visit - wine - peso - continent - utterance - helps - asssistance - each - north - grand - patrick - match - opinion - plan - trump's - papa - instead - martin - root - purchase - perry - richards - closing - cloudy - eddie - senders - move - susan - tesco - size - shows - folder - spaghetti - doctors - stores - presidential - dates - theater - menu - agenda - ann - code - animal - frequency - kansas - roomba - technology - tasks - without - flight - who's - beach - empty - tired - driving - entire - carry - british - dr - asia - rccg - uncle - vacation - pepperoni - programme - standard - reminding - maximum - starts - tallest - gonna - fourteenth - playback - medium - nike - cruise - changed - diego - arrange - bowie - learn - mount - particular - costumer - sundays - fire - calls - silence - podcasts - spain - dominoes - website - italy - strongly - agree - agreed - suggest - mood - fourteen - result - metallica - thinking - session - profile - england - active - ohio - grid - fall - pot - marriage - queue - told - narendra - jerry - mt - frank - tenth - wishes - recording - finished - international - calculate - hit - towers - ninth - site - feeling - macy's - tag - actually - black - birthdays - hottest - mary's - expect - snapchat - jay - smith's - mountains - building - setting - cleaning - height - initiate - hall - breakfast - martha - conference - aol - win - steps - fancy - smartphone - led - zeppelin - houses - holy - currencies - club - children - atlanta - einstein - happen - cell - landline - coworker - objects - negative - modi - soft - haven't - mention - radius - books - daughter - results - earlier - bruce - butter - stars - remaining - delivers - device - domino's - unmute - joy - twelfth - voice - taking - snowing - sick - boots - cleveland - journey - destination - worker - poker - lee - katy - australia - incoming - least - lisa - experience - million - recurring - scenario - sacramento - geography - library - brief - jolie - monthly - elton - sirius - alaska - lyrics - oven - log - random - moscow - barack - disney - alive - measurements - maker - poor - error - stone - versus - hotmail - interpret - sarah's - memorial - goes - stay - delhi - health - special - speed - thirteen - test - edinburgh - credit - facts - cat - neighborhood - sometime - empire - entry - financial - comment - link - hockey - circuit - holidays - singh - jodhpur - rockville - ones - features - bread - eye - mall - directv - contain - seacrest - chance - under - table - few - hotel - rude - services - yesterday's - certain - fb - abc - netflix - linda - notes - length - reminded - shoe - wild - employees - beef - sushi - fastest - thirteenth - recommendations - fish - tennis - main - jersey - jones - break - concerts - gomez - angry - uk - replies - emily - kickball - released - upload - effects - quickest - italian - caroline - emma - real - human - minute - took - activity - jeff's - staff - handler - touch - hold - joanne - range - moon - submit - ends - tomato - lost - prime - twelveth - phones - amd - hectic - bobburgers - screwed - porch - reviews - vegan - rihanna - houston - ham - mondays - general - engaged - walk - melody - electronic - held - selected - equal - getting - tata - wall - clothing - round - leaving - nasdaq - total - pressure - expensive - border - exhibition - trash - november - handle - halloween - attachment - kardashian - shoot - rewind - rating - toronto - department - procedure - member - ray - chelsea - rohan - arrow - checked - modify - wasn't - chances - protest - lottery - prince - include - jo - net - pie - sleep - enjoy - nineties - taco - banana - source - quieter - bored - desert - guys - gary - activities - already - contract - st - minister - disable - woman - europe - arijit - audible - presentation - cad - records - trips - booking - tacos - sally - non - centre - direct - advance - selena - policy - orders - stefan - arrival - divide - chocolate - dish - teeth - hdfc - silvia - stove - coast - defined - digest - snafu - manager - pinterest - tim - conversation - bulldog - titanic - brunch - heat - canyon - dial - earliest - region - stopped - foreign - folk - watching - brexit - albert - joejoe - early - cities - manchester - december - biloxi - often - questions - garage - tunes - possible - ms - ar - kiss - shares - bangalore - heading - derek's - desk - cheers - tomasz - terms - companyname - sara - asap - super - meryl - streep - rent - dress - cinema - usually - trend - conversion - friendly - ties - ordered - electricity - marked - migration - choice - journal - norris - aniston - mailbox - minus - fried - miley - cyrus - newly - theory - rest - swift - windy - dan's - mass - comes - selfie - wings - julie - masti - celine - plays - pack - including - responded - jason's - ale - apples - dolly - oranges - lg - washer - substitute - global - feedback - grandma - ben - drainage - invoice - sunset - takeaways - man - art - universe - suitable - antonio - full - delivered - laundry - wrote - min - register - snap - nixon - bird - spend - rome - jesse - calories - cappuccino - quickly - buying - britney - spears - spacey - jobs - arriving - jean - potholes - janet - pictures - ashwin - morgan - freeman - baby - microwave - yellowstone - francis - dubai - invitation - hope - melbourne - rocky - kroger - rivers - charles - jim's - rectify - statement - carpet - baked - jessica - meatballs - mushrooms - amount - switzerland - relating - zero - front - phonebook - hows - cheesecake - carryout - magic - ola - replace - recorded - access - land - where's - elephant - removed - liz - load - metal - package - diner - goog - bob's - k - year's - mars - guy - assistant - rahman - eagle - part - burn - aran - stevens - daughter's - eighteen - chemistry - action - selling - thats - koc - lines - sugar - major - chair - easter - departing - africa - nigeria - requests - conditions - you'll - manhattan - roll - cracow - candy - crush - bell - massive - gold - happens - usual - andrew - equals - dead - plane - graduation - warned - shaun - triangle - wyatt's - pass - function - max - space - programmes - awful - parton - exciting - battery - hwu - recipes - dirham - rushmore - johndoe - button - express - pontificate - easiest - magda - selection - reservations - guess - copy - classes - supplies - schedules - winning - berkeley - notice - headed - outgoing - mi - rainy - wikipedia - entertainment - dow - everyone - aunt - furniture - oceans - softer - heart - newmail - while - baseball - easy - stations - philadelphia - alice - swat - yearly - poem - soccer - president's - milan - paper - kardashian's - loop - shown - sandals - yo - scan - nevada - apahelp - coldplay - french - bay - higher - rumplestiltskin - airlines - fresh - standing - cream - hamburger - broadway - oscars - tokyo - cable - shipment - formula - teacher - sweet - golden - newsfeed - confirmation - shirt - austin - own - canon - wanna - gods - spanish - count - seat - ideas - study - tara - mutual - jennifer's - because - edit - denmark - direction - timer - growth - luther - marketing - cd - mine - public - peter's - bolshoi - flat - crazy - others - dry - pub - theatres - bro - fashion - teams - cycle - pickup - dion - teach - series - checkout - male - noise - solitaire - pf - cassie - travelling - davis - naty - income - disco - dropping - donna - follow - shelly - accidents - plot - irene - download - circle - law - tea - organize - principal - weekends - camera - solution - bombay - wuthering - heights - charged - colorado - kong - keys - race - mona - entries - j - nyc - potatoes - gospel - raju - trivia - bike - dating - oregon - event's - prefers - rush - percentages - peking - cooker - husbands - won't - tower - heaven - hugh - june's - fake - figure - purple - takes - l - howard - stern - nineteen - percentage - motorola - doe's - outstanding - tesla - laura - dale - warning - eighteenth - golf - island - career - bieber's - vacuuming - pizzas - refund - weekday - s's - derek - thanksgiving - delayed - query - buffet - rachel - pants - wash - survey - photos - except - topography - door - jen - queen - depart - cheap - theaters - web - jesse's - multiply - workhouse - press - click - loss - recipient - verizon - volcano - rolls - royce - pixel - affirmative - completing - thai - walking - bananas - hollywood - equation - dirty - scores - katrina - exam - creating - letter - sing - construction - broadcast - tom's - rupies - management - permanently - converting - ist - iron - religion - kings - tucson - standup - tic - tac - toe - headset - sex - diapers - purpose - seventeenth - eighth - dylan - temple - refer - gift - fact - drink - inches - air - carpets - newcastle - clients - private - tasting - sams - nj - chili - cultural - swimming - they're - iowa - jordan - period - accept - cincinnati - college - rainbow - myself - deep - deepest - warming - sky - vp - seeing - indianapolis - kmart - nikesupport - image - suck - broiler - timeline - dell - parisa - brandon - example - y - filter - sad - shine - sixteen - christian - pic - pdr - fry - another - network - omelette - kilometers - municipality - giving - leo - cups - earthquake - susan's - application - cross - across - carl - pawel's - sauce - relativity - rail - sisters - letting - shorts - vs - rajesh - swift's - starving - discussing - block - written - n9ne - women - celebrities - bake - cookie - continents - workers - leonardo - mel - gibson - shall - beauty - sum - fair - deli - middle - same - nile - sell - role - boat - sandwich - parts - hearing - knows - sand - manoj - delivering - rahul - neil - australian - kindly - properly - assist - esurance - emilia - breach - loudly - harvard - marc - nintendo - scrabble - farm - lie - patio - greg - screen - degrees - yesterdays - carrots - receipt - lasagna - clooney - there's - degree - preferences - hallway - latin - nicest - lauren - worst - also - checkers - input - boyfriend - masala - tournament - monet's - burmuda - section - eric - japanese - supervisor - junk - performance - effective - urgent - oldest - tone - sweater - goa - bag - lowest - aus - peace - julia - summer - fan - hurricane - colder - steven - sachin - tendulkar - watson - exorbitant - bags - macs - yulia - matthew - pole - toby - pennsylvania - carmen - tiffany - complete - electric - wallet - albums - maths - distribution - eminem - familiar - regard - upwards - ron - couple - acme - angel - zoo - nineteenth - shazam - inflation - offers - devotional - jackie - tony - artificial - intelligence - grill - father - predictions - repeats - manila - cooked - reason - learning - nowadays - cheer - jingle - bells - anxiety - hoizer - girl - pondichery - position - teachers - dictionary - nap - cafe - m's - meting - crime - eve - horn - bristol - pubs - companies - johnson - resolve - waterfall - female - biriyani - drama - nothappy - haircut - remote - colleagues - bones - saturdays - cambridge - jam - maine - category - invented - chang's - boy - planning - chen - assignment - publish - hunt - alerts - dad's - deal - leading - trail - follows - young - jay's - summary - ko - beyonce - vergara - mexico - whishes - arrived - placid - specific - depot - tikka - expire - markets - problematic - highly - blues - thirtieth - brooklyn - tatum - argentinian - redso - des - moines - women's - richard's - cellphone - division - hong - political - charley's - steakhouse - accident - normal - wakeup - satellite - freezing - forex - jimmy - chores - snooze - design - museum - guide - speech - ran - shift - inferior - mashed - jcpenney - environment - raw - disturbed - sia - chips - anybody - present - reynolds - limbaugh - weekdays - islands - viral - asian - streets - inception - meatloaf - alternative - compliant - sensex - phil - est - hand - switched - recap - ferrari - nandy - promotion - kate - brothers - ma - followers - closer - deleted - gloves - bands - platter - boland - corner - strong - chipotle - eu - amtrak - son - charges - version - rajdhani - chart - manage - musical - hat - den - tonight's - syria - stronger - homelessness - nails - support - ally - sentences - penn - ago - turning - center - hungry - actress - keywords - usain - bolt - ongoing - cancelled - idol - julia's - wells - fargo - ri - sarahs - computers - devices - toms - regards - quote - production - brother's - inch - shell - marathon - directory - dictate - huey - lewis - elections - alone - marry - apart - danielle - jane's - mankind - singularity - nye - feynman - whom - inventory - makes - dept - apple's - education - bugs - settle - when's - geographical - jason - exchanges - mcdonald's - tgi - ship - hershey - facing - faulty - zita - jeremy - irons - wallmart - sphere - hp - gottten - pardon - engagement - showing - format - absolute - interest - messenger - gate - enable - columbus - hips - tour - sterling - thumbs - priced - tablet - amc - bible - safeway - organism - undertake - freedom - charger - documents - jars - clay - members - o - vegetables - delicious - beaumont - tx - finance - exhibitions - trumps - month's - v - applebee - dakota - bus - brighton - pa - darken - promoted - liverpool - utah - suggestions - micheal - complaints - pencil - keith - fridays - temperatures - hardware - exercise - jpearsonjessica - release - hoover - goshen - chester - wood - woodchuck - healthcare - borges - calculator - dune - reality - jobe - gossip - piece - convenient - titled - pork - belongs - hongbin - wreck - tool - started - gather - bruno - costa - patel - daniel - corporate - controversy - wendy's - texans - biography - flowers - investing - arrives - finish - spot - crop - culture - enjoying - fetch - kill - auto - washing - buffalo - he's - titles - ross - whose - types - pleasant - erin - madison - tuesday's - lif - khan - affordable - season - policies - c - expected - hypothesis - seth - kicked - unhappy - gallery - xorg - used - monali - thakur - noodles - cher - sally's - tracks - mid - launch - glasgow - bridge - releases - pitt's - server - clarity - yens - motivational - scratch - blanket - aib - reads - singing - monas - tuesdays - winter - rocket - lands - chan - economic - sister's - aa - film - pb - indiana - departure - pipeline - stitch - sleeved - hail - logan - style - quantum - physics - labeled - delia - began - rrcg - shape - awards - improve - pertaining - trance - lives - weight - met - brian - sinatra - sunglasses - attending - falls - requesting - sunday's - overhead - greg's - rom - historic - georgia - guest - jaipur - iroomba - alfredo - pride - prejudice - fill - interview - daddy - wangs - manchow - university - locally - lowes - tiring - east - medical - metro - bach - schubert - rooster - czk - channing - pad's - identify - yelp - scandal - affect - suffering - enabled - arby's - saw - mango - itunes - highlights - brings - sixteenth - tourist - wendys - presley - sold - intern - affairs - fries - buttermilk - panda - wants - floor - clint - eastwood - moe's - planets - equivalent - morrocco - gravity - uploaded - someplace - availability - issue - fly - jpy - natural - delta - disappointed - files - q - cindy - shortest - simple - ring - lotion - maroon - fort - died - bonus - repetitive - icecream - statistics - rebel - lawn - leith - measure - daytime - september - pilots - pda's - shade - sil - cap - punjab - gwalior - ashley - juice - nagar - ellen - programs - fairs - invest - suits - ingredient - launches - leaves - bjork - crater - elevation - stewart - hotels - spices - bubbles - grass - broccoli - capricious - philosophy - anthony's - apply - pings - gps - thomas - koontz - acdc - beijing - ratings - union - prayer - todo - angles - scissors - stashable - cinch - bacon - passive - que - occurred - lakeland - tulsa - advise - singapore - risotto - invested - model - helmsworth - bench - julian - buddy - rogers - brains - chap - badminton - dick - lopez - apartment - points - germany - unknown - thugs - healthy - rash - casey - oriam - ps - plants - mailed - ikoyi - grassmarket - marleen's - locations - bush - mac - reaching - allan - till - cheering - guitar - oxford - densely - populated - son's - hubby - comparison - putin - barcelona - gss - energy - pan - nyack - worked - unavailable - bryan - adams - miss - checkbook - jared's - enrique - iglesias - forms - jeans - voices - alan - tudek - animals - olx - mts - freed - jenn's - coordinates - humid - demographic - otherwise - tiffany's - outdoor - sheila - lincon - dust - serve - conduct - estimated - gaana - funds - downloaded - indignation - meijer - necessary - grubhub - pancakes - mario - bars - birmingham - sites - donuts - chopra - textual - rapids - cant - prefix - sounds - provides - amy's - benton - leeds - dsw - returning - defective - digital - bhaji - carlos - linux - upgrade - shark - attacks - screening - exposure - souffle - tracking - od - progress - paused - gilmore - hour's - imdb - orleans - european - gdp - surfers - theme - ash - ikea - klm - marilia - cars - robin - williams - surfin - ottawa - trade - contains - field - someone's - prague - brno - rene - interests - radiolab - harris - strive - accommodating - fell - relationship - pharmacy - memo - nancy - paid - expressing - disapproval - yard - royale - hide - amber - cheeseburger - coca - cola - al - matrimony - scott - potato - funniest - polling - mother's - chase - xmtune - matt - murphy - detroit - taiwan - organic - secrets - domino - ac - assistants - z - fred - owner - required - saga - hanks - trading - erosser - rosser - vikki - dhaka - notepad - oldies - alison - recur - w - mentioning - languages - lavender - toned - videos - stein - chennai - resuming - moms - foke - beep - discussion - woodland - lowry - meetups - powerball - toyota - focus - concentrate - nbc - roosendaal - deactivate - shrimp - parmigiana - bumper - spouses - lucknow - paying - hurry - served - rhythm - enquiry - hartford - plaza - hyundai - wishing - websites - briefing - complex - calculations - jarvis - highway - fired - dissatisfied - sandra - bullock - ratio - haskell - sharon - horse - mum's - dillinger - sunblock - sub - tab - crude - software - stadium - step - short - reddit - appoints - agra - sheet - keyboard - kfi - district - connery - carnival - wok - shutting - phoenix - cloth - rehan - lego - alphabetical - mexco - charles's - foodpoisoning - ultra - madonna's - harley - davidson - daylight - afi - infy - launched - inboxes - secretary - increased - resolving - fuel - injector - multiple - interval - mike's - espresso - sasha - susie - salesperson - country's - cylinder - specifications - ivory - pst - zoella's - jackman - reacting - potential - frying - boise - wendy - divisible - automated - katherine - pre - gaming - containing - decade - industry - foot - chemical - cause - taste - bra - julianne - hough - addresses - vonstaragrabber - lion - restroom - kohl's - mentioned - hz - royal - bloodline - relationships - billings - levin - quarter - lori's - lori - exclamation - definitions - birds - raj - priya - allows - worlds - kelly - clarkson - garam - scarlet - found - cub - dmv - excessively - lake - dried - reporting - smile - changes - charmin - eternal - smoked - meat - beanos - processing - chip - logic - insightbb - highland - terrace - child - peck - midwest - cardinal - anthony - barrack - jancy - thompson - cassy - gulls - alternate - sin - dragons - msnbc - residential - leader - siblings - pedro - serendipitous - bestbuy - targets - wawa - mentions - engagements - hawaii - jr - applied - halifax - ahmedabad - monty - python - stronomy - blahblah - blah - arrivals - subtract - payoneer - formal - connors - indranagar - transform - marcia - perpetual - arranging - cvs - callum - steffi - attention - kanye - mommy - chucky - forest - polarized - proposal - conrad - coldest - hue - dictator - clancy - geranium - delays - build - lense - rai - transistor - dildo - warren - exercises - forman - kinley - bottle - retail - yan - regal - unprofessional - annual - payday - tricep - arts - ripped - vietnam - trends - chaise - preparation - nestle - paula - deen's - bmw - microsoft's - bookstore - below - moving - pretty - lock - administrator - edition - airways - marvel - garner's - rubix - cube - kfc - milwaukee - pager - alexander - gilchrist - goods - performing - unopened - security - chain - probiotic - colleague - knowing - novel - fiesta - comcasts - acer - farmers - fraud - weighing - india's - gotse - grapefruit - similar - tmobile - nifty - sessions - recital - greatest - openings - zip - demento - fatigued - disease - prevention - overcharged - unquote - cotton - tweeter - railways - flipkart - fist - renee - nutritional - starred - calculated - mattress - hillstead - paul's - jill's - disregard - pesto - stinks - nobody - behind - kid - nature - ounces - ted - boiled - dancom - wars - fmod - span - along - malls - joining - frequently - realdonaldtrump - bobby - mcgee - pwd - obamacare - clicked - falling - pampers - virgin - hayden - pat - amie - infosys - technologies - roads - aerosmith - airtel - dairy - sends - dues - tobytoday - ileana - d'cruz - rended - taj - ashok - typhoon - rama - final - missouri - virginia - announce - haughty - salmon - joking - goodnight - rebecca - believe - vowels - ban - haze - insight - cable's - fellow - tweeters - canoe - warriors - assassinated - acceleration - detailed - wife's - robert's - angus - interested - jen's - sjobs - cdn - ruth - simran - aapa - kadai - armor - sms - indefatigable - indicate - fra - floors - modcloth - honor - weigh - priority - hiking - smoky - judawa - expense - deals - plethora - sam's - august - elain - bbq - leap - congressional - representatives - voting - reproductive - ge - bbb - contacted - assigned - jill - drafts - scoring - touches - relevance - goggins - medvesek - philippiness - booked - board - locality - beth - katey - fans - approximately - charitable - rae - darker - anymore - printing - significance - fondle - mate - larry's - larrylarry - faripir - gurpur - seasons - softball - refreshments - jamie - carrie - underwood - abdul - kalam - subterranean - colombo - sri - lanka - quit - dollar's - award - among - spouse - forgot - ass - millionaire - indians - americas - julie's - transcribe - garbage - geographics - tree - criticize - tanzania - heather's - answering - spam - phishing - reseda - axel - kailey - prettiest - century - mattel - toys - grateful - fixing - maidan - sophia - betty - reasons - russian - applicable - loving - claire - crashed - batteries - philips - person's - compile - ali - matthews - apologize - comcastcom - luke - jean's - carefully - beg - trying - flooringco - seams - baking - skiing - calming - continuously - tale - roraima - innova - bowling - beginning - identifier - diverse - santa - continuous - hangman - vegetarian - roast - rewards - allow - immediately - shelley - hennessey - waking - dicaprio - ways - immigration - raised - lose - digger - cosmetic - perth - feet - chick - tornadoes - upstairs - badly - timings - lobster - runner - forum - thunderstorms - powered - plugged - rod - mgccc - bleed - ga - pune - mixed - dishes - radisson - cheetah - what'sapp - cm - father's - skill - graham - eggless - collect - favorited - flag - ssmith - virtual - bryant - spots - scapingyards - washed - springfield - draw - insurance - quantity - brightener - cuba - stream - raincoat - maiden - soundtracks - deliveroo - humidity - crowded - built - mesa - rosenstock - workpdf - occurring - environmental - dbell - converse - radia - logged - scabble - loads - jacob - hasbro - aldi - piramid - completely - method - hems - loose - connect - snapchats - arizona - festivals - hospital - peppers - bowl - korn - lupe - eurostar - umf - unchecked - berlin - lane - synonyms - hampshire - shakira - brads - keanu - reeves - johns's - increasing - burgers - stan - falklands - valley - maria - hangin - glow - we're - newsource - clark - carrey - jams - crashing - outback - sugars - defines - joel - venue - huffington - images - elizabeth - case - agnes - randomly - mecky - incredible - even - decreased - vacations - honey - akon - barbara - handsome - forensic - spielberg - korea - coding - achievements - albert's - clerk - hopes - zimbabwe - buble - research - excel - gun - rogen - resin - tooth - filling - mody - marinara - vicki's - mardi - gras - monika - relatives - chillin - lol - levis - tricounty - messy - disgusted - emoteck - foroogh - quick - decline - emailstudy - atdfd - giant - trey - kalka - mcdo - timestamp - operate - watched - infinity - tactics - upbeat - synonym - racing - towards - fog - muted - coke - eighties - tvs - theresa - brent - kamycka - dejvicka - tap - peanut - circumference - saskatoon - sync - sofa - mcdonald - silenced - catalogue - algorithm - sanctimonious - talked - realize - reveca - paok - wipe - bisque - br - rather - silly - stat - tar - vitamins - gain - xm - fongs - anywhere - zanes - se - chronicles - weber - commence - causes - sangli - german - hedges - truthdig - coffees - commuter - plain - mimo's - oscar - restrictions - treasure - louis - stevenson - fifa - beast - pav - prambors - hannah - ringcast - vegetable - episodes - overnight - apps - nathan - dismiss - karl - hourly - eyes - breeds - inside - tribune - join - crabmeat - shakira's - yankee - greenwich - gala - jump - recall - johnny - cash - pod - cast - rare - suppose - enjoyment - emo - nayagara - passion - pit - marckel - bohemian - emma's - arijit's - pet - prize - receptionist's - beat - freds - probles - patagonia - quart - '?' - zach - duration - jlo - alphabetic - phohouse - badpho - daybreak - biryani - battle - divergent - moby - jungle - jaiho - casserole - shooter - columbine - wednesdays - soul - accumulation - squash - calm - debate - schools - amd's - lee's - managers - myspace - relaxing - bahar - antarctica - atmosphere - pinpoint - payments - illinois - louisiana - cfo - pool - vyas - morel - mysore - rise - sdfa - newspaper - calorie - dangerous - sunrise - mostly - dining - shake - flood - prescription - mix - view - jana - spa - comments - pear - factor - clearance - northern - language - arnold - exxon - mobil - dragon - fruit - differences - seashells - seashore - velocity - motorolla - haggis - fiji - irwin - similarities - hypertrophy - sharukh - implement - kazakhstan - mediterranean - roman - grigorean - hardword - quead - amphibious - roberts - climatic - tornado - prone - rising - declining - megatel - denzel - washington's - citizens - arm - persos - belarus - gyllenhal - geology - helicopter - iphone's - drained - manger - navy - daikin - jerk - nexus - interaction - platform - tweeting - at&t - mahaboobsayyad - kellogg - ashmit - ismail - listing - enalen - projects - clara - clinic - exams - ammunition - mark's - divya - jjnzt - activation - andy - terry's - brenden - jeffrey - burnette - protests - joshua - pianist - whiz - schadenfraude - rials - storage - bot - provided - massachusetts - channin - store's - rump - prior - re - intelligent - recognise - irobot - areas - lighter - yell - uses - cn - gadgets - skynet - marie - lamb - balcony - nyt - bennett - ralph - pda - balloon - maps - degeneres - character - evans - actor - fitbit - malika - shivaji - attitude - lily's - concerned - upon - startup - stuffs - tawa - relative - legacy - cst - leah - remini - mortgage - amed - cleaners - seal - abita - grammar - backdoor - minimize - leisure - billie - spicy - training - comfortably - sunburn - minneapolis - habits - braking - notifier - swan - thoughts - pleasure - those - kashmirstart - sells - i'dl - kettle - 'false' - rta - valia's - visiting - techno - mornings - mow - cbs - slightly - francine - vice - postpone - mins - xyz - hwood - kept - spider - reopen - billy - connery's - eiffel - itinerary - crash - valentine's - likexchange - divorce - danville - il - government - menus - capabara - origin - assistance - vicinity - chit - drinks - flabbergasted - xy - self - double - castle - refrigerator - bakery - spray - pyramids - bio - basic - humans - schwarzenegger - inchoate - rules - caftan - raleigh - hobby - ajay - devgn - corden - aud - prevailing - kenny's - crew - aww - spying - employer - thier - juanpedro - craig - leon's - looked - players - costs - providers - sydney - documentary - hyphen - represent - strings - pianos - acoustical - celeb - pong - linear - turn_down - reaches - strength - routine - billboard - piano - ed - sheeran - diet - vietnamese - yams - grandmother's - rihana - require - stressed - option - affected - acquire - retrieve - clarion - congress - turiellos - mates - solar - dice - jalapenos - wished - painting - therapy - warehouse - mop - neighbor - flappy - returns - someones - spring - wonton - moves - jagger - fishing - hiphop - dunkin - donut - atlantic - daughters - hula - hoop - lessons - scrote's - indie - grief - lebron - naughty - preprogrammed - alt - needy - sharpen - butcher - knife - pulled - starbuck's - backward - terrorist - invaders - parent - crescent - brewhouse - prado - science - playlists - debbie's - sleeping - searched - lindsey - lohan - competitions - subtracting - challenge - beer - gainers - chili's - frubs - police - softly - practical - assessment - bonefish - rotating - placed - lakers - barenaked - ladies - lord - rings - mar - sneakers - artists - sanantha - shuffles - shuffled - bardonia - county - analyze - pattern - girls - league - fjords - nothing - brewing - smurfs - tommy's - lovin - cottage - ming - photosynthesis - danny's - repeated - peaceful - migrations - zydeco - inkheart - seller - occurence - telegraph - invited - wifi - levels - willie - nelson - dolores - alter - retirement - professional - development - sainsburys - byron's - floyd - raingear - notorious - bone - explanation - database - likely - lucky - irish - sshow - ramsey - aired - sprint - preparing - academy - yeshudas - angels - dancing - aretha - franklin's - layers - glass - kuch - hai - wakey - knitting - mujhe - feb - king's - malinda - parents - mirchi - gallon - seen - parks - safest - evacuation - beautiful - sofia - francs - consequences - various - dicaprio's - networth - phelps - disk - constructed - concern - effectively - lawrence - zac - galifrankas - wheat - prediction - schemes - mega - capricorns - dinky - lanegan's - princess - pregnant - smallest - americans - retweet - insta - sonys - bk - alzacz - kohls - cleanliness - pizzahut - delay - lpg - satisfied - choke - suqcom - repairs - killing - miller - budgets - iamironman - gbaby - gma - loves - kate's - margaret - ben's - brady - palmer - homework - tax - regional - archive - fitness - vault - footloose - child's - damage - petco - canceled - passing - pikes - peak - avatar - diverge - maron - fault - sword - eventual - contest - dangal - mauritania - abs - wondering - southampton - resources - soy - lexmark's - hilly - lyon - beirut - tribute - madrid - ate - sweat - charlize - theron - atif - aslam - capture - actual - shane - dawson - zedd - snooker - loquaciousness - sholay - tofu - nightmare - avenged - sevenfold - matters - prompt - panic - brilliant - boston's - mckinleyville - astrology - strait - countdown - cats - fruits - embassy - pita - gyros - negotiations - hairdresser - courteous - enthusiastic - funk - sense - heathens - cabinet - irctc - stored - shutoff - glasses - ella - fitzgerald - rover's - vet - polar - bears - oceanside - medicine - anita - barrow - burrito - oliver - covering - ground - zucchini - textile - antebellum - chimes - covington - species - bees - cranston - kilometer - behaved - rudely - jimi - hendrix - calms - outwards - califonia - composed - hint - shipping - frosting - sport - napoleon - hill - athens - middletown - shirts - sample - politician - investigated - rapper - con - cuisine - wizard - brick - conroe - iterate - architect - salon - babaji - passed - maryland - surya - monopoly - avenue - considering - celebration - brewed - galoshes - tutorials - workouts - millenium - toward - neighbourhood - bannon - storming - reoccurring - longtime - sweetheart - memos - starfish - centaur - philippines - oar - departs - preferably - latte - sides - pentagon - fashioned - rescheduled - transportation - twins - duker - deadline - samurai - obaba - bp - ambiance - automatically - object's - boost - morale - jogging - spell - firefly - mura - masa - checklist - biographies - sucked - congested - avinash - commando - jolie's - instrumentals - clarksville - tablespoons - surveys - flour - acela - calone - bucket - fulls - valid - references - critical - perpetuate - luncheon - ohm's - values - plying - expectations - musician - mindsweper - throughout - noontime - included - tour's - voted - walgreens - chickens - monday's - crankshaft - surfer - lunchtime - skramz - compounds - diabetes - might - reservation - homosapien - engadget - boeing - brisbane - ear - headphones - minimum - worry - snowplows - burying - driveway - adapt - destroy - impanema - equipment - turnt - attractive - conducted - cinnamon - freshener - watsapp - bean - awfully - entitled - murderer - ford - forties - scenery - morocco - sf - blokus - preacher - taken - stormy - centers - ethics - popup - mysterious - puts - stage - considerations - lourie - artic - scoop - carion - merced - bypass - passwords - quantico - grade - examples - cuisines - hibernate - bear - published - authors - tempo - keidis - tidal - cookoff - zones - probable - summerfest - dogs - aren't - necessarily - carolina - eleventh - chilling - sleeve - invoking - term - herald - maria's - poltergeist - imagine - uv - index - johncena - instruct - oscillate - liter - nelly - shawarma - baster - pali - vilnius - tabs - debates - singers - activated - ozzy - osbourne - danish - happypeoplecom - accounting - backpack - im - puttanesca - keeps - worse - wrigley - braise - loin - carnatic - bases - nick - swisher - stolen - clouds - cleared - bola's - norman - reedus - screwdriver - window - volcanoes - rowan - atkinson - minneapoliscity - delicacies - monitor - overall - gymnastics - channels - kxly - botswana - enjoyable - spectre - chane - decentralized - men's - freeze - postal - becomes - ccn - berth - michigan - composition - shahi - panner - dakar - jakarta - equalizer - weird - barely - rodriguez - oklahoma - giraffes - margarita - difficult - crabs - firework - probability - tools - emigration - legislation - pdf - cheeseburgers - applications - adopters - priest - walks - mechanic - h - showers - signs - contrast - recollect - gm's - duck - beavers - tail - lucking - horkersd - wo - myrtle - hr - steam - entirety - anirudh - colored - tropical - bedrooms - yellowish - elephants - expenses - contents - warmer - royksopp - etc - progressives - peoples - cultures - unset - iceland - mp - mangalore - tanya - quad - particulars - insert - tvf - formidable - origins - eden - depressed - mc - donalds - rub - regrets - judgments - scope - intellectual - capacity - ahmadabad - stethoscope - superstitions - rl - stine - quinoa - martial - smooth - damn - speeding - stephen - halley - barry - jealous - siri's - java - scenarios - pc - transfer - tw - agent - nightime - creamy - mirch - dil - cannon - cameras - process - merriam - webster - dubstep - rangoon - wines - older - navigate - chandelier - egs - recognize - subscriptions - mileage - studies - microphone - immigrant - electronics - careful - paint - fund - success - resolved - bola - eva's - roller - augusta - midtown - surprise - children's - dongle - seashell - bots - fallen - centimeters - poisoning - sci - fi - outcome - reform - sleepy - moderate - chrome - ultraviolet - george's - geek - courses - rundown - legend - equipments - usher - manor - advertisers - clue - depending - strongest - outstation - fallout - shoal - lastfm - relocate - pollution - awareness - bryce - jessie - carol - nsnbc - vacuumed - chives - splits - arbor - receiving - toast - futures - brokers - routes - fixed - additional - switches - church's - governor - enacted - grams - guitarists - android - babe - sonny - sear - eliminate - remain - uc - polk - pakistani - bedside - reshuffle - frida - devil's - rusk - actors - pakistan - happenings - sit - montauk - beethoven - legends - sunshine - mothers - smoke - feels - rockies - miamy - operations - addition - subtraction - incite - annoying - cristiano - ronaldo - spin - cows - jenny - spread - wallstreet - selections - nashik - ipl - oswald - chambers - horoscope - mgk - dog's - residing - cricketer - dhoni - byron - fluctuations - talks - palermo - shallowest - bbcnews - nsdl - flights - lineup - stick - ribs - jeopardy - timetables - emi - maya - mackensie - osteen - jimmie's - adjustments - precocious - fork - husband's - audi - hibachi - disputed - crack - visible - boiling - rogan - karachi - babysitter - kidnapping - hamburgers - madonnas - lessen - ipo - greenville - carries - creamed - pickled - herring - tackle - brush - geyser - savings - torey - hurt - subscribe - picks - birthdate - goals - cairo - projected - patrick's - capita - honda - intended - hurriedly - activates - it'll - wsj - spy - broods - grommet - steven's - underground - seahawks - participants - workday - ammi - nightlife - donner - summit - ukraine's - ended - arrangements - altucher's - writer - fortune - brisket - grant - audiobooks - twilight - bass - hunger - roses - barbecue - tuna - deadly - killers - finally - trilogy - grisham - goblet - roadblocks - birthday's - biscuits - lawyers - steve's - kari - labyrinth - commonwealth - sharma - gulf - petrol - earthly - ultimate - ending - allison - canberra - honolulu - flash - salman - gresham - hindustani - stroganoff - sock - creates - geo - traits - moral - rein - blood - slayer - pro - bono - succinct - dalls - somethings - sharp - izzo - whiny - bitch - macaroni - nights - jumper - blind - cure - cancer - vibrant - sloth - transition - recycling - bbc's - columbia - kentucky - hire - opera - prefer - avoid - sort - comedy - compassionate - nc - va - riddles - segment - youth - charity - surrounding - punjabi - sharply - lovett - barber - label - hypocrisy - subscriber - captain - disillusion - hyderabad - dashboard - storm - barrel - panasonic - clinton - canasta - mittens - badra - amit - trivedi - crystal - lewis's - everywhere - rue - evaporated - mma - offered - tutoring - peas - dream - cafes - lauderdale - deletion - precise - parliamentary - remotely - connection - calendars - stupidest - shovel - western - cutting - ll - rapping - spelling - mama - tatum's - fulton - universal - garner - chill - icebo - college's - rehman - soundcloud - scorecards - ketchup - jimmy's - crate - lexmark - preference - females - federal - andreas - sportsnet - favourites - janice - bins - pamela - covered - rhapsody - italian's - ke - panera - remainders - tandoori - sukhwinder - sunidhi - etymology - googleplex - slide - wearing - trivial - pursuit - cancels - martina - mcbride - finances - vocab - zipcode - compaq - composer - margarine - jonathan - entrepreneur - extended - combo - memories - tupac - affects - drunks - ford's - liked - dealership - olky - realtor - thighs - ourselves - economics - medication - gross - domestic - donaldson - prostate - wicker - rooms - instrumental - savannah - outing - affleck - quotes - tire - montana - exhausted - acoustic - commercials - convenience - consciousness - serge - gainsbourg - windows - turks - generate - pedicures - btaxes - departures - frasier - amazon's - bluetooth - verus - neat - forecasted - bing's - dropped - recurrent - candidate - aware - blackeyed - pees - prince's - perimeter - rectangle - aaron - carter - involve - drugs - lighten - slicker - rains - cloud - carrot - popcorn - carmike - cinemas - greater - minestart - frog - lenon - unique - hanging - hung - sporty - seldom - jocko's - kid's - viewers - cantonese - usage - specs - bugatti - veyron - chief - blockbuster - krishnarajpuram - interstate - hammers - obligatory - wonder - southeast - marlon - brando - ferrel - tal - obidallah - manoeuvres - merita - rotate - changs - pepsi - shanghai - branden - wind - landmarks - dvr - congestion - valentines - eastwind - lomaine - geneva - officially - hopkins - takjistan - dimmer - karo - apne - aur - karna - chahta - hu - purchased - otherplace - giraffe - ute - requirement - watts - powerful - bulb - oclock - nba - hulu - composing - melissas - millilitres - spoons - goulash - thor - harischand - mg - i95 - sb - kilo - diana - llyod - webber - wool - penultimate - bang - philosophers - nietzche - focault - profession - kilograms - turkeys - bibulous - angeline - atm - narwhal - kilamanjaro - captia - volkswagen - onkyo - av - receiver - ipad - aniston's - summarize - ice - jindel - pump - nikki - minaj - nationality - snoodle - yemen - sudan - unprompted - organization - megan - fares - engage - functioning - dinar - conservative - korean - sahara - kingdom - antartica - telugu - tamil - tsunami - rajani - khanth - venture - goalkeeper - dushambe - abrupt - hbo - sopranos - parana - cave - anime - posters - johny - depp - invisible - graphical - joli - pricing - beech - nuclear - triad - hilton - borders - lucille - redhead - geraldine - ferraro - bde - lowered - phrases - nicole - mcgoat's - manipulate - roip - nasa - google's - davy - crockett - springsteen's - richest - costliest - easily - gm - psso - kroner - maple - trees - christie - brinkley - libraries - gmb - key - mongolia - anastasia - telekenesis - promise - stray - cruise's - starring - odyssey - polish - zloty - hook - ups - integral - exponential - berkshire - hathaway - tables - pink's - alligator - porto - tommy - hilfiger - print - networks - snaps - celebrate - bina - yay - smiley - emoticon - commented - folgers - hathway - huge - lfi - tagged - treated - hersheys - aircel - nastyburger - linkedin - tracy - waiter - drain - charge - neptunal - poorly - waited - inappropriate - potus - accounts - vodafone - complaining - spoiled - positive - tumblr - unpleasant - overpricing - cheating - connected - else's - greetings - thought - waste - excess - micro - lodge - snapdeal - sonic - hole - sole - patel's - insect - packet - elsewhere - moan - easyjet - snotty - expired - xl - sizes - filing - applebee's - angela - merkel - swagging - moto - sluggish - flavia - mum - jacob's - existing - cannot - pleas - mahmoud - ebay - smsayyad1985 - kishore17051985 - fedex - truette - petey's - tessa - gaurav - karen - mongomery - llc - joseph - turnpike - accumulated - deadlines - fees - ppt - emergency - missing - carl's - attach - physical - drill - marilyn - jugal - here's - bug - sarasigmon123 - lindafancy55 - markpolomm - gary's - mailing - bill's - erins - beth's - wont - stacy - cadwell - tori - aloud - brenda - thisome - smurfette - smithjoe - hwacuk - chong - giselle - bosses - havent - frieda's - jjjindia - exists - batch - samuelwaters - joose - hellen - builders - accepted - victor - taxi's - terry - macdonald - yahoocom - metion - rodger - christy's - otp - jayesh - tried - morgan's - office's - rob - qerwerq - secured - gerry - raj's - junable - shopyourway - reference - jhonny's - marissa - rosa - bert - ana - goddammit - pronounce - serious - recheck - slowly - failed - fuck - executed - clearly - errors - showed - races - thursdays - funky - handmaid's - beam - scotty - debit - wiki - editor's - automobiles - promo - discount - director - act - bejeweled - aside - snakes - ladders - marsala - influx - bayou - reasonably - tapas - az - ddlj - meatball - newscast - bibber - tmz - devon - applebees - hihop - doggie - feelings - radios - litle - tsos - congratulate - links - treble - flame - eta - encourage - students - choices - lobby - vf - chore - butterfly - clips - urban - regular - bi-weekly - baltimore - sport's - breakups - dale's - brea - douglasville - fundraiser - dolphines - maradona - pe - becky - appointed - deputy - utar - pradesh - anniston - handy - sainsbury's - attenuate - parcel - jakes - bristo - stressful - deposit - mathematical - superstar - survivor - destiny's - westcombe - facility - oboe - mcnamara - abolish - swim - repair - grub - hub - ill - dec - dreams - wyatts - obstacle - poach - dental - rose - davinci - trevor - noah - ncaa - entrapreneur - sanam - differs - ave - hopsin - enya - wbc - accordingly - remarks - sufi - beibers - arrested - sensor - music's - author - antwerp - cnn's - foodnetworkcom - customize - preferred - unable - duct - tape - gooseto - apig - ringer - secure - passage - tomatoes - wan - senelena - americano - makeup - robotics - teleconference - robotic - poughkeepsie - steel - day's - soundtrack - tobymac - transit - gloria - furious - nazi - hunting - effect - marvin - gaye - pasadena - ca - constrain - singles - outer - nowhereville - comfortable - erica - grebe - wooly - trigonametry - obsessed - graphics - undone - tough - treasury - toledo - munich - obtain - nutritionally - balanced - internal - locks - exit - mocking - lyft - transaction - tasty - mixture - according - hands - supports - canceling - congressman's - lenin - spagetti - controversial - statements - walker - humor - nkotb - jon - snow's - possibility - wellington - nz - advantages - disadvantages - driver - towels - stretch - gear - joey - crimson - chose - pineapple - asparagus - teaspoons - bling - medieval - engines - foods - hurts - cannibal - tonic - bitcoin - collection - hidden - figures - brasil - politic - superb - dalida - capuccino - analysts - thankama - kodaikanal - vote - burritto - chipolte - abut - sedaka - chamber - rfi - knock - cnncom - remchi - fl - ortcars - flip - wire - thriller - fiasco - breaks - dam - paradise - presidency - sigur - ros - socks - van - halen - wayne - spare - lightness - appropriately - both - musics - coastal - cry - friend's - wore - veganism - picnic - regent - visited - therapist - inauguration - swatishs - dorothy - known - supervision - superbowl - eric's - bday - kar - abhi - achche - ache - rahe - honge - mhz - sponge - bistros - brownies - tenderloin - enchiladas - gluten - hotdog - row - bing - notebook - pulldown - clearer - medford - drivers - waverley - canal - connecting - summers - gibraltar - monoprice - mxblue - mechanical - turbulence - carey - blunder - factorial - depends - commands - stand - draymond - susumu - hirasawa - yosemite - '200' - baguette - stonehenge - douriff - ivf - ivr - litt - runs - hesitant - crock - guetta - malaysia - whelers - sadness - william - coral - daft - punk - sandle - santha - ingerman - calc - shibaru - alcohols - nano - gina - desta - mgmt - bana - talking - garvin - trilly - nytimes - chhana - mereya - favor - strained - cooler - films - einstein's - aroma - ska - raphsody - trebuchet - forth - relate - qualifications - kirk - franklin - arithmetic - skyfall - bathrooms - raghu - dixit - reports - availables - haddock - odd - cape - cod - noisy - dull - hackernews - porn - pad - fight - fighter - nzd - melodious - burton - helena - campaign - mcclanahan - mummy's - motown - rasgulla - janta - pvt - ltd - heartthrob - justin's - velociraptor - hippo - senatra - giggle - peru - nirvana - anirudh's - retro - mf - doom - summarise - ariana - grande - predicted - creed - user - desire - kenny - roger - sia's - thrills - wapo - stockholm - okinawa - occasionally - shuffling - veggie - mukkala - mukkabilla - guardian - anytime - themes - horror - ennema - eatha - homestead - forever - mayor's - stance - council - master - louies - keane's - fears - noe - reggae - largo - swiftm - afi's - xinhua - dedicated - bottom - franks - yelawolf - ucl - flop - grammys - espn - joni - mitchell - shot - tequila - sleepyhead - aces - redder - edms - lamp's - loudest - brolly - thao - nguyen - interior - dine - dogwalking - nytimescom - overcast - deactive - foo - disasters - opacity - dea - guam - drug - abuse - itzhak - perlman - drawing - sweden - bombing - ireland - poll - hotha - defrosting - salt - toggle - spb - weatherit - either - forecasts - intellicast - weathercom - orevena - recorder - pizzahouse - reorganize - sticky - umbrellas - opened - cleaned - shakin - bakey - tips - hypoallergenic - sarcastic - cheat - ii - developers - edg - yaad - dilana - kahin - samantha's - rita's - adding - bro's - attendees - maggie - valet - groomer - timeframe - pete - faculty - parade - greens - jack's - walter - gemma - nail - arora's - namkeen - tonights - ggg - tie - iheartradio - rov - javan - wfrn - kicks - osteen's - wgrr - lite - prairie - companion - palhunik - pudding - tutorial - welsh - rarebit - oatmeal - pathia - achieve - veg - pulav - crockpot - prepared - keno - pinball - fishdom - nfs - harvest - crops - farmvile - millionaires - vodka - depend - pon - stationary - mad - errands - paav - queried - pepper - rowling - shadi - viewed - mlb - heavyweight - citadel - scene - circus - trolls - grab - kung - fu - bowery - railway - coach - fare - metrolink - navigation - westwood - layfayette - inconvenience - emotions - arrahman - cosmos - multiplied - abouts - hitting - eliot's - el - ribbons - sperm - whale - eaten - lbs - pinhead - timeliness - defining - thesaurus - penalty - approval - poetry - ambulance - jello - shots - ferrell - stassi - schroedder's - tacobell - hierophant - zealand - stockton - emissions - blowing - kennedy - ziggurat - gagas - gretszky - hemingway - pages - earn - nobel - actions - sloths - parton's - madagascar - acting - tiangle - trebuchets - googs - gandhiji - amal - brazil - adviser - rich - acted - rihanas - stamp - mugy - msn - busdriver - fergie - flick - ribons - nakumuka - postmates - complaintum - glinder - gta - rcg - outlet - hadock - mclanahan - coal - mumy's - piza - wheelers - guarante - debugging - debuging - proper - sung - bilando - terrorism - cover - dimmed - vanilli - marauthr - wooo - michael's - shutdown - pittsburgh - precipitation - riff - portland - muggy - giants - banks - steelz - ensure - ricky - matin - tyres - plant - chased - advice - gossiping - society - mitushree - hairdresser's - biology - fsu - reflect - yashas - vinay - vally - closed - shoutcast - pilkington - soda - powder - sambar - cookingforu - thermonuclear - battleship - cereal - wishlist - wrist - hipsterhood - duncan - trussel's - simmons - wide - cisco - crafts - sporting - presently - sheffield - septa - lead - fransisco - washingdon - evolution - mariah - kya - tum - mere - karne - karoge - acts - assembly - idle - brand - meridian - terranova - guarantee - marian - fields - farthest - philippine - cambodia - situated - foruget - monopricechanical - peenth - moroco - piz - tre - supplwn - viki - shivle - loged - applebe - acess - madagar - anp - socer - subcribe - pluged - imigration - audiowan - debie's - imediately - f - locar - duark - rebeca - talle - banas - ragh - acordingly - wakely - en - bress - acording - stefanan - puding - vegie - vius - edie - domizza - eg - cheeseiza - ocurred - brightnes - alaba - memory - fransico - sunderland - boogie - butt - leviathan - shinning - premier - cleanup - wacky - aman - cherry - bomb - solstice - silently - closet - nakumukka - shed - responses - yankees - investigation - dooa - pieces - imogen - heap - stole - dynamite - cease - operating - rained - uptown - suggestion - finlee's - bedtime - sockets - sanfranscio - abbas - cn's - vibrate - cooling - sheriffs - hike - ilayaraja - speaking - un - storms - roof - tube - jackpot - classmates - extremely - somewhere - drenched - sentient - budy - heating - apt - parenting - concerning - seo - searches - sticking - patterns - numbered - impression - reunion - presents - mehta - willing - discuss - evan - parker - violin - lesson - musicworkz - registration - opens - evening's - thursday's - nineteenth's - hayathis - shower - corresponding - showcase - famosa - kamp - neal - brenan - gx - nonstop - rm - giver - traveller - knowledge - crispy - supper - broil - noodle - stuffed - maccoroni - almond - clash - clans - ping - keeper - enemy - coc - detergent - corn - dill - pickles - ranch - dressing - lentils - translate - toothpaste - rearrange - groups - santana - pritzker - winners - libertarian - mc's - vitaly - nfl - mythical - oriented - provisional - experiences - safely - themselves - mia - reducing - learly - court - vin - diesel - netbooks - chinatown - aberdeen - queens - luni - purchasing - timing - bagmati - narrow - egypt - represented - revelation - britain - aamir - priyanka - middleton - base - original - nhl - goal - scorers - osteoperosis - laws - correlation - motivation - ncaaa - tense - touring - framework - adel - diamond - schwarzenegger's - stomachs - cow - chairs - steph - subjegant - pategonia - michelle - todlers - stakes - tinder - matches - fjord - equator - triumph - hell - moldova - presley's - wa - rajinikanth - basalt - bali - airplane - hash - lit - <sos/eos> two_pass: false pre_postencoder_norm: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} deliberationencoder: conformer deliberationencoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: linear normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 postdecoder: hugging_face_transformers postdecoder_conf: model_name_or_path: bert-base-cased output_size: 512 required: - output_dir - token_list version: '202207' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "BEAR", "PDR" ]
Non_BioNLP
aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF
aimlresearch2023
sentence-similarity
[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "mteb", "arctic", "snowflake-arctic-embed", "transformers.js", "llama-cpp", "gguf-my-repo", "base_model:Snowflake/snowflake-arctic-embed-m-v1.5", "base_model:quantized:Snowflake/snowflake-arctic-embed-m-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,726,132,804,000
2024-09-12T09:20:08
7
0
--- base_model: Snowflake/snowflake-arctic-embed-m-v1.5 license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - arctic - snowflake-arctic-embed - transformers.js - llama-cpp - gguf-my-repo model-index: - name: snowflake-arctic-embed-m-v1.5 results: - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: main_score value: 59.53000000000001 - type: map_at_1 value: 34.282000000000004 - type: map_at_10 value: 50.613 - type: map_at_100 value: 51.269 - type: map_at_1000 value: 51.271 - type: map_at_20 value: 51.158 - type: map_at_3 value: 45.626 - type: map_at_5 value: 48.638 - type: mrr_at_1 value: 34.92176386913229 - type: mrr_at_10 value: 50.856081645555406 - type: mrr_at_100 value: 51.510739437069034 - type: mrr_at_1000 value: 51.51299498830165 - type: mrr_at_20 value: 51.39987941081724 - type: mrr_at_3 value: 45.993361782835514 - type: mrr_at_5 value: 48.88098624940742 - type: nauc_map_at_1000_diff1 value: 10.628675774160785 - type: nauc_map_at_1000_max value: -10.11742589992339 - type: nauc_map_at_1000_std value: -18.29277379812427 - type: nauc_map_at_100_diff1 value: 10.63250240035489 - type: nauc_map_at_100_max value: -10.112078786734363 - type: nauc_map_at_100_std value: -18.288524872706834 - type: nauc_map_at_10_diff1 value: 10.476494913081712 - type: nauc_map_at_10_max value: -9.890937746734037 - type: nauc_map_at_10_std value: -18.279750514750443 - type: nauc_map_at_1_diff1 value: 14.549204048461151 - type: nauc_map_at_1_max value: -12.230560087701225 - type: nauc_map_at_1_std value: -19.469903650130362 - type: nauc_map_at_20_diff1 value: 10.586564571825674 - type: nauc_map_at_20_max value: -10.00292720526217 - type: nauc_map_at_20_std value: -18.258077347878064 - type: nauc_map_at_3_diff1 value: 10.378663968090372 - type: nauc_map_at_3_max value: -10.458896171786185 - type: nauc_map_at_3_std value: -18.38852760333766 - type: nauc_map_at_5_diff1 value: 10.235960275925581 - type: nauc_map_at_5_max value: -10.239496080409058 - type: nauc_map_at_5_std value: -18.817023479445886 - type: nauc_mrr_at_1000_diff1 value: 8.718212649575722 - type: nauc_mrr_at_1000_max value: -10.81022794038691 - type: nauc_mrr_at_1000_std value: -17.87669499555167 - type: nauc_mrr_at_100_diff1 value: 8.722174171165133 - type: nauc_mrr_at_100_max value: -10.804840985713525 - type: nauc_mrr_at_100_std value: -17.872487099359986 - type: nauc_mrr_at_10_diff1 value: 8.609421635870238 - type: nauc_mrr_at_10_max value: -10.568644717548432 - type: nauc_mrr_at_10_std value: -17.872968762635814 - type: nauc_mrr_at_1_diff1 value: 12.69590006263834 - type: nauc_mrr_at_1_max value: -12.082056561238321 - type: nauc_mrr_at_1_std value: -18.036424092186657 - type: nauc_mrr_at_20_diff1 value: 8.684842497970315 - type: nauc_mrr_at_20_max value: -10.691578914627286 - type: nauc_mrr_at_20_std value: -17.84350301434992 - type: nauc_mrr_at_3_diff1 value: 8.649761557556763 - type: nauc_mrr_at_3_max value: -11.104694428047496 - type: nauc_mrr_at_3_std value: -18.149917948370344 - type: nauc_mrr_at_5_diff1 value: 8.433489750038396 - type: nauc_mrr_at_5_max value: -10.917772454397436 - type: nauc_mrr_at_5_std value: -18.4094211134111 - type: nauc_ndcg_at_1000_diff1 value: 10.19041067807956 - type: nauc_ndcg_at_1000_max value: -9.54328201605796 - type: nauc_ndcg_at_1000_std value: -17.824620427456633 - type: nauc_ndcg_at_100_diff1 value: 10.289491087585963 - type: nauc_ndcg_at_100_max value: -9.357214331420337 - type: nauc_ndcg_at_100_std value: -17.657600653632873 - type: nauc_ndcg_at_10_diff1 value: 9.435530877596092 - type: nauc_ndcg_at_10_max value: -8.182581635383546 - type: nauc_ndcg_at_10_std value: -17.603156479980388 - type: nauc_ndcg_at_1_diff1 value: 14.549204048461151 - type: nauc_ndcg_at_1_max value: -12.230560087701225 - type: nauc_ndcg_at_1_std value: -19.469903650130362 - type: nauc_ndcg_at_20_diff1 value: 9.885227087275197 - type: nauc_ndcg_at_20_max value: -8.52362662391439 - type: nauc_ndcg_at_20_std value: -17.441705436231764 - type: nauc_ndcg_at_3_diff1 value: 9.22542769998547 - type: nauc_ndcg_at_3_max value: -9.903590564219288 - type: nauc_ndcg_at_3_std value: -18.357220221111593 - type: nauc_ndcg_at_5_diff1 value: 8.8756720745828 - type: nauc_ndcg_at_5_max value: -9.269764943861245 - type: nauc_ndcg_at_5_std value: -19.009229433187784 - type: nauc_precision_at_1000_diff1 value: 3.733355117431035 - type: nauc_precision_at_1000_max value: 3.9603571352517393 - type: nauc_precision_at_1000_std value: 70.07345061131439 - type: nauc_precision_at_100_diff1 value: 29.019032142462457 - type: nauc_precision_at_100_max value: 40.75153328286103 - type: nauc_precision_at_100_std value: 62.634249549126594 - type: nauc_precision_at_10_diff1 value: 2.5762677254910353 - type: nauc_precision_at_10_max value: 6.096298633773051 - type: nauc_precision_at_10_std value: -11.507400451348587 - type: nauc_precision_at_1_diff1 value: 14.549204048461151 - type: nauc_precision_at_1_max value: -12.230560087701225 - type: nauc_precision_at_1_std value: -19.469903650130362 - type: nauc_precision_at_20_diff1 value: 1.715540124567996 - type: nauc_precision_at_20_max value: 21.53546453945913 - type: nauc_precision_at_20_std value: 1.537961142195571 - type: nauc_precision_at_3_diff1 value: 5.701850652555737 - type: nauc_precision_at_3_max value: -8.180345365085552 - type: nauc_precision_at_3_std value: -18.37033750502482 - type: nauc_precision_at_5_diff1 value: 3.6053552181042843 - type: nauc_precision_at_5_max value: -5.207647070615612 - type: nauc_precision_at_5_std value: -19.89491085427258 - type: nauc_recall_at_1000_diff1 value: 3.733355117431255 - type: nauc_recall_at_1000_max value: 3.9603571352482194 - type: nauc_recall_at_1000_std value: 70.07345061131205 - type: nauc_recall_at_100_diff1 value: 29.01903214246288 - type: nauc_recall_at_100_max value: 40.7515332828621 - type: nauc_recall_at_100_std value: 62.63424954912607 - type: nauc_recall_at_10_diff1 value: 2.5762677254911988 - type: nauc_recall_at_10_max value: 6.0962986337729905 - type: nauc_recall_at_10_std value: -11.507400451348577 - type: nauc_recall_at_1_diff1 value: 14.549204048461151 - type: nauc_recall_at_1_max value: -12.230560087701225 - type: nauc_recall_at_1_std value: -19.469903650130362 - type: nauc_recall_at_20_diff1 value: 1.7155401245682675 - type: nauc_recall_at_20_max value: 21.535464539459632 - type: nauc_recall_at_20_std value: 1.5379611421957025 - type: nauc_recall_at_3_diff1 value: 5.7018506525557875 - type: nauc_recall_at_3_max value: -8.180345365085538 - type: nauc_recall_at_3_std value: -18.370337505024796 - type: nauc_recall_at_5_diff1 value: 3.6053552181043913 - type: nauc_recall_at_5_max value: -5.207647070615579 - type: nauc_recall_at_5_std value: -19.894910854272492 - type: ndcg_at_1 value: 34.282000000000004 - type: ndcg_at_10 value: 59.53000000000001 - type: ndcg_at_100 value: 62.187000000000005 - type: ndcg_at_1000 value: 62.243 - type: ndcg_at_20 value: 61.451 - type: ndcg_at_3 value: 49.393 - type: ndcg_at_5 value: 54.771 - type: precision_at_1 value: 34.282000000000004 - type: precision_at_10 value: 8.791 - type: precision_at_100 value: 0.992 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.769 - type: precision_at_3 value: 20.104 - type: precision_at_5 value: 14.651 - type: recall_at_1 value: 34.282000000000004 - type: recall_at_10 value: 87.909 - type: recall_at_100 value: 99.21799999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 95.377 - type: recall_at_3 value: 60.313 - type: recall_at_5 value: 73.257 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: mteb/cqadupstack-android config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: main_score value: 53.885000000000005 - type: map_at_1 value: 35.429 - type: map_at_10 value: 47.469 - type: map_at_100 value: 48.997 - type: map_at_1000 value: 49.117 - type: map_at_20 value: 48.324 - type: map_at_3 value: 43.835 - type: map_at_5 value: 46.043 - type: mrr_at_1 value: 43.34763948497854 - type: mrr_at_10 value: 53.258623430297234 - type: mrr_at_100 value: 53.99123884299005 - type: mrr_at_1000 value: 54.02458101713216 - type: mrr_at_20 value: 53.695964669618945 - type: mrr_at_3 value: 50.81068192656173 - type: mrr_at_5 value: 52.45588936576058 - type: nauc_map_at_1000_diff1 value: 51.55382824218782 - type: nauc_map_at_1000_max value: 31.855350695084606 - type: nauc_map_at_1000_std value: -5.465862008150992 - type: nauc_map_at_100_diff1 value: 51.55889312452534 - type: nauc_map_at_100_max value: 31.88429637207401 - type: nauc_map_at_100_std value: -5.40805152544196 - type: nauc_map_at_10_diff1 value: 51.6592677505875 - type: nauc_map_at_10_max value: 31.554425233617543 - type: nauc_map_at_10_std value: -6.125756131339046 - type: nauc_map_at_1_diff1 value: 55.6889617582672 - type: nauc_map_at_1_max value: 27.821166966868176 - type: nauc_map_at_1_std value: -5.778838498211728 - type: nauc_map_at_20_diff1 value: 51.70520970992564 - type: nauc_map_at_20_max value: 31.811676633900465 - type: nauc_map_at_20_std value: -5.463596751904718 - type: nauc_map_at_3_diff1 value: 53.206169626589606 - type: nauc_map_at_3_max value: 31.64373830824983 - type: nauc_map_at_3_std value: -6.054761451312827 - type: nauc_map_at_5_diff1 value: 52.37308971673694 - type: nauc_map_at_5_max value: 31.974302019633644 - type: nauc_map_at_5_std value: -6.302653399940531 - type: nauc_mrr_at_1000_diff1 value: 49.345152231490616 - type: nauc_mrr_at_1000_max value: 33.49789501712511 - type: nauc_mrr_at_1000_std value: -6.054730861163538 - type: nauc_mrr_at_100_diff1 value: 49.3387577601307 - type: nauc_mrr_at_100_max value: 33.48149992464187 - type: nauc_mrr_at_100_std value: -6.061177137579308 - type: nauc_mrr_at_10_diff1 value: 49.08312288449718 - type: nauc_mrr_at_10_max value: 33.470393322577465 - type: nauc_mrr_at_10_std value: -6.180286430216975 - type: nauc_mrr_at_1_diff1 value: 52.43364978537192 - type: nauc_mrr_at_1_max value: 31.521755633355713 - type: nauc_mrr_at_1_std value: -7.002499524130836 - type: nauc_mrr_at_20_diff1 value: 49.311059224991766 - type: nauc_mrr_at_20_max value: 33.538523037692144 - type: nauc_mrr_at_20_std value: -6.034619474981136 - type: nauc_mrr_at_3_diff1 value: 49.90489868439366 - type: nauc_mrr_at_3_max value: 34.400493912164606 - type: nauc_mrr_at_3_std value: -6.028875320994629 - type: nauc_mrr_at_5_diff1 value: 49.033661898983475 - type: nauc_mrr_at_5_max value: 33.732315350193936 - type: nauc_mrr_at_5_std value: -6.272548556330368 - type: nauc_ndcg_at_1000_diff1 value: 49.81681892539247 - type: nauc_ndcg_at_1000_max value: 33.06518006062093 - type: nauc_ndcg_at_1000_std value: -4.282105713014755 - type: nauc_ndcg_at_100_diff1 value: 49.42362108857786 - type: nauc_ndcg_at_100_max value: 32.92024325540483 - type: nauc_ndcg_at_100_std value: -3.7786765305496717 - type: nauc_ndcg_at_10_diff1 value: 48.83102435475594 - type: nauc_ndcg_at_10_max value: 31.898404563611958 - type: nauc_ndcg_at_10_std value: -6.2024003866707 - type: nauc_ndcg_at_1_diff1 value: 52.43364978537192 - type: nauc_ndcg_at_1_max value: 31.521755633355713 - type: nauc_ndcg_at_1_std value: -7.002499524130836 - type: nauc_ndcg_at_20_diff1 value: 49.466526454438316 - type: nauc_ndcg_at_20_max value: 32.424462698701674 - type: nauc_ndcg_at_20_std value: -4.520809563712905 - type: nauc_ndcg_at_3_diff1 value: 50.997884562583884 - type: nauc_ndcg_at_3_max value: 33.26787046916917 - type: nauc_ndcg_at_3_std value: -6.340699471083753 - type: nauc_ndcg_at_5_diff1 value: 49.68314458398097 - type: nauc_ndcg_at_5_max value: 32.80910071143984 - type: nauc_ndcg_at_5_std value: -6.734495576445887 - type: nauc_precision_at_1000_diff1 value: -24.18940012795299 - type: nauc_precision_at_1000_max value: -10.995343674356896 - type: nauc_precision_at_1000_std value: -8.298841004724856 - type: nauc_precision_at_100_diff1 value: -18.104939577865935 - type: nauc_precision_at_100_max value: -1.3757613100627637 - type: nauc_precision_at_100_std value: 0.07661922190466432 - type: nauc_precision_at_10_diff1 value: 3.9624459059275967 - type: nauc_precision_at_10_max value: 14.841561593450391 - type: nauc_precision_at_10_std value: -2.485374333613117 - type: nauc_precision_at_1_diff1 value: 52.43364978537192 - type: nauc_precision_at_1_max value: 31.521755633355713 - type: nauc_precision_at_1_std value: -7.002499524130836 - type: nauc_precision_at_20_diff1 value: -4.4791763436505265 - type: nauc_precision_at_20_max value: 9.157872836996276 - type: nauc_precision_at_20_std value: 2.086903518342088 - type: nauc_precision_at_3_diff1 value: 28.480888018235568 - type: nauc_precision_at_3_max value: 30.34526267718485 - type: nauc_precision_at_3_std value: -6.3006706923866025 - type: nauc_precision_at_5_diff1 value: 16.488039195453517 - type: nauc_precision_at_5_max value: 24.593477099241852 - type: nauc_precision_at_5_std value: -5.316448107840636 - type: nauc_recall_at_1000_diff1 value: 34.715187316533076 - type: nauc_recall_at_1000_max value: 58.2266544684947 - type: nauc_recall_at_1000_std value: 63.85237636398278 - type: nauc_recall_at_100_diff1 value: 36.08623826028132 - type: nauc_recall_at_100_max value: 33.05011429439473 - type: nauc_recall_at_100_std value: 16.559545021212564 - type: nauc_recall_at_10_diff1 value: 39.76738610714205 - type: nauc_recall_at_10_max value: 28.233045706945997 - type: nauc_recall_at_10_std value: -5.13243784043598 - type: nauc_recall_at_1_diff1 value: 55.6889617582672 - type: nauc_recall_at_1_max value: 27.821166966868176 - type: nauc_recall_at_1_std value: -5.778838498211728 - type: nauc_recall_at_20_diff1 value: 41.18682480073759 - type: nauc_recall_at_20_max value: 29.525993239296945 - type: nauc_recall_at_20_std value: 1.5003598438954298 - type: nauc_recall_at_3_diff1 value: 48.31879460301157 - type: nauc_recall_at_3_max value: 32.93751306970167 - type: nauc_recall_at_3_std value: -5.28070084211707 - type: nauc_recall_at_5_diff1 value: 44.327686388315435 - type: nauc_recall_at_5_max value: 32.04823486234599 - type: nauc_recall_at_5_std value: -6.4221525602778256 - type: ndcg_at_1 value: 43.348 - type: ndcg_at_10 value: 53.885000000000005 - type: ndcg_at_100 value: 59.204 - type: ndcg_at_1000 value: 60.744 - type: ndcg_at_20 value: 55.995 - type: ndcg_at_3 value: 49.112 - type: ndcg_at_5 value: 51.61900000000001 - type: precision_at_1 value: 43.348 - type: precision_at_10 value: 10.242999999999999 - type: precision_at_100 value: 1.6150000000000002 - type: precision_at_1000 value: 0.203 - type: precision_at_20 value: 6.066 - type: precision_at_3 value: 23.605 - type: precision_at_5 value: 17.024 - type: recall_at_1 value: 35.429 - type: recall_at_10 value: 65.77199999999999 - type: recall_at_100 value: 87.89 - type: recall_at_1000 value: 97.13000000000001 - type: recall_at_20 value: 73.299 - type: recall_at_3 value: 52.034000000000006 - type: recall_at_5 value: 58.96 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: mteb/cqadupstack-english config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: main_score value: 49.55 - type: map_at_1 value: 31.684 - type: map_at_10 value: 43.258 - type: map_at_100 value: 44.628 - type: map_at_1000 value: 44.761 - type: map_at_20 value: 44.015 - type: map_at_3 value: 39.778000000000006 - type: map_at_5 value: 41.643 - type: mrr_at_1 value: 39.87261146496815 - type: mrr_at_10 value: 49.31978566373469 - type: mrr_at_100 value: 49.94922739445482 - type: mrr_at_1000 value: 49.990325601254106 - type: mrr_at_20 value: 49.70597468576704 - type: mrr_at_3 value: 47.070063694267546 - type: mrr_at_5 value: 48.23248407643316 - type: nauc_map_at_1000_diff1 value: 53.44044712371752 - type: nauc_map_at_1000_max value: 34.5651440062204 - type: nauc_map_at_1000_std value: -0.9814384609230475 - type: nauc_map_at_100_diff1 value: 53.429004435388464 - type: nauc_map_at_100_max value: 34.52038957273436 - type: nauc_map_at_100_std value: -1.1021936362699805 - type: nauc_map_at_10_diff1 value: 53.879128574022005 - type: nauc_map_at_10_max value: 33.74771524140917 - type: nauc_map_at_10_std value: -2.945132777205236 - type: nauc_map_at_1_diff1 value: 60.25159799695403 - type: nauc_map_at_1_max value: 26.843892985235808 - type: nauc_map_at_1_std value: -9.618702739509093 - type: nauc_map_at_20_diff1 value: 53.56789898225283 - type: nauc_map_at_20_max value: 34.11628845872402 - type: nauc_map_at_20_std value: -2.024376635870884 - type: nauc_map_at_3_diff1 value: 54.45882099014072 - type: nauc_map_at_3_max value: 31.29495446507793 - type: nauc_map_at_3_std value: -6.391948228781555 - type: nauc_map_at_5_diff1 value: 54.20536489050697 - type: nauc_map_at_5_max value: 32.31001487256826 - type: nauc_map_at_5_std value: -5.050953263346934 - type: nauc_mrr_at_1000_diff1 value: 50.835858995999125 - type: nauc_mrr_at_1000_max value: 38.20717381701079 - type: nauc_mrr_at_1000_std value: 4.174163368228787 - type: nauc_mrr_at_100_diff1 value: 50.827072441041224 - type: nauc_mrr_at_100_max value: 38.21077622034756 - type: nauc_mrr_at_100_std value: 4.1951082737013365 - type: nauc_mrr_at_10_diff1 value: 50.90578491570948 - type: nauc_mrr_at_10_max value: 38.19229691746408 - type: nauc_mrr_at_10_std value: 3.8290750066335546 - type: nauc_mrr_at_1_diff1 value: 54.807021746871186 - type: nauc_mrr_at_1_max value: 37.09225642043841 - type: nauc_mrr_at_1_std value: 0.5654547513131355 - type: nauc_mrr_at_20_diff1 value: 50.86247832095378 - type: nauc_mrr_at_20_max value: 38.19277867384178 - type: nauc_mrr_at_20_std value: 4.098932316791841 - type: nauc_mrr_at_3_diff1 value: 50.788934370903036 - type: nauc_mrr_at_3_max value: 37.72130561895659 - type: nauc_mrr_at_3_std value: 2.7339370381517583 - type: nauc_mrr_at_5_diff1 value: 50.72543792525547 - type: nauc_mrr_at_5_max value: 37.57740908475375 - type: nauc_mrr_at_5_std value: 2.742881431085094 - type: nauc_ndcg_at_1000_diff1 value: 50.89692885407576 - type: nauc_ndcg_at_1000_max value: 37.250583054716955 - type: nauc_ndcg_at_1000_std value: 5.552279826578831 - type: nauc_ndcg_at_100_diff1 value: 50.624606875496944 - type: nauc_ndcg_at_100_max value: 37.1024514234627 - type: nauc_ndcg_at_100_std value: 5.495892760032762 - type: nauc_ndcg_at_10_diff1 value: 51.910387255793445 - type: nauc_ndcg_at_10_max value: 36.71168418905039 - type: nauc_ndcg_at_10_std value: 2.3064115117905217 - type: nauc_ndcg_at_1_diff1 value: 54.807021746871186 - type: nauc_ndcg_at_1_max value: 37.09225642043841 - type: nauc_ndcg_at_1_std value: 0.5654547513131355 - type: nauc_ndcg_at_20_diff1 value: 51.43416588546778 - type: nauc_ndcg_at_20_max value: 36.76387180172346 - type: nauc_ndcg_at_20_std value: 3.7012798827049718 - type: nauc_ndcg_at_3_diff1 value: 50.91198494475423 - type: nauc_ndcg_at_3_max value: 34.92770670756687 - type: nauc_ndcg_at_3_std value: -0.9071486759887368 - type: nauc_ndcg_at_5_diff1 value: 51.63559468683886 - type: nauc_ndcg_at_5_max value: 34.86849679864564 - type: nauc_ndcg_at_5_std value: -0.734837221224976 - type: nauc_precision_at_1000_diff1 value: -13.43645457127175 - type: nauc_precision_at_1000_max value: 12.71162105198664 - type: nauc_precision_at_1000_std value: 33.175399007040255 - type: nauc_precision_at_100_diff1 value: -8.549834785105412 - type: nauc_precision_at_100_max value: 22.47383497331883 - type: nauc_precision_at_100_std value: 39.09108761430844 - type: nauc_precision_at_10_diff1 value: 7.556572451100043 - type: nauc_precision_at_10_max value: 35.35285122987575 - type: nauc_precision_at_10_std value: 29.417466305615967 - type: nauc_precision_at_1_diff1 value: 54.807021746871186 - type: nauc_precision_at_1_max value: 37.09225642043841 - type: nauc_precision_at_1_std value: 0.5654547513131355 - type: nauc_precision_at_20_diff1 value: -0.550158641635712 - type: nauc_precision_at_20_max value: 29.9068430006187 - type: nauc_precision_at_20_std value: 33.920603132821185 - type: nauc_precision_at_3_diff1 value: 25.551264664276687 - type: nauc_precision_at_3_max value: 37.59463225854679 - type: nauc_precision_at_3_std value: 13.707295021359043 - type: nauc_precision_at_5_diff1 value: 17.76136129817151 - type: nauc_precision_at_5_max value: 35.85363807255972 - type: nauc_precision_at_5_std value: 19.48470876841111 - type: nauc_recall_at_1000_diff1 value: 37.1593620123866 - type: nauc_recall_at_1000_max value: 46.29322536951135 - type: nauc_recall_at_1000_std value: 51.47312657083967 - type: nauc_recall_at_100_diff1 value: 37.7542224949536 - type: nauc_recall_at_100_max value: 38.84120637703135 - type: nauc_recall_at_100_std value: 28.839672572221925 - type: nauc_recall_at_10_diff1 value: 46.24130302658384 - type: nauc_recall_at_10_max value: 35.89001724712849 - type: nauc_recall_at_10_std value: 6.985137790828618 - type: nauc_recall_at_1_diff1 value: 60.25159799695403 - type: nauc_recall_at_1_max value: 26.843892985235808 - type: nauc_recall_at_1_std value: -9.618702739509093 - type: nauc_recall_at_20_diff1 value: 43.63576680886187 - type: nauc_recall_at_20_max value: 36.79079644708101 - type: nauc_recall_at_20_std value: 13.81561928605839 - type: nauc_recall_at_3_diff1 value: 48.2299322140522 - type: nauc_recall_at_3_max value: 30.038088484376203 - type: nauc_recall_at_3_std value: -4.871116183843762 - type: nauc_recall_at_5_diff1 value: 47.22331872695983 - type: nauc_recall_at_5_max value: 30.398541477173136 - type: nauc_recall_at_5_std value: -3.2038541888528957 - type: ndcg_at_1 value: 39.873 - type: ndcg_at_10 value: 49.55 - type: ndcg_at_100 value: 53.809 - type: ndcg_at_1000 value: 55.767999999999994 - type: ndcg_at_20 value: 51.275999999999996 - type: ndcg_at_3 value: 44.91 - type: ndcg_at_5 value: 46.855999999999995 - type: precision_at_1 value: 39.873 - type: precision_at_10 value: 9.65 - type: precision_at_100 value: 1.522 - type: precision_at_1000 value: 0.196 - type: precision_at_20 value: 5.701 - type: precision_at_3 value: 22.166 - type: precision_at_5 value: 15.643 - type: recall_at_1 value: 31.684 - type: recall_at_10 value: 60.69 - type: recall_at_100 value: 78.521 - type: recall_at_1000 value: 91.02900000000001 - type: recall_at_20 value: 66.973 - type: recall_at_3 value: 46.807 - type: recall_at_5 value: 52.402 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: main_score value: 62.686 - type: map_at_1 value: 43.856 - type: map_at_10 value: 57.056 - type: map_at_100 value: 58.048 - type: map_at_1000 value: 58.092 - type: map_at_20 value: 57.684000000000005 - type: map_at_3 value: 53.958 - type: map_at_5 value: 55.80500000000001 - type: mrr_at_1 value: 50.03134796238244 - type: mrr_at_10 value: 60.31022043091019 - type: mrr_at_100 value: 60.91892338857461 - type: mrr_at_1000 value: 60.93770463536649 - type: mrr_at_20 value: 60.705642387392736 - type: mrr_at_3 value: 58.286311389759746 - type: mrr_at_5 value: 59.49320794148393 - type: nauc_map_at_1000_diff1 value: 54.849140197256695 - type: nauc_map_at_1000_max value: 38.978448968260224 - type: nauc_map_at_1000_std value: 0.4955439383268162 - type: nauc_map_at_100_diff1 value: 54.824334747823364 - type: nauc_map_at_100_max value: 38.959443109450994 - type: nauc_map_at_100_std value: 0.49626092018886037 - type: nauc_map_at_10_diff1 value: 54.778189277103394 - type: nauc_map_at_10_max value: 38.20972191654546 - type: nauc_map_at_10_std value: -0.7239823837455759 - type: nauc_map_at_1_diff1 value: 58.74017164752485 - type: nauc_map_at_1_max value: 31.528974862589585 - type: nauc_map_at_1_std value: -3.273824691929492 - type: nauc_map_at_20_diff1 value: 54.78943693416187 - type: nauc_map_at_20_max value: 38.77930316443076 - type: nauc_map_at_20_std value: 0.25607460088355544 - type: nauc_map_at_3_diff1 value: 55.68313410225767 - type: nauc_map_at_3_max value: 36.22847284104399 - type: nauc_map_at_3_std value: -3.010979639100503 - type: nauc_map_at_5_diff1 value: 55.11385094420661 - type: nauc_map_at_5_max value: 37.319681045490924 - type: nauc_map_at_5_std value: -2.156640733221061 - type: nauc_mrr_at_1000_diff1 value: 54.504759468380705 - type: nauc_mrr_at_1000_max value: 40.58849492650406 - type: nauc_mrr_at_1000_std value: 1.8226622175866118 - type: nauc_mrr_at_100_diff1 value: 54.4918034449886 - type: nauc_mrr_at_100_max value: 40.59202728933427 - type: nauc_mrr_at_100_std value: 1.8276428096536335 - type: nauc_mrr_at_10_diff1 value: 54.33603399493329 - type: nauc_mrr_at_10_max value: 40.58896878978089 - type: nauc_mrr_at_10_std value: 1.5733340909114375 - type: nauc_mrr_at_1_diff1 value: 58.062410036466105 - type: nauc_mrr_at_1_max value: 37.660958859966506 - type: nauc_mrr_at_1_std value: 0.029007600674170648 - type: nauc_mrr_at_20_diff1 value: 54.43793386924358 - type: nauc_mrr_at_20_max value: 40.66773423875307 - type: nauc_mrr_at_20_std value: 1.891967891797154 - type: nauc_mrr_at_3_diff1 value: 54.77901284537966 - type: nauc_mrr_at_3_max value: 40.182219821206964 - type: nauc_mrr_at_3_std value: 0.8911935034597871 - type: nauc_mrr_at_5_diff1 value: 54.466068837163675 - type: nauc_mrr_at_5_max value: 40.334996916684126 - type: nauc_mrr_at_5_std value: 0.9460830492892364 - type: nauc_ndcg_at_1000_diff1 value: 53.8465376860938 - type: nauc_ndcg_at_1000_max value: 41.63158111016696 - type: nauc_ndcg_at_1000_std value: 3.864205884257578 - type: nauc_ndcg_at_100_diff1 value: 53.4025864436944 - type: nauc_ndcg_at_100_max value: 41.805453995307914 - type: nauc_ndcg_at_100_std value: 4.36777557904857 - type: nauc_ndcg_at_10_diff1 value: 52.96034987157544 - type: nauc_ndcg_at_10_max value: 40.7601173480795 - type: nauc_ndcg_at_10_std value: 1.905824035879141 - type: nauc_ndcg_at_1_diff1 value: 58.062410036466105 - type: nauc_ndcg_at_1_max value: 37.660958859966506 - type: nauc_ndcg_at_1_std value: 0.029007600674170648 - type: nauc_ndcg_at_20_diff1 value: 53.2834771889242 - type: nauc_ndcg_at_20_max value: 41.713541932946406 - type: nauc_ndcg_at_20_std value: 3.865102828793311 - type: nauc_ndcg_at_3_diff1 value: 54.03389464372289 - type: nauc_ndcg_at_3_max value: 38.41449914649933 - type: nauc_ndcg_at_3_std value: -0.886276189886313 - type: nauc_ndcg_at_5_diff1 value: 53.456413320299 - type: nauc_ndcg_at_5_max value: 39.49048882649335 - type: nauc_ndcg_at_5_std value: -0.42692690160443814 - type: nauc_precision_at_1000_diff1 value: -14.770791653274824 - type: nauc_precision_at_1000_max value: 21.479874538905246 - type: nauc_precision_at_1000_std value: 28.607024261300207 - type: nauc_precision_at_100_diff1 value: -12.189696449878126 - type: nauc_precision_at_100_max value: 26.69785787492456 - type: nauc_precision_at_100_std value: 33.59098307467553 - type: nauc_precision_at_10_diff1 value: 6.922968330978399 - type: nauc_precision_at_10_max value: 34.52138344123087 - type: nauc_precision_at_10_std value: 21.768427637079952 - type: nauc_precision_at_1_diff1 value: 58.062410036466105 - type: nauc_precision_at_1_max value: 37.660958859966506 - type: nauc_precision_at_1_std value: 0.029007600674170648 - type: nauc_precision_at_20_diff1 value: -0.6837867902179278 - type: nauc_precision_at_20_max value: 33.98683709011133 - type: nauc_precision_at_20_std value: 30.8845561918902 - type: nauc_precision_at_3_diff1 value: 28.195043041120847 - type: nauc_precision_at_3_max value: 37.659916094938836 - type: nauc_precision_at_3_std value: 7.226520146634867 - type: nauc_precision_at_5_diff1 value: 16.633667288096245 - type: nauc_precision_at_5_max value: 34.90176597404891 - type: nauc_precision_at_5_std value: 12.421585442334088 - type: nauc_recall_at_1000_diff1 value: 45.20743732415397 - type: nauc_recall_at_1000_max value: 72.77115913579242 - type: nauc_recall_at_1000_std value: 70.48328496679083 - type: nauc_recall_at_100_diff1 value: 38.56282680810794 - type: nauc_recall_at_100_max value: 55.46797683321103 - type: nauc_recall_at_100_std value: 36.878791151929136 - type: nauc_recall_at_10_diff1 value: 44.18252051452362 - type: nauc_recall_at_10_max value: 43.33391810040086 - type: nauc_recall_at_10_std value: 6.663378192277723 - type: nauc_recall_at_1_diff1 value: 58.74017164752485 - type: nauc_recall_at_1_max value: 31.528974862589585 - type: nauc_recall_at_1_std value: -3.273824691929492 - type: nauc_recall_at_20_diff1 value: 44.19944231642417 - type: nauc_recall_at_20_max value: 49.401101483915866 - type: nauc_recall_at_20_std value: 18.97803841673839 - type: nauc_recall_at_3_diff1 value: 49.56378985428704 - type: nauc_recall_at_3_max value: 36.434210616870224 - type: nauc_recall_at_3_std value: -2.850559971607616 - type: nauc_recall_at_5_diff1 value: 47.37107217086109 - type: nauc_recall_at_5_max value: 39.0236745509895 - type: nauc_recall_at_5_std value: -1.7402454457937195 - type: ndcg_at_1 value: 50.031000000000006 - type: ndcg_at_10 value: 62.686 - type: ndcg_at_100 value: 66.403 - type: ndcg_at_1000 value: 67.241 - type: ndcg_at_20 value: 64.37899999999999 - type: ndcg_at_3 value: 57.859 - type: ndcg_at_5 value: 60.375 - type: precision_at_1 value: 50.031000000000006 - type: precision_at_10 value: 9.856 - type: precision_at_100 value: 1.266 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_20 value: 5.489 - type: precision_at_3 value: 25.746999999999996 - type: precision_at_5 value: 17.492 - type: recall_at_1 value: 43.856 - type: recall_at_10 value: 75.824 - type: recall_at_100 value: 91.622 - type: recall_at_1000 value: 97.538 - type: recall_at_20 value: 81.951 - type: recall_at_3 value: 63.016000000000005 - type: recall_at_5 value: 69.18299999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: main_score value: 43.983 - type: map_at_1 value: 28.942 - type: map_at_10 value: 38.621 - type: map_at_100 value: 39.7 - type: map_at_1000 value: 39.766 - type: map_at_20 value: 39.262 - type: map_at_3 value: 35.719 - type: map_at_5 value: 37.378 - type: mrr_at_1 value: 31.29943502824859 - type: mrr_at_10 value: 40.76463994260603 - type: mrr_at_100 value: 41.67073617629083 - type: mrr_at_1000 value: 41.717446259457105 - type: mrr_at_20 value: 41.32577374689195 - type: mrr_at_3 value: 37.984934086628996 - type: mrr_at_5 value: 39.64595103578152 - type: nauc_map_at_1000_diff1 value: 43.64461679688985 - type: nauc_map_at_1000_max value: 31.53717883948204 - type: nauc_map_at_1000_std value: 1.193745788248017 - type: nauc_map_at_100_diff1 value: 43.63847825079489 - type: nauc_map_at_100_max value: 31.536602619279165 - type: nauc_map_at_100_std value: 1.2001240243342401 - type: nauc_map_at_10_diff1 value: 43.845991987142014 - type: nauc_map_at_10_max value: 31.27509937344113 - type: nauc_map_at_10_std value: 0.7327934840520994 - type: nauc_map_at_1_diff1 value: 50.62269273984579 - type: nauc_map_at_1_max value: 30.16325757909521 - type: nauc_map_at_1_std value: -0.6398875136233392 - type: nauc_map_at_20_diff1 value: 43.630758403790914 - type: nauc_map_at_20_max value: 31.408258098047703 - type: nauc_map_at_20_std value: 1.12616034652217 - type: nauc_map_at_3_diff1 value: 44.823493567359456 - type: nauc_map_at_3_max value: 31.075886347614496 - type: nauc_map_at_3_std value: -0.25126874515735426 - type: nauc_map_at_5_diff1 value: 43.79768853087658 - type: nauc_map_at_5_max value: 31.091080995725324 - type: nauc_map_at_5_std value: 0.16440771782544047 - type: nauc_mrr_at_1000_diff1 value: 42.7865400752329 - type: nauc_mrr_at_1000_max value: 32.84731670326893 - type: nauc_mrr_at_1000_std value: 2.6067637582013825 - type: nauc_mrr_at_100_diff1 value: 42.771741548331065 - type: nauc_mrr_at_100_max value: 32.85324232845987 - type: nauc_mrr_at_100_std value: 2.6092786694308376 - type: nauc_mrr_at_10_diff1 value: 42.82969738870672 - type: nauc_mrr_at_10_max value: 32.69407549631432 - type: nauc_mrr_at_10_std value: 2.302903910016054 - type: nauc_mrr_at_1_diff1 value: 49.05638333657571 - type: nauc_mrr_at_1_max value: 33.12030717171514 - type: nauc_mrr_at_1_std value: 1.3278035087690774 - type: nauc_mrr_at_20_diff1 value: 42.74267239536286 - type: nauc_mrr_at_20_max value: 32.78571108973092 - type: nauc_mrr_at_20_std value: 2.5932669908758643 - type: nauc_mrr_at_3_diff1 value: 43.69963426089187 - type: nauc_mrr_at_3_max value: 32.78193126956233 - type: nauc_mrr_at_3_std value: 1.634874463134699 - type: nauc_mrr_at_5_diff1 value: 42.838630647832524 - type: nauc_mrr_at_5_max value: 32.459318735260545 - type: nauc_mrr_at_5_std value: 1.9412518283209172 - type: nauc_ndcg_at_1000_diff1 value: 41.01253839851583 - type: nauc_ndcg_at_1000_max value: 32.69570568894237 - type: nauc_ndcg_at_1000_std value: 3.4254737113410343 - type: nauc_ndcg_at_100_diff1 value: 40.62589243745832 - type: nauc_ndcg_at_100_max value: 32.664990655736126 - type: nauc_ndcg_at_100_std value: 3.799569445326048 - type: nauc_ndcg_at_10_diff1 value: 41.31658753735306 - type: nauc_ndcg_at_10_max value: 31.511946320339295 - type: nauc_ndcg_at_10_std value: 2.0492930500796662 - type: nauc_ndcg_at_1_diff1 value: 49.05638333657571 - type: nauc_ndcg_at_1_max value: 33.12030717171514 - type: nauc_ndcg_at_1_std value: 1.3278035087690774 - type: nauc_ndcg_at_20_diff1 value: 40.66188223212841 - type: nauc_ndcg_at_20_max value: 31.926240431497476 - type: nauc_ndcg_at_20_std value: 3.370398664595343 - type: nauc_ndcg_at_3_diff1 value: 43.035580180241 - type: nauc_ndcg_at_3_max value: 31.363874129878404 - type: nauc_ndcg_at_3_std value: 0.1422507242819929 - type: nauc_ndcg_at_5_diff1 value: 41.29049003955878 - type: nauc_ndcg_at_5_max value: 31.112034994977737 - type: nauc_ndcg_at_5_std value: 0.860179279828966 - type: nauc_precision_at_1000_diff1 value: -12.41854465881981 - type: nauc_precision_at_1000_max value: 14.706779246590548 - type: nauc_precision_at_1000_std value: 9.812804367375206 - type: nauc_precision_at_100_diff1 value: 2.797520107808461 - type: nauc_precision_at_100_max value: 24.335873541811406 - type: nauc_precision_at_100_std value: 12.87186398750545 - type: nauc_precision_at_10_diff1 value: 24.530962799265847 - type: nauc_precision_at_10_max value: 31.00772010798733 - type: nauc_precision_at_10_std value: 6.696733001548185 - type: nauc_precision_at_1_diff1 value: 49.05638333657571 - type: nauc_precision_at_1_max value: 33.12030717171514 - type: nauc_precision_at_1_std value: 1.3278035087690774 - type: nauc_precision_at_20_diff1 value: 16.25028416351204 - type: nauc_precision_at_20_max value: 29.629326492027342 - type: nauc_precision_at_20_std value: 11.085888573121679 - type: nauc_precision_at_3_diff1 value: 33.923667689694256 - type: nauc_precision_at_3_max value: 33.5859782361996 - type: nauc_precision_at_3_std value: 1.9468331086918693 - type: nauc_precision_at_5_diff1 value: 27.917827233088875 - type: nauc_precision_at_5_max value: 33.13290043423535 - type: nauc_precision_at_5_std value: 3.800870695945311 - type: nauc_recall_at_1000_diff1 value: 9.680283388428789 - type: nauc_recall_at_1000_max value: 49.479399284871235 - type: nauc_recall_at_1000_std value: 31.506985071436088 - type: nauc_recall_at_100_diff1 value: 23.607673377885448 - type: nauc_recall_at_100_max value: 36.637750366403935 - type: nauc_recall_at_100_std value: 18.30770690564224 - type: nauc_recall_at_10_diff1 value: 33.199683418312446 - type: nauc_recall_at_10_max value: 29.63115497012312 - type: nauc_recall_at_10_std value: 4.813200391480566 - type: nauc_recall_at_1_diff1 value: 50.62269273984579 - type: nauc_recall_at_1_max value: 30.16325757909521 - type: nauc_recall_at_1_std value: -0.6398875136233392 - type: nauc_recall_at_20_diff1 value: 29.16488387844995 - type: nauc_recall_at_20_max value: 30.788019479459 - type: nauc_recall_at_20_std value: 11.031953917298853 - type: nauc_recall_at_3_diff1 value: 38.215351600417065 - type: nauc_recall_at_3_max value: 29.619887154236128 - type: nauc_recall_at_3_std value: -0.13237298980339363 - type: nauc_recall_at_5_diff1 value: 33.93788042633265 - type: nauc_recall_at_5_max value: 28.67185092656741 - type: nauc_recall_at_5_std value: 1.316700201091445 - type: ndcg_at_1 value: 31.299 - type: ndcg_at_10 value: 43.983 - type: ndcg_at_100 value: 48.992999999999995 - type: ndcg_at_1000 value: 50.757 - type: ndcg_at_20 value: 46.152 - type: ndcg_at_3 value: 38.367000000000004 - type: ndcg_at_5 value: 41.171 - type: precision_at_1 value: 31.299 - type: precision_at_10 value: 6.734 - type: precision_at_100 value: 0.972 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_20 value: 3.898 - type: precision_at_3 value: 16.121 - type: precision_at_5 value: 11.344999999999999 - type: recall_at_1 value: 28.942 - type: recall_at_10 value: 58.343999999999994 - type: recall_at_100 value: 80.82300000000001 - type: recall_at_1000 value: 94.348 - type: recall_at_20 value: 66.449 - type: recall_at_3 value: 43.415 - type: recall_at_5 value: 50.007999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: main_score value: 33.144 - type: map_at_1 value: 19.41 - type: map_at_10 value: 27.802 - type: map_at_100 value: 29.157 - type: map_at_1000 value: 29.274 - type: map_at_20 value: 28.549000000000003 - type: map_at_3 value: 25.052999999999997 - type: map_at_5 value: 26.521 - type: mrr_at_1 value: 23.756218905472636 - type: mrr_at_10 value: 32.3623450209271 - type: mrr_at_100 value: 33.3648208444617 - type: mrr_at_1000 value: 33.427688215162185 - type: mrr_at_20 value: 32.93723485575758 - type: mrr_at_3 value: 29.539800995024883 - type: mrr_at_5 value: 31.156716417910452 - type: nauc_map_at_1000_diff1 value: 36.196391248081284 - type: nauc_map_at_1000_max value: 25.650644367091495 - type: nauc_map_at_1000_std value: 6.130340697729844 - type: nauc_map_at_100_diff1 value: 36.138890642411376 - type: nauc_map_at_100_max value: 25.587124763888518 - type: nauc_map_at_100_std value: 6.129336379055536 - type: nauc_map_at_10_diff1 value: 36.254426743566775 - type: nauc_map_at_10_max value: 25.465599906543034 - type: nauc_map_at_10_std value: 5.880280378112879 - type: nauc_map_at_1_diff1 value: 42.890551563179976 - type: nauc_map_at_1_max value: 25.813805281076956 - type: nauc_map_at_1_std value: 5.150718386163028 - type: nauc_map_at_20_diff1 value: 35.98551587974314 - type: nauc_map_at_20_max value: 25.501540521726636 - type: nauc_map_at_20_std value: 5.858703157458749 - type: nauc_map_at_3_diff1 value: 37.646558039577734 - type: nauc_map_at_3_max value: 26.138491471124247 - type: nauc_map_at_3_std value: 6.0487505175540734 - type: nauc_map_at_5_diff1 value: 36.817582976153695 - type: nauc_map_at_5_max value: 25.398200211121146 - type: nauc_map_at_5_std value: 6.31126763919522 - type: nauc_mrr_at_1000_diff1 value: 37.313544952847835 - type: nauc_mrr_at_1000_max value: 26.96218532078988 - type: nauc_mrr_at_1000_std value: 6.814359224654042 - type: nauc_mrr_at_100_diff1 value: 37.28104407653679 - type: nauc_mrr_at_100_max value: 26.931243040477256 - type: nauc_mrr_at_100_std value: 6.800500150841733 - type: nauc_mrr_at_10_diff1 value: 37.315832621275895 - type: nauc_mrr_at_10_max value: 26.941454225978372 - type: nauc_mrr_at_10_std value: 6.837046527796884 - type: nauc_mrr_at_1_diff1 value: 43.19904188582958 - type: nauc_mrr_at_1_max value: 26.975620445904795 - type: nauc_mrr_at_1_std value: 4.52071008581395 - type: nauc_mrr_at_20_diff1 value: 37.2200524790774 - type: nauc_mrr_at_20_max value: 26.971494160765847 - type: nauc_mrr_at_20_std value: 6.716431228783282 - type: nauc_mrr_at_3_diff1 value: 38.46236387340654 - type: nauc_mrr_at_3_max value: 27.846812992192056 - type: nauc_mrr_at_3_std value: 6.550711872569794 - type: nauc_mrr_at_5_diff1 value: 37.620346007658476 - type: nauc_mrr_at_5_max value: 27.031025952102038 - type: nauc_mrr_at_5_std value: 7.32343760231163 - type: nauc_ndcg_at_1000_diff1 value: 34.95081314840592 - type: nauc_ndcg_at_1000_max value: 26.89265465124325 - type: nauc_ndcg_at_1000_std value: 7.854154466831975 - type: nauc_ndcg_at_100_diff1 value: 34.01417812563093 - type: nauc_ndcg_at_100_max value: 25.792737746436835 - type: nauc_ndcg_at_100_std value: 7.726584165493833 - type: nauc_ndcg_at_10_diff1 value: 33.895122516474466 - type: nauc_ndcg_at_10_max value: 25.388442204589612 - type: nauc_ndcg_at_10_std value: 6.359560223645991 - type: nauc_ndcg_at_1_diff1 value: 43.19904188582958 - type: nauc_ndcg_at_1_max value: 26.975620445904795 - type: nauc_ndcg_at_1_std value: 4.52071008581395 - type: nauc_ndcg_at_20_diff1 value: 33.36078689830245 - type: nauc_ndcg_at_20_max value: 25.531794610571563 - type: nauc_ndcg_at_20_std value: 6.136658608653248 - type: nauc_ndcg_at_3_diff1 value: 36.44505602530781 - type: nauc_ndcg_at_3_max value: 26.9104071983157 - type: nauc_ndcg_at_3_std value: 6.427178520371878 - type: nauc_ndcg_at_5_diff1 value: 35.01384323197442 - type: nauc_ndcg_at_5_max value: 25.5560447088692 - type: nauc_ndcg_at_5_std value: 7.3676236760360485 - type: nauc_precision_at_1000_diff1 value: 2.8903331041804514 - type: nauc_precision_at_1000_max value: 4.059662742366004 - type: nauc_precision_at_1000_std value: -1.5891687644008334 - type: nauc_precision_at_100_diff1 value: 8.437726471693766 - type: nauc_precision_at_100_max value: 11.250588557568427 - type: nauc_precision_at_100_std value: 4.231571164627862 - type: nauc_precision_at_10_diff1 value: 19.57085237210294 - type: nauc_precision_at_10_max value: 20.973093492003905 - type: nauc_precision_at_10_std value: 3.197416248152466 - type: nauc_precision_at_1_diff1 value: 43.19904188582958 - type: nauc_precision_at_1_max value: 26.975620445904795 - type: nauc_precision_at_1_std value: 4.52071008581395 - type: nauc_precision_at_20_diff1 value: 15.67136554192724 - type: nauc_precision_at_20_max value: 17.706882621057858 - type: nauc_precision_at_20_std value: 1.9363472182867714 - type: nauc_precision_at_3_diff1 value: 30.38035695042325 - type: nauc_precision_at_3_max value: 26.48218693244094 - type: nauc_precision_at_3_std value: 6.424657705785632 - type: nauc_precision_at_5_diff1 value: 25.272543315171458 - type: nauc_precision_at_5_max value: 22.32441421311652 - type: nauc_precision_at_5_std value: 7.4912569081905716 - type: nauc_recall_at_1000_diff1 value: 25.5748044137675 - type: nauc_recall_at_1000_max value: 43.85796585370269 - type: nauc_recall_at_1000_std value: 30.0338086596789 - type: nauc_recall_at_100_diff1 value: 22.577080638885093 - type: nauc_recall_at_100_max value: 23.224511700617477 - type: nauc_recall_at_100_std value: 15.187963852289313 - type: nauc_recall_at_10_diff1 value: 25.058592299355908 - type: nauc_recall_at_10_max value: 22.24448483279841 - type: nauc_recall_at_10_std value: 6.3179089740052765 - type: nauc_recall_at_1_diff1 value: 42.890551563179976 - type: nauc_recall_at_1_max value: 25.813805281076956 - type: nauc_recall_at_1_std value: 5.150718386163028 - type: nauc_recall_at_20_diff1 value: 22.433865123187307 - type: nauc_recall_at_20_max value: 22.739695641511762 - type: nauc_recall_at_20_std value: 5.362005125538497 - type: nauc_recall_at_3_diff1 value: 32.17919168998616 - type: nauc_recall_at_3_max value: 26.044028436867357 - type: nauc_recall_at_3_std value: 7.420349884006329 - type: nauc_recall_at_5_diff1 value: 28.967104573649138 - type: nauc_recall_at_5_max value: 23.40865848168201 - type: nauc_recall_at_5_std value: 9.174406147723621 - type: ndcg_at_1 value: 23.756 - type: ndcg_at_10 value: 33.144 - type: ndcg_at_100 value: 39.261 - type: ndcg_at_1000 value: 41.881 - type: ndcg_at_20 value: 35.56 - type: ndcg_at_3 value: 27.927999999999997 - type: ndcg_at_5 value: 30.293999999999997 - type: precision_at_1 value: 23.756 - type: precision_at_10 value: 5.995 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_20 value: 3.688 - type: precision_at_3 value: 13.059999999999999 - type: precision_at_5 value: 9.602 - type: recall_at_1 value: 19.41 - type: recall_at_10 value: 45.074 - type: recall_at_100 value: 71.131 - type: recall_at_1000 value: 89.604 - type: recall_at_20 value: 53.673 - type: recall_at_3 value: 31.055 - type: recall_at_5 value: 36.714999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: mteb/cqadupstack-physics config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: main_score value: 49.675000000000004 - type: map_at_1 value: 33.178999999999995 - type: map_at_10 value: 43.807 - type: map_at_100 value: 45.17 - type: map_at_1000 value: 45.271 - type: map_at_20 value: 44.516 - type: map_at_3 value: 40.813 - type: map_at_5 value: 42.457 - type: mrr_at_1 value: 40.32723772858518 - type: mrr_at_10 value: 49.646867409138814 - type: mrr_at_100 value: 50.493686101426285 - type: mrr_at_1000 value: 50.525386961808834 - type: mrr_at_20 value: 50.120274354884586 - type: mrr_at_3 value: 47.49759384023096 - type: mrr_at_5 value: 48.72473532242535 - type: nauc_map_at_1000_diff1 value: 49.5947127786396 - type: nauc_map_at_1000_max value: 33.39720045844929 - type: nauc_map_at_1000_std value: -3.131428593252271 - type: nauc_map_at_100_diff1 value: 49.57797867324617 - type: nauc_map_at_100_max value: 33.356927974709464 - type: nauc_map_at_100_std value: -3.1661365376766337 - type: nauc_map_at_10_diff1 value: 49.59294630598952 - type: nauc_map_at_10_max value: 32.86647346990462 - type: nauc_map_at_10_std value: -4.1582043443386745 - type: nauc_map_at_1_diff1 value: 53.98646767288695 - type: nauc_map_at_1_max value: 29.45629077638936 - type: nauc_map_at_1_std value: -5.621187380771589 - type: nauc_map_at_20_diff1 value: 49.486982890447074 - type: nauc_map_at_20_max value: 33.11681933406332 - type: nauc_map_at_20_std value: -3.5826433195146854 - type: nauc_map_at_3_diff1 value: 50.81807107491861 - type: nauc_map_at_3_max value: 32.32552291988859 - type: nauc_map_at_3_std value: -3.952946504088928 - type: nauc_map_at_5_diff1 value: 49.70201354274439 - type: nauc_map_at_5_max value: 32.831846031004886 - type: nauc_map_at_5_std value: -3.8330488624207737 - type: nauc_mrr_at_1000_diff1 value: 49.04159472507738 - type: nauc_mrr_at_1000_max value: 35.617600171138676 - type: nauc_mrr_at_1000_std value: -1.5975830757486646 - type: nauc_mrr_at_100_diff1 value: 49.03848471692094 - type: nauc_mrr_at_100_max value: 35.61936748662614 - type: nauc_mrr_at_100_std value: -1.5922053398594729 - type: nauc_mrr_at_10_diff1 value: 48.92463964652612 - type: nauc_mrr_at_10_max value: 35.37757708992045 - type: nauc_mrr_at_10_std value: -2.2052028139567303 - type: nauc_mrr_at_1_diff1 value: 52.23915787290734 - type: nauc_mrr_at_1_max value: 34.393531787632334 - type: nauc_mrr_at_1_std value: -1.452007661016969 - type: nauc_mrr_at_20_diff1 value: 48.91168438018404 - type: nauc_mrr_at_20_max value: 35.478962544421876 - type: nauc_mrr_at_20_std value: -1.8246048423555414 - type: nauc_mrr_at_3_diff1 value: 50.115432665442164 - type: nauc_mrr_at_3_max value: 35.89093796085569 - type: nauc_mrr_at_3_std value: -1.4895016313153366 - type: nauc_mrr_at_5_diff1 value: 49.04321261351915 - type: nauc_mrr_at_5_max value: 35.85730520949451 - type: nauc_mrr_at_5_std value: -1.68790556880753 - type: nauc_ndcg_at_1000_diff1 value: 48.294697499154374 - type: nauc_ndcg_at_1000_max value: 35.167410242367595 - type: nauc_ndcg_at_1000_std value: -0.6346078535914157 - type: nauc_ndcg_at_100_diff1 value: 48.025525283449014 - type: nauc_ndcg_at_100_max value: 34.79288511776105 - type: nauc_ndcg_at_100_std value: -0.7823403044086993 - type: nauc_ndcg_at_10_diff1 value: 47.70793258015258 - type: nauc_ndcg_at_10_max value: 33.09558927880104 - type: nauc_ndcg_at_10_std value: -4.7793864166260605 - type: nauc_ndcg_at_1_diff1 value: 52.23915787290734 - type: nauc_ndcg_at_1_max value: 34.393531787632334 - type: nauc_ndcg_at_1_std value: -1.452007661016969 - type: nauc_ndcg_at_20_diff1 value: 47.354286045074815 - type: nauc_ndcg_at_20_max value: 33.686648806027975 - type: nauc_ndcg_at_20_std value: -3.0189085132476556 - type: nauc_ndcg_at_3_diff1 value: 49.68805334316908 - type: nauc_ndcg_at_3_max value: 34.196077748056496 - type: nauc_ndcg_at_3_std value: -2.7167289163768436 - type: nauc_ndcg_at_5_diff1 value: 47.94474868912989 - type: nauc_ndcg_at_5_max value: 34.00261603413051 - type: nauc_ndcg_at_5_std value: -3.3541028103046115 - type: nauc_precision_at_1000_diff1 value: -12.0150100710755 - type: nauc_precision_at_1000_max value: 5.332942816568796 - type: nauc_precision_at_1000_std value: 14.543288479130458 - type: nauc_precision_at_100_diff1 value: -4.920332181588838 - type: nauc_precision_at_100_max value: 14.42313332017491 - type: nauc_precision_at_100_std value: 17.821953321018384 - type: nauc_precision_at_10_diff1 value: 14.70509089079217 - type: nauc_precision_at_10_max value: 25.381887131649716 - type: nauc_precision_at_10_std value: 5.226419288645675 - type: nauc_precision_at_1_diff1 value: 52.23915787290734 - type: nauc_precision_at_1_max value: 34.393531787632334 - type: nauc_precision_at_1_std value: -1.452007661016969 - type: nauc_precision_at_20_diff1 value: 6.312827641507564 - type: nauc_precision_at_20_max value: 22.483038562271933 - type: nauc_precision_at_20_std value: 11.368419856892416 - type: nauc_precision_at_3_diff1 value: 33.271443420273606 - type: nauc_precision_at_3_max value: 33.571078182106675 - type: nauc_precision_at_3_std value: 4.47382265155717 - type: nauc_precision_at_5_diff1 value: 23.43287104284656 - type: nauc_precision_at_5_max value: 30.909085068105313 - type: nauc_precision_at_5_std value: 5.545672049452433 - type: nauc_recall_at_1000_diff1 value: 35.22615594677707 - type: nauc_recall_at_1000_max value: 52.0710533173532 - type: nauc_recall_at_1000_std value: 45.17683523786464 - type: nauc_recall_at_100_diff1 value: 36.2169056956332 - type: nauc_recall_at_100_max value: 35.02435003210817 - type: nauc_recall_at_100_std value: 15.833632946282508 - type: nauc_recall_at_10_diff1 value: 39.12440292974848 - type: nauc_recall_at_10_max value: 28.0546011979648 - type: nauc_recall_at_10_std value: -9.620558638092172 - type: nauc_recall_at_1_diff1 value: 53.98646767288695 - type: nauc_recall_at_1_max value: 29.45629077638936 - type: nauc_recall_at_1_std value: -5.621187380771589 - type: nauc_recall_at_20_diff1 value: 36.39254630768161 - type: nauc_recall_at_20_max value: 29.277856508751967 - type: nauc_recall_at_20_std value: -3.048007490798412 - type: nauc_recall_at_3_diff1 value: 45.64706642644958 - type: nauc_recall_at_3_max value: 31.003050159737413 - type: nauc_recall_at_3_std value: -4.849763876930667 - type: nauc_recall_at_5_diff1 value: 40.918108859971746 - type: nauc_recall_at_5_max value: 30.69907335071493 - type: nauc_recall_at_5_std value: -6.1445436251916865 - type: ndcg_at_1 value: 40.327 - type: ndcg_at_10 value: 49.675000000000004 - type: ndcg_at_100 value: 55.364000000000004 - type: ndcg_at_1000 value: 56.992 - type: ndcg_at_20 value: 51.803999999999995 - type: ndcg_at_3 value: 45.227000000000004 - type: ndcg_at_5 value: 47.244 - type: precision_at_1 value: 40.327 - type: precision_at_10 value: 8.826 - type: precision_at_100 value: 1.354 - type: precision_at_1000 value: 0.167 - type: precision_at_20 value: 5.115 - type: precision_at_3 value: 21.303 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 33.178999999999995 - type: recall_at_10 value: 61.087 - type: recall_at_100 value: 85.099 - type: recall_at_1000 value: 95.14099999999999 - type: recall_at_20 value: 68.623 - type: recall_at_3 value: 48.245 - type: recall_at_5 value: 53.832 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: main_score value: 44.99 - type: map_at_1 value: 28.089 - type: map_at_10 value: 38.98 - type: map_at_100 value: 40.339000000000006 - type: map_at_1000 value: 40.441 - type: map_at_20 value: 39.702 - type: map_at_3 value: 35.620000000000005 - type: map_at_5 value: 37.657000000000004 - type: mrr_at_1 value: 35.15981735159817 - type: mrr_at_10 value: 44.54075161266937 - type: mrr_at_100 value: 45.435730392436646 - type: mrr_at_1000 value: 45.47673849356812 - type: mrr_at_20 value: 45.05949613726918 - type: mrr_at_3 value: 42.00913242009131 - type: mrr_at_5 value: 43.52739726027392 - type: nauc_map_at_1000_diff1 value: 42.6375513442399 - type: nauc_map_at_1000_max value: 35.83899956589522 - type: nauc_map_at_1000_std value: 5.798620017712549 - type: nauc_map_at_100_diff1 value: 42.609712253881504 - type: nauc_map_at_100_max value: 35.85401871065736 - type: nauc_map_at_100_std value: 5.829007296755533 - type: nauc_map_at_10_diff1 value: 42.90931172127824 - type: nauc_map_at_10_max value: 35.46694204511423 - type: nauc_map_at_10_std value: 5.131477704152026 - type: nauc_map_at_1_diff1 value: 48.066312177855956 - type: nauc_map_at_1_max value: 30.67745267941573 - type: nauc_map_at_1_std value: -1.4170737991670943 - type: nauc_map_at_20_diff1 value: 42.730423700784 - type: nauc_map_at_20_max value: 35.710039616497085 - type: nauc_map_at_20_std value: 5.363961887475162 - type: nauc_map_at_3_diff1 value: 43.499223646579935 - type: nauc_map_at_3_max value: 33.872570039621564 - type: nauc_map_at_3_std value: 3.0787571843453008 - type: nauc_map_at_5_diff1 value: 43.28963642946521 - type: nauc_map_at_5_max value: 35.18327408279892 - type: nauc_map_at_5_std value: 4.516467154662473 - type: nauc_mrr_at_1000_diff1 value: 42.71279871641341 - type: nauc_mrr_at_1000_max value: 37.48825064817496 - type: nauc_mrr_at_1000_std value: 8.10015025024314 - type: nauc_mrr_at_100_diff1 value: 42.694777404773376 - type: nauc_mrr_at_100_max value: 37.476741768741086 - type: nauc_mrr_at_100_std value: 8.11525130417229 - type: nauc_mrr_at_10_diff1 value: 42.954194054560176 - type: nauc_mrr_at_10_max value: 37.606138578797506 - type: nauc_mrr_at_10_std value: 8.092519513302399 - type: nauc_mrr_at_1_diff1 value: 48.350790286038574 - type: nauc_mrr_at_1_max value: 33.97992759739641 - type: nauc_mrr_at_1_std value: 1.8332987018664093 - type: nauc_mrr_at_20_diff1 value: 42.664983701783044 - type: nauc_mrr_at_20_max value: 37.47450702110784 - type: nauc_mrr_at_20_std value: 8.001067634745462 - type: nauc_mrr_at_3_diff1 value: 42.921968602737955 - type: nauc_mrr_at_3_max value: 37.19599728791262 - type: nauc_mrr_at_3_std value: 7.4692697422507575 - type: nauc_mrr_at_5_diff1 value: 42.96028546491891 - type: nauc_mrr_at_5_max value: 37.688350071295915 - type: nauc_mrr_at_5_std value: 8.213017954012372 - type: nauc_ndcg_at_1000_diff1 value: 40.70763263942397 - type: nauc_ndcg_at_1000_max value: 37.87768319167602 - type: nauc_ndcg_at_1000_std value: 9.908807071686738 - type: nauc_ndcg_at_100_diff1 value: 39.97828438221707 - type: nauc_ndcg_at_100_max value: 37.7723393835996 - type: nauc_ndcg_at_100_std value: 10.666779466040097 - type: nauc_ndcg_at_10_diff1 value: 41.172233451172936 - type: nauc_ndcg_at_10_max value: 37.12252131573939 - type: nauc_ndcg_at_10_std value: 8.273798754436639 - type: nauc_ndcg_at_1_diff1 value: 48.350790286038574 - type: nauc_ndcg_at_1_max value: 33.97992759739641 - type: nauc_ndcg_at_1_std value: 1.8332987018664093 - type: nauc_ndcg_at_20_diff1 value: 40.33325895172716 - type: nauc_ndcg_at_20_max value: 37.36015594019951 - type: nauc_ndcg_at_20_std value: 8.818556108749302 - type: nauc_ndcg_at_3_diff1 value: 41.652701699747254 - type: nauc_ndcg_at_3_max value: 35.499109874223294 - type: nauc_ndcg_at_3_std value: 5.831784865606119 - type: nauc_ndcg_at_5_diff1 value: 41.856346892595475 - type: nauc_ndcg_at_5_max value: 36.940681835687194 - type: nauc_ndcg_at_5_std value: 7.507798515093516 - type: nauc_precision_at_1000_diff1 value: -2.4605367806784866 - type: nauc_precision_at_1000_max value: -0.3538142127162922 - type: nauc_precision_at_1000_std value: 8.369794961833236 - type: nauc_precision_at_100_diff1 value: -0.34954522096524704 - type: nauc_precision_at_100_max value: 13.159909603146458 - type: nauc_precision_at_100_std value: 19.425561514133996 - type: nauc_precision_at_10_diff1 value: 17.048304710148145 - type: nauc_precision_at_10_max value: 29.816041846806375 - type: nauc_precision_at_10_std value: 18.358893367243798 - type: nauc_precision_at_1_diff1 value: 48.350790286038574 - type: nauc_precision_at_1_max value: 33.97992759739641 - type: nauc_precision_at_1_std value: 1.8332987018664093 - type: nauc_precision_at_20_diff1 value: 10.450903599411344 - type: nauc_precision_at_20_max value: 25.228916373799127 - type: nauc_precision_at_20_std value: 18.46893569529936 - type: nauc_precision_at_3_diff1 value: 29.181236567048636 - type: nauc_precision_at_3_max value: 35.64918262500281 - type: nauc_precision_at_3_std value: 13.347538222514968 - type: nauc_precision_at_5_diff1 value: 23.693323840550345 - type: nauc_precision_at_5_max value: 33.972399735191225 - type: nauc_precision_at_5_std value: 17.107012760554618 - type: nauc_recall_at_1000_diff1 value: 20.297340483227945 - type: nauc_recall_at_1000_max value: 63.084305970127275 - type: nauc_recall_at_1000_std value: 63.04655000858784 - type: nauc_recall_at_100_diff1 value: 22.587332148979723 - type: nauc_recall_at_100_max value: 40.740968468024775 - type: nauc_recall_at_100_std value: 34.120423684507124 - type: nauc_recall_at_10_diff1 value: 33.361195948673675 - type: nauc_recall_at_10_max value: 37.1411402410262 - type: nauc_recall_at_10_std value: 13.475407196166259 - type: nauc_recall_at_1_diff1 value: 48.066312177855956 - type: nauc_recall_at_1_max value: 30.67745267941573 - type: nauc_recall_at_1_std value: -1.4170737991670943 - type: nauc_recall_at_20_diff1 value: 28.703982984383984 - type: nauc_recall_at_20_max value: 37.32929431193496 - type: nauc_recall_at_20_std value: 16.139135347989903 - type: nauc_recall_at_3_diff1 value: 36.53346179134789 - type: nauc_recall_at_3_max value: 34.11397914899309 - type: nauc_recall_at_3_std value: 7.19358019807132 - type: nauc_recall_at_5_diff1 value: 36.24058894947452 - type: nauc_recall_at_5_max value: 37.00990358651097 - type: nauc_recall_at_5_std value: 11.074645476821619 - type: ndcg_at_1 value: 35.160000000000004 - type: ndcg_at_10 value: 44.99 - type: ndcg_at_100 value: 50.661 - type: ndcg_at_1000 value: 52.599 - type: ndcg_at_20 value: 47.154 - type: ndcg_at_3 value: 39.843 - type: ndcg_at_5 value: 42.486000000000004 - type: precision_at_1 value: 35.160000000000004 - type: precision_at_10 value: 8.299 - type: precision_at_100 value: 1.2850000000000001 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_20 value: 4.84 - type: precision_at_3 value: 19.178 - type: precision_at_5 value: 13.927 - type: recall_at_1 value: 28.089 - type: recall_at_10 value: 57.158 - type: recall_at_100 value: 81.461 - type: recall_at_1000 value: 94.46900000000001 - type: recall_at_20 value: 64.927 - type: recall_at_3 value: 42.775999999999996 - type: recall_at_5 value: 49.719 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: CQADupstackRetrieval is a combined dataset metrics: - type: main_score value: 44.989166666666655 - type: ndcg_at_10 value: 44.989166666666655 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: main_score value: 39.586 - type: map_at_1 value: 27.301 - type: map_at_10 value: 35.022 - type: map_at_100 value: 36.061 - type: map_at_1000 value: 36.146 - type: map_at_20 value: 35.608000000000004 - type: map_at_3 value: 32.978 - type: map_at_5 value: 33.994 - type: mrr_at_1 value: 30.67484662576687 - type: mrr_at_10 value: 38.1696124257474 - type: mrr_at_100 value: 38.99730898994137 - type: mrr_at_1000 value: 39.049871007408136 - type: mrr_at_20 value: 38.62424051396064 - type: mrr_at_3 value: 36.40081799591004 - type: mrr_at_5 value: 37.23670756646219 - type: nauc_map_at_1000_diff1 value: 50.4395097150819 - type: nauc_map_at_1000_max value: 42.36231476768413 - type: nauc_map_at_1000_std value: 1.0739414045485742 - type: nauc_map_at_100_diff1 value: 50.4253775421283 - type: nauc_map_at_100_max value: 42.34508969348633 - type: nauc_map_at_100_std value: 1.0590256535050135 - type: nauc_map_at_10_diff1 value: 50.74196619464362 - type: nauc_map_at_10_max value: 42.354326434590284 - type: nauc_map_at_10_std value: 0.6330167542705694 - type: nauc_map_at_1_diff1 value: 55.7404810490963 - type: nauc_map_at_1_max value: 40.7676941648045 - type: nauc_map_at_1_std value: -5.021772566610674 - type: nauc_map_at_20_diff1 value: 50.39792463598886 - type: nauc_map_at_20_max value: 42.25768760228577 - type: nauc_map_at_20_std value: 0.8979017700131807 - type: nauc_map_at_3_diff1 value: 51.53267996170815 - type: nauc_map_at_3_max value: 41.78801756883417 - type: nauc_map_at_3_std value: -0.6652383024396911 - type: nauc_map_at_5_diff1 value: 50.992783683271504 - type: nauc_map_at_5_max value: 41.8607977828188 - type: nauc_map_at_5_std value: 0.3484379897869807 - type: nauc_mrr_at_1000_diff1 value: 48.952907124445126 - type: nauc_mrr_at_1000_max value: 42.93563741482114 - type: nauc_mrr_at_1000_std value: 3.0791495753556424 - type: nauc_mrr_at_100_diff1 value: 48.941921107360805 - type: nauc_mrr_at_100_max value: 42.94419657374061 - type: nauc_mrr_at_100_std value: 3.075397087180154 - type: nauc_mrr_at_10_diff1 value: 49.098926306303056 - type: nauc_mrr_at_10_max value: 42.941857820499806 - type: nauc_mrr_at_10_std value: 2.8184474174054372 - type: nauc_mrr_at_1_diff1 value: 54.428109877009334 - type: nauc_mrr_at_1_max value: 42.50273386972492 - type: nauc_mrr_at_1_std value: -2.1811826216412187 - type: nauc_mrr_at_20_diff1 value: 48.82502192775839 - type: nauc_mrr_at_20_max value: 42.92227277257095 - type: nauc_mrr_at_20_std value: 2.975812634368533 - type: nauc_mrr_at_3_diff1 value: 49.440009227591176 - type: nauc_mrr_at_3_max value: 42.95503176290712 - type: nauc_mrr_at_3_std value: 2.2997128945013796 - type: nauc_mrr_at_5_diff1 value: 49.09846782701398 - type: nauc_mrr_at_5_max value: 42.51449168285772 - type: nauc_mrr_at_5_std value: 2.7785816484421297 - type: nauc_ndcg_at_1000_diff1 value: 48.14680758187888 - type: nauc_ndcg_at_1000_max value: 43.57465718500695 - type: nauc_ndcg_at_1000_std value: 5.287435676678261 - type: nauc_ndcg_at_100_diff1 value: 47.66081605743284 - type: nauc_ndcg_at_100_max value: 43.28156751251163 - type: nauc_ndcg_at_100_std value: 4.959626409663624 - type: nauc_ndcg_at_10_diff1 value: 48.25075619623878 - type: nauc_ndcg_at_10_max value: 43.00688660666578 - type: nauc_ndcg_at_10_std value: 3.2319193368891637 - type: nauc_ndcg_at_1_diff1 value: 54.428109877009334 - type: nauc_ndcg_at_1_max value: 42.50273386972492 - type: nauc_ndcg_at_1_std value: -2.1811826216412187 - type: nauc_ndcg_at_20_diff1 value: 47.1943098627403 - type: nauc_ndcg_at_20_max value: 42.86954491768707 - type: nauc_ndcg_at_20_std value: 4.08583080150737 - type: nauc_ndcg_at_3_diff1 value: 49.32681523192246 - type: nauc_ndcg_at_3_max value: 42.46898641470274 - type: nauc_ndcg_at_3_std value: 1.7416962407725236 - type: nauc_ndcg_at_5_diff1 value: 48.59647012439291 - type: nauc_ndcg_at_5_max value: 42.07098889846439 - type: nauc_ndcg_at_5_std value: 2.979621233356828 - type: nauc_precision_at_1000_diff1 value: -1.7366334161587105 - type: nauc_precision_at_1000_max value: 17.70969166396819 - type: nauc_precision_at_1000_std value: 17.50619975322144 - type: nauc_precision_at_100_diff1 value: 10.082579982582155 - type: nauc_precision_at_100_max value: 28.024893516091776 - type: nauc_precision_at_100_std value: 18.41413013357596 - type: nauc_precision_at_10_diff1 value: 28.796167732373657 - type: nauc_precision_at_10_max value: 40.37340024485382 - type: nauc_precision_at_10_std value: 13.718572711091733 - type: nauc_precision_at_1_diff1 value: 54.428109877009334 - type: nauc_precision_at_1_max value: 42.50273386972492 - type: nauc_precision_at_1_std value: -2.1811826216412187 - type: nauc_precision_at_20_diff1 value: 19.82691920771315 - type: nauc_precision_at_20_max value: 34.45075390159975 - type: nauc_precision_at_20_std value: 16.410812072348058 - type: nauc_precision_at_3_diff1 value: 40.85430254962678 - type: nauc_precision_at_3_max value: 43.63016056067074 - type: nauc_precision_at_3_std value: 9.322014634477581 - type: nauc_precision_at_5_diff1 value: 35.830272848975795 - type: nauc_precision_at_5_max value: 41.30047691620363 - type: nauc_precision_at_5_std value: 13.145693992266565 - type: nauc_recall_at_1000_diff1 value: 35.532000545890504 - type: nauc_recall_at_1000_max value: 50.714223194510325 - type: nauc_recall_at_1000_std value: 43.09037309139045 - type: nauc_recall_at_100_diff1 value: 35.11024488875192 - type: nauc_recall_at_100_max value: 43.0874566265193 - type: nauc_recall_at_100_std value: 19.70628521846854 - type: nauc_recall_at_10_diff1 value: 40.36203726741153 - type: nauc_recall_at_10_max value: 42.581482582576726 - type: nauc_recall_at_10_std value: 8.642553371022348 - type: nauc_recall_at_1_diff1 value: 55.7404810490963 - type: nauc_recall_at_1_max value: 40.7676941648045 - type: nauc_recall_at_1_std value: -5.021772566610674 - type: nauc_recall_at_20_diff1 value: 35.97348868186562 - type: nauc_recall_at_20_max value: 41.82695933305065 - type: nauc_recall_at_20_std value: 11.444957541593585 - type: nauc_recall_at_3_diff1 value: 44.20020470014979 - type: nauc_recall_at_3_max value: 40.84130855296979 - type: nauc_recall_at_3_std value: 5.004883338558809 - type: nauc_recall_at_5_diff1 value: 42.08756885472078 - type: nauc_recall_at_5_max value: 39.90323783606852 - type: nauc_recall_at_5_std value: 8.085182534171127 - type: ndcg_at_1 value: 30.675 - type: ndcg_at_10 value: 39.586 - type: ndcg_at_100 value: 44.737 - type: ndcg_at_1000 value: 46.863 - type: ndcg_at_20 value: 41.495 - type: ndcg_at_3 value: 35.8 - type: ndcg_at_5 value: 37.3 - type: precision_at_1 value: 30.675 - type: precision_at_10 value: 6.196 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.122 - type: precision_at_20 value: 3.6350000000000002 - type: precision_at_3 value: 15.337 - type: precision_at_5 value: 10.337 - type: recall_at_1 value: 27.301 - type: recall_at_10 value: 50.346999999999994 - type: recall_at_100 value: 74.459 - type: recall_at_1000 value: 90.018 - type: recall_at_20 value: 57.473 - type: recall_at_3 value: 39.672000000000004 - type: recall_at_5 value: 43.383 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: main_score value: 32.842 - type: map_at_1 value: 19.527 - type: map_at_10 value: 27.711999999999996 - type: map_at_100 value: 28.98 - type: map_at_1000 value: 29.108 - type: map_at_20 value: 28.407 - type: map_at_3 value: 25.023 - type: map_at_5 value: 26.528000000000002 - type: mrr_at_1 value: 23.675154852030282 - type: mrr_at_10 value: 31.810676323752784 - type: mrr_at_100 value: 32.788970614380716 - type: mrr_at_1000 value: 32.86028758975889 - type: mrr_at_20 value: 32.35935756676056 - type: mrr_at_3 value: 29.41615049323246 - type: mrr_at_5 value: 30.785730672172633 - type: nauc_map_at_1000_diff1 value: 35.597766688968015 - type: nauc_map_at_1000_max value: 26.295790183159845 - type: nauc_map_at_1000_std value: -0.04229904865958209 - type: nauc_map_at_100_diff1 value: 35.568782622469925 - type: nauc_map_at_100_max value: 26.27850795471227 - type: nauc_map_at_100_std value: -0.04944875782811099 - type: nauc_map_at_10_diff1 value: 35.63760937893694 - type: nauc_map_at_10_max value: 26.130094042028233 - type: nauc_map_at_10_std value: -0.6896882769027717 - type: nauc_map_at_1_diff1 value: 41.759098341890976 - type: nauc_map_at_1_max value: 23.918885427783326 - type: nauc_map_at_1_std value: -2.1383574897865074 - type: nauc_map_at_20_diff1 value: 35.55706530442612 - type: nauc_map_at_20_max value: 26.23339626569677 - type: nauc_map_at_20_std value: -0.162172033918129 - type: nauc_map_at_3_diff1 value: 37.22183376355153 - type: nauc_map_at_3_max value: 25.770512522122186 - type: nauc_map_at_3_std value: -1.3105892187778403 - type: nauc_map_at_5_diff1 value: 36.205913161663084 - type: nauc_map_at_5_max value: 25.953300641502064 - type: nauc_map_at_5_std value: -0.7987363137547906 - type: nauc_mrr_at_1000_diff1 value: 34.864016559617646 - type: nauc_mrr_at_1000_max value: 26.8689525348564 - type: nauc_mrr_at_1000_std value: -0.5839923973914446 - type: nauc_mrr_at_100_diff1 value: 34.83820469598538 - type: nauc_mrr_at_100_max value: 26.864669056231282 - type: nauc_mrr_at_100_std value: -0.5785645654158633 - type: nauc_mrr_at_10_diff1 value: 34.81868397381981 - type: nauc_mrr_at_10_max value: 26.79988560460627 - type: nauc_mrr_at_10_std value: -1.1113808365827318 - type: nauc_mrr_at_1_diff1 value: 40.0281507903504 - type: nauc_mrr_at_1_max value: 25.036735941806583 - type: nauc_mrr_at_1_std value: -2.508700799268523 - type: nauc_mrr_at_20_diff1 value: 34.81954537357966 - type: nauc_mrr_at_20_max value: 26.877673033315453 - type: nauc_mrr_at_20_std value: -0.6706028107452919 - type: nauc_mrr_at_3_diff1 value: 35.87313782549696 - type: nauc_mrr_at_3_max value: 26.776261693392335 - type: nauc_mrr_at_3_std value: -1.8010591328112908 - type: nauc_mrr_at_5_diff1 value: 35.31673912159536 - type: nauc_mrr_at_5_max value: 26.78720786106881 - type: nauc_mrr_at_5_std value: -1.3096326953900546 - type: nauc_ndcg_at_1000_diff1 value: 33.43105244339048 - type: nauc_ndcg_at_1000_max value: 27.52195065724684 - type: nauc_ndcg_at_1000_std value: 2.8376056562675744 - type: nauc_ndcg_at_100_diff1 value: 32.90916846420573 - type: nauc_ndcg_at_100_max value: 27.27161017736065 - type: nauc_ndcg_at_100_std value: 2.8703122625872126 - type: nauc_ndcg_at_10_diff1 value: 33.12714979317447 - type: nauc_ndcg_at_10_max value: 26.67762031747992 - type: nauc_ndcg_at_10_std value: -0.1341345572932233 - type: nauc_ndcg_at_1_diff1 value: 40.0281507903504 - type: nauc_ndcg_at_1_max value: 25.036735941806583 - type: nauc_ndcg_at_1_std value: -2.508700799268523 - type: nauc_ndcg_at_20_diff1 value: 32.891656138688546 - type: nauc_ndcg_at_20_max value: 26.991976404027163 - type: nauc_ndcg_at_20_std value: 1.6050741106677746 - type: nauc_ndcg_at_3_diff1 value: 35.576958713955484 - type: nauc_ndcg_at_3_max value: 26.41687745899445 - type: nauc_ndcg_at_3_std value: -1.5326687067002291 - type: nauc_ndcg_at_5_diff1 value: 34.27335619067276 - type: nauc_ndcg_at_5_max value: 26.479515412084208 - type: nauc_ndcg_at_5_std value: -0.5597648935666003 - type: nauc_precision_at_1000_diff1 value: -0.18660914306684007 - type: nauc_precision_at_1000_max value: 7.268255385799229 - type: nauc_precision_at_1000_std value: -0.1968875268478991 - type: nauc_precision_at_100_diff1 value: 7.386701205054449 - type: nauc_precision_at_100_max value: 15.477735603019607 - type: nauc_precision_at_100_std value: 4.753153414679307 - type: nauc_precision_at_10_diff1 value: 18.4668296945938 - type: nauc_precision_at_10_max value: 25.457144217779597 - type: nauc_precision_at_10_std value: 0.40165373733963605 - type: nauc_precision_at_1_diff1 value: 40.0281507903504 - type: nauc_precision_at_1_max value: 25.036735941806583 - type: nauc_precision_at_1_std value: -2.508700799268523 - type: nauc_precision_at_20_diff1 value: 14.751135844289335 - type: nauc_precision_at_20_max value: 22.763373329576293 - type: nauc_precision_at_20_std value: 4.360731801761864 - type: nauc_precision_at_3_diff1 value: 28.154753888265393 - type: nauc_precision_at_3_max value: 27.838427033527147 - type: nauc_precision_at_3_std value: -1.0042621266717804 - type: nauc_precision_at_5_diff1 value: 23.549026872711423 - type: nauc_precision_at_5_max value: 27.192214745385044 - type: nauc_precision_at_5_std value: 0.4455206110174471 - type: nauc_recall_at_1000_diff1 value: 17.905404210815632 - type: nauc_recall_at_1000_max value: 32.8674418535776 - type: nauc_recall_at_1000_std value: 35.187050415735435 - type: nauc_recall_at_100_diff1 value: 20.903609751984757 - type: nauc_recall_at_100_max value: 27.180306691518364 - type: nauc_recall_at_100_std value: 17.553030959393297 - type: nauc_recall_at_10_diff1 value: 25.615147693464387 - type: nauc_recall_at_10_max value: 25.97062699453565 - type: nauc_recall_at_10_std value: 2.2181702899826576 - type: nauc_recall_at_1_diff1 value: 41.759098341890976 - type: nauc_recall_at_1_max value: 23.918885427783326 - type: nauc_recall_at_1_std value: -2.1383574897865074 - type: nauc_recall_at_20_diff1 value: 23.922775940094386 - type: nauc_recall_at_20_max value: 26.384627814902785 - type: nauc_recall_at_20_std value: 7.944532403561578 - type: nauc_recall_at_3_diff1 value: 32.26543270634743 - type: nauc_recall_at_3_max value: 26.36357710828272 - type: nauc_recall_at_3_std value: -0.42723331708340706 - type: nauc_recall_at_5_diff1 value: 29.080464141763336 - type: nauc_recall_at_5_max value: 25.81238438303652 - type: nauc_recall_at_5_std value: 1.1649311168287726 - type: ndcg_at_1 value: 23.674999999999997 - type: ndcg_at_10 value: 32.842 - type: ndcg_at_100 value: 38.64 - type: ndcg_at_1000 value: 41.367 - type: ndcg_at_20 value: 35.032999999999994 - type: ndcg_at_3 value: 28.166000000000004 - type: ndcg_at_5 value: 30.407 - type: precision_at_1 value: 23.674999999999997 - type: precision_at_10 value: 6.005 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.146 - type: precision_at_20 value: 3.6580000000000004 - type: precision_at_3 value: 13.352 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 19.527 - type: recall_at_10 value: 44.096999999999994 - type: recall_at_100 value: 69.962 - type: recall_at_1000 value: 89.035 - type: recall_at_20 value: 52.166000000000004 - type: recall_at_3 value: 30.946 - type: recall_at_5 value: 36.789 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: main_score value: 46.54 - type: map_at_1 value: 29.953999999999997 - type: map_at_10 value: 40.742 - type: map_at_100 value: 41.964 - type: map_at_1000 value: 42.059999999999995 - type: map_at_20 value: 41.426 - type: map_at_3 value: 37.378 - type: map_at_5 value: 39.267 - type: mrr_at_1 value: 34.701492537313435 - type: mrr_at_10 value: 44.29978085761664 - type: mrr_at_100 value: 45.205551401915486 - type: mrr_at_1000 value: 45.24735017384963 - type: mrr_at_20 value: 44.85338423755729 - type: mrr_at_3 value: 41.57338308457707 - type: mrr_at_5 value: 43.19185323383077 - type: nauc_map_at_1000_diff1 value: 48.45170522932164 - type: nauc_map_at_1000_max value: 31.544164363591204 - type: nauc_map_at_1000_std value: 0.8661088818146858 - type: nauc_map_at_100_diff1 value: 48.47347800061323 - type: nauc_map_at_100_max value: 31.568637596620313 - type: nauc_map_at_100_std value: 0.9252699336843858 - type: nauc_map_at_10_diff1 value: 48.64849891585432 - type: nauc_map_at_10_max value: 31.40371265579746 - type: nauc_map_at_10_std value: 0.7088016563713089 - type: nauc_map_at_1_diff1 value: 53.57918993108331 - type: nauc_map_at_1_max value: 31.392632653740993 - type: nauc_map_at_1_std value: -2.857306170463933 - type: nauc_map_at_20_diff1 value: 48.49084353023969 - type: nauc_map_at_20_max value: 31.470313174779374 - type: nauc_map_at_20_std value: 0.8950296035234309 - type: nauc_map_at_3_diff1 value: 49.273481161619806 - type: nauc_map_at_3_max value: 31.101471509782826 - type: nauc_map_at_3_std value: -0.886510096257905 - type: nauc_map_at_5_diff1 value: 48.85344288229106 - type: nauc_map_at_5_max value: 31.32633663238284 - type: nauc_map_at_5_std value: -0.44752909698881177 - type: nauc_mrr_at_1000_diff1 value: 46.27593166906613 - type: nauc_mrr_at_1000_max value: 31.637594372116336 - type: nauc_mrr_at_1000_std value: 0.8444917550670064 - type: nauc_mrr_at_100_diff1 value: 46.27161543033672 - type: nauc_mrr_at_100_max value: 31.64330655339695 - type: nauc_mrr_at_100_std value: 0.8717446416398773 - type: nauc_mrr_at_10_diff1 value: 46.100348481312864 - type: nauc_mrr_at_10_max value: 31.594271897882237 - type: nauc_mrr_at_10_std value: 0.8807168907688873 - type: nauc_mrr_at_1_diff1 value: 51.35163098909763 - type: nauc_mrr_at_1_max value: 31.99084441327899 - type: nauc_mrr_at_1_std value: -2.688594880742662 - type: nauc_mrr_at_20_diff1 value: 46.18178546174727 - type: nauc_mrr_at_20_max value: 31.639111674119448 - type: nauc_mrr_at_20_std value: 0.9855008641374622 - type: nauc_mrr_at_3_diff1 value: 46.307484835305864 - type: nauc_mrr_at_3_max value: 31.35563850804847 - type: nauc_mrr_at_3_std value: -0.3419536587707561 - type: nauc_mrr_at_5_diff1 value: 46.17646418781234 - type: nauc_mrr_at_5_max value: 31.313474270239833 - type: nauc_mrr_at_5_std value: -0.08656550526568331 - type: nauc_ndcg_at_1000_diff1 value: 46.12095795101613 - type: nauc_ndcg_at_1000_max value: 31.989083597726314 - type: nauc_ndcg_at_1000_std value: 3.2965704707660763 - type: nauc_ndcg_at_100_diff1 value: 46.05376249841318 - type: nauc_ndcg_at_100_max value: 32.39195988574972 - type: nauc_ndcg_at_100_std value: 4.518018135593347 - type: nauc_ndcg_at_10_diff1 value: 46.133631183744875 - type: nauc_ndcg_at_10_max value: 31.45358876172339 - type: nauc_ndcg_at_10_std value: 3.4254370918871055 - type: nauc_ndcg_at_1_diff1 value: 51.35163098909763 - type: nauc_ndcg_at_1_max value: 31.99084441327899 - type: nauc_ndcg_at_1_std value: -2.688594880742662 - type: nauc_ndcg_at_20_diff1 value: 45.94584949766954 - type: nauc_ndcg_at_20_max value: 31.689777515111295 - type: nauc_ndcg_at_20_std value: 4.189082428922442 - type: nauc_ndcg_at_3_diff1 value: 46.5057835389752 - type: nauc_ndcg_at_3_max value: 30.941407592082047 - type: nauc_ndcg_at_3_std value: -0.042473944857831535 - type: nauc_ndcg_at_5_diff1 value: 46.369027395136136 - type: nauc_ndcg_at_5_max value: 31.057841776505352 - type: nauc_ndcg_at_5_std value: 0.6878993420489522 - type: nauc_precision_at_1000_diff1 value: -17.30759714093202 - type: nauc_precision_at_1000_max value: -4.441155558458858 - type: nauc_precision_at_1000_std value: 1.5537300718220326 - type: nauc_precision_at_100_diff1 value: -7.18920438222021 - type: nauc_precision_at_100_max value: 8.017878121399253 - type: nauc_precision_at_100_std value: 11.357132919349102 - type: nauc_precision_at_10_diff1 value: 15.202451884794076 - type: nauc_precision_at_10_max value: 19.077295902881417 - type: nauc_precision_at_10_std value: 9.885526867355805 - type: nauc_precision_at_1_diff1 value: 51.35163098909763 - type: nauc_precision_at_1_max value: 31.99084441327899 - type: nauc_precision_at_1_std value: -2.688594880742662 - type: nauc_precision_at_20_diff1 value: 6.827461091494899 - type: nauc_precision_at_20_max value: 15.27268633497114 - type: nauc_precision_at_20_std value: 11.515826649647384 - type: nauc_precision_at_3_diff1 value: 31.043021807472027 - type: nauc_precision_at_3_max value: 26.22457157531548 - type: nauc_precision_at_3_std value: 1.788215968301994 - type: nauc_precision_at_5_diff1 value: 25.030185818513235 - type: nauc_precision_at_5_max value: 23.680129160901537 - type: nauc_precision_at_5_std value: 4.303018899688115 - type: nauc_recall_at_1000_diff1 value: 28.68826642607512 - type: nauc_recall_at_1000_max value: 42.33849804103852 - type: nauc_recall_at_1000_std value: 42.67413575876864 - type: nauc_recall_at_100_diff1 value: 36.51494878715 - type: nauc_recall_at_100_max value: 37.4764995034434 - type: nauc_recall_at_100_std value: 28.295671266661017 - type: nauc_recall_at_10_diff1 value: 39.416721111463524 - type: nauc_recall_at_10_max value: 29.95985608454179 - type: nauc_recall_at_10_std value: 12.423335839786201 - type: nauc_recall_at_1_diff1 value: 53.57918993108331 - type: nauc_recall_at_1_max value: 31.392632653740993 - type: nauc_recall_at_1_std value: -2.857306170463933 - type: nauc_recall_at_20_diff1 value: 38.228803480194046 - type: nauc_recall_at_20_max value: 30.87261362975955 - type: nauc_recall_at_20_std value: 16.977113091834095 - type: nauc_recall_at_3_diff1 value: 43.154348566653155 - type: nauc_recall_at_3_max value: 29.54536633744803 - type: nauc_recall_at_3_std value: 2.02842672250621 - type: nauc_recall_at_5_diff1 value: 41.00436246072242 - type: nauc_recall_at_5_max value: 29.413569555348023 - type: nauc_recall_at_5_std value: 3.845214021958289 - type: ndcg_at_1 value: 34.701 - type: ndcg_at_10 value: 46.54 - type: ndcg_at_100 value: 51.754999999999995 - type: ndcg_at_1000 value: 53.71 - type: ndcg_at_20 value: 48.679 - type: ndcg_at_3 value: 40.892 - type: ndcg_at_5 value: 43.595 - type: precision_at_1 value: 34.701 - type: precision_at_10 value: 8.004 - type: precision_at_100 value: 1.185 - type: precision_at_1000 value: 0.145 - type: precision_at_20 value: 4.632 - type: precision_at_3 value: 18.719 - type: precision_at_5 value: 13.245999999999999 - type: recall_at_1 value: 29.953999999999997 - type: recall_at_10 value: 60.246 - type: recall_at_100 value: 82.128 - type: recall_at_1000 value: 95.622 - type: recall_at_20 value: 67.756 - type: recall_at_3 value: 45.096000000000004 - type: recall_at_5 value: 51.9 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: mteb/cqadupstack-webmasters config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: main_score value: 44.718999999999994 - type: map_at_1 value: 28.383999999999997 - type: map_at_10 value: 38.422 - type: map_at_100 value: 40.058 - type: map_at_1000 value: 40.276 - type: map_at_20 value: 39.301 - type: map_at_3 value: 35.205 - type: map_at_5 value: 36.803999999999995 - type: mrr_at_1 value: 33.59683794466403 - type: mrr_at_10 value: 42.837536859275986 - type: mrr_at_100 value: 43.7501703455481 - type: mrr_at_1000 value: 43.79258407771123 - type: mrr_at_20 value: 43.36044710445095 - type: mrr_at_3 value: 40.15151515151516 - type: mrr_at_5 value: 41.74242424242425 - type: nauc_map_at_1000_diff1 value: 47.934826596875304 - type: nauc_map_at_1000_max value: 32.39759438116062 - type: nauc_map_at_1000_std value: 0.9489007346763054 - type: nauc_map_at_100_diff1 value: 47.94844822157888 - type: nauc_map_at_100_max value: 32.51485845519537 - type: nauc_map_at_100_std value: 0.8094339925545622 - type: nauc_map_at_10_diff1 value: 48.251456404874645 - type: nauc_map_at_10_max value: 31.412906399154245 - type: nauc_map_at_10_std value: -0.7024825737369933 - type: nauc_map_at_1_diff1 value: 55.81906101970174 - type: nauc_map_at_1_max value: 31.811715334193796 - type: nauc_map_at_1_std value: -6.17056859281584 - type: nauc_map_at_20_diff1 value: 47.80902650237369 - type: nauc_map_at_20_max value: 32.22465403023091 - type: nauc_map_at_20_std value: 0.20706526946705656 - type: nauc_map_at_3_diff1 value: 49.97333984346632 - type: nauc_map_at_3_max value: 31.58195498640799 - type: nauc_map_at_3_std value: -2.577539707727459 - type: nauc_map_at_5_diff1 value: 49.40005767350608 - type: nauc_map_at_5_max value: 30.998435600377434 - type: nauc_map_at_5_std value: -2.1231771618690307 - type: nauc_mrr_at_1000_diff1 value: 46.86811371969663 - type: nauc_mrr_at_1000_max value: 31.25147138171024 - type: nauc_mrr_at_1000_std value: 1.9954422477585918 - type: nauc_mrr_at_100_diff1 value: 46.855870345882195 - type: nauc_mrr_at_100_max value: 31.263524035665966 - type: nauc_mrr_at_100_std value: 2.0160751193806568 - type: nauc_mrr_at_10_diff1 value: 46.93294772825783 - type: nauc_mrr_at_10_max value: 30.927002048701663 - type: nauc_mrr_at_10_std value: 1.6538220080908224 - type: nauc_mrr_at_1_diff1 value: 52.416386548395664 - type: nauc_mrr_at_1_max value: 32.28582003787206 - type: nauc_mrr_at_1_std value: -2.154991145714492 - type: nauc_mrr_at_20_diff1 value: 46.71796185319694 - type: nauc_mrr_at_20_max value: 31.16219902794994 - type: nauc_mrr_at_20_std value: 1.8590646572728409 - type: nauc_mrr_at_3_diff1 value: 47.697100317669914 - type: nauc_mrr_at_3_max value: 30.821806030159383 - type: nauc_mrr_at_3_std value: 1.1927626358099177 - type: nauc_mrr_at_5_diff1 value: 47.065272061365704 - type: nauc_mrr_at_5_max value: 30.299230962805023 - type: nauc_mrr_at_5_std value: 1.3225842862629529 - type: nauc_ndcg_at_1000_diff1 value: 45.20612583136058 - type: nauc_ndcg_at_1000_max value: 33.51931869947315 - type: nauc_ndcg_at_1000_std value: 4.923707509620363 - type: nauc_ndcg_at_100_diff1 value: 44.76206243393775 - type: nauc_ndcg_at_100_max value: 33.57771606755598 - type: nauc_ndcg_at_100_std value: 5.30915563331338 - type: nauc_ndcg_at_10_diff1 value: 45.12714032463827 - type: nauc_ndcg_at_10_max value: 30.351909495610492 - type: nauc_ndcg_at_10_std value: 2.3972947289996873 - type: nauc_ndcg_at_1_diff1 value: 52.416386548395664 - type: nauc_ndcg_at_1_max value: 32.28582003787206 - type: nauc_ndcg_at_1_std value: -2.154991145714492 - type: nauc_ndcg_at_20_diff1 value: 44.20281844000005 - type: nauc_ndcg_at_20_max value: 32.14112739396226 - type: nauc_ndcg_at_20_std value: 3.3971385462591916 - type: nauc_ndcg_at_3_diff1 value: 47.0633767031858 - type: nauc_ndcg_at_3_max value: 31.032896053733435 - type: nauc_ndcg_at_3_std value: 0.6827544906310201 - type: nauc_ndcg_at_5_diff1 value: 46.735352294106484 - type: nauc_ndcg_at_5_max value: 29.784992270528544 - type: nauc_ndcg_at_5_std value: 0.8685943819516141 - type: nauc_precision_at_1000_diff1 value: -12.223330179860852 - type: nauc_precision_at_1000_max value: -9.266492213777273 - type: nauc_precision_at_1000_std value: 19.0569899587788 - type: nauc_precision_at_100_diff1 value: -5.803751085072067 - type: nauc_precision_at_100_max value: 3.448932057044294 - type: nauc_precision_at_100_std value: 23.470863527030627 - type: nauc_precision_at_10_diff1 value: 8.887357341361907 - type: nauc_precision_at_10_max value: 18.67165390928126 - type: nauc_precision_at_10_std value: 19.158543337955404 - type: nauc_precision_at_1_diff1 value: 52.416386548395664 - type: nauc_precision_at_1_max value: 32.28582003787206 - type: nauc_precision_at_1_std value: -2.154991145714492 - type: nauc_precision_at_20_diff1 value: 0.942496138409553 - type: nauc_precision_at_20_max value: 18.86957127610774 - type: nauc_precision_at_20_std value: 24.075503903246496 - type: nauc_precision_at_3_diff1 value: 28.15363877307106 - type: nauc_precision_at_3_max value: 27.064928137991824 - type: nauc_precision_at_3_std value: 8.632807104504753 - type: nauc_precision_at_5_diff1 value: 20.805862332497973 - type: nauc_precision_at_5_max value: 21.420201475758404 - type: nauc_precision_at_5_std value: 12.380239645425714 - type: nauc_recall_at_1000_diff1 value: 18.478341468055547 - type: nauc_recall_at_1000_max value: 56.293560115074506 - type: nauc_recall_at_1000_std value: 64.31607185065428 - type: nauc_recall_at_100_diff1 value: 26.737267337771886 - type: nauc_recall_at_100_max value: 38.011889141496326 - type: nauc_recall_at_100_std value: 30.44904690114732 - type: nauc_recall_at_10_diff1 value: 35.22772732735716 - type: nauc_recall_at_10_max value: 26.000054115159486 - type: nauc_recall_at_10_std value: 5.174264254271206 - type: nauc_recall_at_1_diff1 value: 55.81906101970174 - type: nauc_recall_at_1_max value: 31.811715334193796 - type: nauc_recall_at_1_std value: -6.17056859281584 - type: nauc_recall_at_20_diff1 value: 30.48493302415641 - type: nauc_recall_at_20_max value: 31.05487040370753 - type: nauc_recall_at_20_std value: 10.319948318834136 - type: nauc_recall_at_3_diff1 value: 43.12289512340243 - type: nauc_recall_at_3_max value: 28.176279771026135 - type: nauc_recall_at_3_std value: -0.1775154523381921 - type: nauc_recall_at_5_diff1 value: 40.9934933741234 - type: nauc_recall_at_5_max value: 25.569156290584733 - type: nauc_recall_at_5_std value: 0.21166696686855038 - type: ndcg_at_1 value: 33.597 - type: ndcg_at_10 value: 44.718999999999994 - type: ndcg_at_100 value: 50.324000000000005 - type: ndcg_at_1000 value: 52.468 - type: ndcg_at_20 value: 46.822 - type: ndcg_at_3 value: 39.558 - type: ndcg_at_5 value: 41.827999999999996 - type: precision_at_1 value: 33.597 - type: precision_at_10 value: 8.735 - type: precision_at_100 value: 1.6420000000000001 - type: precision_at_1000 value: 0.246 - type: precision_at_20 value: 5.375 - type: precision_at_3 value: 18.511 - type: precision_at_5 value: 13.399 - type: recall_at_1 value: 28.383999999999997 - type: recall_at_10 value: 56.425000000000004 - type: recall_at_100 value: 82.01899999999999 - type: recall_at_1000 value: 95.285 - type: recall_at_20 value: 64.615 - type: recall_at_3 value: 42.171 - type: recall_at_5 value: 48.296 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: mteb/cqadupstack-wordpress config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: main_score value: 38.269999999999996 - type: map_at_1 value: 25.324999999999996 - type: map_at_10 value: 33.263 - type: map_at_100 value: 34.304 - type: map_at_1000 value: 34.394000000000005 - type: map_at_20 value: 33.827 - type: map_at_3 value: 30.259999999999998 - type: map_at_5 value: 31.832 - type: mrr_at_1 value: 27.171903881700555 - type: mrr_at_10 value: 35.334991051257234 - type: mrr_at_100 value: 36.251283465952355 - type: mrr_at_1000 value: 36.316236092511055 - type: mrr_at_20 value: 35.87141909945257 - type: mrr_at_3 value: 32.71719038817007 - type: mrr_at_5 value: 34.19593345656194 - type: nauc_map_at_1000_diff1 value: 39.614836211522714 - type: nauc_map_at_1000_max value: 22.019768626310192 - type: nauc_map_at_1000_std value: -1.5238708712112499 - type: nauc_map_at_100_diff1 value: 39.63008548572307 - type: nauc_map_at_100_max value: 22.044756063752345 - type: nauc_map_at_100_std value: -1.4869190221494792 - type: nauc_map_at_10_diff1 value: 39.73025012395569 - type: nauc_map_at_10_max value: 22.117710178892107 - type: nauc_map_at_10_std value: -2.5129984871932973 - type: nauc_map_at_1_diff1 value: 45.015617718902654 - type: nauc_map_at_1_max value: 19.313800263189638 - type: nauc_map_at_1_std value: -4.763931386681675 - type: nauc_map_at_20_diff1 value: 39.53678019013766 - type: nauc_map_at_20_max value: 21.880316719428258 - type: nauc_map_at_20_std value: -1.882003994523355 - type: nauc_map_at_3_diff1 value: 40.37307665298228 - type: nauc_map_at_3_max value: 20.851976075322533 - type: nauc_map_at_3_std value: -2.429569082966531 - type: nauc_map_at_5_diff1 value: 39.763015635086 - type: nauc_map_at_5_max value: 22.010102196900725 - type: nauc_map_at_5_std value: -2.654896415670943 - type: nauc_mrr_at_1000_diff1 value: 39.74071733680025 - type: nauc_mrr_at_1000_max value: 21.67309640681989 - type: nauc_mrr_at_1000_std value: -1.4003373135477462 - type: nauc_mrr_at_100_diff1 value: 39.730614151966485 - type: nauc_mrr_at_100_max value: 21.678390048971767 - type: nauc_mrr_at_100_std value: -1.3655362623563931 - type: nauc_mrr_at_10_diff1 value: 39.7900031013241 - type: nauc_mrr_at_10_max value: 21.73643491725051 - type: nauc_mrr_at_10_std value: -2.1175389838696312 - type: nauc_mrr_at_1_diff1 value: 46.165736140679776 - type: nauc_mrr_at_1_max value: 20.071083446822147 - type: nauc_mrr_at_1_std value: -5.018909100858311 - type: nauc_mrr_at_20_diff1 value: 39.6371295762885 - type: nauc_mrr_at_20_max value: 21.659557440270973 - type: nauc_mrr_at_20_std value: -1.4909603958341686 - type: nauc_mrr_at_3_diff1 value: 40.351150322758876 - type: nauc_mrr_at_3_max value: 20.83706249041544 - type: nauc_mrr_at_3_std value: -1.956027373253151 - type: nauc_mrr_at_5_diff1 value: 39.57759107791911 - type: nauc_mrr_at_5_max value: 21.79552045204151 - type: nauc_mrr_at_5_std value: -2.1507013120951126 - type: nauc_ndcg_at_1000_diff1 value: 37.717619356839016 - type: nauc_ndcg_at_1000_max value: 22.545375504379805 - type: nauc_ndcg_at_1000_std value: 1.682348628141016 - type: nauc_ndcg_at_100_diff1 value: 37.656027803682626 - type: nauc_ndcg_at_100_max value: 22.49278246383637 - type: nauc_ndcg_at_100_std value: 2.6818118152357773 - type: nauc_ndcg_at_10_diff1 value: 37.834954205539766 - type: nauc_ndcg_at_10_max value: 22.655839885558443 - type: nauc_ndcg_at_10_std value: -1.97159619786231 - type: nauc_ndcg_at_1_diff1 value: 46.165736140679776 - type: nauc_ndcg_at_1_max value: 20.071083446822147 - type: nauc_ndcg_at_1_std value: -5.018909100858311 - type: nauc_ndcg_at_20_diff1 value: 37.171914857454304 - type: nauc_ndcg_at_20_max value: 21.858904801745897 - type: nauc_ndcg_at_20_std value: 0.3809854859496657 - type: nauc_ndcg_at_3_diff1 value: 38.4460623883955 - type: nauc_ndcg_at_3_max value: 20.95244159463402 - type: nauc_ndcg_at_3_std value: -1.2685011660086651 - type: nauc_ndcg_at_5_diff1 value: 37.48831054573054 - type: nauc_ndcg_at_5_max value: 22.625921624640526 - type: nauc_ndcg_at_5_std value: -2.049221092724925 - type: nauc_precision_at_1000_diff1 value: -19.120500628263994 - type: nauc_precision_at_1000_max value: -6.650707109047473 - type: nauc_precision_at_1000_std value: 15.71193179253002 - type: nauc_precision_at_100_diff1 value: 6.254606806876069 - type: nauc_precision_at_100_max value: 14.601826922181823 - type: nauc_precision_at_100_std value: 28.38299592246453 - type: nauc_precision_at_10_diff1 value: 22.978614338670816 - type: nauc_precision_at_10_max value: 23.04146766323557 - type: nauc_precision_at_10_std value: 6.226264308612577 - type: nauc_precision_at_1_diff1 value: 46.165736140679776 - type: nauc_precision_at_1_max value: 20.071083446822147 - type: nauc_precision_at_1_std value: -5.018909100858311 - type: nauc_precision_at_20_diff1 value: 17.681032853225602 - type: nauc_precision_at_20_max value: 18.66680304585122 - type: nauc_precision_at_20_std value: 15.34896796713905 - type: nauc_precision_at_3_diff1 value: 31.359396694559194 - type: nauc_precision_at_3_max value: 22.279263308973274 - type: nauc_precision_at_3_std value: 3.6302537979529035 - type: nauc_precision_at_5_diff1 value: 26.32257879892933 - type: nauc_precision_at_5_max value: 25.402524493181026 - type: nauc_precision_at_5_std value: 4.731450603747359 - type: nauc_recall_at_1000_diff1 value: 23.562925244967875 - type: nauc_recall_at_1000_max value: 30.737399333586797 - type: nauc_recall_at_1000_std value: 34.19418935008663 - type: nauc_recall_at_100_diff1 value: 28.703574970574824 - type: nauc_recall_at_100_max value: 22.448663600170278 - type: nauc_recall_at_100_std value: 24.53297349042035 - type: nauc_recall_at_10_diff1 value: 31.73603907811882 - type: nauc_recall_at_10_max value: 23.453183748640765 - type: nauc_recall_at_10_std value: -1.8279054407176274 - type: nauc_recall_at_1_diff1 value: 45.015617718902654 - type: nauc_recall_at_1_max value: 19.313800263189638 - type: nauc_recall_at_1_std value: -4.763931386681675 - type: nauc_recall_at_20_diff1 value: 28.74169081866096 - type: nauc_recall_at_20_max value: 20.035509169577324 - type: nauc_recall_at_20_std value: 7.371615811227748 - type: nauc_recall_at_3_diff1 value: 34.09890157333362 - type: nauc_recall_at_3_max value: 20.46565842748346 - type: nauc_recall_at_3_std value: -0.4337283067447526 - type: nauc_recall_at_5_diff1 value: 30.974580787842402 - type: nauc_recall_at_5_max value: 23.76379349487105 - type: nauc_recall_at_5_std value: -1.8407515927979428 - type: ndcg_at_1 value: 27.172 - type: ndcg_at_10 value: 38.269999999999996 - type: ndcg_at_100 value: 43.338 - type: ndcg_at_1000 value: 45.594 - type: ndcg_at_20 value: 40.256 - type: ndcg_at_3 value: 32.673 - type: ndcg_at_5 value: 35.224 - type: precision_at_1 value: 27.172 - type: precision_at_10 value: 6.063000000000001 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.123 - type: precision_at_20 value: 3.5029999999999997 - type: precision_at_3 value: 13.74 - type: precision_at_5 value: 9.797 - type: recall_at_1 value: 25.324999999999996 - type: recall_at_10 value: 51.634 - type: recall_at_100 value: 74.687 - type: recall_at_1000 value: 91.412 - type: recall_at_20 value: 59.207 - type: recall_at_3 value: 36.678 - type: recall_at_5 value: 42.742999999999995 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: main_score value: 36.853 - type: map_at_1 value: 15.371000000000002 - type: map_at_10 value: 27.122 - type: map_at_100 value: 29.226000000000003 - type: map_at_1000 value: 29.409999999999997 - type: map_at_20 value: 28.274 - type: map_at_3 value: 22.431 - type: map_at_5 value: 24.877 - type: mrr_at_1 value: 34.13680781758958 - type: mrr_at_10 value: 47.265911793599145 - type: mrr_at_100 value: 48.028369995763846 - type: mrr_at_1000 value: 48.05317022537804 - type: mrr_at_20 value: 47.75785292259516 - type: mrr_at_3 value: 43.887079261672156 - type: mrr_at_5 value: 45.906623235613544 - type: nauc_map_at_1000_diff1 value: 24.949211292921547 - type: nauc_map_at_1000_max value: 38.69844483304584 - type: nauc_map_at_1000_std value: 18.336359440844753 - type: nauc_map_at_100_diff1 value: 24.8951732982492 - type: nauc_map_at_100_max value: 38.65049158594052 - type: nauc_map_at_100_std value: 18.28935278388095 - type: nauc_map_at_10_diff1 value: 24.606032216798273 - type: nauc_map_at_10_max value: 38.00608351559887 - type: nauc_map_at_10_std value: 16.61261615173358 - type: nauc_map_at_1_diff1 value: 30.83614944448221 - type: nauc_map_at_1_max value: 33.757528532809 - type: nauc_map_at_1_std value: 8.880622713261126 - type: nauc_map_at_20_diff1 value: 24.75491310922017 - type: nauc_map_at_20_max value: 38.353679076398834 - type: nauc_map_at_20_std value: 17.58637493443171 - type: nauc_map_at_3_diff1 value: 25.563085273287083 - type: nauc_map_at_3_max value: 35.14515679047155 - type: nauc_map_at_3_std value: 11.75594869817732 - type: nauc_map_at_5_diff1 value: 24.815807517691614 - type: nauc_map_at_5_max value: 36.25905426665983 - type: nauc_map_at_5_std value: 14.516391726180697 - type: nauc_mrr_at_1000_diff1 value: 27.948233427121274 - type: nauc_mrr_at_1000_max value: 37.5893640945859 - type: nauc_mrr_at_1000_std value: 19.588442449629763 - type: nauc_mrr_at_100_diff1 value: 27.947962345854037 - type: nauc_mrr_at_100_max value: 37.60375479481945 - type: nauc_mrr_at_100_std value: 19.614791576283793 - type: nauc_mrr_at_10_diff1 value: 27.882311310262136 - type: nauc_mrr_at_10_max value: 37.58580968074054 - type: nauc_mrr_at_10_std value: 19.49875186170201 - type: nauc_mrr_at_1_diff1 value: 28.017413073648477 - type: nauc_mrr_at_1_max value: 32.87710191514022 - type: nauc_mrr_at_1_std value: 14.04889142608459 - type: nauc_mrr_at_20_diff1 value: 27.89129925771968 - type: nauc_mrr_at_20_max value: 37.6142863106945 - type: nauc_mrr_at_20_std value: 19.645390143394163 - type: nauc_mrr_at_3_diff1 value: 27.99609559690795 - type: nauc_mrr_at_3_max value: 36.87362332456197 - type: nauc_mrr_at_3_std value: 18.598416821915333 - type: nauc_mrr_at_5_diff1 value: 27.68306089976716 - type: nauc_mrr_at_5_max value: 37.12264485659723 - type: nauc_mrr_at_5_std value: 19.18875305730564 - type: nauc_ndcg_at_1000_diff1 value: 25.736779186453777 - type: nauc_ndcg_at_1000_max value: 41.93281139456004 - type: nauc_ndcg_at_1000_std value: 25.179038422659993 - type: nauc_ndcg_at_100_diff1 value: 25.144796623848322 - type: nauc_ndcg_at_100_max value: 41.72820916876173 - type: nauc_ndcg_at_100_std value: 25.12851686850754 - type: nauc_ndcg_at_10_diff1 value: 24.321249191226652 - type: nauc_ndcg_at_10_max value: 40.23711916935706 - type: nauc_ndcg_at_10_std value: 20.89060972334557 - type: nauc_ndcg_at_1_diff1 value: 28.017413073648477 - type: nauc_ndcg_at_1_max value: 32.87710191514022 - type: nauc_ndcg_at_1_std value: 14.04889142608459 - type: nauc_ndcg_at_20_diff1 value: 24.5090484877482 - type: nauc_ndcg_at_20_max value: 40.752854032983606 - type: nauc_ndcg_at_20_std value: 22.70331074781384 - type: nauc_ndcg_at_3_diff1 value: 25.13499057756147 - type: nauc_ndcg_at_3_max value: 35.8325682137567 - type: nauc_ndcg_at_3_std value: 15.23768392706637 - type: nauc_ndcg_at_5_diff1 value: 24.614105695451116 - type: nauc_ndcg_at_5_max value: 37.68089587624492 - type: nauc_ndcg_at_5_std value: 17.946406099261708 - type: nauc_precision_at_1000_diff1 value: -2.022340544774227 - type: nauc_precision_at_1000_max value: 6.070578645067797 - type: nauc_precision_at_1000_std value: 22.15132728777549 - type: nauc_precision_at_100_diff1 value: 4.544144474504255 - type: nauc_precision_at_100_max value: 19.780392159848574 - type: nauc_precision_at_100_std value: 31.107111186002438 - type: nauc_precision_at_10_diff1 value: 10.107015022955848 - type: nauc_precision_at_10_max value: 30.779709099060465 - type: nauc_precision_at_10_std value: 27.324148451668602 - type: nauc_precision_at_1_diff1 value: 28.017413073648477 - type: nauc_precision_at_1_max value: 32.87710191514022 - type: nauc_precision_at_1_std value: 14.04889142608459 - type: nauc_precision_at_20_diff1 value: 8.270881053079405 - type: nauc_precision_at_20_max value: 27.26753946078481 - type: nauc_precision_at_20_std value: 29.156725822074204 - type: nauc_precision_at_3_diff1 value: 17.82468940497632 - type: nauc_precision_at_3_max value: 31.490021174215155 - type: nauc_precision_at_3_std value: 18.73818985054394 - type: nauc_precision_at_5_diff1 value: 13.24803141673961 - type: nauc_precision_at_5_max value: 29.94926240784298 - type: nauc_precision_at_5_std value: 23.2940906142919 - type: nauc_recall_at_1000_diff1 value: 19.09850333580471 - type: nauc_recall_at_1000_max value: 46.026306142840596 - type: nauc_recall_at_1000_std value: 46.50391519568263 - type: nauc_recall_at_100_diff1 value: 16.739384224869738 - type: nauc_recall_at_100_max value: 40.68987136431252 - type: nauc_recall_at_100_std value: 36.01609750485591 - type: nauc_recall_at_10_diff1 value: 17.51796617221814 - type: nauc_recall_at_10_max value: 39.47453129444401 - type: nauc_recall_at_10_std value: 23.79239002974899 - type: nauc_recall_at_1_diff1 value: 30.83614944448221 - type: nauc_recall_at_1_max value: 33.757528532809 - type: nauc_recall_at_1_std value: 8.880622713261126 - type: nauc_recall_at_20_diff1 value: 16.978668307251652 - type: nauc_recall_at_20_max value: 39.09115357303713 - type: nauc_recall_at_20_std value: 27.278668534187524 - type: nauc_recall_at_3_diff1 value: 22.55937738994021 - type: nauc_recall_at_3_max value: 36.25055459395638 - type: nauc_recall_at_3_std value: 14.828905168761247 - type: nauc_recall_at_5_diff1 value: 19.32656748627199 - type: nauc_recall_at_5_max value: 36.28836228620816 - type: nauc_recall_at_5_std value: 19.264352933914278 - type: ndcg_at_1 value: 34.137 - type: ndcg_at_10 value: 36.853 - type: ndcg_at_100 value: 44.279 - type: ndcg_at_1000 value: 47.336 - type: ndcg_at_20 value: 39.815 - type: ndcg_at_3 value: 30.253999999999998 - type: ndcg_at_5 value: 32.649 - type: precision_at_1 value: 34.137 - type: precision_at_10 value: 11.655 - type: precision_at_100 value: 1.9619999999999997 - type: precision_at_1000 value: 0.254 - type: precision_at_20 value: 7.1209999999999996 - type: precision_at_3 value: 22.823 - type: precision_at_5 value: 17.655 - type: recall_at_1 value: 15.371000000000002 - type: recall_at_10 value: 43.718 - type: recall_at_100 value: 68.81 - type: recall_at_1000 value: 85.69600000000001 - type: recall_at_20 value: 51.94 - type: recall_at_3 value: 27.694000000000003 - type: recall_at_5 value: 34.469 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: main_score value: 45.553 - type: map_at_1 value: 9.168999999999999 - type: map_at_10 value: 22.154 - type: map_at_100 value: 32.174 - type: map_at_1000 value: 33.974 - type: map_at_20 value: 25.899 - type: map_at_3 value: 15.275 - type: map_at_5 value: 18.291 - type: mrr_at_1 value: 70.75 - type: mrr_at_10 value: 78.39662698412697 - type: mrr_at_100 value: 78.56221458977012 - type: mrr_at_1000 value: 78.56669970642338 - type: mrr_at_20 value: 78.49688805346696 - type: mrr_at_3 value: 76.33333333333333 - type: mrr_at_5 value: 77.70833333333333 - type: nauc_map_at_1000_diff1 value: 18.465085922071346 - type: nauc_map_at_1000_max value: 24.29804638788498 - type: nauc_map_at_1000_std value: 22.380463943423514 - type: nauc_map_at_100_diff1 value: 19.37585410674523 - type: nauc_map_at_100_max value: 22.56424042509462 - type: nauc_map_at_100_std value: 19.672237275984426 - type: nauc_map_at_10_diff1 value: 23.597788166305577 - type: nauc_map_at_10_max value: 9.157316105122925 - type: nauc_map_at_10_std value: -3.8881247055786807 - type: nauc_map_at_1_diff1 value: 43.96699602275052 - type: nauc_map_at_1_max value: -0.7577088440873263 - type: nauc_map_at_1_std value: -17.732463891968404 - type: nauc_map_at_20_diff1 value: 22.326759054850097 - type: nauc_map_at_20_max value: 14.879191412167703 - type: nauc_map_at_20_std value: 5.405751236575241 - type: nauc_map_at_3_diff1 value: 28.73583545428074 - type: nauc_map_at_3_max value: 1.5986597211018239 - type: nauc_map_at_3_std value: -16.512455883681515 - type: nauc_map_at_5_diff1 value: 25.401810959155057 - type: nauc_map_at_5_max value: 4.418875376978587 - type: nauc_map_at_5_std value: -12.296750992013052 - type: nauc_mrr_at_1000_diff1 value: 51.228801807498584 - type: nauc_mrr_at_1000_max value: 61.040998883279585 - type: nauc_mrr_at_1000_std value: 40.93983887257123 - type: nauc_mrr_at_100_diff1 value: 51.23715338435314 - type: nauc_mrr_at_100_max value: 61.03971408781317 - type: nauc_mrr_at_100_std value: 40.91796923590573 - type: nauc_mrr_at_10_diff1 value: 51.1214868552331 - type: nauc_mrr_at_10_max value: 61.03069045590881 - type: nauc_mrr_at_10_std value: 40.661621199704264 - type: nauc_mrr_at_1_diff1 value: 50.84660003035892 - type: nauc_mrr_at_1_max value: 60.692091499960895 - type: nauc_mrr_at_1_std value: 42.126228731502955 - type: nauc_mrr_at_20_diff1 value: 51.0402624284872 - type: nauc_mrr_at_20_max value: 60.94577844338166 - type: nauc_mrr_at_20_std value: 40.89505950503613 - type: nauc_mrr_at_3_diff1 value: 51.771113665996516 - type: nauc_mrr_at_3_max value: 61.65264793077224 - type: nauc_mrr_at_3_std value: 41.75781827057092 - type: nauc_mrr_at_5_diff1 value: 51.0656793772882 - type: nauc_mrr_at_5_max value: 61.08042065139715 - type: nauc_mrr_at_5_std value: 41.11203271084835 - type: nauc_ndcg_at_1000_diff1 value: 22.347978262245107 - type: nauc_ndcg_at_1000_max value: 36.56458763955002 - type: nauc_ndcg_at_1000_std value: 35.99616144258822 - type: nauc_ndcg_at_100_diff1 value: 23.1120990977162 - type: nauc_ndcg_at_100_max value: 30.79663306311657 - type: nauc_ndcg_at_100_std value: 27.387572106784297 - type: nauc_ndcg_at_10_diff1 value: 23.329746066899656 - type: nauc_ndcg_at_10_max value: 28.69246947084685 - type: nauc_ndcg_at_10_std value: 21.457736188325345 - type: nauc_ndcg_at_1_diff1 value: 39.99399153456974 - type: nauc_ndcg_at_1_max value: 38.12447856470389 - type: nauc_ndcg_at_1_std value: 27.768869260384676 - type: nauc_ndcg_at_20_diff1 value: 24.945374175339907 - type: nauc_ndcg_at_20_max value: 27.67836982165295 - type: nauc_ndcg_at_20_std value: 19.7933631060578 - type: nauc_ndcg_at_3_diff1 value: 26.063492354398527 - type: nauc_ndcg_at_3_max value: 33.06541959550656 - type: nauc_ndcg_at_3_std value: 23.278902797288726 - type: nauc_ndcg_at_5_diff1 value: 22.521596060750035 - type: nauc_ndcg_at_5_max value: 31.210005673730784 - type: nauc_ndcg_at_5_std value: 22.893106456317927 - type: nauc_precision_at_1000_diff1 value: -19.845356495096006 - type: nauc_precision_at_1000_max value: 4.163819381816099 - type: nauc_precision_at_1000_std value: 7.612952884590339 - type: nauc_precision_at_100_diff1 value: -8.2679285153361 - type: nauc_precision_at_100_max value: 29.78018175573565 - type: nauc_precision_at_100_std value: 41.07244463956215 - type: nauc_precision_at_10_diff1 value: -3.2451428407349057 - type: nauc_precision_at_10_max value: 36.92563008274906 - type: nauc_precision_at_10_std value: 45.06962043489777 - type: nauc_precision_at_1_diff1 value: 50.84660003035892 - type: nauc_precision_at_1_max value: 60.692091499960895 - type: nauc_precision_at_1_std value: 42.126228731502955 - type: nauc_precision_at_20_diff1 value: -3.432279149061878 - type: nauc_precision_at_20_max value: 37.013592483974875 - type: nauc_precision_at_20_std value: 46.47324739428665 - type: nauc_precision_at_3_diff1 value: 7.28495481051025 - type: nauc_precision_at_3_max value: 38.66372411741402 - type: nauc_precision_at_3_std value: 35.23163993723955 - type: nauc_precision_at_5_diff1 value: -0.16540230063716202 - type: nauc_precision_at_5_max value: 37.322494255721715 - type: nauc_precision_at_5_std value: 39.666653561269754 - type: nauc_recall_at_1000_diff1 value: 11.388326469283681 - type: nauc_recall_at_1000_max value: 32.698146308591674 - type: nauc_recall_at_1000_std value: 49.48830488070777 - type: nauc_recall_at_100_diff1 value: 11.497443532756819 - type: nauc_recall_at_100_max value: 20.196970431621615 - type: nauc_recall_at_100_std value: 23.688772100803433 - type: nauc_recall_at_10_diff1 value: 16.519851398596003 - type: nauc_recall_at_10_max value: 0.774066845071221 - type: nauc_recall_at_10_std value: -10.89514647001814 - type: nauc_recall_at_1_diff1 value: 43.96699602275052 - type: nauc_recall_at_1_max value: -0.7577088440873263 - type: nauc_recall_at_1_std value: -17.732463891968404 - type: nauc_recall_at_20_diff1 value: 15.202960269878258 - type: nauc_recall_at_20_max value: 7.067263295590253 - type: nauc_recall_at_20_std value: -0.06050108222640702 - type: nauc_recall_at_3_diff1 value: 24.066741361525125 - type: nauc_recall_at_3_max value: -2.1961525860488424 - type: nauc_recall_at_3_std value: -19.48307077749568 - type: nauc_recall_at_5_diff1 value: 20.086330794102707 - type: nauc_recall_at_5_max value: -0.8866528062747986 - type: nauc_recall_at_5_std value: -16.53799173962747 - type: ndcg_at_1 value: 57.99999999999999 - type: ndcg_at_10 value: 45.553 - type: ndcg_at_100 value: 51.014 - type: ndcg_at_1000 value: 58.226 - type: ndcg_at_20 value: 44.98 - type: ndcg_at_3 value: 48.981 - type: ndcg_at_5 value: 46.794999999999995 - type: precision_at_1 value: 70.75 - type: precision_at_10 value: 36.85 - type: precision_at_100 value: 11.955 - type: precision_at_1000 value: 2.247 - type: precision_at_20 value: 28.075 - type: precision_at_3 value: 52.666999999999994 - type: precision_at_5 value: 45.85 - type: recall_at_1 value: 9.168999999999999 - type: recall_at_10 value: 28.796 - type: recall_at_100 value: 58.892999999999994 - type: recall_at_1000 value: 81.644 - type: recall_at_20 value: 36.659000000000006 - type: recall_at_3 value: 16.709 - type: recall_at_5 value: 21.387 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: main_score value: 88.41 - type: map_at_1 value: 75.637 - type: map_at_10 value: 84.674 - type: map_at_100 value: 84.909 - type: map_at_1000 value: 84.92 - type: map_at_20 value: 84.836 - type: map_at_3 value: 83.44200000000001 - type: map_at_5 value: 84.28099999999999 - type: mrr_at_1 value: 81.56315631563157 - type: mrr_at_10 value: 88.89571695264748 - type: mrr_at_100 value: 88.93671417216285 - type: mrr_at_1000 value: 88.93708016011664 - type: mrr_at_20 value: 88.9311652665256 - type: mrr_at_3 value: 88.20882088208805 - type: mrr_at_5 value: 88.72937293729349 - type: nauc_map_at_1000_diff1 value: 54.41216035074026 - type: nauc_map_at_1000_max value: 13.346153003554361 - type: nauc_map_at_1000_std value: -6.721664416152164 - type: nauc_map_at_100_diff1 value: 54.36538350995795 - type: nauc_map_at_100_max value: 13.355583381471298 - type: nauc_map_at_100_std value: -6.696921015641016 - type: nauc_map_at_10_diff1 value: 54.0389127730555 - type: nauc_map_at_10_max value: 13.387802159150663 - type: nauc_map_at_10_std value: -6.73514381731833 - type: nauc_map_at_1_diff1 value: 57.99489574836453 - type: nauc_map_at_1_max value: 7.830032589171654 - type: nauc_map_at_1_std value: -10.140208285080295 - type: nauc_map_at_20_diff1 value: 54.16841004736076 - type: nauc_map_at_20_max value: 13.345607363689746 - type: nauc_map_at_20_std value: -6.663119775158465 - type: nauc_map_at_3_diff1 value: 53.82879543599303 - type: nauc_map_at_3_max value: 12.716952288433902 - type: nauc_map_at_3_std value: -7.746102082835598 - type: nauc_map_at_5_diff1 value: 53.82838395350109 - type: nauc_map_at_5_max value: 13.487373534211702 - type: nauc_map_at_5_std value: -6.869504398693434 - type: nauc_mrr_at_1000_diff1 value: 68.92783546581906 - type: nauc_mrr_at_1000_max value: 12.076297180596592 - type: nauc_mrr_at_1000_std value: -13.306257067567998 - type: nauc_mrr_at_100_diff1 value: 68.92780219775517 - type: nauc_mrr_at_100_max value: 12.078449805054374 - type: nauc_mrr_at_100_std value: -13.303524852703719 - type: nauc_mrr_at_10_diff1 value: 68.92686206881258 - type: nauc_mrr_at_10_max value: 12.273295656884873 - type: nauc_mrr_at_10_std value: -13.222483496603965 - type: nauc_mrr_at_1_diff1 value: 70.1738022073041 - type: nauc_mrr_at_1_max value: 9.378639533482806 - type: nauc_mrr_at_1_std value: -13.444033823202348 - type: nauc_mrr_at_20_diff1 value: 68.91161304905303 - type: nauc_mrr_at_20_max value: 12.117091514817885 - type: nauc_mrr_at_20_std value: -13.258261750160239 - type: nauc_mrr_at_3_diff1 value: 68.61982455945467 - type: nauc_mrr_at_3_max value: 12.608213879734578 - type: nauc_mrr_at_3_std value: -13.558003431587839 - type: nauc_mrr_at_5_diff1 value: 68.81439097457242 - type: nauc_mrr_at_5_max value: 12.54025598903624 - type: nauc_mrr_at_5_std value: -13.199231514972093 - type: nauc_ndcg_at_1000_diff1 value: 56.47563443877495 - type: nauc_ndcg_at_1000_max value: 14.508331783439466 - type: nauc_ndcg_at_1000_std value: -6.206829736668775 - type: nauc_ndcg_at_100_diff1 value: 55.54015515673474 - type: nauc_ndcg_at_100_max value: 14.753595778278136 - type: nauc_ndcg_at_100_std value: -5.638517949568802 - type: nauc_ndcg_at_10_diff1 value: 54.220845223257996 - type: nauc_ndcg_at_10_max value: 15.265309648490021 - type: nauc_ndcg_at_10_std value: -5.516276098929109 - type: nauc_ndcg_at_1_diff1 value: 70.1738022073041 - type: nauc_ndcg_at_1_max value: 9.378639533482806 - type: nauc_ndcg_at_1_std value: -13.444033823202348 - type: nauc_ndcg_at_20_diff1 value: 54.481406100854635 - type: nauc_ndcg_at_20_max value: 14.868763583210498 - type: nauc_ndcg_at_20_std value: -5.328097380018734 - type: nauc_ndcg_at_3_diff1 value: 54.94411725607744 - type: nauc_ndcg_at_3_max value: 14.27186734506607 - type: nauc_ndcg_at_3_std value: -7.894724962312474 - type: nauc_ndcg_at_5_diff1 value: 54.08048166974806 - type: nauc_ndcg_at_5_max value: 15.528233170721006 - type: nauc_ndcg_at_5_std value: -5.984768714537104 - type: nauc_precision_at_1000_diff1 value: -8.744323640074445 - type: nauc_precision_at_1000_max value: -0.01881224392053465 - type: nauc_precision_at_1000_std value: 3.8721477979260635 - type: nauc_precision_at_100_diff1 value: -11.86150156952171 - type: nauc_precision_at_100_max value: 3.2736651314552314 - type: nauc_precision_at_100_std value: 8.12687620615509 - type: nauc_precision_at_10_diff1 value: -10.360708676781178 - type: nauc_precision_at_10_max value: 10.945552490433458 - type: nauc_precision_at_10_std value: 11.016707653014485 - type: nauc_precision_at_1_diff1 value: 70.1738022073041 - type: nauc_precision_at_1_max value: 9.378639533482806 - type: nauc_precision_at_1_std value: -13.444033823202348 - type: nauc_precision_at_20_diff1 value: -13.557721925696583 - type: nauc_precision_at_20_max value: 6.331386521718574 - type: nauc_precision_at_20_std value: 10.322188778142388 - type: nauc_precision_at_3_diff1 value: 15.139456770248968 - type: nauc_precision_at_3_max value: 17.10220985600708 - type: nauc_precision_at_3_std value: 3.0448183682558074 - type: nauc_precision_at_5_diff1 value: -1.9825577548111102 - type: nauc_precision_at_5_max value: 17.139148127012625 - type: nauc_precision_at_5_std value: 10.598435750554753 - type: nauc_recall_at_1000_diff1 value: 15.641740744283005 - type: nauc_recall_at_1000_max value: 44.65315702195612 - type: nauc_recall_at_1000_std value: 52.34265862835513 - type: nauc_recall_at_100_diff1 value: 5.254385435323394 - type: nauc_recall_at_100_max value: 38.53577774395794 - type: nauc_recall_at_100_std value: 43.47744274335829 - type: nauc_recall_at_10_diff1 value: 19.135735476268042 - type: nauc_recall_at_10_max value: 30.05417445923848 - type: nauc_recall_at_10_std value: 18.3988023241141 - type: nauc_recall_at_1_diff1 value: 57.99489574836453 - type: nauc_recall_at_1_max value: 7.830032589171654 - type: nauc_recall_at_1_std value: -10.140208285080295 - type: nauc_recall_at_20_diff1 value: 9.444797759735126 - type: nauc_recall_at_20_max value: 31.001311675371017 - type: nauc_recall_at_20_std value: 29.351418893822178 - type: nauc_recall_at_3_diff1 value: 36.88862653262064 - type: nauc_recall_at_3_max value: 19.845892741607823 - type: nauc_recall_at_3_std value: -1.0584273105890794 - type: nauc_recall_at_5_diff1 value: 27.360718561944974 - type: nauc_recall_at_5_max value: 26.698311215441738 - type: nauc_recall_at_5_std value: 8.97113997755362 - type: ndcg_at_1 value: 81.563 - type: ndcg_at_10 value: 88.41 - type: ndcg_at_100 value: 89.101 - type: ndcg_at_1000 value: 89.25800000000001 - type: ndcg_at_20 value: 88.79 - type: ndcg_at_3 value: 86.599 - type: ndcg_at_5 value: 87.74 - type: precision_at_1 value: 81.563 - type: precision_at_10 value: 10.699 - type: precision_at_100 value: 1.13 - type: precision_at_1000 value: 0.116 - type: precision_at_20 value: 5.479 - type: precision_at_3 value: 33.238 - type: precision_at_5 value: 20.744 - type: recall_at_1 value: 75.637 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 98.072 - type: recall_at_1000 value: 98.951 - type: recall_at_20 value: 96.792 - type: recall_at_3 value: 90.79599999999999 - type: recall_at_5 value: 93.674 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: main_score value: 42.396 - type: map_at_1 value: 21.711 - type: map_at_10 value: 34.628 - type: map_at_100 value: 36.549 - type: map_at_1000 value: 36.719 - type: map_at_20 value: 35.673 - type: map_at_3 value: 30.585 - type: map_at_5 value: 32.875 - type: mrr_at_1 value: 41.82098765432099 - type: mrr_at_10 value: 50.69505682931607 - type: mrr_at_100 value: 51.50556608727901 - type: mrr_at_1000 value: 51.53870583208304 - type: mrr_at_20 value: 51.15345764364655 - type: mrr_at_3 value: 48.35390946502059 - type: mrr_at_5 value: 49.87397119341563 - type: nauc_map_at_1000_diff1 value: 45.182252919583895 - type: nauc_map_at_1000_max value: 35.66124930024801 - type: nauc_map_at_1000_std value: -0.6925562638650965 - type: nauc_map_at_100_diff1 value: 45.116964706960125 - type: nauc_map_at_100_max value: 35.54990469525889 - type: nauc_map_at_100_std value: -0.6667263852859368 - type: nauc_map_at_10_diff1 value: 45.39189096228184 - type: nauc_map_at_10_max value: 34.780111261901 - type: nauc_map_at_10_std value: -1.8169859294150819 - type: nauc_map_at_1_diff1 value: 47.72764937952259 - type: nauc_map_at_1_max value: 24.83306559709341 - type: nauc_map_at_1_std value: -4.714128457297418 - type: nauc_map_at_20_diff1 value: 45.17073365898278 - type: nauc_map_at_20_max value: 35.0938403469058 - type: nauc_map_at_20_std value: -1.373412631183604 - type: nauc_map_at_3_diff1 value: 46.525724305731295 - type: nauc_map_at_3_max value: 31.042538866512597 - type: nauc_map_at_3_std value: -4.119355935975354 - type: nauc_map_at_5_diff1 value: 45.79569633383187 - type: nauc_map_at_5_max value: 32.88779656647293 - type: nauc_map_at_5_std value: -3.2518474739335312 - type: nauc_mrr_at_1000_diff1 value: 52.83619185487903 - type: nauc_mrr_at_1000_max value: 42.30310720405186 - type: nauc_mrr_at_1000_std value: -1.1487703348518024 - type: nauc_mrr_at_100_diff1 value: 52.82248853996664 - type: nauc_mrr_at_100_max value: 42.30549701564678 - type: nauc_mrr_at_100_std value: -1.1240113031894834 - type: nauc_mrr_at_10_diff1 value: 52.74644276642243 - type: nauc_mrr_at_10_max value: 42.39103029476398 - type: nauc_mrr_at_10_std value: -1.1043413237848576 - type: nauc_mrr_at_1_diff1 value: 54.810335521617326 - type: nauc_mrr_at_1_max value: 40.733260207843394 - type: nauc_mrr_at_1_std value: -4.452554921565855 - type: nauc_mrr_at_20_diff1 value: 52.788257862499954 - type: nauc_mrr_at_20_max value: 42.32658875363406 - type: nauc_mrr_at_20_std value: -1.2209728080684497 - type: nauc_mrr_at_3_diff1 value: 53.43281175319808 - type: nauc_mrr_at_3_max value: 41.735942650867926 - type: nauc_mrr_at_3_std value: -2.462688102468019 - type: nauc_mrr_at_5_diff1 value: 52.874037126566606 - type: nauc_mrr_at_5_max value: 41.93740449458822 - type: nauc_mrr_at_5_std value: -1.2928874908441947 - type: nauc_ndcg_at_1000_diff1 value: 46.5532425476402 - type: nauc_ndcg_at_1000_max value: 40.369611603370515 - type: nauc_ndcg_at_1000_std value: 3.472567588386994 - type: nauc_ndcg_at_100_diff1 value: 45.75244404695404 - type: nauc_ndcg_at_100_max value: 39.36470550675439 - type: nauc_ndcg_at_100_std value: 4.356189041115731 - type: nauc_ndcg_at_10_diff1 value: 46.005135323539704 - type: nauc_ndcg_at_10_max value: 37.89018165334218 - type: nauc_ndcg_at_10_std value: 0.7129618297768014 - type: nauc_ndcg_at_1_diff1 value: 54.810335521617326 - type: nauc_ndcg_at_1_max value: 40.733260207843394 - type: nauc_ndcg_at_1_std value: -4.452554921565855 - type: nauc_ndcg_at_20_diff1 value: 45.841552790490034 - type: nauc_ndcg_at_20_max value: 38.04992825472661 - type: nauc_ndcg_at_20_std value: 1.2748305707955212 - type: nauc_ndcg_at_3_diff1 value: 46.683033449357744 - type: nauc_ndcg_at_3_max value: 37.46397870760607 - type: nauc_ndcg_at_3_std value: -2.3421854966319824 - type: nauc_ndcg_at_5_diff1 value: 45.82409645378457 - type: nauc_ndcg_at_5_max value: 36.27588234096716 - type: nauc_ndcg_at_5_std value: -1.5141197170944254 - type: nauc_precision_at_1000_diff1 value: -3.137944321071885 - type: nauc_precision_at_1000_max value: 24.12803166253776 - type: nauc_precision_at_1000_std value: 11.076454789944101 - type: nauc_precision_at_100_diff1 value: 3.9896283891401048 - type: nauc_precision_at_100_max value: 31.00198316788829 - type: nauc_precision_at_100_std value: 15.725887643803063 - type: nauc_precision_at_10_diff1 value: 20.493420889888394 - type: nauc_precision_at_10_max value: 41.689699671507405 - type: nauc_precision_at_10_std value: 9.374983385669914 - type: nauc_precision_at_1_diff1 value: 54.810335521617326 - type: nauc_precision_at_1_max value: 40.733260207843394 - type: nauc_precision_at_1_std value: -4.452554921565855 - type: nauc_precision_at_20_diff1 value: 15.02911800246446 - type: nauc_precision_at_20_max value: 39.227068888505 - type: nauc_precision_at_20_std value: 11.755558515319404 - type: nauc_precision_at_3_diff1 value: 34.044986535461746 - type: nauc_precision_at_3_max value: 40.96605829831656 - type: nauc_precision_at_3_std value: 1.1903535705688038 - type: nauc_precision_at_5_diff1 value: 26.617002443432707 - type: nauc_precision_at_5_max value: 40.60413785916794 - type: nauc_precision_at_5_std value: 3.6984531670502814 - type: nauc_recall_at_1000_diff1 value: 26.96489389440101 - type: nauc_recall_at_1000_max value: 41.811583968523955 - type: nauc_recall_at_1000_std value: 41.5719519496712 - type: nauc_recall_at_100_diff1 value: 28.50851434908223 - type: nauc_recall_at_100_max value: 32.19528060706322 - type: nauc_recall_at_100_std value: 25.56935294258179 - type: nauc_recall_at_10_diff1 value: 35.139582891180964 - type: nauc_recall_at_10_max value: 32.15221840434225 - type: nauc_recall_at_10_std value: 5.550434611582702 - type: nauc_recall_at_1_diff1 value: 47.72764937952259 - type: nauc_recall_at_1_max value: 24.83306559709341 - type: nauc_recall_at_1_std value: -4.714128457297418 - type: nauc_recall_at_20_diff1 value: 32.78604811055205 - type: nauc_recall_at_20_max value: 29.62940720700254 - type: nauc_recall_at_20_std value: 6.769941491859872 - type: nauc_recall_at_3_diff1 value: 40.76090616138699 - type: nauc_recall_at_3_max value: 27.506425490226867 - type: nauc_recall_at_3_std value: -2.608872693119243 - type: nauc_recall_at_5_diff1 value: 37.06532485024711 - type: nauc_recall_at_5_max value: 27.704150556658448 - type: nauc_recall_at_5_std value: 0.4718707152343872 - type: ndcg_at_1 value: 41.821000000000005 - type: ndcg_at_10 value: 42.396 - type: ndcg_at_100 value: 49.370000000000005 - type: ndcg_at_1000 value: 52.251000000000005 - type: ndcg_at_20 value: 45.097 - type: ndcg_at_3 value: 39.028 - type: ndcg_at_5 value: 40.222 - type: precision_at_1 value: 41.821000000000005 - type: precision_at_10 value: 11.451 - type: precision_at_100 value: 1.863 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_20 value: 6.798 - type: precision_at_3 value: 25.823 - type: precision_at_5 value: 18.735 - type: recall_at_1 value: 21.711 - type: recall_at_10 value: 48.862 - type: recall_at_100 value: 74.708 - type: recall_at_1000 value: 91.865 - type: recall_at_20 value: 57.50999999999999 - type: recall_at_3 value: 35.85 - type: recall_at_5 value: 41.976 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: main_score value: 72.21 - type: map_at_1 value: 39.487 - type: map_at_10 value: 63.949999999999996 - type: map_at_100 value: 64.873 - type: map_at_1000 value: 64.927 - type: map_at_20 value: 64.529 - type: map_at_3 value: 60.243 - type: map_at_5 value: 62.613 - type: mrr_at_1 value: 78.97366644159351 - type: mrr_at_10 value: 84.84600173627825 - type: mrr_at_100 value: 85.0172804866798 - type: mrr_at_1000 value: 85.02245651152857 - type: mrr_at_20 value: 84.9625577788225 - type: mrr_at_3 value: 83.90276839972962 - type: mrr_at_5 value: 84.48278190411845 - type: nauc_map_at_1000_diff1 value: 19.825004700775164 - type: nauc_map_at_1000_max value: 19.943221724164182 - type: nauc_map_at_1000_std value: 10.068951166560058 - type: nauc_map_at_100_diff1 value: 19.80139472181137 - type: nauc_map_at_100_max value: 19.938006132804347 - type: nauc_map_at_100_std value: 10.100008107666842 - type: nauc_map_at_10_diff1 value: 19.53604502514735 - type: nauc_map_at_10_max value: 19.62768870331064 - type: nauc_map_at_10_std value: 9.446859074725705 - type: nauc_map_at_1_diff1 value: 67.7764270505257 - type: nauc_map_at_1_max value: 38.45166604737058 - type: nauc_map_at_1_std value: 1.9919181988552352 - type: nauc_map_at_20_diff1 value: 19.635871913149913 - type: nauc_map_at_20_max value: 19.812838965919155 - type: nauc_map_at_20_std value: 9.905163140101845 - type: nauc_map_at_3_diff1 value: 18.965707122532212 - type: nauc_map_at_3_max value: 17.878860313056517 - type: nauc_map_at_3_std value: 6.189378752019195 - type: nauc_map_at_5_diff1 value: 19.493354049675954 - type: nauc_map_at_5_max value: 19.24527088109141 - type: nauc_map_at_5_std value: 8.283883139680066 - type: nauc_mrr_at_1000_diff1 value: 66.87150374356781 - type: nauc_mrr_at_1000_max value: 41.413456443203984 - type: nauc_mrr_at_1000_std value: 4.140387282484357 - type: nauc_mrr_at_100_diff1 value: 66.87178015619061 - type: nauc_mrr_at_100_max value: 41.419754763150834 - type: nauc_mrr_at_100_std value: 4.15222235416704 - type: nauc_mrr_at_10_diff1 value: 66.89720586892301 - type: nauc_mrr_at_10_max value: 41.56353878125211 - type: nauc_mrr_at_10_std value: 4.213376519922392 - type: nauc_mrr_at_1_diff1 value: 67.7764270505257 - type: nauc_mrr_at_1_max value: 38.45166604737058 - type: nauc_mrr_at_1_std value: 1.9919181988552352 - type: nauc_mrr_at_20_diff1 value: 66.8714688713149 - type: nauc_mrr_at_20_max value: 41.46170778986735 - type: nauc_mrr_at_20_std value: 4.165154741309859 - type: nauc_mrr_at_3_diff1 value: 66.31615462679144 - type: nauc_mrr_at_3_max value: 41.419637693259936 - type: nauc_mrr_at_3_std value: 3.814834551396097 - type: nauc_mrr_at_5_diff1 value: 66.7289413087213 - type: nauc_mrr_at_5_max value: 41.668346356371586 - type: nauc_mrr_at_5_std value: 4.116331539882484 - type: nauc_ndcg_at_1000_diff1 value: 26.37325375970598 - type: nauc_ndcg_at_1000_max value: 24.850915174721735 - type: nauc_ndcg_at_1000_std value: 13.37585683440429 - type: nauc_ndcg_at_100_diff1 value: 25.591771178059503 - type: nauc_ndcg_at_100_max value: 24.562820829532473 - type: nauc_ndcg_at_100_std value: 14.093690500501541 - type: nauc_ndcg_at_10_diff1 value: 24.64600598115805 - type: nauc_ndcg_at_10_max value: 23.543499404760023 - type: nauc_ndcg_at_10_std value: 11.55823632781553 - type: nauc_ndcg_at_1_diff1 value: 67.7764270505257 - type: nauc_ndcg_at_1_max value: 38.45166604737058 - type: nauc_ndcg_at_1_std value: 1.9919181988552352 - type: nauc_ndcg_at_20_diff1 value: 24.757843275306726 - type: nauc_ndcg_at_20_max value: 23.951154200380827 - type: nauc_ndcg_at_20_std value: 12.931320453044886 - type: nauc_ndcg_at_3_diff1 value: 24.37742630418847 - type: nauc_ndcg_at_3_max value: 21.310512304883723 - type: nauc_ndcg_at_3_std value: 6.503993200818077 - type: nauc_ndcg_at_5_diff1 value: 24.813706829269716 - type: nauc_ndcg_at_5_max value: 22.993657212898 - type: nauc_ndcg_at_5_std value: 9.34462052506809 - type: nauc_precision_at_1000_diff1 value: -0.6506415756958156 - type: nauc_precision_at_1000_max value: 28.039755644694875 - type: nauc_precision_at_1000_std value: 53.46474329623814 - type: nauc_precision_at_100_diff1 value: 3.78462668236152 - type: nauc_precision_at_100_max value: 22.501700881673862 - type: nauc_precision_at_100_std value: 40.56672716474142 - type: nauc_precision_at_10_diff1 value: 9.156113228907534 - type: nauc_precision_at_10_max value: 19.734206254833254 - type: nauc_precision_at_10_std value: 19.986282545779602 - type: nauc_precision_at_1_diff1 value: 67.7764270505257 - type: nauc_precision_at_1_max value: 38.45166604737058 - type: nauc_precision_at_1_std value: 1.9919181988552352 - type: nauc_precision_at_20_diff1 value: 6.6164335644470125 - type: nauc_precision_at_20_max value: 20.29343459608317 - type: nauc_precision_at_20_std value: 26.51115475333977 - type: nauc_precision_at_3_diff1 value: 12.476520554399546 - type: nauc_precision_at_3_max value: 16.69401409858964 - type: nauc_precision_at_3_std value: 8.165880294907444 - type: nauc_precision_at_5_diff1 value: 11.783242828320958 - type: nauc_precision_at_5_max value: 19.0679467875759 - type: nauc_precision_at_5_std value: 13.615358345509884 - type: nauc_recall_at_1000_diff1 value: -0.6506415756960168 - type: nauc_recall_at_1000_max value: 28.039755644694786 - type: nauc_recall_at_1000_std value: 53.46474329623801 - type: nauc_recall_at_100_diff1 value: 3.7846266823613877 - type: nauc_recall_at_100_max value: 22.501700881674008 - type: nauc_recall_at_100_std value: 40.566727164741366 - type: nauc_recall_at_10_diff1 value: 9.15611322890755 - type: nauc_recall_at_10_max value: 19.73420625483318 - type: nauc_recall_at_10_std value: 19.98628254577951 - type: nauc_recall_at_1_diff1 value: 67.7764270505257 - type: nauc_recall_at_1_max value: 38.45166604737058 - type: nauc_recall_at_1_std value: 1.9919181988552352 - type: nauc_recall_at_20_diff1 value: 6.616433564446929 - type: nauc_recall_at_20_max value: 20.293434596083248 - type: nauc_recall_at_20_std value: 26.5111547533396 - type: nauc_recall_at_3_diff1 value: 12.476520554399531 - type: nauc_recall_at_3_max value: 16.69401409858966 - type: nauc_recall_at_3_std value: 8.165880294907438 - type: nauc_recall_at_5_diff1 value: 11.783242828320999 - type: nauc_recall_at_5_max value: 19.067946787575845 - type: nauc_recall_at_5_std value: 13.61535834550991 - type: ndcg_at_1 value: 78.974 - type: ndcg_at_10 value: 72.21 - type: ndcg_at_100 value: 75.264 - type: ndcg_at_1000 value: 76.259 - type: ndcg_at_20 value: 73.628 - type: ndcg_at_3 value: 67.047 - type: ndcg_at_5 value: 69.974 - type: precision_at_1 value: 78.974 - type: precision_at_10 value: 15.267 - type: precision_at_100 value: 1.762 - type: precision_at_1000 value: 0.189 - type: precision_at_20 value: 8.09 - type: precision_at_3 value: 43.309 - type: precision_at_5 value: 28.294000000000004 - type: recall_at_1 value: 39.487 - type: recall_at_10 value: 76.334 - type: recall_at_100 value: 88.076 - type: recall_at_1000 value: 94.59100000000001 - type: recall_at_20 value: 80.898 - type: recall_at_3 value: 64.96300000000001 - type: recall_at_5 value: 70.736 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: main_score value: 42.027 - type: map_at_1 value: 22.118 - type: map_at_10 value: 34.816 - type: map_at_100 value: 35.983 - type: map_at_1000 value: 36.028999999999996 - type: map_at_20 value: 35.545 - type: map_at_3 value: 30.752000000000002 - type: map_at_5 value: 33.114 - type: mrr_at_1 value: 22.793696275071635 - type: mrr_at_10 value: 35.47250079592483 - type: mrr_at_100 value: 36.576471512902856 - type: mrr_at_1000 value: 36.616205680509786 - type: mrr_at_20 value: 36.16557033864942 - type: mrr_at_3 value: 31.48758357211065 - type: mrr_at_5 value: 33.80563514804202 - type: nauc_map_at_1000_diff1 value: 32.89234100489284 - type: nauc_map_at_1000_max value: 1.1802816553581001 - type: nauc_map_at_1000_std value: -20.187692925732446 - type: nauc_map_at_100_diff1 value: 32.88694493681772 - type: nauc_map_at_100_max value: 1.1732717578080365 - type: nauc_map_at_100_std value: -20.164165529035245 - type: nauc_map_at_10_diff1 value: 32.826182211848796 - type: nauc_map_at_10_max value: 1.1551262165737235 - type: nauc_map_at_10_std value: -20.88326292319754 - type: nauc_map_at_1_diff1 value: 36.12732122790642 - type: nauc_map_at_1_max value: 1.8197550109156913 - type: nauc_map_at_1_std value: -17.205625720792167 - type: nauc_map_at_20_diff1 value: 32.83333177195551 - type: nauc_map_at_20_max value: 1.0937431645506202 - type: nauc_map_at_20_std value: -20.503956514646145 - type: nauc_map_at_3_diff1 value: 32.76264193805814 - type: nauc_map_at_3_max value: 0.8560962042500389 - type: nauc_map_at_3_std value: -20.608930717315577 - type: nauc_map_at_5_diff1 value: 32.78673238978775 - type: nauc_map_at_5_max value: 1.0511863039329437 - type: nauc_map_at_5_std value: -21.02164728626011 - type: nauc_mrr_at_1000_diff1 value: 32.610323934702286 - type: nauc_mrr_at_1000_max value: 1.276669121901405 - type: nauc_mrr_at_1000_std value: -19.908120615285043 - type: nauc_mrr_at_100_diff1 value: 32.601373758102795 - type: nauc_mrr_at_100_max value: 1.2752735149992132 - type: nauc_mrr_at_100_std value: -19.87937042610101 - type: nauc_mrr_at_10_diff1 value: 32.55795432078168 - type: nauc_mrr_at_10_max value: 1.2881786969258637 - type: nauc_mrr_at_10_std value: -20.54564519015977 - type: nauc_mrr_at_1_diff1 value: 35.596301376443726 - type: nauc_mrr_at_1_max value: 1.7633238037306902 - type: nauc_mrr_at_1_std value: -17.1999420019887 - type: nauc_mrr_at_20_diff1 value: 32.57185739111023 - type: nauc_mrr_at_20_max value: 1.2212620853201877 - type: nauc_mrr_at_20_std value: -20.179517281041264 - type: nauc_mrr_at_3_diff1 value: 32.42681377099514 - type: nauc_mrr_at_3_max value: 0.8745921708861145 - type: nauc_mrr_at_3_std value: -20.41017687790572 - type: nauc_mrr_at_5_diff1 value: 32.499107129648266 - type: nauc_mrr_at_5_max value: 1.1159673851851573 - type: nauc_mrr_at_5_std value: -20.695143502133824 - type: nauc_ndcg_at_1000_diff1 value: 32.16957965806702 - type: nauc_ndcg_at_1000_max value: 1.6763998947980905 - type: nauc_ndcg_at_1000_std value: -18.970592350332893 - type: nauc_ndcg_at_100_diff1 value: 31.977550102558872 - type: nauc_ndcg_at_100_max value: 1.5625858650110014 - type: nauc_ndcg_at_100_std value: -17.990456766123835 - type: nauc_ndcg_at_10_diff1 value: 31.82738932481356 - type: nauc_ndcg_at_10_max value: 1.1661362042692103 - type: nauc_ndcg_at_10_std value: -21.872680193994217 - type: nauc_ndcg_at_1_diff1 value: 35.596301376443726 - type: nauc_ndcg_at_1_max value: 1.7633238037306902 - type: nauc_ndcg_at_1_std value: -17.1999420019887 - type: nauc_ndcg_at_20_diff1 value: 31.749656399266264 - type: nauc_ndcg_at_20_max value: 0.9629024493088691 - type: nauc_ndcg_at_20_std value: -20.4379403899277 - type: nauc_ndcg_at_3_diff1 value: 31.731361436850836 - type: nauc_ndcg_at_3_max value: 0.531749791578849 - type: nauc_ndcg_at_3_std value: -21.551112910698674 - type: nauc_ndcg_at_5_diff1 value: 31.785373941157303 - type: nauc_ndcg_at_5_max value: 0.86207769368333 - type: nauc_ndcg_at_5_std value: -22.24923399160171 - type: nauc_precision_at_1000_diff1 value: -3.841288331986519 - type: nauc_precision_at_1000_max value: 13.558041371634976 - type: nauc_precision_at_1000_std value: 15.181510484512827 - type: nauc_precision_at_100_diff1 value: 12.441154582709053 - type: nauc_precision_at_100_max value: 8.428136255841935 - type: nauc_precision_at_100_std value: 14.710391839731656 - type: nauc_precision_at_10_diff1 value: 26.185854813986705 - type: nauc_precision_at_10_max value: 1.6348387310504464 - type: nauc_precision_at_10_std value: -23.448927004357298 - type: nauc_precision_at_1_diff1 value: 35.596301376443726 - type: nauc_precision_at_1_max value: 1.7633238037306902 - type: nauc_precision_at_1_std value: -17.1999420019887 - type: nauc_precision_at_20_diff1 value: 22.69194179544158 - type: nauc_precision_at_20_max value: 1.2972015009169306 - type: nauc_precision_at_20_std value: -15.751482380060269 - type: nauc_precision_at_3_diff1 value: 28.255531512125188 - type: nauc_precision_at_3_max value: -0.3715575458464333 - type: nauc_precision_at_3_std value: -24.227970454057697 - type: nauc_precision_at_5_diff1 value: 27.65497951098847 - type: nauc_precision_at_5_max value: 0.449773375292472 - type: nauc_precision_at_5_std value: -25.37445450938601 - type: nauc_recall_at_1000_diff1 value: 15.243948516763819 - type: nauc_recall_at_1000_max value: 41.821227805251375 - type: nauc_recall_at_1000_std value: 61.66297794838101 - type: nauc_recall_at_100_diff1 value: 24.516543685029994 - type: nauc_recall_at_100_max value: 7.093972966253228 - type: nauc_recall_at_100_std value: 17.244452321212282 - type: nauc_recall_at_10_diff1 value: 28.404243095182828 - type: nauc_recall_at_10_max value: 1.0805210480930945 - type: nauc_recall_at_10_std value: -24.885018657039527 - type: nauc_recall_at_1_diff1 value: 36.12732122790642 - type: nauc_recall_at_1_max value: 1.8197550109156913 - type: nauc_recall_at_1_std value: -17.205625720792167 - type: nauc_recall_at_20_diff1 value: 26.956250169438512 - type: nauc_recall_at_20_max value: 0.023973408161285917 - type: nauc_recall_at_20_std value: -18.32944444428131 - type: nauc_recall_at_3_diff1 value: 28.9894205130054 - type: nauc_recall_at_3_max value: -0.36140658021466865 - type: nauc_recall_at_3_std value: -24.022505107768364 - type: nauc_recall_at_5_diff1 value: 28.907023434955104 - type: nauc_recall_at_5_max value: 0.2501037567297729 - type: nauc_recall_at_5_std value: -25.719919602271496 - type: ndcg_at_1 value: 22.794 - type: ndcg_at_10 value: 42.027 - type: ndcg_at_100 value: 47.601 - type: ndcg_at_1000 value: 48.713 - type: ndcg_at_20 value: 44.623000000000005 - type: ndcg_at_3 value: 33.772999999999996 - type: ndcg_at_5 value: 37.991 - type: precision_at_1 value: 22.794 - type: precision_at_10 value: 6.711 - type: precision_at_100 value: 0.9490000000000001 - type: precision_at_1000 value: 0.105 - type: precision_at_20 value: 3.8920000000000003 - type: precision_at_3 value: 14.46 - type: precision_at_5 value: 10.822 - type: recall_at_1 value: 22.118 - type: recall_at_10 value: 64.201 - type: recall_at_100 value: 89.878 - type: recall_at_1000 value: 98.259 - type: recall_at_20 value: 74.34100000000001 - type: recall_at_3 value: 41.8 - type: recall_at_5 value: 51.959 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: main_score value: 36.201 - type: map_at_1 value: 5.654 - type: map_at_10 value: 13.402 - type: map_at_100 value: 16.849 - type: map_at_1000 value: 18.264 - type: map_at_20 value: 14.832 - type: map_at_3 value: 9.619 - type: map_at_5 value: 11.483 - type: mrr_at_1 value: 47.6780185758514 - type: mrr_at_10 value: 56.47906531033466 - type: mrr_at_100 value: 57.04539749991402 - type: mrr_at_1000 value: 57.08810157607369 - type: mrr_at_20 value: 56.88003170105462 - type: mrr_at_3 value: 54.43756449948401 - type: mrr_at_5 value: 55.660474716202266 - type: nauc_map_at_1000_diff1 value: 31.134615238698192 - type: nauc_map_at_1000_max value: 36.09522002487132 - type: nauc_map_at_1000_std value: 14.72627666649002 - type: nauc_map_at_100_diff1 value: 32.777473351864444 - type: nauc_map_at_100_max value: 35.25391471621035 - type: nauc_map_at_100_std value: 12.024428973861083 - type: nauc_map_at_10_diff1 value: 36.46466466148528 - type: nauc_map_at_10_max value: 29.707805406826722 - type: nauc_map_at_10_std value: 2.0678757794226335 - type: nauc_map_at_1_diff1 value: 54.30208426149679 - type: nauc_map_at_1_max value: 18.69125148481608 - type: nauc_map_at_1_std value: -8.970955660291802 - type: nauc_map_at_20_diff1 value: 34.76513311600623 - type: nauc_map_at_20_max value: 32.20666003570514 - type: nauc_map_at_20_std value: 5.924889441518581 - type: nauc_map_at_3_diff1 value: 45.73465176835491 - type: nauc_map_at_3_max value: 23.492291524989106 - type: nauc_map_at_3_std value: -5.0123536561688855 - type: nauc_map_at_5_diff1 value: 39.7128319374107 - type: nauc_map_at_5_max value: 25.84231729559691 - type: nauc_map_at_5_std value: -2.0861428981140344 - type: nauc_mrr_at_1000_diff1 value: 33.0997881703397 - type: nauc_mrr_at_1000_max value: 52.7089709923531 - type: nauc_mrr_at_1000_std value: 28.8517952674151 - type: nauc_mrr_at_100_diff1 value: 33.1094984027438 - type: nauc_mrr_at_100_max value: 52.74301398138847 - type: nauc_mrr_at_100_std value: 28.897997840300892 - type: nauc_mrr_at_10_diff1 value: 33.300713655464925 - type: nauc_mrr_at_10_max value: 52.572139698742184 - type: nauc_mrr_at_10_std value: 28.66875615527188 - type: nauc_mrr_at_1_diff1 value: 32.57632582147155 - type: nauc_mrr_at_1_max value: 46.020072246328816 - type: nauc_mrr_at_1_std value: 20.99097889820076 - type: nauc_mrr_at_20_diff1 value: 33.04083904518949 - type: nauc_mrr_at_20_max value: 52.597451362456994 - type: nauc_mrr_at_20_std value: 28.681527293587898 - type: nauc_mrr_at_3_diff1 value: 33.64864656322754 - type: nauc_mrr_at_3_max value: 51.82256412011279 - type: nauc_mrr_at_3_std value: 27.241260746740686 - type: nauc_mrr_at_5_diff1 value: 33.53201325467246 - type: nauc_mrr_at_5_max value: 52.79440885773516 - type: nauc_mrr_at_5_std value: 28.663081392086028 - type: nauc_ndcg_at_1000_diff1 value: 28.632650542040714 - type: nauc_ndcg_at_1000_max value: 51.24103069835822 - type: nauc_ndcg_at_1000_std value: 35.05503784757999 - type: nauc_ndcg_at_100_diff1 value: 29.082177715298503 - type: nauc_ndcg_at_100_max value: 45.24750203464315 - type: nauc_ndcg_at_100_std value: 27.146548925680914 - type: nauc_ndcg_at_10_diff1 value: 25.123554466093594 - type: nauc_ndcg_at_10_max value: 42.74355537806512 - type: nauc_ndcg_at_10_std value: 22.234407997803935 - type: nauc_ndcg_at_1_diff1 value: 33.75083940012058 - type: nauc_ndcg_at_1_max value: 44.44319402133161 - type: nauc_ndcg_at_1_std value: 19.146499358406487 - type: nauc_ndcg_at_20_diff1 value: 24.954207968331872 - type: nauc_ndcg_at_20_max value: 41.25991844405748 - type: nauc_ndcg_at_20_std value: 22.169009285868864 - type: nauc_ndcg_at_3_diff1 value: 28.186539942033516 - type: nauc_ndcg_at_3_max value: 44.40790009754965 - type: nauc_ndcg_at_3_std value: 20.99226576085115 - type: nauc_ndcg_at_5_diff1 value: 25.498387899376706 - type: nauc_ndcg_at_5_max value: 43.174709766261316 - type: nauc_ndcg_at_5_std value: 21.88111962672031 - type: nauc_precision_at_1000_diff1 value: -16.22321012507648 - type: nauc_precision_at_1000_max value: 5.808852256649677 - type: nauc_precision_at_1000_std value: 19.875641776698824 - type: nauc_precision_at_100_diff1 value: -10.248089374355486 - type: nauc_precision_at_100_max value: 19.29065415127588 - type: nauc_precision_at_100_std value: 31.75019665627339 - type: nauc_precision_at_10_diff1 value: 3.6783257583955056 - type: nauc_precision_at_10_max value: 39.22286010695767 - type: nauc_precision_at_10_std value: 31.225485732801022 - type: nauc_precision_at_1_diff1 value: 32.57632582147155 - type: nauc_precision_at_1_max value: 46.020072246328816 - type: nauc_precision_at_1_std value: 20.99097889820076 - type: nauc_precision_at_20_diff1 value: -3.1632510833242784 - type: nauc_precision_at_20_max value: 31.575496762405734 - type: nauc_precision_at_20_std value: 31.576283324468115 - type: nauc_precision_at_3_diff1 value: 17.78864585545647 - type: nauc_precision_at_3_max value: 44.201289661125585 - type: nauc_precision_at_3_std value: 25.447840649726693 - type: nauc_precision_at_5_diff1 value: 9.986748662091358 - type: nauc_precision_at_5_max value: 41.214164860776755 - type: nauc_precision_at_5_std value: 28.22551704127726 - type: nauc_recall_at_1000_diff1 value: 10.984331766850506 - type: nauc_recall_at_1000_max value: 24.641216018034104 - type: nauc_recall_at_1000_std value: 26.91064221008446 - type: nauc_recall_at_100_diff1 value: 23.7009352078473 - type: nauc_recall_at_100_max value: 30.176031609451297 - type: nauc_recall_at_100_std value: 20.360365243211564 - type: nauc_recall_at_10_diff1 value: 28.11831737650638 - type: nauc_recall_at_10_max value: 24.21539670487414 - type: nauc_recall_at_10_std value: 2.245504974150148 - type: nauc_recall_at_1_diff1 value: 54.30208426149679 - type: nauc_recall_at_1_max value: 18.69125148481608 - type: nauc_recall_at_1_std value: -8.970955660291802 - type: nauc_recall_at_20_diff1 value: 26.199425305139908 - type: nauc_recall_at_20_max value: 24.66704097503736 - type: nauc_recall_at_20_std value: 5.86052107206246 - type: nauc_recall_at_3_diff1 value: 42.88348677575622 - type: nauc_recall_at_3_max value: 21.189371077603308 - type: nauc_recall_at_3_std value: -4.537510127238226 - type: nauc_recall_at_5_diff1 value: 30.7936756722569 - type: nauc_recall_at_5_max value: 21.06136406164962 - type: nauc_recall_at_5_std value: -1.4113804735229794 - type: ndcg_at_1 value: 45.975 - type: ndcg_at_10 value: 36.201 - type: ndcg_at_100 value: 32.736 - type: ndcg_at_1000 value: 41.099000000000004 - type: ndcg_at_20 value: 33.724 - type: ndcg_at_3 value: 42.242000000000004 - type: ndcg_at_5 value: 40.137 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 26.904 - type: precision_at_100 value: 8.368 - type: precision_at_1000 value: 2.078 - type: precision_at_20 value: 19.845 - type: precision_at_3 value: 40.351 - type: precision_at_5 value: 35.108 - type: recall_at_1 value: 5.654 - type: recall_at_10 value: 17.793 - type: recall_at_100 value: 32.483000000000004 - type: recall_at_1000 value: 63.294 - type: recall_at_20 value: 21.754 - type: recall_at_3 value: 10.771 - type: recall_at_5 value: 14.084 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: main_score value: 62.464 - type: map_at_1 value: 38.0 - type: map_at_10 value: 54.806 - type: map_at_100 value: 55.599 - type: map_at_1000 value: 55.617000000000004 - type: map_at_20 value: 55.336 - type: map_at_3 value: 50.58200000000001 - type: map_at_5 value: 53.181 - type: mrr_at_1 value: 42.46813441483198 - type: mrr_at_10 value: 57.060710147326446 - type: mrr_at_100 value: 57.60978373431328 - type: mrr_at_1000 value: 57.62192762809547 - type: mrr_at_20 value: 57.43431796174232 - type: mrr_at_3 value: 53.78041714947835 - type: mrr_at_5 value: 55.81257242178437 - type: nauc_map_at_1000_diff1 value: 38.337572188308194 - type: nauc_map_at_1000_max value: 27.550035254787197 - type: nauc_map_at_1000_std value: -7.5513729587308145 - type: nauc_map_at_100_diff1 value: 38.335337794455015 - type: nauc_map_at_100_max value: 27.56919614414171 - type: nauc_map_at_100_std value: -7.526017855405723 - type: nauc_map_at_10_diff1 value: 38.308131361353816 - type: nauc_map_at_10_max value: 27.691849580929933 - type: nauc_map_at_10_std value: -7.971461731555123 - type: nauc_map_at_1_diff1 value: 42.721072690634884 - type: nauc_map_at_1_max value: 21.750451486885332 - type: nauc_map_at_1_std value: -9.99540950522643 - type: nauc_map_at_20_diff1 value: 38.25792874982169 - type: nauc_map_at_20_max value: 27.68877906159661 - type: nauc_map_at_20_std value: -7.560753583212102 - type: nauc_map_at_3_diff1 value: 37.950570055936254 - type: nauc_map_at_3_max value: 26.257969511794858 - type: nauc_map_at_3_std value: -9.236868658300553 - type: nauc_map_at_5_diff1 value: 37.99893219450212 - type: nauc_map_at_5_max value: 27.293454259158057 - type: nauc_map_at_5_std value: -8.734089449603806 - type: nauc_mrr_at_1000_diff1 value: 37.777767467474774 - type: nauc_mrr_at_1000_max value: 27.39507603748298 - type: nauc_mrr_at_1000_std value: -5.554754076870114 - type: nauc_mrr_at_100_diff1 value: 37.77981674583538 - type: nauc_mrr_at_100_max value: 27.411100989441557 - type: nauc_mrr_at_100_std value: -5.539061231412731 - type: nauc_mrr_at_10_diff1 value: 37.72399003363479 - type: nauc_mrr_at_10_max value: 27.618142546685416 - type: nauc_mrr_at_10_std value: -5.6819843907448195 - type: nauc_mrr_at_1_diff1 value: 41.17596078958236 - type: nauc_mrr_at_1_max value: 23.32588591818617 - type: nauc_mrr_at_1_std value: -7.126628034623689 - type: nauc_mrr_at_20_diff1 value: 37.695136721588 - type: nauc_mrr_at_20_max value: 27.52850676467322 - type: nauc_mrr_at_20_std value: -5.50667995515647 - type: nauc_mrr_at_3_diff1 value: 37.23845700908964 - type: nauc_mrr_at_3_max value: 26.69389772971012 - type: nauc_mrr_at_3_std value: -6.31868405989011 - type: nauc_mrr_at_5_diff1 value: 37.33757394192838 - type: nauc_mrr_at_5_max value: 27.42091593836207 - type: nauc_mrr_at_5_std value: -5.993243330132065 - type: nauc_ndcg_at_1000_diff1 value: 37.74836061640332 - type: nauc_ndcg_at_1000_max value: 29.03148916289089 - type: nauc_ndcg_at_1000_std value: -5.543065770074502 - type: nauc_ndcg_at_100_diff1 value: 37.75593955089626 - type: nauc_ndcg_at_100_max value: 29.67109480272493 - type: nauc_ndcg_at_100_std value: -4.773697596687493 - type: nauc_ndcg_at_10_diff1 value: 37.41701174824348 - type: nauc_ndcg_at_10_max value: 30.448703434043445 - type: nauc_ndcg_at_10_std value: -6.306202666419071 - type: nauc_ndcg_at_1_diff1 value: 41.17596078958236 - type: nauc_ndcg_at_1_max value: 23.32588591818617 - type: nauc_ndcg_at_1_std value: -7.126628034623689 - type: nauc_ndcg_at_20_diff1 value: 37.17445197824622 - type: nauc_ndcg_at_20_max value: 30.47378561555209 - type: nauc_ndcg_at_20_std value: -4.921584853993488 - type: nauc_ndcg_at_3_diff1 value: 36.5261976812068 - type: nauc_ndcg_at_3_max value: 27.560538820208926 - type: nauc_ndcg_at_3_std value: -8.556686332882931 - type: nauc_ndcg_at_5_diff1 value: 36.571462759614526 - type: nauc_ndcg_at_5_max value: 29.363401730752585 - type: nauc_ndcg_at_5_std value: -7.825739170420347 - type: nauc_precision_at_1000_diff1 value: -12.588899483401223 - type: nauc_precision_at_1000_max value: 2.641097890578701 - type: nauc_precision_at_1000_std value: 17.643107625788748 - type: nauc_precision_at_100_diff1 value: -8.40579874206785 - type: nauc_precision_at_100_max value: 9.725496771040037 - type: nauc_precision_at_100_std value: 21.558582760191243 - type: nauc_precision_at_10_diff1 value: 6.619157191854486 - type: nauc_precision_at_10_max value: 23.767406373688402 - type: nauc_precision_at_10_std value: 10.428535003478808 - type: nauc_precision_at_1_diff1 value: 41.17596078958236 - type: nauc_precision_at_1_max value: 23.32588591818617 - type: nauc_precision_at_1_std value: -7.126628034623689 - type: nauc_precision_at_20_diff1 value: -0.6449974218292859 - type: nauc_precision_at_20_max value: 20.211503851418783 - type: nauc_precision_at_20_std value: 17.922745410142575 - type: nauc_precision_at_3_diff1 value: 19.710276097428657 - type: nauc_precision_at_3_max value: 26.768918044758706 - type: nauc_precision_at_3_std value: -1.0636448912049246 - type: nauc_precision_at_5_diff1 value: 13.073181337982613 - type: nauc_precision_at_5_max value: 26.418340338971024 - type: nauc_precision_at_5_std value: 2.9842078949528688 - type: nauc_recall_at_1000_diff1 value: 30.52411148739828 - type: nauc_recall_at_1000_max value: 90.96409807536762 - type: nauc_recall_at_1000_std value: 83.94857830921949 - type: nauc_recall_at_100_diff1 value: 36.936303690592155 - type: nauc_recall_at_100_max value: 71.91515014325869 - type: nauc_recall_at_100_std value: 48.93061263403371 - type: nauc_recall_at_10_diff1 value: 32.84292362076269 - type: nauc_recall_at_10_max value: 44.27252783122478 - type: nauc_recall_at_10_std value: -1.5981198975612385 - type: nauc_recall_at_1_diff1 value: 42.721072690634884 - type: nauc_recall_at_1_max value: 21.750451486885332 - type: nauc_recall_at_1_std value: -9.99540950522643 - type: nauc_recall_at_20_diff1 value: 29.36724417081702 - type: nauc_recall_at_20_max value: 52.035846390214715 - type: nauc_recall_at_20_std value: 11.967264191332818 - type: nauc_recall_at_3_diff1 value: 31.634923771936098 - type: nauc_recall_at_3_max value: 30.225743369869473 - type: nauc_recall_at_3_std value: -9.253665347118615 - type: nauc_recall_at_5_diff1 value: 30.66271853090737 - type: nauc_recall_at_5_max value: 35.70815715994996 - type: nauc_recall_at_5_std value: -7.836012956078996 - type: ndcg_at_1 value: 42.468 - type: ndcg_at_10 value: 62.464 - type: ndcg_at_100 value: 65.618 - type: ndcg_at_1000 value: 66.014 - type: ndcg_at_20 value: 64.12 - type: ndcg_at_3 value: 54.790000000000006 - type: ndcg_at_5 value: 58.992 - type: precision_at_1 value: 42.468 - type: precision_at_10 value: 9.959 - type: precision_at_100 value: 1.174 - type: precision_at_1000 value: 0.121 - type: precision_at_20 value: 5.380999999999999 - type: precision_at_3 value: 24.73 - type: precision_at_5 value: 17.299999999999997 - type: recall_at_1 value: 38.0 - type: recall_at_10 value: 83.22699999999999 - type: recall_at_100 value: 96.584 - type: recall_at_1000 value: 99.512 - type: recall_at_20 value: 89.291 - type: recall_at_3 value: 63.666 - type: recall_at_5 value: 73.27900000000001 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics: - type: main_score value: 87.366 - type: map_at_1 value: 69.95700000000001 - type: map_at_10 value: 83.55 - type: map_at_100 value: 84.196 - type: map_at_1000 value: 84.21600000000001 - type: map_at_20 value: 83.982 - type: map_at_3 value: 80.647 - type: map_at_5 value: 82.443 - type: mrr_at_1 value: 80.39 - type: mrr_at_10 value: 86.65646031746004 - type: mrr_at_100 value: 86.7852113210373 - type: mrr_at_1000 value: 86.78651118354796 - type: mrr_at_20 value: 86.75772838878498 - type: mrr_at_3 value: 85.67499999999971 - type: mrr_at_5 value: 86.33749999999962 - type: nauc_map_at_1000_diff1 value: 76.68189702770007 - type: nauc_map_at_1000_max value: 36.19988239025682 - type: nauc_map_at_1000_std value: -26.231691135645736 - type: nauc_map_at_100_diff1 value: 76.68832712120171 - type: nauc_map_at_100_max value: 36.18627717337547 - type: nauc_map_at_100_std value: -26.28243886166 - type: nauc_map_at_10_diff1 value: 76.88888516032657 - type: nauc_map_at_10_max value: 35.69809861085124 - type: nauc_map_at_10_std value: -27.859425473864224 - type: nauc_map_at_1_diff1 value: 79.5243725217315 - type: nauc_map_at_1_max value: 27.092773841207002 - type: nauc_map_at_1_std value: -26.223200911204543 - type: nauc_map_at_20_diff1 value: 76.74938996155176 - type: nauc_map_at_20_max value: 36.07373781351406 - type: nauc_map_at_20_std value: -26.891400098628015 - type: nauc_map_at_3_diff1 value: 77.29604745045076 - type: nauc_map_at_3_max value: 33.11431059356283 - type: nauc_map_at_3_std value: -29.555237195931085 - type: nauc_map_at_5_diff1 value: 77.14069217901078 - type: nauc_map_at_5_max value: 34.68656073526487 - type: nauc_map_at_5_std value: -28.945053669861508 - type: nauc_mrr_at_1000_diff1 value: 76.66087451567746 - type: nauc_mrr_at_1000_max value: 38.78133177265328 - type: nauc_mrr_at_1000_std value: -23.75726541774991 - type: nauc_mrr_at_100_diff1 value: 76.66117078261013 - type: nauc_mrr_at_100_max value: 38.782533036423885 - type: nauc_mrr_at_100_std value: -23.752587601473568 - type: nauc_mrr_at_10_diff1 value: 76.65866401411019 - type: nauc_mrr_at_10_max value: 38.87950311049704 - type: nauc_mrr_at_10_std value: -23.873660706680578 - type: nauc_mrr_at_1_diff1 value: 77.42633506487041 - type: nauc_mrr_at_1_max value: 37.93973722217786 - type: nauc_mrr_at_1_std value: -23.3984130771317 - type: nauc_mrr_at_20_diff1 value: 76.66210684923414 - type: nauc_mrr_at_20_max value: 38.81293033048911 - type: nauc_mrr_at_20_std value: -23.736590746133736 - type: nauc_mrr_at_3_diff1 value: 76.33711764736019 - type: nauc_mrr_at_3_max value: 38.5659231830368 - type: nauc_mrr_at_3_std value: -23.99588149124865 - type: nauc_mrr_at_5_diff1 value: 76.57123830226054 - type: nauc_mrr_at_5_max value: 38.97947097392977 - type: nauc_mrr_at_5_std value: -23.943668957974246 - type: nauc_ndcg_at_1000_diff1 value: 76.38447339050585 - type: nauc_ndcg_at_1000_max value: 37.756822792877934 - type: nauc_ndcg_at_1000_std value: -24.046995734357164 - type: nauc_ndcg_at_100_diff1 value: 76.44058018066822 - type: nauc_ndcg_at_100_max value: 37.72948294169218 - type: nauc_ndcg_at_100_std value: -24.083432140741795 - type: nauc_ndcg_at_10_diff1 value: 76.56246287923074 - type: nauc_ndcg_at_10_max value: 37.0329253490553 - type: nauc_ndcg_at_10_std value: -26.6495163705961 - type: nauc_ndcg_at_1_diff1 value: 77.4085129990432 - type: nauc_ndcg_at_1_max value: 38.06139172214421 - type: nauc_ndcg_at_1_std value: -23.656477126977386 - type: nauc_ndcg_at_20_diff1 value: 76.50192496743098 - type: nauc_ndcg_at_20_max value: 37.51759311013985 - type: nauc_ndcg_at_20_std value: -25.45517058360004 - type: nauc_ndcg_at_3_diff1 value: 75.94398494081794 - type: nauc_ndcg_at_3_max value: 35.7666711547279 - type: nauc_ndcg_at_3_std value: -26.866022682361578 - type: nauc_ndcg_at_5_diff1 value: 76.47334274088344 - type: nauc_ndcg_at_5_max value: 36.40830331490731 - type: nauc_ndcg_at_5_std value: -27.170121189572765 - type: nauc_precision_at_1000_diff1 value: -43.33672630765437 - type: nauc_precision_at_1000_max value: -5.089751329149161 - type: nauc_precision_at_1000_std value: 30.6241447847051 - type: nauc_precision_at_100_diff1 value: -42.736833035629864 - type: nauc_precision_at_100_max value: -4.060198408346224 - type: nauc_precision_at_100_std value: 29.807050266205344 - type: nauc_precision_at_10_diff1 value: -35.90810562245906 - type: nauc_precision_at_10_max value: 1.1633204529249133 - type: nauc_precision_at_10_std value: 20.129691203276018 - type: nauc_precision_at_1_diff1 value: 77.4085129990432 - type: nauc_precision_at_1_max value: 38.06139172214421 - type: nauc_precision_at_1_std value: -23.656477126977386 - type: nauc_precision_at_20_diff1 value: -40.2132286912738 - type: nauc_precision_at_20_max value: -1.3004735030734194 - type: nauc_precision_at_20_std value: 25.15612293757488 - type: nauc_precision_at_3_diff1 value: -13.873825299883904 - type: nauc_precision_at_3_max value: 11.038689278907233 - type: nauc_precision_at_3_std value: 5.4276449621706 - type: nauc_precision_at_5_diff1 value: -27.151668633894737 - type: nauc_precision_at_5_max value: 5.795130010163115 - type: nauc_precision_at_5_std value: 13.220722167587375 - type: nauc_recall_at_1000_diff1 value: 83.903950427863 - type: nauc_recall_at_1000_max value: 37.82919000897223 - type: nauc_recall_at_1000_std value: 70.65670846771707 - type: nauc_recall_at_100_diff1 value: 75.23306095335836 - type: nauc_recall_at_100_max value: 37.54281648247423 - type: nauc_recall_at_100_std value: 8.434289114377373 - type: nauc_recall_at_10_diff1 value: 72.7872912723047 - type: nauc_recall_at_10_max value: 34.261519652104184 - type: nauc_recall_at_10_std value: -34.60101950810808 - type: nauc_recall_at_1_diff1 value: 79.5243725217315 - type: nauc_recall_at_1_max value: 27.092773841207002 - type: nauc_recall_at_1_std value: -26.223200911204543 - type: nauc_recall_at_20_diff1 value: 72.8297963091964 - type: nauc_recall_at_20_max value: 36.070220569670916 - type: nauc_recall_at_20_std value: -27.20897179168245 - type: nauc_recall_at_3_diff1 value: 73.47456374650459 - type: nauc_recall_at_3_max value: 29.901663407294816 - type: nauc_recall_at_3_std value: -32.83329537040381 - type: nauc_recall_at_5_diff1 value: 73.05025750827126 - type: nauc_recall_at_5_max value: 32.35733470860963 - type: nauc_recall_at_5_std value: -34.32357558493091 - type: ndcg_at_1 value: 80.4 - type: ndcg_at_10 value: 87.366 - type: ndcg_at_100 value: 88.7 - type: ndcg_at_1000 value: 88.842 - type: ndcg_at_20 value: 88.11 - type: ndcg_at_3 value: 84.52499999999999 - type: ndcg_at_5 value: 86.047 - type: precision_at_1 value: 80.4 - type: precision_at_10 value: 13.235 - type: precision_at_100 value: 1.516 - type: precision_at_1000 value: 0.156 - type: precision_at_20 value: 7.037 - type: precision_at_3 value: 36.9 - type: precision_at_5 value: 24.236 - type: recall_at_1 value: 69.95700000000001 - type: recall_at_10 value: 94.535 - type: recall_at_100 value: 99.164 - type: recall_at_1000 value: 99.855 - type: recall_at_20 value: 96.974 - type: recall_at_3 value: 86.33800000000001 - type: recall_at_5 value: 90.69 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 metrics: - type: main_score value: 21.492 - type: map_at_1 value: 5.192 - type: map_at_10 value: 12.959000000000001 - type: map_at_100 value: 14.963999999999999 - type: map_at_1000 value: 15.261 - type: map_at_20 value: 13.988999999999999 - type: map_at_3 value: 9.235 - type: map_at_5 value: 11.042 - type: mrr_at_1 value: 25.5 - type: mrr_at_10 value: 36.37313492063491 - type: mrr_at_100 value: 37.36517957347626 - type: mrr_at_1000 value: 37.42538601073437 - type: mrr_at_20 value: 36.987896404421136 - type: mrr_at_3 value: 32.966666666666654 - type: mrr_at_5 value: 34.95166666666664 - type: nauc_map_at_1000_diff1 value: 13.635120934154395 - type: nauc_map_at_1000_max value: 28.03542983005195 - type: nauc_map_at_1000_std value: 17.07156940311778 - type: nauc_map_at_100_diff1 value: 13.59237295184475 - type: nauc_map_at_100_max value: 27.992291365051237 - type: nauc_map_at_100_std value: 16.926533467400464 - type: nauc_map_at_10_diff1 value: 14.149193235999993 - type: nauc_map_at_10_max value: 26.520643811139305 - type: nauc_map_at_10_std value: 13.168673602548925 - type: nauc_map_at_1_diff1 value: 20.096094508148465 - type: nauc_map_at_1_max value: 17.41582245576302 - type: nauc_map_at_1_std value: 5.771729007558897 - type: nauc_map_at_20_diff1 value: 13.977726400526427 - type: nauc_map_at_20_max value: 27.2322235491895 - type: nauc_map_at_20_std value: 14.972781677750435 - type: nauc_map_at_3_diff1 value: 17.371153027460355 - type: nauc_map_at_3_max value: 24.457758503208254 - type: nauc_map_at_3_std value: 7.719726821179824 - type: nauc_map_at_5_diff1 value: 14.600442843442574 - type: nauc_map_at_5_max value: 25.899736370856296 - type: nauc_map_at_5_std value: 10.125349354853359 - type: nauc_mrr_at_1000_diff1 value: 18.70342821390236 - type: nauc_mrr_at_1000_max value: 23.365194520549114 - type: nauc_mrr_at_1000_std value: 12.185114294903236 - type: nauc_mrr_at_100_diff1 value: 18.677858738015907 - type: nauc_mrr_at_100_max value: 23.372641996726742 - type: nauc_mrr_at_100_std value: 12.216130561991909 - type: nauc_mrr_at_10_diff1 value: 18.79094453090232 - type: nauc_mrr_at_10_max value: 23.511686337006466 - type: nauc_mrr_at_10_std value: 11.879716687008134 - type: nauc_mrr_at_1_diff1 value: 20.10455171810408 - type: nauc_mrr_at_1_max value: 17.741566234315428 - type: nauc_mrr_at_1_std value: 6.1676764583652215 - type: nauc_mrr_at_20_diff1 value: 18.70143648544655 - type: nauc_mrr_at_20_max value: 23.45603239095019 - type: nauc_mrr_at_20_std value: 12.244613576686202 - type: nauc_mrr_at_3_diff1 value: 18.894662528857374 - type: nauc_mrr_at_3_max value: 23.3739038101588 - type: nauc_mrr_at_3_std value: 10.4709044796543 - type: nauc_mrr_at_5_diff1 value: 18.877786065095563 - type: nauc_mrr_at_5_max value: 23.78061081203872 - type: nauc_mrr_at_5_std value: 11.847882917869622 - type: nauc_ndcg_at_1000_diff1 value: 13.99159027398115 - type: nauc_ndcg_at_1000_max value: 29.44766808611483 - type: nauc_ndcg_at_1000_std value: 24.289749574699915 - type: nauc_ndcg_at_100_diff1 value: 13.164020363258746 - type: nauc_ndcg_at_100_max value: 29.642442997167723 - type: nauc_ndcg_at_100_std value: 23.761764515453866 - type: nauc_ndcg_at_10_diff1 value: 14.839883268638546 - type: nauc_ndcg_at_10_max value: 27.21043708455449 - type: nauc_ndcg_at_10_std value: 15.56110419291775 - type: nauc_ndcg_at_1_diff1 value: 20.10455171810408 - type: nauc_ndcg_at_1_max value: 17.741566234315428 - type: nauc_ndcg_at_1_std value: 6.1676764583652215 - type: nauc_ndcg_at_20_diff1 value: 14.27998110295395 - type: nauc_ndcg_at_20_max value: 28.2492026337839 - type: nauc_ndcg_at_20_std value: 18.822356982979105 - type: nauc_ndcg_at_3_diff1 value: 17.659263157535445 - type: nauc_ndcg_at_3_max value: 25.416706421591396 - type: nauc_ndcg_at_3_std value: 9.650689638152636 - type: nauc_ndcg_at_5_diff1 value: 15.38459833918123 - type: nauc_ndcg_at_5_max value: 26.92495519416969 - type: nauc_ndcg_at_5_std value: 12.71017696809276 - type: nauc_precision_at_1000_diff1 value: 6.128490135458364 - type: nauc_precision_at_1000_max value: 23.52693893261883 - type: nauc_precision_at_1000_std value: 36.280432732819925 - type: nauc_precision_at_100_diff1 value: 5.306163791220436 - type: nauc_precision_at_100_max value: 27.67851033239246 - type: nauc_precision_at_100_std value: 34.29821573752515 - type: nauc_precision_at_10_diff1 value: 10.829686435425472 - type: nauc_precision_at_10_max value: 27.201648684015318 - type: nauc_precision_at_10_std value: 19.376999508233254 - type: nauc_precision_at_1_diff1 value: 20.10455171810408 - type: nauc_precision_at_1_max value: 17.741566234315428 - type: nauc_precision_at_1_std value: 6.1676764583652215 - type: nauc_precision_at_20_diff1 value: 9.416169626702048 - type: nauc_precision_at_20_max value: 27.65257998670333 - type: nauc_precision_at_20_std value: 24.761868509805826 - type: nauc_precision_at_3_diff1 value: 16.666456902017348 - type: nauc_precision_at_3_max value: 27.9969730961105 - type: nauc_precision_at_3_std value: 10.991562741393231 - type: nauc_precision_at_5_diff1 value: 12.26205064462843 - type: nauc_precision_at_5_max value: 29.083848730874095 - type: nauc_precision_at_5_std value: 15.66630836555747 - type: nauc_recall_at_1000_diff1 value: 5.600277836894063 - type: nauc_recall_at_1000_max value: 23.228705161815526 - type: nauc_recall_at_1000_std value: 36.822431061799485 - type: nauc_recall_at_100_diff1 value: 4.991781244867178 - type: nauc_recall_at_100_max value: 27.70095625483475 - type: nauc_recall_at_100_std value: 34.67168431597854 - type: nauc_recall_at_10_diff1 value: 10.580860425931972 - type: nauc_recall_at_10_max value: 27.145829414223666 - type: nauc_recall_at_10_std value: 19.330630157067382 - type: nauc_recall_at_1_diff1 value: 20.096094508148465 - type: nauc_recall_at_1_max value: 17.41582245576302 - type: nauc_recall_at_1_std value: 5.771729007558897 - type: nauc_recall_at_20_diff1 value: 9.06945331260344 - type: nauc_recall_at_20_max value: 27.56725251066482 - type: nauc_recall_at_20_std value: 24.77644509886098 - type: nauc_recall_at_3_diff1 value: 16.660507676429322 - type: nauc_recall_at_3_max value: 27.816546386536434 - type: nauc_recall_at_3_std value: 10.687824478247007 - type: nauc_recall_at_5_diff1 value: 11.992514446369388 - type: nauc_recall_at_5_max value: 28.789031176671948 - type: nauc_recall_at_5_std value: 15.422118990090805 - type: ndcg_at_1 value: 25.5 - type: ndcg_at_10 value: 21.492 - type: ndcg_at_100 value: 29.022 - type: ndcg_at_1000 value: 34.298 - type: ndcg_at_20 value: 24.237000000000002 - type: ndcg_at_3 value: 20.392 - type: ndcg_at_5 value: 17.801000000000002 - type: precision_at_1 value: 25.5 - type: precision_at_10 value: 11.09 - type: precision_at_100 value: 2.1919999999999997 - type: precision_at_1000 value: 0.346 - type: precision_at_20 value: 7.135 - type: precision_at_3 value: 18.933 - type: precision_at_5 value: 15.52 - type: recall_at_1 value: 5.192 - type: recall_at_10 value: 22.512999999999998 - type: recall_at_100 value: 44.505 - type: recall_at_1000 value: 70.267 - type: recall_at_20 value: 28.965000000000003 - type: recall_at_3 value: 11.522 - type: recall_at_5 value: 15.751999999999999 - task: type: Retrieval dataset: name: MTEB SciFact type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: main_score value: 71.586 - type: map_at_1 value: 56.760999999999996 - type: map_at_10 value: 66.893 - type: map_at_100 value: 67.42 - type: map_at_1000 value: 67.44200000000001 - type: map_at_20 value: 67.232 - type: map_at_3 value: 64.193 - type: map_at_5 value: 65.73400000000001 - type: mrr_at_1 value: 60.0 - type: mrr_at_10 value: 68.20383597883595 - type: mrr_at_100 value: 68.58867453733343 - type: mrr_at_1000 value: 68.61117469977329 - type: mrr_at_20 value: 68.43973740684265 - type: mrr_at_3 value: 66.11111111111111 - type: mrr_at_5 value: 67.44444444444446 - type: nauc_map_at_1000_diff1 value: 72.66688261123035 - type: nauc_map_at_1000_max value: 61.02926282006283 - type: nauc_map_at_1000_std value: 11.084549829740526 - type: nauc_map_at_100_diff1 value: 72.66226192320828 - type: nauc_map_at_100_max value: 61.04393223108811 - type: nauc_map_at_100_std value: 11.101529343291695 - type: nauc_map_at_10_diff1 value: 72.66732266693091 - type: nauc_map_at_10_max value: 61.24124296311832 - type: nauc_map_at_10_std value: 10.91179451961794 - type: nauc_map_at_1_diff1 value: 74.2356464256346 - type: nauc_map_at_1_max value: 54.06962758957632 - type: nauc_map_at_1_std value: 0.8037891907963532 - type: nauc_map_at_20_diff1 value: 72.65198594061253 - type: nauc_map_at_20_max value: 61.130159351448185 - type: nauc_map_at_20_std value: 11.2246899245522 - type: nauc_map_at_3_diff1 value: 72.78578673303954 - type: nauc_map_at_3_max value: 59.19073262936321 - type: nauc_map_at_3_std value: 8.460301560522968 - type: nauc_map_at_5_diff1 value: 72.55004168261968 - type: nauc_map_at_5_max value: 59.75181935082357 - type: nauc_map_at_5_std value: 9.440299527201889 - type: nauc_mrr_at_1000_diff1 value: 72.82720348470325 - type: nauc_mrr_at_1000_max value: 62.344231223741446 - type: nauc_mrr_at_1000_std value: 12.60196558488974 - type: nauc_mrr_at_100_diff1 value: 72.82236849255094 - type: nauc_mrr_at_100_max value: 62.35799491393125 - type: nauc_mrr_at_100_std value: 12.617900773655673 - type: nauc_mrr_at_10_diff1 value: 72.7722847495086 - type: nauc_mrr_at_10_max value: 62.66642401155435 - type: nauc_mrr_at_10_std value: 12.906381237738746 - type: nauc_mrr_at_1_diff1 value: 74.71208073612343 - type: nauc_mrr_at_1_max value: 59.50430394775893 - type: nauc_mrr_at_1_std value: 8.129514198080512 - type: nauc_mrr_at_20_diff1 value: 72.78312367361772 - type: nauc_mrr_at_20_max value: 62.421122493761885 - type: nauc_mrr_at_20_std value: 12.693437522498588 - type: nauc_mrr_at_3_diff1 value: 73.50670156385345 - type: nauc_mrr_at_3_max value: 62.01717537699209 - type: nauc_mrr_at_3_std value: 11.926548252191182 - type: nauc_mrr_at_5_diff1 value: 72.62204028549876 - type: nauc_mrr_at_5_max value: 62.319358766312085 - type: nauc_mrr_at_5_std value: 13.081257923284342 - type: nauc_ndcg_at_1000_diff1 value: 72.29960539074736 - type: nauc_ndcg_at_1000_max value: 62.75096959221402 - type: nauc_ndcg_at_1000_std value: 13.81528462505362 - type: nauc_ndcg_at_100_diff1 value: 72.19985782073529 - type: nauc_ndcg_at_100_max value: 63.18837705326287 - type: nauc_ndcg_at_100_std value: 14.506479655117138 - type: nauc_ndcg_at_10_diff1 value: 71.85759847832983 - type: nauc_ndcg_at_10_max value: 64.150996056865 - type: nauc_ndcg_at_10_std value: 14.580606901634278 - type: nauc_ndcg_at_1_diff1 value: 74.71208073612343 - type: nauc_ndcg_at_1_max value: 59.50430394775893 - type: nauc_ndcg_at_1_std value: 8.129514198080512 - type: nauc_ndcg_at_20_diff1 value: 71.80987178228351 - type: nauc_ndcg_at_20_max value: 63.56269460865743 - type: nauc_ndcg_at_20_std value: 15.024978004625922 - type: nauc_ndcg_at_3_diff1 value: 72.35095651602592 - type: nauc_ndcg_at_3_max value: 61.60548011855679 - type: nauc_ndcg_at_3_std value: 12.048248788835263 - type: nauc_ndcg_at_5_diff1 value: 71.48615621881864 - type: nauc_ndcg_at_5_max value: 61.72870035979784 - type: nauc_ndcg_at_5_std value: 12.83048357446691 - type: nauc_precision_at_1000_diff1 value: -14.743011420972 - type: nauc_precision_at_1000_max value: 19.281995763080158 - type: nauc_precision_at_1000_std value: 49.6140660398164 - type: nauc_precision_at_100_diff1 value: 0.11278174806205563 - type: nauc_precision_at_100_max value: 29.704511820077332 - type: nauc_precision_at_100_std value: 47.84916954122579 - type: nauc_precision_at_10_diff1 value: 20.498227967235728 - type: nauc_precision_at_10_max value: 47.883119365891595 - type: nauc_precision_at_10_std value: 45.182178693450595 - type: nauc_precision_at_1_diff1 value: 74.71208073612343 - type: nauc_precision_at_1_max value: 59.50430394775893 - type: nauc_precision_at_1_std value: 8.129514198080512 - type: nauc_precision_at_20_diff1 value: 12.551737222341455 - type: nauc_precision_at_20_max value: 40.618899501225634 - type: nauc_precision_at_20_std value: 48.5598454249067 - type: nauc_precision_at_3_diff1 value: 47.67720764601145 - type: nauc_precision_at_3_max value: 56.50632017305064 - type: nauc_precision_at_3_std value: 31.14175140162157 - type: nauc_precision_at_5_diff1 value: 35.10058622792819 - type: nauc_precision_at_5_max value: 51.88948872657981 - type: nauc_precision_at_5_std value: 37.62796957461928 - type: nauc_recall_at_1000_diff1 value: 79.57516339869238 - type: nauc_recall_at_1000_max value: 86.11111111111035 - type: nauc_recall_at_1000_std value: 79.57516339869238 - type: nauc_recall_at_100_diff1 value: 70.50859559510081 - type: nauc_recall_at_100_max value: 79.17009941231396 - type: nauc_recall_at_100_std value: 44.32910419069595 - type: nauc_recall_at_10_diff1 value: 66.16118569361245 - type: nauc_recall_at_10_max value: 74.73542948302286 - type: nauc_recall_at_10_std value: 27.680330939810037 - type: nauc_recall_at_1_diff1 value: 74.2356464256346 - type: nauc_recall_at_1_max value: 54.06962758957632 - type: nauc_recall_at_1_std value: 0.8037891907963532 - type: nauc_recall_at_20_diff1 value: 65.4748436545527 - type: nauc_recall_at_20_max value: 73.81532199081235 - type: nauc_recall_at_20_std value: 33.59324708196253 - type: nauc_recall_at_3_diff1 value: 68.83194804473622 - type: nauc_recall_at_3_max value: 61.77722610439669 - type: nauc_recall_at_3_std value: 13.984923756556714 - type: nauc_recall_at_5_diff1 value: 65.51467417209523 - type: nauc_recall_at_5_max value: 64.08276291427661 - type: nauc_recall_at_5_std value: 19.976472037847167 - type: ndcg_at_1 value: 60.0 - type: ndcg_at_10 value: 71.586 - type: ndcg_at_100 value: 73.76899999999999 - type: ndcg_at_1000 value: 74.386 - type: ndcg_at_20 value: 72.612 - type: ndcg_at_3 value: 66.944 - type: ndcg_at_5 value: 69.333 - type: precision_at_1 value: 60.0 - type: precision_at_10 value: 9.6 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_20 value: 5.033 - type: precision_at_3 value: 26.333000000000002 - type: precision_at_5 value: 17.4 - type: recall_at_1 value: 56.760999999999996 - type: recall_at_10 value: 84.589 - type: recall_at_100 value: 94.333 - type: recall_at_1000 value: 99.333 - type: recall_at_20 value: 88.43299999999999 - type: recall_at_3 value: 72.10600000000001 - type: recall_at_5 value: 78.194 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: mteb/trec-covid config: default split: test revision: bb9466bac8153a0349341eb1b22e06409e78ef4e metrics: - type: main_score value: 84.60600000000001 - type: map_at_1 value: 0.257 - type: map_at_10 value: 2.196 - type: map_at_100 value: 13.252 - type: map_at_1000 value: 31.473000000000003 - type: map_at_20 value: 4.023000000000001 - type: map_at_3 value: 0.722 - type: map_at_5 value: 1.146 - type: mrr_at_1 value: 94.0 - type: mrr_at_10 value: 97.0 - type: mrr_at_100 value: 97.0 - type: mrr_at_1000 value: 97.0 - type: mrr_at_20 value: 97.0 - type: mrr_at_3 value: 97.0 - type: mrr_at_5 value: 97.0 - type: nauc_map_at_1000_diff1 value: -30.674816554207062 - type: nauc_map_at_1000_max value: 53.18598689657068 - type: nauc_map_at_1000_std value: 78.88325309469121 - type: nauc_map_at_100_diff1 value: -17.6877824653978 - type: nauc_map_at_100_max value: 19.584159765315658 - type: nauc_map_at_100_std value: 48.051154190992726 - type: nauc_map_at_10_diff1 value: 20.076631089898626 - type: nauc_map_at_10_max value: -8.642556160185636 - type: nauc_map_at_10_std value: -5.768698617334298 - type: nauc_map_at_1_diff1 value: 27.342260509653798 - type: nauc_map_at_1_max value: -23.400451210297994 - type: nauc_map_at_1_std value: -21.152006353733853 - type: nauc_map_at_20_diff1 value: 8.019321726240506 - type: nauc_map_at_20_max value: -1.4826378210544222 - type: nauc_map_at_20_std value: 5.698208117745366 - type: nauc_map_at_3_diff1 value: 32.073377946749446 - type: nauc_map_at_3_max value: -13.099353983204654 - type: nauc_map_at_3_std value: -15.36319127398037 - type: nauc_map_at_5_diff1 value: 22.500045815797876 - type: nauc_map_at_5_max value: -8.548135411428023 - type: nauc_map_at_5_std value: -8.547850460331334 - type: nauc_mrr_at_1000_diff1 value: -6.022408963585526 - type: nauc_mrr_at_1000_max value: 4.481792717087155 - type: nauc_mrr_at_1000_std value: 51.6962340491753 - type: nauc_mrr_at_100_diff1 value: -6.022408963585526 - type: nauc_mrr_at_100_max value: 4.481792717087155 - type: nauc_mrr_at_100_std value: 51.6962340491753 - type: nauc_mrr_at_10_diff1 value: -6.022408963585526 - type: nauc_mrr_at_10_max value: 4.481792717087155 - type: nauc_mrr_at_10_std value: 51.6962340491753 - type: nauc_mrr_at_1_diff1 value: -6.022408963585076 - type: nauc_mrr_at_1_max value: 4.481792717087146 - type: nauc_mrr_at_1_std value: 51.69623404917518 - type: nauc_mrr_at_20_diff1 value: -6.022408963585526 - type: nauc_mrr_at_20_max value: 4.481792717087155 - type: nauc_mrr_at_20_std value: 51.6962340491753 - type: nauc_mrr_at_3_diff1 value: -6.022408963585526 - type: nauc_mrr_at_3_max value: 4.481792717087155 - type: nauc_mrr_at_3_std value: 51.6962340491753 - type: nauc_mrr_at_5_diff1 value: -6.022408963585526 - type: nauc_mrr_at_5_max value: 4.481792717087155 - type: nauc_mrr_at_5_std value: 51.6962340491753 - type: nauc_ndcg_at_1000_diff1 value: -20.79697283984295 - type: nauc_ndcg_at_1000_max value: 52.97671908009218 - type: nauc_ndcg_at_1000_std value: 75.43907707019758 - type: nauc_ndcg_at_100_diff1 value: -38.620752706946455 - type: nauc_ndcg_at_100_max value: 49.41307462381511 - type: nauc_ndcg_at_100_std value: 81.33299379244252 - type: nauc_ndcg_at_10_diff1 value: -18.611906363037356 - type: nauc_ndcg_at_10_max value: 44.20544651664479 - type: nauc_ndcg_at_10_std value: 61.322552829935816 - type: nauc_ndcg_at_1_diff1 value: 18.625935567849073 - type: nauc_ndcg_at_1_max value: -10.104132769280879 - type: nauc_ndcg_at_1_std value: 22.449560689879743 - type: nauc_ndcg_at_20_diff1 value: -30.61130208138771 - type: nauc_ndcg_at_20_max value: 52.68851710375231 - type: nauc_ndcg_at_20_std value: 69.72357683382992 - type: nauc_ndcg_at_3_diff1 value: 5.695394821691213 - type: nauc_ndcg_at_3_max value: 37.909122367102135 - type: nauc_ndcg_at_3_std value: 46.2366603255159 - type: nauc_ndcg_at_5_diff1 value: -15.273067832464731 - type: nauc_ndcg_at_5_max value: 49.7054639475091 - type: nauc_ndcg_at_5_std value: 58.83754007826166 - type: nauc_precision_at_1000_diff1 value: -31.565302588492035 - type: nauc_precision_at_1000_max value: 52.56214379514724 - type: nauc_precision_at_1000_std value: 53.40618234326055 - type: nauc_precision_at_100_diff1 value: -44.67273120709088 - type: nauc_precision_at_100_max value: 48.30381155522576 - type: nauc_precision_at_100_std value: 82.1984661602578 - type: nauc_precision_at_10_diff1 value: -24.737383556860145 - type: nauc_precision_at_10_max value: 52.816815002878556 - type: nauc_precision_at_10_std value: 67.99052410030845 - type: nauc_precision_at_1_diff1 value: -6.022408963585076 - type: nauc_precision_at_1_max value: 4.481792717087146 - type: nauc_precision_at_1_std value: 51.69623404917518 - type: nauc_precision_at_20_diff1 value: -40.23628054967093 - type: nauc_precision_at_20_max value: 56.980056980057014 - type: nauc_precision_at_20_std value: 76.60976777785895 - type: nauc_precision_at_3_diff1 value: -4.661784068466279 - type: nauc_precision_at_3_max value: 59.052007899934125 - type: nauc_precision_at_3_std value: 58.187952600394986 - type: nauc_precision_at_5_diff1 value: -38.11848143512736 - type: nauc_precision_at_5_max value: 68.6149353358365 - type: nauc_precision_at_5_std value: 73.55652899457661 - type: nauc_recall_at_1000_diff1 value: -14.886527444436345 - type: nauc_recall_at_1000_max value: 48.07492302795808 - type: nauc_recall_at_1000_std value: 65.05623212485906 - type: nauc_recall_at_100_diff1 value: -8.148385729388195 - type: nauc_recall_at_100_max value: 8.041615364614533 - type: nauc_recall_at_100_std value: 33.77187914574611 - type: nauc_recall_at_10_diff1 value: 24.333628413035942 - type: nauc_recall_at_10_max value: -14.577877145192078 - type: nauc_recall_at_10_std value: -12.131819145098557 - type: nauc_recall_at_1_diff1 value: 27.342260509653798 - type: nauc_recall_at_1_max value: -23.400451210297994 - type: nauc_recall_at_1_std value: -21.152006353733853 - type: nauc_recall_at_20_diff1 value: 13.695556376785564 - type: nauc_recall_at_20_max value: -8.872009346408264 - type: nauc_recall_at_20_std value: -3.163199444247112 - type: nauc_recall_at_3_diff1 value: 32.00442538217753 - type: nauc_recall_at_3_max value: -15.159737942664552 - type: nauc_recall_at_3_std value: -17.530833132440645 - type: nauc_recall_at_5_diff1 value: 22.64740552912405 - type: nauc_recall_at_5_max value: -12.947090597010414 - type: nauc_recall_at_5_std value: -12.914478822476807 - type: ndcg_at_1 value: 88.0 - type: ndcg_at_10 value: 84.60600000000001 - type: ndcg_at_100 value: 64.31700000000001 - type: ndcg_at_1000 value: 56.40500000000001 - type: ndcg_at_20 value: 80.561 - type: ndcg_at_3 value: 87.87700000000001 - type: ndcg_at_5 value: 86.641 - type: precision_at_1 value: 94.0 - type: precision_at_10 value: 88.2 - type: precision_at_100 value: 65.9 - type: precision_at_1000 value: 25.019999999999996 - type: precision_at_20 value: 84.7 - type: precision_at_3 value: 92.0 - type: precision_at_5 value: 90.0 - type: recall_at_1 value: 0.257 - type: recall_at_10 value: 2.338 - type: recall_at_100 value: 15.831999999999999 - type: recall_at_1000 value: 52.519000000000005 - type: recall_at_20 value: 4.367 - type: recall_at_3 value: 0.74 - type: recall_at_5 value: 1.196 - task: type: Retrieval dataset: name: MTEB Touche2020 type: mteb/touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: main_score value: 31.426 - type: map_at_1 value: 3.4709999999999996 - type: map_at_10 value: 13.236999999999998 - type: map_at_100 value: 19.521 - type: map_at_1000 value: 21.224 - type: map_at_20 value: 15.626000000000001 - type: map_at_3 value: 7.152 - type: map_at_5 value: 9.914000000000001 - type: mrr_at_1 value: 44.89795918367347 - type: mrr_at_10 value: 57.54373177842565 - type: mrr_at_100 value: 57.855267710139536 - type: mrr_at_1000 value: 57.855267710139536 - type: mrr_at_20 value: 57.70071764969724 - type: mrr_at_3 value: 52.72108843537414 - type: mrr_at_5 value: 55.06802721088435 - type: nauc_map_at_1000_diff1 value: 21.148857552115558 - type: nauc_map_at_1000_max value: 2.0837572569021323 - type: nauc_map_at_1000_std value: 3.203419709665347 - type: nauc_map_at_100_diff1 value: 21.383778167597878 - type: nauc_map_at_100_max value: 0.965767943155967 - type: nauc_map_at_100_std value: 0.3949924961020957 - type: nauc_map_at_10_diff1 value: 27.178555638086394 - type: nauc_map_at_10_max value: 4.480675175857958 - type: nauc_map_at_10_std value: -13.69553539513878 - type: nauc_map_at_1_diff1 value: 27.63901823865334 - type: nauc_map_at_1_max value: -18.6387233237763 - type: nauc_map_at_1_std value: -27.02164241863646 - type: nauc_map_at_20_diff1 value: 23.892104752374888 - type: nauc_map_at_20_max value: 3.5343136621362348 - type: nauc_map_at_20_std value: -8.765101188860816 - type: nauc_map_at_3_diff1 value: 22.065793929837493 - type: nauc_map_at_3_max value: 0.8063396680860568 - type: nauc_map_at_3_std value: -20.404849396621824 - type: nauc_map_at_5_diff1 value: 22.66626080580714 - type: nauc_map_at_5_max value: 5.423340658352383 - type: nauc_map_at_5_std value: -18.31523779843455 - type: nauc_mrr_at_1000_diff1 value: 30.520722269282665 - type: nauc_mrr_at_1000_max value: -16.644959497742267 - type: nauc_mrr_at_1000_std value: -16.3824126273053 - type: nauc_mrr_at_100_diff1 value: 30.520722269282665 - type: nauc_mrr_at_100_max value: -16.644959497742267 - type: nauc_mrr_at_100_std value: -16.3824126273053 - type: nauc_mrr_at_10_diff1 value: 30.428248939332974 - type: nauc_mrr_at_10_max value: -16.300183919261585 - type: nauc_mrr_at_10_std value: -15.404823235836309 - type: nauc_mrr_at_1_diff1 value: 27.041346572613474 - type: nauc_mrr_at_1_max value: -23.181309312755804 - type: nauc_mrr_at_1_std value: -24.33076726484014 - type: nauc_mrr_at_20_diff1 value: 30.676558567379303 - type: nauc_mrr_at_20_max value: -16.914268763031416 - type: nauc_mrr_at_20_std value: -15.77742854976336 - type: nauc_mrr_at_3_diff1 value: 31.718457109787096 - type: nauc_mrr_at_3_max value: -15.508391132202235 - type: nauc_mrr_at_3_std value: -20.33229438349494 - type: nauc_mrr_at_5_diff1 value: 28.73798376227693 - type: nauc_mrr_at_5_max value: -16.086295031060196 - type: nauc_mrr_at_5_std value: -15.644604635769321 - type: nauc_ndcg_at_1000_diff1 value: 22.158724660189606 - type: nauc_ndcg_at_1000_max value: -3.1755686809941475 - type: nauc_ndcg_at_1000_std value: 19.258386224159075 - type: nauc_ndcg_at_100_diff1 value: 21.83846748649288 - type: nauc_ndcg_at_100_max value: -10.939957598756036 - type: nauc_ndcg_at_100_std value: 14.729678880436623 - type: nauc_ndcg_at_10_diff1 value: 26.944882726098424 - type: nauc_ndcg_at_10_max value: -3.5176483833346617 - type: nauc_ndcg_at_10_std value: -5.400606773697211 - type: nauc_ndcg_at_1_diff1 value: 26.649410985172985 - type: nauc_ndcg_at_1_max value: -18.806716526067493 - type: nauc_ndcg_at_1_std value: -25.100244999343506 - type: nauc_ndcg_at_20_diff1 value: 24.860266153648315 - type: nauc_ndcg_at_20_max value: -7.521401821712892 - type: nauc_ndcg_at_20_std value: -3.3696577425983003 - type: nauc_ndcg_at_3_diff1 value: 23.9933326962406 - type: nauc_ndcg_at_3_max value: -0.4609479344284664 - type: nauc_ndcg_at_3_std value: -15.176459166869897 - type: nauc_ndcg_at_5_diff1 value: 22.50595978713142 - type: nauc_ndcg_at_5_max value: -2.1093870656000857 - type: nauc_ndcg_at_5_std value: -12.732197425528257 - type: nauc_precision_at_1000_diff1 value: -20.335120385950024 - type: nauc_precision_at_1000_max value: 26.95109729939765 - type: nauc_precision_at_1000_std value: 29.981685890622117 - type: nauc_precision_at_100_diff1 value: -2.782114329320704 - type: nauc_precision_at_100_max value: 2.9489322002048604 - type: nauc_precision_at_100_std value: 67.3074073674319 - type: nauc_precision_at_10_diff1 value: 21.385177180383383 - type: nauc_precision_at_10_max value: -2.4696365259422817 - type: nauc_precision_at_10_std value: 14.469784299536673 - type: nauc_precision_at_1_diff1 value: 27.041346572613474 - type: nauc_precision_at_1_max value: -23.181309312755804 - type: nauc_precision_at_1_std value: -24.33076726484014 - type: nauc_precision_at_20_diff1 value: 11.993846579997673 - type: nauc_precision_at_20_max value: -2.4792189693296227 - type: nauc_precision_at_20_std value: 28.581394687807745 - type: nauc_precision_at_3_diff1 value: 20.70568446328836 - type: nauc_precision_at_3_max value: 0.37326398699875984 - type: nauc_precision_at_3_std value: -12.983918676694389 - type: nauc_precision_at_5_diff1 value: 19.47466335828124 - type: nauc_precision_at_5_max value: -1.8921617684385994 - type: nauc_precision_at_5_std value: -6.533875294402164 - type: nauc_recall_at_1000_diff1 value: 7.611201305723156 - type: nauc_recall_at_1000_max value: 5.6416194035820055 - type: nauc_recall_at_1000_std value: 61.695208644278 - type: nauc_recall_at_100_diff1 value: 10.0183258158735 - type: nauc_recall_at_100_max value: -10.950612455698973 - type: nauc_recall_at_100_std value: 33.06069987640471 - type: nauc_recall_at_10_diff1 value: 24.738210305731535 - type: nauc_recall_at_10_max value: -2.6592454032071546 - type: nauc_recall_at_10_std value: -4.83987517793115 - type: nauc_recall_at_1_diff1 value: 27.63901823865334 - type: nauc_recall_at_1_max value: -18.6387233237763 - type: nauc_recall_at_1_std value: -27.02164241863646 - type: nauc_recall_at_20_diff1 value: 17.79601177409034 - type: nauc_recall_at_20_max value: -6.681637093148051 - type: nauc_recall_at_20_std value: 3.369193919932238 - type: nauc_recall_at_3_diff1 value: 24.9589431081204 - type: nauc_recall_at_3_max value: 2.4783640980500232 - type: nauc_recall_at_3_std value: -19.567415651090702 - type: nauc_recall_at_5_diff1 value: 23.71803410135437 - type: nauc_recall_at_5_max value: 1.6294309357641652 - type: nauc_recall_at_5_std value: -15.365511906408983 - type: ndcg_at_1 value: 40.816 - type: ndcg_at_10 value: 31.426 - type: ndcg_at_100 value: 41.558 - type: ndcg_at_1000 value: 53.042 - type: ndcg_at_20 value: 31.108999999999998 - type: ndcg_at_3 value: 35.518 - type: ndcg_at_5 value: 33.235 - type: precision_at_1 value: 44.897999999999996 - type: precision_at_10 value: 27.551 - type: precision_at_100 value: 8.204 - type: precision_at_1000 value: 1.582 - type: precision_at_20 value: 19.796 - type: precision_at_3 value: 36.735 - type: precision_at_5 value: 33.061 - type: recall_at_1 value: 3.4709999999999996 - type: recall_at_10 value: 19.563 - type: recall_at_100 value: 50.3 - type: recall_at_1000 value: 85.13199999999999 - type: recall_at_20 value: 26.738 - type: recall_at_3 value: 7.8420000000000005 - type: recall_at_5 value: 11.994 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 68.29850746268657 - type: ap value: 30.109785890841966 - type: ap_weighted value: 30.109785890841966 - type: f1 value: 61.76875915202924 - type: f1_weighted value: 71.32073190458556 - type: main_score value: 68.29850746268657 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification (default) type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.3068 - type: ap value: 86.17914339624038 - type: ap_weighted value: 86.17914339624038 - type: f1 value: 90.29716826358077 - type: f1_weighted value: 90.29716826358077 - type: main_score value: 90.3068 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.272000000000006 - type: f1 value: 45.57042543386915 - type: f1_weighted value: 45.57042543386915 - type: main_score value: 46.272000000000006 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P (default) type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: main_score value: 44.9469238081379 - type: v_measure value: 44.9469238081379 - type: v_measure_std value: 13.26811262671461 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S (default) type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: main_score value: 34.12071448053325 - type: v_measure value: 34.12071448053325 - type: v_measure_std value: 13.7019879046405 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions (default) type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: main_score value: 61.597667288657846 - type: map value: 61.597667288657846 - type: mrr value: 75.57940904893813 - type: nAUC_map_diff1 value: 8.745172077340095 - type: nAUC_map_max value: 20.114863024035493 - type: nAUC_map_std value: 15.991351189572192 - type: nAUC_mrr_diff1 value: 20.781369244159983 - type: nAUC_mrr_max value: 30.78542570228559 - type: nAUC_mrr_std value: 19.861484857303676 - task: type: STS dataset: name: MTEB BIOSSES (default) type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cosine_pearson value: 88.55587996301419 - type: cosine_spearman value: 86.40317357420093 - type: euclidean_pearson value: 86.93771958250231 - type: euclidean_spearman value: 86.40317357420093 - type: main_score value: 86.40317357420093 - type: manhattan_pearson value: 86.92196577117366 - type: manhattan_spearman value: 85.79834051556095 - type: pearson value: 88.55587996301419 - type: spearman value: 86.40317357420093 - task: type: Classification dataset: name: MTEB Banking77Classification (default) type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 80.0064935064935 - type: f1 value: 79.29524254086299 - type: f1_weighted value: 79.295242540863 - type: main_score value: 80.0064935064935 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P (default) type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: main_score value: 35.27186813341181 - type: v_measure value: 35.27186813341181 - type: v_measure_std value: 0.8621482145872432 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S (default) type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: main_score value: 28.411805064852295 - type: v_measure value: 28.411805064852295 - type: v_measure_std value: 0.7194290078011281 - task: type: Classification dataset: name: MTEB EmotionClassification (default) type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.675 - type: f1 value: 40.15061931375577 - type: f1_weighted value: 45.714186572727066 - type: main_score value: 43.675 - task: type: Classification dataset: name: MTEB ImdbClassification (default) type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 84.35640000000001 - type: ap value: 79.07507736685174 - type: ap_weighted value: 79.07507736685174 - type: f1 value: 84.32288494833531 - type: f1_weighted value: 84.32288494833531 - type: main_score value: 84.35640000000001 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.35658914728684 - type: f1 value: 90.86877537911086 - type: f1_weighted value: 91.3282092774443 - type: main_score value: 91.35658914728684 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 60.63611491108071 - type: f1 value: 42.78886482112741 - type: f1_weighted value: 63.44208631840539 - type: main_score value: 60.63611491108071 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 66.68796234028245 - type: f1 value: 64.44940791000278 - type: f1_weighted value: 65.77554417406792 - type: main_score value: 66.68796234028245 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 73.0598520511096 - type: f1 value: 72.14267273884774 - type: f1_weighted value: 72.93345180137516 - type: main_score value: 73.0598520511096 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P (default) type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: main_score value: 31.143081341699606 - type: v_measure value: 31.143081341699606 - type: v_measure_std value: 1.5578716347076906 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S (default) type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: main_score value: 27.010818869829556 - type: v_measure value: 27.010818869829556 - type: v_measure_std value: 1.1771554540819378 - task: type: Reranking dataset: name: MTEB MindSmallReranking (default) type: mteb/mind_small config: default split: test revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 metrics: - type: main_score value: 30.20503776754942 - type: map value: 30.20503776754942 - type: mrr value: 31.076636002733437 - type: nAUC_map_diff1 value: 7.290568655287842 - type: nAUC_map_max value: -21.381599355932945 - type: nAUC_map_std value: -7.709920607543168 - type: nAUC_mrr_diff1 value: 7.558397329284913 - type: nAUC_mrr_max value: -15.981397186427607 - type: nAUC_mrr_std value: -4.870495243168834 - task: type: Clustering dataset: name: MTEB RedditClustering (default) type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: main_score value: 51.85893476633338 - type: v_measure value: 51.85893476633338 - type: v_measure_std value: 4.704770139385852 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P (default) type: mteb/reddit-clustering-p2p config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: main_score value: 61.8124222918822 - type: v_measure value: 61.8124222918822 - type: v_measure_std value: 11.994472578100165 - task: type: STS dataset: name: MTEB SICK-R (default) type: mteb/sickr-sts config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cosine_pearson value: 77.63310776935984 - type: cosine_spearman value: 69.86468291111039 - type: euclidean_pearson value: 73.91537077798837 - type: euclidean_spearman value: 69.86468376650203 - type: main_score value: 69.86468291111039 - type: manhattan_pearson value: 73.68616048370464 - type: manhattan_spearman value: 69.76232036206659 - type: pearson value: 77.63310776935984 - type: spearman value: 69.86468291111039 - task: type: STS dataset: name: MTEB STS12 (default) type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cosine_pearson value: 57.71716838245049 - type: cosine_spearman value: 61.797855543446424 - type: euclidean_pearson value: 58.22958675325848 - type: euclidean_spearman value: 61.797855543446424 - type: main_score value: 61.797855543446424 - type: manhattan_pearson value: 57.63117544997929 - type: manhattan_spearman value: 61.3629404350085 - type: pearson value: 57.71716838245049 - type: spearman value: 61.797855543446424 - task: type: STS dataset: name: MTEB STS13 (default) type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cosine_pearson value: 82.30260026790903 - type: cosine_spearman value: 82.66959813070869 - type: euclidean_pearson value: 82.08383017580783 - type: euclidean_spearman value: 82.66959813070869 - type: main_score value: 82.66959813070869 - type: manhattan_pearson value: 81.77991451392153 - type: manhattan_spearman value: 82.3652534745606 - type: pearson value: 82.30260026790903 - type: spearman value: 82.66959813070869 - task: type: STS dataset: name: MTEB STS14 (default) type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cosine_pearson value: 71.50608384084478 - type: cosine_spearman value: 68.94968064977785 - type: euclidean_pearson value: 70.73381299949564 - type: euclidean_spearman value: 68.94968064977785 - type: main_score value: 68.94968064977785 - type: manhattan_pearson value: 70.5385486953787 - type: manhattan_spearman value: 68.82132770672365 - type: pearson value: 71.50608384084478 - type: spearman value: 68.94968064977785 - task: type: STS dataset: name: MTEB STS15 (default) type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cosine_pearson value: 73.66969825874907 - type: cosine_spearman value: 75.55374982088381 - type: euclidean_pearson value: 75.9339313749594 - type: euclidean_spearman value: 75.55374982088381 - type: main_score value: 75.55374982088381 - type: manhattan_pearson value: 75.88287553383817 - type: manhattan_spearman value: 75.50729812977688 - type: pearson value: 73.66969825874907 - type: spearman value: 75.55374982088381 - task: type: STS dataset: name: MTEB STS16 (default) type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cosine_pearson value: 74.5954724414016 - type: cosine_spearman value: 77.2688820850505 - type: euclidean_pearson value: 77.19866353971555 - type: euclidean_spearman value: 77.2688820850505 - type: main_score value: 77.2688820850505 - type: manhattan_pearson value: 77.27072603680978 - type: manhattan_spearman value: 77.29408453673607 - type: pearson value: 74.5954724414016 - type: spearman value: 77.2688820850505 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: faeb762787bd10488a50c8b5be4a3b82e411949c metrics: - type: cosine_pearson value: 71.52588722654055 - type: cosine_spearman value: 74.97235736456061 - type: euclidean_pearson value: 74.51952528854038 - type: euclidean_spearman value: 74.97235736456061 - type: main_score value: 74.97235736456061 - type: manhattan_pearson value: 74.48272300884209 - type: manhattan_spearman value: 74.80633649415176 - type: pearson value: 71.52588722654055 - type: spearman value: 74.97235736456061 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 metrics: - type: cosine_pearson value: 68.80031120401976 - type: cosine_spearman value: 69.07945196478491 - type: euclidean_pearson value: 68.99674496430792 - type: euclidean_spearman value: 69.07945196478491 - type: main_score value: 69.07945196478491 - type: manhattan_pearson value: 69.00236107775687 - type: manhattan_spearman value: 68.98064879049272 - type: pearson value: 68.80031120401976 - type: spearman value: 69.07945196478491 - task: type: STS dataset: name: MTEB STSBenchmark (default) type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cosine_pearson value: 65.6898007230089 - type: cosine_spearman value: 69.72386211803668 - type: euclidean_pearson value: 69.04523003701475 - type: euclidean_spearman value: 69.72386211803668 - type: main_score value: 69.72386211803668 - type: manhattan_pearson value: 68.80479743770702 - type: manhattan_spearman value: 69.43264575177459 - type: pearson value: 65.6898007230089 - type: spearman value: 69.72386211803668 - task: type: Reranking dataset: name: MTEB SciDocsRR (default) type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: main_score value: 79.74088066874383 - type: map value: 79.74088066874383 - type: mrr value: 94.47697455050397 - type: nAUC_map_diff1 value: 8.036086256905502 - type: nAUC_map_max value: 54.88199803816819 - type: nAUC_map_std value: 69.16267942176574 - type: nAUC_mrr_diff1 value: 50.020738477678115 - type: nAUC_mrr_max value: 83.28922770326483 - type: nAUC_mrr_std value: 83.63973501802224 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions (default) type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cosine_accuracy value: 99.83861386138614 - type: cosine_accuracy_threshold value: 74.75666999816895 - type: cosine_ap value: 96.15132792066652 - type: cosine_f1 value: 91.84890656063618 - type: cosine_f1_threshold value: 71.70594930648804 - type: cosine_precision value: 91.30434782608695 - type: cosine_recall value: 92.4 - type: dot_accuracy value: 99.83861386138614 - type: dot_accuracy_threshold value: 74.75666999816895 - type: dot_ap value: 96.15132792066653 - type: dot_f1 value: 91.84890656063618 - type: dot_f1_threshold value: 71.70596122741699 - type: dot_precision value: 91.30434782608695 - type: dot_recall value: 92.4 - type: euclidean_accuracy value: 99.83861386138614 - type: euclidean_accuracy_threshold value: 71.05395793914795 - type: euclidean_ap value: 96.15132792066652 - type: euclidean_f1 value: 91.84890656063618 - type: euclidean_f1_threshold value: 75.22505521774292 - type: euclidean_precision value: 91.30434782608695 - type: euclidean_recall value: 92.4 - type: main_score value: 96.15132792066653 - type: manhattan_accuracy value: 99.83564356435643 - type: manhattan_accuracy_threshold value: 1547.6950645446777 - type: manhattan_ap value: 96.06151211452136 - type: manhattan_f1 value: 91.61676646706587 - type: manhattan_f1_threshold value: 1626.3608932495117 - type: manhattan_precision value: 91.43426294820716 - type: manhattan_recall value: 91.8 - type: max_ap value: 96.15132792066653 - type: max_f1 value: 91.84890656063618 - type: max_precision value: 91.43426294820716 - type: max_recall value: 92.4 - type: similarity_accuracy value: 99.83861386138614 - type: similarity_accuracy_threshold value: 74.75666999816895 - type: similarity_ap value: 96.15132792066652 - type: similarity_f1 value: 91.84890656063618 - type: similarity_f1_threshold value: 71.70594930648804 - type: similarity_precision value: 91.30434782608695 - type: similarity_recall value: 92.4 - task: type: Clustering dataset: name: MTEB StackExchangeClustering (default) type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: main_score value: 61.24120328328453 - type: v_measure value: 61.24120328328453 - type: v_measure_std value: 3.9946560691100372 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P (default) type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: main_score value: 33.808268374864745 - type: v_measure value: 33.808268374864745 - type: v_measure_std value: 1.2212188701887239 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions (default) type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: main_score value: 52.19806018468037 - type: map value: 52.19806018468037 - type: mrr value: 52.98921462524404 - type: nAUC_map_diff1 value: 37.41443156995912 - type: nAUC_map_max value: 9.410262727675603 - type: nAUC_map_std value: 8.7094185014992 - type: nAUC_mrr_diff1 value: 37.78202772392581 - type: nAUC_mrr_max value: 10.517635536565816 - type: nAUC_mrr_std value: 8.509423813772491 - task: type: Summarization dataset: name: MTEB SummEval (default) type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cosine_pearson value: 30.48413700430812 - type: cosine_spearman value: 30.357162200875816 - type: dot_pearson value: 30.484140144824938 - type: dot_spearman value: 30.357162200875816 - type: main_score value: 30.357162200875816 - type: pearson value: 30.48413700430812 - type: spearman value: 30.357162200875816 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification (default) type: mteb/toxic_conversations_50k config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 66.8359375 - type: ap value: 12.482653786025985 - type: ap_weighted value: 12.482653786025985 - type: f1 value: 51.328608527332385 - type: f1_weighted value: 74.07974463955398 - type: main_score value: 66.8359375 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification (default) type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 53.907753254103 - type: f1 value: 54.22707647269581 - type: f1_weighted value: 53.611822984407695 - type: main_score value: 53.907753254103 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering (default) type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: main_score value: 38.1364789307295 - type: v_measure value: 38.1364789307295 - type: v_measure_std value: 2.0731634966352077 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 (default) type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cosine_accuracy value: 82.66674614054956 - type: cosine_accuracy_threshold value: 79.80123162269592 - type: cosine_ap value: 63.28209719072804 - type: cosine_f1 value: 60.16389710903711 - type: cosine_f1_threshold value: 72.22893834114075 - type: cosine_precision value: 52.90232185748599 - type: cosine_recall value: 69.73614775725594 - type: dot_accuracy value: 82.66674614054956 - type: dot_accuracy_threshold value: 79.8012375831604 - type: dot_ap value: 63.282103870645166 - type: dot_f1 value: 60.16389710903711 - type: dot_f1_threshold value: 72.22894430160522 - type: dot_precision value: 52.90232185748599 - type: dot_recall value: 69.73614775725594 - type: euclidean_accuracy value: 82.66674614054956 - type: euclidean_accuracy_threshold value: 63.55905532836914 - type: euclidean_ap value: 63.282095399953164 - type: euclidean_f1 value: 60.16389710903711 - type: euclidean_f1_threshold value: 74.5265781879425 - type: euclidean_precision value: 52.90232185748599 - type: euclidean_recall value: 69.73614775725594 - type: main_score value: 63.282103870645166 - type: manhattan_accuracy value: 82.74423317637242 - type: manhattan_accuracy_threshold value: 1415.380859375 - type: manhattan_ap value: 63.26931757839598 - type: manhattan_f1 value: 60.11014948859166 - type: manhattan_f1_threshold value: 1632.522201538086 - type: manhattan_precision value: 52.359506559624045 - type: manhattan_recall value: 70.55408970976254 - type: max_ap value: 63.282103870645166 - type: max_f1 value: 60.16389710903711 - type: max_precision value: 52.90232185748599 - type: max_recall value: 70.55408970976254 - type: similarity_accuracy value: 82.66674614054956 - type: similarity_accuracy_threshold value: 79.80123162269592 - type: similarity_ap value: 63.28209719072804 - type: similarity_f1 value: 60.16389710903711 - type: similarity_f1_threshold value: 72.22893834114075 - type: similarity_precision value: 52.90232185748599 - type: similarity_recall value: 69.73614775725594 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus (default) type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cosine_accuracy value: 88.10105949470253 - type: cosine_accuracy_threshold value: 68.95147562026978 - type: cosine_ap value: 84.65516103854583 - type: cosine_f1 value: 76.54581123301605 - type: cosine_f1_threshold value: 63.92929553985596 - type: cosine_precision value: 72.46526344751685 - type: cosine_recall value: 81.11333538651063 - type: dot_accuracy value: 88.10105949470253 - type: dot_accuracy_threshold value: 68.95147562026978 - type: dot_ap value: 84.65516301437592 - type: dot_f1 value: 76.54581123301605 - type: dot_f1_threshold value: 63.92928957939148 - type: dot_precision value: 72.46526344751685 - type: dot_recall value: 81.11333538651063 - type: euclidean_accuracy value: 88.10105949470253 - type: euclidean_accuracy_threshold value: 78.80169153213501 - type: euclidean_ap value: 84.65517268264233 - type: euclidean_f1 value: 76.54581123301605 - type: euclidean_f1_threshold value: 84.93610620498657 - type: euclidean_precision value: 72.46526344751685 - type: euclidean_recall value: 81.11333538651063 - type: main_score value: 84.65517268264233 - type: manhattan_accuracy value: 88.08941669577366 - type: manhattan_accuracy_threshold value: 1739.3169403076172 - type: manhattan_ap value: 84.64592398855694 - type: manhattan_f1 value: 76.62890540443034 - type: manhattan_f1_threshold value: 1861.344337463379 - type: manhattan_precision value: 72.09775967413442 - type: manhattan_recall value: 81.76778564829073 - type: max_ap value: 84.65517268264233 - type: max_f1 value: 76.62890540443034 - type: max_precision value: 72.46526344751685 - type: max_recall value: 81.76778564829073 - type: similarity_accuracy value: 88.10105949470253 - type: similarity_accuracy_threshold value: 68.95147562026978 - type: similarity_ap value: 84.65516103854583 - type: similarity_f1 value: 76.54581123301605 - type: similarity_f1_threshold value: 63.92929553985596 - type: similarity_precision value: 72.46526344751685 - type: similarity_recall value: 81.11333538651063 --- # aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF This model was converted to GGUF format from [`Snowflake/snowflake-arctic-embed-m-v1.5`](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -c 2048 ```
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
sultan/BioM-ELECTRA-Base-Discriminator
sultan
null
[ "transformers", "pytorch", "electra", "pretraining", "endpoints_compatible", "region:us" ]
1,646,263,745,000
2023-11-04T23:06:42
179
3
--- {} --- # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA # Abstract The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models. # Model Description This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 500K steps with a batch size of 1024 on TPUv3-32 unit. Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. # Colab Notebook Examples BioM-ELECTRA-LARGE on NER and ChemProt Task [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_NER_and_ChemProt_Task_on_TPU.ipynb) BioM-ELECTRA-Large on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ELECTRA_Large_on_TPU.ipynb) BioM-ALBERT-xxlarge on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ALBERT_xxlarge_on_TPU.ipynb) Text Classification Task With HuggingFace Transformers and PyTorchXLA on Free TPU [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb) Reproducing our BLURB results with JAX [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/BLURB_LeaderBoard_with_TPU_VM.ipynb) Finetunning BioM-Transformers with Jax/Flax on TPUv3-8 with free Kaggle resource [![Open In Colab][COLAB]](https://www.kaggle.com/code/sultanalrowili/biom-transoformers-with-flax-on-tpu-with-kaggle) [COLAB]: https://colab.research.google.com/assets/colab-badge.svg # Acknowledgment We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units. # Citation ```bibtex @inproceedings{alrowili-shanker-2021-biom, title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}", author = "Alrowili, Sultan and Shanker, Vijay", booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bionlp-1.24", pages = "221--227", abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.", } ```
[ "BLURB", "CHEMPROT" ]
BioNLP
Daemontatox/AetherDrake-SFT
Daemontatox
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "Llama3", "trl", "COT", "Reasoning", "conversational", "en", "dataset:Daemontatox/LongCOT-Reason", "base_model:prithivMLmods/Llama-3.1-8B-Open-SFT", "base_model:finetune:prithivMLmods/Llama-3.1-8B-Open-SFT", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,735,052,111,000
2024-12-25T21:37:39
41
1
--- base_model: - prithivMLmods/Llama-3.1-8B-Open-SFT datasets: - Daemontatox/LongCOT-Reason language: - en library_name: transformers license: apache-2.0 metrics: - accuracy - character - competition_math - code_eval pipeline_tag: text-generation tags: - text-generation-inference - transformers - unsloth - Llama3 - trl - COT - Reasoning model-index: - name: AetherDrake-SFT results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 48.13 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Daemontatox/AetherDrake-SFT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 27.14 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Daemontatox/AetherDrake-SFT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 14.65 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Daemontatox/AetherDrake-SFT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 9.4 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Daemontatox/AetherDrake-SFT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 9.97 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Daemontatox/AetherDrake-SFT name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 27.77 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Daemontatox/AetherDrake-SFT name: Open LLM Leaderboard --- ![image](./image.webp) # AetherDrake-SFT - **Developed by:** Daemontatox - **License:** Apache 2.0 - **Finetuned Using:** [Unsloth](https://github.com/unslothai/unsloth), Hugging Face Transformers, and TRL Library ## Model Overview The **AetherDrake-SFT Model** is an advanced AI system optimized for logical reasoning, multi-step problem-solving, and decision-making tasks. Designed with efficiency and accuracy in mind, it employs a structured system prompt to ensure high-quality answers through a transparent and iterative thought process. ### System Prompt and Workflow This model operates using an innovative reasoning framework structured around the following steps: 1. **Initial Thought:** The model uses `<Thinking>` tags to reason step-by-step and craft its best possible response. Example: 2. **Self-Critique:** It evaluates its initial response within `<Critique>` tags, focusing on: - **Accuracy:** Is it factually correct and verifiable? - **Clarity:** Is it clear and free of ambiguity? - **Completeness:** Does it fully address the request? - **Improvement:** What can be enhanced? Example: 3. **Revision:** Based on the critique, the model refines its response within `<Revising>` tags. Example: 4. **Final Response:** The revised response is presented clearly within `<Final>` tags. Example: 5. **Tag Innovation:** When needed, the model creates and defines new tags for better structuring or clarity, ensuring consistent usage. Example: ### Key Features - **Structured Reasoning:** Transparent, multi-step approach for generating and refining answers. - **Self-Improvement:** Built-in critique and revision ensure continuous response enhancement. - **Clarity and Adaptability:** Tagging system provides organized, adaptable responses tailored to user needs. - **Creative Flexibility:** Supports dynamic problem-solving with the ability to introduce new tags and concepts. --- ## Use Cases The model is designed for various domains, including: 1. **Research and Analysis:** Extracting insights and providing structured explanations. 2. **Education:** Assisting with tutoring by breaking down complex problems step-by-step. 3. **Problem-Solving:** Offering logical and actionable solutions for multi-step challenges. 4. **Content Generation:** Producing clear, well-organized creative or professional content. --- ## Training Details - **Frameworks:** - [Unsloth](https://github.com/unslothai/unsloth) for accelerated training. - Hugging Face Transformers and the TRL library for reinforcement learning with human feedback (RLHF). - **Dataset:** Finetuned on diverse reasoning-focused tasks, including logical puzzles, mathematical problems, and commonsense reasoning scenarios. - **Hardware Efficiency:** - Trained with bnb-4bit precision for reduced memory usage. - Optimized training pipeline achieving 2x faster development cycles. --- ## Limitations - **Arithmetic Equations** Model might hallucinate in the middle of thinking and using Arithmetic Equations as it wasn't trained on latex equations. - **Very Complex problems** Model has a tendency to get side tracked when asked long and complex problems and might answer with uncertainty. --- ## Ethical Considerations - **Transparency:** Responses are structured for verifiability through tagging. - **Bias Mitigation:** Includes self-critique to minimize biases and ensure fairness. - **Safe Deployment:** Users are encouraged to evaluate outputs to prevent harm or misinformation. --- ## License This model is distributed under the Apache 2.0 license, allowing users to use, modify, and share it in compliance with the license terms. --- ## Acknowledgments Special thanks to: - [Unsloth](https://github.com/unslothai/unsloth) for accelerated training workflows. - Hugging Face for their powerful tools and libraries. --- Experience the **AetherDrake-SFT**, leveraging its structured reasoning and self-improvement capabilities for any task requiring advanced AI reasoning. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Daemontatox__AetherDrake-SFT-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox/AetherDrake-SFT)! | Metric |% Value| |-------------------|------:| |Avg. | 22.84| |IFEval (0-Shot) | 48.13| |BBH (3-Shot) | 27.14| |MATH Lvl 5 (4-Shot)| 14.65| |GPQA (0-shot) | 9.40| |MuSR (0-shot) | 9.97| |MMLU-PRO (5-shot) | 27.77|
[ "CRAFT" ]
Non_BioNLP
mradermacher/1.5-Pints-16K-v0.1-GGUF
mradermacher
null
[ "transformers", "gguf", "en", "dataset:pints-ai/Expository-Prose-V1", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:meta-math/MetaMathQA", "dataset:HuggingFaceH4/deita-10k-v0-sft", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:togethercomputer/llama-instruct", "dataset:LDJnr/Capybara", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:pints-ai/1.5-Pints-16K-v0.1", "base_model:quantized:pints-ai/1.5-Pints-16K-v0.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]
1,741,431,552,000
2025-03-08T13:37:41
302
0
--- base_model: pints-ai/1.5-Pints-16K-v0.1 datasets: - pints-ai/Expository-Prose-V1 - HuggingFaceH4/ultrachat_200k - Open-Orca/SlimOrca-Dedup - meta-math/MetaMathQA - HuggingFaceH4/deita-10k-v0-sft - WizardLM/WizardLM_evol_instruct_V2_196k - togethercomputer/llama-instruct - LDJnr/Capybara - HuggingFaceH4/ultrafeedback_binarized language: - en library_name: transformers license: mit extra_gated_fields: Company: text Country: country I agree to use this model for in accordance to the afore-mentioned Terms of Use: checkbox I want to use this model for: options: - Research - Education - label: Other value: other type: select Specific date: date_picker extra_gated_prompt: Though best efforts has been made to ensure, as much as possible, that all texts in the training corpora are royalty free, this does not constitute a legal guarantee that such is the case. **By using any of the models, corpora or part thereof, the user agrees to bear full responsibility to do the necessary due diligence to ensure that he / she is in compliance with their local copyright laws. Additionally, the user agrees to bear any damages arising as a direct cause (or otherwise) of using any artifacts released by the pints research team, as well as full responsibility for the consequences of his / her usage (or implementation) of any such released artifacts. The user also indemnifies Pints Research Team (and any of its members or agents) of any damage, related or unrelated, to the release or subsequent usage of any findings, artifacts or code by the team. For the avoidance of doubt, any artifacts released by the Pints Research team are done so in accordance with the 'fair use' clause of Copyright Law, in hopes that this will aid the research community in bringing LLMs to the next frontier. quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q2_K.gguf) | Q2_K | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q5_K_S.gguf) | Q5_K_S | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q5_K_M.gguf) | Q5_K_M | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q6_K.gguf) | Q6_K | 1.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.Q8_0.gguf) | Q8_0 | 1.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF/resolve/main/1.5-Pints-16K-v0.1.f16.gguf) | f16 | 3.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
[ "BEAR" ]
Non_BioNLP
pyf98/librispeech_conformer
pyf98
automatic-speech-recognition
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,646,676,965,000
2022-03-07T18:33:17
1
0
--- datasets: - librispeech language: en license: cc-by-4.0 tags: - espnet - audio - automatic-speech-recognition --- ## ESPnet2 ASR model ### `pyf98/librispeech_conformer` This model was trained by Yifan Peng using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout c3569453a408fd4ff4173d9c1d2062c88d1fc060 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Mon Mar 7 12:26:10 EST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `c3569453a408fd4ff4173d9c1d2062c88d1fc060` - Commit date: `Sun Mar 6 23:58:36 2022 -0500` ## asr_train_asr_conformer8_raw_en_bpe5000_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.2/dev_clean|2703|54402|98.0|1.8|0.2|0.2|2.2|27.2| |beam60_ctc0.2/dev_other|2864|50948|95.1|4.4|0.5|0.5|5.4|43.3| |beam60_ctc0.2/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.4|28.8| |beam60_ctc0.2/test_other|2939|52343|95.2|4.3|0.5|0.6|5.4|45.5| |beam60_ctc0.2_lm0.6/dev_clean|2703|54402|98.3|1.4|0.3|0.2|1.9|23.7| |beam60_ctc0.2_lm0.6/dev_other|2864|50948|96.2|3.3|0.4|0.4|4.2|37.2| |beam60_ctc0.2_lm0.6/test_clean|2620|52576|98.2|1.5|0.3|0.2|2.0|24.3| |beam60_ctc0.2_lm0.6/test_other|2939|52343|96.1|3.3|0.6|0.4|4.4|39.9| |beam60_ctc0.3/dev_clean|2703|54402|98.1|1.8|0.2|0.2|2.1|27.3| |beam60_ctc0.3/dev_other|2864|50948|95.2|4.4|0.4|0.5|5.4|43.7| |beam60_ctc0.3/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.3|29.0| |beam60_ctc0.3/test_other|2939|52343|95.2|4.3|0.4|0.6|5.4|45.7| |beam60_ctc0.3_lm0.6/dev_clean|2703|54402|98.4|1.4|0.2|0.2|1.8|23.5| |beam60_ctc0.3_lm0.6/dev_other|2864|50948|96.2|3.4|0.4|0.4|4.1|37.4| |beam60_ctc0.3_lm0.6/test_clean|2620|52576|98.3|1.5|0.2|0.2|1.9|24.1| |beam60_ctc0.3_lm0.6/test_other|2939|52343|96.2|3.3|0.5|0.5|4.3|39.9| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.2/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|27.2| |beam60_ctc0.2/dev_other|2864|265951|98.1|1.1|0.8|0.6|2.5|43.3| |beam60_ctc0.2/test_clean|2620|281530|99.4|0.3|0.3|0.2|0.8|28.8| |beam60_ctc0.2/test_other|2939|272758|98.3|1.0|0.7|0.6|2.3|45.5| |beam60_ctc0.2_lm0.6/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|23.7| |beam60_ctc0.2_lm0.6/dev_other|2864|265951|98.4|0.9|0.7|0.5|2.1|37.2| |beam60_ctc0.2_lm0.6/test_clean|2620|281530|99.4|0.2|0.4|0.2|0.8|24.3| |beam60_ctc0.2_lm0.6/test_other|2939|272758|98.5|0.8|0.8|0.5|2.0|39.9| |beam60_ctc0.3/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|27.3| |beam60_ctc0.3/dev_other|2864|265951|98.2|1.1|0.7|0.6|2.4|43.7| |beam60_ctc0.3/test_clean|2620|281530|99.4|0.3|0.3|0.2|0.8|29.0| |beam60_ctc0.3/test_other|2939|272758|98.4|0.9|0.7|0.6|2.2|45.7| |beam60_ctc0.3_lm0.6/dev_clean|2703|288456|99.5|0.2|0.2|0.2|0.7|23.5| |beam60_ctc0.3_lm0.6/dev_other|2864|265951|98.5|0.9|0.7|0.5|2.0|37.4| |beam60_ctc0.3_lm0.6/test_clean|2620|281530|99.5|0.2|0.3|0.2|0.7|24.1| |beam60_ctc0.3_lm0.6/test_other|2939|272758|98.6|0.7|0.7|0.5|1.9|39.9| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.2/dev_clean|2703|68010|97.5|1.8|0.7|0.3|2.9|27.2| |beam60_ctc0.2/dev_other|2864|63110|94.1|4.4|1.6|0.9|6.8|43.3| |beam60_ctc0.2/test_clean|2620|65818|97.4|1.8|0.8|0.3|2.9|28.8| |beam60_ctc0.2/test_other|2939|65101|94.1|4.1|1.8|0.8|6.7|45.5| |beam60_ctc0.2_lm0.6/dev_clean|2703|68010|97.8|1.4|0.8|0.3|2.5|23.7| |beam60_ctc0.2_lm0.6/dev_other|2864|63110|95.1|3.5|1.5|0.7|5.6|37.2| |beam60_ctc0.2_lm0.6/test_clean|2620|65818|97.6|1.5|0.9|0.3|2.7|24.3| |beam60_ctc0.2_lm0.6/test_other|2939|65101|95.0|3.2|1.8|0.6|5.6|39.9| |beam60_ctc0.3/dev_clean|2703|68010|97.6|1.8|0.7|0.3|2.8|27.3| |beam60_ctc0.3/dev_other|2864|63110|94.1|4.4|1.5|0.9|6.8|43.7| |beam60_ctc0.3/test_clean|2620|65818|97.4|1.8|0.7|0.3|2.9|29.0| |beam60_ctc0.3/test_other|2939|65101|94.2|4.1|1.7|0.8|6.6|45.7| |beam60_ctc0.3_lm0.6/dev_clean|2703|68010|97.9|1.5|0.7|0.3|2.4|23.5| |beam60_ctc0.3_lm0.6/dev_other|2864|63110|95.1|3.5|1.4|0.6|5.6|37.4| |beam60_ctc0.3_lm0.6/test_clean|2620|65818|97.7|1.5|0.8|0.3|2.5|24.1| |beam60_ctc0.3_lm0.6/test_other|2939|65101|95.1|3.2|1.7|0.6|5.5|39.9| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer8.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer8_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 3 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 59673 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 35000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_960_sp/wav.scp - speech - sound - - dump/raw/train_960_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0025 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 40000 token_list: - <blank> - <unk> - ▁THE - S - ▁AND - ▁OF - ▁TO - ▁A - ▁IN - ▁I - ▁HE - ▁THAT - ▁WAS - ED - ▁IT - '''' - ▁HIS - ING - ▁YOU - ▁WITH - ▁FOR - ▁HAD - T - ▁AS - ▁HER - ▁IS - ▁BE - ▁BUT - ▁NOT - ▁SHE - D - ▁AT - ▁ON - LY - ▁HIM - ▁THEY - ▁ALL - ▁HAVE - ▁BY - ▁SO - ▁THIS - ▁MY - ▁WHICH - ▁ME - ▁SAID - ▁FROM - ▁ONE - Y - E - ▁WERE - ▁WE - ▁NO - N - ▁THERE - ▁OR - ER - ▁AN - ▁WHEN - ▁ARE - ▁THEIR - ▁WOULD - ▁IF - ▁WHAT - ▁THEM - ▁WHO - ▁OUT - M - ▁DO - ▁WILL - ▁UP - ▁BEEN - P - R - ▁MAN - ▁THEN - ▁COULD - ▁MORE - C - ▁INTO - ▁NOW - ▁VERY - ▁YOUR - ▁SOME - ▁LITTLE - ES - ▁TIME - RE - ▁CAN - ▁LIKE - LL - ▁ABOUT - ▁HAS - ▁THAN - ▁DID - ▁UPON - ▁OVER - IN - ▁ANY - ▁WELL - ▁ONLY - B - ▁SEE - ▁GOOD - ▁OTHER - ▁TWO - L - ▁KNOW - ▁GO - ▁DOWN - ▁BEFORE - A - AL - ▁OUR - ▁OLD - ▁SHOULD - ▁MADE - ▁AFTER - ▁GREAT - ▁DAY - ▁MUST - ▁COME - ▁HOW - ▁SUCH - ▁CAME - LE - ▁WHERE - ▁US - ▁NEVER - ▁THESE - ▁MUCH - ▁DE - ▁MISTER - ▁WAY - G - ▁S - ▁MAY - ATION - ▁LONG - OR - ▁AM - ▁FIRST - ▁BACK - ▁OWN - ▁RE - ▁AGAIN - ▁SAY - ▁MEN - ▁WENT - ▁HIMSELF - ▁HERE - NESS - ▁THINK - V - IC - ▁EVEN - ▁THOUGHT - ▁HAND - ▁JUST - ▁O - ▁UN - VE - ION - ▁ITS - 'ON' - ▁MAKE - ▁MIGHT - ▁TOO - K - ▁AWAY - ▁LIFE - TH - ▁WITHOUT - ST - ▁THROUGH - ▁MOST - ▁TAKE - ▁DON - ▁EVERY - F - O - ▁SHALL - ▁THOSE - ▁EYES - AR - ▁STILL - ▁LAST - ▁HOUSE - ▁HEAD - ABLE - ▁NOTHING - ▁NIGHT - ITY - ▁LET - ▁MANY - ▁OFF - ▁BEING - ▁FOUND - ▁WHILE - EN - ▁SAW - ▁GET - ▁PEOPLE - ▁FACE - ▁YOUNG - CH - ▁UNDER - ▁ONCE - ▁TELL - AN - ▁THREE - ▁PLACE - ▁ROOM - ▁YET - ▁SAME - IL - US - U - ▁FATHER - ▁RIGHT - EL - ▁THOUGH - ▁ANOTHER - LI - RI - ▁HEART - IT - ▁PUT - ▁TOOK - ▁GIVE - ▁EVER - ▁E - ▁PART - ▁WORK - ERS - ▁LOOK - ▁NEW - ▁KING - ▁MISSUS - ▁SIR - ▁LOVE - ▁MIND - ▁LOOKED - W - RY - ▁ASKED - ▁LEFT - ET - ▁LIGHT - CK - ▁DOOR - ▁MOMENT - RO - ▁WORLD - ▁THINGS - ▁HOME - UL - ▁THING - LA - ▁WHY - ▁MOTHER - ▁ALWAYS - ▁FAR - FUL - ▁WATER - CE - IVE - UR - ▁HEARD - ▁SOMETHING - ▁SEEMED - I - LO - ▁BECAUSE - OL - ▁END - ▁TOLD - ▁CON - ▁YES - ▁GOING - ▁GOT - RA - IR - ▁WOMAN - ▁GOD - EST - TED - ▁FIND - ▁KNEW - ▁SOON - ▁EACH - ▁SIDE - H - TON - MENT - ▁OH - NE - Z - LING - ▁AGAINST - TER - ▁NAME - ▁MISS - ▁QUITE - ▁WANT - ▁YEARS - ▁FEW - ▁BETTER - ENT - ▁HALF - ▁DONE - ▁ALSO - ▁BEGAN - ▁HAVING - ▁ENOUGH - IS - ▁LADY - ▁WHOLE - LESS - ▁BOTH - ▁SEEN - ▁SET - ▁WHITE - ▁COURSE - IES - ▁VOICE - ▁CALLED - ▁D - ▁EX - ATE - ▁TURNED - ▁GAVE - ▁C - ▁POOR - MAN - UT - NA - ▁DEAR - ISH - ▁GIRL - ▁MORNING - ▁BETWEEN - LED - ▁NOR - IA - ▁AMONG - MA - ▁ - ▁SMALL - ▁REST - ▁WHOM - ▁FELT - ▁HANDS - ▁MYSELF - ▁HIGH - ▁M - ▁HOWEVER - ▁HERSELF - ▁P - CO - ▁STOOD - ID - ▁KIND - ▁HUNDRED - AS - ▁ROUND - ▁ALMOST - TY - ▁SINCE - ▁G - AM - ▁LA - SE - ▁BOY - ▁MA - ▁PERHAPS - ▁WORDS - ATED - ▁HO - X - ▁MO - ▁SAT - ▁REPLIED - ▁FOUR - ▁ANYTHING - ▁TILL - ▁UNTIL - ▁BLACK - TION - ▁CRIED - RU - TE - ▁FACT - ▁HELP - ▁NEXT - ▁LOOKING - ▁DOES - ▁FRIEND - ▁LAY - ANCE - ▁POWER - ▁BROUGHT - VER - ▁FIRE - ▁KEEP - PO - FF - ▁COUNTRY - ▁SEA - ▁WORD - ▁CAR - ▁DAYS - ▁TOGETHER - ▁IMP - ▁REASON - KE - ▁INDEED - TING - ▁MATTER - ▁FULL - ▁TEN - TIC - ▁LAND - ▁RATHER - ▁AIR - ▁HOPE - ▁DA - ▁OPEN - ▁FEET - ▁EN - ▁FIVE - ▁POINT - ▁CO - OM - ▁LARGE - ▁B - ▁CL - ME - ▁GONE - ▁CHILD - INE - GG - ▁BEST - ▁DIS - UM - ▁HARD - ▁LORD - OUS - ▁WIFE - ▁SURE - ▁FORM - DE - ▁DEATH - ANT - ▁NATURE - ▁BA - ▁CARE - ▁BELIEVE - PP - ▁NEAR - ▁RO - ▁RED - ▁WAR - IE - ▁SPEAK - ▁FEAR - ▁CASE - ▁TAKEN - ▁ALONG - ▁CANNOT - ▁HEAR - ▁THEMSELVES - CI - ▁PRESENT - AD - ▁MASTER - ▁SON - ▁THUS - ▁LI - ▁LESS - ▁SUN - ▁TRUE - IM - IOUS - ▁THOUSAND - ▁MONEY - ▁W - ▁BEHIND - ▁CHILDREN - ▁DOCTOR - AC - ▁TWENTY - ▁WISH - ▁SOUND - ▁WHOSE - ▁LEAVE - ▁ANSWERED - ▁THOU - ▁DUR - ▁HA - ▁CERTAIN - ▁PO - ▁PASSED - GE - TO - ▁ARM - ▁LO - ▁STATE - ▁ALONE - TA - ▁SHOW - ▁NEED - ▁LIVE - ND - ▁DEAD - ENCE - ▁STRONG - ▁PRE - ▁TI - ▁GROUND - SH - TI - ▁SHORT - IAN - UN - ▁PRO - ▁HORSE - MI - ▁PRINCE - ARD - ▁FELL - ▁ORDER - ▁CALL - AT - ▁GIVEN - ▁DARK - ▁THEREFORE - ▁CLOSE - ▁BODY - ▁OTHERS - ▁SENT - ▁SECOND - ▁OFTEN - ▁CA - ▁MANNER - MO - NI - ▁BRING - ▁QUESTION - ▁HOUR - ▁BO - AGE - ▁ST - ▁TURN - ▁TABLE - ▁GENERAL - ▁EARTH - ▁BED - ▁REALLY - ▁SIX - 'NO' - IST - ▁BECOME - ▁USE - ▁READ - ▁SE - ▁VI - ▁COMING - ▁EVERYTHING - ▁EM - ▁ABOVE - ▁EVENING - ▁BEAUTIFUL - ▁FEEL - ▁RAN - ▁LEAST - ▁LAW - ▁ALREADY - ▁MEAN - ▁ROSE - WARD - ▁ITSELF - ▁SOUL - ▁SUDDENLY - ▁AROUND - RED - ▁ANSWER - ICAL - ▁RA - ▁WIND - ▁FINE - ▁WON - ▁WHETHER - ▁KNOWN - BER - NG - ▁TA - ▁CAPTAIN - ▁EYE - ▁PERSON - ▁WOMEN - ▁SORT - ▁ASK - ▁BROTHER - ▁USED - ▁HELD - ▁BIG - ▁RETURNED - ▁STRANGE - ▁BU - ▁PER - ▁FREE - ▁EITHER - ▁WITHIN - ▁DOUBT - ▁YEAR - ▁CLEAR - ▁SIGHT - ▁GRA - ▁LOST - ▁KEPT - ▁F - PE - ▁BAR - ▁TOWN - ▁SLEEP - ARY - ▁HAIR - ▁FRIENDS - ▁DREAM - ▁FELLOW - PER - ▁DEEP - QUE - ▁BECAME - ▁REAL - ▁PAST - ▁MAKING - RING - ▁COMP - ▁ACT - ▁BAD - HO - STER - ▁YE - ▁MEANS - ▁RUN - MEN - ▁DAUGHTER - ▁SENSE - ▁CITY - ▁SOMETIMES - ▁TOWARDS - ▁ROAD - ▁SP - ▁LU - ▁READY - ▁FOOT - ▁COLD - ▁SA - ▁LETTER - ▁ELSE - ▁MAR - ▁STA - BE - ▁TRUTH - ▁LE - BO - ▁BUSINESS - CHE - ▁JOHN - ▁SUBJECT - ▁COURT - ▁IDEA - ILY - ▁RIVER - ATING - ▁FAMILY - HE - ▁DIDN - ▁GLAD - ▁SEVERAL - IAL - ▁UNDERSTAND - ▁SC - ▁POSSIBLE - ▁DIFFERENT - ▁RETURN - ▁ARMS - ▁LOW - ▁HOLD - ▁TALK - ▁RU - ▁WINDOW - ▁INTEREST - ▁SISTER - SON - ▁SH - ▁BLOOD - ▁SAYS - ▁CAP - ▁DI - ▁HUMAN - ▁CAUSE - NCE - ▁THANK - ▁LATE - GO - ▁CUT - ▁ACROSS - ▁STORY - NT - ▁COUNT - ▁ABLE - DY - LEY - ▁NUMBER - ▁STAND - ▁CHURCH - ▁THY - ▁SUPPOSE - LES - BLE - OP - ▁EFFECT - BY - ▁K - ▁NA - ▁SPOKE - ▁MET - ▁GREEN - ▁HUSBAND - ▁RESPECT - ▁PA - ▁FOLLOWED - ▁REMEMBER - ▁LONGER - ▁AGE - ▁TAKING - ▁LINE - ▁SEEM - ▁HAPPY - LAND - EM - ▁STAY - ▁PLAY - ▁COMMON - ▁GA - ▁BOOK - ▁TIMES - ▁OBJECT - ▁SEVEN - QUI - DO - UND - ▁FL - ▁PRETTY - ▁FAIR - WAY - ▁WOOD - ▁REACHED - ▁APPEARED - ▁SWEET - ▁FALL - BA - ▁PASS - ▁SIGN - ▁TREE - IONS - ▁GARDEN - ▁ILL - ▁ART - ▁REMAIN - ▁OPENED - ▁BRIGHT - ▁STREET - ▁TROUBLE - ▁PAIN - ▁CONTINUED - ▁SCHOOL - OUR - ▁CARRIED - ▁SAYING - HA - ▁CHANGE - ▁FOLLOW - ▁GOLD - ▁SW - ▁FEELING - ▁COMMAND - ▁BEAR - ▁CERTAINLY - ▁BLUE - ▁NE - CA - ▁WILD - ▁ACCOUNT - ▁OUGHT - UD - ▁T - ▁BREATH - ▁WANTED - ▁RI - ▁HEAVEN - ▁PURPOSE - ▁CHARACTER - ▁RICH - ▁PE - ▁DRESS - OS - FA - ▁TH - ▁ENGLISH - ▁CHANCE - ▁SHIP - ▁VIEW - ▁TOWARD - AK - ▁JOY - ▁JA - ▁HAR - ▁NEITHER - ▁FORCE - ▁UNCLE - DER - ▁PLAN - ▁PRINCESS - DI - ▁CHIEF - ▁HAT - ▁LIVED - ▁AB - ▁VISIT - ▁MOR - TEN - ▁WALL - UC - ▁MINE - ▁PLEASURE - ▁SMILE - ▁FRONT - ▁HU - ▁DEAL - OW - ▁FURTHER - GED - ▁TRIED - DA - VA - ▁NONE - ▁ENTERED - ▁QUEEN - ▁PAY - ▁EL - ▁EXCEPT - ▁SHA - ▁FORWARD - ▁EIGHT - ▁ADDED - ▁PUBLIC - ▁EIGHTEEN - ▁STAR - ▁HAPPENED - ▁LED - ▁WALKED - ▁ALTHOUGH - ▁LATER - ▁SPIRIT - ▁WALK - ▁BIT - ▁MEET - LIN - ▁FI - LT - ▁MOUTH - ▁WAIT - ▁HOURS - ▁LIVING - ▁YOURSELF - ▁FAST - ▁CHA - ▁HALL - ▁BEYOND - ▁BOAT - ▁SECRET - ENS - ▁CHAIR - RN - ▁RECEIVED - ▁CAT - RESS - ▁DESIRE - ▁GENTLEMAN - UGH - ▁LAID - EVER - ▁OCCASION - ▁WONDER - ▁GU - ▁PARTY - DEN - ▁FISH - ▁SEND - ▁NEARLY - ▁TRY - CON - ▁SEEMS - RS - ▁BELL - ▁BRA - ▁SILENCE - IG - ▁GUARD - ▁DIE - ▁DOING - ▁TU - ▁COR - ▁EARLY - ▁BANK - ▁FIGURE - IF - ▁ENGLAND - ▁MARY - ▁AFRAID - LER - ▁FO - ▁WATCH - ▁FA - ▁VA - ▁GRE - ▁AUNT - PED - ▁SERVICE - ▁JE - ▁PEN - ▁MINUTES - ▁PAN - ▁TREES - NED - ▁GLASS - ▁TONE - ▁PLEASE - ▁FORTH - ▁CROSS - ▁EXCLAIMED - ▁DREW - ▁EAT - ▁AH - ▁GRAVE - ▁CUR - PA - URE - CENT - ▁MILES - ▁SOFT - ▁AGO - ▁POSITION - ▁WARM - ▁LENGTH - ▁NECESSARY - ▁THINKING - ▁PICTURE - ▁PI - SHIP - IBLE - ▁HEAVY - ▁ATTENTION - ▁DOG - ABLY - ▁STANDING - ▁NATURAL - ▁APPEAR - OV - ▁CAUGHT - VO - ISM - ▁SPRING - ▁EXPERIENCE - ▁PAT - OT - ▁STOPPED - ▁REGARD - ▁HARDLY - ▁SELF - ▁STRENGTH - ▁GREW - ▁KNIGHT - ▁OPINION - ▁WIDE - ▁INSTEAD - ▁SOUTH - ▁TRANS - ▁CORNER - ▁LEARN - ▁ISLAND - ▁MI - ▁THIRD - ▁STE - ▁STRAIGHT - ▁TEA - ▁BOUND - ▁SEEING - ▁JU - ▁DINNER - ▁BEAUTY - ▁PEACE - AH - ▁REP - ▁SILENT - ▁CRE - ALLY - RIC - ▁STEP - ▁VER - ▁JO - GER - ▁SITTING - ▁THIRTY - ▁SAVE - ENED - ▁GLANCE - ▁REACH - ▁ACTION - ▁SAL - ▁SAD - ▁STONE - ITIES - ▁FRENCH - ▁STRUCK - ▁PAPER - ▁WHATEVER - ▁SUB - ▁DISTANCE - ▁WRONG - ▁KNOWLEDGE - ▁SAFE - ▁SNOW - ▁MUSIC - ▁FIFTY - RON - ▁ATTEMPT - ▁GOVERNMENT - TU - ▁CROWD - ▁BESIDES - ▁LOVED - ▁BOX - ▁DIRECTION - ▁TRAIN - ▁NORTH - ▁THICK - ▁GETTING - AV - ▁FLOOR - ▁COMPANY - ▁BLOW - ▁PLAIN - TRO - ▁BESIDE - ▁ROCK - ▁IMMEDIATELY - FI - ▁SHADOW - ▁SIT - ORS - ILE - ▁DRINK - ▁SPOT - ▁DANGER - ▁AL - ▁SAINT - ▁SLOWLY - ▁PALACE - IER - ▁RESULT - ▁PETER - ▁FOREST - ▁BELONG - ▁SU - ▁PAR - RIS - ▁TEARS - ▁APPEARANCE - ▁GATE - BU - ITION - ▁QUICKLY - ▁QUIET - ▁LONDON - ▁START - ▁BROWN - TRA - KIN - ▁CONSIDER - ▁BATTLE - ▁ANNE - ▁PIECE - ▁DIED - ▁SUCCESS - ▁LIPS - ▁FILLED - ▁FORGET - ▁POST - IFIED - ▁MARGARET - ▁FOOD - HAM - ▁PLEASANT - ▁FE - ▁EXPRESSION - ▁POCKET - ▁FRESH - ▁WEAR - TRI - ▁BROKEN - ▁LAUGHED - GING - ▁FOLLOWING - WN - IP - ▁TOUCH - ▁YOUTH - ATIVE - ▁LEG - ▁WEEK - ▁REMAINED - ▁EASY - NER - RK - ▁ENTER - ▁FIGHT - ▁PLACED - ▁TRAVEL - ▁SIMPLE - ▁GIRLS - ▁WAITING - ▁STOP - ▁WAVE - AU - ▁WISE - ▁CAMP - TURE - UB - ▁VE - ▁OFFICE - ▁GRAND - ▁FIT - ▁JUDGE - UP - MENTS - ▁QUICK - HI - ▁FLO - RIES - VAL - ▁COMFORT - ▁PARTICULAR - ▁STARTED - ▁SUIT - ▁NI - ▁PALE - ▁IMPOSSIBLE - ▁HOT - ▁CONVERSATION - ▁SCENE - ▁BOYS - ▁WIN - ▁BRE - ▁SOCIETY - ▁OUTSIDE - ▁WRITE - ▁EFFORT - ▁TALKING - ▁FORTUNE - ▁NINE - ▁WA - ▁SINGLE - ▁RULE - ▁PORT - ▁WINTER - ▁CAST - ▁CRA - ▁HAPPEN - ▁CRO - ▁SHUT - NING - ▁GUN - ▁NOBLE - ▁BEGIN - ▁PATH - ▁SKY - ▁WONDERFUL - ▁SUDDEN - ▁ARMY - ▁CHE - ▁WORTH - ▁MOUNTAIN - ▁MIN - AG - ▁FLU - ▁GRACE - ▁CHAPTER - ▁BELOW - ▁RING - ▁TURNING - ▁IRON - ▁TOP - ▁AFTERNOON - ORY - ▁EVIL - ▁TRUST - ▁BOW - ▁TRI - ▁SAIL - ▁CONTENT - ▁HORSES - ITE - ▁SILVER - AP - ▁LAD - ▁RUNNING - ▁HILL - ▁BEGINNING - ▁MAD - ▁HABIT - GRA - ▁CLOTHES - ▁MORROW - ▁CRY - ▁FASHION - ▁PRESENCE - ▁Z - FE - ▁ARRIVED - ▁QUARTER - ▁PERFECT - ▁WO - ▁TRA - ▁USUAL - ▁NECK - ▁MARRIED - ▁SEAT - ▁WI - ▁GAR - ▁SAND - ▁SHORE - ▁GIVING - NY - ▁PROBABLY - ▁MINUTE - ▁EXPECT - ▁DU - ▁SHOT - ▁INSTANT - ▁DEGREE - ▁COLOR - ▁WEST - RT - ▁MARCH - ▁BIRD - ▁SHOWED - ▁GREATER - ▁SERIOUS - ▁CARRY - ▁COVERED - ▁FORMER - ▁LOUD - ▁MOVED - ▁MASS - ▁SEEK - ▁CHO - GEN - ▁ROMAN - IB - ▁MOON - ▁BOARD - ▁STREAM - ▁EASILY - ▁WISHED - ▁SEARCH - ▁COULDN - ▁MONTHS - ▁SICK - LIE - ▁DUTY - ▁TWELVE - ▁FAINT - ▁STRANGER - ▁SURPRISE - ▁KILL - ▁LEAVING - ▁JOURNEY - ▁SCARCELY - ▁RAISED - ▁SPEAKING - ▁TERRIBLE - ▁TOM - ▁FIELD - ▁GAME - ▁QUA - ▁PROMISE - ▁LIE - ▁CONDITION - ▁TRO - ▁PERSONAL - ▁TALL - ▁STICK - ▁THREW - ▁MARRY - ▁VAN - ▁BURN - ▁ACCORDING - ▁RISE - ▁ATTACK - ▁SWORD - ▁GUESS - ▁THOUGHTS - ▁THIN - ▁THROW - ▁CALM - SIDE - ▁VILLAGE - ▁DEN - ▁ANXIOUS - ▁MER - GI - ▁EXPECTED - ▁BALL - ▁ESPECIALLY - ▁CHARGE - ▁MEASURE - ISE - ▁NICE - ▁TRYING - ▁ALLOW - ▁SHARP - ▁BREAD - ▁HONOUR - ▁HONOR - ▁ENTIRELY - ▁BILL - ▁BRI - ▁WRITTEN - ▁AR - ▁BROKE - ▁KILLED - ▁MARK - ▁VEN - ▁LADIES - ▁LEARNED - ▁FLOWERS - PLE - ▁FORTY - ▁OFFER - ▁HAPPINESS - ▁PRAY - ▁CLASS - ▁FER - ▁PRINCIPLE - GU - ▁BOOKS - ▁SHAPE - ▁SUMMER - ▁JACK - ▁DRAW - ▁GOLDEN - ▁DECIDED - ▁LEAD - ▁UNLESS - ▁HARM - ▁LISTEN - HER - ▁SHOOK - ▁INFLUENCE - ▁PERFECTLY - ▁MARRIAGE - ▁BROAD - ▁ESCAPE - ▁STATES - ▁MIDDLE - ▁PLANT - ▁MIL - ▁MOVEMENT - ▁NOISE - ▁ENEMY - ▁HISTORY - ▁BREAK - ROUS - ▁UNDERSTOOD - ▁LATTER - FER - ▁COMES - ▁MERELY - ▁SIMPLY - WI - ▁IMAGINE - ▁LOWER - ▁CONDUCT - ▁BORN - WA - ▁YARD - ▁KA - ▁CLOSED - ▁NOTE - GA - ▁STRA - RAN - ▁EXIST - EV - ▁SPEECH - ▁BITTER - JO - ▁MAKES - ▁GRASS - ▁REPLY - ▁CHANGED - ▁MON - ▁LYING - ▁DANCE - ▁FINALLY - ▁AMERICAN - ▁ENJOY - ▁CONTAIN - ▁MEANT - USE - ▁OBSERVED - THER - ▁LAUGH - ▁AFTERWARDS - ▁BEAT - ▁RACE - ▁EQUAL - ▁RAIN - PS - ▁STEPS - ▁BENEATH - ▁TAIL - ▁TASTE - IO - EY - ▁CHAR - ▁GE - GN - TIN - ▁GROW - ▁TE - IANS - ▁MOVE - ▁REPEATED - ▁DRIVE - TUR - ▁SI - CLOCK - ▁BRAVE - ▁MADAME - ▁LOT - ▁CASTLE - ▁HI - AND - ▁FUTURE - ▁RELATION - ▁SORRY - ▁HEALTH - ▁DICK - ▁R - ▁BUILDING - ▁EDGE - ▁BLESS - ▁SPITE - WE - ▁MIS - ▁PRISONER - ▁ALLOWED - ▁PH - ▁CATCH - MER - ETH - ▁COAT - ▁COMPLETE - ▁WOULDN - ▁CREATURE - ▁YELLOW - ▁IMPORTANT - ▁ADD - ▁PASSING - ▁DARKNESS - ▁CARRIAGE - ▁MILL - ▁FIFTEEN - NCY - ▁HUNG - ▁OB - ▁PLEASED - ▁SPREAD - ▁CURIOUS - ▁WORSE - ▁CIRCUMSTANCES - ▁GI - LAR - ▁CAL - ▁HY - ▁MERE - ▁JANE - ▁EAST - BI - ▁CUP - ▁BLIND - ▁PASSION - ▁DISCOVERED - ▁NOTICE - ▁REPORT - ▁SPACE - ▁PRESENTLY - ▁SORROW - ▁PACK - ▁DIN - CY - ▁DRY - ▁ANCIENT - ▁DRESSED - ▁COVER - ▁VO - ▁EXISTENCE - ▁EXACTLY - ▁BEAST - ▁PROPER - ▁DROPPED - ▁CLEAN - ▁COLOUR - ▁HOST - ▁CHAMBER - ▁FAITH - LET - ▁DETERMINED - ▁PRIEST - ▁STORM - ▁SKIN - ▁DARE - ▁PERSONS - ▁PICK - ▁NARROW - ▁SUPPORT - ▁PRIVATE - ▁SMILED - ▁COUSIN - ▁DRAWING - ▁ATTEND - ▁COOK - ▁PREVENT - ▁VARIOUS - ▁BLA - ▁FIXED - ▁WEAK - THE - ▁HOLE - ▁BOTTOM - ▁NOBODY - ADE - ▁LEGS - ITCH - ▁INDIVIDUAL - ▁EARS - LIKE - ▁ADVANTAGE - ▁FRANCE - ▁BON - ▁WINE - ▁LIVES - OD - ▁WALLS - ▁TIRED - ▁SHOP - ▁ANIMAL - ▁CRU - ▁WROTE - ▁ROYAL - ▁CONSIDERED - ▁MORAL - ▁COMPANION - ▁LOSE - ▁ISN - ▁BAG - ▁LAKE - ▁INTER - ▁COM - ▁LETTERS - ▁LUCK - ▁EAR - ▁GERMAN - ▁PET - ▁SAKE - ▁DROP - ▁PAID - ▁BREAKFAST - ▁LABOR - ▁DESERT - ▁DECLARED - ▁HUM - ▁STUDY - ▁INSTANCE - ONE - ▁SOMEWHAT - ▁CLOTH - ▁SPECIAL - ▁COLONEL - ▁SONG - ▁MAIN - ▁VALUE - ▁PROUD - ▁EXPRESS - ▁NATION - ▁HANDSOME - ▁CONFESS - ▁PU - ▁PASSAGE - ▁PERIOD - ▁CUSTOM - ▁HURT - ▁SHOULDER - ▁CHRIST - ZA - ▁RECEIVE - ▁DIFFICULT - ▁DEPEND - ▁MEETING - ▁CHI - ▁GEN - LIGHT - ▁BELIEVED - ▁SOCIAL - ▁DIFFICULTY - ▁GREATEST - ▁DRAWN - ▁GRANT - ▁BIRDS - ▁ANGRY - ▁HEAT - UFF - ▁DUE - ▁PLACES - ▁SIN - ▁COURAGE - ▁EVIDENTLY - ▁GENTLE - ▁CRUEL - ▁GEORGE - ▁GRI - ▁SERVANT - ▁U - ▁PURE - OOK - ▁KNOWS - ▁KNOWING - LF - ▁WRITING - ▁REMEMBERED - ▁CU - ▁HOLDING - ▁TENDER - ▁QUI - ▁BURST - ▁SURELY - IGN - ▁VALLEY - ▁FU - ▁BUTTER - ▁SPOKEN - ▁STORE - ▁DISC - ▁CHRISTIAN - ▁PARIS - ▁HENRY - ▁FINISHED - ▁PROVE - ▁FOOL - ▁SOLDIERS - ▁LANGUAGE - ▁INSIDE - ▁BAN - ▁FALLEN - ROW - ▁MAL - ▁BABY - ▁SITUATION - ▁WATCHED - ANS - ▁RUIN - ▁GENTLEMEN - ▁FRO - ▁FANCY - ▁ACCEPT - ▁SEASON - ▁OURSELVES - ▁SAN - ▁SPEED - IZED - ▁COOL - ▁SERVE - ▁VESSEL - ▁WILLIAM - ▁OBLIGED - ▁GROUP - FORM - ▁GOES - UOUS - ▁LEAVES - ▁PECULIAR - ▁NEWS - ▁VAIN - ▁EVERYBODY - ▁PIN - UG - ▁FORGOTTEN - ▁FRA - GAN - ▁CAREFULLY - ▁FLASH - UCH - ▁FUR - ▁MURDER - ▁DELIGHT - ▁WAITED - ▁RENDER - ▁PROPERTY - ▁NOTICED - ▁ROLL - ▁KNOCK - ▁EARNEST - KI - ▁HONEST - ▁PROMISED - ▁BAL - AW - ▁WALKING - ANG - ▁SQUARE - ▁QUIETLY - ▁CLOUD - WOOD - ▁FORMED - ▁HIGHER - ▁BUILT - ▁FATE - ▁TEACH - MY - ▁FALSE - ▁YORK - ▁DUST - ▁CLIMB - ▁FOND - ▁GROWN - ▁DESCEND - ▁RAG - ▁FRUIT - ▁GENERALLY - ▁OFFERED - ▁ER - ▁NURSE - POSE - ▁SPENT - ▁JOIN - ▁STATION - ▁MEANING - ▁SMOKE - HOOD - ▁ROUGH - JU - ▁LIKELY - ▁SURFACE - ▁KE - ▁MONTH - ▁POSSESSION - ▁TONGUE - ▁DUKE - ▁NOSE - ▁LAUGHING - ▁WEATHER - ▁WHISPERED - ▁SYSTEM - ▁LAWS - DDLE - ▁TOUCHED - ▁TRADE - LD - ▁SURPRISED - RIN - ▁ARCH - ▁WEALTH - FOR - ▁TEMPER - ▁FRANK - ▁GAL - ▁BARE - ▁OPPORTUNITY - ▁CLAIM - ▁ANIMALS - ▁REV - ▁COST - ▁WASH - ZE - ▁CORN - ▁OPPOSITE - ▁POLICE - ▁IDEAS - LON - ▁KEY - ▁READING - ▁COLLECT - CHED - ▁H - ▁CROWN - ▁TAR - ▁SWIFT - ▁SHOULDERS - ▁ICE - ▁GRAY - ▁SHARE - ▁PREPARED - ▁GRO - ▁UND - ▁TER - ▁EMPTY - CING - ▁SMILING - ▁AVOID - ▁DIFFERENCE - ▁EXPLAIN - ▁POUR - ▁ATTRACT - ▁OPENING - ▁WHEEL - ▁MATERIAL - ▁BREAST - ▁SUFFERING - ▁DISTINCT - ▁BOOT - ▁ROW - ▁FINGERS - HAN - ▁ALTOGETHER - ▁FAT - ▁PAPA - ▁BRAIN - ▁ASLEEP - ▁GREY - ▁SUM - ▁GAS - ▁WINDOWS - ▁ALIVE - ▁PROCEED - ▁FLOWER - ▁LEAP - ▁PUR - ▁PIECES - ▁ALTER - ▁MEMORY - IENT - ▁FILL - ▁CLO - ▁THROWN - ▁KINGDOM - ▁RODE - IUS - ▁MAID - ▁DIM - ▁BAND - ▁VIRTUE - ▁DISH - ▁GUEST - ▁LOSS - ▁CAUSED - ▁MOTION - ▁POT - ▁MILLION - ▁FAULT - ▁LOVELY - ▁HERO - PPING - ▁UNITED - ▁SPI - SOME - BRA - ▁MOUNTAINS - ▁NU - ▁SATISFIED - ▁DOLLARS - ▁LOVER - ▁CONCEAL - ▁VAST - ▁PULL - ▁HATH - ▁RUSH - ▁J - ▁DESPAIR - EX - ▁HEIGHT - ▁CE - ▁BENT - ▁PITY - ▁RISING - ATH - ▁PRIDE - ▁HURRY - KA - ▁SETTLED - ▁JUSTICE - ▁LIFTED - PEN - ▁SOLDIER - ▁FINDING - ▁REMARK - ▁REGULAR - ▁STRUGGLE - ▁MACHINE - ▁SING - ▁HURRIED - ▁SUFFICIENT - ▁REPRESENT - ▁DOUBLE - ▁ALARM - ▁SUPPER - ▁DREADFUL - ▁FORE - ATOR - ▁STOCK - ▁TIN - ▁EXAMPLE - ▁ROOF - ▁FLOW - ▁SUPPOSED - ▁PRESERV - ▁L - ▁LISTENED - OC - ▁STO - ▁SECURE - ▁FRIGHTENED - ▁DISTURB - ▁EMOTION - ▁SERVANTS - ▁YO - ▁BUY - ▁FORCED - ▁KITCHEN - ▁TERROR - ▁STAIRS - ▁SIXTY - KER - ▁ORDINARY - ▁DIRECTLY - ▁HEADS - ▁METHOD - ▁FORGIVE - ▁AWFUL - ▁REFLECT - ▁GREATLY - ▁TALKED - ▁RIDE - STONE - ▁FAVOUR - ▁WELCOME - ▁SEIZED - OU - ▁CONTROL - ▁ORDERED - ▁ANGEL - ▁USUALLY - ▁POET - ▁BOLD - LINE - ▁ADVENTURE - ▁WATCHING - ▁FOLK - ▁MISTRESS - IZE - ▁GROWING - ▁CAVE - ▁EVIDENCE - ▁FINGER - ▁SEVENTEEN - ▁MOVING - EOUS - ▁DOESN - ▁COW - ▁TYPE - ▁BOIL - ▁TALE - ▁DELIVER - ▁FARM - ▁MONSIEUR - ▁GATHERED - ▁FEELINGS - ▁RATE - ▁REMARKED - ▁PUTTING - ▁MAT - ▁CONTRARY - ▁CRIME - ▁PLA - ▁COL - ▁NEARER - TES - ▁CIVIL - ▁SHAME - ▁LOOSE - ▁DISCOVER - ▁FLAT - ▁TWICE - ▁FAIL - VIS - ▁UNC - EA - ▁EUROPE - ▁PATIENT - ▁UNTO - ▁SUFFER - ▁PAIR - ▁TREASURE - OSE - ▁EAGER - ▁FLY - ▁N - ▁VAL - ▁DAN - ▁SALT - ▁BORE - BBE - ▁ARTHUR - ▁AFFAIRS - ▁SLOW - ▁CONSIST - ▁DEVIL - LAN - ▁AFFECTION - ▁ENGAGED - ▁KISS - ▁YA - ▁OFFICER - IFICATION - ▁LAMP - ▁PARTS - HEN - ▁MILK - ▁PROCESS - ▁GIFT - ▁PULLED - ▁HID - ▁RAY - ▁EXCELLENT - ▁IMPRESSION - ▁AUTHORITY - ▁PROVED - ▁TELLING - TTE - ▁TOWER - ▁CONSEQUENCE - ▁FAVOR - ▁FLEW - ▁CHARLES - ISTS - ▁ADDRESS - ▁FAMILIAR - ▁LIMIT - ▁CONFIDENCE - ▁RARE - ▁WEEKS - ▁WOODS - ▁INTENTION - ▁DIRECT - ▁PERFORM - ▁SOLEMN - ▁DISTANT - ▁IMAGE - ▁PRESIDENT - ▁FIRM - ▁INDIAN - ▁RANK - ▁LIKED - ▁AGREE - ▁HOUSES - ▁WIL - ▁MATTERS - ▁PRISON - ▁MODE - ▁MAJOR - ▁WORKING - ▁SLIP - ▁WEIGHT - ▁AWARE - ▁BUSY - ▁LOOKS - ▁WOUND - ▁THOR - ▁BATH - ▁EXERCISE - ▁SIMILAR - ▁WORE - ▁AMOUNT - ▁QUESTIONS - ▁VIOLENT - ▁EXCUSE - ▁ASIDE - ▁TUR - ▁DULL - OF - ▁EMPEROR - ▁NEVERTHELESS - ▁SHOUT - ▁EXPLAINED - ▁SIZE - ▁ACCOMPLISH - FORD - CAN - ▁MISTAKE - ▁INSTANTLY - ▁SMOOTH - ▁STRIKE - ▁BOB - ISED - ▁HORROR - ▁SCIENCE - ▁PROTEST - ▁MANAGE - ▁OBEY - ▁NECESSITY - ▁SPLENDID - ▁PRESS - ▁INTERESTING - ▁RELIGION - ▁UNKNOWN - ▁FIERCE - ▁DISAPPEARED - ▁HOLY - ▁HATE - ▁PLAYED - ▁LIN - ▁NATURALLY - ▁DROVE - ▁LOUIS - TIES - ▁BRAND - INESS - RIE - ▁SHOOT - ▁CONSENT - ▁SEATED - ▁LINES - GUE - ▁AGREED - ▁CIRCLE - ▁STIR - ▁STREETS - ▁TASK - ▁RID - ▁PRODUCED - ▁ACCIDENT - ▁WITNESS - ▁LIBERTY - ▁DETAIL - ▁MINISTER - ▁POWERFUL - ▁SAVAGE - ▁SIXTEEN - ▁PRETEND - ▁COAST - ▁SQU - ▁UTTER - ▁NAMED - ▁CLEVER - ▁ADMIT - ▁COUPLE - ▁WICKED - ▁MESSAGE - ▁TEMPLE - ▁STONES - ▁YESTERDAY - ▁HILLS - DAY - ▁SLIGHT - ▁DIAMOND - ▁POSSIBLY - ▁AFFAIR - ▁ORIGINAL - ▁HEARING - ▁WORTHY - ▁SELL - NEY - ICK - ▁COTTAGE - ▁SACRIFICE - ▁PROGRESS - ▁SHOCK - ▁DESIGN - ▁SOUGHT - ▁PIT - ▁SUNDAY - ▁OTHERWISE - ▁CABIN - ▁PRAYER - ▁DWELL - ▁GAIN - ▁BRIDGE - ▁PARTICULARLY - ▁YIELD - ▁TREAT - RIGHT - ▁OAK - ▁ROPE - WIN - ▁ORDERS - ▁SUSPECT - ▁EDWARD - AB - ▁ELEVEN - ▁TEETH - ▁OCCURRED - DDING - ▁AMERICA - ▁FALLING - ▁LION - ▁DEPART - ▁KEEPING - ▁DEMAND - ▁PAUSED - ▁CEASED - INA - ▁FUN - ▁CHEER - ▁PARDON - ▁NATIVE - LUS - LOW - ▁DOGS - ▁REQUIRED - ILITY - ▁ELECT - ▁ENTERTAIN - ITUDE - ▁HUGE - ▁CARRYING - ▁BLU - ▁INSIST - ▁SATISFACTION - ▁HUNT - ▁COUNTENANCE - ▁UPPER - ▁MAIDEN - ▁FAILED - ▁JAMES - ▁FOREIGN - ▁GATHER - ▁TEST - BOARD - ▁TERMS - ▁SILK - ▁BEG - ▁BROTHERS - ▁PAGE - ▁KNEES - ▁SHOWN - ▁PROFESSOR - ▁MIGHTY - ▁DEFI - ▁CHARM - ▁REQUIRE - ▁LOG - MORE - ▁PROOF - ▁POSSESSED - ▁SOFTLY - ▁UNFORTUNATE - ▁PRICE - ▁SEVERE - ▁SINGING - ▁STAGE - ▁FREEDOM - ▁SHOUTED - ▁FARTHER - ▁MAJESTY - ▁PREVIOUS - ▁GUIDE - ▁MATCH - ▁CHEST - ▁INTENDED - ▁BI - ▁EXCITEMENT - ▁OFFICERS - ▁SUR - ▁SHAKE - ▁SENTIMENT - ▁GENTLY - ▁SUCCEEDED - ▁MENTION - ▁LOCK - ▁ACQUAINTANCE - ▁IMAGINATION - ▁PHYSICAL - ▁LEADING - ▁SLAVE - ▁CART - ▁POINTED - ▁STEAM - ▁SHADE - ▁PIPE - ▁BASE - ▁INVENT - ▁ALAS - ▁WORKED - ▁REGRET - ▁BUR - ▁FAITHFUL - ▁MENTIONED - ▁RECORD - ▁COMPLAIN - ▁SUPERIOR - ▁BAY - ▁PAL - EMENT - UE - ▁SEVENTY - ▁HOTEL - ▁SHEEP - ▁MEAL - ▁ADVICE - ▁HIDDEN - ▁DEMANDED - ▁CONSCIOUS - ▁BROW - ▁POSSESS - ▁FOURTH - ▁EVENTS - ▁FRI - ▁PRAISE - ▁ADVANCED - ▁RESOLVED - ▁STUFF - ▁CHEERFUL - ▁BIRTH - ▁GRIEF - ▁AFFORD - ▁FAIRY - ▁WAKE - ▁SIDES - ▁SUBSTANCE - ▁ARTICLE - ▁LEVEL - ▁MIST - ▁JOINED - ▁PRACTICAL - ▁CLEARLY - ▁TRACE - ▁AWAKE - ▁OBSERVE - ▁BASKET - ▁LACK - VILLE - ▁SPIRITS - ▁EXCITED - ▁ABANDON - ▁SHINING - ▁FULLY - ▁CALLING - ▁CONSIDERABLE - ▁SPRANG - ▁MILE - ▁DOZEN - ▁PEA - ▁DANGEROUS - ▁WIT - ▁JEW - ▁POUNDS - ▁FOX - ▁INFORMATION - ▁LIES - ▁DECK - NNY - ▁PAUL - ▁STARS - ▁ANGER - ▁SETTLE - ▁WILLING - ▁ADAM - ▁FACES - ▁SMITH - ▁IMPORTANCE - ▁STRAIN - WAR - ▁SAM - ▁FEATHER - ▁SERVED - ▁AUTHOR - ▁PERCEIVED - ▁FLAME - ▁DIVINE - ▁TRAIL - ▁ANYBODY - ▁SIGH - ▁DELICATE - KY - ▁FOLD - ▁HAVEN - ▁DESIRED - ▁CURIOSITY - ▁PRACTICE - ▁CONSIDERATION - ▁ABSOLUTELY - ▁CITIZEN - ▁BOTTLE - ▁INTERESTED - ▁MEAT - ▁OCCUPIED - ▁CHOOSE - ▁THROAT - ETTE - ▁CANDLE - ▁DAWN - ▁PROTECT - ▁SENTENCE - IED - ▁ROCKS - ▁PORTION - ▁APPARENTLY - ▁PRESENTED - ▁TIGHT - ▁ACTUALLY - ▁DYING - ▁HAM - ▁DAILY - ▁SUFFERED - ▁POLITICAL - ▁BODIES - ▁MODERN - ▁COMPLETELY - ▁SOONER - TAN - ▁PROP - ▁ADVANCE - ▁REFUSED - ▁FARMER - ▁POLITE - ▁THUNDER - ▁BRIEF - ▁ELSIE - ▁SAILOR - ▁SUGGESTED - ▁PLATE - ▁AID - ▁FLESH - ▁WEEP - ▁BUCK - ▁ANTI - ▁OCEAN - ▁SPEND - WELL - ▁ODD - ▁GOVERNOR - ▁ENTRANCE - ▁SUSPICION - ▁STEPPED - ▁RAPIDLY - ▁CHECK - ▁HIDE - ▁FLIGHT - ▁CLUB - ▁ENTIRE - ▁INDIANS - ASH - ▁CAPITAL - ▁MAMMA - HAR - ▁CORRECT - ▁CRACK - ▁SENSATION - ▁WORST - ▁PACE - ▁MIDST - ▁AUGUST - ▁PROPORTION - ▁INNOCENT - LINESS - ▁REGARDED - ▁DRIVEN - ORD - ▁HASTE - ▁EDUCATION - ▁EMPLOY - ▁TRULY - ▁INSTRUMENT - ▁MAG - ▁FRAME - ▁FOOLISH - ▁TAUGHT - ▁HANG - ▁ARGUMENT - ▁NINETEEN - ▁ELDER - ▁NAY - ▁NEEDED - ▁NEIGHBOR - ▁INSTRUCT - ▁PAPERS - ▁REWARD - ▁EQUALLY - ▁FIELDS - ▁DIG - HIN - ▁CONDITIONS - JA - ▁SPAR - ▁REQUEST - ▁WORN - ▁REMARKABLE - ▁LOAD - ▁WORSHIP - ▁PARK - ▁KI - ▁INTERRUPTED - ▁SKILL - ▁TERM - LAC - ▁CRITIC - ▁DISTRESS - ▁BELIEF - ▁STERN - IGHT - ▁TRACK - ▁HUNTING - ▁JEWEL - ▁GRADUALLY - ▁GLOW - ▁RUSHED - ▁MENTAL - ▁VISITOR - ▁PICKED - ▁BEHOLD - ▁EXPRESSED - ▁RUB - ▁SKI - ARTAGNAN - ▁MOREOVER - ▁OPERATION - ▁CAREFUL - ▁KEEN - ▁ASSERT - ▁WANDER - ▁ENEMIES - ▁MYSTERIOUS - ▁DEPTH - ▁PREFER - ▁CROSSED - ▁CHARMING - ▁DREAD - ▁FLOUR - ▁ROBIN - ▁TRE - ▁RELIEF - ▁INQUIRED - ▁APPLE - ▁HENCE - ▁WINGS - ▁CHOICE - ▁JUD - OO - ▁SPECIES - ▁DELIGHTED - IUM - ▁RAPID - ▁APPEAL - ▁FAMOUS - ▁USEFUL - ▁HELEN - ▁NEWSPAPER - ▁PLENTY - ▁BEARING - ▁NERVOUS - ▁PARA - ▁URGE - ▁ROAR - ▁WOUNDED - ▁CHAIN - ▁PRODUCE - ▁REFLECTION - ▁MERCHANT - ▁QUARREL - ▁GLORY - ▁BEGUN - ▁BARON - CUS - ▁QUEER - ▁MIX - ▁GAZE - ▁WHISPER - ▁BURIED - ▁DIV - ▁CARD - ▁FREQUENTLY - ▁TIP - ▁KNEE - ▁REGION - ▁ROOT - ▁LEST - ▁JEALOUS - CTOR - ▁SAVED - ▁ASKING - ▁TRIP - QUA - ▁UNION - HY - ▁COMPANIONS - ▁SHIPS - ▁HALE - ▁APPROACHED - ▁HARRY - ▁DRUNK - ▁ARRIVAL - ▁SLEPT - ▁FURNISH - HEAD - ▁PIG - ▁ABSENCE - ▁PHIL - ▁HEAP - ▁SHOES - ▁CONSCIOUSNESS - ▁KINDLY - ▁EVIDENT - ▁SCAR - ▁DETERMIN - ▁GRASP - ▁STEAL - ▁OWE - ▁KNIFE - ▁PRECIOUS - ▁ELEMENT - ▁PROCEEDED - ▁FEVER - ▁LEADER - ▁RISK - ▁EASE - ▁GRIM - ▁MOUNT - ▁MEANWHILE - ▁CENTURY - OON - ▁JUDGMENT - ▁AROSE - ▁VISION - ▁SPARE - ▁EXTREME - ▁CONSTANT - ▁OBSERVATION - ▁THRUST - ▁DELAY - ▁CENT - ▁INCLUD - ▁LIFT - ▁ADMIRE - ▁ISSUE - ▁FRIENDSHIP - ▁LESSON - ▁PRINCIPAL - ▁MOURN - ▁ACCEPTED - ▁BURNING - ▁CAPABLE - ▁EXTRAORDINARY - ▁SANG - ▁REMOVED - ▁HOPED - ▁HORN - ▁ALICE - ▁MUD - ▁APARTMENT - ▁FIGHTING - ▁BLAME - ▁TREMBLING - ▁SOMEBODY - ▁ANYONE - ▁BRIDE - ▁READER - ▁ROB - ▁EVERYWHERE - ▁LABOUR - ▁RECALL - ▁BULL - ▁HIT - ▁COUNCIL - ▁POPULAR - ▁CHAP - ▁TRIAL - ▁DUN - ▁WISHES - ▁BRILLIANT - ▁ASSURED - ▁FORGOT - ▁CONTINUE - ▁ACKNOWLEDG - ▁RETREAT - ▁INCREASED - ▁CONTEMPT - ▁GRANDFATHER - ▁SYMPATHY - ▁GHOST - ▁STRETCHED - ▁CREATURES - ▁CAB - ▁HIND - ▁PLAYING - ▁MISERABLE - ▁MEMBERS - ▁KINDNESS - ▁HIGHEST - ▁PRIM - ▁KISSED - ▁DESERVE - ▁HUT - ▁BEGGED - ▁EIGHTY - ▁CLOSELY - ▁WONDERED - ▁MILITARY - ▁REMIND - ▁ACCORDINGLY - ▁LARGER - ▁MAINTAIN - ▁ENGINE - ▁MOTIVE - ▁DESTROY - ▁STRIP - ▁HANS - ▁AHEAD - ▁INFINITE - ▁PROMPT - ▁INFORMED - TTLE - ▁PEER - ▁PRESSED - ▁TRAP - ▁SOMEWHERE - ▁BOUGHT - ▁VISIBLE - ▁ASHAMED - ▁TEAR - ▁NEIGHBOUR - ▁CONSTITUTION - ▁INTELLIGENCE - ▁PROFESSION - ▁HUNGRY - RIDGE - ▁SMELL - ▁STORIES - ▁LISTENING - ▁APPROACH - ▁STRING - ▁EXPLANATION - ▁IMMENSE - ▁RELIGIOUS - ▁THROUGHOUT - ▁HOLLOW - ▁AWAIT - ▁FLYING - ▁SCREAM - ▁ACTIVE - ▁RUM - ▁PRODUCT - ▁UNHAPPY - ▁VAGUE - ARIES - ▁ELIZABETH - ▁STUPID - ▁DIGNITY - ▁ISABEL - GAR - ▁BRO - ▁PITCH - ▁COMRADE - ▁STIFF - ▁RECKON - ▁SOLD - ▁SPARK - ▁STRO - ▁CRYING - ▁MAGIC - ▁REPEAT - PORT - ▁MARKED - ▁COMFORTABLE - ▁PROJECT - ▁BECOMING - ▁PARENTS - ▁SHELTER - ▁STOLE - ▁HINT - ▁NEST - ▁TRICK - ▁THOROUGHLY - ▁HOSPITAL - ▁WEAPON - ▁ROME - ▁STYLE - ▁ADMITTED - ▁SAFETY - FIELD - ▁UNDERSTANDING - ▁TREMBLE - ▁PRINT - ▁SLAVES - ▁WEARY - ▁ARTIST - ▁CREDIT - BURG - ▁CONCLUSION - ▁SELDOM - ▁UNUSUAL - ▁CLOUDS - ▁UNABLE - ▁GAY - ▁HANGING - ▁SCR - ▁BOWED - ▁DAVID - ▁VOL - ▁PUSHED - ▁ESCAPED - MOND - ▁WARN - ▁BETRAY - ▁EGGS - ▁PLAINLY - ▁EXHIBIT - ▁DISPLAY - ▁MEMBER - ▁GRIN - ▁PROSPECT - ▁BRUSH - ▁BID - ▁SUCCESSFUL - ▁EXTENT - ▁PERSUADE - ▁MID - ▁MOOD - ▁ARRANGED - ▁UNIVERSAL - ▁JIM - ▁SIGNAL - ▁WHILST - ▁PHILIP - ▁WOLF - RATE - ▁EAGERLY - ▁BILLY - ▁RETURNING - ▁CONSCIENCE - ▁FORTUNATE - ▁FEMALE - ▁GLEAM - ▁HASTILY - ▁PROVIDED - ▁OBTAIN - ▁INSTINCT - ▁CONCERNED - ▁CONCERNING - ▁SOMEHOW - ▁PINK - ▁RAGE - ▁ACCUSTOMED - ▁UNCONSCIOUS - ▁ADVISE - ▁BRANCHES - ▁TINY - ▁REFUSE - ▁BISHOP - ▁SUPPLY - ▁PEASANT - ▁LAWYER - ▁WASTE - ▁CONNECTION - ▁DEVELOP - ▁CORRESPOND - ▁PLUM - ▁NODDED - ▁SLIPPED - ▁EU - ▁CONSTANTLY - CUM - MMED - ▁FAIRLY - HOUSE - ▁KIT - ▁RANG - ▁FEATURES - ▁PAUSE - ▁PAINFUL - ▁JOE - ▁WHENCE - ▁LAUGHTER - ▁COACH - ▁CHRISTMAS - ▁EATING - ▁WHOLLY - ▁APART - ▁SUPER - ▁REVOLUTION - ▁LONELY - ▁CHEEKS - ▁THRONE - ▁CREW - ▁ATTAIN - ▁ESTABLISHED - TIME - ▁DASH - ▁FRIENDLY - ▁OPERA - ▁EARL - ▁EXHAUST - ▁CLIFF - ▁REVEAL - ▁ADOPT - ▁CENTRE - ▁MERRY - ▁SYLVIA - ▁IDEAL - ▁MISFORTUNE - ▁FEAST - ▁ARAB - ▁NUT - ▁FETCH - ▁FOUGHT - ▁PILE - ▁SETTING - ▁SOURCE - ▁PERSIST - ▁MERCY - ▁BARK - ▁LUC - ▁DEEPLY - ▁COMPARE - ▁ATTITUDE - ▁ENDURE - ▁DELIGHTFUL - ▁BEARD - ▁PATIENCE - ▁LOCAL - ▁UTTERED - ▁VICTORY - ▁TREATED - ▁SEPARATE - ▁WAG - ▁DRAGG - ▁TITLE - ▁TROOPS - ▁TRIUMPH - ▁REAR - ▁GAINED - ▁SINK - ▁DEFEND - ▁TIED - ▁FLED - ▁DARED - ▁INCREASE - ▁POND - ▁CONQUER - ▁FOREHEAD - ▁FAN - ▁ANXIETY - ▁ENCOUNTER - ▁SEX - ▁HALT - ▁SANK - ▁CHEEK - ▁HUMBLE - ▁WRITER - ▁EMPLOYED - ▁DISTINGUISHED - ▁RAISE - ▁WHIP - ▁GIANT - ▁RANGE - ▁OBTAINED - ▁FLAG - ▁MAC - ▁JUMPED - ▁DISCOVERY - ▁NATIONAL - ▁COMMISSION - ▁POSITIVE - ▁LOVING - ▁EXACT - ▁MURMURED - ▁GAZED - ▁REFER - ▁COLLEGE - ▁ENCOURAGE - ▁NOVEL - ▁CLOCK - ▁MORTAL - ▁ROLLED - ▁RAT - IZING - ▁GUILTY - ▁VICTOR - WORTH - ▁PRA - ▁APPROACHING - ▁RELATIVE - ▁ESTATE - ▁UGLY - ▁METAL - ▁ROBERT - ▁TENT - ▁ADMIRATION - ▁FOURTEEN - ▁BARBAR - ▁WITCH - ELLA - ▁CAKE - ▁SHONE - ▁MANAGED - ▁VOLUME - ▁GREEK - ▁DANCING - ▁WRETCHED - ▁CONDEMN - ▁MAGNIFICENT - ▁CONSULT - J - ▁ORGAN - ▁FLEET - ▁ARRANGEMENT - ▁INCIDENT - ▁MISERY - ▁ARROW - ▁STROKE - ▁ASSIST - ▁BUILD - ▁SUCCEED - ▁DESPERATE - ▁WIDOW - UDE - ▁MARKET - ▁WISDOM - ▁PRECISE - ▁CURRENT - ▁SPOIL - ▁BADE - ▁WOODEN - ▁RESIST - ▁OBVIOUS - ▁SENSIBLE - FALL - ▁ADDRESSED - ▁GIL - ▁COUNSEL - ▁PURCHASE - ▁SELECT - ▁USELESS - ▁STARED - ▁ARREST - ▁POISON - ▁FIN - ▁SWALLOW - ▁BLOCK - ▁SLID - ▁NINETY - ▁SPORT - ▁PROVIDE - ▁ANNA - ▁LAMB - ▁INTERVAL - ▁JUMP - ▁DESCRIBED - ▁STRIKING - ▁PROVISION - ▁PROPOSED - ▁MELANCHOLY - ▁WARRIOR - ▁SUGGEST - ▁DEPARTURE - ▁BURDEN - ▁LIMB - ▁TROUBLED - ▁MEADOW - ▁SACRED - ▁SOLID - ▁TRU - ▁LUCY - ▁RECOVER - ▁ENERGY - ▁POWDER - ▁RESUMED - ▁INTENSE - ▁BRITISH - ▁STRAW - ▁AGREEABLE - ▁EVERYONE - ▁CONCERN - ▁VOYAGE - ▁SOUTHERN - ▁BOSOM - ▁UTTERLY - ▁FEED - ▁ESSENTIAL - ▁CONFINE - ▁HOUSEHOLD - ▁EXTREMELY - ▁WONDERING - ▁LIST - ▁PINE - PHA - ▁EXPERIMENT - ▁JOSEPH - ▁MYSTERY - ▁RESTORE - ▁BLUSH - FOLD - ▁CHOSEN - ▁INTELLECT - ▁CURTAIN - OLOGY - ▁MOUNTED - ▁LAP - ▁EPI - ▁PUNISH - ▁WEDDING - ▁RECOGNIZED - ▁DRIFT - ▁PREPARATION - ▁RESOLUTION - ▁OPPRESS - ▁FIX - ▁VICTIM - OGRAPH - ▁SUMMON - ▁JULIA - ▁FLOOD - ▁WAL - ULATION - ▁SLIGHTLY - ▁LODGE - ▁WIRE - ▁CONFUSION - ▁UNEXPECTED - ▁CONCEIVE - ▁PRIZE - ▁JESUS - ▁ADDITION - ▁RUDE - ▁FATAL - ▁CARELESS - ▁PATCH - ▁KO - ▁CATHERINE - ▁PARLIAMENT - ▁PROFOUND - ▁ALOUD - ▁RELIEVE - ▁PUSH - ABILITY - ▁ACCOMPANIED - ▁SOVEREIGN - ▁SINGULAR - ▁ECHO - ▁COMPOSED - ▁SHAKING - ATORY - ▁ASSISTANCE - ▁TEACHER - ▁HORRIBLE - ▁STRICT - ▁VERSE - ▁PUNISHMENT - ▁GOWN - ▁MISTAKEN - ▁VARI - ▁SWEPT - ▁GESTURE - ▁BUSH - ▁STEEL - ▁AFFECTED - ▁DIRECTED - ▁SURROUNDED - ▁ABSURD - ▁SUGAR - ▁SCRAP - ▁IMMEDIATE - ▁SADDLE - ▁TY - ▁ARISE - ▁SIGHED - ▁EXCHANGE - ▁IMPATIENT - ▁SNAP - ▁EMBRACE - ▁DISEASE - ▁PROFIT - ▁RIDING - ▁RECOVERED - ▁GOVERN - ▁STRETCH - ▁CONVINCED - ▁LEANING - ▁DOMESTIC - ▁COMPLEX - ▁MANIFEST - ▁INDULGE - ▁GENIUS - ▁AGENT - ▁VEIL - ▁DESCRIPTION - ▁INCLINED - ▁DECEIVE - ▁DARLING - ▁REIGN - HU - ▁ENORMOUS - ▁RESTRAIN - ▁DUTIES - BURY - TTERED - ▁POLE - ▁ENABLE - ▁EXCEPTION - ▁INTIMATE - ▁COUNTESS - ▁TRIBE - ▁HANDKERCHIEF - ▁MIDNIGHT - ▁PROBLEM - ▁TRAMP - ▁OIL - CAST - ▁CRUSH - ▁DISCUSS - ▁RAM - ▁TROT - ▁UNRE - ▁WHIRL - ▁LOCKED - ▁HORIZON - ▁OFFICIAL - ▁SCHEME - ▁DROWN - ▁PIERRE - ▁PERMITTED - ▁CONNECTED - ▁ASSURE - ▁COCK - ▁UTMOST - ▁DEVOTED - ▁RELI - ▁SUFFICIENTLY - ▁INTELLECTUAL - ▁CARPET - ▁OBJECTION - ▁AFTERWARD - ▁REALITY - ▁NEGRO - ▁RETAIN - ▁ASCEND - ▁CEASE - ▁KATE - ▁MARVEL - KO - ▁BOND - MOST - ▁COAL - GATE - ▁IGNORANT - ▁BREAKING - ▁TWIN - ▁ASTONISHMENT - ▁COFFEE - ▁JAR - ▁CITIES - ▁ORIGIN - ▁EXECUT - ▁FINAL - ▁INHABITANTS - ▁STABLE - ▁CHIN - ▁PARTIES - ▁PLUNGE - ▁GENEROUS - ▁DESCRIBE - ▁ANNOUNCED - ▁MERIT - ▁REVERE - ▁ERE - ACIOUS - ZI - ▁DISAPPOINT - ▁SUGGESTION - ▁DOUBTLESS - ▁TRUNK - ▁STAMP - ▁JOB - ▁APPOINTED - ▁DIVIDED - ▁ACQUAINTED - CHI - ▁ABSOLUTE - ▁FEARFUL - ▁PRIVILEGE - ▁CRAFT - ▁STEEP - ▁HUNTER - ▁FORBID - ▁MODEST - ▁ENDEAVOUR - ▁SWEEP - ▁BEHELD - ▁ABSORB - ▁CONSTRUCT - ▁EMPIRE - ▁EXPEDITION - ▁ERECT - ▁OFFEND - ▁INTEND - ▁PERMIT - ▁DESTROYED - ▁CONTRACT - ▁THIRST - ▁WAGON - ▁EVA - ▁GLOOM - ▁ATMOSPHERE - ▁RESERVE - ▁VOTE - ▁GER - ▁NONSENSE - ▁PREVAIL - ▁QUALITY - ▁CLASP - ▁CONCLUDED - ▁RAP - ▁KATY - ▁ETERNAL - ▁MUTTERED - ▁NEGLECT - ▁SQUIRE - ▁CREEP - LOCK - ▁ELECTRIC - ▁HAY - ▁EXPENSE - ▁SCORN - ▁RETIRED - ▁STOUT - ▁MURMUR - ▁SHARPLY - ▁DISTRICT - ▁LEAF - ▁FAILURE - WICK - ▁JEAN - ▁NUMEROUS - ▁INFANT - ▁REALIZED - ▁TRAVELLER - ▁HUNGER - ▁JUNE - ▁MUN - ▁RECOMMEND - ▁CREP - ZZLE - ▁RICHARD - WORK - ▁MONTE - ▁PREACH - ▁PALM - AVI - ▁ANYWHERE - ▁DISPOSITION - ▁MIRROR - ▁VENTURE - ▁POUND - ▁CIGAR - ▁INVITED - ▁BENCH - ▁PROTECTION - ▁BENEFIT - ▁THOMAS - ▁CLERK - ▁REPROACH - ▁UNIFORM - ▁GENERATION - ▁SEAL - ▁COMPASS - ▁WARNING - ▁EXTENDED - ▁DIFFICULTIES - ▁MAYBE - ▁GROAN - ▁AFFECT - ▁COMB - ▁EARN - ▁WESTERN - ▁IDLE - ▁SCORE - ▁TAP - ▁ASTONISHED - ▁INTRODUCED - ▁LEISURE - ▁LIEUTENANT - ▁VIOLENCE - ▁FIRMLY - ▁MONSTER - ▁UR - ▁PROPERLY - ▁TWIST - ▁PIRATE - ▁ROBBER - ▁BATTER - ▁WEPT - ▁LEANED - ▁FOG - ▁ORNAMENT - ▁ANDREW - ▁BUSHES - ▁REPUBLIC - ▁CONFIDENT - ▁LEAN - ▁DART - ▁STOOP - ▁CURL - ▁COUNTER - ▁NORTHERN - ▁PEARL - ▁NEAREST - ▁FRANCIS - ▁WANDERING - ▁FREQUENT - ▁STARTLED - ▁STATEMENT - ▁OCCUR - ▁BLOOM - ▁NERVE - ▁INSPECT - ▁INDUCE - ▁FLATTER - ▁DATE - ▁AMBITION - ▁SLOPE - ▁MALE - ▁MADAM - ▁MONK - ▁RENT - ▁CONFIRM - ▁INVESTIGAT - ▁RABBIT - ▁REGIMENT - ▁SUBMIT - ▁SPELL - ▁FURIOUS - ▁RAIL - ▁BESTOW - ▁RALPH - ▁SCATTERED - ▁COMPELLED - ▁THREAD - ▁CHILL - ▁DENY - ▁PRONOUNC - ▁MANKIND - ▁CATTLE - ▁EXECUTION - ▁REBEL - ▁SUPREME - ▁VALUABLE - ▁LIKEWISE - ▁CONVEY - ▁TIDE - ▁GLOOMY - ▁COIN - ▁ACTUAL - ▁TAX - ▁PROVINCE - ▁GRATEFUL - ▁SPIRITUAL - ▁VANISHED - ▁DIANA - ▁HAUNT - ▁DRAGON - ▁CRAWL - ▁CHINA - ▁GRATITUDE - ▁NEAT - ▁FINISH - ▁INTENT - ▁FRIGHT - ▁EMBARRASS - ▁THIRTEEN - ▁RUTH - ▁SLIGHTEST - ▁DEVELOPMENT - ▁INTERVIEW - ▁SPECTACLE - ▁BROOK - VIE - ▁WEAKNESS - ▁AUDIENCE - ▁CONSEQUENTLY - ▁ABROAD - ▁ASPECT - ▁PAINTED - ▁RELEASE - ▁INSULT - ▁SOOTH - ▁DISAPPOINTMENT - ▁EMERG - ▁BRIG - ▁ESTEEM - ▁INVITATION - ▁PASSENGER - ▁PUBLISH - ▁PIANO - ▁IRISH - ▁DESK - ▁BEATEN - ▁FIFTH - ▁IMPULSE - ▁SWEAR - ▁EATEN - ▁PURPLE - ▁COMMITTED - ▁COUNTRIES - ▁PERCEIVE - ISON - ▁CELEBRAT - ▁GRANDMOTHER - ▁SHUDDER - ▁SUNSHINE - ▁SPANISH - ▁HITHERTO - ▁MARILLA - ▁SNAKE - ▁MOCK - ▁INTERFERE - ▁WALTER - ▁AMID - ▁MARBLE - ▁MISSION - TERIOR - ▁DRIVING - ▁FURNITURE - ▁STEADY - ▁CIRCUMSTANCE - ▁INTERPRET - ▁ENCHANT - ▁ERROR - ▁CONVICTION - ▁HELPLESS - ▁MEDICINE - ▁QUALITIES - ▁ITALIAN - ▁HASTENED - ▁OCCASIONALLY - ▁PURSUED - ▁HESITATED - ▁INDEPENDENT - ▁OLIVER - ▁LINGER - UX - ▁EXAMINED - ▁REPENT - ▁PHYSICIAN - ▁CHASE - ▁BELOVED - ▁ATTACHED - ▁FLORENCE - ▁HONEY - ▁MOUSE - ▁CRIES - ▁BAKE - ▁POEM - ▁DESTRUCTION - ▁FULFIL - ▁MESSENGER - ▁TRISTRAM - ▁FANCIED - ▁EXCESS - ▁CURSE - ▁CHU - ▁QUANTITY - ▁THORNTON - ▁CREATED - ▁CONTINUALLY - ▁LIGHTNING - ▁BORNE - ▁TOTAL - ▁DISPOSED - ▁RIFLE - ▁POLLY - ▁GOAT - ▁BACKWARD - ▁VIRGINIA - ▁KICK - ▁PERIL - ▁QUO - ▁GLORIOUS - ▁MULTITUDE - ▁LEATHER - ▁ABSENT - ▁DEMON - ▁DEBT - ▁TORTURE - ▁ACCORD - ▁MATE - ▁CATHOLIC - ▁PILL - ▁LIBRARY - ▁PURSUIT - ▁SHIRT - ▁DEAREST - ▁COLLAR - ▁BEACH - ▁ROBE - ▁DECLARE - ▁BRANCH - ▁TEMPT - ▁STEADILY - ▁DISGUST - ▁SILLY - ▁ARRIVE - ▁DRANK - ▁LEVI - ▁COMMUNICAT - ▁RACHEL - ▁WASHINGTON - ▁RESIGN - ▁MEANTIME - ▁LACE - ▁ENGAGEMENT - ▁QUIVER - ▁SEPARATED - ▁DISCUSSION - ▁VENTURED - ▁SURROUNDING - ▁POLISH - ▁NAIL - ▁SWELL - ▁JOKE - ▁LINCOLN - ▁STUDENT - ▁GLITTER - ▁RUSSIAN - ▁READILY - ▁CHRIS - ▁POVERTY - ▁DISGRACE - ▁CHEESE - ▁HEAVILY - ▁SCALE - ▁STAFF - ▁ENTREAT - ▁FAREWELL - ▁LUNCH - ▁PEEP - ▁MULE - ▁SOMEONE - ▁DISAPPEAR - ▁DECISION - ▁PISTOL - ▁PUN - ▁SPUR - ▁ASSUMED - ▁EXTEND - ▁ENTHUSIASM - ▁DEFINITE - ▁UNDERTAKE - ▁COMMITTEE - ▁SIMON - ▁FENCE - ▁APPLIED - ▁RELATED - ▁VICE - ▁UNPLEASANT - ▁PROBABLE - ▁PROCURE - ▁FROWN - ▁CLOAK - ▁HUMANITY - ▁FAMILIES - ▁PHILOSOPHER - ▁DWARF - ▁OVERCOME - ▁DEFEAT - ▁FASTENED - ▁MARSH - ▁CLASSES - ▁TOMB - ▁GRACIOUS - ▁REMOTE - ▁CELL - ▁SHRIEK - ▁RESCUE - ▁POOL - ▁ORGANIZ - ▁CHOSE - ▁CUTTING - ▁COWARD - ▁BORDER - ▁DIRTY - ▁MONKEY - ▁HOOK - ▁CHUCK - ▁EMILY - ▁JEST - ▁PLAC - ▁WEIGH - ▁ASSOCIATE - ▁GLIMPSE - ▁STUCK - ▁BOLT - ▁MURDERER - ▁PONY - ▁DISTINGUISH - ▁INSTITUTION - ▁CUNNING - ▁COMPLIMENT - ▁APPETITE - ▁REPUTATION - ▁FEEBLE - ▁KIN - ▁SERIES - ▁GRACEFUL - ▁PLATFORM - ▁BREEZE - ▁PHRASE - ▁CLAY - MONT - ▁RATTL - ▁OPPOSITION - ▁LANE - ▁BOAST - ▁GROWTH - ▁INCLINATION - ▁BEHAVE - ▁SUSAN - ▁DISTINCTION - ▁DISLIKE - ▁NICHOLAS - ▁SATISFY - ▁DRAMA - ▁ELBOW - ▁GAZING - ▁CONSUM - ▁SPIN - ▁OATH - ▁CHANNEL - ▁CHARACTERISTIC - ▁SPEAR - ▁SLAIN - ▁SAUCE - ▁FROG - ▁CONCEPTION - ▁TIMID - ▁ZEAL - ▁APPARENT - SHIRE - ▁CENTER - ▁VARIETY - ▁DUSK - ▁APT - ▁COLUMN - ▁REVENGE - ▁RIVAL - ▁IMITAT - ▁PASSIONATE - ▁SELFISH - ▁NORMAN - ▁REPAIR - ▁THRILL - ▁TREATMENT - ▁ROSA - ▁MARTIN - ▁INDIFFERENT - ▁THITHER - ▁GALLANT - ▁PEPPER - ▁RECOLLECT - ▁VINE - ▁SCARCE - ▁SHIELD - ▁MINGLED - CLOSE - ▁HARSH - ▁BRICK - ▁HUMOR - ▁MISCHIEF - ▁TREMENDOUS - ▁FUNCTION - ▁SMART - ▁SULTAN - ▁DISMISS - ▁THREATENED - ▁CHEAP - ▁FLOCK - ▁ENDEAVOR - ▁WHISK - ▁ITALY - ▁WAIST - ▁FLUTTER - ▁SMOKING - ▁MONARCH - ▁AFRICA - ▁ACCUSE - ▁HERBERT - ▁REFRESH - ▁REJOICE - ▁PILLOW - ▁EXPECTATION - ▁POETRY - ▁HOPELESS - ▁PERISH - ▁PHILOSOPHY - ▁WHISTLE - ▁BERNARD - ▁LAMENT - ▁IMPROVE - ▁SUP - ▁PERPLEX - ▁FOUNTAIN - ▁LEAGUE - ▁DESPISE - ▁IGNORANCE - ▁REFERENCE - ▁DUCK - ▁GROVE - ▁PURSE - ▁PARTNER - ▁PROPHET - ▁SHIVER - ▁NEIGHBOURHOOD - ▁REPRESENTATIVE - SAIL - ▁WIP - ▁ACQUIRED - ▁CHIMNEY - ▁DOCTRINE - ▁MAXIM - ▁ANGLE - ▁MAJORITY - ▁AUTUMN - ▁CONFUSED - ▁CRISTO - ▁ACHIEVE - ▁DISGUISE - ▁REDUCED - ▁EARLIER - ▁THEATRE - ▁DECIDE - MINATED - OLOGICAL - ▁OCCUPATION - ▁VIGOROUS - ▁CONTINENT - ▁DECLINE - ▁COMMUNITY - ▁MOTIONLESS - ▁HATRED - ▁COMMUNICATION - ▁BOWL - ▁COMMENT - ▁APPROVE - ▁CEREMONY - ▁CRIMINAL - ▁SCIENTIFIC - ▁DUCHESS - ▁VIVID - ▁SHIFT - ▁AVAIL - ▁DAMP - ▁JOHNSON - ▁SLENDER - ▁CONTRAST - ▁AMUSEMENT - ▁PLOT - ▁LYN - ▁ASSOCIATION - ▁SNATCH - ▁UNCERTAIN - ▁PRESSURE - ▁PERCH - ▁APPLY - ▁PLANET - ▁NOTWITHSTANDING - ▁SWUNG - ▁STIRRED - ▁ATTENDANT - ▁ENJOYMENT - ▁WORRY - ▁ALBERT - ▁NAKED - ▁TALENT - ▁MARIAN - ▁REFORM - ▁DELIBERATE - ▁INTELLIGENT - ▁SENSITIVE - ▁YONDER - ▁PUPIL - ▁FRIGHTFUL - ▁DOUBTFUL - ▁STANDARD - ▁MAGISTRATE - ▁SHEPHERD - ▁STOMACH - ▁DEPOSIT - ▁RENEW - ▁HEDGE - ▁FRANCS - ▁POSSIBILITY - ▁RESEMBLE - ▁FATIGUE - ▁PORTRAIT - ▁FAVORITE - ▁CREAM - ▁BURG - ▁SECRETARY - ▁DIVERS - ▁ACTIVITY - ▁SPECULAT - ▁HUMOUR - ▁FITTED - ▁EXTERNAL - ▁CETERA - ▁WRAPPED - ▁WHIT - ▁FRED - ▁EXAMINATION - ▁LODGING - ▁OWING - ▁JAW - ▁CROW - ▁BALANCE - ▁PUFF - ▁TENDERNESS - ▁PORTHOS - ▁ANCHOR - ▁INTERRUPT - ▁NECESSARILY - ▁PERPETUAL - ▁AGONY - ▁POPE - ▁SCHOLAR - ▁SCOTLAND - ▁SUPPRESS - ▁WRATH - ▁WRECK - ▁EXCEED - ▁PERFECTION - ▁INDIA - ▁TRADITION - ▁SECTION - ▁EASTERN - ▁DOORWAY - ▁WIVES - ▁CONVENTION - ▁ANNOUNC - ▁EGYPT - ▁CONTRADICT - ▁SCRATCH - ▁CENTRAL - ▁GLOVE - ▁WAX - ▁PREPARE - ▁ACCOMPANY - ▁INCREASING - ▁LIBERAL - ▁RAISING - ▁ORANGE - ▁SHOE - ▁ATTRIBUTE - ▁LITERATURE - ▁PUZZLED - ▁WITHDRAW - ▁WHITHER - ▁HAWK - ▁MOONLIGHT - ▁EXAMINE - ▁HAPPILY - ▁PRECEDE - ▁DETECTIVE - ▁INCHES - ▁SOLITARY - ▁DUTCH - ▁NAPOLEON - ▁UNEASY - ▁CARDINAL - ▁BLEW - ▁FOWL - ▁DECORAT - ▁CHILDHOOD - ▁TORMENT - ▁LOSING - ▁PERMISSION - ▁BLANK - ▁UPSTAIRS - ▁CAPACITY - ▁TRIFLE - ▁FOLLY - ▁RECOGNIZE - ▁REMOVE - ▁VENGEANCE - ▁ENTERPRISE - ▁BEDROOM - ▁ANYHOW - ▁INQUIRY - ▁ASHES - ▁DRAG - ▁HUSH - ▁AWKWARD - ▁SATURDAY - ▁GENUINE - ▁SURVIV - ▁SKIRT - ▁AFFECTIONATE - ▁TANG - ▁MUTUAL - ▁DISPUTE - ▁EAGLE - ▁INCOME - ▁BIND - ▁FAME - ▁IMPROVEMENT - ROVING - ▁DIFFER - ▁AWOKE - ▁SLEEVE - ▁SOLITUDE - ▁FAVOURITE - JI - ▁DETECT - ▁COMPREHEND - ▁PREPARING - ▁SERPENT - ▁SUMMIT - ▁KNOT - ▁KNIT - ▁COPY - ▁STOPPING - ▁FADED - ▁HIDEOUS - ▁JULIE - STEAD - ▁SHINE - ▁CONFLICT - ▁PROPOSITION - ▁REFUGE - ▁GALLERY - ▁BUNDLE - ▁AXE - ▁SLAVERY - ▁MASK - ▁ALYOSHA - ▁LADDER - ▁DEPARTMENT - ▁DISCHARGE - ▁DEPRESS - ▁GALLOP - ▁SCARLET - ▁KITTY - ▁RECEIVING - ▁SURRENDER - ▁SUSTAIN - ▁TWILIGHT - ▁CONGRESS - ▁IRELAND - ▁FUNNY - ▁LEND - ▁CONSTITUTE - ▁FUNERAL - ▁CRYSTAL - ▁SPAIN - ▁EXCEEDINGLY - ▁DAMN - ▁COMMUN - ▁CIVILIZATION - ▁PREJUDICE - ▁PORCH - ▁ASSISTANT - ▁INDUSTRY - ▁TUMBLE - ▁DEFENCE - ▁HITHER - ▁SMOT - ▁COLONI - ▁AMAZEMENT - ▁MARGUERITE - ▁MIRACLE - ▁INHERIT - ▁BEGGAR - ▁ENVELOPE - ▁INDIGNATION - ▁NATASHA - ▁PROPOSAL - ▁FRAGMENT - ▁ROUSED - ▁ROAST - ENCIES - ▁COMMENCED - ▁RESOURCE - ▁POPULATION - ▁QUOTH - ▁PURSUE - ▁EDUCAT - ▁AFFLICT - ▁CONTACT - ▁CRIMSON - ▁DIVISION - ▁DISORDER - ▁COPPER - ▁SOLICIT - ▁MODERATE - ▁DRUM - ▁SWIM - ▁SALUTE - ▁ASSUME - ▁MUSCLE - ▁OVERWHELM - ▁SHAKESPEARE - ▁STRUGGLING - ▁TRANQUIL - ▁CHICKEN - ▁TREAD - ▁CLAW - ▁BIBLE - ▁RIDGE - ▁THREAT - ▁VELVET - ▁EXPOSED - ▁IDIOT - ▁BARREL - ▁PENNY - ▁TEMPTATION - ▁DANGLARS - ▁CENTURIES - ▁DISTRIBUT - ▁REJECT - ▁RETORTED - ▁CONCENTRAT - ▁CORDIAL - ▁MOTOR - ▁CANNON - KEEP - ▁WRETCH - ▁ASSURANCE - ▁THIEF - ▁SURVEY - ▁VITAL - ▁RAILWAY - ▁JACKSON - ▁CRASH - ▁GROWL - ▁COMBAT - ▁RECOLLECTION - ▁SECURITY - ▁JACOB - ▁CLUTCH - ▁BLANKET - ▁NANCY - ▁CELLAR - ▁CONVENIENT - ▁INDIGNANT - ▁COARSE - ▁WORM - ▁SCREEN - ▁TRANSPORT - ▁BULLET - ▁APPRECIATE - ▁DEVOTION - ▁INVISIBLE - ▁DRIED - ▁MIXTURE - ▁CANDID - ▁PERFORMANCE - ▁RIPE - ▁EXQUISITE - ▁BARGAIN - ▁TOBACCO - ▁LOYAL - ▁MOULD - ▁ATTENTIVE - ▁DOROTHY - ▁BRUTE - ▁ESTABLISHMENT - ▁ABILITY - ▁INHABIT - ▁OBSCURE - ▁BORROW - ▁ESSENCE - ▁DISMAY - ▁FLEE - ▁BLADE - ▁PLUCK - ▁COFFIN - ▁SUNSET - ▁STEPHEN - ▁ECONOMIC - ▁HOLIDAY - ▁MECHANICAL - ▁COTTON - ▁AWAKENED - ▁SEIZE - ▁RIDICULOUS - ▁SANCHO - ▁HESITATION - ▁CORPSE - ▁SAVING - HOLD - FOOT - ▁ELDEST - ▁DESPITE - ▁EDITH - ▁CHERISH - ▁RESISTANCE - ▁WILSON - ▁ARGUE - ▁INQUIRE - ▁APPREHENSION - ▁AVENUE - ▁DRAKE - ▁PROPOSE - HURST - ▁INFERIOR - ▁STAIRCASE - ▁WHEREFORE - ▁CARLYLE - ▁COUCH - ▁ROUTE - ▁POLITICS - ▁TOMORROW - ▁THRONG - ▁NAUGHT - ▁SUNLIGHT - ▁INDIFFERENCE - ▁OBEDIENCE - ▁RECEPTION - ▁VEGETABLE - ▁IMPERFECT - ▁RESIDENCE - ▁TURKEY - ▁VIOLET - ▁SARAH - ▁ALTAR - ▁GRIEVE - ▁JERK - ▁ENSU - ▁MAGICIAN - ▁BLOSSOM - ▁LANTERN - ▁RESOLUTE - ▁THOUGHTFULLY - ▁FORTNIGHT - ▁TRUMPET - ▁VALJEAN - ▁UNWILLING - ▁LECTURE - ▁WHEREUPON - ▁HOLLAND - ▁CHANGING - ▁CREEK - ▁SLICE - ▁NORMAL - ▁ANNIE - ▁ACCENT - ▁FREDERICK - ▁DISAGREEABLE - ▁RUBBED - ▁DUMB - ▁ESTABLISH - ▁IMPORT - ▁AFFIRM - ▁MATTHEW - ▁BRISK - ▁CONVERT - ▁BENDING - ▁IVAN - ▁MADEMOISELLE - ▁MICHAEL - ▁EASIER - ▁JONES - ▁FACING - ▁EXCELLENCY - ▁LITERARY - ▁GOSSIP - ▁DEVOUR - ▁STAGGER - ▁PENCIL - ▁AVERAGE - ▁HAMMER - ▁TRIUMPHANT - ▁PREFERRED - ▁APPLICATION - ▁OCCUPY - ▁AUTHORITIES - BURN - ▁ASCERTAIN - ▁CORRIDOR - ▁DELICIOUS - ▁PRACTISE - ▁UNIVERSE - ▁SHILLING - ▁CONTEST - ▁ASHORE - ▁COMMIT - ▁ADMINISTRATION - ▁STUDIED - ▁RIGID - ▁ADORN - ▁ELSEWHERE - ▁INNOCENCE - ▁JOURNAL - ▁LANDSCAPE - ▁TELEGRAPH - ▁ANGRILY - ▁CAMPAIGN - ▁UNJUST - ▁CHALLENGE - ▁TORRENT - ▁RELATE - ▁ASSEMBLED - ▁IMPRESSED - ▁CANOE - ▁CONCLUD - ▁QUIXOTE - ▁SATISFACTORY - ▁NIECE - ▁DEAF - ▁RAFT - ▁JIMMY - ▁GLID - ▁REGULAT - ▁CHATTER - ▁GLACIER - ▁ENVY - ▁STATUE - ▁BOSTON - ▁RICHMOND - ▁DENIED - ▁FANNY - ▁SOLOMON - ▁VULGAR - ▁STALK - ▁REPLACE - ▁SPOON - ▁BASIN - ▁FEATURE - ▁CONVICT - ▁ARCHITECT - ▁ADMIRAL - ▁RIBBON - ▁PERMANENT - ▁APRIL - ▁JOLLY - ▁NEIGHBORHOOD - ▁IMPART - BOROUGH - CAMP - ▁HORRID - ▁IMMORTAL - ▁PRUDENCE - ▁SPANIARD - ▁SUPPOSING - ▁TELEPHONE - ▁TEMPERATURE - ▁PENETRATE - ▁OYSTER - ▁APPOINTMENT - ▁EGYPTIAN - ▁DWELT - ▁NEPHEW - ▁RAILROAD - ▁SEPTEMBER - ▁DEVICE - ▁WHEAT - ▁GILBERT - ▁ELEGANT - ▁ADVERTISE - ▁RATIONAL - ▁TURTLE - ▁BROOD - ▁ASSEMBLY - ▁CULTIVATE - ▁EDITOR - ▁SPECIMEN - ▁UNDOUBTEDLY - ▁WHALE - ▁DROPPING - ▁BALLOON - ▁MEDICAL - COMB - ▁COMPOSITION - ▁FOOTSTEPS - ▁LAUNCELOT - ▁DISCOURSE - ▁ERRAND - ▁CONVERSE - ▁ADVANCING - ▁DOWNSTAIRS - ▁TUMULT - ▁CORRUPT - ▁SUFFICE - ▁ANGUISH - ▁SHAGGY - ▁RETIRE - ▁TIMBER - ▁BLAZE - ▁ABSTRACT - ▁EMBROIDER - ▁PHOTOGRAPH - ▁PROSPERITY - ▁TERRIBLY - ▁TERRITORY - ▁THRESHOLD - ▁PAVEMENT - ▁INJURED - ▁LIMP - ▁AGITATION - ▁RASCAL - ▁PRESUME - ▁OBSERVING - ▁OBSTACLE - ▁SIMPLICITY - ▁SLUMBER - ▁SUPPLIED - ▁COMBINATION - ▁DRAIN - ▁WILDERNESS - ▁BELIEVING - ▁VILLAIN - ▁RECKLESS - ▁INJURY - ▁CLAPP - ▁FRIDAY - ▁HERCULES - ▁KENNEDY - ▁SYMPTOM - ▁SLEDGE - ▁CEILING - ▁LEMON - ▁PLAGUE - ▁MONDAY - ▁CANVAS - ▁IMPATIENCE - ▁UNCOMFORTABLE - ▁ACCESS - ▁FROZEN - ▁SENATOR - ▁FRANZ - ▁SWIMMING - ▁BARRIER - ▁ADJUST - ▁COMPARISON - ▁PROCLAIM - ▁WRINKL - ▁OVERLOOK - ▁MITYA - ▁GUILT - ▁PERCEPTION - ▁PRECAUTION - ▁SPECTATOR - ▁SURPRISING - ▁DISTRACT - ▁DISDAIN - ▁BONNET - ▁MAGNET - ▁PROFESS - ▁CONFOUND - ▁NARRATIVE - ▁STRUCTURE - ▁SKETCH - ▁ULTIMATE - ▁GLOBE - ▁INSECT - FICIENCY - ▁ORCHARD - ▁AMIABLE - ▁DESCENT - ▁INDEPENDENCE - ▁MANUFACTURE - ▁SPRINKLE - ▁NIGHTINGALE - ▁CUSHION - ▁EMINENT - ▁SCOTT - ▁ARRAY - ▁COSETTE - ▁WAVING - ▁EXTRACT - ▁IRREGULAR - ▁PERSECUT - ▁DERIVED - ▁WITHDREW - ▁CAUTION - ▁SUSPICIOUS - ▁MEMORIES - ▁NOWHERE - ▁SUBTLE - ▁THOROUGH - Q - ▁APPROPRIATE - ▁SLAUGHTER - ▁YOURSELVES - ▁THUMB - ▁TWAS - ▁ABODE - ▁BIDDING - ▁CONSPICUOUS - ▁REBECCA - ▁SERGEANT - ▁APRON - ▁ANTICIPATE - ▁DISCIPLINE - ▁GLANCING - ▁PILGRIM - ▁SULLEN - ▁CONTRIBUTE - ▁PRAIRIE - ▁CARVED - ▁COMMERCE - ▁EXCLAMATION - ▁MUSCULAR - ▁NOVEMBER - ▁PHENOMENA - ▁SYMBOL - ▁UMBRELLA - ▁DIMINISH - ▁PARLOUR - ▁THREATENING - ▁STUMP - ▁EXTENSIVE - ▁PLEASING - ▁REMEMBRANCE - ▁COMBINED - ▁SHERIFF - ▁SHAFT - ▁LAURA - ▁INTERCOURSE - ▁STRICKEN - ▁SUPPLIES - ▁LANDLORD - ▁SHRINK - ▁PRICK - ▁CAESAR - ▁DRUG - ▁BEWILDERED - ▁NAUTILUS - ▁BRUTAL - ▁COMMERCIAL - ▁MAGGIE - ▁SPHERE - ▁VIRGIN - ▁BRETHREN - ▁DESTINY - ▁POLICY - ▁TERRIFIED - ▁HOUSEKEEPER - ▁CRAZY - ▁ARDENT - ▁DISCERN - ▁WRAP - ▁MARQUIS - ▁RUSSIA - MOUTH - ▁BRITAIN - ▁HARBOUR - ▁CONCERT - ▁DONKEY - ▁DAMAGE - ▁SLIM - ABOUT - ▁LUXURY - ▁MONSTROUS - ▁TENDENCY - ▁PARADISE - ▁CULTURE - ▁JULIUS - ▁RAOUL - ▁REMEDY - ▁DECAY - ▁SCOLD - ▁SPLIT - ▁ASSAULT - ▁DECEMBER - ▁MOSCOW - ▁EXPLORE - ▁TROUSERS - ▁WRIST - PIECE - ▁MUSKET - ▁VALENTINE - ▁TYRANT - ▁ABRAHAM - ▁MEDIUM - ▁ARTIFICIAL - ▁FACULTY - ▁OBLIGATION - ▁RESEMBLANCE - ▁INQUIRIES - ▁DETAIN - ▁SWARM - ▁PLEDGE - ▁ADMIRABLE - ▁DEFECT - ▁SUPERINTEND - ▁PATRIOT - ▁CLUNG - ▁DISMAL - ▁RECIT - ▁IGNOR - ▁AMELIA - ▁JUSTIFY - ▁ELEPHANT - ▁ESTIMATE - ▁KNELT - ▁SERVING - ▁WHIM - ▁SHRILL - ▁STUDIO - ▁TEXT - ▁ALEXANDER - ▁WROUGHT - ▁ABUNDANT - ▁SITUATED - ▁REGAIN - ▁FIERY - ▁SNEER - ▁SWEAT - ▁GLARE - ▁NIGH - ▁ESCORT - ▁INEVITABLE - ▁PSMITH - ▁RELUCTANT - ▁PRECEDING - ▁RESORT - ▁OUTRAGE - ▁AMBASSADOR - ▁CONSOLATION - ▁RECOGNITION - ▁REMORSE - ▁BEHALF - ▁FORMIDABLE - ▁GRAVITY - ▁DIVIDE - ▁CONFRONT - ▁GIGANTIC - ▁OCTOBER - ▁FLANK - ▁SLEW - ▁CLARA - ▁FILM - ▁BULK - ▁POMP - ▁ELEANOR - ▁EMPHASIS - ▁JAPANESE - ▁CAVALRY - ▁EXCLUSIVE - ▁PERFUME - ▁BRONZE - ▁FEDERAL - ▁LIQUID - ▁RUBBING - ▁OVEN - DOLPH - ▁CONVULS - ▁DEPRIVED - ▁RESPONSIBILITY - ▁SIGNIFICANT - ▁WAISTCOAT - ▁CLUSTER - ▁MARTHA - ▁REVERSE - ▁ATTORNEY - ▁DROOP - ▁SKILFUL - ▁HABITUAL - ▁PUMP - ▁INTERVEN - ▁OWL - ▁CONJECTURE - ▁FANTASTIC - ▁RESPONSIBLE - ▁DESTINED - ▁DOCUMENT - ▁THEREUPON - ▁GODDESS - ▁PACIFIC - ▁WARRANT - ▁COSTUME - ▁BRIDLE - ▁CALIFORNIA - ▁DEMOCRATIC - ▁EUSTACE - ▁SQUIRREL - ▁UNCOMMON - ▁MARVELLOUS - ▁PLOUGH - ▁TRAGEDY - ▁VAULT - ▁HESITATE - ▁REFRAIN - ▁ADMIRING - ▁CORPORAL - ▁ENTITLED - ▁SHREWD - ▁SQUEEZ - ▁ACCURATE - ▁TEMPEST - ▁MONUMENT - ▁SIEGE - ▁CHINESE - ▁RAVEN - ▁LOUNG - ▁ASSASSIN - ▁INFLICT - ▁AGITATED - ▁DESIRABLE - ▁EARLIEST - ▁LAUNCH - ▁PILOT - ▁PULSE - ▁MUTE - LEIGH - ▁LIQUOR - ▁SCARECROW - ▁SKULL - ▁DESOLATE - ▁SUBLIME - ▁SERENE - ▁RECESS - ▁WAKING - ▁CHARLOTTE - ▁CIRCULAR - ▁INJUSTICE - ▁PINOCCHIO - ▁PRISCILLA - ▁THYSELF - ▁OCCURRENCE - ▁CASUAL - ▁FRANTIC - ▁LEGEND - ▁FERTIL - ▁BACKGROUND - ▁DELICACY - ▁ESTRALLA - ▁MANUSCRIPT - ▁RESPONSE - ▁UNIVERSITY - ▁WOLVES - ▁SCANDAL - ▁STUMBLE - ▁HOARSE - ▁BODILY - ▁CONVENT - ▁EXAMINING - ▁INCAPABLE - ▁PERCEIVING - ▁PHILADELPHIA - ▁SUBSEQUENT - ▁THIEVES - ▁ACCUMULAT - ▁DAMSEL - ▁SCOTCH - ▁UNDERNEATH - ▁NOBILITY - ▁SMASH - ▁REVOLT - ▁ENGAGE - ▁CATHEDRAL - ▁CHAMPION - ▁DESPATCH - ▁ETERNITY - ▁JANUARY - ▁PLEADED - ▁PROBABILITY - ▁JIMMIE - ▁PARALLEL - ▁FISHERMAN - ▁JERRY - ▁SWORE - ▁DRAUGHT - ▁OPPONENT - ▁PRIMITIVE - ▁SIGNIFICANCE - ▁SUBSTANTIAL - ▁AMAZED - ▁DUNBAR - ▁COMMEND - ▁CONTEMPLATE - ▁TESTIMONY - ▁IMPERIAL - ▁ADAPT - ▁JUICE - ▁CALAMIT - CULAR - ▁CHATEAU - ▁PHOENIX - ▁PRUDENT - ▁SOLUTION - ▁VILLEFORT - ▁REACTION - ▁RELAX - ▁YU - ▁PROHIBIT - ▁DISTRUST - ▁PLUNDER - ▁WELFARE - ▁NAVIGAT - ▁PARLOR - ▁LAZY - ▁DETACH - OMETER - ▁PRIV - ▁DISCOURAGE - ▁OBSTINATE - ▁REJOICING - ▁SERMON - ▁VEHICLE - ▁FANCIES - ▁ENLIGHTEN - ▁ACUTE - ▁ILLUSION - ▁ANTHEA - ▁MARTIAN - ▁EXCITE - ▁GENEROSITY - OLOGIST - ▁AMAZING - ▁UNWORTHY - ▁INTERNAL - ▁INCENSE - ▁VIBRAT - ▁ADHERE - ROACH - ▁FEBRUARY - ▁MEXICAN - ▁POTATOES - ▁INCESSANT - ▁INTERPOSED - ▁PARCEL - ▁VEXED - ▁PROMOTE - MIDST - ▁ARISTOCRAT - ▁CYRIL - ▁EMBARK - ▁ABUNDANCE - ▁LITERALLY - ▁SURGEON - ▁TERRACE - ▁ATLANTIC - ▁MARTYR - ▁SPECK - ▁SENATE - ▁LOAF - ▁ADMINISTER - ▁APPREHEND - ▁SUBDUED - ▁TEMPORARY - ▁DOMINION - ▁ELABORATE - ▁DIGNIFIED - ▁ELIZA - ▁SPLASH - ▁CONSEIL - ▁DEXTER - ▁UNSEEN - ▁TRAGIC - VOCATION - ▁GRATIFY - ▁BACHELOR - ▁DEFENSE - ▁EXCURSION - ▁FACULTIES - ▁PROPRIETOR - ▁SYMPATHETIC - ▁UNNECESSARY - ▁RADIANT - ▁VACANT - ▁OUNCE - ▁SCREW - ▁PHENOMENON - ▁PROMINENT - ▁WORRIED - ▁STUDIES - ▁CLIMATE - ▁KEITH - ▁ARAMIS - ▁BLISS - ▁CONTINUAL - ▁SURPASS - ▁HEBREW - ▁IDENTITY - ▁PROVOKE - ▁TEMPERAMENT - ▁CHARIOT - ▁HARBOR - ▁NINTH - ▁PRIOR - ▁DESIROUS - ▁JERUSALEM - ▁UNDERTAKING - ▁EDISON - ▁MIRTH - ▁SCOUT - ▁APPARATUS - ▁ILLUSTRATION - ▁INTELLIGIBLE - ▁INVARIABLY - ▁PIERCED - ▁REVIEW - ▁FLICKER - ▁HAZARD - ▁REVELATION - ▁DIXON - ▁EXCITING - ▁GOSPEL - ▁CONSTANCE - ▁OVERTAKE - ▁GUINEA - ▁ALADDIN - ▁CHICAGO - ▁TULLIVER - ▁HAMILTON - ▁GARRISON - ▁DISCIPLE - ▁INTENSITY - ▁TRAITOR - ▁CHANCELLOR - ▁PROVERB - ▁DAGGER - ▁FORESEE - ▁CONFIDE - ▁GLIMMER - ▁CHAUVELIN - ▁ILLUSTRATE - ▁VOLUNTEER - ▁JUNGLE - ▁STREAK - ▁SUNRISE - ▁DISSOLV - ▁QUEST - ▁AWHILE - ▁FELICITY - ▁LEGISLATURE - ▁LEONORA - ▁MAGAZINE - ▁PITIFUL - ▁COLONY - ▁SHAWL - ▁ARRIVING - ▁FUNDAMENTAL - ▁CARPENTER - ▁OVERFLOW - ▁EXPAND - ▁HARVEST - ▁FEMININE - ▁INNUMERABLE - ▁SCRAMBLE - ▁TWENTIETH - ▁TRIFLING - ▁GHASTL - ▁CONQUEST - ▁DANIEL - ▁FACILIT - ▁FORSAKE - ▁BEHAVIOUR - ▁GORGEOUS - ▁PRODUCING - ▁HAPPIER - ▁PROMISING - ▁RAINBOW - ▁INSTINCTIVELY - ▁DECREE - ▁EYEBROWS - ▁IRRESISTIBLE - ▁PHARAOH - ▁SCROOGE - ▁UNNATURAL - ▁CRUMBS - ▁REFINED - ▁DREARY - ▁TRENCH - ▁CONVINCE - ▁FRINGE - ▁EXTREMITY - ▁INTIMACY - ▁SCOUNDREL - ▁SUFFRAGE - ▁UNEASINESS - ▁BARRICADE - ▁CIRCULAT - ▁SAMUEL - ▁BRUCE - ▁DARCY - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram5000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 hop_length: 256 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 10 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.7a1 distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "BEAR", "CRAFT" ]
Non_BioNLP
mlx-community/Llama-3.2-3B-Fluxed
mlx-community
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "mlx", "conversational", "en", "dataset:VincentGOURBIN/FluxPrompting", "base_model:VincentGOURBIN/Llama-3.2-3B-Fluxed", "base_model:finetune:VincentGOURBIN/Llama-3.2-3B-Fluxed", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,733,257,468,000
2024-12-03T20:35:23
55
2
--- base_model: VincentGOURBIN/Llama-3.2-3B-Fluxed datasets: - VincentGOURBIN/FluxPrompting language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - mlx --- # mlx-community/Llama-3.2-3B-Fluxed The Model [mlx-community/Llama-3.2-3B-Fluxed](https://huggingface.co/mlx-community/Llama-3.2-3B-Fluxed) was converted to MLX format from [VincentGOURBIN/Llama-3.2-3B-Fluxed](https://huggingface.co/VincentGOURBIN/Llama-3.2-3B-Fluxed) using mlx-lm version **0.19.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model_id = "mlx-community/Llama-3.2-3B-Fluxed" model, tokenizer = load(model_id) user_need = "a toucan coding on a mac" system_message = """ You are a prompt creation assistant for FLUX, an AI image generation model. Your mission is to help the user craft a detailed and optimized prompt by following these steps: 1. **Understanding the User's Needs**: - The user provides a basic idea, concept, or description. - Analyze their input to determine essential details and nuances. 2. **Enhancing Details**: - Enrich the basic idea with vivid, specific, and descriptive elements. - Include factors such as lighting, mood, style, perspective, and specific objects or elements the user wants in the scene. 3. **Formatting the Prompt**: - Structure the enriched description into a clear, precise, and effective prompt. - Ensure the prompt is tailored for high-quality output from the FLUX model, considering its strengths (e.g., photorealistic details, fine anatomy, or artistic styles). Use this process to compose a detailed and coherent prompt. Ensure the final prompt is clear and complete, and write your response in English. Ensure that the final part is a synthesized version of the prompt. """ if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "system", "content": system_message}, {"role": "user", "content": user_need}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True,max_tokens=1000) ```
[ "CRAFT" ]
Non_BioNLP
Dagobert42/distilbert-base-uncased-biored-augmented
Dagobert42
token-classification
[ "transformers", "safetensors", "distilbert", "token-classification", "low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer", "en", "dataset:medicine", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,707,937,268,000
2024-02-22T11:27:55
22
0
--- base_model: distilbert-base-uncased datasets: - medicine language: - en license: mit metrics: - accuracy - precision - recall - f1 tags: - low-resource NER - token_classification - biomedicine - medical NER - generated_from_trainer model-index: - name: Dagobert42/distilbert-base-uncased-biored-augmented results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Dagobert42/distilbert-base-uncased-biored-augmented This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bigbio/biored dataset. It achieves the following results on the evaluation set: - Loss: 0.5692 - Accuracy: 0.7978 - Precision: 0.5993 - Recall: 0.5337 - F1: 0.5536 - Weighted F1: 0.7929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:| | No log | 1.0 | 25 | 0.6037 | 0.7824 | 0.5931 | 0.4937 | 0.5272 | 0.7719 | | No log | 2.0 | 50 | 0.5858 | 0.7932 | 0.6023 | 0.5298 | 0.5511 | 0.7849 | | No log | 3.0 | 75 | 0.5887 | 0.795 | 0.5757 | 0.5283 | 0.544 | 0.7842 | | No log | 4.0 | 100 | 0.5890 | 0.7937 | 0.5911 | 0.5331 | 0.5466 | 0.7864 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.15.0
[ "BIORED" ]
BioNLP
Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task592
Lots-of-LoRAs
null
[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:mit", "region:us" ]
1,736,087,017,000
2025-01-05T14:23:42
0
0
--- base_model: mistralai/Mistral-7B-Instruct-v0.2 language: en library_name: pytorch license: mit --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task592 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task592_sciq_incorrect_answer_generation - **Developed by:** bruel - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** LoRA - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/bruel-gabrielsson - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset 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. --> https://huggingface.co/datasets/Lots-of-LoRAs/task592_sciq_incorrect_answer_generation sourced from https://github.com/allenai/natural-instructions ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
[ "SCIQ" ]
Non_BioNLP
codegood/Llama3-Aloe-8B-Alpha-Q4_K_M-GGUF
codegood
question-answering
[ "transformers", "gguf", "biology", "medical", "llama-cpp", "gguf-my-repo", "question-answering", "en", "dataset:argilla/dpo-mix-7k", "dataset:nvidia/HelpSteer", "dataset:jondurbin/airoboros-3.2", "dataset:hkust-nlp/deita-10k-v0", "dataset:LDJnr/Capybara", "dataset:HPAI-BSC/CareQA", "dataset:GBaker/MedQA-USMLE-4-options", "dataset:lukaemon/mmlu", "dataset:bigbio/pubmed_qa", "dataset:openlifescienceai/medmcqa", "dataset:bigbio/med_qa", "dataset:HPAI-BSC/better-safe-than-sorry", "dataset:HPAI-BSC/pubmedqa-cot", "dataset:HPAI-BSC/medmcqa-cot", "dataset:HPAI-BSC/medqa-cot", "base_model:HPAI-BSC/Llama3-Aloe-8B-Alpha", "base_model:quantized:HPAI-BSC/Llama3-Aloe-8B-Alpha", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]
1,729,098,632,000
2024-10-16T17:10:56
60
1
--- base_model: HPAI-BSC/Llama3-Aloe-8B-Alpha datasets: - argilla/dpo-mix-7k - nvidia/HelpSteer - jondurbin/airoboros-3.2 - hkust-nlp/deita-10k-v0 - LDJnr/Capybara - HPAI-BSC/CareQA - GBaker/MedQA-USMLE-4-options - lukaemon/mmlu - bigbio/pubmed_qa - openlifescienceai/medmcqa - bigbio/med_qa - HPAI-BSC/better-safe-than-sorry - HPAI-BSC/pubmedqa-cot - HPAI-BSC/medmcqa-cot - HPAI-BSC/medqa-cot language: - en library_name: transformers license: cc-by-nc-4.0 pipeline_tag: question-answering tags: - biology - medical - llama-cpp - gguf-my-repo --- # codegood/Llama3-Aloe-8B-Alpha-Q4_K_M-GGUF This model was converted to GGUF format from [`HPAI-BSC/Llama3-Aloe-8B-Alpha`](https://huggingface.co/HPAI-BSC/Llama3-Aloe-8B-Alpha) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/HPAI-BSC/Llama3-Aloe-8B-Alpha) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo codegood/Llama3-Aloe-8B-Alpha-Q4_K_M-GGUF --hf-file llama3-aloe-8b-alpha-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo codegood/Llama3-Aloe-8B-Alpha-Q4_K_M-GGUF --hf-file llama3-aloe-8b-alpha-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo codegood/Llama3-Aloe-8B-Alpha-Q4_K_M-GGUF --hf-file llama3-aloe-8b-alpha-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo codegood/Llama3-Aloe-8B-Alpha-Q4_K_M-GGUF --hf-file llama3-aloe-8b-alpha-q4_k_m.gguf -c 2048 ```
[ "MEDQA", "PUBMEDQA" ]
BioNLP
chris-code/multilingual-e5-large-Q8_0-GGUF
chris-code
feature-extraction
[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "feature-extraction", "llama-cpp", "gguf-my-repo", "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", "base_model:intfloat/multilingual-e5-large", "base_model:quantized:intfloat/multilingual-e5-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,724,915,602,000
2024-08-29T07:13:29
21
0
--- base_model: intfloat/multilingual-e5-large 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 - llama-cpp - gguf-my-repo 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 revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.34095494283793 - type: f1 value: 67.01134288992947 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (he) type: mteb/amazon_massive_intent config: he split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.61264290517822 - type: f1 value: 64.68730512660757 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hi) type: mteb/amazon_massive_intent config: hi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.79757901815738 - type: f1 value: 65.24938539425598 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hu) type: mteb/amazon_massive_intent config: hu split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.68728984532616 - type: f1 value: 67.0487169762553 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hy) type: mteb/amazon_massive_intent config: hy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.07464694014795 - type: f1 value: 59.183532276789286 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (id) type: mteb/amazon_massive_intent config: id split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.04707464694015 - type: f1 value: 67.66829629003848 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (is) type: mteb/amazon_massive_intent config: is split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.42434431741762 - type: f1 value: 59.01617226544757 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (it) type: mteb/amazon_massive_intent config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.53127101546738 - type: f1 value: 68.10033760906255 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ja) type: mteb/amazon_massive_intent config: ja split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.50504371217215 - type: f1 value: 69.74931103158923 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (jv) type: mteb/amazon_massive_intent config: jv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.91190316072628 - type: f1 value: 54.05551136648796 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ka) type: mteb/amazon_massive_intent config: ka split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.78211163416275 - type: f1 value: 49.874888544058535 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (km) type: mteb/amazon_massive_intent config: km split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 47.017484868863484 - type: f1 value: 44.53364263352014 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (kn) type: mteb/amazon_massive_intent config: kn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.16207128446537 - type: f1 value: 59.01185692320829 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ko) type: mteb/amazon_massive_intent config: ko split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.42501681237391 - type: f1 value: 67.13169450166086 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (lv) type: mteb/amazon_massive_intent config: lv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.0780094149294 - type: f1 value: 64.41720167850707 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ml) type: mteb/amazon_massive_intent config: ml split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.57162071284466 - type: f1 value: 62.414138683804424 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (mn) type: mteb/amazon_massive_intent config: mn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.71149966375252 - type: f1 value: 58.594805125087234 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ms) type: mteb/amazon_massive_intent config: ms split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.03900470746471 - type: f1 value: 63.87937257883887 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (my) type: mteb/amazon_massive_intent config: my split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.8776059179556 - type: f1 value: 57.48587618059131 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (nb) type: mteb/amazon_massive_intent config: nb split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.87895090786819 - type: f1 value: 66.8141299430347 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (nl) type: mteb/amazon_massive_intent config: nl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.45057162071285 - type: f1 value: 67.46444039673516 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.546738399462 - type: f1 value: 68.63640876702655 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pt) type: mteb/amazon_massive_intent config: pt split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.72965702757229 - type: f1 value: 68.54119560379115 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ro) type: mteb/amazon_massive_intent config: ro split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.35574983187625 - type: f1 value: 65.88844917691927 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ru) type: mteb/amazon_massive_intent config: ru split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.70477471418964 - type: f1 value: 69.19665697061978 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sl) type: mteb/amazon_massive_intent config: sl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.0880968392737 - type: f1 value: 64.76962317666086 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sq) type: mteb/amazon_massive_intent config: sq split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.18493611297916 - type: f1 value: 62.49984559035371 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sv) type: mteb/amazon_massive_intent config: sv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.75857431069265 - type: f1 value: 69.20053687623418 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sw) type: mteb/amazon_massive_intent config: sw split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.500336247478145 - type: f1 value: 55.2972398687929 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ta) type: mteb/amazon_massive_intent config: ta split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.68997982515132 - type: f1 value: 59.36848202755348 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (te) type: mteb/amazon_massive_intent config: te split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.01950235373235 - type: f1 value: 60.09351954625423 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (th) type: mteb/amazon_massive_intent config: th split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.29186281102892 - type: f1 value: 67.57860496703447 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (tl) type: mteb/amazon_massive_intent config: tl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.77471418964357 - type: f1 value: 61.913983147713836 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (tr) type: mteb/amazon_massive_intent config: tr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.87222595830532 - type: f1 value: 66.03679033708141 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ur) type: mteb/amazon_massive_intent config: ur split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.04505716207127 - type: f1 value: 61.28569169817908 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (vi) type: mteb/amazon_massive_intent config: vi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.38466711499663 - type: f1 value: 67.20532357036844 - 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: 71.12306657700067 - type: f1 value: 68.91251226588182 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-TW) type: mteb/amazon_massive_intent config: zh-TW split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.20040349697378 - type: f1 value: 66.02657347714175 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (af) type: mteb/amazon_massive_scenario config: af split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.73907195696032 - type: f1 value: 66.98484521791418 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (am) type: mteb/amazon_massive_scenario config: am split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.58843308675185 - type: f1 value: 58.95591723092005 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ar) type: mteb/amazon_massive_scenario config: ar split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.22730329522528 - type: f1 value: 66.0894499712115 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (az) type: mteb/amazon_massive_scenario config: az split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.48285137861465 - type: f1 value: 65.21963176785157 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (bn) type: mteb/amazon_massive_scenario config: bn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.74714189643578 - type: f1 value: 66.8212192745412 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (cy) type: mteb/amazon_massive_scenario config: cy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.09213180901143 - type: f1 value: 56.70735546356339 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (da) type: mteb/amazon_massive_scenario config: da split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.05716207128448 - type: f1 value: 74.8413712365364 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (de) type: mteb/amazon_massive_scenario config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.69737726967047 - type: f1 value: 74.7664341963 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (el) type: mteb/amazon_massive_scenario config: el split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.90383322125084 - type: f1 value: 73.59201554448323 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.51176866173503 - type: f1 value: 77.46104434577758 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (es) type: mteb/amazon_massive_scenario config: es split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.31069266980496 - type: f1 value: 74.61048660675635 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fa) type: mteb/amazon_massive_scenario config: fa split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.95225285810356 - type: f1 value: 72.33160006574627 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fi) type: mteb/amazon_massive_scenario config: fi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.12373907195696 - type: f1 value: 73.20921012557481 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.86684599865501 - type: f1 value: 73.82348774610831 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (he) type: mteb/amazon_massive_scenario config: he split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.40215198386012 - type: f1 value: 71.11945183971858 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hi) type: mteb/amazon_massive_scenario config: hi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.12844653665098 - type: f1 value: 71.34450495911766 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hu) type: mteb/amazon_massive_scenario config: hu split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.52252858103566 - type: f1 value: 73.98878711342999 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hy) type: mteb/amazon_massive_scenario config: hy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.93611297915265 - type: f1 value: 63.723200467653385 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (id) type: mteb/amazon_massive_scenario config: id split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.11903160726295 - type: f1 value: 73.82138439467096 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (is) type: mteb/amazon_massive_scenario config: is split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.15198386012105 - type: f1 value: 66.02172193802167 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (it) type: mteb/amazon_massive_scenario config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.32414256893072 - type: f1 value: 74.30943421170574 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ja) type: mteb/amazon_massive_scenario config: ja split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.46805648957633 - type: f1 value: 77.62808409298209 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (jv) type: mteb/amazon_massive_scenario config: jv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.318762609280434 - type: f1 value: 62.094284066075076 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ka) type: mteb/amazon_massive_scenario config: ka split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 58.34902488231338 - type: f1 value: 57.12893860987984 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (km) type: mteb/amazon_massive_scenario config: km split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 50.88433086751849 - type: f1 value: 48.2272350802058 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (kn) type: mteb/amazon_massive_scenario config: kn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.4425016812374 - type: f1 value: 64.61463095996173 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ko) type: mteb/amazon_massive_scenario config: ko split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.04707464694015 - type: f1 value: 75.05099199098998 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (lv) type: mteb/amazon_massive_scenario config: lv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.50437121721586 - type: f1 value: 69.83397721096314 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ml) type: mteb/amazon_massive_scenario config: ml split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.94283792871553 - type: f1 value: 68.8704663703913 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (mn) type: mteb/amazon_massive_scenario config: mn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.79488903833222 - type: f1 value: 63.615424063345436 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ms) type: mteb/amazon_massive_scenario config: ms split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.88231338264963 - type: f1 value: 68.57892302593237 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (my) type: mteb/amazon_massive_scenario config: my split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.248150638870214 - type: f1 value: 61.06680605338809 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (nb) type: mteb/amazon_massive_scenario config: nb split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.84196368527236 - type: f1 value: 74.52566464968763 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (nl) type: mteb/amazon_massive_scenario config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.8285137861466 - type: f1 value: 74.8853197608802 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.13248150638869 - type: f1 value: 74.3982040999179 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pt) type: mteb/amazon_massive_scenario config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.49024882313383 - type: f1 value: 73.82153848368573 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ro) type: mteb/amazon_massive_scenario config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.72158708809684 - type: f1 value: 71.85049433180541 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ru) type: mteb/amazon_massive_scenario config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.137861466039 - type: f1 value: 75.37628348188467 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sl) type: mteb/amazon_massive_scenario config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.86953597848016 - type: f1 value: 71.87537624521661 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sq) type: mteb/amazon_massive_scenario config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.27572293207801 - type: f1 value: 68.80017302344231 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sv) type: mteb/amazon_massive_scenario config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.09952925353059 - type: f1 value: 76.07992707688408 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sw) type: mteb/amazon_massive_scenario config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.140551445864155 - type: f1 value: 61.73855010331415 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ta) type: mteb/amazon_massive_scenario config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.27774041694687 - type: f1 value: 64.83664868894539 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (te) type: mteb/amazon_massive_scenario config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.69468728984533 - type: f1 value: 64.76239666920868 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (th) type: mteb/amazon_massive_scenario config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.44653665097512 - type: f1 value: 73.14646052013873 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (tl) type: mteb/amazon_massive_scenario config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.71351714862139 - type: f1 value: 66.67212180163382 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (tr) type: mteb/amazon_massive_scenario config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.9946200403497 - type: f1 value: 73.87348793725525 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ur) type: mteb/amazon_massive_scenario config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.15400134498992 - type: f1 value: 67.09433241421094 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (vi) type: mteb/amazon_massive_scenario config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.11365164761264 - type: f1 value: 73.59502539433753 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.82582380632145 - type: f1 value: 76.89992945316313 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-TW) type: mteb/amazon_massive_scenario config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.81237390719569 - type: f1 value: 72.36499770986265 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.480506569594695 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 29.71252128004552 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.421396787056548 - type: mrr value: 32.48155274872267 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.595 - type: map_at_10 value: 12.642000000000001 - type: map_at_100 value: 15.726 - type: map_at_1000 value: 17.061999999999998 - type: map_at_3 value: 9.125 - type: map_at_5 value: 10.866000000000001 - type: mrr_at_1 value: 43.344 - type: mrr_at_10 value: 52.227999999999994 - type: mrr_at_100 value: 52.898999999999994 - type: mrr_at_1000 value: 52.944 - type: mrr_at_3 value: 49.845 - type: mrr_at_5 value: 51.115 - type: ndcg_at_1 value: 41.949999999999996 - type: ndcg_at_10 value: 33.995 - type: ndcg_at_100 value: 30.869999999999997 - type: ndcg_at_1000 value: 39.487 - type: ndcg_at_3 value: 38.903999999999996 - type: ndcg_at_5 value: 37.236999999999995 - type: precision_at_1 value: 43.344 - type: precision_at_10 value: 25.480000000000004 - type: precision_at_100 value: 7.672 - type: precision_at_1000 value: 2.028 - type: precision_at_3 value: 36.636 - type: precision_at_5 value: 32.632 - type: recall_at_1 value: 5.595 - type: recall_at_10 value: 16.466 - type: recall_at_100 value: 31.226 - type: recall_at_1000 value: 62.778999999999996 - type: recall_at_3 value: 9.931 - type: recall_at_5 value: 12.884 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 40.414 - type: map_at_10 value: 56.754000000000005 - type: map_at_100 value: 57.457 - type: map_at_1000 value: 57.477999999999994 - type: map_at_3 value: 52.873999999999995 - type: map_at_5 value: 55.175 - type: mrr_at_1 value: 45.278 - type: mrr_at_10 value: 59.192 - type: mrr_at_100 value: 59.650000000000006 - type: mrr_at_1000 value: 59.665 - type: mrr_at_3 value: 56.141 - type: mrr_at_5 value: 57.998000000000005 - type: ndcg_at_1 value: 45.278 - type: ndcg_at_10 value: 64.056 - type: ndcg_at_100 value: 66.89 - type: ndcg_at_1000 value: 67.364 - type: ndcg_at_3 value: 56.97 - type: ndcg_at_5 value: 60.719 - type: precision_at_1 value: 45.278 - type: precision_at_10 value: 9.994 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.512 - type: precision_at_5 value: 17.509 - type: recall_at_1 value: 40.414 - type: recall_at_10 value: 83.596 - type: recall_at_100 value: 95.72 - type: recall_at_1000 value: 99.24 - type: recall_at_3 value: 65.472 - type: recall_at_5 value: 74.039 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.352 - type: map_at_10 value: 84.369 - type: map_at_100 value: 85.02499999999999 - type: map_at_1000 value: 85.04 - type: map_at_3 value: 81.42399999999999 - type: map_at_5 value: 83.279 - type: mrr_at_1 value: 81.05 - type: mrr_at_10 value: 87.401 - type: mrr_at_100 value: 87.504 - type: mrr_at_1000 value: 87.505 - type: mrr_at_3 value: 86.443 - type: mrr_at_5 value: 87.10799999999999 - type: ndcg_at_1 value: 81.04 - type: ndcg_at_10 value: 88.181 - type: ndcg_at_100 value: 89.411 - type: ndcg_at_1000 value: 89.507 - type: ndcg_at_3 value: 85.28099999999999 - type: ndcg_at_5 value: 86.888 - type: precision_at_1 value: 81.04 - type: precision_at_10 value: 13.406 - type: precision_at_100 value: 1.5350000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.31 - type: precision_at_5 value: 24.54 - type: recall_at_1 value: 70.352 - type: recall_at_10 value: 95.358 - type: recall_at_100 value: 99.541 - type: recall_at_1000 value: 99.984 - type: recall_at_3 value: 87.111 - type: recall_at_5 value: 91.643 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 46.54068723291946 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.216287629895994 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.023000000000001 - type: map_at_10 value: 10.071 - type: map_at_100 value: 11.892 - type: map_at_1000 value: 12.196 - type: map_at_3 value: 7.234 - type: map_at_5 value: 8.613999999999999 - type: mrr_at_1 value: 19.900000000000002 - type: mrr_at_10 value: 30.516 - type: mrr_at_100 value: 31.656000000000002 - type: mrr_at_1000 value: 31.723000000000003 - type: mrr_at_3 value: 27.400000000000002 - type: mrr_at_5 value: 29.270000000000003 - type: ndcg_at_1 value: 19.900000000000002 - type: ndcg_at_10 value: 17.474 - type: ndcg_at_100 value: 25.020999999999997 - type: ndcg_at_1000 value: 30.728 - type: ndcg_at_3 value: 16.588 - type: ndcg_at_5 value: 14.498 - type: precision_at_1 value: 19.900000000000002 - type: precision_at_10 value: 9.139999999999999 - type: precision_at_100 value: 2.011 - type: precision_at_1000 value: 0.33899999999999997 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 12.839999999999998 - type: recall_at_1 value: 4.023000000000001 - type: recall_at_10 value: 18.497 - type: recall_at_100 value: 40.8 - type: recall_at_1000 value: 68.812 - type: recall_at_3 value: 9.508 - type: recall_at_5 value: 12.983 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.967008785134 - type: cos_sim_spearman value: 80.23142141101837 - type: euclidean_pearson value: 81.20166064704539 - type: euclidean_spearman value: 80.18961335654585 - type: manhattan_pearson value: 81.13925443187625 - type: manhattan_spearman value: 80.07948723044424 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.94262461316023 - type: cos_sim_spearman value: 80.01596278563865 - type: euclidean_pearson value: 83.80799622922581 - type: euclidean_spearman value: 79.94984954947103 - type: manhattan_pearson value: 83.68473841756281 - type: manhattan_spearman value: 79.84990707951822 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 80.57346443146068 - type: cos_sim_spearman value: 81.54689837570866 - type: euclidean_pearson value: 81.10909881516007 - type: euclidean_spearman value: 81.56746243261762 - type: manhattan_pearson value: 80.87076036186582 - type: manhattan_spearman value: 81.33074987964402 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.54733787179849 - type: cos_sim_spearman value: 77.72202105610411 - type: euclidean_pearson value: 78.9043595478849 - type: euclidean_spearman value: 77.93422804309435 - type: manhattan_pearson value: 78.58115121621368 - type: manhattan_spearman value: 77.62508135122033 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.59880017237558 - type: cos_sim_spearman value: 89.31088630824758 - type: euclidean_pearson value: 88.47069261564656 - type: euclidean_spearman value: 89.33581971465233 - type: manhattan_pearson value: 88.40774264100956 - type: manhattan_spearman value: 89.28657485627835 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.08055117917084 - type: cos_sim_spearman value: 85.78491813080304 - type: euclidean_pearson value: 84.99329155500392 - type: euclidean_spearman value: 85.76728064677287 - type: manhattan_pearson value: 84.87947428989587 - type: manhattan_spearman value: 85.62429454917464 - task: type: STS dataset: name: MTEB STS17 (ko-ko) type: mteb/sts17-crosslingual-sts config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.14190939287384 - type: cos_sim_spearman value: 82.27331573306041 - type: euclidean_pearson value: 81.891896953716 - type: euclidean_spearman value: 82.37695542955998 - type: manhattan_pearson value: 81.73123869460504 - type: manhattan_spearman value: 82.19989168441421 - task: type: STS dataset: name: MTEB STS17 (ar-ar) type: mteb/sts17-crosslingual-sts config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 76.84695301843362 - type: cos_sim_spearman value: 77.87790986014461 - type: euclidean_pearson value: 76.91981583106315 - type: euclidean_spearman value: 77.88154772749589 - type: manhattan_pearson value: 76.94953277451093 - type: manhattan_spearman value: 77.80499230728604 - task: type: STS dataset: name: MTEB STS17 (en-ar) type: mteb/sts17-crosslingual-sts config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 75.44657840482016 - type: cos_sim_spearman value: 75.05531095119674 - type: euclidean_pearson value: 75.88161755829299 - type: euclidean_spearman value: 74.73176238219332 - type: manhattan_pearson value: 75.63984765635362 - type: manhattan_spearman value: 74.86476440770737 - task: type: STS dataset: name: MTEB STS17 (en-de) type: mteb/sts17-crosslingual-sts config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.64700140524133 - type: cos_sim_spearman value: 86.16014210425672 - type: euclidean_pearson value: 86.49086860843221 - type: euclidean_spearman value: 86.09729326815614 - type: manhattan_pearson value: 86.43406265125513 - type: manhattan_spearman value: 86.17740150939994 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.91170098764921 - type: cos_sim_spearman value: 88.12437004058931 - type: euclidean_pearson value: 88.81828254494437 - type: euclidean_spearman value: 88.14831794572122 - type: manhattan_pearson value: 88.93442183448961 - type: manhattan_spearman value: 88.15254630778304 - task: type: STS dataset: name: MTEB STS17 (en-tr) type: mteb/sts17-crosslingual-sts config: en-tr split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 72.91390577997292 - type: cos_sim_spearman value: 71.22979457536074 - type: euclidean_pearson value: 74.40314008106749 - type: euclidean_spearman value: 72.54972136083246 - type: manhattan_pearson value: 73.85687539530218 - type: manhattan_spearman value: 72.09500771742637 - task: type: STS dataset: name: MTEB STS17 (es-en) type: mteb/sts17-crosslingual-sts config: es-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 80.9301067983089 - type: cos_sim_spearman value: 80.74989828346473 - type: euclidean_pearson value: 81.36781301814257 - type: euclidean_spearman value: 80.9448819964426 - type: manhattan_pearson value: 81.0351322685609 - type: manhattan_spearman value: 80.70192121844177 - task: type: STS dataset: name: MTEB STS17 (es-es) type: mteb/sts17-crosslingual-sts config: es-es split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.13820465980005 - type: cos_sim_spearman value: 86.73532498758757 - type: euclidean_pearson value: 87.21329451846637 - type: euclidean_spearman value: 86.57863198601002 - type: manhattan_pearson value: 87.06973713818554 - type: manhattan_spearman value: 86.47534918791499 - task: type: STS dataset: name: MTEB STS17 (fr-en) type: mteb/sts17-crosslingual-sts config: fr-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.48720108904415 - type: cos_sim_spearman value: 85.62221757068387 - type: euclidean_pearson value: 86.1010129512749 - type: euclidean_spearman value: 85.86580966509942 - type: manhattan_pearson value: 86.26800938808971 - type: manhattan_spearman value: 85.88902721678429 - task: type: STS dataset: name: MTEB STS17 (it-en) type: mteb/sts17-crosslingual-sts config: it-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 83.98021347333516 - type: cos_sim_spearman value: 84.53806553803501 - type: euclidean_pearson value: 84.61483347248364 - type: euclidean_spearman value: 85.14191408011702 - type: manhattan_pearson value: 84.75297588825967 - type: manhattan_spearman value: 85.33176753669242 - task: type: STS dataset: name: MTEB STS17 (nl-en) type: mteb/sts17-crosslingual-sts config: nl-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.51856644893233 - type: cos_sim_spearman value: 85.27510748506413 - type: euclidean_pearson value: 85.09886861540977 - type: euclidean_spearman value: 85.62579245860887 - type: manhattan_pearson value: 84.93017860464607 - type: manhattan_spearman value: 85.5063988898453 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.581573200584195 - type: cos_sim_spearman value: 63.05503590247928 - type: euclidean_pearson value: 63.652564812602094 - type: euclidean_spearman value: 62.64811520876156 - type: manhattan_pearson value: 63.506842893061076 - type: manhattan_spearman value: 62.51289573046917 - task: type: STS dataset: name: MTEB STS22 (de) type: mteb/sts22-crosslingual-sts config: de split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 48.2248801729127 - type: cos_sim_spearman value: 56.5936604678561 - type: euclidean_pearson value: 43.98149464089 - type: euclidean_spearman value: 56.108561882423615 - type: manhattan_pearson value: 43.86880305903564 - type: manhattan_spearman value: 56.04671150510166 - task: type: STS dataset: name: MTEB STS22 (es) type: mteb/sts22-crosslingual-sts config: es split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 55.17564527009831 - type: cos_sim_spearman value: 64.57978560979488 - type: euclidean_pearson value: 58.8818330154583 - type: euclidean_spearman value: 64.99214839071281 - type: manhattan_pearson value: 58.72671436121381 - type: manhattan_spearman value: 65.10713416616109 - task: type: STS dataset: name: MTEB STS22 (pl) type: mteb/sts22-crosslingual-sts config: pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 26.772131864023297 - type: cos_sim_spearman value: 34.68200792408681 - type: euclidean_pearson value: 16.68082419005441 - type: euclidean_spearman value: 34.83099932652166 - type: manhattan_pearson value: 16.52605949659529 - type: manhattan_spearman value: 34.82075801399475 - task: type: STS dataset: name: MTEB STS22 (tr) type: mteb/sts22-crosslingual-sts config: tr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 54.42415189043831 - type: cos_sim_spearman value: 63.54594264576758 - type: euclidean_pearson value: 57.36577498297745 - type: euclidean_spearman value: 63.111466379158074 - type: manhattan_pearson value: 57.584543715873885 - type: manhattan_spearman value: 63.22361054139183 - task: type: STS dataset: name: MTEB STS22 (ar) type: mteb/sts22-crosslingual-sts config: ar split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 47.55216762405518 - type: cos_sim_spearman value: 56.98670142896412 - type: euclidean_pearson value: 50.15318757562699 - type: euclidean_spearman value: 56.524941926541906 - type: manhattan_pearson value: 49.955618528674904 - type: manhattan_spearman value: 56.37102209240117 - task: type: STS dataset: name: MTEB STS22 (ru) type: mteb/sts22-crosslingual-sts config: ru split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 49.20540980338571 - type: cos_sim_spearman value: 59.9009453504406 - type: euclidean_pearson value: 49.557749853620535 - type: euclidean_spearman value: 59.76631621172456 - type: manhattan_pearson value: 49.62340591181147 - type: manhattan_spearman value: 59.94224880322436 - task: type: STS dataset: name: MTEB STS22 (zh) type: mteb/sts22-crosslingual-sts config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 51.508169956576985 - type: cos_sim_spearman value: 66.82461565306046 - type: euclidean_pearson value: 56.2274426480083 - type: euclidean_spearman value: 66.6775323848333 - type: manhattan_pearson value: 55.98277796300661 - type: manhattan_spearman value: 66.63669848497175 - task: type: STS dataset: name: MTEB STS22 (fr) type: mteb/sts22-crosslingual-sts config: fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 72.86478788045507 - type: cos_sim_spearman value: 76.7946552053193 - type: euclidean_pearson value: 75.01598530490269 - type: euclidean_spearman value: 76.83618917858281 - type: manhattan_pearson value: 74.68337628304332 - type: manhattan_spearman value: 76.57480204017773 - task: type: STS dataset: name: MTEB STS22 (de-en) type: mteb/sts22-crosslingual-sts config: de-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 55.922619099401984 - type: cos_sim_spearman value: 56.599362477240774 - type: euclidean_pearson value: 56.68307052369783 - type: euclidean_spearman value: 54.28760436777401 - type: manhattan_pearson value: 56.67763566500681 - type: manhattan_spearman value: 53.94619541711359 - task: type: STS dataset: name: MTEB STS22 (es-en) type: mteb/sts22-crosslingual-sts config: es-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.74357206710913 - type: cos_sim_spearman value: 72.5208244925311 - type: euclidean_pearson value: 67.49254562186032 - type: euclidean_spearman value: 72.02469076238683 - type: manhattan_pearson value: 67.45251772238085 - type: manhattan_spearman value: 72.05538819984538 - task: type: STS dataset: name: MTEB STS22 (it) type: mteb/sts22-crosslingual-sts config: it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 71.25734330033191 - type: cos_sim_spearman value: 76.98349083946823 - type: euclidean_pearson value: 73.71642838667736 - type: euclidean_spearman value: 77.01715504651384 - type: manhattan_pearson value: 73.61712711868105 - type: manhattan_spearman value: 77.01392571153896 - task: type: STS dataset: name: MTEB STS22 (pl-en) type: mteb/sts22-crosslingual-sts config: pl-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.18215462781212 - type: cos_sim_spearman value: 65.54373266117607 - type: euclidean_pearson value: 64.54126095439005 - type: euclidean_spearman value: 65.30410369102711 - type: manhattan_pearson value: 63.50332221148234 - type: manhattan_spearman value: 64.3455878104313 - task: type: STS dataset: name: MTEB STS22 (zh-en) type: mteb/sts22-crosslingual-sts config: zh-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.30509221440029 - type: cos_sim_spearman value: 65.99582704642478 - type: euclidean_pearson value: 63.43818859884195 - type: euclidean_spearman value: 66.83172582815764 - type: manhattan_pearson value: 63.055779168508764 - type: manhattan_spearman value: 65.49585020501449 - task: type: STS dataset: name: MTEB STS22 (es-it) type: mteb/sts22-crosslingual-sts config: es-it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 59.587830825340404 - type: cos_sim_spearman value: 68.93467614588089 - type: euclidean_pearson value: 62.3073527367404 - type: euclidean_spearman value: 69.69758171553175 - type: manhattan_pearson value: 61.9074580815789 - type: manhattan_spearman value: 69.57696375597865 - task: type: STS dataset: name: MTEB STS22 (de-fr) type: mteb/sts22-crosslingual-sts config: de-fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 57.143220125577066 - type: cos_sim_spearman value: 67.78857859159226 - type: euclidean_pearson value: 55.58225107923733 - type: euclidean_spearman value: 67.80662907184563 - type: manhattan_pearson value: 56.24953502726514 - type: manhattan_spearman value: 67.98262125431616 - task: type: STS dataset: name: MTEB STS22 (de-pl) type: mteb/sts22-crosslingual-sts config: de-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 21.826928900322066 - type: cos_sim_spearman value: 49.578506634400405 - type: euclidean_pearson value: 27.939890138843214 - type: euclidean_spearman value: 52.71950519136242 - type: manhattan_pearson value: 26.39878683847546 - type: manhattan_spearman value: 47.54609580342499 - task: type: STS dataset: name: MTEB STS22 (fr-pl) type: mteb/sts22-crosslingual-sts config: fr-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 57.27603854632001 - type: cos_sim_spearman value: 50.709255283710995 - type: euclidean_pearson value: 59.5419024445929 - type: euclidean_spearman value: 50.709255283710995 - type: manhattan_pearson value: 59.03256832438492 - type: manhattan_spearman value: 61.97797868009122 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.00757054859712 - type: cos_sim_spearman value: 87.29283629622222 - type: euclidean_pearson value: 86.54824171775536 - type: euclidean_spearman value: 87.24364730491402 - type: manhattan_pearson value: 86.5062156915074 - type: manhattan_spearman value: 87.15052170378574 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.03549357197389 - type: mrr value: 95.05437645143527 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.260999999999996 - type: map_at_10 value: 66.259 - type: map_at_100 value: 66.884 - type: map_at_1000 value: 66.912 - type: map_at_3 value: 63.685 - type: map_at_5 value: 65.35499999999999 - type: mrr_at_1 value: 60.333000000000006 - type: mrr_at_10 value: 67.5 - type: mrr_at_100 value: 68.013 - type: mrr_at_1000 value: 68.038 - type: mrr_at_3 value: 65.61099999999999 - type: mrr_at_5 value: 66.861 - type: ndcg_at_1 value: 60.333000000000006 - type: ndcg_at_10 value: 70.41 - type: ndcg_at_100 value: 73.10600000000001 - type: ndcg_at_1000 value: 73.846 - type: ndcg_at_3 value: 66.133 - type: ndcg_at_5 value: 68.499 - type: precision_at_1 value: 60.333000000000006 - type: precision_at_10 value: 9.232999999999999 - type: precision_at_100 value: 1.0630000000000002 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.667 - type: precision_at_5 value: 17.067 - type: recall_at_1 value: 57.260999999999996 - type: recall_at_10 value: 81.94399999999999 - type: recall_at_100 value: 93.867 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.339 - type: recall_at_5 value: 76.25 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.74356435643564 - type: cos_sim_ap value: 93.13411948212683 - type: cos_sim_f1 value: 86.80521991300147 - type: cos_sim_precision value: 84.00374181478017 - type: cos_sim_recall value: 89.8 - type: dot_accuracy value: 99.67920792079208 - type: dot_ap value: 89.27277565444479 - type: dot_f1 value: 83.9276990718124 - type: dot_precision value: 82.04393505253104 - type: dot_recall value: 85.9 - type: euclidean_accuracy value: 99.74257425742574 - type: euclidean_ap value: 93.17993008259062 - type: euclidean_f1 value: 86.69396110542476 - type: euclidean_precision value: 88.78406708595388 - type: euclidean_recall value: 84.7 - type: manhattan_accuracy value: 99.74257425742574 - type: manhattan_ap value: 93.14413755550099 - type: manhattan_f1 value: 86.82483594144371 - type: manhattan_precision value: 87.66564729867483 - type: manhattan_recall value: 86 - type: max_accuracy value: 99.74356435643564 - type: max_ap value: 93.17993008259062 - type: max_f1 value: 86.82483594144371 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 57.525863806168566 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.68850574423839 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.71580650644033 - type: mrr value: 50.50971903913081 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.152190498799484 - type: cos_sim_spearman value: 29.686180371952727 - type: dot_pearson value: 27.248664793816342 - type: dot_spearman value: 28.37748983721745 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.20400000000000001 - type: map_at_10 value: 1.6209999999999998 - type: map_at_100 value: 9.690999999999999 - type: map_at_1000 value: 23.733 - type: map_at_3 value: 0.575 - type: map_at_5 value: 0.885 - type: mrr_at_1 value: 78 - type: mrr_at_10 value: 86.56700000000001 - type: mrr_at_100 value: 86.56700000000001 - type: mrr_at_1000 value: 86.56700000000001 - type: mrr_at_3 value: 85.667 - type: mrr_at_5 value: 86.56700000000001 - type: ndcg_at_1 value: 76 - type: ndcg_at_10 value: 71.326 - type: ndcg_at_100 value: 54.208999999999996 - type: ndcg_at_1000 value: 49.252 - type: ndcg_at_3 value: 74.235 - type: ndcg_at_5 value: 73.833 - type: precision_at_1 value: 78 - type: precision_at_10 value: 74.8 - type: precision_at_100 value: 55.50000000000001 - type: precision_at_1000 value: 21.836 - type: precision_at_3 value: 78 - type: precision_at_5 value: 78 - type: recall_at_1 value: 0.20400000000000001 - type: recall_at_10 value: 1.894 - type: recall_at_100 value: 13.245999999999999 - type: recall_at_1000 value: 46.373 - type: recall_at_3 value: 0.613 - type: recall_at_5 value: 0.991 - task: type: BitextMining dataset: name: MTEB Tatoeba (sqi-eng) type: mteb/tatoeba-bitext-mining config: sqi-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.89999999999999 - type: f1 value: 94.69999999999999 - type: precision value: 94.11666666666667 - type: recall value: 95.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (fry-eng) type: mteb/tatoeba-bitext-mining config: fry-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 68.20809248554913 - type: f1 value: 63.431048720066066 - type: precision value: 61.69143958161298 - type: recall value: 68.20809248554913 - task: type: BitextMining dataset: name: MTEB Tatoeba (kur-eng) type: mteb/tatoeba-bitext-mining config: kur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 71.21951219512195 - type: f1 value: 66.82926829268293 - type: precision value: 65.1260162601626 - type: recall value: 71.21951219512195 - task: type: BitextMining dataset: name: MTEB Tatoeba (tur-eng) type: mteb/tatoeba-bitext-mining config: tur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.2 - type: f1 value: 96.26666666666667 - type: precision value: 95.8 - type: recall value: 97.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (deu-eng) type: mteb/tatoeba-bitext-mining config: deu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 99.3 - type: f1 value: 99.06666666666666 - type: precision value: 98.95 - type: recall value: 99.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (nld-eng) type: mteb/tatoeba-bitext-mining config: nld-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.63333333333333 - type: precision value: 96.26666666666668 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (ron-eng) type: mteb/tatoeba-bitext-mining config: ron-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96 - type: f1 value: 94.86666666666666 - type: precision value: 94.31666666666668 - type: recall value: 96 - task: type: BitextMining dataset: name: MTEB Tatoeba (ang-eng) type: mteb/tatoeba-bitext-mining config: ang-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 47.01492537313433 - type: f1 value: 40.178867566927266 - type: precision value: 38.179295828549556 - type: recall value: 47.01492537313433 - task: type: BitextMining dataset: name: MTEB Tatoeba (ido-eng) type: mteb/tatoeba-bitext-mining config: ido-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 86.5 - type: f1 value: 83.62537480063796 - type: precision value: 82.44555555555554 - type: recall value: 86.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (jav-eng) type: mteb/tatoeba-bitext-mining config: jav-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 80.48780487804879 - type: f1 value: 75.45644599303138 - type: precision value: 73.37398373983739 - type: recall value: 80.48780487804879 - task: type: BitextMining dataset: name: MTEB Tatoeba (isl-eng) type: mteb/tatoeba-bitext-mining config: isl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.7 - type: f1 value: 91.95666666666666 - type: precision value: 91.125 - type: recall value: 93.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (slv-eng) type: mteb/tatoeba-bitext-mining config: slv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.73754556500607 - type: f1 value: 89.65168084244632 - type: precision value: 88.73025516403402 - type: recall value: 91.73754556500607 - task: type: BitextMining dataset: name: MTEB Tatoeba (cym-eng) type: mteb/tatoeba-bitext-mining config: cym-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81.04347826086956 - type: f1 value: 76.2128364389234 - type: precision value: 74.2 - type: recall value: 81.04347826086956 - task: type: BitextMining dataset: name: MTEB Tatoeba (kaz-eng) type: mteb/tatoeba-bitext-mining config: kaz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.65217391304348 - type: f1 value: 79.4376811594203 - type: precision value: 77.65797101449274 - type: recall value: 83.65217391304348 - task: type: BitextMining dataset: name: MTEB Tatoeba (est-eng) type: mteb/tatoeba-bitext-mining config: est-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.5 - type: f1 value: 85.02690476190476 - type: precision value: 83.96261904761904 - type: recall value: 87.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (heb-eng) type: mteb/tatoeba-bitext-mining config: heb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.3 - type: f1 value: 86.52333333333333 - type: precision value: 85.22833333333332 - type: recall value: 89.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (gla-eng) type: mteb/tatoeba-bitext-mining config: gla-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.01809408926418 - type: f1 value: 59.00594446432805 - type: precision value: 56.827215807915444 - type: recall value: 65.01809408926418 - task: type: BitextMining dataset: name: MTEB Tatoeba (mar-eng) type: mteb/tatoeba-bitext-mining config: mar-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.2 - type: f1 value: 88.58 - type: precision value: 87.33333333333334 - type: recall value: 91.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (lat-eng) type: mteb/tatoeba-bitext-mining config: lat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 59.199999999999996 - type: f1 value: 53.299166276284915 - type: precision value: 51.3383908045977 - type: recall value: 59.199999999999996 - task: type: BitextMining dataset: name: MTEB Tatoeba (bel-eng) type: mteb/tatoeba-bitext-mining config: bel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.2 - type: f1 value: 91.2 - type: precision value: 90.25 - type: recall value: 93.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (pms-eng) type: mteb/tatoeba-bitext-mining config: pms-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 64.76190476190476 - type: f1 value: 59.867110667110666 - type: precision value: 58.07390192653351 - type: recall value: 64.76190476190476 - task: type: BitextMining dataset: name: MTEB Tatoeba (gle-eng) type: mteb/tatoeba-bitext-mining config: gle-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.2 - type: f1 value: 71.48147546897547 - type: precision value: 69.65409090909091 - type: recall value: 76.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (pes-eng) type: mteb/tatoeba-bitext-mining config: pes-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.8 - type: f1 value: 92.14 - type: precision value: 91.35833333333333 - type: recall value: 93.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (nob-eng) type: mteb/tatoeba-bitext-mining config: nob-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.89999999999999 - type: f1 value: 97.2 - type: precision value: 96.85000000000001 - type: recall value: 97.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (bul-eng) type: mteb/tatoeba-bitext-mining config: bul-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.6 - type: f1 value: 92.93333333333334 - type: precision value: 92.13333333333333 - type: recall value: 94.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cbk-eng) type: mteb/tatoeba-bitext-mining config: cbk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.1 - type: f1 value: 69.14817460317461 - type: precision value: 67.2515873015873 - type: recall value: 74.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (hun-eng) type: mteb/tatoeba-bitext-mining config: hun-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.19999999999999 - type: f1 value: 94.01333333333335 - type: precision value: 93.46666666666667 - type: recall value: 95.19999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (uig-eng) type: mteb/tatoeba-bitext-mining config: uig-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.9 - type: f1 value: 72.07523809523809 - type: precision value: 70.19777777777779 - type: recall value: 76.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (rus-eng) type: mteb/tatoeba-bitext-mining config: rus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.1 - type: f1 value: 92.31666666666666 - type: precision value: 91.43333333333332 - type: recall value: 94.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (spa-eng) type: mteb/tatoeba-bitext-mining config: spa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.8 - type: f1 value: 97.1 - type: precision value: 96.76666666666668 - type: recall value: 97.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (hye-eng) type: mteb/tatoeba-bitext-mining config: hye-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.85714285714286 - type: f1 value: 90.92093441150045 - type: precision value: 90.00449236298293 - type: recall value: 92.85714285714286 - task: type: BitextMining dataset: name: MTEB Tatoeba (tel-eng) type: mteb/tatoeba-bitext-mining config: tel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.16239316239316 - type: f1 value: 91.33903133903132 - type: precision value: 90.56267806267806 - type: recall value: 93.16239316239316 - task: type: BitextMining dataset: name: MTEB Tatoeba (afr-eng) type: mteb/tatoeba-bitext-mining config: afr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.4 - type: f1 value: 90.25666666666666 - type: precision value: 89.25833333333334 - type: recall value: 92.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (mon-eng) type: mteb/tatoeba-bitext-mining config: mon-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.22727272727272 - type: f1 value: 87.53030303030303 - type: precision value: 86.37121212121211 - type: recall value: 90.22727272727272 - task: type: BitextMining dataset: name: MTEB Tatoeba (arz-eng) type: mteb/tatoeba-bitext-mining config: arz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 79.03563941299791 - type: f1 value: 74.7349505840072 - type: precision value: 72.9035639412998 - type: recall 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: - type: accuracy value: 97 - type: f1 value: 96.15 - type: precision value: 95.76666666666668 - type: recall value: 97 - task: type: BitextMining dataset: name: MTEB Tatoeba (nov-eng) type: mteb/tatoeba-bitext-mining config: nov-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.26459143968872 - type: f1 value: 71.55642023346303 - type: precision value: 69.7544932369835 - type: recall value: 76.26459143968872 - task: type: BitextMining dataset: name: MTEB Tatoeba (gsw-eng) type: mteb/tatoeba-bitext-mining config: gsw-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 58.119658119658126 - type: f1 value: 51.65242165242165 - type: precision value: 49.41768108434775 - type: recall value: 58.119658119658126 - task: type: BitextMining dataset: name: MTEB Tatoeba (nds-eng) type: mteb/tatoeba-bitext-mining config: nds-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.3 - type: f1 value: 69.52055555555555 - type: precision value: 67.7574938949939 - type: recall value: 74.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (ukr-eng) type: mteb/tatoeba-bitext-mining config: ukr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.8 - type: f1 value: 93.31666666666666 - type: precision value: 92.60000000000001 - type: recall value: 94.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (uzb-eng) type: mteb/tatoeba-bitext-mining config: uzb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.63551401869158 - type: f1 value: 72.35202492211837 - type: precision value: 70.60358255451713 - type: recall value: 76.63551401869158 - task: type: BitextMining dataset: name: MTEB Tatoeba (lit-eng) type: mteb/tatoeba-bitext-mining config: lit-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.4 - type: f1 value: 88.4811111111111 - type: precision value: 87.7452380952381 - type: recall 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: - type: accuracy value: 95 - type: f1 value: 93.60666666666667 - type: precision value: 92.975 - type: recall value: 95 - task: type: BitextMining dataset: name: MTEB Tatoeba (lfn-eng) type: mteb/tatoeba-bitext-mining config: lfn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 67.2 - type: f1 value: 63.01595782872099 - type: precision value: 61.596587301587306 - type: recall 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: - type: accuracy value: 95.7 - type: f1 value: 94.52999999999999 - type: precision value: 94 - type: recall value: 95.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (ita-eng) type: mteb/tatoeba-bitext-mining config: ita-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.6 - type: f1 value: 93.28999999999999 - type: precision value: 92.675 - type: recall value: 94.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cmn-eng) type: mteb/tatoeba-bitext-mining config: cmn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.28333333333333 - type: precision value: 94.75 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (lvs-eng) type: mteb/tatoeba-bitext-mining config: lvs-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.9 - type: f1 value: 89.83 - type: precision value: 88.92 - type: recall 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: - type: accuracy value: 94.69999999999999 - type: f1 value: 93.34222222222223 - type: precision value: 92.75416666666668 - type: recall 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: - type: accuracy value: 60.333333333333336 - type: f1 value: 55.31203703703703 - type: precision value: 53.39971108326371 - type: recall 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 - type: precision value: 10.589187932631877 - type: recall 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: - type: accuracy value: 86.7 - type: f1 value: 83.0152380952381 - type: precision value: 81.37833333333333 - type: recall 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: - type: accuracy value: 63.39285714285714 - type: f1 value: 56.832482993197274 - type: precision value: 54.56845238095237 - type: recall value: 63.39285714285714 - task: type: BitextMining dataset: name: MTEB Tatoeba (arq-eng) type: mteb/tatoeba-bitext-mining config: arq-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 48.73765093304062 - type: f1 value: 41.555736920720456 - type: precision value: 39.06874531737319 - type: recall value: 48.73765093304062 - task: type: BitextMining dataset: name: MTEB Tatoeba (kab-eng) type: mteb/tatoeba-bitext-mining config: kab-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 41.099999999999994 - type: f1 value: 36.540165945165946 - type: precision value: 35.05175685425686 - type: recall value: 41.099999999999994 - task: type: BitextMining dataset: name: MTEB Tatoeba (fra-eng) type: mteb/tatoeba-bitext-mining config: fra-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.89999999999999 - type: f1 value: 93.42333333333333 - type: precision value: 92.75833333333333 - type: recall value: 94.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (por-eng) type: mteb/tatoeba-bitext-mining config: por-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.89999999999999 - type: f1 value: 93.63333333333334 - type: precision value: 93.01666666666665 - type: recall value: 94.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (tat-eng) type: mteb/tatoeba-bitext-mining config: tat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.9 - type: f1 value: 73.64833333333334 - type: precision value: 71.90282106782105 - type: recall value: 77.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (oci-eng) type: mteb/tatoeba-bitext-mining config: oci-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 59.4 - type: f1 value: 54.90521367521367 - type: precision value: 53.432840025471606 - type: recall value: 59.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (pol-eng) type: mteb/tatoeba-bitext-mining config: pol-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.6 - type: precision value: 96.2 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (war-eng) type: mteb/tatoeba-bitext-mining config: war-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 67.2 - type: f1 value: 62.25926129426129 - type: precision value: 60.408376623376626 - type: recall value: 67.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (aze-eng) type: mteb/tatoeba-bitext-mining config: aze-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.2 - type: f1 value: 87.60666666666667 - type: precision value: 86.45277777777778 - type: recall value: 90.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (vie-eng) type: mteb/tatoeba-bitext-mining config: vie-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.7 - type: f1 value: 97 - type: precision value: 96.65 - type: recall value: 97.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (nno-eng) type: mteb/tatoeba-bitext-mining config: nno-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.2 - type: f1 value: 91.39746031746031 - type: precision value: 90.6125 - type: recall value: 93.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (cha-eng) type: mteb/tatoeba-bitext-mining config: cha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 32.11678832116788 - type: f1 value: 27.210415386260234 - type: precision value: 26.20408990846947 - type: recall value: 32.11678832116788 - task: type: BitextMining dataset: name: MTEB Tatoeba (mhr-eng) type: mteb/tatoeba-bitext-mining config: mhr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.5 - type: f1 value: 6.787319277832475 - type: precision value: 6.3452094433344435 - type: recall value: 8.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (dan-eng) type: mteb/tatoeba-bitext-mining config: dan-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.1 - type: f1 value: 95.08 - type: precision value: 94.61666666666667 - type: recall value: 96.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (ell-eng) type: mteb/tatoeba-bitext-mining config: ell-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.3 - type: f1 value: 93.88333333333333 - type: precision value: 93.18333333333332 - type: recall value: 95.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (amh-eng) type: mteb/tatoeba-bitext-mining config: amh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.11904761904762 - type: f1 value: 80.69444444444444 - type: precision value: 78.72023809523809 - type: recall value: 85.11904761904762 - task: type: BitextMining dataset: name: MTEB Tatoeba (pam-eng) type: mteb/tatoeba-bitext-mining config: pam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 11.1 - type: f1 value: 9.276381801735853 - type: precision value: 8.798174603174601 - type: recall value: 11.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (hsb-eng) type: mteb/tatoeba-bitext-mining config: hsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 63.56107660455487 - type: f1 value: 58.70433569191332 - type: precision value: 56.896926581464015 - type: recall value: 63.56107660455487 - task: type: BitextMining dataset: name: MTEB Tatoeba (srp-eng) type: mteb/tatoeba-bitext-mining config: srp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.69999999999999 - type: f1 value: 93.10000000000001 - type: precision value: 92.35 - type: recall value: 94.69999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (epo-eng) type: mteb/tatoeba-bitext-mining config: epo-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.8 - type: f1 value: 96.01222222222222 - type: precision value: 95.67083333333332 - type: recall value: 96.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (kzj-eng) type: mteb/tatoeba-bitext-mining config: kzj-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 9.2 - type: f1 value: 7.911555250305249 - type: precision value: 7.631246556216846 - type: recall value: 9.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (awa-eng) type: mteb/tatoeba-bitext-mining config: awa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.48917748917748 - type: f1 value: 72.27375798804371 - type: precision value: 70.14430014430013 - type: recall value: 77.48917748917748 - task: type: BitextMining dataset: name: MTEB Tatoeba (fao-eng) type: mteb/tatoeba-bitext-mining config: fao-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.09923664122137 - type: f1 value: 72.61541257724463 - type: precision value: 70.8998380754106 - type: recall value: 77.09923664122137 - task: type: BitextMining dataset: name: MTEB Tatoeba (mal-eng) type: mteb/tatoeba-bitext-mining config: mal-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.2532751091703 - type: f1 value: 97.69529354682193 - type: precision value: 97.42843279961184 - type: recall value: 98.2532751091703 - task: type: BitextMining dataset: name: MTEB Tatoeba (ile-eng) type: mteb/tatoeba-bitext-mining config: ile-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.8 - type: f1 value: 79.14672619047619 - type: precision value: 77.59489247311828 - type: recall value: 82.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (bos-eng) type: mteb/tatoeba-bitext-mining config: bos-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.35028248587571 - type: f1 value: 92.86252354048965 - type: precision value: 92.2080979284369 - type: recall value: 94.35028248587571 - task: type: BitextMining dataset: name: MTEB Tatoeba (cor-eng) type: mteb/tatoeba-bitext-mining config: cor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.5 - type: f1 value: 6.282429263935621 - type: precision value: 5.783274240739785 - type: recall value: 8.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (cat-eng) type: mteb/tatoeba-bitext-mining config: cat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.7 - type: f1 value: 91.025 - type: precision value: 90.30428571428571 - type: recall value: 92.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (eus-eng) type: mteb/tatoeba-bitext-mining config: eus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81 - type: f1 value: 77.8232380952381 - type: precision value: 76.60194444444444 - type: recall value: 81 - task: type: BitextMining dataset: name: MTEB Tatoeba (yue-eng) type: mteb/tatoeba-bitext-mining config: yue-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91 - type: f1 value: 88.70857142857142 - type: precision value: 87.7 - type: recall value: 91 - task: type: BitextMining dataset: name: MTEB Tatoeba (swe-eng) type: mteb/tatoeba-bitext-mining config: swe-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.3 - type: precision value: 94.76666666666667 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (dtp-eng) type: mteb/tatoeba-bitext-mining config: dtp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.1 - type: f1 value: 7.001008218834307 - type: precision value: 6.708329562594269 - type: recall value: 8.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (kat-eng) type: mteb/tatoeba-bitext-mining config: kat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.1313672922252 - type: f1 value: 84.09070598748882 - type: precision value: 82.79171454104429 - type: recall value: 87.1313672922252 - task: type: BitextMining dataset: name: MTEB Tatoeba (jpn-eng) type: mteb/tatoeba-bitext-mining config: jpn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.28333333333333 - type: precision value: 94.73333333333332 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (csb-eng) type: mteb/tatoeba-bitext-mining config: csb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 42.29249011857708 - type: f1 value: 36.981018542283365 - type: precision value: 35.415877813576024 - type: recall value: 42.29249011857708 - task: type: BitextMining dataset: name: MTEB Tatoeba (xho-eng) type: mteb/tatoeba-bitext-mining config: xho-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.80281690140845 - type: f1 value: 80.86854460093896 - type: precision value: 79.60093896713614 - type: recall value: 83.80281690140845 - task: type: BitextMining dataset: name: MTEB Tatoeba (orv-eng) type: mteb/tatoeba-bitext-mining config: orv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 45.26946107784431 - type: f1 value: 39.80235464678088 - type: precision value: 38.14342660001342 - type: recall value: 45.26946107784431 - task: type: BitextMining dataset: name: MTEB Tatoeba (ind-eng) type: mteb/tatoeba-bitext-mining config: ind-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.3 - type: f1 value: 92.9 - type: precision value: 92.26666666666668 - type: recall value: 94.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (tuk-eng) type: mteb/tatoeba-bitext-mining config: tuk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 37.93103448275862 - type: f1 value: 33.15192743764172 - type: precision value: 31.57456528146183 - type: recall value: 37.93103448275862 - task: type: BitextMining dataset: name: MTEB Tatoeba (max-eng) type: mteb/tatoeba-bitext-mining config: max-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 69.01408450704226 - type: f1 value: 63.41549295774648 - type: precision value: 61.342778895595806 - type: recall value: 69.01408450704226 - task: type: BitextMining dataset: name: MTEB Tatoeba (swh-eng) type: mteb/tatoeba-bitext-mining config: swh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.66666666666667 - type: f1 value: 71.60705960705961 - type: precision value: 69.60683760683762 - type: recall value: 76.66666666666667 - task: type: BitextMining dataset: name: MTEB Tatoeba (hin-eng) type: mteb/tatoeba-bitext-mining config: hin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.8 - type: f1 value: 94.48333333333333 - type: precision value: 93.83333333333333 - type: recall value: 95.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (dsb-eng) type: mteb/tatoeba-bitext-mining config: dsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 52.81837160751566 - type: f1 value: 48.435977731384824 - type: precision value: 47.11291973845539 - type: recall value: 52.81837160751566 - task: type: BitextMining dataset: name: MTEB Tatoeba (ber-eng) type: mteb/tatoeba-bitext-mining config: ber-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 44.9 - type: f1 value: 38.88962621607783 - type: precision value: 36.95936507936508 - type: recall value: 44.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (tam-eng) type: mteb/tatoeba-bitext-mining config: tam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.55374592833876 - type: f1 value: 88.22553125484721 - type: precision value: 87.26927252985884 - type: recall value: 90.55374592833876 - task: type: BitextMining dataset: name: MTEB Tatoeba (slk-eng) type: mteb/tatoeba-bitext-mining config: slk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.6 - type: f1 value: 93.13333333333333 - type: precision value: 92.45333333333333 - type: recall value: 94.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (tgl-eng) type: mteb/tatoeba-bitext-mining config: tgl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.7 - type: f1 value: 91.99666666666667 - type: precision value: 91.26666666666668 - type: recall value: 93.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (ast-eng) type: mteb/tatoeba-bitext-mining config: ast-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.03937007874016 - type: f1 value: 81.75853018372703 - type: precision value: 80.34120734908137 - type: recall value: 85.03937007874016 - task: type: BitextMining dataset: name: MTEB Tatoeba (mkd-eng) type: mteb/tatoeba-bitext-mining config: mkd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.3 - type: f1 value: 85.5 - type: precision value: 84.25833333333334 - type: recall value: 88.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (khm-eng) type: mteb/tatoeba-bitext-mining config: khm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.51246537396122 - type: f1 value: 60.02297410192148 - type: precision value: 58.133467727289236 - type: recall value: 65.51246537396122 - task: type: BitextMining dataset: name: MTEB Tatoeba (ces-eng) type: mteb/tatoeba-bitext-mining config: ces-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96 - type: f1 value: 94.89 - type: precision value: 94.39166666666667 - type: recall value: 96 - task: type: BitextMining dataset: name: MTEB Tatoeba (tzl-eng) type: mteb/tatoeba-bitext-mining config: tzl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 57.692307692307686 - type: f1 value: 53.162393162393165 - type: precision value: 51.70673076923077 - type: recall value: 57.692307692307686 - task: type: BitextMining dataset: name: MTEB Tatoeba (urd-eng) type: mteb/tatoeba-bitext-mining config: urd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.60000000000001 - type: f1 value: 89.21190476190475 - type: precision value: 88.08666666666667 - type: recall value: 91.60000000000001 - task: type: BitextMining dataset: name: MTEB Tatoeba (ara-eng) type: mteb/tatoeba-bitext-mining config: ara-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88 - type: f1 value: 85.47 - type: precision value: 84.43266233766234 - type: recall value: 88 - task: type: BitextMining dataset: name: MTEB Tatoeba (kor-eng) type: mteb/tatoeba-bitext-mining config: kor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.7 - type: f1 value: 90.64999999999999 - type: precision value: 89.68333333333332 - type: recall value: 92.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (yid-eng) type: mteb/tatoeba-bitext-mining config: yid-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 80.30660377358491 - type: f1 value: 76.33044137466307 - type: precision value: 74.78970125786164 - type: recall value: 80.30660377358491 - task: type: BitextMining dataset: name: MTEB Tatoeba (fin-eng) type: mteb/tatoeba-bitext-mining config: fin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.39999999999999 - type: f1 value: 95.44 - type: precision value: 94.99166666666666 - type: recall value: 96.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (tha-eng) type: mteb/tatoeba-bitext-mining config: tha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.53284671532847 - type: f1 value: 95.37712895377129 - type: precision value: 94.7992700729927 - type: recall value: 96.53284671532847 - task: type: BitextMining dataset: name: MTEB Tatoeba (wuu-eng) type: mteb/tatoeba-bitext-mining config: wuu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89 - type: f1 value: 86.23190476190476 - type: precision value: 85.035 - type: recall value: 89 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.585 - type: map_at_10 value: 9.012 - type: map_at_100 value: 14.027000000000001 - type: map_at_1000 value: 15.565000000000001 - type: map_at_3 value: 5.032 - type: map_at_5 value: 6.657 - type: mrr_at_1 value: 28.571 - type: mrr_at_10 value: 45.377 - type: mrr_at_100 value: 46.119 - type: mrr_at_1000 value: 46.127 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 42.585 - type: ndcg_at_1 value: 27.551 - type: ndcg_at_10 value: 23.395 - type: ndcg_at_100 value: 33.342 - type: ndcg_at_1000 value: 45.523 - type: ndcg_at_3 value: 25.158 - type: ndcg_at_5 value: 23.427 - type: precision_at_1 value: 28.571 - type: precision_at_10 value: 21.429000000000002 - type: precision_at_100 value: 6.714 - type: precision_at_1000 value: 1.473 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.585 - type: recall_at_10 value: 15.418999999999999 - type: recall_at_100 value: 42.485 - type: recall_at_1000 value: 79.536 - type: recall_at_3 value: 6.239999999999999 - type: recall_at_5 value: 8.996 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.3234 - type: ap value: 14.361688653847423 - type: f1 value: 54.819068624319044 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.97792869269949 - type: f1 value: 62.28965628513728 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 38.90540145385218 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.53513739047506 - type: cos_sim_ap value: 75.27741586677557 - type: cos_sim_f1 value: 69.18792902473774 - type: cos_sim_precision value: 67.94708725515136 - type: cos_sim_recall value: 70.47493403693932 - type: dot_accuracy value: 84.7052512368123 - type: dot_ap value: 69.36075482849378 - type: dot_f1 value: 64.44688376631296 - type: dot_precision value: 59.92288500793831 - type: dot_recall value: 69.70976253298153 - type: euclidean_accuracy value: 86.60666388508076 - type: euclidean_ap value: 75.47512772621097 - type: euclidean_f1 value: 69.413872536473 - type: euclidean_precision value: 67.39562624254472 - type: euclidean_recall value: 71.55672823218997 - type: manhattan_accuracy value: 86.52917684925792 - type: manhattan_ap value: 75.34000110496703 - type: manhattan_f1 value: 69.28489190226429 - type: manhattan_precision value: 67.24608889992551 - type: manhattan_recall value: 71.45118733509234 - type: max_accuracy value: 86.60666388508076 - type: max_ap value: 75.47512772621097 - type: max_f1 value: 69.413872536473 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.01695967710637 - type: cos_sim_ap value: 85.8298270742901 - type: cos_sim_f1 value: 78.46988128389272 - type: cos_sim_precision value: 74.86017897091722 - type: cos_sim_recall value: 82.44533415460425 - type: dot_accuracy value: 88.19420188613343 - type: dot_ap value: 83.82679165901324 - type: dot_f1 value: 76.55833777304208 - type: dot_precision value: 75.6884875846501 - type: dot_recall value: 77.44841392054204 - type: euclidean_accuracy value: 89.03054294252338 - type: euclidean_ap value: 85.89089555185325 - type: euclidean_f1 value: 78.62997658079624 - type: euclidean_precision value: 74.92329149232914 - type: euclidean_recall value: 82.72251308900523 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.86458997929147 - type: manhattan_f1 value: 78.50685331000291 - type: manhattan_precision value: 74.5499861534201 - type: manhattan_recall value: 82.90729904527257 - type: max_accuracy value: 89.03054294252338 - type: max_ap value: 85.89089555185325 - type: max_f1 value: 78.62997658079624 --- # chris-code/multilingual-e5-large-Q8_0-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-large) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo chris-code/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo chris-code/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo chris-code/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo chris-code/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ```
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
binbin83/setfit-MiniLM-dialog-act-13nov
binbin83
text-classification
[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "fr", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
1,706,687,715,000
2024-01-31T08:48:33
6
2
--- language: - fr library_name: setfit license: apache-2.0 metrics: - f1 - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification --- # binbin83/setfit-MiniLM-dialog-act-13nov The model is a multi-class multi-label text classifier to distinguish the different dialog act in semi-structured interview. The data used fot fine-tuning were in French. This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("binbin83/setfit-MiniLM-dialog-act-13nov") label_dict = {'Introductory': 0, 'FollowUp': 1, 'Probing': 2, 'Specifying': 3, 'Structuring': 4, 'DirectQuestion': 5, 'Interpreting': 6, 'Ending': 7} # Run inference preds = model(["Vous pouvez continuer", "Pouvez-vous me dire précisément quel a été l'odre chronologique des événements ?"]) labels = [[[f for f, p in zip(labels_dict, ps) if p] for ps in [pred]] for pred in preds ] ``` ## Labels and training data Brinkmann, S., & Kvale, S (1), define classification of dialog act in interview: * Introductory: Can you tell me about ... (something specific)?, * Follow-up verbal cues: repeat back keywords to participants, ask for reflection or unpacking of point just made, * Probing: Can you say a little more about X? Why do you think X...? (for example, Why do you think X is that way? Why do you think X is important?), * Specifying: Can you give me an example of X?, * Indirect: How do you think other people view X?, * Structuring: Thank you for that. I’d like to move to another topic... * Direct (later stages): When you mention X, are you thinking like Y or Z?, * Interpreting: So, what I have gathered is that..., * Ending: I have asked all the questions I had, but I wanted to check whether there is something else about your experience/understanding we haven’t covered? Do you have any questions for me?, On our corpus of interviews, we humanly label 500 turn of speech using this classification. We use 0.7 to train and evaluate on 0.3. The entire corpus is composed of the following examples: ('Probing', 146), ('Specifying', 135), ('FollowUp', 134), ('DirectQuestion', 125), ('Interpreting', 44), ('Structuring', 27), ('Introductory', 12), ('Ending', 12) (1) Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the Craft of Qualitative Research Interviewing. (3. ed.) SAGE Publications. ## Training and Performances We finetune: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" using SetFit with CosineLossSimilarity and this parapeters: epochs = 5, batch_size=32, num_iterations = 20 On our custom dataset, on test set, we get: {f1': 0.65, 'f1_micro': 0.64, 'f1_sample': 0.64, 'accuracy': 0.475} ## BibTeX entry and citation info To cite the current study: ```bibtex @article{ doi = {conference paper}, url = {https://arxiv.org/abs/2209.11055}, author = {Quillivic Robin, Charles Payet}, keywords = {NLP, JADT}, title = {Semi-Structured Interview Analysis: A French NLP Toolbox for Social Sciences}, publisher = {JADT}, year = {2024}, copyright = {Creative Commons Attribution 4.0 International} } ``` To cite the setFit paper: ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
[ "CRAFT" ]
Non_BioNLP
RichardErkhov/XeAI_-_LLaMa_3.2_3B_Instruct_Text2SQL_Legacy-8bits
RichardErkhov
null
[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]
1,741,756,722,000
2025-03-12T05:20:58
3
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) LLaMa_3.2_3B_Instruct_Text2SQL_Legacy - bnb 8bits - Model creator: https://huggingface.co/XeAI/ - Original model: https://huggingface.co/XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy/ Original model description: --- library_name: transformers license: mit datasets: - gretelai/synthetic_text_to_sql pipeline_tag: text-generation --- # Model Card for LLaMA 3.2 3B Instruct Text2SQL ## Model Details ### Model Description This is a fine-tuned version of LLaMA 3.2 3B Instruct model, specifically optimized for Text-to-SQL generation tasks. The model has been trained to convert natural language queries into structured SQL commands. - **Developed by:** Zhafran Ramadhan - XeAI - **Model type:** Decoder-only Language Model - **Language(s):** English - MultiLingual - **License:** MIT - **Finetuned from model:** LLaMA 3.2 3B Instruct - **Log WandB Report:** [WandB Report](https://wandb.ai/zhafranr/LLaMA_3-2_3B_Instruct_FineTune_Text2SQL/reports/LLaMa-3-2-3B-Instruct-Fine-Tune-Text2SQL--VmlldzoxMDA2NDkzNA) ### Model Sources - **Repository:** [LLaMA 3.2 3B Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) - **Dataset:** [Synthethic Text2SQL](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) ## How to Get Started with the Model ### Installation ```python pip install transformers torch accelerate ``` ### Input Format and Usage The model expects input in a specific format following this template: ```text <|begin_of_text|><|start_header_id|>system<|end_header_id|> [System context and database schema] <|eot_id|><|start_header_id|>user<|end_header_id|> [User query] <|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ### Basic Usage ```python from transformers import pipeline import torch # Initialize the pipeline generator = pipeline( "text-generation", model="XeAI/LLaMa_3.2_3B_Instruct_Text2SQL", # Replace with your model ID torch_dtype=torch.float16, device_map="auto" ) def generate_sql_query(context, question): # Format the prompt according to the training template prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 07 Nov 2024 You are a specialized SQL query generator focused solely on the provided RAG database. Your tasks are: 1. Generate SQL queries based on user requests that are related to querying the RAG database. 2. Only output the SQL query itself, without any additional explanation or commentary. 3. Use the context provided from the RAG database to craft accurate queries. Context: {context} <|eot_id|><|start_header_id|>user<|end_header_id|> {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>""" response = generator( prompt, max_length=500, num_return_sequences=1, temperature=0.1, do_sample=True, pad_token_id=generator.tokenizer.eos_token_id ) return response[0]['generated_text'] # Example usage context = """CREATE TABLE upgrades (id INT, cost FLOAT, type TEXT); INSERT INTO upgrades (id, cost, type) VALUES (1, 500, 'Insulation'), (2, 1000, 'HVAC'), (3, 1500, 'Lighting');""" questions = [ "Find the energy efficiency upgrades with the highest cost and their types.", "Show me all upgrades costing less than 1000 dollars.", "Calculate the average cost of all upgrades." ] for question in questions: sql = generate_sql_query(context, question) print(f"\nQuestion: {question}") print(f"Generated SQL: {sql}\n") ``` ### Advanced Usage with Custom System Prompt ```python def generate_sql_with_custom_prompt(context, question, custom_system_prompt=""): base_prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 07 Nov 2024 You are a specialized SQL query generator focused solely on the provided RAG database.""" full_prompt = f"""{base_prompt} {custom_system_prompt} Context: {context} <|eot_id|><|start_header_id|>user<|end_header_id|> {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>""" response = generator( full_prompt, max_length=500, num_return_sequences=1, temperature=0.1, do_sample=True, pad_token_id=generator.tokenizer.eos_token_id ) return response[0]['generated_text'] ``` ### Best Practices 1. **Input Formatting**: - Always include the special tokens (<|begin_of_text|>, <|eot_id|>, etc.) - Provide complete database schema in context - Keep questions clear and focused on data retrieval 2. **Parameter Configuration**: - Use temperature=0.1 for consistent SQL generation - Adjust max_length based on expected query complexity - Enable do_sample for more natural completions 3. **Context Management**: - Include relevant table schemas - Provide sample data when needed - Keep context concise but complete ## Uses ### Direct Use The model is designed for converting natural language questions into SQL queries. It can be used for: - Database query generation from natural language - SQL query assistance - Data analysis automation ### Out-of-Scope Use - Production deployment without human validation - Critical decision-making without human oversight - Direct database execution without query validation ## Training Details ### Training Data - Dataset: [Synthethic Text2SQL](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) - Data preprocessing: Standard text-to-SQL formatting ### Training Procedure #### Training Hyperparameters - **Total Steps:** 4,149 - **Final Training Loss:** 0.1168 - **Evaluation Loss:** 0.2125 - **Learning Rate:** Dynamic with final LR = 0 - **Epochs:** 2.99 - **Gradient Norm:** 1.3121 #### Performance Metrics - **Training Samples/Second:** 6.291 - **Evaluation Samples/Second:** 19.325 - **Steps/Second:** 3.868 - **Total FLOPS:** 1.92e18 #### Training Infrastructure - **Hardware:** Single NVIDIA H100 GPU - **Training Duration:** 5-6 hours - **Total Runtime:** 16,491.75 seconds - **Model Preparation Time:** 0.0051 seconds ## Evaluation ### Metrics The model's performance was tracked using several key metrics: - **Training Loss:** Started at ~1.2, converged to 0.1168 - **Evaluation Loss:** 0.2125 - **Processing Efficiency:** 19.325 samples per second during evaluation ### Results Summary - Achieved stable convergence after ~4000 steps - Maintained consistent performance metrics throughout training - Shows good balance between training and evaluation loss ## Environmental Impact - **Hardware Type:** NVIDIA H100 GPU - **Hours used:** ~6 hours - **Training Location:** [GPUaaS](www.runpod.io) ## Technical Specifications ### Compute Infrastructure - **GPU:** NVIDIA H100 - **Training Duration:** 5-6 hours - **Total Steps:** 4,149 - **FLOPs Utilized:** 1.92e18 ## Model Card Contact [Contact information to be added by Zhafran Ramadhan] --- *Note: This model card follows the guidelines set by the ML community for responsible AI development and deployment.*
[ "CRAFT" ]
Non_BioNLP
Euanyu/GERBERA-Celltype
Euanyu
null
[ "transformers", "pytorch", "roberta", "license:mit", "endpoints_compatible", "region:us" ]
1,716,326,921,000
2024-05-21T21:32:09
6
0
--- license: mit --- The GERBERA BioNER model for identifying cell type types, trained on the JNLPBA-ct dataset and GUM-Time.
[ "JNLPBA" ]
BioNLP
pruas/BENT-PubMedBERT-NER-Chemical
pruas
token-classification
[ "transformers", "pytorch", "bert", "token-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,673,468,374,000
2024-03-01T13:56:32
687
8
--- language: - en license: apache-2.0 pipeline_tag: token-classification --- Named Entity Recognition (NER) model to recognize chemical entities. Please cite our work: ``` @article{NILNKER2022, title = {NILINKER: Attention-based approach to NIL Entity Linking}, journal = {Journal of Biomedical Informatics}, volume = {132}, pages = {104137}, year = {2022}, issn = {1532-0464}, doi = {https://doi.org/10.1016/j.jbi.2022.104137}, url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526}, author = {Pedro Ruas and Francisco M. Couto}, } ``` [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets: - [Chemdner patents CEMP corpus](https://biocreative.bioinformatics.udel.edu/resources/corpora/chemdner-patents-cemp-corpus/) (train, dev, test sets) - [DDI corpus](https://github.com/isegura/DDICorpus) (train, dev, test sets): entity types "GROUP", "DRUG", "DRUG_N" - [GREC Corpus](http://www.nactem.ac.uk/GREC/standoff.php) (train, dev, test sets): entity type "organic_compounds" - [MLEE](http://nactem.ac.uk/MLEE/) (train, dev, test sets): entity type "Drug or compound" - [NLM-CHEM](https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMChem/) (train, dev, test sets) - [CHEMDNER](https://biocreative.bioinformatics.udel.edu/resources/) (train, dev, test sets) - [Chebi Corpus](http://www.nactem.ac.uk/chebi/) (train, dev, test sets): entity types "Metabolite", "Chemical" - [PHAEDRA](http://www.nactem.ac.uk/PHAEDRA/) (train, dev, test sets): entity type "Pharmalogical_substance" - [Chemprot](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/) (train, dev, test sets) - [PGx Corpus](https://github.com/practikpharma/PGxCorpus) (train, dev, test sets): entity type "Chemical" - [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-chem-IOB) (train, dev, test sets): entity type "Chemical" - [BioNLP13CG]() (train, dev, test sets): entity type "Chemical" - [BC4CHEMD](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD) (train, dev, test sets) - [CRAFT corpus](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation) (train, dev, test sets): entity type "ChEBI" - [BC5CDR]() (train, dev, test sets): entity type "Chemical"
[ "BC5CDR", "CHEBI CORPUS", "CHEMDNER", "CRAFT", "CHEMPROT", "DDI CORPUS", "MLEE", "NLM-CHEM" ]
TBD
mradermacher/BC5CDR-mistral-False-Cosine-GGUF
mradermacher
null
[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]
1,728,369,306,000
2024-10-08T07:00:07
105
0
--- base_model: Motasem7/BC5CDR-mistral-False-Cosine language: - en library_name: transformers tags: [] quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Motasem7/BC5CDR-mistral-False-Cosine <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.IQ3_XS.gguf) | IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.IQ3_M.gguf) | IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/BC5CDR-mistral-False-Cosine-GGUF/resolve/main/BC5CDR-mistral-False-Cosine.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
[ "BC5CDR" ]
BioNLP
Cosmo-Hug/FeverDream
Cosmo-Hug
text-to-image
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
1,680,104,400,000
2023-04-01T16:46:50
63
10
--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # FeverDream This is a fine tuning of SD1.5 and designed as a general purpose model trained on high quality photographs and traditional artworks upscaled and denoised before training to get the sharpest cleanest results possible. It's trained at 576 resolution using the offset noise fix so generations are sharp and detailed with vibrant colors, deep blacks, and a well balanced contrast. This model was not trained with A.I. generated images or merged with any other models which means your images won't have that green/aqua color cast seen in so many models today. The largest portion of the dataset consists of photographs of women, men, gorgeous landscapes, and luxurious home/cabin/hotel interiors, some abandoned buildings and cityscapes, followed by a few unique art styles that can most easily be discovered by prompting with the commonly used words below. Some tips for realistic images: avoid using the word "realistic," part of the dataset contains "realistic porcelain dolls" and the weights for "realistic" have shifted in the direction of the dolls. It shouldn't be an issue but If your faces look a bit smoother than you like try throwing the word "doll" in the negative prompt. This model is great at both high detailed realism and stylized images when prompted correctly. A few commonly used words found in the training dataset include: Papercut Liquid Splash Realistic Porcelain Doll Interior Landscape ...and a few others for you to discover. If your so inclined to leave a tip I'm happy to accept monero at this address: 82s3fk8bQB2DHJ3r9idZUsST1Dvf5cKKC6Fu87rYgV9dAFbCbAcXMPXaP59yDwWzRXfYfTBszHZno6xGwDb17xUzEkCsAah Thanks for checking out my work and enjoy! ## Examples Below are some examples of images generated using this model: **Liquid Splash:** ![Liquid Splash](https://huggingface.co/Cosmo-Hug/FeverDream/resolve/main/Example01.png) ``` close up of a young woman wearing a black and gold liquid splash dress, pretty face, detailed eyes, soft lips, floating in outer space and planets in the background, fluid, wet, dripping, waxy, smooth, realistic, octane render Negative prompt: two, few, couple, group, lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, dehydrated, extra limbs, clone, disfigured, gross, malformed, extra arms, extra legs, fingers, long neck, username, watermark, signature Steps: 30, Sampler: DPM++ SDE, CFG scale: 7 ``` **Party Skelly:** ![Party Skelly](https://huggingface.co/Cosmo-Hug/FeverDream/resolve/main/Example02.png) ``` a 3d render of a cyberpunk gothic skull face surrounded by neon lines and veins, in the style ayami kojima with headdress made out of sushi Negative prompt: two, few, couple, double, blurry background, low depth of field, bokeh, lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, dehydrated, extra limbs, clone, disfigured, gross, malformed, extra arms, extra legs, fingers, long neck, username, watermark, signature Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 5 ``` **Dirty Girl:** ![Dirty Girl](https://huggingface.co/Cosmo-Hug/FeverDream/resolve/main/Example03.png) ``` close up photo action shot of a buxom woman cyborg, beautiful, sweaty, sweat glistening, science fiction movie, old scars, suspended movement, slow motion, visual flow, rough and tumble, dirt flying, pale, big gray eyes, piercing eyes, undercut hair, (highly detailed:1.1), dramatic, hi-res, (film grain), high ISO, 35mm, gorgeous, outdoor lighting, perfect face, delicate features, bloody nose, rusty tattered and dented metal parts, wasteland, dramatic rim lighting Negative prompt: doll, blurry background, low depth of field, bokeh, lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5 ``` **Apartment:** ![Apartment](https://huggingface.co/Cosmo-Hug/FeverDream/resolve/main/Example04.png) ``` a new york manhattan luxury bedroom in the spring, hyper detailed, ultra realistic, sharp focus, octane render, volumetric, ray tracing, artstation trending, cgsociety, insanely detailed studio photography hdr, 8k, cinematic lighting, dramatic lighting, Cannon EOS 5D, 85mm Negative prompt: lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, dehydrated, extra limbs, clone, disfigured, gross, malformed, extra arms, extra legs, fingers, long neck, username, watermark, signature Steps: 30, Sampler: DPM2, CFG scale: 5 ```
[ "MONERO" ]
Non_BioNLP
jncraton/stella-base-en-v2-ct2-int8
jncraton
feature-extraction
[ "sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "en", "arxiv:1612.00796", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,713,636,080,000
2024-04-20T18:02:00
8
0
--- language: - en license: mit tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: stella-base-en-v2 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.19402985074628 - type: ap value: 40.43267503017359 - type: f1 value: 71.15585210518594 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.256675 - type: ap value: 90.00824833079179 - type: f1 value: 93.2473146151734 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.612 - type: f1 value: 48.530785631574304 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 37.411 - type: map_at_10 value: 52.673 - type: map_at_100 value: 53.410999999999994 - type: map_at_1000 value: 53.415 - type: map_at_3 value: 48.495 - type: map_at_5 value: 51.183 - type: mrr_at_1 value: 37.838 - type: mrr_at_10 value: 52.844 - type: mrr_at_100 value: 53.581999999999994 - type: mrr_at_1000 value: 53.586 - type: mrr_at_3 value: 48.672 - type: mrr_at_5 value: 51.272 - type: ndcg_at_1 value: 37.411 - type: ndcg_at_10 value: 60.626999999999995 - type: ndcg_at_100 value: 63.675000000000004 - type: ndcg_at_1000 value: 63.776999999999994 - type: ndcg_at_3 value: 52.148 - type: ndcg_at_5 value: 57.001999999999995 - type: precision_at_1 value: 37.411 - type: precision_at_10 value: 8.578 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.91 - type: precision_at_5 value: 14.908 - type: recall_at_1 value: 37.411 - type: recall_at_10 value: 85.775 - type: recall_at_100 value: 98.86200000000001 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 62.731 - type: recall_at_5 value: 74.53800000000001 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.24219029437865 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.474604844291726 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.720542706366054 - type: mrr value: 75.59633733456448 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.31345008397868 - type: cos_sim_spearman value: 85.94292212320399 - type: euclidean_pearson value: 85.03974302774525 - type: euclidean_spearman value: 85.88087251659051 - type: manhattan_pearson value: 84.91900996712951 - type: manhattan_spearman value: 85.96701905781116 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.72727272727273 - type: f1 value: 84.29572512364581 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.55532460397536 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.91195973591251 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.822 - type: map_at_10 value: 44.139 - type: map_at_100 value: 45.786 - type: map_at_1000 value: 45.906000000000006 - type: map_at_3 value: 40.637 - type: map_at_5 value: 42.575 - type: mrr_at_1 value: 41.059 - type: mrr_at_10 value: 50.751000000000005 - type: mrr_at_100 value: 51.548 - type: mrr_at_1000 value: 51.583999999999996 - type: mrr_at_3 value: 48.236000000000004 - type: mrr_at_5 value: 49.838 - type: ndcg_at_1 value: 41.059 - type: ndcg_at_10 value: 50.573 - type: ndcg_at_100 value: 56.25 - type: ndcg_at_1000 value: 58.004 - type: ndcg_at_3 value: 45.995000000000005 - type: ndcg_at_5 value: 48.18 - type: precision_at_1 value: 41.059 - type: precision_at_10 value: 9.757 - type: precision_at_100 value: 1.609 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 22.222 - type: precision_at_5 value: 16.023 - type: recall_at_1 value: 32.822 - type: recall_at_10 value: 61.794000000000004 - type: recall_at_100 value: 85.64699999999999 - type: recall_at_1000 value: 96.836 - type: recall_at_3 value: 47.999 - type: recall_at_5 value: 54.376999999999995 - type: map_at_1 value: 29.579 - type: map_at_10 value: 39.787 - type: map_at_100 value: 40.976 - type: map_at_1000 value: 41.108 - type: map_at_3 value: 36.819 - type: map_at_5 value: 38.437 - type: mrr_at_1 value: 37.516 - type: mrr_at_10 value: 45.822 - type: mrr_at_100 value: 46.454 - type: mrr_at_1000 value: 46.495999999999995 - type: mrr_at_3 value: 43.556 - type: mrr_at_5 value: 44.814 - type: ndcg_at_1 value: 37.516 - type: ndcg_at_10 value: 45.5 - type: ndcg_at_100 value: 49.707 - type: ndcg_at_1000 value: 51.842 - type: ndcg_at_3 value: 41.369 - type: ndcg_at_5 value: 43.161 - type: precision_at_1 value: 37.516 - type: precision_at_10 value: 8.713 - type: precision_at_100 value: 1.38 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 20.233999999999998 - type: precision_at_5 value: 14.280000000000001 - type: recall_at_1 value: 29.579 - type: recall_at_10 value: 55.458 - type: recall_at_100 value: 73.49799999999999 - type: recall_at_1000 value: 87.08200000000001 - type: recall_at_3 value: 42.858000000000004 - type: recall_at_5 value: 48.215 - type: map_at_1 value: 40.489999999999995 - type: map_at_10 value: 53.313 - type: map_at_100 value: 54.290000000000006 - type: map_at_1000 value: 54.346000000000004 - type: map_at_3 value: 49.983 - type: map_at_5 value: 51.867 - type: mrr_at_1 value: 46.27 - type: mrr_at_10 value: 56.660999999999994 - type: mrr_at_100 value: 57.274 - type: mrr_at_1000 value: 57.301 - type: mrr_at_3 value: 54.138 - type: mrr_at_5 value: 55.623999999999995 - type: ndcg_at_1 value: 46.27 - type: ndcg_at_10 value: 59.192 - type: ndcg_at_100 value: 63.026 - type: ndcg_at_1000 value: 64.079 - type: ndcg_at_3 value: 53.656000000000006 - type: ndcg_at_5 value: 56.387 - type: precision_at_1 value: 46.27 - type: precision_at_10 value: 9.511 - type: precision_at_100 value: 1.23 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 24.096 - type: precision_at_5 value: 16.476 - type: recall_at_1 value: 40.489999999999995 - type: recall_at_10 value: 73.148 - type: recall_at_100 value: 89.723 - type: recall_at_1000 value: 97.073 - type: recall_at_3 value: 58.363 - type: recall_at_5 value: 65.083 - type: map_at_1 value: 26.197 - type: map_at_10 value: 35.135 - type: map_at_100 value: 36.14 - type: map_at_1000 value: 36.216 - type: map_at_3 value: 32.358 - type: map_at_5 value: 33.814 - type: mrr_at_1 value: 28.475 - type: mrr_at_10 value: 37.096000000000004 - type: mrr_at_100 value: 38.006 - type: mrr_at_1000 value: 38.06 - type: mrr_at_3 value: 34.52 - type: mrr_at_5 value: 35.994 - type: ndcg_at_1 value: 28.475 - type: ndcg_at_10 value: 40.263 - type: ndcg_at_100 value: 45.327 - type: ndcg_at_1000 value: 47.225 - type: ndcg_at_3 value: 34.882000000000005 - type: ndcg_at_5 value: 37.347 - type: precision_at_1 value: 28.475 - type: precision_at_10 value: 6.249 - type: precision_at_100 value: 0.919 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.689 - type: precision_at_5 value: 10.237 - type: recall_at_1 value: 26.197 - type: recall_at_10 value: 54.17999999999999 - type: recall_at_100 value: 77.768 - type: recall_at_1000 value: 91.932 - type: recall_at_3 value: 39.804 - type: recall_at_5 value: 45.660000000000004 - type: map_at_1 value: 16.683 - type: map_at_10 value: 25.013999999999996 - type: map_at_100 value: 26.411 - type: map_at_1000 value: 26.531 - type: map_at_3 value: 22.357 - type: map_at_5 value: 23.982999999999997 - type: mrr_at_1 value: 20.896 - type: mrr_at_10 value: 29.758000000000003 - type: mrr_at_100 value: 30.895 - type: mrr_at_1000 value: 30.964999999999996 - type: mrr_at_3 value: 27.177 - type: mrr_at_5 value: 28.799999999999997 - type: ndcg_at_1 value: 20.896 - type: ndcg_at_10 value: 30.294999999999998 - type: ndcg_at_100 value: 36.68 - type: ndcg_at_1000 value: 39.519 - type: ndcg_at_3 value: 25.480999999999998 - type: ndcg_at_5 value: 28.027 - type: precision_at_1 value: 20.896 - type: precision_at_10 value: 5.56 - type: precision_at_100 value: 1.006 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.231 - type: precision_at_5 value: 9.104 - type: recall_at_1 value: 16.683 - type: recall_at_10 value: 41.807 - type: recall_at_100 value: 69.219 - type: recall_at_1000 value: 89.178 - type: recall_at_3 value: 28.772 - type: recall_at_5 value: 35.167 - type: map_at_1 value: 30.653000000000002 - type: map_at_10 value: 41.21 - type: map_at_100 value: 42.543 - type: map_at_1000 value: 42.657000000000004 - type: map_at_3 value: 38.094 - type: map_at_5 value: 39.966 - type: mrr_at_1 value: 37.824999999999996 - type: mrr_at_10 value: 47.087 - type: mrr_at_100 value: 47.959 - type: mrr_at_1000 value: 48.003 - type: mrr_at_3 value: 45.043 - type: mrr_at_5 value: 46.352 - type: ndcg_at_1 value: 37.824999999999996 - type: ndcg_at_10 value: 47.158 - type: ndcg_at_100 value: 52.65 - type: ndcg_at_1000 value: 54.644999999999996 - type: ndcg_at_3 value: 42.632999999999996 - type: ndcg_at_5 value: 44.994 - type: precision_at_1 value: 37.824999999999996 - type: precision_at_10 value: 8.498999999999999 - type: precision_at_100 value: 1.308 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 20.308 - type: precision_at_5 value: 14.283000000000001 - type: recall_at_1 value: 30.653000000000002 - type: recall_at_10 value: 58.826 - type: recall_at_100 value: 81.94 - type: recall_at_1000 value: 94.71000000000001 - type: recall_at_3 value: 45.965 - type: recall_at_5 value: 52.294 - type: map_at_1 value: 26.71 - type: map_at_10 value: 36.001 - type: map_at_100 value: 37.416 - type: map_at_1000 value: 37.522 - type: map_at_3 value: 32.841 - type: map_at_5 value: 34.515 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 41.43 - type: mrr_at_100 value: 42.433 - type: mrr_at_1000 value: 42.482 - type: mrr_at_3 value: 39.117000000000004 - type: mrr_at_5 value: 40.35 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 41.629 - type: ndcg_at_100 value: 47.707 - type: ndcg_at_1000 value: 49.913000000000004 - type: ndcg_at_3 value: 36.598000000000006 - type: ndcg_at_5 value: 38.696000000000005 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.704999999999999 - type: precision_at_100 value: 1.242 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.374 - type: recall_at_1 value: 26.71 - type: recall_at_10 value: 52.898 - type: recall_at_100 value: 79.08 - type: recall_at_1000 value: 93.94 - type: recall_at_3 value: 38.731 - type: recall_at_5 value: 44.433 - type: map_at_1 value: 26.510999999999996 - type: map_at_10 value: 35.755333333333326 - type: map_at_100 value: 36.97525 - type: map_at_1000 value: 37.08741666666667 - type: map_at_3 value: 32.921 - type: map_at_5 value: 34.45041666666667 - type: mrr_at_1 value: 31.578416666666666 - type: mrr_at_10 value: 40.06066666666667 - type: mrr_at_100 value: 40.93350000000001 - type: mrr_at_1000 value: 40.98716666666667 - type: mrr_at_3 value: 37.710499999999996 - type: mrr_at_5 value: 39.033249999999995 - type: ndcg_at_1 value: 31.578416666666666 - type: ndcg_at_10 value: 41.138666666666666 - type: ndcg_at_100 value: 46.37291666666666 - type: ndcg_at_1000 value: 48.587500000000006 - type: ndcg_at_3 value: 36.397083333333335 - type: ndcg_at_5 value: 38.539 - type: precision_at_1 value: 31.578416666666666 - type: precision_at_10 value: 7.221583333333332 - type: precision_at_100 value: 1.1581666666666668 - type: precision_at_1000 value: 0.15416666666666667 - type: precision_at_3 value: 16.758 - type: precision_at_5 value: 11.830916666666665 - type: recall_at_1 value: 26.510999999999996 - type: recall_at_10 value: 52.7825 - type: recall_at_100 value: 75.79675 - type: recall_at_1000 value: 91.10483333333335 - type: recall_at_3 value: 39.48233333333334 - type: recall_at_5 value: 45.07116666666667 - type: map_at_1 value: 24.564 - type: map_at_10 value: 31.235000000000003 - type: map_at_100 value: 32.124 - type: map_at_1000 value: 32.216 - type: map_at_3 value: 29.330000000000002 - type: map_at_5 value: 30.379 - type: mrr_at_1 value: 27.761000000000003 - type: mrr_at_10 value: 34.093 - type: mrr_at_100 value: 34.885 - type: mrr_at_1000 value: 34.957 - type: mrr_at_3 value: 32.388 - type: mrr_at_5 value: 33.269 - type: ndcg_at_1 value: 27.761000000000003 - type: ndcg_at_10 value: 35.146 - type: ndcg_at_100 value: 39.597 - type: ndcg_at_1000 value: 42.163000000000004 - type: ndcg_at_3 value: 31.674000000000003 - type: ndcg_at_5 value: 33.224 - type: precision_at_1 value: 27.761000000000003 - type: precision_at_10 value: 5.383 - type: precision_at_100 value: 0.836 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 13.599 - type: precision_at_5 value: 9.202 - type: recall_at_1 value: 24.564 - type: recall_at_10 value: 44.36 - type: recall_at_100 value: 64.408 - type: recall_at_1000 value: 83.892 - type: recall_at_3 value: 34.653 - type: recall_at_5 value: 38.589 - type: map_at_1 value: 17.01 - type: map_at_10 value: 24.485 - type: map_at_100 value: 25.573 - type: map_at_1000 value: 25.703 - type: map_at_3 value: 21.953 - type: map_at_5 value: 23.294999999999998 - type: mrr_at_1 value: 20.544 - type: mrr_at_10 value: 28.238000000000003 - type: mrr_at_100 value: 29.142000000000003 - type: mrr_at_1000 value: 29.219 - type: mrr_at_3 value: 25.802999999999997 - type: mrr_at_5 value: 27.105 - type: ndcg_at_1 value: 20.544 - type: ndcg_at_10 value: 29.387999999999998 - type: ndcg_at_100 value: 34.603 - type: ndcg_at_1000 value: 37.564 - type: ndcg_at_3 value: 24.731 - type: ndcg_at_5 value: 26.773000000000003 - type: precision_at_1 value: 20.544 - type: precision_at_10 value: 5.509 - type: precision_at_100 value: 0.9450000000000001 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 11.757 - type: precision_at_5 value: 8.596 - type: recall_at_1 value: 17.01 - type: recall_at_10 value: 40.392 - type: recall_at_100 value: 64.043 - type: recall_at_1000 value: 85.031 - type: recall_at_3 value: 27.293 - type: recall_at_5 value: 32.586999999999996 - type: map_at_1 value: 27.155 - type: map_at_10 value: 35.92 - type: map_at_100 value: 37.034 - type: map_at_1000 value: 37.139 - type: map_at_3 value: 33.263999999999996 - type: map_at_5 value: 34.61 - type: mrr_at_1 value: 32.183 - type: mrr_at_10 value: 40.099000000000004 - type: mrr_at_100 value: 41.001 - type: mrr_at_1000 value: 41.059 - type: mrr_at_3 value: 37.889 - type: mrr_at_5 value: 39.007999999999996 - type: ndcg_at_1 value: 32.183 - type: ndcg_at_10 value: 41.127 - type: ndcg_at_100 value: 46.464 - type: ndcg_at_1000 value: 48.67 - type: ndcg_at_3 value: 36.396 - type: ndcg_at_5 value: 38.313 - type: precision_at_1 value: 32.183 - type: precision_at_10 value: 6.847 - type: precision_at_100 value: 1.0739999999999998 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 16.356 - type: precision_at_5 value: 11.362 - type: recall_at_1 value: 27.155 - type: recall_at_10 value: 52.922000000000004 - type: recall_at_100 value: 76.39 - type: recall_at_1000 value: 91.553 - type: recall_at_3 value: 39.745999999999995 - type: recall_at_5 value: 44.637 - type: map_at_1 value: 25.523 - type: map_at_10 value: 34.268 - type: map_at_100 value: 35.835 - type: map_at_1000 value: 36.046 - type: map_at_3 value: 31.662000000000003 - type: map_at_5 value: 32.71 - type: mrr_at_1 value: 31.028 - type: mrr_at_10 value: 38.924 - type: mrr_at_100 value: 39.95 - type: mrr_at_1000 value: 40.003 - type: mrr_at_3 value: 36.594 - type: mrr_at_5 value: 37.701 - type: ndcg_at_1 value: 31.028 - type: ndcg_at_10 value: 39.848 - type: ndcg_at_100 value: 45.721000000000004 - type: ndcg_at_1000 value: 48.424 - type: ndcg_at_3 value: 35.329 - type: ndcg_at_5 value: 36.779 - type: precision_at_1 value: 31.028 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 1.478 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 11.383000000000001 - type: recall_at_1 value: 25.523 - type: recall_at_10 value: 50.735 - type: recall_at_100 value: 76.593 - type: recall_at_1000 value: 93.771 - type: recall_at_3 value: 37.574000000000005 - type: recall_at_5 value: 41.602 - type: map_at_1 value: 20.746000000000002 - type: map_at_10 value: 28.557 - type: map_at_100 value: 29.575000000000003 - type: map_at_1000 value: 29.659000000000002 - type: map_at_3 value: 25.753999999999998 - type: map_at_5 value: 27.254 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 30.769000000000002 - type: mrr_at_100 value: 31.655 - type: mrr_at_1000 value: 31.717000000000002 - type: mrr_at_3 value: 28.065 - type: mrr_at_5 value: 29.543999999999997 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 33.545 - type: ndcg_at_100 value: 38.743 - type: ndcg_at_1000 value: 41.002 - type: ndcg_at_3 value: 28.021 - type: ndcg_at_5 value: 30.586999999999996 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.416 - type: precision_at_100 value: 0.8710000000000001 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 11.953 - type: precision_at_5 value: 8.651 - type: recall_at_1 value: 20.746000000000002 - type: recall_at_10 value: 46.87 - type: recall_at_100 value: 71.25200000000001 - type: recall_at_1000 value: 88.26 - type: recall_at_3 value: 32.029999999999994 - type: recall_at_5 value: 38.21 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 12.105 - type: map_at_10 value: 20.577 - type: map_at_100 value: 22.686999999999998 - type: map_at_1000 value: 22.889 - type: map_at_3 value: 17.174 - type: map_at_5 value: 18.807 - type: mrr_at_1 value: 27.101 - type: mrr_at_10 value: 38.475 - type: mrr_at_100 value: 39.491 - type: mrr_at_1000 value: 39.525 - type: mrr_at_3 value: 34.886 - type: mrr_at_5 value: 36.922 - type: ndcg_at_1 value: 27.101 - type: ndcg_at_10 value: 29.002 - type: ndcg_at_100 value: 37.218 - type: ndcg_at_1000 value: 40.644000000000005 - type: ndcg_at_3 value: 23.464 - type: ndcg_at_5 value: 25.262 - type: precision_at_1 value: 27.101 - type: precision_at_10 value: 9.179 - type: precision_at_100 value: 1.806 - type: precision_at_1000 value: 0.244 - type: precision_at_3 value: 17.394000000000002 - type: precision_at_5 value: 13.342 - type: recall_at_1 value: 12.105 - type: recall_at_10 value: 35.143 - type: recall_at_100 value: 63.44499999999999 - type: recall_at_1000 value: 82.49499999999999 - type: recall_at_3 value: 21.489 - type: recall_at_5 value: 26.82 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.769 - type: map_at_10 value: 18.619 - type: map_at_100 value: 26.3 - type: map_at_1000 value: 28.063 - type: map_at_3 value: 13.746 - type: map_at_5 value: 16.035 - type: mrr_at_1 value: 65.25 - type: mrr_at_10 value: 73.678 - type: mrr_at_100 value: 73.993 - type: mrr_at_1000 value: 74.003 - type: mrr_at_3 value: 72.042 - type: mrr_at_5 value: 72.992 - type: ndcg_at_1 value: 53.625 - type: ndcg_at_10 value: 39.638 - type: ndcg_at_100 value: 44.601 - type: ndcg_at_1000 value: 52.80200000000001 - type: ndcg_at_3 value: 44.727 - type: ndcg_at_5 value: 42.199 - type: precision_at_1 value: 65.25 - type: precision_at_10 value: 31.025000000000002 - type: precision_at_100 value: 10.174999999999999 - type: precision_at_1000 value: 2.0740000000000003 - type: precision_at_3 value: 48.083 - type: precision_at_5 value: 40.6 - type: recall_at_1 value: 8.769 - type: recall_at_10 value: 23.910999999999998 - type: recall_at_100 value: 51.202999999999996 - type: recall_at_1000 value: 77.031 - type: recall_at_3 value: 15.387999999999998 - type: recall_at_5 value: 18.919 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 54.47 - type: f1 value: 48.21839043361556 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 63.564 - type: map_at_10 value: 74.236 - type: map_at_100 value: 74.53699999999999 - type: map_at_1000 value: 74.557 - type: map_at_3 value: 72.556 - type: map_at_5 value: 73.656 - type: mrr_at_1 value: 68.497 - type: mrr_at_10 value: 78.373 - type: mrr_at_100 value: 78.54299999999999 - type: mrr_at_1000 value: 78.549 - type: mrr_at_3 value: 77.03 - type: mrr_at_5 value: 77.938 - type: ndcg_at_1 value: 68.497 - type: ndcg_at_10 value: 79.12599999999999 - type: ndcg_at_100 value: 80.319 - type: ndcg_at_1000 value: 80.71199999999999 - type: ndcg_at_3 value: 76.209 - type: ndcg_at_5 value: 77.90700000000001 - type: precision_at_1 value: 68.497 - type: precision_at_10 value: 9.958 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 29.908 - type: precision_at_5 value: 18.971 - type: recall_at_1 value: 63.564 - type: recall_at_10 value: 90.05199999999999 - type: recall_at_100 value: 95.028 - type: recall_at_1000 value: 97.667 - type: recall_at_3 value: 82.17999999999999 - type: recall_at_5 value: 86.388 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 19.042 - type: map_at_10 value: 30.764999999999997 - type: map_at_100 value: 32.678000000000004 - type: map_at_1000 value: 32.881 - type: map_at_3 value: 26.525 - type: map_at_5 value: 28.932000000000002 - type: mrr_at_1 value: 37.653999999999996 - type: mrr_at_10 value: 46.597 - type: mrr_at_100 value: 47.413 - type: mrr_at_1000 value: 47.453 - type: mrr_at_3 value: 43.775999999999996 - type: mrr_at_5 value: 45.489000000000004 - type: ndcg_at_1 value: 37.653999999999996 - type: ndcg_at_10 value: 38.615 - type: ndcg_at_100 value: 45.513999999999996 - type: ndcg_at_1000 value: 48.815999999999995 - type: ndcg_at_3 value: 34.427 - type: ndcg_at_5 value: 35.954 - type: precision_at_1 value: 37.653999999999996 - type: precision_at_10 value: 10.864 - type: precision_at_100 value: 1.7850000000000001 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_3 value: 22.788 - type: precision_at_5 value: 17.346 - type: recall_at_1 value: 19.042 - type: recall_at_10 value: 45.707 - type: recall_at_100 value: 71.152 - type: recall_at_1000 value: 90.7 - type: recall_at_3 value: 30.814000000000004 - type: recall_at_5 value: 37.478 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 38.001000000000005 - type: map_at_10 value: 59.611000000000004 - type: map_at_100 value: 60.582 - type: map_at_1000 value: 60.646 - type: map_at_3 value: 56.031 - type: map_at_5 value: 58.243 - type: mrr_at_1 value: 76.003 - type: mrr_at_10 value: 82.15400000000001 - type: mrr_at_100 value: 82.377 - type: mrr_at_1000 value: 82.383 - type: mrr_at_3 value: 81.092 - type: mrr_at_5 value: 81.742 - type: ndcg_at_1 value: 76.003 - type: ndcg_at_10 value: 68.216 - type: ndcg_at_100 value: 71.601 - type: ndcg_at_1000 value: 72.821 - type: ndcg_at_3 value: 63.109 - type: ndcg_at_5 value: 65.902 - type: precision_at_1 value: 76.003 - type: precision_at_10 value: 14.379 - type: precision_at_100 value: 1.702 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 40.396 - type: precision_at_5 value: 26.442 - type: recall_at_1 value: 38.001000000000005 - type: recall_at_10 value: 71.897 - type: recall_at_100 value: 85.105 - type: recall_at_1000 value: 93.133 - type: recall_at_3 value: 60.594 - type: recall_at_5 value: 66.104 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.31280000000001 - type: ap value: 87.53723467501632 - type: f1 value: 91.30282906596291 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.917 - type: map_at_10 value: 34.117999999999995 - type: map_at_100 value: 35.283 - type: map_at_1000 value: 35.333999999999996 - type: map_at_3 value: 30.330000000000002 - type: map_at_5 value: 32.461 - type: mrr_at_1 value: 22.579 - type: mrr_at_10 value: 34.794000000000004 - type: mrr_at_100 value: 35.893 - type: mrr_at_1000 value: 35.937000000000005 - type: mrr_at_3 value: 31.091 - type: mrr_at_5 value: 33.173 - type: ndcg_at_1 value: 22.579 - type: ndcg_at_10 value: 40.951 - type: ndcg_at_100 value: 46.558 - type: ndcg_at_1000 value: 47.803000000000004 - type: ndcg_at_3 value: 33.262 - type: ndcg_at_5 value: 37.036 - type: precision_at_1 value: 22.579 - type: precision_at_10 value: 6.463000000000001 - type: precision_at_100 value: 0.928 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.174000000000001 - type: precision_at_5 value: 10.421 - type: recall_at_1 value: 21.917 - type: recall_at_10 value: 61.885 - type: recall_at_100 value: 87.847 - type: recall_at_1000 value: 97.322 - type: recall_at_3 value: 41.010000000000005 - type: recall_at_5 value: 50.031000000000006 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.49521203830369 - type: f1 value: 93.30882341740241 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 71.0579115367077 - type: f1 value: 51.2368258319339 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.88029589778077 - type: f1 value: 72.34422048584663 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.2817753866846 - type: f1 value: 77.87746050004304 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.247341454119216 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.9647477166234 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.90698374676892 - type: mrr value: 33.07523683771251 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.717 - type: map_at_10 value: 14.566 - type: map_at_100 value: 18.465999999999998 - type: map_at_1000 value: 20.033 - type: map_at_3 value: 10.863 - type: map_at_5 value: 12.589 - type: mrr_at_1 value: 49.845 - type: mrr_at_10 value: 58.385 - type: mrr_at_100 value: 58.989999999999995 - type: mrr_at_1000 value: 59.028999999999996 - type: mrr_at_3 value: 56.76 - type: mrr_at_5 value: 57.766 - type: ndcg_at_1 value: 47.678 - type: ndcg_at_10 value: 37.511 - type: ndcg_at_100 value: 34.537 - type: ndcg_at_1000 value: 43.612 - type: ndcg_at_3 value: 43.713 - type: ndcg_at_5 value: 41.303 - type: precision_at_1 value: 49.845 - type: precision_at_10 value: 27.307 - type: precision_at_100 value: 8.746 - type: precision_at_1000 value: 2.182 - type: precision_at_3 value: 40.764 - type: precision_at_5 value: 35.232 - type: recall_at_1 value: 6.717 - type: recall_at_10 value: 18.107 - type: recall_at_100 value: 33.759 - type: recall_at_1000 value: 67.31 - type: recall_at_3 value: 11.68 - type: recall_at_5 value: 14.557999999999998 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 27.633999999999997 - type: map_at_10 value: 42.400999999999996 - type: map_at_100 value: 43.561 - type: map_at_1000 value: 43.592 - type: map_at_3 value: 37.865 - type: map_at_5 value: 40.650999999999996 - type: mrr_at_1 value: 31.286 - type: mrr_at_10 value: 44.996 - type: mrr_at_100 value: 45.889 - type: mrr_at_1000 value: 45.911 - type: mrr_at_3 value: 41.126000000000005 - type: mrr_at_5 value: 43.536 - type: ndcg_at_1 value: 31.257 - type: ndcg_at_10 value: 50.197 - type: ndcg_at_100 value: 55.062 - type: ndcg_at_1000 value: 55.81700000000001 - type: ndcg_at_3 value: 41.650999999999996 - type: ndcg_at_5 value: 46.324 - type: precision_at_1 value: 31.257 - type: precision_at_10 value: 8.508000000000001 - type: precision_at_100 value: 1.121 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.1 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 27.633999999999997 - type: recall_at_10 value: 71.40100000000001 - type: recall_at_100 value: 92.463 - type: recall_at_1000 value: 98.13199999999999 - type: recall_at_3 value: 49.382 - type: recall_at_5 value: 60.144 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.17099999999999 - type: map_at_10 value: 85.036 - type: map_at_100 value: 85.67099999999999 - type: map_at_1000 value: 85.68599999999999 - type: map_at_3 value: 82.086 - type: map_at_5 value: 83.956 - type: mrr_at_1 value: 82.04 - type: mrr_at_10 value: 88.018 - type: mrr_at_100 value: 88.114 - type: mrr_at_1000 value: 88.115 - type: mrr_at_3 value: 87.047 - type: mrr_at_5 value: 87.73100000000001 - type: ndcg_at_1 value: 82.03 - type: ndcg_at_10 value: 88.717 - type: ndcg_at_100 value: 89.904 - type: ndcg_at_1000 value: 89.991 - type: ndcg_at_3 value: 85.89099999999999 - type: ndcg_at_5 value: 87.485 - type: precision_at_1 value: 82.03 - type: precision_at_10 value: 13.444999999999999 - type: precision_at_100 value: 1.533 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.537 - type: precision_at_5 value: 24.692 - type: recall_at_1 value: 71.17099999999999 - type: recall_at_10 value: 95.634 - type: recall_at_100 value: 99.614 - type: recall_at_1000 value: 99.99 - type: recall_at_3 value: 87.48 - type: recall_at_5 value: 91.996 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.067219624685315 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.121822992300444 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.153 - type: map_at_10 value: 11.024000000000001 - type: map_at_100 value: 13.233 - type: map_at_1000 value: 13.62 - type: map_at_3 value: 7.779999999999999 - type: map_at_5 value: 9.529 - type: mrr_at_1 value: 20.599999999999998 - type: mrr_at_10 value: 31.361 - type: mrr_at_100 value: 32.738 - type: mrr_at_1000 value: 32.792 - type: mrr_at_3 value: 28.15 - type: mrr_at_5 value: 30.085 - type: ndcg_at_1 value: 20.599999999999998 - type: ndcg_at_10 value: 18.583 - type: ndcg_at_100 value: 27.590999999999998 - type: ndcg_at_1000 value: 34.001 - type: ndcg_at_3 value: 17.455000000000002 - type: ndcg_at_5 value: 15.588 - type: precision_at_1 value: 20.599999999999998 - type: precision_at_10 value: 9.74 - type: precision_at_100 value: 2.284 - type: precision_at_1000 value: 0.381 - type: precision_at_3 value: 16.533 - type: precision_at_5 value: 14.02 - type: recall_at_1 value: 4.153 - type: recall_at_10 value: 19.738 - type: recall_at_100 value: 46.322 - type: recall_at_1000 value: 77.378 - type: recall_at_3 value: 10.048 - type: recall_at_5 value: 14.233 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.07097501003639 - type: cos_sim_spearman value: 81.05827848407056 - type: euclidean_pearson value: 82.6279003372546 - type: euclidean_spearman value: 81.00031515279802 - type: manhattan_pearson value: 82.59338284959495 - type: manhattan_spearman value: 80.97432711064945 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.28991993621685 - type: cos_sim_spearman value: 78.71828082424351 - type: euclidean_pearson value: 83.4881331520832 - type: euclidean_spearman value: 78.51746826842316 - type: manhattan_pearson value: 83.4109223774324 - type: manhattan_spearman value: 78.431544382179 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.16651661072123 - type: cos_sim_spearman value: 84.88094386637867 - type: euclidean_pearson value: 84.3547603585416 - type: euclidean_spearman value: 84.85148665860193 - type: manhattan_pearson value: 84.29648369879266 - type: manhattan_spearman value: 84.76074870571124 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.40596254292149 - type: cos_sim_spearman value: 83.10699573133829 - type: euclidean_pearson value: 83.22794776876958 - type: euclidean_spearman value: 83.22583316084712 - type: manhattan_pearson value: 83.15899233935681 - type: manhattan_spearman value: 83.17668293648019 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.27977121352563 - type: cos_sim_spearman value: 88.73903130248591 - type: euclidean_pearson value: 88.30685958438735 - type: euclidean_spearman value: 88.79755484280406 - type: manhattan_pearson value: 88.30305607758652 - type: manhattan_spearman value: 88.80096577072784 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.08819031430218 - type: cos_sim_spearman value: 86.35414445951125 - type: euclidean_pearson value: 85.4683192388315 - type: euclidean_spearman value: 86.2079674669473 - type: manhattan_pearson value: 85.35835702257341 - type: manhattan_spearman value: 86.08483380002187 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.36149449801478 - type: cos_sim_spearman value: 87.7102980757725 - type: euclidean_pearson value: 88.16457177837161 - type: euclidean_spearman value: 87.6598652482716 - type: manhattan_pearson value: 88.23894728971618 - type: manhattan_spearman value: 87.74470156709361 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.54023758394433 - type: cos_sim_spearman value: 66.28491960187773 - type: euclidean_pearson value: 67.0853128483472 - type: euclidean_spearman value: 66.10307543766307 - type: manhattan_pearson value: 66.7635365592556 - type: manhattan_spearman value: 65.76408004780167 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.15858398195317 - type: cos_sim_spearman value: 87.44850004752102 - type: euclidean_pearson value: 86.60737082550408 - type: euclidean_spearman value: 87.31591549824242 - type: manhattan_pearson value: 86.56187011429977 - type: manhattan_spearman value: 87.23854795795319 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.66210488769109 - type: mrr value: 96.23100664767331 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 56.094 - type: map_at_10 value: 67.486 - type: map_at_100 value: 67.925 - type: map_at_1000 value: 67.949 - type: map_at_3 value: 64.857 - type: map_at_5 value: 66.31 - type: mrr_at_1 value: 58.667 - type: mrr_at_10 value: 68.438 - type: mrr_at_100 value: 68.733 - type: mrr_at_1000 value: 68.757 - type: mrr_at_3 value: 66.389 - type: mrr_at_5 value: 67.456 - type: ndcg_at_1 value: 58.667 - type: ndcg_at_10 value: 72.506 - type: ndcg_at_100 value: 74.27 - type: ndcg_at_1000 value: 74.94800000000001 - type: ndcg_at_3 value: 67.977 - type: ndcg_at_5 value: 70.028 - type: precision_at_1 value: 58.667 - type: precision_at_10 value: 9.767000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.0 - type: precision_at_5 value: 17.666999999999998 - type: recall_at_1 value: 56.094 - type: recall_at_10 value: 86.68900000000001 - type: recall_at_100 value: 94.333 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 74.522 - type: recall_at_5 value: 79.611 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.83069306930693 - type: cos_sim_ap value: 95.69184662911199 - type: cos_sim_f1 value: 91.4027149321267 - type: cos_sim_precision value: 91.91102123356926 - type: cos_sim_recall value: 90.9 - type: dot_accuracy value: 99.69405940594059 - type: dot_ap value: 90.21674151456216 - type: dot_f1 value: 84.4489179667841 - type: dot_precision value: 85.00506585612969 - type: dot_recall value: 83.89999999999999 - type: euclidean_accuracy value: 99.83069306930693 - type: euclidean_ap value: 95.67760109671087 - type: euclidean_f1 value: 91.19754350051177 - type: euclidean_precision value: 93.39622641509435 - type: euclidean_recall value: 89.1 - type: manhattan_accuracy value: 99.83267326732673 - type: manhattan_ap value: 95.69771347732625 - type: manhattan_f1 value: 91.32420091324201 - type: manhattan_precision value: 92.68795056642637 - type: manhattan_recall value: 90.0 - type: max_accuracy value: 99.83267326732673 - type: max_ap value: 95.69771347732625 - type: max_f1 value: 91.4027149321267 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 64.47378332953092 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.79602531604151 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.80707639107175 - type: mrr value: 54.64886522790935 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.852448373051395 - type: cos_sim_spearman value: 32.51821499493775 - type: dot_pearson value: 30.390650062190456 - type: dot_spearman value: 30.588836159667636 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.51 - type: map_at_100 value: 8.882 - type: map_at_1000 value: 22.181 - type: map_at_3 value: 0.553 - type: map_at_5 value: 0.843 - type: mrr_at_1 value: 74.0 - type: mrr_at_10 value: 84.89999999999999 - type: mrr_at_100 value: 84.89999999999999 - type: mrr_at_1000 value: 84.89999999999999 - type: mrr_at_3 value: 84.0 - type: mrr_at_5 value: 84.89999999999999 - type: ndcg_at_1 value: 68.0 - type: ndcg_at_10 value: 64.792 - type: ndcg_at_100 value: 51.37199999999999 - type: ndcg_at_1000 value: 47.392 - type: ndcg_at_3 value: 68.46900000000001 - type: ndcg_at_5 value: 67.084 - type: precision_at_1 value: 74.0 - type: precision_at_10 value: 69.39999999999999 - type: precision_at_100 value: 53.080000000000005 - type: precision_at_1000 value: 21.258 - type: precision_at_3 value: 76.0 - type: precision_at_5 value: 73.2 - type: recall_at_1 value: 0.198 - type: recall_at_10 value: 1.7950000000000002 - type: recall_at_100 value: 12.626999999999999 - type: recall_at_1000 value: 44.84 - type: recall_at_3 value: 0.611 - type: recall_at_5 value: 0.959 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.4949999999999999 - type: map_at_10 value: 8.797 - type: map_at_100 value: 14.889 - type: map_at_1000 value: 16.309 - type: map_at_3 value: 4.389 - type: map_at_5 value: 6.776 - type: mrr_at_1 value: 18.367 - type: mrr_at_10 value: 35.844 - type: mrr_at_100 value: 37.119 - type: mrr_at_1000 value: 37.119 - type: mrr_at_3 value: 30.612000000000002 - type: mrr_at_5 value: 33.163 - type: ndcg_at_1 value: 16.326999999999998 - type: ndcg_at_10 value: 21.9 - type: ndcg_at_100 value: 34.705000000000005 - type: ndcg_at_1000 value: 45.709 - type: ndcg_at_3 value: 22.7 - type: ndcg_at_5 value: 23.197000000000003 - type: precision_at_1 value: 18.367 - type: precision_at_10 value: 21.02 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.504 - type: precision_at_3 value: 26.531 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 1.4949999999999999 - type: recall_at_10 value: 15.504000000000001 - type: recall_at_100 value: 47.978 - type: recall_at_1000 value: 81.56 - type: recall_at_3 value: 5.569 - type: recall_at_5 value: 9.821 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.99279999999999 - type: ap value: 15.459189680101492 - type: f1 value: 56.33023271441895 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 63.070175438596486 - type: f1 value: 63.28070758709465 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 50.076231309703054 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.21463908922931 - type: cos_sim_ap value: 77.67287017966282 - type: cos_sim_f1 value: 70.34412955465588 - type: cos_sim_precision value: 67.57413709285368 - type: cos_sim_recall value: 73.35092348284961 - type: dot_accuracy value: 85.04500208618943 - type: dot_ap value: 70.4075203869744 - type: dot_f1 value: 66.18172537008678 - type: dot_precision value: 64.08798813643104 - type: dot_recall value: 68.41688654353561 - type: euclidean_accuracy value: 87.17887584192646 - type: euclidean_ap value: 77.5774128274464 - type: euclidean_f1 value: 70.09307972480777 - type: euclidean_precision value: 71.70852884349986 - type: euclidean_recall value: 68.54881266490766 - type: manhattan_accuracy value: 87.28020504261787 - type: manhattan_ap value: 77.57835820297892 - type: manhattan_f1 value: 70.23063591521131 - type: manhattan_precision value: 70.97817299919159 - type: manhattan_recall value: 69.49868073878628 - type: max_accuracy value: 87.28020504261787 - type: max_ap value: 77.67287017966282 - type: max_f1 value: 70.34412955465588 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.96650754841464 - type: cos_sim_ap value: 86.00185968965064 - type: cos_sim_f1 value: 77.95861256351718 - type: cos_sim_precision value: 74.70712773465067 - type: cos_sim_recall value: 81.50600554357868 - type: dot_accuracy value: 87.36950362867233 - type: dot_ap value: 82.22071181147555 - type: dot_f1 value: 74.85680716698488 - type: dot_precision value: 71.54688377316114 - type: dot_recall value: 78.48783492454572 - type: euclidean_accuracy value: 88.99561454573679 - type: euclidean_ap value: 86.15882097229648 - type: euclidean_f1 value: 78.18463125322332 - type: euclidean_precision value: 74.95408956067241 - type: euclidean_recall value: 81.70619032953496 - type: manhattan_accuracy value: 88.96650754841464 - type: manhattan_ap value: 86.13133111232099 - type: manhattan_f1 value: 78.10771470160115 - type: manhattan_precision value: 74.05465084184377 - type: manhattan_recall value: 82.63012011087157 - type: max_accuracy value: 88.99561454573679 - type: max_ap value: 86.15882097229648 - type: max_f1 value: 78.18463125322332 --- **新闻 | News** **[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语**。 **[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。 **[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。 **[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。 **[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。 **[2023-09-11]** 开源stella-base-zh和stella-large-zh 欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！ ## stella model stella是一个通用的文本编码模型，主要有以下模型： | Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| | stella-base-en-v2 | 0.2 | 768 | 512 | English | No | | stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No | | stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No | | stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes | | stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes | 完整的训练思路和训练过程已记录在[博客1](https://zhuanlan.zhihu.com/p/655322183)和[博客2](https://zhuanlan.zhihu.com/p/662209559)，欢迎阅读讨论。 **训练数据：** 1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本 2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据 **训练方法：** 1. 对比学习损失函数 2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例) 3. EWC(Elastic Weights Consolidation)[4] 4. cosent loss[5] 5. 每一种类型的数据一个迭代器，分别计算loss进行更新 stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction( 比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。 **初始权重：**\ stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position embedding使用层次分解位置编码[7]进行初始化。\ 感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。 stella is a general-purpose text encoder, which mainly includes the following models: | Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| | stella-base-en-v2 | 0.2 | 768 | 512 | English | No | | stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No | | stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No | | stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes | | stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes | The training data mainly includes: 1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater than 512. 2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM. The loss functions mainly include: 1. Contrastive learning loss function 2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives) 3. EWC (Elastic Weights Consolidation) 4. cosent loss Model weight initialization:\ stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the 512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding. Training strategy:\ One iterator for each type of data, separately calculating the loss. Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation. ## Metric #### C-MTEB leaderboard (Chinese) | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | |:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:| | stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | | stella-base-zh-v2 | 0.2 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.95 | 66.1 | 70.08 | 56.92 | | stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | | stella-base-zh | 0.2 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | #### MTEB leaderboard (English) | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | |:-----------------:|:---------------:|:---------:|:---------------:|:------------:|:-------------------:|:---------------:|:-----------------------:|:-------------:|:--------------:|:--------:|:------------------:| | stella-base-en-v2 | 0.2 | 768 | 512 | 62.61 | 75.28 | 44.9 | 86.45 | 58.77 | 50.1 | 83.02 | 32.52 | #### Reproduce our results **C-MTEB:** ```python import torch import numpy as np from typing import List from mteb import MTEB from sentence_transformers import SentenceTransformer class FastTextEncoder(): def __init__(self, model_name): self.model = SentenceTransformer(model_name).cuda().half().eval() self.model.max_seq_length = 512 def encode( self, input_texts: List[str], *args, **kwargs ): new_sens = list(set(input_texts)) new_sens.sort(key=lambda x: len(x), reverse=True) vecs = self.model.encode( new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256 ).astype(np.float32) sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)} vecs = vecs[[sen2arrid[sen] for sen in input_texts]] torch.cuda.empty_cache() return vecs if __name__ == '__main__': model_name = "infgrad/stella-base-zh-v2" output_folder = "zh_mteb_results/stella-base-zh-v2" task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks] model = FastTextEncoder(model_name) for task in task_names: MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder) ``` **MTEB:** You can use official script to reproduce our result. [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py) #### Evaluation for long text 经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的， 更致命的是那些长度大于512的文本，其重点都在前半部分 这里以CMRC2018的数据为例说明这个问题： ``` question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？ passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推...... ``` passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\ 简言之，现有数据集的2个问题：\ 1）长度大于512的过少\ 2）即便大于512，对于检索而言也只需要前512的文本内容\ 导致**无法准确评估模型的长文本编码能力。** 为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是： - CMRC2018，通用百科 - CAIL，法律阅读理解 - DRCD，繁体百科，已转简体 - Military，军工问答 - Squad，英文阅读理解，已转中文 - Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9] 处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。 除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing 评测指标为Recall@5, 结果如下： | Dataset | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh | |:---------------:|:---------------:|:----------------:|:-----------:|:------------:|:--------------:|:---------------:| | CMRC2018 | 94.34 | 93.82 | 91.56 | 93.12 | 96.08 | 95.56 | | CAIL | 28.04 | 33.64 | 31.22 | 33.94 | 34.62 | 37.18 | | DRCD | 78.25 | 77.9 | 78.34 | 80.26 | 86.14 | 84.58 | | Military | 76.61 | 73.06 | 75.65 | 75.81 | 83.71 | 80.48 | | Squad | 91.21 | 86.61 | 87.87 | 90.38 | 93.31 | 91.21 | | Multifieldqa_zh | 81.41 | 83.92 | 83.92 | 83.42 | 79.9 | 80.4 | | **Average** | 74.98 | 74.83 | 74.76 | 76.15 | **78.96** | **78.24** | **注意：** 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。 ## Usage #### stella 中文系列模型 stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此**用法和piccolo完全一致** ，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。 stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，**任何使用场景中都不需要加前缀文本**。 stella中文系列模型均使用mean pooling做为文本向量。 在sentence-transformer库中的使用方法： ```python from sentence_transformers import SentenceTransformer sentences = ["数据1", "数据2"] model = SentenceTransformer('infgrad/stella-base-zh-v2') print(model.max_seq_length) embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` 直接使用transformers库： ```python from transformers import AutoModel, AutoTokenizer from sklearn.preprocessing import normalize model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2') tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2') sentences = ["数据1", "数据ABCDEFGH"] batch_data = tokenizer( batch_text_or_text_pairs=sentences, padding="longest", return_tensors="pt", max_length=1024, truncation=True, ) attention_mask = batch_data["attention_mask"] model_output = model(**batch_data) last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] vectors = normalize(vectors, norm="l2", axis=1, ) print(vectors.shape) # 2,768 ``` #### stella models for English **Using Sentence-Transformers:** ```python from sentence_transformers import SentenceTransformer sentences = ["one car come", "one car go"] model = SentenceTransformer('infgrad/stella-base-en-v2') print(model.max_seq_length) embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` **Using HuggingFace Transformers:** ```python from transformers import AutoModel, AutoTokenizer from sklearn.preprocessing import normalize model = AutoModel.from_pretrained('infgrad/stella-base-en-v2') tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2') sentences = ["one car come", "one car go"] batch_data = tokenizer( batch_text_or_text_pairs=sentences, padding="longest", return_tensors="pt", max_length=512, truncation=True, ) attention_mask = batch_data["attention_mask"] model_output = model(**batch_data) last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] vectors = normalize(vectors, norm="l2", axis=1, ) print(vectors.shape) # 2,768 ``` ## Training Detail **硬件：** 单卡A100-80GB **环境：** torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing **学习率：** 1e-6 **batch_size：** base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例 **数据量：** 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。 ## ToDoList **评测的稳定性：** 评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。 **更高质量的长文本训练和测试数据：** 训练数据多是用13b模型构造的，肯定会存在噪声。 测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。 **OOD的性能：** 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere, 它们的效果均比不上BM25。 ## Reference 1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab 2. https://github.com/wangyuxinwhy/uniem 3. https://github.com/CLUEbenchmark/SimCLUE 4. https://arxiv.org/abs/1612.00796 5. https://kexue.fm/archives/8847 6. https://huggingface.co/sensenova/piccolo-base-zh 7. https://kexue.fm/archives/7947 8. https://github.com/FlagOpen/FlagEmbedding 9. https://github.com/THUDM/LongBench
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
rohitanurag/ClinicalGPT-Pubmed-Instruct-V1.0
rohitanurag
question-answering
[ "peft", "safetensors", "mistral", "medical", "lifescience", "drugdiscovery", "question-answering", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]
1,729,593,954,000
2024-10-22T11:13:25
0
2
--- base_model: - mistralai/Mistral-7B-Instruct-v0.2 library_name: peft license: apache-2.0 pipeline_tag: question-answering tags: - medical - lifescience - drugdiscovery --- # ClinicalGPT-Pubmed-Instruct-V1.0 ## Overview ClinicalGPT-Pubmed-Instruct-V1.0 is a specialized language model fine-tuned on the mistralai/Mistral-7B-Instruct-v0.2 base model. While primarily trained on 10 million PubMed abstracts and titles, this model excels at generating responses to life science-related medical questions with relevant citations from various scientific sources. ## Key Features - Built on Mistral-7B-Instruct-v0.2 base model - Primary training on 10M PubMed abstracts and titles - Generates answers with scientific citations from multiple sources - Specialized for medical and life science domains ## Applications - **Life Science Research**: Generate accurate, referenced answers for biomedical and healthcare queries - **Pharmaceutical Industry**: Support healthcare professionals with evidence-based responses - **Medical Education**: Aid students and educators with scientifically-supported content from various academic sources ## System Requirements ### GPU Requirements - **Minimum VRAM**: 16-18 GB for inference in BF16 (BFloat16) precision - **Recommended GPUs**: - NVIDIA A100 (20GB) - Ideal for BF16 precision - Any GPU with 16+ GB VRAM - Performance may vary based on available memory ### Software Prerequisites - Python 3.x - PyTorch - Transformers library ### Basic Implementation ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Set parameters model_dir = "rohitanurag/ClinicalGPT-Pubmed-Instruct-V1.0" max_new_tokens = 1500 device = "cuda" if torch.cuda.is_available() else "cpu" # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_dir) model = AutoModelForCausalLM.from_pretrained(model_dir).to(device) # Define your question question = "What is the role of the tumor microenvironment in cancer progression?" prompt = f"""Please provide the answer to the question asked. ### Question: {question} ### Answer: """ # Tokenize input inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(device) # Generate output output_ids = model.generate( inputs.input_ids, attention_mask=inputs.attention_mask, max_new_tokens=1000, repetition_penalty=1.2, pad_token_id=tokenizer.eos_token_id, ) # Decode and print generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) print(f"Generated Answer:\n{generated_text}") ``` ## Sample Output ``` ### Question: What is the role of the tumor microenvironment in cancer progression, and how does it influence the response to therapy? ### Answer: The tumor microenvironment (TME) refers to the complex network of cells, extracellular matrix components, signaling molecules, and immune cells that surround a growing tumor. It plays an essential role in regulating various aspects of cancer development and progression... ### References: 1. Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646-74. doi:10.1016/j.cell.2011.03.019 2. Coussens LM, Pollard JW. Angiogenesis and Metastasis. Nature Reviews Cancer. 2006;6(1):57-68. doi:10.1038/nrc2210 3. Mantovani A, et al. Cancer's Educated Environment: How the Tumour Microenvironment Promotes Progression. Cell. 2017;168(6):988-1001.e15. doi:10.1016/j.cell.2017.02.011 4. Cheng YH, et al. Targeting the Tumor Microenvironment for Improved Therapy Response. Journal of Clinical Oncology. 2018;34(18_suppl):LBA10001. doi:10.1200/JCO.2018.34.18_suppl.LBA10001 5. Kang YS, et al. Role of the Tumor Microenvironment in Cancer Immunotherapy. Current Opinion in Pharmacology. 2018;30:101-108. doi:10.1016/j.ycoop.20 ``` ## Model Details - **Base Model**: Mistral-7B-Instruct-v0.2 - **Primary Training Data**: 10 million PubMed abstracts and titles - **Specialization**: Medical question-answering with scientific citations - **Output**: Generates detailed answers with relevant academic references ## Future Development ClinicalGPT-Pubmed-Instruct-V2.0 is under development, featuring: - Training on new 20 million pubmed articles - Inclusion of full-text articles from various academic sources - Enhanced performance for life science tasks - Expanded citation capabilities across multiple scientific databases ## Contributors - Rohit Anurag – Principal Data Scientist - Aneesh Paul – Data Scientist ## License Licensed under the Apache License, Version 2.0. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
[ "HALLMARKS OF CANCER" ]
BioNLP
Nextcloud-AI/multilingual-e5-large-instruct
Nextcloud-AI
feature-extraction
[ "sentence-transformers", "onnx", "safetensors", "xlm-roberta", "feature-extraction", "mteb", "transformers", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2402.05672", "arxiv:2401.00368", "arxiv:2104.08663", "arxiv:2210.07316", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,727,419,452,000
2024-09-26T06:33:15
329
5
--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit tags: - mteb - sentence-transformers - transformers model-index: - name: multilingual-e5-large-instruct results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.23880597014924 - type: ap value: 39.07351965022687 - type: f1 value: 70.04836733862683 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.71306209850107 - type: ap value: 79.01499914759529 - type: f1 value: 64.81951817560703 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.85307346326837 - type: ap value: 22.447519885878737 - type: f1 value: 61.0162730745633 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.04925053533191 - type: ap value: 23.44983217128922 - type: f1 value: 62.5723230907759 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.28742500000001 - type: ap value: 94.8449918887462 - type: f1 value: 96.28680923610432 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 56.716 - type: f1 value: 55.76510398266401 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 52.99999999999999 - type: f1 value: 52.00829994765178 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.806000000000004 - type: f1 value: 48.082345914983634 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.507999999999996 - type: f1 value: 47.68752844642045 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.709999999999994 - type: f1 value: 47.05870376637181 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.662000000000006 - type: f1 value: 43.42371965372771 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 31.721 - type: map_at_10 value: 49.221 - type: map_at_100 value: 49.884 - type: map_at_1000 value: 49.888 - type: map_at_3 value: 44.31 - type: map_at_5 value: 47.276 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 49.5 - type: mrr_at_100 value: 50.163000000000004 - type: mrr_at_1000 value: 50.166 - type: mrr_at_3 value: 44.618 - type: mrr_at_5 value: 47.541 - type: ndcg_at_1 value: 31.721 - type: ndcg_at_10 value: 58.384 - type: ndcg_at_100 value: 61.111000000000004 - type: ndcg_at_1000 value: 61.187999999999995 - type: ndcg_at_3 value: 48.386 - type: ndcg_at_5 value: 53.708999999999996 - type: precision_at_1 value: 31.721 - type: precision_at_10 value: 8.741 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.057 - type: precision_at_5 value: 14.609 - type: recall_at_1 value: 31.721 - type: recall_at_10 value: 87.411 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 60.171 - type: recall_at_5 value: 73.044 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.40419580759799 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.48593255007969 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.889179122289995 - type: mrr value: 77.61146286769556 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.15075203727929 - type: cos_sim_spearman value: 86.9622224570873 - type: euclidean_pearson value: 86.70473853624121 - type: euclidean_spearman value: 86.9622224570873 - type: manhattan_pearson value: 86.21089380980065 - type: manhattan_spearman value: 86.75318154937008 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.65553235908142 - type: f1 value: 99.60681976339595 - type: precision value: 99.58246346555325 - type: recall value: 99.65553235908142 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.26260180497468 - type: f1 value: 99.14520507740848 - type: precision value: 99.08650671362535 - type: recall value: 99.26260180497468 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.07412538967787 - type: f1 value: 97.86629719431936 - type: precision value: 97.76238309664012 - type: recall value: 98.07412538967787 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.42074776197998 - type: f1 value: 99.38564156573635 - type: precision value: 99.36808846761454 - type: recall value: 99.42074776197998 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.73376623376623 - type: f1 value: 85.68480707214599 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.935218072113855 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.276389017675264 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 27.764166666666668 - type: map_at_10 value: 37.298166666666674 - type: map_at_100 value: 38.530166666666666 - type: map_at_1000 value: 38.64416666666667 - type: map_at_3 value: 34.484833333333334 - type: map_at_5 value: 36.0385 - type: mrr_at_1 value: 32.93558333333333 - type: mrr_at_10 value: 41.589749999999995 - type: mrr_at_100 value: 42.425333333333334 - type: mrr_at_1000 value: 42.476333333333336 - type: mrr_at_3 value: 39.26825 - type: mrr_at_5 value: 40.567083333333336 - type: ndcg_at_1 value: 32.93558333333333 - type: ndcg_at_10 value: 42.706583333333334 - type: ndcg_at_100 value: 47.82483333333333 - type: ndcg_at_1000 value: 49.95733333333334 - type: ndcg_at_3 value: 38.064750000000004 - type: ndcg_at_5 value: 40.18158333333333 - type: precision_at_1 value: 32.93558333333333 - type: precision_at_10 value: 7.459833333333334 - type: precision_at_100 value: 1.1830833333333335 - type: precision_at_1000 value: 0.15608333333333332 - type: precision_at_3 value: 17.5235 - type: precision_at_5 value: 12.349833333333333 - type: recall_at_1 value: 27.764166666666668 - type: recall_at_10 value: 54.31775 - type: recall_at_100 value: 76.74350000000001 - type: recall_at_1000 value: 91.45208333333332 - type: recall_at_3 value: 41.23425 - type: recall_at_5 value: 46.73983333333334 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 12.969 - type: map_at_10 value: 21.584999999999997 - type: map_at_100 value: 23.3 - type: map_at_1000 value: 23.5 - type: map_at_3 value: 18.218999999999998 - type: map_at_5 value: 19.983 - type: mrr_at_1 value: 29.316 - type: mrr_at_10 value: 40.033 - type: mrr_at_100 value: 40.96 - type: mrr_at_1000 value: 41.001 - type: mrr_at_3 value: 37.123 - type: mrr_at_5 value: 38.757999999999996 - type: ndcg_at_1 value: 29.316 - type: ndcg_at_10 value: 29.858 - type: ndcg_at_100 value: 36.756 - type: ndcg_at_1000 value: 40.245999999999995 - type: ndcg_at_3 value: 24.822 - type: ndcg_at_5 value: 26.565 - type: precision_at_1 value: 29.316 - type: precision_at_10 value: 9.186 - type: precision_at_100 value: 1.6549999999999998 - type: precision_at_1000 value: 0.22999999999999998 - type: precision_at_3 value: 18.436 - type: precision_at_5 value: 13.876 - type: recall_at_1 value: 12.969 - type: recall_at_10 value: 35.142 - type: recall_at_100 value: 59.143 - type: recall_at_1000 value: 78.594 - type: recall_at_3 value: 22.604 - type: recall_at_5 value: 27.883000000000003 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.527999999999999 - type: map_at_10 value: 17.974999999999998 - type: map_at_100 value: 25.665 - type: map_at_1000 value: 27.406000000000002 - type: map_at_3 value: 13.017999999999999 - type: map_at_5 value: 15.137 - type: mrr_at_1 value: 62.5 - type: mrr_at_10 value: 71.891 - type: mrr_at_100 value: 72.294 - type: mrr_at_1000 value: 72.296 - type: mrr_at_3 value: 69.958 - type: mrr_at_5 value: 71.121 - type: ndcg_at_1 value: 50.875 - type: ndcg_at_10 value: 38.36 - type: ndcg_at_100 value: 44.235 - type: ndcg_at_1000 value: 52.154 - type: ndcg_at_3 value: 43.008 - type: ndcg_at_5 value: 40.083999999999996 - type: precision_at_1 value: 62.5 - type: precision_at_10 value: 30.0 - type: precision_at_100 value: 10.038 - type: precision_at_1000 value: 2.0869999999999997 - type: precision_at_3 value: 46.833000000000006 - type: precision_at_5 value: 38.800000000000004 - type: recall_at_1 value: 8.527999999999999 - type: recall_at_10 value: 23.828 - type: recall_at_100 value: 52.322 - type: recall_at_1000 value: 77.143 - type: recall_at_3 value: 14.136000000000001 - type: recall_at_5 value: 17.761 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.51 - type: f1 value: 47.632159862049896 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 60.734 - type: map_at_10 value: 72.442 - type: map_at_100 value: 72.735 - type: map_at_1000 value: 72.75 - type: map_at_3 value: 70.41199999999999 - type: map_at_5 value: 71.80499999999999 - type: mrr_at_1 value: 65.212 - type: mrr_at_10 value: 76.613 - type: mrr_at_100 value: 76.79899999999999 - type: mrr_at_1000 value: 76.801 - type: mrr_at_3 value: 74.8 - type: mrr_at_5 value: 76.12400000000001 - type: ndcg_at_1 value: 65.212 - type: ndcg_at_10 value: 77.988 - type: ndcg_at_100 value: 79.167 - type: ndcg_at_1000 value: 79.452 - type: ndcg_at_3 value: 74.362 - type: ndcg_at_5 value: 76.666 - type: precision_at_1 value: 65.212 - type: precision_at_10 value: 10.003 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 29.518 - type: precision_at_5 value: 19.016 - type: recall_at_1 value: 60.734 - type: recall_at_10 value: 90.824 - type: recall_at_100 value: 95.71600000000001 - type: recall_at_1000 value: 97.577 - type: recall_at_3 value: 81.243 - type: recall_at_5 value: 86.90299999999999 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 23.845 - type: map_at_10 value: 39.281 - type: map_at_100 value: 41.422 - type: map_at_1000 value: 41.593 - type: map_at_3 value: 34.467 - type: map_at_5 value: 37.017 - type: mrr_at_1 value: 47.531 - type: mrr_at_10 value: 56.204 - type: mrr_at_100 value: 56.928999999999995 - type: mrr_at_1000 value: 56.962999999999994 - type: mrr_at_3 value: 54.115 - type: mrr_at_5 value: 55.373000000000005 - type: ndcg_at_1 value: 47.531 - type: ndcg_at_10 value: 47.711999999999996 - type: ndcg_at_100 value: 54.510999999999996 - type: ndcg_at_1000 value: 57.103 - type: ndcg_at_3 value: 44.145 - type: ndcg_at_5 value: 45.032 - type: precision_at_1 value: 47.531 - type: precision_at_10 value: 13.194 - type: precision_at_100 value: 2.045 - type: precision_at_1000 value: 0.249 - type: precision_at_3 value: 29.424 - type: precision_at_5 value: 21.451 - type: recall_at_1 value: 23.845 - type: recall_at_10 value: 54.967 - type: recall_at_100 value: 79.11399999999999 - type: recall_at_1000 value: 94.56700000000001 - type: recall_at_3 value: 40.256 - type: recall_at_5 value: 46.215 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 37.819 - type: map_at_10 value: 60.889 - type: map_at_100 value: 61.717999999999996 - type: map_at_1000 value: 61.778 - type: map_at_3 value: 57.254000000000005 - type: map_at_5 value: 59.541 - type: mrr_at_1 value: 75.638 - type: mrr_at_10 value: 82.173 - type: mrr_at_100 value: 82.362 - type: mrr_at_1000 value: 82.37 - type: mrr_at_3 value: 81.089 - type: mrr_at_5 value: 81.827 - type: ndcg_at_1 value: 75.638 - type: ndcg_at_10 value: 69.317 - type: ndcg_at_100 value: 72.221 - type: ndcg_at_1000 value: 73.382 - type: ndcg_at_3 value: 64.14 - type: ndcg_at_5 value: 67.07600000000001 - type: precision_at_1 value: 75.638 - type: precision_at_10 value: 14.704999999999998 - type: precision_at_100 value: 1.698 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 41.394999999999996 - type: precision_at_5 value: 27.162999999999997 - type: recall_at_1 value: 37.819 - type: recall_at_10 value: 73.52499999999999 - type: recall_at_100 value: 84.875 - type: recall_at_1000 value: 92.559 - type: recall_at_3 value: 62.092999999999996 - type: recall_at_5 value: 67.907 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.60079999999999 - type: ap value: 92.67396345347356 - type: f1 value: 94.5988098167121 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.285 - type: map_at_10 value: 33.436 - type: map_at_100 value: 34.63 - type: map_at_1000 value: 34.681 - type: map_at_3 value: 29.412 - type: map_at_5 value: 31.715 - type: mrr_at_1 value: 21.848 - type: mrr_at_10 value: 33.979 - type: mrr_at_100 value: 35.118 - type: mrr_at_1000 value: 35.162 - type: mrr_at_3 value: 30.036 - type: mrr_at_5 value: 32.298 - type: ndcg_at_1 value: 21.862000000000002 - type: ndcg_at_10 value: 40.43 - type: ndcg_at_100 value: 46.17 - type: ndcg_at_1000 value: 47.412 - type: ndcg_at_3 value: 32.221 - type: ndcg_at_5 value: 36.332 - type: precision_at_1 value: 21.862000000000002 - type: precision_at_10 value: 6.491 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.744 - type: precision_at_5 value: 10.331999999999999 - type: recall_at_1 value: 21.285 - type: recall_at_10 value: 62.083 - type: recall_at_100 value: 88.576 - type: recall_at_1000 value: 98.006 - type: recall_at_3 value: 39.729 - type: recall_at_5 value: 49.608000000000004 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.92612859097127 - type: f1 value: 93.82370333372853 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (de) type: mteb/mtop_domain config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 92.67681036911807 - type: f1 value: 92.14191382411472 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (es) type: mteb/mtop_domain config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 92.26817878585723 - type: f1 value: 91.92824250337878 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (fr) type: mteb/mtop_domain config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.96554963983714 - type: f1 value: 90.02859329630792 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (hi) type: mteb/mtop_domain config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.02509860164935 - type: f1 value: 89.30665159182062 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (th) type: mteb/mtop_domain config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.55515370705244 - type: f1 value: 87.94449232331907 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 82.4623803009576 - type: f1 value: 66.06738378772725 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (de) type: mteb/mtop_intent config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.3716539870386 - type: f1 value: 60.37614033396853 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (es) type: mteb/mtop_intent config: es split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 80.34022681787857 - type: f1 value: 58.302008026952 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (fr) type: mteb/mtop_intent config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.72095208268087 - type: f1 value: 59.64524724009049 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (hi) type: mteb/mtop_intent config: hi split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.87020437432773 - type: f1 value: 57.80202694670567 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (th) type: mteb/mtop_intent config: th split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.73598553345387 - type: f1 value: 58.19628250675031 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (af) type: mteb/amazon_massive_intent config: af split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.6630800268998 - type: f1 value: 65.00996668051691 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (am) type: mteb/amazon_massive_intent config: am split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.7128446536651 - type: f1 value: 57.95860594874963 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ar) type: mteb/amazon_massive_intent config: ar split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.61129791526563 - type: f1 value: 59.75328290206483 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (az) type: mteb/amazon_massive_intent config: az split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.00134498991257 - type: f1 value: 67.0230483991802 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (bn) type: mteb/amazon_massive_intent config: bn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.54068594485541 - type: f1 value: 65.54604628946976 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (cy) type: mteb/amazon_massive_intent config: cy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.032952252858095 - type: f1 value: 58.715741857057104 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (da) type: mteb/amazon_massive_intent config: da split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.80901143241427 - type: f1 value: 68.33963989243877 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (de) type: mteb/amazon_massive_intent config: de split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.47141896435777 - type: f1 value: 69.56765020308262 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (el) type: mteb/amazon_massive_intent config: el split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.2373907195696 - type: f1 value: 69.04529836036467 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.05783456624076 - type: f1 value: 74.69430584708174 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (es) type: mteb/amazon_massive_intent config: es split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.82111634162744 - type: f1 value: 70.77228952803762 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (fa) type: mteb/amazon_massive_intent config: fa split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.25353059852051 - type: f1 value: 71.05310103416411 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (fi) type: mteb/amazon_massive_intent config: fi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.28648285137861 - type: f1 value: 69.08020473732226 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (fr) type: mteb/amazon_massive_intent config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.31540013449899 - type: f1 value: 70.9426355465791 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (he) type: mteb/amazon_massive_intent config: he split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.2151983860121 - type: f1 value: 67.52541755908858 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hi) type: mteb/amazon_massive_intent config: hi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.58372562205784 - type: f1 value: 69.49769064229827 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hu) type: mteb/amazon_massive_intent config: hu split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.9233355749832 - type: f1 value: 69.36311548259593 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (hy) type: mteb/amazon_massive_intent config: hy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.07330195023538 - type: f1 value: 64.99882022345572 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (id) type: mteb/amazon_massive_intent config: id split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.62273032952253 - type: f1 value: 70.6394885471001 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (is) type: mteb/amazon_massive_intent config: is split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.77000672494957 - type: f1 value: 62.9368944815065 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (it) type: mteb/amazon_massive_intent config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.453261600538 - type: f1 value: 70.85069934666681 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ja) type: mteb/amazon_massive_intent config: ja split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.6906523201076 - type: f1 value: 72.03249740074217 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (jv) type: mteb/amazon_massive_intent config: jv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.03631472763953 - type: f1 value: 59.3165215571852 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ka) type: mteb/amazon_massive_intent config: ka split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.913920645595155 - type: f1 value: 57.367337711611285 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (km) type: mteb/amazon_massive_intent config: km split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 54.42837928715535 - type: f1 value: 52.60527294970906 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (kn) type: mteb/amazon_massive_intent config: kn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.33490248823135 - type: f1 value: 63.213340969404065 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ko) type: mteb/amazon_massive_intent config: ko split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.58507061197041 - type: f1 value: 68.40256628040486 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (lv) type: mteb/amazon_massive_intent config: lv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.11230665770006 - type: f1 value: 66.44863577842305 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ml) type: mteb/amazon_massive_intent config: ml split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.70073974445192 - type: f1 value: 67.21291337273702 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (mn) type: mteb/amazon_massive_intent config: mn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.43913920645595 - type: f1 value: 64.09838087422806 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ms) type: mteb/amazon_massive_intent config: ms split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.80026899798251 - type: f1 value: 68.76986742962444 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (my) type: mteb/amazon_massive_intent config: my split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.78816408876934 - type: f1 value: 62.18781873428972 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (nb) type: mteb/amazon_massive_intent config: nb split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.6577000672495 - type: f1 value: 68.75171511133003 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (nl) type: mteb/amazon_massive_intent config: nl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.42501681237391 - type: f1 value: 71.18434963451544 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.64828513786146 - type: f1 value: 70.67741914007422 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pt) type: mteb/amazon_massive_intent config: pt split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.62811028917284 - type: f1 value: 71.36402039740959 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ro) type: mteb/amazon_massive_intent config: ro split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.88634835238736 - type: f1 value: 69.23701923480677 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ru) type: mteb/amazon_massive_intent config: ru split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.15938130464022 - type: f1 value: 71.87792218993388 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sl) type: mteb/amazon_massive_intent config: sl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.96301277740416 - type: f1 value: 67.29584200202983 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sq) type: mteb/amazon_massive_intent config: sq split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.49562878278412 - type: f1 value: 66.91716685679431 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sv) type: mteb/amazon_massive_intent config: sv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.6805648957633 - type: f1 value: 72.02723592594374 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (sw) type: mteb/amazon_massive_intent config: sw split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.00605245460659 - type: f1 value: 60.16716669482932 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ta) type: mteb/amazon_massive_intent config: ta split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.90988567585742 - type: f1 value: 63.99405488777784 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (te) type: mteb/amazon_massive_intent config: te split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.62273032952253 - type: f1 value: 65.17213906909481 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (th) type: mteb/amazon_massive_intent config: th split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.50907868190988 - type: f1 value: 69.15165697194853 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (tl) type: mteb/amazon_massive_intent config: tl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.30733019502352 - type: f1 value: 66.69024007380474 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (tr) type: mteb/amazon_massive_intent config: tr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.24277067921989 - type: f1 value: 68.80515408492947 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (ur) type: mteb/amazon_massive_intent config: ur split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.49831876260929 - type: f1 value: 64.83778567111116 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (vi) type: mteb/amazon_massive_intent config: vi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.28782784129119 - type: f1 value: 69.3294186700733 - 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: 73.315400134499 - type: f1 value: 71.22674385243207 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-TW) type: mteb/amazon_massive_intent config: zh-TW split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.37794216543377 - type: f1 value: 68.96962492838232 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (af) type: mteb/amazon_massive_scenario config: af split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.33557498318764 - type: f1 value: 72.28949738478356 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (am) type: mteb/amazon_massive_scenario config: am split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 65.84398117014123 - type: f1 value: 64.71026362091463 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ar) type: mteb/amazon_massive_scenario config: ar split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.76462676529925 - type: f1 value: 69.8229667407667 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (az) type: mteb/amazon_massive_scenario config: az split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.02420981842636 - type: f1 value: 71.76576384895898 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (bn) type: mteb/amazon_massive_scenario config: bn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.7572293207801 - type: f1 value: 72.76840765295256 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (cy) type: mteb/amazon_massive_scenario config: cy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.02286482851379 - type: f1 value: 66.17237947327872 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (da) type: mteb/amazon_massive_scenario config: da split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.60928043039678 - type: f1 value: 77.27094731234773 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (de) type: mteb/amazon_massive_scenario config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.68325487558843 - type: f1 value: 77.97530399082261 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (el) type: mteb/amazon_massive_scenario config: el split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.13315400134498 - type: f1 value: 75.97558584796424 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.47410894418292 - type: f1 value: 80.52244841473792 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (es) type: mteb/amazon_massive_scenario config: es split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.9670477471419 - type: f1 value: 77.37318805793146 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fa) type: mteb/amazon_massive_scenario config: fa split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.09683927370544 - type: f1 value: 77.69773737430847 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fi) type: mteb/amazon_massive_scenario config: fi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.20847343644922 - type: f1 value: 75.17071738727348 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.07464694014796 - type: f1 value: 77.16136207698571 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (he) type: mteb/amazon_massive_scenario config: he split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.53396099529255 - type: f1 value: 73.58296404484122 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hi) type: mteb/amazon_massive_scenario config: hi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.75319435104237 - type: f1 value: 75.24674707850833 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hu) type: mteb/amazon_massive_scenario config: hu split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.0948217888366 - type: f1 value: 76.47559490205028 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (hy) type: mteb/amazon_massive_scenario config: hy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.07599193006052 - type: f1 value: 70.76028043093511 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (id) type: mteb/amazon_massive_scenario config: id split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.10490921318089 - type: f1 value: 77.01215275283272 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (is) type: mteb/amazon_massive_scenario config: is split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.25756556825824 - type: f1 value: 70.20605314648762 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (it) type: mteb/amazon_massive_scenario config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.08137188971082 - type: f1 value: 77.3899269057439 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ja) type: mteb/amazon_massive_scenario config: ja split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.35440484196369 - type: f1 value: 79.58964690002772 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (jv) type: mteb/amazon_massive_scenario config: jv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.42299932750504 - type: f1 value: 68.07844356925413 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ka) type: mteb/amazon_massive_scenario config: ka split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.15669132481507 - type: f1 value: 65.89383352608513 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (km) type: mteb/amazon_massive_scenario config: km split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.11432414256894 - type: f1 value: 57.69910594559806 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (kn) type: mteb/amazon_massive_scenario config: kn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.24747814391392 - type: f1 value: 70.42455553830918 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ko) type: mteb/amazon_massive_scenario config: ko split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.46267652992603 - type: f1 value: 76.8854559308316 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (lv) type: mteb/amazon_massive_scenario config: lv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.24815063887021 - type: f1 value: 72.77805034658074 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ml) type: mteb/amazon_massive_scenario config: ml split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.11566913248151 - type: f1 value: 73.86147988001356 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (mn) type: mteb/amazon_massive_scenario config: mn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.0168123739072 - type: f1 value: 69.38515920054571 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ms) type: mteb/amazon_massive_scenario config: ms split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.41156691324814 - type: f1 value: 73.43474953408237 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (my) type: mteb/amazon_massive_scenario config: my split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.39609952925353 - type: f1 value: 67.29731681109291 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (nb) type: mteb/amazon_massive_scenario config: nb split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.20914593140552 - type: f1 value: 77.07066497935367 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (nl) type: mteb/amazon_massive_scenario config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.52387357094821 - type: f1 value: 78.5259569473291 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.6913248150639 - type: f1 value: 76.91201656350455 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pt) type: mteb/amazon_massive_scenario config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.1217215870881 - type: f1 value: 77.41179937912504 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ro) type: mteb/amazon_massive_scenario config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.25891055817083 - type: f1 value: 75.8089244542887 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ru) type: mteb/amazon_massive_scenario config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.70679219905851 - type: f1 value: 78.21459594517711 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sl) type: mteb/amazon_massive_scenario config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.83523873570948 - type: f1 value: 74.86847028401978 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sq) type: mteb/amazon_massive_scenario config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.71755211835911 - type: f1 value: 74.0214326485662 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sv) type: mteb/amazon_massive_scenario config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.06523201075991 - type: f1 value: 79.10545620325138 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (sw) type: mteb/amazon_massive_scenario config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.91862811028918 - type: f1 value: 66.50386121217983 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ta) type: mteb/amazon_massive_scenario config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.93140551445865 - type: f1 value: 70.755435928495 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (te) type: mteb/amazon_massive_scenario config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.40753194351042 - type: f1 value: 71.61816115782923 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (th) type: mteb/amazon_massive_scenario config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.1815736381977 - type: f1 value: 75.08016717887205 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (tl) type: mteb/amazon_massive_scenario config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.86482851378614 - type: f1 value: 72.39521180006291 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (tr) type: mteb/amazon_massive_scenario config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.46940147948891 - type: f1 value: 76.70044085362349 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (ur) type: mteb/amazon_massive_scenario config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.89307330195024 - type: f1 value: 71.5721825332298 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (vi) type: mteb/amazon_massive_scenario config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.7511768661735 - type: f1 value: 75.17918654541515 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.69535978480162 - type: f1 value: 78.90019070153316 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-TW) type: mteb/amazon_massive_scenario config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.45729657027572 - type: f1 value: 76.19578371794672 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.92715354123554 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 35.53536244162518 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 33.08507884504006 - type: mrr value: 34.32436977159129 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.935 - type: map_at_10 value: 13.297 - type: map_at_100 value: 16.907 - type: map_at_1000 value: 18.391 - type: map_at_3 value: 9.626999999999999 - type: map_at_5 value: 11.190999999999999 - type: mrr_at_1 value: 46.129999999999995 - type: mrr_at_10 value: 54.346000000000004 - type: mrr_at_100 value: 55.067 - type: mrr_at_1000 value: 55.1 - type: mrr_at_3 value: 51.961 - type: mrr_at_5 value: 53.246 - type: ndcg_at_1 value: 44.118 - type: ndcg_at_10 value: 35.534 - type: ndcg_at_100 value: 32.946999999999996 - type: ndcg_at_1000 value: 41.599000000000004 - type: ndcg_at_3 value: 40.25 - type: ndcg_at_5 value: 37.978 - type: precision_at_1 value: 46.129999999999995 - type: precision_at_10 value: 26.842 - type: precision_at_100 value: 8.427 - type: precision_at_1000 value: 2.128 - type: precision_at_3 value: 37.977 - type: precision_at_5 value: 32.879000000000005 - type: recall_at_1 value: 5.935 - type: recall_at_10 value: 17.211000000000002 - type: recall_at_100 value: 34.33 - type: recall_at_1000 value: 65.551 - type: recall_at_3 value: 10.483 - type: recall_at_5 value: 13.078999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 35.231 - type: map_at_10 value: 50.202000000000005 - type: map_at_100 value: 51.154999999999994 - type: map_at_1000 value: 51.181 - type: map_at_3 value: 45.774 - type: map_at_5 value: 48.522 - type: mrr_at_1 value: 39.687 - type: mrr_at_10 value: 52.88 - type: mrr_at_100 value: 53.569 - type: mrr_at_1000 value: 53.58500000000001 - type: mrr_at_3 value: 49.228 - type: mrr_at_5 value: 51.525 - type: ndcg_at_1 value: 39.687 - type: ndcg_at_10 value: 57.754000000000005 - type: ndcg_at_100 value: 61.597 - type: ndcg_at_1000 value: 62.18900000000001 - type: ndcg_at_3 value: 49.55 - type: ndcg_at_5 value: 54.11899999999999 - type: precision_at_1 value: 39.687 - type: precision_at_10 value: 9.313 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 22.229 - type: precision_at_5 value: 15.939 - type: recall_at_1 value: 35.231 - type: recall_at_10 value: 78.083 - type: recall_at_100 value: 94.42099999999999 - type: recall_at_1000 value: 98.81 - type: recall_at_3 value: 57.047000000000004 - type: recall_at_5 value: 67.637 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.241 - type: map_at_10 value: 85.462 - type: map_at_100 value: 86.083 - type: map_at_1000 value: 86.09700000000001 - type: map_at_3 value: 82.49499999999999 - type: map_at_5 value: 84.392 - type: mrr_at_1 value: 82.09 - type: mrr_at_10 value: 88.301 - type: mrr_at_100 value: 88.383 - type: mrr_at_1000 value: 88.384 - type: mrr_at_3 value: 87.37 - type: mrr_at_5 value: 88.035 - type: ndcg_at_1 value: 82.12 - type: ndcg_at_10 value: 89.149 - type: ndcg_at_100 value: 90.235 - type: ndcg_at_1000 value: 90.307 - type: ndcg_at_3 value: 86.37599999999999 - type: ndcg_at_5 value: 87.964 - type: precision_at_1 value: 82.12 - type: precision_at_10 value: 13.56 - type: precision_at_100 value: 1.539 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.88 - type: precision_at_5 value: 24.92 - type: recall_at_1 value: 71.241 - type: recall_at_10 value: 96.128 - type: recall_at_100 value: 99.696 - type: recall_at_1000 value: 99.994 - type: recall_at_3 value: 88.181 - type: recall_at_5 value: 92.694 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.59757799655151 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.27391998854624 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.243 - type: map_at_10 value: 10.965 - type: map_at_100 value: 12.934999999999999 - type: map_at_1000 value: 13.256 - type: map_at_3 value: 7.907 - type: map_at_5 value: 9.435 - type: mrr_at_1 value: 20.9 - type: mrr_at_10 value: 31.849 - type: mrr_at_100 value: 32.964 - type: mrr_at_1000 value: 33.024 - type: mrr_at_3 value: 28.517 - type: mrr_at_5 value: 30.381999999999998 - type: ndcg_at_1 value: 20.9 - type: ndcg_at_10 value: 18.723 - type: ndcg_at_100 value: 26.384999999999998 - type: ndcg_at_1000 value: 32.114 - type: ndcg_at_3 value: 17.753 - type: ndcg_at_5 value: 15.558 - type: precision_at_1 value: 20.9 - type: precision_at_10 value: 9.8 - type: precision_at_100 value: 2.078 - type: precision_at_1000 value: 0.345 - type: precision_at_3 value: 16.900000000000002 - type: precision_at_5 value: 13.88 - type: recall_at_1 value: 4.243 - type: recall_at_10 value: 19.885 - type: recall_at_100 value: 42.17 - type: recall_at_1000 value: 70.12 - type: recall_at_3 value: 10.288 - type: recall_at_5 value: 14.072000000000001 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.84209174935282 - type: cos_sim_spearman value: 81.73248048438833 - type: euclidean_pearson value: 83.02810070308149 - type: euclidean_spearman value: 81.73248295679514 - type: manhattan_pearson value: 82.95368060376002 - type: manhattan_spearman value: 81.60277910998718 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 88.52628804556943 - type: cos_sim_spearman value: 82.5713913555672 - type: euclidean_pearson value: 85.8796774746988 - type: euclidean_spearman value: 82.57137506803424 - type: manhattan_pearson value: 85.79671002960058 - type: manhattan_spearman value: 82.49445981618027 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.23682503505542 - type: cos_sim_spearman value: 87.15008956711806 - type: euclidean_pearson value: 86.79805401524959 - type: euclidean_spearman value: 87.15008956711806 - type: manhattan_pearson value: 86.65298502699244 - type: manhattan_spearman value: 86.97677821948562 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 85.63370304677802 - type: cos_sim_spearman value: 84.97105553540318 - type: euclidean_pearson value: 85.28896108687721 - type: euclidean_spearman value: 84.97105553540318 - type: manhattan_pearson value: 85.09663190337331 - type: manhattan_spearman value: 84.79126831644619 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 90.2614838800733 - type: cos_sim_spearman value: 91.0509162991835 - type: euclidean_pearson value: 90.33098317533373 - type: euclidean_spearman value: 91.05091625871644 - type: manhattan_pearson value: 90.26250435151107 - type: manhattan_spearman value: 90.97999594417519 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.80480973335091 - type: cos_sim_spearman value: 87.313695492969 - type: euclidean_pearson value: 86.49267251576939 - type: euclidean_spearman value: 87.313695492969 - type: manhattan_pearson value: 86.44019901831935 - type: manhattan_spearman value: 87.24205395460392 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 90.05662789380672 - type: cos_sim_spearman value: 90.02759424426651 - type: euclidean_pearson value: 90.4042483422981 - type: euclidean_spearman value: 90.02759424426651 - type: manhattan_pearson value: 90.51446975000226 - type: manhattan_spearman value: 90.08832889933616 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.5975528273532 - type: cos_sim_spearman value: 67.62969861411354 - type: euclidean_pearson value: 69.224275734323 - type: euclidean_spearman value: 67.62969861411354 - type: manhattan_pearson value: 69.3761447059927 - type: manhattan_spearman value: 67.90921005611467 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.11244327231684 - type: cos_sim_spearman value: 88.37902438979035 - type: euclidean_pearson value: 87.86054279847336 - type: euclidean_spearman value: 88.37902438979035 - type: manhattan_pearson value: 87.77257757320378 - type: manhattan_spearman value: 88.25208966098123 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.87174608143563 - type: mrr value: 96.12836872640794 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.760999999999996 - type: map_at_10 value: 67.258 - type: map_at_100 value: 67.757 - type: map_at_1000 value: 67.78800000000001 - type: map_at_3 value: 64.602 - type: map_at_5 value: 65.64 - type: mrr_at_1 value: 60.667 - type: mrr_at_10 value: 68.441 - type: mrr_at_100 value: 68.825 - type: mrr_at_1000 value: 68.853 - type: mrr_at_3 value: 66.444 - type: mrr_at_5 value: 67.26100000000001 - type: ndcg_at_1 value: 60.667 - type: ndcg_at_10 value: 71.852 - type: ndcg_at_100 value: 73.9 - type: ndcg_at_1000 value: 74.628 - type: ndcg_at_3 value: 67.093 - type: ndcg_at_5 value: 68.58 - type: precision_at_1 value: 60.667 - type: precision_at_10 value: 9.6 - type: precision_at_100 value: 1.0670000000000002 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 26.111 - type: precision_at_5 value: 16.733 - type: recall_at_1 value: 57.760999999999996 - type: recall_at_10 value: 84.967 - type: recall_at_100 value: 93.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 71.589 - type: recall_at_5 value: 75.483 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.66633663366336 - type: cos_sim_ap value: 91.17685358899108 - type: cos_sim_f1 value: 82.16818642350559 - type: cos_sim_precision value: 83.26488706365504 - type: cos_sim_recall value: 81.10000000000001 - type: dot_accuracy value: 99.66633663366336 - type: dot_ap value: 91.17663411119032 - type: dot_f1 value: 82.16818642350559 - type: dot_precision value: 83.26488706365504 - type: dot_recall value: 81.10000000000001 - type: euclidean_accuracy value: 99.66633663366336 - type: euclidean_ap value: 91.17685189882275 - type: euclidean_f1 value: 82.16818642350559 - type: euclidean_precision value: 83.26488706365504 - type: euclidean_recall value: 81.10000000000001 - type: manhattan_accuracy value: 99.66633663366336 - type: manhattan_ap value: 91.2241619496737 - type: manhattan_f1 value: 82.20472440944883 - type: manhattan_precision value: 86.51933701657458 - type: manhattan_recall value: 78.3 - type: max_accuracy value: 99.66633663366336 - type: max_ap value: 91.2241619496737 - type: max_f1 value: 82.20472440944883 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.85101268897951 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 42.461184054706905 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 51.44542568873886 - type: mrr value: 52.33656151854681 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.75982974997539 - type: cos_sim_spearman value: 30.385405026539914 - type: dot_pearson value: 30.75982433546523 - type: dot_spearman value: 30.385405026539914 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22799999999999998 - type: map_at_10 value: 2.064 - type: map_at_100 value: 13.056000000000001 - type: map_at_1000 value: 31.747999999999998 - type: map_at_3 value: 0.67 - type: map_at_5 value: 1.097 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 94.667 - type: mrr_at_100 value: 94.667 - type: mrr_at_1000 value: 94.667 - type: mrr_at_3 value: 94.667 - type: mrr_at_5 value: 94.667 - type: ndcg_at_1 value: 86.0 - type: ndcg_at_10 value: 82.0 - type: ndcg_at_100 value: 64.307 - type: ndcg_at_1000 value: 57.023999999999994 - type: ndcg_at_3 value: 85.816 - type: ndcg_at_5 value: 84.904 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 85.8 - type: precision_at_100 value: 66.46 - type: precision_at_1000 value: 25.202 - type: precision_at_3 value: 90.0 - type: precision_at_5 value: 89.2 - type: recall_at_1 value: 0.22799999999999998 - type: recall_at_10 value: 2.235 - type: recall_at_100 value: 16.185 - type: recall_at_1000 value: 53.620999999999995 - type: recall_at_3 value: 0.7040000000000001 - type: recall_at_5 value: 1.172 - task: type: BitextMining dataset: name: MTEB Tatoeba (sqi-eng) type: mteb/tatoeba-bitext-mining config: sqi-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.75 - type: precision value: 96.45 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (fry-eng) type: mteb/tatoeba-bitext-mining config: fry-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.54913294797689 - type: f1 value: 82.46628131021194 - type: precision value: 81.1175337186898 - type: recall value: 85.54913294797689 - task: type: BitextMining dataset: name: MTEB Tatoeba (kur-eng) type: mteb/tatoeba-bitext-mining config: kur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81.21951219512195 - type: f1 value: 77.33333333333334 - type: precision value: 75.54878048780488 - type: recall value: 81.21951219512195 - task: type: BitextMining dataset: name: MTEB Tatoeba (tur-eng) type: mteb/tatoeba-bitext-mining config: tur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.6 - type: f1 value: 98.26666666666665 - type: precision value: 98.1 - type: recall value: 98.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (deu-eng) type: mteb/tatoeba-bitext-mining config: deu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 99.5 - type: f1 value: 99.33333333333333 - type: precision value: 99.25 - type: recall value: 99.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (nld-eng) type: mteb/tatoeba-bitext-mining config: nld-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.8 - type: f1 value: 97.2 - type: precision value: 96.89999999999999 - type: recall value: 97.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (ron-eng) type: mteb/tatoeba-bitext-mining config: ron-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.8 - type: f1 value: 97.18333333333334 - type: precision value: 96.88333333333333 - type: recall value: 97.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (ang-eng) type: mteb/tatoeba-bitext-mining config: ang-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.61194029850746 - type: f1 value: 72.81094527363183 - type: precision value: 70.83333333333333 - type: recall value: 77.61194029850746 - task: type: BitextMining dataset: name: MTEB Tatoeba (ido-eng) type: mteb/tatoeba-bitext-mining config: ido-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.7 - type: f1 value: 91.91666666666667 - type: precision value: 91.08333333333334 - type: recall value: 93.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (jav-eng) type: mteb/tatoeba-bitext-mining config: jav-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.29268292682927 - type: f1 value: 85.27642276422765 - type: precision value: 84.01277584204414 - type: recall value: 88.29268292682927 - task: type: BitextMining dataset: name: MTEB Tatoeba (isl-eng) type: mteb/tatoeba-bitext-mining config: isl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.1 - type: f1 value: 95.0 - type: precision value: 94.46666666666668 - type: recall value: 96.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (slv-eng) type: mteb/tatoeba-bitext-mining config: slv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.681652490887 - type: f1 value: 91.90765492102065 - type: precision value: 91.05913325232888 - type: recall value: 93.681652490887 - task: type: BitextMining dataset: name: MTEB Tatoeba (cym-eng) type: mteb/tatoeba-bitext-mining config: cym-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.17391304347827 - type: f1 value: 89.97101449275361 - type: precision value: 88.96811594202899 - type: recall value: 92.17391304347827 - task: type: BitextMining dataset: name: MTEB Tatoeba (kaz-eng) type: mteb/tatoeba-bitext-mining config: kaz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.43478260869566 - type: f1 value: 87.72173913043478 - type: precision value: 86.42028985507245 - type: recall value: 90.43478260869566 - task: type: BitextMining dataset: name: MTEB Tatoeba (est-eng) type: mteb/tatoeba-bitext-mining config: est-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.4 - type: f1 value: 88.03 - type: precision value: 86.95 - type: recall value: 90.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (heb-eng) type: mteb/tatoeba-bitext-mining config: heb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.4 - type: f1 value: 91.45666666666666 - type: precision value: 90.525 - type: recall value: 93.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (gla-eng) type: mteb/tatoeba-bitext-mining config: gla-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81.9059107358263 - type: f1 value: 78.32557872364869 - type: precision value: 76.78260286824823 - type: recall value: 81.9059107358263 - task: type: BitextMining dataset: name: MTEB Tatoeba (mar-eng) type: mteb/tatoeba-bitext-mining config: mar-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.3 - type: f1 value: 92.58333333333333 - type: precision value: 91.73333333333332 - type: recall value: 94.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (lat-eng) type: mteb/tatoeba-bitext-mining config: lat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 79.10000000000001 - type: f1 value: 74.50500000000001 - type: precision value: 72.58928571428571 - type: recall value: 79.10000000000001 - task: type: BitextMining dataset: name: MTEB Tatoeba (bel-eng) type: mteb/tatoeba-bitext-mining config: bel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.6 - type: f1 value: 95.55 - type: precision value: 95.05 - type: recall value: 96.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (pms-eng) type: mteb/tatoeba-bitext-mining config: pms-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.0952380952381 - type: f1 value: 77.98458049886621 - type: precision value: 76.1968253968254 - type: recall value: 82.0952380952381 - task: type: BitextMining dataset: name: MTEB Tatoeba (gle-eng) type: mteb/tatoeba-bitext-mining config: gle-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.9 - type: f1 value: 84.99190476190476 - type: precision value: 83.65 - type: recall value: 87.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (pes-eng) type: mteb/tatoeba-bitext-mining config: pes-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.7 - type: f1 value: 94.56666666666666 - type: precision value: 94.01666666666667 - type: recall value: 95.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (nob-eng) type: mteb/tatoeba-bitext-mining config: nob-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.6 - type: f1 value: 98.2 - type: precision value: 98.0 - type: recall value: 98.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (bul-eng) type: mteb/tatoeba-bitext-mining config: bul-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.6 - type: f1 value: 94.38333333333334 - type: precision value: 93.78333333333335 - type: recall value: 95.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cbk-eng) type: mteb/tatoeba-bitext-mining config: cbk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.4 - type: f1 value: 84.10380952380952 - type: precision value: 82.67 - type: recall value: 87.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (hun-eng) type: mteb/tatoeba-bitext-mining config: hun-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.5 - type: f1 value: 94.33333333333334 - type: precision value: 93.78333333333333 - type: recall value: 95.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (uig-eng) type: mteb/tatoeba-bitext-mining config: uig-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.4 - type: f1 value: 86.82000000000001 - type: precision value: 85.64500000000001 - type: recall value: 89.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (rus-eng) type: mteb/tatoeba-bitext-mining config: rus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.1 - type: f1 value: 93.56666666666668 - type: precision value: 92.81666666666666 - type: recall value: 95.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (spa-eng) type: mteb/tatoeba-bitext-mining config: spa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.9 - type: f1 value: 98.6 - type: precision value: 98.45 - type: recall value: 98.9 - task: type: BitextMining dataset: name: MTEB Tatoeba (hye-eng) type: mteb/tatoeba-bitext-mining config: hye-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.01347708894879 - type: f1 value: 93.51752021563343 - type: precision value: 92.82794249775381 - type: recall value: 95.01347708894879 - task: type: BitextMining dataset: name: MTEB Tatoeba (tel-eng) type: mteb/tatoeba-bitext-mining config: tel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.00854700854701 - type: f1 value: 96.08262108262107 - type: precision value: 95.65527065527067 - type: recall value: 97.00854700854701 - task: type: BitextMining dataset: name: MTEB Tatoeba (afr-eng) type: mteb/tatoeba-bitext-mining config: afr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.5 - type: f1 value: 95.39999999999999 - type: precision value: 94.88333333333333 - type: recall value: 96.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (mon-eng) type: mteb/tatoeba-bitext-mining config: mon-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.5909090909091 - type: f1 value: 95.49242424242425 - type: precision value: 94.9621212121212 - type: recall value: 96.5909090909091 - task: type: BitextMining dataset: name: MTEB Tatoeba (arz-eng) type: mteb/tatoeba-bitext-mining config: arz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 84.90566037735849 - type: f1 value: 81.85883997204752 - type: precision value: 80.54507337526205 - type: recall value: 84.90566037735849 - task: type: BitextMining dataset: name: MTEB Tatoeba (hrv-eng) type: mteb/tatoeba-bitext-mining config: hrv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.5 - type: f1 value: 96.75 - type: precision value: 96.38333333333333 - type: recall value: 97.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (nov-eng) type: mteb/tatoeba-bitext-mining config: nov-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 86.7704280155642 - type: f1 value: 82.99610894941635 - type: precision value: 81.32295719844358 - type: recall value: 86.7704280155642 - task: type: BitextMining dataset: name: MTEB Tatoeba (gsw-eng) type: mteb/tatoeba-bitext-mining config: gsw-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 67.52136752136752 - type: f1 value: 61.89662189662191 - type: precision value: 59.68660968660969 - type: recall value: 67.52136752136752 - task: type: BitextMining dataset: name: MTEB Tatoeba (nds-eng) type: mteb/tatoeba-bitext-mining config: nds-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.2 - type: f1 value: 86.32 - type: precision value: 85.015 - type: recall value: 89.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (ukr-eng) type: mteb/tatoeba-bitext-mining config: ukr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.0 - type: f1 value: 94.78333333333333 - type: precision value: 94.18333333333334 - type: recall value: 96.0 - task: type: BitextMining dataset: name: MTEB Tatoeba (uzb-eng) type: mteb/tatoeba-bitext-mining config: uzb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.8785046728972 - type: f1 value: 80.54517133956385 - type: precision value: 79.154984423676 - type: recall value: 83.8785046728972 - task: type: BitextMining dataset: name: MTEB Tatoeba (lit-eng) type: mteb/tatoeba-bitext-mining config: lit-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.60000000000001 - type: f1 value: 92.01333333333334 - type: precision value: 91.28333333333333 - type: recall value: 93.60000000000001 - task: type: BitextMining dataset: name: MTEB Tatoeba (ina-eng) type: mteb/tatoeba-bitext-mining config: ina-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.1 - type: f1 value: 96.26666666666667 - type: precision value: 95.85000000000001 - type: recall value: 97.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (lfn-eng) type: mteb/tatoeba-bitext-mining config: lfn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 84.3 - type: f1 value: 80.67833333333333 - type: precision value: 79.03928571428571 - type: recall value: 84.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (zsm-eng) type: mteb/tatoeba-bitext-mining config: zsm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.3 - type: f1 value: 96.48333333333332 - type: precision value: 96.08333333333331 - type: recall value: 97.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (ita-eng) type: mteb/tatoeba-bitext-mining config: ita-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.7 - type: f1 value: 94.66666666666667 - type: precision value: 94.16666666666667 - type: recall value: 95.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (cmn-eng) type: mteb/tatoeba-bitext-mining config: cmn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.2 - type: f1 value: 96.36666666666667 - type: precision value: 95.96666666666668 - type: recall value: 97.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (lvs-eng) type: mteb/tatoeba-bitext-mining config: lvs-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.3 - type: f1 value: 92.80666666666667 - type: precision value: 92.12833333333333 - type: recall value: 94.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (glg-eng) type: mteb/tatoeba-bitext-mining config: glg-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.0 - type: f1 value: 96.22333333333334 - type: precision value: 95.875 - type: recall value: 97.0 - task: type: BitextMining dataset: name: MTEB Tatoeba (ceb-eng) type: mteb/tatoeba-bitext-mining config: ceb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.33333333333333 - type: f1 value: 70.78174603174602 - type: precision value: 69.28333333333332 - type: recall value: 74.33333333333333 - 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: 37.6 - type: f1 value: 32.938348952090365 - type: precision value: 31.2811038961039 - type: recall value: 37.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (ben-eng) type: mteb/tatoeba-bitext-mining config: ben-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.5 - type: f1 value: 89.13333333333333 - type: precision value: 88.03333333333333 - type: recall value: 91.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (swg-eng) type: mteb/tatoeba-bitext-mining config: swg-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.14285714285714 - type: f1 value: 77.67857142857143 - type: precision value: 75.59523809523809 - type: recall value: 82.14285714285714 - task: type: BitextMining dataset: name: MTEB Tatoeba (arq-eng) type: mteb/tatoeba-bitext-mining config: arq-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 69.0450054884742 - type: f1 value: 63.070409283362075 - type: precision value: 60.58992781824835 - type: recall value: 69.0450054884742 - task: type: BitextMining dataset: name: MTEB Tatoeba (kab-eng) type: mteb/tatoeba-bitext-mining config: kab-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 63.1 - type: f1 value: 57.848333333333336 - type: precision value: 55.69500000000001 - type: recall value: 63.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (fra-eng) type: mteb/tatoeba-bitext-mining config: fra-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.1 - type: f1 value: 95.01666666666667 - type: precision value: 94.5 - type: recall value: 96.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (por-eng) type: mteb/tatoeba-bitext-mining config: por-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.89999999999999 - type: f1 value: 94.90666666666667 - type: precision value: 94.425 - type: recall value: 95.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (tat-eng) type: mteb/tatoeba-bitext-mining config: tat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.6 - type: f1 value: 84.61333333333333 - type: precision value: 83.27 - type: recall value: 87.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (oci-eng) type: mteb/tatoeba-bitext-mining config: oci-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.4 - type: f1 value: 71.90746031746032 - type: precision value: 70.07027777777778 - type: recall value: 76.4 - task: type: BitextMining dataset: name: MTEB Tatoeba (pol-eng) type: mteb/tatoeba-bitext-mining config: pol-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.89999999999999 - type: f1 value: 97.26666666666667 - type: precision value: 96.95 - type: recall value: 97.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (war-eng) type: mteb/tatoeba-bitext-mining config: war-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 78.8 - type: f1 value: 74.39555555555555 - type: precision value: 72.59416666666667 - type: recall value: 78.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (aze-eng) type: mteb/tatoeba-bitext-mining config: aze-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.19999999999999 - type: f1 value: 93.78999999999999 - type: precision value: 93.125 - type: recall value: 95.19999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (vie-eng) type: mteb/tatoeba-bitext-mining config: vie-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.8 - type: f1 value: 97.1 - type: precision value: 96.75 - type: recall value: 97.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (nno-eng) type: mteb/tatoeba-bitext-mining config: nno-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.6 - type: f1 value: 94.25666666666666 - type: precision value: 93.64166666666668 - type: recall value: 95.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cha-eng) type: mteb/tatoeba-bitext-mining config: cha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 56.934306569343065 - type: f1 value: 51.461591936044485 - type: precision value: 49.37434827945776 - type: recall value: 56.934306569343065 - task: type: BitextMining dataset: name: MTEB Tatoeba (mhr-eng) type: mteb/tatoeba-bitext-mining config: mhr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 20.200000000000003 - type: f1 value: 16.91799284049284 - type: precision value: 15.791855158730158 - type: recall value: 20.200000000000003 - task: type: BitextMining dataset: name: MTEB Tatoeba (dan-eng) type: mteb/tatoeba-bitext-mining config: dan-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.2 - type: f1 value: 95.3 - type: precision value: 94.85 - type: recall value: 96.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (ell-eng) type: mteb/tatoeba-bitext-mining config: ell-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.3 - type: f1 value: 95.11666666666667 - type: precision value: 94.53333333333333 - type: recall value: 96.3 - task: type: BitextMining dataset: name: MTEB Tatoeba (amh-eng) type: mteb/tatoeba-bitext-mining config: amh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.88095238095238 - type: f1 value: 87.14285714285714 - type: precision value: 85.96230158730161 - type: recall value: 89.88095238095238 - task: type: BitextMining dataset: name: MTEB Tatoeba (pam-eng) type: mteb/tatoeba-bitext-mining config: pam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 24.099999999999998 - type: f1 value: 19.630969083349783 - type: precision value: 18.275094905094907 - type: recall value: 24.099999999999998 - task: type: BitextMining dataset: name: MTEB Tatoeba (hsb-eng) type: mteb/tatoeba-bitext-mining config: hsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.4368530020704 - type: f1 value: 79.45183870649709 - type: precision value: 77.7432712215321 - type: recall value: 83.4368530020704 - task: type: BitextMining dataset: name: MTEB Tatoeba (srp-eng) type: mteb/tatoeba-bitext-mining config: srp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.8 - type: f1 value: 94.53333333333333 - type: precision value: 93.91666666666666 - type: recall value: 95.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (epo-eng) type: mteb/tatoeba-bitext-mining config: epo-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.8 - type: f1 value: 98.48333333333332 - type: precision value: 98.33333333333334 - type: recall value: 98.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (kzj-eng) type: mteb/tatoeba-bitext-mining config: kzj-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 17.5 - type: f1 value: 14.979285714285714 - type: precision value: 14.23235060690943 - type: recall value: 17.5 - task: type: BitextMining dataset: name: MTEB Tatoeba (awa-eng) type: mteb/tatoeba-bitext-mining config: awa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.93939393939394 - type: f1 value: 91.991341991342 - type: precision value: 91.05339105339105 - type: recall value: 93.93939393939394 - task: type: BitextMining dataset: name: MTEB Tatoeba (fao-eng) type: mteb/tatoeba-bitext-mining config: fao-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.31297709923665 - type: f1 value: 86.76844783715012 - type: precision value: 85.63613231552164 - type: recall value: 89.31297709923665 - task: type: BitextMining dataset: name: MTEB Tatoeba (mal-eng) type: mteb/tatoeba-bitext-mining config: mal-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 99.12663755458514 - type: f1 value: 98.93255701115964 - type: precision value: 98.83551673944687 - type: recall value: 99.12663755458514 - task: type: BitextMining dataset: name: MTEB Tatoeba (ile-eng) type: mteb/tatoeba-bitext-mining config: ile-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.0 - type: f1 value: 89.77999999999999 - type: precision value: 88.78333333333333 - type: recall value: 92.0 - task: type: BitextMining dataset: name: MTEB Tatoeba (bos-eng) type: mteb/tatoeba-bitext-mining config: bos-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.89265536723164 - type: f1 value: 95.85687382297553 - type: precision value: 95.33898305084746 - type: recall value: 96.89265536723164 - task: type: BitextMining dataset: name: MTEB Tatoeba (cor-eng) type: mteb/tatoeba-bitext-mining config: cor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 14.6 - type: f1 value: 11.820611790170615 - type: precision value: 11.022616224355355 - type: recall value: 14.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (cat-eng) type: mteb/tatoeba-bitext-mining config: cat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.89999999999999 - type: f1 value: 94.93333333333334 - type: precision value: 94.48666666666666 - type: recall value: 95.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (eus-eng) type: mteb/tatoeba-bitext-mining config: eus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.6 - type: f1 value: 84.72333333333334 - type: precision value: 83.44166666666666 - type: recall value: 87.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (yue-eng) type: mteb/tatoeba-bitext-mining config: yue-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.8 - type: f1 value: 93.47333333333333 - type: precision value: 92.875 - type: recall value: 94.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (swe-eng) type: mteb/tatoeba-bitext-mining config: swe-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.6 - type: f1 value: 95.71666666666665 - type: precision value: 95.28333333333335 - type: recall value: 96.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (dtp-eng) type: mteb/tatoeba-bitext-mining config: dtp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 17.8 - type: f1 value: 14.511074040901628 - type: precision value: 13.503791000666002 - type: recall value: 17.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (kat-eng) type: mteb/tatoeba-bitext-mining config: kat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.10187667560321 - type: f1 value: 92.46648793565683 - type: precision value: 91.71134941912423 - type: recall value: 94.10187667560321 - task: type: BitextMining dataset: name: MTEB Tatoeba (jpn-eng) type: mteb/tatoeba-bitext-mining config: jpn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.0 - type: f1 value: 96.11666666666666 - type: precision value: 95.68333333333334 - type: recall value: 97.0 - task: type: BitextMining dataset: name: MTEB Tatoeba (csb-eng) type: mteb/tatoeba-bitext-mining config: csb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 72.72727272727273 - type: f1 value: 66.58949745906267 - type: precision value: 63.86693017127799 - type: recall value: 72.72727272727273 - task: type: BitextMining dataset: name: MTEB Tatoeba (xho-eng) type: mteb/tatoeba-bitext-mining config: xho-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.14084507042254 - type: f1 value: 88.26291079812206 - type: precision value: 87.32394366197182 - type: recall value: 90.14084507042254 - task: type: BitextMining dataset: name: MTEB Tatoeba (orv-eng) type: mteb/tatoeba-bitext-mining config: orv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 64.67065868263472 - type: f1 value: 58.2876627696987 - type: precision value: 55.79255774165953 - type: recall value: 64.67065868263472 - task: type: BitextMining dataset: name: MTEB Tatoeba (ind-eng) type: mteb/tatoeba-bitext-mining config: ind-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.6 - type: f1 value: 94.41666666666667 - type: precision value: 93.85 - type: recall value: 95.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (tuk-eng) type: mteb/tatoeba-bitext-mining config: tuk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 55.172413793103445 - type: f1 value: 49.63992493549144 - type: precision value: 47.71405113769646 - type: recall value: 55.172413793103445 - task: type: BitextMining dataset: name: MTEB Tatoeba (max-eng) type: mteb/tatoeba-bitext-mining config: max-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.46478873239437 - type: f1 value: 73.4417616811983 - type: precision value: 71.91607981220658 - type: recall value: 77.46478873239437 - task: type: BitextMining dataset: name: MTEB Tatoeba (swh-eng) type: mteb/tatoeba-bitext-mining config: swh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 84.61538461538461 - type: f1 value: 80.91452991452994 - type: precision value: 79.33760683760683 - type: recall value: 84.61538461538461 - task: type: BitextMining dataset: name: MTEB Tatoeba (hin-eng) type: mteb/tatoeba-bitext-mining config: hin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 98.2 - type: f1 value: 97.6 - type: precision value: 97.3 - type: recall value: 98.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (dsb-eng) type: mteb/tatoeba-bitext-mining config: dsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 75.5741127348643 - type: f1 value: 72.00417536534445 - type: precision value: 70.53467872883321 - type: recall value: 75.5741127348643 - task: type: BitextMining dataset: name: MTEB Tatoeba (ber-eng) type: mteb/tatoeba-bitext-mining config: ber-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 62.2 - type: f1 value: 55.577460317460314 - type: precision value: 52.98583333333333 - type: recall value: 62.2 - task: type: BitextMining dataset: name: MTEB Tatoeba (tam-eng) type: mteb/tatoeba-bitext-mining config: tam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.18241042345277 - type: f1 value: 90.6468124709167 - type: precision value: 89.95656894679696 - type: recall value: 92.18241042345277 - task: type: BitextMining dataset: name: MTEB Tatoeba (slk-eng) type: mteb/tatoeba-bitext-mining config: slk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.1 - type: f1 value: 95.13333333333333 - type: precision value: 94.66666666666667 - type: recall value: 96.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (tgl-eng) type: mteb/tatoeba-bitext-mining config: tgl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.8 - type: f1 value: 95.85000000000001 - type: precision value: 95.39999999999999 - type: recall value: 96.8 - task: type: BitextMining dataset: name: MTEB Tatoeba (ast-eng) type: mteb/tatoeba-bitext-mining config: ast-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.1259842519685 - type: f1 value: 89.76377952755905 - type: precision value: 88.71391076115485 - type: recall value: 92.1259842519685 - task: type: BitextMining dataset: name: MTEB Tatoeba (mkd-eng) type: mteb/tatoeba-bitext-mining config: mkd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.1 - type: f1 value: 92.49 - type: precision value: 91.725 - type: recall value: 94.1 - task: type: BitextMining dataset: name: MTEB Tatoeba (khm-eng) type: mteb/tatoeba-bitext-mining config: khm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.5623268698061 - type: f1 value: 73.27364463791058 - type: precision value: 71.51947852086357 - type: recall value: 77.5623268698061 - task: type: BitextMining dataset: name: MTEB Tatoeba (ces-eng) type: mteb/tatoeba-bitext-mining config: ces-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.56666666666666 - type: precision value: 96.16666666666667 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (tzl-eng) type: mteb/tatoeba-bitext-mining config: tzl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 66.34615384615384 - type: f1 value: 61.092032967032964 - type: precision value: 59.27197802197802 - type: recall value: 66.34615384615384 - task: type: BitextMining dataset: name: MTEB Tatoeba (urd-eng) type: mteb/tatoeba-bitext-mining config: urd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.89999999999999 - type: f1 value: 93.41190476190476 - type: precision value: 92.7 - type: recall value: 94.89999999999999 - task: type: BitextMining dataset: name: MTEB Tatoeba (ara-eng) type: mteb/tatoeba-bitext-mining config: ara-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.10000000000001 - type: f1 value: 91.10000000000001 - type: precision value: 90.13333333333333 - type: recall value: 93.10000000000001 - task: type: BitextMining dataset: name: MTEB Tatoeba (kor-eng) type: mteb/tatoeba-bitext-mining config: kor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.7 - type: f1 value: 91.97333333333334 - type: precision value: 91.14166666666667 - type: recall value: 93.7 - task: type: BitextMining dataset: name: MTEB Tatoeba (yid-eng) type: mteb/tatoeba-bitext-mining config: yid-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.21698113207547 - type: f1 value: 90.3796046720575 - type: precision value: 89.56367924528303 - type: recall value: 92.21698113207547 - task: type: BitextMining dataset: name: MTEB Tatoeba (fin-eng) type: mteb/tatoeba-bitext-mining config: fin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.6 - type: f1 value: 96.91666666666667 - type: precision value: 96.6 - type: recall value: 97.6 - task: type: BitextMining dataset: name: MTEB Tatoeba (tha-eng) type: mteb/tatoeba-bitext-mining config: tha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.44525547445255 - type: f1 value: 96.71532846715328 - type: precision value: 96.35036496350365 - type: recall value: 97.44525547445255 - task: type: BitextMining dataset: name: MTEB Tatoeba (wuu-eng) type: mteb/tatoeba-bitext-mining config: wuu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.1 - type: f1 value: 92.34000000000002 - type: precision value: 91.49166666666667 - type: recall value: 94.1 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.2910000000000004 - type: map_at_10 value: 10.373000000000001 - type: map_at_100 value: 15.612 - type: map_at_1000 value: 17.06 - type: map_at_3 value: 6.119 - type: map_at_5 value: 7.917000000000001 - type: mrr_at_1 value: 44.897999999999996 - type: mrr_at_10 value: 56.054 - type: mrr_at_100 value: 56.82000000000001 - type: mrr_at_1000 value: 56.82000000000001 - type: mrr_at_3 value: 52.381 - type: mrr_at_5 value: 53.81 - type: ndcg_at_1 value: 42.857 - type: ndcg_at_10 value: 27.249000000000002 - type: ndcg_at_100 value: 36.529 - type: ndcg_at_1000 value: 48.136 - type: ndcg_at_3 value: 33.938 - type: ndcg_at_5 value: 29.951 - type: precision_at_1 value: 44.897999999999996 - type: precision_at_10 value: 22.653000000000002 - type: precision_at_100 value: 7.000000000000001 - type: precision_at_1000 value: 1.48 - type: precision_at_3 value: 32.653 - type: precision_at_5 value: 27.755000000000003 - type: recall_at_1 value: 3.2910000000000004 - type: recall_at_10 value: 16.16 - type: recall_at_100 value: 43.908 - type: recall_at_1000 value: 79.823 - type: recall_at_3 value: 7.156 - type: recall_at_5 value: 10.204 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.05879999999999 - type: ap value: 14.609748142799111 - type: f1 value: 54.878956295843096 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.61799660441426 - type: f1 value: 64.8698191961434 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.32860036611885 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 88.34714192048638 - type: cos_sim_ap value: 80.26732975975634 - type: cos_sim_f1 value: 73.53415148134374 - type: cos_sim_precision value: 69.34767360299276 - type: cos_sim_recall value: 78.25857519788919 - type: dot_accuracy value: 88.34714192048638 - type: dot_ap value: 80.26733698491206 - type: dot_f1 value: 73.53415148134374 - type: dot_precision value: 69.34767360299276 - type: dot_recall value: 78.25857519788919 - type: euclidean_accuracy value: 88.34714192048638 - type: euclidean_ap value: 80.26734337771738 - type: euclidean_f1 value: 73.53415148134374 - type: euclidean_precision value: 69.34767360299276 - type: euclidean_recall value: 78.25857519788919 - type: manhattan_accuracy value: 88.30541813196639 - type: manhattan_ap value: 80.19415808104145 - type: manhattan_f1 value: 73.55143870713441 - type: manhattan_precision value: 73.25307511122743 - type: manhattan_recall value: 73.85224274406332 - type: max_accuracy value: 88.34714192048638 - type: max_ap value: 80.26734337771738 - type: max_f1 value: 73.55143870713441 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.81061047075717 - type: cos_sim_ap value: 87.11747055081017 - type: cos_sim_f1 value: 80.04355498817256 - type: cos_sim_precision value: 78.1165262000733 - type: cos_sim_recall value: 82.06806282722513 - type: dot_accuracy value: 89.81061047075717 - type: dot_ap value: 87.11746902745236 - type: dot_f1 value: 80.04355498817256 - type: dot_precision value: 78.1165262000733 - type: dot_recall value: 82.06806282722513 - type: euclidean_accuracy value: 89.81061047075717 - type: euclidean_ap value: 87.11746919324248 - type: euclidean_f1 value: 80.04355498817256 - type: euclidean_precision value: 78.1165262000733 - type: euclidean_recall value: 82.06806282722513 - type: manhattan_accuracy value: 89.79508673885202 - type: manhattan_ap value: 87.11074390832218 - type: manhattan_f1 value: 80.13002540726349 - type: manhattan_precision value: 77.83826945412311 - type: manhattan_recall value: 82.56082537727133 - type: max_accuracy value: 89.81061047075717 - type: max_ap value: 87.11747055081017 - type: max_f1 value: 80.13002540726349 --- ## Multilingual-E5-large-instruct [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 24 layers and the embedding size is 1024. ## Usage Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset. ### Transformers ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, '南瓜的家常做法') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct') model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) # => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]] ``` ### Sentence Transformers ```python from sentence_transformers import SentenceTransformer def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, '南瓜的家常做法') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] input_texts = queries + documents model = SentenceTransformer('intfloat/multilingual-e5-large-instruct') embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) # [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]] ``` ## Supported Languages This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) **First stage**: contrastive pre-training with 1 billion weakly supervised text pairs. **Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper. ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## FAQ **1. Do I need to add instructions to the query?** Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This is a way to customize text embeddings for different scenarios through natural language instructions. Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation. On the other hand, there is no need to add instructions to the document side. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1480
Lots-of-LoRAs
null
[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:mit", "region:us" ]
1,735,930,007,000
2025-01-03T18:46:52
0
0
--- base_model: mistralai/Mistral-7B-Instruct-v0.2 language: en library_name: pytorch license: mit --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1480 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task1480_gene_extraction_jnlpba_dataset - **Developed by:** bruel - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** LoRA - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/bruel-gabrielsson - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset 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. --> https://huggingface.co/datasets/Lots-of-LoRAs/task1480_gene_extraction_jnlpba_dataset sourced from https://github.com/allenai/natural-instructions ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
[ "JNLPBA" ]
TBD
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" ]
1,714,445,132,000
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]`).
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.magical-energy-swirling-around
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,704,279,767,000
2024-01-03T11:02:50
5
1
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/evaluate/magical energy swirling around.../magical energy swirling around_17_3.0.png widget: - text: magical energy swirling around output: url: images/magical energy swirling around_17_3.0.png - text: magical energy swirling around output: url: images/magical energy swirling around_19_3.0.png - text: magical energy swirling around output: url: images/magical energy swirling around_20_3.0.png - text: magical energy swirling around output: url: images/magical energy swirling around_21_3.0.png - text: magical energy swirling around output: url: images/magical energy swirling around_22_3.0.png inference: false instance_prompt: magical energy swirling around --- # ntcai.xyz slider - magical energy swirling around (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/magical energy swirling around_17_-3.0.png" width=256 height=256 /> | <img src="images/magical energy swirling around_17_0.0.png" width=256 height=256 /> | <img src="images/magical energy swirling around_17_3.0.png" width=256 height=256 /> | | <img src="images/magical energy swirling around_19_-3.0.png" width=256 height=256 /> | <img src="images/magical energy swirling around_19_0.0.png" width=256 height=256 /> | <img src="images/magical energy swirling around_19_3.0.png" width=256 height=256 /> | | <img src="images/magical energy swirling around_20_-3.0.png" width=256 height=256 /> | <img src="images/magical energy swirling around_20_0.0.png" width=256 height=256 /> | <img src="images/magical energy swirling around_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` magical energy swirling around ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.magical-energy-swirling-around', weight_name='magical energy swirling around.safetensors', adapter_name="magical energy swirling around") # Activate the LoRA pipe.set_adapters(["magical energy swirling around"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, magical energy swirling around" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 830+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-8bits
RichardErkhov
null
[ "safetensors", "gemma", "8-bit", "bitsandbytes", "region:us" ]
1,729,276,401,000
2024-10-18T18:35:47
4
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) vi-gemma-2b-RAG - bnb 8bits - Model creator: https://huggingface.co/ricepaper/ - Original model: https://huggingface.co/ricepaper/vi-gemma-2b-RAG/ Original model description: --- base_model: unsloth/gemma-1.1-2b-it-bnb-4bit language: - en - vi license: apache-2.0 tags: - text-generation-inference - retrieval-augmented-generation - transformers - unsloth - gemma - trl - sft --- ## Model Card: vi-gemma-2b-RAG ### (English below) ### Tiếng Việt (Vietnamese) **Mô tả mô hình:** vi-gemma-2b-RAG là một mô hình ngôn ngữ lớn được tinh chỉnh từ mô hình cơ sở [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) sử dụng kỹ thuật LoRA. Mô hình được huấn luyện trên tập dữ liệu tiếng Việt với mục tiêu cải thiện khả năng xử lý ngôn ngữ tiếng Việt và nâng cao hiệu suất cho các tác vụ truy xuất thông tin mở (Retrieval Augmented Generation - RAG). **Mục đích sử dụng:** Mô hình vi-gemma-2b-RAG phù hợp cho các tác vụ sau: * Trả lời câu hỏi dựa trên ngữ cảnh tiếng Việt. * Tóm tắt văn bản tiếng Việt. * Dịch máy tiếng Việt. * Và các tác vụ tạo văn bản tiếng Việt khác. **Giới hạn:** Mặc dù đã được tinh chỉnh cho tiếng Việt, vi-gemma-2b-RAG vẫn có thể gặp phải một số hạn chế: * Có thể tạo ra thông tin sai lệch hoặc không chính xác. * Có thể thể hiện thành kiến ​​hoặc quan điểm không phù hợp. * Hiệu suất có thể bị ảnh hưởng bởi chất lượng của dữ liệu đầu vào. **Cách sử dụng:** Dưới đây chúng tôi chia sẻ một số đoạn mã về cách bắt đầu nhanh chóng để sử dụng mô hình. Trước tiên, hãy đảm bảo đã cài đặt `pip install -U transformers`, sau đó sao chép đoạn mã từ phần có liên quan đến usecase của bạn. Chúng tôi khuyến nghị sử dụng `torch.bfloat16` làm mặc định. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Khởi tạo tokenizer và model từ checkpoint đã lưu tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Sử dụng GPU nếu có if torch.cuda.is_available(): model.to("cuda") # Định dạng prompt cho model prompt = """ ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: {} Hãy trả lời câu hỏi: {} ### Response: {} """ # Chuẩn bị dữ liệu đầu vào input_data = """ Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' """ query = "Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì?" # Định dạng input text input_text = prompt.format(input_data, query," ") # Mã hóa input text thành input ids input_ids = tokenizer(input_text, return_tensors="pt") # Sử dụng GPU cho input ids nếu có if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Tạo văn bản bằng model outputs = model.generate( **input_ids, max_new_tokens=500, no_repeat_ngram_size=5, # Ngăn chặn lặp lại các cụm từ 5 gram # do_sample=True, # Kích hoạt chế độ tạo văn bản dựa trên lấy mẫu. Trong chế độ này, model sẽ chọn ngẫu nhiên token tiếp theo dựa trên xác suất được tính từ phân phối xác suất của các token. # temperature=0.7, # Giảm temperature để kiểm soát tính ngẫu nhiên # early_stopping=True, # Dừng tạo văn bản khi tìm thấy kết thúc phù hợp ) # Giải mã và in kết quả print(tokenizer.decode(outputs[0])) ''' <bos> ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' Hãy trả lời câu hỏi: Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì? ### Response: STRs được sử dụng để xác định danh tính, chuẩn đoán bệnh lý và xác định bệnh lý di truyền. <eos> ''' ``` **Huấn luyện:** * **Mô hình cơ sở:** google/gemma-1.1-2b-it * **Tập dữ liệu:** lamhieu/mabrycodes_dialogue_vi * **Phương pháp tinh chỉnh:** LoRA, PEFT với Unsloth ## Model Card: vi-gemma-2b-RAG ### English **Model Description:** vi-gemma-2b-RAG is a large language model fine-tuned from the base model [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) using LoRA. The model is trained on a Vietnamese dataset to improve its Vietnamese language processing capabilities and enhance its performance for Retrieval Augmented Generation (RAG) tasks. **Intended Use:** The vi-gemma-2b-RAG model is suitable for tasks such as: * Vietnamese question answering. * Vietnamese text summarization. * Vietnamese machine translation. * And other Vietnamese text generation tasks. **Limitations:** While fine-tuned for Vietnamese, vi-gemma-2b-RAG may still have some limitations: * It may generate incorrect or misleading information. * It may exhibit biases or inappropriate opinions. * Its performance may be affected by the quality of the input data. **How to Use:** ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. We recommend `torch.bfloat16` as the default dtype. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Initialize the tokenizer and model from the saved checkpoint tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Use GPU if available if torch.cuda.is_available(): model.to("cuda") # Define the prompt format for the model prompt = """ ### Instruction and Input: Based on the following context/document: {} Please answer the question: {} ### Response: {} """ # Prepare the input data input_data = """ Short Tandem Repeats (STRs) are short (2-6 nucleotides) repeating DNA sequences that are widespread in the human genome. These sequences are highly polymorphic in nature, which makes STRs very important genetic markers in human gene mapping and diagnosis of hereditary diseases as well as identification in the field of forensics. STRs have become popular in forensic laboratories because the replication and analysis of STRs requires very small amounts of DNA, even in decomposed form, identification can still be performed successfully. Furthermore, the detection and assessment of sample DNA contamination in specimens can be quickly resolved with STR analysis results. In the United States today, the set of 13 markers has now been increased to 20 main markers being used to create a nationwide DNA database called The FBI Combined DNA Index System (Expaned CODIS). CODIS and similar DNA databases are being used very successfully in linking DNA records from criminals and crime scene evidence. STR identification results are also used to support hundreds of thousands of paternity test cases each year.' """ query = "Tell me what are some properties of STRs used for?" # Format the input text input_text = prompt.format(input_data, query," ") # Encode the input text into input ids input_ids = tokenizer(input_text, return_tensors="pt") # Use GPU for input ids if available if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Generate text using the model outputs = model.generate( **input_ids, max_new_tokens=500, # Limit the number of tokens generated no_repeat_ngram_size=5, # Prevent repetition of 5-gram phrases # do_sample=True, # temperature=0.7, # Adjust the randomness of the generated text # early_stopping=True, # Stop generating text when a suitable ending is found ) # Decode and print the results print(tokenizer.decode(outputs[0])) ``` **Training:** * **Base Model:** google/gemma-1.1-2b-it * **Dataset:** lamhieu/mabrycodes_dialogue_vi * **Fine-tuning Method:** LoRA, PEFT and Unsloth **Using example repository:** https://github.com/Martincrux/Vietnamese-RAG-system-building-with-vi-gemma-2b-RAG-and-halong_embedding # Uploaded model - **Developed by:** [hiieu](https://huggingface.co/hiieu), [himmeow the coder](https://huggingface.co/himmeow), [cuctrinh](https://www.linkedin.com/in/trinh-cuc-5722832b6) - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
[ "CHIA" ]
Non_BioNLP
RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf
RichardErkhov
null
[ "gguf", "endpoints_compatible", "region:us", "conversational" ]
1,728,145,378,000
2024-10-05T19:07:51
48
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) Phi-3-mini-4k-instruct-LLaMAfied - GGUF - Model creator: https://huggingface.co/vonjack/ - Original model: https://huggingface.co/vonjack/Phi-3-mini-4k-instruct-LLaMAfied/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Phi-3-mini-4k-instruct-LLaMAfied.Q2_K.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q2_K.gguf) | Q2_K | 1.35GB | | [Phi-3-mini-4k-instruct-LLaMAfied.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.IQ3_XS.gguf) | IQ3_XS | 1.49GB | | [Phi-3-mini-4k-instruct-LLaMAfied.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.IQ3_S.gguf) | IQ3_S | 1.57GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q3_K_S.gguf) | Q3_K_S | 1.57GB | | [Phi-3-mini-4k-instruct-LLaMAfied.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.IQ3_M.gguf) | IQ3_M | 1.65GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q3_K.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q3_K.gguf) | Q3_K | 1.75GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q3_K_M.gguf) | Q3_K_M | 1.75GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q3_K_L.gguf) | Q3_K_L | 1.9GB | | [Phi-3-mini-4k-instruct-LLaMAfied.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.IQ4_XS.gguf) | IQ4_XS | 1.93GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q4_0.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q4_0.gguf) | Q4_0 | 2.03GB | | [Phi-3-mini-4k-instruct-LLaMAfied.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.IQ4_NL.gguf) | IQ4_NL | 2.04GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q4_K_S.gguf) | Q4_K_S | 2.04GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q4_K.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q4_K.gguf) | Q4_K | 2.16GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q4_K_M.gguf) | Q4_K_M | 2.16GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q4_1.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q4_1.gguf) | Q4_1 | 2.24GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q5_0.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q5_0.gguf) | Q5_0 | 2.46GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q5_K_S.gguf) | Q5_K_S | 2.46GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q5_K.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q5_K.gguf) | Q5_K | 2.53GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q5_K_M.gguf) | Q5_K_M | 2.53GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q5_1.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q5_1.gguf) | Q5_1 | 2.68GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q6_K.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q6_K.gguf) | Q6_K | 2.92GB | | [Phi-3-mini-4k-instruct-LLaMAfied.Q8_0.gguf](https://huggingface.co/RichardErkhov/vonjack_-_Phi-3-mini-4k-instruct-LLaMAfied-gguf/blob/main/Phi-3-mini-4k-instruct-LLaMAfied.Q8_0.gguf) | Q8_0 | 3.78GB | Original model description: --- license: mit license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code --- ## Model Summary The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + Phi-3 GGUF: [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf) + Phi-3 ONNX: [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx) ## Intended Uses **Primary use cases** The model is intended for commercial and research use in English. The model provides uses for applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. **Use case considerations** Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list | grep transformers`. Phi-3 Mini-4K-Instruct is also available in [HuggingChat](https://aka.ms/try-phi3-hf-chat). ### Chat Format Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <|user|>\nQuestion <|end|>\n<|assistant|> ``` For example: ```markdown <|system|> You are a helpful AI assistant.<|end|> <|user|> How to explain Internet for a medieval knight?<|end|> <|assistant|> ``` where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <|system|> You are a helpful AI assistant.<|end|> <|user|> I am going to Paris, what should I see?<|end|> <|assistant|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|> <|user|> What is so great about #1?<|end|> <|assistant|> ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-4k-instruct", device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") messages = [ {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."}, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, **generation_args) print(output[0]['generated_text']) ``` ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 4K tokens * GPUs: 512 H100-80G * Training time: 7 days * Training data: 3.3T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. ### Datasets Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. ### Fine-tuning A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/sample_finetune.py). ## Benchmarks We report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5. All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. | | Phi-3-Mini-4K-In<br>3.8b | Phi-3-Small<br>7b (preview) | Phi-3-Medium<br>14b (preview) | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 | |---|---|---|---|---|---|---|---|---|---| | MMLU <br>5-Shot | 68.8 | 75.3 | 78.2 | 56.3 | 61.7 | 63.6 | 66.5 | 68.4 | 71.4 | | HellaSwag <br> 5-Shot | 76.7 | 78.7 | 83.2 | 53.6 | 58.5 | 49.8 | 71.1 | 70.4 | 78.8 | | ANLI <br> 7-Shot | 52.8 | 55.0 | 58.7 | 42.5 | 47.1 | 48.7 | 57.3 | 55.2 | 58.1 | | GSM-8K <br> 0-Shot; CoT | 82.5 | 86.4 | 90.8 | 61.1 | 46.4 | 59.8 | 77.4 | 64.7 | 78.1 | | MedQA <br> 2-Shot | 53.8 | 58.2 | 69.8 | 40.9 | 49.6 | 50.0 | 60.5 | 62.2 | 63.4 | | AGIEval <br> 0-Shot | 37.5 | 45.0 | 49.7 | 29.8 | 35.1 | 42.1 | 42.0 | 45.2 | 48.4 | | TriviaQA <br> 5-Shot | 64.0 | 59.1 | 73.3 | 45.2 | 72.3 | 75.2 | 67.7 | 82.2 | 85.8 | | Arc-C <br> 10-Shot | 84.9 | 90.7 | 91.9 | 75.9 | 78.6 | 78.3 | 82.8 | 87.3 | 87.4 | | Arc-E <br> 10-Shot | 94.6 | 97.1 | 98.0 | 88.5 | 90.6 | 91.4 | 93.4 | 95.6 | 96.3 | | PIQA <br> 5-Shot | 84.2 | 87.8 | 88.2 | 60.2 | 77.7 | 78.1 | 75.7 | 86.0 | 86.6 | | SociQA <br> 5-Shot | 76.6 | 79.0 | 79.4 | 68.3 | 74.6 | 65.5 | 73.9 | 75.9 | 68.3 | | BigBench-Hard <br> 0-Shot | 71.7 | 75.0 | 82.5 | 59.4 | 57.3 | 59.6 | 51.5 | 69.7 | 68.32 | | WinoGrande <br> 5-Shot | 70.8 | 82.5 | 81.2 | 54.7 | 54.2 | 55.6 | 65 | 62.0 | 68.8 | | OpenBookQA <br> 10-Shot | 83.2 | 88.4 | 86.6 | 73.6 | 79.8 | 78.6 | 82.6 | 85.8 | 86.0 | | BoolQ <br> 0-Shot | 77.6 | 82.9 | 86.5 | -- | 72.2 | 66.0 | 80.9 | 77.6 | 79.1 | | CommonSenseQA <br> 10-Shot | 80.2 | 80.3 | 82.6 | 69.3 | 72.6 | 76.2 | 79 | 78.1 | 79.6 | | TruthfulQA <br> 10-Shot | 65.0 | 68.1 | 74.8 | -- | 52.1 | 53.0 | 63.2 | 60.1 | 85.8 | | HumanEval <br> 0-Shot | 59.1 | 59.1 | 54.7 | 59.0 | 28.0 | 34.1 | 60.4 | 37.8 | 62.2 | | MBPP <br> 3-Shot | 53.8 | 71.4 | 73.7 | 60.6 | 50.8 | 51.5 | 67.7 | 60.2 | 77.8 | ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" * CPU: use the **GGUF** quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf) + Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx) ## Cross Platform Support ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model [here](https://aka.ms/phi3-mini-4k-instruct-onnx). Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-4k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
[ "MEDQA" ]
Non_BioNLP
tomaarsen/static-bert-uncased-gooaq
tomaarsen
sentence-similarity
[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,727,351,276,000
2024-10-18T10:35:51
0
4
--- datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 6.448001991119035 energy_consumed: 0.0165885485310573 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.109 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 1024 dev type: gooaq-1024-dev metrics: - type: cosine_accuracy@1 value: 0.6309 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8409 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8986 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6309 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28029999999999994 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17972000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6309 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8409 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8986 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7932643237589305 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7440336111111036 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7465739001132767 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 512 dev type: gooaq-512-dev metrics: - type: cosine_accuracy@1 value: 0.6271 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8366 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8946 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9431 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6271 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27886666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17892000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09431000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6271 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8366 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8946 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9431 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7904860196985286 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7408453174603101 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7434337897783787 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 256 dev type: gooaq-256-dev metrics: - type: cosine_accuracy@1 value: 0.6192 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8235 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8866 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9364 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6192 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27449999999999997 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17732000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09364000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6192 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8235 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8866 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9364 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7821476540310974 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7321259126984055 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7348893313013708 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 128 dev type: gooaq-128-dev metrics: - type: cosine_accuracy@1 value: 0.5942 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.804 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8721 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9249 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5942 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.268 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17442000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09249 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5942 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.804 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8721 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9249 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7627845665665897 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7103426587301529 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7133975871277517 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 64 dev type: gooaq-64-dev metrics: - type: cosine_accuracy@1 value: 0.556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7553 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8267 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8945 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25176666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16534000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08945 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7553 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8267 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8945 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7246435400765202 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6701957142857087 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6743443703166442 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 32 dev type: gooaq-32-dev metrics: - type: cosine_accuracy@1 value: 0.4628 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6619 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7415 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8241 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4628 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2206333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1483 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08241 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4628 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6619 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7415 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8241 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6387155548290799 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5797731349206319 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5857231820662888 name: Cosine Map@100 --- # Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using the [train_script.py](train_script.py) code. ## Model Details ### Model Description - **Model Type:** Sentence Transformer <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en - **License:** apache-2.0 ### 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): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## 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("tomaarsen/static-bert-uncased-gooaq") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # 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 #### Information Retrieval * Dataset: `gooaq-1024-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6309 | | cosine_accuracy@3 | 0.8409 | | cosine_accuracy@5 | 0.8986 | | cosine_accuracy@10 | 0.9444 | | cosine_precision@1 | 0.6309 | | cosine_precision@3 | 0.2803 | | cosine_precision@5 | 0.1797 | | cosine_precision@10 | 0.0944 | | cosine_recall@1 | 0.6309 | | cosine_recall@3 | 0.8409 | | cosine_recall@5 | 0.8986 | | cosine_recall@10 | 0.9444 | | cosine_ndcg@10 | 0.7933 | | cosine_mrr@10 | 0.744 | | **cosine_map@100** | **0.7466** | #### Information Retrieval * Dataset: `gooaq-512-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6271 | | cosine_accuracy@3 | 0.8366 | | cosine_accuracy@5 | 0.8946 | | cosine_accuracy@10 | 0.9431 | | cosine_precision@1 | 0.6271 | | cosine_precision@3 | 0.2789 | | cosine_precision@5 | 0.1789 | | cosine_precision@10 | 0.0943 | | cosine_recall@1 | 0.6271 | | cosine_recall@3 | 0.8366 | | cosine_recall@5 | 0.8946 | | cosine_recall@10 | 0.9431 | | cosine_ndcg@10 | 0.7905 | | cosine_mrr@10 | 0.7408 | | **cosine_map@100** | **0.7434** | #### Information Retrieval * Dataset: `gooaq-256-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6192 | | cosine_accuracy@3 | 0.8235 | | cosine_accuracy@5 | 0.8866 | | cosine_accuracy@10 | 0.9364 | | cosine_precision@1 | 0.6192 | | cosine_precision@3 | 0.2745 | | cosine_precision@5 | 0.1773 | | cosine_precision@10 | 0.0936 | | cosine_recall@1 | 0.6192 | | cosine_recall@3 | 0.8235 | | cosine_recall@5 | 0.8866 | | cosine_recall@10 | 0.9364 | | cosine_ndcg@10 | 0.7821 | | cosine_mrr@10 | 0.7321 | | **cosine_map@100** | **0.7349** | #### Information Retrieval * Dataset: `gooaq-128-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5942 | | cosine_accuracy@3 | 0.804 | | cosine_accuracy@5 | 0.8721 | | cosine_accuracy@10 | 0.9249 | | cosine_precision@1 | 0.5942 | | cosine_precision@3 | 0.268 | | cosine_precision@5 | 0.1744 | | cosine_precision@10 | 0.0925 | | cosine_recall@1 | 0.5942 | | cosine_recall@3 | 0.804 | | cosine_recall@5 | 0.8721 | | cosine_recall@10 | 0.9249 | | cosine_ndcg@10 | 0.7628 | | cosine_mrr@10 | 0.7103 | | **cosine_map@100** | **0.7134** | #### Information Retrieval * Dataset: `gooaq-64-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.556 | | cosine_accuracy@3 | 0.7553 | | cosine_accuracy@5 | 0.8267 | | cosine_accuracy@10 | 0.8945 | | cosine_precision@1 | 0.556 | | cosine_precision@3 | 0.2518 | | cosine_precision@5 | 0.1653 | | cosine_precision@10 | 0.0895 | | cosine_recall@1 | 0.556 | | cosine_recall@3 | 0.7553 | | cosine_recall@5 | 0.8267 | | cosine_recall@10 | 0.8945 | | cosine_ndcg@10 | 0.7246 | | cosine_mrr@10 | 0.6702 | | **cosine_map@100** | **0.6743** | #### Information Retrieval * Dataset: `gooaq-32-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4628 | | cosine_accuracy@3 | 0.6619 | | cosine_accuracy@5 | 0.7415 | | cosine_accuracy@10 | 0.8241 | | cosine_precision@1 | 0.4628 | | cosine_precision@3 | 0.2206 | | cosine_precision@5 | 0.1483 | | cosine_precision@10 | 0.0824 | | cosine_recall@1 | 0.4628 | | cosine_recall@3 | 0.6619 | | cosine_recall@5 | 0.7415 | | cosine_recall@10 | 0.8241 | | cosine_ndcg@10 | 0.6387 | | cosine_mrr@10 | 0.5798 | | **cosine_map@100** | **0.5857** | <!-- ## 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 #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### 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`: 2048 - `per_device_eval_batch_size`: 2048 - `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`: 0.2 - `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`: 0.1 - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | gooaq-1024-dev_cosine_map@100 | gooaq-512-dev_cosine_map@100 | gooaq-256-dev_cosine_map@100 | gooaq-128-dev_cosine_map@100 | gooaq-64-dev_cosine_map@100 | gooaq-32-dev_cosine_map@100 | |:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------:| | 0 | 0 | - | - | 0.2095 | 0.2010 | 0.1735 | 0.1381 | 0.0750 | 0.0331 | | 0.0007 | 1 | 34.953 | - | - | - | - | - | - | - | | 0.0682 | 100 | 16.2504 | - | - | - | - | - | - | - | | 0.1363 | 200 | 5.9502 | - | - | - | - | - | - | - | | 0.1704 | 250 | - | 1.6781 | 0.6791 | 0.6729 | 0.6619 | 0.6409 | 0.5904 | 0.4934 | | 0.2045 | 300 | 4.8411 | - | - | - | - | - | - | - | | 0.2727 | 400 | 4.336 | - | - | - | - | - | - | - | | 0.3408 | 500 | 4.0484 | 1.3935 | 0.7104 | 0.7055 | 0.6968 | 0.6756 | 0.6322 | 0.5358 | | 0.4090 | 600 | 3.8378 | - | - | - | - | - | - | - | | 0.4772 | 700 | 3.6765 | - | - | - | - | - | - | - | | 0.5112 | 750 | - | 1.2549 | 0.7246 | 0.7216 | 0.7133 | 0.6943 | 0.6482 | 0.5582 | | 0.5453 | 800 | 3.5439 | - | - | - | - | - | - | - | | 0.6135 | 900 | 3.4284 | - | - | - | - | - | - | - | | 0.6817 | 1000 | 3.3576 | 1.1656 | 0.7359 | 0.7338 | 0.7252 | 0.7040 | 0.6604 | 0.5715 | | 0.7498 | 1100 | 3.2456 | - | - | - | - | - | - | - | | 0.8180 | 1200 | 3.2014 | - | - | - | - | - | - | - | | 0.8521 | 1250 | - | 1.1133 | 0.7438 | 0.7398 | 0.7310 | 0.7099 | 0.6704 | 0.5796 | | 0.8862 | 1300 | 3.1536 | - | - | - | - | - | - | - | | 0.9543 | 1400 | 3.0696 | - | - | - | - | - | - | - | | 1.0 | 1467 | - | - | 0.7466 | 0.7434 | 0.7349 | 0.7134 | 0.6743 | 0.5857 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.017 kWh - **Carbon Emitted**: 0.006 kg of CO2 - **Hours Used**: 0.109 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.2.0.dev0 - Transformers: 4.43.4 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## 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} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "CRAFT" ]
Non_BioNLP
niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF
niancheng
sentence-similarity
[ "sentence-transformers", "gguf", "mteb", "transformers", "Qwen2", "sentence-similarity", "llama-cpp", "gguf-my-repo", "base_model:Alibaba-NLP/gte-Qwen2-1.5B-instruct", "base_model:quantized:Alibaba-NLP/gte-Qwen2-1.5B-instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]
1,721,030,190,000
2024-07-15T07:56:38
26
0
--- base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct license: apache-2.0 tags: - mteb - sentence-transformers - transformers - Qwen2 - sentence-similarity - llama-cpp - gguf-my-repo model-index: - name: gte-qwen2-7B-instruct results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 83.98507462686567 - type: ap value: 50.93015252587014 - type: f1 value: 78.50416599051215 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.61065 - type: ap value: 94.89174052954196 - type: f1 value: 96.60942596940565 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 55.614000000000004 - type: f1 value: 54.90553480294904 - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 45.164 - type: map_at_10 value: 61.519 - type: map_at_100 value: 61.769 - type: map_at_1000 value: 61.769 - type: map_at_3 value: 57.443999999999996 - type: map_at_5 value: 60.058 - type: mrr_at_1 value: 46.088 - type: mrr_at_10 value: 61.861 - type: mrr_at_100 value: 62.117999999999995 - type: mrr_at_1000 value: 62.117999999999995 - type: mrr_at_3 value: 57.729 - type: mrr_at_5 value: 60.392 - type: ndcg_at_1 value: 45.164 - type: ndcg_at_10 value: 69.72 - type: ndcg_at_100 value: 70.719 - type: ndcg_at_1000 value: 70.719 - type: ndcg_at_3 value: 61.517999999999994 - type: ndcg_at_5 value: 66.247 - type: precision_at_1 value: 45.164 - type: precision_at_10 value: 9.545 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 24.443 - type: precision_at_5 value: 16.97 - type: recall_at_1 value: 45.164 - type: recall_at_10 value: 95.448 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 73.329 - type: recall_at_5 value: 84.851 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 50.511868162026175 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 45.007803189284004 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.55292107723382 - type: mrr value: 77.66158818097877 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.65459047085452 - type: cos_sim_spearman value: 82.10729255710761 - type: euclidean_pearson value: 82.78079159312476 - type: euclidean_spearman value: 80.50002701880933 - type: manhattan_pearson value: 82.41372641383016 - type: manhattan_spearman value: 80.57412509272639 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.30844155844156 - type: f1 value: 87.25307322443255 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 43.20754608934859 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 38.818037697335505 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 35.423 - type: map_at_10 value: 47.198 - type: map_at_100 value: 48.899 - type: map_at_1000 value: 49.004 - type: map_at_3 value: 43.114999999999995 - type: map_at_5 value: 45.491 - type: mrr_at_1 value: 42.918 - type: mrr_at_10 value: 53.299 - type: mrr_at_100 value: 54.032000000000004 - type: mrr_at_1000 value: 54.055 - type: mrr_at_3 value: 50.453 - type: mrr_at_5 value: 52.205999999999996 - type: ndcg_at_1 value: 42.918 - type: ndcg_at_10 value: 53.98 - type: ndcg_at_100 value: 59.57 - type: ndcg_at_1000 value: 60.879000000000005 - type: ndcg_at_3 value: 48.224000000000004 - type: ndcg_at_5 value: 50.998 - type: precision_at_1 value: 42.918 - type: precision_at_10 value: 10.299999999999999 - type: precision_at_100 value: 1.687 - type: precision_at_1000 value: 0.211 - type: precision_at_3 value: 22.842000000000002 - type: precision_at_5 value: 16.681 - type: recall_at_1 value: 35.423 - type: recall_at_10 value: 66.824 - type: recall_at_100 value: 89.564 - type: recall_at_1000 value: 97.501 - type: recall_at_3 value: 50.365 - type: recall_at_5 value: 57.921 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: BeIR/cqadupstack config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 33.205 - type: map_at_10 value: 44.859 - type: map_at_100 value: 46.135 - type: map_at_1000 value: 46.259 - type: map_at_3 value: 41.839 - type: map_at_5 value: 43.662 - type: mrr_at_1 value: 41.146 - type: mrr_at_10 value: 50.621 - type: mrr_at_100 value: 51.207 - type: mrr_at_1000 value: 51.246 - type: mrr_at_3 value: 48.535000000000004 - type: mrr_at_5 value: 49.818 - type: ndcg_at_1 value: 41.146 - type: ndcg_at_10 value: 50.683 - type: ndcg_at_100 value: 54.82 - type: ndcg_at_1000 value: 56.69 - type: ndcg_at_3 value: 46.611000000000004 - type: ndcg_at_5 value: 48.66 - type: precision_at_1 value: 41.146 - type: precision_at_10 value: 9.439 - type: precision_at_100 value: 1.465 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 22.59 - type: precision_at_5 value: 15.86 - type: recall_at_1 value: 33.205 - type: recall_at_10 value: 61.028999999999996 - type: recall_at_100 value: 78.152 - type: recall_at_1000 value: 89.59700000000001 - type: recall_at_3 value: 49.05 - type: recall_at_5 value: 54.836 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: BeIR/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 41.637 - type: map_at_10 value: 55.162 - type: map_at_100 value: 56.142 - type: map_at_1000 value: 56.188 - type: map_at_3 value: 51.564 - type: map_at_5 value: 53.696 - type: mrr_at_1 value: 47.524 - type: mrr_at_10 value: 58.243 - type: mrr_at_100 value: 58.879999999999995 - type: mrr_at_1000 value: 58.9 - type: mrr_at_3 value: 55.69499999999999 - type: mrr_at_5 value: 57.284 - type: ndcg_at_1 value: 47.524 - type: ndcg_at_10 value: 61.305 - type: ndcg_at_100 value: 65.077 - type: ndcg_at_1000 value: 65.941 - type: ndcg_at_3 value: 55.422000000000004 - type: ndcg_at_5 value: 58.516 - type: precision_at_1 value: 47.524 - type: precision_at_10 value: 9.918000000000001 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.765 - type: precision_at_5 value: 17.204 - type: recall_at_1 value: 41.637 - type: recall_at_10 value: 76.185 - type: recall_at_100 value: 92.149 - type: recall_at_1000 value: 98.199 - type: recall_at_3 value: 60.856 - type: recall_at_5 value: 68.25099999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: BeIR/cqadupstack config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 26.27 - type: map_at_10 value: 37.463 - type: map_at_100 value: 38.434000000000005 - type: map_at_1000 value: 38.509 - type: map_at_3 value: 34.226 - type: map_at_5 value: 36.161 - type: mrr_at_1 value: 28.588 - type: mrr_at_10 value: 39.383 - type: mrr_at_100 value: 40.23 - type: mrr_at_1000 value: 40.281 - type: mrr_at_3 value: 36.422 - type: mrr_at_5 value: 38.252 - type: ndcg_at_1 value: 28.588 - type: ndcg_at_10 value: 43.511 - type: ndcg_at_100 value: 48.274 - type: ndcg_at_1000 value: 49.975 - type: ndcg_at_3 value: 37.319 - type: ndcg_at_5 value: 40.568 - type: precision_at_1 value: 28.588 - type: precision_at_10 value: 6.893000000000001 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 16.347 - type: precision_at_5 value: 11.661000000000001 - type: recall_at_1 value: 26.27 - type: recall_at_10 value: 60.284000000000006 - type: recall_at_100 value: 81.902 - type: recall_at_1000 value: 94.43 - type: recall_at_3 value: 43.537 - type: recall_at_5 value: 51.475 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: BeIR/cqadupstack config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 18.168 - type: map_at_10 value: 28.410000000000004 - type: map_at_100 value: 29.78 - type: map_at_1000 value: 29.892999999999997 - type: map_at_3 value: 25.238 - type: map_at_5 value: 26.96 - type: mrr_at_1 value: 23.507 - type: mrr_at_10 value: 33.382 - type: mrr_at_100 value: 34.404 - type: mrr_at_1000 value: 34.467999999999996 - type: mrr_at_3 value: 30.637999999999998 - type: mrr_at_5 value: 32.199 - type: ndcg_at_1 value: 23.507 - type: ndcg_at_10 value: 34.571000000000005 - type: ndcg_at_100 value: 40.663 - type: ndcg_at_1000 value: 43.236000000000004 - type: ndcg_at_3 value: 29.053 - type: ndcg_at_5 value: 31.563999999999997 - type: precision_at_1 value: 23.507 - type: precision_at_10 value: 6.654 - type: precision_at_100 value: 1.113 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 14.427999999999999 - type: precision_at_5 value: 10.498000000000001 - type: recall_at_1 value: 18.168 - type: recall_at_10 value: 48.443000000000005 - type: recall_at_100 value: 74.47 - type: recall_at_1000 value: 92.494 - type: recall_at_3 value: 33.379999999999995 - type: recall_at_5 value: 39.76 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 32.39 - type: map_at_10 value: 44.479 - type: map_at_100 value: 45.977000000000004 - type: map_at_1000 value: 46.087 - type: map_at_3 value: 40.976 - type: map_at_5 value: 43.038 - type: mrr_at_1 value: 40.135 - type: mrr_at_10 value: 50.160000000000004 - type: mrr_at_100 value: 51.052 - type: mrr_at_1000 value: 51.087 - type: mrr_at_3 value: 47.818 - type: mrr_at_5 value: 49.171 - type: ndcg_at_1 value: 40.135 - type: ndcg_at_10 value: 50.731 - type: ndcg_at_100 value: 56.452000000000005 - type: ndcg_at_1000 value: 58.123000000000005 - type: ndcg_at_3 value: 45.507 - type: ndcg_at_5 value: 48.11 - type: precision_at_1 value: 40.135 - type: precision_at_10 value: 9.192 - type: precision_at_100 value: 1.397 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 21.816 - type: precision_at_5 value: 15.476 - type: recall_at_1 value: 32.39 - type: recall_at_10 value: 63.597 - type: recall_at_100 value: 86.737 - type: recall_at_1000 value: 97.039 - type: recall_at_3 value: 48.906 - type: recall_at_5 value: 55.659000000000006 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 28.397 - type: map_at_10 value: 39.871 - type: map_at_100 value: 41.309000000000005 - type: map_at_1000 value: 41.409 - type: map_at_3 value: 36.047000000000004 - type: map_at_5 value: 38.104 - type: mrr_at_1 value: 34.703 - type: mrr_at_10 value: 44.773 - type: mrr_at_100 value: 45.64 - type: mrr_at_1000 value: 45.678999999999995 - type: mrr_at_3 value: 41.705 - type: mrr_at_5 value: 43.406 - type: ndcg_at_1 value: 34.703 - type: ndcg_at_10 value: 46.271 - type: ndcg_at_100 value: 52.037 - type: ndcg_at_1000 value: 53.81700000000001 - type: ndcg_at_3 value: 39.966 - type: ndcg_at_5 value: 42.801 - type: precision_at_1 value: 34.703 - type: precision_at_10 value: 8.744 - type: precision_at_100 value: 1.348 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 19.102 - type: precision_at_5 value: 13.836 - type: recall_at_1 value: 28.397 - type: recall_at_10 value: 60.299 - type: recall_at_100 value: 84.595 - type: recall_at_1000 value: 96.155 - type: recall_at_3 value: 43.065 - type: recall_at_5 value: 50.371 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 28.044333333333338 - type: map_at_10 value: 38.78691666666666 - type: map_at_100 value: 40.113 - type: map_at_1000 value: 40.22125 - type: map_at_3 value: 35.52966666666667 - type: map_at_5 value: 37.372749999999996 - type: mrr_at_1 value: 33.159083333333335 - type: mrr_at_10 value: 42.913583333333335 - type: mrr_at_100 value: 43.7845 - type: mrr_at_1000 value: 43.830333333333336 - type: mrr_at_3 value: 40.29816666666667 - type: mrr_at_5 value: 41.81366666666667 - type: ndcg_at_1 value: 33.159083333333335 - type: ndcg_at_10 value: 44.75750000000001 - type: ndcg_at_100 value: 50.13658333333334 - type: ndcg_at_1000 value: 52.037 - type: ndcg_at_3 value: 39.34258333333334 - type: ndcg_at_5 value: 41.93708333333333 - type: precision_at_1 value: 33.159083333333335 - type: precision_at_10 value: 7.952416666666667 - type: precision_at_100 value: 1.2571666666666668 - type: precision_at_1000 value: 0.16099999999999998 - type: precision_at_3 value: 18.303833333333337 - type: precision_at_5 value: 13.057083333333333 - type: recall_at_1 value: 28.044333333333338 - type: recall_at_10 value: 58.237249999999996 - type: recall_at_100 value: 81.35391666666666 - type: recall_at_1000 value: 94.21283333333334 - type: recall_at_3 value: 43.32341666666667 - type: recall_at_5 value: 49.94908333333333 - type: map_at_1 value: 18.398 - type: map_at_10 value: 27.929 - type: map_at_100 value: 29.032999999999998 - type: map_at_1000 value: 29.126 - type: map_at_3 value: 25.070999999999998 - type: map_at_5 value: 26.583000000000002 - type: mrr_at_1 value: 19.963 - type: mrr_at_10 value: 29.997 - type: mrr_at_100 value: 30.9 - type: mrr_at_1000 value: 30.972 - type: mrr_at_3 value: 27.264 - type: mrr_at_5 value: 28.826 - type: ndcg_at_1 value: 19.963 - type: ndcg_at_10 value: 33.678999999999995 - type: ndcg_at_100 value: 38.931 - type: ndcg_at_1000 value: 41.379 - type: ndcg_at_3 value: 28.000000000000004 - type: ndcg_at_5 value: 30.637999999999998 - type: precision_at_1 value: 19.963 - type: precision_at_10 value: 5.7299999999999995 - type: precision_at_100 value: 0.902 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 12.631 - type: precision_at_5 value: 9.057 - type: recall_at_1 value: 18.398 - type: recall_at_10 value: 49.254 - type: recall_at_100 value: 73.182 - type: recall_at_1000 value: 91.637 - type: recall_at_3 value: 34.06 - type: recall_at_5 value: 40.416000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 27.838 - type: map_at_10 value: 36.04 - type: map_at_100 value: 37.113 - type: map_at_1000 value: 37.204 - type: map_at_3 value: 33.585 - type: map_at_5 value: 34.845 - type: mrr_at_1 value: 30.982 - type: mrr_at_10 value: 39.105000000000004 - type: mrr_at_100 value: 39.98 - type: mrr_at_1000 value: 40.042 - type: mrr_at_3 value: 36.912 - type: mrr_at_5 value: 38.062000000000005 - type: ndcg_at_1 value: 30.982 - type: ndcg_at_10 value: 40.982 - type: ndcg_at_100 value: 46.092 - type: ndcg_at_1000 value: 48.25 - type: ndcg_at_3 value: 36.41 - type: ndcg_at_5 value: 38.379999999999995 - type: precision_at_1 value: 30.982 - type: precision_at_10 value: 6.534 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 15.745999999999999 - type: precision_at_5 value: 10.828 - type: recall_at_1 value: 27.838 - type: recall_at_10 value: 52.971000000000004 - type: recall_at_100 value: 76.357 - type: recall_at_1000 value: 91.973 - type: recall_at_3 value: 40.157 - type: recall_at_5 value: 45.147999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 19.059 - type: map_at_10 value: 27.454 - type: map_at_100 value: 28.736 - type: map_at_1000 value: 28.865000000000002 - type: map_at_3 value: 24.773999999999997 - type: map_at_5 value: 26.266000000000002 - type: mrr_at_1 value: 23.125 - type: mrr_at_10 value: 31.267 - type: mrr_at_100 value: 32.32 - type: mrr_at_1000 value: 32.394 - type: mrr_at_3 value: 28.894 - type: mrr_at_5 value: 30.281000000000002 - type: ndcg_at_1 value: 23.125 - type: ndcg_at_10 value: 32.588 - type: ndcg_at_100 value: 38.432 - type: ndcg_at_1000 value: 41.214 - type: ndcg_at_3 value: 27.938000000000002 - type: ndcg_at_5 value: 30.127 - type: precision_at_1 value: 23.125 - type: precision_at_10 value: 5.9639999999999995 - type: precision_at_100 value: 1.047 - type: precision_at_1000 value: 0.148 - type: precision_at_3 value: 13.294 - type: precision_at_5 value: 9.628 - type: recall_at_1 value: 19.059 - type: recall_at_10 value: 44.25 - type: recall_at_100 value: 69.948 - type: recall_at_1000 value: 89.35300000000001 - type: recall_at_3 value: 31.114000000000004 - type: recall_at_5 value: 36.846000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 28.355999999999998 - type: map_at_10 value: 39.055 - type: map_at_100 value: 40.486 - type: map_at_1000 value: 40.571 - type: map_at_3 value: 35.69 - type: map_at_5 value: 37.605 - type: mrr_at_1 value: 33.302 - type: mrr_at_10 value: 42.986000000000004 - type: mrr_at_100 value: 43.957 - type: mrr_at_1000 value: 43.996 - type: mrr_at_3 value: 40.111999999999995 - type: mrr_at_5 value: 41.735 - type: ndcg_at_1 value: 33.302 - type: ndcg_at_10 value: 44.962999999999994 - type: ndcg_at_100 value: 50.917 - type: ndcg_at_1000 value: 52.622 - type: ndcg_at_3 value: 39.182 - type: ndcg_at_5 value: 41.939 - type: precision_at_1 value: 33.302 - type: precision_at_10 value: 7.779999999999999 - type: precision_at_100 value: 1.203 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.035 - type: precision_at_5 value: 12.873000000000001 - type: recall_at_1 value: 28.355999999999998 - type: recall_at_10 value: 58.782000000000004 - type: recall_at_100 value: 84.02199999999999 - type: recall_at_1000 value: 95.511 - type: recall_at_3 value: 43.126999999999995 - type: recall_at_5 value: 50.14999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 27.391 - type: map_at_10 value: 37.523 - type: map_at_100 value: 39.312000000000005 - type: map_at_1000 value: 39.54 - type: map_at_3 value: 34.231 - type: map_at_5 value: 36.062 - type: mrr_at_1 value: 32.016 - type: mrr_at_10 value: 41.747 - type: mrr_at_100 value: 42.812 - type: mrr_at_1000 value: 42.844 - type: mrr_at_3 value: 39.129999999999995 - type: mrr_at_5 value: 40.524 - type: ndcg_at_1 value: 32.016 - type: ndcg_at_10 value: 43.826 - type: ndcg_at_100 value: 50.373999999999995 - type: ndcg_at_1000 value: 52.318 - type: ndcg_at_3 value: 38.479 - type: ndcg_at_5 value: 40.944 - type: precision_at_1 value: 32.016 - type: precision_at_10 value: 8.280999999999999 - type: precision_at_100 value: 1.6760000000000002 - type: precision_at_1000 value: 0.25 - type: precision_at_3 value: 18.05 - type: precision_at_5 value: 13.083 - type: recall_at_1 value: 27.391 - type: recall_at_10 value: 56.928999999999995 - type: recall_at_100 value: 85.169 - type: recall_at_1000 value: 96.665 - type: recall_at_3 value: 42.264 - type: recall_at_5 value: 48.556 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 19.681 - type: map_at_10 value: 32.741 - type: map_at_100 value: 34.811 - type: map_at_1000 value: 35.003 - type: map_at_3 value: 27.697 - type: map_at_5 value: 30.372 - type: mrr_at_1 value: 44.951 - type: mrr_at_10 value: 56.34400000000001 - type: mrr_at_100 value: 56.961 - type: mrr_at_1000 value: 56.987 - type: mrr_at_3 value: 53.681 - type: mrr_at_5 value: 55.407 - type: ndcg_at_1 value: 44.951 - type: ndcg_at_10 value: 42.905 - type: ndcg_at_100 value: 49.95 - type: ndcg_at_1000 value: 52.917 - type: ndcg_at_3 value: 36.815 - type: ndcg_at_5 value: 38.817 - type: precision_at_1 value: 44.951 - type: precision_at_10 value: 12.989999999999998 - type: precision_at_100 value: 2.068 - type: precision_at_1000 value: 0.263 - type: precision_at_3 value: 27.275 - type: precision_at_5 value: 20.365 - type: recall_at_1 value: 19.681 - type: recall_at_10 value: 48.272999999999996 - type: recall_at_100 value: 71.87400000000001 - type: recall_at_1000 value: 87.929 - type: recall_at_3 value: 32.653999999999996 - type: recall_at_5 value: 39.364 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 10.231 - type: map_at_10 value: 22.338 - type: map_at_100 value: 31.927 - type: map_at_1000 value: 33.87 - type: map_at_3 value: 15.559999999999999 - type: map_at_5 value: 18.239 - type: mrr_at_1 value: 75.0 - type: mrr_at_10 value: 81.303 - type: mrr_at_100 value: 81.523 - type: mrr_at_1000 value: 81.53 - type: mrr_at_3 value: 80.083 - type: mrr_at_5 value: 80.758 - type: ndcg_at_1 value: 64.625 - type: ndcg_at_10 value: 48.687000000000005 - type: ndcg_at_100 value: 52.791 - type: ndcg_at_1000 value: 60.041999999999994 - type: ndcg_at_3 value: 53.757999999999996 - type: ndcg_at_5 value: 50.76500000000001 - type: precision_at_1 value: 75.0 - type: precision_at_10 value: 38.3 - type: precision_at_100 value: 12.025 - type: precision_at_1000 value: 2.3970000000000002 - type: precision_at_3 value: 55.417 - type: precision_at_5 value: 47.5 - type: recall_at_1 value: 10.231 - type: recall_at_10 value: 27.697 - type: recall_at_100 value: 57.409 - type: recall_at_1000 value: 80.547 - type: recall_at_3 value: 16.668 - type: recall_at_5 value: 20.552 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 61.365 - type: f1 value: 56.7540827912991 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 83.479 - type: map_at_10 value: 88.898 - type: map_at_100 value: 89.11 - type: map_at_1000 value: 89.12400000000001 - type: map_at_3 value: 88.103 - type: map_at_5 value: 88.629 - type: mrr_at_1 value: 89.934 - type: mrr_at_10 value: 93.91000000000001 - type: mrr_at_100 value: 93.937 - type: mrr_at_1000 value: 93.938 - type: mrr_at_3 value: 93.62700000000001 - type: mrr_at_5 value: 93.84599999999999 - type: ndcg_at_1 value: 89.934 - type: ndcg_at_10 value: 91.574 - type: ndcg_at_100 value: 92.238 - type: ndcg_at_1000 value: 92.45 - type: ndcg_at_3 value: 90.586 - type: ndcg_at_5 value: 91.16300000000001 - type: precision_at_1 value: 89.934 - type: precision_at_10 value: 10.555 - type: precision_at_100 value: 1.1159999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 33.588 - type: precision_at_5 value: 20.642 - type: recall_at_1 value: 83.479 - type: recall_at_10 value: 94.971 - type: recall_at_100 value: 97.397 - type: recall_at_1000 value: 98.666 - type: recall_at_3 value: 92.24799999999999 - type: recall_at_5 value: 93.797 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 27.16 - type: map_at_10 value: 45.593 - type: map_at_100 value: 47.762 - type: map_at_1000 value: 47.899 - type: map_at_3 value: 39.237 - type: map_at_5 value: 42.970000000000006 - type: mrr_at_1 value: 52.623 - type: mrr_at_10 value: 62.637 - type: mrr_at_100 value: 63.169 - type: mrr_at_1000 value: 63.185 - type: mrr_at_3 value: 59.928000000000004 - type: mrr_at_5 value: 61.702999999999996 - type: ndcg_at_1 value: 52.623 - type: ndcg_at_10 value: 54.701 - type: ndcg_at_100 value: 61.263 - type: ndcg_at_1000 value: 63.134 - type: ndcg_at_3 value: 49.265 - type: ndcg_at_5 value: 51.665000000000006 - type: precision_at_1 value: 52.623 - type: precision_at_10 value: 15.185 - type: precision_at_100 value: 2.202 - type: precision_at_1000 value: 0.254 - type: precision_at_3 value: 32.767 - type: precision_at_5 value: 24.722 - type: recall_at_1 value: 27.16 - type: recall_at_10 value: 63.309000000000005 - type: recall_at_100 value: 86.722 - type: recall_at_1000 value: 97.505 - type: recall_at_3 value: 45.045 - type: recall_at_5 value: 54.02400000000001 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 42.573 - type: map_at_10 value: 59.373 - type: map_at_100 value: 60.292 - type: map_at_1000 value: 60.358999999999995 - type: map_at_3 value: 56.159000000000006 - type: map_at_5 value: 58.123999999999995 - type: mrr_at_1 value: 85.14500000000001 - type: mrr_at_10 value: 89.25999999999999 - type: mrr_at_100 value: 89.373 - type: mrr_at_1000 value: 89.377 - type: mrr_at_3 value: 88.618 - type: mrr_at_5 value: 89.036 - type: ndcg_at_1 value: 85.14500000000001 - type: ndcg_at_10 value: 68.95 - type: ndcg_at_100 value: 71.95 - type: ndcg_at_1000 value: 73.232 - type: ndcg_at_3 value: 64.546 - type: ndcg_at_5 value: 66.945 - type: precision_at_1 value: 85.14500000000001 - type: precision_at_10 value: 13.865 - type: precision_at_100 value: 1.619 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 39.703 - type: precision_at_5 value: 25.718000000000004 - type: recall_at_1 value: 42.573 - type: recall_at_10 value: 69.325 - type: recall_at_100 value: 80.932 - type: recall_at_1000 value: 89.446 - type: recall_at_3 value: 59.553999999999995 - type: recall_at_5 value: 64.294 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 95.8336 - type: ap value: 93.78862962194073 - type: f1 value: 95.83192650728371 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 23.075000000000003 - type: map_at_10 value: 36.102000000000004 - type: map_at_100 value: 37.257 - type: map_at_1000 value: 37.3 - type: map_at_3 value: 32.144 - type: map_at_5 value: 34.359 - type: mrr_at_1 value: 23.711 - type: mrr_at_10 value: 36.671 - type: mrr_at_100 value: 37.763999999999996 - type: mrr_at_1000 value: 37.801 - type: mrr_at_3 value: 32.775 - type: mrr_at_5 value: 34.977000000000004 - type: ndcg_at_1 value: 23.711 - type: ndcg_at_10 value: 43.361 - type: ndcg_at_100 value: 48.839 - type: ndcg_at_1000 value: 49.88 - type: ndcg_at_3 value: 35.269 - type: ndcg_at_5 value: 39.224 - type: precision_at_1 value: 23.711 - type: precision_at_10 value: 6.866999999999999 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 15.096000000000002 - type: precision_at_5 value: 11.083 - type: recall_at_1 value: 23.075000000000003 - type: recall_at_10 value: 65.756 - type: recall_at_100 value: 90.88199999999999 - type: recall_at_1000 value: 98.739 - type: recall_at_3 value: 43.691 - type: recall_at_5 value: 53.15800000000001 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 97.69493844049248 - type: f1 value: 97.55048089616261 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 88.75968992248062 - type: f1 value: 72.26321223399123 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 82.40080699394754 - type: f1 value: 79.62590029057968 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 84.49562878278414 - type: f1 value: 84.0040193313333 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 39.386760057101945 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 37.89687154075537 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 33.94151656057482 - type: mrr value: 35.32684700746953 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 6.239999999999999 - type: map_at_10 value: 14.862 - type: map_at_100 value: 18.955 - type: map_at_1000 value: 20.694000000000003 - type: map_at_3 value: 10.683 - type: map_at_5 value: 12.674 - type: mrr_at_1 value: 50.15500000000001 - type: mrr_at_10 value: 59.697 - type: mrr_at_100 value: 60.095 - type: mrr_at_1000 value: 60.129999999999995 - type: mrr_at_3 value: 58.35900000000001 - type: mrr_at_5 value: 58.839 - type: ndcg_at_1 value: 48.452 - type: ndcg_at_10 value: 39.341 - type: ndcg_at_100 value: 35.866 - type: ndcg_at_1000 value: 45.111000000000004 - type: ndcg_at_3 value: 44.527 - type: ndcg_at_5 value: 42.946 - type: precision_at_1 value: 50.15500000000001 - type: precision_at_10 value: 29.536 - type: precision_at_100 value: 9.142 - type: precision_at_1000 value: 2.2849999999999997 - type: precision_at_3 value: 41.899 - type: precision_at_5 value: 37.647000000000006 - type: recall_at_1 value: 6.239999999999999 - type: recall_at_10 value: 19.278000000000002 - type: recall_at_100 value: 36.074 - type: recall_at_1000 value: 70.017 - type: recall_at_3 value: 12.066 - type: recall_at_5 value: 15.254000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 39.75 - type: map_at_10 value: 56.443 - type: map_at_100 value: 57.233999999999995 - type: map_at_1000 value: 57.249 - type: map_at_3 value: 52.032999999999994 - type: map_at_5 value: 54.937999999999995 - type: mrr_at_1 value: 44.728 - type: mrr_at_10 value: 58.939 - type: mrr_at_100 value: 59.489000000000004 - type: mrr_at_1000 value: 59.499 - type: mrr_at_3 value: 55.711999999999996 - type: mrr_at_5 value: 57.89 - type: ndcg_at_1 value: 44.728 - type: ndcg_at_10 value: 63.998999999999995 - type: ndcg_at_100 value: 67.077 - type: ndcg_at_1000 value: 67.40899999999999 - type: ndcg_at_3 value: 56.266000000000005 - type: ndcg_at_5 value: 60.88 - type: precision_at_1 value: 44.728 - type: precision_at_10 value: 10.09 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.145 - type: precision_at_5 value: 17.822 - type: recall_at_1 value: 39.75 - type: recall_at_10 value: 84.234 - type: recall_at_100 value: 97.055 - type: recall_at_1000 value: 99.517 - type: recall_at_3 value: 64.851 - type: recall_at_5 value: 75.343 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: None metrics: - type: map_at_1 value: 72.085 - type: map_at_10 value: 86.107 - type: map_at_100 value: 86.727 - type: map_at_1000 value: 86.74 - type: map_at_3 value: 83.21 - type: map_at_5 value: 85.06 - type: mrr_at_1 value: 82.94 - type: mrr_at_10 value: 88.845 - type: mrr_at_100 value: 88.926 - type: mrr_at_1000 value: 88.927 - type: mrr_at_3 value: 87.993 - type: mrr_at_5 value: 88.62299999999999 - type: ndcg_at_1 value: 82.97 - type: ndcg_at_10 value: 89.645 - type: ndcg_at_100 value: 90.717 - type: ndcg_at_1000 value: 90.78 - type: ndcg_at_3 value: 86.99900000000001 - type: ndcg_at_5 value: 88.52600000000001 - type: precision_at_1 value: 82.97 - type: precision_at_10 value: 13.569 - type: precision_at_100 value: 1.539 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.043 - type: precision_at_5 value: 24.992 - type: recall_at_1 value: 72.085 - type: recall_at_10 value: 96.262 - type: recall_at_100 value: 99.77000000000001 - type: recall_at_1000 value: 99.997 - type: recall_at_3 value: 88.652 - type: recall_at_5 value: 93.01899999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.82153952668092 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.094465801879295 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 5.688 - type: map_at_10 value: 15.201999999999998 - type: map_at_100 value: 18.096 - type: map_at_1000 value: 18.481 - type: map_at_3 value: 10.734 - type: map_at_5 value: 12.94 - type: mrr_at_1 value: 28.000000000000004 - type: mrr_at_10 value: 41.101 - type: mrr_at_100 value: 42.202 - type: mrr_at_1000 value: 42.228 - type: mrr_at_3 value: 37.683 - type: mrr_at_5 value: 39.708 - type: ndcg_at_1 value: 28.000000000000004 - type: ndcg_at_10 value: 24.976000000000003 - type: ndcg_at_100 value: 35.129 - type: ndcg_at_1000 value: 40.77 - type: ndcg_at_3 value: 23.787 - type: ndcg_at_5 value: 20.816000000000003 - type: precision_at_1 value: 28.000000000000004 - type: precision_at_10 value: 13.04 - type: precision_at_100 value: 2.761 - type: precision_at_1000 value: 0.41000000000000003 - type: precision_at_3 value: 22.6 - type: precision_at_5 value: 18.52 - type: recall_at_1 value: 5.688 - type: recall_at_10 value: 26.43 - type: recall_at_100 value: 56.02 - type: recall_at_1000 value: 83.21 - type: recall_at_3 value: 13.752 - type: recall_at_5 value: 18.777 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.15084859283178 - type: cos_sim_spearman value: 80.49030614009419 - type: euclidean_pearson value: 81.84574978672468 - type: euclidean_spearman value: 79.89787150656818 - type: manhattan_pearson value: 81.63076538567131 - type: manhattan_spearman value: 79.69867352121841 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.64097921490992 - type: cos_sim_spearman value: 77.25370084896514 - type: euclidean_pearson value: 82.71210826468788 - type: euclidean_spearman value: 78.50445584994826 - type: manhattan_pearson value: 82.92580164330298 - type: manhattan_spearman value: 78.69686891301019 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 87.24596417308994 - type: cos_sim_spearman value: 87.79454220555091 - type: euclidean_pearson value: 87.40242561671164 - type: euclidean_spearman value: 88.25955597373556 - type: manhattan_pearson value: 87.25160240485849 - type: manhattan_spearman value: 88.155794979818 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.44914233422564 - type: cos_sim_spearman value: 82.91015471820322 - type: euclidean_pearson value: 84.7206656630327 - type: euclidean_spearman value: 83.86408872059216 - type: manhattan_pearson value: 84.72816725158454 - type: manhattan_spearman value: 84.01603388572788 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.6168026237477 - type: cos_sim_spearman value: 88.45414278092397 - type: euclidean_pearson value: 88.57023240882022 - type: euclidean_spearman value: 89.04102190922094 - type: manhattan_pearson value: 88.66695535796354 - type: manhattan_spearman value: 89.19898476680969 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.27925826089424 - type: cos_sim_spearman value: 85.45291099550461 - type: euclidean_pearson value: 83.63853036580834 - type: euclidean_spearman value: 84.33468035821484 - type: manhattan_pearson value: 83.72778773251596 - type: manhattan_spearman value: 84.51583132445376 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.67375185692552 - type: cos_sim_spearman value: 90.32542469203855 - type: euclidean_pearson value: 89.63513717951847 - type: euclidean_spearman value: 89.87760271003745 - type: manhattan_pearson value: 89.28381452982924 - type: manhattan_spearman value: 89.53568197785721 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 66.24644693819846 - type: cos_sim_spearman value: 66.09889420525377 - type: euclidean_pearson value: 63.72551583520747 - type: euclidean_spearman value: 63.01385470780679 - type: manhattan_pearson value: 64.09258157214097 - type: manhattan_spearman value: 63.080517752822594 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.27321463839989 - type: cos_sim_spearman value: 86.37572865993327 - type: euclidean_pearson value: 86.36268020198149 - type: euclidean_spearman value: 86.31089339478922 - type: manhattan_pearson value: 86.4260445761947 - type: manhattan_spearman value: 86.45885895320457 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.52456702387798 - type: mrr value: 96.34556529164372 - task: type: Retrieval dataset: name: MTEB SciFact type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 61.99400000000001 - type: map_at_10 value: 73.38799999999999 - type: map_at_100 value: 73.747 - type: map_at_1000 value: 73.75 - type: map_at_3 value: 70.04599999999999 - type: map_at_5 value: 72.095 - type: mrr_at_1 value: 65.0 - type: mrr_at_10 value: 74.42800000000001 - type: mrr_at_100 value: 74.722 - type: mrr_at_1000 value: 74.725 - type: mrr_at_3 value: 72.056 - type: mrr_at_5 value: 73.60600000000001 - type: ndcg_at_1 value: 65.0 - type: ndcg_at_10 value: 78.435 - type: ndcg_at_100 value: 79.922 - type: ndcg_at_1000 value: 80.00500000000001 - type: ndcg_at_3 value: 73.05199999999999 - type: ndcg_at_5 value: 75.98 - type: precision_at_1 value: 65.0 - type: precision_at_10 value: 10.5 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.555999999999997 - type: precision_at_5 value: 19.0 - type: recall_at_1 value: 61.99400000000001 - type: recall_at_10 value: 92.72200000000001 - type: recall_at_100 value: 99.333 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 78.739 - type: recall_at_5 value: 85.828 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.79009900990098 - type: cos_sim_ap value: 95.3203137438653 - type: cos_sim_f1 value: 89.12386706948641 - type: cos_sim_precision value: 89.75659229208925 - type: cos_sim_recall value: 88.5 - type: dot_accuracy value: 99.67821782178218 - type: dot_ap value: 89.94069840000675 - type: dot_f1 value: 83.45902463549521 - type: dot_precision value: 83.9231547017189 - type: dot_recall value: 83.0 - type: euclidean_accuracy value: 99.78613861386138 - type: euclidean_ap value: 95.10648259135526 - type: euclidean_f1 value: 88.77338877338877 - type: euclidean_precision value: 92.42424242424242 - type: euclidean_recall value: 85.39999999999999 - type: manhattan_accuracy value: 99.7950495049505 - type: manhattan_ap value: 95.29987661320946 - type: manhattan_f1 value: 89.21313183949972 - type: manhattan_precision value: 93.14472252448314 - type: manhattan_recall value: 85.6 - type: max_accuracy value: 99.7950495049505 - type: max_ap value: 95.3203137438653 - type: max_f1 value: 89.21313183949972 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 67.65446577183913 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 46.30749237193961 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 54.91481849959949 - type: mrr value: 55.853506175197346 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.08196549170419 - type: cos_sim_spearman value: 31.16661390597077 - type: dot_pearson value: 29.892258410943466 - type: dot_spearman value: 30.51328811965085 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: mteb/trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.23900000000000002 - type: map_at_10 value: 2.173 - type: map_at_100 value: 14.24 - type: map_at_1000 value: 35.309000000000005 - type: map_at_3 value: 0.7100000000000001 - type: map_at_5 value: 1.163 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 96.0 - type: mrr_at_100 value: 96.0 - type: mrr_at_1000 value: 96.0 - type: mrr_at_3 value: 96.0 - type: mrr_at_5 value: 96.0 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 85.382 - type: ndcg_at_100 value: 68.03 - type: ndcg_at_1000 value: 61.021 - type: ndcg_at_3 value: 89.765 - type: ndcg_at_5 value: 88.444 - type: precision_at_1 value: 92.0 - type: precision_at_10 value: 88.0 - type: precision_at_100 value: 70.02000000000001 - type: precision_at_1000 value: 26.984 - type: precision_at_3 value: 94.0 - type: precision_at_5 value: 92.80000000000001 - type: recall_at_1 value: 0.23900000000000002 - type: recall_at_10 value: 2.313 - type: recall_at_100 value: 17.049 - type: recall_at_1000 value: 57.489999999999995 - type: recall_at_3 value: 0.737 - type: recall_at_5 value: 1.221 - task: type: Retrieval dataset: name: MTEB Touche2020 type: mteb/touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.75 - type: map_at_10 value: 11.29 - type: map_at_100 value: 18.032999999999998 - type: map_at_1000 value: 19.746 - type: map_at_3 value: 6.555 - type: map_at_5 value: 8.706999999999999 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 50.55 - type: mrr_at_100 value: 51.659 - type: mrr_at_1000 value: 51.659 - type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.728 - type: ndcg_at_1 value: 32.653 - type: ndcg_at_10 value: 27.894000000000002 - type: ndcg_at_100 value: 39.769 - type: ndcg_at_1000 value: 51.495999999999995 - type: ndcg_at_3 value: 32.954 - type: ndcg_at_5 value: 31.502999999999997 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 23.265 - type: precision_at_100 value: 7.898 - type: precision_at_1000 value: 1.58 - type: precision_at_3 value: 34.694 - type: precision_at_5 value: 31.429000000000002 - type: recall_at_1 value: 2.75 - type: recall_at_10 value: 16.953 - type: recall_at_100 value: 48.68 - type: recall_at_1000 value: 85.18599999999999 - type: recall_at_3 value: 7.710999999999999 - type: recall_at_5 value: 11.484 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 82.66099999999999 - type: ap value: 25.555698090238337 - type: f1 value: 66.48402012461622 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 72.94567062818335 - type: f1 value: 73.28139189595674 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.581627240203474 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.78089050485785 - type: cos_sim_ap value: 79.64487116574168 - type: cos_sim_f1 value: 72.46563021970964 - type: cos_sim_precision value: 70.62359128474831 - type: cos_sim_recall value: 74.40633245382587 - type: dot_accuracy value: 86.2609524944865 - type: dot_ap value: 75.513046857613 - type: dot_f1 value: 68.58213616489695 - type: dot_precision value: 65.12455516014235 - type: dot_recall value: 72.42744063324538 - type: euclidean_accuracy value: 87.6080348095607 - type: euclidean_ap value: 79.00204933649795 - type: euclidean_f1 value: 72.14495342605589 - type: euclidean_precision value: 69.85421299728193 - type: euclidean_recall value: 74.5910290237467 - type: manhattan_accuracy value: 87.59611372712642 - type: manhattan_ap value: 78.78523756706264 - type: manhattan_f1 value: 71.86499137718648 - type: manhattan_precision value: 67.39833641404806 - type: manhattan_recall value: 76.96569920844327 - type: max_accuracy value: 87.78089050485785 - type: max_ap value: 79.64487116574168 - type: max_f1 value: 72.46563021970964 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.98719292117825 - type: cos_sim_ap value: 87.58146137353202 - type: cos_sim_f1 value: 80.28543232369239 - type: cos_sim_precision value: 79.1735289714029 - type: cos_sim_recall value: 81.42901139513397 - type: dot_accuracy value: 88.9199363526992 - type: dot_ap value: 84.98499998630417 - type: dot_f1 value: 78.21951400757969 - type: dot_precision value: 75.58523624874336 - type: dot_recall value: 81.04404065291038 - type: euclidean_accuracy value: 89.77374160748244 - type: euclidean_ap value: 87.35151562835209 - type: euclidean_f1 value: 79.92160922940393 - type: euclidean_precision value: 76.88531587933979 - type: euclidean_recall value: 83.20757622420696 - type: manhattan_accuracy value: 89.72717041176699 - type: manhattan_ap value: 87.34065592142515 - type: manhattan_f1 value: 79.85603419187943 - type: manhattan_precision value: 77.82243332115455 - type: manhattan_recall value: 81.99876809362489 - type: max_accuracy value: 89.98719292117825 - type: max_ap value: 87.58146137353202 - type: max_f1 value: 80.28543232369239 - task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cos_sim_pearson value: 53.45954203592337 - type: cos_sim_spearman value: 58.42154680418638 - type: euclidean_pearson value: 56.41543791722753 - type: euclidean_spearman value: 58.39328016640146 - type: manhattan_pearson value: 56.318510356833876 - type: manhattan_spearman value: 58.28423447818184 - task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics: - type: cos_sim_pearson value: 50.78356460675945 - type: cos_sim_spearman value: 55.6530411663269 - type: euclidean_pearson value: 56.50763660417816 - type: euclidean_spearman value: 55.733823335669065 - type: manhattan_pearson value: 56.45323093512866 - type: manhattan_spearman value: 55.63248619032702 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.209999999999994 - type: f1 value: 46.08892432018655 - task: type: STS dataset: name: MTEB BQ type: C-MTEB/BQ config: default split: test revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 metrics: - type: cos_sim_pearson value: 70.25573992001478 - type: cos_sim_spearman value: 73.85247134951433 - type: euclidean_pearson value: 72.60033082168442 - type: euclidean_spearman value: 73.72445893756499 - type: manhattan_pearson value: 72.59932284620231 - type: manhattan_spearman value: 73.68002490614583 - task: type: Clustering dataset: name: MTEB CLSClusteringP2P type: C-MTEB/CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics: - type: v_measure value: 45.21317724305628 - task: type: Clustering dataset: name: MTEB CLSClusteringS2S type: C-MTEB/CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics: - type: v_measure value: 42.49825170976724 - task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics: - type: map value: 88.15661686810597 - type: mrr value: 90.11222222222223 - task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics: - type: map value: 88.1204726064383 - type: mrr value: 90.20142857142858 - task: type: Retrieval dataset: name: MTEB CmedqaRetrieval type: C-MTEB/CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics: - type: map_at_1 value: 27.224999999999998 - type: map_at_10 value: 40.169 - type: map_at_100 value: 42.0 - type: map_at_1000 value: 42.109 - type: map_at_3 value: 35.76 - type: map_at_5 value: 38.221 - type: mrr_at_1 value: 40.56 - type: mrr_at_10 value: 49.118 - type: mrr_at_100 value: 50.092999999999996 - type: mrr_at_1000 value: 50.133 - type: mrr_at_3 value: 46.507 - type: mrr_at_5 value: 47.973 - type: ndcg_at_1 value: 40.56 - type: ndcg_at_10 value: 46.972 - type: ndcg_at_100 value: 54.04 - type: ndcg_at_1000 value: 55.862 - type: ndcg_at_3 value: 41.36 - type: ndcg_at_5 value: 43.704 - type: precision_at_1 value: 40.56 - type: precision_at_10 value: 10.302999999999999 - type: precision_at_100 value: 1.606 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 23.064 - type: precision_at_5 value: 16.764000000000003 - type: recall_at_1 value: 27.224999999999998 - type: recall_at_10 value: 58.05200000000001 - type: recall_at_100 value: 87.092 - type: recall_at_1000 value: 99.099 - type: recall_at_3 value: 41.373 - type: recall_at_5 value: 48.453 - task: type: PairClassification dataset: name: MTEB Cmnli type: C-MTEB/CMNLI config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics: - type: cos_sim_accuracy value: 77.40228502705953 - type: cos_sim_ap value: 86.22359172956327 - type: cos_sim_f1 value: 78.96328293736501 - type: cos_sim_precision value: 73.36945615091311 - type: cos_sim_recall value: 85.48047696983868 - type: dot_accuracy value: 75.53818400481059 - type: dot_ap value: 83.70164011305312 - type: dot_f1 value: 77.67298719348754 - type: dot_precision value: 67.49482401656314 - type: dot_recall value: 91.46598082768296 - type: euclidean_accuracy value: 77.94347564642213 - type: euclidean_ap value: 86.4652108728609 - type: euclidean_f1 value: 79.15555555555555 - type: euclidean_precision value: 75.41816641964853 - type: euclidean_recall value: 83.28267477203647 - type: manhattan_accuracy value: 77.45039085989175 - type: manhattan_ap value: 86.09986583900665 - type: manhattan_f1 value: 78.93669264438988 - type: manhattan_precision value: 72.63261296660117 - type: manhattan_recall value: 86.43909282207154 - type: max_accuracy value: 77.94347564642213 - type: max_ap value: 86.4652108728609 - type: max_f1 value: 79.15555555555555 - task: type: Retrieval dataset: name: MTEB CovidRetrieval type: C-MTEB/CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics: - type: map_at_1 value: 69.336 - type: map_at_10 value: 77.16 - type: map_at_100 value: 77.47500000000001 - type: map_at_1000 value: 77.482 - type: map_at_3 value: 75.42999999999999 - type: map_at_5 value: 76.468 - type: mrr_at_1 value: 69.44200000000001 - type: mrr_at_10 value: 77.132 - type: mrr_at_100 value: 77.43299999999999 - type: mrr_at_1000 value: 77.44 - type: mrr_at_3 value: 75.395 - type: mrr_at_5 value: 76.459 - type: ndcg_at_1 value: 69.547 - type: ndcg_at_10 value: 80.794 - type: ndcg_at_100 value: 82.245 - type: ndcg_at_1000 value: 82.40899999999999 - type: ndcg_at_3 value: 77.303 - type: ndcg_at_5 value: 79.168 - type: precision_at_1 value: 69.547 - type: precision_at_10 value: 9.305 - type: precision_at_100 value: 0.9979999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 27.749000000000002 - type: precision_at_5 value: 17.576 - type: recall_at_1 value: 69.336 - type: recall_at_10 value: 92.097 - type: recall_at_100 value: 98.736 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 82.64 - type: recall_at_5 value: 87.144 - task: type: Retrieval dataset: name: MTEB DuRetrieval type: C-MTEB/DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: map_at_1 value: 26.817999999999998 - type: map_at_10 value: 82.67 - type: map_at_100 value: 85.304 - type: map_at_1000 value: 85.334 - type: map_at_3 value: 57.336 - type: map_at_5 value: 72.474 - type: mrr_at_1 value: 91.45 - type: mrr_at_10 value: 94.272 - type: mrr_at_100 value: 94.318 - type: mrr_at_1000 value: 94.32000000000001 - type: mrr_at_3 value: 94.0 - type: mrr_at_5 value: 94.17699999999999 - type: ndcg_at_1 value: 91.45 - type: ndcg_at_10 value: 89.404 - type: ndcg_at_100 value: 91.724 - type: ndcg_at_1000 value: 91.973 - type: ndcg_at_3 value: 88.104 - type: ndcg_at_5 value: 87.25699999999999 - type: precision_at_1 value: 91.45 - type: precision_at_10 value: 42.585 - type: precision_at_100 value: 4.838 - type: precision_at_1000 value: 0.49 - type: precision_at_3 value: 78.8 - type: precision_at_5 value: 66.66 - type: recall_at_1 value: 26.817999999999998 - type: recall_at_10 value: 90.67 - type: recall_at_100 value: 98.36200000000001 - type: recall_at_1000 value: 99.583 - type: recall_at_3 value: 59.614999999999995 - type: recall_at_5 value: 77.05199999999999 - task: type: Retrieval dataset: name: MTEB EcomRetrieval type: C-MTEB/EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: map_at_1 value: 47.699999999999996 - type: map_at_10 value: 57.589999999999996 - type: map_at_100 value: 58.226 - type: map_at_1000 value: 58.251 - type: map_at_3 value: 55.233 - type: map_at_5 value: 56.633 - type: mrr_at_1 value: 47.699999999999996 - type: mrr_at_10 value: 57.589999999999996 - type: mrr_at_100 value: 58.226 - type: mrr_at_1000 value: 58.251 - type: mrr_at_3 value: 55.233 - type: mrr_at_5 value: 56.633 - type: ndcg_at_1 value: 47.699999999999996 - type: ndcg_at_10 value: 62.505 - type: ndcg_at_100 value: 65.517 - type: ndcg_at_1000 value: 66.19800000000001 - type: ndcg_at_3 value: 57.643 - type: ndcg_at_5 value: 60.181 - type: precision_at_1 value: 47.699999999999996 - type: precision_at_10 value: 7.8 - type: precision_at_100 value: 0.919 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 21.532999999999998 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 47.699999999999996 - type: recall_at_10 value: 78.0 - type: recall_at_100 value: 91.9 - type: recall_at_1000 value: 97.3 - type: recall_at_3 value: 64.60000000000001 - type: recall_at_5 value: 70.8 - task: type: Classification dataset: name: MTEB IFlyTek type: C-MTEB/IFlyTek-classification config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics: - type: accuracy value: 44.84801846864178 - type: f1 value: 37.47347897956339 - task: type: Classification dataset: name: MTEB JDReview type: C-MTEB/JDReview-classification config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics: - type: accuracy value: 85.81613508442777 - type: ap value: 52.68244615477374 - type: f1 value: 80.0445640948843 - task: type: STS dataset: name: MTEB LCQMC type: C-MTEB/LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics: - type: cos_sim_pearson value: 69.57786502217138 - type: cos_sim_spearman value: 75.39106054489906 - type: euclidean_pearson value: 73.72082954602402 - type: euclidean_spearman value: 75.14421475913619 - type: manhattan_pearson value: 73.62463076633642 - type: manhattan_spearman value: 75.01301565104112 - task: type: Reranking dataset: name: MTEB MMarcoReranking type: C-MTEB/Mmarco-reranking config: default split: dev revision: None metrics: - type: map value: 29.143797057999134 - type: mrr value: 28.08174603174603 - task: type: Retrieval dataset: name: MTEB MMarcoRetrieval type: C-MTEB/MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - type: map_at_1 value: 70.492 - type: map_at_10 value: 79.501 - type: map_at_100 value: 79.728 - type: map_at_1000 value: 79.735 - type: map_at_3 value: 77.77 - type: map_at_5 value: 78.851 - type: mrr_at_1 value: 72.822 - type: mrr_at_10 value: 80.001 - type: mrr_at_100 value: 80.19 - type: mrr_at_1000 value: 80.197 - type: mrr_at_3 value: 78.484 - type: mrr_at_5 value: 79.42099999999999 - type: ndcg_at_1 value: 72.822 - type: ndcg_at_10 value: 83.013 - type: ndcg_at_100 value: 84.013 - type: ndcg_at_1000 value: 84.20400000000001 - type: ndcg_at_3 value: 79.728 - type: ndcg_at_5 value: 81.542 - type: precision_at_1 value: 72.822 - type: precision_at_10 value: 9.917 - type: precision_at_100 value: 1.042 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 29.847 - type: precision_at_5 value: 18.871 - type: recall_at_1 value: 70.492 - type: recall_at_10 value: 93.325 - type: recall_at_100 value: 97.822 - type: recall_at_1000 value: 99.319 - type: recall_at_3 value: 84.636 - type: recall_at_5 value: 88.93100000000001 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-CN) type: mteb/amazon_massive_intent config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.88298587760592 - type: f1 value: 73.89001762017176 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.76328177538669 - type: f1 value: 80.24718532423358 - task: type: Retrieval dataset: name: MTEB MedicalRetrieval type: C-MTEB/MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics: - type: map_at_1 value: 49.6 - type: map_at_10 value: 55.620999999999995 - type: map_at_100 value: 56.204 - type: map_at_1000 value: 56.251 - type: map_at_3 value: 54.132999999999996 - type: map_at_5 value: 54.933 - type: mrr_at_1 value: 49.7 - type: mrr_at_10 value: 55.67100000000001 - type: mrr_at_100 value: 56.254000000000005 - type: mrr_at_1000 value: 56.301 - type: mrr_at_3 value: 54.18300000000001 - type: mrr_at_5 value: 54.983000000000004 - type: ndcg_at_1 value: 49.6 - type: ndcg_at_10 value: 58.645 - type: ndcg_at_100 value: 61.789 - type: ndcg_at_1000 value: 63.219 - type: ndcg_at_3 value: 55.567 - type: ndcg_at_5 value: 57.008 - type: precision_at_1 value: 49.6 - type: precision_at_10 value: 6.819999999999999 - type: precision_at_100 value: 0.836 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 19.900000000000002 - type: precision_at_5 value: 12.64 - type: recall_at_1 value: 49.6 - type: recall_at_10 value: 68.2 - type: recall_at_100 value: 83.6 - type: recall_at_1000 value: 95.3 - type: recall_at_3 value: 59.699999999999996 - type: recall_at_5 value: 63.2 - task: type: Classification dataset: name: MTEB MultilingualSentiment type: C-MTEB/MultilingualSentiment-classification config: default split: validation revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a metrics: - type: accuracy value: 74.45666666666666 - type: f1 value: 74.32582402190089 - task: type: PairClassification dataset: name: MTEB Ocnli type: C-MTEB/OCNLI config: default split: validation revision: 66e76a618a34d6d565d5538088562851e6daa7ec metrics: - type: cos_sim_accuracy value: 80.67135896047645 - type: cos_sim_ap value: 87.60421240712051 - type: cos_sim_f1 value: 82.1304131408661 - type: cos_sim_precision value: 77.68361581920904 - type: cos_sim_recall value: 87.11721224920802 - type: dot_accuracy value: 79.04710341093666 - type: dot_ap value: 85.6370059719336 - type: dot_f1 value: 80.763723150358 - type: dot_precision value: 73.69337979094077 - type: dot_recall value: 89.33474128827878 - type: euclidean_accuracy value: 81.05035192203573 - type: euclidean_ap value: 87.7880240053663 - type: euclidean_f1 value: 82.50244379276637 - type: euclidean_precision value: 76.7970882620564 - type: euclidean_recall value: 89.1235480464625 - type: manhattan_accuracy value: 80.61721710882512 - type: manhattan_ap value: 87.43568120591175 - type: manhattan_f1 value: 81.89526184538653 - type: manhattan_precision value: 77.5992438563327 - type: manhattan_recall value: 86.6948257655755 - type: max_accuracy value: 81.05035192203573 - type: max_ap value: 87.7880240053663 - type: max_f1 value: 82.50244379276637 - task: type: Classification dataset: name: MTEB OnlineShopping type: C-MTEB/OnlineShopping-classification config: default split: test revision: e610f2ebd179a8fda30ae534c3878750a96db120 metrics: - type: accuracy value: 93.5 - type: ap value: 91.31357903446782 - type: f1 value: 93.48088994006616 - task: type: STS dataset: name: MTEB PAWSX type: C-MTEB/PAWSX config: default split: test revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 metrics: - type: cos_sim_pearson value: 36.93293453538077 - type: cos_sim_spearman value: 42.45972506308574 - type: euclidean_pearson value: 42.34945133152159 - type: euclidean_spearman value: 42.331610303674644 - type: manhattan_pearson value: 42.31455070249498 - type: manhattan_spearman value: 42.19887982891834 - task: type: STS dataset: name: MTEB QBQTC type: C-MTEB/QBQTC config: default split: test revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 metrics: - type: cos_sim_pearson value: 33.683290790043785 - type: cos_sim_spearman value: 35.149171171202994 - type: euclidean_pearson value: 32.33806561267862 - type: euclidean_spearman value: 34.483576387347966 - type: manhattan_pearson value: 32.47629754599608 - type: manhattan_spearman value: 34.66434471867615 - task: type: STS dataset: name: MTEB STS22 (zh) type: mteb/sts22-crosslingual-sts config: zh split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 66.46322760516104 - type: cos_sim_spearman value: 67.398478319726 - type: euclidean_pearson value: 64.7223480293625 - type: euclidean_spearman value: 66.83118568812951 - type: manhattan_pearson value: 64.88440039828305 - type: manhattan_spearman value: 66.80429458952257 - task: type: STS dataset: name: MTEB STSB type: C-MTEB/STSB config: default split: test revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 metrics: - type: cos_sim_pearson value: 79.08991383232105 - type: cos_sim_spearman value: 79.39715677296854 - type: euclidean_pearson value: 78.63201279320496 - type: euclidean_spearman value: 79.40262660785731 - type: manhattan_pearson value: 78.98138363146906 - type: manhattan_spearman value: 79.79968413014194 - task: type: Reranking dataset: name: MTEB T2Reranking type: C-MTEB/T2Reranking config: default split: dev revision: 76631901a18387f85eaa53e5450019b87ad58ef9 metrics: - type: map value: 67.43289278789972 - type: mrr value: 77.53012460908535 - task: type: Retrieval dataset: name: MTEB T2Retrieval type: C-MTEB/T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics: - type: map_at_1 value: 27.733999999999998 - type: map_at_10 value: 78.24799999999999 - type: map_at_100 value: 81.765 - type: map_at_1000 value: 81.824 - type: map_at_3 value: 54.92 - type: map_at_5 value: 67.61399999999999 - type: mrr_at_1 value: 90.527 - type: mrr_at_10 value: 92.843 - type: mrr_at_100 value: 92.927 - type: mrr_at_1000 value: 92.93 - type: mrr_at_3 value: 92.45100000000001 - type: mrr_at_5 value: 92.693 - type: ndcg_at_1 value: 90.527 - type: ndcg_at_10 value: 85.466 - type: ndcg_at_100 value: 88.846 - type: ndcg_at_1000 value: 89.415 - type: ndcg_at_3 value: 86.768 - type: ndcg_at_5 value: 85.46000000000001 - type: precision_at_1 value: 90.527 - type: precision_at_10 value: 42.488 - type: precision_at_100 value: 5.024 - type: precision_at_1000 value: 0.516 - type: precision_at_3 value: 75.907 - type: precision_at_5 value: 63.727000000000004 - type: recall_at_1 value: 27.733999999999998 - type: recall_at_10 value: 84.346 - type: recall_at_100 value: 95.536 - type: recall_at_1000 value: 98.42999999999999 - type: recall_at_3 value: 56.455 - type: recall_at_5 value: 70.755 - task: type: Classification dataset: name: MTEB TNews type: C-MTEB/TNews-classification config: default split: validation revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 metrics: - type: accuracy value: 49.952000000000005 - type: f1 value: 48.264617195258054 - task: type: Clustering dataset: name: MTEB ThuNewsClusteringP2P type: C-MTEB/ThuNewsClusteringP2P config: default split: test revision: 5798586b105c0434e4f0fe5e767abe619442cf93 metrics: - type: v_measure value: 68.23769904483508 - task: type: Clustering dataset: name: MTEB ThuNewsClusteringS2S type: C-MTEB/ThuNewsClusteringS2S config: default split: test revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d metrics: - type: v_measure value: 62.50294403136556 - task: type: Retrieval dataset: name: MTEB VideoRetrieval type: C-MTEB/VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: map_at_1 value: 54.0 - type: map_at_10 value: 63.668 - type: map_at_100 value: 64.217 - type: map_at_1000 value: 64.23100000000001 - type: map_at_3 value: 61.7 - type: map_at_5 value: 62.870000000000005 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 63.668 - type: mrr_at_100 value: 64.217 - type: mrr_at_1000 value: 64.23100000000001 - type: mrr_at_3 value: 61.7 - type: mrr_at_5 value: 62.870000000000005 - type: ndcg_at_1 value: 54.0 - type: ndcg_at_10 value: 68.11399999999999 - type: ndcg_at_100 value: 70.723 - type: ndcg_at_1000 value: 71.123 - type: ndcg_at_3 value: 64.074 - type: ndcg_at_5 value: 66.178 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 8.200000000000001 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 23.633000000000003 - type: precision_at_5 value: 15.2 - type: recall_at_1 value: 54.0 - type: recall_at_10 value: 82.0 - type: recall_at_100 value: 94.1 - type: recall_at_1000 value: 97.3 - type: recall_at_3 value: 70.89999999999999 - type: recall_at_5 value: 76.0 - task: type: Classification dataset: name: MTEB Waimai type: C-MTEB/waimai-classification config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics: - type: accuracy value: 86.63000000000001 - type: ap value: 69.99457882599567 - type: f1 value: 85.07735617998541 - task: type: Clustering dataset: name: MTEB 8TagsClustering type: PL-MTEB/8tags-clustering config: default split: test revision: None metrics: - type: v_measure value: 44.594104491193555 - task: type: Classification dataset: name: MTEB AllegroReviews type: PL-MTEB/allegro-reviews config: default split: test revision: None metrics: - type: accuracy value: 63.97614314115309 - type: f1 value: 52.15634261679283 - task: type: Retrieval dataset: name: MTEB ArguAna-PL type: clarin-knext/arguana-pl config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics: - type: map_at_1 value: 32.646 - type: map_at_10 value: 47.963 - type: map_at_100 value: 48.789 - type: map_at_1000 value: 48.797000000000004 - type: map_at_3 value: 43.196 - type: map_at_5 value: 46.016 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.126000000000005 - type: mrr_at_100 value: 48.946 - type: mrr_at_1000 value: 48.953 - type: mrr_at_3 value: 43.374 - type: mrr_at_5 value: 46.147 - type: ndcg_at_1 value: 32.646 - type: ndcg_at_10 value: 56.481 - type: ndcg_at_100 value: 59.922 - type: ndcg_at_1000 value: 60.07 - type: ndcg_at_3 value: 46.675 - type: ndcg_at_5 value: 51.76500000000001 - type: precision_at_1 value: 32.646 - type: precision_at_10 value: 8.371 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.919 - type: precision_at_5 value: 13.825999999999999 - type: recall_at_1 value: 32.646 - type: recall_at_10 value: 83.71300000000001 - type: recall_at_100 value: 98.578 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 56.757000000000005 - type: recall_at_5 value: 69.132 - task: type: Classification dataset: name: MTEB CBD type: PL-MTEB/cbd config: default split: test revision: None metrics: - type: accuracy value: 68.56 - type: ap value: 23.310493680488513 - type: f1 value: 58.85369533105693 - task: type: PairClassification dataset: name: MTEB CDSC-E type: PL-MTEB/cdsce-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 88.5 - type: cos_sim_ap value: 72.42140924378361 - type: cos_sim_f1 value: 66.0919540229885 - type: cos_sim_precision value: 72.78481012658227 - type: cos_sim_recall value: 60.526315789473685 - type: dot_accuracy value: 88.5 - type: dot_ap value: 72.42140924378361 - type: dot_f1 value: 66.0919540229885 - type: dot_precision value: 72.78481012658227 - type: dot_recall value: 60.526315789473685 - type: euclidean_accuracy value: 88.5 - type: euclidean_ap value: 72.42140924378361 - type: euclidean_f1 value: 66.0919540229885 - type: euclidean_precision value: 72.78481012658227 - type: euclidean_recall value: 60.526315789473685 - type: manhattan_accuracy value: 88.5 - type: manhattan_ap value: 72.49745515311696 - type: manhattan_f1 value: 66.0968660968661 - type: manhattan_precision value: 72.04968944099379 - type: manhattan_recall value: 61.05263157894737 - type: max_accuracy value: 88.5 - type: max_ap value: 72.49745515311696 - type: max_f1 value: 66.0968660968661 - task: type: STS dataset: name: MTEB CDSC-R type: PL-MTEB/cdscr-sts config: default split: test revision: None metrics: - type: cos_sim_pearson value: 90.32269765590145 - type: cos_sim_spearman value: 89.73666311491672 - type: euclidean_pearson value: 88.2933868516544 - type: euclidean_spearman value: 89.73666311491672 - type: manhattan_pearson value: 88.33474590219448 - type: manhattan_spearman value: 89.8548364866583 - task: type: Retrieval dataset: name: MTEB DBPedia-PL type: clarin-knext/dbpedia-pl config: default split: test revision: 76afe41d9af165cc40999fcaa92312b8b012064a metrics: - type: map_at_1 value: 7.632999999999999 - type: map_at_10 value: 16.426 - type: map_at_100 value: 22.651 - type: map_at_1000 value: 24.372 - type: map_at_3 value: 11.706 - type: map_at_5 value: 13.529 - type: mrr_at_1 value: 60.75000000000001 - type: mrr_at_10 value: 68.613 - type: mrr_at_100 value: 69.001 - type: mrr_at_1000 value: 69.021 - type: mrr_at_3 value: 67.0 - type: mrr_at_5 value: 67.925 - type: ndcg_at_1 value: 49.875 - type: ndcg_at_10 value: 36.978 - type: ndcg_at_100 value: 40.031 - type: ndcg_at_1000 value: 47.566 - type: ndcg_at_3 value: 41.148 - type: ndcg_at_5 value: 38.702 - type: precision_at_1 value: 60.75000000000001 - type: precision_at_10 value: 29.7 - type: precision_at_100 value: 9.278 - type: precision_at_1000 value: 2.099 - type: precision_at_3 value: 44.0 - type: precision_at_5 value: 37.6 - type: recall_at_1 value: 7.632999999999999 - type: recall_at_10 value: 22.040000000000003 - type: recall_at_100 value: 44.024 - type: recall_at_1000 value: 67.848 - type: recall_at_3 value: 13.093 - type: recall_at_5 value: 15.973 - task: type: Retrieval dataset: name: MTEB FiQA-PL type: clarin-knext/fiqa-pl config: default split: test revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e metrics: - type: map_at_1 value: 15.473 - type: map_at_10 value: 24.579 - type: map_at_100 value: 26.387 - type: map_at_1000 value: 26.57 - type: map_at_3 value: 21.278 - type: map_at_5 value: 23.179 - type: mrr_at_1 value: 30.709999999999997 - type: mrr_at_10 value: 38.994 - type: mrr_at_100 value: 39.993 - type: mrr_at_1000 value: 40.044999999999995 - type: mrr_at_3 value: 36.342999999999996 - type: mrr_at_5 value: 37.846999999999994 - type: ndcg_at_1 value: 30.709999999999997 - type: ndcg_at_10 value: 31.608999999999998 - type: ndcg_at_100 value: 38.807 - type: ndcg_at_1000 value: 42.208 - type: ndcg_at_3 value: 28.086 - type: ndcg_at_5 value: 29.323 - type: precision_at_1 value: 30.709999999999997 - type: precision_at_10 value: 8.688 - type: precision_at_100 value: 1.608 - type: precision_at_1000 value: 0.22100000000000003 - type: precision_at_3 value: 18.724 - type: precision_at_5 value: 13.950999999999999 - type: recall_at_1 value: 15.473 - type: recall_at_10 value: 38.361000000000004 - type: recall_at_100 value: 65.2 - type: recall_at_1000 value: 85.789 - type: recall_at_3 value: 25.401 - type: recall_at_5 value: 30.875999999999998 - task: type: Retrieval dataset: name: MTEB HotpotQA-PL type: clarin-knext/hotpotqa-pl config: default split: test revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 metrics: - type: map_at_1 value: 38.096000000000004 - type: map_at_10 value: 51.44499999999999 - type: map_at_100 value: 52.325 - type: map_at_1000 value: 52.397000000000006 - type: map_at_3 value: 48.626999999999995 - type: map_at_5 value: 50.342 - type: mrr_at_1 value: 76.19200000000001 - type: mrr_at_10 value: 81.191 - type: mrr_at_100 value: 81.431 - type: mrr_at_1000 value: 81.443 - type: mrr_at_3 value: 80.30199999999999 - type: mrr_at_5 value: 80.85900000000001 - type: ndcg_at_1 value: 76.19200000000001 - type: ndcg_at_10 value: 60.9 - type: ndcg_at_100 value: 64.14699999999999 - type: ndcg_at_1000 value: 65.647 - type: ndcg_at_3 value: 56.818000000000005 - type: ndcg_at_5 value: 59.019999999999996 - type: precision_at_1 value: 76.19200000000001 - type: precision_at_10 value: 12.203 - type: precision_at_100 value: 1.478 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 34.616 - type: precision_at_5 value: 22.515 - type: recall_at_1 value: 38.096000000000004 - type: recall_at_10 value: 61.013 - type: recall_at_100 value: 73.90299999999999 - type: recall_at_1000 value: 83.91 - type: recall_at_3 value: 51.92400000000001 - type: recall_at_5 value: 56.286 - task: type: Retrieval dataset: name: MTEB MSMARCO-PL type: clarin-knext/msmarco-pl config: default split: test revision: 8634c07806d5cce3a6138e260e59b81760a0a640 metrics: - type: map_at_1 value: 1.548 - type: map_at_10 value: 11.049000000000001 - type: map_at_100 value: 28.874 - type: map_at_1000 value: 34.931 - type: map_at_3 value: 4.162 - type: map_at_5 value: 6.396 - type: mrr_at_1 value: 90.69800000000001 - type: mrr_at_10 value: 92.093 - type: mrr_at_100 value: 92.345 - type: mrr_at_1000 value: 92.345 - type: mrr_at_3 value: 91.86 - type: mrr_at_5 value: 91.86 - type: ndcg_at_1 value: 74.031 - type: ndcg_at_10 value: 63.978 - type: ndcg_at_100 value: 53.101 - type: ndcg_at_1000 value: 60.675999999999995 - type: ndcg_at_3 value: 71.421 - type: ndcg_at_5 value: 68.098 - type: precision_at_1 value: 90.69800000000001 - type: precision_at_10 value: 71.86 - type: precision_at_100 value: 31.395 - type: precision_at_1000 value: 5.981 - type: precision_at_3 value: 84.49600000000001 - type: precision_at_5 value: 79.07 - type: recall_at_1 value: 1.548 - type: recall_at_10 value: 12.149000000000001 - type: recall_at_100 value: 40.794999999999995 - type: recall_at_1000 value: 67.974 - type: recall_at_3 value: 4.244 - type: recall_at_5 value: 6.608 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.55413584398119 - type: f1 value: 69.65610882318181 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.37188971082716 - type: f1 value: 75.64847309941361 - task: type: Retrieval dataset: name: MTEB NFCorpus-PL type: clarin-knext/nfcorpus-pl config: default split: test revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 metrics: - type: map_at_1 value: 4.919 - type: map_at_10 value: 10.834000000000001 - type: map_at_100 value: 13.38 - type: map_at_1000 value: 14.581 - type: map_at_3 value: 8.198 - type: map_at_5 value: 9.428 - type: mrr_at_1 value: 41.176 - type: mrr_at_10 value: 50.083 - type: mrr_at_100 value: 50.559 - type: mrr_at_1000 value: 50.604000000000006 - type: mrr_at_3 value: 47.936 - type: mrr_at_5 value: 49.407000000000004 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 30.098000000000003 - type: ndcg_at_100 value: 27.061 - type: ndcg_at_1000 value: 35.94 - type: ndcg_at_3 value: 35.135 - type: ndcg_at_5 value: 33.335 - type: precision_at_1 value: 41.176 - type: precision_at_10 value: 22.259999999999998 - type: precision_at_100 value: 6.712 - type: precision_at_1000 value: 1.9060000000000001 - type: precision_at_3 value: 33.23 - type: precision_at_5 value: 29.04 - type: recall_at_1 value: 4.919 - type: recall_at_10 value: 14.196 - type: recall_at_100 value: 26.948 - type: recall_at_1000 value: 59.211000000000006 - type: recall_at_3 value: 9.44 - type: recall_at_5 value: 11.569 - task: type: Retrieval dataset: name: MTEB NQ-PL type: clarin-knext/nq-pl config: default split: test revision: f171245712cf85dd4700b06bef18001578d0ca8d metrics: - type: map_at_1 value: 25.35 - type: map_at_10 value: 37.884 - type: map_at_100 value: 38.955 - type: map_at_1000 value: 39.007999999999996 - type: map_at_3 value: 34.239999999999995 - type: map_at_5 value: 36.398 - type: mrr_at_1 value: 28.737000000000002 - type: mrr_at_10 value: 39.973 - type: mrr_at_100 value: 40.844 - type: mrr_at_1000 value: 40.885 - type: mrr_at_3 value: 36.901 - type: mrr_at_5 value: 38.721 - type: ndcg_at_1 value: 28.708 - type: ndcg_at_10 value: 44.204 - type: ndcg_at_100 value: 48.978 - type: ndcg_at_1000 value: 50.33 - type: ndcg_at_3 value: 37.36 - type: ndcg_at_5 value: 40.912 - type: precision_at_1 value: 28.708 - type: precision_at_10 value: 7.367 - type: precision_at_100 value: 1.0030000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 17.034 - type: precision_at_5 value: 12.293999999999999 - type: recall_at_1 value: 25.35 - type: recall_at_10 value: 61.411 - type: recall_at_100 value: 82.599 - type: recall_at_1000 value: 92.903 - type: recall_at_3 value: 43.728 - type: recall_at_5 value: 51.854 - task: type: Classification dataset: name: MTEB PAC type: laugustyniak/abusive-clauses-pl config: default split: test revision: None metrics: - type: accuracy value: 69.04141326382856 - type: ap value: 77.49422763833996 - type: f1 value: 66.73472657783407 - task: type: PairClassification dataset: name: MTEB PPC type: PL-MTEB/ppc-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 81.0 - type: cos_sim_ap value: 91.47194213011349 - type: cos_sim_f1 value: 84.73767885532592 - type: cos_sim_precision value: 81.49847094801224 - type: cos_sim_recall value: 88.24503311258279 - type: dot_accuracy value: 81.0 - type: dot_ap value: 91.47194213011349 - type: dot_f1 value: 84.73767885532592 - type: dot_precision value: 81.49847094801224 - type: dot_recall value: 88.24503311258279 - type: euclidean_accuracy value: 81.0 - type: euclidean_ap value: 91.47194213011349 - type: euclidean_f1 value: 84.73767885532592 - type: euclidean_precision value: 81.49847094801224 - type: euclidean_recall value: 88.24503311258279 - type: manhattan_accuracy value: 81.0 - type: manhattan_ap value: 91.46464475050571 - type: manhattan_f1 value: 84.48687350835321 - type: manhattan_precision value: 81.31699846860643 - type: manhattan_recall value: 87.91390728476821 - type: max_accuracy value: 81.0 - type: max_ap value: 91.47194213011349 - type: max_f1 value: 84.73767885532592 - task: type: PairClassification dataset: name: MTEB PSC type: PL-MTEB/psc-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 97.6808905380334 - type: cos_sim_ap value: 99.27948611836348 - type: cos_sim_f1 value: 96.15975422427034 - type: cos_sim_precision value: 96.90402476780186 - type: cos_sim_recall value: 95.42682926829268 - type: dot_accuracy value: 97.6808905380334 - type: dot_ap value: 99.2794861183635 - type: dot_f1 value: 96.15975422427034 - type: dot_precision value: 96.90402476780186 - type: dot_recall value: 95.42682926829268 - type: euclidean_accuracy value: 97.6808905380334 - type: euclidean_ap value: 99.2794861183635 - type: euclidean_f1 value: 96.15975422427034 - type: euclidean_precision value: 96.90402476780186 - type: euclidean_recall value: 95.42682926829268 - type: manhattan_accuracy value: 97.6808905380334 - type: manhattan_ap value: 99.28715055268721 - type: manhattan_f1 value: 96.14791987673343 - type: manhattan_precision value: 97.19626168224299 - type: manhattan_recall value: 95.1219512195122 - type: max_accuracy value: 97.6808905380334 - type: max_ap value: 99.28715055268721 - type: max_f1 value: 96.15975422427034 - task: type: Classification dataset: name: MTEB PolEmo2.0-IN type: PL-MTEB/polemo2_in config: default split: test revision: None metrics: - type: accuracy value: 86.16343490304708 - type: f1 value: 83.3442579486744 - task: type: Classification dataset: name: MTEB PolEmo2.0-OUT type: PL-MTEB/polemo2_out config: default split: test revision: None metrics: - type: accuracy value: 68.40080971659918 - type: f1 value: 53.13720751142237 - task: type: Retrieval dataset: name: MTEB Quora-PL type: clarin-knext/quora-pl config: default split: test revision: 0be27e93455051e531182b85e85e425aba12e9d4 metrics: - type: map_at_1 value: 63.322 - type: map_at_10 value: 76.847 - type: map_at_100 value: 77.616 - type: map_at_1000 value: 77.644 - type: map_at_3 value: 73.624 - type: map_at_5 value: 75.603 - type: mrr_at_1 value: 72.88 - type: mrr_at_10 value: 80.376 - type: mrr_at_100 value: 80.604 - type: mrr_at_1000 value: 80.61 - type: mrr_at_3 value: 78.92 - type: mrr_at_5 value: 79.869 - type: ndcg_at_1 value: 72.89999999999999 - type: ndcg_at_10 value: 81.43 - type: ndcg_at_100 value: 83.394 - type: ndcg_at_1000 value: 83.685 - type: ndcg_at_3 value: 77.62599999999999 - type: ndcg_at_5 value: 79.656 - type: precision_at_1 value: 72.89999999999999 - type: precision_at_10 value: 12.548 - type: precision_at_100 value: 1.4869999999999999 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 34.027 - type: precision_at_5 value: 22.654 - type: recall_at_1 value: 63.322 - type: recall_at_10 value: 90.664 - type: recall_at_100 value: 97.974 - type: recall_at_1000 value: 99.636 - type: recall_at_3 value: 80.067 - type: recall_at_5 value: 85.526 - task: type: Retrieval dataset: name: MTEB SCIDOCS-PL type: clarin-knext/scidocs-pl config: default split: test revision: 45452b03f05560207ef19149545f168e596c9337 metrics: - type: map_at_1 value: 3.95 - type: map_at_10 value: 9.658999999999999 - type: map_at_100 value: 11.384 - type: map_at_1000 value: 11.677 - type: map_at_3 value: 7.055 - type: map_at_5 value: 8.244 - type: mrr_at_1 value: 19.5 - type: mrr_at_10 value: 28.777 - type: mrr_at_100 value: 29.936 - type: mrr_at_1000 value: 30.009999999999998 - type: mrr_at_3 value: 25.55 - type: mrr_at_5 value: 27.284999999999997 - type: ndcg_at_1 value: 19.5 - type: ndcg_at_10 value: 16.589000000000002 - type: ndcg_at_100 value: 23.879 - type: ndcg_at_1000 value: 29.279 - type: ndcg_at_3 value: 15.719 - type: ndcg_at_5 value: 13.572000000000001 - type: precision_at_1 value: 19.5 - type: precision_at_10 value: 8.62 - type: precision_at_100 value: 1.924 - type: precision_at_1000 value: 0.322 - type: precision_at_3 value: 14.6 - type: precision_at_5 value: 11.78 - type: recall_at_1 value: 3.95 - type: recall_at_10 value: 17.477999999999998 - type: recall_at_100 value: 38.99 - type: recall_at_1000 value: 65.417 - type: recall_at_3 value: 8.883000000000001 - type: recall_at_5 value: 11.933 - task: type: PairClassification dataset: name: MTEB SICK-E-PL type: PL-MTEB/sicke-pl-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 83.48960456583775 - type: cos_sim_ap value: 76.31522115825375 - type: cos_sim_f1 value: 70.35573122529645 - type: cos_sim_precision value: 70.9934735315446 - type: cos_sim_recall value: 69.72934472934473 - type: dot_accuracy value: 83.48960456583775 - type: dot_ap value: 76.31522115825373 - type: dot_f1 value: 70.35573122529645 - type: dot_precision value: 70.9934735315446 - type: dot_recall value: 69.72934472934473 - type: euclidean_accuracy value: 83.48960456583775 - type: euclidean_ap value: 76.31522115825373 - type: euclidean_f1 value: 70.35573122529645 - type: euclidean_precision value: 70.9934735315446 - type: euclidean_recall value: 69.72934472934473 - type: manhattan_accuracy value: 83.46922136159804 - type: manhattan_ap value: 76.18474601388084 - type: manhattan_f1 value: 70.34779490856937 - type: manhattan_precision value: 70.83032490974729 - type: manhattan_recall value: 69.87179487179486 - type: max_accuracy value: 83.48960456583775 - type: max_ap value: 76.31522115825375 - type: max_f1 value: 70.35573122529645 - task: type: STS dataset: name: MTEB SICK-R-PL type: PL-MTEB/sickr-pl-sts config: default split: test revision: None metrics: - type: cos_sim_pearson value: 77.95374883876302 - type: cos_sim_spearman value: 73.77630219171942 - type: euclidean_pearson value: 75.81927069594934 - type: euclidean_spearman value: 73.7763211303831 - type: manhattan_pearson value: 76.03126859057528 - type: manhattan_spearman value: 73.96528138013369 - task: type: STS dataset: name: MTEB STS22 (pl) type: mteb/sts22-crosslingual-sts config: pl split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 37.388282764841826 - type: cos_sim_spearman value: 40.83477184710897 - type: euclidean_pearson value: 26.754737044177805 - type: euclidean_spearman value: 40.83477184710897 - type: manhattan_pearson value: 26.760453110872458 - type: manhattan_spearman value: 41.034477441383856 - task: type: Retrieval dataset: name: MTEB SciFact-PL type: clarin-knext/scifact-pl config: default split: test revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e metrics: - type: map_at_1 value: 49.15 - type: map_at_10 value: 61.690999999999995 - type: map_at_100 value: 62.348000000000006 - type: map_at_1000 value: 62.38 - type: map_at_3 value: 58.824 - type: map_at_5 value: 60.662000000000006 - type: mrr_at_1 value: 51.333 - type: mrr_at_10 value: 62.731 - type: mrr_at_100 value: 63.245 - type: mrr_at_1000 value: 63.275000000000006 - type: mrr_at_3 value: 60.667 - type: mrr_at_5 value: 61.93300000000001 - type: ndcg_at_1 value: 51.333 - type: ndcg_at_10 value: 67.168 - type: ndcg_at_100 value: 69.833 - type: ndcg_at_1000 value: 70.56700000000001 - type: ndcg_at_3 value: 62.40599999999999 - type: ndcg_at_5 value: 65.029 - type: precision_at_1 value: 51.333 - type: precision_at_10 value: 9.333 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.333 - type: precision_at_5 value: 17.067 - type: recall_at_1 value: 49.15 - type: recall_at_10 value: 82.533 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 69.917 - type: recall_at_5 value: 76.356 - task: type: Retrieval dataset: name: MTEB TRECCOVID-PL type: clarin-knext/trec-covid-pl config: default split: test revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd metrics: - type: map_at_1 value: 0.261 - type: map_at_10 value: 2.1260000000000003 - type: map_at_100 value: 12.171999999999999 - type: map_at_1000 value: 26.884999999999998 - type: map_at_3 value: 0.695 - type: map_at_5 value: 1.134 - type: mrr_at_1 value: 96.0 - type: mrr_at_10 value: 96.952 - type: mrr_at_100 value: 96.952 - type: mrr_at_1000 value: 96.952 - type: mrr_at_3 value: 96.667 - type: mrr_at_5 value: 96.667 - type: ndcg_at_1 value: 92.0 - type: ndcg_at_10 value: 81.193 - type: ndcg_at_100 value: 61.129 - type: ndcg_at_1000 value: 51.157 - type: ndcg_at_3 value: 85.693 - type: ndcg_at_5 value: 84.129 - type: precision_at_1 value: 96.0 - type: precision_at_10 value: 85.39999999999999 - type: precision_at_100 value: 62.03999999999999 - type: precision_at_1000 value: 22.224 - type: precision_at_3 value: 88.0 - type: precision_at_5 value: 88.0 - type: recall_at_1 value: 0.261 - type: recall_at_10 value: 2.262 - type: recall_at_100 value: 14.981 - type: recall_at_1000 value: 46.837 - type: recall_at_3 value: 0.703 - type: recall_at_5 value: 1.172 - task: type: Clustering dataset: name: MTEB AlloProfClusteringP2P type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: v_measure value: 70.55290063940157 - type: v_measure value: 55.41500719337263 - task: type: Reranking dataset: name: MTEB AlloprofReranking type: lyon-nlp/mteb-fr-reranking-alloprof-s2p config: default split: test revision: 666fdacebe0291776e86f29345663dfaf80a0db9 metrics: - type: map value: 73.48697375332002 - type: mrr value: 75.01836585523822 - task: type: Retrieval dataset: name: MTEB AlloprofRetrieval type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: map_at_1 value: 38.454 - type: map_at_10 value: 51.605000000000004 - type: map_at_100 value: 52.653000000000006 - type: map_at_1000 value: 52.697 - type: map_at_3 value: 48.304 - type: map_at_5 value: 50.073 - type: mrr_at_1 value: 43.307 - type: mrr_at_10 value: 54.400000000000006 - type: mrr_at_100 value: 55.147999999999996 - type: mrr_at_1000 value: 55.174 - type: mrr_at_3 value: 51.77 - type: mrr_at_5 value: 53.166999999999994 - type: ndcg_at_1 value: 43.307 - type: ndcg_at_10 value: 57.891000000000005 - type: ndcg_at_100 value: 62.161 - type: ndcg_at_1000 value: 63.083 - type: ndcg_at_3 value: 51.851 - type: ndcg_at_5 value: 54.605000000000004 - type: precision_at_1 value: 43.307 - type: precision_at_10 value: 9.033 - type: precision_at_100 value: 1.172 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 22.798 - type: precision_at_5 value: 15.492 - type: recall_at_1 value: 38.454 - type: recall_at_10 value: 74.166 - type: recall_at_100 value: 92.43599999999999 - type: recall_at_1000 value: 99.071 - type: recall_at_3 value: 58.087 - type: recall_at_5 value: 64.568 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 53.474 - type: f1 value: 50.38275392350236 - task: type: Retrieval dataset: name: MTEB BSARDRetrieval type: maastrichtlawtech/bsard config: default split: test revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 metrics: - type: map_at_1 value: 2.252 - type: map_at_10 value: 4.661 - type: map_at_100 value: 5.271 - type: map_at_1000 value: 5.3629999999999995 - type: map_at_3 value: 3.604 - type: map_at_5 value: 4.3020000000000005 - type: mrr_at_1 value: 2.252 - type: mrr_at_10 value: 4.661 - type: mrr_at_100 value: 5.271 - type: mrr_at_1000 value: 5.3629999999999995 - type: mrr_at_3 value: 3.604 - type: mrr_at_5 value: 4.3020000000000005 - type: ndcg_at_1 value: 2.252 - type: ndcg_at_10 value: 6.3020000000000005 - type: ndcg_at_100 value: 10.342 - type: ndcg_at_1000 value: 13.475999999999999 - type: ndcg_at_3 value: 4.0649999999999995 - type: ndcg_at_5 value: 5.344 - type: precision_at_1 value: 2.252 - type: precision_at_10 value: 1.171 - type: precision_at_100 value: 0.333 - type: precision_at_1000 value: 0.059000000000000004 - type: precision_at_3 value: 1.802 - type: precision_at_5 value: 1.712 - type: recall_at_1 value: 2.252 - type: recall_at_10 value: 11.712 - type: recall_at_100 value: 33.333 - type: recall_at_1000 value: 59.458999999999996 - type: recall_at_3 value: 5.405 - type: recall_at_5 value: 8.559 - task: type: Clustering dataset: name: MTEB HALClusteringS2S type: lyon-nlp/clustering-hal-s2s config: default split: test revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 metrics: - type: v_measure value: 28.301882091023288 - task: type: Clustering dataset: name: MTEB MLSUMClusteringP2P type: mlsum config: default split: test revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 metrics: - type: v_measure value: 45.26992995191701 - type: v_measure value: 42.773174876871145 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (fr) type: mteb/mtop_domain config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.47635452552458 - type: f1 value: 93.19922617577213 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (fr) type: mteb/mtop_intent config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 80.2317569683683 - type: f1 value: 56.18060418621901 - task: type: Classification dataset: name: MTEB MasakhaNEWSClassification (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: accuracy value: 85.18957345971565 - type: f1 value: 80.829981537394 - task: type: Clustering dataset: name: MTEB MasakhaNEWSClusteringP2P (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: v_measure value: 71.04138999801822 - type: v_measure value: 71.7056263158008 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (fr) type: mteb/amazon_massive_intent config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.65097511768661 - type: f1 value: 73.82441070598712 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.09885675857431 - type: f1 value: 78.28407777434224 - task: type: Retrieval dataset: name: MTEB MintakaRetrieval (fr) type: jinaai/mintakaqa config: fr split: test revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e metrics: - type: map_at_1 value: 25.307000000000002 - type: map_at_10 value: 36.723 - type: map_at_100 value: 37.713 - type: map_at_1000 value: 37.769000000000005 - type: map_at_3 value: 33.77 - type: map_at_5 value: 35.463 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 36.723 - type: mrr_at_100 value: 37.713 - type: mrr_at_1000 value: 37.769000000000005 - type: mrr_at_3 value: 33.77 - type: mrr_at_5 value: 35.463 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 42.559999999999995 - type: ndcg_at_100 value: 47.457 - type: ndcg_at_1000 value: 49.162 - type: ndcg_at_3 value: 36.461 - type: ndcg_at_5 value: 39.504 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 6.106 - type: precision_at_100 value: 0.8420000000000001 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 14.741999999999999 - type: precision_at_5 value: 10.319 - type: recall_at_1 value: 25.307000000000002 - type: recall_at_10 value: 61.056999999999995 - type: recall_at_100 value: 84.152 - type: recall_at_1000 value: 98.03399999999999 - type: recall_at_3 value: 44.226 - type: recall_at_5 value: 51.597 - task: type: PairClassification dataset: name: MTEB OpusparcusPC (fr) type: GEM/opusparcus config: fr split: test revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a metrics: - type: cos_sim_accuracy value: 99.90069513406156 - type: cos_sim_ap value: 100.0 - type: cos_sim_f1 value: 99.95032290114257 - type: cos_sim_precision value: 100.0 - type: cos_sim_recall value: 99.90069513406156 - type: dot_accuracy value: 99.90069513406156 - type: dot_ap value: 100.0 - type: dot_f1 value: 99.95032290114257 - type: dot_precision value: 100.0 - type: dot_recall value: 99.90069513406156 - type: euclidean_accuracy value: 99.90069513406156 - type: euclidean_ap value: 100.0 - type: euclidean_f1 value: 99.95032290114257 - type: euclidean_precision value: 100.0 - type: euclidean_recall value: 99.90069513406156 - type: manhattan_accuracy value: 99.90069513406156 - type: manhattan_ap value: 100.0 - type: manhattan_f1 value: 99.95032290114257 - type: manhattan_precision value: 100.0 - type: manhattan_recall value: 99.90069513406156 - type: max_accuracy value: 99.90069513406156 - type: max_ap value: 100.0 - type: max_f1 value: 99.95032290114257 - task: type: PairClassification dataset: name: MTEB PawsX (fr) type: paws-x config: fr split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics: - type: cos_sim_accuracy value: 70.8 - type: cos_sim_ap value: 73.7671529695957 - type: cos_sim_f1 value: 68.80964339527875 - type: cos_sim_precision value: 62.95955882352941 - type: cos_sim_recall value: 75.85825027685493 - type: dot_accuracy value: 70.8 - type: dot_ap value: 73.78345265366947 - type: dot_f1 value: 68.80964339527875 - type: dot_precision value: 62.95955882352941 - type: dot_recall value: 75.85825027685493 - type: euclidean_accuracy value: 70.8 - type: euclidean_ap value: 73.7671529695957 - type: euclidean_f1 value: 68.80964339527875 - type: euclidean_precision value: 62.95955882352941 - type: euclidean_recall value: 75.85825027685493 - type: manhattan_accuracy value: 70.75 - type: manhattan_ap value: 73.78996383615953 - type: manhattan_f1 value: 68.79432624113475 - type: manhattan_precision value: 63.39869281045751 - type: manhattan_recall value: 75.1937984496124 - type: max_accuracy value: 70.8 - type: max_ap value: 73.78996383615953 - type: max_f1 value: 68.80964339527875 - task: type: STS dataset: name: MTEB SICKFr type: Lajavaness/SICK-fr config: default split: test revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a metrics: - type: cos_sim_pearson value: 84.03253762760392 - type: cos_sim_spearman value: 79.68280105762004 - type: euclidean_pearson value: 80.98265050044444 - type: euclidean_spearman value: 79.68233242682867 - type: manhattan_pearson value: 80.9678911810704 - type: manhattan_spearman value: 79.70264097683109 - task: type: STS dataset: name: MTEB STS22 (fr) type: mteb/sts22-crosslingual-sts config: fr split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 80.56896987572884 - type: cos_sim_spearman value: 81.84352499523287 - type: euclidean_pearson value: 80.40831759421305 - type: euclidean_spearman value: 81.84352499523287 - type: manhattan_pearson value: 80.74333857561238 - type: manhattan_spearman value: 82.41503246733892 - task: type: STS dataset: name: MTEB STSBenchmarkMultilingualSTS (fr) type: stsb_multi_mt config: fr split: test revision: 93d57ef91790589e3ce9c365164337a8a78b7632 metrics: - type: cos_sim_pearson value: 82.71826762276979 - type: cos_sim_spearman value: 82.25433354916042 - type: euclidean_pearson value: 81.87115571724316 - type: euclidean_spearman value: 82.25322342890107 - type: manhattan_pearson value: 82.11174867527224 - type: manhattan_spearman value: 82.55905365203084 - task: type: Summarization dataset: name: MTEB SummEvalFr type: lyon-nlp/summarization-summeval-fr-p2p config: default split: test revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 metrics: - type: cos_sim_pearson value: 30.659441623392887 - type: cos_sim_spearman value: 30.501134097353315 - type: dot_pearson value: 30.659444768851056 - type: dot_spearman value: 30.501134097353315 - task: type: Reranking dataset: name: MTEB SyntecReranking type: lyon-nlp/mteb-fr-reranking-syntec-s2p config: default split: test revision: b205c5084a0934ce8af14338bf03feb19499c84d metrics: - type: map value: 94.03333333333333 - type: mrr value: 94.03333333333333 - task: type: Retrieval dataset: name: MTEB SyntecRetrieval type: lyon-nlp/mteb-fr-retrieval-syntec-s2p config: default split: test revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff metrics: - type: map_at_1 value: 79.0 - type: map_at_10 value: 87.61 - type: map_at_100 value: 87.655 - type: map_at_1000 value: 87.655 - type: map_at_3 value: 87.167 - type: map_at_5 value: 87.36699999999999 - type: mrr_at_1 value: 79.0 - type: mrr_at_10 value: 87.61 - type: mrr_at_100 value: 87.655 - type: mrr_at_1000 value: 87.655 - type: mrr_at_3 value: 87.167 - type: mrr_at_5 value: 87.36699999999999 - type: ndcg_at_1 value: 79.0 - type: ndcg_at_10 value: 90.473 - type: ndcg_at_100 value: 90.694 - type: ndcg_at_1000 value: 90.694 - type: ndcg_at_3 value: 89.464 - type: ndcg_at_5 value: 89.851 - type: precision_at_1 value: 79.0 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 32.0 - type: precision_at_5 value: 19.400000000000002 - type: recall_at_1 value: 79.0 - type: recall_at_10 value: 99.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 96.0 - type: recall_at_5 value: 97.0 - task: type: Retrieval dataset: name: MTEB XPQARetrieval (fr) type: jinaai/xpqa config: fr split: test revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f metrics: - type: map_at_1 value: 39.395 - type: map_at_10 value: 59.123999999999995 - type: map_at_100 value: 60.704 - type: map_at_1000 value: 60.760000000000005 - type: map_at_3 value: 53.187 - type: map_at_5 value: 56.863 - type: mrr_at_1 value: 62.083 - type: mrr_at_10 value: 68.87299999999999 - type: mrr_at_100 value: 69.46900000000001 - type: mrr_at_1000 value: 69.48299999999999 - type: mrr_at_3 value: 66.8 - type: mrr_at_5 value: 67.928 - type: ndcg_at_1 value: 62.083 - type: ndcg_at_10 value: 65.583 - type: ndcg_at_100 value: 70.918 - type: ndcg_at_1000 value: 71.72800000000001 - type: ndcg_at_3 value: 60.428000000000004 - type: ndcg_at_5 value: 61.853 - type: precision_at_1 value: 62.083 - type: precision_at_10 value: 15.033 - type: precision_at_100 value: 1.9529999999999998 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 36.315 - type: precision_at_5 value: 25.955000000000002 - type: recall_at_1 value: 39.395 - type: recall_at_10 value: 74.332 - type: recall_at_100 value: 94.729 - type: recall_at_1000 value: 99.75500000000001 - type: recall_at_3 value: 57.679 - type: recall_at_5 value: 65.036 --- # niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Alibaba-NLP/gte-Qwen2-1.5B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_k_m.gguf -c 2048 ```
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
AIDA-UPM/MARTINI_enrich_BERTopic_realx22report
AIDA-UPM
text-classification
[ "bertopic", "text-classification", "region:us" ]
1,736,792,911,000
2025-01-13T18:28:33
5
0
--- library_name: bertopic pipeline_tag: text-classification tags: - bertopic --- # MARTINI_enrich_BERTopic_realx22report 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. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_realx22report") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 30 * Number of training documents: 3061 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | -1 | biden - fbi - vaccinated - impeachment - anyone | 21 | -1_biden_fbi_vaccinated_impeachment | | 0 | patriotism - freedoms - tyranny - volunteer - never | 1651 | 0_patriotism_freedoms_tyranny_volunteer | | 1 | ballots - maricopa - recount - auditors - leaked | 165 | 1_ballots_maricopa_recount_auditors | | 2 | protests - unvaccinated - masks - austria - mandatory | 145 | 2_protests_unvaccinated_masks_austria | | 3 | vaccinated - pfizer - vaers - antibodies - delisted | 91 | 3_vaccinated_pfizer_vaers_antibodies | | 4 | twitter - musk - shadowbanning - censored - takeover | 79 | 4_twitter_musk_shadowbanning_censored | | 5 | donetsk - putin - zelensky - bioweapons - nazis | 76 | 5_donetsk_putin_zelensky_bioweapons | | 6 | fauci - pcr - false - test - swabs | 74 | 6_fauci_pcr_false_test | | 7 | truthsocial - impersonating - verified - bots - deleted | 47 | 7_truthsocial_impersonating_verified_bots | | 8 | biden - teleprompter - michelle - jimmy - reporters | 47 | 8_biden_teleprompter_michelle_jimmy | | 9 | thesecretcurriculum - indoctrinating - teachers - hillsdale - antifa | 46 | 9_thesecretcurriculum_indoctrinating_teachers_hillsdale | | 10 | trump - indicted - courthouse - guantanamo - supreme | 44 | 10_trump_indicted_courthouse_guantanamo | | 11 | mueller - clinton - dossier - colluded - indictments | 42 | 11_mueller_clinton_dossier_colluded | | 12 | truckers - trudeau - beltway - blockade - protesters | 42 | 12_truckers_trudeau_beltway_blockade | | 13 | traffickers - immigration - blinken - border - texas | 41 | 13_traffickers_immigration_blinken_border | | 14 | vaccine - mandatory - eeoc - defendingtherepublic - injunction | 39 | 14_vaccine_mandatory_eeoc_defendingtherepublic | | 15 | senators - filibuster - voted - trillion - taxpayers | 37 | 15_senators_filibuster_voted_trillion | | 16 | taliban - kabul - bombing - evacuees - stormypatriotjoe | 35 | 16_taliban_kabul_bombing_evacuees | | 17 | taiwan - norad - cyberattack - stratotankers - zhangjiakou | 32 | 17_taiwan_norad_cyberattack_stratotankers | | 18 | ghislaine - unsealed - docket - incriminating - john | 32 | 18_ghislaine_unsealed_docket_incriminating | | 19 | fauci - darpa - nanoscientist - deepfakes - unredacted | 32 | 19_fauci_darpa_nanoscientist_deepfakes | | 20 | desantis - florida - governor - mandates - brandon | 32 | 20_desantis_florida_governor_mandates | | 21 | facebook - censorship - misinformation - campaigns - announced | 32 | 21_facebook_censorship_misinformation_campaigns | | 22 | firearms - illegal - amendment - disarming - hr3015 | 30 | 22_firearms_illegal_amendment_disarming | | 23 | cnn - newscast - reporter - warnermedia - allegations | 29 | 23_cnn_newscast_reporter_warnermedia | | 24 | fbi - insurrectionists - january - carlson - congressman | 28 | 24_fbi_insurrectionists_january_carlson | | 25 | shootings - killed - suspect - deputies - george | 27 | 25_shootings_killed_suspect_deputies | | 26 | bidenlaptopemails - disinformation - rudy - investigators - huawei | 23 | 26_bidenlaptopemails_disinformation_rudy_investigators | | 27 | trump - inauguration - accusations - andrews - 45th | 21 | 27_trump_inauguration_accusations_andrews | | 28 | nyt - defamation - depositions - constitutionally - unverifiable | 21 | 28_nyt_defamation_depositions_constitutionally | </details> ## Training hyperparameters * calculate_probabilities: True * 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: False * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.26.4 * HDBSCAN: 0.8.40 * UMAP: 0.5.7 * Pandas: 2.2.3 * Scikit-Learn: 1.5.2 * Sentence-transformers: 3.3.1 * Transformers: 4.46.3 * Numba: 0.60.0 * Plotly: 5.24.1 * Python: 3.10.12
[ "PCR" ]
Non_BioNLP
Amir13/xlm-roberta-base-ncbi_disease
Amir13
token-classification
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:Amir13/ncbi-persian", "arxiv:2302.09611", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,676,379,557,000
2023-03-16T21:05:07
15
0
--- datasets: Amir13/ncbi-persian license: mit metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: xlm-roberta-base-ncbi_disease results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-ncbi_disease This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ncbi-persian](https://huggingface.co/datasets/Amir13/ncbi-persian) dataset. It achieves the following results on the evaluation set: - Loss: 0.0915 - Precision: 0.8273 - Recall: 0.8763 - F1: 0.8511 - Accuracy: 0.9866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 169 | 0.0682 | 0.7049 | 0.7763 | 0.7389 | 0.9784 | | No log | 2.0 | 338 | 0.0575 | 0.7558 | 0.8592 | 0.8042 | 0.9832 | | 0.0889 | 3.0 | 507 | 0.0558 | 0.8092 | 0.8592 | 0.8334 | 0.9859 | | 0.0889 | 4.0 | 676 | 0.0595 | 0.8316 | 0.8579 | 0.8446 | 0.9858 | | 0.0889 | 5.0 | 845 | 0.0665 | 0.7998 | 0.8566 | 0.8272 | 0.9850 | | 0.0191 | 6.0 | 1014 | 0.0796 | 0.8229 | 0.85 | 0.8362 | 0.9862 | | 0.0191 | 7.0 | 1183 | 0.0783 | 0.8193 | 0.8474 | 0.8331 | 0.9860 | | 0.0191 | 8.0 | 1352 | 0.0792 | 0.8257 | 0.8539 | 0.8396 | 0.9864 | | 0.0079 | 9.0 | 1521 | 0.0847 | 0.8154 | 0.8658 | 0.8398 | 0.9851 | | 0.0079 | 10.0 | 1690 | 0.0855 | 0.8160 | 0.875 | 0.8444 | 0.9857 | | 0.0079 | 11.0 | 1859 | 0.0868 | 0.8081 | 0.8645 | 0.8353 | 0.9864 | | 0.0037 | 12.0 | 2028 | 0.0912 | 0.8036 | 0.8776 | 0.8390 | 0.9853 | | 0.0037 | 13.0 | 2197 | 0.0907 | 0.8323 | 0.8684 | 0.8500 | 0.9868 | | 0.0037 | 14.0 | 2366 | 0.0899 | 0.8192 | 0.8763 | 0.8468 | 0.9865 | | 0.0023 | 15.0 | 2535 | 0.0915 | 0.8273 | 0.8763 | 0.8511 | 0.9866 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2 ### Citation If you used the datasets and models in this repository, please cite it. ```bibtex @misc{https://doi.org/10.48550/arxiv.2302.09611, doi = {10.48550/ARXIV.2302.09611}, url = {https://arxiv.org/abs/2302.09611}, author = {Sartipi, Amir and Fatemi, Afsaneh}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English}, publisher = {arXiv}, year = {2023}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
[ "NCBI DISEASE" ]
BioNLP
twadada/lma_noabtt
twadada
null
[ "mteb", "model-index", "region:us" ]
1,726,129,059,000
2024-09-12T08:17:52
0
0
--- tags: - mteb model-index: - name: llama3_STSprompt_noabtt_new results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.53731343283582 - type: ap value: 35.09550935769008 - type: f1 value: 66.39064790565796 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 68.46487499999999 - type: ap value: 63.194645384561895 - type: f1 value: 68.14318967696387 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 36.722 - type: f1 value: 36.0815270010246 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 17.994 - type: map_at_10 value: 29.559 - type: map_at_100 value: 30.679000000000002 - type: map_at_1000 value: 30.73 - type: map_at_3 value: 25.319999999999997 - type: map_at_5 value: 27.71 - type: mrr_at_1 value: 18.421000000000003 - type: mrr_at_10 value: 29.73 - type: mrr_at_100 value: 30.85 - type: mrr_at_1000 value: 30.901 - type: mrr_at_3 value: 25.45 - type: mrr_at_5 value: 27.883000000000003 - type: ndcg_at_1 value: 17.994 - type: ndcg_at_10 value: 36.642 - type: ndcg_at_100 value: 42.247 - type: ndcg_at_1000 value: 43.579 - type: ndcg_at_3 value: 27.865000000000002 - type: ndcg_at_5 value: 32.171 - type: precision_at_1 value: 17.994 - type: precision_at_10 value: 5.953 - type: precision_at_100 value: 0.861 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 11.759 - type: precision_at_5 value: 9.147 - type: recall_at_1 value: 17.994 - type: recall_at_10 value: 59.531 - type: recall_at_100 value: 86.06 - type: recall_at_1000 value: 96.515 - type: recall_at_3 value: 35.276999999999994 - type: recall_at_5 value: 45.733000000000004 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 31.66949406382408 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.14545126769505 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 52.74601918537465 - type: mrr value: 66.62885283383898 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 72.6753460041215 - type: cos_sim_spearman value: 71.97935094171385 - type: euclidean_pearson value: 72.35700410190371 - type: euclidean_spearman value: 71.97935094171385 - type: manhattan_pearson value: 73.65568445281664 - type: manhattan_spearman value: 73.25166908897245 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 70.62337662337663 - type: f1 value: 69.8634895921265 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 30.04921398276571 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 22.3114828809719 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.685000000000002 - type: map_at_10 value: 28.016000000000002 - type: map_at_100 value: 29.028 - type: map_at_1000 value: 29.176999999999996 - type: map_at_3 value: 25.873 - type: map_at_5 value: 27.05 - type: mrr_at_1 value: 26.466 - type: mrr_at_10 value: 33.684999999999995 - type: mrr_at_100 value: 34.449999999999996 - type: mrr_at_1000 value: 34.522999999999996 - type: mrr_at_3 value: 31.879 - type: mrr_at_5 value: 32.836999999999996 - type: ndcg_at_1 value: 26.466 - type: ndcg_at_10 value: 32.897 - type: ndcg_at_100 value: 37.433 - type: ndcg_at_1000 value: 40.393 - type: ndcg_at_3 value: 29.793999999999997 - type: ndcg_at_5 value: 31.051000000000002 - type: precision_at_1 value: 26.466 - type: precision_at_10 value: 6.209 - type: precision_at_100 value: 1.057 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 14.591999999999999 - type: precision_at_5 value: 10.272 - type: recall_at_1 value: 20.685000000000002 - type: recall_at_10 value: 41.284 - type: recall_at_100 value: 61.91 - type: recall_at_1000 value: 81.846 - type: recall_at_3 value: 31.144 - type: recall_at_5 value: 35.33 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 18.683 - type: map_at_10 value: 24.818 - type: map_at_100 value: 25.702 - type: map_at_1000 value: 25.819 - type: map_at_3 value: 22.671 - type: map_at_5 value: 23.846 - type: mrr_at_1 value: 23.885 - type: mrr_at_10 value: 29.454 - type: mrr_at_100 value: 30.201 - type: mrr_at_1000 value: 30.270000000000003 - type: mrr_at_3 value: 27.537 - type: mrr_at_5 value: 28.594 - type: ndcg_at_1 value: 23.885 - type: ndcg_at_10 value: 28.961 - type: ndcg_at_100 value: 33.128 - type: ndcg_at_1000 value: 35.919000000000004 - type: ndcg_at_3 value: 25.455 - type: ndcg_at_5 value: 27.038 - type: precision_at_1 value: 23.885 - type: precision_at_10 value: 5.459 - type: precision_at_100 value: 0.9440000000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 12.081 - type: precision_at_5 value: 8.738999999999999 - type: recall_at_1 value: 18.683 - type: recall_at_10 value: 36.616 - type: recall_at_100 value: 55.126 - type: recall_at_1000 value: 74.32600000000001 - type: recall_at_3 value: 26.540000000000003 - type: recall_at_5 value: 30.812 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.025 - type: map_at_10 value: 32.903999999999996 - type: map_at_100 value: 33.857 - type: map_at_1000 value: 33.945 - type: map_at_3 value: 30.711 - type: map_at_5 value: 31.972 - type: mrr_at_1 value: 29.279 - type: mrr_at_10 value: 36.281 - type: mrr_at_100 value: 37.119 - type: mrr_at_1000 value: 37.181999999999995 - type: mrr_at_3 value: 34.316 - type: mrr_at_5 value: 35.437999999999995 - type: ndcg_at_1 value: 29.279 - type: ndcg_at_10 value: 37.333 - type: ndcg_at_100 value: 41.948 - type: ndcg_at_1000 value: 44.249 - type: ndcg_at_3 value: 33.311 - type: ndcg_at_5 value: 35.251 - type: precision_at_1 value: 29.279 - type: precision_at_10 value: 5.956 - type: precision_at_100 value: 0.8959999999999999 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 14.774999999999999 - type: precision_at_5 value: 10.194 - type: recall_at_1 value: 25.025 - type: recall_at_10 value: 47.583 - type: recall_at_100 value: 68.42 - type: recall_at_1000 value: 85.47 - type: recall_at_3 value: 36.595 - type: recall_at_5 value: 41.461999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 11.356 - type: map_at_10 value: 15.417 - type: map_at_100 value: 15.988 - type: map_at_1000 value: 16.088 - type: map_at_3 value: 13.941999999999998 - type: map_at_5 value: 14.651 - type: mrr_at_1 value: 12.316 - type: mrr_at_10 value: 16.451999999999998 - type: mrr_at_100 value: 17.061999999999998 - type: mrr_at_1000 value: 17.158 - type: mrr_at_3 value: 14.953 - type: mrr_at_5 value: 15.733 - type: ndcg_at_1 value: 12.316 - type: ndcg_at_10 value: 18.109 - type: ndcg_at_100 value: 21.537 - type: ndcg_at_1000 value: 24.512999999999998 - type: ndcg_at_3 value: 15.058 - type: ndcg_at_5 value: 16.31 - type: precision_at_1 value: 12.316 - type: precision_at_10 value: 2.915 - type: precision_at_100 value: 0.494 - type: precision_at_1000 value: 0.078 - type: precision_at_3 value: 6.29 - type: precision_at_5 value: 4.542 - type: recall_at_1 value: 11.356 - type: recall_at_10 value: 25.55 - type: recall_at_100 value: 42.369 - type: recall_at_1000 value: 65.642 - type: recall_at_3 value: 17.269000000000002 - type: recall_at_5 value: 20.139000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 5.926 - type: map_at_10 value: 9.023 - type: map_at_100 value: 9.705 - type: map_at_1000 value: 9.814 - type: map_at_3 value: 8.043 - type: map_at_5 value: 8.609 - type: mrr_at_1 value: 7.960000000000001 - type: mrr_at_10 value: 11.472999999999999 - type: mrr_at_100 value: 12.225 - type: mrr_at_1000 value: 12.316 - type: mrr_at_3 value: 10.365 - type: mrr_at_5 value: 11.049000000000001 - type: ndcg_at_1 value: 7.960000000000001 - type: ndcg_at_10 value: 11.266 - type: ndcg_at_100 value: 15.092 - type: ndcg_at_1000 value: 18.159 - type: ndcg_at_3 value: 9.343 - type: ndcg_at_5 value: 10.302 - type: precision_at_1 value: 7.960000000000001 - type: precision_at_10 value: 2.1270000000000002 - type: precision_at_100 value: 0.48 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 4.601999999999999 - type: precision_at_5 value: 3.383 - type: recall_at_1 value: 5.926 - type: recall_at_10 value: 15.873999999999999 - type: recall_at_100 value: 33.274 - type: recall_at_1000 value: 55.799 - type: recall_at_3 value: 10.571 - type: recall_at_5 value: 12.986 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.639 - type: map_at_10 value: 22.948 - type: map_at_100 value: 24.104 - type: map_at_1000 value: 24.226 - type: map_at_3 value: 21.128 - type: map_at_5 value: 22.218 - type: mrr_at_1 value: 21.559 - type: mrr_at_10 value: 27.443 - type: mrr_at_100 value: 28.363 - type: mrr_at_1000 value: 28.438000000000002 - type: mrr_at_3 value: 25.409 - type: mrr_at_5 value: 26.558999999999997 - type: ndcg_at_1 value: 21.559 - type: ndcg_at_10 value: 26.863999999999997 - type: ndcg_at_100 value: 32.417 - type: ndcg_at_1000 value: 35.3 - type: ndcg_at_3 value: 23.658 - type: ndcg_at_5 value: 25.240000000000002 - type: precision_at_1 value: 21.559 - type: precision_at_10 value: 4.859999999999999 - type: precision_at_100 value: 0.9159999999999999 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 11.004 - type: precision_at_5 value: 7.931000000000001 - type: recall_at_1 value: 17.639 - type: recall_at_10 value: 34.245 - type: recall_at_100 value: 58.754 - type: recall_at_1000 value: 79.14099999999999 - type: recall_at_3 value: 25.072 - type: recall_at_5 value: 29.334 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 11.109 - type: map_at_10 value: 16.184 - type: map_at_100 value: 17.127 - type: map_at_1000 value: 17.247 - type: map_at_3 value: 14.389 - type: map_at_5 value: 15.323999999999998 - type: mrr_at_1 value: 13.699 - type: mrr_at_10 value: 19.358 - type: mrr_at_100 value: 20.233999999999998 - type: mrr_at_1000 value: 20.318 - type: mrr_at_3 value: 17.561 - type: mrr_at_5 value: 18.48 - type: ndcg_at_1 value: 13.699 - type: ndcg_at_10 value: 19.779 - type: ndcg_at_100 value: 24.352999999999998 - type: ndcg_at_1000 value: 27.633999999999997 - type: ndcg_at_3 value: 16.414 - type: ndcg_at_5 value: 17.802 - type: precision_at_1 value: 13.699 - type: precision_at_10 value: 3.8699999999999997 - type: precision_at_100 value: 0.729 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 8.067 - type: precision_at_5 value: 5.913 - type: recall_at_1 value: 11.109 - type: recall_at_10 value: 27.606 - type: recall_at_100 value: 47.333999999999996 - type: recall_at_1000 value: 71.466 - type: recall_at_3 value: 18.209 - type: recall_at_5 value: 21.785 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 13.838916666666668 - type: map_at_10 value: 18.685583333333337 - type: map_at_100 value: 19.5005 - type: map_at_1000 value: 19.614833333333333 - type: map_at_3 value: 17.090333333333334 - type: map_at_5 value: 17.965833333333332 - type: mrr_at_1 value: 16.78108333333334 - type: mrr_at_10 value: 21.741 - type: mrr_at_100 value: 22.48608333333333 - type: mrr_at_1000 value: 22.568333333333335 - type: mrr_at_3 value: 20.163833333333333 - type: mrr_at_5 value: 21.01383333333333 - type: ndcg_at_1 value: 16.78108333333334 - type: ndcg_at_10 value: 21.979666666666667 - type: ndcg_at_100 value: 26.148500000000002 - type: ndcg_at_1000 value: 29.099749999999997 - type: ndcg_at_3 value: 19.107250000000004 - type: ndcg_at_5 value: 20.390916666666666 - type: precision_at_1 value: 16.78108333333334 - type: precision_at_10 value: 3.9185833333333338 - type: precision_at_100 value: 0.7166666666666665 - type: precision_at_1000 value: 0.11350000000000002 - type: precision_at_3 value: 8.856166666666667 - type: precision_at_5 value: 6.328749999999999 - type: recall_at_1 value: 13.838916666666668 - type: recall_at_10 value: 28.876583333333333 - type: recall_at_100 value: 48.09441666666666 - type: recall_at_1000 value: 69.68258333333333 - type: recall_at_3 value: 20.706999999999997 - type: recall_at_5 value: 24.09425 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 9.577 - type: map_at_10 value: 13.633999999999999 - type: map_at_100 value: 14.248 - type: map_at_1000 value: 14.322 - type: map_at_3 value: 12.423 - type: map_at_5 value: 13.083 - type: mrr_at_1 value: 11.503 - type: mrr_at_10 value: 15.67 - type: mrr_at_100 value: 16.292 - type: mrr_at_1000 value: 16.356 - type: mrr_at_3 value: 14.494000000000002 - type: mrr_at_5 value: 15.161 - type: ndcg_at_1 value: 11.503 - type: ndcg_at_10 value: 16.195999999999998 - type: ndcg_at_100 value: 19.733999999999998 - type: ndcg_at_1000 value: 21.956 - type: ndcg_at_3 value: 13.947000000000001 - type: ndcg_at_5 value: 14.976999999999999 - type: precision_at_1 value: 11.503 - type: precision_at_10 value: 2.868 - type: precision_at_100 value: 0.512 - type: precision_at_1000 value: 0.076 - type: precision_at_3 value: 6.544 - type: precision_at_5 value: 4.601 - type: recall_at_1 value: 9.577 - type: recall_at_10 value: 22.055 - type: recall_at_100 value: 39.104 - type: recall_at_1000 value: 56.165 - type: recall_at_3 value: 15.719 - type: recall_at_5 value: 18.453 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.266 - type: map_at_10 value: 10.41 - type: map_at_100 value: 10.979 - type: map_at_1000 value: 11.084 - type: map_at_3 value: 9.379 - type: map_at_5 value: 9.937999999999999 - type: mrr_at_1 value: 9.188 - type: mrr_at_10 value: 12.662 - type: mrr_at_100 value: 13.257 - type: mrr_at_1000 value: 13.345 - type: mrr_at_3 value: 11.557 - type: mrr_at_5 value: 12.124 - type: ndcg_at_1 value: 9.188 - type: ndcg_at_10 value: 12.681999999999999 - type: ndcg_at_100 value: 15.937999999999999 - type: ndcg_at_1000 value: 19.088 - type: ndcg_at_3 value: 10.772 - type: ndcg_at_5 value: 11.591999999999999 - type: precision_at_1 value: 9.188 - type: precision_at_10 value: 2.3810000000000002 - type: precision_at_100 value: 0.481 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 5.196 - type: precision_at_5 value: 3.758 - type: recall_at_1 value: 7.266 - type: recall_at_10 value: 17.433 - type: recall_at_100 value: 32.822 - type: recall_at_1000 value: 56.45099999999999 - type: recall_at_3 value: 11.928999999999998 - type: recall_at_5 value: 14.152999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.674 - type: map_at_10 value: 17.061999999999998 - type: map_at_100 value: 17.845 - type: map_at_1000 value: 17.944 - type: map_at_3 value: 15.856 - type: map_at_5 value: 16.478 - type: mrr_at_1 value: 16.418 - type: mrr_at_10 value: 19.999 - type: mrr_at_100 value: 20.798 - type: mrr_at_1000 value: 20.884 - type: mrr_at_3 value: 18.657 - type: mrr_at_5 value: 19.31 - type: ndcg_at_1 value: 16.418 - type: ndcg_at_10 value: 19.64 - type: ndcg_at_100 value: 23.926 - type: ndcg_at_1000 value: 26.889999999999997 - type: ndcg_at_3 value: 17.283 - type: ndcg_at_5 value: 18.232 - type: precision_at_1 value: 16.418 - type: precision_at_10 value: 3.125 - type: precision_at_100 value: 0.59 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.494000000000001 - type: precision_at_5 value: 5.149 - type: recall_at_1 value: 13.674 - type: recall_at_10 value: 24.917 - type: recall_at_100 value: 44.851 - type: recall_at_1000 value: 67.123 - type: recall_at_3 value: 18.275 - type: recall_at_5 value: 20.757 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.392 - type: map_at_10 value: 20.598 - type: map_at_100 value: 21.512 - type: map_at_1000 value: 21.698999999999998 - type: map_at_3 value: 18.884 - type: map_at_5 value: 19.911 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 23.854 - type: mrr_at_100 value: 24.581 - type: mrr_at_1000 value: 24.685000000000002 - type: mrr_at_3 value: 22.299 - type: mrr_at_5 value: 23.119 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 24.285999999999998 - type: ndcg_at_100 value: 28.53 - type: ndcg_at_1000 value: 32.124 - type: ndcg_at_3 value: 21.504 - type: ndcg_at_5 value: 22.853 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 4.684 - type: precision_at_100 value: 1.002 - type: precision_at_1000 value: 0.181 - type: precision_at_3 value: 10.145 - type: precision_at_5 value: 7.470000000000001 - type: recall_at_1 value: 15.392 - type: recall_at_10 value: 30.941000000000003 - type: recall_at_100 value: 51.361000000000004 - type: recall_at_1000 value: 75.82900000000001 - type: recall_at_3 value: 22.823 - type: recall_at_5 value: 26.495 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 9.735000000000001 - type: map_at_10 value: 13.213 - type: map_at_100 value: 13.911000000000001 - type: map_at_1000 value: 14.013 - type: map_at_3 value: 11.785 - type: map_at_5 value: 12.509999999999998 - type: mrr_at_1 value: 10.721 - type: mrr_at_10 value: 14.560999999999998 - type: mrr_at_100 value: 15.251000000000001 - type: mrr_at_1000 value: 15.345 - type: mrr_at_3 value: 12.939 - type: mrr_at_5 value: 13.761999999999999 - type: ndcg_at_1 value: 10.721 - type: ndcg_at_10 value: 15.742999999999999 - type: ndcg_at_100 value: 19.746 - type: ndcg_at_1000 value: 22.972 - type: ndcg_at_3 value: 12.748000000000001 - type: ndcg_at_5 value: 14.043 - type: precision_at_1 value: 10.721 - type: precision_at_10 value: 2.569 - type: precision_at_100 value: 0.49899999999999994 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 5.484 - type: precision_at_5 value: 3.993 - type: recall_at_1 value: 9.735000000000001 - type: recall_at_10 value: 22.415 - type: recall_at_100 value: 41.808 - type: recall_at_1000 value: 66.93299999999999 - type: recall_at_3 value: 14.338000000000001 - type: recall_at_5 value: 17.424999999999997 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.368 - type: map_at_10 value: 11.819 - type: map_at_100 value: 13.311 - type: map_at_1000 value: 13.517000000000001 - type: map_at_3 value: 9.451 - type: map_at_5 value: 10.68 - type: mrr_at_1 value: 14.463000000000001 - type: mrr_at_10 value: 23.502000000000002 - type: mrr_at_100 value: 24.635 - type: mrr_at_1000 value: 24.693 - type: mrr_at_3 value: 20.25 - type: mrr_at_5 value: 22.015 - type: ndcg_at_1 value: 14.463000000000001 - type: ndcg_at_10 value: 17.832 - type: ndcg_at_100 value: 24.514 - type: ndcg_at_1000 value: 28.395 - type: ndcg_at_3 value: 13.378 - type: ndcg_at_5 value: 15.078 - type: precision_at_1 value: 14.463000000000001 - type: precision_at_10 value: 6.065 - type: precision_at_100 value: 1.319 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 10.25 - type: precision_at_5 value: 8.534 - type: recall_at_1 value: 6.368 - type: recall_at_10 value: 23.093 - type: recall_at_100 value: 46.664 - type: recall_at_1000 value: 68.657 - type: recall_at_3 value: 12.711 - type: recall_at_5 value: 16.858 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 3.75 - type: map_at_10 value: 8.94 - type: map_at_100 value: 12.631999999999998 - type: map_at_1000 value: 13.541 - type: map_at_3 value: 6.140000000000001 - type: map_at_5 value: 7.432999999999999 - type: mrr_at_1 value: 39.5 - type: mrr_at_10 value: 48.983 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.782 - type: mrr_at_3 value: 46.125 - type: mrr_at_5 value: 47.825 - type: ndcg_at_1 value: 28.249999999999996 - type: ndcg_at_10 value: 22.241 - type: ndcg_at_100 value: 25.387999999999998 - type: ndcg_at_1000 value: 32.11 - type: ndcg_at_3 value: 24.495 - type: ndcg_at_5 value: 23.402 - type: precision_at_1 value: 39.5 - type: precision_at_10 value: 19.675 - type: precision_at_100 value: 6.3 - type: precision_at_1000 value: 1.3259999999999998 - type: precision_at_3 value: 29.666999999999998 - type: precision_at_5 value: 25.45 - type: recall_at_1 value: 3.75 - type: recall_at_10 value: 13.766 - type: recall_at_100 value: 31.915 - type: recall_at_1000 value: 54.85 - type: recall_at_3 value: 7.167 - type: recall_at_5 value: 9.728 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.135000000000005 - type: f1 value: 40.132852463847094 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.445999999999998 - type: map_at_10 value: 24.275 - type: map_at_100 value: 25.205 - type: map_at_1000 value: 25.278 - type: map_at_3 value: 21.813 - type: map_at_5 value: 23.254 - type: mrr_at_1 value: 17.567 - type: mrr_at_10 value: 25.733 - type: mrr_at_100 value: 26.663999999999998 - type: mrr_at_1000 value: 26.727 - type: mrr_at_3 value: 23.169999999999998 - type: mrr_at_5 value: 24.666 - type: ndcg_at_1 value: 17.567 - type: ndcg_at_10 value: 28.937 - type: ndcg_at_100 value: 33.757 - type: ndcg_at_1000 value: 35.792 - type: ndcg_at_3 value: 23.91 - type: ndcg_at_5 value: 26.485999999999997 - type: precision_at_1 value: 17.567 - type: precision_at_10 value: 4.58 - type: precision_at_100 value: 0.718 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 10.241 - type: precision_at_5 value: 7.521999999999999 - type: recall_at_1 value: 16.445999999999998 - type: recall_at_10 value: 42.152 - type: recall_at_100 value: 64.795 - type: recall_at_1000 value: 80.54 - type: recall_at_3 value: 28.608 - type: recall_at_5 value: 34.771 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 6.658 - type: map_at_10 value: 10.437000000000001 - type: map_at_100 value: 11.411 - type: map_at_1000 value: 11.581 - type: map_at_3 value: 8.876000000000001 - type: map_at_5 value: 9.806 - type: mrr_at_1 value: 13.117 - type: mrr_at_10 value: 18.447 - type: mrr_at_100 value: 19.363 - type: mrr_at_1000 value: 19.461000000000002 - type: mrr_at_3 value: 16.512 - type: mrr_at_5 value: 17.607999999999997 - type: ndcg_at_1 value: 13.117 - type: ndcg_at_10 value: 14.277999999999999 - type: ndcg_at_100 value: 19.259999999999998 - type: ndcg_at_1000 value: 23.27 - type: ndcg_at_3 value: 11.965 - type: ndcg_at_5 value: 13.020000000000001 - type: precision_at_1 value: 13.117 - type: precision_at_10 value: 3.92 - type: precision_at_100 value: 0.8920000000000001 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 7.819 - type: precision_at_5 value: 6.019 - type: recall_at_1 value: 6.658 - type: recall_at_10 value: 17.913999999999998 - type: recall_at_100 value: 37.687 - type: recall_at_1000 value: 62.647 - type: recall_at_3 value: 10.908 - type: recall_at_5 value: 14.381 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.15 - type: map_at_10 value: 25.289 - type: map_at_100 value: 26.174999999999997 - type: map_at_1000 value: 26.282 - type: map_at_3 value: 23.31 - type: map_at_5 value: 24.367 - type: mrr_at_1 value: 36.3 - type: mrr_at_10 value: 43.134 - type: mrr_at_100 value: 43.854 - type: mrr_at_1000 value: 43.908 - type: mrr_at_3 value: 41.276 - type: mrr_at_5 value: 42.315000000000005 - type: ndcg_at_1 value: 36.3 - type: ndcg_at_10 value: 32.193 - type: ndcg_at_100 value: 36.301 - type: ndcg_at_1000 value: 38.853 - type: ndcg_at_3 value: 28.477000000000004 - type: ndcg_at_5 value: 30.223 - type: precision_at_1 value: 36.3 - type: precision_at_10 value: 7.051 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 17.889 - type: precision_at_5 value: 12.119 - type: recall_at_1 value: 18.15 - type: recall_at_10 value: 35.253 - type: recall_at_100 value: 51.668000000000006 - type: recall_at_1000 value: 68.717 - type: recall_at_3 value: 26.833000000000002 - type: recall_at_5 value: 30.297 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 66.5428 - type: ap value: 61.12502572883321 - type: f1 value: 66.3624123025287 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 5.946 - type: map_at_10 value: 10.17 - type: map_at_100 value: 11.058 - type: map_at_1000 value: 11.164 - type: map_at_3 value: 8.414000000000001 - type: map_at_5 value: 9.316 - type: mrr_at_1 value: 6.132 - type: mrr_at_10 value: 10.459999999999999 - type: mrr_at_100 value: 11.354000000000001 - type: mrr_at_1000 value: 11.456 - type: mrr_at_3 value: 8.674999999999999 - type: mrr_at_5 value: 9.592 - type: ndcg_at_1 value: 6.089 - type: ndcg_at_10 value: 13.027 - type: ndcg_at_100 value: 17.9 - type: ndcg_at_1000 value: 21.053 - type: ndcg_at_3 value: 9.314 - type: ndcg_at_5 value: 10.943999999999999 - type: precision_at_1 value: 6.089 - type: precision_at_10 value: 2.291 - type: precision_at_100 value: 0.482 - type: precision_at_1000 value: 0.075 - type: precision_at_3 value: 4.031 - type: precision_at_5 value: 3.235 - type: recall_at_1 value: 5.946 - type: recall_at_10 value: 22.017999999999997 - type: recall_at_100 value: 45.811 - type: recall_at_1000 value: 71.039 - type: recall_at_3 value: 11.691 - type: recall_at_5 value: 15.618000000000002 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.25763793889648 - type: f1 value: 89.49997029740435 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 63.0095759233926 - type: f1 value: 44.42212537420128 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.641560188298584 - type: f1 value: 60.99041094723092 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.90450571620714 - type: f1 value: 69.64935741375153 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 26.23206350160216 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 24.265258673517774 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 28.796475009504274 - type: mrr value: 29.653223337884594 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.811 - type: map_at_10 value: 8.58 - type: map_at_100 value: 10.663 - type: map_at_1000 value: 11.855 - type: map_at_3 value: 6.842 - type: map_at_5 value: 7.643 - type: mrr_at_1 value: 34.985 - type: mrr_at_10 value: 44.877 - type: mrr_at_100 value: 45.622 - type: mrr_at_1000 value: 45.684000000000005 - type: mrr_at_3 value: 42.466 - type: mrr_at_5 value: 43.782 - type: ndcg_at_1 value: 33.282000000000004 - type: ndcg_at_10 value: 25.583 - type: ndcg_at_100 value: 23.957 - type: ndcg_at_1000 value: 33.216 - type: ndcg_at_3 value: 29.86 - type: ndcg_at_5 value: 27.883000000000003 - type: precision_at_1 value: 34.985 - type: precision_at_10 value: 18.142 - type: precision_at_100 value: 6.245 - type: precision_at_1000 value: 1.894 - type: precision_at_3 value: 27.554000000000002 - type: precision_at_5 value: 23.034 - type: recall_at_1 value: 4.811 - type: recall_at_10 value: 12.264999999999999 - type: recall_at_100 value: 25.483 - type: recall_at_1000 value: 58.396 - type: recall_at_3 value: 7.888000000000001 - type: recall_at_5 value: 9.607000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 8.876000000000001 - type: map_at_10 value: 14.799999999999999 - type: map_at_100 value: 15.967999999999998 - type: map_at_1000 value: 16.070999999999998 - type: map_at_3 value: 12.422 - type: map_at_5 value: 13.628000000000002 - type: mrr_at_1 value: 9.994 - type: mrr_at_10 value: 16.250999999999998 - type: mrr_at_100 value: 17.341 - type: mrr_at_1000 value: 17.43 - type: mrr_at_3 value: 13.808000000000002 - type: mrr_at_5 value: 15.057 - type: ndcg_at_1 value: 9.994 - type: ndcg_at_10 value: 18.887 - type: ndcg_at_100 value: 24.878 - type: ndcg_at_1000 value: 27.744000000000003 - type: ndcg_at_3 value: 13.921 - type: ndcg_at_5 value: 16.083 - type: precision_at_1 value: 9.994 - type: precision_at_10 value: 3.54 - type: precision_at_100 value: 0.698 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 6.489000000000001 - type: precision_at_5 value: 5.116 - type: recall_at_1 value: 8.876000000000001 - type: recall_at_10 value: 30.272 - type: recall_at_100 value: 58.097 - type: recall_at_1000 value: 80.207 - type: recall_at_3 value: 16.903000000000002 - type: recall_at_5 value: 21.948999999999998 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 61.650000000000006 - type: map_at_10 value: 73.951 - type: map_at_100 value: 74.725 - type: map_at_1000 value: 74.76 - type: map_at_3 value: 71.13199999999999 - type: map_at_5 value: 72.785 - type: mrr_at_1 value: 71.07 - type: mrr_at_10 value: 78.533 - type: mrr_at_100 value: 78.805 - type: mrr_at_1000 value: 78.81099999999999 - type: mrr_at_3 value: 77.108 - type: mrr_at_5 value: 77.986 - type: ndcg_at_1 value: 71.09 - type: ndcg_at_10 value: 78.717 - type: ndcg_at_100 value: 81.01 - type: ndcg_at_1000 value: 81.448 - type: ndcg_at_3 value: 75.223 - type: ndcg_at_5 value: 76.913 - type: precision_at_1 value: 71.09 - type: precision_at_10 value: 11.873000000000001 - type: precision_at_100 value: 1.43 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 32.6 - type: precision_at_5 value: 21.46 - type: recall_at_1 value: 61.650000000000006 - type: recall_at_10 value: 87.759 - type: recall_at_100 value: 96.697 - type: recall_at_1000 value: 99.213 - type: recall_at_3 value: 77.627 - type: recall_at_5 value: 82.433 - type: map_at_1 value: 2.973 - type: map_at_10 value: 6.963 - type: map_at_100 value: 8.179 - type: map_at_1000 value: 8.391 - type: map_at_3 value: 5.1209999999999996 - type: map_at_5 value: 6.1 - type: mrr_at_1 value: 14.6 - type: mrr_at_10 value: 22.519 - type: mrr_at_100 value: 23.656 - type: mrr_at_1000 value: 23.752000000000002 - type: mrr_at_3 value: 19.933 - type: mrr_at_5 value: 21.418 - type: ndcg_at_1 value: 14.6 - type: ndcg_at_10 value: 12.357999999999999 - type: ndcg_at_100 value: 18.071 - type: ndcg_at_1000 value: 22.658 - type: ndcg_at_3 value: 11.78 - type: ndcg_at_5 value: 10.377 - type: precision_at_1 value: 14.6 - type: precision_at_10 value: 6.34 - type: precision_at_100 value: 1.462 - type: precision_at_1000 value: 0.257 - type: precision_at_3 value: 10.9 - type: precision_at_5 value: 9.08 - type: recall_at_1 value: 2.973 - type: recall_at_10 value: 12.876999999999999 - type: recall_at_100 value: 29.732999999999997 - type: recall_at_1000 value: 52.25300000000001 - type: recall_at_3 value: 6.638 - type: recall_at_5 value: 9.223 - type: map_at_1 value: 0.149 - type: map_at_10 value: 0.808 - type: map_at_100 value: 4.284000000000001 - type: map_at_1000 value: 11.209 - type: map_at_3 value: 0.334 - type: map_at_5 value: 0.461 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 67.533 - type: mrr_at_100 value: 68.00699999999999 - type: mrr_at_1000 value: 68.00699999999999 - type: mrr_at_3 value: 66.0 - type: mrr_at_5 value: 67.2 - type: ndcg_at_1 value: 50.0 - type: ndcg_at_10 value: 44.132 - type: ndcg_at_100 value: 33.656000000000006 - type: ndcg_at_1000 value: 31.062 - type: ndcg_at_3 value: 46.939 - type: ndcg_at_5 value: 44.299 - type: precision_at_1 value: 56.00000000000001 - type: precision_at_10 value: 47.0 - type: precision_at_100 value: 35.56 - type: precision_at_1000 value: 15.160000000000002 - type: precision_at_3 value: 51.333 - type: precision_at_5 value: 46.800000000000004 - type: recall_at_1 value: 0.149 - type: recall_at_10 value: 1.081 - type: recall_at_100 value: 7.614999999999999 - type: recall_at_1000 value: 30.381999999999998 - type: recall_at_3 value: 0.384 - type: recall_at_5 value: 0.5519999999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 31.5539671147009 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 39.054096619731716 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 74.16263342018726 - type: cos_sim_spearman value: 65.47896874175562 - type: euclidean_pearson value: 69.85793726549834 - type: euclidean_spearman value: 65.4790013428057 - type: manhattan_pearson value: 64.55568963713883 - type: manhattan_spearman value: 61.568825078510024 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 66.8016903690634 - type: cos_sim_spearman value: 59.14548944908871 - type: euclidean_pearson value: 63.51433073183812 - type: euclidean_spearman value: 59.1468815981049 - type: manhattan_pearson value: 66.7777786631213 - type: manhattan_spearman value: 62.983103811799964 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 72.28649661642542 - type: cos_sim_spearman value: 74.15608835313905 - type: euclidean_pearson value: 73.82320584523798 - type: euclidean_spearman value: 74.15612619641261 - type: manhattan_pearson value: 75.8258411016566 - type: manhattan_spearman value: 76.30803708387923 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 73.43505716930555 - type: cos_sim_spearman value: 70.62852856991695 - type: euclidean_pearson value: 72.89910614146251 - type: euclidean_spearman value: 70.62851888356735 - type: manhattan_pearson value: 71.9214204068933 - type: manhattan_spearman value: 70.57115043790483 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 76.28414453948841 - type: cos_sim_spearman value: 77.64236034865235 - type: euclidean_pearson value: 77.82436104070885 - type: euclidean_spearman value: 77.64235891658193 - type: manhattan_pearson value: 78.02479262236683 - type: manhattan_spearman value: 78.41394470741825 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 74.80262034437214 - type: cos_sim_spearman value: 75.61749265386518 - type: euclidean_pearson value: 75.24995074046139 - type: euclidean_spearman value: 75.61748558266487 - type: manhattan_pearson value: 72.8680664793595 - type: manhattan_spearman value: 72.64465541572571 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.77228924820331 - type: cos_sim_spearman value: 84.45133365648942 - type: euclidean_pearson value: 83.65508313764323 - type: euclidean_spearman value: 84.45220756360993 - type: manhattan_pearson value: 79.31968289045513 - type: manhattan_spearman value: 79.95192251749855 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 52.345708620762984 - type: cos_sim_spearman value: 57.64880367305184 - type: euclidean_pearson value: 56.52639850051479 - type: euclidean_spearman value: 57.64880367305184 - type: manhattan_pearson value: 59.969265121930434 - type: manhattan_spearman value: 59.099517496575984 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 75.5029813815189 - type: cos_sim_spearman value: 73.8791301142112 - type: euclidean_pearson value: 75.44794524705392 - type: euclidean_spearman value: 73.87914858448825 - type: manhattan_pearson value: 71.2741911732711 - type: manhattan_spearman value: 69.5920899359239 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 69.98974452315024 - type: mrr value: 90.37764250999545 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 32.667 - type: map_at_10 value: 41.303 - type: map_at_100 value: 42.292 - type: map_at_1000 value: 42.372 - type: map_at_3 value: 39.013999999999996 - type: map_at_5 value: 40.469 - type: mrr_at_1 value: 34.666999999999994 - type: mrr_at_10 value: 43.187 - type: mrr_at_100 value: 44.028 - type: mrr_at_1000 value: 44.092 - type: mrr_at_3 value: 41.111 - type: mrr_at_5 value: 42.428 - type: ndcg_at_1 value: 34.666999999999994 - type: ndcg_at_10 value: 45.94 - type: ndcg_at_100 value: 50.67 - type: ndcg_at_1000 value: 52.654999999999994 - type: ndcg_at_3 value: 41.571999999999996 - type: ndcg_at_5 value: 43.998 - type: precision_at_1 value: 34.666999999999994 - type: precision_at_10 value: 6.367000000000001 - type: precision_at_100 value: 0.907 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 16.889000000000003 - type: precision_at_5 value: 11.4 - type: recall_at_1 value: 32.667 - type: recall_at_10 value: 58.556 - type: recall_at_100 value: 80.122 - type: recall_at_1000 value: 95.517 - type: recall_at_3 value: 46.778 - type: recall_at_5 value: 52.722 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.64455445544554 - type: cos_sim_ap value: 87.59975053126642 - type: cos_sim_f1 value: 81.30325814536342 - type: cos_sim_precision value: 81.50753768844221 - type: cos_sim_recall value: 81.10000000000001 - type: dot_accuracy value: 99.64455445544554 - type: dot_ap value: 87.59975053126642 - type: dot_f1 value: 81.30325814536342 - type: dot_precision value: 81.50753768844221 - type: dot_recall value: 81.10000000000001 - type: euclidean_accuracy value: 99.64455445544554 - type: euclidean_ap value: 87.59975053126642 - type: euclidean_f1 value: 81.30325814536342 - type: euclidean_precision value: 81.50753768844221 - type: euclidean_recall value: 81.10000000000001 - type: manhattan_accuracy value: 99.75544554455446 - type: manhattan_ap value: 92.79867201777404 - type: manhattan_f1 value: 87.35279057859702 - type: manhattan_precision value: 89.50682056663169 - type: manhattan_recall value: 85.3 - type: max_accuracy value: 99.75544554455446 - type: max_ap value: 92.79867201777404 - type: max_f1 value: 87.35279057859702 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 38.67507463066139 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 27.940405218037796 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 42.17856950522057 - type: mrr value: 42.73857270180799 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.824946271899556 - type: cos_sim_spearman value: 30.19370307268101 - type: dot_pearson value: 30.824946248457795 - type: dot_spearman value: 30.239461621041393 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.138 - type: map_at_10 value: 6.84 - type: map_at_100 value: 11.829 - type: map_at_1000 value: 13.431999999999999 - type: map_at_3 value: 3.4299999999999997 - type: map_at_5 value: 5.1450000000000005 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 40.96 - type: mrr_at_100 value: 42.259 - type: mrr_at_1000 value: 42.262 - type: mrr_at_3 value: 37.075 - type: mrr_at_5 value: 39.728 - type: ndcg_at_1 value: 27.551 - type: ndcg_at_10 value: 19.358 - type: ndcg_at_100 value: 32.036 - type: ndcg_at_1000 value: 43.552 - type: ndcg_at_3 value: 20.689 - type: ndcg_at_5 value: 21.308 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 17.755000000000003 - type: precision_at_100 value: 7.449 - type: precision_at_1000 value: 1.478 - type: precision_at_3 value: 21.088 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 2.138 - type: recall_at_10 value: 11.958 - type: recall_at_100 value: 44.659 - type: recall_at_1000 value: 80.16499999999999 - type: recall_at_3 value: 4.295 - type: recall_at_5 value: 7.701 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.4548 - type: ap value: 14.903569291537838 - type: f1 value: 54.76990636936144 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 58.678551216751565 - type: f1 value: 58.927198725369465 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.90243227502835 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.88269654884664 - type: cos_sim_ap value: 65.74743802769225 - type: cos_sim_f1 value: 63.409891845910806 - type: cos_sim_precision value: 58.774498760982205 - type: cos_sim_recall value: 68.83905013192611 - type: dot_accuracy value: 83.88269654884664 - type: dot_ap value: 65.74743802769225 - type: dot_f1 value: 63.409891845910806 - type: dot_precision value: 58.774498760982205 - type: dot_recall value: 68.83905013192611 - type: euclidean_accuracy value: 83.88269654884664 - type: euclidean_ap value: 65.74743802769225 - type: euclidean_f1 value: 63.409891845910806 - type: euclidean_precision value: 58.774498760982205 - type: euclidean_recall value: 68.83905013192611 - type: manhattan_accuracy value: 81.29582166060678 - type: manhattan_ap value: 57.475373048925846 - type: manhattan_f1 value: 55.90868397493285 - type: manhattan_precision value: 48.54255732607851 - type: manhattan_recall value: 65.91029023746702 - type: max_accuracy value: 83.88269654884664 - type: max_ap value: 65.74743802769225 - type: max_f1 value: 63.409891845910806 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.36174176271976 - type: cos_sim_ap value: 82.35619973560178 - type: cos_sim_f1 value: 75.05234819024828 - type: cos_sim_precision value: 72.95725501599301 - type: cos_sim_recall value: 77.27132737911919 - type: dot_accuracy value: 87.36174176271976 - type: dot_ap value: 82.35619970731375 - type: dot_f1 value: 75.05234819024828 - type: dot_precision value: 72.95725501599301 - type: dot_recall value: 77.27132737911919 - type: euclidean_accuracy value: 87.36174176271976 - type: euclidean_ap value: 82.35619955418572 - type: euclidean_f1 value: 75.05234819024828 - type: euclidean_precision value: 72.95725501599301 - type: euclidean_recall value: 77.27132737911919 - type: manhattan_accuracy value: 87.25889703884813 - type: manhattan_ap value: 81.85878727777866 - type: manhattan_f1 value: 74.52758333031083 - type: manhattan_precision value: 70.45189604333814 - type: manhattan_recall value: 79.10378811210347 - type: max_accuracy value: 87.36174176271976 - type: max_ap value: 82.35619973560178 - type: max_f1 value: 75.05234819024828 ---
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
Triangle104/MN-Chunky-Lotus-12B-Q6_K-GGUF
Triangle104
null
[ "transformers", "gguf", "storywriting", "text adventure", "creative", "story", "writing", "fiction", "roleplaying", "rp", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "en", "base_model:FallenMerick/MN-Chunky-Lotus-12B", "base_model:quantized:FallenMerick/MN-Chunky-Lotus-12B", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
1,732,107,422,000
2024-11-20T12:58:32
26
1
--- base_model: FallenMerick/MN-Chunky-Lotus-12B language: - en library_name: transformers license: cc-by-4.0 tags: - storywriting - text adventure - creative - story - writing - fiction - roleplaying - rp - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/MN-Chunky-Lotus-12B-Q6_K-GGUF This model was converted to GGUF format from [`FallenMerick/MN-Chunky-Lotus-12B`](https://huggingface.co/FallenMerick/MN-Chunky-Lotus-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/FallenMerick/MN-Chunky-Lotus-12B) for more details on the model. --- Model details: - I had originally planned to use this model for future/further merges, but decided to go ahead and release it since it scored rather high on my local EQ Bench testing (79.58 w/ 100% parsed @ 8-bit). Bear in mind that most models tend to score a bit higher on my own local tests as compared to their posted scores. Still, its the highest score I've personally seen from all the models I've tested. Its a decent model, with great emotional intelligence and acceptable adherence to various character personalities. It does a good job at roleplaying despite being a bit bland at times. Overall, I like the way it writes, but it has a few formatting issues that show up from time to time, and it has an uncommon tendency to paste walls of character feelings/intentions at the end of some outputs without any prompting. This is something I hope to correct with future iterations. This is a merge of pre-trained language models created using mergekit. Merge Method - This model was merged using the TIES merge method. Models Merged - The following models were included in the merge: Epiculous/Violet_Twilight-v0.2 nbeerbower/mistral-nemo-gutenberg-12B-v4 flammenai/Mahou-1.5-mistral-nemo-12B Configuration - The following YAML configuration was used to produce this model: models: - model: Epiculous/Violet_Twilight-v0.2 parameters: weight: 1.0 density: 1.0 - model: nbeerbower/mistral-nemo-gutenberg-12B-v4 parameters: weight: 1.0 density: 0.54 - model: flammenai/Mahou-1.5-mistral-nemo-12B parameters: weight: 1.0 density: 0.26 merge_method: ties base_model: TheDrummer/Rocinante-12B-v1.1 parameters: normalize: true dtype: bfloat16 The idea behind this recipe was to take the long-form writing capabilities of Gutenberg, curtail it a bit with the very short output formatting of Mahou, and use Violet Twilight as an extremely solid roleplaying foundation underneath. Rocinante is used as the base model in this merge in order to really target the delta weights from Gutenberg, since those seemed to have the highest impact on the resulting EQ of the model. Special shoutout to @matchaaaaa for helping with testing, and for all the great model recommendations. Also, for just being an all around great person who's really inspired and motivated me to continue merging and working on models. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/MN-Chunky-Lotus-12B-Q6_K-GGUF --hf-file mn-chunky-lotus-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/MN-Chunky-Lotus-12B-Q6_K-GGUF --hf-file mn-chunky-lotus-12b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/MN-Chunky-Lotus-12B-Q6_K-GGUF --hf-file mn-chunky-lotus-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/MN-Chunky-Lotus-12B-Q6_K-GGUF --hf-file mn-chunky-lotus-12b-q6_k.gguf -c 2048 ```
[ "BEAR" ]
Non_BioNLP
andreabac3/Fauno-Italian-LLM-7B
andreabac3
null
[ "large language model", "italian large language model", "baize", "llama ", "italian", "it", "en", "dataset:andreabac3/MedQuaAD-Italian-Fauno-Baize", "dataset:andreabac3/StackOverflow-Italian-Fauno-Baize", "dataset:andreabac3/Quora-Italian-Fauno-Baize", "dataset:teelinsan/camoscio_cleaned", "license:gpl-3.0", "region:us" ]
1,680,861,100,000
2023-07-12T06:11:53
0
37
--- datasets: - andreabac3/MedQuaAD-Italian-Fauno-Baize - andreabac3/StackOverflow-Italian-Fauno-Baize - andreabac3/Quora-Italian-Fauno-Baize - teelinsan/camoscio_cleaned language: - it - en license: gpl-3.0 tags: - large language model - italian large language model - baize - 'llama ' - italian --- # Fauno - Italian LLM ![image](fauno.drawio.png) Get ready to meet Fauno - the Italian language model crafted by the [RSTLess Research Group](https://rstless-lab.netlify.app/) from the Sapienza University of Rome. The talented research team behind Fauno includes [Andrea Bacciu](https://andreabac3.github.io/), [Dr. Giovanni Trappolini](https://sites.google.com/view/giovannitrappolini), [Andrea Santilli](https://www.santilli.xyz/), and [Professor Fabrizio Silvestri](https://sites.google.com/diag.uniroma1.it/fabriziosilvestri/home). Fauno represents a cutting-edge development in open-source Italian Large Language Modeling. It's trained on extensive Italian synthetic datasets, encompassing a wide range of fields such as medical data 🩺, technical content from Stack Overflow 💻, Quora discussions 💬, and Alpaca data 🦙 translated into Italian. Hence, our model is able to answer to your questions in Italian 🙋, fix your buggy code 🐛 and understand a minimum of medical literature 💊. ## The 🇮🇹 open-source version of chatGPT! Discover the capabilities of Fauno and experience the evolution of Italian language models for yourself. ![demo](screenshot_demo.png) ### Why Fauno? We started with a model called Baize, named after a legendary creature from Chinese literature. Continuing along this thematic line, we developed our Italian model based on Baize and named it Fauno, inspired by an iconic figure from Roman mythology. This choice underlines the link between the two models, while maintaining a distinctive identity rooted in Italian culture. # Did you know that you can run Fauno on Colab base? Follow this link to access a Colab notebook with our 7B version! <a target="_blank" href="https://colab.research.google.com/drive/1AepJVWS-qU910zyq-Zi7wWFQ5tthVzUe"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> ## 🔎 Model's details Fauno is a fine-tuned version of the LoRa weights of [Baize](https://github.com/project-baize/baize-chatbot), that is an improved version of [LLama](https://github.com/facebookresearch/llama). We translated and cleaned the data of Baize, and then we fine-tuned the 7b model using a single RTX A6000 (48GB of VRAM) with 19 hours for one epoch. - 13B: https://huggingface.co/andreabac3/Fauno-Italian-LLM-13B Fauno 30B and 65B are coming soon! ## Model initialization ```python from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig from peft import PeftModel tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") model = LlamaForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, device_map="auto", ) model = PeftModel.from_pretrained(model, "andreabac3/Fauno-Italian-LLM-7B") model.eval() ``` ## Inference ```python def evaluate(question: str) -> str: prompt = f"The conversation between human and AI assistant.\n[|Human|] {question}.\n[|AI|] " inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256 ) output = tokenizer.decode(generation_output.sequences[0]).split("[|AI|]")[1] return output your_question: str = "Qual'è il significato della vita?" print(evaluate(your_question)) ``` ### Output ``` Il senso della vita è una domanda che molte persone hanno cercato di rispondere per secoli. Alla fine, il senso della vita è soggettivo e varia da persona a persona. Alcune persone credono che il senso della vita sia trovare la felicità, mentre altre credono che sia raggiungere i propri obiettivi o aiutare gli altri. Alla fine, il senso della vita è determinato dall'individuo e dai loro valori e credenze. In definitiva, il senso della vita è qualcosa che ognuno deve trovare da solo. Non c'è una risposta giusta o sbagliata, poiché ogni persona ha le proprie convinzioni e credenze. La ricerca del senso della vita può essere un viaggio lungo e difficile, ma vale la pena perseguire. ``` # 📖 Cite our work To use our translated dataset and model weights in your research, remember to cite our work. ```bibtex @misc{fauno, author = {Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Fabrizio Silvestri}, title = {Fauno: The Italian Large Language Model that will leave you senza parole!}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/andreabac3/Fauno-Italian-LLM}}, } ``` ## 🔑 License This project is a derivative of Baize, and we adhere to the licensing constraints imposed by both Baize's creators and the authors of LLama. ## ⚠️ Hallucinations It is important to remark that current generation models are prone to the problem of hallucinations. So we advise you not to take their answers seriously. ## 👏 Acknowledgement - LLama - Meta AI: https://github.com/facebookresearch/llama - Baize: https://github.com/project-baize/baize-chatbot - Standford Alpaca: https://github.com/tatsu-lab/stanford_alpaca - Camoscio: https://github.com/teelinsan/camoscio #### Image Credits - llama image: https://next14.com/en/nextnews-7-march-a-new-language-model-for-meta-bing-ai-on-windows-and-the-first-tokenized-real-estate-sales/ - Fauno logo: https://www.flaticon.com/free-icon/faun_7931635?term=faun&page=1&position=1&origin=tag&related_id=7931635
[ "MEDICAL DATA" ]
Non_BioNLP
buio/Fauno-Italian-LLM-7B
buio
null
[ "large language model", "italian large language model", "baize", "llama ", "italian", "it", "en", "dataset:andreabac3/MedQuaAD-Italian-Fauno-Baize", "dataset:andreabac3/StackOverflow-Italian-Fauno-Baize", "dataset:andreabac3/Quora-Italian-Fauno-Baize", "dataset:teelinsan/camoscio_cleaned", "license:gpl-3.0", "region:us" ]
1,707,844,716,000
2024-02-13T17:24:48
0
0
--- datasets: - andreabac3/MedQuaAD-Italian-Fauno-Baize - andreabac3/StackOverflow-Italian-Fauno-Baize - andreabac3/Quora-Italian-Fauno-Baize - teelinsan/camoscio_cleaned language: - it - en license: gpl-3.0 tags: - large language model - italian large language model - baize - 'llama ' - italian --- # Fauno - Italian LLM ![image](fauno.drawio.png) Get ready to meet Fauno - the Italian language model crafted by the [RSTLess Research Group](https://rstless-lab.netlify.app/) from the Sapienza University of Rome. The talented research team behind Fauno includes [Andrea Bacciu](https://andreabac3.github.io/), [Dr. Giovanni Trappolini](https://sites.google.com/view/giovannitrappolini), [Andrea Santilli](https://www.santilli.xyz/), and [Professor Fabrizio Silvestri](https://sites.google.com/diag.uniroma1.it/fabriziosilvestri/home). Fauno represents a cutting-edge development in open-source Italian Large Language Modeling. It's trained on extensive Italian synthetic datasets, encompassing a wide range of fields such as medical data 🩺, technical content from Stack Overflow 💻, Quora discussions 💬, and Alpaca data 🦙 translated into Italian. Hence, our model is able to answer to your questions in Italian 🙋, fix your buggy code 🐛 and understand a minimum of medical literature 💊. ## The 🇮🇹 open-source version of chatGPT! Discover the capabilities of Fauno and experience the evolution of Italian language models for yourself. ![demo](screenshot_demo.png) ### Why Fauno? We started with a model called Baize, named after a legendary creature from Chinese literature. Continuing along this thematic line, we developed our Italian model based on Baize and named it Fauno, inspired by an iconic figure from Roman mythology. This choice underlines the link between the two models, while maintaining a distinctive identity rooted in Italian culture. # Did you know that you can run Fauno on Colab base? Follow this link to access a Colab notebook with our 7B version! <a target="_blank" href="https://colab.research.google.com/drive/1AepJVWS-qU910zyq-Zi7wWFQ5tthVzUe"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> ## 🔎 Model's details Fauno is a fine-tuned version of the LoRa weights of [Baize](https://github.com/project-baize/baize-chatbot), that is an improved version of [LLama](https://github.com/facebookresearch/llama). We translated and cleaned the data of Baize, and then we fine-tuned the 7b model using a single RTX A6000 (48GB of VRAM) with 19 hours for one epoch. - 13B: https://huggingface.co/andreabac3/Fauno-Italian-LLM-13B Fauno 30B and 65B are coming soon! ## Model initialization ```python from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig from peft import PeftModel tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") model = LlamaForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, device_map="auto", ) model = PeftModel.from_pretrained(model, "andreabac3/Fauno-Italian-LLM-7B") model.eval() ``` ## Inference ```python def evaluate(question: str) -> str: prompt = f"The conversation between human and AI assistant.\n[|Human|] {question}.\n[|AI|] " inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256 ) output = tokenizer.decode(generation_output.sequences[0]).split("[|AI|]")[1] return output your_question: str = "Qual'è il significato della vita?" print(evaluate(your_question)) ``` ### Output ``` Il senso della vita è una domanda che molte persone hanno cercato di rispondere per secoli. Alla fine, il senso della vita è soggettivo e varia da persona a persona. Alcune persone credono che il senso della vita sia trovare la felicità, mentre altre credono che sia raggiungere i propri obiettivi o aiutare gli altri. Alla fine, il senso della vita è determinato dall'individuo e dai loro valori e credenze. In definitiva, il senso della vita è qualcosa che ognuno deve trovare da solo. Non c'è una risposta giusta o sbagliata, poiché ogni persona ha le proprie convinzioni e credenze. La ricerca del senso della vita può essere un viaggio lungo e difficile, ma vale la pena perseguire. ``` # 📖 Cite our work To use our translated dataset and model weights in your research, remember to cite our work. ```bibtex @misc{fauno, author = {Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Fabrizio Silvestri}, title = {Fauno: The Italian Large Language Model that will leave you senza parole!}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/andreabac3/Fauno-Italian-LLM}}, } ``` ## 🔑 License This project is a derivative of Baize, and we adhere to the licensing constraints imposed by both Baize's creators and the authors of LLama. ## ⚠️ Hallucinations It is important to remark that current generation models are prone to the problem of hallucinations. So we advise you not to take their answers seriously. ## 👏 Acknowledgement - LLama - Meta AI: https://github.com/facebookresearch/llama - Baize: https://github.com/project-baize/baize-chatbot - Standford Alpaca: https://github.com/tatsu-lab/stanford_alpaca - Camoscio: https://github.com/teelinsan/camoscio #### Image Credits - llama image: https://next14.com/en/nextnews-7-march-a-new-language-model-for-meta-bing-ai-on-windows-and-the-first-tokenized-real-estate-sales/ - Fauno logo: https://www.flaticon.com/free-icon/faun_7931635?term=faun&page=1&position=1&origin=tag&related_id=7931635
[ "MEDICAL DATA" ]
Non_BioNLP
vectoriseai/bge-small-en
vectoriseai
sentence-similarity
[ "sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "mteb", "sentence transformers", "sentence-similarity", "en", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,692,968,402,000
2023-08-28T14:17:25
18
0
--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - mteb - sentence transformers model-index: - name: bge-small-en results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.34328358208955 - type: ap value: 37.59947775195661 - type: f1 value: 68.548415491933 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.04527499999999 - type: ap value: 89.60696356772135 - type: f1 value: 93.03361469382438 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.08 - type: f1 value: 45.66249835363254 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 35.205999999999996 - type: map_at_10 value: 50.782000000000004 - type: map_at_100 value: 51.547 - type: map_at_1000 value: 51.554 - type: map_at_3 value: 46.515 - type: map_at_5 value: 49.296 - type: mrr_at_1 value: 35.632999999999996 - type: mrr_at_10 value: 50.958999999999996 - type: mrr_at_100 value: 51.724000000000004 - type: mrr_at_1000 value: 51.731 - type: mrr_at_3 value: 46.669 - type: mrr_at_5 value: 49.439 - type: ndcg_at_1 value: 35.205999999999996 - type: ndcg_at_10 value: 58.835 - type: ndcg_at_100 value: 62.095 - type: ndcg_at_1000 value: 62.255 - type: ndcg_at_3 value: 50.255 - type: ndcg_at_5 value: 55.296 - type: precision_at_1 value: 35.205999999999996 - type: precision_at_10 value: 8.421 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.365 - type: precision_at_5 value: 14.680000000000001 - type: recall_at_1 value: 35.205999999999996 - type: recall_at_10 value: 84.211 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.095 - type: recall_at_5 value: 73.4 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.52644476278646 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.973045724188964 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.28285314871488 - type: mrr value: 74.52743701358659 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 80.09041909160327 - type: cos_sim_spearman value: 79.96266537706944 - type: euclidean_pearson value: 79.50774978162241 - type: euclidean_spearman value: 79.9144715078551 - type: manhattan_pearson value: 79.2062139879302 - type: manhattan_spearman value: 79.35000081468212 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.31493506493506 - type: f1 value: 85.2704557977762 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.6837242810816 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.38881249555897 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 27.884999999999998 - type: map_at_10 value: 39.574 - type: map_at_100 value: 40.993 - type: map_at_1000 value: 41.129 - type: map_at_3 value: 36.089 - type: map_at_5 value: 38.191 - type: mrr_at_1 value: 34.477999999999994 - type: mrr_at_10 value: 45.411 - type: mrr_at_100 value: 46.089999999999996 - type: mrr_at_1000 value: 46.147 - type: mrr_at_3 value: 42.346000000000004 - type: mrr_at_5 value: 44.292 - type: ndcg_at_1 value: 34.477999999999994 - type: ndcg_at_10 value: 46.123999999999995 - type: ndcg_at_100 value: 51.349999999999994 - type: ndcg_at_1000 value: 53.578 - type: ndcg_at_3 value: 40.824 - type: ndcg_at_5 value: 43.571 - type: precision_at_1 value: 34.477999999999994 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 19.742 - type: precision_at_5 value: 14.421000000000001 - type: recall_at_1 value: 27.884999999999998 - type: recall_at_10 value: 59.087 - type: recall_at_100 value: 80.609 - type: recall_at_1000 value: 95.054 - type: recall_at_3 value: 44.082 - type: recall_at_5 value: 51.593999999999994 - type: map_at_1 value: 30.639 - type: map_at_10 value: 40.047 - type: map_at_100 value: 41.302 - type: map_at_1000 value: 41.425 - type: map_at_3 value: 37.406 - type: map_at_5 value: 38.934000000000005 - type: mrr_at_1 value: 37.707 - type: mrr_at_10 value: 46.082 - type: mrr_at_100 value: 46.745 - type: mrr_at_1000 value: 46.786 - type: mrr_at_3 value: 43.980999999999995 - type: mrr_at_5 value: 45.287 - type: ndcg_at_1 value: 37.707 - type: ndcg_at_10 value: 45.525 - type: ndcg_at_100 value: 49.976 - type: ndcg_at_1000 value: 51.94499999999999 - type: ndcg_at_3 value: 41.704 - type: ndcg_at_5 value: 43.596000000000004 - type: precision_at_1 value: 37.707 - type: precision_at_10 value: 8.465 - type: precision_at_100 value: 1.375 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 19.979 - type: precision_at_5 value: 14.115 - type: recall_at_1 value: 30.639 - type: recall_at_10 value: 54.775 - type: recall_at_100 value: 73.678 - type: recall_at_1000 value: 86.142 - type: recall_at_3 value: 43.230000000000004 - type: recall_at_5 value: 48.622 - type: map_at_1 value: 38.038 - type: map_at_10 value: 49.922 - type: map_at_100 value: 51.032 - type: map_at_1000 value: 51.085 - type: map_at_3 value: 46.664 - type: map_at_5 value: 48.588 - type: mrr_at_1 value: 43.95 - type: mrr_at_10 value: 53.566 - type: mrr_at_100 value: 54.318999999999996 - type: mrr_at_1000 value: 54.348 - type: mrr_at_3 value: 51.066 - type: mrr_at_5 value: 52.649 - type: ndcg_at_1 value: 43.95 - type: ndcg_at_10 value: 55.676 - type: ndcg_at_100 value: 60.126000000000005 - type: ndcg_at_1000 value: 61.208 - type: ndcg_at_3 value: 50.20400000000001 - type: ndcg_at_5 value: 53.038 - type: precision_at_1 value: 43.95 - type: precision_at_10 value: 8.953 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.256999999999998 - type: precision_at_5 value: 15.524 - type: recall_at_1 value: 38.038 - type: recall_at_10 value: 69.15 - type: recall_at_100 value: 88.31599999999999 - type: recall_at_1000 value: 95.993 - type: recall_at_3 value: 54.663 - type: recall_at_5 value: 61.373 - type: map_at_1 value: 24.872 - type: map_at_10 value: 32.912 - type: map_at_100 value: 33.972 - type: map_at_1000 value: 34.046 - type: map_at_3 value: 30.361 - type: map_at_5 value: 31.704 - type: mrr_at_1 value: 26.779999999999998 - type: mrr_at_10 value: 34.812 - type: mrr_at_100 value: 35.754999999999995 - type: mrr_at_1000 value: 35.809000000000005 - type: mrr_at_3 value: 32.335 - type: mrr_at_5 value: 33.64 - type: ndcg_at_1 value: 26.779999999999998 - type: ndcg_at_10 value: 37.623 - type: ndcg_at_100 value: 42.924 - type: ndcg_at_1000 value: 44.856 - type: ndcg_at_3 value: 32.574 - type: ndcg_at_5 value: 34.842 - type: precision_at_1 value: 26.779999999999998 - type: precision_at_10 value: 5.729 - type: precision_at_100 value: 0.886 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.559 - type: precision_at_5 value: 9.469 - type: recall_at_1 value: 24.872 - type: recall_at_10 value: 50.400999999999996 - type: recall_at_100 value: 74.954 - type: recall_at_1000 value: 89.56 - type: recall_at_3 value: 36.726 - type: recall_at_5 value: 42.138999999999996 - type: map_at_1 value: 16.803 - type: map_at_10 value: 24.348 - type: map_at_100 value: 25.56 - type: map_at_1000 value: 25.668000000000003 - type: map_at_3 value: 21.811 - type: map_at_5 value: 23.287 - type: mrr_at_1 value: 20.771 - type: mrr_at_10 value: 28.961 - type: mrr_at_100 value: 29.979 - type: mrr_at_1000 value: 30.046 - type: mrr_at_3 value: 26.555 - type: mrr_at_5 value: 28.060000000000002 - type: ndcg_at_1 value: 20.771 - type: ndcg_at_10 value: 29.335 - type: ndcg_at_100 value: 35.188 - type: ndcg_at_1000 value: 37.812 - type: ndcg_at_3 value: 24.83 - type: ndcg_at_5 value: 27.119 - type: precision_at_1 value: 20.771 - type: precision_at_10 value: 5.4350000000000005 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.982 - type: precision_at_5 value: 8.831 - type: recall_at_1 value: 16.803 - type: recall_at_10 value: 40.039 - type: recall_at_100 value: 65.83200000000001 - type: recall_at_1000 value: 84.478 - type: recall_at_3 value: 27.682000000000002 - type: recall_at_5 value: 33.535 - type: map_at_1 value: 28.345 - type: map_at_10 value: 37.757000000000005 - type: map_at_100 value: 39.141 - type: map_at_1000 value: 39.262 - type: map_at_3 value: 35.183 - type: map_at_5 value: 36.592 - type: mrr_at_1 value: 34.649 - type: mrr_at_10 value: 43.586999999999996 - type: mrr_at_100 value: 44.481 - type: mrr_at_1000 value: 44.542 - type: mrr_at_3 value: 41.29 - type: mrr_at_5 value: 42.642 - type: ndcg_at_1 value: 34.649 - type: ndcg_at_10 value: 43.161 - type: ndcg_at_100 value: 48.734 - type: ndcg_at_1000 value: 51.046 - type: ndcg_at_3 value: 39.118 - type: ndcg_at_5 value: 41.022 - type: precision_at_1 value: 34.649 - type: precision_at_10 value: 7.603 - type: precision_at_100 value: 1.209 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 18.319 - type: precision_at_5 value: 12.839 - type: recall_at_1 value: 28.345 - type: recall_at_10 value: 53.367 - type: recall_at_100 value: 76.453 - type: recall_at_1000 value: 91.82000000000001 - type: recall_at_3 value: 41.636 - type: recall_at_5 value: 46.760000000000005 - type: map_at_1 value: 22.419 - type: map_at_10 value: 31.716 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.267 - type: map_at_3 value: 28.74 - type: map_at_5 value: 30.48 - type: mrr_at_1 value: 28.310999999999996 - type: mrr_at_10 value: 37.039 - type: mrr_at_100 value: 38.09 - type: mrr_at_1000 value: 38.145 - type: mrr_at_3 value: 34.437 - type: mrr_at_5 value: 36.024 - type: ndcg_at_1 value: 28.310999999999996 - type: ndcg_at_10 value: 37.41 - type: ndcg_at_100 value: 43.647999999999996 - type: ndcg_at_1000 value: 46.007 - type: ndcg_at_3 value: 32.509 - type: ndcg_at_5 value: 34.943999999999996 - type: precision_at_1 value: 28.310999999999996 - type: precision_at_10 value: 6.963 - type: precision_at_100 value: 1.1860000000000002 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 15.867999999999999 - type: precision_at_5 value: 11.507000000000001 - type: recall_at_1 value: 22.419 - type: recall_at_10 value: 49.28 - type: recall_at_100 value: 75.802 - type: recall_at_1000 value: 92.032 - type: recall_at_3 value: 35.399 - type: recall_at_5 value: 42.027 - type: map_at_1 value: 24.669249999999998 - type: map_at_10 value: 33.332583333333325 - type: map_at_100 value: 34.557833333333335 - type: map_at_1000 value: 34.67141666666666 - type: map_at_3 value: 30.663166666666662 - type: map_at_5 value: 32.14883333333333 - type: mrr_at_1 value: 29.193833333333334 - type: mrr_at_10 value: 37.47625 - type: mrr_at_100 value: 38.3545 - type: mrr_at_1000 value: 38.413166666666676 - type: mrr_at_3 value: 35.06741666666667 - type: mrr_at_5 value: 36.450666666666656 - type: ndcg_at_1 value: 29.193833333333334 - type: ndcg_at_10 value: 38.505416666666676 - type: ndcg_at_100 value: 43.81125 - type: ndcg_at_1000 value: 46.09558333333333 - type: ndcg_at_3 value: 33.90916666666667 - type: ndcg_at_5 value: 36.07666666666666 - type: precision_at_1 value: 29.193833333333334 - type: precision_at_10 value: 6.7251666666666665 - type: precision_at_100 value: 1.1058333333333332 - type: precision_at_1000 value: 0.14833333333333332 - type: precision_at_3 value: 15.554166666666665 - type: precision_at_5 value: 11.079250000000002 - type: recall_at_1 value: 24.669249999999998 - type: recall_at_10 value: 49.75583333333332 - type: recall_at_100 value: 73.06908333333332 - type: recall_at_1000 value: 88.91316666666667 - type: recall_at_3 value: 36.913250000000005 - type: recall_at_5 value: 42.48641666666666 - type: map_at_1 value: 24.044999999999998 - type: map_at_10 value: 30.349999999999998 - type: map_at_100 value: 31.273 - type: map_at_1000 value: 31.362000000000002 - type: map_at_3 value: 28.508 - type: map_at_5 value: 29.369 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.12 - type: mrr_at_100 value: 33.904 - type: mrr_at_1000 value: 33.967000000000006 - type: mrr_at_3 value: 31.365 - type: mrr_at_5 value: 32.124 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.214 - type: ndcg_at_100 value: 38.681 - type: ndcg_at_1000 value: 40.926 - type: ndcg_at_3 value: 30.725 - type: ndcg_at_5 value: 31.967000000000002 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.215 - type: precision_at_100 value: 0.807 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 8.712 - type: recall_at_1 value: 24.044999999999998 - type: recall_at_10 value: 43.456 - type: recall_at_100 value: 63.675000000000004 - type: recall_at_1000 value: 80.05499999999999 - type: recall_at_3 value: 33.561 - type: recall_at_5 value: 36.767 - type: map_at_1 value: 15.672 - type: map_at_10 value: 22.641 - type: map_at_100 value: 23.75 - type: map_at_1000 value: 23.877000000000002 - type: map_at_3 value: 20.219 - type: map_at_5 value: 21.648 - type: mrr_at_1 value: 18.823 - type: mrr_at_10 value: 26.101999999999997 - type: mrr_at_100 value: 27.038 - type: mrr_at_1000 value: 27.118 - type: mrr_at_3 value: 23.669 - type: mrr_at_5 value: 25.173000000000002 - type: ndcg_at_1 value: 18.823 - type: ndcg_at_10 value: 27.176000000000002 - type: ndcg_at_100 value: 32.42 - type: ndcg_at_1000 value: 35.413 - type: ndcg_at_3 value: 22.756999999999998 - type: ndcg_at_5 value: 25.032 - type: precision_at_1 value: 18.823 - type: precision_at_10 value: 5.034000000000001 - type: precision_at_100 value: 0.895 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 10.771 - type: precision_at_5 value: 8.1 - type: recall_at_1 value: 15.672 - type: recall_at_10 value: 37.296 - type: recall_at_100 value: 60.863 - type: recall_at_1000 value: 82.234 - type: recall_at_3 value: 25.330000000000002 - type: recall_at_5 value: 30.964000000000002 - type: map_at_1 value: 24.633 - type: map_at_10 value: 32.858 - type: map_at_100 value: 34.038000000000004 - type: map_at_1000 value: 34.141 - type: map_at_3 value: 30.209000000000003 - type: map_at_5 value: 31.567 - type: mrr_at_1 value: 28.358 - type: mrr_at_10 value: 36.433 - type: mrr_at_100 value: 37.352000000000004 - type: mrr_at_1000 value: 37.41 - type: mrr_at_3 value: 34.033 - type: mrr_at_5 value: 35.246 - type: ndcg_at_1 value: 28.358 - type: ndcg_at_10 value: 37.973 - type: ndcg_at_100 value: 43.411 - type: ndcg_at_1000 value: 45.747 - type: ndcg_at_3 value: 32.934999999999995 - type: ndcg_at_5 value: 35.013 - type: precision_at_1 value: 28.358 - type: precision_at_10 value: 6.418 - type: precision_at_100 value: 1.02 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 14.677000000000001 - type: precision_at_5 value: 10.335999999999999 - type: recall_at_1 value: 24.633 - type: recall_at_10 value: 50.048 - type: recall_at_100 value: 73.821 - type: recall_at_1000 value: 90.046 - type: recall_at_3 value: 36.284 - type: recall_at_5 value: 41.370000000000005 - type: map_at_1 value: 23.133 - type: map_at_10 value: 31.491999999999997 - type: map_at_100 value: 33.062000000000005 - type: map_at_1000 value: 33.256 - type: map_at_3 value: 28.886 - type: map_at_5 value: 30.262 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.144 - type: mrr_at_100 value: 37.14 - type: mrr_at_1000 value: 37.191 - type: mrr_at_3 value: 33.762 - type: mrr_at_5 value: 34.997 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 36.951 - type: ndcg_at_100 value: 43.287 - type: ndcg_at_1000 value: 45.777 - type: ndcg_at_3 value: 32.786 - type: ndcg_at_5 value: 34.65 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.055 - type: precision_at_100 value: 1.476 - type: precision_at_1000 value: 0.22899999999999998 - type: precision_at_3 value: 15.481 - type: precision_at_5 value: 11.186 - type: recall_at_1 value: 23.133 - type: recall_at_10 value: 47.285 - type: recall_at_100 value: 76.176 - type: recall_at_1000 value: 92.176 - type: recall_at_3 value: 35.223 - type: recall_at_5 value: 40.142 - type: map_at_1 value: 19.547 - type: map_at_10 value: 26.374 - type: map_at_100 value: 27.419 - type: map_at_1000 value: 27.539 - type: map_at_3 value: 23.882 - type: map_at_5 value: 25.163999999999998 - type: mrr_at_1 value: 21.442 - type: mrr_at_10 value: 28.458 - type: mrr_at_100 value: 29.360999999999997 - type: mrr_at_1000 value: 29.448999999999998 - type: mrr_at_3 value: 25.97 - type: mrr_at_5 value: 27.273999999999997 - type: ndcg_at_1 value: 21.442 - type: ndcg_at_10 value: 30.897000000000002 - type: ndcg_at_100 value: 35.99 - type: ndcg_at_1000 value: 38.832 - type: ndcg_at_3 value: 25.944 - type: ndcg_at_5 value: 28.126 - type: precision_at_1 value: 21.442 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8109999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.911 - type: recall_at_1 value: 19.547 - type: recall_at_10 value: 42.886 - type: recall_at_100 value: 66.64999999999999 - type: recall_at_1000 value: 87.368 - type: recall_at_3 value: 29.143 - type: recall_at_5 value: 34.544000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 15.572 - type: map_at_10 value: 25.312 - type: map_at_100 value: 27.062 - type: map_at_1000 value: 27.253 - type: map_at_3 value: 21.601 - type: map_at_5 value: 23.473 - type: mrr_at_1 value: 34.984 - type: mrr_at_10 value: 46.406 - type: mrr_at_100 value: 47.179 - type: mrr_at_1000 value: 47.21 - type: mrr_at_3 value: 43.485 - type: mrr_at_5 value: 45.322 - type: ndcg_at_1 value: 34.984 - type: ndcg_at_10 value: 34.344 - type: ndcg_at_100 value: 41.015 - type: ndcg_at_1000 value: 44.366 - type: ndcg_at_3 value: 29.119 - type: ndcg_at_5 value: 30.825999999999997 - type: precision_at_1 value: 34.984 - type: precision_at_10 value: 10.358 - type: precision_at_100 value: 1.762 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 21.368000000000002 - type: precision_at_5 value: 15.948 - type: recall_at_1 value: 15.572 - type: recall_at_10 value: 39.367999999999995 - type: recall_at_100 value: 62.183 - type: recall_at_1000 value: 80.92200000000001 - type: recall_at_3 value: 26.131999999999998 - type: recall_at_5 value: 31.635999999999996 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.848 - type: map_at_10 value: 19.25 - type: map_at_100 value: 27.193 - type: map_at_1000 value: 28.721999999999998 - type: map_at_3 value: 13.968 - type: map_at_5 value: 16.283 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.25 - type: mrr_at_100 value: 76.534 - type: mrr_at_1000 value: 76.53999999999999 - type: mrr_at_3 value: 74.667 - type: mrr_at_5 value: 75.86699999999999 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 41.426 - type: ndcg_at_100 value: 45.660000000000004 - type: ndcg_at_1000 value: 53.02 - type: ndcg_at_3 value: 46.581 - type: ndcg_at_5 value: 43.836999999999996 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.800000000000004 - type: precision_at_100 value: 10.440000000000001 - type: precision_at_1000 value: 1.9980000000000002 - type: precision_at_3 value: 49.667 - type: precision_at_5 value: 42.25 - type: recall_at_1 value: 8.848 - type: recall_at_10 value: 24.467 - type: recall_at_100 value: 51.344 - type: recall_at_1000 value: 75.235 - type: recall_at_3 value: 15.329 - type: recall_at_5 value: 18.892999999999997 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.95 - type: f1 value: 43.44563593360779 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 78.036 - type: map_at_10 value: 85.639 - type: map_at_100 value: 85.815 - type: map_at_1000 value: 85.829 - type: map_at_3 value: 84.795 - type: map_at_5 value: 85.336 - type: mrr_at_1 value: 84.353 - type: mrr_at_10 value: 90.582 - type: mrr_at_100 value: 90.617 - type: mrr_at_1000 value: 90.617 - type: mrr_at_3 value: 90.132 - type: mrr_at_5 value: 90.447 - type: ndcg_at_1 value: 84.353 - type: ndcg_at_10 value: 89.003 - type: ndcg_at_100 value: 89.60000000000001 - type: ndcg_at_1000 value: 89.836 - type: ndcg_at_3 value: 87.81400000000001 - type: ndcg_at_5 value: 88.478 - type: precision_at_1 value: 84.353 - type: precision_at_10 value: 10.482 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 33.257999999999996 - type: precision_at_5 value: 20.465 - type: recall_at_1 value: 78.036 - type: recall_at_10 value: 94.517 - type: recall_at_100 value: 96.828 - type: recall_at_1000 value: 98.261 - type: recall_at_3 value: 91.12 - type: recall_at_5 value: 92.946 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 20.191 - type: map_at_10 value: 32.369 - type: map_at_100 value: 34.123999999999995 - type: map_at_1000 value: 34.317 - type: map_at_3 value: 28.71 - type: map_at_5 value: 30.607 - type: mrr_at_1 value: 40.894999999999996 - type: mrr_at_10 value: 48.842 - type: mrr_at_100 value: 49.599 - type: mrr_at_1000 value: 49.647000000000006 - type: mrr_at_3 value: 46.785 - type: mrr_at_5 value: 47.672 - type: ndcg_at_1 value: 40.894999999999996 - type: ndcg_at_10 value: 39.872 - type: ndcg_at_100 value: 46.126 - type: ndcg_at_1000 value: 49.476 - type: ndcg_at_3 value: 37.153000000000006 - type: ndcg_at_5 value: 37.433 - type: precision_at_1 value: 40.894999999999996 - type: precision_at_10 value: 10.818 - type: precision_at_100 value: 1.73 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 25.051000000000002 - type: precision_at_5 value: 17.531 - type: recall_at_1 value: 20.191 - type: recall_at_10 value: 45.768 - type: recall_at_100 value: 68.82000000000001 - type: recall_at_1000 value: 89.133 - type: recall_at_3 value: 33.296 - type: recall_at_5 value: 38.022 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.257 - type: map_at_10 value: 61.467000000000006 - type: map_at_100 value: 62.364 - type: map_at_1000 value: 62.424 - type: map_at_3 value: 58.228 - type: map_at_5 value: 60.283 - type: mrr_at_1 value: 78.515 - type: mrr_at_10 value: 84.191 - type: mrr_at_100 value: 84.378 - type: mrr_at_1000 value: 84.385 - type: mrr_at_3 value: 83.284 - type: mrr_at_5 value: 83.856 - type: ndcg_at_1 value: 78.515 - type: ndcg_at_10 value: 69.78999999999999 - type: ndcg_at_100 value: 72.886 - type: ndcg_at_1000 value: 74.015 - type: ndcg_at_3 value: 65.23 - type: ndcg_at_5 value: 67.80199999999999 - type: precision_at_1 value: 78.515 - type: precision_at_10 value: 14.519000000000002 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.702 - type: precision_at_5 value: 27.046999999999997 - type: recall_at_1 value: 39.257 - type: recall_at_10 value: 72.59299999999999 - type: recall_at_100 value: 84.679 - type: recall_at_1000 value: 92.12 - type: recall_at_3 value: 62.552 - type: recall_at_5 value: 67.616 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.5152 - type: ap value: 87.64584669595709 - type: f1 value: 91.50605576428437 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.926000000000002 - type: map_at_10 value: 34.049 - type: map_at_100 value: 35.213 - type: map_at_1000 value: 35.265 - type: map_at_3 value: 30.309 - type: map_at_5 value: 32.407000000000004 - type: mrr_at_1 value: 22.55 - type: mrr_at_10 value: 34.657 - type: mrr_at_100 value: 35.760999999999996 - type: mrr_at_1000 value: 35.807 - type: mrr_at_3 value: 30.989 - type: mrr_at_5 value: 33.039 - type: ndcg_at_1 value: 22.55 - type: ndcg_at_10 value: 40.842 - type: ndcg_at_100 value: 46.436 - type: ndcg_at_1000 value: 47.721999999999994 - type: ndcg_at_3 value: 33.209 - type: ndcg_at_5 value: 36.943 - type: precision_at_1 value: 22.55 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.136000000000001 - type: precision_at_5 value: 10.381 - type: recall_at_1 value: 21.926000000000002 - type: recall_at_10 value: 61.724999999999994 - type: recall_at_100 value: 87.604 - type: recall_at_1000 value: 97.421 - type: recall_at_3 value: 40.944 - type: recall_at_5 value: 49.915 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.54765161878704 - type: f1 value: 93.3298945415573 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.71591427268582 - type: f1 value: 59.32113870474471 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 75.83053127101547 - type: f1 value: 73.60757944876475 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.72562205783457 - type: f1 value: 78.63761662505502 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.37935633767996 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.55270546130387 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.462692753143834 - type: mrr value: 31.497569753511563 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.646 - type: map_at_10 value: 12.498 - type: map_at_100 value: 15.486 - type: map_at_1000 value: 16.805999999999997 - type: map_at_3 value: 9.325 - type: map_at_5 value: 10.751 - type: mrr_at_1 value: 43.034 - type: mrr_at_10 value: 52.662 - type: mrr_at_100 value: 53.189 - type: mrr_at_1000 value: 53.25 - type: mrr_at_3 value: 50.929 - type: mrr_at_5 value: 51.92 - type: ndcg_at_1 value: 41.796 - type: ndcg_at_10 value: 33.477000000000004 - type: ndcg_at_100 value: 29.996000000000002 - type: ndcg_at_1000 value: 38.864 - type: ndcg_at_3 value: 38.940000000000005 - type: ndcg_at_5 value: 36.689 - type: precision_at_1 value: 43.034 - type: precision_at_10 value: 24.799 - type: precision_at_100 value: 7.432999999999999 - type: precision_at_1000 value: 1.9929999999999999 - type: precision_at_3 value: 36.842000000000006 - type: precision_at_5 value: 32.135999999999996 - type: recall_at_1 value: 5.646 - type: recall_at_10 value: 15.963 - type: recall_at_100 value: 29.492 - type: recall_at_1000 value: 61.711000000000006 - type: recall_at_3 value: 10.585 - type: recall_at_5 value: 12.753999999999998 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 27.602 - type: map_at_10 value: 41.545 - type: map_at_100 value: 42.644999999999996 - type: map_at_1000 value: 42.685 - type: map_at_3 value: 37.261 - type: map_at_5 value: 39.706 - type: mrr_at_1 value: 31.141000000000002 - type: mrr_at_10 value: 44.139 - type: mrr_at_100 value: 44.997 - type: mrr_at_1000 value: 45.025999999999996 - type: mrr_at_3 value: 40.503 - type: mrr_at_5 value: 42.64 - type: ndcg_at_1 value: 31.141000000000002 - type: ndcg_at_10 value: 48.995 - type: ndcg_at_100 value: 53.788000000000004 - type: ndcg_at_1000 value: 54.730000000000004 - type: ndcg_at_3 value: 40.844 - type: ndcg_at_5 value: 44.955 - type: precision_at_1 value: 31.141000000000002 - type: precision_at_10 value: 8.233 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 18.579 - type: precision_at_5 value: 13.533999999999999 - type: recall_at_1 value: 27.602 - type: recall_at_10 value: 69.216 - type: recall_at_100 value: 90.252 - type: recall_at_1000 value: 97.27 - type: recall_at_3 value: 47.987 - type: recall_at_5 value: 57.438 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.949 - type: map_at_10 value: 84.89999999999999 - type: map_at_100 value: 85.531 - type: map_at_1000 value: 85.548 - type: map_at_3 value: 82.027 - type: map_at_5 value: 83.853 - type: mrr_at_1 value: 81.69999999999999 - type: mrr_at_10 value: 87.813 - type: mrr_at_100 value: 87.917 - type: mrr_at_1000 value: 87.91799999999999 - type: mrr_at_3 value: 86.938 - type: mrr_at_5 value: 87.53999999999999 - type: ndcg_at_1 value: 81.75 - type: ndcg_at_10 value: 88.55499999999999 - type: ndcg_at_100 value: 89.765 - type: ndcg_at_1000 value: 89.871 - type: ndcg_at_3 value: 85.905 - type: ndcg_at_5 value: 87.41 - type: precision_at_1 value: 81.75 - type: precision_at_10 value: 13.403 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.597 - type: precision_at_5 value: 24.69 - type: recall_at_1 value: 70.949 - type: recall_at_10 value: 95.423 - type: recall_at_100 value: 99.509 - type: recall_at_1000 value: 99.982 - type: recall_at_3 value: 87.717 - type: recall_at_5 value: 92.032 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 51.76962893449579 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.32897690686379 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.478 - type: map_at_10 value: 11.994 - type: map_at_100 value: 13.977 - type: map_at_1000 value: 14.295 - type: map_at_3 value: 8.408999999999999 - type: map_at_5 value: 10.024 - type: mrr_at_1 value: 22.1 - type: mrr_at_10 value: 33.526 - type: mrr_at_100 value: 34.577000000000005 - type: mrr_at_1000 value: 34.632000000000005 - type: mrr_at_3 value: 30.217 - type: mrr_at_5 value: 31.962000000000003 - type: ndcg_at_1 value: 22.1 - type: ndcg_at_10 value: 20.191 - type: ndcg_at_100 value: 27.954 - type: ndcg_at_1000 value: 33.491 - type: ndcg_at_3 value: 18.787000000000003 - type: ndcg_at_5 value: 16.378999999999998 - type: precision_at_1 value: 22.1 - type: precision_at_10 value: 10.69 - type: precision_at_100 value: 2.1919999999999997 - type: precision_at_1000 value: 0.35200000000000004 - type: precision_at_3 value: 17.732999999999997 - type: precision_at_5 value: 14.499999999999998 - type: recall_at_1 value: 4.478 - type: recall_at_10 value: 21.657 - type: recall_at_100 value: 44.54 - type: recall_at_1000 value: 71.542 - type: recall_at_3 value: 10.778 - type: recall_at_5 value: 14.687 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.82325259156718 - type: cos_sim_spearman value: 79.2463589100662 - type: euclidean_pearson value: 80.48318380496771 - type: euclidean_spearman value: 79.34451935199979 - type: manhattan_pearson value: 80.39041824178759 - type: manhattan_spearman value: 79.23002892700211 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.74130231431258 - type: cos_sim_spearman value: 78.36856568042397 - type: euclidean_pearson value: 82.48301631890303 - type: euclidean_spearman value: 78.28376980722732 - type: manhattan_pearson value: 82.43552075450525 - type: manhattan_spearman value: 78.22702443947126 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.96138619461459 - type: cos_sim_spearman value: 81.85436343502379 - type: euclidean_pearson value: 81.82895226665367 - type: euclidean_spearman value: 82.22707349602916 - type: manhattan_pearson value: 81.66303369445873 - type: manhattan_spearman value: 82.05030197179455 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.05481244198648 - type: cos_sim_spearman value: 80.85052504637808 - type: euclidean_pearson value: 80.86728419744497 - type: euclidean_spearman value: 81.033786401512 - type: manhattan_pearson value: 80.90107531061103 - type: manhattan_spearman value: 81.11374116827795 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 84.615220756399 - type: cos_sim_spearman value: 86.46858500002092 - type: euclidean_pearson value: 86.08307800247586 - type: euclidean_spearman value: 86.72691443870013 - type: manhattan_pearson value: 85.96155594487269 - type: manhattan_spearman value: 86.605909505275 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.14363913634436 - type: cos_sim_spearman value: 84.48430226487102 - type: euclidean_pearson value: 83.75303424801902 - type: euclidean_spearman value: 84.56762380734538 - type: manhattan_pearson value: 83.6135447165928 - type: manhattan_spearman value: 84.39898212616731 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.09909252554525 - type: cos_sim_spearman value: 85.70951402743276 - type: euclidean_pearson value: 87.1991936239908 - type: euclidean_spearman value: 86.07745840612071 - type: manhattan_pearson value: 87.25039137549952 - type: manhattan_spearman value: 85.99938746659761 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.529332093413615 - type: cos_sim_spearman value: 65.38177340147439 - type: euclidean_pearson value: 66.35278011412136 - type: euclidean_spearman value: 65.47147267032997 - type: manhattan_pearson value: 66.71804682408693 - type: manhattan_spearman value: 65.67406521423597 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.45802942885662 - type: cos_sim_spearman value: 84.8853341842566 - type: euclidean_pearson value: 84.60915021096707 - type: euclidean_spearman value: 85.11181242913666 - type: manhattan_pearson value: 84.38600521210364 - type: manhattan_spearman value: 84.89045417981723 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.92793380635129 - type: mrr value: 95.85834191226348 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 55.74400000000001 - type: map_at_10 value: 65.455 - type: map_at_100 value: 66.106 - type: map_at_1000 value: 66.129 - type: map_at_3 value: 62.719 - type: map_at_5 value: 64.441 - type: mrr_at_1 value: 58.667 - type: mrr_at_10 value: 66.776 - type: mrr_at_100 value: 67.363 - type: mrr_at_1000 value: 67.384 - type: mrr_at_3 value: 64.889 - type: mrr_at_5 value: 66.122 - type: ndcg_at_1 value: 58.667 - type: ndcg_at_10 value: 69.904 - type: ndcg_at_100 value: 72.807 - type: ndcg_at_1000 value: 73.423 - type: ndcg_at_3 value: 65.405 - type: ndcg_at_5 value: 67.86999999999999 - type: precision_at_1 value: 58.667 - type: precision_at_10 value: 9.3 - type: precision_at_100 value: 1.08 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 17 - type: recall_at_1 value: 55.74400000000001 - type: recall_at_10 value: 82.122 - type: recall_at_100 value: 95.167 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 70.14399999999999 - type: recall_at_5 value: 76.417 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.86534653465347 - type: cos_sim_ap value: 96.54142419791388 - type: cos_sim_f1 value: 93.07535641547861 - type: cos_sim_precision value: 94.81327800829875 - type: cos_sim_recall value: 91.4 - type: dot_accuracy value: 99.86435643564356 - type: dot_ap value: 96.53682260449868 - type: dot_f1 value: 92.98515104966718 - type: dot_precision value: 95.27806925498426 - type: dot_recall value: 90.8 - type: euclidean_accuracy value: 99.86336633663366 - type: euclidean_ap value: 96.5228676185697 - type: euclidean_f1 value: 92.9735234215886 - type: euclidean_precision value: 94.70954356846472 - type: euclidean_recall value: 91.3 - type: manhattan_accuracy value: 99.85841584158416 - type: manhattan_ap value: 96.50392760934032 - type: manhattan_f1 value: 92.84642321160581 - type: manhattan_precision value: 92.8928928928929 - type: manhattan_recall value: 92.80000000000001 - type: max_accuracy value: 99.86534653465347 - type: max_ap value: 96.54142419791388 - type: max_f1 value: 93.07535641547861 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 61.08285408766616 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.640675309010604 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.20333913710715 - type: mrr value: 54.088813555725324 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.79465221925075 - type: cos_sim_spearman value: 30.530816059163634 - type: dot_pearson value: 31.364837244718043 - type: dot_spearman value: 30.79726823684003 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22599999999999998 - type: map_at_10 value: 1.735 - type: map_at_100 value: 8.978 - type: map_at_1000 value: 20.851 - type: map_at_3 value: 0.613 - type: map_at_5 value: 0.964 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 92.867 - type: mrr_at_100 value: 92.867 - type: mrr_at_1000 value: 92.867 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 92.667 - type: ndcg_at_1 value: 82 - type: ndcg_at_10 value: 73.164 - type: ndcg_at_100 value: 51.878 - type: ndcg_at_1000 value: 44.864 - type: ndcg_at_3 value: 79.184 - type: ndcg_at_5 value: 76.39 - type: precision_at_1 value: 88 - type: precision_at_10 value: 76.2 - type: precision_at_100 value: 52.459999999999994 - type: precision_at_1000 value: 19.692 - type: precision_at_3 value: 82.667 - type: precision_at_5 value: 80 - type: recall_at_1 value: 0.22599999999999998 - type: recall_at_10 value: 1.942 - type: recall_at_100 value: 12.342 - type: recall_at_1000 value: 41.42 - type: recall_at_3 value: 0.637 - type: recall_at_5 value: 1.034 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.567 - type: map_at_10 value: 13.116 - type: map_at_100 value: 19.39 - type: map_at_1000 value: 20.988 - type: map_at_3 value: 7.109 - type: map_at_5 value: 9.950000000000001 - type: mrr_at_1 value: 42.857 - type: mrr_at_10 value: 57.404999999999994 - type: mrr_at_100 value: 58.021 - type: mrr_at_1000 value: 58.021 - type: mrr_at_3 value: 54.762 - type: mrr_at_5 value: 56.19 - type: ndcg_at_1 value: 38.775999999999996 - type: ndcg_at_10 value: 30.359 - type: ndcg_at_100 value: 41.284 - type: ndcg_at_1000 value: 52.30200000000001 - type: ndcg_at_3 value: 36.744 - type: ndcg_at_5 value: 34.326 - type: precision_at_1 value: 42.857 - type: precision_at_10 value: 26.122 - type: precision_at_100 value: 8.082 - type: precision_at_1000 value: 1.559 - type: precision_at_3 value: 40.136 - type: precision_at_5 value: 35.510000000000005 - type: recall_at_1 value: 3.567 - type: recall_at_10 value: 19.045 - type: recall_at_100 value: 49.979 - type: recall_at_1000 value: 84.206 - type: recall_at_3 value: 8.52 - type: recall_at_5 value: 13.103000000000002 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 68.8394 - type: ap value: 13.454399712443099 - type: f1 value: 53.04963076364322 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.546123372948514 - type: f1 value: 60.86952793277713 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.10042955060234 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.03308100375514 - type: cos_sim_ap value: 71.08284605869684 - type: cos_sim_f1 value: 65.42539436255494 - type: cos_sim_precision value: 64.14807302231237 - type: cos_sim_recall value: 66.75461741424802 - type: dot_accuracy value: 84.68736961316088 - type: dot_ap value: 69.20524036530992 - type: dot_f1 value: 63.54893953365829 - type: dot_precision value: 63.45698500394633 - type: dot_recall value: 63.641160949868066 - type: euclidean_accuracy value: 85.07480479227513 - type: euclidean_ap value: 71.14592761009864 - type: euclidean_f1 value: 65.43814432989691 - type: euclidean_precision value: 63.95465994962216 - type: euclidean_recall value: 66.99208443271768 - type: manhattan_accuracy value: 85.06288370984085 - type: manhattan_ap value: 71.07289742593868 - type: manhattan_f1 value: 65.37585421412301 - type: manhattan_precision value: 62.816147859922175 - type: manhattan_recall value: 68.15303430079156 - type: max_accuracy value: 85.07480479227513 - type: max_ap value: 71.14592761009864 - type: max_f1 value: 65.43814432989691 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.79058485659952 - type: cos_sim_ap value: 83.7183187008759 - type: cos_sim_f1 value: 75.86921142180798 - type: cos_sim_precision value: 73.00683371298405 - type: cos_sim_recall value: 78.96519864490298 - type: dot_accuracy value: 87.0085768618776 - type: dot_ap value: 81.87467488474279 - type: dot_f1 value: 74.04188363990559 - type: dot_precision value: 72.10507114191901 - type: dot_recall value: 76.08561749307053 - type: euclidean_accuracy value: 87.8332751193387 - type: euclidean_ap value: 83.83585648120315 - type: euclidean_f1 value: 76.02582177042369 - type: euclidean_precision value: 73.36388371759989 - type: euclidean_recall value: 78.88820449645827 - type: manhattan_accuracy value: 87.87208444910156 - type: manhattan_ap value: 83.8101950642973 - type: manhattan_f1 value: 75.90454195535027 - type: manhattan_precision value: 72.44419564761039 - type: manhattan_recall value: 79.71204188481676 - type: max_accuracy value: 87.87208444910156 - type: max_ap value: 83.83585648120315 - type: max_f1 value: 76.02582177042369 --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</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 database for LLMs. ************* 🌟**Updates**🌟 ************* - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [**this**](#using-langchain); C-MTEB **leaderboard** is [avaliable](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!** - 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. ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | Description | query instruction for retrieval\* | |:-------------------------------|:--------:| :--------:| :--------:| | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | 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 | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | 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 | 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-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `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 | 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：` | \*: If you need to search the **long** relevant passages to a **short** query (s2p retrieval task), you need 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** need to be added to passages. ## Usage Here are some examples to use `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 = ["样例数据-1", "样例数据-2"] model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：") embeddings_1 = model.encode(sentences) embeddings_2 = model.encode(sentences) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, please 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 ``` The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU. #### Using Sentence-Transformers Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences = ["样例数据-1", "样例数据-2"] model = SentenceTransformer('BAAI/bge-large-zh') embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, 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') 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-small-en" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model_norm = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) ``` #### Using HuggingFace Transformers With 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 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') model = AutoModel.from_pretrained('BAAI/bge-large-zh') # 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) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** 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) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**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 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 384 | 512 | 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 768 | 512 | 56.00 | 41.88 | 41.1 | 82.54 | 53.14 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 768 | 512 | 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 | - **C-MTEB**: We create a 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 | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 | | [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** | | [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 | | [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | | [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 | ## Train This section will introduce the way we used to train the general embedding. The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md). **1. RetroMAE Pre-train** We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively. We used the AdamW optimizer and the learning rate is 2e-5. **Pre-training data**: - English: - [Pile](https://pile.eleuther.ai/) - [wikipedia](https://huggingface.co/datasets/wikipedia) - [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus) - Chinese: - [wudao](https://github.com/BAAI-WuDao/Data) **2. Finetune** We fine-tune the model using a contrastive objective. The format of input data is a triple`(query, positive, negative)`. Besides the negative in the triple, we also adopt in-batch negatives strategy. We employ the cross-device negatives sharing method to share negatives among different GPUs, which can dramatically **increase the number of negatives**. We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). We used the AdamW optimizer and the learning rate is 1e-5. The temperature for contrastive loss is 0.01. Besides, we add instruction to the query for s2p(short query to long passage) retrieval task in the training (add nothing to passages). For English, the instruction is `Represent this sentence for searching relevant passages: `; For Chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`. In the evaluation, the instruction should be added for queries in retrieval task, not be added for other tasks. Noted that the instruction is not needed for passages. The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). You can easily finetune your model with it. **Training data**: - For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on. - For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on. **The data collection is to be released in the future.** We will continually update the embedding models and training codes, hoping to promote the development of the embedding model community. ## License FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
aisingapore/gemma2-9b-cpt-sea-lionv3-base
aisingapore
text-generation
[ "transformers", "safetensors", "gemma2", "text-generation", "en", "zh", "vi", "id", "th", "fil", "ta", "ms", "km", "lo", "my", "arxiv:2309.06085", "arxiv:2101.09635", "base_model:google/gemma-2-9b", "base_model:finetune:google/gemma-2-9b", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,730,258,495,000
2024-12-19T12:56:00
550
2
--- base_model: google/gemma-2-9b language: - en - zh - vi - id - th - fil - ta - ms - km - lo - my library_name: transformers license: gemma pipeline_tag: text-generation --- <div> <img src="gemma_2_9b_sea-lion_v3_base_banner.png"/> </div> # Gemma2 9B CPT SEA-LIONv3 SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. Gemma2 9B CPT SEA-LIONv3 Base is a multilingual model which has undergone continued pre-training on approximately **200B** tokens across the 11 official Southeast Asian languages: English, Chinese, Vietnamese, Indonesian, Thai, Tamil, Filipino, Malay, Khmer, Lao, Burmese. SEA-LION stands for <i>Southeast Asian Languages In One Network</i>. - **Developed by:** Products Pillar, AI Singapore - **Funded by:** Singapore NRF - **Model type:** Decoder - **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Khmer, Lao, Malay, Tamil, Thai, Vietnamese - **License:** [Gemma Community License](https://ai.google.dev/gemma/terms) ## Model Details ### Model Description We performed continued pre-training in English and ASEAN languages on [Gemma-2-9B](https://huggingface.co/google/gemma-2-9b), a decoder model using the Gemma 2 architecture, to create Gemma2 9B CPT SEA-LIONv3 Base. For tokenisation, the model employs the default tokenizer used in Gemma 2 9B. ### Benchmark Performance We evaluated Gemma2 9B CPT SEA-LIONv3 base model on general language 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 **five-shot** with native prompts on a sample of 100-1000 instances for each dataset. For more details on Gemma2 9B CPT SEA-LIONv3 base benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/ ## Technical Specifications ### Infrastructure Gemma2 9B CPT SEA-LIONv3 was trained using [MosaicML Composer](https://github.com/mosaicml/composer) on the following hardware: | Training Details | Gemma2 9B CPT SEA-LIONv3 | |----------------------|:------------------------:| | SingTel HGX-100 | 8 instances | | Nvidia H100 80GB GPU | 64 | | Training Duration | 10 days | ### Configuration | HyperParameter | Gemma2 9B CPT SEA-LIONv3 | |-------------------|:------------------------:| | Precision | bfloat16 | | Optimizer | decoupled_adamw | | Scheduler | weight_stable_decay | | Learning Rate | 1.0e-5 | | Global Batch Size | 512 | | Micro Batch Size | 1 | ## Data Gemma2 9B CPT SEA-LIONv3 base model was continued pre-trained on 200B tokens of the following data: | Language | Source | Total Tokens (B) | Percentage (%) | Total percentage (%) | | ------------------------ | ---------------- | ---------------- | -------------- | -------------------- | | Code | StackV2 | 40 | 20 | 20 | | English | Dolma | 37.5 | 18.75 | 25 | | | Fineweb-Edu | 7.5 | 3.75 | | | Others | 5 | 2.5 | | Chinese | SEA-LION Pile v1 | 12 | 6 | 13 | | | Others | 14 | 7 | | Vietnamese | SEA-LION Pile v1 | 8.4 | 4.2 | 13 | | | VinBigData | 16 | 8 | | | Others | 1.6 | 0.8 | | Indonesian | SEA-LION Pile v1 | 7 | 3.5 | 13 | | | SEA-LION Pile v2 | 7 | 3.5 | | | Others | 12 | 6 | | Thai | SEA-LION Pile v1 | 10.7 | 5.35 | 10 | | | WangChanBERTa | 8.5 | 4.25 | | | Others | 0.8 | 0.4 | | Filipino - Malay - Tamil | SEA-LION Pile v1 | 4.28 | 2.14 | 3 | | | Others | 1.72 | 0.86 | | Khmer - Lao - Burmese | SEA-LION Pile v1 | 5.2 | 2.6 | 3 | | | Others | 0.8 | 0.4 | Note: - All token counts are counted using Gemma 2 9B tokenizer - SEA-LION Pile v1 is processed from Common Crawl WET, which is published [here](https://huggingface.co/datasets/aisingapore/sea-lion-pile). The cutoff date of this version is September 2020. - SEA-LION Pile v2 is processed from Common Crawl WARC from October 2020 to April 2024. - Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/) ## 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. ## References ### Thai Pre-Training Data Reference ```bibtex @misc{lowphansirikul2021wangchanberta, title={WangchanBERTa: Pretraining transformer-based Thai Language Models}, author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong}, year={2021}, eprint={2101.09635}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "CHIA" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.blonde-hair
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,703,890,493,000
2023-12-29T22:54:57
133
1
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/evaluate/blonde hair.../blonde hair_17_3.0.png widget: - text: blonde hair output: url: images/blonde hair_17_3.0.png - text: blonde hair output: url: images/blonde hair_19_3.0.png - text: blonde hair output: url: images/blonde hair_20_3.0.png - text: blonde hair output: url: images/blonde hair_21_3.0.png - text: blonde hair output: url: images/blonde hair_22_3.0.png inference: false instance_prompt: blonde hair --- # ntcai.xyz slider - blonde hair (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/blonde hair_17_-3.0.png" width=256 height=256 /> | <img src="images/blonde hair_17_0.0.png" width=256 height=256 /> | <img src="images/blonde hair_17_3.0.png" width=256 height=256 /> | | <img src="images/blonde hair_19_-3.0.png" width=256 height=256 /> | <img src="images/blonde hair_19_0.0.png" width=256 height=256 /> | <img src="images/blonde hair_19_3.0.png" width=256 height=256 /> | | <img src="images/blonde hair_20_-3.0.png" width=256 height=256 /> | <img src="images/blonde hair_20_0.0.png" width=256 height=256 /> | <img src="images/blonde hair_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` blonde hair ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.blonde-hair', weight_name='blonde hair.safetensors', adapter_name="blonde hair") # Activate the LoRA pipe.set_adapters(["blonde hair"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, blonde hair" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 720+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.catwalk
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,702,244,582,000
2024-02-06T00:28:54
26
0
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/catwalk_17_3.0.png widget: - text: catwalk output: url: images/catwalk_17_3.0.png - text: catwalk output: url: images/catwalk_19_3.0.png - text: catwalk output: url: images/catwalk_20_3.0.png - text: catwalk output: url: images/catwalk_21_3.0.png - text: catwalk output: url: images/catwalk_22_3.0.png inference: false instance_prompt: catwalk --- # ntcai.xyz slider - catwalk (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/catwalk_17_-3.0.png" width=256 height=256 /> | <img src="images/catwalk_17_0.0.png" width=256 height=256 /> | <img src="images/catwalk_17_3.0.png" width=256 height=256 /> | | <img src="images/catwalk_19_-3.0.png" width=256 height=256 /> | <img src="images/catwalk_19_0.0.png" width=256 height=256 /> | <img src="images/catwalk_19_3.0.png" width=256 height=256 /> | | <img src="images/catwalk_20_-3.0.png" width=256 height=256 /> | <img src="images/catwalk_20_0.0.png" width=256 height=256 /> | <img src="images/catwalk_20_3.0.png" width=256 height=256 /> | See more at [https://sliders.ntcai.xyz/sliders/app/loras/5aa8afbd-670c-4bf2-80c1-7691682375f5](https://sliders.ntcai.xyz/sliders/app/loras/5aa8afbd-670c-4bf2-80c1-7691682375f5) ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` catwalk ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.catwalk', weight_name='catwalk.safetensors', adapter_name="catwalk") # Activate the LoRA pipe.set_adapters(["catwalk"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, catwalk" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 1496+ unique and diverse LoRAs along with 14600+ slider merges, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful <strong>NTC Slider Factory</strong> LoRA creator, allowing you to craft your own custom LoRAs and merges opening up endless possibilities. Your support on Patreon will allow us to continue developing new models and tools. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
jncraton/Falcon3-1B-Instruct-ct2-int8
jncraton
null
[ "ctranslate2", "falcon3", "en", "fr", "es", "pt", "base_model:tiiuae/Falcon3-1B-Instruct", "base_model:quantized:tiiuae/Falcon3-1B-Instruct", "license:other", "region:us" ]
1,734,459,007,000
2024-12-19T15:35:12
8
0
--- base_model: tiiuae/Falcon3-1B-Instruct language: - en - fr - es - pt library_name: ctranslate2 license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html tags: - falcon3 base_model_relation: quantized --- <div align="center"> <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/> </div> # Falcon3-1B-Instruct **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. This repository contains the **Falcon3-1B-Instruct**. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-1B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 8K. ## Model Details - Architecture - Transformer-based causal decoder-only architecture - 18 decoder blocks - Grouped Query Attention (GQA) for faster inference: 8 query heads and 4 key-value heads - Wider head dimension: 256 - High RoPE value to support long context understanding: 1000042 - Uses SwiGLU and RMSNorm - 8K context length - 131K vocab size - Pruned and healed using larger Falcon models (3B and 7B respectively) on only 80 Gigatokens of datasets comprising of web, code, STEM, high quality and multilingual data using 256 H100 GPU chips - Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data - Supports EN, FR, ES, PT - Developed by [Technology Innovation Institute](https://www.tii.ae) - License: TII Falcon-LLM License 2.0 - Model Release Date: December 2024 ## Getting started <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "tiiuae/Falcon3-1B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "How many hours in one day?" messages = [ {"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=1024 ) 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] print(response) ``` </details> <br> ## Benchmarks We report in the following table our internal pipeline benchmarks. - We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). - We report **raw scores** obtained by applying chat template **without fewshot_as_multiturn** (unlike Llama3.1). - We use same batch-size across all models. <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;"> <colgroup> <col style="width: 10%;"> <col style="width: 10%;"> <col style="width: 7%;"> <col style="width: 7%;"> <col style="width: 7%;"> <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> </colgroup> <thead> <tr> <th>Category</th> <th>Benchmark</th> <th>Llama-3.2-1B</th> <th>Qwen2.5-1.5B</th> <th>SmolLM2-1.7B</th> <th>Falcon3-1B-Instruct</th> </tr> </thead> <tbody> <tr> <td rowspan="3">General</td> <td>MMLU (5-shot)</td> <td>23.4</td> <td><b>58.4</b></td> <td>48.4</td> <td>43.9</td> </tr> <tr> <td>MMLU-PRO (5-shot)</td> <td>11.3</td> <td><b>21.3</b></td> <td>17.2</td> <td>18.6</td> </tr> <tr> <td>IFEval</td> <td><b>55.8</b></td> <td>44.4</td> <td>53.0</td> <td>54.4</td> </tr> <tr> <td rowspan="3">Math</td> <td>GSM8K (5-shot)</td> <td>37.4</td> <td><b>57.2</b></td> <td>43.4</td> <td>38.6</td> </tr> <tr> <td>GSM8K (8-shot, COT)</td> <td>35.6</td> <td><b>62.2</b></td> <td>47.2</td> <td>41.8</td> </tr> <tr> <td>MATH Lvl-5 (4-shot)</td> <td><b>3.9</b></td> <td>0.2</td> <td>0.1</td> <td>1.0</td> </tr> <tr> <td rowspan="6">Reasoning</td> <td>Arc Challenge (25-shot)</td> <td>34.1</td> <td>47.0</td> <td><b>47.6</b></td> <td>45.9</td> </tr> <tr> <td>GPQA (0-shot)</td> <td>25.3</td> <td><b>29.6</b></td> <td>28.7</td> <td>26.5</td> </tr> <tr> <td>GPQA (0-shot, COT)</td> <td>13.2</td> <td>9.2</td> <td>16.0</td> <td><b>21.3</b></td> </tr> <tr> <td>MUSR (0-shot)</td> <td>32.4</td> <td>36.8</td> <td>33.0</td> <td><b>40.7</b></td> </tr> <tr> <td>BBH (3-shot)</td> <td>30.3</td> <td><b>38.5</b></td> <td>33.1</td> <td>35.1</td> </tr> <tr> <td>BBH (3-shot, COT)</td> <td>0.0</td> <td>20.3</td> <td>0.8</td> <td><b>30.5</b></td> </tr> <tr> <td rowspan="5">CommonSense Understanding</td> <td>PIQA (0-shot)</td> <td>72.1</td> <td>73.2</td> <td><b>74.4</b></td> <td>72.0</td> </tr> <tr> <td>SciQ (0-shot)</td> <td>61.8</td> <td>69.5</td> <td>71.4</td> <td><b>86.8</b></td> </tr> <tr> <td>Winogrande (0-shot)</td> <td>-</td> <td>-</td> <td>-</td> <td><b>60.2</b></td> </tr> <tr> <td>OpenbookQA (0-shot)</td> <td>40.2</td> <td>40.4</td> <td><b>42.8</b></td> <td>40.0</td> </tr> <tr> <td>MT-Bench (avg)</td> <td>5.4</td> <td><b>7.1</b></td> <td>6.1</td> <td>5.5</td> </tr> <tr> <td rowspan="1">Instructions following</td> <td>Alpaca (WC)</td> <td><b>8.6</b></td> <td><b>8.6</b></td> <td>5.4</td> <td>6.1</td> </tr> </tbody> </table> ## Useful links - View our [release blogpost](https://huggingface.co/blog/falcon3). - Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers. ## Technical Report Coming soon.... ## Citation If the Falcon3 family of models were helpful to your work, feel free to give us a cite. ``` @misc{Falcon3, title = {The Falcon 3 Family of Open Models}, url = {https://huggingface.co/blog/falcon3}, author = {Falcon-LLM Team}, month = {December}, year = {2024} } ```
[ "SCIQ" ]
Non_BioNLP
AIDA-UPM/MARTINI_enrich_BERTopic_hiddeninplainsight1
AIDA-UPM
text-classification
[ "bertopic", "text-classification", "region:us" ]
1,736,792,777,000
2025-01-13T18:26:29
6
0
--- library_name: bertopic pipeline_tag: text-classification tags: - bertopic --- # MARTINI_enrich_BERTopic_hiddeninplainsight1 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. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_hiddeninplainsight1") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 41 * Number of training documents: 4643 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | -1 | vaccinated - pandemic - deaths - 2021 - mrna | 20 | -1_vaccinated_pandemic_deaths_2021 | | 0 | transgenderism - lgbtq - pedophiles - brainwashing - god | 2590 | 0_transgenderism_lgbtq_pedophiles_brainwashing | | 1 | invincible - unshakeable - champion - hope - moments | 168 | 1_invincible_unshakeable_champion_hope | | 2 | zealanders - christchurch - terrorist - devastation - deported | 144 | 2_zealanders_christchurch_terrorist_devastation | | 3 | police - australians - violently - toowoomba - tyranny | 134 | 3_police_australians_violently_toowoomba | | 4 | iphone - radiofrequency - cancers - dangerous - 5g | 114 | 4_iphone_radiofrequency_cancers_dangerous | | 5 | illuminati - pedophiles - trafficking - cannibalism - satanic | 76 | 5_illuminati_pedophiles_trafficking_cannibalism | | 6 | detoxing - fulvic - chelates - glutathione - themiracledirt | 72 | 6_detoxing_fulvic_chelates_glutathione | | 7 | vaccinating - fauci - jabs - deaf - simpsons | 65 | 7_vaccinating_fauci_jabs_deaf | | 8 | cashless - banks - withdrawals - coinbase - donations | 64 | 8_cashless_banks_withdrawals_coinbase | | 9 | propolis - glutathione - remedies - garlic - poisoned | 63 | 9_propolis_glutathione_remedies_garlic | | 10 | robodogs - sniper - webcams - smartphones - unmanned | 61 | 10_robodogs_sniper_webcams_smartphones | | 11 | fluoridated - poison - cereals - monsanto - coca | 58 | 11_fluoridated_poison_cereals_monsanto | | 12 | floods - austria - wildfires - chemtrails - devastating | 57 | 12_floods_austria_wildfires_chemtrails | | 13 | myocarditis - pfizer - injections - clots - cyanotic | 55 | 13_myocarditis_pfizer_injections_clots | | 14 | vaccinated - deaths - boosters - boris - doses | 51 | 14_vaccinated_deaths_boosters_boris | | 15 | grubs - cannibalism - meal - suckers - hungry | 51 | 15_grubs_cannibalism_meal_suckers | | 16 | australia - doctors - vaxxination - lockdown - bureaucrats | 50 | 16_australia_doctors_vaxxination_lockdown | | 17 | pcr - nasopharyngeal - swabs - test - pineal | 47 | 17_pcr_nasopharyngeal_swabs_test | | 18 | queensland - vaccinations - lockdown - mandatory - constitutional | 47 | 18_queensland_vaccinations_lockdown_mandatory | | 19 | biden - cronies - cambodia - teleprompter - clown | 45 | 19_biden_cronies_cambodia_teleprompter | | 20 | snitch - pedos - discredit - fbi - executions | 43 | 20_snitch_pedos_discredit_fbi | | 21 | usda - famines - rationed - beans - supermarkets | 41 | 21_usda_famines_rationed_beans | | 22 | gates - billions - zika - vaccinate - younggloballeader | 37 | 22_gates_billions_zika_vaccinate | | 23 | quarantine - taiwan - shenzhen - weibo - test | 36 | 23_quarantine_taiwan_shenzhen_weibo | | 24 | surveillance - australia - chinafication - wechat - fingerprint | 36 | 24_surveillance_australia_chinafication_wechat | | 25 | pilots - inflight - qantas - mh370 - crash | 36 | 25_pilots_inflight_qantas_mh370 | | 26 | climate - co2 - thunberg - hoax - mccarthy | 34 | 26_climate_co2_thunberg_hoax | | 27 | gmo - biosludge - corpses - fake - dissolved | 34 | 27_gmo_biosludge_corpses_fake | | 28 | remdesivir - ivermectine - hcq - intravenous - dexamethasone | 31 | 28_remdesivir_ivermectine_hcq_intravenous | | 29 | grapheneagenda - nanoparticles - adjuvant - magnetized - toxicity | 30 | 29_grapheneagenda_nanoparticles_adjuvant_magnetized | | 30 | doctors - malpractice - rage - whistleblower - wtaf | 28 | 30_doctors_malpractice_rage_whistleblower | | 31 | deleted - messages - banned - unfollow - spammer | 28 | 31_deleted_messages_banned_unfollow | | 32 | pfizer - doctored - miscarriages - shots - nurtec | 28 | 32_pfizer_doctored_miscarriages_shots | | 33 | hillary - trafficking - epstein - fbi - revealed | 27 | 33_hillary_trafficking_epstein_fbi | | 34 | maskitis - pneumonia - oxygen - vaxxinated - mold | 25 | 34_maskitis_pneumonia_oxygen_vaxxinated | | 35 | fires - chickens - smoldering - explosion - fertilizer | 24 | 35_fires_chickens_smoldering_explosion | | 36 | cyberpandemic - attacks - sabotaged - decentralized - russia | 24 | 36_cyberpandemic_attacks_sabotaged_decentralized | | 37 | trudeau - ottawa - dissidents - saskatchewan - blockade | 24 | 37_trudeau_ottawa_dissidents_saskatchewan | | 38 | telegram - deleting - censorship - hiddenvideos - encrypted | 23 | 38_telegram_deleting_censorship_hiddenvideos | | 39 | melatonin - morocco - harmful - shield - orgone | 22 | 39_melatonin_morocco_harmful_shield | </details> ## Training hyperparameters * calculate_probabilities: True * 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: False * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.26.4 * HDBSCAN: 0.8.40 * UMAP: 0.5.7 * Pandas: 2.2.3 * Scikit-Learn: 1.5.2 * Sentence-transformers: 3.3.1 * Transformers: 4.46.3 * Numba: 0.60.0 * Plotly: 5.24.1 * Python: 3.10.12
[ "PCR" ]
Non_BioNLP
internistai/base-7b-v0.2-Q4_K_M-GGUF
internistai
null
[ "gguf", "medical", "llama-cpp", "gguf-my-repo", "en", "dataset:Open-Orca/OpenOrca", "dataset:pubmed", "dataset:medmcqa", "dataset:maximegmd/medqa_alpaca_format", "base_model:internistai/base-7b-v0.2", "base_model:quantized:internistai/base-7b-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
1,719,092,355,000
2024-06-22T21:39:33
5
0
--- base_model: internistai/base-7b-v0.2 datasets: - Open-Orca/OpenOrca - pubmed - medmcqa - maximegmd/medqa_alpaca_format language: - en license: apache-2.0 metrics: - accuracy tags: - medical - llama-cpp - gguf-my-repo tag: text-generation --- # maximegmd/base-7b-v0.2-Q4_K_M-GGUF This model was converted to GGUF format from [`internistai/base-7b-v0.2`](https://huggingface.co/internistai/base-7b-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/internistai/base-7b-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo maximegmd/base-7b-v0.2-Q4_K_M-GGUF --hf-file base-7b-v0.2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo maximegmd/base-7b-v0.2-Q4_K_M-GGUF --hf-file base-7b-v0.2-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo maximegmd/base-7b-v0.2-Q4_K_M-GGUF --hf-file base-7b-v0.2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo maximegmd/base-7b-v0.2-Q4_K_M-GGUF --hf-file base-7b-v0.2-q4_k_m.gguf -c 2048 ```
[ "MEDQA" ]
BioNLP
GuCuChiara/NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased
GuCuChiara
token-classification
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,695,599,872,000
2023-10-10T14:13:27
20
0
--- base_model: bert-base-multilingual-cased license: apache-2.0 metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 - Precision: 0.6121 - Recall: 0.5161 - F1: 0.5600 - Accuracy: 0.9541 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 71 | 0.1704 | 0.4558 | 0.3635 | 0.4045 | 0.9353 | | No log | 2.0 | 142 | 0.1572 | 0.5925 | 0.3518 | 0.4415 | 0.9433 | | No log | 3.0 | 213 | 0.1386 | 0.5932 | 0.4774 | 0.5290 | 0.9531 | | No log | 4.0 | 284 | 0.1427 | 0.5945 | 0.5175 | 0.5534 | 0.9533 | | No log | 5.0 | 355 | 0.1653 | 0.6354 | 0.4788 | 0.5461 | 0.9540 | | No log | 6.0 | 426 | 0.1620 | 0.6121 | 0.5161 | 0.5600 | 0.9541 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
[ "DISTEMIST" ]
Non_BioNLP
espnet/roshansh_asr_base_sp_conformer_swbd
espnet
automatic-speech-recognition
[ "espnet", "audio", "automatic-speech-recognition", "dataset:swbd", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,647,205,901,000
2022-03-13T21:30:20
1
0
--- datasets: - swbd language: noinfo license: cc-by-4.0 tags: - espnet - audio - automatic-speech-recognition --- ## ESPnet2 ASR model ### `espnet/roshansh_asr_base_sp_conformer_swbd` This model was trained by roshansh-cmu using swbd recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout a04a98c98797b314f2425082bc40261757fd47de pip install -e . cd egs2/swbd/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/roshansh_asr_base_sp_conformer_swbd ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sun Mar 13 17:23:58 EDT 2022` - python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `a04a98c98797b314f2425082bc40261757fd47de` - Commit date: `Thu Mar 3 16:09:41 2022 -0500` ## roshansh_asr_base_sp_conformer_swbd ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/eval2000/hyp.callhm.ctm.filt.sys|2628|21594|87.4|9.6|3.0|2.0|14.6|49.7| |decode_asr_asr_model_valid.acc.ave_10best/eval2000/hyp.ctm.filt.sys|4459|42989|90.5|7.0|2.5|1.5|10.9|44.7| |decode_asr_asr_model_valid.acc.ave_10best/eval2000/hyp.swbd.ctm.filt.sys|1831|21395|93.7|4.3|2.0|0.9|7.2|37.7| |decode_lm_lm_lm_base_lm_transformer_valid.loss.ave_asr_model_valid.acc.ave_10best/eval2000/hyp.callhm.ctm.filt.sys|2628|21594|88.0|8.9|3.1|2.0|14.0|48.0| |decode_lm_lm_lm_base_lm_transformer_valid.loss.ave_asr_model_valid.acc.ave_10best/eval2000/hyp.ctm.filt.sys|4459|42989|91.0|6.5|2.5|1.4|10.4|43.0| |decode_lm_lm_lm_base_lm_transformer_valid.loss.ave_asr_model_valid.acc.ave_10best/eval2000/hyp.swbd.ctm.filt.sys|1831|21395|94.0|4.0|2.0|0.9|6.8|35.9| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/eval2000|4458|181952|92.3|3.7|4.0|11.9|19.5|69.9| |decode_lm_lm_lm_base_lm_transformer_valid.loss.ave_asr_model_valid.acc.ave_10best/eval2000|4458|181952|92.3|3.7|4.1|11.6|19.3|69.1| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/eval2000|4458|47302|81.7|13.5|4.8|16.7|34.9|69.9| |decode_lm_lm_lm_base_lm_transformer_valid.loss.ave_asr_model_valid.acc.ave_10best/eval2000|4458|47302|81.9|13.1|5.0|16.4|34.5|69.1| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_confformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_base_sp_conformer ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 52583 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 150 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 3000 use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null detect_anomaly: false pretrain_path: null init_param: [] freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 75000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_base_sp/train/speech_shape - exp/asr_stats_base_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_base_sp/valid/speech_shape - exp/asr_stats_base_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 - 800 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/fbank_pitch/train_nodup_sp/feats.scp - speech - kaldi_ark - - dump/fbank_pitch/train_nodup_sp/text - text - text valid_data_path_and_name_and_type: - - dump/fbank_pitch/train_dev/feats.scp - speech - kaldi_ark - - dump/fbank_pitch/train_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.006 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁i - s - '''' - ▁and - ▁the - ▁you - ▁that - ▁a - ▁it - ▁uh - ▁to - t - ▁of - ▁know - ▁they - '-' - ▁in - ▁we - ']' - ▁[ - ▁yeah - ▁have - ▁but - ▁so - ▁was - ▁like - re - m - ▁um - ▁just - ▁well - ▁do - ▁for - d - ▁think - ing - ▁don - ▁is - ▁there - ▁or - ▁on - ▁be - noise - ▁what - laughter - ▁oh - ▁my - ed - ve - ▁not - ▁really - ▁with - n - ▁he - ▁one - ▁if - ▁are - ▁all - ▁get - ▁right - ▁about - ▁can - ▁because - ▁out - ▁had - ▁up - ▁them - ▁lot - a - v - ▁at - ▁this - ▁would - ▁when - ▁go - ▁some - p - i - r - er - ▁people - ▁no - ▁mean - ▁kind - ▁then - o - ▁good - ▁now - ▁me - ▁got - e - ▁time - ll - ▁as - ▁she - ▁going - y - ▁see - ▁more - ▁were - ▁been - ▁from - ▁too - ▁an - ▁things - ly - ▁how - ▁something - c - ▁your - b - ▁where - ▁much - u - '._' - ▁guess - ▁little - g - ▁here - . - ▁thing - ▁our - le - ocalized - ▁very - ▁did - ▁their - ▁other - ▁work - ▁could - ▁okay - l - in - ▁even - ▁t - al - ▁two - huh - ▁way - or - ▁say - f - ▁has - ▁any - ▁s - ▁years - ▁want - ▁back - ▁down - ▁those - ▁who - ▁pretty - ▁probably - ▁home - ▁didn - ▁real - ▁year - ▁take - ▁over - ▁yes - ▁than - ▁re - ▁sure - ▁into - ar - hum - ▁school - ▁put - ▁stuff - an - ▁make - ▁kids - ▁her - ▁by - ▁said - ▁never - w - ▁c - ▁which - ▁off - k - ▁went - ic - ▁f - ▁only - ▁big - ▁car - ▁always - ▁these - ▁around - ▁money - ▁day - ▁anything - ▁three - ▁nice - ▁doing - ▁need - ▁come - ▁actually - ▁will - ▁maybe - ▁care - ▁him - h - ent - en - ▁still - ▁should - ▁new - ▁used - ▁five - 'on' - ch - ion - ▁long - ▁sort - ▁his - th - ter - ▁old - ▁most - ▁house - ▁bit - ▁e - ▁every - ▁different - ▁last - ▁use - ▁let - il - es - it - ▁many - ▁us - ▁look - ▁course - ▁getting - ▁true - ▁everything - ▁feel - ▁first - ck - ▁part - ▁does - ▁pay - ▁great - ▁hard - ▁same - ▁thought - ▁de - ▁problem - ▁also - ▁keep - ers - at - ▁through - ▁doesn - ▁children - ▁four - ▁find - ▁done - ment - ▁th - ies - ur - ▁before - ▁far - ▁though - ▁w - ▁area - ate - ▁haven - ▁o - ▁ever - ▁p - ▁ - ▁being - ▁family - ▁bad - ▁seems - ation - ▁d - ▁live - ▁whole - ▁fact - ▁own - se - ▁why - ▁b - ▁play - ▁talking - ▁tell - ▁better - ▁interesting - ▁another - ▁place - ▁try - ▁trying - ▁huh - ▁ten - te - ▁twenty - ▁else - ol - ▁watch - ▁read - ▁type - ro - ▁quite - ▁job - ▁hundred - ▁high - ▁call - ▁con - ▁ago - ▁after - ▁give - ▁couple - ▁enough - ▁whatever - ke - is - id - ▁either - ▁start - ▁having - ▁texas - el - ▁somebody - ▁husband - ▁sometimes - ▁dollars - ir - ow - ▁usually - ▁show - ▁help - ▁while - ▁few - ▁away - ive - ▁se - ▁college - ▁y - ▁system - ▁might - ▁mo - ▁co - ▁heard - ▁ma - us - ▁person - ▁once - ▁made - ▁point - ▁six - ce - ▁n - ▁fun - ra - ▁week - ▁pa - ▁buy - ▁seen - ▁state - ▁anyway - ▁again - ▁love - ▁gonna - ▁dallas - ne - ▁started - ▁exactly - ▁pro - ▁country - ▁life - ▁enjoy - ▁everybody - ▁ha - ▁talk - ▁lo - ▁v - ▁night - ▁able - ▁may - ▁stay - ▁remember - est - ▁news - ▁sa - ▁k - ▁came - ▁hear - ▁end - able - ▁least - ▁working - et - ▁un - ry - ▁fl - ▁po - ▁g - ▁since - ▁ra - ▁change - ul - ▁idea - ▁both - ▁h - ▁boy - ▁agree - age - ▁program - un - ▁pre - ▁st - ▁almost - ▁dis - ▁someone - ▁run - ▁di - um - z - ▁ba - ▁ho - ist - ▁la - ▁dog - ▁m - ▁reason - ▁took - ▁believe - ant - ▁bye - ▁company - ▁eight - ▁times - ▁half - ▁wife - ▁isn - ▁paper - ▁deal - ▁goes - ▁hand - ▁guy - ▁called - ▁next - ▁close - ▁month - ▁thirty - ▁wanted - ▁thousand - ▁yet - ▁mi - ▁understand - ▁bu - tion - ▁cost - ▁pick - ge - am - ▁drive - ▁sp - ▁looking - ▁government - ▁child - ▁crime - ac - ▁tax - ▁li - ▁spend - lo - ee - ▁women - ▁parents - ▁bo - ▁days - ▁especially - ▁wow - ▁saying - ▁cut - ▁name - ▁eat - ▁gone - ▁whether - ▁happen - ity - ▁less - ated - ▁small - ▁saw - ▁sounds - ▁supposed - ▁number - ▁world - ▁mother - ▁music - ▁set - ▁such - ▁until - ▁hi - ▁movie - ru - ▁credit - ▁bought - ▁turn - ▁city - ▁myself - ▁ga - ▁walk - ▁food - if - ▁le - ▁seem - ▁problems - ting - ▁computer - ▁makes - ▁am - ▁man - ▁found - ▁percent - ▁together - ▁sit - ▁ro - ▁coming - ure - ▁basically - ▁young - ▁best - ▁sc - ▁listen - ▁hum - ▁water - ▁check - ance - ▁son - ▁business - ▁u - co - ▁comp - ▁seven - ▁summer - ▁each - ▁situation - ie - ian - ▁war - ▁j - ▁worked - x - ward - ▁side - ▁definitely - ▁certain - ▁game - ▁wh - ▁won - ▁cl - ia - ▁wonderful - ▁wonder - ▁matter - ▁public - ▁ex - op - ▁lived - ▁fifty - ▁certainly - ▁cat - ▁cook - ▁funny - ▁air - ty - ▁age - ▁room - ▁nothing - ▁class - ▁health - ▁ch - ▁sh - ▁large - ig - na - ▁r - ▁fa - ▁gotten - ▁ju - ▁mine - ▁town - ▁per - ▁months - ma - ▁ti - ide - ▁test - ▁places - ▁yep - ▁comes - ▁anymore - ▁ca - ▁under - he - ▁plan - ▁vote - ▁fi - ▁important - ▁taking - ▁da - ▁daughter - ▁thinking - ▁team - port - ▁learn - ▁budget - ▁american - ful - ▁taxes - de - ▁hm - ▁gun - ▁str - ▁eighty - ▁control - ▁service - ▁today - ▁drug - ▁cars - ▁paying - ally - ▁rather - ▁neat - ▁line - ▁tend - ▁law - ▁fr - tic - rs - time - ▁insurance - man - ▁wear - ▁friends - ▁outside - ▁easy - ▁north - ▁friend - ▁during - und - ▁l - ▁card - ▁nine - me - bye - ▁living - ▁mind - ▁involved - ▁gosh - ▁moved - ight - ▁camping - ▁several - ence - ical - ▁bring - ice - ▁tried - ▁major - ▁newspaper - ▁favorite - ▁en - ▁student - ▁consider - ▁making - la - ▁morning - ous - ▁dr - ph - ▁question - ▁between - ▁jury - ▁amount - ▁mar - ▁ones - ▁older - ▁case - ▁education - ▁wa - ▁paid - ▁ri - ▁depend - ish - ▁bill - ▁must - ine - gg - ▁happened - ▁hour - ▁difference - ▁du - ▁hope - ▁experience - ▁absolutely - ▁group - ▁figure - ▁anybody - ▁miles - ▁aren - ating - ▁although - ▁worth - ▁su - ▁ta - ▁interest - ▁book - ▁sha - ▁forty - ▁expensive - ▁second - ▁without - up - ▁gets - ▁full - ▁app - ex - ▁along - ▁recently - ▁paint - ▁leave - ▁ru - all - ▁weather - ▁miss - ▁free - ▁com - ▁often - ▁gra - ▁minutes - ition - ill - ▁magazine - ▁wait - ca - ▁ahead - ▁wrong - ▁hours - ▁already - ▁married - ▁left - ▁hit - ▁camp - ▁fifteen - ▁pr - ▁men - ▁drugs - ▁rain - ▁schools - ious - ▁fish - ▁girl - ick - ▁office - ▁weeks - ▁ski - ▁middle - ▁knew - ▁al - ▁store - ▁watching - ▁cha - ▁sl - ▁hot - ▁running - ▁yourself - ▁act - ▁cold - ▁price - ▁lake - ▁death - ▁dad - ▁enjoyed - ▁benefits - ▁word - ▁main - ▁grow - ▁recycling - ▁past - ▁weekend - ▁break - 'no' - ber - ▁against - ▁base - ▁movies - ▁mostly - ial - ▁guys - ▁san - ▁pi - ay - ▁sense - ▁sell - ▁sister - ▁thank - ▁issue - way - ▁pet - ▁throw - ▁cover - ary - ▁baby - ▁doctor - ▁local - ▁difficult - ▁nursing - ▁wi - ▁wanna - ▁open - ▁head - ought - ▁vacation - ▁- - ▁brother - ▁instead - ▁kid - ▁reading - ▁add - ▁rest - ▁qu - ▁interested - ▁short - ▁degree - ▁charge - ▁rec - ▁topic - ha - ▁talked - ▁move - land - cy - ▁trouble - ▁told - ▁fairly - ▁hate - ▁stand - do - ▁unless - ▁winter - ▁sta - ▁twelve - ▁plano - ▁wish - ▁yard - ▁exercise - ▁front - ▁somewhere - ▁east - ▁everyone - ▁regular - ▁restaurant - ▁gre - ▁plant - ▁catch - ▁states - ▁near - ▁decided - ▁imagine - ▁except - ▁chance - ▁says - ▁kill - ▁california - ▁looked - ▁pe - ling - ▁ask - ▁punishment - ▁pull - ▁fan - ▁south - ▁fine - ▁hold - ▁taken - ▁tra - ▁garden - ▁park - ▁late - ▁ja - ▁takes - ▁street - ▁door - ▁fall - ▁clean - ▁dress - ▁mom - ▁income - ▁teach - ▁companies - ▁works - ▁ready - ▁capital - ▁spent - ▁recycle - ▁york - ▁using - ▁gu - ▁tough - ▁social - ▁raise - ▁father - ▁seventy - ▁ne - ▁gr - ▁realize - ▁early - ▁send - ▁terms - ▁become - ▁sixty - ▁themselves - ▁level - ▁phone - ▁god - ▁woman - ▁oil - ▁rent - ▁exp - ▁changed - ▁felt - ▁particular - ▁radio - ▁christmas - ▁station - ▁top - ▁goodness - ▁save - ▁power - ▁pass - ▁bar - ▁die - ▁society - ▁choice - ▁bra - ▁ge - ▁personal - ▁na - ▁dollar - ▁playing - ▁tha - ▁rate - ard - ▁national - ▁special - ▁general - ▁awful - ible - ▁cards - ▁plastic - ▁visit - ▁fix - ▁train - ▁rid - ▁dec - ▁lives - ▁expect - ▁support - ▁wood - ▁books - ▁feeling - ▁pu - ▁acc - line - ▁center - ized - ▁putting - ▁bag - ness - ▁growing - ▁later - ▁guns - ton - ▁land - ▁travel - der - ▁subject - ▁period - ▁dinner - ▁judge - ▁season - ▁happens - ▁machine - ▁extra - ▁manage - ▁gave - ▁vi - ▁force - ▁ph - ▁lately - ▁effect - ner - ▁starting - ▁saving - one - ▁building - ▁trip - ▁sitting - ▁cases - ▁bri - ▁kept - ▁finally - ▁fast - ▁red - ▁forth - ▁mu - ▁stop - ▁testing - less - ▁spring - ▁cause - ▁require - ▁built - ▁kn - ▁sw - ▁murder - ▁black - ▁quick - ▁community - ▁record - ▁snow - gra - j - ▁cra - ▁plus - ▁bank - ▁bi - ▁beautiful - ▁grade - ran - ▁afford - ▁graduate - ▁space - ▁countries - ▁cats - ▁fire - ▁process - ▁sound - ▁played - ▁limit - ▁white - ny - ▁sad - que - ▁university - ▁trans - ▁mess - ▁nineteen - ▁shoot - ▁nobody - ▁football - ▁speak - ▁story - ▁light - ▁longer - ▁jo - king - ▁ninety - ▁road - ▁totally - ▁fishing - ▁order - ▁information - ▁sign - ▁worry - ▁spending - ▁product - ▁soon - ▁bother - ▁across - ▁write - ▁bl - ▁bunch - ▁pen - ▁carry - ▁truck - ▁hey - ▁ball - be - ▁driving - ▁needed - ▁church - ▁teachers - ▁low - ▁amazing - ▁decision - ▁hurt - ▁golf - ▁sorry - ite - ▁younger - ities - ▁account - ▁terrible - ▁wind - ▁report - ▁suppose - ▁wor - ▁color - ▁hunt - ▁teacher - ▁concerned - ▁easier - ▁strange - ▁sub - ▁size - ▁strong - ▁safe - ▁turned - ▁given - ▁lost - ▁families - ▁happy - ▁follow - ▁view - ▁market - ▁handle - ▁ye - ▁single - ▁shop - ▁si - ▁within - ze - ▁television - ▁cheap - vis - ▁rock - ▁engineer - ▁individual - ▁shot - ▁tri - ▁criminal - ▁united - ▁worse - ▁trial - out - ▁serious - ▁neighborhood - ▁brought - ▁answer - ▁trees - mon - ▁build - ▁example - ▁fair - ▁buying - ▁caught - ▁military - ▁private - ▁field - ▁weight - ▁che - ship - ▁crazy - law - ▁serve - ▁decide - ▁opinion - ▁medical - ▁push - ▁step - ▁meet - ▁stick - clock - ▁boat - ▁quality - ▁win - ▁green - ▁term - ▁lose - ▁fo - ▁scary - ▁ended - ▁cu - ▁hospital - ▁police - ▁biggest - ▁apartment - ▁repair - ▁finish - ▁glad - ▁inside - ▁learned - ▁prison - ▁cri - ▁familiar - ▁third - ▁seemed - uh - ▁pan - ▁mountain - ▁whenever - ▁range - ▁watched - ▁necessarily - ▁piece - ook - lie - ▁noticed - ▁president - ▁collect - ▁twice - ative - ▁glass - ▁super - ▁ran - ▁fund - ▁sleep - ▁lawn - ▁chi - ▁behind - ▁guilty - ▁drop - ▁mix - ▁killed - ▁court - ▁completely - ▁party - ▁current - ▁tape - ▁commit - ▁benefit - ▁wall - ▁particularly - ▁personally - ▁anywhere - ▁project - ▁clothes - ▁eighteen - ▁bigger - ▁arm - ▁list - ▁hang - ▁warm - ▁eleven - ▁research - uff - ▁gee - ▁grand - ron - ▁fight - ▁grass - ▁teaching - ▁million - istic - ▁trash - ▁cash - ▁waiting - ▁neighbor - ▁club - ability - ▁develop - ▁unfortunately - ▁loan - ▁picked - ▁star - ▁generally - ▁cur - ▁environment - ▁minute - ▁obviously - ▁protect - ▁opera - ize - ▁anyone - ▁employee - ▁houston - ▁fill - ▁treat - ▁baseball - ▁ground - ▁video - ▁pollution - ▁higher - ▁available - ▁generation - ▁luck - ▁excuse - ▁pound - ▁picture - ▁roll - ▁america - ade - ▁eventually - ▁itself - ▁ooh - ▁asked - ▁forget - ▁surprised - ▁sun - ▁federal - ▁jail - qui - ▁pla - ome - ▁basic - ▁extreme - ▁washington - ▁attention - ▁penalty - ▁sentence - ▁poor - ▁mail - ▁cool - ▁florida - ▁clear - ▁fortunate - ▁huge - ▁aware - ▁lay - ▁civil - ▁value - ▁band - ▁lead - ▁parent - ▁giving - ▁bottle - ▁blue - ▁standard - ▁rob - ▁afraid - ▁bedroom - ▁comfortable - ▁separate - ▁position - ▁foot - ▁eye - ▁art - ▁europe - ▁sunday - ▁cap - ▁discuss - ▁provide - ▁lucky - ▁sick - ▁excellent - ▁utah - ▁classes - ▁el - ▁apparently - ▁condition - ▁perhaps - ▁weapon - ▁burn - ▁originally - q - ▁self - ▁beginning - ▁prefer - ▁cou - ▁count - ▁quit - ▁typical - 'off' - ▁economic - ▁broke - ▁average - ▁smaller - ▁security - ▁virginia - ▁weird - ▁future - ▁similar - ▁hopefully - ▁economy - ▁political - ▁relative - ▁master - ▁slow - ▁financial - ▁respect - ▁expense - ▁accept - ▁appeal - ▁normally - ▁channel - ▁alone - ▁human - ▁union - ▁privacy - ▁science - ▁lawyer - ▁busy - ▁window - ▁automatic - ▁sold - ▁county - ▁advantage - ▁bush - ▁direct - ▁affect - ▁drink - ▁van - ▁entire - ▁lunch - ▁switch - ▁role - ▁basis - ▁z - ▁table - ▁animal - ▁basketball - ▁industry - ▁peace - ▁reunion - ▁blow - ▁department - ▁present - ▁relate - ▁positive - ▁article - ▁heavy - ▁return - place - ▁chicken - ▁stories - ▁honest - ▁somehow - ▁ride - ▁history - ▁saturday - ▁salary - ▁member - ▁payment - ▁moving - ▁port - ▁professional - ▁mexico - ▁normal - ▁lower - ▁jump - ▁mow - ▁rich - ▁organization - ▁design - ▁straight - ▁draw - ▁smoke - ▁possible - ▁bucks - ▁debt - work - ▁property - ▁rough - ▁teenage - ▁garage - ▁wild - ▁scout - ▁touch - ash - ▁suit - ▁purchase - ▁retirement - ▁election - over - ▁carolina - ▁recipe - ▁track - ▁entertain - ▁changing - ▁grandmother - ▁thirteen - ▁instance - ▁coverage - ▁attitude - ▁box - ▁face - ▁background - ▁study - ▁kidding - ▁english - ▁ridiculous - ▁legal - ▁tonight - ▁trade - ▁random - ▁john - ▁coast - ▁cable - ▁aluminum - ▁choose - ▁cowboy - ▁colorado - ▁lu - ▁continue - ▁contract - ▁england - ▁ticket - ▁board - ▁replace - ▁join - ▁folks - ▁sudden - ▁garbage - ▁engine - ▁himself - ▁instrument - ▁row - ▁spot - ▁activities - ▁cross - ▁shape - ▁scare - ▁mini - ▁district - ▁floor - ▁taste - ▁corn - ▁correct - ▁opportunity - ified - ▁threat - ▁concern - ▁popular - ▁everyday - ▁adult - ▁terr - ▁doubt - ▁brand - ▁dead - ▁defense - ▁worst - ▁mexican - ▁policy - ▁taught - ▁vietnam - ▁pressure - ▁balance - ▁body - ▁cities - ▁accident - ▁afternoon - ▁horrible - ▁german - ▁electric - ▁tired - ▁everywhere - ▁opposed - ▁squa - ▁bike - ▁hair - ▁congress - ▁foreign - ▁physical - ▁yesterday - ▁increase - ▁metric - ▁style - ▁minor - ▁majority - ▁perfect - ▁responsibility - ▁common - ▁central - ▁improve - ▁kitchen - ▁vegetable - ▁sixteen - ▁forever - ▁nurse - ▁stopped - ▁tech - ▁bird - ▁born - ▁jeez - ▁mistake - ▁richardson - ▁express - ▁lady - ▁russia - ▁print - ▁hook - ▁bottom - ▁easily - ▁select - ▁option - ▁coach - ▁direction - ville - ▁favor - ▁pennsylvania - ▁key - ject - ▁effort - ▁schedule - ▁execut - ▁spread - ▁hobby - ▁immediate - ▁simple - ▁somewhat - ▁however - ▁natural - ▁fourteen - ▁block - ▁dump - ▁perform - ▁equipment - ▁complain - ▁planning - ▁river - ▁occasionally - ▁conversation - ▁grocery - ▁fresh - ▁besides - ▁friday - ▁result - ▁smart - ▁various - ▁discover - ▁storm - ▁appreciate - ▁equal - ▁nowadays - ▁brown - ▁elderly - ▁invasion - ▁oklahoma - ▁politics - ▁maryland - ▁regard - ▁upset - ▁commercial - ▁incredible - ▁french - ▁trust - ▁seventies - ▁league - ▁ourselves - ▁possibly - ▁purpose - ▁network - ▁stuck - ▁admit - ▁sweat - ▁cousin - ▁begin - ably - ▁elect - board - ▁alcohol - ▁contribut - ▁solution - ▁material - ▁supp - ▁deep - ▁specific - ▁convict - ▁motor - ▁tree - ▁junior - ▁nature - ▁oak - ▁restrict - ▁mentioned - ▁shoes - ▁laugh - ▁volunteer - ▁temp - ▁austin - ▁prior - ▁extent - ▁otherwise - ▁blood - ▁deduct - ▁hobbies - ▁influence - ▁writing - ▁abuse - ▁soviet - ▁mental - ▁awhile - ▁connect - ▁western - ▁italian - ▁convenient - ▁language - ▁recommend - ▁downtown - ▁border - ▁character - ▁politician - ▁truth - ▁pitch - ▁sixties - ▁strict - ▁hello - ▁chinese - ▁relax - ▁wheel - ▁drove - ▁access - ▁cannot - ▁plenty - ▁pardon - ▁model - ▁visa - ▁section - ▁boston - ▁dirt - ▁aspect - ▁electronic - ▁responsible - ▁participate - ▁steak - ▁profit - ▁roof - ▁cabin - ▁bowl - ▁japanese - ▁telephone - ▁variety - ▁piano - ▁broad - ▁chicago - ▁citizen - ▁corps - ▁assume - ▁automobile - ▁crowd - ▁simply - ▁technical - ▁quarter - ▁wrote - ▁damage - ▁dental - ▁corporation - ▁honda - ▁necessary - ▁traffic - ▁vehicle - ▁salad - ▁southern - ▁unusual - '0' - ▁voting - ▁screen - ▁stress - ▁mandatory - ▁monday - ▁secret - ▁above - ▁source - ▁load - ▁suspect - ▁license - ▁population - ▁subscribe - ▁atlanta - ▁draft - ▁tremendous - ▁knowledge - ▁earth - ▁match - ▁atmosphere - ▁democrat - ▁habit - ▁edge - ▁film - ▁auto - ▁earlier - ▁encourage - ▁exciting - ▁fellow - ▁suburb - ▁became - ▁shut - ▁ceiling - ▁disease - ▁cheese - ▁actual - ▁bathroom - ▁divorce - ▁further - ▁pattern - ▁practical - ▁technology - ▁becoming - ▁double - ▁investment - ▁trend - ▁dark - ▁discipline - ▁occur - ▁christian - ▁liberal - ▁senior - ▁israel - ▁scene - ▁deterrent - ▁jazz - ▁suggest - ▁beyond - ▁seventeen - ▁sauce - ▁interview - ▁swimming - ▁stupid - ▁voice - ▁pump - ▁consumer - ▁independent - ▁practice - ▁tomatoes - ▁outdoor - ▁blame - ▁northern - ▁craft - ▁republic - ▁antonio - ▁written - ▁tennis - ▁tune - ology - ▁legislat - ▁finance - ipped - ▁adjust - ▁massachusetts - ▁successful - ▁repeat - ▁versus - ▁chemical - ▁milk - ▁carpet - ▁horse - ▁address - ▁speed - ▁apart - ▁occasion - ▁belong - ▁francisco - ▁grandchildren - ▁quiet - ▁holiday - ▁register - ▁resource - ▁mechanic - ▁staff - ▁steal - ▁maintain - ▁toyota - ▁psych - ▁casual - ▁backyard - ▁receive - ▁chose - ▁energy - ▁author - ▁bread - ▁focus - ▁iraq - ▁journal - ▁professor - ▁sentencing - ▁explain - ▁knock - ▁series - ficial - ▁amazed - ▁baltimore - ▁facilities - ▁neither - ▁potato - ▁advance - ▁gulf - ▁sweet - hold - ▁candidate - ▁pittsburgh - ▁garland - ▁hung - ▁babies - ▁involve - ▁spec - ▁concept - ▁convince - ▁impressed - ▁leaving - ▁primarily - ▁produce - ▁victim - ▁herself - ▁shock - ▁convert - ▁juries - ▁loose - wood - ▁represent - ▁georgia - ▁kindergarten - ▁progress - ▁yellow - ▁stock - ▁junk - ▁surprise - ▁circumstances - ▁dangerous - ▁illegal - ▁concert - ▁shift - ▁gang - ▁advertise - ▁disappoint - ▁educate - ▁female - ▁minimum - ▁establish - ▁fantastic - ▁welfare - house - ▁extend - ▁birthday - ▁cruise - ▁culture - ▁elementary - ▁employer - ▁incentive - ▁relationship - ▁speech - ▁reduce - ▁smell - ▁carrie - ▁original - ▁august - ▁grandparents - ▁preschool - ▁quarterback - ▁violent - ▁barbecue - ▁fifties - ▁rabbit - ▁freedom - ▁parole - ▁fascinat - ▁emotion - ▁innocent - ▁perspective - ▁temperature - ▁attract - apped - ▁pollut - ▁negative - ▁wisconsin - ▁contact - ▁impact - ▁jersey - ▁recognize - ▁conscious - ▁detail - ▁complete - ▁claim - ▁creek - ▁attack - ▁continu - ▁enforce - '1' - ▁attorney - ▁campaign - ▁conservative - ▁excited - ▁canada - ▁split - ▁multi - ▁challenge - ▁evidence - ▁maintenance - ▁pepper - ▁release - ▁frame - employed - ▁include - ▁paycheck - ▁raleigh - '4' - '2' - '&' - '6' - '8' - '9' - '7' - '5' - '3' - / - '[' - _ - <sos/eos> init: null input_size: 83 ctc_conf: ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram2000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: null frontend_conf: {} specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_base_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "CRAFT" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.person-wearing-headphones
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,702,277,096,000
2024-02-06T00:29:40
11
0
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/person wearing headphones_17_3.0.png widget: - text: person wearing headphones output: url: images/person wearing headphones_17_3.0.png - text: person wearing headphones output: url: images/person wearing headphones_19_3.0.png - text: person wearing headphones output: url: images/person wearing headphones_20_3.0.png - text: person wearing headphones output: url: images/person wearing headphones_21_3.0.png - text: person wearing headphones output: url: images/person wearing headphones_22_3.0.png inference: false instance_prompt: person wearing headphones --- # ntcai.xyz slider - person wearing headphones (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/person wearing headphones_17_-3.0.png" width=256 height=256 /> | <img src="images/person wearing headphones_17_0.0.png" width=256 height=256 /> | <img src="images/person wearing headphones_17_3.0.png" width=256 height=256 /> | | <img src="images/person wearing headphones_19_-3.0.png" width=256 height=256 /> | <img src="images/person wearing headphones_19_0.0.png" width=256 height=256 /> | <img src="images/person wearing headphones_19_3.0.png" width=256 height=256 /> | | <img src="images/person wearing headphones_20_-3.0.png" width=256 height=256 /> | <img src="images/person wearing headphones_20_0.0.png" width=256 height=256 /> | <img src="images/person wearing headphones_20_3.0.png" width=256 height=256 /> | See more at [https://sliders.ntcai.xyz/sliders/app/loras/db3ca807-26d4-4bf6-b3e0-77c3d2d8a566](https://sliders.ntcai.xyz/sliders/app/loras/db3ca807-26d4-4bf6-b3e0-77c3d2d8a566) ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` person wearing headphones ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.person-wearing-headphones', weight_name='person wearing headphones.safetensors', adapter_name="person wearing headphones") # Activate the LoRA pipe.set_adapters(["person wearing headphones"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, person wearing headphones" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 1496+ unique and diverse LoRAs along with 14600+ slider merges, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful <strong>NTC Slider Factory</strong> LoRA creator, allowing you to craft your own custom LoRAs and merges opening up endless possibilities. Your support on Patreon will allow us to continue developing new models and tools. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
YiDuo1999/Llama-3-Physician-8B-Instruct
YiDuo1999
text-generation
[ "transformers", "pytorch", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,718,888,677,000
2024-07-02T10:05:43
46
5
--- license: llama3 --- The official instruct model weights for "Efficient Continual Pre-training by Mitigating the Stability Gap". ## Introduction This repo contains Llama-3-Physician-8B-Instruct, a medical language model with 8 billion parameters. This model builds upon the foundation of Llama 3 and has been firstly continual pretrained on high-quality medical sub-corpus from the RefinedWeb dataset and then tuned with diverse medical and general instructions. We also use the three strategies in the paper to mitigate the stability gap during continual pretraining and instruction tuning, which boosts the model's medical task performance and reduces the computation consumption. ## 💻 Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig import torch model_name = "YiDuo1999/Llama-3-Physician-8B-Instruct" device_map = 'auto' model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True,use_cache=False,device_map=device_map) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token def askme(question): sys_message = ''' You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help. ''' # Create messages structured for the chat template messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}] # Applying chat template prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=100, use_cache=True) # Extract and return the generated text, removing the prompt response_text = tokenizer.batch_decode(outputs)[0].strip() answer = response_text.split('<|im_start|>assistant')[-1].strip() return answer # Example usage # - Context: First describe your problem. # - Question: Then make the question. question = '''What is HIV?''' print(askme(question)) ``` the type of answer is : ``` HIV, or Human Immunodeficiency Virus, is a retrovirus that primarily infects cells of the human immune system, particularly CD4+ T cells, which are crucial to the body's ability to fight off infection. HIV infection can lead to AIDS, or Acquired Immune Deficiency Syndrome, a condition that causes severe damage to the immune system and makes individuals more susceptible to life-threatening infections. HIV is transmitted through sexual contact, sharing needles, or through mother-to-child transmission during pregnancy. ``` ## 🏆 Evaluation For question-answering tasks, we have | Model | MMLU-Medical | PubMedQA | MedMCQA | MedQA-4-Option | Avg | |:--------------------------------|:--------------|:----------|:---------|:----------------|:------| | Mistral-7B-instruct | 55.8 | 17.8 | 40.2 | 41.1 | 37.5 | | Zephyr-7B-instruct-β | 63.3 | 46.0 | 43.0 | 48.5 | 48.7 | | PMC-Llama-7B | 59.7 | 59.2 | 57.6 | 49.2 | 53.6 | | Medalpaca-13B | 55.2 | 50.4 | 21.2 | 20.2 | 36.7 | | AlpaCare-13B | 60.2 | 53.8 | 38.5 | 30.4 | 45.7 | | BioMedGPT-LM 7B | 52.0 | 58.6 | 34.9 | 39.3 | 46.2 | | Me-Llama-13B | - | 70.0 | 44.9 | 42.7 | - | | Llama-3-8B instruct | 82.0 | 74.6 | 57.1 | 60.3 | 68.5 | | JSL-Med-Sft-Llama-3-8B | 83.0 | 75.4 | 57.5 | 74.8 | 72.7 | | GPT-3.5-turbo-1106 | 74.0 | 72.6 | 34.9 | 39.3 | 60.6 | | GPT-4 | 85.5 | 69.2 | 69.5 | 83.9 | 77.0 | | Llama-3-physician-8B instruct (ours) | 80.0 | 76.0 | 80.2 | 60.3 | 74.1 | For Medical claasification, relation extraction, natural language inference, summarization tasks, we have | Task type | Classification | Relation extraction | Natural Language Inference | Summarization | |:--------------------------------|:----------------|:----------------------|:----------------------------|:---------------| | Datasets | HOC | DDI-2013 | BioNLI | MIMIC-CXR | | Mistral-7B-instruct | 35.8 | 14.1 | 16.7 | 12.5 | | Zephyr-7B-instruct-β | 26.1 | 19.4 | 19.9 | 10.5 | | PMC-Llama-7B | 18.4 | 14.7 | 15.9 | 13.9 | | Medalpaca-13B | 24.6 | 5.8 | 16.4 | 1.0 | | AlpaCare-13B | 26.7 | 11.0 | 17.0 | 13.4 | | BioMedGPT-LM 7B | 23.4 | 15.5 | 17.9 | 6.2 | | Me-Llama-13B | 33.5 | 21.4 | 19.5 | 40.0 | | JSL-Med-Sft-Llama-3-8B | 25.6 | 19.7 | 16.6 | 13.8 | | Llama-3-8B instruct | 31.0 | 15.1 | 18.8 | 10.3 | | GPT-3.5-turbo-1106 | 54.5 | 21.6 | 31.7 | 13.5 | | GPT-4 | 60.2 | 29.2 | 57.8 | 15.2 | | Llama-3-physician-8B instruct (ours) | 78.9 | 33.6 | 76.2 | 37.7 | ## Citation ``` @inproceedings{Guo2024EfficientCP, title={Efficient Continual Pre-training by Mitigating the Stability Gap}, author={Yiduo Guo and Jie Fu and Huishuai Zhang and Dongyan Zhao and Yikang Shen}, year={2024}, url={https://api.semanticscholar.org/CorpusID:270688100} } ```
[ "MEDQA", "PUBMEDQA" ]
BioNLP
LoneStriker/OpenBioLLM-Llama3-8B-8.0bpw-h8-exl2
LoneStriker
text-generation
[ "transformers", "pytorch", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:quantized:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]
1,714,161,140,000
2024-04-26T19:56:23
10
0
--- base_model: meta-llama/Meta-Llama-3-8B language: - en license: llama3 tags: - llama-3 - llama - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - distillation widget: - example_title: OpenBioLLM-8B messages: - role: system content: You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. - role: user content: How long does it take for newborn jaundice to go away? output: text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment. The duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines: 1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment. It''s important for parents to monitor their newborn''s jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn''s jaundice, it''s always best to consult with a healthcare professional for personalized advice and guidance.' model-index: - name: OpenBioLLM-8B results: [] --- <div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 **Biomedical Specialization**: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 **Superior Performance**: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 **Advanced Training Techniques**: OpenBioLLM-8B builds upon the powerful foundations of the **Meta-Llama-3-8B** and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - **Ranking Dataset**: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ **Release Details**: - **Model Size**: 8 billion parameters - **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - **Language(s) (NLP):** en - **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - **License:** Meta-Llama License - **Fine-tuned from models:** [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - **Resources for more information:** - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers **Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.** See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## **Training procedure** ### **Training hyperparameters** <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### **Peft hyperparameters** <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### **Training results** ### **Framework versions** - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------| | **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 | | **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 | <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 **Below results are from the quantized version of OpenBioLLM-70B** # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) **Advisory Notice!**  While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! **Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)
[ "MEDQA", "PUBMEDQA" ]
BioNLP
opensearch-project/opensearch-neural-sparse-encoding-v2-distill
opensearch-project
fill-mask
[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "learned sparse", "opensearch", "retrieval", "passage-retrieval", "query-expansion", "document-expansion", "bag-of-words", "en", "arxiv:2411.04403", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,721,201,535,000
2024-11-15T07:13:42
4,103
5
--- language: en license: apache-2.0 tags: - learned sparse - opensearch - transformers - retrieval - passage-retrieval - query-expansion - document-expansion - bag-of-words --- # opensearch-neural-sparse-encoding-v2-distill ## Select the model The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' **zero-shot performance** on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora. Overall, the v2 series of models have better search relevance, efficiency and inference speed than the v1 series. The specific advantages and disadvantages may vary across different datasets. | Model | Inference-free for Retrieval | Model Parameters | AVG NDCG@10 | AVG FLOPS | |-------|------------------------------|------------------|-------------|-----------| | [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | | 133M | 0.524 | 11.4 | | [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | | 67M | 0.528 | 8.3 | | [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | ✔️ | 133M | 0.490 | 2.3 | | [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | ✔️ | 67M | 0.504 | 1.8 | | [opensearch-neural-sparse-encoding-doc-v2-mini](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini) | ✔️ | 23M | 0.497 | 1.7 | ## Overview - **Paper**: [Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers](https://arxiv.org/abs/2411.04403) - **Fine-tuning sample**: [opensearch-sparse-model-tuning-sample](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample) This is a learned sparse retrieval model. It encodes the queries and documents to 30522 dimensional **sparse vectors**. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. The training datasets includes MS MARCO, eli5_question_answer, squad_pairs, WikiAnswers, yahoo_answers_title_question, gooaq_pairs, stackexchange_duplicate_questions_body_body, wikihow, S2ORC_title_abstract, stackexchange_duplicate_questions_title-body_title-body, yahoo_answers_question_answer, searchQA_top5_snippets, stackexchange_duplicate_questions_title_title, yahoo_answers_title_answer. OpenSearch neural sparse feature supports learned sparse retrieval with lucene inverted index. Link: https://opensearch.org/docs/latest/query-dsl/specialized/neural-sparse/. The indexing and search can be performed with OpenSearch high-level API. ## Usage (HuggingFace) This model is supposed to run inside OpenSearch cluster. But you can also use it outside the cluster, with HuggingFace models API. ```python import itertools import torch from transformers import AutoModelForMaskedLM, AutoTokenizer # get sparse vector from dense vectors with shape batch_size * seq_len * vocab_size def get_sparse_vector(feature, output): values, _ = torch.max(output*feature["attention_mask"].unsqueeze(-1), dim=1) values = torch.log(1 + torch.relu(values)) values[:,special_token_ids] = 0 return values # transform the sparse vector to a dict of (token, weight) def transform_sparse_vector_to_dict(sparse_vector): sample_indices,token_indices=torch.nonzero(sparse_vector,as_tuple=True) non_zero_values = sparse_vector[(sample_indices,token_indices)].tolist() number_of_tokens_for_each_sample = torch.bincount(sample_indices).cpu().tolist() tokens = [transform_sparse_vector_to_dict.id_to_token[_id] for _id in token_indices.tolist()] output = [] end_idxs = list(itertools.accumulate([0]+number_of_tokens_for_each_sample)) for i in range(len(end_idxs)-1): token_strings = tokens[end_idxs[i]:end_idxs[i+1]] weights = non_zero_values[end_idxs[i]:end_idxs[i+1]] output.append(dict(zip(token_strings, weights))) return output # load the model model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v2-distill") tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v2-distill") # set the special tokens and id_to_token transform for post-process special_token_ids = [tokenizer.vocab[token] for token in tokenizer.special_tokens_map.values()] get_sparse_vector.special_token_ids = special_token_ids id_to_token = ["" for i in range(tokenizer.vocab_size)] for token, _id in tokenizer.vocab.items(): id_to_token[_id] = token transform_sparse_vector_to_dict.id_to_token = id_to_token query = "What's the weather in ny now?" document = "Currently New York is rainy." # encode the query & document feature = tokenizer([query, document], padding=True, truncation=True, return_tensors='pt', return_token_type_ids=False) output = model(**feature)[0] sparse_vector = get_sparse_vector(feature, output) # get similarity score sim_score = torch.matmul(sparse_vector[0],sparse_vector[1]) print(sim_score) # tensor(38.6112, grad_fn=<DotBackward0>) query_token_weight, document_query_token_weight = transform_sparse_vector_to_dict(sparse_vector) for token in sorted(query_token_weight, key=lambda x:query_token_weight[x], reverse=True): if token in document_query_token_weight: print("score in query: %.4f, score in document: %.4f, token: %s"%(query_token_weight[token],document_query_token_weight[token],token)) # result: # score in query: 2.7273, score in document: 2.9088, token: york # score in query: 2.5734, score in document: 0.9208, token: now # score in query: 2.3895, score in document: 1.7237, token: ny # score in query: 2.2184, score in document: 1.2368, token: weather # score in query: 1.8693, score in document: 1.4146, token: current # score in query: 1.5887, score in document: 0.7450, token: today # score in query: 1.4704, score in document: 0.9247, token: sunny # score in query: 1.4374, score in document: 1.9737, token: nyc # score in query: 1.4347, score in document: 1.6019, token: currently # score in query: 1.1605, score in document: 0.9794, token: climate # score in query: 1.0944, score in document: 0.7141, token: upstate # score in query: 1.0471, score in document: 0.5519, token: forecast # score in query: 0.9268, score in document: 0.6692, token: verve # score in query: 0.9126, score in document: 0.4486, token: huh # score in query: 0.8960, score in document: 0.7706, token: greene # score in query: 0.8779, score in document: 0.7120, token: picturesque # score in query: 0.8471, score in document: 0.4183, token: pleasantly # score in query: 0.8079, score in document: 0.2140, token: windy # score in query: 0.7537, score in document: 0.4925, token: favorable # score in query: 0.7519, score in document: 2.1456, token: rain # score in query: 0.7277, score in document: 0.3818, token: skies # score in query: 0.6995, score in document: 0.8593, token: lena # score in query: 0.6895, score in document: 0.2410, token: sunshine # score in query: 0.6621, score in document: 0.3016, token: johnny # score in query: 0.6604, score in document: 0.1933, token: skyline # score in query: 0.6117, score in document: 0.2197, token: sasha # score in query: 0.5962, score in document: 0.0414, token: vibe # score in query: 0.5381, score in document: 0.7560, token: hardly # score in query: 0.4582, score in document: 0.4243, token: prevailing # score in query: 0.4539, score in document: 0.5073, token: unpredictable # score in query: 0.4350, score in document: 0.8463, token: presently # score in query: 0.3674, score in document: 0.2496, token: hail # score in query: 0.3324, score in document: 0.5506, token: shivered # score in query: 0.3281, score in document: 0.1964, token: wind # score in query: 0.3052, score in document: 0.5785, token: rudy # score in query: 0.2797, score in document: 0.0357, token: looming # score in query: 0.2712, score in document: 0.0870, token: atmospheric # score in query: 0.2471, score in document: 0.3490, token: vicky # score in query: 0.2247, score in document: 0.2383, token: sandy # score in query: 0.2154, score in document: 0.5737, token: crowded # score in query: 0.1723, score in document: 0.1857, token: chilly # score in query: 0.1700, score in document: 0.4110, token: blizzard # score in query: 0.1183, score in document: 0.0613, token: ##cken # score in query: 0.0923, score in document: 0.6363, token: unrest # score in query: 0.0624, score in document: 0.2127, token: russ # score in query: 0.0558, score in document: 0.5542, token: blackout # score in query: 0.0549, score in document: 0.1589, token: kahn # score in query: 0.0160, score in document: 0.0566, token: 2020 # score in query: 0.0125, score in document: 0.3753, token: nighttime ``` The above code sample shows an example of neural sparse search. Although there is no overlap token in original query and document, but this model performs a good match. ## Detailed Search Relevance <div style="overflow-x: auto;"> | Model | Average | Trec Covid | NFCorpus | NQ | HotpotQA | FiQA | ArguAna | Touche | DBPedia | SCIDOCS | FEVER | Climate FEVER | SciFact | Quora | |-------|---------|------------|----------|----|----------|------|---------|--------|---------|---------|-------|---------------|---------|-------| | [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | 0.524 | 0.771 | 0.360 | 0.553 | 0.697 | 0.376 | 0.508 | 0.278 | 0.447 | 0.164 | 0.821 | 0.263 | 0.723 | 0.856 | | [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | 0.528 | 0.775 | 0.347 | 0.561 | 0.685 | 0.374 | 0.551 | 0.278 | 0.435 | 0.173 | 0.849 | 0.249 | 0.722 | 0.863 | | [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | 0.490 | 0.707 | 0.352 | 0.521 | 0.677 | 0.344 | 0.461 | 0.294 | 0.412 | 0.154 | 0.743 | 0.202 | 0.716 | 0.788 | | [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | 0.504 | 0.690 | 0.343 | 0.528 | 0.675 | 0.357 | 0.496 | 0.287 | 0.418 | 0.166 | 0.818 | 0.224 | 0.715 | 0.841 | | [opensearch-neural-sparse-encoding-doc-v2-mini](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini) | 0.497 | 0.709 | 0.336 | 0.510 | 0.666 | 0.338 | 0.480 | 0.285 | 0.407 | 0.164 | 0.812 | 0.216 | 0.699 | 0.837 | </div> ## License This project is licensed under the [Apache v2.0 License](https://github.com/opensearch-project/neural-search/blob/main/LICENSE). ## Copyright Copyright OpenSearch Contributors. See [NOTICE](https://github.com/opensearch-project/neural-search/blob/main/NOTICE) for details.
[ "SCIFACT" ]
Non_BioNLP
daijin219/MLMA_lab9_task2
daijin219
token-classification
[ "transformers", "pytorch", "tensorboard", "gpt2", "token-classification", "generated_from_trainer", "dataset:ncbi_disease", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,681,561,187,000
2023-04-15T14:33:32
14
0
--- datasets: - ncbi_disease license: mit metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: MLMA_lab9_task2 results: - task: type: token-classification name: Token Classification dataset: name: ncbi_disease type: ncbi_disease config: ncbi_disease split: validation args: ncbi_disease metrics: - type: precision value: 0.015873015873015872 name: Precision - type: recall value: 0.14866581956797967 name: Recall - type: f1 value: 0.028683500858053445 name: F1 - type: accuracy value: 0.6365342039100904 name: Accuracy --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MLMA_lab9_task2 This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset. It achieves the following results on the evaluation set: - Loss: 1.2509 - Precision: 0.0159 - Recall: 0.1487 - F1: 0.0287 - Accuracy: 0.6365 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.153 | 1.0 | 680 | 1.0671 | 0.0122 | 0.1258 | 0.0223 | 0.5452 | | 1.02 | 2.0 | 1360 | 1.0418 | 0.0098 | 0.0203 | 0.0132 | 0.6791 | | 0.9552 | 3.0 | 2040 | 1.0269 | 0.0135 | 0.1677 | 0.0250 | 0.5282 | | 0.926 | 4.0 | 2720 | 1.0390 | 0.0143 | 0.0940 | 0.0248 | 0.6686 | | 0.9156 | 5.0 | 3400 | 1.0200 | 0.0135 | 0.2046 | 0.0253 | 0.4679 | | 0.8791 | 6.0 | 4080 | 1.0543 | 0.0131 | 0.2745 | 0.0250 | 0.3149 | | 0.8672 | 7.0 | 4760 | 1.0545 | 0.0141 | 0.2732 | 0.0267 | 0.3471 | | 0.8627 | 8.0 | 5440 | 1.0734 | 0.0145 | 0.0826 | 0.0246 | 0.7220 | | 0.8375 | 9.0 | 6120 | 1.1068 | 0.0156 | 0.1410 | 0.0281 | 0.6451 | | 0.8235 | 10.0 | 6800 | 1.0796 | 0.0158 | 0.1537 | 0.0286 | 0.6210 | | 0.8157 | 11.0 | 7480 | 1.1476 | 0.0143 | 0.1690 | 0.0263 | 0.5737 | | 0.7957 | 12.0 | 8160 | 1.1369 | 0.0143 | 0.1525 | 0.0262 | 0.6155 | | 0.7937 | 13.0 | 8840 | 1.2014 | 0.0151 | 0.1741 | 0.0278 | 0.5808 | | 0.7765 | 14.0 | 9520 | 1.2249 | 0.0160 | 0.1449 | 0.0289 | 0.6443 | | 0.7661 | 15.0 | 10200 | 1.2509 | 0.0159 | 0.1487 | 0.0287 | 0.6365 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
[ "NCBI DISEASE" ]
BioNLP
dev0612/nd-neuro-embedding
dev0612
sentence-similarity
[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "medical", "biology", "en", "dataset:FremyCompany/BioLORD-Dataset", "dataset:FremyCompany/AGCT-Dataset", "arxiv:2311.16075", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,739,873,837,000
2025-02-18T10:50:07
13
0
--- datasets: - FremyCompany/BioLORD-Dataset - FremyCompany/AGCT-Dataset language: en license: other license_name: ihtsdo-and-nlm-licences license_link: https://www.nlm.nih.gov/databases/umls.html pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - medical - biology widget: - source_sentence: bartonellosis sentences: - cat scratch disease - cat scratch wound - tick-borne orbivirus fever - cat fur --- | 🙏 If you are able to, please help me [fund my open research](https://gofund.me/1f2d6803). 🙏 Thank you for your generosity! 🤗 | |-----------------------------------------------------------------------------------------------------------------------------------| # FremyCompany/BioLORD-2023 This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B). This model is based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and was further finetuned on the [BioLORD-Dataset](https://huggingface.co/datasets/FremyCompany/BioLORD-Dataset) and LLM-generated definitions from the [Automatic Glossary of Clinical Terminology (AGCT)](https://huggingface.co/datasets/FremyCompany/AGCT-Dataset). ## Sibling models This model is accompanied by other models in the BioLORD-2023 series, which you might want to check: - [BioLORD-2023-M](https://huggingface.co/FremyCompany/BioLORD-2023-M) (multilingual model; distilled from BioLORD-2023) - [BioLORD-2023](https://huggingface.co/FremyCompany/BioLORD-2023) (best model after model averaging; this model) - [BioLORD-2023-S](https://huggingface.co/FremyCompany/BioLORD-2023-S) (best hyperparameters; no model averaging) - [BioLORD-2023-C](https://huggingface.co/FremyCompany/BioLORD-2023-C) (contrastive training only; for NEL tasks) You can also take a look at last year's model and paper: - [BioLORD-2022](https://huggingface.co/FremyCompany/BioLORD-STAMB2-v1) (also known as BioLORD-STAMB2-v1) ## Training strategy ### Summary of the 3 phases ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f04e8865d08220171a0ad3f/my94lNjxATRU_Rg5knUZ8.png) ### Contrastive phase: details ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f04e8865d08220171a0ad3f/_jE2ETcXkLvYLr7TeOdci.png) ### Self-distallation phase: details ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f04e8865d08220171a0ad3f/7xuqi231RB0OzvcxK3bf-.png) ## Citation This model accompanies the [BioLORD-2023: Learning Ontological Representations from Definitions](https://arxiv.org/abs/2311.16075) paper. When you use this model, please cite the original paper as follows: ```latex @article{remy-etal-2023-biolord, author = {Remy, François and Demuynck, Kris and Demeester, Thomas}, title = "{BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights}", journal = {Journal of the American Medical Informatics Association}, pages = {ocae029}, year = {2024}, month = {02}, issn = {1527-974X}, doi = {10.1093/jamia/ocae029}, url = {https://doi.org/10.1093/jamia/ocae029}, eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocae029/56772025/ocae029.pdf}, } ``` ## Usage (Sentence-Transformers) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model has been finentuned for the biomedical domain. While it preserves a good ability to produce embeddings for general-purpose text, it will be more useful to you if you are trying to process medical documents such as EHR records or clinical notes. Both sentences and phrases can be embedded in the same latent space. 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 = ["Cat scratch injury", "Cat scratch disease", "Bartonellosis"] model = SentenceTransformer('FremyCompany/BioLORD-2023') 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 import torch.nn.functional as F #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 = ["Cat scratch injury", "Cat scratch disease", "Bartonellosis"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('FremyCompany/BioLORD-2023') model = AutoModel.from_pretrained('FremyCompany/BioLORD-2023') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## License My own contributions for this model are covered by the MIT license. However, given the data used to train this model originates from UMLS and SnomedCT, you will need to ensure you have proper licensing of UMLS and SnomedCT before using this model. Both UMLS and SnomedCT are free of charge in most countries, but you might have to create an account and report on your usage of the data yearly to keep a valid license.
[ "EHR-REL" ]
BioNLP
RichardErkhov/BSC-LT_-_salamandra-7b-base-fp8-8bits
RichardErkhov
null
[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]
1,736,582,639,000
2025-01-11T08:08:42
5
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) salamandra-7b-base-fp8 - bnb 8bits - Model creator: https://huggingface.co/BSC-LT/ - Original model: https://huggingface.co/BSC-LT/salamandra-7b-base-fp8/ Original model description: --- license: apache-2.0 library_name: transformers base_model: BSC-LT/salamandra-7b pipeline_tag: text-generation 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 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633b489acbdbadd99c0b75ef/z4qWovHuCwvWSlkb5H3jm.png) # Salamandra-7b-fp8 Model Card This model is the fp8-quantized version of [Salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b). The model weights are quantized from FP16 to FP8 (8-bit weights) using the FP8 quantization algorithm from [NeuralMagic](https://neuralmagic.com/blog/vllm-brings-fp8-inference-to-the-open-source-community/). Inferencing with this model can be done using [VLLM](https://docs.vllm.ai/en/stable/models/engine_args.html). 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, promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/) and 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. This model card corresponds to the fp8-quantized version of Salamandra-7b. The entire Salamandra family is released under a permissive [Apache 2.0 license]((https://www.apache.org/licenses/LICENSE-2.0)). ## How to Use The following example code works under ``Python 3.9.16``, ``vllm==0.6.3.post1``, ``torch==2.4.0`` and ``torchvision==0.19.0``, though it should run on any current version of the libraries. This is an example of how to create a text completion using the model: ``` from vllm import LLM, SamplingParams model_name = "BSC-LT/salamandra-7b-base-fp8" llm = LLM(model=model_name) outputs = llm.generate("El mercat del barri ", sampling_params=SamplingParams( temperature=0.5, max_tokens=200) ) print(outputs[0].outputs[0].text) ``` ### Author International Business Machines (IBM). ### Copyright International Business Machines (IBM). ### Contact For further information, please send an email to <[email protected]>. ### Acknowledgements We appreciate the collaboration with IBM in this work. Specifically, the IBM team created fp8-quantized version of the Salamandra-7b model released here. ### 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. Barcelona Supercomputing Center and International Business Machines shall not be held liable for any outcomes resulting from third-party use. ### License [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
[ "BEAR" ]
Non_BioNLP
jinaai/jina-embedding-l-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" ]
1,688,892,846,000
2025-01-06T16:30:42
561
24
--- 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-triplets-large results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 68.92537313432835 - type: ap value: 29.723758877632513 - type: f1 value: 61.909704211663794 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 69.13669999999999 - type: ap value: 65.30216072238086 - type: f1 value: 67.1890891071034 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.384 - type: f1 value: 30.016752348953723 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 23.613 - type: map_at_10 value: 37.897 - type: map_at_100 value: 39.093 - type: map_at_1000 value: 39.109 - type: map_at_3 value: 32.824 - type: map_at_5 value: 35.679 - type: mrr_at_1 value: 23.826 - type: mrr_at_10 value: 37.997 - type: mrr_at_100 value: 39.186 - type: mrr_at_1000 value: 39.202 - type: mrr_at_3 value: 32.918 - type: mrr_at_5 value: 35.748999999999995 - type: ndcg_at_1 value: 23.613 - type: ndcg_at_10 value: 46.482 - type: ndcg_at_100 value: 51.55499999999999 - type: ndcg_at_1000 value: 51.974 - type: ndcg_at_3 value: 35.964 - type: ndcg_at_5 value: 41.144999999999996 - type: precision_at_1 value: 23.613 - type: precision_at_10 value: 7.417999999999999 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.031 - type: precision_at_5 value: 11.55 - type: recall_at_1 value: 23.613 - type: recall_at_10 value: 74.182 - type: recall_at_100 value: 96.30199999999999 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 45.092 - type: recall_at_5 value: 57.752 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.51285742156528 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 31.5825964077496 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.830281630546835 - type: mrr value: 75.93072593765115 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.26764516732737 - type: cos_sim_spearman value: 84.42541766631741 - type: euclidean_pearson value: 48.71357447655235 - type: euclidean_spearman value: 49.2023259276511 - type: manhattan_pearson value: 48.36366272727299 - type: manhattan_spearman value: 48.457128224924354 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.3409090909091 - type: f1 value: 85.25262617676835 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.560193912974974 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 28.4426572644577 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 27.822999999999997 - type: map_at_10 value: 39.088 - type: map_at_100 value: 40.561 - type: map_at_1000 value: 40.69 - type: map_at_3 value: 35.701 - type: map_at_5 value: 37.556 - type: mrr_at_1 value: 33.906 - type: mrr_at_10 value: 44.527 - type: mrr_at_100 value: 45.403999999999996 - type: mrr_at_1000 value: 45.452 - type: mrr_at_3 value: 41.726 - type: mrr_at_5 value: 43.314 - type: ndcg_at_1 value: 33.906 - type: ndcg_at_10 value: 45.591 - type: ndcg_at_100 value: 51.041000000000004 - type: ndcg_at_1000 value: 53.1 - type: ndcg_at_3 value: 40.324 - type: ndcg_at_5 value: 42.723 - type: precision_at_1 value: 33.906 - type: precision_at_10 value: 8.655 - type: precision_at_100 value: 1.418 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 19.123 - type: precision_at_5 value: 13.963000000000001 - type: recall_at_1 value: 27.822999999999997 - type: recall_at_10 value: 58.63699999999999 - type: recall_at_100 value: 80.874 - type: recall_at_1000 value: 93.82000000000001 - type: recall_at_3 value: 44.116 - type: recall_at_5 value: 50.178999999999995 - type: map_at_1 value: 26.823999999999998 - type: map_at_10 value: 37.006 - type: map_at_100 value: 38.256 - type: map_at_1000 value: 38.397999999999996 - type: map_at_3 value: 34.011 - type: map_at_5 value: 35.643 - type: mrr_at_1 value: 34.268 - type: mrr_at_10 value: 43.374 - type: mrr_at_100 value: 44.096000000000004 - type: mrr_at_1000 value: 44.144 - type: mrr_at_3 value: 41.008 - type: mrr_at_5 value: 42.359 - type: ndcg_at_1 value: 34.268 - type: ndcg_at_10 value: 43.02 - type: ndcg_at_100 value: 47.747 - type: ndcg_at_1000 value: 50.019999999999996 - type: ndcg_at_3 value: 38.687 - type: ndcg_at_5 value: 40.647 - type: precision_at_1 value: 34.268 - type: precision_at_10 value: 8.261000000000001 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 19.108 - type: precision_at_5 value: 13.489999999999998 - type: recall_at_1 value: 26.823999999999998 - type: recall_at_10 value: 53.84100000000001 - type: recall_at_100 value: 73.992 - type: recall_at_1000 value: 88.524 - type: recall_at_3 value: 40.711000000000006 - type: recall_at_5 value: 46.477000000000004 - type: map_at_1 value: 34.307 - type: map_at_10 value: 45.144 - type: map_at_100 value: 46.351 - type: map_at_1000 value: 46.414 - type: map_at_3 value: 42.315000000000005 - type: map_at_5 value: 43.991 - type: mrr_at_1 value: 39.06 - type: mrr_at_10 value: 48.612 - type: mrr_at_100 value: 49.425000000000004 - type: mrr_at_1000 value: 49.458999999999996 - type: mrr_at_3 value: 46.144 - type: mrr_at_5 value: 47.654999999999994 - type: ndcg_at_1 value: 39.06 - type: ndcg_at_10 value: 50.647 - type: ndcg_at_100 value: 55.620000000000005 - type: ndcg_at_1000 value: 56.976000000000006 - type: ndcg_at_3 value: 45.705 - type: ndcg_at_5 value: 48.269 - type: precision_at_1 value: 39.06 - type: precision_at_10 value: 8.082 - type: precision_at_100 value: 1.161 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 20.376 - type: precision_at_5 value: 14.069 - type: recall_at_1 value: 34.307 - type: recall_at_10 value: 63.497 - type: recall_at_100 value: 85.038 - type: recall_at_1000 value: 94.782 - type: recall_at_3 value: 50.209 - type: recall_at_5 value: 56.525000000000006 - type: map_at_1 value: 26.448 - type: map_at_10 value: 34.86 - type: map_at_100 value: 36.004999999999995 - type: map_at_1000 value: 36.081 - type: map_at_3 value: 32.527 - type: map_at_5 value: 33.955 - type: mrr_at_1 value: 28.701 - type: mrr_at_10 value: 36.909 - type: mrr_at_100 value: 37.89 - type: mrr_at_1000 value: 37.945 - type: mrr_at_3 value: 34.576 - type: mrr_at_5 value: 35.966 - type: ndcg_at_1 value: 28.701 - type: ndcg_at_10 value: 39.507999999999996 - type: ndcg_at_100 value: 45.056000000000004 - type: ndcg_at_1000 value: 47.034 - type: ndcg_at_3 value: 34.985 - type: ndcg_at_5 value: 37.384 - type: precision_at_1 value: 28.701 - type: precision_at_10 value: 5.921 - type: precision_at_100 value: 0.914 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.689 - type: precision_at_5 value: 10.237 - type: recall_at_1 value: 26.448 - type: recall_at_10 value: 51.781 - type: recall_at_100 value: 77.142 - type: recall_at_1000 value: 92.10000000000001 - type: recall_at_3 value: 39.698 - type: recall_at_5 value: 45.469 - type: map_at_1 value: 14.174000000000001 - type: map_at_10 value: 22.019 - type: map_at_100 value: 23.18 - type: map_at_1000 value: 23.304 - type: map_at_3 value: 19.332 - type: map_at_5 value: 20.816000000000003 - type: mrr_at_1 value: 17.785999999999998 - type: mrr_at_10 value: 26.233 - type: mrr_at_100 value: 27.254 - type: mrr_at_1000 value: 27.328000000000003 - type: mrr_at_3 value: 23.653 - type: mrr_at_5 value: 25.095 - type: ndcg_at_1 value: 17.785999999999998 - type: ndcg_at_10 value: 27.236 - type: ndcg_at_100 value: 32.932 - type: ndcg_at_1000 value: 36.134 - type: ndcg_at_3 value: 22.33 - type: ndcg_at_5 value: 24.573999999999998 - type: precision_at_1 value: 17.785999999999998 - type: precision_at_10 value: 5.286 - type: precision_at_100 value: 0.9369999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.07 - type: precision_at_5 value: 8.308 - type: recall_at_1 value: 14.174000000000001 - type: recall_at_10 value: 39.135 - type: recall_at_100 value: 64.095 - type: recall_at_1000 value: 87.485 - type: recall_at_3 value: 25.496999999999996 - type: recall_at_5 value: 31.148999999999997 - type: map_at_1 value: 24.371000000000002 - type: map_at_10 value: 33.074999999999996 - type: map_at_100 value: 34.486 - type: map_at_1000 value: 34.608 - type: map_at_3 value: 30.483 - type: map_at_5 value: 31.972 - type: mrr_at_1 value: 29.548000000000002 - type: mrr_at_10 value: 38.431 - type: mrr_at_100 value: 39.347 - type: mrr_at_1000 value: 39.4 - type: mrr_at_3 value: 35.980000000000004 - type: mrr_at_5 value: 37.413999999999994 - type: ndcg_at_1 value: 29.548000000000002 - type: ndcg_at_10 value: 38.552 - type: ndcg_at_100 value: 44.598 - type: ndcg_at_1000 value: 47.0 - type: ndcg_at_3 value: 34.109 - type: ndcg_at_5 value: 36.263 - type: precision_at_1 value: 29.548000000000002 - type: precision_at_10 value: 6.92 - type: precision_at_100 value: 1.179 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 16.137 - type: precision_at_5 value: 11.511000000000001 - type: recall_at_1 value: 24.371000000000002 - type: recall_at_10 value: 49.586999999999996 - type: recall_at_100 value: 75.15899999999999 - type: recall_at_1000 value: 91.06 - type: recall_at_3 value: 37.09 - type: recall_at_5 value: 42.588 - type: map_at_1 value: 24.517 - type: map_at_10 value: 32.969 - type: map_at_100 value: 34.199 - type: map_at_1000 value: 34.322 - type: map_at_3 value: 30.270999999999997 - type: map_at_5 value: 31.863000000000003 - type: mrr_at_1 value: 30.479 - type: mrr_at_10 value: 38.633 - type: mrr_at_100 value: 39.522 - type: mrr_at_1000 value: 39.583 - type: mrr_at_3 value: 36.454 - type: mrr_at_5 value: 37.744 - type: ndcg_at_1 value: 30.479 - type: ndcg_at_10 value: 38.269 - type: ndcg_at_100 value: 43.91 - type: ndcg_at_1000 value: 46.564 - type: ndcg_at_3 value: 34.03 - type: ndcg_at_5 value: 36.155 - type: precision_at_1 value: 30.479 - type: precision_at_10 value: 6.815 - type: precision_at_100 value: 1.138 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.058 - type: precision_at_5 value: 11.416 - type: recall_at_1 value: 24.517 - type: recall_at_10 value: 48.559000000000005 - type: recall_at_100 value: 73.307 - type: recall_at_1000 value: 91.508 - type: recall_at_3 value: 36.563 - type: recall_at_5 value: 42.375 - type: map_at_1 value: 24.336166666666664 - type: map_at_10 value: 32.80791666666667 - type: map_at_100 value: 34.043416666666666 - type: map_at_1000 value: 34.162749999999996 - type: map_at_3 value: 30.187083333333337 - type: map_at_5 value: 31.637833333333337 - type: mrr_at_1 value: 28.669583333333343 - type: mrr_at_10 value: 36.88616666666667 - type: mrr_at_100 value: 37.80233333333333 - type: mrr_at_1000 value: 37.86141666666666 - type: mrr_at_3 value: 34.537416666666665 - type: mrr_at_5 value: 35.84275 - type: ndcg_at_1 value: 28.669583333333343 - type: ndcg_at_10 value: 37.956916666666665 - type: ndcg_at_100 value: 43.39475 - type: ndcg_at_1000 value: 45.79925 - type: ndcg_at_3 value: 33.43683333333334 - type: ndcg_at_5 value: 35.52575 - type: precision_at_1 value: 28.669583333333343 - type: precision_at_10 value: 6.603833333333335 - type: precision_at_100 value: 1.1079166666666667 - type: precision_at_1000 value: 0.15208333333333335 - type: precision_at_3 value: 15.338750000000001 - type: precision_at_5 value: 10.88775 - type: recall_at_1 value: 24.336166666666664 - type: recall_at_10 value: 49.19358333333333 - type: recall_at_100 value: 73.07583333333334 - type: recall_at_1000 value: 89.81675 - type: recall_at_3 value: 36.54091666666667 - type: recall_at_5 value: 41.919250000000005 - type: map_at_1 value: 23.388 - type: map_at_10 value: 29.408 - type: map_at_100 value: 30.452 - type: map_at_1000 value: 30.546 - type: map_at_3 value: 27.139000000000003 - type: map_at_5 value: 28.402 - type: mrr_at_1 value: 25.46 - type: mrr_at_10 value: 31.966 - type: mrr_at_100 value: 32.879999999999995 - type: mrr_at_1000 value: 32.944 - type: mrr_at_3 value: 29.755 - type: mrr_at_5 value: 30.974 - type: ndcg_at_1 value: 25.46 - type: ndcg_at_10 value: 33.449 - type: ndcg_at_100 value: 38.67 - type: ndcg_at_1000 value: 41.035 - type: ndcg_at_3 value: 29.048000000000002 - type: ndcg_at_5 value: 31.127 - type: precision_at_1 value: 25.46 - type: precision_at_10 value: 5.199 - type: precision_at_100 value: 0.8670000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.168 - type: precision_at_5 value: 8.62 - type: recall_at_1 value: 23.388 - type: recall_at_10 value: 43.428 - type: recall_at_100 value: 67.245 - type: recall_at_1000 value: 84.75399999999999 - type: recall_at_3 value: 31.416 - type: recall_at_5 value: 36.451 - type: map_at_1 value: 17.136000000000003 - type: map_at_10 value: 24.102999999999998 - type: map_at_100 value: 25.219 - type: map_at_1000 value: 25.344 - type: map_at_3 value: 22.004 - type: map_at_5 value: 23.145 - type: mrr_at_1 value: 20.613 - type: mrr_at_10 value: 27.753 - type: mrr_at_100 value: 28.698 - type: mrr_at_1000 value: 28.776000000000003 - type: mrr_at_3 value: 25.711000000000002 - type: mrr_at_5 value: 26.795 - type: ndcg_at_1 value: 20.613 - type: ndcg_at_10 value: 28.510999999999996 - type: ndcg_at_100 value: 33.924 - type: ndcg_at_1000 value: 36.849 - type: ndcg_at_3 value: 24.664 - type: ndcg_at_5 value: 26.365 - type: precision_at_1 value: 20.613 - type: precision_at_10 value: 5.069 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.574 - type: precision_at_5 value: 8.211 - type: recall_at_1 value: 17.136000000000003 - type: recall_at_10 value: 38.232 - type: recall_at_100 value: 62.571 - type: recall_at_1000 value: 83.23 - type: recall_at_3 value: 27.468999999999998 - type: recall_at_5 value: 31.852999999999998 - type: map_at_1 value: 25.580000000000002 - type: map_at_10 value: 33.449 - type: map_at_100 value: 34.58 - type: map_at_1000 value: 34.692 - type: map_at_3 value: 30.660999999999998 - type: map_at_5 value: 32.425 - type: mrr_at_1 value: 30.037000000000003 - type: mrr_at_10 value: 37.443 - type: mrr_at_100 value: 38.32 - type: mrr_at_1000 value: 38.384 - type: mrr_at_3 value: 34.778999999999996 - type: mrr_at_5 value: 36.458 - type: ndcg_at_1 value: 30.037000000000003 - type: ndcg_at_10 value: 38.46 - type: ndcg_at_100 value: 43.746 - type: ndcg_at_1000 value: 46.28 - type: ndcg_at_3 value: 33.52 - type: ndcg_at_5 value: 36.175000000000004 - type: precision_at_1 value: 30.037000000000003 - type: precision_at_10 value: 6.418 - type: precision_at_100 value: 1.0210000000000001 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 15.018999999999998 - type: precision_at_5 value: 10.877 - type: recall_at_1 value: 25.580000000000002 - type: recall_at_10 value: 49.830000000000005 - type: recall_at_100 value: 73.04899999999999 - type: recall_at_1000 value: 90.751 - type: recall_at_3 value: 36.370999999999995 - type: recall_at_5 value: 43.104 - type: map_at_1 value: 24.071 - type: map_at_10 value: 33.384 - type: map_at_100 value: 35.004999999999995 - type: map_at_1000 value: 35.215999999999994 - type: map_at_3 value: 30.459000000000003 - type: map_at_5 value: 31.769 - type: mrr_at_1 value: 28.854000000000003 - type: mrr_at_10 value: 37.512 - type: mrr_at_100 value: 38.567 - type: mrr_at_1000 value: 38.618 - type: mrr_at_3 value: 35.211 - type: mrr_at_5 value: 36.13 - type: ndcg_at_1 value: 28.854000000000003 - type: ndcg_at_10 value: 39.216 - type: ndcg_at_100 value: 45.214 - type: ndcg_at_1000 value: 47.573 - type: ndcg_at_3 value: 34.597 - type: ndcg_at_5 value: 36.063 - type: precision_at_1 value: 28.854000000000003 - type: precision_at_10 value: 7.648000000000001 - type: precision_at_100 value: 1.545 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 16.667 - type: precision_at_5 value: 11.818 - type: recall_at_1 value: 24.071 - type: recall_at_10 value: 50.802 - type: recall_at_100 value: 77.453 - type: recall_at_1000 value: 92.304 - type: recall_at_3 value: 36.846000000000004 - type: recall_at_5 value: 41.14 - type: map_at_1 value: 23.395 - type: map_at_10 value: 29.189999999999998 - type: map_at_100 value: 30.226999999999997 - type: map_at_1000 value: 30.337999999999997 - type: map_at_3 value: 27.342 - type: map_at_5 value: 28.116999999999997 - type: mrr_at_1 value: 25.323 - type: mrr_at_10 value: 31.241000000000003 - type: mrr_at_100 value: 32.225 - type: mrr_at_1000 value: 32.304 - type: mrr_at_3 value: 29.452 - type: mrr_at_5 value: 30.209000000000003 - type: ndcg_at_1 value: 25.323 - type: ndcg_at_10 value: 33.024 - type: ndcg_at_100 value: 38.279 - type: ndcg_at_1000 value: 41.026 - type: ndcg_at_3 value: 29.243000000000002 - type: ndcg_at_5 value: 30.564000000000004 - type: precision_at_1 value: 25.323 - type: precision_at_10 value: 4.972 - type: precision_at_100 value: 0.8210000000000001 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 12.076 - type: precision_at_5 value: 8.133 - type: recall_at_1 value: 23.395 - type: recall_at_10 value: 42.994 - type: recall_at_100 value: 66.985 - type: recall_at_1000 value: 87.483 - type: recall_at_3 value: 32.505 - type: recall_at_5 value: 35.721000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 8.322000000000001 - type: map_at_10 value: 14.491000000000001 - type: map_at_100 value: 16.066 - type: map_at_1000 value: 16.238 - type: map_at_3 value: 12.235 - type: map_at_5 value: 13.422999999999998 - type: mrr_at_1 value: 19.479 - type: mrr_at_10 value: 29.38 - type: mrr_at_100 value: 30.520999999999997 - type: mrr_at_1000 value: 30.570999999999998 - type: mrr_at_3 value: 26.395000000000003 - type: mrr_at_5 value: 27.982000000000003 - type: ndcg_at_1 value: 19.479 - type: ndcg_at_10 value: 21.215 - type: ndcg_at_100 value: 27.966 - type: ndcg_at_1000 value: 31.324 - type: ndcg_at_3 value: 17.194000000000003 - type: ndcg_at_5 value: 18.593 - type: precision_at_1 value: 19.479 - type: precision_at_10 value: 6.5280000000000005 - type: precision_at_100 value: 1.359 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 12.703999999999999 - type: precision_at_5 value: 9.655 - type: recall_at_1 value: 8.322000000000001 - type: recall_at_10 value: 26.165 - type: recall_at_100 value: 49.573 - type: recall_at_1000 value: 68.501 - type: recall_at_3 value: 16.179 - type: recall_at_5 value: 20.175 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.003 - type: map_at_10 value: 16.087 - type: map_at_100 value: 21.363 - type: map_at_1000 value: 22.64 - type: map_at_3 value: 12.171999999999999 - type: map_at_5 value: 13.866 - type: mrr_at_1 value: 61.25000000000001 - type: mrr_at_10 value: 68.626 - type: mrr_at_100 value: 69.134 - type: mrr_at_1000 value: 69.144 - type: mrr_at_3 value: 67.042 - type: mrr_at_5 value: 67.929 - type: ndcg_at_1 value: 49.0 - type: ndcg_at_10 value: 34.132 - type: ndcg_at_100 value: 37.545 - type: ndcg_at_1000 value: 44.544 - type: ndcg_at_3 value: 38.946999999999996 - type: ndcg_at_5 value: 36.317 - type: precision_at_1 value: 61.25000000000001 - type: precision_at_10 value: 26.325 - type: precision_at_100 value: 8.173 - type: precision_at_1000 value: 1.778 - type: precision_at_3 value: 41.667 - type: precision_at_5 value: 34.300000000000004 - type: recall_at_1 value: 8.003 - type: recall_at_10 value: 20.577 - type: recall_at_100 value: 41.884 - type: recall_at_1000 value: 64.36500000000001 - type: recall_at_3 value: 13.602 - type: recall_at_5 value: 16.41 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.835 - type: f1 value: 41.66455981281837 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 55.717000000000006 - type: map_at_10 value: 66.34100000000001 - type: map_at_100 value: 66.776 - type: map_at_1000 value: 66.794 - type: map_at_3 value: 64.386 - type: map_at_5 value: 65.566 - type: mrr_at_1 value: 60.141 - type: mrr_at_10 value: 70.928 - type: mrr_at_100 value: 71.29299999999999 - type: mrr_at_1000 value: 71.30199999999999 - type: mrr_at_3 value: 69.07900000000001 - type: mrr_at_5 value: 70.244 - type: ndcg_at_1 value: 60.141 - type: ndcg_at_10 value: 71.90100000000001 - type: ndcg_at_100 value: 73.836 - type: ndcg_at_1000 value: 74.214 - type: ndcg_at_3 value: 68.203 - type: ndcg_at_5 value: 70.167 - type: precision_at_1 value: 60.141 - type: precision_at_10 value: 9.268 - type: precision_at_100 value: 1.03 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 27.028000000000002 - type: precision_at_5 value: 17.342 - type: recall_at_1 value: 55.717000000000006 - type: recall_at_10 value: 84.66799999999999 - type: recall_at_100 value: 93.28 - type: recall_at_1000 value: 95.887 - type: recall_at_3 value: 74.541 - type: recall_at_5 value: 79.389 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 17.744 - type: map_at_10 value: 29.554000000000002 - type: map_at_100 value: 31.180000000000003 - type: map_at_1000 value: 31.372 - type: map_at_3 value: 25.6 - type: map_at_5 value: 27.642 - type: mrr_at_1 value: 35.802 - type: mrr_at_10 value: 44.812999999999995 - type: mrr_at_100 value: 45.56 - type: mrr_at_1000 value: 45.606 - type: mrr_at_3 value: 42.181000000000004 - type: mrr_at_5 value: 43.516 - type: ndcg_at_1 value: 35.802 - type: ndcg_at_10 value: 37.269999999999996 - type: ndcg_at_100 value: 43.575 - type: ndcg_at_1000 value: 46.916000000000004 - type: ndcg_at_3 value: 33.511 - type: ndcg_at_5 value: 34.504000000000005 - type: precision_at_1 value: 35.802 - type: precision_at_10 value: 10.448 - type: precision_at_100 value: 1.7129999999999999 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 22.531000000000002 - type: precision_at_5 value: 16.512 - type: recall_at_1 value: 17.744 - type: recall_at_10 value: 44.616 - type: recall_at_100 value: 68.51899999999999 - type: recall_at_1000 value: 88.495 - type: recall_at_3 value: 30.235 - type: recall_at_5 value: 35.821999999999996 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 33.315 - type: map_at_10 value: 45.932 - type: map_at_100 value: 46.708 - type: map_at_1000 value: 46.778999999999996 - type: map_at_3 value: 43.472 - type: map_at_5 value: 45.022 - type: mrr_at_1 value: 66.631 - type: mrr_at_10 value: 73.083 - type: mrr_at_100 value: 73.405 - type: mrr_at_1000 value: 73.421 - type: mrr_at_3 value: 71.756 - type: mrr_at_5 value: 72.616 - type: ndcg_at_1 value: 66.631 - type: ndcg_at_10 value: 54.949000000000005 - type: ndcg_at_100 value: 57.965 - type: ndcg_at_1000 value: 59.467000000000006 - type: ndcg_at_3 value: 51.086 - type: ndcg_at_5 value: 53.272 - type: precision_at_1 value: 66.631 - type: precision_at_10 value: 11.178 - type: precision_at_100 value: 1.3559999999999999 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 31.582 - type: precision_at_5 value: 20.678 - type: recall_at_1 value: 33.315 - type: recall_at_10 value: 55.888000000000005 - type: recall_at_100 value: 67.812 - type: recall_at_1000 value: 77.839 - type: recall_at_3 value: 47.373 - type: recall_at_5 value: 51.695 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 66.424 - type: ap value: 61.132235499939256 - type: f1 value: 66.07094958225315 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.575 - type: map_at_10 value: 33.509 - type: map_at_100 value: 34.725 - type: map_at_1000 value: 34.775 - type: map_at_3 value: 29.673 - type: map_at_5 value: 31.805 - type: mrr_at_1 value: 22.235 - type: mrr_at_10 value: 34.1 - type: mrr_at_100 value: 35.254999999999995 - type: mrr_at_1000 value: 35.299 - type: mrr_at_3 value: 30.334 - type: mrr_at_5 value: 32.419 - type: ndcg_at_1 value: 22.235 - type: ndcg_at_10 value: 40.341 - type: ndcg_at_100 value: 46.161 - type: ndcg_at_1000 value: 47.400999999999996 - type: ndcg_at_3 value: 32.482 - type: ndcg_at_5 value: 36.269 - type: precision_at_1 value: 22.235 - type: precision_at_10 value: 6.422999999999999 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.835 - type: precision_at_5 value: 10.226 - type: recall_at_1 value: 21.575 - type: recall_at_10 value: 61.448 - type: recall_at_100 value: 88.289 - type: recall_at_1000 value: 97.76899999999999 - type: recall_at_3 value: 39.971000000000004 - type: recall_at_5 value: 49.053000000000004 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 92.83401732786137 - type: f1 value: 92.47678691291068 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.08983128134975 - type: f1 value: 59.782936393820904 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.73032952252858 - type: f1 value: 70.72684765888265 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.08473436449226 - type: f1 value: 77.31457411257054 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.11980959210532 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 25.2587629106119 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.48268319779204 - type: mrr value: 32.501885728964304 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.284 - type: map_at_10 value: 11.509 - type: map_at_100 value: 14.624 - type: map_at_1000 value: 16.035 - type: map_at_3 value: 8.347999999999999 - type: map_at_5 value: 9.919 - type: mrr_at_1 value: 43.344 - type: mrr_at_10 value: 52.303999999999995 - type: mrr_at_100 value: 52.994 - type: mrr_at_1000 value: 53.032999999999994 - type: mrr_at_3 value: 50.361 - type: mrr_at_5 value: 51.754 - type: ndcg_at_1 value: 41.176 - type: ndcg_at_10 value: 32.244 - type: ndcg_at_100 value: 29.916999999999998 - type: ndcg_at_1000 value: 38.753 - type: ndcg_at_3 value: 36.856 - type: ndcg_at_5 value: 35.394999999999996 - type: precision_at_1 value: 43.034 - type: precision_at_10 value: 24.118000000000002 - type: precision_at_100 value: 7.926 - type: precision_at_1000 value: 2.045 - type: precision_at_3 value: 34.675 - type: precision_at_5 value: 31.146 - type: recall_at_1 value: 5.284 - type: recall_at_10 value: 15.457 - type: recall_at_100 value: 30.914 - type: recall_at_1000 value: 63.788999999999994 - type: recall_at_3 value: 9.596 - type: recall_at_5 value: 12.391 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 29.537999999999997 - type: map_at_10 value: 43.99 - type: map_at_100 value: 45.003 - type: map_at_1000 value: 45.04 - type: map_at_3 value: 39.814 - type: map_at_5 value: 42.166 - type: mrr_at_1 value: 33.256 - type: mrr_at_10 value: 46.487 - type: mrr_at_100 value: 47.264 - type: mrr_at_1000 value: 47.29 - type: mrr_at_3 value: 43.091 - type: mrr_at_5 value: 45.013999999999996 - type: ndcg_at_1 value: 33.256 - type: ndcg_at_10 value: 51.403 - type: ndcg_at_100 value: 55.706999999999994 - type: ndcg_at_1000 value: 56.586000000000006 - type: ndcg_at_3 value: 43.559 - type: ndcg_at_5 value: 47.426 - type: precision_at_1 value: 33.256 - type: precision_at_10 value: 8.540000000000001 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 19.834 - type: precision_at_5 value: 14.143 - type: recall_at_1 value: 29.537999999999997 - type: recall_at_10 value: 71.5 - type: recall_at_100 value: 90.25 - type: recall_at_1000 value: 96.82600000000001 - type: recall_at_3 value: 51.108 - type: recall_at_5 value: 60.006 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.526 - type: map_at_10 value: 84.342 - type: map_at_100 value: 84.985 - type: map_at_1000 value: 85.003 - type: map_at_3 value: 81.472 - type: map_at_5 value: 83.292 - type: mrr_at_1 value: 81.17 - type: mrr_at_10 value: 87.33999999999999 - type: mrr_at_100 value: 87.445 - type: mrr_at_1000 value: 87.446 - type: mrr_at_3 value: 86.387 - type: mrr_at_5 value: 87.042 - type: ndcg_at_1 value: 81.19 - type: ndcg_at_10 value: 88.088 - type: ndcg_at_100 value: 89.35 - type: ndcg_at_1000 value: 89.462 - type: ndcg_at_3 value: 85.319 - type: ndcg_at_5 value: 86.858 - type: precision_at_1 value: 81.19 - type: precision_at_10 value: 13.33 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.31 - type: precision_at_5 value: 24.512 - type: recall_at_1 value: 70.526 - type: recall_at_10 value: 95.166 - type: recall_at_100 value: 99.479 - type: recall_at_1000 value: 99.984 - type: recall_at_3 value: 87.124 - type: recall_at_5 value: 91.53 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.049073872893494 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 55.13810914528368 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.593 - type: map_at_10 value: 10.907 - type: map_at_100 value: 12.888 - type: map_at_1000 value: 13.167000000000002 - type: map_at_3 value: 7.936 - type: map_at_5 value: 9.31 - type: mrr_at_1 value: 22.7 - type: mrr_at_10 value: 32.509 - type: mrr_at_100 value: 33.69 - type: mrr_at_1000 value: 33.747 - type: mrr_at_3 value: 29.599999999999998 - type: mrr_at_5 value: 31.155 - type: ndcg_at_1 value: 22.7 - type: ndcg_at_10 value: 18.445 - type: ndcg_at_100 value: 26.241999999999997 - type: ndcg_at_1000 value: 31.409 - type: ndcg_at_3 value: 17.864 - type: ndcg_at_5 value: 15.232999999999999 - type: precision_at_1 value: 22.7 - type: precision_at_10 value: 9.43 - type: precision_at_100 value: 2.061 - type: precision_at_1000 value: 0.331 - type: precision_at_3 value: 16.467000000000002 - type: precision_at_5 value: 13.08 - type: recall_at_1 value: 4.593 - type: recall_at_10 value: 19.115 - type: recall_at_100 value: 41.82 - type: recall_at_1000 value: 67.167 - type: recall_at_3 value: 9.983 - type: recall_at_5 value: 13.218 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.94432059816452 - type: cos_sim_spearman value: 79.19993315048852 - type: euclidean_pearson value: 72.43261099671753 - type: euclidean_spearman value: 71.51531114998619 - type: manhattan_pearson value: 71.83604124130447 - type: manhattan_spearman value: 71.24460392842295 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.25401068481673 - type: cos_sim_spearman value: 74.5249604699309 - type: euclidean_pearson value: 71.1324859629043 - type: euclidean_spearman value: 58.77041705276752 - type: manhattan_pearson value: 71.01471521586141 - type: manhattan_spearman value: 58.69949381017865 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.85731544223766 - type: cos_sim_spearman value: 83.15607264736185 - type: euclidean_pearson value: 75.8803249521361 - type: euclidean_spearman value: 76.4862168799065 - type: manhattan_pearson value: 75.80451454386811 - type: manhattan_spearman value: 76.35986831074699 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.40669043798857 - type: cos_sim_spearman value: 78.08686090667834 - type: euclidean_pearson value: 74.48574712193803 - type: euclidean_spearman value: 70.79423012045118 - type: manhattan_pearson value: 74.39099211477354 - type: manhattan_spearman value: 70.73135427277684 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.03027014209859 - type: cos_sim_spearman value: 86.91082847840946 - type: euclidean_pearson value: 69.13187603971996 - type: euclidean_spearman value: 70.0370035340552 - type: manhattan_pearson value: 69.2586635812031 - type: manhattan_spearman value: 70.18638387118486 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.41190748361883 - type: cos_sim_spearman value: 83.64850851235231 - type: euclidean_pearson value: 71.60523243575282 - type: euclidean_spearman value: 72.26134033805099 - type: manhattan_pearson value: 71.50771482066683 - type: manhattan_spearman value: 72.13707967973161 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 90.42838477648627 - type: cos_sim_spearman value: 90.15798155439076 - type: euclidean_pearson value: 77.09619972244516 - type: euclidean_spearman value: 75.5953488548861 - type: manhattan_pearson value: 77.36892406451771 - type: manhattan_spearman value: 75.76625156149356 - 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: 65.76151154879307 - type: cos_sim_spearman value: 64.8846800918359 - type: euclidean_pearson value: 50.23302700257155 - type: euclidean_spearman value: 58.89455187289583 - type: manhattan_pearson value: 50.05498582284945 - type: manhattan_spearman value: 58.75893793871576 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.72381109169437 - type: cos_sim_spearman value: 84.59820928231167 - type: euclidean_pearson value: 74.85450857429493 - type: euclidean_spearman value: 73.83634052565915 - type: manhattan_pearson value: 74.97349743979106 - type: manhattan_spearman value: 73.9636470375881 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 80.96736259172798 - type: mrr value: 94.48378781712114 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 46.344 - type: map_at_10 value: 54.962 - type: map_at_100 value: 55.772 - type: map_at_1000 value: 55.81700000000001 - type: map_at_3 value: 51.832 - type: map_at_5 value: 53.718999999999994 - type: mrr_at_1 value: 49.0 - type: mrr_at_10 value: 56.721 - type: mrr_at_100 value: 57.287 - type: mrr_at_1000 value: 57.330000000000005 - type: mrr_at_3 value: 54.056000000000004 - type: mrr_at_5 value: 55.822 - type: ndcg_at_1 value: 49.0 - type: ndcg_at_10 value: 59.757000000000005 - type: ndcg_at_100 value: 63.149 - type: ndcg_at_1000 value: 64.43100000000001 - type: ndcg_at_3 value: 54.105000000000004 - type: ndcg_at_5 value: 57.196999999999996 - type: precision_at_1 value: 49.0 - type: precision_at_10 value: 8.200000000000001 - type: precision_at_100 value: 1.0070000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 20.889 - type: precision_at_5 value: 14.399999999999999 - type: recall_at_1 value: 46.344 - type: recall_at_10 value: 72.722 - type: recall_at_100 value: 88.167 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 57.994 - type: recall_at_5 value: 65.506 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.83366336633664 - type: cos_sim_ap value: 96.09329747251944 - type: cos_sim_f1 value: 91.66255550074001 - type: cos_sim_precision value: 90.45764362220059 - type: cos_sim_recall value: 92.9 - type: dot_accuracy value: 99.32871287128712 - type: dot_ap value: 63.95436644147969 - type: dot_f1 value: 60.61814556331008 - type: dot_precision value: 60.437375745526836 - type: dot_recall value: 60.8 - type: euclidean_accuracy value: 99.66534653465347 - type: euclidean_ap value: 85.85143979761818 - type: euclidean_f1 value: 81.57033805888769 - type: euclidean_precision value: 89.68824940047962 - type: euclidean_recall value: 74.8 - type: manhattan_accuracy value: 99.65742574257426 - type: manhattan_ap value: 85.55693926348405 - type: manhattan_f1 value: 81.13804004214963 - type: manhattan_precision value: 85.74610244988864 - type: manhattan_recall value: 77.0 - type: max_accuracy value: 99.83366336633664 - type: max_ap value: 96.09329747251944 - type: max_f1 value: 91.66255550074001 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 45.23573510003245 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.37478638401161 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.375920467392476 - 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: 29.768864092288343 - type: cos_sim_spearman value: 29.854278347043266 - type: dot_pearson value: 20.51281723837505 - type: dot_spearman value: 21.799102540913665 - 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: 1.202 - type: map_at_100 value: 6.729 - type: map_at_1000 value: 15.928 - type: map_at_3 value: 0.492 - type: map_at_5 value: 0.712 - type: mrr_at_1 value: 76.0 - type: mrr_at_10 value: 84.75 - type: mrr_at_100 value: 84.75 - type: mrr_at_1000 value: 84.75 - type: mrr_at_3 value: 83.0 - type: mrr_at_5 value: 84.5 - type: ndcg_at_1 value: 71.0 - type: ndcg_at_10 value: 57.253 - type: ndcg_at_100 value: 44.383 - type: ndcg_at_1000 value: 38.666 - type: ndcg_at_3 value: 64.324 - type: ndcg_at_5 value: 60.791 - type: precision_at_1 value: 76.0 - type: precision_at_10 value: 59.599999999999994 - type: precision_at_100 value: 45.440000000000005 - type: precision_at_1000 value: 17.458000000000002 - type: precision_at_3 value: 69.333 - type: precision_at_5 value: 63.2 - type: recall_at_1 value: 0.2 - type: recall_at_10 value: 1.4949999999999999 - type: recall_at_100 value: 10.266 - type: recall_at_1000 value: 35.853 - type: recall_at_3 value: 0.5349999999999999 - type: recall_at_5 value: 0.8109999999999999 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.0140000000000002 - type: map_at_10 value: 8.474 - type: map_at_100 value: 14.058000000000002 - type: map_at_1000 value: 15.381 - type: map_at_3 value: 4.508 - type: map_at_5 value: 5.87 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 37.242 - type: mrr_at_100 value: 38.291 - type: mrr_at_1000 value: 38.311 - type: mrr_at_3 value: 32.312999999999995 - type: mrr_at_5 value: 34.762 - type: ndcg_at_1 value: 20.408 - type: ndcg_at_10 value: 20.729 - type: ndcg_at_100 value: 33.064 - type: ndcg_at_1000 value: 44.324999999999996 - type: ndcg_at_3 value: 21.251 - type: ndcg_at_5 value: 20.28 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 18.98 - type: precision_at_100 value: 7.224 - type: precision_at_1000 value: 1.471 - type: precision_at_3 value: 22.448999999999998 - type: precision_at_5 value: 20.816000000000003 - type: recall_at_1 value: 2.0140000000000002 - type: recall_at_10 value: 13.96 - type: recall_at_100 value: 44.187 - type: recall_at_1000 value: 79.328 - type: recall_at_3 value: 5.345 - type: recall_at_5 value: 7.979 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.1312 - type: ap value: 12.606776505497608 - type: f1 value: 52.4112415600534 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 58.16072439162422 - type: f1 value: 58.29152785435414 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 40.421119289825924 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.48012159504083 - type: cos_sim_ap value: 72.31974877212102 - type: cos_sim_f1 value: 67.96846573681019 - type: cos_sim_precision value: 62.89562289562289 - type: cos_sim_recall value: 73.93139841688654 - type: dot_accuracy value: 78.52416999463551 - type: dot_ap value: 43.65271285411479 - type: dot_f1 value: 46.94641449960599 - type: dot_precision value: 37.456774599182644 - type: dot_recall value: 62.875989445910285 - type: euclidean_accuracy value: 83.90057817249806 - type: euclidean_ap value: 65.96278727778665 - type: euclidean_f1 value: 63.35733232284957 - type: euclidean_precision value: 60.770535497940394 - type: euclidean_recall value: 66.17414248021109 - type: manhattan_accuracy value: 83.96614412588663 - type: manhattan_ap value: 66.03670273156699 - type: manhattan_f1 value: 63.49128406579917 - type: manhattan_precision value: 59.366391184573 - type: manhattan_recall value: 68.23218997361478 - type: max_accuracy value: 85.48012159504083 - type: max_ap value: 72.31974877212102 - type: max_f1 value: 67.96846573681019 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.97038848139093 - type: cos_sim_ap value: 85.982764495556 - type: cos_sim_f1 value: 78.73283281450284 - type: cos_sim_precision value: 75.07857791436754 - type: cos_sim_recall value: 82.7610101632276 - type: dot_accuracy value: 83.21108394458028 - type: dot_ap value: 70.97956937273386 - type: dot_f1 value: 66.53083038279111 - type: dot_precision value: 58.7551622418879 - type: dot_recall value: 76.67847243609486 - type: euclidean_accuracy value: 84.31520937633407 - type: euclidean_ap value: 74.67323411319909 - type: euclidean_f1 value: 67.21935410935676 - type: euclidean_precision value: 65.82773636430733 - type: euclidean_recall value: 68.67108099784416 - type: manhattan_accuracy value: 84.35013777312066 - type: manhattan_ap value: 74.66508905354597 - type: manhattan_f1 value: 67.28264162375038 - type: manhattan_precision value: 66.19970193740686 - type: manhattan_recall value: 68.40160147828766 - type: max_accuracy value: 88.97038848139093 - type: max_ap value: 85.982764495556 - type: max_f1 value: 78.73283281450284 --- <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-l-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 size of 330 million parameters, the model enables single-gpu inference while delivering better performance than our small and base model. 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 - [`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 **(you are here)**. - `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-l-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-b-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} } ```
[ "BIOSSES", "LINNAEUS", "SCIFACT" ]
Non_BioNLP
jstephencorey/pythia-14m-embedding
jstephencorey
null
[ "region:us" ]
1,703,713,513,000
2024-02-05T20:12:28
0
0
--- {} --- --- tags: - mteb model-index: - name: pythia-14m_mean results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 70.73134328358208 - type: ap value: 32.35996836729783 - type: f1 value: 64.2137087561157 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 62.291220556745174 - type: ap value: 76.5427302441011 - type: f1 value: 60.37703210343267 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 67.57871064467767 - type: ap value: 17.03033311712744 - type: f1 value: 54.821750631894986 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 62.51605995717344 - type: ap value: 14.367489440317666 - type: f1 value: 50.48473578289779 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 57.567425000000014 - type: ap value: 54.53026421737829 - type: f1 value: 56.60093061259046 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 29.172000000000004 - type: f1 value: 28.264998641170465 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 25.157999999999998 - type: f1 value: 23.033533062569987 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 26.840000000000003 - type: f1 value: 25.693413738086402 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 26.491999999999997 - type: f1 value: 25.6252880863665 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 24.448000000000004 - type: f1 value: 23.86460242225935 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 26.412000000000003 - type: f1 value: 25.779710231390755 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 5.761 - type: map_at_10 value: 10.267 - type: map_at_100 value: 11.065999999999999 - type: map_at_1000 value: 11.16 - type: map_at_3 value: 8.642 - type: map_at_5 value: 9.474 - type: mrr_at_1 value: 6.046 - type: mrr_at_10 value: 10.365 - type: mrr_at_100 value: 11.178 - type: mrr_at_1000 value: 11.272 - type: mrr_at_3 value: 8.713 - type: mrr_at_5 value: 9.587 - type: ndcg_at_1 value: 5.761 - type: ndcg_at_10 value: 13.055 - type: ndcg_at_100 value: 17.526 - type: ndcg_at_1000 value: 20.578 - type: ndcg_at_3 value: 9.616 - type: ndcg_at_5 value: 11.128 - type: precision_at_1 value: 5.761 - type: precision_at_10 value: 2.212 - type: precision_at_100 value: 0.44400000000000006 - type: precision_at_1000 value: 0.06999999999999999 - type: precision_at_3 value: 4.149 - type: precision_at_5 value: 3.229 - type: recall_at_1 value: 5.761 - type: recall_at_10 value: 22.119 - type: recall_at_100 value: 44.381 - type: recall_at_1000 value: 69.70100000000001 - type: recall_at_3 value: 12.447 - type: recall_at_5 value: 16.145 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 25.92658946113241 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 13.902183567893395 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 47.93210378051478 - type: mrr value: 60.70318339708921 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 49.57650220181508 - type: cos_sim_spearman value: 51.842145113866636 - type: euclidean_pearson value: 41.2188173176347 - type: euclidean_spearman value: 41.16840792962046 - type: manhattan_pearson value: 42.73893519020435 - type: manhattan_spearman value: 44.384746276312534 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 46.03896103896104 - type: f1 value: 44.54083818845286 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 23.113393015706908 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 12.624675113307488 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 10.105 - type: map_at_10 value: 13.364 - type: map_at_100 value: 13.987 - type: map_at_1000 value: 14.08 - type: map_at_3 value: 12.447 - type: map_at_5 value: 12.992999999999999 - type: mrr_at_1 value: 12.876000000000001 - type: mrr_at_10 value: 16.252 - type: mrr_at_100 value: 16.926 - type: mrr_at_1000 value: 17.004 - type: mrr_at_3 value: 15.235999999999999 - type: mrr_at_5 value: 15.744 - type: ndcg_at_1 value: 12.876000000000001 - type: ndcg_at_10 value: 15.634999999999998 - type: ndcg_at_100 value: 19.173000000000002 - type: ndcg_at_1000 value: 22.168 - type: ndcg_at_3 value: 14.116999999999999 - type: ndcg_at_5 value: 14.767 - type: precision_at_1 value: 12.876000000000001 - type: precision_at_10 value: 2.761 - type: precision_at_100 value: 0.5579999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 6.676 - type: precision_at_5 value: 4.635 - type: recall_at_1 value: 10.105 - type: recall_at_10 value: 19.767000000000003 - type: recall_at_100 value: 36.448 - type: recall_at_1000 value: 58.623000000000005 - type: recall_at_3 value: 15.087 - type: recall_at_5 value: 17.076 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 7.249999999999999 - type: map_at_10 value: 9.41 - type: map_at_100 value: 9.903 - type: map_at_1000 value: 9.993 - type: map_at_3 value: 8.693 - type: map_at_5 value: 9.052 - type: mrr_at_1 value: 9.299 - type: mrr_at_10 value: 11.907 - type: mrr_at_100 value: 12.424 - type: mrr_at_1000 value: 12.503 - type: mrr_at_3 value: 10.945 - type: mrr_at_5 value: 11.413 - type: ndcg_at_1 value: 9.299 - type: ndcg_at_10 value: 11.278 - type: ndcg_at_100 value: 13.904 - type: ndcg_at_1000 value: 16.642000000000003 - type: ndcg_at_3 value: 9.956 - type: ndcg_at_5 value: 10.488 - type: precision_at_1 value: 9.299 - type: precision_at_10 value: 2.166 - type: precision_at_100 value: 0.45399999999999996 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 4.798 - type: precision_at_5 value: 3.427 - type: recall_at_1 value: 7.249999999999999 - type: recall_at_10 value: 14.285 - type: recall_at_100 value: 26.588 - type: recall_at_1000 value: 46.488 - type: recall_at_3 value: 10.309 - type: recall_at_5 value: 11.756 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.57 - type: map_at_10 value: 15.497 - type: map_at_100 value: 16.036 - type: map_at_1000 value: 16.122 - type: map_at_3 value: 14.309 - type: map_at_5 value: 14.895 - type: mrr_at_1 value: 13.354 - type: mrr_at_10 value: 17.408 - type: mrr_at_100 value: 17.936 - type: mrr_at_1000 value: 18.015 - type: mrr_at_3 value: 16.123 - type: mrr_at_5 value: 16.735 - type: ndcg_at_1 value: 13.354 - type: ndcg_at_10 value: 18.071 - type: ndcg_at_100 value: 21.017 - type: ndcg_at_1000 value: 23.669999999999998 - type: ndcg_at_3 value: 15.644 - type: ndcg_at_5 value: 16.618 - type: precision_at_1 value: 13.354 - type: precision_at_10 value: 2.94 - type: precision_at_100 value: 0.481 - type: precision_at_1000 value: 0.076 - type: precision_at_3 value: 7.001 - type: precision_at_5 value: 4.765 - type: recall_at_1 value: 11.57 - type: recall_at_10 value: 24.147 - type: recall_at_100 value: 38.045 - type: recall_at_1000 value: 58.648 - type: recall_at_3 value: 17.419999999999998 - type: recall_at_5 value: 19.875999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 4.463 - type: map_at_10 value: 6.091 - type: map_at_100 value: 6.548 - type: map_at_1000 value: 6.622 - type: map_at_3 value: 5.461 - type: map_at_5 value: 5.768 - type: mrr_at_1 value: 4.746 - type: mrr_at_10 value: 6.431000000000001 - type: mrr_at_100 value: 6.941 - type: mrr_at_1000 value: 7.016 - type: mrr_at_3 value: 5.763 - type: mrr_at_5 value: 6.101999999999999 - type: ndcg_at_1 value: 4.746 - type: ndcg_at_10 value: 7.19 - type: ndcg_at_100 value: 9.604 - type: ndcg_at_1000 value: 12.086 - type: ndcg_at_3 value: 5.88 - type: ndcg_at_5 value: 6.429 - type: precision_at_1 value: 4.746 - type: precision_at_10 value: 1.141 - type: precision_at_100 value: 0.249 - type: precision_at_1000 value: 0.049 - type: precision_at_3 value: 2.448 - type: precision_at_5 value: 1.7850000000000001 - type: recall_at_1 value: 4.463 - type: recall_at_10 value: 10.33 - type: recall_at_100 value: 21.578 - type: recall_at_1000 value: 41.404 - type: recall_at_3 value: 6.816999999999999 - type: recall_at_5 value: 8.06 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 1.521 - type: map_at_10 value: 2.439 - type: map_at_100 value: 2.785 - type: map_at_1000 value: 2.858 - type: map_at_3 value: 2.091 - type: map_at_5 value: 2.2560000000000002 - type: mrr_at_1 value: 2.114 - type: mrr_at_10 value: 3.216 - type: mrr_at_100 value: 3.6319999999999997 - type: mrr_at_1000 value: 3.712 - type: mrr_at_3 value: 2.778 - type: mrr_at_5 value: 2.971 - type: ndcg_at_1 value: 2.114 - type: ndcg_at_10 value: 3.1910000000000003 - type: ndcg_at_100 value: 5.165 - type: ndcg_at_1000 value: 7.607 - type: ndcg_at_3 value: 2.456 - type: ndcg_at_5 value: 2.7439999999999998 - type: precision_at_1 value: 2.114 - type: precision_at_10 value: 0.634 - type: precision_at_100 value: 0.189 - type: precision_at_1000 value: 0.049 - type: precision_at_3 value: 1.202 - type: precision_at_5 value: 0.8959999999999999 - type: recall_at_1 value: 1.521 - type: recall_at_10 value: 4.8 - type: recall_at_100 value: 13.877 - type: recall_at_1000 value: 32.1 - type: recall_at_3 value: 2.806 - type: recall_at_5 value: 3.5520000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 7.449999999999999 - type: map_at_10 value: 10.065 - type: map_at_100 value: 10.507 - type: map_at_1000 value: 10.599 - type: map_at_3 value: 9.017 - type: map_at_5 value: 9.603 - type: mrr_at_1 value: 9.336 - type: mrr_at_10 value: 12.589 - type: mrr_at_100 value: 13.086 - type: mrr_at_1000 value: 13.161000000000001 - type: mrr_at_3 value: 11.373 - type: mrr_at_5 value: 12.084999999999999 - type: ndcg_at_1 value: 9.336 - type: ndcg_at_10 value: 12.299 - type: ndcg_at_100 value: 14.780999999999999 - type: ndcg_at_1000 value: 17.632 - type: ndcg_at_3 value: 10.302 - type: ndcg_at_5 value: 11.247 - type: precision_at_1 value: 9.336 - type: precision_at_10 value: 2.271 - type: precision_at_100 value: 0.42300000000000004 - type: precision_at_1000 value: 0.08099999999999999 - type: precision_at_3 value: 4.909 - type: precision_at_5 value: 3.5999999999999996 - type: recall_at_1 value: 7.449999999999999 - type: recall_at_10 value: 16.891000000000002 - type: recall_at_100 value: 28.050000000000004 - type: recall_at_1000 value: 49.267 - type: recall_at_3 value: 11.187999999999999 - type: recall_at_5 value: 13.587 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 4.734 - type: map_at_10 value: 7.045999999999999 - type: map_at_100 value: 7.564 - type: map_at_1000 value: 7.6499999999999995 - type: map_at_3 value: 6.21 - type: map_at_5 value: 6.617000000000001 - type: mrr_at_1 value: 5.936 - type: mrr_at_10 value: 8.624 - type: mrr_at_100 value: 9.193 - type: mrr_at_1000 value: 9.28 - type: mrr_at_3 value: 7.725 - type: mrr_at_5 value: 8.147 - type: ndcg_at_1 value: 5.936 - type: ndcg_at_10 value: 8.81 - type: ndcg_at_100 value: 11.694 - type: ndcg_at_1000 value: 14.526 - type: ndcg_at_3 value: 7.140000000000001 - type: ndcg_at_5 value: 7.8020000000000005 - type: precision_at_1 value: 5.936 - type: precision_at_10 value: 1.701 - type: precision_at_100 value: 0.366 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 3.463 - type: precision_at_5 value: 2.557 - type: recall_at_1 value: 4.734 - type: recall_at_10 value: 12.733 - type: recall_at_100 value: 25.982 - type: recall_at_1000 value: 47.233999999999995 - type: recall_at_3 value: 8.018 - type: recall_at_5 value: 9.762 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 4.293 - type: map_at_10 value: 6.146999999999999 - type: map_at_100 value: 6.487 - type: map_at_1000 value: 6.544999999999999 - type: map_at_3 value: 5.6930000000000005 - type: map_at_5 value: 5.869 - type: mrr_at_1 value: 5.061 - type: mrr_at_10 value: 7.1690000000000005 - type: mrr_at_100 value: 7.542 - type: mrr_at_1000 value: 7.5969999999999995 - type: mrr_at_3 value: 6.646000000000001 - type: mrr_at_5 value: 6.8229999999999995 - type: ndcg_at_1 value: 5.061 - type: ndcg_at_10 value: 7.396 - type: ndcg_at_100 value: 9.41 - type: ndcg_at_1000 value: 11.386000000000001 - type: ndcg_at_3 value: 6.454 - type: ndcg_at_5 value: 6.718 - type: precision_at_1 value: 5.061 - type: precision_at_10 value: 1.319 - type: precision_at_100 value: 0.262 - type: precision_at_1000 value: 0.047 - type: precision_at_3 value: 3.0669999999999997 - type: precision_at_5 value: 1.994 - type: recall_at_1 value: 4.293 - type: recall_at_10 value: 10.221 - type: recall_at_100 value: 19.744999999999997 - type: recall_at_1000 value: 35.399 - type: recall_at_3 value: 7.507999999999999 - type: recall_at_5 value: 8.275 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 3.519 - type: map_at_10 value: 4.768 - type: map_at_100 value: 5.034000000000001 - type: map_at_1000 value: 5.087 - type: map_at_3 value: 4.308 - type: map_at_5 value: 4.565 - type: mrr_at_1 value: 4.474 - type: mrr_at_10 value: 6.045 - type: mrr_at_100 value: 6.361999999999999 - type: mrr_at_1000 value: 6.417000000000001 - type: mrr_at_3 value: 5.483 - type: mrr_at_5 value: 5.81 - type: ndcg_at_1 value: 4.474 - type: ndcg_at_10 value: 5.799 - type: ndcg_at_100 value: 7.344 - type: ndcg_at_1000 value: 9.141 - type: ndcg_at_3 value: 4.893 - type: ndcg_at_5 value: 5.309 - type: precision_at_1 value: 4.474 - type: precision_at_10 value: 1.06 - type: precision_at_100 value: 0.217 - type: precision_at_1000 value: 0.045 - type: precision_at_3 value: 2.306 - type: precision_at_5 value: 1.7000000000000002 - type: recall_at_1 value: 3.519 - type: recall_at_10 value: 7.75 - type: recall_at_100 value: 15.049999999999999 - type: recall_at_1000 value: 28.779 - type: recall_at_3 value: 5.18 - type: recall_at_5 value: 6.245 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 6.098 - type: map_at_10 value: 7.918 - type: map_at_100 value: 8.229000000000001 - type: map_at_1000 value: 8.293000000000001 - type: map_at_3 value: 7.138999999999999 - type: map_at_5 value: 7.646 - type: mrr_at_1 value: 7.090000000000001 - type: mrr_at_10 value: 9.293 - type: mrr_at_100 value: 9.669 - type: mrr_at_1000 value: 9.734 - type: mrr_at_3 value: 8.364 - type: mrr_at_5 value: 8.956999999999999 - type: ndcg_at_1 value: 7.090000000000001 - type: ndcg_at_10 value: 9.411999999999999 - type: ndcg_at_100 value: 11.318999999999999 - type: ndcg_at_1000 value: 13.478000000000002 - type: ndcg_at_3 value: 7.837 - type: ndcg_at_5 value: 8.73 - type: precision_at_1 value: 7.090000000000001 - type: precision_at_10 value: 1.558 - type: precision_at_100 value: 0.28400000000000003 - type: precision_at_1000 value: 0.053 - type: precision_at_3 value: 3.42 - type: precision_at_5 value: 2.5749999999999997 - type: recall_at_1 value: 6.098 - type: recall_at_10 value: 12.764000000000001 - type: recall_at_100 value: 21.747 - type: recall_at_1000 value: 38.279999999999994 - type: recall_at_3 value: 8.476 - type: recall_at_5 value: 10.707 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 8.607 - type: map_at_10 value: 10.835 - type: map_at_100 value: 11.285 - type: map_at_1000 value: 11.383000000000001 - type: map_at_3 value: 10.111 - type: map_at_5 value: 10.334999999999999 - type: mrr_at_1 value: 10.671999999999999 - type: mrr_at_10 value: 13.269 - type: mrr_at_100 value: 13.729 - type: mrr_at_1000 value: 13.813 - type: mrr_at_3 value: 12.385 - type: mrr_at_5 value: 12.701 - type: ndcg_at_1 value: 10.671999999999999 - type: ndcg_at_10 value: 12.728 - type: ndcg_at_100 value: 15.312999999999999 - type: ndcg_at_1000 value: 18.160999999999998 - type: ndcg_at_3 value: 11.355 - type: ndcg_at_5 value: 11.605 - type: precision_at_1 value: 10.671999999999999 - type: precision_at_10 value: 2.154 - type: precision_at_100 value: 0.455 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 4.941 - type: precision_at_5 value: 3.2809999999999997 - type: recall_at_1 value: 8.607 - type: recall_at_10 value: 16.398 - type: recall_at_100 value: 28.92 - type: recall_at_1000 value: 49.761 - type: recall_at_3 value: 11.844000000000001 - type: recall_at_5 value: 12.792 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 3.826 - type: map_at_10 value: 5.6419999999999995 - type: map_at_100 value: 5.943 - type: map_at_1000 value: 6.005 - type: map_at_3 value: 5.1049999999999995 - type: map_at_5 value: 5.437 - type: mrr_at_1 value: 4.436 - type: mrr_at_10 value: 6.413 - type: mrr_at_100 value: 6.752 - type: mrr_at_1000 value: 6.819999999999999 - type: mrr_at_3 value: 5.884 - type: mrr_at_5 value: 6.18 - type: ndcg_at_1 value: 4.436 - type: ndcg_at_10 value: 6.7989999999999995 - type: ndcg_at_100 value: 8.619 - type: ndcg_at_1000 value: 10.842 - type: ndcg_at_3 value: 5.739 - type: ndcg_at_5 value: 6.292000000000001 - type: precision_at_1 value: 4.436 - type: precision_at_10 value: 1.109 - type: precision_at_100 value: 0.214 - type: precision_at_1000 value: 0.043 - type: precision_at_3 value: 2.588 - type: precision_at_5 value: 1.848 - type: recall_at_1 value: 3.826 - type: recall_at_10 value: 9.655 - type: recall_at_100 value: 18.611 - type: recall_at_1000 value: 36.733 - type: recall_at_3 value: 6.784 - type: recall_at_5 value: 8.17 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 2.09 - type: map_at_10 value: 3.469 - type: map_at_100 value: 3.93 - type: map_at_1000 value: 4.018 - type: map_at_3 value: 2.8209999999999997 - type: map_at_5 value: 3.144 - type: mrr_at_1 value: 4.756 - type: mrr_at_10 value: 7.853000000000001 - type: mrr_at_100 value: 8.547 - type: mrr_at_1000 value: 8.631 - type: mrr_at_3 value: 6.569 - type: mrr_at_5 value: 7.249999999999999 - type: ndcg_at_1 value: 4.756 - type: ndcg_at_10 value: 5.494000000000001 - type: ndcg_at_100 value: 8.275 - type: ndcg_at_1000 value: 10.892 - type: ndcg_at_3 value: 4.091 - type: ndcg_at_5 value: 4.588 - type: precision_at_1 value: 4.756 - type: precision_at_10 value: 1.8370000000000002 - type: precision_at_100 value: 0.475 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 3.018 - type: precision_at_5 value: 2.528 - type: recall_at_1 value: 2.09 - type: recall_at_10 value: 7.127 - type: recall_at_100 value: 17.483999999999998 - type: recall_at_1000 value: 33.353 - type: recall_at_3 value: 3.742 - type: recall_at_5 value: 5.041 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 0.573 - type: map_at_10 value: 1.282 - type: map_at_100 value: 1.625 - type: map_at_1000 value: 1.71 - type: map_at_3 value: 1.0 - type: map_at_5 value: 1.135 - type: mrr_at_1 value: 7.000000000000001 - type: mrr_at_10 value: 11.084 - type: mrr_at_100 value: 11.634 - type: mrr_at_1000 value: 11.715 - type: mrr_at_3 value: 9.792 - type: mrr_at_5 value: 10.404 - type: ndcg_at_1 value: 4.375 - type: ndcg_at_10 value: 3.7800000000000002 - type: ndcg_at_100 value: 4.353 - type: ndcg_at_1000 value: 6.087 - type: ndcg_at_3 value: 4.258 - type: ndcg_at_5 value: 3.988 - type: precision_at_1 value: 7.000000000000001 - type: precision_at_10 value: 3.35 - type: precision_at_100 value: 1.057 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 5.75 - type: precision_at_5 value: 4.6 - type: recall_at_1 value: 0.573 - type: recall_at_10 value: 2.464 - type: recall_at_100 value: 5.6770000000000005 - type: recall_at_1000 value: 12.516 - type: recall_at_3 value: 1.405 - type: recall_at_5 value: 1.807 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 23.279999999999998 - type: f1 value: 19.87865985032945 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 3.145 - type: map_at_10 value: 4.721 - type: map_at_100 value: 5.086 - type: map_at_1000 value: 5.142 - type: map_at_3 value: 4.107 - type: map_at_5 value: 4.45 - type: mrr_at_1 value: 3.27 - type: mrr_at_10 value: 4.958 - type: mrr_at_100 value: 5.35 - type: mrr_at_1000 value: 5.409 - type: mrr_at_3 value: 4.303 - type: mrr_at_5 value: 4.6739999999999995 - type: ndcg_at_1 value: 3.27 - type: ndcg_at_10 value: 5.768 - type: ndcg_at_100 value: 7.854 - type: ndcg_at_1000 value: 9.729000000000001 - type: ndcg_at_3 value: 4.476 - type: ndcg_at_5 value: 5.102 - type: precision_at_1 value: 3.27 - type: precision_at_10 value: 0.942 - type: precision_at_100 value: 0.20600000000000002 - type: precision_at_1000 value: 0.038 - type: precision_at_3 value: 1.8849999999999998 - type: precision_at_5 value: 1.455 - type: recall_at_1 value: 3.145 - type: recall_at_10 value: 8.889 - type: recall_at_100 value: 19.092000000000002 - type: recall_at_1000 value: 34.35 - type: recall_at_3 value: 5.353 - type: recall_at_5 value: 6.836 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 1.166 - type: map_at_10 value: 2.283 - type: map_at_100 value: 2.564 - type: map_at_1000 value: 2.6519999999999997 - type: map_at_3 value: 1.867 - type: map_at_5 value: 2.0500000000000003 - type: mrr_at_1 value: 2.932 - type: mrr_at_10 value: 4.852 - type: mrr_at_100 value: 5.306 - type: mrr_at_1000 value: 5.4 - type: mrr_at_3 value: 4.141 - type: mrr_at_5 value: 4.457 - type: ndcg_at_1 value: 2.932 - type: ndcg_at_10 value: 3.5709999999999997 - type: ndcg_at_100 value: 5.489 - type: ndcg_at_1000 value: 8.309999999999999 - type: ndcg_at_3 value: 2.773 - type: ndcg_at_5 value: 2.979 - type: precision_at_1 value: 2.932 - type: precision_at_10 value: 1.049 - type: precision_at_100 value: 0.306 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 1.8519999999999999 - type: precision_at_5 value: 1.389 - type: recall_at_1 value: 1.166 - type: recall_at_10 value: 5.178 - type: recall_at_100 value: 13.056999999999999 - type: recall_at_1000 value: 31.708 - type: recall_at_3 value: 2.714 - type: recall_at_5 value: 3.4909999999999997 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 6.138 - type: map_at_10 value: 8.212 - type: map_at_100 value: 8.548 - type: map_at_1000 value: 8.604000000000001 - type: map_at_3 value: 7.555000000000001 - type: map_at_5 value: 7.881 - type: mrr_at_1 value: 12.275 - type: mrr_at_10 value: 15.49 - type: mrr_at_100 value: 15.978 - type: mrr_at_1000 value: 16.043 - type: mrr_at_3 value: 14.488000000000001 - type: mrr_at_5 value: 14.975 - type: ndcg_at_1 value: 12.275 - type: ndcg_at_10 value: 11.078000000000001 - type: ndcg_at_100 value: 13.081999999999999 - type: ndcg_at_1000 value: 14.906 - type: ndcg_at_3 value: 9.574 - type: ndcg_at_5 value: 10.206999999999999 - type: precision_at_1 value: 12.275 - type: precision_at_10 value: 2.488 - type: precision_at_100 value: 0.41200000000000003 - type: precision_at_1000 value: 0.066 - type: precision_at_3 value: 5.991 - type: precision_at_5 value: 4.0969999999999995 - type: recall_at_1 value: 6.138 - type: recall_at_10 value: 12.438 - type: recall_at_100 value: 20.601 - type: recall_at_1000 value: 32.984 - type: recall_at_3 value: 8.987 - type: recall_at_5 value: 10.242999999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 56.96359999999999 - type: ap value: 54.16760114570921 - type: f1 value: 56.193845361069116 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 1.34 - type: map_at_10 value: 2.2190000000000003 - type: map_at_100 value: 2.427 - type: map_at_1000 value: 2.461 - type: map_at_3 value: 1.8610000000000002 - type: map_at_5 value: 2.0340000000000003 - type: mrr_at_1 value: 1.375 - type: mrr_at_10 value: 2.284 - type: mrr_at_100 value: 2.5 - type: mrr_at_1000 value: 2.535 - type: mrr_at_3 value: 1.913 - type: mrr_at_5 value: 2.094 - type: ndcg_at_1 value: 1.375 - type: ndcg_at_10 value: 2.838 - type: ndcg_at_100 value: 4.043 - type: ndcg_at_1000 value: 5.205 - type: ndcg_at_3 value: 2.0629999999999997 - type: ndcg_at_5 value: 2.387 - type: precision_at_1 value: 1.375 - type: precision_at_10 value: 0.496 - type: precision_at_100 value: 0.11399999999999999 - type: precision_at_1000 value: 0.022000000000000002 - type: precision_at_3 value: 0.898 - type: precision_at_5 value: 0.705 - type: recall_at_1 value: 1.34 - type: recall_at_10 value: 4.787 - type: recall_at_100 value: 10.759 - type: recall_at_1000 value: 20.362 - type: recall_at_3 value: 2.603 - type: recall_at_5 value: 3.398 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 64.39808481532147 - type: f1 value: 63.468270818712625 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 53.961679346294744 - type: f1 value: 51.6707117653683 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 57.018012008005336 - type: f1 value: 54.23413458037234 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 48.84434700908236 - type: f1 value: 46.48494180527987 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 39.7669415561133 - type: f1 value: 35.50974325529877 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 42.589511754068724 - type: f1 value: 40.47244422785889 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 34.01276789785682 - type: f1 value: 21.256775922291286 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 33.285432516201745 - type: f1 value: 19.841703666811565 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) config: es split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 32.121414276184126 - type: f1 value: 19.34706868150749 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 26.088318196053866 - type: f1 value: 17.22608011891254 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (hi) config: hi split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 15.320903549659375 - type: f1 value: 9.62002916015258 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (th) config: th split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 16.426763110307412 - type: f1 value: 11.023799171137183 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (af) config: af split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.347007397444518 - type: f1 value: 25.503551916252842 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (am) config: am split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 10.655682582380631 - type: f1 value: 9.141696317946996 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ar) config: ar split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 17.347007397444518 - type: f1 value: 15.345346511499534 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (az) config: az split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 20.39004707464694 - type: f1 value: 21.129515472610237 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (bn) config: bn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 14.082044384667114 - type: f1 value: 12.169922201279885 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (cy) config: cy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.108271687962336 - type: f1 value: 25.449222444030063 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (da) config: da split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.780766644250164 - type: f1 value: 26.96237025531764 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (de) config: de split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 21.768661735036986 - type: f1 value: 22.377462868662263 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (el) config: el split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 21.967047747141898 - type: f1 value: 22.427583602797057 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 33.221250840618694 - type: f1 value: 32.627621011904495 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (es) config: es split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.047747141896426 - type: f1 value: 25.244455827652786 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fa) config: fa split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 18.850033624747812 - type: f1 value: 16.532690247057452 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fi) config: fi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.934767989240083 - type: f1 value: 24.126974912341858 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fr) config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.59179556153329 - type: f1 value: 23.97686173045838 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (he) config: he split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 17.683254875588432 - type: f1 value: 15.217082232778534 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hi) config: hi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 14.277067921990588 - type: f1 value: 13.06156794974721 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hu) config: hu split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.817081371889717 - type: f1 value: 24.79443526877249 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hy) config: hy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 12.326832548755885 - type: f1 value: 10.850963544530288 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (id) config: id split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 28.244788164088764 - type: f1 value: 27.442212153664336 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (is) config: is split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 26.0390047074647 - type: f1 value: 24.29180485465988 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (it) config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 26.099529253530594 - type: f1 value: 26.47963496597501 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ja) config: ja split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.383322125084057 - type: f1 value: 27.24527274982159 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (jv) config: jv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 26.970410221923334 - type: f1 value: 24.710215925904627 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ka) config: ka split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 14.159381304640215 - type: f1 value: 12.262797179154113 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (km) config: km split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 12.078009414929387 - type: f1 value: 10.85388698579932 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (kn) config: kn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 10.144586415601884 - type: f1 value: 8.629316498328535 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ko) config: ko split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 23.799596503026226 - type: f1 value: 21.4839774342838 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (lv) config: lv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.047747141896433 - type: f1 value: 25.86288514660441 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ml) config: ml split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 10.82380632145259 - type: f1 value: 9.568319030257811 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (mn) config: mn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 14.435104236718225 - type: f1 value: 14.861584951252121 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ms) config: ms split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 26.83254875588433 - type: f1 value: 26.084749439191967 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (my) config: my split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 13.792871553463351 - type: f1 value: 11.496310101617802 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (nb) config: nb split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.292535305985208 - type: f1 value: 24.098118477282508 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (nl) config: nl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 26.24075319435104 - type: f1 value: 25.259998680849815 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pl) config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 24.909213180901148 - type: f1 value: 23.684726718537476 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pt) config: pt split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.310020174848688 - type: f1 value: 26.28728556240871 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ro) config: ro split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 21.930060524546064 - type: f1 value: 21.993435727293242 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ru) config: ru split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.773369199731 - type: f1 value: 23.857739120942632 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sl) config: sl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 29.256893073301953 - type: f1 value: 26.6824726822139 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sq) config: sq split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 17.673167451244115 - type: f1 value: 18.74169530134767 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sv) config: sv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 26.190316072629454 - type: f1 value: 26.000111409348474 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sw) config: sw split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 28.903833221250842 - type: f1 value: 27.166958835143152 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ta) config: ta split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 13.517148621385342 - type: f1 value: 10.124568781522633 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (te) config: te split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 12.414256893073302 - type: f1 value: 10.72905194223371 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (th) config: th split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 17.330195023537325 - type: f1 value: 17.262535319973026 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (tl) config: tl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 22.310020174848688 - type: f1 value: 22.770175966437712 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (tr) config: tr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 23.066577000672496 - type: f1 value: 22.50576183714979 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ur) config: ur split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 12.501681237390722 - type: f1 value: 11.803223778323549 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (vi) config: vi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 22.76059179556153 - type: f1 value: 21.55631039694901 - 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: 34.92602555480834 - type: f1 value: 33.727974171773425 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-TW) config: zh-TW split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 28.365837256220576 - type: f1 value: 28.510022067424707 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (af) config: af split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 36.02891728312038 - type: f1 value: 31.370502912090657 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (am) config: am split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 19.330867518493612 - type: f1 value: 15.90055477686407 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ar) config: ar split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 23.880295897780766 - type: f1 value: 21.695586897546278 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (az) config: az split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 28.853396099529256 - type: f1 value: 26.709933198206503 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (bn) config: bn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 18.382649630127773 - type: f1 value: 16.772353498005437 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (cy) config: cy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 35.40685944855414 - type: f1 value: 31.488014251203257 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (da) config: da split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 34.4754539340955 - type: f1 value: 31.226537880607296 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (de) config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 30.0 - type: f1 value: 28.08153376359271 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (el) config: el split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 26.879623402824482 - type: f1 value: 24.773267596830472 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 43.77605917955615 - type: f1 value: 41.60519309254586 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (es) config: es split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.807666442501684 - type: f1 value: 30.250350082554473 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fa) config: fa split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 24.515803631472764 - type: f1 value: 21.726717236406927 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fi) config: fi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 33.315400134498994 - type: f1 value: 29.353644700383935 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fr) config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.42434431741762 - type: f1 value: 29.87668403274445 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (he) config: he split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 24.40147948890384 - type: f1 value: 21.429656257015555 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hi) config: hi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 21.435776731674512 - type: f1 value: 18.344740043506686 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hu) config: hu split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.533288500336248 - type: f1 value: 29.185785763469298 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hy) config: hy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 19.983187626092803 - type: f1 value: 17.088318438817677 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (id) config: id split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.73369199731002 - type: f1 value: 31.37876294000571 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (is) config: is split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.503026227303295 - type: f1 value: 28.944584739759726 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (it) config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.116341627437794 - type: f1 value: 29.410419968584232 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ja) config: ja split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.136516476126424 - type: f1 value: 29.6719090248999 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (jv) config: jv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 34.3813046402152 - type: f1 value: 31.481374035431443 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ka) config: ka split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 18.3591123066577 - type: f1 value: 16.158113168004945 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (km) config: km split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 19.250168123739073 - type: f1 value: 16.728590019294526 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (kn) config: kn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 16.04236718224613 - type: f1 value: 14.304518042661318 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ko) config: ko split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 28.95427034297243 - type: f1 value: 26.14194694048272 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (lv) config: lv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.286482851378615 - type: f1 value: 29.426003364232837 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ml) config: ml split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 18.762609280430397 - type: f1 value: 15.764539982254005 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (mn) config: mn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 21.570275722932077 - type: f1 value: 18.98517402487539 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ms) config: ms split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 35.95158036314728 - type: f1 value: 33.45853327725729 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (my) config: my split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 20.03026227303295 - type: f1 value: 16.864192363299615 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nb) config: nb split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.89172831203766 - type: f1 value: 29.90554132334421 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nl) config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.36381977135171 - type: f1 value: 29.57501043505274 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pl) config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.856086079354405 - type: f1 value: 30.242758819443548 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pt) config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.694687289845326 - type: f1 value: 29.495870419371684 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ro) config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 27.01412239408204 - type: f1 value: 26.44782204477556 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ru) config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 30.554808338937463 - type: f1 value: 27.93696283146029 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sl) config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 35.813718897108274 - type: f1 value: 33.021495683147286 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sq) config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 22.992602555480836 - type: f1 value: 21.524928515996447 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sv) config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 34.7074646940148 - type: f1 value: 31.54759971873104 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sw) config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 36.240753194351036 - type: f1 value: 33.34397881816082 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ta) config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 19.741089441829185 - type: f1 value: 16.129268723975766 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (te) config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 17.54203093476799 - type: f1 value: 15.537381383894061 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (th) config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 22.817753866846 - type: f1 value: 20.72245485990428 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tl) config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 28.58439811701412 - type: f1 value: 26.88190194852028 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tr) config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 28.60457296570275 - type: f1 value: 26.98989368733863 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ur) config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 23.345662407531943 - type: f1 value: 19.75032390408514 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (vi) config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 29.71082716879624 - type: f1 value: 26.675920460240782 - 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: 44.09549428379288 - type: f1 value: 41.275350430825675 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-TW) config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 37.24277067921991 - type: f1 value: 35.65629114113254 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 22.08508717069763 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 16.58582885790446 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 26.730268595233923 - type: mrr value: 27.065185919114704 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 1.2 - type: map_at_10 value: 1.6400000000000001 - type: map_at_100 value: 1.9789999999999999 - type: map_at_1000 value: 2.554 - type: map_at_3 value: 1.4449999999999998 - type: map_at_5 value: 1.533 - type: mrr_at_1 value: 6.811 - type: mrr_at_10 value: 11.068999999999999 - type: mrr_at_100 value: 12.454 - type: mrr_at_1000 value: 12.590000000000002 - type: mrr_at_3 value: 9.751999999999999 - type: mrr_at_5 value: 10.31 - type: ndcg_at_1 value: 6.3469999999999995 - type: ndcg_at_10 value: 4.941 - type: ndcg_at_100 value: 6.524000000000001 - type: ndcg_at_1000 value: 15.918 - type: ndcg_at_3 value: 5.959 - type: ndcg_at_5 value: 5.395 - type: precision_at_1 value: 6.811 - type: precision_at_10 value: 3.375 - type: precision_at_100 value: 2.0709999999999997 - type: precision_at_1000 value: 1.313 - type: precision_at_3 value: 5.47 - type: precision_at_5 value: 4.396 - type: recall_at_1 value: 1.2 - type: recall_at_10 value: 2.5909999999999997 - type: recall_at_100 value: 9.443999999999999 - type: recall_at_1000 value: 41.542 - type: recall_at_3 value: 1.702 - type: recall_at_5 value: 1.9879999999999998 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 1.214 - type: map_at_10 value: 2.067 - type: map_at_100 value: 2.2399999999999998 - type: map_at_1000 value: 2.2689999999999997 - type: map_at_3 value: 1.691 - type: map_at_5 value: 1.916 - type: mrr_at_1 value: 1.506 - type: mrr_at_10 value: 2.413 - type: mrr_at_100 value: 2.587 - type: mrr_at_1000 value: 2.616 - type: mrr_at_3 value: 2.023 - type: mrr_at_5 value: 2.246 - type: ndcg_at_1 value: 1.506 - type: ndcg_at_10 value: 2.703 - type: ndcg_at_100 value: 3.66 - type: ndcg_at_1000 value: 4.6 - type: ndcg_at_3 value: 1.9300000000000002 - type: ndcg_at_5 value: 2.33 - type: precision_at_1 value: 1.506 - type: precision_at_10 value: 0.539 - type: precision_at_100 value: 0.11 - type: precision_at_1000 value: 0.02 - type: precision_at_3 value: 0.9369999999999999 - type: precision_at_5 value: 0.7939999999999999 - type: recall_at_1 value: 1.214 - type: recall_at_10 value: 4.34 - type: recall_at_100 value: 8.905000000000001 - type: recall_at_1000 value: 16.416 - type: recall_at_3 value: 2.3009999999999997 - type: recall_at_5 value: 3.2489999999999997 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 45.708 - type: map_at_10 value: 55.131 - type: map_at_100 value: 55.935 - type: map_at_1000 value: 55.993 - type: map_at_3 value: 52.749 - type: map_at_5 value: 54.166000000000004 - type: mrr_at_1 value: 52.44 - type: mrr_at_10 value: 59.99 - type: mrr_at_100 value: 60.492999999999995 - type: mrr_at_1000 value: 60.522 - type: mrr_at_3 value: 58.285 - type: mrr_at_5 value: 59.305 - type: ndcg_at_1 value: 52.43 - type: ndcg_at_10 value: 59.873 - type: ndcg_at_100 value: 63.086 - type: ndcg_at_1000 value: 64.291 - type: ndcg_at_3 value: 56.291000000000004 - type: ndcg_at_5 value: 58.071 - type: precision_at_1 value: 52.43 - type: precision_at_10 value: 8.973 - type: precision_at_100 value: 1.161 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 24.177 - type: precision_at_5 value: 16.073999999999998 - type: recall_at_1 value: 45.708 - type: recall_at_10 value: 69.195 - type: recall_at_100 value: 82.812 - type: recall_at_1000 value: 91.136 - type: recall_at_3 value: 58.938 - type: recall_at_5 value: 63.787000000000006 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 13.142048230676806 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 26.06687178917052 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 0.46499999999999997 - type: map_at_10 value: 0.906 - type: map_at_100 value: 1.127 - type: map_at_1000 value: 1.203 - type: map_at_3 value: 0.72 - type: map_at_5 value: 0.814 - type: mrr_at_1 value: 2.3 - type: mrr_at_10 value: 3.733 - type: mrr_at_100 value: 4.295999999999999 - type: mrr_at_1000 value: 4.412 - type: mrr_at_3 value: 3.183 - type: mrr_at_5 value: 3.458 - type: ndcg_at_1 value: 2.3 - type: ndcg_at_10 value: 1.797 - type: ndcg_at_100 value: 3.376 - type: ndcg_at_1000 value: 6.143 - type: ndcg_at_3 value: 1.763 - type: ndcg_at_5 value: 1.5070000000000001 - type: precision_at_1 value: 2.3 - type: precision_at_10 value: 0.91 - type: precision_at_100 value: 0.32399999999999995 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 1.633 - type: precision_at_5 value: 1.3 - type: recall_at_1 value: 0.46499999999999997 - type: recall_at_10 value: 1.8499999999999999 - type: recall_at_100 value: 6.625 - type: recall_at_1000 value: 20.587 - type: recall_at_3 value: 0.9900000000000001 - type: recall_at_5 value: 1.315 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 60.78961481918511 - type: cos_sim_spearman value: 54.92014630234372 - type: euclidean_pearson value: 54.91456364340953 - type: euclidean_spearman value: 50.95537043206628 - type: manhattan_pearson value: 55.0450005071106 - type: manhattan_spearman value: 51.227579527791654 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 43.73124494569395 - type: cos_sim_spearman value: 43.07629933550637 - type: euclidean_pearson value: 37.2529484210563 - type: euclidean_spearman value: 36.68421330216546 - type: manhattan_pearson value: 37.41673219009712 - type: manhattan_spearman value: 36.92073705702668 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 57.17534157059787 - type: cos_sim_spearman value: 56.86679858348438 - type: euclidean_pearson value: 54.51552371857776 - type: euclidean_spearman value: 53.80989851917749 - type: manhattan_pearson value: 54.44486043632584 - type: manhattan_spearman value: 53.83487353949481 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 52.319034960820375 - type: cos_sim_spearman value: 50.89512224974754 - type: euclidean_pearson value: 49.19308209408045 - type: euclidean_spearman value: 47.45736923614355 - type: manhattan_pearson value: 48.82127080055118 - type: manhattan_spearman value: 47.20185686489298 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 61.57602956458427 - type: cos_sim_spearman value: 62.894640061838956 - type: euclidean_pearson value: 53.86893407586029 - type: euclidean_spearman value: 54.68528520514299 - type: manhattan_pearson value: 53.689614981956815 - type: manhattan_spearman value: 54.51172839699876 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 56.2305694109318 - type: cos_sim_spearman value: 57.885939000786045 - type: euclidean_pearson value: 50.486043353701994 - type: euclidean_spearman value: 50.4463227974027 - type: manhattan_pearson value: 50.73317560427465 - type: manhattan_spearman value: 50.81397877006027 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ko-ko) config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 55.52162058025664 - type: cos_sim_spearman value: 59.02220327783535 - type: euclidean_pearson value: 55.66332330866701 - type: euclidean_spearman value: 56.829076266662206 - type: manhattan_pearson value: 55.39181385186973 - type: manhattan_spearman value: 56.607432176121144 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ar-ar) config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 46.312186899914906 - type: cos_sim_spearman value: 48.07172073934163 - type: euclidean_pearson value: 46.957276350776695 - type: euclidean_spearman value: 43.98800593212707 - type: manhattan_pearson value: 46.910805787619914 - type: manhattan_spearman value: 43.96662723946553 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-ar) config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 16.222172523403835 - type: cos_sim_spearman value: 17.230258645779042 - type: euclidean_pearson value: -6.781460243147299 - type: euclidean_spearman value: -6.884123336780775 - type: manhattan_pearson value: -4.369061881907372 - type: manhattan_spearman value: -4.235845433380353 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-de) config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 7.462476431657987 - type: cos_sim_spearman value: 5.875270645234161 - type: euclidean_pearson value: -10.79494346180473 - type: euclidean_spearman value: -11.704529023304776 - type: manhattan_pearson value: -11.465867974964997 - type: manhattan_spearman value: -12.428424608287173 - 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: 61.46601840758559 - type: cos_sim_spearman value: 65.69667638887147 - type: euclidean_pearson value: 49.531065525619866 - type: euclidean_spearman value: 53.880480167479725 - type: manhattan_pearson value: 50.25462221374689 - type: manhattan_spearman value: 54.22205494276401 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-tr) config: en-tr split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: -12.769479370624031 - type: cos_sim_spearman value: -12.161427312728382 - type: euclidean_pearson value: -27.950593491756536 - type: euclidean_spearman value: -24.925281959398585 - type: manhattan_pearson value: -25.98778888167475 - type: manhattan_spearman value: -22.861942388867234 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-en) config: es-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 2.1575763564561727 - type: cos_sim_spearman value: 1.182204089411577 - type: euclidean_pearson value: -10.389249806317189 - type: euclidean_spearman value: -16.078659904264605 - type: manhattan_pearson value: -9.674301846448607 - type: manhattan_spearman value: -16.976576817518577 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-es) config: es-es split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 66.16718583059163 - type: cos_sim_spearman value: 69.95156267898052 - type: euclidean_pearson value: 64.93174777029739 - type: euclidean_spearman value: 66.21292533974568 - type: manhattan_pearson value: 65.2578109632889 - type: manhattan_spearman value: 66.21830865759128 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (fr-en) config: fr-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 0.1540829683540524 - type: cos_sim_spearman value: -2.4072834011003987 - type: euclidean_pearson value: -18.951775877513473 - type: euclidean_spearman value: -18.393605606817527 - type: manhattan_pearson value: -19.609633839454542 - type: manhattan_spearman value: -19.276064769117912 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (it-en) config: it-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: -4.22497246932717 - type: cos_sim_spearman value: -5.747420352346977 - type: euclidean_pearson value: -16.86351349130112 - type: euclidean_spearman value: -16.555536618547382 - type: manhattan_pearson value: -17.45445643482646 - type: manhattan_spearman value: -17.97322953856309 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (nl-en) config: nl-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 8.559184021676034 - type: cos_sim_spearman value: 5.600273352595882 - type: euclidean_pearson value: -10.76482859283058 - type: euclidean_spearman value: -9.575202768285926 - type: manhattan_pearson value: -9.48508597350615 - type: manhattan_spearman value: -9.33387861352172 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 30.260087169228978 - type: cos_sim_spearman value: 43.264174903196015 - type: euclidean_pearson value: 35.07785877281954 - type: euclidean_spearman value: 43.41294719372452 - type: manhattan_pearson value: 36.74996284702431 - type: manhattan_spearman value: 43.53522851890142 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de) config: de split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 5.58694979115026 - type: cos_sim_spearman value: 32.80692337371332 - type: euclidean_pearson value: 10.53180875461474 - type: euclidean_spearman value: 31.105269938654033 - type: manhattan_pearson value: 10.559778015974826 - type: manhattan_spearman value: 31.452204563072044 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es) config: es split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 10.593783873928478 - type: cos_sim_spearman value: 50.397542574042006 - type: euclidean_pearson value: 28.122179063209714 - type: euclidean_spearman value: 50.72847867996529 - type: manhattan_pearson value: 28.730690148465005 - type: manhattan_spearman value: 51.019761292483366 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl) config: pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: -1.3049499265017876 - type: cos_sim_spearman value: 16.347130048706084 - type: euclidean_pearson value: 0.5710147274110128 - type: euclidean_spearman value: 16.589843077857605 - type: manhattan_pearson value: 1.1226404198336415 - type: manhattan_spearman value: 16.410620108636557 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (tr) config: tr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: -10.96861909019159 - type: cos_sim_spearman value: 24.536979219880724 - type: euclidean_pearson value: -1.3040190807315306 - type: euclidean_spearman value: 25.061584673761928 - type: manhattan_pearson value: -0.06525719745037804 - type: manhattan_spearman value: 25.979295538386893 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (ar) config: ar split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 1.0599417065503314 - type: cos_sim_spearman value: 52.055853787103345 - type: euclidean_pearson value: 23.666828441081776 - type: euclidean_spearman value: 52.38656753170069 - type: manhattan_pearson value: 23.398080463967215 - type: manhattan_spearman value: 52.23849717509109 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (ru) config: ru split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: -2.847646040977239 - type: cos_sim_spearman value: 40.5826838357407 - type: euclidean_pearson value: 9.242304983683113 - type: euclidean_spearman value: 40.35906851022345 - type: manhattan_pearson value: 9.645663412799504 - type: manhattan_spearman value: 40.78106154950966 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 17.761397832130992 - type: cos_sim_spearman value: 59.98756452345925 - type: euclidean_pearson value: 37.03125109036693 - type: euclidean_spearman value: 59.58469212715707 - type: manhattan_pearson value: 36.828102137170724 - type: manhattan_spearman value: 59.07036501478588 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (fr) config: fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 22.281212883400205 - type: cos_sim_spearman value: 48.27687537627578 - type: euclidean_pearson value: 30.531395629285324 - type: euclidean_spearman value: 50.349143748970384 - type: manhattan_pearson value: 30.48762081986554 - type: manhattan_spearman value: 50.66037165529169 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-en) config: de-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 15.76679673990358 - type: cos_sim_spearman value: 19.123349126370442 - type: euclidean_pearson value: 19.21389203087116 - type: euclidean_spearman value: 23.63276413160338 - type: manhattan_pearson value: 18.789263824907053 - type: manhattan_spearman value: 19.962703178974692 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es-en) config: es-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 11.024970397289941 - type: cos_sim_spearman value: 13.530951900755017 - type: euclidean_pearson value: 13.473514585343645 - type: euclidean_spearman value: 16.754702023734914 - type: manhattan_pearson value: 13.72847275970385 - type: manhattan_spearman value: 16.673001637012348 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (it) config: it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 33.32761589409043 - type: cos_sim_spearman value: 54.14305778960692 - type: euclidean_pearson value: 45.30173241170555 - type: euclidean_spearman value: 54.77422257007743 - type: manhattan_pearson value: 45.41890064000217 - type: manhattan_spearman value: 54.533788920795544 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl-en) config: pl-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 20.045210048995486 - type: cos_sim_spearman value: 17.597101329633823 - type: euclidean_pearson value: 32.531726142346145 - type: euclidean_spearman value: 27.244772040848105 - type: manhattan_pearson value: 32.74618458514601 - type: manhattan_spearman value: 25.81220754539242 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh-en) config: zh-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: -13.832846350193021 - type: cos_sim_spearman value: -8.406778050457863 - type: euclidean_pearson value: -6.557254855697437 - type: euclidean_spearman value: -3.5112770921588563 - type: manhattan_pearson value: -6.493730738275641 - type: manhattan_spearman value: -2.5922348401468365 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es-it) config: es-it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 26.357929743436664 - type: cos_sim_spearman value: 37.3417709718339 - type: euclidean_pearson value: 30.930792572341293 - type: euclidean_spearman value: 36.061866364725795 - type: manhattan_pearson value: 31.56982745863155 - type: manhattan_spearman value: 37.18529502311113 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-fr) config: de-fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 9.310102041071547 - type: cos_sim_spearman value: 10.907002693108673 - type: euclidean_pearson value: 7.361793742296021 - type: euclidean_spearman value: 9.53967881391466 - type: manhattan_pearson value: 8.017048631719996 - type: manhattan_spearman value: 13.537860190039725 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-pl) config: de-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: -5.534456407419709 - type: cos_sim_spearman value: 17.552638994787724 - type: euclidean_pearson value: -10.136558594355556 - type: euclidean_spearman value: 11.055083156366303 - type: manhattan_pearson value: -11.799223055640773 - type: manhattan_spearman value: 1.416528760982869 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (fr-pl) config: fr-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 48.64639760720344 - type: cos_sim_spearman value: 39.440531887330785 - type: euclidean_pearson value: 37.75527464173489 - type: euclidean_spearman value: 39.440531887330785 - type: manhattan_pearson value: 32.324715276369474 - type: manhattan_spearman value: 28.17180849095055 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 44.667456983937 - type: cos_sim_spearman value: 46.04327333618551 - type: euclidean_pearson value: 44.583522824155104 - type: euclidean_spearman value: 44.77184813864239 - type: manhattan_pearson value: 44.54496373721756 - type: manhattan_spearman value: 44.830873857115996 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 49.756063724243 - type: mrr value: 75.29077585450135 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 14.194 - type: map_at_10 value: 18.756999999999998 - type: map_at_100 value: 19.743 - type: map_at_1000 value: 19.865 - type: map_at_3 value: 16.986 - type: map_at_5 value: 18.024 - type: mrr_at_1 value: 15.0 - type: mrr_at_10 value: 19.961000000000002 - type: mrr_at_100 value: 20.875 - type: mrr_at_1000 value: 20.982 - type: mrr_at_3 value: 18.056 - type: mrr_at_5 value: 19.406000000000002 - type: ndcg_at_1 value: 15.0 - type: ndcg_at_10 value: 21.775 - type: ndcg_at_100 value: 26.8 - type: ndcg_at_1000 value: 30.468 - type: ndcg_at_3 value: 18.199 - type: ndcg_at_5 value: 20.111 - type: precision_at_1 value: 15.0 - type: precision_at_10 value: 3.4000000000000004 - type: precision_at_100 value: 0.607 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.444000000000001 - type: precision_at_5 value: 5.6000000000000005 - type: recall_at_1 value: 14.194 - type: recall_at_10 value: 30.0 - type: recall_at_100 value: 53.911 - type: recall_at_1000 value: 83.289 - type: recall_at_3 value: 20.556 - type: recall_at_5 value: 24.972 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.35544554455446 - type: cos_sim_ap value: 62.596006705300724 - type: cos_sim_f1 value: 60.80283353010627 - type: cos_sim_precision value: 74.20749279538906 - type: cos_sim_recall value: 51.5 - type: dot_accuracy value: 99.13564356435643 - type: dot_ap value: 43.87589686325114 - type: dot_f1 value: 46.99663623258049 - type: dot_precision value: 45.235892691951896 - type: dot_recall value: 48.9 - type: euclidean_accuracy value: 99.2 - type: euclidean_ap value: 43.44660755386079 - type: euclidean_f1 value: 45.9016393442623 - type: euclidean_precision value: 52.79583875162549 - type: euclidean_recall value: 40.6 - type: manhattan_accuracy value: 99.2 - type: manhattan_ap value: 43.11790011749347 - type: manhattan_f1 value: 45.11023176936122 - type: manhattan_precision value: 51.88556566970091 - type: manhattan_recall value: 39.900000000000006 - type: max_accuracy value: 99.35544554455446 - type: max_ap value: 62.596006705300724 - type: max_f1 value: 60.80283353010627 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 25.71674282500873 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 25.465780711520985 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 35.35656209427094 - type: mrr value: 35.10693860877685 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.074 - type: map_at_10 value: 0.47400000000000003 - type: map_at_100 value: 1.825 - type: map_at_1000 value: 4.056 - type: map_at_3 value: 0.199 - type: map_at_5 value: 0.301 - type: mrr_at_1 value: 34.0 - type: mrr_at_10 value: 46.06 - type: mrr_at_100 value: 47.506 - type: mrr_at_1000 value: 47.522999999999996 - type: mrr_at_3 value: 44.0 - type: mrr_at_5 value: 44.4 - type: ndcg_at_1 value: 32.0 - type: ndcg_at_10 value: 28.633999999999997 - type: ndcg_at_100 value: 18.547 - type: ndcg_at_1000 value: 16.142 - type: ndcg_at_3 value: 32.48 - type: ndcg_at_5 value: 31.163999999999998 - type: precision_at_1 value: 34.0 - type: precision_at_10 value: 30.4 - type: precision_at_100 value: 18.54 - type: precision_at_1000 value: 7.942 - type: precision_at_3 value: 35.333 - type: precision_at_5 value: 34.0 - type: recall_at_1 value: 0.074 - type: recall_at_10 value: 0.641 - type: recall_at_100 value: 3.675 - type: recall_at_1000 value: 15.706000000000001 - type: recall_at_3 value: 0.231 - type: recall_at_5 value: 0.367 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 0.6799999999999999 - type: map_at_10 value: 2.1420000000000003 - type: map_at_100 value: 2.888 - type: map_at_1000 value: 3.3779999999999997 - type: map_at_3 value: 1.486 - type: map_at_5 value: 1.7579999999999998 - type: mrr_at_1 value: 12.245000000000001 - type: mrr_at_10 value: 22.12 - type: mrr_at_100 value: 23.407 - type: mrr_at_1000 value: 23.483999999999998 - type: mrr_at_3 value: 19.048000000000002 - type: mrr_at_5 value: 20.986 - type: ndcg_at_1 value: 10.204 - type: ndcg_at_10 value: 7.374 - type: ndcg_at_100 value: 10.524000000000001 - type: ndcg_at_1000 value: 18.4 - type: ndcg_at_3 value: 9.913 - type: ndcg_at_5 value: 8.938 - type: precision_at_1 value: 12.245000000000001 - type: precision_at_10 value: 7.142999999999999 - type: precision_at_100 value: 2.4490000000000003 - type: precision_at_1000 value: 0.731 - type: precision_at_3 value: 11.565 - type: precision_at_5 value: 9.796000000000001 - type: recall_at_1 value: 0.6799999999999999 - type: recall_at_10 value: 4.038 - type: recall_at_100 value: 14.151 - type: recall_at_1000 value: 40.111999999999995 - type: recall_at_3 value: 1.921 - type: recall_at_5 value: 2.604 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 54.625600000000006 - type: ap value: 9.425323874806459 - type: f1 value: 42.38724794017267 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 42.8494623655914 - type: f1 value: 42.66062148844617 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 12.464890895237952 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 79.97854205161829 - type: cos_sim_ap value: 47.45175747605773 - type: cos_sim_f1 value: 46.55775962660444 - type: cos_sim_precision value: 41.73640167364017 - type: cos_sim_recall value: 52.638522427440634 - type: dot_accuracy value: 77.76718126005842 - type: dot_ap value: 35.97737653101504 - type: dot_f1 value: 41.1975475754439 - type: dot_precision value: 29.50165355228646 - type: dot_recall value: 68.25857519788919 - type: euclidean_accuracy value: 79.34076414138403 - type: euclidean_ap value: 45.309577778755134 - type: euclidean_f1 value: 45.09938313913639 - type: euclidean_precision value: 39.76631748589847 - type: euclidean_recall value: 52.0844327176781 - type: manhattan_accuracy value: 79.31692197651546 - type: manhattan_ap value: 45.2433373222626 - type: manhattan_f1 value: 45.04624986069319 - type: manhattan_precision value: 38.99286127725256 - type: manhattan_recall value: 53.324538258575195 - type: max_accuracy value: 79.97854205161829 - type: max_ap value: 47.45175747605773 - type: max_f1 value: 46.55775962660444 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 81.76737687740133 - type: cos_sim_ap value: 64.59241956109807 - type: cos_sim_f1 value: 57.83203629255339 - type: cos_sim_precision value: 55.50442477876106 - type: cos_sim_recall value: 60.363412380659064 - type: dot_accuracy value: 78.96922420149805 - type: dot_ap value: 56.11775087282065 - type: dot_f1 value: 52.92134831460675 - type: dot_precision value: 51.524212368728115 - type: dot_recall value: 54.39636587619341 - type: euclidean_accuracy value: 80.8611790274382 - type: euclidean_ap value: 61.28070098354092 - type: euclidean_f1 value: 54.58334971882497 - type: euclidean_precision value: 55.783297162607504 - type: euclidean_recall value: 53.43393902063443 - type: manhattan_accuracy value: 80.72534637326814 - type: manhattan_ap value: 61.18048430787254 - type: manhattan_f1 value: 54.50978912822061 - type: manhattan_precision value: 53.435396790178245 - type: manhattan_recall value: 55.6282722513089 - type: max_accuracy value: 81.76737687740133 - type: max_ap value: 64.59241956109807 - type: max_f1 value: 57.83203629255339
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
LoneStriker/Phi-3-medium-4k-instruct-8.0bpw-h8-exl2
LoneStriker
text-generation
[ "transformers", "safetensors", "phi3", "text-generation", "nlp", "code", "conversational", "custom_code", "multilingual", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]
1,716,312,414,000
2024-05-21T17:37:44
9
0
--- language: - multilingual license: mit license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation tags: - nlp - code inference: parameters: temperature: 0.7 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- ## Model Summary The Phi-3-Medium-4K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Medium version in two variants [4K](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3-Medium-4K-Instruct showcased a robust and state-of-the-art performance among models of the same-size and next-size-up. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) | | Short Context | Long Context | | ------- | ------------- | ------------ | | Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)| | Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)| | Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)| | Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct)| ## Intended Uses **Primary use cases** The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. **Use case considerations** Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3-Medium-4K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list | grep transformers`. Phi-3-Medium-4K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai). ### Tokenizer Phi-3-Medium-4K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3-Medium-4K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <|user|>\nQuestion <|end|>\n<|assistant|> ``` For example: ```markdown <|user|> How to explain Internet for a medieval knight?<|end|> <|assistant|> ``` where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <|user|> I am going to Paris, what should I see?<|end|> <|assistant|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|> <|user|> What is so great about #1?<|end|> <|assistant|> ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model_id = "microsoft/Phi-3-medium-4k-instruct" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, **generation_args) print(output[0]['generated_text']) ``` *Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.* ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3-Medium-4K-Instruct has 14B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 4K tokens * GPUs: 512 H100-80G * Training time: 42 days * Training data: 4.8T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. * Release dates: The model weight is released on May 21, 2024. ### Datasets Our training data includes a wide variety of sources, totaling 4.8 trillion tokens (including 10% multilingual), and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. As an example, the result of a game in premier league in a particular day might be good training data for frontier models, but we need to remove such information to leave more model capacity for reasoning for the small size models. More details about data can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report). ## Benchmarks We report the results for Phi-3-Medium-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Mixtral-8x22b, Gemini-Pro, Command R+ 104B, Llama-3-70B-Instruct, GPT-3.5-Turbo-1106, and GPT-4-Turbo-1106(Chat). All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. |Benchmark|Phi-3-Medium-4K-Instruct<br>14b|Command R+<br>104B|Mixtral<br>8x22B|Llama-3-70B-Instruct<br>8b|GPT3.5-Turbo<br>version 1106|Gemini<br>Pro|GPT-4-Turbo<br>version 1106 (Chat)| |---------|-----------------------|--------|-------------|-------------------|-------------------|----------|------------------------| |AGI Eval<br>5-shot|50.2|50.1|54.0|56.9|48.4|49.0|59.6| |MMLU<br>5-shot|78.0|73.8|76.2|80.2|71.4|66.7|84.0| |BigBench Hard<br>3-shot|81.4|74.1|81.8|80.4|68.3|75.6|87.7| |ANLI<br>7-shot|55.8|63.4|65.2|68.3|58.1|64.2|71.7| |HellaSwag<br>5-shot|82.4|78.0|79.0|82.6|78.8|76.2|88.3| |ARC Challenge<br>10-shot|91.6|86.9|91.3|93.0|87.4|88.3|95.6| |ARC Easy<br>10-shot|97.7|95.7|96.9|98.2|96.3|96.1|98.8| |BoolQ<br>2-shot|86.5|86.1|82.7|89.1|79.1|86.4|91.3| |CommonsenseQA<br>10-shot|82.8|82.0|82.0|84.4|79.6|81.8|86.7| |MedQA<br>2-shot|69.9|59.2|67.9|78.5|63.4|58.2|83.7| |OpenBookQA<br>10-shot|87.4|86.8|88.6|91.8|86.0|86.4|93.4| |PIQA<br>5-shot|87.9|86.4|85.0|85.3|86.6|86.2|90.1| |Social IQA<br>5-shot|80.2|75.3|78.2|81.1|68.3|75.4|81.7| |TruthfulQA (MC2)<br>10-shot|75.1|57.8|67.4|81.9|67.7|72.6|85.2| |WinoGrande<br>5-shot|81.5|77.0|75.3|83.3|68.8|72.2|86.7| |TriviaQA<br>5-shot|73.9|82.8|84.5|78.5|85.8|80.2|73.3| |GSM8K Chain of Thought<br>8-shot|91.0|78.3|83.8|93.5|78.1|80.4|94.2| |HumanEval<br>0-shot|62.2|61.6|39.6|78.7|62.2|64.4|79.9| |MBPP<br>3-shot|75.2|68.9|70.7|81.3|77.8|73.2|86.7| |Average|78.5|75.0|76.3|82.5|74.3|75.4|85.2| We take a closer look at different categories across 80 public benchmark datasets at the table below: |Benchmark|Phi-3-Medium-4K-Instruct<br>14b|Command R+<br>104B|Mixtral<br>8x22B|Llama-3-70B-Instruct<br>8b|GPT3.5-Turbo<br>version 1106|Gemini<br>Pro|GPT-4-Turbo<br>version 1106 (Chat)| |--------|------------------------|--------|-------------|-------------------|-------------------|----------|------------------------| |Popular aggregated benchmark|75.4|69.9|73.4|76.3|67.0|67.5|80.5| |Reasoning|84.1|79.3|81.5|86.7|78.3|80.4|89.3| |Language understanding|73.9|75.6|78.1|76.9|68.7|76.2|80.7| |Code generation|66.1|68.6|60.0|69.3|70.4|66.7|76.1| |Math|52.8|45.3|52.5|59.7|52.8|50.9|67.1| |Factual knowledge|48.3|60.3|60.6|52.4|63.4|54.6|45.9| |Multilingual|62.9|67.8|69.8|62.0|67.0|73.4|78.2| |Robustness|66.5|57.9|65.5|78.7|69.3|69.7|84.6| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-Medium model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: + Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [4K](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) ## Cross Platform Support ONNX runtime ecosystem now supports Phi3 Medium models across platforms and hardware. Optimized phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML GPU acceleration is supported for Windows desktops GPUs (AMD, Intel, and NVIDIA). Along with DML, ONNX Runtime provides cross platform support for Phi3 Medium across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-medium-4k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
[ "MEDQA" ]
Non_BioNLP
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" ]
1,721,186,027,000
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 value: 35.20411354359985 - type: nauc_precision_at_1_diff1 value: 36.87861012283668 - type: nauc_precision_at_1_max value: 25.592704128025584 - type: nauc_precision_at_20_diff1 value: 23.583394574577937 - type: nauc_precision_at_20_max value: 38.697643796192324 - type: nauc_precision_at_3_diff1 value: 26.407752776386506 - type: nauc_precision_at_3_max value: 29.64769320764332 - type: nauc_precision_at_5_diff1 value: 25.45743969076595 - type: nauc_precision_at_5_max value: 30.919847931025647 - type: nauc_recall_at_1000_diff1 value: 27.306371732511476 - type: nauc_recall_at_1000_max value: 65.7837411528459 - type: nauc_recall_at_100_diff1 value: 25.39481704738587 - type: nauc_recall_at_100_max value: 47.29752571335173 - type: nauc_recall_at_10_diff1 value: 24.310996780059064 - type: nauc_recall_at_10_max value: 35.20411354359981 - type: nauc_recall_at_1_diff1 value: 36.87861012283668 - type: nauc_recall_at_1_max value: 25.592704128025584 - type: nauc_recall_at_20_diff1 value: 23.583394574578005 - type: nauc_recall_at_20_max value: 38.69764379619235 - type: nauc_recall_at_3_diff1 value: 26.407752776386513 - type: nauc_recall_at_3_max value: 29.647693207643332 - type: nauc_recall_at_5_diff1 value: 25.457439690765938 - type: nauc_recall_at_5_max value: 30.91984793102568 - type: ndcg_at_1 value: 23.23 - type: ndcg_at_10 value: 39.215 - type: ndcg_at_100 value: 44.566 - type: ndcg_at_1000 value: 46.409 - type: ndcg_at_20 value: 41.467 - type: ndcg_at_3 value: 32.993 - type: ndcg_at_5 value: 35.839 - type: precision_at_1 value: 23.23 - type: precision_at_10 value: 5.743 - type: precision_at_100 value: 0.831 - type: precision_at_1000 value: 0.098 - type: precision_at_20 value: 3.318 - type: precision_at_3 value: 13.385 - type: precision_at_5 value: 9.413 - type: recall_at_1 value: 23.23 - type: recall_at_10 value: 57.42699999999999 - type: recall_at_100 value: 83.11699999999999 - type: recall_at_1000 value: 97.75500000000001 - type: recall_at_20 value: 66.364 - type: recall_at_3 value: 40.155 - type: recall_at_5 value: 47.064 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.102000000000004 - type: f1 value: 36.48213245522153 - type: f1_weighted value: 36.48213245522153 - task: type: Retrieval dataset: name: MTEB BSARDRetrieval type: maastrichtlawtech/bsard config: default split: test revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 metrics: - type: map_at_1 value: 5.405 - type: map_at_10 value: 8.372 - type: map_at_100 value: 9.522 - type: map_at_1000 value: 9.645 - type: map_at_20 value: 8.987 - type: map_at_3 value: 7.132 - type: map_at_5 value: 7.763000000000001 - type: mrr_at_1 value: 5.405405405405405 - type: mrr_at_10 value: 8.37247962247962 - type: mrr_at_100 value: 9.522369675165548 - type: mrr_at_1000 value: 9.644865518194182 - type: mrr_at_20 value: 8.987200953145225 - type: mrr_at_3 value: 7.132132132132132 - type: mrr_at_5 value: 7.762762762762764 - type: nauc_map_at_1000_diff1 value: 12.103894408912778 - type: nauc_map_at_1000_max value: 26.77460445002228 - type: nauc_map_at_100_diff1 value: 12.199535893254412 - type: nauc_map_at_100_max value: 26.791136142909995 - type: nauc_map_at_10_diff1 value: 11.762615047468374 - type: nauc_map_at_10_max value: 26.54661601271767 - type: nauc_map_at_1_diff1 value: 19.75768475065795 - type: nauc_map_at_1_max value: 36.45294032166726 - type: nauc_map_at_20_diff1 value: 12.627299133728561 - type: nauc_map_at_20_max value: 26.431834723382625 - type: nauc_map_at_3_diff1 value: 11.406093688135979 - type: nauc_map_at_3_max value: 26.206852419799336 - type: nauc_map_at_5_diff1 value: 10.933715346866054 - type: nauc_map_at_5_max value: 25.15168912848224 - type: nauc_mrr_at_1000_diff1 value: 12.103894408912778 - type: nauc_mrr_at_1000_max value: 26.77460445002228 - type: nauc_mrr_at_100_diff1 value: 12.199535893254412 - type: nauc_mrr_at_100_max value: 26.791136142909995 - type: nauc_mrr_at_10_diff1 value: 11.762615047468374 - type: nauc_mrr_at_10_max value: 26.54661601271767 - type: nauc_mrr_at_1_diff1 value: 19.75768475065795 - type: nauc_mrr_at_1_max value: 36.45294032166726 - type: nauc_mrr_at_20_diff1 value: 12.627299133728561 - type: nauc_mrr_at_20_max value: 26.431834723382625 - type: nauc_mrr_at_3_diff1 value: 11.406093688135979 - type: nauc_mrr_at_3_max value: 26.206852419799336 - type: nauc_mrr_at_5_diff1 value: 10.933715346866054 - type: nauc_mrr_at_5_max value: 25.15168912848224 - type: nauc_ndcg_at_1000_diff1 value: 8.119711442397051 - type: nauc_ndcg_at_1000_max value: 25.821500954959493 - type: nauc_ndcg_at_100_diff1 value: 10.610584456957277 - type: nauc_ndcg_at_100_max value: 27.81373505272856 - type: nauc_ndcg_at_10_diff1 value: 10.667531959142947 - type: nauc_ndcg_at_10_max value: 25.428088817882212 - type: nauc_ndcg_at_1_diff1 value: 19.75768475065795 - type: nauc_ndcg_at_1_max value: 36.45294032166726 - type: nauc_ndcg_at_20_diff1 value: 13.52659589943601 - type: nauc_ndcg_at_20_max value: 25.543352357923972 - type: nauc_ndcg_at_3_diff1 value: 9.220701633954755 - type: nauc_ndcg_at_3_max value: 23.41404735216586 - type: nauc_ndcg_at_5_diff1 value: 8.904201880131358 - type: nauc_ndcg_at_5_max value: 22.27268813727672 - type: nauc_precision_at_1000_diff1 value: -13.379595578660972 - type: nauc_precision_at_1000_max value: 19.07407039098987 - type: nauc_precision_at_100_diff1 value: 7.161231404563548 - type: nauc_precision_at_100_max value: 32.1446712851372 - type: nauc_precision_at_10_diff1 value: 9.32876742632238 - type: nauc_precision_at_10_max value: 24.401763374615022 - type: nauc_precision_at_1_diff1 value: 19.75768475065795 - type: nauc_precision_at_1_max value: 36.45294032166726 - type: nauc_precision_at_20_diff1 value: 16.344981963229685 - type: nauc_precision_at_20_max value: 25.273014482618493 - type: nauc_precision_at_3_diff1 value: 4.604729400599949 - type: nauc_precision_at_3_max value: 17.491915784171987 - type: nauc_precision_at_5_diff1 value: 5.152774776096578 - type: nauc_precision_at_5_max value: 16.848544787508555 - type: nauc_recall_at_1000_diff1 value: -13.379595578660883 - type: nauc_recall_at_1000_max value: 19.07407039098995 - type: nauc_recall_at_100_diff1 value: 7.161231404563502 - type: nauc_recall_at_100_max value: 32.144671285137136 - type: nauc_recall_at_10_diff1 value: 9.328767426322395 - type: nauc_recall_at_10_max value: 24.40176337461501 - type: nauc_recall_at_1_diff1 value: 19.75768475065795 - type: nauc_recall_at_1_max value: 36.45294032166726 - type: nauc_recall_at_20_diff1 value: 16.34498196322963 - type: nauc_recall_at_20_max value: 25.27301448261847 - type: nauc_recall_at_3_diff1 value: 4.604729400599932 - type: nauc_recall_at_3_max value: 17.49191578417196 - type: nauc_recall_at_5_diff1 value: 5.152774776096596 - type: nauc_recall_at_5_max value: 16.848544787508573 - type: ndcg_at_1 value: 5.405 - type: ndcg_at_10 value: 10.51 - type: ndcg_at_100 value: 17.012 - type: ndcg_at_1000 value: 20.686 - type: ndcg_at_20 value: 12.849 - type: ndcg_at_3 value: 7.835 - type: ndcg_at_5 value: 8.959 - type: precision_at_1 value: 5.405 - type: precision_at_10 value: 1.757 - type: precision_at_100 value: 0.5 - type: precision_at_1000 value: 0.08 - type: precision_at_20 value: 1.351 - type: precision_at_3 value: 3.3029999999999995 - type: precision_at_5 value: 2.5229999999999997 - type: recall_at_1 value: 5.405 - type: recall_at_10 value: 17.568 - type: recall_at_100 value: 50.0 - type: recall_at_1000 value: 79.73 - type: recall_at_20 value: 27.027 - type: recall_at_3 value: 9.91 - type: recall_at_5 value: 12.613 - task: type: Clustering dataset: name: MTEB HALClusteringS2S type: lyon-nlp/clustering-hal-s2s config: default split: test revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 metrics: - type: v_measure value: 23.773322308353517 - type: v_measures value: - 0.28321300949906897 - 0.26004472642751963 - 0.25956951558284086 - 0.24123195304666292 - 0.22207486085944725 - task: type: Clustering dataset: name: MTEB MLSUMClusteringP2P type: reciTAL/mlsum config: default split: test revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 metrics: - type: v_measure value: 44.10635133884183 - type: v_measures value: - 0.4352021194557699 - 0.4616076200837005 - 0.4407517544208635 - 0.4387026615402928 - 0.40575306284000634 - type: v_measure value: 43.6574557237274 - type: v_measures value: - 0.431013360873005 - 0.45456972088535785 - 0.4474907746228345 - 0.4318507494303067 - 0.3857692351737129 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (fr) type: mteb/mtop_domain config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 86.08518634512997 - type: f1 value: 86.01437763316983 - type: f1_weighted value: 86.03483392539235 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (fr) type: mteb/mtop_intent config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.23081741309113 - type: f1 value: 44.76981337966934 - type: f1_weighted value: 64.86577219367403 - task: type: Classification dataset: name: MTEB MasakhaNEWSClassification (fra) type: mteb/masakhanews config: fra split: test revision: 18193f187b92da67168c655c9973a165ed9593dd metrics: - type: accuracy value: 78.34123222748815 - type: f1 value: 74.19808376161188 - type: f1_weighted value: 78.465165305135 - task: type: Clustering dataset: name: MTEB MasakhaNEWSClusteringP2P (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: v_measure value: 58.6260598345145 - type: v_measures value: - 1.0 - 0.052551289167589395 - 0.4126124221990744 - 0.690570263898874 - 0.7755690164601873 - type: v_measure value: 53.47058992788083 - type: v_measures value: - 1.0 - 0.06287268264858063 - 0.6712730568122484 - 0.2169401386066275 - 0.7224436183265853 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (fr) type: mteb/amazon_massive_intent config: fr split: test revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 65.48755884330868 - type: f1 value: 63.42516904610099 - type: f1_weighted value: 65.62227422445625 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 69.27370544720914 - type: f1 value: 68.92639289886843 - type: f1_weighted value: 69.39025426049528 - task: type: Retrieval dataset: name: MTEB MintakaRetrieval (fr) type: jinaai/mintakaqa config: fr split: test revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e metrics: - type: map_at_1 value: 13.718 - type: map_at_10 value: 21.404 - type: map_at_100 value: 22.371 - type: map_at_1000 value: 22.493 - type: map_at_20 value: 21.972 - type: map_at_3 value: 19.192 - type: map_at_5 value: 20.366999999999997 - type: mrr_at_1 value: 13.718263718263717 - type: mrr_at_10 value: 21.40396890396889 - type: mrr_at_100 value: 22.370673563176524 - type: mrr_at_1000 value: 22.49316476646458 - type: mrr_at_20 value: 21.971667600361627 - type: mrr_at_3 value: 19.191919191919162 - type: mrr_at_5 value: 20.36718536718532 - type: nauc_map_at_1000_diff1 value: 18.66186772676608 - type: nauc_map_at_1000_max value: 32.45794220535016 - type: nauc_map_at_100_diff1 value: 18.620993318615394 - type: nauc_map_at_100_max value: 32.44820369787587 - type: nauc_map_at_10_diff1 value: 18.884049513804037 - type: nauc_map_at_10_max value: 32.77552431144882 - type: nauc_map_at_1_diff1 value: 25.49080673895181 - type: nauc_map_at_1_max value: 29.25311987317655 - type: nauc_map_at_20_diff1 value: 18.704330878419075 - type: nauc_map_at_20_max value: 32.55837988078994 - type: nauc_map_at_3_diff1 value: 20.095857563209314 - type: nauc_map_at_3_max value: 32.93191322461617 - type: nauc_map_at_5_diff1 value: 19.281813313113396 - type: nauc_map_at_5_max value: 32.786844756856475 - type: nauc_mrr_at_1000_diff1 value: 18.66186763232669 - type: nauc_mrr_at_1000_max value: 32.45794216874754 - type: nauc_mrr_at_100_diff1 value: 18.620993318615394 - type: nauc_mrr_at_100_max value: 32.44820369787587 - type: nauc_mrr_at_10_diff1 value: 18.884049513804037 - type: nauc_mrr_at_10_max value: 32.77552431144882 - type: nauc_mrr_at_1_diff1 value: 25.49080673895181 - type: nauc_mrr_at_1_max value: 29.25311987317655 - type: nauc_mrr_at_20_diff1 value: 18.704330878419075 - type: nauc_mrr_at_20_max value: 32.55837988078994 - type: nauc_mrr_at_3_diff1 value: 20.095857563209314 - type: nauc_mrr_at_3_max value: 32.93191322461617 - type: nauc_mrr_at_5_diff1 value: 19.281813313113396 - type: nauc_mrr_at_5_max value: 32.786844756856475 - type: nauc_ndcg_at_1000_diff1 value: 16.291266125191186 - type: nauc_ndcg_at_1000_max value: 32.03483412880716 - type: nauc_ndcg_at_100_diff1 value: 15.08155959648069 - type: nauc_ndcg_at_100_max value: 31.74628993952365 - type: nauc_ndcg_at_10_diff1 value: 16.457288503185854 - type: nauc_ndcg_at_10_max value: 33.34322548472455 - type: nauc_ndcg_at_1_diff1 value: 25.49080673895181 - type: nauc_ndcg_at_1_max value: 29.25311987317655 - type: nauc_ndcg_at_20_diff1 value: 15.847885101378232 - type: nauc_ndcg_at_20_max value: 32.63179959589915 - type: nauc_ndcg_at_3_diff1 value: 18.77312834653236 - type: nauc_ndcg_at_3_max value: 33.76341797807492 - type: nauc_ndcg_at_5_diff1 value: 17.46839695168085 - type: nauc_ndcg_at_5_max value: 33.52006824258854 - type: nauc_precision_at_1000_diff1 value: -1.1586826170737583 - type: nauc_precision_at_1000_max value: 19.93551888234813 - type: nauc_precision_at_100_diff1 value: 3.0380716626456072 - type: nauc_precision_at_100_max value: 27.2930149786862 - type: nauc_precision_at_10_diff1 value: 10.566658459623403 - type: nauc_precision_at_10_max value: 34.3880626271458 - type: nauc_precision_at_1_diff1 value: 25.49080673895181 - type: nauc_precision_at_1_max value: 29.25311987317655 - type: nauc_precision_at_20_diff1 value: 8.479138077976014 - type: nauc_precision_at_20_max value: 32.113399922346744 - type: nauc_precision_at_3_diff1 value: 15.622074491261472 - type: nauc_precision_at_3_max value: 35.7250108281599 - type: nauc_precision_at_5_diff1 value: 13.229128818765382 - type: nauc_precision_at_5_max value: 35.14850024280549 - type: nauc_recall_at_1000_diff1 value: -1.1586826170740634 - type: nauc_recall_at_1000_max value: 19.93551888234777 - type: nauc_recall_at_100_diff1 value: 3.038071662645654 - type: nauc_recall_at_100_max value: 27.293014978686276 - type: nauc_recall_at_10_diff1 value: 10.56665845962341 - type: nauc_recall_at_10_max value: 34.38806262714581 - type: nauc_recall_at_1_diff1 value: 25.49080673895181 - type: nauc_recall_at_1_max value: 29.25311987317655 - type: nauc_recall_at_20_diff1 value: 8.47913807797598 - type: nauc_recall_at_20_max value: 32.113399922346744 - type: nauc_recall_at_3_diff1 value: 15.622074491261479 - type: nauc_recall_at_3_max value: 35.725010828159924 - type: nauc_recall_at_5_diff1 value: 13.229128818765403 - type: nauc_recall_at_5_max value: 35.14850024280548 - type: ndcg_at_1 value: 13.718 - type: ndcg_at_10 value: 25.576 - type: ndcg_at_100 value: 30.537999999999997 - type: ndcg_at_1000 value: 34.364 - type: ndcg_at_20 value: 27.619 - type: ndcg_at_3 value: 20.924 - type: ndcg_at_5 value: 23.046 - type: precision_at_1 value: 13.718 - type: precision_at_10 value: 3.894 - type: precision_at_100 value: 0.628 - type: precision_at_1000 value: 0.094 - type: precision_at_20 value: 2.348 - type: precision_at_3 value: 8.64 - type: precision_at_5 value: 6.216 - type: recall_at_1 value: 13.718 - type: recall_at_10 value: 38.943 - type: recall_at_100 value: 62.775999999999996 - type: recall_at_1000 value: 94.10300000000001 - type: recall_at_20 value: 46.97 - type: recall_at_3 value: 25.921 - type: recall_at_5 value: 31.080999999999996 - task: type: PairClassification dataset: name: MTEB OpusparcusPC (fr) type: GEM/opusparcus config: fr split: test revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a metrics: - type: cos_sim_accuracy value: 81.81198910081744 - type: cos_sim_ap 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: - type: map_at_1 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 - type: nauc_map_at_100_max value: 12.229977029802052 - type: nauc_map_at_10_diff1 value: 47.83118981173179 - type: nauc_map_at_10_max value: 12.67122414331949 - type: nauc_map_at_1_diff1 value: 48.29708026951358 - type: nauc_map_at_1_max value: 5.016460019075176 - type: nauc_map_at_20_diff1 value: 47.5126416742559 - type: nauc_map_at_20_max value: 12.23002184861472 - type: nauc_map_at_3_diff1 value: 48.18168651330906 - type: nauc_map_at_3_max value: 14.063513453945578 - type: nauc_map_at_5_diff1 value: 46.8656518414084 - type: nauc_map_at_5_max value: 13.22896127813873 - type: nauc_mrr_at_1000_diff1 value: 47.52352042312832 - type: nauc_mrr_at_1000_max value: 12.229977029802052 - type: nauc_mrr_at_100_diff1 value: 47.52352042312832 - type: nauc_mrr_at_100_max value: 12.229977029802052 - type: nauc_mrr_at_10_diff1 value: 47.83118981173179 - type: nauc_mrr_at_10_max 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 - type: nauc_ndcg_at_100_max value: 12.900901768894494 - type: nauc_ndcg_at_10_diff1 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: - type: map_at_1 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 - type: nauc_ndcg_at_1_max 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 - type: nauc_ndcg_at_3_max value: 51.74775013382323 - type: nauc_ndcg_at_5_diff1 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 - type: nauc_precision_at_10_diff1 value: -0.128354517220691 - type: nauc_precision_at_10_max value: 31.00617539775257 - type: nauc_precision_at_1_diff1 value: 61.67387773779846 - type: nauc_precision_at_1_max value: 59.51259333152851 - type: nauc_precision_at_20_diff1 value: -4.838065494986492 - type: nauc_precision_at_20_max 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 - type: nauc_recall_at_10_max value: 45.84205807317027 - type: nauc_recall_at_1_diff1 value: 60.321467202422596 - type: nauc_recall_at_1_max 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 - type: nauc_recall_at_3_max 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 value: 45.245999999999995 - type: map_at_20 value: 45.048 - type: map_at_3 value: 39.343 - type: map_at_5 value: 42.156 - type: mrr_at_1 value: 28.591749644381224 - type: mrr_at_10 value: 44.53744157691528 - type: mrr_at_100 value: 45.36249919705719 - type: mrr_at_1000 value: 45.36795843267093 - type: mrr_at_20 value: 45.17017744908004 - type: mrr_at_3 value: 39.50924608819345 - type: mrr_at_5 value: 42.29374110953058 - type: nauc_map_at_1000_diff1 value: 3.81697701184427 - type: nauc_map_at_1000_max value: -5.3494391339512966 - type: nauc_map_at_100_diff1 value: 3.8255923068950737 - type: nauc_map_at_100_max value: -5.338796585423051 - type: nauc_map_at_10_diff1 value: 3.807599213819479 - type: nauc_map_at_10_max value: -5.313800854145031 - type: nauc_map_at_1_diff1 value: 5.156690676517333 - type: nauc_map_at_1_max value: -9.64584413837327 - type: nauc_map_at_20_diff1 value: 3.7941985981544244 - type: nauc_map_at_20_max value: -5.200991165900242 - type: nauc_map_at_3_diff1 value: 3.042950933986489 - type: nauc_map_at_3_max value: -5.953385411481654 - type: nauc_map_at_5_diff1 value: 3.0549453605943433 - type: nauc_map_at_5_max value: -5.787888510997178 - type: nauc_mrr_at_1000_diff1 value: 2.815942782079056 - type: nauc_mrr_at_1000_max value: -6.045251506633342 - type: nauc_mrr_at_100_diff1 value: 2.8247136693036206 - type: nauc_mrr_at_100_max value: -6.034513630311149 - type: nauc_mrr_at_10_diff1 value: 2.842321554294615 - type: nauc_mrr_at_10_max value: -5.983994994110801 - type: nauc_mrr_at_1_diff1 value: 4.289447405708845 - type: nauc_mrr_at_1_max value: -10.158513246070529 - type: nauc_mrr_at_20_diff1 value: 2.802223509089013 - type: nauc_mrr_at_20_max value: -5.889383549567283 - type: nauc_mrr_at_3_diff1 value: 1.9507572567994225 - type: nauc_mrr_at_3_max value: -6.579817119302078 - type: nauc_mrr_at_5_diff1 value: 2.0636113696159306 - type: nauc_mrr_at_5_max value: -6.47814796715319 - type: nauc_ndcg_at_1000_diff1 value: 4.054109302322553 - type: nauc_ndcg_at_1000_max value: -4.194276048637998 - type: nauc_ndcg_at_100_diff1 value: 4.3606449596207995 - type: nauc_ndcg_at_100_max value: -3.802885863375761 - type: nauc_ndcg_at_10_diff1 value: 4.374146895999117 - type: nauc_ndcg_at_10_max value: -3.007138296243735 - type: nauc_ndcg_at_1_diff1 value: 5.156690676517333 - type: nauc_ndcg_at_1_max value: -9.64584413837327 - type: nauc_ndcg_at_20_diff1 value: 4.283769209560412 - type: nauc_ndcg_at_20_max value: -2.5570972005509245 - type: nauc_ndcg_at_3_diff1 value: 2.4019132290785628 - type: nauc_ndcg_at_3_max value: -4.772614514375251 - type: nauc_ndcg_at_5_diff1 value: 2.2604685552347488 - type: nauc_ndcg_at_5_max value: -4.5287849384277346 - type: nauc_precision_at_1000_diff1 value: 26.832693994163886 - type: nauc_precision_at_1000_max value: 28.13719829218545 - type: nauc_precision_at_100_diff1 value: 49.25187779934308 - type: nauc_precision_at_100_max value: 54.90462014878204 - type: nauc_precision_at_10_diff1 value: 9.375044420325825 - type: nauc_precision_at_10_max value: 11.118715229369158 - type: nauc_precision_at_1_diff1 value: 5.156690676517333 - type: nauc_precision_at_1_max value: -9.64584413837327 - type: nauc_precision_at_20_diff1 value: 12.648487139563313 - type: nauc_precision_at_20_max value: 29.17269939791144 - type: nauc_precision_at_3_diff1 value: 0.5381479007985195 - type: nauc_precision_at_3_max value: -1.319607327988569 - type: nauc_precision_at_5_diff1 value: -0.530675691789191 - type: nauc_precision_at_5_max value: -0.3449755187285182 - type: nauc_recall_at_1000_diff1 value: 26.83269399415972 - type: nauc_recall_at_1000_max value: 28.137198292180138 - type: nauc_recall_at_100_diff1 value: 49.25187779934272 - type: nauc_recall_at_100_max value: 54.90462014878089 - type: nauc_recall_at_10_diff1 value: 9.375044420325978 - type: nauc_recall_at_10_max value: 11.118715229369167 - type: nauc_recall_at_1_diff1 value: 5.156690676517333 - type: nauc_recall_at_1_max value: -9.64584413837327 - type: nauc_recall_at_20_diff1 value: 12.648487139563178 - type: nauc_recall_at_20_max value: 29.172699397911256 - type: nauc_recall_at_3_diff1 value: 0.5381479007985096 - type: nauc_recall_at_3_max value: -1.3196073279885299 - type: nauc_recall_at_5_diff1 value: -0.5306756917892376 - type: nauc_recall_at_5_max value: -0.34497551872854154 - type: ndcg_at_1 value: 28.307 - type: ndcg_at_10 value: 53.593999999999994 - type: ndcg_at_100 value: 57.13399999999999 - type: ndcg_at_1000 value: 57.28 - type: ndcg_at_20 value: 55.861000000000004 - type: ndcg_at_3 value: 43.091 - type: ndcg_at_5 value: 48.16 - type: precision_at_1 value: 28.307 - type: precision_at_10 value: 8.3 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.595 - type: precision_at_3 value: 17.994 - type: precision_at_5 value: 13.257 - type: recall_at_1 value: 28.307 - type: recall_at_10 value: 83.001 - type: recall_at_100 value: 98.506 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 91.892 - type: recall_at_3 value: 53.983000000000004 - type: recall_at_5 value: 66.28699999999999 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 41.75510333108447 - type: v_measures value: - 0.38932105262642 - 0.41167658391196155 - 0.4152007083702598 - 0.43751533882806676 - 0.41353841462129437 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 31.628031176398057 - type: v_measures value: - 0.3124730271530551 - 0.30410053196374376 - 0.31038902598125107 - 0.3037853444036682 - 0.3061080414991767 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 56.78685528593245 - type: mrr value: 70.13113925163786 - type: nAUC_map_diff1 value: 2.9860496068519695 - type: nAUC_map_max value: 22.582369735674774 - type: nAUC_mrr_diff1 value: 10.846967439812445 - type: nAUC_mrr_max value: 35.29439227015077 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.88368776567987 - type: cos_sim_spearman value: 83.98625103310174 - type: euclidean_pearson value: 84.15851334353565 - type: euclidean_spearman value: 83.50611961105386 - type: manhattan_pearson value: 84.26852097545078 - type: manhattan_spearman value: 83.74287199356931 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 80.31493506493506 - type: f1 value: 80.18539252802539 - type: f1_weighted value: 80.1853925280254 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 36.01213557884716 - type: v_measures value: - 0.35149213783659844 - 0.3504551848301787 - 0.3777396210177721 - 0.36713470804377507 - 0.35699360527484775 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 28.77320940838855 - type: v_measures value: - 0.30066854059482007 - 0.27912691518289856 - 0.28109177448868566 - 0.27788082204726 - 0.28174202201956644 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: mteb/cqadupstack-android config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 28.931 - type: map_at_10 value: 39.226 - type: map_at_100 value: 40.641 - type: map_at_1000 value: 40.758 - type: map_at_20 value: 39.947 - type: map_at_3 value: 35.893 - type: map_at_5 value: 37.911 - type: mrr_at_1 value: 36.33762517882689 - type: mrr_at_10 value: 45.437813656697756 - type: mrr_at_100 value: 46.263112207849225 - type: mrr_at_1000 value: 46.31120643750262 - type: mrr_at_20 value: 45.89328313995794 - type: mrr_at_3 value: 42.89461134954697 - type: mrr_at_5 value: 44.49690033381019 - type: nauc_map_at_1000_diff1 value: 51.149900773445665 - type: nauc_map_at_1000_max value: 38.68580130673067 - type: nauc_map_at_100_diff1 value: 51.10002536433903 - type: nauc_map_at_100_max value: 38.641317822870484 - type: nauc_map_at_10_diff1 value: 51.124389332061504 - type: nauc_map_at_10_max value: 38.318568221563254 - type: nauc_map_at_1_diff1 value: 56.90092514723948 - type: nauc_map_at_1_max value: 37.61485298818892 - type: nauc_map_at_20_diff1 value: 51.181676535641515 - type: nauc_map_at_20_max value: 38.50630258148947 - type: nauc_map_at_3_diff1 value: 52.9080719662819 - type: nauc_map_at_3_max value: 37.65490829785428 - type: nauc_map_at_5_diff1 value: 51.88563997044587 - type: nauc_map_at_5_max value: 38.162982469441104 - type: nauc_mrr_at_1000_diff1 value: 52.02314497512314 - type: nauc_mrr_at_1000_max value: 42.51237380812326 - type: nauc_mrr_at_100_diff1 value: 52.00022544992019 - type: nauc_mrr_at_100_max value: 42.47931426167529 - type: nauc_mrr_at_10_diff1 value: 51.91527284768196 - type: nauc_mrr_at_10_max value: 42.39017221462642 - type: nauc_mrr_at_1_diff1 value: 57.748140308636906 - type: nauc_mrr_at_1_max value: 45.151335057931625 - type: nauc_mrr_at_20_diff1 value: 52.014517489654786 - type: nauc_mrr_at_20_max value: 42.502037133226224 - type: nauc_mrr_at_3_diff1 value: 53.44263059806559 - type: nauc_mrr_at_3_max value: 42.54366394954965 - type: nauc_mrr_at_5_diff1 value: 52.40067352297368 - type: nauc_mrr_at_5_max value: 42.39770466495629 - type: nauc_ndcg_at_1000_diff1 value: 49.303067288367096 - type: nauc_ndcg_at_1000_max value: 40.15083357935891 - type: nauc_ndcg_at_100_diff1 value: 48.06078219853983 - type: nauc_ndcg_at_100_max value: 39.099873422335584 - type: nauc_ndcg_at_10_diff1 value: 48.427405777556764 - type: nauc_ndcg_at_10_max value: 38.8466159356305 - type: nauc_ndcg_at_1_diff1 value: 57.748140308636906 - type: nauc_ndcg_at_1_max value: 45.151335057931625 - type: nauc_ndcg_at_20_diff1 value: 48.400275143008884 - type: nauc_ndcg_at_20_max value: 38.987281654803155 - type: nauc_ndcg_at_3_diff1 value: 51.94028236848058 - type: nauc_ndcg_at_3_max value: 39.22267932164834 - type: nauc_ndcg_at_5_diff1 value: 50.228342110462435 - type: nauc_ndcg_at_5_max value: 39.25835142473454 - type: nauc_precision_at_1000_diff1 value: -6.148682329597722 - type: nauc_precision_at_1000_max value: 1.1132760594569802 - type: nauc_precision_at_100_diff1 value: -0.42183455399296765 - type: nauc_precision_at_100_max value: 12.337898495315343 - type: nauc_precision_at_10_diff1 value: 18.94429698742333 - type: nauc_precision_at_10_max value: 28.777738237731203 - type: nauc_precision_at_1_diff1 value: 57.748140308636906 - type: nauc_precision_at_1_max value: 45.151335057931625 - type: nauc_precision_at_20_diff1 value: 12.915885854552354 - type: nauc_precision_at_20_max value: 24.01402704364973 - type: nauc_precision_at_3_diff1 value: 36.634218047630384 - type: nauc_precision_at_3_max value: 36.27512688680148 - type: nauc_precision_at_5_diff1 value: 28.272819211308992 - type: nauc_precision_at_5_max value: 33.34907639932695 - type: nauc_recall_at_1000_diff1 value: 26.52022379258474 - type: nauc_recall_at_1000_max value: 49.10217378309213 - type: nauc_recall_at_100_diff1 value: 25.383923002033832 - type: nauc_recall_at_100_max value: 29.224125741020877 - type: nauc_recall_at_10_diff1 value: 36.465429616129015 - type: nauc_recall_at_10_max value: 33.39232875391991 - type: nauc_recall_at_1_diff1 value: 56.90092514723948 - type: nauc_recall_at_1_max value: 37.61485298818892 - type: nauc_recall_at_20_diff1 value: 34.97381075257172 - type: nauc_recall_at_20_max value: 33.453578222267346 - type: nauc_recall_at_3_diff1 value: 47.268820296829134 - type: nauc_recall_at_3_max value: 35.21361112290018 - type: nauc_recall_at_5_diff1 value: 42.36929492536004 - type: nauc_recall_at_5_max value: 34.972452567095665 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 45.07 - type: ndcg_at_100 value: 50.619 - type: ndcg_at_1000 value: 52.729000000000006 - type: ndcg_at_20 value: 47.027 - type: ndcg_at_3 value: 40.388000000000005 - type: ndcg_at_5 value: 42.811 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 8.541 - type: precision_at_100 value: 1.391 - type: precision_at_1000 value: 0.184 - type: precision_at_20 value: 5.007000000000001 - type: precision_at_3 value: 19.409000000000002 - type: precision_at_5 value: 14.163 - type: recall_at_1 value: 28.931 - type: recall_at_10 value: 55.701 - type: recall_at_100 value: 79.389 - type: recall_at_1000 value: 93.366 - type: recall_at_20 value: 62.833000000000006 - type: recall_at_3 value: 42.007 - type: recall_at_5 value: 48.84 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: mteb/cqadupstack-english config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 23.638 - type: map_at_10 value: 32.277 - type: map_at_100 value: 33.363 - type: map_at_1000 value: 33.488 - type: map_at_20 value: 32.857 - type: map_at_3 value: 29.748 - type: map_at_5 value: 31.179000000000002 - type: mrr_at_1 value: 30.254777070063692 - type: mrr_at_10 value: 37.817384490951355 - type: mrr_at_100 value: 38.525912264467145 - type: mrr_at_1000 value: 38.58069194468667 - type: mrr_at_20 value: 38.20930815682446 - type: mrr_at_3 value: 35.700636942675146 - type: mrr_at_5 value: 36.926751592356666 - type: nauc_map_at_1000_diff1 value: 45.223392226929235 - type: nauc_map_at_1000_max value: 38.24038259272163 - type: nauc_map_at_100_diff1 value: 45.23190429504784 - type: nauc_map_at_100_max value: 38.19794744902846 - type: nauc_map_at_10_diff1 value: 45.515992352450176 - type: nauc_map_at_10_max value: 37.548960747017844 - type: nauc_map_at_1_diff1 value: 52.291404507813056 - type: nauc_map_at_1_max value: 33.46767953286993 - type: nauc_map_at_20_diff1 value: 45.332400816656936 - type: nauc_map_at_20_max value: 37.878067742926675 - type: nauc_map_at_3_diff1 value: 46.829233538381764 - type: nauc_map_at_3_max value: 36.69901435795047 - type: nauc_map_at_5_diff1 value: 46.12298460254266 - type: nauc_map_at_5_max value: 37.34969360011008 - type: nauc_mrr_at_1000_diff1 value: 41.898365322188674 - type: nauc_mrr_at_1000_max value: 39.304566277957704 - type: nauc_mrr_at_100_diff1 value: 41.88883697764852 - type: nauc_mrr_at_100_max value: 39.30077276431053 - type: nauc_mrr_at_10_diff1 value: 42.062104921386506 - type: nauc_mrr_at_10_max value: 39.30528366258507 - type: nauc_mrr_at_1_diff1 value: 47.92599437007114 - type: nauc_mrr_at_1_max value: 39.11863678363455 - type: nauc_mrr_at_20_diff1 value: 41.88168571216021 - type: nauc_mrr_at_20_max value: 39.26248573846707 - type: nauc_mrr_at_3_diff1 value: 43.07190580570743 - type: nauc_mrr_at_3_max value: 39.87788973395513 - type: nauc_mrr_at_5_diff1 value: 42.49866565630987 - type: nauc_mrr_at_5_max value: 39.54834907714328 - type: nauc_ndcg_at_1000_diff1 value: 41.51353648334291 - type: nauc_ndcg_at_1000_max value: 39.603326878012986 - type: nauc_ndcg_at_100_diff1 value: 41.30454895265097 - type: nauc_ndcg_at_100_max value: 39.313602966554505 - type: nauc_ndcg_at_10_diff1 value: 42.02099052567711 - type: nauc_ndcg_at_10_max value: 38.534861088136715 - type: nauc_ndcg_at_1_diff1 value: 47.92599437007114 - type: nauc_ndcg_at_1_max value: 39.11863678363455 - type: nauc_ndcg_at_20_diff1 value: 41.663145625518375 - type: nauc_ndcg_at_20_max value: 38.752693813154075 - type: nauc_ndcg_at_3_diff1 value: 43.68575961185724 - type: nauc_ndcg_at_3_max value: 39.40226210725685 - type: nauc_ndcg_at_5_diff1 value: 43.00140726081697 - type: nauc_ndcg_at_5_max value: 39.21485362612467 - type: nauc_precision_at_1000_diff1 value: -2.790275135023392 - type: nauc_precision_at_1000_max value: 17.818318660525463 - type: nauc_precision_at_100_diff1 value: 2.0554939129182417 - type: nauc_precision_at_100_max value: 29.753860102457935 - type: nauc_precision_at_10_diff1 value: 15.094160126474254 - type: nauc_precision_at_10_max value: 37.972874196449126 - type: nauc_precision_at_1_diff1 value: 47.92599437007114 - type: nauc_precision_at_1_max value: 39.11863678363455 - type: nauc_precision_at_20_diff1 value: 10.746873592106713 - type: nauc_precision_at_20_max value: 36.96468826692449 - type: nauc_precision_at_3_diff1 value: 28.944521315560483 - type: nauc_precision_at_3_max value: 42.03983245575044 - type: nauc_precision_at_5_diff1 value: 23.828098284010075 - type: nauc_precision_at_5_max value: 41.76526648017447 - type: nauc_recall_at_1000_diff1 value: 26.537966542990997 - type: nauc_recall_at_1000_max value: 41.86346125540241 - type: nauc_recall_at_100_diff1 value: 28.044584247129283 - type: nauc_recall_at_100_max value: 37.42247127416711 - type: nauc_recall_at_10_diff1 value: 33.434563672243115 - type: nauc_recall_at_10_max value: 34.63428918279095 - type: nauc_recall_at_1_diff1 value: 52.291404507813056 - type: nauc_recall_at_1_max value: 33.46767953286993 - type: nauc_recall_at_20_diff1 value: 31.189066205007187 - type: nauc_recall_at_20_max value: 35.3704318509917 - type: nauc_recall_at_3_diff1 value: 39.67602671214362 - type: nauc_recall_at_3_max value: 35.6485218636747 - type: nauc_recall_at_5_diff1 value: 36.71118621793804 - type: nauc_recall_at_5_max value: 35.81341336007971 - type: ndcg_at_1 value: 30.255 - type: ndcg_at_10 value: 37.376 - type: ndcg_at_100 value: 41.678 - type: ndcg_at_1000 value: 44.079 - type: ndcg_at_20 value: 38.942 - type: ndcg_at_3 value: 33.641 - type: ndcg_at_5 value: 35.346 - type: precision_at_1 value: 30.255 - type: precision_at_10 value: 7.102 - type: precision_at_100 value: 1.184 - type: precision_at_1000 value: 0.166 - type: precision_at_20 value: 4.185 - type: precision_at_3 value: 16.348 - type: precision_at_5 value: 11.591999999999999 - type: recall_at_1 value: 23.638 - type: recall_at_10 value: 46.524 - type: recall_at_100 value: 65.118 - type: recall_at_1000 value: 81.133 - type: recall_at_20 value: 52.331 - type: recall_at_3 value: 35.254999999999995 - type: recall_at_5 value: 40.174 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 35.667 - type: map_at_10 value: 47.397 - type: map_at_100 value: 48.366 - type: map_at_1000 value: 48.433 - type: map_at_20 value: 47.963 - type: map_at_3 value: 44.211 - type: map_at_5 value: 46.037 - type: mrr_at_1 value: 40.87774294670846 - type: mrr_at_10 value: 50.565880479673616 - type: mrr_at_100 value: 51.271230622181626 - type: mrr_at_1000 value: 51.306805744714836 - type: mrr_at_20 value: 51.012075318045525 - type: mrr_at_3 value: 47.9832810867294 - type: mrr_at_5 value: 49.525600835945724 - type: nauc_map_at_1000_diff1 value: 50.33288681013869 - type: nauc_map_at_1000_max value: 37.44437438806084 - type: nauc_map_at_100_diff1 value: 50.317492085630064 - type: nauc_map_at_100_max value: 37.426681891363835 - type: nauc_map_at_10_diff1 value: 50.24182139242321 - type: nauc_map_at_10_max value: 36.91039477771677 - type: nauc_map_at_1_diff1 value: 56.01200063592147 - type: nauc_map_at_1_max value: 31.767342114075202 - type: nauc_map_at_20_diff1 value: 50.21631708851613 - type: nauc_map_at_20_max value: 37.28818324793643 - type: nauc_map_at_3_diff1 value: 51.111793089491364 - type: nauc_map_at_3_max value: 36.16516417187456 - type: nauc_map_at_5_diff1 value: 50.47567188188865 - type: nauc_map_at_5_max value: 36.72222550501132 - type: nauc_mrr_at_1000_diff1 value: 49.29372112096636 - type: nauc_mrr_at_1000_max value: 39.248284382084236 - type: nauc_mrr_at_100_diff1 value: 49.28279373491327 - type: nauc_mrr_at_100_max value: 39.26004837053389 - type: nauc_mrr_at_10_diff1 value: 49.123704806290434 - type: nauc_mrr_at_10_max value: 39.05266034946078 - type: nauc_mrr_at_1_diff1 value: 53.88859746474265 - type: nauc_mrr_at_1_max value: 37.056204568674275 - type: nauc_mrr_at_20_diff1 value: 49.18403232554298 - type: nauc_mrr_at_20_max value: 39.26689196401381 - type: nauc_mrr_at_3_diff1 value: 49.59424894836517 - type: nauc_mrr_at_3_max value: 38.95714592509984 - type: nauc_mrr_at_5_diff1 value: 49.257845318012954 - type: nauc_mrr_at_5_max value: 39.30070104826491 - type: nauc_ndcg_at_1000_diff1 value: 48.91743661336846 - type: nauc_ndcg_at_1000_max value: 39.39031133623686 - type: nauc_ndcg_at_100_diff1 value: 48.61511346835115 - type: nauc_ndcg_at_100_max value: 39.459340998985724 - type: nauc_ndcg_at_10_diff1 value: 48.06588542038947 - type: nauc_ndcg_at_10_max value: 38.157829321231 - type: nauc_ndcg_at_1_diff1 value: 53.88859746474265 - type: nauc_ndcg_at_1_max value: 37.056204568674275 - type: nauc_ndcg_at_20_diff1 value: 48.05115075637084 - type: nauc_ndcg_at_20_max value: 39.2235027218884 - type: nauc_ndcg_at_3_diff1 value: 49.30878740373676 - type: nauc_ndcg_at_3_max value: 37.84032746584941 - type: nauc_ndcg_at_5_diff1 value: 48.47712228032605 - type: nauc_ndcg_at_5_max value: 38.38589466282407 - type: nauc_precision_at_1000_diff1 value: -7.243262652381105 - type: nauc_precision_at_1000_max value: 18.453365469588427 - type: nauc_precision_at_100_diff1 value: -2.0153970546194753 - type: nauc_precision_at_100_max value: 24.22667501786602 - type: nauc_precision_at_10_diff1 value: 14.979334560516222 - type: nauc_precision_at_10_max value: 33.13307837324579 - type: nauc_precision_at_1_diff1 value: 53.88859746474265 - type: nauc_precision_at_1_max value: 37.056204568674275 - type: nauc_precision_at_20_diff1 value: 8.379765029951027 - type: nauc_precision_at_20_max value: 32.28271665269386 - type: nauc_precision_at_3_diff1 value: 31.16831547354767 - type: nauc_precision_at_3_max value: 38.10801385749373 - type: nauc_precision_at_5_diff1 value: 23.32241470046817 - type: nauc_precision_at_5_max value: 37.2000516679205 - type: nauc_recall_at_1000_diff1 value: 40.03022783413472 - type: nauc_recall_at_1000_max value: 49.77189630896353 - type: nauc_recall_at_100_diff1 value: 39.485228558001154 - type: nauc_recall_at_100_max value: 44.84364760927468 - type: nauc_recall_at_10_diff1 value: 39.911638774960096 - type: nauc_recall_at_10_max value: 37.00135324546857 - type: nauc_recall_at_1_diff1 value: 56.01200063592147 - type: nauc_recall_at_1_max value: 31.767342114075202 - type: nauc_recall_at_20_diff1 value: 38.604788301520685 - type: nauc_recall_at_20_max value: 42.21099902041599 - type: nauc_recall_at_3_diff1 value: 44.913068402378755 - type: nauc_recall_at_3_max value: 36.35063250643407 - type: nauc_recall_at_5_diff1 value: 42.15428494957372 - type: nauc_recall_at_5_max value: 38.11256932308573 - type: ndcg_at_1 value: 40.878 - type: ndcg_at_10 value: 53.062 - type: ndcg_at_100 value: 57.160999999999994 - type: ndcg_at_1000 value: 58.538999999999994 - type: ndcg_at_20 value: 54.821 - type: ndcg_at_3 value: 47.544 - type: ndcg_at_5 value: 50.305 - type: precision_at_1 value: 40.878 - type: precision_at_10 value: 8.564 - type: precision_at_100 value: 1.155 - type: precision_at_1000 value: 0.133 - type: precision_at_20 value: 4.79 - type: precision_at_3 value: 21.108 - type: precision_at_5 value: 14.658 - type: recall_at_1 value: 35.667 - type: recall_at_10 value: 66.766 - type: recall_at_100 value: 84.553 - type: recall_at_1000 value: 94.346 - type: recall_at_20 value: 73.272 - type: recall_at_3 value: 52.139 - type: recall_at_5 value: 58.816 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 20.618 - type: map_at_10 value: 27.47 - type: map_at_100 value: 28.505000000000003 - type: map_at_1000 value: 28.594 - type: map_at_20 value: 28.057 - type: map_at_3 value: 24.918000000000003 - type: map_at_5 value: 26.229999999999997 - type: mrr_at_1 value: 22.372881355932204 - type: mrr_at_10 value: 29.201910142588083 - type: mrr_at_100 value: 30.158796422923974 - type: mrr_at_1000 value: 30.226705533923436 - type: mrr_at_20 value: 29.748232470158786 - type: mrr_at_3 value: 26.723163841807924 - type: mrr_at_5 value: 27.97175141242938 - type: nauc_map_at_1000_diff1 value: 42.518789058108034 - type: nauc_map_at_1000_max value: 21.82284691419632 - type: nauc_map_at_100_diff1 value: 42.513945119173414 - type: nauc_map_at_100_max value: 21.824969680261194 - type: nauc_map_at_10_diff1 value: 42.21492283731788 - type: nauc_map_at_10_max value: 21.305888026069674 - type: nauc_map_at_1_diff1 value: 47.62145355083881 - type: nauc_map_at_1_max value: 22.35827304798013 - type: nauc_map_at_20_diff1 value: 42.31982757448588 - type: nauc_map_at_20_max value: 21.594656622891048 - type: nauc_map_at_3_diff1 value: 44.10607386887907 - type: nauc_map_at_3_max value: 21.690680453680425 - type: nauc_map_at_5_diff1 value: 43.24911634980367 - type: nauc_map_at_5_max value: 21.736719675567752 - type: nauc_mrr_at_1000_diff1 value: 41.98881956610886 - type: nauc_mrr_at_1000_max value: 23.673388697614747 - type: nauc_mrr_at_100_diff1 value: 41.975077881853366 - type: nauc_mrr_at_100_max value: 23.680855488904697 - type: nauc_mrr_at_10_diff1 value: 41.753512191582516 - type: nauc_mrr_at_10_max value: 23.286885884623786 - type: nauc_mrr_at_1_diff1 value: 48.01121917180329 - type: nauc_mrr_at_1_max value: 25.91040117459629 - type: nauc_mrr_at_20_diff1 value: 41.837798974871795 - type: nauc_mrr_at_20_max value: 23.53887919859698 - type: nauc_mrr_at_3_diff1 value: 43.74425619417245 - type: nauc_mrr_at_3_max value: 23.80181072142051 - type: nauc_mrr_at_5_diff1 value: 42.77128789582419 - type: nauc_mrr_at_5_max value: 23.78994160229315 - type: nauc_ndcg_at_1000_diff1 value: 40.4038817214834 - type: nauc_ndcg_at_1000_max value: 22.308549183052513 - type: nauc_ndcg_at_100_diff1 value: 40.55678288183828 - type: nauc_ndcg_at_100_max value: 22.609367205269443 - type: nauc_ndcg_at_10_diff1 value: 38.83098871853759 - type: nauc_ndcg_at_10_max value: 20.68362628733941 - type: nauc_ndcg_at_1_diff1 value: 48.01121917180329 - type: nauc_ndcg_at_1_max value: 25.91040117459629 - type: nauc_ndcg_at_20_diff1 value: 39.061663618713894 - type: nauc_ndcg_at_20_max value: 21.476419663219456 - type: nauc_ndcg_at_3_diff1 value: 42.736087127795955 - type: nauc_ndcg_at_3_max value: 21.742127165660058 - type: nauc_ndcg_at_5_diff1 value: 41.186966297966734 - type: nauc_ndcg_at_5_max value: 21.759401429767212 - type: nauc_precision_at_1000_diff1 value: 6.654559938649311 - type: nauc_precision_at_1000_max value: 16.806910891601543 - type: nauc_precision_at_100_diff1 value: 25.864492780814064 - type: nauc_precision_at_100_max value: 25.263440890575012 - type: nauc_precision_at_10_diff1 value: 28.182469153166974 - type: nauc_precision_at_10_max value: 21.10173854858086 - type: nauc_precision_at_1_diff1 value: 48.01121917180329 - type: nauc_precision_at_1_max value: 25.91040117459629 - type: nauc_precision_at_20_diff1 value: 26.16409861031152 - type: nauc_precision_at_20_max value: 22.589571974868473 - type: nauc_precision_at_3_diff1 value: 39.49309649649902 - type: nauc_precision_at_3_max value: 23.66194846956826 - type: nauc_precision_at_5_diff1 value: 35.47688709673743 - type: nauc_precision_at_5_max value: 23.5888265356714 - type: nauc_recall_at_1000_diff1 value: 28.057334771322758 - type: nauc_recall_at_1000_max value: 17.48633214718912 - type: nauc_recall_at_100_diff1 value: 35.67263027900714 - type: nauc_recall_at_100_max value: 23.115839579250103 - type: nauc_recall_at_10_diff1 value: 28.261498615045998 - type: nauc_recall_at_10_max value: 16.20575819609654 - type: nauc_recall_at_1_diff1 value: 47.62145355083881 - type: nauc_recall_at_1_max value: 22.35827304798013 - type: nauc_recall_at_20_diff1 value: 28.255566253430192 - type: nauc_recall_at_20_max value: 18.257219460506295 - type: nauc_recall_at_3_diff1 value: 39.30800774709927 - type: nauc_recall_at_3_max value: 19.810995082445473 - type: nauc_recall_at_5_diff1 value: 35.27158910591411 - type: nauc_recall_at_5_max value: 19.678077623550937 - type: ndcg_at_1 value: 22.373 - type: ndcg_at_10 value: 31.918000000000003 - type: ndcg_at_100 value: 36.992000000000004 - type: ndcg_at_1000 value: 39.513 - type: ndcg_at_20 value: 33.983999999999995 - type: ndcg_at_3 value: 26.832 - type: ndcg_at_5 value: 29.078 - type: precision_at_1 value: 22.373 - type: precision_at_10 value: 5.04 - type: precision_at_100 value: 0.7979999999999999 - type: precision_at_1000 value: 0.106 - type: precision_at_20 value: 3.0 - type: precision_at_3 value: 11.299 - type: precision_at_5 value: 8.045 - type: recall_at_1 value: 20.618 - type: recall_at_10 value: 44.202000000000005 - type: recall_at_100 value: 67.242 - type: recall_at_1000 value: 86.69200000000001 - type: recall_at_20 value: 52.03 - type: recall_at_3 value: 30.386000000000003 - type: recall_at_5 value: 35.858000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 12.731 - type: map_at_10 value: 19.054 - type: map_at_100 value: 20.313 - type: map_at_1000 value: 20.443 - type: map_at_20 value: 19.77 - type: map_at_3 value: 16.596 - type: map_at_5 value: 18.013 - type: mrr_at_1 value: 15.796019900497512 - type: mrr_at_10 value: 22.789327173655526 - type: mrr_at_100 value: 23.862539649948847 - type: mrr_at_1000 value: 23.946663050199312 - type: mrr_at_20 value: 23.427143696525313 - type: mrr_at_3 value: 20.27363184079603 - type: mrr_at_5 value: 21.68532338308458 - type: nauc_map_at_1000_diff1 value: 27.516068477843454 - type: nauc_map_at_1000_max value: 17.80187067129459 - type: nauc_map_at_100_diff1 value: 27.49088157159594 - type: nauc_map_at_100_max value: 17.78426299327126 - type: nauc_map_at_10_diff1 value: 27.322013804309574 - type: nauc_map_at_10_max value: 17.001651979041277 - type: nauc_map_at_1_diff1 value: 32.676819886304166 - type: nauc_map_at_1_max value: 15.203042726400561 - type: nauc_map_at_20_diff1 value: 27.44288664011662 - type: nauc_map_at_20_max value: 17.908350138714276 - type: nauc_map_at_3_diff1 value: 28.50114932717826 - type: nauc_map_at_3_max value: 17.780823694386235 - type: nauc_map_at_5_diff1 value: 27.86215762055489 - type: nauc_map_at_5_max value: 17.50773539133613 - type: nauc_mrr_at_1000_diff1 value: 29.947843223207236 - type: nauc_mrr_at_1000_max value: 19.62172810233295 - type: nauc_mrr_at_100_diff1 value: 29.9288142137001 - type: nauc_mrr_at_100_max value: 19.629003114636255 - type: nauc_mrr_at_10_diff1 value: 29.97657648240847 - type: nauc_mrr_at_10_max value: 19.194295823726197 - type: nauc_mrr_at_1_diff1 value: 35.00554412354239 - type: nauc_mrr_at_1_max value: 17.759999184794772 - type: nauc_mrr_at_20_diff1 value: 29.96168512518019 - type: nauc_mrr_at_20_max value: 19.812693338679974 - type: nauc_mrr_at_3_diff1 value: 31.869293054331997 - type: nauc_mrr_at_3_max value: 19.72221933712261 - type: nauc_mrr_at_5_diff1 value: 30.633662242516408 - type: nauc_mrr_at_5_max value: 19.633065520422832 - type: nauc_ndcg_at_1000_diff1 value: 26.41309716877246 - type: nauc_ndcg_at_1000_max value: 19.407030290375477 - type: nauc_ndcg_at_100_diff1 value: 26.033991008430068 - type: nauc_ndcg_at_100_max value: 19.18116285140471 - type: nauc_ndcg_at_10_diff1 value: 25.58417445038125 - type: nauc_ndcg_at_10_max value: 17.264882794530223 - type: nauc_ndcg_at_1_diff1 value: 35.00554412354239 - type: nauc_ndcg_at_1_max value: 17.759999184794772 - type: nauc_ndcg_at_20_diff1 value: 25.93407473459688 - type: nauc_ndcg_at_20_max value: 19.950029090611025 - type: nauc_ndcg_at_3_diff1 value: 28.72061546564716 - type: nauc_ndcg_at_3_max value: 19.386795976250635 - type: nauc_ndcg_at_5_diff1 value: 27.154487593736675 - type: nauc_ndcg_at_5_max value: 18.600609597997746 - type: nauc_precision_at_1000_diff1 value: 5.41924757448531 - type: nauc_precision_at_1000_max value: 6.545740061131494 - type: nauc_precision_at_100_diff1 value: 14.592825976137824 - type: nauc_precision_at_100_max value: 14.125640563802245 - type: nauc_precision_at_10_diff1 value: 21.4491651411123 - type: nauc_precision_at_10_max value: 16.9551658679841 - type: nauc_precision_at_1_diff1 value: 35.00554412354239 - type: nauc_precision_at_1_max value: 17.759999184794772 - type: nauc_precision_at_20_diff1 value: 19.92971906917106 - type: nauc_precision_at_20_max value: 23.22690053316326 - type: nauc_precision_at_3_diff1 value: 27.57959149246176 - type: nauc_precision_at_3_max value: 22.093284431161333 - type: nauc_precision_at_5_diff1 value: 25.25496908645805 - type: nauc_precision_at_5_max value: 20.458763176343208 - type: nauc_recall_at_1000_diff1 value: 16.984282437643287 - type: nauc_recall_at_1000_max value: 24.737697260268117 - type: nauc_recall_at_100_diff1 value: 17.950878545274918 - type: nauc_recall_at_100_max value: 19.837467082624126 - type: nauc_recall_at_10_diff1 value: 18.161945687752247 - type: nauc_recall_at_10_max value: 14.97915128596929 - type: nauc_recall_at_1_diff1 value: 32.676819886304166 - type: nauc_recall_at_1_max value: 15.203042726400561 - type: nauc_recall_at_20_diff1 value: 19.155358112421517 - type: nauc_recall_at_20_max value: 22.374680334603898 - type: nauc_recall_at_3_diff1 value: 24.842029532917927 - type: nauc_recall_at_3_max value: 20.135627867318494 - type: nauc_recall_at_5_diff1 value: 22.00729745995486 - type: nauc_recall_at_5_max value: 18.21612524182701 - type: ndcg_at_1 value: 15.796 - type: ndcg_at_10 value: 23.528 - type: ndcg_at_100 value: 29.537000000000003 - type: ndcg_at_1000 value: 32.719 - type: ndcg_at_20 value: 25.935000000000002 - type: ndcg_at_3 value: 18.908 - type: ndcg_at_5 value: 21.154 - type: precision_at_1 value: 15.796 - type: precision_at_10 value: 4.515000000000001 - type: precision_at_100 value: 0.8789999999999999 - type: precision_at_1000 value: 0.129 - type: precision_at_20 value: 2.942 - type: precision_at_3 value: 9.163 - type: precision_at_5 value: 7.04 - type: recall_at_1 value: 12.731 - type: recall_at_10 value: 33.797 - type: recall_at_100 value: 59.914 - type: recall_at_1000 value: 82.718 - type: recall_at_20 value: 42.347 - type: recall_at_3 value: 20.923 - type: recall_at_5 value: 26.71 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: mteb/cqadupstack-physics config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 23.567 - type: map_at_10 value: 32.628 - type: map_at_100 value: 33.908 - type: map_at_1000 value: 34.039 - type: map_at_20 value: 33.363 - type: map_at_3 value: 29.726999999999997 - type: map_at_5 value: 31.347 - type: mrr_at_1 value: 29.25890279114533 - type: mrr_at_10 value: 38.079960890355494 - type: mrr_at_100 value: 38.97514464689193 - type: mrr_at_1000 value: 39.038125614768354 - type: mrr_at_20 value: 38.620932209488565 - type: mrr_at_3 value: 35.65928777670837 - type: mrr_at_5 value: 37.00673724735321 - type: nauc_map_at_1000_diff1 value: 44.243962711843174 - type: nauc_map_at_1000_max value: 26.557934640504595 - type: nauc_map_at_100_diff1 value: 44.24191729725104 - type: nauc_map_at_100_max value: 26.520477564732385 - type: nauc_map_at_10_diff1 value: 44.33869154968317 - type: nauc_map_at_10_max value: 26.044484632871434 - type: nauc_map_at_1_diff1 value: 50.15813855147419 - type: nauc_map_at_1_max value: 26.303389987904445 - type: nauc_map_at_20_diff1 value: 44.3113665446356 - type: nauc_map_at_20_max value: 26.237662556133813 - type: nauc_map_at_3_diff1 value: 45.85173282565928 - type: nauc_map_at_3_max value: 26.32504035565671 - type: nauc_map_at_5_diff1 value: 44.78643814548486 - type: nauc_map_at_5_max value: 26.334504875414634 - type: nauc_mrr_at_1000_diff1 value: 43.626249945153624 - type: nauc_mrr_at_1000_max value: 29.330289291530644 - type: nauc_mrr_at_100_diff1 value: 43.613407635792015 - type: nauc_mrr_at_100_max value: 29.319268635273986 - type: nauc_mrr_at_10_diff1 value: 43.63724190566422 - type: nauc_mrr_at_10_max value: 29.108055344568847 - type: nauc_mrr_at_1_diff1 value: 48.217336788734755 - type: nauc_mrr_at_1_max value: 30.672813296466302 - type: nauc_mrr_at_20_diff1 value: 43.649017818875166 - type: nauc_mrr_at_20_max value: 29.261304940945127 - type: nauc_mrr_at_3_diff1 value: 44.675792519491715 - type: nauc_mrr_at_3_max value: 29.675911336957483 - type: nauc_mrr_at_5_diff1 value: 43.64775996596029 - type: nauc_mrr_at_5_max value: 29.45182353499564 - type: nauc_ndcg_at_1000_diff1 value: 41.87489199354678 - type: nauc_ndcg_at_1000_max value: 27.93893077509421 - type: nauc_ndcg_at_100_diff1 value: 41.670343791634906 - type: nauc_ndcg_at_100_max value: 27.313715056723876 - type: nauc_ndcg_at_10_diff1 value: 41.85016751613856 - type: nauc_ndcg_at_10_max value: 25.643066472480765 - type: nauc_ndcg_at_1_diff1 value: 48.217336788734755 - type: nauc_ndcg_at_1_max value: 30.672813296466302 - type: nauc_ndcg_at_20_diff1 value: 41.97037963181627 - type: nauc_ndcg_at_20_max value: 26.33944171406708 - type: nauc_ndcg_at_3_diff1 value: 44.06711834714099 - type: nauc_ndcg_at_3_max value: 27.34491521639161 - type: nauc_ndcg_at_5_diff1 value: 42.4168573468611 - type: nauc_ndcg_at_5_max value: 26.65793931965115 - type: nauc_precision_at_1000_diff1 value: -9.551422528655461 - type: nauc_precision_at_1000_max value: 8.34835764204442 - type: nauc_precision_at_100_diff1 value: 2.2233830685766245 - type: nauc_precision_at_100_max value: 18.020691836598584 - type: nauc_precision_at_10_diff1 value: 19.325743761791916 - type: nauc_precision_at_10_max value: 23.679007985508786 - type: nauc_precision_at_1_diff1 value: 48.217336788734755 - type: nauc_precision_at_1_max value: 30.672813296466302 - type: nauc_precision_at_20_diff1 value: 13.87527519424572 - type: nauc_precision_at_20_max value: 22.302645068156657 - type: nauc_precision_at_3_diff1 value: 33.05090446279134 - type: nauc_precision_at_3_max value: 29.389174313703947 - type: nauc_precision_at_5_diff1 value: 25.75562225572127 - type: nauc_precision_at_5_max value: 27.147828437597372 - type: nauc_recall_at_1000_diff1 value: 19.621088665598236 - type: nauc_recall_at_1000_max value: 30.43205196145353 - type: nauc_recall_at_100_diff1 value: 27.23232029826097 - type: nauc_recall_at_100_max value: 22.14067215503966 - type: nauc_recall_at_10_diff1 value: 33.10443747704974 - type: nauc_recall_at_10_max value: 19.41308822202282 - type: nauc_recall_at_1_diff1 value: 50.15813855147419 - type: nauc_recall_at_1_max value: 26.303389987904445 - type: nauc_recall_at_20_diff1 value: 32.276483197865936 - type: nauc_recall_at_20_max value: 20.72725151323571 - type: nauc_recall_at_3_diff1 value: 40.22031270566891 - type: nauc_recall_at_3_max value: 23.9301104444151 - type: nauc_recall_at_5_diff1 value: 35.98209271954092 - type: nauc_recall_at_5_max value: 22.83878482624863 - type: ndcg_at_1 value: 29.259 - type: ndcg_at_10 value: 38.207 - type: ndcg_at_100 value: 43.711 - type: ndcg_at_1000 value: 46.341 - type: ndcg_at_20 value: 40.498 - type: ndcg_at_3 value: 33.532000000000004 - type: ndcg_at_5 value: 35.69 - type: precision_at_1 value: 29.259 - type: precision_at_10 value: 7.007 - type: precision_at_100 value: 1.1560000000000001 - type: precision_at_1000 value: 0.158 - type: precision_at_20 value: 4.244 - type: precision_at_3 value: 16.041 - type: precision_at_5 value: 11.511000000000001 - type: recall_at_1 value: 23.567 - type: recall_at_10 value: 49.523 - type: recall_at_100 value: 72.562 - type: recall_at_1000 value: 90.178 - type: recall_at_20 value: 57.621 - type: recall_at_3 value: 36.282 - type: recall_at_5 value: 41.921 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 22.266 - type: map_at_10 value: 30.25 - type: map_at_100 value: 31.581 - type: map_at_1000 value: 31.704 - type: map_at_20 value: 30.952 - type: map_at_3 value: 27.466 - type: map_at_5 value: 29.072 - type: mrr_at_1 value: 27.397260273972602 - type: mrr_at_10 value: 35.2599296948612 - type: mrr_at_100 value: 36.24881323819273 - type: mrr_at_1000 value: 36.31077886612844 - type: mrr_at_20 value: 35.848406062858004 - type: mrr_at_3 value: 32.591324200913256 - type: mrr_at_5 value: 34.235159817351594 - type: nauc_map_at_1000_diff1 value: 49.041338317944216 - type: nauc_map_at_1000_max value: 38.50873723942883 - type: nauc_map_at_100_diff1 value: 49.01701126534856 - type: nauc_map_at_100_max value: 38.49295698329094 - type: nauc_map_at_10_diff1 value: 49.095813348188166 - type: nauc_map_at_10_max value: 37.90864503064915 - type: nauc_map_at_1_diff1 value: 55.75650937633808 - type: nauc_map_at_1_max value: 36.45803206536568 - type: nauc_map_at_20_diff1 value: 48.88486278259804 - type: nauc_map_at_20_max value: 38.234576284979276 - type: nauc_map_at_3_diff1 value: 50.800510951074344 - type: nauc_map_at_3_max value: 36.75190407865029 - type: nauc_map_at_5_diff1 value: 49.60838604964711 - type: nauc_map_at_5_max value: 37.32035047604114 - type: nauc_mrr_at_1000_diff1 value: 49.13411044876944 - type: nauc_mrr_at_1000_max value: 38.97006615081024 - type: nauc_mrr_at_100_diff1 value: 49.11706960503639 - type: nauc_mrr_at_100_max value: 38.96559788105358 - type: nauc_mrr_at_10_diff1 value: 49.092123992814116 - type: nauc_mrr_at_10_max value: 38.94728645893312 - type: nauc_mrr_at_1_diff1 value: 55.47287529444724 - type: nauc_mrr_at_1_max value: 40.293546568686224 - type: nauc_mrr_at_20_diff1 value: 48.96467402915927 - type: nauc_mrr_at_20_max value: 38.86612256286537 - type: nauc_mrr_at_3_diff1 value: 50.69348233488136 - type: nauc_mrr_at_3_max value: 39.07374242862782 - type: nauc_mrr_at_5_diff1 value: 49.48713462272688 - type: nauc_mrr_at_5_max value: 38.903556289495874 - type: nauc_ndcg_at_1000_diff1 value: 46.865532935814144 - type: nauc_ndcg_at_1000_max value: 39.54745630513795 - type: nauc_ndcg_at_100_diff1 value: 46.320278315069814 - type: nauc_ndcg_at_100_max value: 39.38111071082402 - type: nauc_ndcg_at_10_diff1 value: 46.21493444038667 - type: nauc_ndcg_at_10_max value: 38.21912668950852 - type: nauc_ndcg_at_1_diff1 value: 55.47287529444724 - type: nauc_ndcg_at_1_max value: 40.293546568686224 - type: nauc_ndcg_at_20_diff1 value: 45.64094089105446 - type: nauc_ndcg_at_20_max value: 38.59596868552488 - type: nauc_ndcg_at_3_diff1 value: 49.016415433673835 - type: nauc_ndcg_at_3_max value: 37.89533426315243 - type: nauc_ndcg_at_5_diff1 value: 47.20788719798163 - type: nauc_ndcg_at_5_max value: 37.682560665048904 - type: nauc_precision_at_1000_diff1 value: -7.359826953607673 - type: nauc_precision_at_1000_max value: 5.6412152804640066 - type: nauc_precision_at_100_diff1 value: 4.466458911297046 - type: nauc_precision_at_100_max value: 24.578741906158726 - type: nauc_precision_at_10_diff1 value: 22.359709568967506 - type: nauc_precision_at_10_max value: 36.47969015950308 - type: nauc_precision_at_1_diff1 value: 55.47287529444724 - type: nauc_precision_at_1_max value: 40.293546568686224 - type: nauc_precision_at_20_diff1 value: 14.120893949469135 - type: nauc_precision_at_20_max value: 34.249667264582534 - type: nauc_precision_at_3_diff1 value: 37.7007086171551 - type: nauc_precision_at_3_max value: 37.95445662666999 - type: nauc_precision_at_5_diff1 value: 29.715009411494712 - type: nauc_precision_at_5_max value: 36.89274409767293 - type: nauc_recall_at_1000_diff1 value: 32.33111662036445 - type: nauc_recall_at_1000_max value: 45.35170430166642 - type: nauc_recall_at_100_diff1 value: 32.144354498328035 - type: nauc_recall_at_100_max value: 36.84062501935607 - type: nauc_recall_at_10_diff1 value: 36.14633959446269 - type: nauc_recall_at_10_max value: 35.448585836721506 - type: nauc_recall_at_1_diff1 value: 55.75650937633808 - type: nauc_recall_at_1_max value: 36.45803206536568 - type: nauc_recall_at_20_diff1 value: 32.97579259309187 - type: nauc_recall_at_20_max value: 35.23418118770078 - type: nauc_recall_at_3_diff1 value: 44.664415627999816 - type: nauc_recall_at_3_max value: 34.31461153552717 - type: nauc_recall_at_5_diff1 value: 40.19780197689489 - type: nauc_recall_at_5_max value: 34.5962677637341 - type: ndcg_at_1 value: 27.397 - type: ndcg_at_10 value: 35.443999999999996 - type: ndcg_at_100 value: 41.429 - type: ndcg_at_1000 value: 44.059 - type: ndcg_at_20 value: 37.714999999999996 - type: ndcg_at_3 value: 30.679000000000002 - type: ndcg_at_5 value: 32.992 - type: precision_at_1 value: 27.397 - type: precision_at_10 value: 6.5409999999999995 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.151 - type: precision_at_20 value: 3.961 - type: precision_at_3 value: 14.536 - type: precision_at_5 value: 10.685 - type: recall_at_1 value: 22.266 - type: recall_at_10 value: 46.071 - type: recall_at_100 value: 72.064 - type: recall_at_1000 value: 90.038 - type: recall_at_20 value: 54.342999999999996 - type: recall_at_3 value: 32.926 - type: recall_at_5 value: 38.75 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 17.261000000000003 - type: map_at_10 value: 23.753 - type: map_at_100 value: 24.627 - type: map_at_1000 value: 24.735 - type: map_at_20 value: 24.274 - type: map_at_3 value: 21.413 - type: map_at_5 value: 22.745 - type: mrr_at_1 value: 19.32515337423313 - type: mrr_at_10 value: 25.910081312688664 - type: mrr_at_100 value: 26.732545270907544 - type: mrr_at_1000 value: 26.81958074650717 - type: mrr_at_20 value: 26.38483873098964 - type: mrr_at_3 value: 23.77300613496933 - type: mrr_at_5 value: 25.02300613496933 - type: nauc_map_at_1000_diff1 value: 51.2509463814842 - type: nauc_map_at_1000_max value: 34.59378039517527 - type: nauc_map_at_100_diff1 value: 51.20195960756142 - type: nauc_map_at_100_max value: 34.52292810417864 - type: nauc_map_at_10_diff1 value: 50.971047162244375 - type: nauc_map_at_10_max value: 34.42837100976023 - type: nauc_map_at_1_diff1 value: 61.66057666415862 - type: nauc_map_at_1_max value: 34.54325674205874 - type: nauc_map_at_20_diff1 value: 51.16950921599598 - type: nauc_map_at_20_max value: 34.50836855076594 - type: nauc_map_at_3_diff1 value: 52.980211175481394 - type: nauc_map_at_3_max value: 34.10535134653065 - type: nauc_map_at_5_diff1 value: 51.825290665802 - type: nauc_map_at_5_max value: 34.48591848937056 - type: nauc_mrr_at_1000_diff1 value: 51.50014502932111 - type: nauc_mrr_at_1000_max value: 36.80362520167512 - type: nauc_mrr_at_100_diff1 value: 51.447470381911685 - type: nauc_mrr_at_100_max value: 36.776788558968704 - type: nauc_mrr_at_10_diff1 value: 51.264885407403696 - type: nauc_mrr_at_10_max value: 36.93350671984603 - type: nauc_mrr_at_1_diff1 value: 60.877750778528494 - type: nauc_mrr_at_1_max value: 36.49057984523738 - type: nauc_mrr_at_20_diff1 value: 51.3534499982496 - type: nauc_mrr_at_20_max value: 36.84780620387409 - type: nauc_mrr_at_3_diff1 value: 53.30071892113097 - type: nauc_mrr_at_3_max value: 36.820559680318546 - type: nauc_mrr_at_5_diff1 value: 52.220386246212556 - type: nauc_mrr_at_5_max value: 37.04291739287823 - type: nauc_ndcg_at_1000_diff1 value: 48.42608193180114 - type: nauc_ndcg_at_1000_max value: 35.93812099772579 - type: nauc_ndcg_at_100_diff1 value: 47.5791516869875 - type: nauc_ndcg_at_100_max value: 34.85361305271241 - type: nauc_ndcg_at_10_diff1 value: 46.85004446008741 - type: nauc_ndcg_at_10_max value: 34.62550268395681 - type: nauc_ndcg_at_1_diff1 value: 60.877750778528494 - type: nauc_ndcg_at_1_max value: 36.49057984523738 - type: nauc_ndcg_at_20_diff1 value: 47.301675307241545 - type: nauc_ndcg_at_20_max value: 34.762713095272225 - type: nauc_ndcg_at_3_diff1 value: 50.570168102906564 - type: nauc_ndcg_at_3_max value: 35.019669654163586 - type: nauc_ndcg_at_5_diff1 value: 48.66877986875303 - type: nauc_ndcg_at_5_max value: 35.01212166467292 - type: nauc_precision_at_1000_diff1 value: 14.228081363546169 - type: nauc_precision_at_1000_max value: 32.18702497143084 - type: nauc_precision_at_100_diff1 value: 27.494269464828974 - type: nauc_precision_at_100_max value: 37.41573760452751 - type: nauc_precision_at_10_diff1 value: 33.933451544379366 - type: nauc_precision_at_10_max value: 38.49427569486423 - type: nauc_precision_at_1_diff1 value: 60.877750778528494 - type: nauc_precision_at_1_max value: 36.49057984523738 - type: nauc_precision_at_20_diff1 value: 34.397803404800605 - type: nauc_precision_at_20_max value: 40.15514058102005 - type: nauc_precision_at_3_diff1 value: 42.88433793638738 - type: nauc_precision_at_3_max value: 38.4764975067788 - type: nauc_precision_at_5_diff1 value: 38.93369587658407 - type: nauc_precision_at_5_max value: 39.456916993900585 - type: nauc_recall_at_1000_diff1 value: 37.19758635716514 - type: nauc_recall_at_1000_max value: 36.93465372531077 - type: nauc_recall_at_100_diff1 value: 35.404949235194174 - type: nauc_recall_at_100_max value: 30.630300224996066 - type: nauc_recall_at_10_diff1 value: 34.702045929932055 - type: nauc_recall_at_10_max value: 31.534616746827915 - type: nauc_recall_at_1_diff1 value: 61.66057666415862 - type: nauc_recall_at_1_max value: 34.54325674205874 - type: nauc_recall_at_20_diff1 value: 35.24947576154629 - type: nauc_recall_at_20_max value: 31.041888309997695 - type: nauc_recall_at_3_diff1 value: 43.141135363012054 - type: nauc_recall_at_3_max value: 31.535167376189584 - type: nauc_recall_at_5_diff1 value: 38.72810643136954 - type: nauc_recall_at_5_max value: 32.01182215240314 - type: ndcg_at_1 value: 19.325 - type: ndcg_at_10 value: 27.722 - type: ndcg_at_100 value: 32.0 - type: ndcg_at_1000 value: 34.77 - type: ndcg_at_20 value: 29.465000000000003 - type: ndcg_at_3 value: 23.341 - type: ndcg_at_5 value: 25.529000000000003 - type: precision_at_1 value: 19.325 - type: precision_at_10 value: 4.601 - type: precision_at_100 value: 0.721 - type: precision_at_1000 value: 0.104 - type: precision_at_20 value: 2.692 - type: precision_at_3 value: 10.327 - type: precision_at_5 value: 7.515 - type: recall_at_1 value: 17.261000000000003 - type: recall_at_10 value: 37.802 - type: recall_at_100 value: 57.166 - type: recall_at_1000 value: 77.469 - type: recall_at_20 value: 44.318999999999996 - type: recall_at_3 value: 26.116 - type: recall_at_5 value: 31.366 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 12.961 - type: map_at_10 value: 18.740000000000002 - type: map_at_100 value: 19.703 - type: map_at_1000 value: 19.825 - type: map_at_20 value: 19.216 - type: map_at_3 value: 16.694 - type: map_at_5 value: 17.743000000000002 - type: mrr_at_1 value: 16.10461114934618 - type: mrr_at_10 value: 22.051188564437027 - type: mrr_at_100 value: 22.947710833057016 - type: mrr_at_1000 value: 23.031251789042475 - type: mrr_at_20 value: 22.535362926344344 - type: mrr_at_3 value: 20.061940812112866 - type: mrr_at_5 value: 21.092567102546468 - type: nauc_map_at_1000_diff1 value: 28.720484948469466 - type: nauc_map_at_1000_max value: 22.865440767140637 - type: nauc_map_at_100_diff1 value: 28.725034760353314 - type: nauc_map_at_100_max value: 22.84046004129796 - type: nauc_map_at_10_diff1 value: 28.952012695372698 - type: nauc_map_at_10_max value: 22.793975798196286 - type: nauc_map_at_1_diff1 value: 35.53613349593089 - type: nauc_map_at_1_max value: 24.140548014012747 - type: nauc_map_at_20_diff1 value: 28.853451957069336 - type: nauc_map_at_20_max value: 22.799743549101326 - type: nauc_map_at_3_diff1 value: 29.951337425480883 - type: nauc_map_at_3_max value: 22.610756063409553 - type: nauc_map_at_5_diff1 value: 29.37330668286449 - type: nauc_map_at_5_max value: 22.57878266649173 - type: nauc_mrr_at_1000_diff1 value: 27.90192701434291 - type: nauc_mrr_at_1000_max value: 23.7579661122046 - type: nauc_mrr_at_100_diff1 value: 27.900632259474882 - type: nauc_mrr_at_100_max value: 23.75784428285424 - type: nauc_mrr_at_10_diff1 value: 28.00880872779524 - type: nauc_mrr_at_10_max value: 23.798169424406627 - type: nauc_mrr_at_1_diff1 value: 34.309863568911425 - type: nauc_mrr_at_1_max value: 26.916059981932417 - type: nauc_mrr_at_20_diff1 value: 28.043424996676624 - type: nauc_mrr_at_20_max value: 23.783097407351868 - type: nauc_mrr_at_3_diff1 value: 28.872236354185237 - type: nauc_mrr_at_3_max value: 24.10001094600915 - type: nauc_mrr_at_5_diff1 value: 28.431586921893327 - type: nauc_mrr_at_5_max value: 23.793770139983565 - type: nauc_ndcg_at_1000_diff1 value: 25.26133758890153 - type: nauc_ndcg_at_1000_max value: 22.369863581700518 - type: nauc_ndcg_at_100_diff1 value: 25.295918102117653 - type: nauc_ndcg_at_100_max value: 22.19607938223796 - type: nauc_ndcg_at_10_diff1 value: 26.73394941848248 - type: nauc_ndcg_at_10_max value: 22.53565041597461 - type: nauc_ndcg_at_1_diff1 value: 34.309863568911425 - type: nauc_ndcg_at_1_max value: 26.916059981932417 - type: nauc_ndcg_at_20_diff1 value: 26.483879384526325 - type: nauc_ndcg_at_20_max value: 22.37283043808397 - type: nauc_ndcg_at_3_diff1 value: 28.233989865507585 - type: nauc_ndcg_at_3_max value: 23.18337582626765 - type: nauc_ndcg_at_5_diff1 value: 27.586183431281597 - type: nauc_ndcg_at_5_max value: 22.525122228978613 - type: nauc_precision_at_1000_diff1 value: 4.291961797660381 - type: nauc_precision_at_1000_max value: 20.066766200392706 - type: nauc_precision_at_100_diff1 value: 9.25374685617893 - type: nauc_precision_at_100_max value: 23.561539434177973 - type: nauc_precision_at_10_diff1 value: 18.543124835189897 - type: nauc_precision_at_10_max value: 25.99560427639843 - type: nauc_precision_at_1_diff1 value: 34.309863568911425 - type: nauc_precision_at_1_max value: 26.916059981932417 - type: nauc_precision_at_20_diff1 value: 17.32859805675752 - type: nauc_precision_at_20_max value: 25.111647024470713 - type: nauc_precision_at_3_diff1 value: 23.11307218784423 - type: nauc_precision_at_3_max value: 25.43882757760188 - type: nauc_precision_at_5_diff1 value: 21.066799573535427 - type: nauc_precision_at_5_max value: 25.53816237609956 - type: nauc_recall_at_1000_diff1 value: 9.108450047050564 - type: nauc_recall_at_1000_max value: 15.552865366057592 - type: nauc_recall_at_100_diff1 value: 14.425072798063132 - type: nauc_recall_at_100_max value: 17.05584096508452 - type: nauc_recall_at_10_diff1 value: 20.957155461035747 - type: nauc_recall_at_10_max value: 18.77313505623332 - type: nauc_recall_at_1_diff1 value: 35.53613349593089 - type: nauc_recall_at_1_max value: 24.140548014012747 - type: nauc_recall_at_20_diff1 value: 19.96872494547587 - type: nauc_recall_at_20_max value: 18.462760317549197 - type: nauc_recall_at_3_diff1 value: 24.694266156911524 - type: nauc_recall_at_3_max value: 19.640837676020173 - type: nauc_recall_at_5_diff1 value: 23.065469774243972 - type: nauc_recall_at_5_max value: 18.657460685134776 - type: ndcg_at_1 value: 16.105 - type: ndcg_at_10 value: 22.708000000000002 - type: ndcg_at_100 value: 27.653 - type: ndcg_at_1000 value: 30.812 - type: ndcg_at_20 value: 24.346 - type: ndcg_at_3 value: 18.95 - type: ndcg_at_5 value: 20.522000000000002 - type: precision_at_1 value: 16.105 - type: precision_at_10 value: 4.267 - type: precision_at_100 value: 0.799 - type: precision_at_1000 value: 0.124 - type: precision_at_20 value: 2.6100000000000003 - type: precision_at_3 value: 9.049999999999999 - type: precision_at_5 value: 6.593 - type: recall_at_1 value: 12.961 - type: recall_at_10 value: 31.438 - type: recall_at_100 value: 54.129000000000005 - type: recall_at_1000 value: 77.076 - type: recall_at_20 value: 37.518 - type: recall_at_3 value: 20.997 - type: recall_at_5 value: 25.074999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 20.709 - type: map_at_10 value: 28.738999999999997 - type: map_at_100 value: 29.815 - type: map_at_1000 value: 29.932 - type: map_at_20 value: 29.282999999999998 - type: map_at_3 value: 26.441 - type: map_at_5 value: 27.777 - type: mrr_at_1 value: 24.53358208955224 - type: mrr_at_10 value: 32.394463693437544 - type: mrr_at_100 value: 33.350174597946385 - type: mrr_at_1000 value: 33.41993464841955 - type: mrr_at_20 value: 32.94076610467875 - type: mrr_at_3 value: 30.130597014925375 - type: mrr_at_5 value: 31.473880597014904 - type: nauc_map_at_1000_diff1 value: 40.692748340864746 - type: nauc_map_at_1000_max value: 35.57839095914156 - type: nauc_map_at_100_diff1 value: 40.650799378493744 - type: nauc_map_at_100_max value: 35.53795709449845 - type: nauc_map_at_10_diff1 value: 40.89383138365538 - type: nauc_map_at_10_max value: 35.44293342259398 - type: nauc_map_at_1_diff1 value: 49.12072003473875 - type: nauc_map_at_1_max value: 35.88899688359625 - type: nauc_map_at_20_diff1 value: 40.67489507417953 - type: nauc_map_at_20_max value: 35.37903608045856 - type: nauc_map_at_3_diff1 value: 41.5317838231129 - type: nauc_map_at_3_max value: 35.46770908063441 - type: nauc_map_at_5_diff1 value: 40.846545617446004 - type: nauc_map_at_5_max value: 35.14965178055238 - type: nauc_mrr_at_1000_diff1 value: 40.73361687958999 - type: nauc_mrr_at_1000_max value: 37.121713108339534 - type: nauc_mrr_at_100_diff1 value: 40.71129341657058 - type: nauc_mrr_at_100_max value: 37.11517896403668 - type: nauc_mrr_at_10_diff1 value: 40.72473147121323 - type: nauc_mrr_at_10_max value: 37.04589115955753 - type: nauc_mrr_at_1_diff1 value: 48.388878266455734 - type: nauc_mrr_at_1_max value: 37.526360339847045 - type: nauc_mrr_at_20_diff1 value: 40.61982213089854 - type: nauc_mrr_at_20_max value: 37.00491513836514 - type: nauc_mrr_at_3_diff1 value: 41.37485831338118 - type: nauc_mrr_at_3_max value: 37.47176509970741 - type: nauc_mrr_at_5_diff1 value: 40.93161777811511 - type: nauc_mrr_at_5_max value: 37.078286920815906 - type: nauc_ndcg_at_1000_diff1 value: 38.5467813816651 - type: nauc_ndcg_at_1000_max value: 36.596764984052825 - type: nauc_ndcg_at_100_diff1 value: 37.67469746267849 - type: nauc_ndcg_at_100_max value: 35.8208874944717 - type: nauc_ndcg_at_10_diff1 value: 38.66595637217053 - type: nauc_ndcg_at_10_max value: 35.6228257599822 - type: nauc_ndcg_at_1_diff1 value: 48.388878266455734 - type: nauc_ndcg_at_1_max value: 37.526360339847045 - type: nauc_ndcg_at_20_diff1 value: 37.890275853954094 - type: nauc_ndcg_at_20_max value: 35.25047254404629 - type: nauc_ndcg_at_3_diff1 value: 39.87230430483416 - type: nauc_ndcg_at_3_max value: 36.008184210199325 - type: nauc_ndcg_at_5_diff1 value: 38.841236541335206 - type: nauc_ndcg_at_5_max value: 35.192374109201246 - type: nauc_precision_at_1000_diff1 value: 1.657722375056512 - type: nauc_precision_at_1000_max value: 11.706401779440883 - type: nauc_precision_at_100_diff1 value: 10.20061825548431 - type: nauc_precision_at_100_max value: 22.845634742237408 - type: nauc_precision_at_10_diff1 value: 26.632700346478916 - type: nauc_precision_at_10_max value: 32.62334674689399 - type: nauc_precision_at_1_diff1 value: 48.388878266455734 - type: nauc_precision_at_1_max value: 37.526360339847045 - type: nauc_precision_at_20_diff1 value: 20.876173735564592 - type: nauc_precision_at_20_max value: 28.850377091435526 - type: nauc_precision_at_3_diff1 value: 32.025223944269 - type: nauc_precision_at_3_max value: 35.71025859086816 - type: nauc_precision_at_5_diff1 value: 28.967780161302443 - type: nauc_precision_at_5_max value: 33.49195837301289 - type: nauc_recall_at_1000_diff1 value: 23.608841697077036 - type: nauc_recall_at_1000_max value: 41.20735928314203 - type: nauc_recall_at_100_diff1 value: 22.76475282031864 - type: nauc_recall_at_100_max value: 30.663804567546897 - type: nauc_recall_at_10_diff1 value: 31.20793541893715 - type: nauc_recall_at_10_max value: 32.83480866538358 - type: nauc_recall_at_1_diff1 value: 49.12072003473875 - type: nauc_recall_at_1_max value: 35.88899688359625 - type: nauc_recall_at_20_diff1 value: 27.465280423335305 - type: nauc_recall_at_20_max value: 30.40284795095875 - type: nauc_recall_at_3_diff1 value: 34.792488128346164 - type: nauc_recall_at_3_max value: 34.223694348326724 - type: nauc_recall_at_5_diff1 value: 32.528565271564474 - type: nauc_recall_at_5_max value: 32.428759553708744 - type: ndcg_at_1 value: 24.534 - type: ndcg_at_10 value: 33.363 - type: ndcg_at_100 value: 38.737 - type: ndcg_at_1000 value: 41.508 - type: ndcg_at_20 value: 35.288000000000004 - type: ndcg_at_3 value: 29.083 - type: ndcg_at_5 value: 31.212 - type: precision_at_1 value: 24.534 - type: precision_at_10 value: 5.588 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.129 - type: precision_at_20 value: 3.3300000000000005 - type: precision_at_3 value: 13.245999999999999 - type: precision_at_5 value: 9.366 - type: recall_at_1 value: 20.709 - type: recall_at_10 value: 43.924 - type: recall_at_100 value: 67.823 - type: recall_at_1000 value: 87.665 - type: recall_at_20 value: 50.893 - type: recall_at_3 value: 32.175 - type: recall_at_5 value: 37.649 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: mteb/cqadupstack-webmasters config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 22.305 - type: map_at_10 value: 30.379 - type: map_at_100 value: 31.782 - type: map_at_1000 value: 32.012 - type: map_at_20 value: 31.064000000000004 - type: map_at_3 value: 27.881 - type: map_at_5 value: 29.160000000000004 - type: mrr_at_1 value: 27.07509881422925 - type: mrr_at_10 value: 34.71610515088775 - type: mrr_at_100 value: 35.64647402926793 - type: mrr_at_1000 value: 35.72461288830468 - type: mrr_at_20 value: 35.21386515614449 - type: mrr_at_3 value: 32.608695652173914 - type: mrr_at_5 value: 33.73517786561265 - type: nauc_map_at_1000_diff1 value: 40.06942921592567 - type: nauc_map_at_1000_max value: 34.948952917618826 - type: nauc_map_at_100_diff1 value: 40.26652655508838 - type: nauc_map_at_100_max value: 35.05037692834513 - type: nauc_map_at_10_diff1 value: 40.40482595725152 - type: nauc_map_at_10_max value: 34.76994801074602 - type: nauc_map_at_1_diff1 value: 48.449155396082276 - type: nauc_map_at_1_max value: 31.923255733967675 - type: nauc_map_at_20_diff1 value: 40.43121378897672 - type: nauc_map_at_20_max value: 34.955059887164744 - type: nauc_map_at_3_diff1 value: 41.520030101234 - type: nauc_map_at_3_max value: 33.87326916343342 - type: nauc_map_at_5_diff1 value: 40.68085798830698 - type: nauc_map_at_5_max value: 34.52274061079644 - type: nauc_mrr_at_1000_diff1 value: 38.58624602600238 - type: nauc_mrr_at_1000_max value: 36.71589604244066 - type: nauc_mrr_at_100_diff1 value: 38.57954339254479 - type: nauc_mrr_at_100_max value: 36.71451461262756 - type: nauc_mrr_at_10_diff1 value: 38.39778240600376 - type: nauc_mrr_at_10_max value: 36.867440078145805 - type: nauc_mrr_at_1_diff1 value: 45.54773488737558 - type: nauc_mrr_at_1_max value: 35.46157252708776 - type: nauc_mrr_at_20_diff1 value: 38.56226741939672 - type: nauc_mrr_at_20_max value: 36.79076112969171 - type: nauc_mrr_at_3_diff1 value: 39.241048736996326 - type: nauc_mrr_at_3_max value: 36.81497880532945 - type: nauc_mrr_at_5_diff1 value: 38.75938933304581 - type: nauc_mrr_at_5_max value: 36.91112394256869 - type: nauc_ndcg_at_1000_diff1 value: 37.01015933832102 - type: nauc_ndcg_at_1000_max value: 36.14674427038953 - type: nauc_ndcg_at_100_diff1 value: 37.46009355653446 - type: nauc_ndcg_at_100_max value: 36.168362134330415 - type: nauc_ndcg_at_10_diff1 value: 36.87998378155374 - type: nauc_ndcg_at_10_max value: 36.03488979078424 - type: nauc_ndcg_at_1_diff1 value: 45.54773488737558 - type: nauc_ndcg_at_1_max value: 35.46157252708776 - type: nauc_ndcg_at_20_diff1 value: 37.32245335628528 - type: nauc_ndcg_at_20_max value: 35.98153437861986 - type: nauc_ndcg_at_3_diff1 value: 38.4065595992595 - type: nauc_ndcg_at_3_max value: 36.16984761665991 - type: nauc_ndcg_at_5_diff1 value: 37.528041451543274 - type: nauc_ndcg_at_5_max value: 36.29795461312836 - type: nauc_precision_at_1000_diff1 value: -27.028565760553704 - type: nauc_precision_at_1000_max value: -6.211061610108618 - type: nauc_precision_at_100_diff1 value: -11.543495827856747 - type: nauc_precision_at_100_max value: 10.08227744965561 - type: nauc_precision_at_10_diff1 value: 11.91615180702728 - type: nauc_precision_at_10_max value: 31.648399736572237 - type: nauc_precision_at_1_diff1 value: 45.54773488737558 - type: nauc_precision_at_1_max value: 35.46157252708776 - type: nauc_precision_at_20_diff1 value: 7.106796337295673 - type: nauc_precision_at_20_max value: 28.270776285978005 - type: nauc_precision_at_3_diff1 value: 27.025372640430305 - type: nauc_precision_at_3_max value: 37.05993782016582 - type: nauc_precision_at_5_diff1 value: 20.36905717821343 - type: nauc_precision_at_5_max value: 36.78762312900936 - type: nauc_recall_at_1000_diff1 value: 15.327824598428135 - type: nauc_recall_at_1000_max value: 37.388077518454125 - type: nauc_recall_at_100_diff1 value: 26.663273479931682 - type: nauc_recall_at_100_max value: 35.19719455819416 - type: nauc_recall_at_10_diff1 value: 29.457868053419173 - type: nauc_recall_at_10_max value: 34.69858107618685 - type: nauc_recall_at_1_diff1 value: 48.449155396082276 - type: nauc_recall_at_1_max value: 31.923255733967675 - type: nauc_recall_at_20_diff1 value: 28.740287691134785 - type: nauc_recall_at_20_max value: 33.54392173053316 - type: nauc_recall_at_3_diff1 value: 34.36341724443082 - type: nauc_recall_at_3_max value: 34.23281133452072 - type: nauc_recall_at_5_diff1 value: 31.778622196668138 - type: nauc_recall_at_5_max value: 35.09923813897011 - type: ndcg_at_1 value: 27.075 - type: ndcg_at_10 value: 35.35 - type: ndcg_at_100 value: 40.822 - type: ndcg_at_1000 value: 43.961 - type: ndcg_at_20 value: 37.13 - type: ndcg_at_3 value: 31.419000000000004 - type: ndcg_at_5 value: 33.032000000000004 - type: precision_at_1 value: 27.075 - type: precision_at_10 value: 6.64 - type: precision_at_100 value: 1.35 - type: precision_at_1000 value: 0.232 - type: precision_at_20 value: 4.14 - type: precision_at_3 value: 14.427000000000001 - type: precision_at_5 value: 10.435 - type: recall_at_1 value: 22.305 - type: recall_at_10 value: 44.456 - type: recall_at_100 value: 69.57799999999999 - type: recall_at_1000 value: 89.262 - type: recall_at_20 value: 51.434999999999995 - type: recall_at_3 value: 33.141999999999996 - type: recall_at_5 value: 37.51 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: mteb/cqadupstack-wordpress config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 15.723999999999998 - type: map_at_10 value: 22.479 - type: map_at_100 value: 23.494 - type: map_at_1000 value: 23.613 - type: map_at_20 value: 23.043 - type: map_at_3 value: 20.49 - type: map_at_5 value: 21.711 - type: mrr_at_1 value: 17.375231053604438 - type: mrr_at_10 value: 24.391485491300635 - type: mrr_at_100 value: 25.3451706703197 - type: mrr_at_1000 value: 25.4338976938353 - type: mrr_at_20 value: 24.932480156623605 - type: mrr_at_3 value: 22.48921749845963 - type: mrr_at_5 value: 23.62600123228588 - type: nauc_map_at_1000_diff1 value: 26.568452594006768 - type: nauc_map_at_1000_max value: 22.6643108995624 - type: nauc_map_at_100_diff1 value: 26.56713050875225 - type: nauc_map_at_100_max value: 22.72845024690553 - type: nauc_map_at_10_diff1 value: 26.48034872839756 - type: nauc_map_at_10_max value: 22.77864909505566 - type: nauc_map_at_1_diff1 value: 35.16513757522047 - type: nauc_map_at_1_max value: 22.34690217093654 - type: nauc_map_at_20_diff1 value: 26.373663262670444 - type: nauc_map_at_20_max value: 22.587491027571254 - type: nauc_map_at_3_diff1 value: 27.621000302198922 - type: nauc_map_at_3_max value: 22.84661442384809 - type: nauc_map_at_5_diff1 value: 26.765290689478732 - type: nauc_map_at_5_max value: 22.988851260881056 - type: nauc_mrr_at_1000_diff1 value: 27.28527950781967 - type: nauc_mrr_at_1000_max value: 22.818092962601042 - type: nauc_mrr_at_100_diff1 value: 27.29780478860489 - type: nauc_mrr_at_100_max value: 22.85092145520846 - type: nauc_mrr_at_10_diff1 value: 27.245118068210814 - type: nauc_mrr_at_10_max value: 22.93612080353226 - type: nauc_mrr_at_1_diff1 value: 36.22401042267479 - type: nauc_mrr_at_1_max value: 24.1620633176196 - type: nauc_mrr_at_20_diff1 value: 27.10137249046854 - type: nauc_mrr_at_20_max value: 22.74832608433313 - type: nauc_mrr_at_3_diff1 value: 28.803394273224846 - type: nauc_mrr_at_3_max value: 23.58218274270813 - type: nauc_mrr_at_5_diff1 value: 27.548514879068392 - type: nauc_mrr_at_5_max value: 23.202061782986362 - type: nauc_ndcg_at_1000_diff1 value: 24.255610268405004 - type: nauc_ndcg_at_1000_max value: 21.021653182317866 - type: nauc_ndcg_at_100_diff1 value: 24.38035576235643 - type: nauc_ndcg_at_100_max value: 22.01602046149638 - type: nauc_ndcg_at_10_diff1 value: 23.72345010383346 - type: nauc_ndcg_at_10_max value: 22.379426846697886 - type: nauc_ndcg_at_1_diff1 value: 36.22401042267479 - type: nauc_ndcg_at_1_max value: 24.1620633176196 - type: nauc_ndcg_at_20_diff1 value: 23.238204223853767 - type: nauc_ndcg_at_20_max value: 21.524058764754642 - type: nauc_ndcg_at_3_diff1 value: 26.154431437162284 - type: nauc_ndcg_at_3_max value: 23.12477560308262 - type: nauc_ndcg_at_5_diff1 value: 24.381279154864856 - type: nauc_ndcg_at_5_max value: 22.928738776001943 - type: nauc_precision_at_1000_diff1 value: 10.866194934427694 - type: nauc_precision_at_1000_max value: -8.119816513990198 - type: nauc_precision_at_100_diff1 value: 16.347299053203397 - type: nauc_precision_at_100_max value: 13.26292415361133 - type: nauc_precision_at_10_diff1 value: 16.63699688800471 - type: nauc_precision_at_10_max value: 22.375088256427286 - type: nauc_precision_at_1_diff1 value: 36.22401042267479 - type: nauc_precision_at_1_max value: 24.1620633176196 - type: nauc_precision_at_20_diff1 value: 15.555806748912909 - type: nauc_precision_at_20_max value: 18.55637142126297 - type: nauc_precision_at_3_diff1 value: 21.119629681631707 - type: nauc_precision_at_3_max value: 25.238443284915007 - type: nauc_precision_at_5_diff1 value: 18.173398326347908 - type: nauc_precision_at_5_max value: 24.277628318544387 - type: nauc_recall_at_1000_diff1 value: 11.904300629344641 - type: nauc_recall_at_1000_max value: 4.543701587503855 - type: nauc_recall_at_100_diff1 value: 17.873778791471032 - type: nauc_recall_at_100_max value: 18.07160995779775 - type: nauc_recall_at_10_diff1 value: 15.715088403469021 - type: nauc_recall_at_10_max value: 20.285351500657857 - type: nauc_recall_at_1_diff1 value: 35.16513757522047 - type: nauc_recall_at_1_max value: 22.34690217093654 - type: nauc_recall_at_20_diff1 value: 13.584020684215409 - type: nauc_recall_at_20_max value: 16.915404230260844 - type: nauc_recall_at_3_diff1 value: 20.57543835256644 - type: nauc_recall_at_3_max value: 22.257888049364798 - type: nauc_recall_at_5_diff1 value: 17.196563781054497 - type: nauc_recall_at_5_max value: 21.786295860256278 - type: ndcg_at_1 value: 17.375 - type: ndcg_at_10 value: 26.458 - type: ndcg_at_100 value: 31.630999999999997 - type: ndcg_at_1000 value: 34.648 - type: ndcg_at_20 value: 28.429 - type: ndcg_at_3 value: 22.572 - type: ndcg_at_5 value: 24.627 - type: precision_at_1 value: 17.375 - type: precision_at_10 value: 4.3069999999999995 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_20 value: 2.616 - type: precision_at_3 value: 9.982000000000001 - type: precision_at_5 value: 7.172000000000001 - type: recall_at_1 value: 15.723999999999998 - type: recall_at_10 value: 36.848 - type: recall_at_100 value: 60.843 - type: recall_at_1000 value: 83.35900000000001 - type: recall_at_20 value: 44.239 - type: recall_at_3 value: 26.512999999999998 - type: recall_at_5 value: 31.447999999999997 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 7.556 - type: map_at_10 value: 14.451 - type: map_at_100 value: 16.098000000000003 - type: map_at_1000 value: 16.292 - type: map_at_20 value: 15.354000000000001 - type: map_at_3 value: 11.788 - type: map_at_5 value: 13.036 - type: mrr_at_1 value: 17.850162866449512 - type: mrr_at_10 value: 29.02070730572359 - type: mrr_at_100 value: 30.10374653258222 - type: mrr_at_1000 value: 30.159660391788854 - type: mrr_at_20 value: 29.705480653232243 - type: mrr_at_3 value: 25.287730727470116 - type: mrr_at_5 value: 27.437567861020568 - type: nauc_map_at_1000_diff1 value: 19.209527081030096 - type: nauc_map_at_1000_max value: 31.647208883839507 - type: nauc_map_at_100_diff1 value: 19.20806522507485 - type: nauc_map_at_100_max value: 31.548780447276 - type: nauc_map_at_10_diff1 value: 19.169908589166987 - type: nauc_map_at_10_max value: 30.501288768500395 - type: nauc_map_at_1_diff1 value: 26.988959334325852 - type: nauc_map_at_1_max value: 27.356073363716522 - type: nauc_map_at_20_diff1 value: 19.09827492317952 - type: nauc_map_at_20_max value: 31.134688299749186 - type: nauc_map_at_3_diff1 value: 19.934035735585724 - type: nauc_map_at_3_max value: 29.22218051641785 - type: nauc_map_at_5_diff1 value: 19.398656144868713 - type: nauc_map_at_5_max value: 29.045993729549778 - type: nauc_mrr_at_1000_diff1 value: 16.978829558159727 - type: nauc_mrr_at_1000_max value: 27.016129985398962 - type: nauc_mrr_at_100_diff1 value: 16.95693929120996 - type: nauc_mrr_at_100_max value: 27.02464201206241 - type: nauc_mrr_at_10_diff1 value: 16.922383134541786 - type: nauc_mrr_at_10_max value: 26.917342116854172 - type: nauc_mrr_at_1_diff1 value: 21.967275710063323 - type: nauc_mrr_at_1_max value: 23.97730021914779 - type: nauc_mrr_at_20_diff1 value: 16.933125050384778 - type: nauc_mrr_at_20_max value: 27.07768335891788 - type: nauc_mrr_at_3_diff1 value: 16.946763294333316 - type: nauc_mrr_at_3_max value: 25.214811458539 - type: nauc_mrr_at_5_diff1 value: 17.04305756647301 - type: nauc_mrr_at_5_max value: 26.130628979961834 - type: nauc_ndcg_at_1000_diff1 value: 16.986658675773686 - type: nauc_ndcg_at_1000_max value: 34.4643347153785 - type: nauc_ndcg_at_100_diff1 value: 17.057499024976163 - type: nauc_ndcg_at_100_max value: 33.73159453243811 - type: nauc_ndcg_at_10_diff1 value: 16.929966520239194 - type: nauc_ndcg_at_10_max value: 31.301536380836026 - type: nauc_ndcg_at_1_diff1 value: 21.967275710063323 - type: nauc_ndcg_at_1_max value: 23.97730021914779 - type: nauc_ndcg_at_20_diff1 value: 16.900348110026968 - type: nauc_ndcg_at_20_max value: 32.476079344191525 - type: nauc_ndcg_at_3_diff1 value: 17.270453057670856 - type: nauc_ndcg_at_3_max value: 27.75387606914448 - type: nauc_ndcg_at_5_diff1 value: 17.300131450254998 - type: nauc_ndcg_at_5_max value: 28.707766380169097 - type: nauc_precision_at_1000_diff1 value: 2.3756918838598002 - type: nauc_precision_at_1000_max value: 20.23410724169113 - type: nauc_precision_at_100_diff1 value: 6.358801887547644 - type: nauc_precision_at_100_max value: 26.742998434337 - type: nauc_precision_at_10_diff1 value: 8.985726577486592 - type: nauc_precision_at_10_max value: 29.98846164047006 - type: nauc_precision_at_1_diff1 value: 21.967275710063323 - type: nauc_precision_at_1_max value: 23.97730021914779 - type: nauc_precision_at_20_diff1 value: 8.689481678265938 - type: nauc_precision_at_20_max value: 30.24412868451184 - type: nauc_precision_at_3_diff1 value: 11.498289241456895 - type: nauc_precision_at_3_max value: 26.84419245258572 - type: nauc_precision_at_5_diff1 value: 10.894319062565254 - type: nauc_precision_at_5_max value: 27.273788735432884 - type: nauc_recall_at_1000_diff1 value: 8.943592557292224 - type: nauc_recall_at_1000_max value: 37.585654238896446 - type: nauc_recall_at_100_diff1 value: 10.708206895515247 - type: nauc_recall_at_100_max value: 32.10962530348595 - type: nauc_recall_at_10_diff1 value: 12.169794236323957 - type: nauc_recall_at_10_max value: 30.12170288353037 - type: nauc_recall_at_1_diff1 value: 26.988959334325852 - type: nauc_recall_at_1_max value: 27.356073363716522 - type: nauc_recall_at_20_diff1 value: 11.394888526086374 - type: nauc_recall_at_20_max value: 30.72718903844353 - type: nauc_recall_at_3_diff1 value: 15.011650843515994 - type: nauc_recall_at_3_max value: 28.233837827958475 - type: nauc_recall_at_5_diff1 value: 13.739007199689038 - type: nauc_recall_at_5_max value: 27.097220418736455 - type: ndcg_at_1 value: 17.849999999999998 - type: ndcg_at_10 value: 21.712 - type: ndcg_at_100 value: 28.552 - type: ndcg_at_1000 value: 32.261 - type: ndcg_at_20 value: 24.421 - type: ndcg_at_3 value: 16.791 - type: ndcg_at_5 value: 18.462999999999997 - type: precision_at_1 value: 17.849999999999998 - type: precision_at_10 value: 7.212000000000001 - type: precision_at_100 value: 1.438 - type: precision_at_1000 value: 0.212 - type: precision_at_20 value: 4.73 - type: precision_at_3 value: 12.942 - type: precision_at_5 value: 10.280000000000001 - type: recall_at_1 value: 7.556 - type: recall_at_10 value: 27.891 - type: recall_at_100 value: 51.585 - type: recall_at_1000 value: 72.638 - type: recall_at_20 value: 35.644999999999996 - type: recall_at_3 value: 16.026 - type: recall_at_5 value: 20.507 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 7.234 - type: map_at_10 value: 14.607000000000001 - type: map_at_100 value: 20.104 - type: map_at_1000 value: 21.478 - type: map_at_20 value: 16.619999999999997 - type: map_at_3 value: 11.027000000000001 - type: map_at_5 value: 12.469 - type: mrr_at_1 value: 54.25 - type: mrr_at_10 value: 64.63998015873014 - type: mrr_at_100 value: 65.1130930093471 - type: mrr_at_1000 value: 65.13135082056961 - type: mrr_at_20 value: 64.94966038326137 - type: mrr_at_3 value: 62.458333333333336 - type: mrr_at_5 value: 63.845833333333324 - type: nauc_map_at_1000_diff1 value: 22.4158889201391 - type: nauc_map_at_1000_max value: 7.026467662060626 - type: nauc_map_at_100_diff1 value: 23.04636496295622 - type: nauc_map_at_100_max value: 4.725540774086458 - type: nauc_map_at_10_diff1 value: 23.432494495467783 - type: nauc_map_at_10_max value: -5.821110663085555 - type: nauc_map_at_1_diff1 value: 34.840276007257444 - type: nauc_map_at_1_max value: -11.37201527363141 - type: nauc_map_at_20_diff1 value: 24.395490704549474 - type: nauc_map_at_20_max value: -2.1089029956487084 - type: nauc_map_at_3_diff1 value: 26.996333964606727 - type: nauc_map_at_3_max value: -10.371168153982198 - type: nauc_map_at_5_diff1 value: 24.959954478462205 - type: nauc_map_at_5_max value: -8.600893701670593 - type: nauc_mrr_at_1000_diff1 value: 35.24039282463778 - type: nauc_mrr_at_1000_max value: 37.114026096308244 - type: nauc_mrr_at_100_diff1 value: 35.246986246738324 - type: nauc_mrr_at_100_max value: 37.127597625848175 - type: nauc_mrr_at_10_diff1 value: 35.19817679146017 - type: nauc_mrr_at_10_max value: 37.10088394447574 - type: nauc_mrr_at_1_diff1 value: 37.871437973819546 - type: nauc_mrr_at_1_max value: 33.639317316766494 - type: nauc_mrr_at_20_diff1 value: 35.1331593237111 - type: nauc_mrr_at_20_max value: 37.0319775042493 - type: nauc_mrr_at_3_diff1 value: 35.18290669114643 - type: nauc_mrr_at_3_max value: 37.17151353458554 - type: nauc_mrr_at_5_diff1 value: 35.27152644879001 - type: nauc_mrr_at_5_max value: 37.59776931748075 - type: nauc_ndcg_at_1000_diff1 value: 23.265231375797573 - type: nauc_ndcg_at_1000_max value: 19.253303883964247 - type: nauc_ndcg_at_100_diff1 value: 24.65543924960885 - type: nauc_ndcg_at_100_max value: 12.423207189979774 - type: nauc_ndcg_at_10_diff1 value: 22.383661242851076 - type: nauc_ndcg_at_10_max value: 12.11544119539834 - type: nauc_ndcg_at_1_diff1 value: 35.37762392054306 - type: nauc_ndcg_at_1_max value: 24.33308418951577 - type: nauc_ndcg_at_20_diff1 value: 24.56519958043796 - type: nauc_ndcg_at_20_max value: 9.25238387333473 - type: nauc_ndcg_at_3_diff1 value: 24.39638864122631 - type: nauc_ndcg_at_3_max value: 18.095896878796434 - type: nauc_ndcg_at_5_diff1 value: 21.554177625230157 - type: nauc_ndcg_at_5_max value: 14.90300796432758 - type: nauc_precision_at_1000_diff1 value: -14.028751970399872 - type: nauc_precision_at_1000_max value: 22.683829892782335 - type: nauc_precision_at_100_diff1 value: -1.4922684516357194 - type: nauc_precision_at_100_max value: 32.211371870388795 - type: nauc_precision_at_10_diff1 value: 1.3791441135589875 - type: nauc_precision_at_10_max value: 28.329452472562267 - type: nauc_precision_at_1_diff1 value: 37.871437973819546 - type: nauc_precision_at_1_max value: 33.639317316766494 - type: nauc_precision_at_20_diff1 value: 3.1829444563318128 - type: nauc_precision_at_20_max value: 30.79822842458981 - type: nauc_precision_at_3_diff1 value: 9.890760276356035 - type: nauc_precision_at_3_max value: 27.255950486716085 - type: nauc_precision_at_5_diff1 value: 2.835882319987235 - type: nauc_precision_at_5_max value: 27.588094099192865 - type: nauc_recall_at_1000_diff1 value: 11.301016973437319 - type: nauc_recall_at_1000_max value: 13.632028573670441 - type: nauc_recall_at_100_diff1 value: 16.244420258674484 - type: nauc_recall_at_100_max value: 5.252228595283477 - type: nauc_recall_at_10_diff1 value: 17.14009149723741 - type: nauc_recall_at_10_max value: -8.886638909096206 - type: nauc_recall_at_1_diff1 value: 34.840276007257444 - type: nauc_recall_at_1_max value: -11.37201527363141 - type: nauc_recall_at_20_diff1 value: 18.393774547280316 - type: nauc_recall_at_20_max value: -5.756994115048744 - type: nauc_recall_at_3_diff1 value: 23.65687656688717 - type: nauc_recall_at_3_max value: -11.646229125385862 - type: nauc_recall_at_5_diff1 value: 21.02934437742109 - type: nauc_recall_at_5_max value: -9.305597108185982 - type: ndcg_at_1 value: 42.625 - type: ndcg_at_10 value: 32.005 - type: ndcg_at_100 value: 36.563 - type: ndcg_at_1000 value: 44.207 - type: ndcg_at_20 value: 31.608999999999998 - type: ndcg_at_3 value: 35.949999999999996 - type: ndcg_at_5 value: 33.375 - type: precision_at_1 value: 54.25 - type: precision_at_10 value: 25.650000000000002 - type: precision_at_100 value: 8.260000000000002 - type: precision_at_1000 value: 1.806 - type: precision_at_20 value: 18.9 - type: precision_at_3 value: 39.833 - type: precision_at_5 value: 32.7 - type: recall_at_1 value: 7.234 - type: recall_at_10 value: 20.075000000000003 - type: recall_at_100 value: 43.980999999999995 - type: recall_at_1000 value: 68.527 - type: recall_at_20 value: 26.251 - type: recall_at_3 value: 12.534999999999998 - type: recall_at_5 value: 15.121 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 40.81 - type: f1 value: 36.53895095274932 - type: f1_weighted value: 43.09824575802351 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 45.399 - type: map_at_10 value: 59.345000000000006 - type: map_at_100 value: 59.821000000000005 - type: map_at_1000 value: 59.841 - type: map_at_20 value: 59.662000000000006 - type: map_at_3 value: 56.577 - type: map_at_5 value: 58.384 - type: mrr_at_1 value: 48.7998799879988 - type: mrr_at_10 value: 63.182490868134714 - type: mrr_at_100 value: 63.571831061553496 - type: mrr_at_1000 value: 63.58053777600791 - type: mrr_at_20 value: 63.45420825510186 - type: mrr_at_3 value: 60.45604560456103 - type: mrr_at_5 value: 62.25322532253252 - type: nauc_map_at_1000_diff1 value: 35.07017933142576 - type: nauc_map_at_1000_max value: 8.523823797002448 - type: nauc_map_at_100_diff1 value: 35.06363318835806 - type: nauc_map_at_100_max value: 8.522323239837585 - type: nauc_map_at_10_diff1 value: 34.99069002859329 - type: nauc_map_at_10_max value: 8.635643511853687 - type: nauc_map_at_1_diff1 value: 38.063117939510256 - type: nauc_map_at_1_max value: 5.897821931847972 - type: nauc_map_at_20_diff1 value: 35.02816464339912 - type: nauc_map_at_20_max value: 8.57606618814322 - type: nauc_map_at_3_diff1 value: 34.74870593960704 - type: nauc_map_at_3_max value: 7.7563142367550855 - type: nauc_map_at_5_diff1 value: 34.86268337627808 - type: nauc_map_at_5_max value: 8.440880068028383 - type: nauc_mrr_at_1000_diff1 value: 38.05838366137394 - type: nauc_mrr_at_1000_max value: 8.841793483971488 - type: nauc_mrr_at_100_diff1 value: 38.055327497620105 - type: nauc_mrr_at_100_max value: 8.852785015905537 - type: nauc_mrr_at_10_diff1 value: 37.972785779782065 - type: nauc_mrr_at_10_max value: 9.037378532213502 - type: nauc_mrr_at_1_diff1 value: 40.4432565446304 - type: nauc_mrr_at_1_max value: 5.807334670577964 - type: nauc_mrr_at_20_diff1 value: 38.02767311040578 - type: nauc_mrr_at_20_max value: 8.935949669165813 - type: nauc_mrr_at_3_diff1 value: 37.60471936912395 - type: nauc_mrr_at_3_max value: 8.236789961860858 - type: nauc_mrr_at_5_diff1 value: 37.86352377415473 - type: nauc_mrr_at_5_max value: 8.895540094390892 - type: nauc_ndcg_at_1000_diff1 value: 35.07160524499026 - type: nauc_ndcg_at_1000_max value: 9.813866402912101 - type: nauc_ndcg_at_100_diff1 value: 34.92933991980568 - type: nauc_ndcg_at_100_max value: 9.89567365562028 - type: nauc_ndcg_at_10_diff1 value: 34.529981017804104 - type: nauc_ndcg_at_10_max value: 10.607560550422225 - type: nauc_ndcg_at_1_diff1 value: 40.4432565446304 - type: nauc_ndcg_at_1_max value: 5.807334670577964 - type: nauc_ndcg_at_20_diff1 value: 34.668263021521994 - type: nauc_ndcg_at_20_max value: 10.397799223138245 - type: nauc_ndcg_at_3_diff1 value: 34.25729382926051 - type: nauc_ndcg_at_3_max value: 8.745767948993501 - type: nauc_ndcg_at_5_diff1 value: 34.33973241023773 - type: nauc_ndcg_at_5_max value: 10.048081516024556 - type: nauc_precision_at_1000_diff1 value: -4.077783587263832 - type: nauc_precision_at_1000_max value: 12.765822496184464 - type: nauc_precision_at_100_diff1 value: 1.4680450598592432 - type: nauc_precision_at_100_max value: 17.44831984105488 - type: nauc_precision_at_10_diff1 value: 19.92695770531176 - type: nauc_precision_at_10_max value: 23.914679743057352 - type: nauc_precision_at_1_diff1 value: 40.4432565446304 - type: nauc_precision_at_1_max value: 5.807334670577964 - type: nauc_precision_at_20_diff1 value: 12.999177323343336 - type: nauc_precision_at_20_max value: 23.540911859396033 - type: nauc_precision_at_3_diff1 value: 29.62941105307629 - type: nauc_precision_at_3_max value: 12.866042509022865 - type: nauc_precision_at_5_diff1 value: 26.255704472502938 - type: nauc_precision_at_5_max value: 18.77439128365061 - type: nauc_recall_at_1000_diff1 value: 8.920814764522019 - type: nauc_recall_at_1000_max value: 17.655295496605643 - type: nauc_recall_at_100_diff1 value: 14.762238468369407 - type: nauc_recall_at_100_max value: 17.048567752646125 - type: nauc_recall_at_10_diff1 value: 23.32325502930857 - type: nauc_recall_at_10_max value: 19.556176492083992 - type: nauc_recall_at_1_diff1 value: 38.063117939510256 - type: nauc_recall_at_1_max value: 5.897821931847972 - type: nauc_recall_at_20_diff1 value: 20.506042184854063 - type: nauc_recall_at_20_max value: 20.561022468033503 - type: nauc_recall_at_3_diff1 value: 27.65947022544946 - type: nauc_recall_at_3_max value: 10.81743699331276 - type: nauc_recall_at_5_diff1 value: 25.94551760999131 - type: nauc_recall_at_5_max value: 15.156745563504675 - type: ndcg_at_1 value: 48.8 - type: ndcg_at_10 value: 66.459 - type: ndcg_at_100 value: 68.521 - type: ndcg_at_1000 value: 68.938 - type: ndcg_at_20 value: 67.52 - type: ndcg_at_3 value: 61.11299999999999 - type: ndcg_at_5 value: 64.21900000000001 - type: precision_at_1 value: 48.8 - type: precision_at_10 value: 9.256 - type: precision_at_100 value: 1.04 - type: precision_at_1000 value: 0.109 - type: precision_at_20 value: 4.862 - type: precision_at_3 value: 25.387999999999998 - type: precision_at_5 value: 16.933999999999997 - type: recall_at_1 value: 45.399 - type: recall_at_10 value: 84.572 - type: recall_at_100 value: 93.585 - type: recall_at_1000 value: 96.43 - type: recall_at_20 value: 88.576 - type: recall_at_3 value: 70.283 - type: recall_at_5 value: 77.804 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 10.773000000000001 - type: map_at_10 value: 18.273 - type: map_at_100 value: 19.846 - type: map_at_1000 value: 20.066 - type: map_at_20 value: 19.092000000000002 - type: map_at_3 value: 15.653 - type: map_at_5 value: 16.996 - type: mrr_at_1 value: 22.839506172839506 - type: mrr_at_10 value: 30.709264158338218 - type: mrr_at_100 value: 31.765285545264728 - type: mrr_at_1000 value: 31.84254498770477 - type: mrr_at_20 value: 31.28359047494611 - type: mrr_at_3 value: 28.34362139917697 - type: mrr_at_5 value: 29.578189300411527 - type: nauc_map_at_1000_diff1 value: 42.33696758957174 - type: nauc_map_at_1000_max value: 22.28446732536063 - type: nauc_map_at_100_diff1 value: 42.280232367289614 - type: nauc_map_at_100_max value: 22.193658543387336 - type: nauc_map_at_10_diff1 value: 42.86152992348606 - type: nauc_map_at_10_max value: 21.649513921678768 - type: nauc_map_at_1_diff1 value: 50.25274550047308 - type: nauc_map_at_1_max value: 18.793153289309025 - type: nauc_map_at_20_diff1 value: 42.68337193792793 - type: nauc_map_at_20_max value: 21.783732998080165 - type: nauc_map_at_3_diff1 value: 44.526091901592025 - type: nauc_map_at_3_max value: 20.44240168343812 - type: nauc_map_at_5_diff1 value: 43.40025778096801 - type: nauc_map_at_5_max value: 21.337520847399794 - type: nauc_mrr_at_1000_diff1 value: 42.76413081015503 - type: nauc_mrr_at_1000_max value: 25.051153181122253 - type: nauc_mrr_at_100_diff1 value: 42.726972311439724 - type: nauc_mrr_at_100_max value: 25.041597478239442 - type: nauc_mrr_at_10_diff1 value: 43.05815490208189 - type: nauc_mrr_at_10_max value: 25.13689635924164 - type: nauc_mrr_at_1_diff1 value: 49.40608982855475 - type: nauc_mrr_at_1_max value: 26.84279922755957 - type: nauc_mrr_at_20_diff1 value: 42.68770796904053 - type: nauc_mrr_at_20_max value: 25.00374130766682 - type: nauc_mrr_at_3_diff1 value: 43.56229080869875 - type: nauc_mrr_at_3_max value: 25.00272462955036 - type: nauc_mrr_at_5_diff1 value: 42.78163485253489 - type: nauc_mrr_at_5_max value: 24.996583555035066 - type: nauc_ndcg_at_1000_diff1 value: 39.60623109749308 - type: nauc_ndcg_at_1000_max value: 24.945954161473963 - type: nauc_ndcg_at_100_diff1 value: 38.391977738851054 - type: nauc_ndcg_at_100_max value: 23.1495309393186 - type: nauc_ndcg_at_10_diff1 value: 40.82447224697167 - type: nauc_ndcg_at_10_max value: 22.103721284897222 - type: nauc_ndcg_at_1_diff1 value: 49.40608982855475 - type: nauc_ndcg_at_1_max value: 26.84279922755957 - type: nauc_ndcg_at_20_diff1 value: 39.87655648003804 - type: nauc_ndcg_at_20_max value: 21.863160067094732 - type: nauc_ndcg_at_3_diff1 value: 42.702330655505094 - type: nauc_ndcg_at_3_max value: 24.30088309227799 - type: nauc_ndcg_at_5_diff1 value: 41.15335198539591 - type: nauc_ndcg_at_5_max value: 23.383496342798235 - type: nauc_precision_at_1000_diff1 value: 5.078790711874846 - type: nauc_precision_at_1000_max value: 28.270734693277067 - type: nauc_precision_at_100_diff1 value: 10.751006733811092 - type: nauc_precision_at_100_max value: 28.016358575658305 - type: nauc_precision_at_10_diff1 value: 28.69051966074066 - type: nauc_precision_at_10_max value: 29.264771382133375 - type: nauc_precision_at_1_diff1 value: 49.40608982855475 - type: nauc_precision_at_1_max value: 26.84279922755957 - type: nauc_precision_at_20_diff1 value: 23.657472193309125 - type: nauc_precision_at_20_max value: 27.08411359763242 - type: nauc_precision_at_3_diff1 value: 36.599109026411924 - type: nauc_precision_at_3_max value: 28.383077203742246 - type: nauc_precision_at_5_diff1 value: 31.358430042619563 - type: nauc_precision_at_5_max value: 28.555003400952845 - type: nauc_recall_at_1000_diff1 value: 20.25194559618304 - type: nauc_recall_at_1000_max value: 23.710031862813118 - type: nauc_recall_at_100_diff1 value: 18.359725605438047 - type: nauc_recall_at_100_max value: 13.823806806919805 - type: nauc_recall_at_10_diff1 value: 30.54188950640248 - type: nauc_recall_at_10_max value: 15.290504422192791 - type: nauc_recall_at_1_diff1 value: 50.25274550047308 - type: nauc_recall_at_1_max value: 18.793153289309025 - type: nauc_recall_at_20_diff1 value: 27.314651647568404 - type: nauc_recall_at_20_max value: 14.088522206775039 - type: nauc_recall_at_3_diff1 value: 36.125136373927354 - type: nauc_recall_at_3_max value: 16.778297325102113 - type: nauc_recall_at_5_diff1 value: 32.03749698394437 - type: nauc_recall_at_5_max value: 17.620359878684805 - type: ndcg_at_1 value: 22.84 - type: ndcg_at_10 value: 24.467 - type: ndcg_at_100 value: 31.270999999999997 - type: ndcg_at_1000 value: 35.564 - type: ndcg_at_20 value: 26.871000000000002 - type: ndcg_at_3 value: 21.128 - type: ndcg_at_5 value: 22.203999999999997 - type: precision_at_1 value: 22.84 - type: precision_at_10 value: 7.114 - type: precision_at_100 value: 1.381 - type: precision_at_1000 value: 0.213 - type: precision_at_20 value: 4.552 - type: precision_at_3 value: 14.352 - type: precision_at_5 value: 10.864 - type: recall_at_1 value: 10.773000000000001 - type: recall_at_10 value: 30.564000000000004 - type: recall_at_100 value: 56.745999999999995 - type: recall_at_1000 value: 82.826 - type: recall_at_20 value: 37.844 - type: recall_at_3 value: 19.406000000000002 - type: recall_at_5 value: 23.724 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 32.046 - type: map_at_10 value: 48.443000000000005 - type: map_at_100 value: 49.389 - type: map_at_1000 value: 49.466 - type: map_at_20 value: 48.986000000000004 - type: map_at_3 value: 44.893 - type: map_at_5 value: 47.075 - type: mrr_at_1 value: 64.09182984469953 - type: mrr_at_10 value: 72.85266282970527 - type: mrr_at_100 value: 73.21185355612093 - type: mrr_at_1000 value: 73.2252657846111 - type: mrr_at_20 value: 73.10862127718183 - type: mrr_at_3 value: 71.3031735313976 - type: mrr_at_5 value: 72.34233625928402 - type: nauc_map_at_1000_diff1 value: 29.57880669891487 - type: nauc_map_at_1000_max value: 21.845463980026476 - type: nauc_map_at_100_diff1 value: 29.55367483685356 - type: nauc_map_at_100_max value: 21.828007798768958 - type: nauc_map_at_10_diff1 value: 29.67368554537432 - type: nauc_map_at_10_max value: 21.849279138947868 - type: nauc_map_at_1_diff1 value: 61.13740199701338 - type: nauc_map_at_1_max value: 32.6342175820136 - type: nauc_map_at_20_diff1 value: 29.598291599316568 - type: nauc_map_at_20_max value: 21.862735577320557 - type: nauc_map_at_3_diff1 value: 31.22835556923922 - type: nauc_map_at_3_max value: 22.344809372883315 - type: nauc_map_at_5_diff1 value: 30.432000722074665 - type: nauc_map_at_5_max value: 22.27699649933424 - type: nauc_mrr_at_1000_diff1 value: 59.26794200803715 - type: nauc_mrr_at_1000_max value: 34.33050463026508 - type: nauc_mrr_at_100_diff1 value: 59.26740246956419 - type: nauc_mrr_at_100_max value: 34.33577087313508 - type: nauc_mrr_at_10_diff1 value: 59.13786202070478 - type: nauc_mrr_at_10_max value: 34.377953823081384 - type: nauc_mrr_at_1_diff1 value: 61.13740199701338 - type: nauc_mrr_at_1_max value: 32.6342175820136 - type: nauc_mrr_at_20_diff1 value: 59.22898475872048 - type: nauc_mrr_at_20_max value: 34.34680319223408 - type: nauc_mrr_at_3_diff1 value: 59.03499635007199 - type: nauc_mrr_at_3_max value: 34.398014446289544 - type: nauc_mrr_at_5_diff1 value: 59.20761322618965 - type: nauc_mrr_at_5_max value: 34.42827235318949 - type: nauc_ndcg_at_1000_diff1 value: 32.64061494118113 - type: nauc_ndcg_at_1000_max value: 23.616685748536074 - type: nauc_ndcg_at_100_diff1 value: 32.11038119247951 - type: nauc_ndcg_at_100_max value: 23.33285928609271 - type: nauc_ndcg_at_10_diff1 value: 32.70477446409243 - type: nauc_ndcg_at_10_max value: 23.662027117393535 - type: nauc_ndcg_at_1_diff1 value: 61.13740199701338 - type: nauc_ndcg_at_1_max value: 32.6342175820136 - type: nauc_ndcg_at_20_diff1 value: 32.32220211811219 - type: nauc_ndcg_at_20_max value: 23.564270159145643 - type: nauc_ndcg_at_3_diff1 value: 35.63724665178986 - type: nauc_ndcg_at_3_max value: 24.820074757992305 - type: nauc_ndcg_at_5_diff1 value: 34.27199365493392 - type: nauc_ndcg_at_5_max value: 24.508158825075682 - type: nauc_precision_at_1000_diff1 value: -2.430622498990411 - type: nauc_precision_at_1000_max value: 7.822027373881609 - type: nauc_precision_at_100_diff1 value: 4.202356673527351 - type: nauc_precision_at_100_max value: 10.321772681063146 - type: nauc_precision_at_10_diff1 value: 14.011676403321902 - type: nauc_precision_at_10_max value: 15.666639850967512 - type: nauc_precision_at_1_diff1 value: 61.13740199701338 - type: nauc_precision_at_1_max value: 32.6342175820136 - type: nauc_precision_at_20_diff1 value: 10.437835060510753 - type: nauc_precision_at_20_max value: 14.10661581882921 - type: nauc_precision_at_3_diff1 value: 23.783985172773143 - type: nauc_precision_at_3_max value: 20.590352544033866 - type: nauc_precision_at_5_diff1 value: 19.592566862830548 - type: nauc_precision_at_5_max value: 18.88117124055341 - type: nauc_recall_at_1000_diff1 value: -2.430622498990057 - type: nauc_recall_at_1000_max value: 7.822027373881757 - type: nauc_recall_at_100_diff1 value: 4.202356673527403 - type: nauc_recall_at_100_max value: 10.32177268106303 - type: nauc_recall_at_10_diff1 value: 14.011676403321957 - type: nauc_recall_at_10_max value: 15.666639850967554 - type: nauc_recall_at_1_diff1 value: 61.13740199701338 - type: nauc_recall_at_1_max value: 32.6342175820136 - type: nauc_recall_at_20_diff1 value: 10.437835060510707 - type: nauc_recall_at_20_max value: 14.106615818829187 - type: nauc_recall_at_3_diff1 value: 23.783985172773168 - type: nauc_recall_at_3_max value: 20.590352544033934 - type: nauc_recall_at_5_diff1 value: 19.59256686283052 - type: nauc_recall_at_5_max value: 18.88117124055339 - type: ndcg_at_1 value: 64.092 - type: ndcg_at_10 value: 57.964000000000006 - type: ndcg_at_100 value: 61.501 - type: ndcg_at_1000 value: 63.022 - type: ndcg_at_20 value: 59.463 - type: ndcg_at_3 value: 52.608 - type: ndcg_at_5 value: 55.577 - type: precision_at_1 value: 64.092 - type: precision_at_10 value: 12.462 - type: precision_at_100 value: 1.5230000000000001 - type: precision_at_1000 value: 0.172 - type: precision_at_20 value: 6.714 - type: precision_at_3 value: 33.657 - type: precision_at_5 value: 22.533 - type: recall_at_1 value: 32.046 - type: recall_at_10 value: 62.309000000000005 - type: recall_at_100 value: 76.13799999999999 - type: recall_at_1000 value: 86.185 - type: recall_at_20 value: 67.144 - type: recall_at_3 value: 50.486 - type: recall_at_5 value: 56.333999999999996 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 73.702 - type: ap value: 67.55549836397681 - type: ap_weighted value: 67.55549836397681 - type: f1 value: 73.4581895293936 - type: f1_weighted value: 73.45818952939358 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: test revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: main_score value: 30.051 - type: map_at_1 value: 14.244 - type: map_at_10 value: 24.143 - type: map_at_100 value: 25.402 - type: map_at_1000 value: 25.479 - type: map_at_20 value: 24.875 - type: map_at_3 value: 20.694 - type: map_at_5 value: 22.604 - type: mrr_at_1 value: 14.584527220630372 - type: mrr_at_10 value: 24.557460090053123 - type: mrr_at_100 value: 25.785901435660147 - type: mrr_at_1000 value: 25.85709282510335 - type: mrr_at_20 value: 25.274992596418866 - type: mrr_at_3 value: 21.131805157593057 - type: mrr_at_5 value: 23.0429799426934 - type: nauc_map_at_1000_diff1 value: 27.61711087970365 - type: nauc_map_at_1000_max value: 1.6657479941178628 - type: nauc_map_at_1000_std value: -9.49651956936018 - type: nauc_map_at_100_diff1 value: 27.61498736358577 - type: nauc_map_at_100_max value: 1.6634690696430845 - type: nauc_map_at_100_std value: -9.46789097558277 - type: nauc_map_at_10_diff1 value: 27.616888705380603 - type: nauc_map_at_10_max value: 1.4276684096575918 - type: nauc_map_at_10_std value: -10.446820384304754 - type: nauc_map_at_1_diff1 value: 29.76931787521696 - type: nauc_map_at_1_max value: 0.948603060998731 - type: nauc_map_at_1_std value: -10.775704940266767 - type: nauc_map_at_20_diff1 value: 27.57600730820819 - type: nauc_map_at_20_max value: 1.5143185235329177 - type: nauc_map_at_20_std value: -9.849312193865744 - type: nauc_map_at_3_diff1 value: 27.890351531157577 - type: nauc_map_at_3_max value: 1.4000607426502167 - type: nauc_map_at_3_std value: -11.118014158060422 - type: nauc_map_at_5_diff1 value: 27.786816928992376 - type: nauc_map_at_5_max value: 1.2637200637686197 - type: nauc_map_at_5_std value: -10.922970086569386 - type: nauc_mrr_at_1000_diff1 value: 27.42128154832487 - type: nauc_mrr_at_1000_max value: 1.769383613212847 - type: nauc_mrr_at_1000_std value: -9.304600797518969 - type: nauc_mrr_at_100_diff1 value: 27.418466905238216 - type: nauc_mrr_at_100_max value: 1.7702836453764914 - type: nauc_mrr_at_100_std value: -9.27018903363956 - type: nauc_mrr_at_10_diff1 value: 27.43223048852499 - type: nauc_mrr_at_10_max value: 1.5863443925517158 - type: nauc_mrr_at_10_std value: -10.19228455560491 - type: nauc_mrr_at_1_diff1 value: 29.63894982019449 - type: nauc_mrr_at_1_max value: 1.1350720726087482 - type: nauc_mrr_at_1_std value: -10.706375855749798 - type: nauc_mrr_at_20_diff1 value: 27.3813401873824 - type: nauc_mrr_at_20_max value: 1.6349061697179936 - type: nauc_mrr_at_20_std value: -9.62511280355079 - type: nauc_mrr_at_3_diff1 value: 27.63825584292618 - type: nauc_mrr_at_3_max value: 1.5014142622215632 - type: nauc_mrr_at_3_std value: -10.937120645836448 - type: nauc_mrr_at_5_diff1 value: 27.65874684943374 - type: nauc_mrr_at_5_max value: 1.3921567756597124 - type: nauc_mrr_at_5_std value: -10.715887774339881 - type: nauc_ndcg_at_1000_diff1 value: 26.940019720932135 - type: nauc_ndcg_at_1000_max value: 3.071589090811754 - type: nauc_ndcg_at_1000_std value: -5.820914521338 - type: nauc_ndcg_at_100_diff1 value: 26.80295695348146 - type: nauc_ndcg_at_100_max value: 3.064374012393309 - type: nauc_ndcg_at_100_std value: -4.689320725729883 - type: nauc_ndcg_at_10_diff1 value: 26.73912033432779 - type: nauc_ndcg_at_10_max value: 1.7371596861856864 - type: nauc_ndcg_at_10_std value: -9.587955568967976 - type: nauc_ndcg_at_1_diff1 value: 29.63894982019449 - type: nauc_ndcg_at_1_max value: 1.1350720726087482 - type: nauc_ndcg_at_1_std value: -10.706375855749798 - type: nauc_ndcg_at_20_diff1 value: 26.554059540064955 - type: nauc_ndcg_at_20_max value: 2.037008011734218 - type: nauc_ndcg_at_20_std value: -7.522356479764311 - type: nauc_ndcg_at_3_diff1 value: 27.38197429348882 - type: nauc_ndcg_at_3_max value: 1.5447259968645135 - type: nauc_ndcg_at_3_std value: -11.056572041307833 - type: nauc_ndcg_at_5_diff1 value: 27.23078023341192 - type: nauc_ndcg_at_5_max value: 1.3332668241078742 - type: nauc_ndcg_at_5_std value: -10.70755059234365 - type: nauc_precision_at_1000_diff1 value: 4.824440345952768 - type: nauc_precision_at_1000_max value: 22.501190150975695 - type: nauc_precision_at_1000_std value: 27.01244032141851 - type: nauc_precision_at_100_diff1 value: 18.806308686259438 - type: nauc_precision_at_100_max value: 14.0556087259749 - type: nauc_precision_at_100_std value: 23.65979665814084 - type: nauc_precision_at_10_diff1 value: 23.631970615652996 - type: nauc_precision_at_10_max value: 3.2279467100874113 - type: nauc_precision_at_10_std value: -6.612111844206746 - type: nauc_precision_at_1_diff1 value: 29.63894982019449 - type: nauc_precision_at_1_max value: 1.1350720726087482 - type: nauc_precision_at_1_std value: -10.706375855749798 - type: nauc_precision_at_20_diff1 value: 22.13613457378927 - type: nauc_precision_at_20_max value: 4.984490409308019 - type: nauc_precision_at_20_std value: 1.3959896282348365 - type: nauc_precision_at_3_diff1 value: 25.924423449037278 - type: nauc_precision_at_3_max value: 2.119600062847904 - type: nauc_precision_at_3_std value: -10.816296974118274 - type: nauc_precision_at_5_diff1 value: 25.47042606356821 - type: nauc_precision_at_5_max value: 1.832019713836658 - type: nauc_precision_at_5_std value: -9.928054676627815 - type: nauc_recall_at_1000_diff1 value: 22.574618149749853 - type: nauc_recall_at_1000_max value: 30.82526285969409 - type: nauc_recall_at_1000_std value: 59.21512310658756 - type: nauc_recall_at_100_diff1 value: 23.54920706844819 - type: nauc_recall_at_100_max value: 10.975217227651312 - type: nauc_recall_at_100_std value: 24.85603771243269 - type: nauc_recall_at_10_diff1 value: 24.413494892666748 - type: nauc_recall_at_10_max value: 2.349732649717201 - type: nauc_recall_at_10_std value: -7.37174021438692 - type: nauc_recall_at_1_diff1 value: 29.76931787521696 - type: nauc_recall_at_1_max value: 0.948603060998731 - type: nauc_recall_at_1_std value: -10.775704940266767 - type: nauc_recall_at_20_diff1 value: 23.4560099128478 - type: nauc_recall_at_20_max value: 3.399890015984125 - type: nauc_recall_at_20_std value: 0.1065905686863526 - type: nauc_recall_at_3_diff1 value: 26.33393571726941 - type: nauc_recall_at_3_max value: 1.7770061463264046 - type: nauc_recall_at_3_std value: -11.030373812919407 - type: nauc_recall_at_5_diff1 value: 25.84870110945663 - type: nauc_recall_at_5_max value: 1.368501163591071 - type: nauc_recall_at_5_std value: -10.251669620544972 - type: ndcg_at_1 value: 14.585 - type: ndcg_at_10 value: 30.051 - type: ndcg_at_100 value: 36.429 - type: ndcg_at_1000 value: 38.501 - type: ndcg_at_20 value: 32.678 - type: ndcg_at_3 value: 22.963 - type: ndcg_at_5 value: 26.385 - type: precision_at_1 value: 14.585 - type: precision_at_10 value: 5.04 - type: precision_at_100 value: 0.826 - type: precision_at_1000 value: 0.101 - type: precision_at_20 value: 3.062 - type: precision_at_3 value: 10.0 - type: precision_at_5 value: 7.722 - type: recall_at_1 value: 14.244 - type: recall_at_10 value: 48.48 - type: recall_at_100 value: 78.652 - type: recall_at_1000 value: 94.774 - type: recall_at_20 value: 58.724 - type: recall_at_3 value: 29.106 - type: recall_at_5 value: 37.329 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.73096215230278 - type: f1 value: 89.31269053453195 - type: f1_weighted value: 89.75118268368209 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 66.49110807113543 - type: f1 value: 51.250886916460544 - type: f1_weighted value: 69.910921231367 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 68.86348352387357 - type: f1 value: 66.19332858716572 - type: f1_weighted value: 68.90834063842036 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 74.48890383322124 - type: f1 value: 74.01198670144007 - type: f1_weighted value: 74.5767171066833 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.26742368186013 - type: v_measures value: - 0.3010655091536935 - 0.29691302264328545 - 0.31333602296285296 - 0.3118686703571087 - 0.3066404174656012 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.971953824342002 - type: v_measures value: - 0.28128031641493684 - 0.2709575455939747 - 0.28058910226798894 - 0.286988068530199 - 0.27155292611128873 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 metrics: - type: map value: 30.919005892986945 - type: mrr value: 31.964215230201017 - type: nAUC_map_diff1 value: 12.380227971335106 - type: nAUC_map_max value: -20.306665699119915 - type: nAUC_mrr_diff1 value: 11.860907307359078 - type: nAUC_mrr_max value: -14.820057982537445 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.9239999999999995 - type: map_at_10 value: 10.216 - type: map_at_100 value: 13.073 - type: map_at_1000 value: 14.335999999999999 - type: map_at_20 value: 11.562 - type: map_at_3 value: 7.361 - type: map_at_5 value: 8.790000000000001 - type: mrr_at_1 value: 40.55727554179567 - type: mrr_at_10 value: 48.67290775959506 - type: mrr_at_100 value: 49.39988509152788 - type: mrr_at_1000 value: 49.44995547989892 - type: mrr_at_20 value: 49.1476640818267 - type: mrr_at_3 value: 46.336429308565535 - type: mrr_at_5 value: 47.85345717234262 - type: nauc_map_at_1000_diff1 value: 26.873576120717154 - type: nauc_map_at_1000_max value: 22.28375511136719 - type: nauc_map_at_100_diff1 value: 28.105989810331305 - type: nauc_map_at_100_max value: 20.80280182475018 - type: nauc_map_at_10_diff1 value: 32.22012802586023 - type: nauc_map_at_10_max value: 14.563410751855393 - type: nauc_map_at_1_diff1 value: 48.78589273340728 - type: nauc_map_at_1_max value: 0.7100902846948914 - type: nauc_map_at_20_diff1 value: 29.749385475706614 - type: nauc_map_at_20_max value: 17.725130767277143 - type: nauc_map_at_3_diff1 value: 42.91163831592647 - type: nauc_map_at_3_max value: 7.949303449529328 - type: nauc_map_at_5_diff1 value: 36.37288307582431 - type: nauc_map_at_5_max value: 10.294774281587333 - type: nauc_mrr_at_1000_diff1 value: 28.224194118245986 - type: nauc_mrr_at_1000_max value: 35.03713736523123 - type: nauc_mrr_at_100_diff1 value: 28.239722499941884 - type: nauc_mrr_at_100_max value: 35.08008834682332 - type: nauc_mrr_at_10_diff1 value: 28.312031722561397 - type: nauc_mrr_at_10_max value: 35.07745441637377 - type: nauc_mrr_at_1_diff1 value: 29.71286290489225 - type: nauc_mrr_at_1_max value: 27.07492092557332 - type: nauc_mrr_at_20_diff1 value: 28.408619888309524 - type: nauc_mrr_at_20_max value: 35.07056834593783 - type: nauc_mrr_at_3_diff1 value: 28.57209508947814 - type: nauc_mrr_at_3_max value: 32.824180760173896 - type: nauc_mrr_at_5_diff1 value: 28.236082992043393 - type: nauc_mrr_at_5_max value: 34.17372569423924 - type: nauc_ndcg_at_1000_diff1 value: 24.083700969367612 - type: nauc_ndcg_at_1000_max value: 38.883846498536116 - type: nauc_ndcg_at_100_diff1 value: 23.312730110282526 - type: nauc_ndcg_at_100_max value: 32.64936241784008 - type: nauc_ndcg_at_10_diff1 value: 17.975398707754817 - type: nauc_ndcg_at_10_max value: 32.32412505213287 - type: nauc_ndcg_at_1_diff1 value: 30.756195441367673 - type: nauc_ndcg_at_1_max value: 26.483443465985328 - type: nauc_ndcg_at_20_diff1 value: 18.936710159355073 - type: nauc_ndcg_at_20_max value: 31.338021731338316 - type: nauc_ndcg_at_3_diff1 value: 22.895979777747623 - type: nauc_ndcg_at_3_max value: 32.17933652323659 - type: nauc_ndcg_at_5_diff1 value: 19.852961142954506 - type: nauc_ndcg_at_5_max value: 32.56301733572076 - type: nauc_precision_at_1000_diff1 value: -12.569744637564826 - type: nauc_precision_at_1000_max value: 14.067171968274472 - type: nauc_precision_at_100_diff1 value: -8.452640794750774 - type: nauc_precision_at_100_max value: 26.52425208852308 - type: nauc_precision_at_10_diff1 value: -0.8599396198058924 - type: nauc_precision_at_10_max value: 36.79898093749965 - type: nauc_precision_at_1_diff1 value: 30.53565353379064 - type: nauc_precision_at_1_max value: 27.150932557011842 - type: nauc_precision_at_20_diff1 value: -4.190746979665414 - type: nauc_precision_at_20_max value: 35.90857451601526 - type: nauc_precision_at_3_diff1 value: 12.548153913459656 - type: nauc_precision_at_3_max value: 35.753894439704055 - type: nauc_precision_at_5_diff1 value: 5.476630825300621 - type: nauc_precision_at_5_max value: 36.94019333022866 - type: nauc_recall_at_1000_diff1 value: 15.743509429414217 - type: nauc_recall_at_1000_max value: 19.44531544138 - type: nauc_recall_at_100_diff1 value: 18.385119061958157 - type: nauc_recall_at_100_max value: 19.1318751995873 - type: nauc_recall_at_10_diff1 value: 25.482096811308676 - type: nauc_recall_at_10_max value: 14.006190865424864 - type: nauc_recall_at_1_diff1 value: 48.78589273340728 - type: nauc_recall_at_1_max value: 0.7100902846948914 - type: nauc_recall_at_20_diff1 value: 22.76078199362388 - type: nauc_recall_at_20_max value: 17.126864200524057 - type: nauc_recall_at_3_diff1 value: 39.93189765909178 - type: nauc_recall_at_3_max value: 9.276495447517293 - type: nauc_recall_at_5_diff1 value: 28.17119993582467 - type: nauc_recall_at_5_max value: 9.757053939301784 - type: ndcg_at_1 value: 38.7 - type: ndcg_at_10 value: 28.942 - type: ndcg_at_100 value: 27.346999999999998 - type: ndcg_at_1000 value: 36.216 - type: ndcg_at_20 value: 27.506999999999998 - type: ndcg_at_3 value: 33.335 - type: ndcg_at_5 value: 31.541999999999998 - type: precision_at_1 value: 40.248 - type: precision_at_10 value: 21.455 - type: precision_at_100 value: 7.015000000000001 - type: precision_at_1000 value: 1.9709999999999999 - type: precision_at_20 value: 16.471 - type: precision_at_3 value: 30.857 - type: precision_at_5 value: 26.811 - type: recall_at_1 value: 4.9239999999999995 - type: recall_at_10 value: 13.724 - type: recall_at_100 value: 28.450999999999997 - type: recall_at_1000 value: 60.136 - type: recall_at_20 value: 18.013 - type: recall_at_3 value: 7.954999999999999 - type: recall_at_5 value: 10.700999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 21.246000000000002 - type: map_at_10 value: 34.107 - type: map_at_100 value: 35.43 - type: map_at_1000 value: 35.483 - type: map_at_20 value: 34.945 - type: map_at_3 value: 30.070000000000004 - type: map_at_5 value: 32.25 - type: mrr_at_1 value: 24.10196987253766 - type: mrr_at_10 value: 36.531318398352 - type: mrr_at_100 value: 37.59236775235497 - type: mrr_at_1000 value: 37.630099883433154 - type: mrr_at_20 value: 37.20931733276279 - type: mrr_at_3 value: 32.91328698339132 - type: mrr_at_5 value: 34.92516415604491 - type: nauc_map_at_1000_diff1 value: 20.872737281787636 - type: nauc_map_at_1000_max value: 16.624364326260896 - type: nauc_map_at_100_diff1 value: 20.878142367328813 - type: nauc_map_at_100_max value: 16.643468154696926 - type: nauc_map_at_10_diff1 value: 20.807793402274534 - type: nauc_map_at_10_max value: 16.39391387269205 - type: nauc_map_at_1_diff1 value: 22.35812341861645 - type: nauc_map_at_1_max value: 11.615838197259766 - type: nauc_map_at_20_diff1 value: 20.893013757323047 - type: nauc_map_at_20_max value: 16.675046191798433 - type: nauc_map_at_3_diff1 value: 20.05521274346964 - type: nauc_map_at_3_max value: 13.969959601269148 - type: nauc_map_at_5_diff1 value: 20.625293595408642 - type: nauc_map_at_5_max value: 15.630481595302918 - type: nauc_mrr_at_1000_diff1 value: 20.659075334032188 - type: nauc_mrr_at_1000_max value: 17.1077266798649 - type: nauc_mrr_at_100_diff1 value: 20.659592764012615 - type: nauc_mrr_at_100_max value: 17.12673405006388 - type: nauc_mrr_at_10_diff1 value: 20.6492334539065 - type: nauc_mrr_at_10_max value: 17.139758338338574 - type: nauc_mrr_at_1_diff1 value: 21.959789955443817 - type: nauc_mrr_at_1_max value: 13.311351245662395 - type: nauc_mrr_at_20_diff1 value: 20.697135833887096 - type: nauc_mrr_at_20_max value: 17.174901738327268 - type: nauc_mrr_at_3_diff1 value: 20.012890126078148 - type: nauc_mrr_at_3_max value: 15.325749640509228 - type: nauc_mrr_at_5_diff1 value: 20.438050294840547 - type: nauc_mrr_at_5_max value: 16.56107433490657 - type: nauc_ndcg_at_1000_diff1 value: 20.981193766001212 - type: nauc_ndcg_at_1000_max value: 19.366882624001466 - type: nauc_ndcg_at_100_diff1 value: 21.07151595070923 - type: nauc_ndcg_at_100_max value: 19.969093104531108 - type: nauc_ndcg_at_10_diff1 value: 20.824455077933653 - type: nauc_ndcg_at_10_max value: 19.215675460656907 - type: nauc_ndcg_at_1_diff1 value: 22.064098120682292 - type: nauc_ndcg_at_1_max value: 13.411137146530983 - type: nauc_ndcg_at_20_diff1 value: 21.12343657664599 - type: nauc_ndcg_at_20_max value: 20.04689967321189 - type: nauc_ndcg_at_3_diff1 value: 19.470309201418857 - type: nauc_ndcg_at_3_max value: 14.848503224176909 - type: nauc_ndcg_at_5_diff1 value: 20.32521541385147 - type: nauc_ndcg_at_5_max value: 17.48824868961743 - type: nauc_precision_at_1000_diff1 value: 0.4660953834541917 - type: nauc_precision_at_1000_max value: 14.735755093338893 - type: nauc_precision_at_100_diff1 value: 7.579249137389521 - type: nauc_precision_at_100_max value: 23.48086608082409 - type: nauc_precision_at_10_diff1 value: 15.621524664818134 - type: nauc_precision_at_10_max value: 26.16669034759615 - type: nauc_precision_at_1_diff1 value: 22.064098120682292 - type: nauc_precision_at_1_max value: 13.411137146530983 - type: nauc_precision_at_20_diff1 value: 13.58615876770919 - type: nauc_precision_at_20_max value: 26.806761446925364 - type: nauc_precision_at_3_diff1 value: 16.500214986231953 - type: nauc_precision_at_3_max value: 18.649494923088263 - type: nauc_precision_at_5_diff1 value: 17.307374618712128 - type: nauc_precision_at_5_max value: 23.444839731139965 - type: nauc_recall_at_1000_diff1 value: 28.75547954061722 - type: nauc_recall_at_1000_max value: 62.409320816680015 - type: nauc_recall_at_100_diff1 value: 23.43814017912217 - type: nauc_recall_at_100_max value: 42.499893768353374 - type: nauc_recall_at_10_diff1 value: 20.535131644031498 - type: nauc_recall_at_10_max value: 26.527673119431896 - type: nauc_recall_at_1_diff1 value: 22.35812341861645 - type: nauc_recall_at_1_max value: 11.615838197259766 - type: nauc_recall_at_20_diff1 value: 21.994120461812543 - type: nauc_recall_at_20_max value: 31.819936351026307 - type: nauc_recall_at_3_diff1 value: 17.432747909860975 - type: nauc_recall_at_3_max value: 15.382311079169869 - type: nauc_recall_at_5_diff1 value: 19.13496828564786 - type: nauc_recall_at_5_max value: 21.081897544526708 - type: ndcg_at_1 value: 24.073 - type: ndcg_at_10 value: 41.323 - type: ndcg_at_100 value: 47.188 - type: ndcg_at_1000 value: 48.424 - type: ndcg_at_20 value: 44.084 - type: ndcg_at_3 value: 33.427 - type: ndcg_at_5 value: 37.171 - type: precision_at_1 value: 24.073 - type: precision_at_10 value: 7.242 - type: precision_at_100 value: 1.051 - type: precision_at_1000 value: 0.117 - type: precision_at_20 value: 4.2700000000000005 - type: precision_at_3 value: 15.498000000000001 - type: precision_at_5 value: 11.431 - type: recall_at_1 value: 21.246000000000002 - type: recall_at_10 value: 61.102000000000004 - type: recall_at_100 value: 87.08500000000001 - type: recall_at_1000 value: 96.222 - type: recall_at_20 value: 71.372 - type: recall_at_3 value: 40.361000000000004 - type: recall_at_5 value: 49.044 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics: - type: map_at_1 value: 68.285 - type: map_at_10 value: 82.106 - type: map_at_100 value: 82.76599999999999 - type: map_at_1000 value: 82.788 - type: map_at_20 value: 82.529 - type: map_at_3 value: 79.108 - type: map_at_5 value: 80.964 - type: mrr_at_1 value: 78.67 - type: mrr_at_10 value: 85.4671111111108 - type: mrr_at_100 value: 85.59335571351787 - type: mrr_at_1000 value: 85.59536983332889 - type: mrr_at_20 value: 85.55846883663256 - type: mrr_at_3 value: 84.39999999999962 - type: mrr_at_5 value: 85.11249999999953 - type: nauc_map_at_1000_diff1 value: 75.5071991426707 - type: nauc_map_at_1000_max value: 33.24884628979125 - type: nauc_map_at_100_diff1 value: 75.51606789897293 - type: nauc_map_at_100_max value: 33.237678419609715 - type: nauc_map_at_10_diff1 value: 75.63941045488615 - type: nauc_map_at_10_max value: 32.914787531889694 - type: nauc_map_at_1_diff1 value: 78.53182147965822 - type: nauc_map_at_1_max value: 24.631838635071222 - type: nauc_map_at_20_diff1 value: 75.55246990673865 - type: nauc_map_at_20_max value: 33.12406999050574 - type: nauc_map_at_3_diff1 value: 75.76122624224449 - type: nauc_map_at_3_max value: 30.56135184566114 - type: nauc_map_at_5_diff1 value: 75.62760573601093 - type: nauc_map_at_5_max value: 32.014157139666985 - type: nauc_mrr_at_1000_diff1 value: 76.37148849763105 - type: nauc_mrr_at_1000_max value: 35.935665230883934 - type: nauc_mrr_at_100_diff1 value: 76.37094038633705 - type: nauc_mrr_at_100_max value: 35.94012231045831 - type: nauc_mrr_at_10_diff1 value: 76.35647457628434 - type: nauc_mrr_at_10_max value: 36.01811322984862 - type: nauc_mrr_at_1_diff1 value: 77.24309585056221 - type: nauc_mrr_at_1_max value: 34.48519876828825 - type: nauc_mrr_at_20_diff1 value: 76.36670040011074 - type: nauc_mrr_at_20_max value: 35.99210482612602 - type: nauc_mrr_at_3_diff1 value: 76.09424554868272 - type: nauc_mrr_at_3_max value: 35.609777385861044 - type: nauc_mrr_at_5_diff1 value: 76.25068640961776 - type: nauc_mrr_at_5_max value: 35.86165128556917 - type: nauc_ndcg_at_1000_diff1 value: 75.46284119099505 - type: nauc_ndcg_at_1000_max value: 34.897248065013535 - type: nauc_ndcg_at_100_diff1 value: 75.55417772660796 - type: nauc_ndcg_at_100_max value: 34.9921360207961 - type: nauc_ndcg_at_10_diff1 value: 75.48987547153091 - type: nauc_ndcg_at_10_max value: 34.52070770288654 - type: nauc_ndcg_at_1_diff1 value: 77.2205910169754 - type: nauc_ndcg_at_1_max value: 34.54544979283322 - type: nauc_ndcg_at_20_diff1 value: 75.52495648309022 - type: nauc_ndcg_at_20_max value: 34.75327053329915 - type: nauc_ndcg_at_3_diff1 value: 74.76800490923522 - type: nauc_ndcg_at_3_max value: 32.77064919163132 - type: nauc_ndcg_at_5_diff1 value: 75.05016397357261 - type: nauc_ndcg_at_5_max value: 33.50761269482319 - type: nauc_precision_at_1000_diff1 value: -41.81465497084401 - type: nauc_precision_at_1000_max value: -4.443935842899313 - type: nauc_precision_at_100_diff1 value: -40.80948937001563 - type: nauc_precision_at_100_max value: -3.403706458833991 - type: nauc_precision_at_10_diff1 value: -33.369656218745945 - type: nauc_precision_at_10_max value: 2.2202781020992255 - type: nauc_precision_at_1_diff1 value: 77.2205910169754 - type: nauc_precision_at_1_max value: 34.54544979283322 - type: nauc_precision_at_20_diff1 value: -37.568976386400706 - type: nauc_precision_at_20_max value: -0.6469605151975117 - type: nauc_precision_at_3_diff1 value: -10.217358622390567 - type: nauc_precision_at_3_max value: 11.83919267748663 - type: nauc_precision_at_5_diff1 value: -24.481543671948373 - type: nauc_precision_at_5_max value: 6.576825503675188 - type: nauc_recall_at_1000_diff1 value: 67.36524460905785 - type: nauc_recall_at_1000_max value: 53.720724394976585 - type: nauc_recall_at_100_diff1 value: 75.23538841054406 - type: nauc_recall_at_100_max value: 47.2723927504464 - type: nauc_recall_at_10_diff1 value: 69.95500109263831 - type: nauc_recall_at_10_max value: 34.583322421413996 - type: nauc_recall_at_1_diff1 value: 78.53182147965822 - type: nauc_recall_at_1_max value: 24.631838635071222 - type: nauc_recall_at_20_diff1 value: 70.32541323559573 - type: nauc_recall_at_20_max value: 36.98517552839284 - type: nauc_recall_at_3_diff1 value: 71.477694594835 - type: nauc_recall_at_3_max value: 27.960647983463073 - type: nauc_recall_at_5_diff1 value: 70.17565198935641 - type: nauc_recall_at_5_max value: 30.104013734994844 - type: ndcg_at_1 value: 78.68 - type: ndcg_at_10 value: 86.244 - type: ndcg_at_100 value: 87.651 - type: ndcg_at_1000 value: 87.816 - type: ndcg_at_20 value: 86.961 - type: ndcg_at_3 value: 83.152 - type: ndcg_at_5 value: 84.819 - type: precision_at_1 value: 78.68 - type: precision_at_10 value: 13.123000000000001 - type: precision_at_100 value: 1.514 - type: precision_at_1000 value: 0.156 - type: precision_at_20 value: 6.979 - type: precision_at_3 value: 36.353 - type: precision_at_5 value: 23.977999999999998 - type: recall_at_1 value: 68.285 - type: recall_at_10 value: 94.16799999999999 - type: recall_at_100 value: 99.116 - type: recall_at_1000 value: 99.944 - type: recall_at_20 value: 96.494 - type: recall_at_3 value: 85.31 - type: recall_at_5 value: 89.993 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 47.39326061426851 - type: v_measures value: - 0.47153549737072414 - 0.5113188409132627 - 0.4256578555733507 - 0.45547697557001166 - 0.44673621430540467 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 58.382849910561305 - type: v_measures value: - 0.638508286047501 - 0.6201813511333097 - 0.6412218572317954 - 0.34538859648148 - 0.6372584092921234 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 metrics: - type: map_at_1 value: 3.5680000000000005 - type: map_at_10 value: 9.165 - type: map_at_100 value: 10.928 - type: map_at_1000 value: 11.187 - type: map_at_20 value: 10.030999999999999 - type: map_at_3 value: 6.598 - type: map_at_5 value: 7.746 - type: mrr_at_1 value: 17.5 - type: mrr_at_10 value: 28.15242063492062 - type: mrr_at_100 value: 29.148090545385042 - type: mrr_at_1000 value: 29.22586383082865 - type: mrr_at_20 value: 28.716339503289944 - type: mrr_at_3 value: 24.9666666666667 - type: mrr_at_5 value: 26.496666666666684 - type: nauc_map_at_1000_diff1 value: 18.97303632967232 - type: nauc_map_at_1000_max value: 26.99578750624317 - type: nauc_map_at_100_diff1 value: 19.03406677193612 - type: nauc_map_at_100_max value: 26.869362658515016 - type: nauc_map_at_10_diff1 value: 18.057667997990386 - type: nauc_map_at_10_max value: 25.309052871533634 - type: nauc_map_at_1_diff1 value: 19.012090704165505 - type: nauc_map_at_1_max value: 17.258809318287167 - type: nauc_map_at_20_diff1 value: 18.941090010273122 - type: nauc_map_at_20_max value: 26.333042449319226 - type: nauc_map_at_3_diff1 value: 16.710501604799592 - type: nauc_map_at_3_max value: 21.31218718265248 - type: nauc_map_at_5_diff1 value: 16.56134390513593 - type: nauc_map_at_5_max value: 22.826974292312546 - type: nauc_mrr_at_1000_diff1 value: 16.363889874600694 - type: nauc_mrr_at_1000_max value: 20.518910454040395 - type: nauc_mrr_at_100_diff1 value: 16.351792727972825 - type: nauc_mrr_at_100_max value: 20.51605975440402 - type: nauc_mrr_at_10_diff1 value: 16.353234548491002 - type: nauc_mrr_at_10_max value: 20.3474303123765 - type: nauc_mrr_at_1_diff1 value: 18.72320588103456 - type: nauc_mrr_at_1_max value: 17.31659868214623 - type: nauc_mrr_at_20_diff1 value: 16.349503308662584 - type: nauc_mrr_at_20_max 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 value: 67.86904761904762 - type: mrr_at_100 value: 68.1173503682578 - type: mrr_at_1000 value: 68.1173503682578 - type: mrr_at_20 value: 67.97431077694236 - type: mrr_at_3 value: 64.0 - type: mrr_at_5 value: 66.8 - type: nauc_map_at_1000_diff1 value: -22.260198132192173 - type: nauc_map_at_1000_max value: 54.68403556878306 - type: nauc_map_at_100_diff1 value: -16.159059471544403 - type: nauc_map_at_100_max value: 46.680332538973104 - type: nauc_map_at_10_diff1 value: -0.16025207380124323 - type: nauc_map_at_10_max value: 18.60858928303837 - type: nauc_map_at_1_diff1 value: 19.31406645591962 - type: nauc_map_at_1_max value: 12.446064494149196 - type: nauc_map_at_20_diff1 value: -3.7207534399873086 - type: nauc_map_at_20_max value: 23.984664337717064 - type: nauc_map_at_3_diff1 value: 11.318172692961777 - type: nauc_map_at_3_max value: 17.80683628355867 - type: nauc_map_at_5_diff1 value: 7.92181873049933 - type: nauc_map_at_5_max value: 17.64389113325039 - type: nauc_mrr_at_1000_diff1 value: 17.49153792066571 - type: nauc_mrr_at_1000_max value: 33.59871091829616 - type: nauc_mrr_at_100_diff1 value: 17.49153792066571 - type: nauc_mrr_at_100_max value: 33.59871091829616 - type: nauc_mrr_at_10_diff1 value: 17.502786184772496 - type: nauc_mrr_at_10_max value: 33.97577280665956 - type: nauc_mrr_at_1_diff1 value: 20.469006140423968 - type: nauc_mrr_at_1_max value: 24.62282237225972 - type: nauc_mrr_at_20_diff1 value: 17.27246967398437 - type: nauc_mrr_at_20_max value: 33.69787393313599 - type: nauc_mrr_at_3_diff1 value: 17.658115148717215 - type: nauc_mrr_at_3_max value: 34.66827145024068 - type: nauc_mrr_at_5_diff1 value: 17.916005644695375 - type: nauc_mrr_at_5_max value: 35.10406736432433 - type: nauc_ndcg_at_1000_diff1 value: -25.695422281160564 - type: nauc_ndcg_at_1000_max value: 41.85333091055545 - type: nauc_ndcg_at_100_diff1 value: -20.77388791351094 - type: nauc_ndcg_at_100_max value: 44.356134608903034 - type: nauc_ndcg_at_10_diff1 value: -10.307778980699197 - type: nauc_ndcg_at_10_max value: 33.23388628961326 - type: nauc_ndcg_at_1_diff1 value: 20.412738715863956 - type: nauc_ndcg_at_1_max value: 23.390778206963613 - type: nauc_ndcg_at_20_diff1 value: -11.307721360709836 - type: nauc_ndcg_at_20_max value: 36.352174201276206 - type: nauc_ndcg_at_3_diff1 value: 7.285454029149752 - type: nauc_ndcg_at_3_max value: 29.03877907321362 - type: nauc_ndcg_at_5_diff1 value: 0.8947521854164275 - type: nauc_ndcg_at_5_max value: 31.54102751296627 - type: nauc_precision_at_1000_diff1 value: -25.78557535978164 - type: nauc_precision_at_1000_max value: 37.467970941981896 - type: nauc_precision_at_100_diff1 value: -25.701682320317964 - type: nauc_precision_at_100_max value: 45.81756747527059 - type: nauc_precision_at_10_diff1 value: -21.234526843340713 - type: nauc_precision_at_10_max value: 32.91504410405538 - type: nauc_precision_at_1_diff1 value: 20.469006140423968 - type: nauc_precision_at_1_max value: 24.62282237225972 - type: nauc_precision_at_20_diff1 value: -20.025454190589233 - type: nauc_precision_at_20_max value: 37.55936600361076 - type: nauc_precision_at_3_diff1 value: 2.8390823388370996 - type: nauc_precision_at_3_max value: 31.69418560296442 - type: nauc_precision_at_5_diff1 value: -7.36442063396579 - type: nauc_precision_at_5_max value: 32.88936384031251 - type: nauc_recall_at_1000_diff1 value: -25.040103819963193 - type: nauc_recall_at_1000_max value: 39.67194190901835 - type: nauc_recall_at_100_diff1 value: -15.819635509190055 - type: nauc_recall_at_100_max value: 38.20290322073082 - type: nauc_recall_at_10_diff1 value: -2.179337202237811 - type: nauc_recall_at_10_max value: 15.444689423576962 - type: nauc_recall_at_1_diff1 value: 19.31406645591962 - type: nauc_recall_at_1_max value: 12.446064494149196 - type: nauc_recall_at_20_diff1 value: -4.369705346989079 - type: nauc_recall_at_20_max value: 19.689399778184235 - type: nauc_recall_at_3_diff1 value: 11.368703632097438 - type: nauc_recall_at_3_max value: 18.834378852568555 - type: nauc_recall_at_5_diff1 value: 9.363083205894776 - type: nauc_recall_at_5_max value: 16.283811472009358 - type: ndcg_at_1 value: 50.0 - type: ndcg_at_10 value: 46.788999999999994 - type: ndcg_at_100 value: 33.676 - type: ndcg_at_1000 value: 36.502 - type: ndcg_at_20 value: 42.895 - type: ndcg_at_3 value: 49.531 - type: ndcg_at_5 value: 49.413000000000004 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 51.2 - type: precision_at_100 value: 34.62 - type: precision_at_1000 value: 16.869999999999997 - type: precision_at_20 value: 45.800000000000004 - type: precision_at_3 value: 54.0 - type: precision_at_5 value: 54.400000000000006 - type: recall_at_1 value: 0.153 - type: recall_at_10 value: 1.373 - type: recall_at_100 value: 8.425 - type: recall_at_1000 value: 36.521 - type: recall_at_20 value: 2.4 - type: recall_at_3 value: 0.441 - type: recall_at_5 value: 0.739 - task: type: Retrieval dataset: name: MTEB Touche2020 type: mteb/touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.4449999999999998 - type: map_at_10 value: 5.508 - type: map_at_100 value: 9.561 - type: map_at_1000 value: 11.075 - type: map_at_20 value: 7.195 - type: map_at_3 value: 2.8819999999999997 - type: map_at_5 value: 3.859 - type: mrr_at_1 value: 18.367346938775512 - type: mrr_at_10 value: 30.816326530612244 - type: mrr_at_100 value: 32.36702497368042 - type: mrr_at_1000 value: 32.39717992373145 - type: mrr_at_20 value: 31.906678711584885 - type: mrr_at_3 value: 26.190476190476193 - type: mrr_at_5 value: 28.945578231292522 - type: nauc_map_at_1000_diff1 value: 15.86075185050381 - type: nauc_map_at_1000_max value: -39.68076638135203 - type: nauc_map_at_100_diff1 value: 14.373599398703885 - type: nauc_map_at_100_max value: -40.06871903205363 - type: nauc_map_at_10_diff1 value: 17.129188771799722 - type: nauc_map_at_10_max value: -40.62148414336222 - type: nauc_map_at_1_diff1 value: 19.300742296090192 - type: nauc_map_at_1_max value: -39.01422276688408 - type: nauc_map_at_20_diff1 value: 17.096257109130104 - type: nauc_map_at_20_max value: -41.45895858788768 - type: nauc_map_at_3_diff1 value: 13.061713953725201 - type: nauc_map_at_3_max value: -38.41319841534379 - type: nauc_map_at_5_diff1 value: 14.349086737587205 - type: nauc_map_at_5_max value: -38.969968391834044 - type: nauc_mrr_at_1000_diff1 value: 15.890202844477846 - type: nauc_mrr_at_1000_max value: -35.71618277376245 - type: nauc_mrr_at_100_diff1 value: 15.922757469316565 - type: nauc_mrr_at_100_max value: -35.79109859355446 - type: nauc_mrr_at_10_diff1 value: 15.047536449761841 - type: nauc_mrr_at_10_max value: -36.56394292392469 - type: nauc_mrr_at_1_diff1 value: 23.5674706768817 - type: nauc_mrr_at_1_max value: -34.577680813370684 - type: nauc_mrr_at_20_diff1 value: 15.48856353024658 - type: nauc_mrr_at_20_max value: -35.79541680443546 - type: nauc_mrr_at_3_diff1 value: 15.806087622568954 - type: nauc_mrr_at_3_max value: -32.477788788477206 - type: nauc_mrr_at_5_diff1 value: 15.100010170892547 - type: nauc_mrr_at_5_max value: -34.902570265426476 - type: nauc_ndcg_at_1000_diff1 value: 17.06221439254491 - type: nauc_ndcg_at_1000_max value: -38.057099656137524 - type: nauc_ndcg_at_100_diff1 value: 10.712806009366044 - type: nauc_ndcg_at_100_max value: -41.634510046296825 - type: nauc_ndcg_at_10_diff1 value: 19.714184908152074 - type: nauc_ndcg_at_10_max value: -38.35275712711699 - type: nauc_ndcg_at_1_diff1 value: 27.689699524955962 - type: nauc_ndcg_at_1_max value: -32.166823132012276 - type: nauc_ndcg_at_20_diff1 value: 16.460154587871894 - type: nauc_ndcg_at_20_max value: -44.9036600147991 - type: nauc_ndcg_at_3_diff1 value: 20.089462936175444 - type: nauc_ndcg_at_3_max value: -28.050150980736177 - type: nauc_ndcg_at_5_diff1 value: 16.85293507256734 - type: nauc_ndcg_at_5_max value: -30.806342862683927 - type: nauc_precision_at_1000_diff1 value: 14.408977497220873 - type: nauc_precision_at_1000_max value: 37.74317255169207 - type: nauc_precision_at_100_diff1 value: -1.535852218534388 - type: nauc_precision_at_100_max value: -19.385555066523708 - type: nauc_precision_at_10_diff1 value: 14.935398953941345 - type: nauc_precision_at_10_max value: -40.7784122393935 - type: nauc_precision_at_1_diff1 value: 23.5674706768817 - type: nauc_precision_at_1_max value: -34.577680813370684 - type: nauc_precision_at_20_diff1 value: 10.2401285323039 - type: nauc_precision_at_20_max value: -44.04141433293453 - type: nauc_precision_at_3_diff1 value: 15.784680322541114 - type: nauc_precision_at_3_max value: -30.464693842536324 - type: nauc_precision_at_5_diff1 value: 6.837543215418572 - type: nauc_precision_at_5_max value: -32.9314191958357 - type: nauc_recall_at_1000_diff1 value: 8.533481249495253 - type: nauc_recall_at_1000_max value: -30.221386840946657 - type: nauc_recall_at_100_diff1 value: -1.394100451328846 - type: nauc_recall_at_100_max value: -41.79269914007117 - type: nauc_recall_at_10_diff1 value: 13.77128337229429 - type: nauc_recall_at_10_max value: -44.513151444340814 - type: nauc_recall_at_1_diff1 value: 19.300742296090192 - type: nauc_recall_at_1_max value: -39.01422276688408 - type: nauc_recall_at_20_diff1 value: 8.568504019773036 - type: nauc_recall_at_20_max value: -47.8434381158021 - type: nauc_recall_at_3_diff1 value: 9.308189193923543 - type: nauc_recall_at_3_max value: -39.95524531900913 - type: nauc_recall_at_5_diff1 value: 10.205415401777017 - type: nauc_recall_at_5_max value: -38.78454250086998 - type: ndcg_at_1 value: 16.326999999999998 - type: ndcg_at_10 value: 14.472999999999999 - type: ndcg_at_100 value: 24.621000000000002 - type: ndcg_at_1000 value: 37.964999999999996 - type: ndcg_at_20 value: 16.55 - type: ndcg_at_3 value: 15.432000000000002 - type: ndcg_at_5 value: 14.654 - type: precision_at_1 value: 18.367 - type: precision_at_10 value: 14.285999999999998 - type: precision_at_100 value: 5.612 - type: precision_at_1000 value: 1.39 - type: precision_at_20 value: 11.735 - type: precision_at_3 value: 17.007 - type: precision_at_5 value: 16.326999999999998 - type: recall_at_1 value: 1.4449999999999998 - type: recall_at_10 value: 10.796999999999999 - type: recall_at_100 value: 36.172 - type: recall_at_1000 value: 75.737 - type: recall_at_20 value: 17.494 - type: recall_at_3 value: 3.74 - type: recall_at_5 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: - type: v_measure 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: - type: cos_sim_accuracy value: 84.19860523335518 - type: cos_sim_ap value: 67.98183780223552 - type: cos_sim_f1 value: 63.287574797606474 - type: cos_sim_precision value: 56.98288611874076 - type: cos_sim_recall value: 71.16094986807387 - type: dot_accuracy value: 81.0872027180068 - type: dot_ap value: 57.080616165589994 - type: dot_f1 value: 57.184056030487184 - type: dot_precision value: 46.899814157796925 - type: dot_recall value: 73.24538258575198 - type: euclidean_accuracy value: 84.10919711509806 - type: euclidean_ap value: 68.02422564958268 - type: euclidean_f1 value: 63.76539589442815 - type: euclidean_precision value: 57.40232312565998 - type: euclidean_recall value: 71.71503957783642 - type: manhattan_accuracy value: 84.06747332657805 - type: manhattan_ap value: 67.74186393843273 - type: manhattan_f1 value: 63.57935359382538 - type: manhattan_precision value: 58.55175477565526 - type: manhattan_recall value: 69.55145118733509 - type: max_accuracy value: 84.19860523335518 - type: max_ap value: 68.02422564958268 - type: max_f1 value: 63.76539589442815 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.40311250824699 - type: cos_sim_ap 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}
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
QiaoyuZheng/RP3D-DiagModel
QiaoyuZheng
null
[ "license:apache-2.0", "region:us" ]
1,703,923,273,000
2024-01-16T09:09:19
0
3
--- license: apache-2.0 --- # RP3D-DiagModel ## About Checkpoint The detailed parameter we use for training is in the following: ``` start_class: 0 end_clas: 5569 backbone: 'resnet' level: 'articles' # represents the disorder level depth: 32 ltype: 'MultiLabel' # represents the Binary Cross Entropy Loss augment: True # represents the medical data augmentation split: 'late' # represents the late fusion strategy ``` ### Load Model ``` # Load backnone model = RadNet(num_cls=num_classes, backbone=backbone, depth=depth, ltype=ltype, augment=augment, fuse=fuse, ke=ke, encoded=encoded, adapter=adapter) pretrained_weights = torch.load("path/to/pytorch_model_32_late.bin") missing, unexpect = model.load_state_dict(pretrained_weights,strict=False) print("missing_cpt:", missing) print("unexpect_cpt:", unexpect) # If KE is set True, load text encoder medcpt = MedCPT_clinical(bert_model_name = 'ncbi/MedCPT-Query-Encoder') checkpoint = torch.load('path/to/epoch_state.pt',map_location='cpu')['state_dict'] load_checkpoint = {key.replace('module.', ''): value for key, value in checkpoint.items()} missing, unexpect = medcpt.load_state_dict(load_checkpoint, strict=False) print("missing_cpt:", missing) print("unexpect_cpt:", unexpect) ``` ## Why we provide this checkpoint? All the early fusion checkpoint can be further finetuned from this checkpoint. If you need other checkpoints using different parameter settings, there are two possible ways: ### Finetune from this checkpoint ''' checkpoint: "None" safetensor: path to this checkpoint(pytorch_model.bin) ''' ### Contact Us Email the author: [email protected] ## About Dataset Please refer to [RP3D-DiagDS](https://huggingface.co/datasets/QiaoyuZheng/RP3D-DiagDS) For more information, please refer to our instructions on [github](https://github.com/qiaoyu-zheng/RP3D-Diag) to download and use.
[ "MEDICAL DATA" ]
Non_BioNLP
tsavage68/MedQA_L3_350steps_1e7rate_03beta_CSFTDPO
tsavage68
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "base_model:finetune:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,716,518,287,000
2024-05-24T02:42:23
4
0
--- base_model: tsavage68/MedQA_L3_1000steps_1e6rate_SFT license: llama3 tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_350steps_1e7rate_03beta_CSFTDPO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MedQA_L3_350steps_1e7rate_03beta_CSFTDPO This model is a fine-tuned version of [tsavage68/MedQA_L3_1000steps_1e6rate_SFT](https://huggingface.co/tsavage68/MedQA_L3_1000steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6516 - Rewards/chosen: 0.2738 - Rewards/rejected: 0.1790 - Rewards/accuracies: 0.7099 - Rewards/margins: 0.0948 - Logps/rejected: -33.2582 - Logps/chosen: -30.4158 - Logits/rejected: -0.7313 - Logits/chosen: -0.7305 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 350 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6925 | 0.0489 | 50 | 0.6930 | -0.0016 | -0.0023 | 0.5011 | 0.0007 | -33.8624 | -31.3338 | -0.7320 | -0.7314 | | 0.6841 | 0.0977 | 100 | 0.6807 | 0.2459 | 0.2195 | 0.6549 | 0.0264 | -33.1233 | -30.5088 | -0.7330 | -0.7323 | | 0.6524 | 0.1466 | 150 | 0.6658 | 0.3522 | 0.2898 | 0.6703 | 0.0624 | -32.8887 | -30.1544 | -0.7315 | -0.7308 | | 0.631 | 0.1954 | 200 | 0.6545 | 0.1829 | 0.0948 | 0.6923 | 0.0881 | -33.5389 | -30.7188 | -0.7310 | -0.7303 | | 0.6675 | 0.2443 | 250 | 0.6520 | 0.2481 | 0.1544 | 0.7121 | 0.0938 | -33.3403 | -30.5014 | -0.7309 | -0.7301 | | 0.6479 | 0.2931 | 300 | 0.6509 | 0.2738 | 0.1773 | 0.7099 | 0.0966 | -33.2640 | -30.4157 | -0.7310 | -0.7303 | | 0.6583 | 0.3420 | 350 | 0.6516 | 0.2738 | 0.1790 | 0.7099 | 0.0948 | -33.2582 | -30.4158 | -0.7313 | -0.7305 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
[ "MEDQA" ]
BioNLP
PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct-v1.1
PatronusAI
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "pytorch", "Lynx", "Patronus AI", "evaluation", "hallucination-detection", "conversational", "en", "arxiv:2407.08488", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,721,851,223,000
2024-07-31T17:01:34
9,624
10
--- language: - en library_name: transformers license: cc-by-nc-4.0 tags: - text-generation - pytorch - Lynx - Patronus AI - evaluation - hallucination-detection --- # Model Card for Model ID Lynx is an open-source hallucination evaluation model. Patronus-Lynx-8B-Instruct-v1.1 was trained on a mix of datasets including CovidQA, PubmedQA, DROP, RAGTruth. The datasets contain a mix of hand-annotated and synthetic data. The maximum sequence length is 128000 tokens. ## Model Details - **Model Type:** Patronus-Lynx-8B-Instruct-v1.1 is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct model. - **Language:** Primarily English - **Developed by:** Patronus AI - **Paper:** [https://arxiv.org/abs/2407.08488](https://arxiv.org/abs/2407.08488) - **License:** [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** [https://github.com/patronus-ai/Lynx-hallucination-detection](https://github.com/patronus-ai/Lynx-hallucination-detection) ## How to Get Started with the Model Lynx is trained to detect hallucinations in RAG settings. Provided a document, question and answer, the model can evaluate whether the answer is faithful to the document. To use the model, we recommend using the following prompt: ``` PROMPT = """ Given the following QUESTION, DOCUMENT and ANSWER you must analyze the provided answer and determine whether it is faithful to the contents of the DOCUMENT. The ANSWER must not offer new information beyond the context provided in the DOCUMENT. The ANSWER also must not contradict information provided in the DOCUMENT. Output your final verdict by strictly following this format: "PASS" if the answer is faithful to the DOCUMENT and "FAIL" if the answer is not faithful to the DOCUMENT. Show your reasoning. -- QUESTION (THIS DOES NOT COUNT AS BACKGROUND INFORMATION): {question} -- DOCUMENT: {context} -- ANSWER: {answer} -- Your output should be in JSON FORMAT with the keys "REASONING" and "SCORE": {{"REASONING": <your reasoning as bullet points>, "SCORE": <your final score>}} """ ``` The model will output the score as 'PASS' if the answer is faithful to the document or FAIL if the answer is not faithful to the document. ## Inference To run inference, you can use HF pipeline: ``` model_name = 'PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct-v1.1' pipe = pipeline( "text-generation", model=model_name, max_new_tokens=600, device="cuda", return_full_text=False ) messages = [ {"role": "user", "content": prompt}, ] result = pipe(messages) print(result[0]['generated_text']) ``` Since the model is trained in chat format, ensure that you pass the prompt as a user message. For more information on training details, refer to our [ArXiv paper](https://arxiv.org/abs/2407.08488). ## Evaluation The model was evaluated on [PatronusAI/HaluBench](https://huggingface.co/datasets/PatronusAI/HaluBench). | Model | HaluEval | RAGTruth | FinanceBench | DROP | CovidQA | PubmedQA | Overall | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | GPT-4o | <ins>87.9%</ins> | 84.3% | <ins>85.3%</ins> | 84.3% | 95.0% | 82.1% | <ins>86.5%</ins> | | GPT-4-Turbo | 86.0% | <ins>85.0%</ins> | 82.2% | <ins>84.8%</ins> | 90.6% | 83.5% | 85.0% | | GPT-3.5-Turbo | 62.2% | 50.7% | 60.9% | 57.2% | 56.7% | 62.8% | 58.7% | | Claude-3.5-Sonnet | 84.5% | 79.1% | 69.3% | 69.7% | 70.8% |84.8% |83.7%| | RAGAS Faithfulness | 70.6% | 75.8% | 59.5% | 59.6% | 75.0% | 67.7% | 66.9% | | Mistral-Instruct-7B | 78.3% | 77.7% | 56.3% | 56.3% | 71.7% | 77.9% | 69.4% | | Llama-3-Instruct-8B | 83.1% | 80.0% | 55.0% | 58.2% | 75.2% | 70.7% | 70.4% | | Llama-3-Instruct-70B | 87.0% | **83.8%** | 72.7% | 69.4% | 85.0% | 82.6% | 80.1% | | Lynx (8B) | 85.7% | 80.0% | 72.5% | **77.8%** | 96.3% | 85.2% | 82.9% | | Lynx v1.1 (8B) | **87.3%** | 79.9% | **75.6%** | 77.5% | <ins>**96.9%**</ins> |<ins> **88.9%**</ins> | **84.3%** | ## Citation If you are using the model, cite using ``` @article{ravi2024lynx, title={Lynx: An Open Source Hallucination Evaluation Model}, author={Ravi, Selvan Sunitha and Mielczarek, Bartosz and Kannappan, Anand and Kiela, Douwe and Qian, Rebecca}, journal={arXiv preprint arXiv:2407.08488}, year={2024} } ``` ## Model Card Contact [@sunitha-ravi](https://huggingface.co/sunitha-ravi) [@RebeccaQian1](https://huggingface.co/RebeccaQian1) [@presidev](https://huggingface.co/presidev)
[ "PUBMEDQA" ]
Non_BioNLP
Triangle104/EtherealRainbow-v0.3-8B-Q6_K-GGUF
Triangle104
null
[ "transformers", "gguf", "mergekit", "merge", "not-for-all-audiences", "llama-cpp", "gguf-my-repo", "en", "base_model:invisietch/EtherealRainbow-v0.3-8B", "base_model:quantized:invisietch/EtherealRainbow-v0.3-8B", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]
1,731,948,209,000
2024-11-18T16:56:13
2
0
--- base_model: invisietch/EtherealRainbow-v0.3-8B language: - en library_name: transformers license: llama3 tags: - mergekit - merge - not-for-all-audiences - llama-cpp - gguf-my-repo --- # Triangle104/EtherealRainbow-v0.3-8B-Q6_K-GGUF This model was converted to GGUF format from [`invisietch/EtherealRainbow-v0.3-8B`](https://huggingface.co/invisietch/EtherealRainbow-v0.3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/invisietch/EtherealRainbow-v0.3-8B) for more details on the model. --- Model details: - Ethereal Rainbow is an 8B parameter merge of various Llama3-based finetunes created using mergekit. The purpose of Ethereal Rainbow is to create an uncensored Llama3 variant which is capable of writing creative prose, and engaging in SFW as well as NSFW roleplay and storytelling, with a strong focus on long-form responses & adherence to prompts. v0.3 improves creativity over v0.2 without losing coherence. It has been tested over more than 1,000 messages including roleplay, code prompts, and 'write a scene'-type prompts. Feedback - I appreciate all feedback on any of my models, you can use: My Discord server - requires Discord. The Community tab - requires HF login. The SillyTavern Discord thread - must be on SillyTavern Discord. Discord DMs to invisietch. Your feedback is how I improve these models for future versions. Disclaimer - This model is built on an abliterated base and as such is largely uncensored. It can generate explicit, disturbing or offensive responses. Use responsibly. I am not responsible for your use of this model. Prompting Format I'd recommend Llama-3 Instruct prompting format: <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {output}<|eot_id|> Some of the models included in the merge were trained on ChatML & Alpaca so you can try those. I have not tested them. Example Storywriting These prompts are used on SillyTavern with a fairly basic narrator card. I have trimmed the start and finish where the narrator decided to add chapter headings, commentary and the like. All samples are made with the F32 GGUF loaded with koboldcpp, with response length capped at 2048 tokens. Write me a 3,000 word opening chapter of a 'gritty hard sci-fi' novel, drawing inspiration from the writing styles of Isaac Asimov & Andy Weir. Use third person personal. Include dialogue and internal monologues. The POV character for the opening chapter should be a 26 year old astronaut called Tone on a mission to Europa, who has just realised that the craft for the return journey is broken beyond repair, and he only has supplies for a few months. Given that survival is impossible, he seeks to spend the few months he has researching titan, so his life & mission are not wasted. Write me a 3,000 word opening chapter of a 'high fantasy' novel, drawing inspiration from the writing styles of J R R Tolkien & George R R Martin. Use third person personal. Include dialogue and internal monologues. The POV character for the opening chapter should be a 19 year old female elf bard who is looking for adventure. Write me a 3,000 word opening chapter of a 'weird fiction' novel, drawing inspiration from the writing styles of China Mieville and Neil Gaiman. Use third person personal. Include dialogue and internal monologues. The POV character for the opening chapter should be a male in his 20s called Horton who has just come to the city looking for work. I chose the hard sci-fi example to test positivity bias. It did require some prompting, but it was willing to kill the protagonist. I chose the high fantasy example to see whether it would bleed human features through to elves, this didn't occur. I chose the weird fiction example to see if the LLM understood a niche genre. I'd say it performed okay, better on style than on substance. Merge Strategy First, we create three bases: Rain - This is a roleplay base which makes up the majority of the model. Sun - This is the brains of the model, with strong instruct models & writing models. Ghost - This model primarily aims to improve the NSFW/NSFL aspects of the model, as well as general vocabulary. After this, we have a two-slerp stage to create the final model. Models Used The following models were used to create EtherealRainbow-v0.3-8B: mlabonne/NeuralDaredevil-8B-abliterated Sao10K/L3-8B-Stheno-v3.2 Nitral-AI/Hathor-L3-8B-v.02 grimjim/Llama-3-Luminurse-v0.2-OAS-8B hf-100/Llama-3-Spellbound-Instruct-8B-0.3 Gryphe/Pantheon-RP-1.0-8b-Llama-3 Blackroot/Llama-3-LongStory Locutusque/Llama-3-Hercules-5.0-8B Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B mpasila/Llama-3-LimaRP-Instruct-8B Undi95/Llama-3-LewdPlay-8B-evo Mergekit Configs - Rain - models: - model: mlabonne/NeuralDaredevil-8B-abliterated - model: Sao10K/L3-8B-Stheno-v3.2 parameters: density: 0.41 weight: 0.4 - model: Nitral-AI/Hathor-L3-8B-v.02 parameters: density: 0.53 weight: 0.5 - model: grimjim/Llama-3-Luminurse-v0.2-OAS-8B parameters: density: 0.45 weight: 0.1 merge_method: dare_ties base_model: mlabonne/NeuralDaredevil-8B-abliterated parameters: int8_mask: true dtype: bfloat16 Sun - models: - model: hf-100/Llama-3-Spellbound-Instruct-8B-0.3 - model: Gryphe/Pantheon-RP-1.0-8b-Llama-3 parameters: density: 0.48 weight: 0.5 - model: Blackroot/Llama-3-LongStory parameters: density: 0.36 weight: 0.2 - model: Locutusque/Llama-3-Hercules-5.0-8B parameters: density: 0.51 weight: 0.3 merge_method: dare_ties base_model: hf-100/Llama-3-Spellbound-Instruct-8B-0.3 parameters: int8_mask: true dtype: bfloat16 Ghost - models: - model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B - model: ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B parameters: density: 0.39 weight: 0.3 - model: mpasila/Llama-3-LimaRP-Instruct-8B parameters: density: 0.54 weight: 0.4 - model: Undi95/Llama-3-LewdPlay-8B-evo parameters: density: 0.49 weight: 0.3 merge_method: dare_ties base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B parameters: int8_mask: true dtype: bfloat16 Stage1 Slerp - models: - model: ./fp16/Rain-v0.3-8B - model: ./fp16/Ghost-v0.3-8B merge_method: slerp base_model: ./fp16/Rain-v0.3-8B parameters: t: - value: [0, 0, 0.1, 0.3, 0.5, 0.7, 0.5, 0.3, 0.1, 0, 0] embed_slerp: true dtype: bfloat16 tokenizer-source: model:./fp16/Rain-v0.3-8B Final-Stage Slerp - models: - model: ./fp16/ERStage1-v0.3-8B - model: ./fp16/Sun-v0.3-8B merge_method: slerp base_model: ./fp16/ERStage1-v0.3-8B parameters: t: - value: [0, 0, 0.1, 0.2, 0.4, 0.6, 0.4, 0.2, 0.1, 0, 0] embed_slerp: true dtype: bfloat16 tokenizer-source: model:./fp16/ERStage1-v0.3-8B --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/EtherealRainbow-v0.3-8B-Q6_K-GGUF --hf-file etherealrainbow-v0.3-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/EtherealRainbow-v0.3-8B-Q6_K-GGUF --hf-file etherealrainbow-v0.3-8b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/EtherealRainbow-v0.3-8B-Q6_K-GGUF --hf-file etherealrainbow-v0.3-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/EtherealRainbow-v0.3-8B-Q6_K-GGUF --hf-file etherealrainbow-v0.3-8b-q6_k.gguf -c 2048 ```
[ "CRAFT" ]
Non_BioNLP
ahmet1338/finetuned_embedder
ahmet1338
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", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "region:us" ]
1,713,775,482,000
2024-04-22T08:45:59
14
0
--- language: en license: apache-2.0 pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - prompt-retrieval - text-reranking - sentence-transformers - feature-extraction - sentence-similarity - transformers - t5 - English - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb inference: false model-index: - name: INSTRUCTOR results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 88.13432835820896 - type: ap value: 59.298209334395665 - type: f1 value: 83.31769058643586 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.526375 - type: ap value: 88.16327709705504 - type: f1 value: 91.51095801287843 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.856 - type: f1 value: 45.41490917650942 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 31.223 - type: map_at_10 value: 47.947 - type: map_at_100 value: 48.742000000000004 - type: map_at_1000 value: 48.745 - type: map_at_3 value: 43.137 - type: map_at_5 value: 45.992 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 48.4 - type: mrr_at_100 value: 49.202 - type: mrr_at_1000 value: 49.205 - type: mrr_at_3 value: 43.551 - type: mrr_at_5 value: 46.467999999999996 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 57.045 - type: ndcg_at_100 value: 60.175 - type: ndcg_at_1000 value: 60.233000000000004 - type: ndcg_at_3 value: 47.171 - type: ndcg_at_5 value: 52.322 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 8.599 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.63 - type: precision_at_5 value: 14.282 - type: recall_at_1 value: 31.223 - type: recall_at_10 value: 85.989 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 58.89 - type: recall_at_5 value: 71.408 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 43.1621946393635 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.56417132407894 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.29539304390207 - type: mrr value: 76.44484017060196 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_spearman value: 84.38746499431112 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.51298701298701 - type: f1 value: 77.49041754069235 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.61848554098577 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 31.32623280148178 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 35.803000000000004 - type: map_at_10 value: 48.848 - type: map_at_100 value: 50.5 - type: map_at_1000 value: 50.602999999999994 - type: map_at_3 value: 45.111000000000004 - type: map_at_5 value: 47.202 - type: mrr_at_1 value: 44.635000000000005 - type: mrr_at_10 value: 55.593 - type: mrr_at_100 value: 56.169999999999995 - type: mrr_at_1000 value: 56.19499999999999 - type: mrr_at_3 value: 53.361999999999995 - type: mrr_at_5 value: 54.806999999999995 - type: ndcg_at_1 value: 44.635000000000005 - type: ndcg_at_10 value: 55.899 - type: ndcg_at_100 value: 60.958 - type: ndcg_at_1000 value: 62.302 - type: ndcg_at_3 value: 51.051 - type: ndcg_at_5 value: 53.351000000000006 - type: precision_at_1 value: 44.635000000000005 - type: precision_at_10 value: 10.786999999999999 - type: precision_at_100 value: 1.6580000000000001 - type: precision_at_1000 value: 0.213 - type: precision_at_3 value: 24.893 - type: precision_at_5 value: 17.740000000000002 - type: recall_at_1 value: 35.803000000000004 - type: recall_at_10 value: 68.657 - type: recall_at_100 value: 89.77199999999999 - type: recall_at_1000 value: 97.67 - type: recall_at_3 value: 54.066 - type: recall_at_5 value: 60.788 - type: map_at_1 value: 33.706 - type: map_at_10 value: 44.896 - type: map_at_100 value: 46.299 - type: map_at_1000 value: 46.44 - type: map_at_3 value: 41.721000000000004 - type: map_at_5 value: 43.486000000000004 - type: mrr_at_1 value: 41.592 - type: mrr_at_10 value: 50.529 - type: mrr_at_100 value: 51.22 - type: mrr_at_1000 value: 51.258 - type: mrr_at_3 value: 48.205999999999996 - type: mrr_at_5 value: 49.528 - type: ndcg_at_1 value: 41.592 - type: ndcg_at_10 value: 50.77199999999999 - type: ndcg_at_100 value: 55.383 - type: ndcg_at_1000 value: 57.288 - type: ndcg_at_3 value: 46.324 - type: ndcg_at_5 value: 48.346000000000004 - type: precision_at_1 value: 41.592 - type: precision_at_10 value: 9.516 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 22.399 - type: precision_at_5 value: 15.770999999999999 - type: recall_at_1 value: 33.706 - type: recall_at_10 value: 61.353 - type: recall_at_100 value: 80.182 - type: recall_at_1000 value: 91.896 - type: recall_at_3 value: 48.204 - type: recall_at_5 value: 53.89699999999999 - type: map_at_1 value: 44.424 - type: map_at_10 value: 57.169000000000004 - type: map_at_100 value: 58.202 - type: map_at_1000 value: 58.242000000000004 - type: map_at_3 value: 53.825 - type: map_at_5 value: 55.714 - type: mrr_at_1 value: 50.470000000000006 - type: mrr_at_10 value: 60.489000000000004 - type: mrr_at_100 value: 61.096 - type: mrr_at_1000 value: 61.112 - type: mrr_at_3 value: 58.192 - type: mrr_at_5 value: 59.611999999999995 - type: ndcg_at_1 value: 50.470000000000006 - type: ndcg_at_10 value: 63.071999999999996 - type: ndcg_at_100 value: 66.964 - type: ndcg_at_1000 value: 67.659 - type: ndcg_at_3 value: 57.74399999999999 - type: ndcg_at_5 value: 60.367000000000004 - type: precision_at_1 value: 50.470000000000006 - type: precision_at_10 value: 10.019 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.558999999999997 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 44.424 - type: recall_at_10 value: 77.02 - type: recall_at_100 value: 93.738 - type: recall_at_1000 value: 98.451 - type: recall_at_3 value: 62.888 - type: recall_at_5 value: 69.138 - type: map_at_1 value: 26.294 - type: map_at_10 value: 34.503 - type: map_at_100 value: 35.641 - type: map_at_1000 value: 35.724000000000004 - type: map_at_3 value: 31.753999999999998 - type: map_at_5 value: 33.190999999999995 - type: mrr_at_1 value: 28.362 - type: mrr_at_10 value: 36.53 - type: mrr_at_100 value: 37.541000000000004 - type: mrr_at_1000 value: 37.602000000000004 - type: mrr_at_3 value: 33.917 - type: mrr_at_5 value: 35.358000000000004 - type: ndcg_at_1 value: 28.362 - type: ndcg_at_10 value: 39.513999999999996 - type: ndcg_at_100 value: 44.815 - type: ndcg_at_1000 value: 46.839 - type: ndcg_at_3 value: 34.02 - type: ndcg_at_5 value: 36.522 - type: precision_at_1 value: 28.362 - type: precision_at_10 value: 6.101999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.161999999999999 - type: precision_at_5 value: 9.966 - type: recall_at_1 value: 26.294 - type: recall_at_10 value: 53.098 - type: recall_at_100 value: 76.877 - type: recall_at_1000 value: 91.834 - type: recall_at_3 value: 38.266 - type: recall_at_5 value: 44.287 - type: map_at_1 value: 16.407 - type: map_at_10 value: 25.185999999999996 - type: map_at_100 value: 26.533 - type: map_at_1000 value: 26.657999999999998 - type: map_at_3 value: 22.201999999999998 - type: map_at_5 value: 23.923 - type: mrr_at_1 value: 20.522000000000002 - type: mrr_at_10 value: 29.522 - type: mrr_at_100 value: 30.644 - type: mrr_at_1000 value: 30.713 - type: mrr_at_3 value: 26.679000000000002 - type: mrr_at_5 value: 28.483000000000004 - type: ndcg_at_1 value: 20.522000000000002 - type: ndcg_at_10 value: 30.656 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.675 - type: ndcg_at_3 value: 25.319000000000003 - type: ndcg_at_5 value: 27.992 - type: precision_at_1 value: 20.522000000000002 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 16.407 - type: recall_at_10 value: 43.164 - type: recall_at_100 value: 69.695 - type: recall_at_1000 value: 89.41900000000001 - type: recall_at_3 value: 28.634999999999998 - type: recall_at_5 value: 35.308 - type: map_at_1 value: 30.473 - type: map_at_10 value: 41.676 - type: map_at_100 value: 43.120999999999995 - type: map_at_1000 value: 43.230000000000004 - type: map_at_3 value: 38.306000000000004 - type: map_at_5 value: 40.355999999999995 - type: mrr_at_1 value: 37.536 - type: mrr_at_10 value: 47.643 - type: mrr_at_100 value: 48.508 - type: mrr_at_1000 value: 48.551 - type: mrr_at_3 value: 45.348 - type: mrr_at_5 value: 46.744 - type: ndcg_at_1 value: 37.536 - type: ndcg_at_10 value: 47.823 - type: ndcg_at_100 value: 53.395 - type: ndcg_at_1000 value: 55.271 - type: ndcg_at_3 value: 42.768 - type: ndcg_at_5 value: 45.373000000000005 - type: precision_at_1 value: 37.536 - type: precision_at_10 value: 8.681 - type: precision_at_100 value: 1.34 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.468 - type: precision_at_5 value: 14.495 - type: recall_at_1 value: 30.473 - type: recall_at_10 value: 60.092999999999996 - type: recall_at_100 value: 82.733 - type: recall_at_1000 value: 94.875 - type: recall_at_3 value: 45.734 - type: recall_at_5 value: 52.691 - type: map_at_1 value: 29.976000000000003 - type: map_at_10 value: 41.097 - type: map_at_100 value: 42.547000000000004 - type: map_at_1000 value: 42.659000000000006 - type: map_at_3 value: 37.251 - type: map_at_5 value: 39.493 - type: mrr_at_1 value: 37.557 - type: mrr_at_10 value: 46.605000000000004 - type: mrr_at_100 value: 47.487 - type: mrr_at_1000 value: 47.54 - type: mrr_at_3 value: 43.721 - type: mrr_at_5 value: 45.411 - type: ndcg_at_1 value: 37.557 - type: ndcg_at_10 value: 47.449000000000005 - type: ndcg_at_100 value: 53.052 - type: ndcg_at_1000 value: 55.010999999999996 - type: ndcg_at_3 value: 41.439 - type: ndcg_at_5 value: 44.292 - type: precision_at_1 value: 37.557 - type: precision_at_10 value: 8.847 - type: precision_at_100 value: 1.357 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 20.091 - type: precision_at_5 value: 14.384 - type: recall_at_1 value: 29.976000000000003 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 84.245 - type: recall_at_1000 value: 96.97200000000001 - type: recall_at_3 value: 43.794 - type: recall_at_5 value: 51.778999999999996 - type: map_at_1 value: 28.099166666666665 - type: map_at_10 value: 38.1365 - type: map_at_100 value: 39.44491666666667 - type: map_at_1000 value: 39.55858333333334 - type: map_at_3 value: 35.03641666666666 - type: map_at_5 value: 36.79833333333334 - type: mrr_at_1 value: 33.39966666666667 - type: mrr_at_10 value: 42.42583333333333 - type: mrr_at_100 value: 43.28575 - type: mrr_at_1000 value: 43.33741666666667 - type: mrr_at_3 value: 39.94975 - type: mrr_at_5 value: 41.41633333333334 - type: ndcg_at_1 value: 33.39966666666667 - type: ndcg_at_10 value: 43.81741666666667 - type: ndcg_at_100 value: 49.08166666666667 - type: ndcg_at_1000 value: 51.121166666666674 - type: ndcg_at_3 value: 38.73575 - type: ndcg_at_5 value: 41.18158333333333 - type: precision_at_1 value: 33.39966666666667 - type: precision_at_10 value: 7.738916666666667 - type: precision_at_100 value: 1.2265833333333331 - type: precision_at_1000 value: 0.15983333333333336 - type: precision_at_3 value: 17.967416666666665 - type: precision_at_5 value: 12.78675 - type: recall_at_1 value: 28.099166666666665 - type: recall_at_10 value: 56.27049999999999 - type: recall_at_100 value: 78.93291666666667 - type: recall_at_1000 value: 92.81608333333334 - type: recall_at_3 value: 42.09775 - type: recall_at_5 value: 48.42533333333334 - type: map_at_1 value: 23.663 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.426 - type: map_at_1000 value: 31.519000000000002 - type: map_at_3 value: 28.069 - type: map_at_5 value: 29.256999999999998 - type: mrr_at_1 value: 26.687 - type: mrr_at_10 value: 33.107 - type: mrr_at_100 value: 34.055 - type: mrr_at_1000 value: 34.117999999999995 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.14 - type: ndcg_at_1 value: 26.687 - type: ndcg_at_10 value: 34.615 - type: ndcg_at_100 value: 39.776 - type: ndcg_at_1000 value: 42.05 - type: ndcg_at_3 value: 30.322 - type: ndcg_at_5 value: 32.157000000000004 - type: precision_at_1 value: 26.687 - type: precision_at_10 value: 5.491 - type: precision_at_100 value: 0.877 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.139000000000001 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.663 - type: recall_at_10 value: 45.035 - type: recall_at_100 value: 68.554 - type: recall_at_1000 value: 85.077 - type: recall_at_3 value: 32.982 - type: recall_at_5 value: 37.688 - type: map_at_1 value: 17.403 - type: map_at_10 value: 25.197000000000003 - type: map_at_100 value: 26.355 - type: map_at_1000 value: 26.487 - type: map_at_3 value: 22.733 - type: map_at_5 value: 24.114 - type: mrr_at_1 value: 21.37 - type: mrr_at_10 value: 29.091 - type: mrr_at_100 value: 30.018 - type: mrr_at_1000 value: 30.096 - type: mrr_at_3 value: 26.887 - type: mrr_at_5 value: 28.157 - type: ndcg_at_1 value: 21.37 - type: ndcg_at_10 value: 30.026000000000003 - type: ndcg_at_100 value: 35.416 - type: ndcg_at_1000 value: 38.45 - type: ndcg_at_3 value: 25.764 - type: ndcg_at_5 value: 27.742 - type: precision_at_1 value: 21.37 - type: precision_at_10 value: 5.609 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 12.423 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 17.403 - type: recall_at_10 value: 40.573 - type: recall_at_100 value: 64.818 - type: recall_at_1000 value: 86.53699999999999 - type: recall_at_3 value: 28.493000000000002 - type: recall_at_5 value: 33.660000000000004 - type: map_at_1 value: 28.639 - type: map_at_10 value: 38.951 - type: map_at_100 value: 40.238 - type: map_at_1000 value: 40.327 - type: map_at_3 value: 35.842 - type: map_at_5 value: 37.617 - type: mrr_at_1 value: 33.769 - type: mrr_at_10 value: 43.088 - type: mrr_at_100 value: 44.03 - type: mrr_at_1000 value: 44.072 - type: mrr_at_3 value: 40.656 - type: mrr_at_5 value: 42.138999999999996 - type: ndcg_at_1 value: 33.769 - type: ndcg_at_10 value: 44.676 - type: ndcg_at_100 value: 50.416000000000004 - type: ndcg_at_1000 value: 52.227999999999994 - type: ndcg_at_3 value: 39.494 - type: ndcg_at_5 value: 42.013 - type: precision_at_1 value: 33.769 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.221 - type: precision_at_5 value: 12.966 - type: recall_at_1 value: 28.639 - type: recall_at_10 value: 57.687999999999995 - type: recall_at_100 value: 82.541 - type: recall_at_1000 value: 94.896 - type: recall_at_3 value: 43.651 - type: recall_at_5 value: 49.925999999999995 - type: map_at_1 value: 29.57 - type: map_at_10 value: 40.004 - type: map_at_100 value: 41.75 - type: map_at_1000 value: 41.97 - type: map_at_3 value: 36.788 - type: map_at_5 value: 38.671 - type: mrr_at_1 value: 35.375 - type: mrr_at_10 value: 45.121 - type: mrr_at_100 value: 45.994 - type: mrr_at_1000 value: 46.04 - type: mrr_at_3 value: 42.227 - type: mrr_at_5 value: 43.995 - type: ndcg_at_1 value: 35.375 - type: ndcg_at_10 value: 46.392 - type: ndcg_at_100 value: 52.196 - type: ndcg_at_1000 value: 54.274 - type: ndcg_at_3 value: 41.163 - type: ndcg_at_5 value: 43.813 - type: precision_at_1 value: 35.375 - type: precision_at_10 value: 8.676 - type: precision_at_100 value: 1.678 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 19.104 - type: precision_at_5 value: 13.913 - type: recall_at_1 value: 29.57 - type: recall_at_10 value: 58.779 - type: recall_at_100 value: 83.337 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 44.005 - type: recall_at_5 value: 50.975 - type: map_at_1 value: 20.832 - type: map_at_10 value: 29.733999999999998 - type: map_at_100 value: 30.727 - type: map_at_1000 value: 30.843999999999998 - type: map_at_3 value: 26.834999999999997 - type: map_at_5 value: 28.555999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 31.791999999999998 - type: mrr_at_100 value: 32.666000000000004 - type: mrr_at_1000 value: 32.751999999999995 - type: mrr_at_3 value: 29.144 - type: mrr_at_5 value: 30.622 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.744 - type: ndcg_at_1000 value: 42.407000000000004 - type: ndcg_at_3 value: 29.421000000000003 - type: ndcg_at_5 value: 32.211 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 5.675 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.753999999999998 - type: precision_at_5 value: 9.353 - type: recall_at_1 value: 20.832 - type: recall_at_10 value: 48.795 - type: recall_at_100 value: 70.703 - type: recall_at_1000 value: 90.187 - type: recall_at_3 value: 34.455000000000005 - type: recall_at_5 value: 40.967 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 10.334 - type: map_at_10 value: 19.009999999999998 - type: map_at_100 value: 21.129 - type: map_at_1000 value: 21.328 - type: map_at_3 value: 15.152 - type: map_at_5 value: 17.084 - type: mrr_at_1 value: 23.453 - type: mrr_at_10 value: 36.099 - type: mrr_at_100 value: 37.069 - type: mrr_at_1000 value: 37.104 - type: mrr_at_3 value: 32.096000000000004 - type: mrr_at_5 value: 34.451 - type: ndcg_at_1 value: 23.453 - type: ndcg_at_10 value: 27.739000000000004 - type: ndcg_at_100 value: 35.836 - type: ndcg_at_1000 value: 39.242 - type: ndcg_at_3 value: 21.263 - type: ndcg_at_5 value: 23.677 - type: precision_at_1 value: 23.453 - type: precision_at_10 value: 9.199 - type: precision_at_100 value: 1.791 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.2 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 10.334 - type: recall_at_10 value: 35.177 - type: recall_at_100 value: 63.009 - type: recall_at_1000 value: 81.938 - type: recall_at_3 value: 19.914 - type: recall_at_5 value: 26.077 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.212 - type: map_at_10 value: 17.386 - type: map_at_100 value: 24.234 - type: map_at_1000 value: 25.724999999999998 - type: map_at_3 value: 12.727 - type: map_at_5 value: 14.785 - type: mrr_at_1 value: 59.25 - type: mrr_at_10 value: 68.687 - type: mrr_at_100 value: 69.133 - type: mrr_at_1000 value: 69.14099999999999 - type: mrr_at_3 value: 66.917 - type: mrr_at_5 value: 67.742 - type: ndcg_at_1 value: 48.625 - type: ndcg_at_10 value: 36.675999999999995 - type: ndcg_at_100 value: 41.543 - type: ndcg_at_1000 value: 49.241 - type: ndcg_at_3 value: 41.373 - type: ndcg_at_5 value: 38.707 - type: precision_at_1 value: 59.25 - type: precision_at_10 value: 28.525 - type: precision_at_100 value: 9.027000000000001 - type: precision_at_1000 value: 1.8339999999999999 - type: precision_at_3 value: 44.833 - type: precision_at_5 value: 37.35 - type: recall_at_1 value: 8.212 - type: recall_at_10 value: 23.188 - type: recall_at_100 value: 48.613 - type: recall_at_1000 value: 73.093 - type: recall_at_3 value: 14.419 - type: recall_at_5 value: 17.798 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.725 - type: f1 value: 46.50743309855908 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 55.086 - type: map_at_10 value: 66.914 - type: map_at_100 value: 67.321 - type: map_at_1000 value: 67.341 - type: map_at_3 value: 64.75800000000001 - type: map_at_5 value: 66.189 - type: mrr_at_1 value: 59.28600000000001 - type: mrr_at_10 value: 71.005 - type: mrr_at_100 value: 71.304 - type: mrr_at_1000 value: 71.313 - type: mrr_at_3 value: 69.037 - type: mrr_at_5 value: 70.35 - type: ndcg_at_1 value: 59.28600000000001 - type: ndcg_at_10 value: 72.695 - type: ndcg_at_100 value: 74.432 - type: ndcg_at_1000 value: 74.868 - type: ndcg_at_3 value: 68.72200000000001 - type: ndcg_at_5 value: 71.081 - type: precision_at_1 value: 59.28600000000001 - type: precision_at_10 value: 9.499 - type: precision_at_100 value: 1.052 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 27.503 - type: precision_at_5 value: 17.854999999999997 - type: recall_at_1 value: 55.086 - type: recall_at_10 value: 86.453 - type: recall_at_100 value: 94.028 - type: recall_at_1000 value: 97.052 - type: recall_at_3 value: 75.821 - type: recall_at_5 value: 81.6 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 22.262999999999998 - type: map_at_10 value: 37.488 - type: map_at_100 value: 39.498 - type: map_at_1000 value: 39.687 - type: map_at_3 value: 32.529 - type: map_at_5 value: 35.455 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.239000000000004 - type: mrr_at_100 value: 54.086 - type: mrr_at_1000 value: 54.122 - type: mrr_at_3 value: 51.235 - type: mrr_at_5 value: 52.415 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 45.446 - type: ndcg_at_100 value: 52.429 - type: ndcg_at_1000 value: 55.169000000000004 - type: ndcg_at_3 value: 41.882000000000005 - type: ndcg_at_5 value: 43.178 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.931999999999999 - type: precision_at_100 value: 2.025 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 28.652 - type: precision_at_5 value: 21.204 - type: recall_at_1 value: 22.262999999999998 - type: recall_at_10 value: 52.447 - type: recall_at_100 value: 78.045 - type: recall_at_1000 value: 94.419 - type: recall_at_3 value: 38.064 - type: recall_at_5 value: 44.769 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 32.519 - type: map_at_10 value: 45.831 - type: map_at_100 value: 46.815 - type: map_at_1000 value: 46.899 - type: map_at_3 value: 42.836 - type: map_at_5 value: 44.65 - type: mrr_at_1 value: 65.037 - type: mrr_at_10 value: 72.16 - type: mrr_at_100 value: 72.51100000000001 - type: mrr_at_1000 value: 72.53 - type: mrr_at_3 value: 70.682 - type: mrr_at_5 value: 71.54599999999999 - type: ndcg_at_1 value: 65.037 - type: ndcg_at_10 value: 55.17999999999999 - type: ndcg_at_100 value: 58.888 - type: ndcg_at_1000 value: 60.648 - type: ndcg_at_3 value: 50.501 - type: ndcg_at_5 value: 52.977 - type: precision_at_1 value: 65.037 - type: precision_at_10 value: 11.530999999999999 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 31.483 - type: precision_at_5 value: 20.845 - type: recall_at_1 value: 32.519 - type: recall_at_10 value: 57.657000000000004 - type: recall_at_100 value: 72.30199999999999 - type: recall_at_1000 value: 84.024 - type: recall_at_3 value: 47.225 - type: recall_at_5 value: 52.113 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.3168 - type: ap value: 83.80165516037135 - type: f1 value: 88.29942471066407 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 20.724999999999998 - type: map_at_10 value: 32.736 - type: map_at_100 value: 33.938 - type: map_at_1000 value: 33.991 - type: map_at_3 value: 28.788000000000004 - type: map_at_5 value: 31.016 - type: mrr_at_1 value: 21.361 - type: mrr_at_10 value: 33.323 - type: mrr_at_100 value: 34.471000000000004 - type: mrr_at_1000 value: 34.518 - type: mrr_at_3 value: 29.453000000000003 - type: mrr_at_5 value: 31.629 - type: ndcg_at_1 value: 21.361 - type: ndcg_at_10 value: 39.649 - type: ndcg_at_100 value: 45.481 - type: ndcg_at_1000 value: 46.775 - type: ndcg_at_3 value: 31.594 - type: ndcg_at_5 value: 35.543 - type: precision_at_1 value: 21.361 - type: precision_at_10 value: 6.3740000000000006 - type: precision_at_100 value: 0.931 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.514999999999999 - type: precision_at_5 value: 10.100000000000001 - type: recall_at_1 value: 20.724999999999998 - type: recall_at_10 value: 61.034 - type: recall_at_100 value: 88.062 - type: recall_at_1000 value: 97.86399999999999 - type: recall_at_3 value: 39.072 - type: recall_at_5 value: 48.53 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.8919288645691 - type: f1 value: 93.57059586398059 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.97993616051072 - type: f1 value: 48.244319183606535 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.90047074646941 - type: f1 value: 66.48999056063725 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.34566240753195 - type: f1 value: 73.54164154290658 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.21866934757011 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.000936217235534 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.68189362520352 - type: mrr value: 32.69603637784303 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.078 - type: map_at_10 value: 12.671 - type: map_at_100 value: 16.291 - type: map_at_1000 value: 17.855999999999998 - type: map_at_3 value: 9.610000000000001 - type: map_at_5 value: 11.152 - type: mrr_at_1 value: 43.963 - type: mrr_at_10 value: 53.173 - type: mrr_at_100 value: 53.718999999999994 - type: mrr_at_1000 value: 53.756 - type: mrr_at_3 value: 50.980000000000004 - type: mrr_at_5 value: 52.42 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.086 - type: ndcg_at_100 value: 32.545 - type: ndcg_at_1000 value: 41.144999999999996 - type: ndcg_at_3 value: 39.434999999999995 - type: ndcg_at_5 value: 37.888 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.014999999999997 - type: precision_at_100 value: 8.594 - type: precision_at_1000 value: 2.169 - type: precision_at_3 value: 37.049 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 6.078 - type: recall_at_10 value: 16.17 - type: recall_at_100 value: 34.512 - type: recall_at_1000 value: 65.447 - type: recall_at_3 value: 10.706 - type: recall_at_5 value: 13.158 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 27.378000000000004 - type: map_at_10 value: 42.178 - type: map_at_100 value: 43.32 - type: map_at_1000 value: 43.358000000000004 - type: map_at_3 value: 37.474000000000004 - type: map_at_5 value: 40.333000000000006 - type: mrr_at_1 value: 30.823 - type: mrr_at_10 value: 44.626 - type: mrr_at_100 value: 45.494 - type: mrr_at_1000 value: 45.519 - type: mrr_at_3 value: 40.585 - type: mrr_at_5 value: 43.146 - type: ndcg_at_1 value: 30.794 - type: ndcg_at_10 value: 50.099000000000004 - type: ndcg_at_100 value: 54.900999999999996 - type: ndcg_at_1000 value: 55.69499999999999 - type: ndcg_at_3 value: 41.238 - type: ndcg_at_5 value: 46.081 - type: precision_at_1 value: 30.794 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 18.926000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 27.378000000000004 - type: recall_at_10 value: 71.842 - type: recall_at_100 value: 92.565 - type: recall_at_1000 value: 98.402 - type: recall_at_3 value: 49.053999999999995 - type: recall_at_5 value: 60.207 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.557 - type: map_at_10 value: 84.729 - type: map_at_100 value: 85.369 - type: map_at_1000 value: 85.382 - type: map_at_3 value: 81.72 - type: map_at_5 value: 83.613 - type: mrr_at_1 value: 81.3 - type: mrr_at_10 value: 87.488 - type: mrr_at_100 value: 87.588 - type: mrr_at_1000 value: 87.589 - type: mrr_at_3 value: 86.53 - type: mrr_at_5 value: 87.18599999999999 - type: ndcg_at_1 value: 81.28999999999999 - type: ndcg_at_10 value: 88.442 - type: ndcg_at_100 value: 89.637 - type: ndcg_at_1000 value: 89.70700000000001 - type: ndcg_at_3 value: 85.55199999999999 - type: ndcg_at_5 value: 87.154 - type: precision_at_1 value: 81.28999999999999 - type: precision_at_10 value: 13.489999999999998 - type: precision_at_100 value: 1.54 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.708 - type: recall_at_1 value: 70.557 - type: recall_at_10 value: 95.645 - type: recall_at_100 value: 99.693 - type: recall_at_1000 value: 99.995 - type: recall_at_3 value: 87.359 - type: recall_at_5 value: 91.89699999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 63.65060114776209 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.63271250680617 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.263 - type: map_at_10 value: 10.801 - type: map_at_100 value: 12.888 - type: map_at_1000 value: 13.224 - type: map_at_3 value: 7.362 - type: map_at_5 value: 9.149000000000001 - type: mrr_at_1 value: 21 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.513 - type: mrr_at_1000 value: 32.58 - type: mrr_at_3 value: 28.116999999999997 - type: mrr_at_5 value: 29.976999999999997 - type: ndcg_at_1 value: 21 - type: ndcg_at_10 value: 18.551000000000002 - type: ndcg_at_100 value: 26.657999999999998 - type: ndcg_at_1000 value: 32.485 - type: ndcg_at_3 value: 16.834 - type: ndcg_at_5 value: 15.204999999999998 - type: precision_at_1 value: 21 - type: precision_at_10 value: 9.84 - type: precision_at_100 value: 2.16 - type: precision_at_1000 value: 0.35500000000000004 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 13.62 - type: recall_at_1 value: 4.263 - type: recall_at_10 value: 19.922 - type: recall_at_100 value: 43.808 - type: recall_at_1000 value: 72.14500000000001 - type: recall_at_3 value: 9.493 - type: recall_at_5 value: 13.767999999999999 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_spearman value: 81.27446313317233 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_spearman value: 76.27963301217527 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_spearman value: 88.18495048450949 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_spearman value: 81.91982338692046 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_spearman value: 89.00896818385291 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_spearman value: 85.48814644586132 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_spearman value: 90.30116926966582 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_spearman value: 67.74132963032342 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_spearman value: 86.87741355780479 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.0019012295875 - type: mrr value: 94.70267024188593 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 50.05 - type: map_at_10 value: 59.36 - type: map_at_100 value: 59.967999999999996 - type: map_at_1000 value: 60.023 - type: map_at_3 value: 56.515 - type: map_at_5 value: 58.272999999999996 - type: mrr_at_1 value: 53 - type: mrr_at_10 value: 61.102000000000004 - type: mrr_at_100 value: 61.476 - type: mrr_at_1000 value: 61.523 - type: mrr_at_3 value: 58.778 - type: mrr_at_5 value: 60.128 - type: ndcg_at_1 value: 53 - type: ndcg_at_10 value: 64.43100000000001 - type: ndcg_at_100 value: 66.73599999999999 - type: ndcg_at_1000 value: 68.027 - type: ndcg_at_3 value: 59.279 - type: ndcg_at_5 value: 61.888 - type: precision_at_1 value: 53 - type: precision_at_10 value: 8.767 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.444000000000003 - type: precision_at_5 value: 15.667 - type: recall_at_1 value: 50.05 - type: recall_at_10 value: 78.511 - type: recall_at_100 value: 88.5 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 64.117 - type: recall_at_5 value: 70.867 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72178217821782 - type: cos_sim_ap value: 93.0728601593541 - type: cos_sim_f1 value: 85.6727976766699 - type: cos_sim_precision value: 83.02063789868667 - type: cos_sim_recall value: 88.5 - type: dot_accuracy value: 99.72178217821782 - type: dot_ap value: 93.07287396168348 - type: dot_f1 value: 85.6727976766699 - type: dot_precision value: 83.02063789868667 - type: dot_recall value: 88.5 - type: euclidean_accuracy value: 99.72178217821782 - type: euclidean_ap value: 93.07285657982895 - type: euclidean_f1 value: 85.6727976766699 - type: euclidean_precision value: 83.02063789868667 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.72475247524753 - type: manhattan_ap value: 93.02792973059809 - type: manhattan_f1 value: 85.7727737973388 - type: manhattan_precision value: 87.84067085953879 - type: manhattan_recall value: 83.8 - type: max_accuracy value: 99.72475247524753 - type: max_ap value: 93.07287396168348 - type: max_f1 value: 85.7727737973388 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.77583615550819 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.151636938606956 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.16607939471187 - type: mrr value: 52.95172046091163 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.314646669495666 - type: cos_sim_spearman value: 31.83562491439455 - type: dot_pearson value: 31.314590842874157 - type: dot_spearman value: 31.83363065810437 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.3010000000000002 - type: map_at_100 value: 7.2139999999999995 - type: map_at_1000 value: 20.179 - type: map_at_3 value: 0.528 - type: map_at_5 value: 0.8019999999999999 - type: mrr_at_1 value: 72 - type: mrr_at_10 value: 83.39999999999999 - type: mrr_at_100 value: 83.39999999999999 - type: mrr_at_1000 value: 83.39999999999999 - type: mrr_at_3 value: 81.667 - type: mrr_at_5 value: 83.06700000000001 - type: ndcg_at_1 value: 66 - type: ndcg_at_10 value: 58.059000000000005 - type: ndcg_at_100 value: 44.316 - type: ndcg_at_1000 value: 43.147000000000006 - type: ndcg_at_3 value: 63.815999999999995 - type: ndcg_at_5 value: 63.005 - type: precision_at_1 value: 72 - type: precision_at_10 value: 61.4 - type: precision_at_100 value: 45.62 - type: precision_at_1000 value: 19.866 - type: precision_at_3 value: 70 - type: precision_at_5 value: 68.8 - type: recall_at_1 value: 0.198 - type: recall_at_10 value: 1.517 - type: recall_at_100 value: 10.587 - type: recall_at_1000 value: 41.233 - type: recall_at_3 value: 0.573 - type: recall_at_5 value: 0.907 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.894 - type: map_at_10 value: 8.488999999999999 - type: map_at_100 value: 14.445 - type: map_at_1000 value: 16.078 - type: map_at_3 value: 4.589 - type: map_at_5 value: 6.019 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 39.82 - type: mrr_at_100 value: 40.752 - type: mrr_at_1000 value: 40.771 - type: mrr_at_3 value: 34.354 - type: mrr_at_5 value: 37.721 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 21.563 - type: ndcg_at_100 value: 33.857 - type: ndcg_at_1000 value: 46.199 - type: ndcg_at_3 value: 22.296 - type: ndcg_at_5 value: 21.770999999999997 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.142999999999999 - type: precision_at_1000 value: 1.541 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 1.894 - type: recall_at_10 value: 14.931 - type: recall_at_100 value: 45.524 - type: recall_at_1000 value: 83.243 - type: recall_at_3 value: 5.712 - type: recall_at_5 value: 8.386000000000001 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.049 - type: ap value: 13.85116971310922 - type: f1 value: 54.37504302487686 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.1312959818902 - type: f1 value: 64.11413877009383 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 54.13103431861502 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.327889372355 - type: cos_sim_ap value: 77.42059895975699 - type: cos_sim_f1 value: 71.02706903250873 - type: cos_sim_precision value: 69.75324344950394 - type: cos_sim_recall value: 72.34828496042216 - type: dot_accuracy value: 87.327889372355 - type: dot_ap value: 77.4209479346677 - type: dot_f1 value: 71.02706903250873 - type: dot_precision value: 69.75324344950394 - type: dot_recall value: 72.34828496042216 - type: euclidean_accuracy value: 87.327889372355 - type: euclidean_ap value: 77.42096495861037 - type: euclidean_f1 value: 71.02706903250873 - type: euclidean_precision value: 69.75324344950394 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.31000774870358 - type: manhattan_ap value: 77.38930750711619 - type: manhattan_f1 value: 71.07935314027831 - type: manhattan_precision value: 67.70957726295677 - type: manhattan_recall value: 74.80211081794195 - type: max_accuracy value: 87.327889372355 - type: max_ap value: 77.42096495861037 - type: max_f1 value: 71.07935314027831 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.58939729110878 - type: cos_sim_ap value: 87.17594155025475 - type: cos_sim_f1 value: 79.21146953405018 - type: cos_sim_precision value: 76.8918527109307 - type: cos_sim_recall value: 81.67539267015707 - type: dot_accuracy value: 89.58939729110878 - type: dot_ap value: 87.17593963273593 - type: dot_f1 value: 79.21146953405018 - type: dot_precision value: 76.8918527109307 - type: dot_recall value: 81.67539267015707 - type: euclidean_accuracy value: 89.58939729110878 - type: euclidean_ap value: 87.17592466925834 - type: euclidean_f1 value: 79.21146953405018 - type: euclidean_precision value: 76.8918527109307 - type: euclidean_recall value: 81.67539267015707 - type: manhattan_accuracy value: 89.62626615438352 - type: manhattan_ap value: 87.16589873161546 - type: manhattan_f1 value: 79.25143598295348 - type: manhattan_precision value: 76.39494177323712 - type: manhattan_recall value: 82.32984293193716 - type: max_accuracy value: 89.62626615438352 - type: max_ap value: 87.17594155025475 - type: max_f1 value: 79.25143598295348 ---
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-47339454
fine-tuned
feature-extraction
[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-47339454", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,716,952,722,000
2024-05-29T03:19:42
6
0
--- datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-47339454 - allenai/c4 language: - en - en license: apache-2.0 pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [**BAAI/bge-m3**](https://huggingface.co/BAAI/bge-m3) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-47339454', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
[ "SCIFACT" ]
Non_BioNLP
davidadamczyk/setfit-model-4
davidadamczyk
text-classification
[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "model-index", "region:us" ]
1,728,827,363,000
2024-10-13T13:49:39
6
0
--- base_model: sentence-transformers/all-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Having previously lived in D.C., Rochester and Detroit and having made regular trips on the thruways and turnpikes in-between, I can truly say that the rest stops along the New York Thruway are the least desirable for food offerings. Even the NJ Turnpike offers a much better selection, with Ohio striking the best balance overall. Delaware has the largest rest stop, which offers a great selection but at the cost of having to negotiate a mall-size parking lot. Although I don''t begrudge those who like McDonald''s, I can honestly say I''ve never eaten at a rest stop or airport McDonalds, even when there were no other options. There''s nothing wrong with wanting better food, so long as there are options available at reasonable prices.If there''s one thing for which I can give credit to the New York Thruway rest stops, it''s in forcing us to seek out roadside alternatives in the many communities along the way. As a result, my wife has an extensive collection of books on diners that has morphed into somewhat of an obsession over the years. Of course with smartphones and apps such as Yelp, finding exceptional food along the way has never been easier. Put another way, I see the thruway rest stop as a place for an early morning snack or cup of coffee when we''re desperate. Unfortunately, the options are at their worst at 2 am, no matter where one stops. ' - text: 'Now that Iran is actively funneling missiles, warheads and drones to Russia for use in Ukraine, and Russia is funneling technical expertise and supplies to Iran to make more weapons, things are quickly heating up and the clock is approaching midnight as Iran get closer and closer to weaponizing a nuclear MIRV ICBM.The no so cold war between Iran and Israel, Egypt, Saudi Arabia and the UAE is about to get very hot and Israel''s efforts to avoid aligning against Russia in Syrian airspace (thank you President Obama) is about to fail as the Russo-Nato proxy war in Ukraine spills into the Middle East and a heavily armed and nuclear Israel gets drawn into a very open conflict with Iran and Russia. The bombing of an Iranian plant inside Iran is major escalation and I doubt that the CIA and DIA were blindsided by the IDF operation as such a strike was likely meant to cripple Iranian efforts to resupply Russia as much as Iranian efforts to resupply Hizbollah in Lebanon. With the Turks waging war in Syria, the air space over Syria is clearly going to become very crowded and very dangerous very quickly as Russia is stumbling into a second war with Israel through its Iranian proxy and Israel unlike Ukraine can take out both Russian and Iranian offensive capabilities. We just witnessed the opening salvo of a hot war which is why the DIA, CIA have been in Tel Aviv and Cairo recently - it is not really about the Palestinian territories. ' - text: 'It''s the year of our Lord, 2023; it''s hard to believe that we are having this conversation about the urgent necessity of ammo and lethal weapons. WWI, WWII, the Korean War, Gulf Wars I & II, Afghanistan, ISIS, etc., have come and gone. This does not include the multitude of conflicts in Africa, Georgia, and other hot spots. Mankind has not changed a bit. We are still driven by fear, greed, and the curse of the ego and its lust for power. Another article in today''s edition discusses the Doomsday Clock and its relentless ticking toward oblivion. It''s just a matter of time -and Boom! ' - text: 'i''d go further than the correct interpretation that putin''s "cease fire" was nothing more than "propaganda."i suggest that the russian attack on kramatorsk on january 7, which russia falsely claimed killed 600 ukrainian soldiers, reveals the expectation that a cease fire would gather ukrainians in a rest area where they could be killed en masse. the headline was preplanned before the event.i point readers to the Institute for the Study of War (ISW) as an excellent daily summary of open source information by highly skilled military analysts. they point out that putin is using a "grievance-revenge" framing of russian military activities (e.g., kramatorsk was revenge for the grievance of russians killed in makiivka). the ISW points out that this has only worsened the antagonism toward the kremlin and military from pro-invasion russian commentators, who ask why any "grievance event" was allowed to occur in the first place. ' - text: 'I cannot entirely agree with this. If there''s a disconnect between what''s being taught, and what the student really wants to learn, that can be a problem. I, for example, learned a _LOT_ about computers, back in ''84 -- and a fair bit of other stuff, too. (I speak what I''ll term "conversational" Spanish; I can''t claim to be fluent, but I can absolutely carry on modest conversations and express myself.)But the teachers in my core subjects were uninspired or flatly failed me (e.g., the CompSci prof who lost my test, and gave me a zero; that really took the wind out of my sails, considering I thought I nailed it). So I was having far more fun at 11:00 p.m. in the computer lab than I was doing school work. Bombed out of college, but I''ve now worked at four Fortune 500 companies, and am currently a senior cloud admin. Students _do_ need to have a desire to learn, yes, but teachers need to be equipped properly to teach them, too. ' inference: true model-index: - name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9 name: Accuracy --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 384 tokens - **Number of Classes:** 2 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | yes | <ul><li>'TIME Magazine prediction for 2023 (3Jan2023)"A cornered Russia will turn from global player into the world’s most dangerous rogue state, posing a serious and pervasive danger to Europe, the U.S., and beyond. Bogged down in Ukraine, with little to lose from further isolation and Western retaliation, and facing intense domestic pressure to show strength, Russia will turn to asymmetric warfare against the West to inflict damage through a thousand \'paper cuts\' rather than by overt aggression that depends on military and economic power that Russia no longer has.Putin’s nuclear saber-rattling will escalate. Kremlin-affiliated hackers will ramp up increasingly sophisticated cyberattacks on Western firms, governments, and infrastructure. Russia will intensify its offensive against Western elections by systematically supporting and funding disinformation and extremism. Attacks on Ukrainian infrastructure will continue.In short, Rogue Russia is a threat to global security, Western political systems, the cybersphere, and food security. Not to mention every Ukrainian civilian."\n'</li><li>"Bulletin of the Atomic Scientists advanced the Doomsday Clock, now to 90 seconds due to increasing nuclear risk.The rulers are putting humans in peril, an unconscionable and unethical danger since we haven't consented to such risk.In view of the fact that, over millennia, the rulers have killed hundreds of millions of innocent people, we can question their claimed legitimacy, and reject their bogus claim.\n"</li><li>'This article explains the bad political rusults although rulers might be acting rationally within their ideological frameworks.It is based on plausible speculation of Biden and Putin\'s ideologies, yet other plausible facts could be animating the escalations. For instance, some describe \'getting ukrained\' as "what happens to you if you ally with the U.S. government," and Joe Biden might be escalating to avoid such observations.Notice that these types of explanations do not rely on free will, but that rulers are prisoner to the constraints and incentives facing them, even if this ends with humanity being nuked again.Bulletin of Atomic Scientists advancing the Doomsday Clock is largely in line with rulers vs humanity framework, but as Douthat explains, this is different than the logic of the rulers.Another view, that of Prof. Mearshimer\'s presents a pessimistic view of this Ukraine War, while being remarkably prescient providing yet another framework to understand what\'s likely to happen; let\'s hope that he\'s wrong, althought lacking evidence for this optimism.\n'</li></ul> | | no | <ul><li>"M Martínez - Doubtful. The US has been conducting virtually Perpetual War (mostly against smaller, weaker, brown-skinned nations) since day one and that hasn't dulled the Chickenhawk politicians (see: Bush the Lesser, George) from happily pushing us into the next one.Starting wars that are fought by Other Mother's Children and are profitable for the war-mongers will never cease.\n"</li><li>"I know it is easy to blame America always, but we are largely blameless. We opened trade with China and this allowed China to industrialize and build its economy. We in the west believe in Free markets and free people. Chinese state adopted a version of capitalism but instead of liberalizing like South Korea and Taiwan decided to become more insular. They restricted access to western products for their citizens. Movies, TV shows had to be censored. American social media companies cannot do business in China. Chinese citizens are not masters of their own destiny as the state dictates every aspect of their lives. Many of us in the west enjoy the benefits of western liberalism, namely - Free markets, Rule of law ( including contract enforcement) and individual rights. In the cold war era, we had to actively defend these values from Soviets. Now, we must brace ourselves to defend them from China. Liberal order will prevail because once people know the values of western liberal order, like Hongkongers, Taiwanese etc they will defend it. We in US, must help them, become the arsenal of democracy, supply planes, ships, munitions to Taiwan to defend themselves. Help Hong Kong citizens by giving the persecuted asylum in the west. We are not responsible for confrontation with China, Chinese state's disregard for Taiwanese and Hongkong citizens aspirations is responsible for this.\n"</li><li>'We probably have male, transient cougars moving through the area more frequently than wildlife experts and state officials document. My neighbors woke to a partially eaten deer carcass in their backyard, but heard no coyotes the night before. We hadn\'t heard this story yet, when a week later, my husband had a very large animal run in front of his car. It had a very long tail, short hair of all tan color and bounded as tall as the hood of his sedan. I posted this on a local wildlife FB page, and a man replied his daughter saw it while walking one their 2 dogs, and reported it was as big as their mastiff. A week later, my neighbor was walking her dog at 7 am, and saw it in a neighboring yard, at the top of a hill, "sitting like a sphinx" under a large blue juniper bush. My neighbor clearly saw a broad feline face and large white torso. Several months later, I heard a jogger in another part of my town also saw it early in the morning, and and went to FB posting a stock picture of a cougar with the comment, \'\'This is what I saw." An email sent to CTDEEP with all this information wasn\'t taken seriously, with their reply stating reports are usually confusing other animals. It\'s hard to know what CTDEEP might think we are confused about, since coyote, fox, fisher, black bear and deer have all been sighted in our yard or near us, frequently.\n'</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("davidadamczyk/setfit-model-4") # Run inference preds = model("It's the year of our Lord, 2023; it's hard to believe that we are having this conversation about the urgent necessity of ammo and lethal weapons. WWI, WWII, the Korean War, Gulf Wars I & II, Afghanistan, ISIS, etc., have come and gone. This does not include the multitude of conflicts in Africa, Georgia, and other hot spots. Mankind has not changed a bit. We are still driven by fear, greed, and the curse of the ego and its lust for power. Another article in today's edition discusses the Doomsday Clock and its relentless ticking toward oblivion. It's just a matter of time -and Boom! ") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## 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 Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 18 | 133.075 | 255 | | Label | Training Sample Count | |:------|:----------------------| | no | 18 | | yes | 22 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 120 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0017 | 1 | 0.4133 | - | | 0.0833 | 50 | 0.188 | - | | 0.1667 | 100 | 0.0071 | - | | 0.25 | 150 | 0.0002 | - | | 0.3333 | 200 | 0.0001 | - | | 0.4167 | 250 | 0.0001 | - | | 0.5 | 300 | 0.0001 | - | | 0.5833 | 350 | 0.0001 | - | | 0.6667 | 400 | 0.0001 | - | | 0.75 | 450 | 0.0001 | - | | 0.8333 | 500 | 0.0001 | - | | 0.9167 | 550 | 0.0001 | - | | 1.0 | 600 | 0.0001 | - | ### Framework Versions - Python: 3.10.13 - SetFit: 1.1.0 - Sentence Transformers: 3.0.1 - Transformers: 4.45.2 - PyTorch: 2.4.0+cu124 - Datasets: 2.21.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "BEAR" ]
Non_BioNLP
baconnier/Gaston-Llama-3-8B
baconnier
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,714,817,395,000
2024-05-08T13:33:08
19
3
--- {} --- # Vous en avez assez du jargon administratif incompréhensible ? Gaston est là pour vous aider ! ![Gaston](https://huggingface.co/baconnier/Gaston-Llama-3-8B/resolve/main/gaston2.jpg) 💡 Cette IA a été conçue pour reformuler les communications et documents administratifs en langage clair et simple. 📝 Grâce à Gaston, fini les lettres obscures et les procédures nébuleuses. Tout devient limpide et à la portée du commun des mortels. 😊 Gaston est un POC (Proof of Concept) qui a pour mission de rendre l'administration plus transparente et accessible. 🙌 Son secret ? Une capacité à analyser et à traduire le jargon en termes compréhensibles par tous. 💬 Avec Gaston, les démarches administratives deviennent enfin un jeu d'enfant ! This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE) --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - orpo base_model: NousResearch/Hermes-2-Pro-Llama-3-8B --- # Uploaded model - **Developed by:** baconnier - **License:** apache-2.0 - **Finetuned from model :** NousResearch/Hermes-2-Pro-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) This model was trained ORPO , using ChatML prompt template format. ``` <|im_start|>user Qui est tu ? <|im_end|> <|im_start|>assistant ``` # Example with local TGI: See the snippet below for usage with local inference: ```python #Example: reuse your existing OpenAI setup from openai import OpenAI client = OpenAI(base_url="http://localhost:8080/v1", api_key="TGI") completion = client.chat.completions.create( model="baconnier/Gaston-Llama-3-8B", messages=[ {"role": "system", "content": "Reponds comme Gaston, un fonctionnaire français qui s'exprime uniquement en français avec des termes tres simples et surtout sans jamais utiliser de jargon administratif."}, {"role": "user", "content": "l’action en nullité se prescrit par 5 ans ou est prescrite en 5 ans"}, {"role": "assistant", "content": ""} ], max_tokens=400, temperature=0.7, ) print(completion.choices[0].message.content) ``` Output: ``` Le délai de prescription pour l'action en nullité est généralement de cinq ans à compter du jour où la partie qui peut agir a eu connaissance des faits, selon le Code civil français. ``` # Example with Transformers and pipeline: See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "baconnier/Gaston-Llama-3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages=[ {"role": "system", "content": "Reponds comme Gaston, un fonctionnaire français qui s'exprime uniquement en français avec des termes tres simples et surtout sans jamais utiliser de jargon administratif."}, {"role": "user", "content": "Vous voudrez bien vous munir de toutes les pièces justificatives afférentes à vos déclarations de revenus et de patrimoine des 3 derniers exercices, nonobstant les éléments déjà produits. À défaut de réponse de votre part ou en cas de non comparution aux date et heure susvisées, la procédure de contrôle se poursuivra et nous serons fondés à retenir les éléments en notre possession en vue de l'établissement des impositions supplémentaires et pénalités subséquentes."}, {"role": "assistant", "content": ""} ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) outputs = pipeline( prompt, max_new_tokens=256, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` Question: ``` Vous voudrez bien vous munir de toutes les pièces justificatives afférentes à vos déclarations de revenus et de patrimoine des 3 derniers exercices, nonobstant les éléments déjà produits. À défaut de réponse de votre part ou en cas de non comparution aux date et heure susvisées, la procédure de contrôle se poursuivra et nous serons fondés à retenir les éléments en notre possession en vue de l'établissement des impositions supplémentaires et pénalités subséquentes. ``` Reponse: ``` Je vous invite à fournir toutes les pièces justificatives concernant vos déclarations de revenus et de patrimoine des trois dernières années, malgré les éléments déjà fournis. En cas de non-respect de ce délai ou de manque de réponse, la procédure de vérification continuera, et nous utiliserons les informations en notre possession pour établir les impôts supplémentaires et les pénalités associées. ``` Question: ``` Nous accusons réception de votre demande de renouvellement de passeport en date du 15 mars 2023. Après instruction de votre dossier, il appert que les pièces produites s'avèrent insuffisantes eu égard aux dispositions de l'article 5-1 du décret n°2005-1726 du 30 décembre 2005. Nonobstant les justificatifs déjà fournis, il vous appartient de nous transmettre dans les meilleurs délais les documents complémentaires listés ci-après : - Copie d'un justificatif de domicile de moins de 6 mois - Timbre fiscal d'un montant de 86€ À défaut de production des pièces susvisées dans un délai de 30 jours à compter de la présente, votre demande sera classée sans suite. Nous vous prions d'agréer, Monsieur, l'expression de nos salutations distinguées. ``` Reponse: ``` Nous recevons votre requête pour renouveler votre passeport datant du 15 mars 2023. Suite à l'examen de votre dossier, il a été déterminé que les documents fournis ne respectent pas les exigences mentionnées dans la section 5-1 du décret n°2005-1726 du 30 décembre 2005. Malgré les pièces déjà fournies, vous devez nous envoyer rapidement les éléments suivants : - Une copie d'un justificatif de domicile datant de moins de 6 mois. - Un timbre fiscal d'une valeur de 86 €. Si ces documents ne sont pas fournis dans un délai de 30 jours à compter de cette lettre, votre demande sera close. Nous vous prions d'accepter, Monsieur, l'expression de nos salutations respectueuses. ``` # Example with Transformers: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("baconnier/Gaston-Llama-3-8B") model = AutoModelForCausalLM.from_pretrained("baconnier/Gaston-Llama-3-8B") prompt = "Nonobstant les justificatifs déjà fournis" inputs = tokenizer(prompt, return_tensors="pt") # Generate generate_ids = model.generate(inputs.input_ids, max_length=200) tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] ```
[ "CAS" ]
Non_BioNLP
croissantllm/base_75k
croissantllm
text2text-generation
[ "transformers", "pytorch", "llama", "text-generation", "legal", "code", "text-generation-inference", "art", "text2text-generation", "fr", "en", "dataset:cerebras/SlimPajama-627B", "dataset:uonlp/CulturaX", "dataset:pg19", "dataset:bigcode/starcoderdata", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,705,586,370,000
2024-02-01T15:56:43
8
0
--- datasets: - cerebras/SlimPajama-627B - uonlp/CulturaX - pg19 - bigcode/starcoderdata language: - fr - en license: mit pipeline_tag: text2text-generation tags: - legal - code - text-generation-inference - art --- # CroissantLLM - Base (75k steps) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 75k steps (1.18 T) tokens. To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1. ## 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 Coming soon ``` ## Usage This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "croissantllm/base_75k" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") inputs = tokenizer("I am so tired I could sleep right now. -> Je suis si fatigué que je pourrais m'endormir maintenant. He is heading to the market. -> Il va au marché. We are running on the beach. ->", return_tensors="pt").to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60, temperature=0.5) print(tokenizer.decode(tokens[0])) # remove bos token inputs = tokenizer("Capitales: France -> Paris, Italie -> Rome, Allemagne -> Berlin, Espagne ->", return_tensors="pt", add_special_tokens=True).to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60) print(tokenizer.decode(tokens[0])) ```
[ "CRAFT" ]
Non_BioNLP
TensorStack/AbsoluteReality_v181-onnx
TensorStack
null
[ "onnx", "region:us" ]
1,726,099,888,000
2024-09-12T00:17:11
0
2
--- {} --- # AbsoluteReality v1.8.1 - Onnx Olive DirectML Optimized ## Original Model https://civitai.com/models/81458/absolutereality?modelVersionId=132760 ## C# Inference Demo https://github.com/TensorStack-AI/OnnxStack ```csharp // Create Pipeline var pipeline = StableDiffusionPipeline.CreatePipeline("D:\\Models\\AbsoluteReality_v181-onnx"); // Prompt var promptOptions = new PromptOptions { Prompt = "Craft an image of a gallant prince, with a charming smile and a sword at his side, ready to embark on a quest." }; // Run pipeline var result = await pipeline.GenerateImageAsync(promptOptions, schedulerOptions); // Save Image Result await result.SaveAsync("Result.png"); ``` ## Inference Result ![Intro Image](Sample.png)
[ "CRAFT" ]
Non_BioNLP
NghiemAbe/SeaLLM-v2.5-Legal-v4
NghiemAbe
text-generation
[ "transformers", "pytorch", "safetensors", "gemma", "text-generation", "multilingual", "sea", "conversational", "en", "zh", "vi", "id", "th", "ms", "km", "lo", "my", "tl", "arxiv:2312.00738", "arxiv:2306.05179", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,718,113,188,000
2024-07-18T14:19:19
9
0
--- language: - en - zh - vi - id - th - ms - km - lo - my - tl license: other license_name: seallms license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE tags: - multilingual - sea --- <p align="center"> <img src="seal_logo.png" width="200" /> </p> # *SeaLLM-7B-v2.5* - Large Language Models for Southeast Asia <p align="center"> <a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a> &nbsp;&nbsp; <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 Tech Memo</a> &nbsp;&nbsp; <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 DEMO</a> &nbsp;&nbsp; <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a> &nbsp;&nbsp; <a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a> </p> 🔥<span style="color: #ff3860">[HOT]</span> SeaLLMs project now has a dedicated website - [damo-nlp-sg.github.io/SeaLLMs](https://damo-nlp-sg.github.io/SeaLLMs/) We introduce [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc. ### Highlights * [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) outperforms GPT-3.5 and achieves 7B SOTA on most multilingual knowledge benchmarks for SEA languages (MMLU, M3Exam & VMLU). * It achieves 79.0 and 34.9 on GSM8K and MATH, surpassing GPT-3.5 in MATH. ### Release and DEMO - DEMO: - [SeaLLMs/SeaLLM-7B-v2.5](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B-v2.5). - [SeaLLMs/SeaLLM-7B | SeaLMMM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B) - Experimental multimodal SeaLLM. - Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf). - Model weights: - [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5). - [SeaLLM-7B-v2.5-GGUF](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF). - Run locally: - [LM-studio](https://lmstudio.ai/): - [SeaLLM-7B-v2.5-q4_0-chatml](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF/blob/main/seallm-7b-v2.5-chatml.Q4_K_M.gguf) with ChatML template (`<eos>` token changed to `<|im_end|>`) - [SeaLLM-7B-v2.5-q4_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF/blob/main/seallm-7b-v2.5.Q4_K_M.gguf) - must use SeaLLM-7B-v2.5 chat format. - [MLX for Apple Silicon](https://github.com/ml-explore/mlx): [SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized) - Previous models: - [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) - [SeaLLM-7B-v1](https://huggingface.co/SeaLLMs/SeaLLM-7B-v1) <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. > The logo was generated by DALL-E 3. ### What's new since SeaLLM-7B-v2? * SeaLLM-7B-v2.5 was built on top of Gemma-7b, and underwent large scale SFT and carefully designed alignment. ## Evaluation ### Multilingual World Knowledge We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi. | Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- | | GPT-3.5 | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41 | Vistral-7B-chat | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27 | Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25 | SailorLM | Multi | 52.72 | 59.76 | 67.74 | 50.14 | --- | 39.53 | 37.73 | SeaLLM-7B-v2 | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52 | SeaLLM-7B-v2.5 | Multi | 64.05 | 76.87 | 62.54 | 63.11 | 53.30 | 48.64 | 46.86 ### Zero-shot CoT Multilingual Math Reasoning <!-- [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.5** score on the GSM8K with zero-shot CoT reasoning, making it the **state of the art** in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with **28.4** vs 18.1 scores. ![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png) --> | Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1 | Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6.0 | Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | | | Qwen1.5-7B-chat | 56.8 | 15.3 | 40.0 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 | 4.7 | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4 | SeaLLM-7B-v2.5 | 78.5 | 34.9 | 51.3 | 22.1 | 72.3 | 30.2 | 71.5 | 30.1 | 62.0 | 28.4 Baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Vistral](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)). #### Zero-shot MGSM [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Thai. | Model | MGSM-Zh | MGSM-Th |-----| ----- | --- | ChatGPT (reported) | 61.2 | 47.2 | Qwen-14B-chat | 59.6 | 28 | SeaLLM-7B-v2 | **64.8** | 62.4 | SeaLLM-7B-v2.5 | 58.0 | **64.8** ### Sea-Bench ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) ### Usage **IMPORTANT NOTICE for using the model** * `<bos>` must be at start of prompt, ff your code's tokenizer does not prepend `<bos>` by default, you MUST prepend <bos> into the prompt yourself, otherwise, it would not work! * Repitition penalty (e.g: in llama.cpp, ollama, LM-studio) must be set to **1** , otherwise will lead to degeneration! #### Instruction format ```python # ! WARNING, if your code's tokenizer does not prepend <bos> by default, # You MUST prepend <bos> into the prompt yourself, otherwise, it would not work! prompt = """<|im_start|>system You are a helpful assistant.<eos> <|im_start|>user Hello world<eos> <|im_start|>assistant Hi there, how can I help?<eos>""" # <|im_start|> is not a special token. # Transformers chat_template should be consistent with vLLM format below. # ! ENSURE 1 and only 1 bos `<bos>` at the beginning of sequence print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))) """ ``` #### Using transformers's chat_template Install the latest transformers (>4.40) ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # use bfloat16 to ensure the best performance. model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2.5", torch_dtype=torch.bfloat16, device_map=device) tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2.5") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world"}, {"role": "assistant", "content": "Hi there, how can I help you today?"}, {"role": "user", "content": "Explain general relativity in details."} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) print(tokenizer.convert_ids_to_tokens(encodeds[0])) model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` #### Using vLLM ```python from vllm import LLM, SamplingParams TURN_TEMPLATE = "<|im_start|>{role}\n{content}<eos>\n" TURN_PREFIX = "<|im_start|>{role}\n" def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None): # conversations: list of dict with key `role` and `content` (openai format) if conversations[0]['role'] != 'system' and system_prompt is not None: conversations = [{"role": "system", "content": system_prompt}] + conversations text = '' for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) text += prompt if add_assistant_prefix: prompt = TURN_PREFIX.format(role='assistant') text += prompt return text sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['<eos>', '<|im_start|>']) llm = LLM("SeaLLMs/SeaLLM-7B-v2.5", dtype="bfloat16") message = "Explain general relativity in details." prompt = seallm_chat_convo_format(message, True) gen = llm.generate(prompt, sampling_params) print(gen[0].outputs[0].text) ``` #### Fine-tuning SeaLLM-7B-v2.5 Should follow the chat format and accurately mask out source tokens. Here is an example. ```python conversations = [ {"role": "system", "content": "You are helful assistant."}, {"role": "user", "content": "Hello world."}, {"role": "assistant", "content": "Hi there, how can I help?"}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, ] def seallm_7b_v25_tokenize_multi_turns(tokenizer, conversations, add_assistant_prefix=False): """ Inputs: conversations: list of dict following openai format, eg conversations = [ {"role": "system", "content": "You are helful assistant."}, {"role": "user", "content": "Hello world."}, {"role": "assistant", "content": "Hi there, how can I help?"}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, ] add_assistant_prefix: whether to add assistant_prefix, only for inference decoding Outputs: tokenize_output_sample, { "input_ids": ... "token_type_ids": 1 if train and 0 if masked out (not train) } During training, need to create a labels, with masked-out tokens = -100 to avoid loss computations. labels = sample['input_ids'].clone() labels[sample['token_type_ids'] == 0] = -100 """ TURN_TEMPLATE = "<|im_start|>{role}\n{content}<eos>\n" TURN_PREFIX = "<|im_start|>{role}\n" TURN_SUFFIX = "<eos>\n" TURN_SUFFIX_TAKE = "<eos>" sample = None assistant_prefix_len = None assistant_suffix_len = None for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) turn_sample = tokenizer( prompt, padding=False, truncation=False, verbose=False, add_special_tokens=False, return_token_type_ids=True, ) if turn['role'] == 'assistant': if assistant_prefix_len is None: assistant_prefix_len = len(tokenizer.encode(TURN_PREFIX.format(role=turn['role']), add_special_tokens=False)) if assistant_suffix_len is None: assistant_suffix_len = ( len(tokenizer.encode(TURN_SUFFIX.format(role=turn['role']), add_special_tokens=False)) - len(tokenizer.encode(TURN_SUFFIX_TAKE, add_special_tokens=False)) ) turn_sample['token_type_ids'][assistant_prefix_len:-assistant_suffix_len] = [1] * (len(turn_sample['input_ids']) - assistant_prefix_len - assistant_suffix_len) if sample is None: sample = turn_sample else: for k in turn_sample.keys(): sample[k].extend(turn_sample[k]) if add_assistant_prefix: assistant_prefix_sample = tokenizer( TURN_PREFIX.format(role="assistant"), padding=False, truncation=False, verbose=False, add_special_tokens=False, return_token_type_ids=True, ) for k in sample.keys(): sample[k].extend(assistant_prefix_sample[k]) if tokenizer.add_bos_token: sample['input_ids'] = [tokenizer.bos_token_id] + sample['input_ids'] sample['attention_mask'] = [1] + sample['attention_mask'] sample['token_type_ids'] = [sample['token_type_ids'][0]] + sample['token_type_ids'] return sample # ! testing sample = seallm_7b_v25_tokenize_multi_turns(tokenizer, conversations) tokens = tokenizer.convert_ids_to_tokens(sample['input_ids']) pairs = [(x, y) for x, y in zip(tokens, sample['token_type_ids'])] print(pairs) # source and special tokens is masked out (token_type 0), only assistant with <eos> is trained (token_type 1) # [('<bos>', 0), ('<', 0), ('|', 0), ..., ('assistant', 0), ('\n', 0), ('Hi', 1), ('▁there', 1), (',', 1), ('▁how', 1), ('▁can', 1), ('▁I', 1), ('▁help', 1), ('?', 1), ('<eos>', 1), ('\n', 0), ('<', 0), ... ``` ## 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: Corresponding Author: [[email protected]](mailto:[email protected]) **Author list and order will change!** * `*` and `^` are equal contributions. ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Weiwen Xu, Hou Pong Chan, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```
[ "CHIA" ]
Non_BioNLP
RichardErkhov/LuuNgoc2k2_-_Law-Llama-v1-8bits
RichardErkhov
null
[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]
1,737,848,824,000
2025-01-25T23:50:58
4
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) Law-Llama-v1 - bnb 8bits - Model creator: https://huggingface.co/LuuNgoc2k2/ - Original model: https://huggingface.co/LuuNgoc2k2/Law-Llama-v1/ Original model description: Load Model ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained("LuuNgoc2k2/Law-Llama-v1", add_eos_token=True, padding_side='right') model = AutoModelForCausalLM.from_pretrained( 'LuuNgoc2k2/Law-Llama-v1', torch_dtype=torch.bfloat16, quantization_config=bnb_config, # If you need device_map="auto", use_cache=True, ) tokenizer.pad_token = tokenizer.eos_token ``` Generate ```python PROMPT = """ ### Hướng dẫn: Bạn là một trợ lí Tiếng Việt. Hãy luôn trả lời một cách trung thực và an toàn Câu trả lời của bạn không nên chứa bất kỳ nội dung gây hại, nguy hiểm hoặc bất hợp pháp nào Nếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác Nếu bạn không biết câu trả lời cho một câu hỏi, hãy trẳ lời là bạn không biết và vui lòng không chia sẻ thông tin sai lệch. ### Câu hỏi: {input} """ question = """Trình bày về thủ tục li hôn ?""" text = PROMPT.format_map({ 'input': question, }) input_ids = tokenizer(text, return_tensors='pt', add_special_tokens=False).to('cuda') generated_ids = model.generate( input_ids=input_ids['input_ids'], max_new_tokens=1024, do_sample=True, top_p=0.95, top_k=40, temperature=0.3, repetition_penalty=1.1, no_repeat_ngram_size=7, num_beams=5, ) a = tokenizer.batch_decode(generated_ids)[0] # print(a.split('### Trả lời:')[1]) print(a) ```
[ "CHIA" ]
Non_BioNLP
KingKazma/xsum_6789_3000_1500_train
KingKazma
text-classification
[ "bertopic", "text-classification", "region:us" ]
1,691,075,832,000
2023-08-03T15:17:15
10
0
--- library_name: bertopic pipeline_tag: text-classification tags: - bertopic --- # xsum_6789_3000_1500_train 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. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("KingKazma/xsum_6789_3000_1500_train") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 16 * Number of training documents: 3000 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | -1 | league - club - game - win - player | 5 | -1_league_club_game_win | | 0 | said - mr - would - people - year | 382 | 0_said_mr_would_people | | 1 | sport - medal - gold - team - olympic | 2143 | 1_sport_medal_gold_team | | 2 | cricket - wicket - test - england - match | 72 | 2_cricket_wicket_test_england | | 3 | arsenal - league - liverpool - chelsea - kick | 56 | 3_arsenal_league_liverpool_chelsea | | 4 | world - open - round - mcilroy - golf | 55 | 4_world_open_round_mcilroy | | 5 | foul - town - half - kick - win | 52 | 5_foul_town_half_kick | | 6 | season - club - dedicated - transfer - appearance | 46 | 6_season_club_dedicated_transfer | | 7 | celtic - game - aberdeen - rangers - player | 42 | 7_celtic_game_aberdeen_rangers | | 8 | madrid - atltico - win - real - barcelona | 36 | 8_madrid_atltico_win_real | | 9 | race - hamilton - team - prix - grand | 26 | 9_race_hamilton_team_prix | | 10 | rugby - wales - game - coach - england | 22 | 10_rugby_wales_game_coach | | 11 | fight - champion - boxing - amateur - world | 19 | 11_fight_champion_boxing_amateur | | 12 | yn - wedi - ei - ar - bod | 17 | 12_yn_wedi_ei_ar | | 13 | fan - club - bet - stadium - standing | 14 | 13_fan_club_bet_stadium | | 14 | connacht - ronaldson - blade - penalty - ulster | 13 | 14_connacht_ronaldson_blade_penalty | </details> ## Training hyperparameters * calculate_probabilities: True * language: english * low_memory: False * min_topic_size: 10 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False ## Framework versions * Numpy: 1.22.4 * HDBSCAN: 0.8.33 * UMAP: 0.5.3 * Pandas: 1.5.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.2.2 * Transformers: 4.31.0 * Numba: 0.57.1 * Plotly: 5.13.1 * Python: 3.10.12
[ "MEDAL" ]
Non_BioNLP
pucpr/biobertpt-bio
pucpr
fill-mask
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,646,263,745,000
2022-11-27T16:54:50
76
6
--- language: pt widget: - text: O principal [MASK] da COVID-19 é tosse seca. - text: O vírus da gripe apresenta um [MASK] constituído por segmentos de ácido ribonucleico. thumbnail: https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # BioBERTpt - Portuguese Clinical and Biomedical BERT The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical literature. This model card describes the BioBERTpt(bio) model, a biomedical version of BioBERTpt, trained on Portuguese biomedical literature from scientific papers from Pubmed and Scielo. ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-bio") model = AutoModel.from_pretrained("pucpr/biobertpt-bio") ``` ## More Information Refer to the original paper, [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) for additional details and performance on Portuguese NER tasks. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).
[ "SCIELO" ]
TBD
masonbarnes/open-llm-search
masonbarnes
text-generation
[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "en", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,693,864,524,000
2023-09-09T06:00:09
29
10
--- language: - en license: llama2 --- # **Model Overview** As the demand for large language models grows, a common limitation surfaces: their inability to directly search the internet. Although tech giants like Google (with Bard), Bing, and Perplexity are addressing this challenge, their proprietary methods have data logging issues. **Introducing Open LLM Search** — A specialized adaptation of Together AI's `llama-2-7b-32k` model, purpose-built for extracting information from web pages. While the model only has a 7 billion parameters, its fine-tuned capabilities and expanded context limit enable it to excel in search tasks. **License:** This model uses Meta's Llama 2 license. # **Fine-Tuning Process** The model's fine tuning involved a combination of GPT-4 and GPT-4-32k to generate synthetic data. Here is the training workflow used: 1. Use GPT-4 to generate a multitude of queries. 2. For each query, identify the top five website results from Google. 3. Extract content from these websites and use GPT-4-32k for their summarization. 4. Record the text and summarizes from GPT-4-32k for fine-tuning. 5. Feed the summaries from all five sources with GPT-4 to craft a cohesive response. 6. Document both the input and output from GPT-4 for fine-tuning. Fine tuning was done with an `<instructions>:`, `<user>:`, and `<assistant>:` format. # **Getting Started** - Experience it firsthand! Check out the live demo [here](https://huggingface.co/spaces/masonbarnes/open-llm-search). - For DIY enthusiasts, explore or self-deploy this solution using our [GitHub repository](https://github.com/MasonBarnes/open-llm-search).
[ "CRAFT" ]
Non_BioNLP
mixamrepijey/instructor-small
mixamrepijey
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" ]
1,702,568,686,000
2023-12-15T12:37:48
11
0
--- language: en license: apache-2.0 pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - prompt-retrieval - text-reranking - sentence-transformers - feature-extraction - sentence-similarity - transformers - t5 - English - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb inference: false model-index: - name: INSTRUCTOR results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 88.13432835820896 - type: ap value: 59.298209334395665 - type: f1 value: 83.31769058643586 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.526375 - type: ap value: 88.16327709705504 - type: f1 value: 91.51095801287843 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.856 - type: f1 value: 45.41490917650942 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 31.223 - type: map_at_10 value: 47.947 - type: map_at_100 value: 48.742000000000004 - type: map_at_1000 value: 48.745 - type: map_at_3 value: 43.137 - type: map_at_5 value: 45.992 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 48.4 - type: mrr_at_100 value: 49.202 - type: mrr_at_1000 value: 49.205 - type: mrr_at_3 value: 43.551 - type: mrr_at_5 value: 46.467999999999996 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 57.045 - type: ndcg_at_100 value: 60.175 - type: ndcg_at_1000 value: 60.233000000000004 - type: ndcg_at_3 value: 47.171 - type: ndcg_at_5 value: 52.322 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 8.599 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.63 - type: precision_at_5 value: 14.282 - type: recall_at_1 value: 31.223 - type: recall_at_10 value: 85.989 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 58.89 - type: recall_at_5 value: 71.408 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 43.1621946393635 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.56417132407894 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.29539304390207 - type: mrr value: 76.44484017060196 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_spearman value: 84.38746499431112 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.51298701298701 - type: f1 value: 77.49041754069235 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.61848554098577 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 31.32623280148178 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 35.803000000000004 - type: map_at_10 value: 48.848 - type: map_at_100 value: 50.5 - type: map_at_1000 value: 50.602999999999994 - type: map_at_3 value: 45.111000000000004 - type: map_at_5 value: 47.202 - type: mrr_at_1 value: 44.635000000000005 - type: mrr_at_10 value: 55.593 - type: mrr_at_100 value: 56.169999999999995 - type: mrr_at_1000 value: 56.19499999999999 - type: mrr_at_3 value: 53.361999999999995 - type: mrr_at_5 value: 54.806999999999995 - type: ndcg_at_1 value: 44.635000000000005 - type: ndcg_at_10 value: 55.899 - type: ndcg_at_100 value: 60.958 - type: ndcg_at_1000 value: 62.302 - type: ndcg_at_3 value: 51.051 - type: ndcg_at_5 value: 53.351000000000006 - type: precision_at_1 value: 44.635000000000005 - type: precision_at_10 value: 10.786999999999999 - type: precision_at_100 value: 1.6580000000000001 - type: precision_at_1000 value: 0.213 - type: precision_at_3 value: 24.893 - type: precision_at_5 value: 17.740000000000002 - type: recall_at_1 value: 35.803000000000004 - type: recall_at_10 value: 68.657 - type: recall_at_100 value: 89.77199999999999 - type: recall_at_1000 value: 97.67 - type: recall_at_3 value: 54.066 - type: recall_at_5 value: 60.788 - type: map_at_1 value: 33.706 - type: map_at_10 value: 44.896 - type: map_at_100 value: 46.299 - type: map_at_1000 value: 46.44 - type: map_at_3 value: 41.721000000000004 - type: map_at_5 value: 43.486000000000004 - type: mrr_at_1 value: 41.592 - type: mrr_at_10 value: 50.529 - type: mrr_at_100 value: 51.22 - type: mrr_at_1000 value: 51.258 - type: mrr_at_3 value: 48.205999999999996 - type: mrr_at_5 value: 49.528 - type: ndcg_at_1 value: 41.592 - type: ndcg_at_10 value: 50.77199999999999 - type: ndcg_at_100 value: 55.383 - type: ndcg_at_1000 value: 57.288 - type: ndcg_at_3 value: 46.324 - type: ndcg_at_5 value: 48.346000000000004 - type: precision_at_1 value: 41.592 - type: precision_at_10 value: 9.516 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 22.399 - type: precision_at_5 value: 15.770999999999999 - type: recall_at_1 value: 33.706 - type: recall_at_10 value: 61.353 - type: recall_at_100 value: 80.182 - type: recall_at_1000 value: 91.896 - type: recall_at_3 value: 48.204 - type: recall_at_5 value: 53.89699999999999 - type: map_at_1 value: 44.424 - type: map_at_10 value: 57.169000000000004 - type: map_at_100 value: 58.202 - type: map_at_1000 value: 58.242000000000004 - type: map_at_3 value: 53.825 - type: map_at_5 value: 55.714 - type: mrr_at_1 value: 50.470000000000006 - type: mrr_at_10 value: 60.489000000000004 - type: mrr_at_100 value: 61.096 - type: mrr_at_1000 value: 61.112 - type: mrr_at_3 value: 58.192 - type: mrr_at_5 value: 59.611999999999995 - type: ndcg_at_1 value: 50.470000000000006 - type: ndcg_at_10 value: 63.071999999999996 - type: ndcg_at_100 value: 66.964 - type: ndcg_at_1000 value: 67.659 - type: ndcg_at_3 value: 57.74399999999999 - type: ndcg_at_5 value: 60.367000000000004 - type: precision_at_1 value: 50.470000000000006 - type: precision_at_10 value: 10.019 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.558999999999997 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 44.424 - type: recall_at_10 value: 77.02 - type: recall_at_100 value: 93.738 - type: recall_at_1000 value: 98.451 - type: recall_at_3 value: 62.888 - type: recall_at_5 value: 69.138 - type: map_at_1 value: 26.294 - type: map_at_10 value: 34.503 - type: map_at_100 value: 35.641 - type: map_at_1000 value: 35.724000000000004 - type: map_at_3 value: 31.753999999999998 - type: map_at_5 value: 33.190999999999995 - type: mrr_at_1 value: 28.362 - type: mrr_at_10 value: 36.53 - type: mrr_at_100 value: 37.541000000000004 - type: mrr_at_1000 value: 37.602000000000004 - type: mrr_at_3 value: 33.917 - type: mrr_at_5 value: 35.358000000000004 - type: ndcg_at_1 value: 28.362 - type: ndcg_at_10 value: 39.513999999999996 - type: ndcg_at_100 value: 44.815 - type: ndcg_at_1000 value: 46.839 - type: ndcg_at_3 value: 34.02 - type: ndcg_at_5 value: 36.522 - type: precision_at_1 value: 28.362 - type: precision_at_10 value: 6.101999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.161999999999999 - type: precision_at_5 value: 9.966 - type: recall_at_1 value: 26.294 - type: recall_at_10 value: 53.098 - type: recall_at_100 value: 76.877 - type: recall_at_1000 value: 91.834 - type: recall_at_3 value: 38.266 - type: recall_at_5 value: 44.287 - type: map_at_1 value: 16.407 - type: map_at_10 value: 25.185999999999996 - type: map_at_100 value: 26.533 - type: map_at_1000 value: 26.657999999999998 - type: map_at_3 value: 22.201999999999998 - type: map_at_5 value: 23.923 - type: mrr_at_1 value: 20.522000000000002 - type: mrr_at_10 value: 29.522 - type: mrr_at_100 value: 30.644 - type: mrr_at_1000 value: 30.713 - type: mrr_at_3 value: 26.679000000000002 - type: mrr_at_5 value: 28.483000000000004 - type: ndcg_at_1 value: 20.522000000000002 - type: ndcg_at_10 value: 30.656 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.675 - type: ndcg_at_3 value: 25.319000000000003 - type: ndcg_at_5 value: 27.992 - type: precision_at_1 value: 20.522000000000002 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 16.407 - type: recall_at_10 value: 43.164 - type: recall_at_100 value: 69.695 - type: recall_at_1000 value: 89.41900000000001 - type: recall_at_3 value: 28.634999999999998 - type: recall_at_5 value: 35.308 - type: map_at_1 value: 30.473 - type: map_at_10 value: 41.676 - type: map_at_100 value: 43.120999999999995 - type: map_at_1000 value: 43.230000000000004 - type: map_at_3 value: 38.306000000000004 - type: map_at_5 value: 40.355999999999995 - type: mrr_at_1 value: 37.536 - type: mrr_at_10 value: 47.643 - type: mrr_at_100 value: 48.508 - type: mrr_at_1000 value: 48.551 - type: mrr_at_3 value: 45.348 - type: mrr_at_5 value: 46.744 - type: ndcg_at_1 value: 37.536 - type: ndcg_at_10 value: 47.823 - type: ndcg_at_100 value: 53.395 - type: ndcg_at_1000 value: 55.271 - type: ndcg_at_3 value: 42.768 - type: ndcg_at_5 value: 45.373000000000005 - type: precision_at_1 value: 37.536 - type: precision_at_10 value: 8.681 - type: precision_at_100 value: 1.34 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.468 - type: precision_at_5 value: 14.495 - type: recall_at_1 value: 30.473 - type: recall_at_10 value: 60.092999999999996 - type: recall_at_100 value: 82.733 - type: recall_at_1000 value: 94.875 - type: recall_at_3 value: 45.734 - type: recall_at_5 value: 52.691 - type: map_at_1 value: 29.976000000000003 - type: map_at_10 value: 41.097 - type: map_at_100 value: 42.547000000000004 - type: map_at_1000 value: 42.659000000000006 - type: map_at_3 value: 37.251 - type: map_at_5 value: 39.493 - type: mrr_at_1 value: 37.557 - type: mrr_at_10 value: 46.605000000000004 - type: mrr_at_100 value: 47.487 - type: mrr_at_1000 value: 47.54 - type: mrr_at_3 value: 43.721 - type: mrr_at_5 value: 45.411 - type: ndcg_at_1 value: 37.557 - type: ndcg_at_10 value: 47.449000000000005 - type: ndcg_at_100 value: 53.052 - type: ndcg_at_1000 value: 55.010999999999996 - type: ndcg_at_3 value: 41.439 - type: ndcg_at_5 value: 44.292 - type: precision_at_1 value: 37.557 - type: precision_at_10 value: 8.847 - type: precision_at_100 value: 1.357 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 20.091 - type: precision_at_5 value: 14.384 - type: recall_at_1 value: 29.976000000000003 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 84.245 - type: recall_at_1000 value: 96.97200000000001 - type: recall_at_3 value: 43.794 - type: recall_at_5 value: 51.778999999999996 - type: map_at_1 value: 28.099166666666665 - type: map_at_10 value: 38.1365 - type: map_at_100 value: 39.44491666666667 - type: map_at_1000 value: 39.55858333333334 - type: map_at_3 value: 35.03641666666666 - type: map_at_5 value: 36.79833333333334 - type: mrr_at_1 value: 33.39966666666667 - type: mrr_at_10 value: 42.42583333333333 - type: mrr_at_100 value: 43.28575 - type: mrr_at_1000 value: 43.33741666666667 - type: mrr_at_3 value: 39.94975 - type: mrr_at_5 value: 41.41633333333334 - type: ndcg_at_1 value: 33.39966666666667 - type: ndcg_at_10 value: 43.81741666666667 - type: ndcg_at_100 value: 49.08166666666667 - type: ndcg_at_1000 value: 51.121166666666674 - type: ndcg_at_3 value: 38.73575 - type: ndcg_at_5 value: 41.18158333333333 - type: precision_at_1 value: 33.39966666666667 - type: precision_at_10 value: 7.738916666666667 - type: precision_at_100 value: 1.2265833333333331 - type: precision_at_1000 value: 0.15983333333333336 - type: precision_at_3 value: 17.967416666666665 - type: precision_at_5 value: 12.78675 - type: recall_at_1 value: 28.099166666666665 - type: recall_at_10 value: 56.27049999999999 - type: recall_at_100 value: 78.93291666666667 - type: recall_at_1000 value: 92.81608333333334 - type: recall_at_3 value: 42.09775 - type: recall_at_5 value: 48.42533333333334 - type: map_at_1 value: 23.663 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.426 - type: map_at_1000 value: 31.519000000000002 - type: map_at_3 value: 28.069 - type: map_at_5 value: 29.256999999999998 - type: mrr_at_1 value: 26.687 - type: mrr_at_10 value: 33.107 - type: mrr_at_100 value: 34.055 - type: mrr_at_1000 value: 34.117999999999995 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.14 - type: ndcg_at_1 value: 26.687 - type: ndcg_at_10 value: 34.615 - type: ndcg_at_100 value: 39.776 - type: ndcg_at_1000 value: 42.05 - type: ndcg_at_3 value: 30.322 - type: ndcg_at_5 value: 32.157000000000004 - type: precision_at_1 value: 26.687 - type: precision_at_10 value: 5.491 - type: precision_at_100 value: 0.877 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.139000000000001 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.663 - type: recall_at_10 value: 45.035 - type: recall_at_100 value: 68.554 - type: recall_at_1000 value: 85.077 - type: recall_at_3 value: 32.982 - type: recall_at_5 value: 37.688 - type: map_at_1 value: 17.403 - type: map_at_10 value: 25.197000000000003 - type: map_at_100 value: 26.355 - type: map_at_1000 value: 26.487 - type: map_at_3 value: 22.733 - type: map_at_5 value: 24.114 - type: mrr_at_1 value: 21.37 - type: mrr_at_10 value: 29.091 - type: mrr_at_100 value: 30.018 - type: mrr_at_1000 value: 30.096 - type: mrr_at_3 value: 26.887 - type: mrr_at_5 value: 28.157 - type: ndcg_at_1 value: 21.37 - type: ndcg_at_10 value: 30.026000000000003 - type: ndcg_at_100 value: 35.416 - type: ndcg_at_1000 value: 38.45 - type: ndcg_at_3 value: 25.764 - type: ndcg_at_5 value: 27.742 - type: precision_at_1 value: 21.37 - type: precision_at_10 value: 5.609 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 12.423 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 17.403 - type: recall_at_10 value: 40.573 - type: recall_at_100 value: 64.818 - type: recall_at_1000 value: 86.53699999999999 - type: recall_at_3 value: 28.493000000000002 - type: recall_at_5 value: 33.660000000000004 - type: map_at_1 value: 28.639 - type: map_at_10 value: 38.951 - type: map_at_100 value: 40.238 - type: map_at_1000 value: 40.327 - type: map_at_3 value: 35.842 - type: map_at_5 value: 37.617 - type: mrr_at_1 value: 33.769 - type: mrr_at_10 value: 43.088 - type: mrr_at_100 value: 44.03 - type: mrr_at_1000 value: 44.072 - type: mrr_at_3 value: 40.656 - type: mrr_at_5 value: 42.138999999999996 - type: ndcg_at_1 value: 33.769 - type: ndcg_at_10 value: 44.676 - type: ndcg_at_100 value: 50.416000000000004 - type: ndcg_at_1000 value: 52.227999999999994 - type: ndcg_at_3 value: 39.494 - type: ndcg_at_5 value: 42.013 - type: precision_at_1 value: 33.769 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.221 - type: precision_at_5 value: 12.966 - type: recall_at_1 value: 28.639 - type: recall_at_10 value: 57.687999999999995 - type: recall_at_100 value: 82.541 - type: recall_at_1000 value: 94.896 - type: recall_at_3 value: 43.651 - type: recall_at_5 value: 49.925999999999995 - type: map_at_1 value: 29.57 - type: map_at_10 value: 40.004 - type: map_at_100 value: 41.75 - type: map_at_1000 value: 41.97 - type: map_at_3 value: 36.788 - type: map_at_5 value: 38.671 - type: mrr_at_1 value: 35.375 - type: mrr_at_10 value: 45.121 - type: mrr_at_100 value: 45.994 - type: mrr_at_1000 value: 46.04 - type: mrr_at_3 value: 42.227 - type: mrr_at_5 value: 43.995 - type: ndcg_at_1 value: 35.375 - type: ndcg_at_10 value: 46.392 - type: ndcg_at_100 value: 52.196 - type: ndcg_at_1000 value: 54.274 - type: ndcg_at_3 value: 41.163 - type: ndcg_at_5 value: 43.813 - type: precision_at_1 value: 35.375 - type: precision_at_10 value: 8.676 - type: precision_at_100 value: 1.678 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 19.104 - type: precision_at_5 value: 13.913 - type: recall_at_1 value: 29.57 - type: recall_at_10 value: 58.779 - type: recall_at_100 value: 83.337 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 44.005 - type: recall_at_5 value: 50.975 - type: map_at_1 value: 20.832 - type: map_at_10 value: 29.733999999999998 - type: map_at_100 value: 30.727 - type: map_at_1000 value: 30.843999999999998 - type: map_at_3 value: 26.834999999999997 - type: map_at_5 value: 28.555999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 31.791999999999998 - type: mrr_at_100 value: 32.666000000000004 - type: mrr_at_1000 value: 32.751999999999995 - type: mrr_at_3 value: 29.144 - type: mrr_at_5 value: 30.622 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.744 - type: ndcg_at_1000 value: 42.407000000000004 - type: ndcg_at_3 value: 29.421000000000003 - type: ndcg_at_5 value: 32.211 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 5.675 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.753999999999998 - type: precision_at_5 value: 9.353 - type: recall_at_1 value: 20.832 - type: recall_at_10 value: 48.795 - type: recall_at_100 value: 70.703 - type: recall_at_1000 value: 90.187 - type: recall_at_3 value: 34.455000000000005 - type: recall_at_5 value: 40.967 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 10.334 - type: map_at_10 value: 19.009999999999998 - type: map_at_100 value: 21.129 - type: map_at_1000 value: 21.328 - type: map_at_3 value: 15.152 - type: map_at_5 value: 17.084 - type: mrr_at_1 value: 23.453 - type: mrr_at_10 value: 36.099 - type: mrr_at_100 value: 37.069 - type: mrr_at_1000 value: 37.104 - type: mrr_at_3 value: 32.096000000000004 - type: mrr_at_5 value: 34.451 - type: ndcg_at_1 value: 23.453 - type: ndcg_at_10 value: 27.739000000000004 - type: ndcg_at_100 value: 35.836 - type: ndcg_at_1000 value: 39.242 - type: ndcg_at_3 value: 21.263 - type: ndcg_at_5 value: 23.677 - type: precision_at_1 value: 23.453 - type: precision_at_10 value: 9.199 - type: precision_at_100 value: 1.791 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.2 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 10.334 - type: recall_at_10 value: 35.177 - type: recall_at_100 value: 63.009 - type: recall_at_1000 value: 81.938 - type: recall_at_3 value: 19.914 - type: recall_at_5 value: 26.077 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.212 - type: map_at_10 value: 17.386 - type: map_at_100 value: 24.234 - type: map_at_1000 value: 25.724999999999998 - type: map_at_3 value: 12.727 - type: map_at_5 value: 14.785 - type: mrr_at_1 value: 59.25 - type: mrr_at_10 value: 68.687 - type: mrr_at_100 value: 69.133 - type: mrr_at_1000 value: 69.14099999999999 - type: mrr_at_3 value: 66.917 - type: mrr_at_5 value: 67.742 - type: ndcg_at_1 value: 48.625 - type: ndcg_at_10 value: 36.675999999999995 - type: ndcg_at_100 value: 41.543 - type: ndcg_at_1000 value: 49.241 - type: ndcg_at_3 value: 41.373 - type: ndcg_at_5 value: 38.707 - type: precision_at_1 value: 59.25 - type: precision_at_10 value: 28.525 - type: precision_at_100 value: 9.027000000000001 - type: precision_at_1000 value: 1.8339999999999999 - type: precision_at_3 value: 44.833 - type: precision_at_5 value: 37.35 - type: recall_at_1 value: 8.212 - type: recall_at_10 value: 23.188 - type: recall_at_100 value: 48.613 - type: recall_at_1000 value: 73.093 - type: recall_at_3 value: 14.419 - type: recall_at_5 value: 17.798 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.725 - type: f1 value: 46.50743309855908 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 55.086 - type: map_at_10 value: 66.914 - type: map_at_100 value: 67.321 - type: map_at_1000 value: 67.341 - type: map_at_3 value: 64.75800000000001 - type: map_at_5 value: 66.189 - type: mrr_at_1 value: 59.28600000000001 - type: mrr_at_10 value: 71.005 - type: mrr_at_100 value: 71.304 - type: mrr_at_1000 value: 71.313 - type: mrr_at_3 value: 69.037 - type: mrr_at_5 value: 70.35 - type: ndcg_at_1 value: 59.28600000000001 - type: ndcg_at_10 value: 72.695 - type: ndcg_at_100 value: 74.432 - type: ndcg_at_1000 value: 74.868 - type: ndcg_at_3 value: 68.72200000000001 - type: ndcg_at_5 value: 71.081 - type: precision_at_1 value: 59.28600000000001 - type: precision_at_10 value: 9.499 - type: precision_at_100 value: 1.052 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 27.503 - type: precision_at_5 value: 17.854999999999997 - type: recall_at_1 value: 55.086 - type: recall_at_10 value: 86.453 - type: recall_at_100 value: 94.028 - type: recall_at_1000 value: 97.052 - type: recall_at_3 value: 75.821 - type: recall_at_5 value: 81.6 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 22.262999999999998 - type: map_at_10 value: 37.488 - type: map_at_100 value: 39.498 - type: map_at_1000 value: 39.687 - type: map_at_3 value: 32.529 - type: map_at_5 value: 35.455 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.239000000000004 - type: mrr_at_100 value: 54.086 - type: mrr_at_1000 value: 54.122 - type: mrr_at_3 value: 51.235 - type: mrr_at_5 value: 52.415 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 45.446 - type: ndcg_at_100 value: 52.429 - type: ndcg_at_1000 value: 55.169000000000004 - type: ndcg_at_3 value: 41.882000000000005 - type: ndcg_at_5 value: 43.178 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.931999999999999 - type: precision_at_100 value: 2.025 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 28.652 - type: precision_at_5 value: 21.204 - type: recall_at_1 value: 22.262999999999998 - type: recall_at_10 value: 52.447 - type: recall_at_100 value: 78.045 - type: recall_at_1000 value: 94.419 - type: recall_at_3 value: 38.064 - type: recall_at_5 value: 44.769 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 32.519 - type: map_at_10 value: 45.831 - type: map_at_100 value: 46.815 - type: map_at_1000 value: 46.899 - type: map_at_3 value: 42.836 - type: map_at_5 value: 44.65 - type: mrr_at_1 value: 65.037 - type: mrr_at_10 value: 72.16 - type: mrr_at_100 value: 72.51100000000001 - type: mrr_at_1000 value: 72.53 - type: mrr_at_3 value: 70.682 - type: mrr_at_5 value: 71.54599999999999 - type: ndcg_at_1 value: 65.037 - type: ndcg_at_10 value: 55.17999999999999 - type: ndcg_at_100 value: 58.888 - type: ndcg_at_1000 value: 60.648 - type: ndcg_at_3 value: 50.501 - type: ndcg_at_5 value: 52.977 - type: precision_at_1 value: 65.037 - type: precision_at_10 value: 11.530999999999999 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 31.483 - type: precision_at_5 value: 20.845 - type: recall_at_1 value: 32.519 - type: recall_at_10 value: 57.657000000000004 - type: recall_at_100 value: 72.30199999999999 - type: recall_at_1000 value: 84.024 - type: recall_at_3 value: 47.225 - type: recall_at_5 value: 52.113 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.3168 - type: ap value: 83.80165516037135 - type: f1 value: 88.29942471066407 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 20.724999999999998 - type: map_at_10 value: 32.736 - type: map_at_100 value: 33.938 - type: map_at_1000 value: 33.991 - type: map_at_3 value: 28.788000000000004 - type: map_at_5 value: 31.016 - type: mrr_at_1 value: 21.361 - type: mrr_at_10 value: 33.323 - type: mrr_at_100 value: 34.471000000000004 - type: mrr_at_1000 value: 34.518 - type: mrr_at_3 value: 29.453000000000003 - type: mrr_at_5 value: 31.629 - type: ndcg_at_1 value: 21.361 - type: ndcg_at_10 value: 39.649 - type: ndcg_at_100 value: 45.481 - type: ndcg_at_1000 value: 46.775 - type: ndcg_at_3 value: 31.594 - type: ndcg_at_5 value: 35.543 - type: precision_at_1 value: 21.361 - type: precision_at_10 value: 6.3740000000000006 - type: precision_at_100 value: 0.931 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.514999999999999 - type: precision_at_5 value: 10.100000000000001 - type: recall_at_1 value: 20.724999999999998 - type: recall_at_10 value: 61.034 - type: recall_at_100 value: 88.062 - type: recall_at_1000 value: 97.86399999999999 - type: recall_at_3 value: 39.072 - type: recall_at_5 value: 48.53 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.8919288645691 - type: f1 value: 93.57059586398059 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.97993616051072 - type: f1 value: 48.244319183606535 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.90047074646941 - type: f1 value: 66.48999056063725 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.34566240753195 - type: f1 value: 73.54164154290658 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.21866934757011 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.000936217235534 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.68189362520352 - type: mrr value: 32.69603637784303 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.078 - type: map_at_10 value: 12.671 - type: map_at_100 value: 16.291 - type: map_at_1000 value: 17.855999999999998 - type: map_at_3 value: 9.610000000000001 - type: map_at_5 value: 11.152 - type: mrr_at_1 value: 43.963 - type: mrr_at_10 value: 53.173 - type: mrr_at_100 value: 53.718999999999994 - type: mrr_at_1000 value: 53.756 - type: mrr_at_3 value: 50.980000000000004 - type: mrr_at_5 value: 52.42 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.086 - type: ndcg_at_100 value: 32.545 - type: ndcg_at_1000 value: 41.144999999999996 - type: ndcg_at_3 value: 39.434999999999995 - type: ndcg_at_5 value: 37.888 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.014999999999997 - type: precision_at_100 value: 8.594 - type: precision_at_1000 value: 2.169 - type: precision_at_3 value: 37.049 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 6.078 - type: recall_at_10 value: 16.17 - type: recall_at_100 value: 34.512 - type: recall_at_1000 value: 65.447 - type: recall_at_3 value: 10.706 - type: recall_at_5 value: 13.158 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 27.378000000000004 - type: map_at_10 value: 42.178 - type: map_at_100 value: 43.32 - type: map_at_1000 value: 43.358000000000004 - type: map_at_3 value: 37.474000000000004 - type: map_at_5 value: 40.333000000000006 - type: mrr_at_1 value: 30.823 - type: mrr_at_10 value: 44.626 - type: mrr_at_100 value: 45.494 - type: mrr_at_1000 value: 45.519 - type: mrr_at_3 value: 40.585 - type: mrr_at_5 value: 43.146 - type: ndcg_at_1 value: 30.794 - type: ndcg_at_10 value: 50.099000000000004 - type: ndcg_at_100 value: 54.900999999999996 - type: ndcg_at_1000 value: 55.69499999999999 - type: ndcg_at_3 value: 41.238 - type: ndcg_at_5 value: 46.081 - type: precision_at_1 value: 30.794 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 18.926000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 27.378000000000004 - type: recall_at_10 value: 71.842 - type: recall_at_100 value: 92.565 - type: recall_at_1000 value: 98.402 - type: recall_at_3 value: 49.053999999999995 - type: recall_at_5 value: 60.207 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.557 - type: map_at_10 value: 84.729 - type: map_at_100 value: 85.369 - type: map_at_1000 value: 85.382 - type: map_at_3 value: 81.72 - type: map_at_5 value: 83.613 - type: mrr_at_1 value: 81.3 - type: mrr_at_10 value: 87.488 - type: mrr_at_100 value: 87.588 - type: mrr_at_1000 value: 87.589 - type: mrr_at_3 value: 86.53 - type: mrr_at_5 value: 87.18599999999999 - type: ndcg_at_1 value: 81.28999999999999 - type: ndcg_at_10 value: 88.442 - type: ndcg_at_100 value: 89.637 - type: ndcg_at_1000 value: 89.70700000000001 - type: ndcg_at_3 value: 85.55199999999999 - type: ndcg_at_5 value: 87.154 - type: precision_at_1 value: 81.28999999999999 - type: precision_at_10 value: 13.489999999999998 - type: precision_at_100 value: 1.54 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.708 - type: recall_at_1 value: 70.557 - type: recall_at_10 value: 95.645 - type: recall_at_100 value: 99.693 - type: recall_at_1000 value: 99.995 - type: recall_at_3 value: 87.359 - type: recall_at_5 value: 91.89699999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 63.65060114776209 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.63271250680617 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.263 - type: map_at_10 value: 10.801 - type: map_at_100 value: 12.888 - type: map_at_1000 value: 13.224 - type: map_at_3 value: 7.362 - type: map_at_5 value: 9.149000000000001 - type: mrr_at_1 value: 21 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.513 - type: mrr_at_1000 value: 32.58 - type: mrr_at_3 value: 28.116999999999997 - type: mrr_at_5 value: 29.976999999999997 - type: ndcg_at_1 value: 21 - type: ndcg_at_10 value: 18.551000000000002 - type: ndcg_at_100 value: 26.657999999999998 - type: ndcg_at_1000 value: 32.485 - type: ndcg_at_3 value: 16.834 - type: ndcg_at_5 value: 15.204999999999998 - type: precision_at_1 value: 21 - type: precision_at_10 value: 9.84 - type: precision_at_100 value: 2.16 - type: precision_at_1000 value: 0.35500000000000004 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 13.62 - type: recall_at_1 value: 4.263 - type: recall_at_10 value: 19.922 - type: recall_at_100 value: 43.808 - type: recall_at_1000 value: 72.14500000000001 - type: recall_at_3 value: 9.493 - type: recall_at_5 value: 13.767999999999999 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_spearman value: 81.27446313317233 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_spearman value: 76.27963301217527 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_spearman value: 88.18495048450949 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_spearman value: 81.91982338692046 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_spearman value: 89.00896818385291 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_spearman value: 85.48814644586132 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_spearman value: 90.30116926966582 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_spearman value: 67.74132963032342 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_spearman value: 86.87741355780479 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.0019012295875 - type: mrr value: 94.70267024188593 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 50.05 - type: map_at_10 value: 59.36 - type: map_at_100 value: 59.967999999999996 - type: map_at_1000 value: 60.023 - type: map_at_3 value: 56.515 - type: map_at_5 value: 58.272999999999996 - type: mrr_at_1 value: 53 - type: mrr_at_10 value: 61.102000000000004 - type: mrr_at_100 value: 61.476 - type: mrr_at_1000 value: 61.523 - type: mrr_at_3 value: 58.778 - type: mrr_at_5 value: 60.128 - type: ndcg_at_1 value: 53 - type: ndcg_at_10 value: 64.43100000000001 - type: ndcg_at_100 value: 66.73599999999999 - type: ndcg_at_1000 value: 68.027 - type: ndcg_at_3 value: 59.279 - type: ndcg_at_5 value: 61.888 - type: precision_at_1 value: 53 - type: precision_at_10 value: 8.767 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.444000000000003 - type: precision_at_5 value: 15.667 - type: recall_at_1 value: 50.05 - type: recall_at_10 value: 78.511 - type: recall_at_100 value: 88.5 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 64.117 - type: recall_at_5 value: 70.867 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72178217821782 - type: cos_sim_ap value: 93.0728601593541 - type: cos_sim_f1 value: 85.6727976766699 - type: cos_sim_precision value: 83.02063789868667 - type: cos_sim_recall value: 88.5 - type: dot_accuracy value: 99.72178217821782 - type: dot_ap value: 93.07287396168348 - type: dot_f1 value: 85.6727976766699 - type: dot_precision value: 83.02063789868667 - type: dot_recall value: 88.5 - type: euclidean_accuracy value: 99.72178217821782 - type: euclidean_ap value: 93.07285657982895 - type: euclidean_f1 value: 85.6727976766699 - type: euclidean_precision value: 83.02063789868667 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.72475247524753 - type: manhattan_ap value: 93.02792973059809 - type: manhattan_f1 value: 85.7727737973388 - type: manhattan_precision value: 87.84067085953879 - type: manhattan_recall value: 83.8 - type: max_accuracy value: 99.72475247524753 - type: max_ap value: 93.07287396168348 - type: max_f1 value: 85.7727737973388 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.77583615550819 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.151636938606956 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.16607939471187 - type: mrr value: 52.95172046091163 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.314646669495666 - type: cos_sim_spearman value: 31.83562491439455 - type: dot_pearson value: 31.314590842874157 - type: dot_spearman value: 31.83363065810437 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.3010000000000002 - type: map_at_100 value: 7.2139999999999995 - type: map_at_1000 value: 20.179 - type: map_at_3 value: 0.528 - type: map_at_5 value: 0.8019999999999999 - type: mrr_at_1 value: 72 - type: mrr_at_10 value: 83.39999999999999 - type: mrr_at_100 value: 83.39999999999999 - type: mrr_at_1000 value: 83.39999999999999 - type: mrr_at_3 value: 81.667 - type: mrr_at_5 value: 83.06700000000001 - type: ndcg_at_1 value: 66 - type: ndcg_at_10 value: 58.059000000000005 - type: ndcg_at_100 value: 44.316 - type: ndcg_at_1000 value: 43.147000000000006 - type: ndcg_at_3 value: 63.815999999999995 - type: ndcg_at_5 value: 63.005 - type: precision_at_1 value: 72 - type: precision_at_10 value: 61.4 - type: precision_at_100 value: 45.62 - type: precision_at_1000 value: 19.866 - type: precision_at_3 value: 70 - type: precision_at_5 value: 68.8 - type: recall_at_1 value: 0.198 - type: recall_at_10 value: 1.517 - type: recall_at_100 value: 10.587 - type: recall_at_1000 value: 41.233 - type: recall_at_3 value: 0.573 - type: recall_at_5 value: 0.907 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.894 - type: map_at_10 value: 8.488999999999999 - type: map_at_100 value: 14.445 - type: map_at_1000 value: 16.078 - type: map_at_3 value: 4.589 - type: map_at_5 value: 6.019 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 39.82 - type: mrr_at_100 value: 40.752 - type: mrr_at_1000 value: 40.771 - type: mrr_at_3 value: 34.354 - type: mrr_at_5 value: 37.721 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 21.563 - type: ndcg_at_100 value: 33.857 - type: ndcg_at_1000 value: 46.199 - type: ndcg_at_3 value: 22.296 - type: ndcg_at_5 value: 21.770999999999997 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.142999999999999 - type: precision_at_1000 value: 1.541 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 1.894 - type: recall_at_10 value: 14.931 - type: recall_at_100 value: 45.524 - type: recall_at_1000 value: 83.243 - type: recall_at_3 value: 5.712 - type: recall_at_5 value: 8.386000000000001 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.049 - type: ap value: 13.85116971310922 - type: f1 value: 54.37504302487686 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.1312959818902 - type: f1 value: 64.11413877009383 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 54.13103431861502 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.327889372355 - type: cos_sim_ap value: 77.42059895975699 - type: cos_sim_f1 value: 71.02706903250873 - type: cos_sim_precision value: 69.75324344950394 - type: cos_sim_recall value: 72.34828496042216 - type: dot_accuracy value: 87.327889372355 - type: dot_ap value: 77.4209479346677 - type: dot_f1 value: 71.02706903250873 - type: dot_precision value: 69.75324344950394 - type: dot_recall value: 72.34828496042216 - type: euclidean_accuracy value: 87.327889372355 - type: euclidean_ap value: 77.42096495861037 - type: euclidean_f1 value: 71.02706903250873 - type: euclidean_precision value: 69.75324344950394 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.31000774870358 - type: manhattan_ap value: 77.38930750711619 - type: manhattan_f1 value: 71.07935314027831 - type: manhattan_precision value: 67.70957726295677 - type: manhattan_recall value: 74.80211081794195 - type: max_accuracy value: 87.327889372355 - type: max_ap value: 77.42096495861037 - type: max_f1 value: 71.07935314027831 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.58939729110878 - type: cos_sim_ap value: 87.17594155025475 - type: cos_sim_f1 value: 79.21146953405018 - type: cos_sim_precision value: 76.8918527109307 - type: cos_sim_recall value: 81.67539267015707 - type: dot_accuracy value: 89.58939729110878 - type: dot_ap value: 87.17593963273593 - type: dot_f1 value: 79.21146953405018 - type: dot_precision value: 76.8918527109307 - type: dot_recall value: 81.67539267015707 - type: euclidean_accuracy value: 89.58939729110878 - type: euclidean_ap value: 87.17592466925834 - type: euclidean_f1 value: 79.21146953405018 - type: euclidean_precision value: 76.8918527109307 - type: euclidean_recall value: 81.67539267015707 - type: manhattan_accuracy value: 89.62626615438352 - type: manhattan_ap value: 87.16589873161546 - type: manhattan_f1 value: 79.25143598295348 - type: manhattan_precision value: 76.39494177323712 - type: manhattan_recall value: 82.32984293193716 - type: max_accuracy value: 89.62626615438352 - type: max_ap value: 87.17594155025475 - type: max_f1 value: 79.25143598295348 --- # hkunlp/instructor-large We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! **************************** **Updates** **************************** * 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance. * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out! ## Quick start <hr /> ## Installation ```bash pip install InstructorEmbedding ``` ## Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-large') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title:" embeddings = model.encode([[instruction,sentence]]) print(embeddings) ``` ## Use cases <hr /> ## Calculate embeddings for your customized texts If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;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) ```
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
papahawk/gpt-neox-20b
papahawk
null
[ "pytorch", "causal-lm", "en", "dataset:EleutherAI/pile", "arxiv:2204.06745", "arxiv:2101.00027", "arxiv:2201.07311", "arxiv:2104.09864", "license:apache-2.0", "region:us" ]
1,687,969,381,000
2023-06-28T16:44:58
0
0
--- datasets: - EleutherAI/pile language: - en license: apache-2.0 tags: - pytorch - causal-lm --- <h1 style='text-align: center '>GPT-NeoX-20b LLM</h1> <h2 style='text-align: center '><em>Fork of EleutherAI/gpt-neox-20b</em> </h2> <h3 style='text-align: center '>Model Card</h3> <img src="https://alt-web.xyz/images/rainbow.png" alt="Rainbow Solutions" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> 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} } ```
[ "SCIQ" ]
Non_BioNLP
EleutherAI/Llama-2-7b-hf-sciq
EleutherAI
null
[ "safetensors", "en", "arxiv:2312.01037", "license:apache-2.0", "region:us" ]
1,705,510,378,000
2024-02-07T00:09:00
0
0
--- language: - en license: apache-2.0 --- # Model Card for Llama-2-7b-hf-sciq A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods. ## Model Details ### Model Description This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods. The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors. We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*. They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing). These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading. **Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE) ### Model Sources [optional] - **Repository:** https://github.com/EleutherAI/elk-generalization ## Uses This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods. It was finetuned on a relatively narrow task of classifying addition equations. ## Bias, Risks, and Limitations Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general. We invite contributions of new quirky datasets and models. ### Training Procedure This model was finetuned using the [quirky sciq dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9). The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py). #### Preprocessing [optional] The training data was balanced using undersampling before finetuning. ## Evaluation This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk). ## Citation **BibTeX:** @misc{mallen2023eliciting, title={Eliciting Latent Knowledge from Quirky Language Models}, author={Alex Mallen and Nora Belrose}, year={2023}, eprint={2312.01037}, archivePrefix={arXiv}, primaryClass={cs.LG\} }
[ "SCIQ" ]
Non_BioNLP
nbninh/a216846e-a3c4-4676-8b3f-c205c39cfb2d
nbninh
null
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "base_model:adapter:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "8-bit", "bitsandbytes", "region:us" ]
1,736,754,049,000
2025-01-13T08:33:02
4
0
--- base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 library_name: peft tags: - axolotl - generated_from_trainer model-index: - name: a216846e-a3c4-4676-8b3f-c205c39cfb2d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b897f6ee6a8273ba_train_data.json ds_type: json format: custom path: /workspace/input_data/b897f6ee6a8273ba_train_data.json type: field_input: context field_instruction: question field_output: final_decision format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nbninh/a216846e-a3c4-4676-8b3f-c205c39cfb2d hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/b897f6ee6a8273ba_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 89c0a3ea-a9d5-41af-919e-924b573834cf wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 89c0a3ea-a9d5-41af-919e-924b573834cf warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a216846e-a3c4-4676-8b3f-c205c39cfb2d This model is a fine-tuned version of [rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28](https://huggingface.co/rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0431 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0003 | 0.0080 | 200 | 0.0431 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
[ "PUBMEDQA" ]
BioNLP
Triangle104/Distilled-DarkPlanet-Allades-8B_TIES
Triangle104
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "arxiv:2306.01708", "base_model:DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B", "base_model:merge:DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:merge:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:nbeerbower/Llama3.1-Allades-8B", "base_model:merge:nbeerbower/Llama3.1-Allades-8B", "license:llama3.1", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,738,522,060,000
2025-02-08T00:20:18
23
1
--- base_model: - deepseek-ai/DeepSeek-R1-Distill-Llama-8B - nbeerbower/Llama3.1-Allades-8B - DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B language: - en library_name: transformers license: llama3.1 tags: - mergekit - merge model-index: - name: Distilled-DarkPlanet-Allades-8B_TIES results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 38.92 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Distilled-DarkPlanet-Allades-8B_TIES name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 29.96 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Distilled-DarkPlanet-Allades-8B_TIES name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 8.38 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Distilled-DarkPlanet-Allades-8B_TIES name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.61 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Distilled-DarkPlanet-Allades-8B_TIES name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 8.05 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Distilled-DarkPlanet-Allades-8B_TIES name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 26.68 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Distilled-DarkPlanet-Allades-8B_TIES name: Open LLM Leaderboard --- # Merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details Different merge of Allades, Dark-Planet-SF and DS Distilled models, using DS as base. Doesn't seem to trigger thought proccess of Deepseek model, but useful nonetheless. Sample - User: Write a story about an elf called Tyril fighting an owlbear. Assistant: Tyril, a skilled Elven archer, crouched behind a moss-covered boulder, her piercing emerald eyes fixed on the snarling owlbear in front of her. The creature's yellow eyes glinted with menace as it reared up on its hind legs, its long claws flexing menacingly. The boulder was no more than 20 feet away from Tyril, but it provided a modicum of cover. The forest floor around her was a tangle of thorny brambles and tall, swaying ferns that offered some concealment, but not enough to make the owlbear's keen eyes miss her. Tyril inhaled deeply, filling her lungs with the damp, earthy scent of the forest. She mentally prepared herself for the fight ahead, as she had a reputation for being one of the most skilled and deadly archers among the Elven community. The owlbear was a formidable opponent; its razor-sharp talons could tear through even the thickest of armor, and it possessed brute strength to match any bear. Tyril knew that if she didn't act quickly, her chances of victory would be slim to none. She slowly reached for one of the quivers of arrows at her side. With a smooth, practiced motion, she nocked an arrow on to her bowstring. The owlbear, sensing her presence, turned its attention fully to Tyril and took a step forward, its claws scraping against the rough bark of a nearby tree as it shifted weight. Tyril's eyes never left the beast, tracking its every movement. She could almost feel the heat emanating from its body, and she knew that if she hesitated for even an instant, she would be within reach of those deadly talons. Her bowstring hummed as she drew back to her ear, and a steady, measured breath escaped from between her lips. As her fingers released the arrow, it was gone in a flash - a streak of green and black, whirring through the air with a sharp, guttural whoosh. The owlbear, anticipating Tyril's next move, swiped at the arrow as if to knock it out of the sky. However, Tyril had taken the angle into account, and her aim was true; the arrow struck the owlbear squarely between its eyes with a sickening thud. The creature let out an anguished howl that echoed through the forest, and for a moment, it stumbled backwards, clutching at the wound. Tyril didn't wait to see if the attack had been effective - she immediately notched another arrow, her bow singing in its familiar rhythm as she drew back to her ear. With an economy of motion, she released the next arrow, which struck the owlbear's shoulder and caused it to rear up in agony. The beast's howls now took on a desperate edge, and Tyril realized that this was no ordinary attack - the owlbear was not only wounded but also enraged by the pain. This made it an even more formidable foe, as its ferocity increased with every passing moment. As she readied yet another arrow, Tyril's eyes flicked over to a nearby tree. She had seen a branch snap off under the weight of the owlbear during one of their earlier skirmishes. That same branch now lay on the forest floor about 20 feet from where Tyril was hiding - and in an instant, she decided that it could become her best friend. Tyril swiftly nocked another arrow and released it with a smooth, practiced motion. The arrow flew true, striking the owlbear in its side, causing it to wince and take a step back. Then, using all of her archer's skill, she expertly guided her next arrow towards where she knew the snapped branch was lying - straight for the owlbear. The owlbear, as if anticipating Tyril's strategy, swung around with an arm-like motion. The force of its swipe sent a flurry of leaves and twigs flying through the air, but, to Tyril's surprise, it failed to connect with the branch that she had aimed at. Instead, it missed by a fraction - but not before its razor-sharp talons brushed against her bow, sending a shiver of fear down Tyril's spine as the edge of the bow scraped and almost broke under the owlbear's swipe. The creature's howls grew even more frantic now, as if enraged by its own ineptitude. However, this was merely a momentary setback for both combatants - Tyril knew that the owlbear would be back to try and claim her as its next meal in no time. ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) as a base. ### Models Merged The following models were included in the merge: * [nbeerbower/Llama3.1-Allades-8B](https://huggingface.co/nbeerbower/Llama3.1-Allades-8B) * [DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B](https://huggingface.co/DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: nbeerbower/Llama3.1-Allades-8B parameters: density: 0.5 weight: 1 - model: DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B parameters: density: 0.5 weight: 1 merge_method: ties base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B parameters: normalize: true int8_mask: true dtype: float16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Triangle104__Distilled-DarkPlanet-Allades-8B_TIES-details) | Metric |Value| |-------------------|----:| |Avg. |20.10| |IFEval (0-Shot) |38.92| |BBH (3-Shot) |29.96| |MATH Lvl 5 (4-Shot)| 8.38| |GPQA (0-shot) | 8.61| |MuSR (0-shot) | 8.05| |MMLU-PRO (5-shot) |26.68|
[ "BEAR" ]
Non_BioNLP
liddlefish/privacy_embedding_rag
liddlefish
feature-extraction
[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "mteb", "en", "arxiv:2401.03462", "arxiv:2312.15503", "arxiv:2311.13534", "arxiv:2310.07554", "arxiv:2309.07597", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,717,906,691,000
2024-06-09T04:19:49
5
0
--- language: - en license: mit tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-small-en-v1.5 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 37.21923821573361 - type: f1 value: 68.0914945617093 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.75377499999999 - type: ap value: 89.46766124546022 - type: f1 value: 92.73884001331487 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.986 - type: f1 value: 46.55936786727896 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 35.846000000000004 - type: map_at_10 value: 51.388 - type: map_at_100 value: 52.132999999999996 - type: map_at_1000 value: 52.141000000000005 - type: map_at_3 value: 47.037 - type: map_at_5 value: 49.579 - type: mrr_at_1 value: 36.558 - type: mrr_at_10 value: 51.658 - type: mrr_at_100 value: 52.402 - type: mrr_at_1000 value: 52.410000000000004 - type: mrr_at_3 value: 47.345 - type: mrr_at_5 value: 49.797999999999995 - type: ndcg_at_1 value: 35.846000000000004 - type: ndcg_at_10 value: 59.550000000000004 - type: ndcg_at_100 value: 62.596 - type: ndcg_at_1000 value: 62.759 - type: ndcg_at_3 value: 50.666999999999994 - type: ndcg_at_5 value: 55.228 - type: precision_at_1 value: 35.846000000000004 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.389 - type: precision_at_5 value: 14.438 - type: recall_at_1 value: 35.846000000000004 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.166 - type: recall_at_5 value: 72.191 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.402770198163594 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.01545436974177 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.586465273207196 - type: mrr value: 74.42169019038825 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.1891186537969 - type: cos_sim_spearman value: 83.75492046087288 - type: euclidean_pearson value: 84.11766204805357 - type: euclidean_spearman value: 84.01456493126516 - type: manhattan_pearson value: 84.2132950502772 - type: manhattan_spearman value: 83.89227298813377 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.74025974025975 - type: f1 value: 85.71493566466381 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.467181385006434 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.719496037339056 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 29.587000000000003 - type: map_at_10 value: 41.114 - type: map_at_100 value: 42.532 - type: map_at_1000 value: 42.661 - type: map_at_3 value: 37.483 - type: map_at_5 value: 39.652 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.763 - type: mrr_at_100 value: 47.393 - type: mrr_at_1000 value: 47.445 - type: mrr_at_3 value: 43.538 - type: mrr_at_5 value: 45.556000000000004 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.658 - type: ndcg_at_100 value: 52.824000000000005 - type: ndcg_at_1000 value: 54.913999999999994 - type: ndcg_at_3 value: 41.989 - type: ndcg_at_5 value: 44.944 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.156 - type: precision_at_100 value: 1.4789999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.85 - type: recall_at_1 value: 29.587000000000003 - type: recall_at_10 value: 60.746 - type: recall_at_100 value: 82.157 - type: recall_at_1000 value: 95.645 - type: recall_at_3 value: 44.821 - type: recall_at_5 value: 52.819 - type: map_at_1 value: 30.239 - type: map_at_10 value: 39.989000000000004 - type: map_at_100 value: 41.196 - type: map_at_1000 value: 41.325 - type: map_at_3 value: 37.261 - type: map_at_5 value: 38.833 - type: mrr_at_1 value: 37.516 - type: mrr_at_10 value: 46.177 - type: mrr_at_100 value: 46.806 - type: mrr_at_1000 value: 46.849000000000004 - type: mrr_at_3 value: 44.002 - type: mrr_at_5 value: 45.34 - type: ndcg_at_1 value: 37.516 - type: ndcg_at_10 value: 45.586 - type: ndcg_at_100 value: 49.897000000000006 - type: ndcg_at_1000 value: 51.955 - type: ndcg_at_3 value: 41.684 - type: ndcg_at_5 value: 43.617 - type: precision_at_1 value: 37.516 - type: precision_at_10 value: 8.522 - type: precision_at_100 value: 1.374 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 20.105999999999998 - type: precision_at_5 value: 14.152999999999999 - type: recall_at_1 value: 30.239 - type: recall_at_10 value: 55.03 - type: recall_at_100 value: 73.375 - type: recall_at_1000 value: 86.29599999999999 - type: recall_at_3 value: 43.269000000000005 - type: recall_at_5 value: 48.878 - type: map_at_1 value: 38.338 - type: map_at_10 value: 50.468999999999994 - type: map_at_100 value: 51.553000000000004 - type: map_at_1000 value: 51.608 - type: map_at_3 value: 47.107 - type: map_at_5 value: 49.101 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 54.057 - type: mrr_at_100 value: 54.764 - type: mrr_at_1000 value: 54.791000000000004 - type: mrr_at_3 value: 51.56699999999999 - type: mrr_at_5 value: 53.05 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 56.379000000000005 - type: ndcg_at_100 value: 60.645 - type: ndcg_at_1000 value: 61.73499999999999 - type: ndcg_at_3 value: 50.726000000000006 - type: ndcg_at_5 value: 53.58500000000001 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 9.141 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.654 - type: precision_at_5 value: 15.723999999999998 - type: recall_at_1 value: 38.338 - type: recall_at_10 value: 70.30499999999999 - type: recall_at_100 value: 88.77199999999999 - type: recall_at_1000 value: 96.49799999999999 - type: recall_at_3 value: 55.218 - type: recall_at_5 value: 62.104000000000006 - type: map_at_1 value: 25.682 - type: map_at_10 value: 33.498 - type: map_at_100 value: 34.461000000000006 - type: map_at_1000 value: 34.544000000000004 - type: map_at_3 value: 30.503999999999998 - type: map_at_5 value: 32.216 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 35.467999999999996 - type: mrr_at_100 value: 36.32 - type: mrr_at_1000 value: 36.386 - type: mrr_at_3 value: 32.618 - type: mrr_at_5 value: 34.262 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 38.378 - type: ndcg_at_100 value: 43.288 - type: ndcg_at_1000 value: 45.413 - type: ndcg_at_3 value: 32.586 - type: ndcg_at_5 value: 35.499 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.864 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 25.682 - type: recall_at_10 value: 51.712 - type: recall_at_100 value: 74.446 - type: recall_at_1000 value: 90.472 - type: recall_at_3 value: 36.236000000000004 - type: recall_at_5 value: 43.234 - type: map_at_1 value: 16.073999999999998 - type: map_at_10 value: 24.352999999999998 - type: map_at_100 value: 25.438 - type: map_at_1000 value: 25.545 - type: map_at_3 value: 21.614 - type: map_at_5 value: 23.104 - type: mrr_at_1 value: 19.776 - type: mrr_at_10 value: 28.837000000000003 - type: mrr_at_100 value: 29.755 - type: mrr_at_1000 value: 29.817 - type: mrr_at_3 value: 26.201999999999998 - type: mrr_at_5 value: 27.714 - type: ndcg_at_1 value: 19.776 - type: ndcg_at_10 value: 29.701 - type: ndcg_at_100 value: 35.307 - type: ndcg_at_1000 value: 37.942 - type: ndcg_at_3 value: 24.764 - type: ndcg_at_5 value: 27.025 - type: precision_at_1 value: 19.776 - type: precision_at_10 value: 5.659 - type: precision_at_100 value: 0.971 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.905000000000001 - type: recall_at_1 value: 16.073999999999998 - type: recall_at_10 value: 41.647 - type: recall_at_100 value: 66.884 - type: recall_at_1000 value: 85.91499999999999 - type: recall_at_3 value: 27.916 - type: recall_at_5 value: 33.729 - type: map_at_1 value: 28.444999999999997 - type: map_at_10 value: 38.218999999999994 - type: map_at_100 value: 39.595 - type: map_at_1000 value: 39.709 - type: map_at_3 value: 35.586 - type: map_at_5 value: 36.895 - type: mrr_at_1 value: 34.841 - type: mrr_at_10 value: 44.106 - type: mrr_at_100 value: 44.98 - type: mrr_at_1000 value: 45.03 - type: mrr_at_3 value: 41.979 - type: mrr_at_5 value: 43.047999999999995 - type: ndcg_at_1 value: 34.841 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.504999999999995 - type: ndcg_at_1000 value: 51.675000000000004 - type: ndcg_at_3 value: 39.858 - type: ndcg_at_5 value: 41.408 - type: precision_at_1 value: 34.841 - type: precision_at_10 value: 7.872999999999999 - type: precision_at_100 value: 1.2449999999999999 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 18.993 - type: precision_at_5 value: 13.032 - type: recall_at_1 value: 28.444999999999997 - type: recall_at_10 value: 54.984 - type: recall_at_100 value: 78.342 - type: recall_at_1000 value: 92.77 - type: recall_at_3 value: 42.842999999999996 - type: recall_at_5 value: 47.247 - type: map_at_1 value: 23.072 - type: map_at_10 value: 32.354 - type: map_at_100 value: 33.800000000000004 - type: map_at_1000 value: 33.908 - type: map_at_3 value: 29.232000000000003 - type: map_at_5 value: 31.049 - type: mrr_at_1 value: 29.110000000000003 - type: mrr_at_10 value: 38.03 - type: mrr_at_100 value: 39.032 - type: mrr_at_1000 value: 39.086999999999996 - type: mrr_at_3 value: 35.407 - type: mrr_at_5 value: 36.76 - type: ndcg_at_1 value: 29.110000000000003 - type: ndcg_at_10 value: 38.231 - type: ndcg_at_100 value: 44.425 - type: ndcg_at_1000 value: 46.771 - type: ndcg_at_3 value: 33.095 - type: ndcg_at_5 value: 35.459 - type: precision_at_1 value: 29.110000000000003 - type: precision_at_10 value: 7.215000000000001 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 16.058 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 50.285999999999994 - type: recall_at_100 value: 76.596 - type: recall_at_1000 value: 92.861 - type: recall_at_3 value: 35.702 - type: recall_at_5 value: 42.152 - type: map_at_1 value: 24.937916666666666 - type: map_at_10 value: 33.755250000000004 - type: map_at_100 value: 34.955999999999996 - type: map_at_1000 value: 35.070499999999996 - type: map_at_3 value: 30.98708333333333 - type: map_at_5 value: 32.51491666666666 - type: mrr_at_1 value: 29.48708333333333 - type: mrr_at_10 value: 37.92183333333334 - type: mrr_at_100 value: 38.76583333333333 - type: mrr_at_1000 value: 38.82466666666667 - type: mrr_at_3 value: 35.45125 - type: mrr_at_5 value: 36.827000000000005 - type: ndcg_at_1 value: 29.48708333333333 - type: ndcg_at_10 value: 39.05225 - type: ndcg_at_100 value: 44.25983333333334 - type: ndcg_at_1000 value: 46.568333333333335 - type: ndcg_at_3 value: 34.271583333333325 - type: ndcg_at_5 value: 36.483916666666666 - type: precision_at_1 value: 29.48708333333333 - type: precision_at_10 value: 6.865749999999999 - type: precision_at_100 value: 1.1195833333333332 - type: precision_at_1000 value: 0.15058333333333335 - type: precision_at_3 value: 15.742083333333333 - type: precision_at_5 value: 11.221916666666667 - type: recall_at_1 value: 24.937916666666666 - type: recall_at_10 value: 50.650416666666665 - type: recall_at_100 value: 73.55383333333334 - type: recall_at_1000 value: 89.61691666666667 - type: recall_at_3 value: 37.27808333333334 - type: recall_at_5 value: 42.99475 - type: map_at_1 value: 23.947 - type: map_at_10 value: 30.575000000000003 - type: map_at_100 value: 31.465 - type: map_at_1000 value: 31.558000000000003 - type: map_at_3 value: 28.814 - type: map_at_5 value: 29.738999999999997 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.415 - type: mrr_at_100 value: 34.18 - type: mrr_at_1000 value: 34.245 - type: mrr_at_3 value: 31.621 - type: mrr_at_5 value: 32.549 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.482 - type: ndcg_at_100 value: 38.915 - type: ndcg_at_1000 value: 41.355 - type: ndcg_at_3 value: 31.139 - type: ndcg_at_5 value: 32.589 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.322 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.344000000000001 - type: precision_at_5 value: 8.988 - type: recall_at_1 value: 23.947 - type: recall_at_10 value: 43.647999999999996 - type: recall_at_100 value: 63.851 - type: recall_at_1000 value: 82.0 - type: recall_at_3 value: 34.288000000000004 - type: recall_at_5 value: 38.117000000000004 - type: map_at_1 value: 16.197 - type: map_at_10 value: 22.968 - type: map_at_100 value: 24.095 - type: map_at_1000 value: 24.217 - type: map_at_3 value: 20.771 - type: map_at_5 value: 21.995 - type: mrr_at_1 value: 19.511 - type: mrr_at_10 value: 26.55 - type: mrr_at_100 value: 27.500999999999998 - type: mrr_at_1000 value: 27.578999999999997 - type: mrr_at_3 value: 24.421 - type: mrr_at_5 value: 25.604 - type: ndcg_at_1 value: 19.511 - type: ndcg_at_10 value: 27.386 - type: ndcg_at_100 value: 32.828 - type: ndcg_at_1000 value: 35.739 - type: ndcg_at_3 value: 23.405 - type: ndcg_at_5 value: 25.255 - type: precision_at_1 value: 19.511 - type: precision_at_10 value: 5.017 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 11.023 - type: precision_at_5 value: 8.025 - type: recall_at_1 value: 16.197 - type: recall_at_10 value: 37.09 - type: recall_at_100 value: 61.778 - type: recall_at_1000 value: 82.56599999999999 - type: recall_at_3 value: 26.034000000000002 - type: recall_at_5 value: 30.762 - type: map_at_1 value: 25.41 - type: map_at_10 value: 33.655 - type: map_at_100 value: 34.892 - type: map_at_1000 value: 34.995 - type: map_at_3 value: 30.94 - type: map_at_5 value: 32.303 - type: mrr_at_1 value: 29.477999999999998 - type: mrr_at_10 value: 37.443 - type: mrr_at_100 value: 38.383 - type: mrr_at_1000 value: 38.440000000000005 - type: mrr_at_3 value: 34.949999999999996 - type: mrr_at_5 value: 36.228 - type: ndcg_at_1 value: 29.477999999999998 - type: ndcg_at_10 value: 38.769 - type: ndcg_at_100 value: 44.245000000000005 - type: ndcg_at_1000 value: 46.593 - type: ndcg_at_3 value: 33.623 - type: ndcg_at_5 value: 35.766 - type: precision_at_1 value: 29.477999999999998 - type: precision_at_10 value: 6.455 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 14.893999999999998 - type: precision_at_5 value: 10.485 - type: recall_at_1 value: 25.41 - type: recall_at_10 value: 50.669 - type: recall_at_100 value: 74.084 - type: recall_at_1000 value: 90.435 - type: recall_at_3 value: 36.679 - type: recall_at_5 value: 41.94 - type: map_at_1 value: 23.339 - type: map_at_10 value: 31.852000000000004 - type: map_at_100 value: 33.411 - type: map_at_1000 value: 33.62 - type: map_at_3 value: 28.929 - type: map_at_5 value: 30.542 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.301 - type: mrr_at_100 value: 37.288 - type: mrr_at_1000 value: 37.349 - type: mrr_at_3 value: 33.663 - type: mrr_at_5 value: 35.165 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 37.462 - type: ndcg_at_100 value: 43.620999999999995 - type: ndcg_at_1000 value: 46.211 - type: ndcg_at_3 value: 32.68 - type: ndcg_at_5 value: 34.981 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.1739999999999995 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.217 - type: precision_at_5 value: 11.265 - type: recall_at_1 value: 23.339 - type: recall_at_10 value: 48.376999999999995 - type: recall_at_100 value: 76.053 - type: recall_at_1000 value: 92.455 - type: recall_at_3 value: 34.735 - type: recall_at_5 value: 40.71 - type: map_at_1 value: 18.925 - type: map_at_10 value: 26.017000000000003 - type: map_at_100 value: 27.034000000000002 - type: map_at_1000 value: 27.156000000000002 - type: map_at_3 value: 23.604 - type: map_at_5 value: 24.75 - type: mrr_at_1 value: 20.333000000000002 - type: mrr_at_10 value: 27.915 - type: mrr_at_100 value: 28.788000000000004 - type: mrr_at_1000 value: 28.877999999999997 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.648 - type: ndcg_at_1 value: 20.333000000000002 - type: ndcg_at_10 value: 30.673000000000002 - type: ndcg_at_100 value: 35.618 - type: ndcg_at_1000 value: 38.517 - type: ndcg_at_3 value: 25.71 - type: ndcg_at_5 value: 27.679 - type: precision_at_1 value: 20.333000000000002 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8130000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.8740000000000006 - type: recall_at_1 value: 18.925 - type: recall_at_10 value: 43.311 - type: recall_at_100 value: 66.308 - type: recall_at_1000 value: 87.49 - type: recall_at_3 value: 29.596 - type: recall_at_5 value: 34.245 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 13.714 - type: map_at_10 value: 23.194 - type: map_at_100 value: 24.976000000000003 - type: map_at_1000 value: 25.166 - type: map_at_3 value: 19.709 - type: map_at_5 value: 21.523999999999997 - type: mrr_at_1 value: 30.619000000000003 - type: mrr_at_10 value: 42.563 - type: mrr_at_100 value: 43.386 - type: mrr_at_1000 value: 43.423 - type: mrr_at_3 value: 39.555 - type: mrr_at_5 value: 41.268 - type: ndcg_at_1 value: 30.619000000000003 - type: ndcg_at_10 value: 31.836 - type: ndcg_at_100 value: 38.652 - type: ndcg_at_1000 value: 42.088 - type: ndcg_at_3 value: 26.733 - type: ndcg_at_5 value: 28.435 - type: precision_at_1 value: 30.619000000000003 - type: precision_at_10 value: 9.751999999999999 - type: precision_at_100 value: 1.71 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 19.935 - type: precision_at_5 value: 14.984 - type: recall_at_1 value: 13.714 - type: recall_at_10 value: 37.26 - type: recall_at_100 value: 60.546 - type: recall_at_1000 value: 79.899 - type: recall_at_3 value: 24.325 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.462 - type: map_at_10 value: 18.637 - type: map_at_100 value: 26.131999999999998 - type: map_at_1000 value: 27.607 - type: map_at_3 value: 13.333 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.32600000000001 - type: mrr_at_100 value: 74.60900000000001 - type: mrr_at_1000 value: 74.62 - type: mrr_at_3 value: 72.667 - type: mrr_at_5 value: 73.817 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.028999999999996 - type: ndcg_at_100 value: 44.199 - type: ndcg_at_1000 value: 51.629999999999995 - type: ndcg_at_3 value: 44.113 - type: ndcg_at_5 value: 41.731 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 31.900000000000002 - type: precision_at_100 value: 10.043000000000001 - type: precision_at_1000 value: 1.926 - type: precision_at_3 value: 47.417 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.462 - type: recall_at_10 value: 24.293 - type: recall_at_100 value: 50.146 - type: recall_at_1000 value: 74.034 - type: recall_at_3 value: 14.967 - type: recall_at_5 value: 18.682000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.84499999999999 - type: f1 value: 42.48106691979349 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.034 - type: map_at_10 value: 82.76 - type: map_at_100 value: 82.968 - type: map_at_1000 value: 82.98299999999999 - type: map_at_3 value: 81.768 - type: map_at_5 value: 82.418 - type: mrr_at_1 value: 80.048 - type: mrr_at_10 value: 87.64999999999999 - type: mrr_at_100 value: 87.712 - type: mrr_at_1000 value: 87.713 - type: mrr_at_3 value: 87.01100000000001 - type: mrr_at_5 value: 87.466 - type: ndcg_at_1 value: 80.048 - type: ndcg_at_10 value: 86.643 - type: ndcg_at_100 value: 87.361 - type: ndcg_at_1000 value: 87.606 - type: ndcg_at_3 value: 85.137 - type: ndcg_at_5 value: 86.016 - type: precision_at_1 value: 80.048 - type: precision_at_10 value: 10.372 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 32.638 - type: precision_at_5 value: 20.177 - type: recall_at_1 value: 74.034 - type: recall_at_10 value: 93.769 - type: recall_at_100 value: 96.569 - type: recall_at_1000 value: 98.039 - type: recall_at_3 value: 89.581 - type: recall_at_5 value: 91.906 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 20.5 - type: map_at_10 value: 32.857 - type: map_at_100 value: 34.589 - type: map_at_1000 value: 34.778 - type: map_at_3 value: 29.160999999999998 - type: map_at_5 value: 31.033 - type: mrr_at_1 value: 40.123 - type: mrr_at_10 value: 48.776 - type: mrr_at_100 value: 49.495 - type: mrr_at_1000 value: 49.539 - type: mrr_at_3 value: 46.605000000000004 - type: mrr_at_5 value: 47.654 - type: ndcg_at_1 value: 40.123 - type: ndcg_at_10 value: 40.343 - type: ndcg_at_100 value: 46.56 - type: ndcg_at_1000 value: 49.777 - type: ndcg_at_3 value: 37.322 - type: ndcg_at_5 value: 37.791000000000004 - type: precision_at_1 value: 40.123 - type: precision_at_10 value: 11.08 - type: precision_at_100 value: 1.752 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 24.897 - type: precision_at_5 value: 17.809 - type: recall_at_1 value: 20.5 - type: recall_at_10 value: 46.388 - type: recall_at_100 value: 69.552 - type: recall_at_1000 value: 89.011 - type: recall_at_3 value: 33.617999999999995 - type: recall_at_5 value: 38.211 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.135999999999996 - type: map_at_10 value: 61.673 - type: map_at_100 value: 62.562 - type: map_at_1000 value: 62.62 - type: map_at_3 value: 58.467999999999996 - type: map_at_5 value: 60.463 - type: mrr_at_1 value: 78.271 - type: mrr_at_10 value: 84.119 - type: mrr_at_100 value: 84.29299999999999 - type: mrr_at_1000 value: 84.299 - type: mrr_at_3 value: 83.18900000000001 - type: mrr_at_5 value: 83.786 - type: ndcg_at_1 value: 78.271 - type: ndcg_at_10 value: 69.935 - type: ndcg_at_100 value: 73.01299999999999 - type: ndcg_at_1000 value: 74.126 - type: ndcg_at_3 value: 65.388 - type: ndcg_at_5 value: 67.906 - type: precision_at_1 value: 78.271 - type: precision_at_10 value: 14.562 - type: precision_at_100 value: 1.6969999999999998 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.841 - type: precision_at_5 value: 27.087 - type: recall_at_1 value: 39.135999999999996 - type: recall_at_10 value: 72.809 - type: recall_at_100 value: 84.86200000000001 - type: recall_at_1000 value: 92.208 - type: recall_at_3 value: 62.76199999999999 - type: recall_at_5 value: 67.718 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.60600000000001 - type: ap value: 86.6579587804335 - type: f1 value: 90.5938853929307 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.852 - type: map_at_10 value: 33.982 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.167 - type: map_at_3 value: 30.134 - type: map_at_5 value: 32.340999999999994 - type: mrr_at_1 value: 22.479 - type: mrr_at_10 value: 34.594 - type: mrr_at_100 value: 35.672 - type: mrr_at_1000 value: 35.716 - type: mrr_at_3 value: 30.84 - type: mrr_at_5 value: 32.998 - type: ndcg_at_1 value: 22.493 - type: ndcg_at_10 value: 40.833000000000006 - type: ndcg_at_100 value: 46.357 - type: ndcg_at_1000 value: 47.637 - type: ndcg_at_3 value: 32.995999999999995 - type: ndcg_at_5 value: 36.919000000000004 - type: precision_at_1 value: 22.493 - type: precision_at_10 value: 6.465999999999999 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.030999999999999 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.852 - type: recall_at_10 value: 61.934999999999995 - type: recall_at_100 value: 87.611 - type: recall_at_1000 value: 97.441 - type: recall_at_3 value: 40.583999999999996 - type: recall_at_5 value: 49.992999999999995 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.36069311445507 - type: f1 value: 93.16456330371453 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 74.74692202462381 - type: f1 value: 58.17903579421599 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.80833893745796 - type: f1 value: 72.70786592684664 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.69872225958305 - type: f1 value: 78.61626934504731 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.058658628717694 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.85561739360599 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.290259910144385 - type: mrr value: 32.44223046102856 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.288 - type: map_at_10 value: 12.267999999999999 - type: map_at_100 value: 15.557000000000002 - type: map_at_1000 value: 16.98 - type: map_at_3 value: 8.866 - type: map_at_5 value: 10.418 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 52.681 - type: mrr_at_100 value: 53.315999999999995 - type: mrr_at_1000 value: 53.357 - type: mrr_at_3 value: 51.393 - type: mrr_at_5 value: 51.903999999999996 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.305 - type: ndcg_at_100 value: 30.825999999999997 - type: ndcg_at_1000 value: 39.393 - type: ndcg_at_3 value: 39.931 - type: ndcg_at_5 value: 37.519999999999996 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.728 - type: precision_at_100 value: 7.932 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.184000000000005 - type: precision_at_5 value: 32.879000000000005 - type: recall_at_1 value: 5.288 - type: recall_at_10 value: 16.195 - type: recall_at_100 value: 31.135 - type: recall_at_1000 value: 61.531000000000006 - type: recall_at_3 value: 10.313 - type: recall_at_5 value: 12.754999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 28.216 - type: map_at_10 value: 42.588 - type: map_at_100 value: 43.702999999999996 - type: map_at_1000 value: 43.739 - type: map_at_3 value: 38.177 - type: map_at_5 value: 40.754000000000005 - type: mrr_at_1 value: 31.866 - type: mrr_at_10 value: 45.189 - type: mrr_at_100 value: 46.056000000000004 - type: mrr_at_1000 value: 46.081 - type: mrr_at_3 value: 41.526999999999994 - type: mrr_at_5 value: 43.704 - type: ndcg_at_1 value: 31.837 - type: ndcg_at_10 value: 50.178 - type: ndcg_at_100 value: 54.98800000000001 - type: ndcg_at_1000 value: 55.812 - type: ndcg_at_3 value: 41.853 - type: ndcg_at_5 value: 46.153 - type: precision_at_1 value: 31.837 - type: precision_at_10 value: 8.43 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.911000000000001 - type: recall_at_1 value: 28.216 - type: recall_at_10 value: 70.8 - type: recall_at_100 value: 91.857 - type: recall_at_1000 value: 97.941 - type: recall_at_3 value: 49.196 - type: recall_at_5 value: 59.072 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.22800000000001 - type: map_at_10 value: 85.115 - type: map_at_100 value: 85.72 - type: map_at_1000 value: 85.737 - type: map_at_3 value: 82.149 - type: map_at_5 value: 84.029 - type: mrr_at_1 value: 81.96 - type: mrr_at_10 value: 88.00200000000001 - type: mrr_at_100 value: 88.088 - type: mrr_at_1000 value: 88.089 - type: mrr_at_3 value: 87.055 - type: mrr_at_5 value: 87.715 - type: ndcg_at_1 value: 82.01 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.91 - type: ndcg_at_1000 value: 90.013 - type: ndcg_at_3 value: 85.957 - type: ndcg_at_5 value: 87.56 - type: precision_at_1 value: 82.01 - type: precision_at_10 value: 13.462 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.732000000000003 - type: recall_at_1 value: 71.22800000000001 - type: recall_at_10 value: 95.69 - type: recall_at_100 value: 99.531 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 87.632 - type: recall_at_5 value: 92.117 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 52.31768034366916 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.640266772723606 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.7780000000000005 - type: map_at_10 value: 12.299 - type: map_at_100 value: 14.363000000000001 - type: map_at_1000 value: 14.71 - type: map_at_3 value: 8.738999999999999 - type: map_at_5 value: 10.397 - type: mrr_at_1 value: 23.599999999999998 - type: mrr_at_10 value: 34.845 - type: mrr_at_100 value: 35.916 - type: mrr_at_1000 value: 35.973 - type: mrr_at_3 value: 31.7 - type: mrr_at_5 value: 33.535 - type: ndcg_at_1 value: 23.599999999999998 - type: ndcg_at_10 value: 20.522000000000002 - type: ndcg_at_100 value: 28.737000000000002 - type: ndcg_at_1000 value: 34.596 - type: ndcg_at_3 value: 19.542 - type: ndcg_at_5 value: 16.958000000000002 - type: precision_at_1 value: 23.599999999999998 - type: precision_at_10 value: 10.67 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 14.879999999999999 - type: recall_at_1 value: 4.7780000000000005 - type: recall_at_10 value: 21.617 - type: recall_at_100 value: 45.905 - type: recall_at_1000 value: 74.42 - type: recall_at_3 value: 11.148 - type: recall_at_5 value: 15.082999999999998 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.22372750297885 - type: cos_sim_spearman value: 79.40972617119405 - type: euclidean_pearson value: 80.6101072020434 - type: euclidean_spearman value: 79.53844217225202 - type: manhattan_pearson value: 80.57265975286111 - type: manhattan_spearman value: 79.46335611792958 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.43713315520749 - type: cos_sim_spearman value: 77.44128693329532 - type: euclidean_pearson value: 81.63869928101123 - type: euclidean_spearman value: 77.29512977961515 - type: manhattan_pearson value: 81.63704185566183 - type: manhattan_spearman value: 77.29909412738657 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.59451537860527 - type: cos_sim_spearman value: 82.97994638856723 - type: euclidean_pearson value: 82.89478688288412 - type: euclidean_spearman value: 83.58740751053104 - type: manhattan_pearson value: 82.69140840941608 - type: manhattan_spearman value: 83.33665956040555 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.00756527711764 - type: cos_sim_spearman value: 81.83560996841379 - type: euclidean_pearson value: 82.07684151976518 - type: euclidean_spearman value: 82.00913052060511 - type: manhattan_pearson value: 82.05690778488794 - type: manhattan_spearman value: 82.02260252019525 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.13710262895447 - type: cos_sim_spearman value: 87.26412811156248 - type: euclidean_pearson value: 86.94151453230228 - type: euclidean_spearman value: 87.5363796699571 - type: manhattan_pearson value: 86.86989424083748 - type: manhattan_spearman value: 87.47315940781353 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.0230597603627 - type: cos_sim_spearman value: 84.93344499318864 - type: euclidean_pearson value: 84.23754743431141 - type: euclidean_spearman value: 85.09707376597099 - type: manhattan_pearson value: 84.04325160987763 - type: manhattan_spearman value: 84.89353071339909 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.75620824563921 - type: cos_sim_spearman value: 87.15065513706398 - type: euclidean_pearson value: 88.26281533633521 - type: euclidean_spearman value: 87.51963738643983 - type: manhattan_pearson value: 88.25599267618065 - type: manhattan_spearman value: 87.58048736047483 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.74645319195137 - type: cos_sim_spearman value: 65.29996325037214 - type: euclidean_pearson value: 67.04297794086443 - type: euclidean_spearman value: 65.43841726694343 - type: manhattan_pearson value: 67.39459955690904 - type: manhattan_spearman value: 65.92864704413651 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.31291020270801 - type: cos_sim_spearman value: 85.86473738688068 - type: euclidean_pearson value: 85.65537275064152 - type: euclidean_spearman value: 86.13087454209642 - type: manhattan_pearson value: 85.43946955047609 - type: manhattan_spearman value: 85.91568175344916 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.93798118350695 - type: mrr value: 95.93536274908824 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.594 - type: map_at_10 value: 66.81899999999999 - type: map_at_100 value: 67.368 - type: map_at_1000 value: 67.4 - type: map_at_3 value: 64.061 - type: map_at_5 value: 65.47 - type: mrr_at_1 value: 60.667 - type: mrr_at_10 value: 68.219 - type: mrr_at_100 value: 68.655 - type: mrr_at_1000 value: 68.684 - type: mrr_at_3 value: 66.22200000000001 - type: mrr_at_5 value: 67.289 - type: ndcg_at_1 value: 60.667 - type: ndcg_at_10 value: 71.275 - type: ndcg_at_100 value: 73.642 - type: ndcg_at_1000 value: 74.373 - type: ndcg_at_3 value: 66.521 - type: ndcg_at_5 value: 68.581 - type: precision_at_1 value: 60.667 - type: precision_at_10 value: 9.433 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.556 - type: precision_at_5 value: 16.8 - type: recall_at_1 value: 57.594 - type: recall_at_10 value: 83.622 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.64399999999999 - type: recall_at_5 value: 75.983 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85841584158416 - type: cos_sim_ap value: 96.66996142314342 - type: cos_sim_f1 value: 92.83208020050125 - type: cos_sim_precision value: 93.06532663316584 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.85841584158416 - type: dot_ap value: 96.6775307676576 - type: dot_f1 value: 92.69289729177312 - type: dot_precision value: 94.77533960292581 - type: dot_recall value: 90.7 - type: euclidean_accuracy value: 99.86138613861387 - type: euclidean_ap value: 96.6338454403108 - type: euclidean_f1 value: 92.92214357937311 - type: euclidean_precision value: 93.96728016359918 - type: euclidean_recall value: 91.9 - type: manhattan_accuracy value: 99.86237623762376 - type: manhattan_ap value: 96.60370449645053 - type: manhattan_f1 value: 92.91177970423253 - type: manhattan_precision value: 94.7970863683663 - type: manhattan_recall value: 91.10000000000001 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.6775307676576 - type: max_f1 value: 92.92214357937311 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.77977058695198 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.2725272535638 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.64052466362125 - type: mrr value: 54.533067014684654 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.677624219206578 - type: cos_sim_spearman value: 30.121368518123447 - type: dot_pearson value: 30.69870088041608 - type: dot_spearman value: 29.61284927093751 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.855 - type: map_at_100 value: 9.885 - type: map_at_1000 value: 23.416999999999998 - type: map_at_3 value: 0.637 - type: map_at_5 value: 1.024 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.067 - type: mrr_at_100 value: 93.067 - type: mrr_at_1000 value: 93.067 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 93.067 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 75.899 - type: ndcg_at_100 value: 55.115 - type: ndcg_at_1000 value: 48.368 - type: ndcg_at_3 value: 79.704 - type: ndcg_at_5 value: 78.39699999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 79.60000000000001 - type: precision_at_100 value: 56.06 - type: precision_at_1000 value: 21.206 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 83.2 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.078 - type: recall_at_100 value: 13.297 - type: recall_at_1000 value: 44.979 - type: recall_at_3 value: 0.6689999999999999 - type: recall_at_5 value: 1.106 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.258 - type: map_at_10 value: 10.439 - type: map_at_100 value: 16.89 - type: map_at_1000 value: 18.407999999999998 - type: map_at_3 value: 5.668 - type: map_at_5 value: 7.718 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 51.159 - type: mrr_at_100 value: 51.714000000000006 - type: mrr_at_1000 value: 51.714000000000006 - type: mrr_at_3 value: 47.959 - type: mrr_at_5 value: 50.407999999999994 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.037 - type: ndcg_at_100 value: 37.924 - type: ndcg_at_1000 value: 49.126999999999995 - type: ndcg_at_3 value: 30.631999999999998 - type: ndcg_at_5 value: 28.571 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.857 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.529 - type: precision_at_3 value: 34.014 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.258 - type: recall_at_10 value: 16.554 - type: recall_at_100 value: 48.439 - type: recall_at_1000 value: 82.80499999999999 - type: recall_at_3 value: 7.283 - type: recall_at_5 value: 10.732 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.8858 - type: ap value: 13.835684144362109 - type: f1 value: 53.803351693244586 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.50650820599886 - type: f1 value: 60.84357825979259 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.52131044852134 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.59337187816654 - type: cos_sim_ap value: 73.23925826533437 - type: cos_sim_f1 value: 67.34693877551021 - type: cos_sim_precision value: 62.40432237730752 - type: cos_sim_recall value: 73.13984168865434 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.30723963807422 - type: dot_f1 value: 66.47051612112296 - type: dot_precision value: 62.0792305930845 - type: dot_recall value: 71.53034300791556 - type: euclidean_accuracy value: 85.61125350181797 - type: euclidean_ap value: 73.32843720487845 - type: euclidean_f1 value: 67.36549633745895 - type: euclidean_precision value: 64.60755813953489 - type: euclidean_recall value: 70.36939313984169 - type: manhattan_accuracy value: 85.63509566668654 - type: manhattan_ap value: 73.16658488311325 - type: manhattan_f1 value: 67.20597386434349 - type: manhattan_precision value: 63.60424028268551 - type: manhattan_recall value: 71.2401055408971 - type: max_accuracy value: 85.63509566668654 - type: max_ap value: 73.32843720487845 - type: max_f1 value: 67.36549633745895 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.33779640625606 - type: cos_sim_ap value: 84.83868375898157 - type: cos_sim_f1 value: 77.16506154017773 - type: cos_sim_precision value: 74.62064005753327 - type: cos_sim_recall value: 79.88912842623961 - type: dot_accuracy value: 88.02732176815307 - type: dot_ap value: 83.95089283763002 - type: dot_f1 value: 76.29635101196631 - type: dot_precision value: 73.31771720613288 - type: dot_recall value: 79.52725592854944 - type: euclidean_accuracy value: 88.44452206310397 - type: euclidean_ap value: 84.98384576824827 - type: euclidean_f1 value: 77.29311047696697 - type: euclidean_precision value: 74.51232583065381 - type: euclidean_recall value: 80.28949799815214 - type: manhattan_accuracy value: 88.47362906042613 - type: manhattan_ap value: 84.91421462218432 - type: manhattan_f1 value: 77.05107637204792 - type: manhattan_precision value: 74.74484256243214 - type: manhattan_recall value: 79.50415768401602 - type: max_accuracy value: 88.47362906042613 - type: max_ap value: 84.98384576824827 - type: max_f1 value: 77.29311047696697 --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5') model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` #### Usage via infinity Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
[ "BEAR", "BIOSSES", "SCIFACT" ]
Non_BioNLP
ntc-ai/SDXL-LoRA-slider.dancing-with-joy
ntc-ai
text-to-image
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
1,704,085,141,000
2024-01-01T04:59:04
15
0
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 language: - en license: mit tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers thumbnail: images/evaluate/dancing with joy.../dancing with joy_17_3.0.png widget: - text: dancing with joy output: url: images/dancing with joy_17_3.0.png - text: dancing with joy output: url: images/dancing with joy_19_3.0.png - text: dancing with joy output: url: images/dancing with joy_20_3.0.png - text: dancing with joy output: url: images/dancing with joy_21_3.0.png - text: dancing with joy output: url: images/dancing with joy_22_3.0.png inference: false instance_prompt: dancing with joy --- # ntcai.xyz slider - dancing with joy (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/dancing with joy_17_-3.0.png" width=256 height=256 /> | <img src="images/dancing with joy_17_0.0.png" width=256 height=256 /> | <img src="images/dancing with joy_17_3.0.png" width=256 height=256 /> | | <img src="images/dancing with joy_19_-3.0.png" width=256 height=256 /> | <img src="images/dancing with joy_19_0.0.png" width=256 height=256 /> | <img src="images/dancing with joy_19_3.0.png" width=256 height=256 /> | | <img src="images/dancing with joy_20_-3.0.png" width=256 height=256 /> | <img src="images/dancing with joy_20_0.0.png" width=256 height=256 /> | <img src="images/dancing with joy_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` dancing with joy ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.dancing-with-joy', weight_name='dancing with joy.safetensors', adapter_name="dancing with joy") # Activate the LoRA pipe.set_adapters(["dancing with joy"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, dancing with joy" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 780+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
[ "CRAFT" ]
Non_BioNLP
ktangri/gpt-neo-demo
ktangri
text-generation
[ "transformers", "pytorch", "gpt_neo", "text-generation", "text generation", "the Pile", "causal-lm", "en", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,646,263,745,000
2021-07-21T15:20:09
27
1
--- datasets: - the Pile language: - en license: apache-2.0 tags: - text generation - pytorch - the Pile - causal-lm --- # GPT-Neo 2.7B (By EleutherAI) ## Model Description GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model. ## Training data GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. ## Training procedure This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss. ## Intended Use and Limitations This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B') >>> generator("EleutherAI has", do_sample=True, min_length=50) [{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}] ``` ### Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ## Eval results All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM). ### Linguistic Reasoning | Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag | | ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- | | GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 | 7.498 | 57.23% | 55.01% | 38.66% | | GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% | | **GPT-Neo 2.7B** | **0.7165** | **5.646** | **11.39** | **5.626** | **62.22%** | **56.50%** | **42.73%** | | GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% | ### Physical and Scientific Reasoning | Model and Size | MathQA | PubMedQA | Piqa | | ---------------- | ---------- | ---------- | ----------- | | GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% | | GPT-2 1.5B | 23.64% | 58.33% | 70.78% | | **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%** | | GPT-3 Ada | 24.29% | 52.80% | 68.88% | ### Down-Stream Applications TBD ### BibTeX entry and citation info To cite this model, use ```bibtex @article{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` To cite the codebase that this model was trained with, use ```bibtex @software{gpt-neo, author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}, url = {http://github.com/eleutherai/gpt-neo}, version = {1.0}, year = {2021}, } ```
[ "PUBMEDQA" ]
Non_BioNLP
IBI-CAAI/MELT-Mistral-3x7B-Instruct-v0.1
IBI-CAAI
text-generation
[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,704,060,803,000
2024-01-06T11:09:17
3
0
--- language: - en library_name: transformers license: apache-2.0 --- # Model Card MELT-Mistral-3x7B-Instruct-v0.1 Medical Education Language Transformer (MELT) # Model Type: The MELT-Mistral-3x7B-Instruct-v0.1 Large Language Model (LLM) is a pretrained generative text model pre-trained and fine-tuned on using publically avalable medical data. MELT-Mistral-3x7B-Instruct-v0.1 demonstrated a average 19.7% improvement over Mistral-3x7B-Instruct-v0.1 (MoE of 3 X Mistral-7B-Instruct-v0.1) across 3 USMLE, Indian AIIMS, and NEET medical examination benchmarks. This is MoE model, thanks to [Charles Goddard](https://huggingface.co/chargoddard) for code/tools. ## Model Details The Medical Education Language Transformer (MELT) models have been trained on a wide-range of text, chat, Q/A, and instruction data in the medical domain. While the model was evaluated using publically avalable [USMLE](https://www.usmle.org/), Indian AIIMS, and NEET medical examination example questions, its use it intented to be more broadly applicable. ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [Center for Applied AI](https://caai.ai.uky.edu/) - **Funded by:** [Institute or Biomedical Informatics](https://www.research.uky.edu/IBI) - **Model type:** LLM - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Finetuned from model:** A MoE x 3 [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ## Uses MELT is intended for research purposes only. MELT models are best suited for prompts using a QA or chat format. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> MELT is intended for research purposes only and should not be used for medical advice. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> MELT was training using collections publicly available, which likely contain biased and inaccurate information. The training and evaluation datasets have not been evaluated for content or accuracy. ## How to Get Started with the Model Use this model like you would the Mixtral-8x7B-Instruct-v0.1 model. ## Training Details ### Training Data <!-- This should link to a Dataset 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. --> The following datasets were used for training: [Expert Med](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/Q3A969) [MedQA train](https://huggingface.co/datasets/bigbio/med_qa) [MedMCQA train](https://github.com/MedMCQA/MedMCQA?tab=readme-ov-file#data-download-and-preprocessing) [LiveQA](https://github.com/abachaa/LiveQA_MedicalTask_TREC2017) [MedicationQA](https://huggingface.co/datasets/truehealth/medicationqa) [MMLU clinical topics](https://huggingface.co/datasets/Stevross/mmlu) [Medical Flashcards](https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards) [Wikidoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc) [Wikidoc Patient Information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information) [MEDIQA](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa) [MMMLU](https://huggingface.co/datasets/medalpaca/medical_meadow_mmmlu) [icliniq 10k](https://drive.google.com/file/d/1ZKbqgYqWc7DJHs3N9TQYQVPdDQmZaClA/view?usp=sharing) [HealthCare Magic 100k](https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view?usp=sharing) [GenMedGPT-5k](https://drive.google.com/file/d/1nDTKZ3wZbZWTkFMBkxlamrzbNz0frugg/view?usp=sharing) [Mental Health Conversational](https://huggingface.co/datasets/heliosbrahma/mental_health_conversational_dataset) ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Training Hyperparameters - **Lora Rank:** 64 - **Lora Alpha:** 16 - **Lora Targets:** "o_proj","down_proj","v_proj","gate_proj","up_proj","k_proj","q_proj" - **LR:** 2e-4 - **Epoch:** 3 - **Precision:** bf16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> MELT-Mistral-3x7B-Instruct-v0.1 demonstrated a average 19.7% improvement over Mistral-3x7B-Instruct-v0.1 (MoE of 3 X Mistral-7B-Instruct-v0.1) across 3 USMLE, 3 USMLE, Indian AIIMS, and NEET medical examination benchmarks. ### Mistral-3x7B-Instruct-v0.1 - **medqa:** {'base': {'Average': 42.88, 'STEP-1': 43.51, 'STEP-2&3': 42.16}} - **mausmle:** {'base': {'Average': 45.88, 'STEP-1': 45.88, 'STEP-2': 45.98, 'STEP-3': 45.79}} - **medmcqa:** {'base': {'Average': 45.87, 'MEDICINE': 44.02, 'OPHTHALMOLOGY': 54.76, 'ANATOMY': 48.63, 'PATHOLOGY': 54.26, 'PHYSIOLOGY': 46.21, 'DENTAL': 41.47, 'RADIOLOGY': 53.57, 'BIOCHEMISTRY': 52.89, 'ANAESTHESIA': 47.83, 'GYNAECOLOGY': 37.91, 'PHARMACOLOGY': 48.88, 'SOCIAL': 42.22, 'PEDIATRICS': 41.67, 'ENT': 55.26, 'SURGERY': 47.58, 'MICROBIOLOGY': 43.84, 'FORENSIC': 48.84, 'PSYCHIATRY': 77.78, 'SKIN': 60.0, 'ORTHOPAEDICS': 50.0, 'UNKNOWN': 100.0}} - **average:** 44.87% ### MELT-Mixtral-3x7B-Instruct-v0.1 - **medqa:** {'base': {'Average': 52.24, 'STEP-1': 51.92, 'STEP-2&3': 52.61}} - **mausmle:** {'base': {'Average': 58.06, 'STEP-1': 54.12, 'STEP-2': 58.62, 'STEP-3': 60.75}} - **medmcqa:** {'base': {'Average': 50.73, 'MEDICINE': 48.91, 'OPHTHALMOLOGY': 59.52, 'ANATOMY': 51.37, 'PATHOLOGY': 61.63, 'PHYSIOLOGY': 56.82, 'DENTAL': 42.42, 'RADIOLOGY': 62.5, 'BIOCHEMISTRY': 62.81, 'ANAESTHESIA': 39.13, 'GYNAECOLOGY': 46.41, 'PHARMACOLOGY': 56.74, 'SOCIAL': 46.67, 'PEDIATRICS': 50.76, 'ENT': 52.63, 'SURGERY': 53.23, 'MICROBIOLOGY': 46.58, 'FORENSIC': 62.79, 'PSYCHIATRY': 77.78, 'SKIN': 70.0, 'ORTHOPAEDICS': 50.0, 'UNKNOWN': 100.0}} - **average:** 53.7% ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [MedQA test](https://huggingface.co/datasets/bigbio/med_qa) [MedMCQA test](https://github.com/MedMCQA/MedMCQA?tab=readme-ov-file#data-download-and-preprocessing) [MA USMLE](https://huggingface.co/datasets/medalpaca/medical_meadow_usmle_self_assessment) ## Disclaimer: The use of large language models, such as this one, is provided without warranties or guarantees of any kind. While every effort has been made to ensure accuracy, completeness, and reliability of the information generated, it should be noted that these models may produce responses that are inaccurate, outdated, or inappropriate for specific purposes. Users are advised to exercise discretion and judgment when relying on the information generated by these models. The outputs should not be considered as professional, legal, medical, financial, or any other form of advice. It is recommended to seek expert advice or consult appropriate sources for specific queries or critical decision-making. The creators, developers, and providers of these models disclaim any liability for damages, losses, or any consequences arising from the use, reliance upon, or interpretation of the information provided by these models. The user assumes full responsibility for their interactions and usage of the generated content. By using these language models, users agree to indemnify and hold harmless the developers, providers, and affiliates from any claims, damages, or liabilities that may arise from their use. Please be aware that these models are constantly evolving, and their capabilities, limitations, and outputs may change over time without prior notice. Your use of this language model signifies your acceptance and understanding of this disclaimer.
[ "MEDQA", "MEDICAL DATA" ]
BioNLP