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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01mteb /resultstext1M<n<10M18 likes314k downloads19h agoHugging Face02mteb /sts12-sts STS12 An MTEB dataset Massive Text Embedding Benchmark SemEval-2012 Task 6. Task category t2t Domains Encyclopaedic, News, Written Reference https://www.aclweb.org/anthology/S12-1051.pdf How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STS12"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how to… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts12-sts.textsentence-similarity1K<n<10K8 likes84k downloads7mo agoHugging Face03morteza20 /mteb_leaderboard [!NOTE]Previously it was possible to submit models results to MTEB by adding the results to the model metadata. This is no longer an option as we want to ensure high quality metadata. This repository contain the results of the embedding benchmark evaluated using the package mteb. Reference 🦾 Leaderboard An up to date leaderboard of embedding models 📚 mteb Guides and instructions on how to use mteb, including running, submitting scores, etc. 🙋 Questions Questions about the… See the full description on the dataset page: https://huggingface.co/datasets/morteza20/mteb_leaderboard.0 likes56k downloads2y agoHugging Face04mteb /nfcorpus NFCorpus An MTEB dataset Massive Text Embedding Benchmark NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval Task category t2t Domains Medical, Academic, Written Reference https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/ How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["NFCorpus"]) evaluator = mteb.MTEB(task) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/nfcorpus.texttext-retrieval100K<n<1M3 likes46k downloads1y agoHugging Face05mteb /amazon_massive_intent MassiveIntentClassification An MTEB dataset Massive Text Embedding Benchmark MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages Task category t2c Domains Spoken Reference https://arxiv.org/abs/2204.08582 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["MassiveIntentClassification"]) evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_massive_intent.texttext-classification100K<n<1M27 likes37k downloads7mo agoHugging Face06mteb /sts22-crosslingual-sts STS22.v2 An MTEB dataset Massive Text Embedding Benchmark SemEval 2022 Task 8: Multilingual News Article Similarity. Version 2 filters updated on STS22 by removing pairs where one of entries contain empty sentences. Task category t2t Domains News, Written Reference https://competitions.codalab.org/competitions/33835 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts22-crosslingual-sts.textsentence-similarity10K<n<100K16 likes30k downloads7mo agoHugging Face07mteb /arguana ArguAna An MTEB dataset Massive Text Embedding Benchmark ArguAna: Retrieval of the Best Counterargument without Prior Topic Knowledge Task category Retrieval (text-to-text) Domains Social, Web, Written Reference ACL Source datasets: mteb/arguana How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_task("ArguAna") evaluator = mteb.MTEB([task]) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/arguana.texttext-retrieval10K<n<100K7 likes27k downloads5mo agoHugging Face08mteb /banking77 Banking77Classification An MTEB dataset Massive Text Embedding Benchmark Dataset composed of online banking queries annotated with their corresponding intents. Task category t2c Domains Written Reference https://arxiv.org/abs/2003.04807 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["Banking77Classification"]) evaluator = mteb.MTEB(task) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/banking77.texttext-classification10K<n<100K17 likes25k downloads1y agoHugging Face09mteb /sickr-sts SICK-R An MTEB dataset Massive Text Embedding Benchmark Semantic Textual Similarity SICK-R dataset Task category t2t Domains Web, Written Reference https://aclanthology.org/L14-1314/ How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["SICK-R"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how to run models… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sickr-sts.textsentence-similarity1K<n<10K5 likes23k downloads7mo agoHugging Face10mteb /sts13-sts STS13 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2013 dataset. Task category t2t Domains Web, News, Non-fiction, Written Reference https://www.aclweb.org/anthology/S13-1004/ How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STS13"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts13-sts.textsentence-similarity1K<n<10K1 likes23k downloads7mo agoHugging Face11mteb /sts14-sts STS14 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2014 dataset. Currently only the English dataset Task category t2t Domains Blog, Web, Spoken Reference https://www.aclweb.org/anthology/S14-1002 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STS14"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts14-sts.textsentence-similarity1K<n<10K2 likes22k downloads7mo agoHugging Face12mteb /scidocs SCIDOCS An MTEB dataset Massive Text Embedding Benchmark SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. Task category t2t Domains Academic, Written, Non-fiction Reference https://allenai.org/data/scidocs How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/scidocs.texttext-retrieval10K<n<100K6 likes22k downloads7mo agoHugging Face13mteb /scifact SciFact An MTEB dataset Massive Text Embedding Benchmark SciFact verifies scientific claims using evidence from the research literature containing scientific paper abstracts. Task category t2t Domains Academic, Medical, Written Reference https://github.com/allenai/scifact How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["SciFact"]) evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/scifact.texttext-retrieval1K<n<10K5 likes22k downloads1y agoHugging Face14mteb /biosses-sts BIOSSES An MTEB dataset Massive Text Embedding Benchmark Biomedical Semantic Similarity Estimation. Task category t2t Domains Medical Reference https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["BIOSSES"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biosses-sts.textsentence-similarityn<1K2 likes21k downloads1y agoHugging Face15mteb /stsbenchmark-sts STSBenchmark An MTEB dataset Massive Text Embedding Benchmark Semantic Textual Similarity Benchmark (STSbenchmark) dataset. Task category t2t Domains Blog, News, Written Reference https://github.com/PhilipMay/stsb-multi-mt/ How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STSBenchmark"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/stsbenchmark-sts.textsentence-similarity1K<n<10K19 likes20k downloads7mo agoHugging Face16philschmid /mt-benchtextn<1K4 likes17k downloads3y agoHugging Face17mteb /ClimateFEVER_test_top_250_only_w_correct-v2 ClimateFEVERHardNegatives An MTEB dataset Massive Text Embedding Benchmark CLIMATE-FEVER is a dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct. Task category t2t Domains Encyclopaedic, Written Reference https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html… See the full description on the dataset page: https://huggingface.co/datasets/mteb/ClimateFEVER_test_top_250_only_w_correct-v2.texttext-retrieval10K<n<100K0 likes17k downloads1y agoHugging Face18mteb /twentynewsgroups-clustering TwentyNewsgroupsClustering.v2 An MTEB dataset Massive Text Embedding Benchmark Clustering of the 20 Newsgroups dataset (subject only). Task category t2c Domains News, Written Reference https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["TwentyNewsgroupsClustering.v2"]) evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/twentynewsgroups-clustering.texttext-classificationn<1K1 likes16k downloads1y agoHugging Face19HuggingFaceH4 /mt_bench_prompts MT Bench by LMSYS This set of evaluation prompts is created by the LMSYS org for better evaluation of chat models. For more information, see the paper. Dataset loading To load this dataset, use 🤗 datasets: from datasets import load_dataset data = load_dataset(HuggingFaceH4/mt_bench_prompts, split="train") Dataset creation To create the dataset, we do the following for our internal tooling. rename turns to prompts, add empty reference to remaining prompts… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts.textquestion-answeringn<1K26 likes16k downloads3y agoHugging Face20mteb /summeval SummEvalSummarization.v2 An MTEB dataset Massive Text Embedding Benchmark News Article Summary Semantic Similarity Estimation. This version fixes a bug in the evaluation script that caused the main score to be computed incorrectly. Task category t2t Domains News, Written Reference https://github.com/Yale-LILY/SummEval How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/summeval.textsummarizationn<1K11 likes14k downloads1y agoHugging Face21code-philia /mtpnet_image_models 模型训练过程汇总[该仓库只含有image model的训练过程] 本仓库采用扁平化的目录结构和标签系统来组织模型，具体说明如下： 仓库结构 一级目录：直接以模型名称-数据集，例如 ResNet-CIFAR-10、GraphMAE_QM9-Cora 等 二级目录：包含该模型在该数据集下的不同训练任务或变体，例如 normal、noisy、backdoor_invisible 等 训练过程目录结构：每个模型目录下包含： scripts/：存放模型相关代码和训练脚本 epochs/：存放模型训练过程和权重文件 每个epoch的权重文件(model.pth)和embedding(.npy) dataset/：模型需要的数据集 仓库结构展示 文件结构展示 2 likes13k downloads1y agoHugging Face22mteb /DBPedia_test_top_250_only_w_correct-v2 DBPediaHardNegatives An MTEB dataset Massive Text Embedding Benchmark DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct. Task category t2t Domains Written, Encyclopaedic Reference https://github.com/iai-group/DBpedia-Entity/ How to evaluate on this task You can evaluate… See the full description on the dataset page: https://huggingface.co/datasets/mteb/DBPedia_test_top_250_only_w_correct-v2.texttext-retrieval100K<n<1M0 likes12k downloads1y agoHugging Face23mteb /fiqa FiQA2018 An MTEB dataset Massive Text Embedding Benchmark Financial Opinion Mining and Question Answering Task category t2t Domains Written, Financial Reference https://sites.google.com/view/fiqa/ How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["FiQA2018"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more… See the full description on the dataset page: https://huggingface.co/datasets/mteb/fiqa.texttext-retrieval10K<n<100K5 likes11k downloads1y agoHugging Face24mteb /BornholmBitextMining BornholmBitextMining An MTEB dataset Massive Text Embedding Benchmark Danish Bornholmsk Parallel Corpus. Bornholmsk is a Danish dialect spoken on the island of Bornholm, Denmark. Historically it is a part of east Danish which was also spoken in Scania and Halland, Sweden. Task categoryt2t Domains Web, Social, Fiction, Written Reference https://aclanthology.org/W19-6138/ Source datasets: strombergnlp/bornholmsk_parallel How to evaluate on this task… See the full description on the dataset page: https://huggingface.co/datasets/mteb/BornholmBitextMining.texttranslation1K<n<10K0 likes9.8k downloads7mo agoHugging Face25mteb /arena-resultsThis dataset contains the saved results from MTEB-Arena tabular1K<n<10K4 likes9k downloads1y agoHugging Face26mteb /MIRACLRetrievalHardNegatives MIRACLRetrievalHardNegatives An MTEB dataset Massive Text Embedding Benchmark MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct. Task category t2t Domains Encyclopaedic, Written Reference http://miracl.ai/… See the full description on the dataset page: https://huggingface.co/datasets/mteb/MIRACLRetrievalHardNegatives.texttext-retrieval1M<n<10M3 likes8.6k downloads7mo agoHugging Face27mteb /SciDocsRR SciDocsRR An MTEB dataset Massive Text Embedding Benchmark Ranking of related scientific papers based on their title. Task category t2t Domains Academic, Non-fiction, Written Reference https://allenai.org/data/scidocs How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["SciDocsRR"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/SciDocsRR.texttext-ranking100K<n<1M0 likes8.6k downloads1y agoHugging Face28mteb /sts15-sts STS15 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2015 dataset Task category t2t Domains Blog, News, Web, Written, Spoken Reference https://www.aclweb.org/anthology/S15-2010 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STS15"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts15-sts.textsentence-similarity1K<n<10K2 likes8.3k downloads7mo agoHugging Face29mteb /common_voice_21_00 likes7.9k downloads1y agoHugging Face30mteb /trec-covid TRECCOVID An MTEB dataset Massive Text Embedding Benchmark TRECCOVID is an ad-hoc search challenge based on the COVID-19 dataset containing scientific articles related to the COVID-19 pandemic. Task category t2t Domains Medical, Academic, Written Reference https://ir.nist.gov/covidSubmit/index.html How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["TRECCOVID"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/trec-covid.texttext-retrieval100K<n<1M5 likes7.9k downloads7mo agoHugging Face

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