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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 /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 Face02mteb /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 likes48k downloads1y agoHugging Face03mteb /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 likes32k downloads7mo agoHugging Face04mteb /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 likes26k downloads5mo agoHugging Face05mteb /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 likes24k downloads7mo agoHugging Face06mteb /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 Face07mteb /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 likes23k downloads7mo agoHugging Face08mteb /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 Face09mteb /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 likes21k downloads7mo agoHugging Face10mteb /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 Face11mteb /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 Face12mteb /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 likes17k downloads1y agoHugging Face13mteb /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 Face14mteb /arena-resultsThis dataset contains the saved results from MTEB-Arena tabular1K<n<10K4 likes8.8k downloads1y agoHugging Face15mteb /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 likes8.5k downloads7mo agoHugging Face16mteb /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 likes7.4k downloads7mo agoHugging Face17mteb /amazon_massive_scenario MassiveScenarioClassification 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(["MassiveScenarioClassification"]) evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_massive_scenario.texttext-classification1M<n<10M6 likes6.3k downloads1y agoHugging Face18mteb /cqadupstack-gaming CQADupstackGamingRetrieval An MTEB dataset Massive Text Embedding Benchmark CQADupStack: A Benchmark Data Set for Community Question-Answering Research Task category t2t Domains Web, Written Reference http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ 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(["CQADupstackGamingRetrieval"]) evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-gaming.texttext-retrieval10K<n<100K0 likes6.1k downloads1y agoHugging Face19mteb /cqadupstack-unix CQADupstackUnixRetrieval An MTEB dataset Massive Text Embedding Benchmark CQADupStack: A Benchmark Data Set for Community Question-Answering Research Task category t2t Domains Written, Web, Programming Reference http://nlp.cis.unimelb.edu.au/resources/cqadupstack/ 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(["CQADupstackUnixRetrieval"]) evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-unix.texttext-retrieval10K<n<100K0 likes6k downloads1y agoHugging Face20mteb /stackexchange-clustering StackExchangeClustering.v2 An MTEB dataset Massive Text Embedding Benchmark Clustering of titles from 121 stackexchanges. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences. Task category t2c Domains Web, Written Reference https://arxiv.org/abs/2104.07081 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/stackexchange-clustering.texttext-classificationn<1K1 likes5.8k downloads7mo agoHugging Face21mteb /sts17-crosslingual-sts STS17 An MTEB dataset Massive Text Embedding Benchmark Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation Task category t2t Domains News, Web, Written Reference https://alt.qcri.org/semeval2017/task1/ 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(["STS17"]) evaluator = mteb.MTEB(task) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts17-crosslingual-sts.textsentence-similarity10K<n<100K4 likes5.7k downloads7mo agoHugging Face22mteb /sts16-sts STS16 An MTEB dataset Massive Text Embedding Benchmark SemEval-2016 Task 4 Task category t2t Domains Blog, Web, Spoken Reference https://www.aclweb.org/anthology/S16-1001 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(["STS16"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how to run models on mteb task… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts16-sts.textsentence-similarity1K<n<10K2 likes4.8k downloads1y agoHugging Face23mteb /arxiv-clustering-s2s ArXivHierarchicalClusteringS2S An MTEB dataset Massive Text Embedding Benchmark Clustering of titles from arxiv. Clustering of 30 sets, either on the main or secondary category Task category t2c Domains Academic, Written Reference https://www.kaggle.com/Cornell-University/arxiv 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(["ArXivHierarchicalClusteringS2S"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/arxiv-clustering-s2s.texttext-classificationn<1K1 likes4k downloads7mo agoHugging Face24mteb /biorxiv-clustering-p2p BiorxivClusteringP2P.v2 An MTEB dataset Massive Text Embedding Benchmark Clustering of titles+abstract from biorxiv across 26 categories. Task category t2c Domains Academic, Written Reference https://api.biorxiv.org/ 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(["BiorxivClusteringP2P.v2"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biorxiv-clustering-p2p.texttext-classification10K<n<100K0 likes3.9k downloads7mo agoHugging Face25mteb /medrxiv-clustering-s2s MedrxivClusteringS2S.v2 An MTEB dataset Massive Text Embedding Benchmark Clustering of titles from medrxiv across 51 categories. Task category t2c Domains Academic, Medical, Written Reference https://api.medrxiv.org/ 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(["MedrxivClusteringS2S.v2"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/medrxiv-clustering-s2s.texttext-classification10K<n<100K1 likes3.7k downloads1y agoHugging Face26mteb /arxiv-clustering-p2p ArXivHierarchicalClusteringP2P An MTEB dataset Massive Text Embedding Benchmark Clustering of titles+abstract from arxiv. Clustering of 30 sets, either on the main or secondary category Task category t2c Domains Academic, Written Reference https://www.kaggle.com/Cornell-University/arxiv 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/arxiv-clustering-p2p.texttext-classificationn<1K3 likes3.6k downloads7mo agoHugging Face27mteb /quora QuoraRetrieval An MTEB dataset Massive Text Embedding Benchmark QuoraRetrieval is based on questions that are marked as duplicates on the Quora platform. Given a question, find other (duplicate) questions. Task category t2t Domains Written, Web, Blog Reference https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs 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/quora.texttext-retrieval100K<n<1M1 likes3.1k downloads1y agoHugging Face28mteb /toxic_conversations_50k ToxicConversationsClassification An MTEB dataset Massive Text Embedding Benchmark Collection of comments from the Civil Comments platform together with annotations if the comment is toxic or not. Task category t2c Domains Social, Written Reference https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/overview How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import… See the full description on the dataset page: https://huggingface.co/datasets/mteb/toxic_conversations_50k.texttext-classification100K<n<1M19 likes3.1k downloads7mo agoHugging Face29mteb /medrxiv-clustering-p2p MedrxivClusteringP2P.v2 An MTEB dataset Massive Text Embedding Benchmark Clustering of titles+abstract from medrxiv across 51 categories. Task category t2c Domains Academic, Medical, Written Reference https://api.medrxiv.org/ 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(["MedrxivClusteringP2P.v2"]) evaluator = mteb.MTEB(task) model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/medrxiv-clustering-p2p.texttext-classification10K<n<100K3 likes3k downloads7mo agoHugging Face30mteb /stackexchange-clustering-p2p StackExchangeClusteringP2P.v2 An MTEB dataset Massive Text Embedding Benchmark Clustering of title+body from stackexchange. Clustering of 5 sets of 10k paragraphs and 5 sets of 5k paragraphs. Task category t2c Domains Web, Written Reference https://arxiv.org/abs/2104.07081 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(["StackExchangeClusteringP2P.v2"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/stackexchange-clustering-p2p.texttext-classificationn<1K1 likes2.8k downloads1y agoHugging Face

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