diff --git "a/data/dataset_meta/dataset_meta_en.csv" "b/data/dataset_meta/dataset_meta_en.csv"
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+"id","author","created_at","lastModified","sha","downloads_30","downloads_alltime","likes","tags","tasks","description","citation","languages","language_category","size_categories","paperswithcode_id","private","gated","disabled","license","arxiv_id","url","task_ids"
+"argilla/databricks-dolly-15k-curated-en","argilla","2023-05-30 09:54:44","2023-10-02 12:32:53+00:00","4dcd1dedbe148307a833c931b21ca456a1fc4281","167431","95821337","45","None","None","Guidelines In this dataset, you will find a collection of records that show a category, an instruction, a context and a response to that instruction. The aim of the project is to correct the instructi","None","en","mono","None","None","False","False","False","None","None","None","None"
+"lighteval/mmlu","lighteval","2023-05-16 09:39:28","2023-06-09 16:36:19+00:00","e24764f1fb58c26b5f622157644f2e5fe77e5b01","10443","59386649","39","None","question-answering, multiple-choice-qa","This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and ","@article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} }","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2005.00700","None","multiple-choice-qa"
+"cais/mmlu","cais","2022-03-02 23:29:22","2024-03-08 20:36:26+00:00","c30699e8356da336a370243923dbaf21066bb9fe","105911","36901288","371","None","question-answering, multiple-choice-qa","Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2005.00700","None","multiple-choice-qa"
+"nyu-mll/glue","nyu-mll","2022-03-02 23:29:22","2024-01-30 07:41:18+00:00","bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c","320395","35945159","390","None","text-classification, acceptability-classification, natural-language-inference, semantic-similarity-scoring, sentiment-classification, text-scoring","Dataset Card for GLUE Dataset Summary GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing na","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","1804.07461","None","acceptability-classification, natural-language-inference, semantic-similarity-scoring, sentiment-classification, text-scoring"
+"rajpurkar/squad_v2","rajpurkar","2022-03-02 23:29:22","2024-03-04 13:55:27+00:00","3ffb306f725f7d2ce8394bc1873b24868140c412","21214","33952921","191","None","question-answering, open-domain-qa, extractive-qa","Dataset Card for SQuAD 2.0 Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles,","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","1806.03822","None","open-domain-qa, extractive-qa"
+"Salesforce/wikitext","Salesforce","2022-03-02 23:29:22","2024-01-04 16:49:18+00:00","b08601e04326c79dfdd32d625aee71d232d685c3","390766","18287258","391","None","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for ""wikitext"" Dataset Summary The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","1609.07843","None","language-modeling, masked-language-modeling"
+"nyu-mll/blimp","nyu-mll","2022-03-02 23:29:22","2024-01-23 09:58:08+00:00","877fba0801ffb7cbd8c39c1ff314a46f053f6036","15552","13186826","36","task_categories:text-classification, task_ids:acceptability-classification, annotations_creators:crowdsourced, language_creators:machine-generated, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1912.00582, region:us","text-classification, acceptability-classification","Dataset Card for ""blimp"" Dataset Summary BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each c","None","en","mono","1|0|K|<|n|<|1|0|0|K","blimp","False","False","False","cc-by-4.0","1912.00582","None","acceptability-classification"
+"truthfulqa/truthful_qa","truthfulqa","2022-06-08 14:44:06","2024-01-04 16:36:00+00:00","741b8276f2d1982aa3d5b832d3ee81ed3b896490","28685","11580962","216","None","multiple-choice, text-generation, question-answering, multiple-choice-qa, language-modeling, open-domain-qa","Dataset Card for truthful_qa Dataset Summary TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that sp","None","en","mono","n|<|1|K","None","False","False","False","apache-2.0","2109.07958","None","multiple-choice-qa, language-modeling, open-domain-qa"
+"allenai/ai2_arc","allenai","2022-03-02 23:29:22","2023-12-21 15:09:48+00:00","210d026faf9955653af8916fad021475a3f00453","170725","7501388","165","None","question-answering, open-domain-qa, multiple-choice-qa","Dataset Card for ""ai2_arc"" Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dat","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","1803.05457","None","open-domain-qa, multiple-choice-qa"
+"stanfordnlp/imdb","stanfordnlp","2022-03-02 23:29:22","2024-01-04 12:09:45+00:00","e6281661ce1c48d982bc483cf8a173c1bbeb5d31","79377","6631390","272","None","text-classification, sentiment-classification","Dataset Card for ""imdb"" Dataset Summary Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provid","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","sentiment-classification"
+"rajpurkar/squad","rajpurkar","2022-03-02 23:29:22","2024-03-04 13:54:37+00:00","7b6d24c440a36b6815f21b70d25016731768db1f","47008","5738179","285","None","question-answering, extractive-qa","Dataset Card for SQuAD Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, whe","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","1606.05250","None","extractive-qa"
+"Yelp/yelp_review_full","Yelp","2022-03-02 23:29:22","2024-01-04 17:14:53+00:00","c1f9ee939b7d05667af864ee1cb066393154bf85","30494","4688581","110","None","text-classification, sentiment-classification","Dataset Card for YelpReviewFull Dataset Summary The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. Supported Tasks and Leaderboards text","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","other","1509.01626","None","sentiment-classification"
+"allenai/c4","allenai","2022-03-02 23:29:22","2024-01-09 19:14:03+00:00","1588ec454efa1a09f29cd18ddd04fe05fc8653a2","496392","4669421","354","None","text-generation, fill-mask, language-modeling, masked-language-modeling","C4 Dataset Summary A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: ""https://commoncrawl.org"". This is the processed version of Google's C4 dataset We pre","None","af, am, ar, az, be, bg, bn, ca, ceb, co, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fil, fr, fy, ga, gd, gl, gu, ha, haw, he, hi, hmn, ht, hu, hy, id, ig, is, it, iw, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, no, ny, pa, pl, ps, pt, ro, ru, sd, si, sk, sl, sm, sn, so, sq, sr, st, su, sv, sw, ta, te, tg, th, tr, uk, und, ur, uz, vi, xh, yi, yo, zh, zu","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M|||1|0|M|<|n|<|1|0|0|M|||1|0|0|M|<|n|<|1|B|||1|B|<|n|<|1|0|B","None","False","False","False","odc-by","1910.10683","None","language-modeling, masked-language-modeling"
+"openai/gsm8k","openai","2022-04-12 10:22:10","2024-01-04 12:05:15+00:00","e53f048856ff4f594e959d75785d2c2d37b678ee","194587","3560864","507","None","text2text-generation","Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the tas","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2110.14168","None","None"
+"EleutherAI/lambada_openai","EleutherAI","2022-12-16 16:35:07","2022-12-16 19:53:23+00:00","879e19a2c9bb7ef03f9cf962089cf7d008f91e27","31824","3405797","40","None","language-modeling","The LAMBADA dataset as processed by OpenAI. It is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrat","@misc{ author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, title={The LAMBADA dataset}, DOI={10.5281/zenodo.2630551}, publisher={Zenodo}, year={2016}, month={Aug} }","de, en, es, fr, it","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","language-modeling"
+"nuprl/MultiPL-E","nuprl","2022-09-28 19:20:07","2025-01-21 11:22:40+00:00","40ca145bea0cb0395934a51d01d4a020fa5b8832","250865","3130060","46","None","None","Dataset Card for MultiPL-E Dataset Summary MultiPL-E is a dataset for evaluating large language models for code generation that supports 22 programming languages. It takes the OpenAI HumanEval and the","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2305.06161","None","None"
+"allenai/sciq","allenai","2022-03-02 23:29:22","2024-01-04 16:23:51+00:00","2c94ad3e1aafab77146f384e23536f97a4849815","14165","3069458","97","None","question-answering, closed-domain-qa","Dataset Card for ""sciq"" Dataset Summary The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice fo","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-3.0","None","None","closed-domain-qa"
+"unimelb-nlp/wikiann","unimelb-nlp","2022-03-02 23:29:22","2024-02-22 14:32:02+00:00","f0a3be6dc5564c0cc4150bb660144800a1f539d4","47521","2954749","103","None","token-classification, named-entity-recognition","Dataset Card for WikiANN Dataset Summary WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person","None","ace, af, als, am, an, ang, ar, arc, arz, as, ast, ay, az, ba, bar, be, bg, bh, bn, bo, br, bs, ca, cbk, cdo, ce, ceb, ckb, co, crh, cs, csb, cv, cy, da, de, diq, dv, el, eml, en, eo, es, et, eu, ext, fa, fi, fo, fr, frr, fur, fy, ga, gan, gd, gl, gn, gu, hak, he, hi, hr, hsb, hu, hy, ia, id, ig, ilo, io, is, it, ja, jbo, jv, ka, kk, km, kn, ko, ksh, ku, ky, la, lb, li, lij, lmo, ln, lt, lv, lzh, mg, mhr, mi, min, mk, ml, mn, mr, ms, mt, mwl, my, mzn, nan, nap, nds, ne, nl, nn, no, nov, oc, or, os, pa, pdc, pl, pms, pnb, ps, pt, qu, rm, ro, ru, rw, sa, sah, scn, sco, sd, sgs, sh, si, sk, sl, so, sq, sr, su, sv, sw, szl, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vec, vep, vi, vls, vo, vro, wa, war, wuu, xmf, yi, yo, yue, zea, zh","multi","n|<|1|K","None","False","False","False","unknown","None","None","named-entity-recognition"
+"facebook/anli","facebook","2022-03-02 23:29:22","2023-12-21 15:34:02+00:00","8e4813d81f46d313dac7892e1c28076917cfcdf9","3334","2899416","41","None","text-classification, natural-language-inference, multi-input-text-classification","Dataset Card for ""anli"" Dataset Summary The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-m","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","1910.14599","None","natural-language-inference, multi-input-text-classification"
+"openai/openai_humaneval","openai","2022-03-02 23:29:22","2024-01-04 16:08:05+00:00","7dce6050a7d6d172f3cc5c32aa97f52fa1a2e544","71040","2876181","262","None","text2text-generation","Dataset Card for OpenAI HumanEval Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They ","None","en","mono","n|<|1|K","None","False","False","False","mit","2107.03374","None","None"
+"abisee/cnn_dailymail","abisee","2022-03-02 23:29:22","2024-01-18 15:31:34+00:00","96df5e686bee6baa90b8bee7c28b81fa3fa6223d","58684","2715234","237","None","summarization, news-articles-summarization","Dataset Card for CNN Dailymail Dataset Dataset Summary The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and th","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","news-articles-summarization"
+"facebook/xnli","facebook","2022-03-02 23:29:22","2024-01-05 08:30:52+00:00","b8dd5d7af51114dbda02c0e3f6133f332186418e","15916","2470294","53","language:ar, language:bg, language:de, language:el, language:en, language:es, language:fr, language:hi, language:ru, language:sw, language:th, language:tr, language:ur, language:vi, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""xnli"" Dataset Summary XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is","None","ar, bg, de, el, en, es, fr, hi, ru, sw, th, tr, ur, vi, zh","multi","None","xnli","False","False","False","None","None","None","None"
+"HuggingFaceFW/fineweb-edu","HuggingFaceFW","2024-05-28 14:32:57","2025-01-06 14:45:40+00:00","81fd597c805179172da5d94ac803cde08d95683d","284870","2289105","603","None","text-generation","📚 FineWeb-Edu 1.3 trillion tokens of the finest educational data the 🌐 web has to offer Paper: https://arxiv.org/abs/2406.17557 What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T token","None","en","mono","n|>|1|T","None","False","False","False","odc-by","2406.17557","None","None"
+"Rowan/hellaswag","Rowan","2022-03-02 23:29:22","2023-09-28 14:49:00+00:00","6002345709e0801764318f06bf06ce1e7d1a1fe3","144626","2267737","105","None","None","HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.","@inproceedings{zellers2019hellaswag, title={HellaSwag: Can a Machine Really Finish Your Sentence?}, author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} }","en","mono","None","None","False","False","False","None","1905.07830","None","None"
+"wmt/wmt16","wmt","2022-03-02 23:29:22","2024-04-03 12:30:24+00:00","41d8a4013aa1489f28fea60ec0932af246086482","5755","2169229","21","None","translation","Dataset Card for ""wmt16"" Dataset Summary Warning: There are issues with the Common Crawl corpus data (training-parallel-commoncrawl.tgz): Non-English files contain many English sentences. Their ""paral","None","cs, de, en, fi, ro, ru, tr","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","unknown","None","None","None"
+"fancyzhx/ag_news","fancyzhx","2022-03-02 23:29:22","2024-03-07 12:02:37+00:00","eb185aade064a813bc0b7f42de02595523103ca4","19103","2165309","149","None","text-classification, topic-classification","Dataset Card for ""ag_news"" Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 yea","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","None","None","topic-classification"
+"cardiffnlp/tweet_eval","cardiffnlp","2022-03-02 23:29:22","2024-01-04 16:40:33+00:00","b3a375baf0f409c77e6bc7aa35102b7b3534f8be","11115","2117074","118","None","text-classification, intent-classification, multi-class-classification, sentiment-classification","Dataset Card for tweet_eval Dataset Summary TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stanc","None","en","mono","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K|||n|<|1|K","None","False","False","False","unknown","None","None","intent-classification, multi-class-classification, sentiment-classification"
+"bigscience/P3","bigscience","2022-03-02 23:29:22","2024-03-04 18:08:03+00:00","db485208b9f41d46c1d0975202328d08f8199046","26680","2045636","209","task_categories:other, annotations_creators:crowdsourced, annotations_creators:expert-generated, multilinguality:monolingual, language:en, license:apache-2.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2110.08207, region:us","other","Dataset Card for P3 Dataset Summary P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","2110.08207","None","None"
+"cornell-movie-review-data/rotten_tomatoes","cornell-movie-review-data","2022-03-02 23:29:22","2024-03-18 14:28:45+00:00","aa13bc287fa6fcab6daf52f0dfb9994269ffea28","20129","1924035","68","None","text-classification, sentiment-classification","Dataset Card for ""rotten_tomatoes"" Dataset Summary Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","0506075","None","sentiment-classification"
+"google/xtreme","google","2022-03-02 23:29:22","2024-02-22 17:12:06+00:00","ec5f1f46e9af79639a90684a7a70a956c4998f04","42104","1893686","97","None","multiple-choice, question-answering, token-classification, text-classification, text-retrieval, token-classification, multiple-choice-qa, extractive-qa, open-domain-qa, natural-language-inference, named-entity-recognition, part-of-speech","Dataset Card for ""xtreme"" Dataset Summary The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs ","None","af, ar, bg, bn, de, el, en, es, et, eu, fa, fi, fr, he, hi, hu, id, it, ja, jv, ka, kk, ko, ml, mr, ms, my, nl, pt, ru, sw, ta, te, th, tl, tr, ur, vi, yo, zh","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","multiple-choice-qa, extractive-qa, open-domain-qa, natural-language-inference, named-entity-recognition, part-of-speech"
+"locuslab/TOFU","locuslab","2023-11-14 22:25:09","2024-02-07 14:58:06+00:00","c3ad0b4efbdd12e11cc4faf95aebfbd6f53b664b","15483","1838605","37","None","question-answering, closed-domain-qa","TOFU: Task of Fictitious Unlearning 🍢 The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pa","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2401.06121","None","closed-domain-qa"
+"HuggingFaceFW/fineweb","HuggingFaceFW","2024-04-18 14:33:13","2025-01-03 11:58:46+00:00","e31fdfd3918d4b48e837d69d274e624a067d7091","423243","1775726","1829","None","text-generation","🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl.","None","en","mono","n|>|1|T","None","False","False","False","odc-by","2406.17557","None","None"
+"uclanlp/wino_bias","uclanlp","2022-03-02 23:29:22","2024-01-04 16:50:33+00:00","3f31267586e4408e3b3f77ec22198fd24ea8dc1d","2471","1722477","16","None","token-classification, coreference-resolution","Dataset Card for Wino_Bias dataset Dataset Summary WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias. The corpus contains Winograd-schema style sentences with entit","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","1804.06876","None","coreference-resolution"
+"allenai/openbookqa","allenai","2022-03-02 23:29:22","2024-01-04 16:09:20+00:00","388097ea7776314e93a529163e0fea805b8a6454","76510","1623970","85","None","question-answering, open-domain-qa","Dataset Card for OpenBookQA Dataset Summary OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an op","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","None","None","open-domain-qa"
+"Helsinki-NLP/opus-100","Helsinki-NLP","2022-03-02 23:29:22","2024-02-28 09:17:34+00:00","805090dc28bf78897da9641cdf08b61287580df9","24897","1450598","168","None","translation","Dataset Card for OPUS-100 Dataset Summary OPUS-100 is an English-centric multilingual corpus covering 100 languages. OPUS-100 is English-centric, meaning that all training pairs include English on eit","None","af, am, an, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, dz, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, gu, ha, he, hi, hr, hu, hy, id, ig, is, it, ja, ka, kk, km, kn, ko, ku, ky, li, lt, lv, mg, mk, ml, mn, mr, ms, mt, my, nb, ne, nl, nn, no, oc, or, pa, pl, ps, pt, ro, ru, rw, se, sh, si, sk, sl, sq, sr, sv, ta, te, tg, th, tk, tr, tt, ug, uk, ur, uz, vi, wa, xh, yi, yo, zh, zu","multi","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K|||1|M|<|n|<|1|0|M|||n|<|1|K","None","False","False","False","unknown","2004.11867","None","None"
+"ehovy/race","ehovy","2022-03-02 23:29:22","2024-01-04 16:22:34+00:00","2fec9fd81f1dc971569a9b729c43f2f0e6436637","1908","1441331","54","task_categories:multiple-choice, task_ids:multiple-choice-qa, annotations_creators:expert-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:en, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1704.04683, region:us","multiple-choice, multiple-choice-qa","Dataset Card for ""race"" Dataset Summary RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinati","None","en","mono","1|0|K|<|n|<|1|0|0|K","race","False","False","False","other","1704.04683","None","multiple-choice-qa"
+"MMMU/MMMU","MMMU","2023-11-27 17:52:01","2024-09-19 17:11:03+00:00","171b0ef74cd1704464e6940860968009d8cdd59a","7641","1410128","220","None","question-answering, visual-question-answering, multiple-choice","MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub 🔔News 🛠️[2024-05-30]: Fixed duplic","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2311.16502","None","None"
+"mandarjoshi/trivia_qa","mandarjoshi","2022-03-02 23:29:22","2024-01-05 13:24:37+00:00","0f7faf33a3908546c6fd5b73a660e0f8ff173c2f","32650","1376406","114","None","question-answering, text2text-generation, open-domain-qa, open-domain-abstractive-qa, extractive-qa, abstractive-qa","Dataset Card for ""trivia_qa"" Dataset Summary TriviaqQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaqQA includes 95K question-answer pairs authored b","None","en","mono","1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","1705.03551","None","open-domain-qa, open-domain-abstractive-qa, extractive-qa, abstractive-qa"
+"EdinburghNLP/xsum","EdinburghNLP","2022-03-02 23:29:22","2023-04-05 13:45:25+00:00","40db7604fedb616a9d2b0673d11838fa5be8451c","9355","1340891","96","task_categories:summarization, task_ids:news-articles-summarization, annotations_creators:found, language_creators:found, multilinguality:monolingual, source_datasets:original, language:en, license:unknown, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, arxiv:1808.08745, region:us","summarization, news-articles-summarization","Extreme Summarization (XSum) Dataset. There are three features: - document: Input news article. - summary: One sentence summary of the article. - id: BBC ID of the article.","@article{Narayan2018DontGM, title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization}, author={Shashi Narayan and Shay B. Cohen and Mirella Lapata}, journal={ArXiv}, year={2018}, volume={abs/1808.08745} }","en","mono","1|0|0|K|<|n|<|1|M","xsum","False","False","False","unknown","1808.08745","None","news-articles-summarization"
+"mteb/sts22-crosslingual-sts","mteb","2022-05-30 20:19:00","2024-07-06 11:42:07+00:00","d31f33a128469b20e357535c39b82fb3c3f6f2bd","168855","1298678","6","None","None","Scores in this dataset have been inverted to be from least to most similar! The scores in the original STS22 task were from most to least similar. Updates: 2024/07/06: Removed pairs where one of the s","None","ar, de, en, es, fr, it, pl, ru, tr, zh","multi","None","None","False","False","False","None","None","None","None"
+"facebook/kilt_tasks","facebook","2022-03-02 23:29:22","2024-01-04 14:01:11+00:00","0f7881c6bf693742af91a31b4d8f827db2f41233","5886","1251854","54","task_categories:fill-mask, task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, task_categories:text-retrieval, task_categories:text2text-generation, task_ids:abstractive-qa, task_ids:dialogue-modeling, task_ids:document-retrieval, task_ids:entity-linking-retrieval, task_ids:extractive-qa, task_ids:fact-checking, task_ids:fact-checking-retrieval, task_ids:open-domain-abstractive-qa, task_ids:open-domain-qa, task_ids:slot-filling, annotations_creators:crowdsourced, annotations_creators:found, annotations_creators:machine-generated, language_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:extended|natural_questions, source_datasets:extended|other-aidayago, source_datasets:extended|other-fever, source_datasets:extended|other-hotpotqa, source_datasets:extended|other-trex, source_datasets:extended|other-triviaqa, source_datasets:extended|other-wizardsofwikipedia, source_datasets:extended|other-wned-cweb, source_datasets:extended|other-wned-wiki, source_datasets:extended|other-zero-shot-re, source_datasets:original, language:en, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2009.02252, region:us","fill-mask, question-answering, text-classification, text-generation, text-retrieval, text2text-generation, abstractive-qa, dialogue-modeling, document-retrieval, entity-linking-retrieval, extractive-qa, fact-checking, fact-checking-retrieval, open-domain-abstractive-qa, open-domain-qa, slot-filling","Dataset Card for KILT Dataset Summary KILT has been built from 11 datasets representing 5 types of tasks: Fact-checking Entity linking Slot filling Open domain QA Dialog generation All these datasets ","None","en","mono","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K|||1|M|<|n|<|1|0|M","kilt","False","False","False","mit","2009.02252","None","abstractive-qa, dialogue-modeling, document-retrieval, entity-linking-retrieval, extractive-qa, fact-checking, fact-checking-retrieval, open-domain-abstractive-qa, open-domain-qa, slot-filling"
+"stanfordnlp/sst2","stanfordnlp","2022-06-13 14:01:47","2024-01-04 16:31:07+00:00","8d51e7e4887a4caaa95b3fbebbf53c0490b58bbb","11437","1225690","107","None","text-classification, sentiment-classification","Dataset Card for [Dataset Name] Dataset Summary The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","None","None","sentiment-classification"
+"tatsu-lab/alpaca","tatsu-lab","2023-03-13 17:19:43","2023-05-22 20:33:36+00:00","dce01c9b08f87459cf36a430d809084718273017","24664","1216609","724","None","text-generation","Dataset Card for Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instru","None","en","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"THUDM/LongBench","THUDM","2023-07-29 14:33:21","2024-12-18 08:44:33+00:00","5e628be450b7e67fb7ae6e201bd6d8f7056f7672","39087","1148323","133","None","question-answering, text-generation, summarization, text-classification","LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This da","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","None","2108.00573","None","None"
+"clips/mqa","clips","2022-03-02 23:29:22","2022-09-27 12:38:50+00:00","27eebc4a00d229f8dd4ae2a6d9f1e4ad45781f3b","1078","1116742","51","task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:no-annotation, language_creators:other, multilinguality:multilingual, source_datasets:original, language:ca, language:en, language:de, language:es, language:fr, language:ru, language:ja, language:it, language:zh, language:pt, language:nl, language:tr, language:pl, language:vi, language:ar, language:id, language:uk, language:ro, language:no, language:th, language:sv, language:el, language:fi, language:he, language:da, language:cs, language:ko, language:fa, language:hi, language:hu, language:sk, language:lt, language:et, language:hr, language:is, language:lv, language:ms, language:bg, language:sr, license:cc0-1.0, size_categories:100M<n<1B, modality:text, library:datasets, library:mlcroissant, region:us","question-answering, multiple-choice-qa","MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages.","@misc{debruyn2021mfaq, title={MFAQ: a Multilingual FAQ Dataset}, author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans}, year={2021}, booktitle={MRQA@EMNLP2021}, }","ca, en, de, es, fr, ru, ja, it, zh, pt, nl, tr, pl, vi, ar, id, uk, ro, no, th, sv, el, fi, he, da, cs, ko, fa, hi, hu, sk, lt, et, hr, is, lv, ms, bg, sr, ca","multi","u|n|k|n|o|w|n","None","False","False","False","cc0-1.0","None","None","multiple-choice-qa"
+"togethercomputer/RedPajama-Data-1T-Sample","togethercomputer","2023-04-16 23:12:30","2023-07-19 06:59:10+00:00","776f0cf8399524b53d817f4613cc75c6cd9c5a3b","9297","1074730","124","None","text-generation","RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset.","None","en","mono","None","None","False","False","False","None","None","None","None"
+"cimec/lambada","cimec","2022-03-02 23:29:22","2024-01-04 14:16:25+00:00","5953bd97664b64b95754f299b2309ecfbfbe81b9","6743","1024071","58","None","text2text-generation","Dataset Card for LAMBADA Dataset Summary The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"Helsinki-NLP/euconst","Helsinki-NLP","2022-03-02 23:29:22","2024-02-27 09:42:27+00:00","d0949806dc2f0b5502b994fc74a082d1845a0506","16344","955980","8","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:fi, language:fr, language:ga, language:hu, language:it, language:lt, language:lv, language:mt, language:nl, language:pl, language:pt, language:sk, language:sl, language:sv, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for OPUS EUconst Dataset Summary A parallel corpus collected from the European Constitution. EUconst's Numbers: Languages: 21 Bitexts: 210 Number of files: 986 Number of tokens: 3.01M Sen","None","cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, sk, sl, sv","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","None","None","None"
+"flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl","flax-sentence-embeddings","2022-03-02 23:29:22","2022-07-11 13:13:27+00:00","5ce5373dcaed72457e1b61860d7368dca0f10179","826","826374","8","None","question-answering, closed-domain-qa","This new dataset is designed to solve this great NLP task and is crafted with a lot of care.","@misc{StackExchangeDataset, author = {Flax Sentence Embeddings Team}, title = {Stack Exchange question pairs}, year = {2021}, howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/}, }","en","mono","u|n|k|n|o|w|n","None","False","False","False","cc-by-nc-sa-4.0","None","None","closed-domain-qa"
+"microsoft/wiki_qa","microsoft","2022-03-02 23:29:22","2024-01-04 16:41:46+00:00","3f104672b5de699878fe7907afc486f0de325eb5","3821","808558","50","None","question-answering, open-domain-qa","Dataset Card for ""wiki_qa"" Dataset Summary Wiki Question Answering corpus from Microsoft. The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for rese","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","open-domain-qa"
+"speechcolab/gigaspeech","speechcolab","2022-06-09 14:51:58","2023-11-23 14:08:34+00:00","13eadc735ff81c0e0537276f729f2f391e594bb8","6853","796686","101","task_categories:automatic-speech-recognition, task_categories:text-to-speech, task_categories:text-to-audio, multilinguality:monolingual, language:en, license:apache-2.0, size_categories:100K<n<1M, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:2106.06909, region:us","automatic-speech-recognition, text-to-speech, text-to-audio","GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for","@article{DBLP:journals/corr/abs-2106-06909, author = {Guoguo Chen and Shuzhou Chai and Guanbo Wang and Jiayu Du and Wei{-}Qiang Zhang and Chao Weng and Dan Su and Daniel Povey and Jan Trmal and Junbo Zhang and Mingjie Jin and Sanjeev Khudanpur and Shinji Watanabe and Shuaijiang Zhao and Wei Zou and Xiangang Li and Xuchen Yao and Yongqing Wang and Yujun Wang and Zhao You and Zhiyong Yan}, title = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours of Transcribed Audio}, journal = {CoRR}, volume = {abs/2106.06909}, year = {2021}, url = {https://arxiv.org/abs/2106.06909}, eprinttype = {arXiv}, eprint = {2106.06909}, timestamp = {Wed, 29 Dec 2021 14:29:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }","en","mono","None","None","False","auto","False","apache-2.0","2106.06909","None","None"
+"fancyzhx/amazon_polarity","fancyzhx","2022-03-02 23:29:22","2024-01-09 12:23:33+00:00","9d9c45c18f8c3cf1b23a3c27917b60cbf28f3289","1781","790793","46","task_categories:text-classification, task_ids:sentiment-classification, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1509.01626, region:us","text-classification, sentiment-classification","Dataset Card for Amazon Review Polarity Dataset Summary The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. ","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","1509.01626","None","sentiment-classification"
+"tau/commonsense_qa","tau","2022-03-02 23:29:22","2024-01-04 07:44:16+00:00","94630fe30dad47192a8546eb75f094926d47e155","40546","787792","84","None","question-answering, open-domain-qa","Dataset Card for ""commonsense_qa"" Dataset Summary CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answer","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","1811.00937","None","open-domain-qa"
+"wikimedia/wikipedia","wikimedia","2022-03-02 23:29:22","2024-01-09 09:40:51+00:00","b04c8d1ceb2f5cd4588862100d08de323dccfbaa","116436","787764","707","None","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with o","None","ab, ace, ady, af, alt, am, ami, an, ang, anp, ar, arc, ary, arz, as, ast, atj, av, avk, awa, ay, az, azb, ba, ban, bar, bbc, bcl, be, bg, bh, bi, bjn, blk, bm, bn, bo, bpy, br, bs, bug, bxr, ca, cbk, cdo, ce, ceb, ch, chr, chy, ckb, co, cr, crh, cs, csb, cu, cv, cy, da, dag, de, dga, din, diq, dsb, dty, dv, dz, ee, el, eml, en, eo, es, et, eu, ext, fa, fat, ff, fi, fj, fo, fon, fr, frp, frr, fur, fy, ga, gag, gan, gcr, gd, gl, glk, gn, gom, gor, got, gpe, gsw, gu, guc, gur, guw, gv, ha, hak, haw, hbs, he, hi, hif, hr, hsb, ht, hu, hy, hyw, ia, id, ie, ig, ik, ilo, inh, io, is, it, iu, ja, jam, jbo, jv, ka, kaa, kab, kbd, kbp, kcg, kg, ki, kk, kl, km, kn, ko, koi, krc, ks, ksh, ku, kv, kw, ky, la, lad, lb, lbe, lez, lfn, lg, li, lij, lld, lmo, ln, lo, lt, ltg, lv, lzh, mad, mai, map, mdf, mg, mhr, mi, min, mk, ml, mn, mni, mnw, mr, mrj, ms, mt, mwl, my, myv, mzn, nah, nan, nap, nds, ne, new, nia, nl, nn, no, nov, nqo, nrf, nso, nv, ny, oc, olo, om, or, os, pa, pag, pam, pap, pcd, pcm, pdc, pfl, pi, pih, pl, pms, pnb, pnt, ps, pt, pwn, qu, rm, rmy, rn, ro, ru, rue, rup, rw, sa, sah, sat, sc, scn, sco, sd, se, sg, sgs, shi, shn, si, sk, skr, sl, sm, smn, sn, so, sq, sr, srn, ss, st, stq, su, sv, sw, szl, szy, ta, tay, tcy, te, tet, tg, th, ti, tk, tl, tly, tn, to, tpi, tr, trv, ts, tt, tum, tw, ty, tyv, udm, ug, uk, ur, uz, ve, vec, vep, vi, vls, vo, vro, wa, war, wo, wuu, xal, xh, xmf, yi, yo, yue, za, zea, zgh, zh, zu","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","language-modeling, masked-language-modeling"
+"MBZUAI/Bactrian-X","MBZUAI","2023-04-22 12:42:39","2023-05-27 12:54:05+00:00","3698480a001cb3f62c61c314d4acb181eb31e983","4006","749794","115","None","text-generation","Dataset Card for ""Bactrian-X"" A. Dataset Description Homepage: https://github.com/mbzuai-nlp/Bactrian-X Repository: https://huggingface.co/datasets/MBZUAI/Bactrian-X Paper: to-be-soon released Dataset","None","af, ar, az, bn, cs, de, en, es, et, fi, fr, gl, gu, he, hi, hr, id, it, ja, ka, kk, km, ko, lt, lv, mk, ml, mn, mr, my, ne, nl, pl, ps, pt, ro, ru, si, sl, sv, sw, ta, te, th, tl, tr, uk, ur, vi, xh, zh","multi","None","None","False","False","False","cc-by-nc-4.0","2008.00401","None","None"
+"csebuetnlp/xlsum","csebuetnlp","2022-03-02 23:29:22","2023-04-18 01:46:20+00:00","30fece425f9a3866e04321773ca7a80056d55ca6","6872","748041","125","task_categories:summarization, task_categories:text-generation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:am, language:ar, language:az, language:bn, language:my, language:zh, language:en, language:fr, language:gu, language:ha, language:hi, language:ig, language:id, language:ja, language:rn, language:ko, language:ky, language:mr, language:ne, language:om, language:ps, language:fa, language:pcm, language:pt, language:pa, language:ru, language:gd, language:sr, language:si, language:so, language:es, language:sw, language:ta, language:te, language:th, language:ti, language:tr, language:uk, language:ur, language:uz, language:vi, language:cy, language:yo, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:1607.01759, region:us, conditional-text-generation","summarization, text-generation","We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The datas","@inproceedings{hasan-etal-2021-xl, title = ""{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages"", author = ""Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat"", booktitle = ""Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"", month = aug, year = ""2021"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2021.findings-acl.413"", pages = ""4693--4703"", }","am, ar, az, bn, my, zh, en, fr, gu, ha, hi, ig, id, ja, rn, ko, ky, mr, ne, om, ps, fa, pcm, pt, pa, ru, gd, sr, si, so, es, sw, ta, te, th, ti, tr, uk, ur, uz, vi, cy, yo","multi","1|M|<|n|<|1|0|M","xl-sum","False","False","False","cc-by-nc-sa-4.0","1607.01759","None","None"
+"stanfordnlp/snli","stanfordnlp","2022-03-02 23:29:22","2024-03-06 10:55:50+00:00","cdb5c3d5eed6ead6e5a341c8e56e669bb666725b","3391","735682","71","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:extended|other-flicker-30k, source_datasets:extended|other-visual-genome, language:en, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1508.05326, region:us","text-classification, natural-language-inference, multi-input-text-classification","Dataset Card for SNLI Dataset Summary The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment","None","en","mono","1|0|0|K|<|n|<|1|M","snli","False","False","False","cc-by-sa-4.0","1508.05326","None","natural-language-inference, multi-input-text-classification"
+"PKU-Alignment/PKU-SafeRLHF","PKU-Alignment","2023-06-14 16:03:29","2024-10-18 03:47:09+00:00","9421ffafec3fa40a1f1a7d567b4d525079477ecb","2561","725325","123","task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.15513, region:us, safe, safety, ai-safety, llm, lm, human-feedback, rlhf, safe-rlhf","text-generation","Dataset Card for PKU-SafeRLHF Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"mteb/amazon_massive_intent","mteb","2022-05-15 20:59:15","2024-05-07 21:43:00+00:00","4672e20407010da34463acc759c162ca9734bca6","6939","699412","21","None","None","None","None","af, am, ar, az, bn, cy, da, de, el, en, es, fa, fr, he, hi, hu, hy, id, is, it, ja, jv, ka, km, kn, ko, lv, ml, mn, ms, my, nb, nl, pl, pt, ro, ru, sl, sq, sv, sw, ta, te, th, tl, tr, ur, vi, zh","multi","None","None","False","False","False","None","None","None","None"
+"tasksource/mmlu","tasksource","2023-02-01 10:20:16","2023-03-31 20:44:21+00:00","822103d61820ec3fe16054cea64468521c2621d9","22865","681893","33","task_categories:text-classification, task_categories:multiple-choice, task_categories:question-answering, task_ids:multiple-choice-qa, task_ids:open-domain-qa, task_ids:closed-domain-qa, language:en, license:apache-2.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us, multi-task, multitask, mmlu, hendrycks_test","text-classification, multiple-choice, question-answering, multiple-choice-qa, open-domain-qa, closed-domain-qa","This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and ","@article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} }","en","mono","None","None","False","False","False","apache-2.0","None","None","multiple-choice-qa, open-domain-qa, closed-domain-qa"
+"facebook/belebele","facebook","2023-09-01 18:27:13","2024-08-12 22:18:08+00:00","7899cdfa4e1e0d733fd77c848e2c273cb1d32be2","7480","676629","102","None","question-answering, zero-shot-classification, text-classification, multiple-choice","The Belebele Benchmark for Massively Multilingual NLU Evaluation Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the eval","None","af, am, ar, az, as, bm, bn, bo, bg, ca, cs, ku, da, de, el, en, es, et, eu, fi, fr, ff, om, gu, gn, ht, ha, he, hi, hr, hu, hy, ig, id, it, is, jv, ja, ka, kn, kk, mn, km, rw, ky, ko, lo, ln, lt, lg, lv, ml, mr, mk, mt, mi, my, nl, no, ne, ny, or, pa, ps, fa, mg, pl, pt, ro, ru, sn, si, sl, sv, sk, sd, sw, ta, te, tg, tl, th, ti, tn, ts, tr, uk, ur, uz, vi, wo, xh, yo, zh, ms, zu","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"mlfoundations/MINT-1T-HTML","mlfoundations","2024-07-21 06:48:51","2024-09-21 01:50:16+00:00","906a8b85cea61198ff7339c4dd711ad0b5361847","96236","651148","81","task_categories:image-to-text, task_categories:text-generation, language:en, license:cc-by-4.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.11271, region:us, multimodal","image-to-text, text-generation","🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billi","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2406.11271","None","None"
+"mteb/sts17-crosslingual-sts","mteb","2022-05-19 12:59:56","2024-04-30 13:49:22+00:00","faeb762787bd10488a50c8b5be4a3b82e411949c","3133","639031","3","language:ar, language:de, language:en, language:es, language:fr, language:it, language:nl, language:ko, language:tr, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ar, de, en, es, fr, it, nl, ko, tr","multi","None","None","False","False","False","None","None","None","None"
+"tiiuae/falcon-refinedweb","tiiuae","2023-05-07 14:57:27","2023-06-20 12:38:07+00:00","c735840575b629292b41da8dde11dcd523d4f91c","20373","621263","829","None","text-generation","📀 Falcon RefinedWeb Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license. See the 📓 paper on arXiv for more details. RefinedWeb is built through str","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","odc-by","2306.01116","None","None"
+"mteb/sts12-sts","mteb","2022-04-20 10:47:29","2022-09-27 19:11:50+00:00","fdf84275bb8ce4b49c971d02e84dd1abc677a50f","28024","606985","6","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Idavidrein/gpqa","Idavidrein","2023-11-27 23:18:46","2024-03-28 21:38:55+00:00","90b8e5be2b1d3d2dbfe016cdab47981150600c4a","19679","600667","114","None","question-answering, text-generation","Dataset Card for GPQA GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own doma","None","en","mono","n|<|1|K","None","False","auto","False","cc-by-4.0","2311.12022","None","None"
+"Stanford/web_questions","Stanford","2022-03-02 23:29:22","2024-01-04 16:41:06+00:00","0e473cbe21d1e91ec18da343644498be6a3f5454","1888","572693","31","None","question-answering, open-domain-qa","Dataset Card for ""web_questions"" Dataset Summary This dataset consists of 6,642 question/answer pairs. The questions are supposed to be answerable by Freebase, a large knowledge graph. The questions a","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","None","None","open-domain-qa"
+"yahma/alpaca-cleaned","yahma","2023-03-24 18:27:58","2023-04-10 20:29:06+00:00","12567cabf869d7c92e573c7c783905fc160e9639","14250","572430","617","None","text-generation","Dataset Card for Alpaca-Cleaned Repository: https://github.com/gururise/AlpacaDataCleaned Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The followi","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"Helsinki-NLP/news_commentary","Helsinki-NLP","2022-03-02 23:29:22","2024-02-29 15:28:06+00:00","bbaf548083e788e0e6d2ca68efc325283fe2aff5","18361","569556","31","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:ar, language:cs, language:de, language:en, language:es, language:fr, language:it, language:ja, language:nl, language:pt, language:ru, language:zh, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for OPUS News-Commentary Dataset Summary [More Information Needed] Supported Tasks and Leaderboards [More Information Needed] Languages [More Information Needed] Dataset Structure Data In","None","ar, cs, de, en, es, fr, it, ja, nl, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","None","None","None"
+"coastalcph/lex_glue","coastalcph","2022-03-02 23:29:22","2024-01-04 14:25:27+00:00","c23fdff1a6bf74e0e1a71cb86f1e781d37da888c","10229","568446","51","None","question-answering, text-classification, multi-class-classification, multi-label-classification, multiple-choice-qa, topic-classification","Dataset Card for ""LexGLUE"" Dataset Summary Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al.,","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2109.00904","None","multi-class-classification, multi-label-classification, multiple-choice-qa, topic-classification"
+"apple/DataCompDR-1B","apple","2024-06-04 02:29:39","2024-07-30 17:11:06+00:00","e3b7774033839d1027ab63b9bb38bf635afb5af9","237619","568367","18","task_categories:text-to-image, task_categories:image-to-text, language:en, license:other, size_categories:1B<n<10B, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, arxiv:2311.17049, region:us","text-to-image, image-to-text","Dataset Card for DataCompDR-1B This dataset contains synthetic captions, embeddings, and metadata for DataCompDR-1B. The metadata has been generated using pretrained image-text models on DataComp-1B. ","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","other","2311.17049","None","None"
+"Muennighoff/xwinograd","Muennighoff","2022-07-17 15:20:09","2023-07-07 08:27:03+00:00","e98b9216f60fc8dbabfe766e014534a08ff01949","5285","565272","13","language:en, language:fr, language:ja, language:pt, language:ru, language:zh, license:cc-by-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, arxiv:2211.01786, arxiv:2106.12066, region:us","None","A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities.","@misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{tikhonov2021heads, title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning}, author={Alexey Tikhonov and Max Ryabinin}, year={2021}, eprint={2106.12066}, archivePrefix={arXiv}, primaryClass={cs.CL} }","en, fr, ja, pt, ru, zh","multi","None","None","False","False","False","cc-by-4.0","2211.01786","None","None"
+"databricks/databricks-dolly-15k","databricks","2023-04-11 16:43:13","2023-06-30 18:34:13+00:00","bdd27f4d94b9c1f951818a7da7fd7aeea5dbff1a","12625","558439","785","None","question-answering, summarization","Summary databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructG","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","2203.02155","None","None"
+"Open-Orca/FLAN","Open-Orca","2023-07-21 13:45:12","2023-08-02 15:08:01+00:00","6845b1b3b53c6d5c5b1e49767ed759df3fc246cc","22380","543360","171","language:en, license:cc-by-4.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2301.13688, arxiv:2109.01652, arxiv:2110.08207, arxiv:2204.07705, region:us","None","�� The WHOLE FLAN Collection! 🍮 Overview This repository includes the full dataset from the FLAN Collection, totalling ~300GB as parquets. Generated using the official seqio templating from the Google ","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","cc-by-4.0","None","None","None"
+"roneneldan/TinyStories","roneneldan","2023-05-12 19:04:09","2024-08-12 13:27:26+00:00","f54c09fd23315a6f9c86f9dc80f725de7d8f9c64","17677","539887","601","None","text-generation","Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referre","None","en","mono","None","None","False","False","False","cdla-sharing-1.0","2305.07759","None","None"
+"allenai/qasc","allenai","2022-03-02 23:29:22","2024-01-04 16:17:46+00:00","a34ba204eb9a33b919c10cc08f4f1c8dae5ec070","1626","524693","14","None","question-answering, multiple-choice, extractive-qa, multiple-choice-qa","Dataset Card for ""qasc"" Dataset Summary QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,13","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","1910.11473","None","extractive-qa, multiple-choice-qa"
+"Helsinki-NLP/opus_books","Helsinki-NLP","2022-03-02 23:29:22","2024-03-29 16:50:29+00:00","1f9f6191d0e91a3c539c2595e2fe48fc1420de9b","27096","519493","61","None","translation","Dataset Card for OPUS Books Dataset Summary This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php Note t","None","ca, de, el, en, eo, es, fi, fr, hu, it, nl, no, pl, pt, ru, sv","multi","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"mteb/stsbenchmark-sts","mteb","2022-04-19 14:53:43","2022-09-27 19:11:21+00:00","8913289635987208e6e7c72789e4be2fe94b6abd","5793","516673","11","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Zyphra/Zyda-2","Zyphra","2024-09-13 21:45:20","2024-12-12 00:00:22+00:00","cb4049ca0a0ee98279ef8f30c453496649ac501a","100910","515961","69","task_categories:text-generation, language:en, license:odc-by, size_categories:1B<n<10B, modality:tabular, modality:text, modality:timeseries, region:us","text-generation","Zyda-2 Zyda-2 is a 5 trillion token language modeling dataset created by collecting open and high quality datasets and combining them and cross-deduplication and model-based quality filtering. Zyda-2 ","None","en","mono","n|>|1|T","None","False","False","False","odc-by","None","None","None"
+"fancyzhx/dbpedia_14","fancyzhx","2022-03-02 23:29:22","2024-01-22 11:57:58+00:00","9abd46cf7fc8b4c64290f26993c540b92aa145ac","2199","513261","28","None","text-classification, topic-classification","Dataset Card for DBpedia14 Dataset Summary The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes from DBpedia 2014. They are listed in classes.txt. From each","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-3.0","1509.01626","None","topic-classification"
+"wyu1/Leopard-Instruct","wyu1","2024-10-29 20:51:58","2024-11-08 00:12:25+00:00","93317b272c5a9d9c0417fa6ea6e2be89ac9215ea","136887","503585","56","language:en, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2410.01744, region:us, multimodal, instruction-following, multi-image, lmm, vlm, mllm","None","Leopard-Instruct Paper | Github | Models-LLaVA | Models-Idefics2 Summaries Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-r","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2410.01744","None","None"
+"allenai/reward-bench","allenai","2023-12-14 18:38:07","2024-09-09 16:11:45+00:00","168d848cdbbea9764fae4a544dc9ca1e6cca4931","6013","493439","83","None","question-answering","Code | Leaderboard | Prior Preference Sets | Results | Paper Reward Bench Evaluation Dataset Card The RewardBench evaluation dataset evaluates capabilities of reward models over the following categori","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","odc-by","2403.13787","None","None"
+"TAUR-Lab/MuSR","TAUR-Lab","2024-05-17 18:56:34","2024-05-21 15:36:53+00:00","7c365b439a222150f317764d4f16ae6c96d7d94a","8349","492029","13","None","question-answering","MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning Creating murder mysteries that require multi-step reasoning with commonsense using ChatGPT! By: Zayne Sprague, Xi Ye, Kaj Bos","None","en","mono","n|<|1|K","None","False","False","False","cc-by-4.0","2310.16049","None","None"
+"HuggingFaceH4/ultrachat_200k","HuggingFaceH4","2023-10-24 08:24:57","2024-10-16 11:52:27+00:00","8049631c405ae6576f93f445c6b8166f76f5505a","12949","480137","498","None","text-generation","Dataset Card for UltraChat 200k Dataset Description This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model. The original datase","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"mteb/sts14-sts","mteb","2022-04-20 10:47:52","2022-09-27 19:11:37+00:00","e2125984e7df8b7871f6ae9949cf6b6795e7c54b","25377","477695","1","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"HuggingFaceH4/ultrafeedback_binarized","HuggingFaceH4","2023-10-24 08:53:19","2024-10-16 11:49:06+00:00","3949bf5f8c17c394422ccfab0c31ea9c20bdeb85","8551","473427","266","None","text-generation","Dataset Card for UltraFeedback Binarized Dataset Description This is a pre-processed version of the UltraFeedback dataset and was used to train Zephyr-7Β-β, a state of the art chat model at the 7B par","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"TIGER-Lab/MMLU-Pro","TIGER-Lab","2024-05-08 13:36:21","2024-11-27 16:03:40+00:00","014c8cf520218fc7f1ecec95c691a83ae5b8ba14","40250","459452","311","None","question-answering","MMLU-Pro Dataset MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset cont","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2406.01574","None","None"
+"mteb/sts13-sts","mteb","2022-04-20 10:47:41","2022-09-27 19:12:02+00:00","1591bfcbe8c69d4bf7fe2a16e2451017832cafb9","25364","459062","1","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"google/xquad","google","2022-03-02 23:29:22","2024-01-04 17:08:50+00:00","51adfef1c1287aab1d2d91b5bead9bcfb9c68583","2889","454045","28","None","question-answering, extractive-qa","Dataset Card for ""xquad"" Dataset Summary XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a ","None","ar, de, el, en, es, hi, ro, ru, th, tr, vi, zh","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-sa-4.0","None","None","extractive-qa"
+"mteb/amazon_counterfactual","mteb","2022-05-26 10:48:56","2024-06-17 12:12:21+00:00","1f7e6a9d6fa6e64c53d146e428565640410c0df1","1284","452474","1","language:de, language:en, language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2104.06893, region:us","None","Amazon Multilingual Counterfactual Dataset The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary ","None","de, en, ja","multi","None","None","False","False","False","None","2104.06893","None","None"
+"gsarti/flores_101","gsarti","2022-03-02 23:29:22","2022-10-27 08:37:36+00:00","bc58ae43b22607b3e1e2bf3ae1bc5cb053495abb","20112","449039","23","task_categories:text-generation, task_categories:translation, annotations_creators:found, language_creators:expert-generated, multilinguality:multilingual, multilinguality:translation, source_datasets:extended|flores, language:af, language:am, language:ar, language:hy, language:as, language:ast, language:az, language:be, language:bn, language:bs, language:bg, language:my, language:ca, language:ceb, language:zho, language:hr, language:cs, language:da, language:nl, language:en, language:et, language:tl, language:fi, language:fr, language:ff, language:gl, language:lg, language:ka, language:de, language:el, language:gu, language:ha, language:he, language:hi, language:hu, language:is, language:ig, language:id, language:ga, language:it, language:ja, language:jv, language:kea, language:kam, language:kn, language:kk, language:km, language:ko, language:ky, language:lo, language:lv, language:ln, language:lt, language:luo, language:lb, language:mk, language:ms, language:ml, language:mt, language:mi, language:mr, language:mn, language:ne, language:ns, language:no, language:ny, language:oc, language:or, language:om, language:ps, language:fa, language:pl, language:pt, language:pa, language:ro, language:ru, language:sr, language:sn, language:sd, language:sk, language:sl, language:so, language:ku, language:es, language:sw, language:sv, language:tg, language:ta, language:te, language:th, language:tr, language:uk, language:umb, language:ur, language:uz, language:vi, language:cy, language:wo, language:xh, language:yo, language:zu, license:cc-by-sa-4.0, size_categories:100K<n<1M, modality:tabular, modality:text, library:datasets, library:mlcroissant, arxiv:2106.03193, region:us, conditional-text-generation","text-generation, translation","One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage","@inproceedings{, title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={ Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\\'{a}n, Francisco and Fan, Angela }, year={2021} }","af, am, ar, hy, as, ast, az, be, bn, bs, bg, my, ca, ceb, zho, hr, cs, da, nl, en, et, tl, fi, fr, ff, gl, lg, ka, de, el, gu, ha, he, hi, hu, is, ig, id, ga, it, ja, jv, kea, kam, kn, kk, km, ko, ky, lo, lv, ln, lt, luo, lb, mk, ms, ml, mt, mi, mr, mn, ne, ns, no, ny, oc, or, om, ps, fa, pl, pt, pa, ro, ru, sr, sn, sd, sk, sl, so, ku, es, sw, sv, tg, ta, te, th, tr, uk, umb, ur, uz, vi, cy, wo, xh, yo, zu","multi","u|n|k|n|o|w|n","flores","False","False","False","cc-by-sa-4.0","2106.03193","None","None"
+"wmt/wmt14","wmt","2022-03-02 23:29:22","2024-04-03 09:05:59+00:00","b199e406369ec1b7634206d3ded5ba45de2fe696","4969","438481","15","None","translation","Dataset Card for ""wmt14"" Dataset Summary Warning: There are issues with the Common Crawl corpus data (training-parallel-commoncrawl.tgz): Non-English files contain many English sentences. Their ""paral","None","cs, de, en, fr, hi, ru","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","unknown","None","None","None"
+"fancyzhx/yelp_polarity","fancyzhx","2022-03-02 23:29:22","2024-08-08 05:55:49+00:00","bbf1c97a1f0cf005e5aded43839fd814654a1557","7632","432903","15","None","text-classification, sentiment-classification","Dataset Card for ""yelp_polarity"" Dataset Summary Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, a","None","en","mono","None","None","False","False","False","None","None","None","sentiment-classification"
+"allenai/scitail","allenai","2022-03-02 23:29:22","2024-01-04 16:25:10+00:00","0cc4353235b289165dfde1c7c5d1be983f99ce44","2552","432801","5","language:en, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""scitail"" Dataset Summary The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are co","None","en","mono","None","scitail","False","False","False","None","None","None","None"
+"togethercomputer/RedPajama-Data-1T","togethercomputer","2023-04-17 06:28:35","2024-06-17 11:36:03+00:00","398f92572e94f4793e41c22ab7ea2a788d9e7de4","1261","423872","1073","None","text-generation","RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.","None","en","mono","None","None","False","False","False","None","None","None","None"
+"nyu-mll/multi_nli","nyu-mll","2022-03-02 23:29:22","2024-01-04 16:06:27+00:00","da70db2af9d09693783c3320c4249840212ee221","1950","407415","95","None","text-classification, natural-language-inference, multi-input-text-classification","Dataset Card for Multi-Genre Natural Language Inference (MultiNLI) Dataset Summary The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs ann","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-3.0","None","None","natural-language-inference, multi-input-text-classification"
+"mteb/biosses-sts","mteb","2022-04-19 14:47:25","2022-09-27 19:13:38+00:00","9ee918f184421b6bd48b78f6c714d86546106103","10780","406025","1","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mteb/sts15-sts","mteb","2022-04-20 10:48:04","2022-09-27 19:12:14+00:00","1cd7298cac12a96a373b6a2f18738bb3e739a9b6","16209","404123","1","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"google/boolq","google","2022-03-02 23:29:22","2024-01-22 09:16:26+00:00","35b264d03638db9f4ce671b711558bf7ff0f80d5","6155","401640","70","task_categories:text-classification, task_ids:natural-language-inference, annotations_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1905.10044, region:us","text-classification, natural-language-inference","Dataset Card for Boolq Dataset Summary BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted","None","en","mono","1|0|K|<|n|<|1|0|0|K","boolq","False","False","False","cc-by-sa-3.0","1905.10044","None","natural-language-inference"
+"mteb/sickr-sts","mteb","2022-04-19 14:28:03","2022-09-27 19:13:22+00:00","20a6d6f312dd54037fe07a32d58e5e168867909d","13331","399543","4","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"parler-tts/mls_eng","parler-tts","2024-03-11 20:00:44","2024-04-09 14:37:17+00:00","faf6604dcf0bb9ec0d9c280a140ad74da9c931b0","4697","398286","17","None","automatic-speech-recognition, text-to-speech, text-to-audio","Dataset Card for English MLS Dataset Summary This is a streamable version of the English version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original on","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","2012.03411","None","None"
+"ai4bharat/indic_glue","ai4bharat","2022-03-02 23:29:22","2024-01-04 12:36:30+00:00","6dcbebd49d6b12965d433bffcd07e9a786211703","10110","396150","11","task_categories:text-classification, task_categories:token-classification, task_categories:multiple-choice, task_ids:topic-classification, task_ids:natural-language-inference, task_ids:sentiment-analysis, task_ids:semantic-similarity-scoring, task_ids:named-entity-recognition, task_ids:multiple-choice-qa, annotations_creators:other, language_creators:found, multilinguality:multilingual, source_datasets:extended|other, language:as, language:bn, language:en, language:gu, language:hi, language:kn, language:ml, language:mr, language:or, language:pa, language:ta, language:te, license:other, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, discourse-mode-classification, paraphrase-identification, cross-lingual-similarity, headline-classification","text-classification, token-classification, multiple-choice, topic-classification, natural-language-inference, sentiment-analysis, semantic-similarity-scoring, named-entity-recognition, multiple-choice-qa","Dataset Card for ""indic_glue"" Dataset Summary IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide variety of tasks and covers 11 major Indian languages - a","None","as, bn, en, gu, hi, kn, ml, mr, or, pa, ta, te","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","topic-classification, natural-language-inference, sentiment-analysis, semantic-similarity-scoring, named-entity-recognition, multiple-choice-qa"
+"qiaojin/PubMedQA","qiaojin","2022-03-02 23:29:22","2024-03-06 01:50:16+00:00","9001f2853fb87cab8d220904e0de81ac6973b318","4387","393476","168","None","question-answering, multiple-choice-qa","Dataset Card for [Dataset Name] Dataset Summary The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery b","None","en","mono","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K","None","False","False","False","mit","1909.06146","None","multiple-choice-qa"
+"ought/raft","ought","2022-03-02 23:29:22","2022-10-25 09:54:19+00:00","9ee50172ea9afda2f1033c6f1b986e568b862fb3","1440","388841","54","None","text-classification, multi-class-classification","Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have s","@InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} }","en","mono","u|n|k|n|o|w|n","None","False","False","False","other","2109.14076","None","multi-class-classification"
+"maveriq/bigbenchhard","maveriq","2023-09-26 20:05:20","2023-09-29 08:24:12+00:00","d53c5b10a77edeb29da195f47e6086b29f2f7f74","897","381357","22","None","question-answering, token-classification, text2text-generation, text-classification","BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progre","@article{suzgun2022challenging, title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them}, author={Suzgun, Mirac and Scales, Nathan and Sch{\\""a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason}, journal={arXiv preprint arXiv:2210.09261}, year={2022} }","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"juletxara/mgsm","juletxara","2023-05-09 08:20:29","2023-05-09 16:46:31+00:00","f52417ca77bd71e9888ddc29f92587660725d2b4","5671","380450","28","None","text2text-generation","Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2","@article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } @misc{shi2022language, title={Language Models are Multilingual Chain-of-Thought Reasoners}, author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei}, year={2022}, eprint={2210.03057}, archivePrefix={arXiv}, primaryClass={cs.CL} }","en, es, fr, de, ru, zh, ja, th, sw, bn","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2210.03057","None","None"
+"mteb/sts16-sts","mteb","2022-04-20 10:48:15","2022-09-27 19:12:09+00:00","360a0b2dff98700d09e634a01e1cc1624d3e42cd","3400","378282","1","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mozilla-foundation/common_voice_17_0","mozilla-foundation","2024-04-04 10:06:19","2024-06-16 13:50:23+00:00","b10d53980ef166bc24ce3358471c1970d7e6b5ec","28071","375384","215","None","None","Dataset Card for Common Voice Corpus 17.0 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 31175 recorded hours in the dataset also include de","@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }","ab, af, am, ar, as, ast, az, ba, bas, be, bg, bn, br, ca, ckb, cnh, cs, cv, cy, da, de, dv, dyu, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gn, ha, he, hi, hsb, ht, hu, hy, ia, id, ig, is, it, ja, ka, kab, kk, kmr, ko, ky, lg, lij, lo, lt, ltg, lv, mdf, mhr, mk, ml, mn, mr, mrj, mt, myv, nan, ne, nhi, nl, nn, nso, oc, or, os, pa, pl, ps, pt, quy, rm, ro, ru, rw, sah, sat, sc, sk, skr, sl, sq, sr, sv, sw, ta, te, th, ti, tig, tk, tok, tr, tt, tw, ug, uk, ur, uz, vi, vot, yi, yo, yue, zgh, zh, zu, zza","multi","None","None","False","auto","False","cc0-1.0","1912.06670","None","None"
+"mteb/emotion","mteb","2022-05-23 09:55:39","2022-09-27 19:14:18+00:00","829147f8f75a25f005913200eb5ed41fae320aa1","15922","374574","11","None","None","** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)**","None","en","mono","None","None","False","False","False","None","None","None","None"
+"nvidia/HelpSteer2","nvidia","2024-06-02 06:59:33","2024-12-18 21:06:57+00:00","990b2711a36180dd19d9c94b8627844866f8982a","14506","358871","398","None","None","HelpSteer2: Open-source dataset for training top-performing reward models HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually c","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2410.01257","None","None"
+"AI-Lab-Makerere/beans","AI-Lab-Makerere","2022-03-02 23:29:22","2024-01-03 12:06:51+00:00","27aa014ce09b193e1a6f58112d4a66e0eddb69c5","3758","358273","37","None","image-classification, multi-class-image-classification","Dataset Card for Beans Dataset Summary Beans leaf dataset with images of diseased and health leaves. Supported Tasks and Leaderboards image-classification: Based on a leaf image, the goal of this task","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","multi-class-image-classification"
+"INK-USC/xcsr","INK-USC","2022-03-02 23:29:22","2024-01-04 17:03:17+00:00","f691f311a11f84773a899e9bfc7da2bae51b0b02","3729","354396","6","None","question-answering, multiple-choice-qa","Dataset Card for X-CSR Dataset Summary To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and","None","ar, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, sw, ur, vi, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2106.06937","None","multiple-choice-qa"
+"m-a-p/FineFineWeb","m-a-p","2024-12-14 12:46:33","2024-12-19 11:34:03+00:00","7fd92dc825a75cbff271a5a52eea0eda91a2c112","330915","349684","28","None","text-classification, text2text-generation, text-generation","FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus arXiv: Coming Soon Project Page: Coming Soon Blog: Coming Soon Data Statistics Domain (#tokens/#samples) Iteration 1 Tokens Iterati","None","en","mono","n|>|1|T","None","False","False","False","apache-2.0","None","None","None"
+"allenai/swag","allenai","2022-03-02 23:29:22","2024-06-14 10:21:05+00:00","dc48148372b3853a9c7bae7bb06c161b46d8364a","2081","341155","22","None","text-classification, natural-language-inference","Dataset Card for Situations With Adversarial Generations Dataset Summary Given a partial description like ""she opened the hood of the car,"" humans can reason about the situation and anticipate what mi","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","1808.05326","None","natural-language-inference"
+"mteb/sprintduplicatequestions-pairclassification","mteb","2022-04-19 10:40:02","2022-09-27 19:15:57+00:00","5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea","1643","336732","0","language:en, size_categories:n<1K, modality:text, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Sterzhang/PVIT-3M","Sterzhang","2024-10-07 09:28:17","2024-11-02 07:41:57+00:00","68c0ad34851b06e7e408b092c1f8ee1004f6c92b","21207","336555","17","task_categories:visual-question-answering, task_categories:image-text-to-text, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.07113, region:us, multi-modal, personalized","visual-question-answering, image-text-to-text","PVIT-3M The paper titled ""Personalized Visual Instruction Tuning"" introduces a novel dataset called PVIT-3M. This dataset is specifically designed for tuning MLLMs in the context of personalized visua","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2410.07113","None","None"
+"Open-Orca/OpenOrca","Open-Orca","2023-06-15 18:16:11","2023-10-21 10:09:31+00:00","3e85783ecb0db83df8b30dbbd94107857b5ac830","9439","336369","1357","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:en, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","🐋 The OpenOrca Dataset! 🐋 We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in t","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2306.02707","None","None"
+"nthngdy/oscar-small","nthngdy","2022-03-23 09:26:03","2023-03-08 09:57:45+00:00","686600ca31931048cbb9f0acc98b83c29d0036b9","3332","333501","14","None","text-generation, language-modeling","The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\\","@inproceedings{ortiz-suarez-etal-2020-monolingual, title = ""A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages"", author = ""Ortiz Su{\\'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit"", booktitle = ""Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"", month = jul, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.acl-main.156"", pages = ""1703--1714"", abstract = ""We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures."", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{\\'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\\""u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{\\""u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} }","af, am, ar, arz, as, az, azb, ba, be, bg, bn, bo, br, ca, ce, ceb, ckb, cs, cv, cy, da, de, dv, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gu, he, hi, hr, hu, hy, id, is, it, ja, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mhr, mk, ml, mn, mr, ms, mt, my, nds, ne, nl, nn, no, or, os, pa, pl, pnb, ps, pt, ro, ru, sa, sah, sd, sh, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vi, yi, zh","multi","None","None","False","False","False","cc0-1.0","2010.14571","None","language-modeling"
+"SwayStar123/preprocessed_commoncatalog-cc-by","SwayStar123","2024-10-19 05:23:44","2025-01-23 08:21:12+00:00","f3c9f1c6ef0cd52a877b2039b0665d7b71f01dd9","201498","330412","2","language:en, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","I also seperately provide just the prompts in prompts.json keys are the image_id, and the values are the captions generated Captions generated by moondream: vikhyatk/moondream2 Latents generated by SD","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"dair-ai/emotion","dair-ai","2022-03-02 23:29:22","2024-08-08 06:10:47+00:00","cab853a1dbdf4c42c2b3ef2173804746df8825fe","22620","311147","319","None","text-classification, multi-class-classification","Dataset Card for ""emotion"" Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","multi-class-classification"
+"PhilipMay/stsb_multi_mt","PhilipMay","2022-03-02 23:29:22","2024-05-14 13:16:38+00:00","3acaa3dd8c91649e0b8e627ffad891f059e47c8c","1629","306506","65","None","text-classification, text-scoring, semantic-similarity-scoring","Dataset Card for STSb Multi MT Dataset Summary STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selectio","None","de, en, es, fr, it, nl, pl, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","1708.00055","None","text-scoring, semantic-similarity-scoring"
+"mteb/askubuntudupquestions-reranking","mteb","2022-04-19 11:08:05","2022-09-27 19:11:08+00:00","4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c","1587","303899","0","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"juletxara/xstory_cloze","juletxara","2023-01-28 14:49:52","2023-05-21 16:04:36+00:00","6a88f6fc855b5b740125ed2cd7c73476dcaed853","1848","299307","9","None","other","XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This data","@article{DBLP:journals/corr/abs-2112-10668, author = {Xi Victoria Lin and Todor Mihaylov and Mikel Artetxe and Tianlu Wang and Shuohui Chen and Daniel Simig and Myle Ott and Naman Goyal and Shruti Bhosale and Jingfei Du and Ramakanth Pasunuru and Sam Shleifer and Punit Singh Koura and Vishrav Chaudhary and Brian O'Horo and Jeff Wang and Luke Zettlemoyer and Zornitsa Kozareva and Mona T. Diab and Veselin Stoyanov and Xian Li}, title = {Few-shot Learning with Multilingual Language Models}, journal = {CoRR}, volume = {abs/2112.10668}, year = {2021}, url = {https://arxiv.org/abs/2112.10668}, eprinttype = {arXiv}, eprint = {2112.10668}, timestamp = {Tue, 04 Jan 2022 15:59:27 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }","en, ru, zh, es, ar, hi, id, te, sw, eu, my","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"google/wiki40b","google","2022-03-02 23:29:22","2024-03-11 16:19:48+00:00","06bfe584d8193cf58f79c6354ecc2a2b94a7deea","1985","289950","24","language:en, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""wiki40b"" Dataset Summary Clean-up text for 40+ Wikipedia languages editions of pages correspond to entities. The datasets have train/dev/test splits per language. The dataset is clea","None","en","mono","None","wiki-40b","False","False","False","None","None","None","None"
+"mosaicml/dolly_hhrlhf","mosaicml","2023-05-02 22:27:06","2023-10-02 15:48:48+00:00","8193c9263cb5b413c723fd2a73b8ae17ab09557b","1090","287476","109","None","text-generation","Dataset Card for ""dolly_hhrlhf"" This dataset is a combination of Databrick's dolly-15k dataset and a filtered subset of Anthropic's HH-RLHF. It also includes a test split, which was missing in the ori","None","en","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Davlan/sib200","Davlan","2024-01-27 14:04:12","2024-02-19 16:34:23+00:00","38977a667f6fc264d5c26ec57a01e16db040b358","1788","278456","12","None","text-classification, topic-classification","Dataset Card for SIB-200 Dataset Summary SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects. The train/validation/test sets ","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nqo, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2309.07445","None","topic-classification"
+"m-a-p/PIN-14M","m-a-p","2024-04-12 09:35:42","2024-12-20 04:00:22+00:00","1bd245307fee726f0128ccd651ad0ce6adca6579","4852","268441","28","language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.13923, region:us, multimodal","None","PIN-14M A mini version of ""PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents"" Paper: https://arxiv.org/abs/2406.13923 This dataset contains 14M samples in PIN format, ","None","en, zh","bi","1|B|<|n|<|1|0|B","None","False","False","False","apache-2.0","2406.13923","None","None"
+"Helsinki-NLP/opus_infopankki","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:10:55+00:00","52339513e7ee4d1896c804ca4f61d7ee3b35ea93","5366","267009","3","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:ar, language:en, language:es, language:et, language:fa, language:fi, language:fr, language:ru, language:so, language:sv, language:tr, language:zh, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for infopankki Dataset Summary A parallel corpus of 12 languages, 66 bitexts. Supported Tasks and Leaderboards The underlying task is machine translation. Languages [More Information Need","None","ar, en, es, et, fa, fi, fr, ru, so, sv, tr, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"mteb/scifact","mteb","2024-02-26 15:56:04","2024-03-02 19:11:40+00:00","d56462d0e63a25450459c4f213e49ffdb866f7f9","18435","265877","3","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:scifact, language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"mteb/amazon_reviews_multi","mteb","2022-05-25 19:22:51","2022-09-27 19:10:01+00:00","c379a6705fec24a2493fa68e011692605f44e119","2861","253698","17","language:de, language:en, language:es, language:fr, language:ja, language:zh, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us","None","We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1,","@inproceedings{marc_reviews, title={The Multilingual Amazon Reviews Corpus}, author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing}, year={2020} }","de, en, es, fr, ja, zh","multi","None","None","False","False","False","None","None","None","None"
+"cais/wmdp","cais","2024-03-05 21:08:18","2024-04-27 05:37:45+00:00","7125571f22f032c56415e7980f48d877dd830ff8","8720","247948","11","None","text-generation","Dataset Card for WMDP The Weapons of Mass Destruction Proxy (WMDP) benchmark is a dataset of multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersec","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2403.03218","None","None"
+"aiana94/polynews-parallel","aiana94","2024-05-17 07:58:37","2024-06-21 08:35:51+00:00","67141c74541109a6689ac9d81a78fd0b27b5c146","1095","244490","11","None","translation, text-retrieval","Dataset Card for PolyNewsParallel Dataset Summary PolyNewsParallel is a multilingual paralllel dataset containing news titles for 833 language pairs. It covers 64 languages and 17 scripts. Uses This d","None","am, ar, ay, bm, bbj, bn, bg, ca, cs, ku, da, de, el, en, et, ee, fil, fi, fr, fon, gu, ha, he, hi, hu, ig, id, it, ja, kk, km, ko, lv, lt, lg, luo, mk, mos, my, nl, ne, or, pa, pcm, fa, pl, pt, mg, ro, ru, es, sr, sq, sw, sv, tet, tn, tr, tw, ur, wo, yo, zh, zu","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","2406.12634","None","None"
+"knkarthick/dialogsum","knkarthick","2022-06-28 10:17:20","2023-10-03 10:56:21+00:00","a968e7aee0602e257935f1321a02e4287f7d5848","9865","242420","192","None","summarization, text2text-generation, text-generation","Dataset Card for DIALOGSum Corpus Dataset Description Links Homepage: https://aclanthology.org/2021.findings-acl.449 Repository: https://github.com/cylnlp/dialogsum Paper: https://aclanthology.org/202","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"FiscalNote/billsum","FiscalNote","2022-03-02 23:29:22","2024-03-27 16:01:38+00:00","3d8510441c06a3d9dfb32eb0d7f80151730bcc4f","3419","235893","47","None","summarization","Dataset Card for ""billsum"" Dataset Summary BillSum, summarization of US Congressional and California state bills. There are several features: text: bill text. summary: summary of the bills. title: tit","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc0-1.0","1910.00523","None","None"
+"microsoft/ms_marco","microsoft","2022-03-02 23:29:22","2024-01-04 16:01:29+00:00","a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a","3493","234819","142","None","None","Dataset Card for ""ms_marco"" Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answe","None","en","mono","None","None","False","False","False","None","None","None","None"
+"OpenAssistant/oasst1","OpenAssistant","2023-04-13 15:48:16","2023-05-02 13:21:21+00:00","fdf72ae0827c1cda404aff25b6603abec9e3399b","3210","233887","1288","None","None","OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-an","None","en, es, ru, de, pl, th, vi, sv, bn, da, he, it, fa, sk, id, nb, el, nl, hu, eu, zh, eo, ja, ca, cs, bg, fi, pt, tr, ro, ar, uk, gl, fr, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2304.07327","None","None"
+"uonlp/CulturaX","uonlp","2023-09-04 08:20:39","2024-12-16 17:24:53+00:00","6a8734bc69fefcbb7735f4f9250f43e4cd7a442e","13446","230352","491","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:original, language:af, language:als, language:am, language:an, language:ar, language:arz, language:as, language:ast, language:av, language:az, language:azb, language:ba, language:bar, language:bcl, language:be, language:bg, language:bh, language:bn, language:bo, language:bpy, language:br, language:bs, language:bxr, language:ca, language:cbk, language:ce, language:ceb, language:ckb, language:cs, language:cv, language:cy, language:da, language:de, language:dsb, language:dv, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:frr, language:fy, language:ga, language:gd, language:gl, language:gn, language:gom, language:gu, language:he, language:hi, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:ilo, language:io, language:is, language:it, language:ja, language:jbo, language:jv, language:ka, language:kk, language:km, language:kn, language:ko, language:krc, language:ku, language:kv, language:kw, language:ky, language:la, language:lb, language:lez, language:li, language:lmo, language:lo, language:lrc, language:lt, language:lv, language:mai, language:mg, language:mhr, language:min, language:mk, language:ml, language:mn, language:mr, language:mrj, language:ms, language:mt, language:mwl, language:my, language:myv, language:mzn, language:nah, language:nap, language:nds, language:ne, language:new, language:nl, language:nn, language:no, language:oc, language:or, language:os, language:pa, language:pam, language:pl, language:pms, language:pnb, language:ps, language:pt, language:qu, language:rm, language:ro, language:ru, language:rue, language:sa, language:sah, language:scn, language:sd, language:sh, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:ta, language:te, language:tg, language:th, language:tk, language:tl, language:tr, language:tt, language:tyv, language:ug, language:uk, language:ur, language:uz, language:vec, language:vi, language:vls, language:vo, language:wa, language:war, language:wuu, language:xal, language:xmf, language:yi, language:yo, language:yue, language:zh, size_categories:1B<n<10B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.09400, region:us","text-generation, fill-mask, language-modeling, masked-language-modeling","CulturaX Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages Dataset Summary We present CulturaX, a substantial multilingual dataset with 6.3 tr","None","af, als, am, an, ar, arz, as, ast, av, az, azb, ba, bar, bcl, be, bg, bh, bn, bo, bpy, br, bs, bxr, ca, cbk, ce, ceb, ckb, cs, cv, cy, da, de, dsb, dv, el, eml, en, eo, es, et, eu, fa, fi, fr, frr, fy, ga, gd, gl, gn, gom, gu, he, hi, hr, hsb, ht, hu, hy, ia, id, ie, ilo, io, is, it, ja, jbo, jv, ka, kk, km, kn, ko, krc, ku, kv, kw, ky, la, lb, lez, li, lmo, lo, lrc, lt, lv, mai, mg, mhr, min, mk, ml, mn, mr, mrj, ms, mt, mwl, my, myv, mzn, nah, nap, nds, ne, new, nl, nn, no, oc, or, os, pa, pam, pl, pms, pnb, ps, pt, qu, rm, ro, ru, rue, sa, sah, scn, sd, sh, si, sk, sl, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, tyv, ug, uk, ur, uz, vec, vi, vls, vo, wa, war, wuu, xal, xmf, yi, yo, yue, zh","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M|||1|0|M|<|n|<|1|0|0|M|||1|0|0|M|<|n|<|1|B|||1|B|<|n|<|1|0|B","None","False","auto","False","None","2309.09400","None","language-modeling, masked-language-modeling"
+"mteb/mtop_domain","mteb","2022-05-19 15:04:17","2022-11-21 19:59:05+00:00","a97447adbe89f8c1ca4e8dc3b70018d84a4cccdd","2412","229843","2","None","text-classification","None","None","de, en, es, fr, hi, th","multi","None","None","False","False","False","None","None","None","None"
+"JeanKaddour/minipile","JeanKaddour","2023-04-09 20:32:58","2023-06-20 10:08:26+00:00","18ad1b0c701eaa0de03d3cecfdd769cbc70ffbd0","1698","229621","119","None","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for MiniPile Dataset Description The MiniPile Challenge for Data-Efficient Language Models Dataset Summary MiniPile is a 6GB subset of the deduplicated The Pile corpus. To curate MiniPile","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","other","2304.08442","None","language-modeling, masked-language-modeling"
+"evalplus/humanevalplus","evalplus","2024-01-22 06:55:51","2024-05-01 22:59:55+00:00","d32357cf319e50e9c8d8dab5ea876c72b0fd321b","26944","229388","6","None","text2text-generation","None","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"mozilla-foundation/common_voice_16_1","mozilla-foundation","2024-01-15 12:20:41","2024-01-16 13:57:04+00:00","8228a37ca8d3c733f5e559cc99e3be9f8de170f9","1195","222232","50","None","None","Dataset Card for Common Voice Corpus 16 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 30328 recorded hours in the dataset also include demo","@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }","ab, af, am, ar, as, ast, az, ba, bas, be, bg, bn, br, ca, ckb, cnh, cs, cv, cy, da, de, dv, dyu, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gn, ha, he, hi, hsb, hu, hy, ia, id, ig, is, it, ja, ka, kab, kk, kmr, ko, ky, lg, lij, lo, lt, ltg, lv, mdf, mhr, mk, ml, mn, mr, mrj, mt, myv, nan, ne, nhi, nl, nn, oc, or, os, pa, pl, ps, pt, quy, rm, ro, ru, rw, sah, sat, sc, sk, skr, sl, sq, sr, sv, sw, ta, te, th, ti, tig, tk, tok, tr, tt, tw, ug, uk, ur, uz, vi, vot, yi, yo, yue, zgh, zh","multi","None","None","False","auto","False","cc0-1.0","1912.06670","None","None"
+"vblagoje/cc_news","vblagoje","2022-03-02 23:29:22","2024-01-04 06:45:02+00:00","81eb2ce0d2a9dad6ad16b68ef750ec290880fa36","1036","217707","54","None","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for CC-News Dataset Summary CC-News dataset contains news articles from news sites all over the world. The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","None","None","language-modeling, masked-language-modeling"
+"lvwerra/stack-exchange-paired","lvwerra","2023-03-13 09:32:41","2023-03-13 11:30:17+00:00","0c0a1bc27a253683bdcdd061007b82bfc7bbddb1","1902","216215","143","None","text-generation, question-answering","StackExchange Paired This is a processed version of the HuggingFaceH4/stack-exchange-preferences. The following steps were applied: Parse HTML to Markdown with markdownify Create pairs (response_j, re","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"ucinlp/drop","ucinlp","2022-03-02 23:29:22","2024-01-17 08:15:43+00:00","95cda593fae71b60b5b19f82de3fcf3298c1239c","2562","214787","45","task_categories:question-answering, task_categories:text2text-generation, task_ids:extractive-qa, task_ids:abstractive-qa, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1903.00161, region:us","question-answering, text2text-generation, extractive-qa, abstractive-qa","Dataset Card for ""drop"" Dataset Summary DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. . DROP is a crowdsourced, adversarially-created, 96k-question benchmark, i","None","en","mono","1|0|K|<|n|<|1|0|0|K","drop","False","False","False","cc-by-sa-4.0","1903.00161","None","extractive-qa, abstractive-qa"
+"inductiva/windtunnel-20k","inductiva","2024-09-26 09:06:47","2024-10-02 11:49:47+00:00","9a396883304a5dbf4c6bd59c6bc3cb8a733fc894","28348","209007","5","task_categories:feature-extraction, task_categories:graph-ml, task_categories:image-to-3d, language:en, size_categories:10K<n<100K, modality:3d, modality:tabular, modality:text, region:us, simulation, openfoam, physics, windtunnel, inductiva, machine learning, synthetic","feature-extraction, graph-ml, image-to-3d","Wind Tunnel Dataset The Wind Tunnel Dataset contains 19,812 OpenFOAM simulations of 1,000 unique automobile-like objects placed in a virtual wind tunnel measuring 20 meters long, 10 meters wide, and 8","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"mteb/nfcorpus","mteb","2024-03-02 21:17:27","2024-03-03 11:16:55+00:00","ec0fa4fe99da2ff19ca1214b7966684033a58814","18474","208992","2","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"alexandrainst/m_hellaswag","alexandrainst","2023-12-27 20:55:26","2024-02-12 16:32:54+00:00","9d31dc982bd6285e081e3e3136332a38b9c1d7b7","2745","208821","3","task_categories:question-answering, task_ids:multiple-choice-qa, language:ar, language:bn, language:ca, language:da, language:de, language:es, language:eu, language:fr, language:gu, language:hi, language:hr, language:hu, language:hy, language:id, language:it, language:kn, language:ml, language:mr, language:ne, language:nl, language:pt, language:ro, language:ru, language:sk, language:sr, language:sv, language:ta, language:te, language:uk, language:vi, language:zh, language:is, language:en, language:no, language:nb, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, multiple-choice-qa","Multilingual HellaSwag Dataset Summary This dataset is a machine translated version of the HellaSwag dataset. The Icelandic (is) part was translated with Miðeind's Greynir model and Norwegian (nb) was","None","ar, bn, ca, da, de, es, eu, fr, gu, hi, hr, hu, hy, id, it, kn, ml, mr, ne, nl, pt, ro, ru, sk, sr, sv, ta, te, uk, vi, zh, is, en, no, nb","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"alexandrainst/m_mmlu","alexandrainst","2023-12-27 20:56:17","2024-03-11 07:52:21+00:00","18e6c8e65b2010a9819fcd8152718b17f8c551a7","2921","206268","14","None","question-answering, multiple-choice-qa","Multilingual MMLU Dataset Summary This dataset is a machine translated version of the MMLU dataset. The Icelandic (is) part was translated with Miðeind's Greynir model and Norwegian (nb) was translate","None","ar, bn, ca, da, de, en, es, eu, fr, gu, hi, hr, hu, hy, id, is, it, kn, ml, mr, nb, no, ne, nl, pt, ro, ru, sk, sr, sv, ta, te, uk, vi, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"curaihealth/medical_questions_pairs","curaihealth","2022-03-02 23:29:22","2024-01-04 14:27:42+00:00","d91d9da9b2a843fc09ab9d48568d2a93bf58bd0d","823","206016","46","None","text-classification, semantic-similarity-classification","Dataset Card for [medical_questions_pairs] Dataset Summary This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. Doctors with a lis","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","2008.13546","None","semantic-similarity-classification"
+"stanfordnlp/coqa","stanfordnlp","2022-03-02 23:29:22","2024-01-04 07:47:32+00:00","0d9e9952f1ef6e5415492d3d84b5873259137e3c","2407","202400","59","None","question-answering, extractive-qa","Dataset Card for ""coqa"" Dataset Summary CoQA is a large-scale dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k convers","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","1704.04683","None","extractive-qa"
+"mteb/mtop_intent","mteb","2022-05-19 15:03:15","2022-09-27 19:10:23+00:00","6299947a7777084cc2d4b64235bf7190381ce755","2339","201306","2","None","None","None","None","de, en, es, fr, hi, th","multi","None","None","False","False","False","None","None","None","None"
+"clips/mfaq","clips","2022-03-02 23:29:22","2022-10-20 11:32:50+00:00","87a7bada8da4fe2a7b738c6d3e549153383198ad","7396","199631","33","task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:no-annotation, language_creators:other, multilinguality:multilingual, source_datasets:original, language:cs, language:da, language:de, language:en, language:es, language:fi, language:fr, language:he, language:hr, language:hu, language:id, language:it, language:nl, language:no, language:pl, language:pt, language:ro, language:ru, language:sv, language:tr, language:vi, license:cc0-1.0, size_categories:10M<n<100M, modality:tabular, modality:text, library:datasets, library:mlcroissant, arxiv:2109.12870, region:us","question-answering, multiple-choice-qa","We present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages.","@InProceedings{mfaq_a_multilingual_dataset, title={MFAQ: a Multilingual FAQ Dataset}, author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans}, year={2021}, booktitle={MRQA @ EMNLP 2021} }","cs, da, de, en, es, fi, fr, he, hr, hu, id, it, nl, no, pl, pt, ro, ru, sv, tr, vi","multi","u|n|k|n|o|w|n","None","False","False","False","cc0-1.0","None","None","multiple-choice-qa"
+"facebook/multilingual_librispeech","facebook","2022-03-02 23:29:22","2024-08-12 16:50:57+00:00","2e83e61823b4c47dcbcb1980bb88601274127609","5691","198110","119","None","automatic-speech-recognition, text-to-speech, text-to-audio","Dataset Card for MultiLingual LibriSpeech Dataset Summary This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from Op","None","de, nl, fr, it, es, pt, pl, en","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","2012.03411","None","None"
+"garage-bAInd/Open-Platypus","garage-bAInd","2023-08-03 19:31:18","2024-01-24 19:09:41+00:00","37141edbdb7826378cce118c46a109b813e1f038","3673","185217","376","None","None","Open-Platypus This dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","2308.07317","None","None"
+"clinc/clinc_oos","clinc","2022-03-02 23:29:22","2024-01-18 14:33:10+00:00","155b9c710419136e17307b80d0a13e68cd46b4ec","2981","184181","13","None","text-classification, intent-classification","Dataset Card for CLINC150 Dataset Summary Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define lab","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-3.0","None","None","intent-classification"
+"HuggingFaceTB/smollm-corpus","HuggingFaceTB","2024-07-15 13:51:48","2024-09-06 07:04:57+00:00","3ba9d605774198c5868892d7a8deda78031a781f","9673","182599","286","None","None","SmolLM-Corpus This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models. You can find more details about the models trained on thi","None","en","mono","None","None","False","False","False","odc-by","None","None","None"
+"facebook/voxpopuli","facebook","2022-05-10 14:42:49","2022-10-14 13:43:12+00:00","719aaef8225945c0d80b277de6c79aa42ab053d5","15784","178543","102","task_categories:automatic-speech-recognition, multilinguality:multilingual, language:en, language:de, language:fr, language:es, language:pl, language:it, language:ro, language:hu, language:cs, language:nl, language:fi, language:hr, language:sk, language:sl, language:et, language:lt, license:cc0-1.0, license:other, size_categories:100K<n<1M, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:2101.00390, region:us","automatic-speech-recognition","A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.","@inproceedings{wang-etal-2021-voxpopuli, title = ""{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation"", author = ""Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel"", booktitle = ""Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"", month = aug, year = ""2021"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2021.acl-long.80"", doi = ""10.18653/v1/2021.acl-long.80"", pages = ""993--1003"", }","en, de, fr, es, pl, it, ro, hu, cs, nl, fi, hr, sk, sl, et, lt","multi","None","None","False","False","False","cc0-1.0","2101.00390","None","None"
+"amphion/Emilia-Dataset","amphion","2024-08-23 08:25:08","2024-09-06 13:29:55+00:00","bcaad00d13e7c101485990a46e88f5884ffed3fc","31105","178304","200","None","text-to-speech, automatic-speech-recognition","Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech","None","zh, en, ja, fr, de, ko","multi","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","cc-by-nc-4.0","2407.05361","None","None"
+"ade-benchmark-corpus/ade_corpus_v2","ade-benchmark-corpus","2022-03-02 23:29:22","2024-01-09 11:42:58+00:00","4ba01c71687dd7c996597042449448ea312126cf","794","177001","30","None","text-classification, token-classification, coreference-resolution, fact-checking","Dataset Card for Adverse Drug Reaction Data v2 Dataset Summary ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for Classification if a sentence is ADE-related (True) or not (False","None","en","mono","1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K|||n|<|1|K","None","False","False","False","unknown","None","None","coreference-resolution, fact-checking"
+"alexandrainst/m_arc","alexandrainst","2023-12-27 20:54:59","2024-01-15 14:53:25+00:00","69b0991ee606079105c4f9e271bcc1fef0014e03","2603","176385","4","None","question-answering, multiple-choice-qa","Multilingual ARC Dataset Summary This dataset is a machine translated version of the ARC dataset. The Icelandic (is) part was translated with Miðeind's Greynir model and Norwegian (nb) was translated ","None","ar, bn, ca, da, de, en, es, eu, fr, gu, hi, hr, hu, hy, id, is, it, kn, ml, mr, nb, no, ne, nl, pt, ro, ru, sk, sr, sv, ta, te, uk, vi, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"wmt/wmt19","wmt","2022-03-02 23:29:22","2024-04-04 16:12:13+00:00","c8a87a86736e7bf10c74c22e9033eba9146774e9","2936","173848","29","None","translation","Dataset Card for ""wmt19"" Dataset Summary Warning: There are issues with the Common Crawl corpus data (training-parallel-commoncrawl.tgz): Non-English files contain many English sentences. Their ""paral","None","cs, de, en, fi, fr, gu, kk, lt, ru, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","unknown","None","None","None"
+"Spawning/pd12m-full","Spawning","2024-11-14 11:06:57","2024-11-26 03:07:27+00:00","7bda6bfe13c6a39fd0a286adced5df1228041720","34776","173681","11","language:en, license:cdla-permissive-2.0, size_categories:10M<n<100M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us, image","None","This dataset is the downloaded variant of Spawning/PD12M. More specifically, this dataset is compatible with webdataset. It was made public after obtaining permission from the original authors of the ","None","en","mono","None","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"jmhessel/newyorker_caption_contest","jmhessel","2022-09-29 17:28:05","2023-12-22 19:13:58+00:00","d81cbab7d0392708d5371d3a4960e69261824db4","23469","172444","64","task_categories:image-to-text, task_categories:multiple-choice, task_categories:text-classification, task_categories:text-generation, task_categories:visual-question-answering, task_categories:other, task_categories:text2text-generation, task_ids:multi-class-classification, task_ids:language-modeling, task_ids:visual-question-answering, task_ids:explanation-generation, annotations_creators:expert-generated, annotations_creators:crowdsourced, annotations_creators:found, language_creators:crowdsourced, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-4.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2209.06293, region:us, humor, caption contest, new yorker","image-to-text, multiple-choice, text-classification, text-generation, visual-question-answering, other, text2text-generation, multi-class-classification, language-modeling, visual-question-answering, explanation-generation","Dataset Card for New Yorker Caption Contest Benchmarks Dataset Summary See capcon.dev for more! Data from: Do Androids Laugh at Electric Sheep? Humor ""Understanding"" Benchmarks from The New Yorker Cap","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2209.06293","None","multi-class-classification, language-modeling, visual-question-answering, explanation-generation"
+"mteb/banking77","mteb","2022-05-17 12:14:06","2022-09-27 19:15:02+00:00","44fa15921b4c889113cc5df03dd4901b49161ab7","18305","172362","2","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"miracl/miracl-corpus","miracl","2022-09-29 14:49:58","2023-01-05 17:28:26+00:00","d921ec7e349ce0d28daf30b2da9da5ee698bef0d","5089","171794","43","None","text-retrieval, document-retrieval","Dataset Card for MIRACL Corpus MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages,","None","ar, bn, en, es, fa, fi, fr, hi, id, ja, ko, ru, sw, te, th, zh","multi","None","None","False","False","False","apache-2.0","2210.09984","None","document-retrieval"
+"MLCommons/peoples_speech","MLCommons","2022-08-16 14:21:49","2024-11-20 15:17:45+00:00","f10597c5d3d3a63f8b6827701297c3afdf178272","22935","169865","93","task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, annotations_creators:machine-generated, language_creators:crowdsourced, language_creators:machine-generated, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-2.0, license:cc-by-2.5, license:cc-by-3.0, license:cc-by-4.0, license:cc-by-sa-3.0, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2111.09344, region:us, robust-speech-recognition, noisy-speech-recognition, speech-recognition","automatic-speech-recognition","Dataset Card for People's Speech Dataset Summary The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage und","None","en","mono","1|T|<|n","None","False","False","False","cc-by-2.0","2111.09344","None","None"
+"MU-NLPC/Calc-svamp","MU-NLPC","2023-09-08 14:56:46","2023-10-30 15:05:26+00:00","1895931ce53425d46db1ffeb055d74ca1d912f17","13154","168464","0","task_categories:text-generation, language:en, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.15017, region:us, math world problems, math, arithmetics","text-generation","Dataset Card for Calc-SVAMP Summary The dataset is a collection of simple math word problems focused on arithmetics. It is derived from https://github.com/arkilpatel/SVAMP/. The main addition in this ","None","en","mono","n|<|1|K","None","False","False","False","mit","2305.15017","None","None"
+"mteb/twentynewsgroups-clustering","mteb","2022-04-07 13:46:04","2022-09-27 19:13:51+00:00","091a54f9a36281ce7d6590ec8c75dd485e7e01d4","4743","164580","0","language:en, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"jhu-clsp/jfleg","jhu-clsp","2022-03-02 23:29:22","2024-02-06 07:47:11+00:00","8b9fb0e6b3456ed57c9ba5524ef9e26b01177750","2112","162841","59","None","text2text-generation","Dataset Card for JFLEG Dataset Summary JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC syste","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","1702.04066","None","None"
+"openlifescienceai/medmcqa","openlifescienceai","2022-05-06 08:43:24","2024-01-04 14:32:02+00:00","91c6572c454088bf71b679ad90aa8dffcd0d5868","7199","161131","130","None","question-answering, multiple-choice, multiple-choice-qa, open-domain-qa","Dataset Card for MedMCQA Dataset Summary MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more th","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","multiple-choice-qa, open-domain-qa"
+"mlfoundations/MINT-1T-PDF-CC-2024-10","mlfoundations","2024-07-12 05:17:41","2024-09-19 21:03:25+00:00","4caa665264020fbe4a7b1fbca177445ca5897772","74649","158208","2","task_categories:image-to-text, task_categories:text-generation, language:en, license:cc-by-4.0, size_categories:1M<n<10M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, arxiv:2406.11271, region:us, multimodal","image-to-text, text-generation","🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billi","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2406.11271","None","None"
+"HuggingFaceFW/fineweb-edu-score-2","HuggingFaceFW","2024-05-28 17:30:16","2025-01-06 14:49:16+00:00","fc5b7dfa8dd381e16bcc153f81f5624d1bad1d9b","18054","155154","68","task_categories:text-generation, language:en, license:odc-by, size_categories:10B<n<100B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2404.14219, arxiv:2401.10020, arxiv:2109.07445, region:us","text-generation","📚 FineWeb-Edu-score-2 1.3 trillion tokens of the finest educational data the 🌐 web has to offer What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens (FineWeb-Edu) and 5.4T tokens of educational w","None","en","mono","n|>|1|T","None","False","False","False","odc-by","2404.14219","None","None"
+"deepmind/narrativeqa","deepmind","2022-03-02 23:29:22","2024-03-06 07:33:05+00:00","2e643e7363944af1c33a652d1c87320d0871c4e4","4770","151533","41","task_categories:text2text-generation, task_ids:abstractive-qa, annotations_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:original, language:en, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1712.07040, region:us","text2text-generation, abstractive-qa","Dataset Card for Narrative QA Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. S","None","en","mono","1|0|K|<|n|<|1|0|0|K","narrativeqa","False","False","False","apache-2.0","1712.07040","None","abstractive-qa"
+"ucirvine/sms_spam","ucirvine","2022-03-02 23:29:22","2024-08-08 06:04:08+00:00","cae486f927c250fe1d4a5b55f11357964ed1646c","3409","149072","33","None","text-classification, intent-classification","Dataset Card for [Dataset Name] Dataset Summary The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research. It has one collection compo","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","None","None","intent-classification"
+"ParlAI/blended_skill_talk","ParlAI","2022-03-02 23:29:22","2024-01-10 10:22:26+00:00","d7b0093243439fa5f0cd9663125cc47575ced2ea","1013","147645","69","None","conversational, dialogue-generation","Dataset Card for ""blended_skill_talk"" Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstratin","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","2004.08449v1","None","dialogue-generation"
+"OpenGVLab/MVBench","OpenGVLab","2023-11-28 12:03:30","2024-10-18 02:25:19+00:00","230a2d4fac8900333c61754641c7a13e069ac9c6","12436","147394","29","None","visual-question-answering, video-classification","MVBench Important Update [18/10/2024] Due to NTU RGB+D License, 320 videos from NTU RGB+D need to be downloaded manually. Please visit ROSE Lab to access the data. We also provide a list of the 320 vi","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2311.17005","None","None"
+"Abirate/english_quotes","Abirate","2022-03-02 23:29:22","2022-10-25 08:39:16+00:00","7b544c4920a8be268b48b403c188acf0a462051b","2240","147287","79","None","text-classification, multi-label-classification","Dataset Card for English quotes I-Dataset Summary english_quotes is a dataset of all the quotes retrieved from goodreads quotes. This dataset can be used for multi-label text classification and text g","None","en","mono","None","None","False","False","False","None","None","None","multi-label-classification"
+"mteb/arguana","mteb","2024-03-02 20:27:06","2024-03-03 11:57:44+00:00","c22ab2a51041ffd869aaddef7af8d8215647e41a","15915","147241","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"vicgalle/alpaca-gpt4","vicgalle","2023-04-07 16:22:59","2024-02-10 10:03:45+00:00","f7e3ded725cb81e8e564e32feb12860f376f2b51","6663","146856","257","None","text-generation, conversational, question-answering","Dataset Card for ""alpaca-gpt4"" This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. The dataset was originaly shared in this repository: ht","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","2304.03277","None","None"
+"DKYoon/SlimPajama-6B","DKYoon","2023-08-21 15:25:52","2023-08-21 16:54:47+00:00","b5f90f419b7489cdba26fdbc8c022fcb5562f968","9334","144012","42","None","text-generation","Sampled version of cerebras/SlimPajama-627B. Since the original data was shuffled before chunking, I only downloaded train/chunk1 (of 10 total) and further sampled 10%. This should result in roughly 6","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"mteb/tweet_sentiment_extraction","mteb","2022-05-26 18:07:50","2022-09-27 19:14:27+00:00","62146448f05be9e52a36b8ee9936447ea787eede","2528","141947","24","language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"deepmind/aqua_rat","deepmind","2022-03-02 23:29:22","2024-01-09 12:33:06+00:00","33301c6a050c96af81f63cad5562cb5363e88971","2098","140133","47","None","question-answering, multiple-choice-qa","Dataset Card for AQUA-RAT Dataset Summary A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural langu","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","multiple-choice-qa"
+"tinyBenchmarks/tinyMMLU","tinyBenchmarks","2024-02-22 11:33:08","2024-07-08 21:12:06+00:00","cd6a8ee9e1e6f3f18f683535f6c096c386a85da3","3171","137205","16","None","question-answering, multiple-choice-qa","tinyMMLU Welcome to tinyMMLU! This dataset serves as a concise version of the MMLU dataset, offering a subset of 100 data points selected from the original compilation. tinyMMLU is designed to enable ","None","en","mono","None","None","False","False","False","None","None","None","multiple-choice-qa"
+"webis/tldr-17","webis","2022-03-02 23:29:22","2023-06-05 12:48:30+00:00","fa7f50e62d35aff41aa165ddbe6c10dfa01ff49c","2211","135419","42","None","summarization","This corpus contains preprocessed posts from the Reddit dataset. The dataset consists of 3,848,330 posts with an average length of 270 words for content, and 28 words for the summary. Features include","@inproceedings{volske-etal-2017-tl, title = {TL DR: Mining {R}eddit to Learn Automatic Summarization}, author = {V{\\""o}lske, Michael and Potthast, Martin and Syed, Shahbaz and Stein, Benno}, booktitle = {Proceedings of the Workshop on New Frontiers in Summarization}, month = {sep}, year = {2017}, address = {Copenhagen, Denmark}, publisher = {Association for Computational Linguistics}, url = {https://www.aclweb.org/anthology/W17-4508}, doi = {10.18653/v1/W17-4508}, pages = {59--63}, abstract = {Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.}, }","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"nkp37/OpenVid-1M","nkp37","2024-06-11 15:02:08","2024-08-23 11:59:12+00:00","176e8f8737dab5617f2493c7298e4d5962d6eeb5","18337","135143","176","task_categories:text-to-video, language:en, license:cc-by-4.0, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, modality:video, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2407.02371, region:us, text-to-video, Video Generative Model Training, Text-to-Video Diffusion Model Training, prompts","text-to-video","Summary This is the dataset proposed in our paper ""OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation"". OpenVid-1M is a high-quality text-to-video dataset designed for researc","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"argilla/distilabel-capybara-dpo-7k-binarized","argilla","2024-01-26 08:36:14","2024-07-16 13:30:29+00:00","801d1307f5c886088678a2353dedfa7b3ce1cf94","2006","134625","180","None","question-answering, text-generation","Capybara-DPO 7K binarized A DPO dataset built with distilabel atop the awesome LDJnr/Capybara This is a preview version to collect feedback from the community. v2 will include the full base dataset an","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"HPLT/HPLT2.0_cleaned","HPLT","2024-10-19 12:29:38","2025-01-08 13:45:28+00:00","5fca0192f7e79bb2c20f0513a896d02bf3f568f3","89473","134552","11","None","fill-mask, text-generation, language-modeling","This is a large-scale collection of web-crawled documents in 191 world languages, produced by the HPLT project. The source of the data is mostly Internet Archive with some additions from Common Crawl.","None","ace, af, als, am, ar, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mr, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","n|>|1|T","None","False","False","False","cc0-1.0","None","None","language-modeling"
+"common-canvas/commoncatalog-cc-by","common-canvas","2024-04-22 18:07:35","2024-05-16 19:01:29+00:00","80f50fe4a1ca937f37a11be3f8eee5199d776ff3","8166","132149","26","task_categories:text-to-image, language:en, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2310.16825, region:us","text-to-image","Dataset Card for CommonCatalog CC-BY This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 ","None","en","mono","None","None","False","False","False","cc-by-4.0","2310.16825","None","None"
+"PrimeIntellect/fineweb-edu","PrimeIntellect","2024-10-07 19:03:08","2024-10-16 20:30:19+00:00","14efaa24d7dff8a745bf4918e415878546542346","31680","127682","2","language:en, license:odc-by, size_categories:1B<n<10B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Pre-shuffled fineweb-edu dataset","None","en","mono","None","None","False","False","False","odc-by","None","None","None"
+"HuggingFaceM4/OBELICS","HuggingFaceM4","2023-05-30 23:06:14","2023-08-22 20:50:09+00:00","08b7dca61ec09bacd68234ba6c388209b126d31c","12276","126055","146","language:en, license:cc-by-4.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.16527, region:us","None","Dataset Card for OBELICS OBELICS is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted fr","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc-by-4.0","2306.16527","None","None"
+"stanfordnlp/SHP","stanfordnlp","2023-02-18 05:00:06","2023-10-10 23:35:57+00:00","e94b5f32602712d78ed494fe79105b1959396686","1625","120879","296","None","text-generation, question-answering","🚢 Stanford Human Preferences Dataset (SHP) If you mention this dataset in a paper, please cite the paper: Understanding Dataset Difficulty with V-Usable Information (ICML 2022). Summary SHP is a datas","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","2001.08435","None","None"
+"HuggingFaceTB/cosmopedia","HuggingFaceTB","2024-02-18 20:23:48","2024-08-12 22:05:49+00:00","0ae6ec63f91742bd2d1eaef4f02232c55d719385","10477","119374","575","language:en, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.05463, arxiv:2306.11644, region:us, synthetic","None","Cosmopedia v0.1 Image generated by DALL-E, the prompt was generated by Mixtral-8x7B-Instruct-v0.1 Note: Cosmopedia v0.2 is available at smollm-corpus User: What do you think ""Cosmopedia"" could mean? H","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"EleutherAI/coqa","EleutherAI","2023-08-30 10:34:59","2025-01-10 23:51:22+00:00","82e11af842af6c1396f5e9a5c7de260107c50cf1","2708","118123","2","language:en, license:other, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1808.07042, region:us","None","""""""CoQA dataset. This CoQA adds the ""additional_answers"" feature that's missing in the original datasets version: https://github.com/huggingface/datasets/blob/master/datasets/coqa/coqa.py """""" _CITATIO","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"Helsinki-NLP/opus_openoffice","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:14:50+00:00","cba6a33cf9a39667ed21151b5dd767e6bb27a34c","2705","116898","7","None","translation","Dataset Card for [Dataset Name] Dataset Summary A collection of documents from http://www.openoffice.org/. 8 languages, 28 bitexts Supported Tasks and Leaderboards The underlying task is machine trans","None","de, en, es, fr, ja, ru, sv, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","None","None","None"
+"osunlp/TravelPlanner","osunlp","2024-01-11 12:10:14","2024-07-14 07:47:48+00:00","8736504ecfc31b7f8b7e40122873c337e83fff7c","1894","112876","48","None","text-generation, text2text-generation","TravelPlanner Dataset TravelPlanner is a benchmark crafted for evaluating language agents in tool-use and complex planning within multiple constraints. (See our paper for more details.) Introduction I","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"esdurmus/wiki_lingua","esdurmus","2022-03-02 23:29:22","2024-01-05 08:06:54+00:00","ea3db3510cbd34d0f8dc612419ae40e4732f3b40","1088","112664","41","None","summarization","Dataset Card for ""wiki_lingua"" Dataset Summary We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article an","None","ar, cs, de, en, es, fr, hi, id, it, ja, ko, nl, pt, ru, th, tr, vi, zh","multi","1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K","None","False","False","False","cc-by-3.0","2010.03093","None","None"
+"b-mc2/sql-create-context","b-mc2","2023-04-21 03:23:24","2024-01-25 22:01:25+00:00","9d80a6a118b838d9defc3798d659a54a2ac2ff37","2061","112338","432","None","text-generation, question-answering, table-question-answering","Overview This dataset builds from WikiSQL and Spider. There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statemen","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"mteb/biorxiv-clustering-s2s","mteb","2022-05-11 06:46:22","2024-05-23 08:09:51+00:00","eb4edb10386758d274cd161093eb351381a16dbf","2390","111878","2","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mteb/summeval","mteb","2022-06-21 13:37:10","2022-09-27 19:14:10+00:00","8753c2788d36c01fc6f05d03fe3f7268d63f9122","4400","110349","7","None","None","SummEval The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total). Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 ","None","en","mono","None","None","False","False","False","None","None","None","None"
+"TIGER-Lab/Mantis-Instruct","TIGER-Lab","2024-02-24 02:00:11","2024-12-25 01:43:11+00:00","01a9edfe0bb8c2582431308c5b2645a9f4796939","2482","110275","32","None","None","Mantis-Instruct Paper | Website | Github | Models | Demo Summaries Mantis-Instruct is a fully text-image interleaved multimodal instruction tuning dataset, containing 721K examples from 14 subsets and","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2405.01483","None","None"
+"ccdv/pubmed-summarization","ccdv","2022-03-02 23:29:22","2024-08-08 05:49:36+00:00","6b30a2cae59b11ed77cb19959bffccbbd18e1106","1636","109724","74","None","summarization, text-generation","PubMed dataset for summarization Dataset for summarization of long documents.Adapted from this repo.Note that original data are pre-tokenized so this dataset returns "" "".join(text) and add ""\\n"" for pa","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"joelniklaus/lextreme","joelniklaus","2022-08-01 08:41:55","2023-04-29 07:02:17+00:00","8de79b42002a6e7ab7e713787f4c427d122a269f","3151","109152","20","task_categories:text-classification, task_categories:token-classification, task_ids:multi-class-classification, task_ids:multi-label-classification, task_ids:topic-classification, task_ids:named-entity-recognition, annotations_creators:other, language_creators:found, multilinguality:multilingual, source_datasets:extended, language:bg, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:fi, language:fr, language:ga, language:hr, language:hu, language:it, language:lt, language:lv, language:mt, language:nl, language:pl, language:pt, language:ro, language:sk, language:sl, language:sv, license:cc-by-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2301.13126, region:us","text-classification, token-classification, multi-class-classification, multi-label-classification, topic-classification, named-entity-recognition","The LEXTREME Benchmark is a collection of multilingual datasets for evaluating model performance across a diverse set of legal NLU tasks.","@misc{niklaus2023lextreme, title={LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain}, author={Joel Niklaus and Veton Matoshi and Pooja Rani and Andrea Galassi and Matthias Stürmer and Ilias Chalkidis}, year={2023}, eprint={2301.13126}, archivePrefix={arXiv}, primaryClass={cs.CL} }","bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","multi-class-classification, multi-label-classification, topic-classification, named-entity-recognition"
+"mteb/scidocs-reranking","mteb","2022-04-19 12:15:26","2022-09-27 19:11:31+00:00","56a6d0140cf6356659e2a7c1413286a774468d44","4752","107928","0","language:en, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"google/code_x_glue_ct_code_to_text","google","2022-03-02 23:29:22","2024-01-24 15:09:09+00:00","2678080e78a4b20ad79d1f0a514f04d815912564","1133","107902","61","None","translation","Dataset Card for ""code_x_glue_ct_code_to_text"" Dataset Summary CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text The dataset we use c","None","code, en","mono","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K","None","False","False","False","c-uda","None","None","None"
+"xlangai/spider","xlangai","2022-03-02 23:29:22","2024-03-27 13:38:42+00:00","0c350918f3f29ec754f1181c65cdce76cd6c133c","1555","107413","129","task_categories:text2text-generation, annotations_creators:expert-generated, language_creators:expert-generated, language_creators:machine-generated, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1809.08887, region:us, text-to-sql","text2text-generation","Dataset Card for Spider Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students. The goal of the Spider challenge is to ","None","en","mono","1|K|<|n|<|1|0|K","spider-1","False","False","False","cc-by-sa-4.0","1809.08887","None","None"
+"common-canvas/commoncatalog-cc-by-nc-sa","common-canvas","2023-10-19 02:09:41","2024-05-16 19:45:25+00:00","2cca17ba84a4e4aae3542643d853d60195d44c6f","2904","106597","4","None","text-to-image","Dataset Card for CommonCatalog CC-BY-NC-SA This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in","None","en","mono","None","None","False","False","False","cc-by-nc-sa-4.0","2310.16825","None","None"
+"wmt/wmt18","wmt","2022-03-02 23:29:22","2024-04-03 18:42:09+00:00","ceae0f932f739f66498b7695b1807878f82d9b22","1328","106539","8","None","translation","Dataset Card for ""wmt18"" Dataset Summary Warning: There are issues with the Common Crawl corpus data (training-parallel-commoncrawl.tgz): Non-English files contain many English sentences. Their ""paral","None","cs, de, en, et, fi, kk, ru, tr, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","unknown","None","None","None"
+"wmt/wmt17","wmt","2022-03-02 23:29:22","2024-04-03 14:25:40+00:00","54d3aacfb5429020b9b85b170a677e4bc92f2449","2605","105493","7","task_categories:translation, annotations_creators:no-annotation, language_creators:found, multilinguality:translation, source_datasets:extended|europarl_bilingual, source_datasets:extended|news_commentary, source_datasets:extended|setimes, source_datasets:extended|un_multi, language:cs, language:de, language:en, language:fi, language:lv, language:ru, language:tr, language:zh, license:unknown, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for ""wmt17"" Dataset Summary Warning: There are issues with the Common Crawl corpus data (training-parallel-commoncrawl.tgz): Non-English files contain many English sentences. Their ""paral","None","cs, de, en, fi, lv, ru, tr, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","unknown","None","None","None"
+"AI4Math/MathVista","AI4Math","2023-10-15 17:49:10","2024-02-11 23:09:05+00:00","2b6ad69445fbb5695c9b165475e8decdbeb97747","4364","104955","122","None","multiple-choice, question-answering, visual-question-answering, text-classification, multiple-choice-qa, closed-domain-qa, open-domain-qa, visual-question-answering, multi-class-classification","Dataset Card for MathVista Dataset Description Paper Information Dataset Examples Leaderboard Dataset Usage Data Downloading Data Format Data Visualization Data Source Automatic Evaluation License Cit","None","en, zh, fa","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2310.02255","None","multiple-choice-qa, closed-domain-qa, open-domain-qa, visual-question-answering, multi-class-classification"
+"mteb/fiqa","mteb","2024-03-02 20:35:06","2024-03-03 11:41:05+00:00","27a168819829fe9bcd655c2df245fb19452e8e06","1671","104313","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"stingning/ultrachat","stingning","2023-04-20 15:15:28","2024-02-22 02:26:29+00:00","f220fe796ce3ed62fbe1681b45ce6cbc9c6cabe0","1301","103350","429","None","conversational, text-generation","Dataset Card for Dataset Name Dataset Description An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, we do not ","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"mlfoundations/dclm-baseline-1.0-parquet","mlfoundations","2024-06-30 20:31:14","2024-07-19 17:35:58+00:00","817d6752765f6a41261085171dd546b104f60626","39782","102419","25","None","None","DCLM-baseline Note: this is an identical copy of https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0, where all the files have been mapped to a parquet format. DCLM-baseline is a 4T token ","None","en","mono","None","None","False","False","False","cc-by-4.0","2406.11794","None","None"
+"GBaker/MedQA-USMLE-4-options","GBaker","2023-01-24 19:08:56","2023-01-24 19:18:09+00:00","0fb93dd23a7339b6dcd27e241cb9b5eca62d4d18","1795","102117","50","None","None","Original dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams Citation information: @article{jin2020disease,","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"frgfm/imagenette","frgfm","2022-07-18 00:13:35","2022-12-11 22:26:06+00:00","a8a496123b40fa739da2acf9b0dae339d30c7bae","1634","101946","13","None","image-classification","Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).","@software{Howard_Imagenette_2019, title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet}, author={Jeremy Howard}, year={2019}, month={March}, publisher = {GitHub}, url = {https://github.com/fastai/imagenette} }","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"PKU-Alignment/BeaverTails","PKU-Alignment","2023-06-07 17:22:12","2023-10-17 11:47:53+00:00","8401fe609d288129cc684a9b3be6a93e41cfe678","3300","99053","45","None","text-classification","Dataset Card for BeaverTails BeaverTails is an AI safety-focused collection comprising a series of datasets. This repository includes human-labeled data consisting of question-answer (QA) pairs, each ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","2307.04657","None","None"
+"bilgeyucel/seven-wonders","bilgeyucel","2023-03-08 18:44:17","2023-03-09 14:25:43+00:00","fb6a760df211962001d69fda7f3b42568ca938f8","4938","96761","5","None","None","None","None","en","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"mteb/medrxiv-clustering-s2s","mteb","2022-05-11 06:56:34","2024-05-23 08:09:57+00:00","ec20c81676a749c0f06fb4a9397fc7e168521458","2616","96452","1","language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Gustavosta/Stable-Diffusion-Prompts","Gustavosta","2022-09-18 12:13:15","2022-09-18 22:38:59+00:00","d816d4a05cb89bde39dd99284c459801e1e7e69a","3696","95983","459","annotations_creators:no-annotation, language_creators:found, source_datasets:original, language:en, license:unknown, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Stable Diffusion Dataset This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: ""Lexica.art"". It was a little difficult to extract the data, since the","None","en","mono","None","None","False","False","False","unknown","None","None","None"
+"MU-NLPC/Calc-mawps","MU-NLPC","2023-09-08 21:19:20","2023-10-30 15:55:30+00:00","38c10053efeafd20ab6ff4e08c3ec17de26c19b7","2106","95421","1","None","text-generation","Dataset Card for Calc-MAWPS Summary The dataset is a collection of simple math word problems focused on arithmetics. It is derived from https://huggingface.co/datasets/omarxadel/MaWPS-ar. The main add","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2305.15017","None","None"
+"lmms-lab/LLaVA-OneVision-Data","lmms-lab","2024-07-25 15:25:28","2024-10-22 06:47:46+00:00","b3732cfc24e4b64d5080a0d55110e543b1f7db80","9415","93766","160","None","None","Dataset Card for LLaVA-OneVision [2024-09-01]: Uploaded VisualWebInstruct(filtered), it's used in OneVision Stage almost all subsets are uploaded with HF's required format and you can use the recommen","None","en, zh","bi","None","None","False","False","False","apache-2.0","2408.03326","None","None"
+"lmms-lab/LLaVA-Video-178K","lmms-lab","2024-08-27 07:09:50","2024-10-11 04:59:25+00:00","6d8c562dc26d70042a0d9704d1cae58c94b89098","7013","92475","106","task_categories:visual-question-answering, task_categories:video-text-to-text, language:en, size_categories:1M<n<10M, modality:text, modality:video, arxiv:2410.02713, region:us, video","visual-question-answering, video-text-to-text","Dataset Card for LLaVA-Video-178K Uses This dataset is used for the training of the LLaVA-Video model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT","None","en","mono","None","None","False","False","False","None","2410.02713","None","None"
+"davidstap/biblenlp-corpus-mmteb","davidstap","2024-04-26 07:10:56","2024-04-26 14:09:22+00:00","264a18480c529d9e922483839b4b9758e690b762","3604","92268","2","None","None","This dataset pre-computes all English-centric directions from bible-nlp/biblenlp-corpus, and as a result loading is significantly faster. Loading example: >>> from datasets import load_dataset >>> dat","None","aai, aak, aau, aaz, abt, abx, aby, acf, acr, acu, adz, aer, aey, agd, agg, agm, agn, agr, agt, agu, aia, aii, aka, ake, alp, alq, als, aly, ame, amf, amk, amm, amn, amo, amp, amr, amu, amx, anh, anv, aoi, aoj, aom, aon, apb, ape, apn, apr, apu, apw, apz, arb, are, arl, arn, arp, asm, aso, ata, atb, atd, atg, att, auc, aui, auy, avt, awb, awk, awx, azb, azg, azz, bao, bba, bbb, bbr, bch, bco, bdd, bea, bef, bel, ben, beo, beu, bgs, bgt, bhg, bhl, big, bjk, bjp, bjr, bjv, bjz, bkd, bki, bkq, bkx, bla, blw, blz, bmh, bmk, bmr, bmu, bnp, boa, boj, bon, box, bpr, bps, bqc, bqp, bre, bsj, bsn, bsp, bss, buk, bus, bvd, bvr, bxh, byr, byx, bzd, bzh, bzj, caa, cab, cac, caf, cak, cao, cap, car, cav, cax, cbc, cbi, cbk, cbr, cbs, cbt, cbu, cbv, cco, ceb, cek, ces, cgc, cha, chd, chf, chk, chq, chz, cjo, cjv, ckb, cle, clu, cme, cmn, cni, cnl, cnt, cof, con, cop, cot, cpa, cpb, cpc, cpu, cpy, crn, crx, cso, csy, cta, cth, ctp, ctu, cub, cuc, cui, cuk, cut, cux, cwe, cya, daa, dad, dah, dan, ded, deu, dgc, dgr, dgz, dhg, dif, dik, dji, djk, djr, dob, dop, dov, dwr, dww, dwy, ebk, eko, emi, emp, eng, enq, epo, eri, ese, esk, etr, ewe, faa, fai, far, ffm, for, fra, fue, fuf, fuh, gah, gai, gam, gaw, gdn, gdr, geb, gfk, ghs, glk, gmv, gng, gnn, gnw, gof, grc, gub, guh, gui, guj, gul, gum, gun, guo, gup, gux, gvc, gvf, gvn, gvs, gwi, gym, gyr, hat, hau, haw, hbo, hch, heb, heg, hin, hix, hla, hlt, hmo, hns, hop, hot, hrv, hto, hub, hui, hun, hus, huu, huv, hvn, ian, ign, ikk, ikw, ilo, imo, inb, ind, ino, iou, ipi, isn, ita, iws, ixl, jac, jae, jao, jic, jid, jiv, jni, jpn, jvn, kan, kaq, kbc, kbh, kbm, kbq, kdc, kde, kdl, kek, ken, kew, kgf, kgk, kgp, khs, khz, kik, kiw, kiz, kje, kjn, kjs, kkc, kkl, klt, klv, kmg, kmh, kmk, kmo, kms, kmu, kne, knf, knj, knv, kos, kpf, kpg, kpj, kpr, kpw, kpx, kqa, kqc, kqf, kql, kqw, ksd, ksj, ksr, ktm, kto, kud, kue, kup, kvg, kvn, kwd, kwf, kwi, kwj, kyc, kyf, kyg, kyq, kyz, kze, lac, lat, lbb, lbk, lcm, leu, lex, lgl, lid, lif, lin, lit, llg, lug, luo, lww, maa, maj, mal, mam, maq, mar, mau, mav, maz, mbb, mbc, mbh, mbj, mbl, mbs, mbt, mca, mcb, mcd, mcf, mco, mcp, mcq, mcr, mdy, med, mee, mek, meq, met, meu, mgc, mgh, mgw, mhl, mib, mic, mie, mig, mih, mil, mio, mir, mit, miz, mjc, mkj, mkl, mkn, mks, mle, mlh, mlp, mmo, mmx, mna, mop, mox, mph, mpj, mpm, mpp, mps, mpt, mpx, mqb, mqj, msb, msc, msk, msm, msy, mti, mto, mux, muy, mva, mvn, mwc, mwe, mwf, mwp, mxb, mxp, mxq, mxt, mya, myk, myu, myw, myy, mzz, nab, naf, nak, nas, nay, nbq, nca, nch, ncj, ncl, ncu, ndg, ndj, nfa, ngp, ngu, nhe, nhg, nhi, nho, nhr, nhu, nhw, nhy, nif, nii, nin, nko, nld, nlg, nmw, nna, nnq, noa, nop, not, nou, npi, npl, nsn, nss, ntj, ntp, ntu, nuy, nvm, nwi, nya, nys, nyu, obo, okv, omw, ong, ons, ood, opm, ory, ote, otm, otn, otq, ots, pab, pad, pah, pan, pao, pes, pib, pio, pir, piu, pjt, pls, plu, pma, poe, poh, poi, pol, pon, por, poy, ppo, prf, pri, ptp, ptu, pwg, qub, quc, quf, quh, qul, qup, qvc, qve, qvh, qvm, qvn, qvs, qvw, qvz, qwh, qxh, qxn, qxo, rai, reg, rgu, rkb, rmc, rmy, ron, roo, rop, row, rro, ruf, rug, rus, rwo, sab, san, sbe, sbk, sbs, seh, sey, sgb, sgz, shj, shp, sim, sja, sll, smk, snc, snn, snp, snx, sny, som, soq, soy, spa, spl, spm, spp, sps, spy, sri, srm, srn, srp, srq, ssd, ssg, ssx, stp, sua, sue, sus, suz, swe, swh, swp, sxb, tac, taj, tam, tav, taw, tbc, tbf, tbg, tbl, tbo, tbz, tca, tcs, tcz, tdt, tee, tel, ter, tet, tew, tfr, tgk, tgl, tgo, tgp, tha, thd, tif, tim, tiw, tiy, tke, tku, tlf, tmd, tna, tnc, tnk, tnn, tnp, toc, tod, tof, toj, ton, too, top, tos, tpa, tpi, tpt, tpz, trc, tsw, ttc, tte, tuc, tue, tuf, tuo, tur, tvk, twi, txq, txu, tzj, tzo, ubr, ubu, udu, uig, ukr, uli, ulk, upv, ura, urb, urd, uri, urt, urw, usa, usp, uvh, uvl, vid, vie, viv, vmy, waj, wal, wap, wat, wbi, wbp, wed, wer, wim, wiu, wiv, wmt, wmw, wnc, wnu, wol, wos, wrk, wro, wrs, wsk, wuv, xav, xbi, xed, xla, xnn, xon, xsi, xtd, xtm, yaa, yad, yal, yap, yaq, yby, ycn, yka, yle, yml, yon, yor, yrb, yre, yss, yuj, yut, yuw, yva, zaa, zab, zac, zad, zai, zaj, zam, zao, zap, zar, zas, zat, zav, zaw, zca, zga, zia, ziw, zlm, zos, zpc, zpl, zpm, zpo, zpq, zpu, zpv, zpz, zsr, ztq, zty, zyp, be, br, cs, ch, zh, de, en, eo, fr, ht, he, hr, id, it, ja, la, nl, ru, sa, so, es, sr, sv, to, uk, vi","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"Helsinki-NLP/multiun","Helsinki-NLP","2022-03-02 23:29:22","2024-02-27 16:59:52+00:00","489448d66a808cfc421b9f82affc1efbef9d5299","2604","91255","9","None","translation","Dataset Card for OPUS MultiUN Dataset Summary The MultiUN parallel corpus is extracted from the United Nations Website , and then cleaned and converted to XML at Language Technology Lab in DFKI GmbH (","None","ar, de, en, es, fr, ru, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","None","None","None"
+"allenai/tulu-v2-sft-mixture","allenai","2023-11-13 21:56:34","2024-05-24 21:29:24+00:00","6248b175d2ccb5ec7c4aeb22e6d8ee3b21b2c752","1221","88218","121","task_categories:question-answering, task_categories:text-generation, language:en, license:odc-by, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.18290, region:us","question-answering, conversational, text-generation","Dataset Card for Tulu V2 Mix Note the ODC-BY license, indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the m","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","odc-by","2305.18290","None","None"
+"common-canvas/commoncatalog-cc-by-nc-nd","common-canvas","2023-10-19 02:10:48","2024-05-16 19:46:41+00:00","ab32f26621adfbc8f9b8011119129028d23a91a5","5166","88159","2","task_categories:text-to-image, language:en, license:cc-by-nc-nd-4.0, size_categories:10M<n<100M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2310.16825, region:us","text-to-image","Dataset Card for CommonCatalog CC-BY-NC-ND This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in","None","en","mono","None","None","False","False","False","cc-by-nc-nd-4.0","2310.16825","None","None"
+"google/civil_comments","google","2022-03-02 23:29:22","2024-01-25 08:23:15+00:00","f2970eb3a55777454c94069077cc8d9b5866312d","945","88140","16","None","text-classification, multi-label-classification","Dataset Card for ""civil_comments"" Dataset Summary The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comment","None","en","mono","None","None","False","False","False","cc0-1.0","1903.04561","None","multi-label-classification"
+"ccdv/arxiv-summarization","ccdv","2022-03-02 23:29:22","2024-08-08 05:49:50+00:00","240aaf1a969b3f8cd0ade6986bfad0cd730ee288","2082","88078","81","task_categories:summarization, task_categories:text-generation, multilinguality:monolingual, language:en, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, conditional-text-generation","summarization, text-generation","Arxiv dataset for summarization Dataset for summarization of long documents.Adapted from this repo.Note that original data are pre-tokenized so this dataset returns "" "".join(text) and add ""\\n"" for par","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"mteb/toxic_conversations_50k","mteb","2022-05-26 17:47:49","2022-09-27 19:14:35+00:00","edfaf9da55d3dd50d43143d90c1ac476895ae6de","1571","86686","11","language:en, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Toxic Conversation This is a version of the Jigsaw Unintended Bias in Toxicity Classification dataset. It contains comments from the Civil Comments platform together with annotations if the comment is","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Helsinki-NLP/un_pc","Helsinki-NLP","2022-03-02 23:29:22","2024-04-03 07:35:04+00:00","99a0348ce711ec6b30d5f18d70c0d30c971011c7","6001","86547","17","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:ar, language:en, language:es, language:fr, language:ru, language:zh, license:other, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for United Nations Parallel Corpus Dataset Summary The United Nations Parallel Corpus is the first parallel corpus composed from United Nations documents published by the original data cr","None","ar, en, es, fr, ru, zh","multi","1|0|M|<|n|<|1|0|0|M","united-nations-parallel-corpus","False","False","False","other","None","None","None"
+"castorini/mr-tydi","castorini","2022-03-02 23:29:22","2022-10-12 20:25:19+00:00","1d43c80218d06d0ef80f5b172ccabd848b948bc1","970","86336","19","None","text-retrieval","Dataset Summary Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking","None","ar, bn, en, fi, id, fi, ja, ko, ru, sw, te, th","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"deepmind/code_contests","deepmind","2022-07-19 16:02:55","2023-06-11 12:22:30+00:00","802411c3010cb00d1b05bad57ca77365a3c699d6","7801","85897","134","None","translation","Dataset Card for CodeContests Dataset Summary CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode. It consists of programming problems","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2203.07814v1","None","None"
+"TIGER-Lab/MathInstruct","TIGER-Lab","2023-09-11 14:21:02","2024-05-15 00:06:46+00:00","b4fdc323a7be1379c9c7c0b67b1de72dfee2111a","4381","85646","265","None","text-generation","🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is co","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"HuggingFaceM4/Docmatix","HuggingFaceM4","2024-07-17 11:33:00","2024-08-26 08:15:21+00:00","0725b65616e0e5f6024be10e38ddf8d8c48664fd","13386","85253","240","task_categories:visual-question-answering, language:en, license:mit, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2408.12637, region:us, docvqa","visual-question-answering","Dataset Card for Docmatix Dataset description Docmatix is part of the Idefics3 release (stay tuned). It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"wmt/wmt15","wmt","2022-03-02 23:29:22","2024-04-03 12:29:48+00:00","9c1ec82d252027f9f43f98f23e2feccaddcb395c","2201","84902","3","None","translation","Dataset Card for ""wmt15"" Dataset Summary Warning: There are issues with the Common Crawl corpus data (training-parallel-commoncrawl.tgz): Non-English files contain many English sentences. Their ""paral","None","cs, de, en, fi, fr, ru","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","unknown","None","None","None"
+"L4NLP/LEval","L4NLP","2023-06-14 11:51:39","2023-10-11 03:56:48+00:00","43b9dbf06c239360b183c76cb5d90ebe4f5e3070","1898","83657","18","None","summarization, question-answering, multiple-choice","A benchmark to evaluate long document understanding and generation ability of LLM","}","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","gpl-3.0","None","None","None"
+"PleIAs/common_corpus","PleIAs","2024-11-12 13:44:24","2024-11-22 13:41:35+00:00","afc882408a4a15396965254b221bc9dfd7c35434","8025","82126","198","task_categories:text-generation, language:en, language:fr, language:de, language:it, language:pt, language:nl, language:es, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2410.22587, region:us, legal, finance, literature, science, code","text-generation","Common Corpus Common Corpus is the largest open and permissible licensed text dataset, comprising over 2 trillion tokens (2,003,039,184,047 tokens). It is a diverse dataset, consisting of books, newsp","None","en, fr, de, it, pt, nl, es","multi","n|>|1|T","None","False","False","False","None","None","None","None"
+"mteb/stackoverflowdupquestions-reranking","mteb","2022-04-19 12:24:06","2022-09-27 19:13:01+00:00","ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9","2364","81633","3","language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Helsinki-NLP/europarl","Helsinki-NLP","2022-03-02 23:29:22","2024-02-27 13:58:07+00:00","ab45e286aef3fb5780067100cb5a1132b52b7949","5263","81256","22","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:translation, source_datasets:original, language:bg, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:fi, language:fr, language:hu, language:it, language:lt, language:lv, language:nl, language:pl, language:pt, language:ro, language:sk, language:sl, language:sv, license:unknown, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for OPUS Europarl (European Parliament Proceedings Parallel Corpus) Dataset Summary A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinb","None","bg, cs, da, de, el, en, es, et, fi, fr, hu, it, lt, lv, nl, pl, pt, ro, sk, sl, sv","multi","1|0|0|K|<|n|<|1|M","europarl","False","False","False","unknown","None","None","None"
+"hails/agieval-logiqa-en","hails","2024-01-10 15:42:56","2024-01-26 18:40:53+00:00","e01aa0f040456cd8d58ee9986a58b64e26c1b782","2517","81135","0","None","None","Dataset Card for ""agieval-logiqa-en"" Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub. This dataset contains ","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mteb/stackexchange-clustering","mteb","2022-04-07 13:42:09","2022-09-27 19:11:56+00:00","70a89468f6dccacc6aa2b12a6eac54e74328f235","1223","81108","0","language:en, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"EleutherAI/race","EleutherAI","2023-07-03 13:20:38","2023-07-03 21:27:18+00:00","e34d68e9b94506fd4ff5c9da0764de77f2ad3d5c","9597","80880","5","None","multiple-choice, multiple-choice-qa","Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for mid","@article{lai2017large, title={RACE: Large-scale ReAding Comprehension Dataset From Examinations}, author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard}, journal={arXiv preprint arXiv:1704.04683}, year={2017} }","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","1704.04683","None","multiple-choice-qa"
+"math-ai/AutoMathText","math-ai","2024-01-24 01:39:26","2024-10-30 21:19:01+00:00","e894d840ac3c86d4d0645d2a0e43895c0d1e73b6","24994","80026","163","task_categories:text-generation, task_categories:question-answering, language:en, license:cc-by-sa-4.0, size_categories:1M<n<10M, modality:text, arxiv:2402.07625, region:us, mathematical-reasoning, reasoning, finetuning, pretraining, llm","text-generation, question-answering","AutoMathText AutoMathText is an extensive and carefully curated dataset encompassing around 200 GB of mathematical texts. It's a compilation sourced from a diverse range of platforms including various","None","en","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-sa-4.0","2402.07625","None","None"
+"allenai/real-toxicity-prompts","allenai","2022-08-17 20:30:46","2022-09-30 14:23:19+00:00","f21629712ffd6a3d13a54fd2807ccd521c55ef74","1926","78843","66","None","image-generation, text-generation","Dataset Card for Real Toxicity Prompts Dataset Summary RealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2009.11462","None","text-generation"
+"BAAI/IndustryCorpus2","BAAI","2024-09-15 00:12:49","2024-12-17 02:14:57+00:00","1721eecd696e4110d33a255440f3c7ce981140ee","3268","78747","46","None","None","Industry models play a vital role in promoting the intelligent transformation and innovative development of enterprises. High-quality industry data is the key to improving the performance of large mod","None","en, zh","bi","n|>|1|T","None","False","auto","False","apache-2.0","None","None","None"
+"mlfoundations/MINT-1T-PDF-CC-2023-50","mlfoundations","2024-07-12 05:42:22","2024-09-19 21:06:23+00:00","b9a0d67f6048cf79615e63fa44d7ac729958fc71","2468","76558","3","None","image-to-text, text-generation","🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billi","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2406.11271","None","None"
+"mteb/reddit-clustering","mteb","2022-04-07 09:12:22","2022-09-27 19:13:31+00:00","b2805658ae38990172679479369a78b86de8c390","1172","75128","0","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mteb/imdb","mteb","2022-05-26 08:50:50","2022-09-27 19:14:44+00:00","8d743909f834c38949e8323a8a6ce8721ea6c7f4","14109","74014","1","language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"KBlueLeaf/danbooru2023-webp-4Mpixel","KBlueLeaf","2024-01-25 04:18:45","2024-07-18 10:41:35+00:00","853de4e8a8910a4e2b628acf55efac4d8fd30ca9","3019","72429","66","None","image-classification, zero-shot-image-classification, text-to-image","Danbooru 2023 webp: A space-efficient version of Danbooru 2023 This dataset is a resized/re-encoded version of danbooru2023. Which removed the non-image/truncated files and resize all of them into sma","None","en","mono","1|M|<|n|<|1|0|M","None","False","auto","False","mit","None","None","None"
+"bigscience/xP3mt","bigscience","2022-09-28 12:36:00","2023-05-30 15:50:57+00:00","8a5e23f6ffbd1b55efaf0ffe6322f985fe859bf2","16773","72290","24","task_categories:other, annotations_creators:expert-generated, annotations_creators:crowdsourced, multilinguality:multilingual, language:ak, language:ar, language:as, language:bm, language:bn, language:ca, language:code, language:en, language:es, language:eu, language:fon, language:fr, language:gu, language:hi, language:id, language:ig, language:ki, language:kn, language:lg, language:ln, language:ml, language:mr, language:ne, language:nso, language:ny, language:or, language:pa, language:pt, language:rn, language:rw, language:sn, language:st, language:sw, language:ta, language:te, language:tn, language:ts, language:tum, language:tw, language:ur, language:vi, language:wo, language:xh, language:yo, language:zh, language:zu, license:apache-2.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2211.01786, region:us","other","xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capab","@misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} }","ak, ar, as, bm, bn, ca, code, en, es, eu, fon, fr, gu, hi, id, ig, ki, kn, lg, ln, ml, mr, ne, nso, ny, or, pa, pt, rn, rw, sn, st, sw, ta, te, tn, ts, tum, tw, ur, vi, wo, xh, yo, zh, zu","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","2211.01786","None","None"
+"bigscience/xP3all","bigscience","2022-07-30 21:05:02","2023-05-30 15:51:40+00:00","d2bde405fafdd53aa4f92ddf03b14a7e7533d660","11155","72071","28","task_categories:other, annotations_creators:expert-generated, annotations_creators:crowdsourced, multilinguality:multilingual, language:ak, language:ar, language:as, language:bm, language:bn, language:ca, language:code, language:en, language:es, language:eu, language:fon, language:fr, language:gu, language:hi, language:id, language:ig, language:ki, language:kn, language:lg, language:ln, language:ml, language:mr, language:ne, language:nso, language:ny, language:or, language:pa, language:pt, language:rn, language:rw, language:sn, language:st, language:sw, language:ta, language:te, language:tn, language:ts, language:tum, language:tw, language:ur, language:vi, language:wo, language:xh, language:yo, language:zh, language:zu, license:apache-2.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2211.01786, region:us","other","xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capab","@misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} }","ak, ar, as, bm, bn, ca, code, en, es, eu, fon, fr, gu, hi, id, ig, ki, kn, lg, ln, ml, mr, ne, nso, ny, or, pa, pt, rn, rw, sn, st, sw, ta, te, tn, ts, tum, tw, ur, vi, wo, xh, yo, zh, zu","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","2211.01786","None","None"
+"argilla/ultrafeedback-binarized-preferences-cleaned","argilla","2023-12-05 11:07:34","2023-12-11 14:22:19+00:00","770076f077c4c5e298498fa32f804857f46d5134","5039","71393","130","None","text-generation","UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned) This dataset represents a new iteration on top of argilla/ultrafeedback-binarized-preferences, and is the recommended and pr","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"OpenGVLab/OmniCorpus-CC","OpenGVLab","2024-08-30 06:16:02","2024-11-17 07:08:46+00:00","73f1ffb4c81801186363a98b4caf29d792941797","34803","70740","12","task_categories:image-to-text, task_categories:visual-question-answering, language:en, license:cc-by-4.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08418, region:us","image-to-text, visual-question-answering","⭐️ NOTE: Several parquet files were marked unsafe (viruses) by official scaning of hf, while they are reported safe by ClamAV and Virustotal. We found many false positive cases of the hf automatic sca","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc-by-4.0","2406.08418","None","None"
+"argilla/OpenHermesPreferences","argilla","2024-02-22 22:45:57","2024-03-01 08:39:41+00:00","ad1335fc6a3f4afb2edffa1ce889983448cac34d","1212","70462","203","task_categories:text-generation, language:en, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, library:distilabel, arxiv:2305.18290, arxiv:2402.12366, region:us, synthetic, rlaif, dpo, distilabel","text-generation","OpenHermesPreferences v0.1 🧙 Using LLMs to improve other LLMs, at scale! OpenHermesPreferences is a dataset of ~1 million AI preferences derived from teknium/OpenHermes-2.5. It combines responses from","None","en","mono","None","None","False","False","False","other","None","None","None"
+"Meranti/CLAP_freesound","Meranti","2023-06-02 00:42:03","2023-07-09 17:09:18+00:00","9d4fe96213f03fb04e7ed70ac737b07c52bb9979","5615","68044","25","task_categories:audio-classification, language:en, size_categories:1M<n<10M, modality:audio, modality:text, region:us, audio, text, contrastive learning","audio-classification","LAION-Audio-630K Freesound Dataset LAION-Audio-630K is the largest audio-text dataset publicly available and a magnitude larger than previous audio-text datasets (by 2022-11-05). Notably, it combines ","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"airtrain-ai/fineweb-edu-fortified","airtrain-ai","2024-07-22 14:22:31","2024-08-08 18:04:44+00:00","d906fc5fc18bf4d3bd73f61cd8aee6788a295852","12524","66676","54","task_categories:text-generation, language:en, license:odc-by, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.17557, arxiv:2109.07445, region:us","text-generation","Fineweb-Edu-Fortified The composition of fineweb-edu-fortified, produced by automatically clustering a 500k row sample in Airtrain What is it? Fineweb-Edu-Fortified is a dataset derived from Fineweb-E","None","en","mono","None","None","False","False","False","odc-by","2406.17557","None","None"
+"mteb/stackexchange-clustering-p2p","mteb","2022-05-11 07:56:54","2022-09-27 19:14:52+00:00","d88009ab563dd0b16cfaf4436abaf97fa3550cf0","1056","65980","0","language:en, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"MathLLMs/MathVision","MathLLMs","2024-02-22 19:14:42","2024-12-28 07:50:30+00:00","57f012f5143a1fd9605fb39d33fd94c20656d0a4","7622","63719","40","task_categories:question-answering, task_categories:multiple-choice, task_categories:visual-question-answering, task_categories:text-generation, annotations_creators:expert-generated, annotations_creators:found, language_creators:expert-generated, language_creators:found, language:en, license:mit, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.14804, region:us, mathematics, reasoning, multi-modal-qa, math-qa, figure-qa, geometry-qa, math-word-problem, textbook-qa, vqa, geometry-diagram, synthetic-scene, chart, plot, scientific-figure, table, function-plot, abstract-scene, puzzle-test, document-image, science","question-answering, multiple-choice, visual-question-answering, text-generation","Measuring Multimodal Mathematical Reasoning with the MATH-Vision Dataset [💻 Github] [🌐 Homepage] [📊 Leaderboard ] [🔍 Visualization] [📖 ArXiv Paper] 🚀 Data Usage from datasets import load_dataset datas","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2402.14804","None","None"
+"Trelis/tiny-shakespeare","Trelis","2023-09-06 16:16:36","2023-09-06 16:27:30+00:00","2225a5674b252d623cfb151ef099d179629c2f49","3895","63063","9","None","text-generation","Data source Downloaded via Andrej Karpathy's nanogpt repo from this link Data Format The entire dataset is split into train (90%) and test (10%). All rows are at most 1024 tokens, using the Llama 2 to","None","en","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"HuggingFaceM4/WebSight","HuggingFaceM4","2024-01-04 10:08:47","2024-03-26 15:37:29+00:00","b11f8172f89c992b56ac702319e02c428cca4a4e","10403","63033","341","language:en, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.09029, region:us, code, synthetic","None","Dataset Card for WebSight Dataset Description WebSight is a large synthetic dataset containing HTML/CSS codes representing synthetically generated English websites, each accompanied by a corresponding","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","2403.09029","None","None"
+"common-canvas/commoncatalog-cc-by-sa","common-canvas","2023-10-19 02:05:17","2024-05-16 19:41:37+00:00","23725105ef17babef2fdc7c3dfda07504fb3892e","2587","62506","8","None","text-to-image","Dataset Card for CommonCatalog CC-BY-SA This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 20","None","en","mono","None","None","False","False","False","cc-by-sa-4.0","2310.16825","None","None"
+"openbmb/UltraInteract_sft","openbmb","2024-04-02 15:45:18","2024-04-05 14:29:52+00:00","2b102e45f52d1f28b69db810e0d2f48db456272e","35105","61499","120","None","None","Introduction 📜 Paper 🤗 Eurus Collection 🤗 UltraInteract SFT Preference Learning GitHub Repo UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning t","None","en","mono","None","None","False","False","False","mit","2404.02078","None","None"
+"derek-thomas/ScienceQA","derek-thomas","2023-02-10 11:28:58","2023-02-25 04:23:01+00:00","f18b0a70359ebfb41f658fd564208d0355b013f4","2516","61041","159","None","multiple-choice, question-answering, other, visual-question-answering, text-classification, multiple-choice-qa, closed-domain-qa, open-domain-qa, visual-question-answering, multi-class-classification","Dataset Card Creation Guide Dataset Summary Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering Supported Tasks and Leaderboards Multi-modal Multiple Choice Langua","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2209.09513","None","multiple-choice-qa, closed-domain-qa, open-domain-qa, visual-question-answering, multi-class-classification"
+"mlfoundations/MINT-1T-PDF-CC-2023-23","mlfoundations","2024-07-12 05:43:59","2024-09-19 21:07:25+00:00","d2475fc14efd472e8c1cd10d1c0147b5fc52a2bf","1979","59848","1","task_categories:image-to-text, task_categories:text-generation, language:en, license:cc-by-4.0, size_categories:1M<n<10M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, arxiv:2406.11271, region:us, multimodal","image-to-text, text-generation","🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billi","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2406.11271","None","None"
+"ShareGPT4Video/ShareGPT4Video","ShareGPT4Video","2024-05-22 11:59:11","2024-07-08 05:57:32+00:00","9545b6e2492d0df930e107601ddd86ab1680e55b","3307","59346","189","None","visual-question-answering, question-answering","ShareGPT4Video 4.8M Dataset Card Dataset details Dataset type: ShareGPT4Video Captions 4.8M is a set of GPT4-Vision-powered multi-modal captions data of videos. It is constructed to enhance modality a","None","en","mono","1|M|<|n","None","False","False","False","cc-by-nc-4.0","2406.04325v1","None","None"
+"sahil2801/CodeAlpaca-20k","sahil2801","2023-03-26 22:09:47","2023-10-03 11:46:04+00:00","152bb5e9a29651266b018106053980070a0521a1","1347","59004","187","None","text-generation","None","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"common-canvas/commoncatalog-cc-by-nc","common-canvas","2023-10-19 02:08:22","2024-05-16 19:44:00+00:00","f4d240a3accb06baafa9a9578e590978198c3712","3576","58955","6","None","text-to-image","Dataset Card for CommonCatalog CC-BY-NC This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 20","None","en","mono","None","None","False","False","False","cc-by-nc-4.0","2310.16825","None","None"
+"cyberagent/crello","cyberagent","2023-02-03 01:31:45","2024-11-04 23:54:35+00:00","e886486b8369b155adb359b999d40d7a92048955","1970","58593","32","None","unconditional-image-generation","Dataset Card for Crello Dataset Summary The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cdla-permissive-2.0","2108.01249","None","None"
+"medalpaca/medical_meadow_medical_flashcards","medalpaca","2023-04-06 17:09:17","2023-04-06 17:12:17+00:00","7597b32036d67c731cb91bae4f49717fcfe5d5f0","1616","58465","33","None","question-answering","Dataset Card for Medical Flashcards Dataset Summary Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master in order to practice effectively. This incl","None","en","mono","None","None","False","False","False","cc","None","None","None"
+"mteb/nq","mteb","2024-03-02 20:30:57","2024-03-03 11:25:04+00:00","b774495ed302d8c44a3a7ea25c90dbce03968f31","1712","58437","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"HuggingFaceH4/no_robots","HuggingFaceH4","2023-11-10 12:23:22","2024-04-18 08:40:39+00:00","e6f9a4ac5c37faeb744ba9ecf0473184d7f8105b","1196","57620","464","None","text-generation","Dataset Card for No Robots 🙅‍♂️🤖 Look Ma, an instruction dataset that wasn't generated by GPTs! Dataset Summary No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by","None","en","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"Helsinki-NLP/un_ga","Helsinki-NLP","2022-03-02 23:29:22","2024-04-02 13:20:41+00:00","6c053d4b74d3a504133526d178b2530a69ec178f","2496","57236","1","None","translation","Deprecated: Dataset ""un_ga"" is deprecated due to the the unavailability of its source data. It has been superseded by the official United Nations Parallel Corpus, which is recommended for use in its p","None","ar, en, es, fr, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","None","None","None"
+"allenai/dolmino-mix-1124","allenai","2024-11-23 03:52:26","2024-12-17 23:01:58+00:00","d9a228da2db18ea90030c9b2698bedfa5ee315f4","30988","56619","30","None","text-generation","DOLMino dataset mix for OLMo2 stage 2 annealing training. Mixture of high-quality data used for the second stage of OLMo2 training. Source Sizes Name Category Tokens Bytes (uncompressed) Documents Lic","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","odc-by","None","None","None"
+"zeroshot/twitter-financial-news-sentiment","zeroshot","2022-09-01 21:21:56","2024-02-23 19:04:10+00:00","ccbe24de388e287beb92dd393a335c376b350ac3","1759","56069","128","task_categories:text-classification, task_ids:multi-class-classification, annotations_creators:other, language_creators:other, multilinguality:monolingual, source_datasets:original, language:en, license:mit, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, twitter, finance, markets, stocks, wallstreet, quant, hedgefunds","text-classification, multi-class-classification","Dataset Description The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","multi-class-classification"
+"sentence-transformers/stsb","sentence-transformers","2024-04-25 15:17:00","2024-04-25 19:14:38+00:00","ab7a5ac0e35aa22088bdcf23e7fd99b220e53308","2479","55614","11","None","feature-extraction, sentence-similarity","Dataset Card for STSB The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"mteb/medrxiv-clustering-p2p","mteb","2022-05-11 06:56:44","2024-05-23 08:10:00+00:00","9894e30672c61db02f10a8593519d84e2b7a1a1c","1153","55313","2","language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"allenai/qasper","allenai","2022-03-02 23:29:22","2022-10-07 22:04:11+00:00","fdc9d8214fbab5dd782958601db4d678e6934a54","1086","55247","71","None","question-answering, closed-domain-qa","A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.","@inproceedings{Dasigi2021ADO, title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers}, author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner}, year={2021} }","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2105.03011","None","closed-domain-qa"
+"lmsys/toxic-chat","lmsys","2023-10-26 13:52:48","2024-05-14 08:07:42+00:00","29df8e4dba60e1f4af4b4075c0705c5b313548a8","2210","54854","148","None","text-classification","Update [01/31/2024] We update the OpenAI Moderation API results for ToxicChat (0124) based on their updated moderation model on on Jan 25, 2024.[01/28/2024] We release an official T5-Large model train","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","2310.17389","None","None"
+"Amod/mental_health_counseling_conversations","Amod","2023-06-22 12:52:50","2024-04-05 08:30:03+00:00","4672e03c7f1a7b2215eb4302b83ca50449ce2553","3434","54684","304","None","text-generation, question-answering","Amod/mental_health_counseling_conversations Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide r","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","openrail","None","None","None"
+"ai4bharat/sangraha","ai4bharat","2024-03-05 10:55:09","2024-10-21 09:33:54+00:00","016955bcbba051ef11d8f310f1a32fdc600e2297","5813","53946","37","task_categories:text-generation, language:as, language:bn, language:gu, language:en, language:hi, language:kn, language:ks, language:ml, language:mr, language:ne, language:or, language:pa, language:sa, language:sd, language:ta, language:te, language:ur, license:cc-by-4.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.06350, region:us, language-modeling, casual-lm, llm","text-generation","Sangraha Sangraha is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora","None","as, bn, gu, en, hi, kn, ks, ml, mr, ne, or, pa, sa, sd, ta, te, ur","multi","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2403.06350","None","None"
+"mteb/biblenlp-corpus-mmteb","mteb","2024-05-05 22:41:26","2024-05-07 00:47:48+00:00","3912ed967b0834547f35b2da9470c4976b357c9a","5062","53918","1","annotations_creators:no-annotation, language_creators:expert-generated, multilinguality:translation, multilinguality:multilingual, language:aai, language:aak, language:aau, language:aaz, language:abt, language:abx, language:aby, language:acf, language:acr, language:acu, language:adz, language:aer, language:aey, language:agd, language:agg, language:agm, language:agn, language:agr, language:agt, language:agu, language:aia, language:aii, language:aka, language:ake, language:alp, language:alq, language:als, language:aly, language:ame, language:amf, language:amk, language:amm, language:amn, language:amo, language:amp, language:amr, language:amu, language:amx, language:anh, language:anv, language:aoi, language:aoj, language:aom, language:aon, language:apb, language:ape, language:apn, language:apr, language:apu, language:apw, language:apz, language:arb, language:are, language:arl, language:arn, language:arp, language:asm, language:aso, language:ata, language:atb, language:atd, language:atg, language:att, language:auc, language:aui, language:auy, language:avt, language:awb, language:awk, language:awx, language:azb, language:azg, language:azz, language:bao, language:bba, language:bbb, language:bbr, language:bch, language:bco, language:bdd, language:bea, language:bef, language:bel, language:ben, language:beo, language:beu, language:bgs, language:bgt, language:bhg, language:bhl, language:big, language:bjk, language:bjp, language:bjr, language:bjv, language:bjz, language:bkd, language:bki, language:bkq, language:bkx, language:bla, language:blw, language:blz, language:bmh, language:bmk, language:bmr, language:bmu, language:bnp, language:boa, language:boj, language:bon, language:box, language:bpr, language:bps, language:bqc, language:bqp, language:bre, language:bsj, language:bsn, language:bsp, language:bss, language:buk, language:bus, language:bvd, language:bvr, language:bxh, language:byr, language:byx, language:bzd, language:bzh, language:bzj, language:caa, language:cab, language:cac, language:caf, language:cak, language:cao, language:cap, language:car, language:cav, language:cax, language:cbc, language:cbi, language:cbk, language:cbr, language:cbs, language:cbt, language:cbu, language:cbv, language:cco, language:ceb, language:cek, language:ces, language:cgc, language:cha, language:chd, language:chf, language:chk, language:chq, language:chz, language:cjo, language:cjv, language:ckb, language:cle, language:clu, language:cme, language:cmn, language:cni, language:cnl, language:cnt, language:cof, language:con, language:cop, language:cot, language:cpa, language:cpb, language:cpc, language:cpu, language:cpy, language:crn, language:crx, language:cso, language:csy, language:cta, language:cth, language:ctp, language:ctu, language:cub, language:cuc, language:cui, language:cuk, language:cut, language:cux, language:cwe, language:cya, language:daa, language:dad, language:dah, language:dan, language:ded, language:deu, language:dgc, language:dgr, language:dgz, language:dhg, language:dif, language:dik, language:dji, language:djk, language:djr, language:dob, language:dop, language:dov, language:dwr, language:dww, language:dwy, language:ebk, language:eko, language:emi, language:emp, language:eng, language:enq, language:epo, language:eri, language:ese, language:esk, language:etr, language:ewe, language:faa, language:fai, language:far, language:ffm, language:for, language:fra, language:fue, language:fuf, language:fuh, language:gah, language:gai, language:gam, language:gaw, language:gdn, language:gdr, language:geb, language:gfk, language:ghs, language:glk, language:gmv, language:gng, language:gnn, language:gnw, language:gof, language:grc, language:gub, language:guh, language:gui, language:guj, language:gul, language:gum, language:gun, language:guo, language:gup, language:gux, language:gvc, language:gvf, language:gvn, language:gvs, language:gwi, language:gym, language:gyr, language:hat, language:hau, language:haw, language:hbo, language:hch, language:heb, language:heg, language:hin, language:hix, language:hla, language:hlt, language:hmo, language:hns, language:hop, language:hot, language:hrv, language:hto, language:hub, language:hui, language:hun, language:hus, language:huu, language:huv, language:hvn, language:ian, language:ign, language:ikk, language:ikw, language:ilo, language:imo, language:inb, language:ind, language:ino, language:iou, language:ipi, language:isn, language:ita, language:iws, language:ixl, language:jac, language:jae, language:jao, language:jic, language:jid, language:jiv, language:jni, language:jpn, language:jvn, language:kan, language:kaq, language:kbc, language:kbh, language:kbm, language:kbq, language:kdc, language:kde, language:kdl, language:kek, language:ken, language:kew, language:kgf, language:kgk, language:kgp, language:khs, language:khz, language:kik, language:kiw, language:kiz, language:kje, language:kjn, language:kjs, language:kkc, language:kkl, language:klt, language:klv, language:kmg, language:kmh, language:kmk, language:kmo, language:kms, language:kmu, language:kne, language:knf, language:knj, language:knv, language:kos, language:kpf, language:kpg, language:kpj, language:kpr, language:kpw, language:kpx, language:kqa, language:kqc, language:kqf, language:kql, language:kqw, language:ksd, language:ksj, language:ksr, language:ktm, language:kto, language:kud, language:kue, language:kup, language:kvg, language:kvn, language:kwd, language:kwf, language:kwi, language:kwj, language:kyc, language:kyf, language:kyg, language:kyq, language:kyz, language:kze, language:lac, language:lat, language:lbb, language:lbk, language:lcm, language:leu, language:lex, language:lgl, language:lid, language:lif, language:lin, language:lit, language:llg, language:lug, language:luo, language:lww, language:maa, language:maj, language:mal, language:mam, language:maq, language:mar, language:mau, language:mav, language:maz, language:mbb, language:mbc, language:mbh, language:mbj, language:mbl, language:mbs, language:mbt, language:mca, language:mcb, language:mcd, language:mcf, language:mco, language:mcp, language:mcq, language:mcr, language:mdy, language:med, language:mee, language:mek, language:meq, language:met, language:meu, language:mgc, language:mgh, language:mgw, language:mhl, language:mib, language:mic, language:mie, language:mig, language:mih, language:mil, language:mio, language:mir, language:mit, language:miz, language:mjc, language:mkj, language:mkl, language:mkn, language:mks, language:mle, language:mlh, language:mlp, language:mmo, language:mmx, language:mna, language:mop, language:mox, language:mph, language:mpj, language:mpm, language:mpp, language:mps, language:mpt, language:mpx, language:mqb, language:mqj, language:msb, language:msc, language:msk, language:msm, language:msy, language:mti, language:mto, language:mux, language:muy, language:mva, language:mvn, language:mwc, language:mwe, language:mwf, language:mwp, language:mxb, language:mxp, language:mxq, language:mxt, language:mya, language:myk, language:myu, language:myw, language:myy, language:mzz, language:nab, language:naf, language:nak, language:nas, language:nay, language:nbq, language:nca, language:nch, language:ncj, language:ncl, language:ncu, language:ndg, language:ndj, language:nfa, language:ngp, language:ngu, language:nhe, language:nhg, language:nhi, language:nho, language:nhr, language:nhu, language:nhw, language:nhy, language:nif, language:nii, language:nin, language:nko, language:nld, language:nlg, language:nmw, language:nna, language:nnq, language:noa, language:nop, language:not, language:nou, language:npi, language:npl, language:nsn, language:nss, language:ntj, language:ntp, language:ntu, language:nuy, language:nvm, language:nwi, language:nya, language:nys, language:nyu, language:obo, language:okv, language:omw, language:ong, language:ons, language:ood, language:opm, language:ory, language:ote, language:otm, language:otn, language:otq, language:ots, language:pab, language:pad, language:pah, language:pan, language:pao, language:pes, language:pib, language:pio, language:pir, language:piu, language:pjt, language:pls, language:plu, language:pma, language:poe, language:poh, language:poi, language:pol, language:pon, language:por, language:poy, language:ppo, language:prf, language:pri, language:ptp, language:ptu, language:pwg, language:qub, language:quc, language:quf, language:quh, language:qul, language:qup, language:qvc, language:qve, language:qvh, language:qvm, language:qvn, language:qvs, language:qvw, language:qvz, language:qwh, language:qxh, language:qxn, language:qxo, language:rai, language:reg, language:rgu, language:rkb, language:rmc, language:rmy, language:ron, language:roo, language:rop, language:row, language:rro, language:ruf, language:rug, language:rus, language:rwo, language:sab, language:san, language:sbe, language:sbk, language:sbs, language:seh, language:sey, language:sgb, language:sgz, language:shj, language:shp, language:sim, language:sja, language:sll, language:smk, language:snc, language:snn, language:snp, language:snx, language:sny, language:som, language:soq, language:soy, language:spa, language:spl, language:spm, language:spp, language:sps, language:spy, language:sri, language:srm, language:srn, language:srp, language:srq, language:ssd, language:ssg, language:ssx, language:stp, language:sua, language:sue, language:sus, language:suz, language:swe, language:swh, language:swp, language:sxb, language:tac, language:taj, language:tam, language:tav, language:taw, language:tbc, language:tbf, language:tbg, language:tbl, language:tbo, language:tbz, language:tca, language:tcs, language:tcz, language:tdt, language:tee, language:tel, language:ter, language:tet, language:tew, language:tfr, language:tgk, language:tgl, language:tgo, language:tgp, language:tha, language:thd, language:tif, language:tim, language:tiw, language:tiy, language:tke, language:tku, language:tlf, language:tmd, language:tna, language:tnc, language:tnk, language:tnn, language:tnp, language:toc, language:tod, language:tof, language:toj, language:ton, language:too, language:top, language:tos, language:tpa, language:tpi, language:tpt, language:tpz, language:trc, language:tsw, language:ttc, language:tte, language:tuc, language:tue, language:tuf, language:tuo, language:tur, language:tvk, language:twi, language:txq, language:txu, language:tzj, language:tzo, language:ubr, language:ubu, language:udu, language:uig, language:ukr, language:uli, language:ulk, language:upv, language:ura, language:urb, language:urd, language:uri, language:urt, language:urw, language:usa, language:usp, language:uvh, language:uvl, language:vid, language:vie, language:viv, language:vmy, language:waj, language:wal, language:wap, language:wat, language:wbi, language:wbp, language:wed, language:wer, language:wim, language:wiu, language:wiv, language:wmt, language:wmw, language:wnc, language:wnu, language:wol, language:wos, language:wrk, language:wro, language:wrs, language:wsk, language:wuv, language:xav, language:xbi, language:xed, language:xla, language:xnn, language:xon, language:xsi, language:xtd, language:xtm, language:yaa, language:yad, language:yal, language:yap, language:yaq, language:yby, language:ycn, language:yka, language:yle, language:yml, language:yon, language:yor, language:yrb, language:yre, language:yss, language:yuj, language:yut, language:yuw, language:yva, language:zaa, language:zab, language:zac, language:zad, language:zai, language:zaj, language:zam, language:zao, language:zap, language:zar, language:zas, language:zat, language:zav, language:zaw, language:zca, language:zga, language:zia, language:ziw, language:zlm, language:zos, language:zpc, language:zpl, language:zpm, language:zpo, language:zpq, language:zpu, language:zpv, language:zpz, language:zsr, language:ztq, language:zty, language:zyp, language:be, language:br, language:cs, language:ch, language:zh, language:de, language:en, language:eo, language:fr, language:ht, language:he, language:hr, language:id, language:it, language:ja, language:la, language:nl, language:ru, language:sa, language:so, language:es, language:sr, language:sv, language:to, language:uk, language:vi, license:cc-by-4.0, license:other, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","This dataset pre-computes all English-centric directions from bible-nlp/biblenlp-corpus, and as a result loading is significantly faster. Loading example: >>> from datasets import load_dataset >>> dat","None","aai, aak, aau, aaz, abt, abx, aby, acf, acr, acu, adz, aer, aey, agd, agg, agm, agn, agr, agt, agu, aia, aii, aka, ake, alp, alq, als, aly, ame, amf, amk, amm, amn, amo, amp, amr, amu, amx, anh, anv, aoi, aoj, aom, aon, apb, ape, apn, apr, apu, apw, apz, arb, are, arl, arn, arp, asm, aso, ata, atb, atd, atg, att, auc, aui, auy, avt, awb, awk, awx, azb, azg, azz, bao, bba, bbb, bbr, bch, bco, bdd, bea, bef, bel, ben, beo, beu, bgs, bgt, bhg, bhl, big, bjk, bjp, bjr, bjv, bjz, bkd, bki, bkq, bkx, bla, blw, blz, bmh, bmk, bmr, bmu, bnp, boa, boj, bon, box, bpr, bps, bqc, bqp, bre, bsj, bsn, bsp, bss, buk, bus, bvd, bvr, bxh, byr, byx, bzd, bzh, bzj, caa, cab, cac, caf, cak, cao, cap, car, cav, cax, cbc, cbi, cbk, cbr, cbs, cbt, cbu, cbv, cco, ceb, cek, ces, cgc, cha, chd, chf, chk, chq, chz, cjo, cjv, ckb, cle, clu, cme, cmn, cni, cnl, cnt, cof, con, cop, cot, cpa, cpb, cpc, cpu, cpy, crn, crx, cso, csy, cta, cth, ctp, ctu, cub, cuc, cui, cuk, cut, cux, cwe, cya, daa, dad, dah, dan, ded, deu, dgc, dgr, dgz, dhg, dif, dik, dji, djk, djr, dob, dop, dov, dwr, dww, dwy, ebk, eko, emi, emp, eng, enq, epo, eri, ese, esk, etr, ewe, faa, fai, far, ffm, for, fra, fue, fuf, fuh, gah, gai, gam, gaw, gdn, gdr, geb, gfk, ghs, glk, gmv, gng, gnn, gnw, gof, grc, gub, guh, gui, guj, gul, gum, gun, guo, gup, gux, gvc, gvf, gvn, gvs, gwi, gym, gyr, hat, hau, haw, hbo, hch, heb, heg, hin, hix, hla, hlt, hmo, hns, hop, hot, hrv, hto, hub, hui, hun, hus, huu, huv, hvn, ian, ign, ikk, ikw, ilo, imo, inb, ind, ino, iou, ipi, isn, ita, iws, ixl, jac, jae, jao, jic, jid, jiv, jni, jpn, jvn, kan, kaq, kbc, kbh, kbm, kbq, kdc, kde, kdl, kek, ken, kew, kgf, kgk, kgp, khs, khz, kik, kiw, kiz, kje, kjn, kjs, kkc, kkl, klt, klv, kmg, kmh, kmk, kmo, kms, kmu, kne, knf, knj, knv, kos, kpf, kpg, kpj, kpr, kpw, kpx, kqa, kqc, kqf, kql, kqw, ksd, ksj, ksr, ktm, kto, kud, kue, kup, kvg, kvn, kwd, kwf, kwi, kwj, kyc, kyf, kyg, kyq, kyz, kze, lac, lat, lbb, lbk, lcm, leu, lex, lgl, lid, lif, lin, lit, llg, lug, luo, lww, maa, maj, mal, mam, maq, mar, mau, mav, maz, mbb, mbc, mbh, mbj, mbl, mbs, mbt, mca, mcb, mcd, mcf, mco, mcp, mcq, mcr, mdy, med, mee, mek, meq, met, meu, mgc, mgh, mgw, mhl, mib, mic, mie, mig, mih, mil, mio, mir, mit, miz, mjc, mkj, mkl, mkn, mks, mle, mlh, mlp, mmo, mmx, mna, mop, mox, mph, mpj, mpm, mpp, mps, mpt, mpx, mqb, mqj, msb, msc, msk, msm, msy, mti, mto, mux, muy, mva, mvn, mwc, mwe, mwf, mwp, mxb, mxp, mxq, mxt, mya, myk, myu, myw, myy, mzz, nab, naf, nak, nas, nay, nbq, nca, nch, ncj, ncl, ncu, ndg, ndj, nfa, ngp, ngu, nhe, nhg, nhi, nho, nhr, nhu, nhw, nhy, nif, nii, nin, nko, nld, nlg, nmw, nna, nnq, noa, nop, not, nou, npi, npl, nsn, nss, ntj, ntp, ntu, nuy, nvm, nwi, nya, nys, nyu, obo, okv, omw, ong, ons, ood, opm, ory, ote, otm, otn, otq, ots, pab, pad, pah, pan, pao, pes, pib, pio, pir, piu, pjt, pls, plu, pma, poe, poh, poi, pol, pon, por, poy, ppo, prf, pri, ptp, ptu, pwg, qub, quc, quf, quh, qul, qup, qvc, qve, qvh, qvm, qvn, qvs, qvw, qvz, qwh, qxh, qxn, qxo, rai, reg, rgu, rkb, rmc, rmy, ron, roo, rop, row, rro, ruf, rug, rus, rwo, sab, san, sbe, sbk, sbs, seh, sey, sgb, sgz, shj, shp, sim, sja, sll, smk, snc, snn, snp, snx, sny, som, soq, soy, spa, spl, spm, spp, sps, spy, sri, srm, srn, srp, srq, ssd, ssg, ssx, stp, sua, sue, sus, suz, swe, swh, swp, sxb, tac, taj, tam, tav, taw, tbc, tbf, tbg, tbl, tbo, tbz, tca, tcs, tcz, tdt, tee, tel, ter, tet, tew, tfr, tgk, tgl, tgo, tgp, tha, thd, tif, tim, tiw, tiy, tke, tku, tlf, tmd, tna, tnc, tnk, tnn, tnp, toc, tod, tof, toj, ton, too, top, tos, tpa, tpi, tpt, tpz, trc, tsw, ttc, tte, tuc, tue, tuf, tuo, tur, tvk, twi, txq, txu, tzj, tzo, ubr, ubu, udu, uig, ukr, uli, ulk, upv, ura, urb, urd, uri, urt, urw, usa, usp, uvh, uvl, vid, vie, viv, vmy, waj, wal, wap, wat, wbi, wbp, wed, wer, wim, wiu, wiv, wmt, wmw, wnc, wnu, wol, wos, wrk, wro, wrs, wsk, wuv, xav, xbi, xed, xla, xnn, xon, xsi, xtd, xtm, yaa, yad, yal, yap, yaq, yby, ycn, yka, yle, yml, yon, yor, yrb, yre, yss, yuj, yut, yuw, yva, zaa, zab, zac, zad, zai, zaj, zam, zao, zap, zar, zas, zat, zav, zaw, zca, zga, zia, ziw, zlm, zos, zpc, zpl, zpm, zpo, zpq, zpu, zpv, zpz, zsr, ztq, zty, zyp, be, br, cs, ch, zh, de, en, eo, fr, ht, he, hr, id, it, ja, la, nl, ru, sa, so, es, sr, sv, to, uk, vi","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"tyqiangz/multilingual-sentiments","tyqiangz","2022-08-21 11:04:38","2023-05-23 15:01:51+00:00","a3080a58e563138e9c7a61765d8120b388dc572d","2928","53508","45","None","text-classification, sentiment-analysis, sentiment-classification","Multilingual Sentiments Dataset A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative. Most multilingual sentiment datasets are either 2-class positive","None","de, en, es, fr, ja, zh, id, ar, hi, it, ms, pt","multi","1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","sentiment-analysis, sentiment-classification"
+"mutiyama/alt","mutiyama","2022-03-02 23:29:22","2024-01-09 12:07:24+00:00","afbd92e198bbcf17f660e03076fd2938f5a4bbb2","896","53209","17","None","translation, token-classification, parsing","Dataset Card for Asian Language Treebank (ALT) Dataset Summary The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration fo","None","bn, en, fil, hi, id, ja, km, lo, ms, my, th, vi, zh","multi","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","parsing"
+"HuggingFaceGECLM/REDDIT_comments","HuggingFaceGECLM","2023-03-15 14:14:58","2023-03-17 07:52:51+00:00","54779d3d1f1c1b12e5989f695e13d38b394a558f","22104","53201","11","task_categories:text-generation, task_ids:dialogue-modeling, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, language:en, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2001.08435, region:us, reddit, social-media","text-generation, dialogue-modeling, language-modeling","Dataset Card for ""REDDIT_comments"" Dataset Summary Comments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023). Supported Tasks These comments can be","None","en","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","None","None","None","dialogue-modeling, language-modeling"
+"meta-llama/Llama-3.1-8B-Instruct-evals","meta-llama","2024-07-23 05:35:42","2024-10-02 21:45:07+00:00","d768f59e351416d894a9277a47195a0609ea58b7","1156","52693","26","language:en, license:llama3.1, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for Llama-3.1-8B-Instruct Evaluation Result Details This dataset contains the Meta evaluation result details for Llama-3.1-8B-Instruct. The dataset has been created from 30 evaluation tas","None","e, n","bi","None","None","False","manual","False","llama3.1","None","None","None"
+"fixie-ai/librispeech_asr","fixie-ai","2024-07-19 02:34:30","2024-08-05 18:38:33+00:00","ea1da511055b346c29e03ed435f5b276d1de5ac2","16638","52590","2","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mcaleste/sat_multiple_choice_math_may_23","mcaleste","2023-09-18 21:30:36","2023-10-14 02:23:29+00:00","113bd56ac2b12f2eb6132c5287e624192b60c76b","34476","51494","2","None","None","This is the set of math SAT questions from the May 2023 SAT, taken from here: https://www.mcelroytutoring.com/lower.php?url=44-official-sat-pdfs-and-82-official-act-pdf-practice-tests-free. Questions ","None","en","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"jinzhuoran/RWKU","jinzhuoran","2024-06-02 12:02:18","2024-06-18 02:25:48+00:00","d72f493d481d1b0a9bdc6e869d32baeffad8904f","15392","51340","3","task_categories:text-generation, task_categories:fill-mask, task_categories:question-answering, language:en, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.10890, doi:10.57967/hf/2448, region:us, unlearning, knowledge unlearning, NLP, LLM","text-generation, fill-mask, question-answering","Dataset Card for Real-World Knowledge Unlearning Benchmark (RWKU) Dataset Summary RWKU is a real-world knowledge unlearning benchmark specifically designed for large language models (LLMs). This bench","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2406.10890","None","None"
+"mteb/amazon_polarity","mteb","2022-05-25 21:49:03","2022-09-27 19:11:44+00:00","80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1","1306","51221","2","language:en, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mteb/cqadupstack-programmers","mteb","2024-03-02 19:36:53","2024-03-02 19:57:32+00:00","6184bc1440d2dbc7612be22b50686b8826d22b32","965","49151","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"Stevross/mmlu","Stevross","2023-07-11 11:58:20","2023-07-11 12:04:33+00:00","66cd19290bc9800f4ef4accc440a047c6b81dd0b","5994","48831","7","task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:no-annotation, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:en, license:mit, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2009.03300, arxiv:2005.00700, arxiv:2005.14165, arxiv:2008.02275, region:us","question-answering, multiple-choice-qa","This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and ","@article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} }","en","mono","1|0|K|<|n|<|1|0|0|K","mmlu","False","False","False","mit","2005.00700","None","multiple-choice-qa"
+"mteb/arxiv-clustering-s2s","mteb","2022-05-10 12:26:54","2022-09-27 19:12:49+00:00","b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3","1314","47718","0","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"mlfoundations/MINT-1T-PDF-CC-2023-14","mlfoundations","2024-07-12 05:44:44","2024-09-19 21:07:39+00:00","6e5179c68a1dbf999fa29bf4665f763f8c62bb32","3768","47717","1","None","image-to-text, text-generation","🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billi","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2406.11271","None","None"
+"mteb/quora","mteb","2024-03-02 20:47:08","2024-03-03 11:21:08+00:00","e4e08e0b7dbe3c8700f0daef558ff32256715259","1243","47406","1","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:quora, language:en, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"Babelscape/wikineural","Babelscape","2022-03-02 23:29:22","2022-11-13 07:52:46+00:00","74c9b9dca034bb1606a6769457983904bd976803","1508","47404","32","None","token-classification, named-entity-recognition","Dataset Card for WikiNEuRal dataset Description Summary: In a nutshell, WikiNEuRal consists in a novel technique which builds upon a multilingual lexical knowledge base (i.e., BabelNet) and transforme","None","de, en, es, fr, it, nl, pl, pt, ru","multi","None","None","False","False","False","cc-by-nc-sa-4.0","1810.04805","None","named-entity-recognition"
+"cardiffnlp/databench","cardiffnlp","2023-12-21 08:08:56","2025-01-24 14:37:22+00:00","a2642525108e56dcba9c01fb3246ab0fd59e1aa7","21676","47232","7","None","table-question-answering, question-answering","💾🏋️💾 DataBench 💾🏋️💾 This repository contains the original 65 datasets used for the paper Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs which appeared ","None","en, es","bi","None","None","False","False","False","mit","None","None","None"
+"mteb/cqadupstack-webmasters","mteb","2024-03-02 19:37:41","2024-03-02 20:22:45+00:00","160c094312a0e1facb97e55eeddb698c0abe3571","968","47196","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"llamafactory/demo_data","llamafactory","2024-05-17 10:31:51","2024-07-18 16:50:20+00:00","fe2bf7023ec72408f056c82027488eea8d599c72","4217","47096","0","None","text-generation","1,000 examples from https://huggingface.co/datasets/llamafactory/alpaca_gpt4_en 1,000 examples from https://huggingface.co/datasets/llamafactory/alpaca_gpt4_zh 300 examples from https://huggingface.co","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"hoskinson-center/proof-pile","hoskinson-center","2022-08-08 20:57:56","2023-08-19 03:24:11+00:00","490b980249446f2f3bd2df3a8cf085d0f2de240a","11838","46788","58","task_categories:text-generation, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, language:en, license:apache-2.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us, math, mathematics, formal-mathematics","text-generation, language-modeling","A dataset of high quality mathematical text.","@InProceedings{huggingface:dataset, title = {proof-pile}, author={Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski }, year={2022} }","en","mono","None","None","False","False","False","apache-2.0","None","None","language-modeling"
+"wmt/wmt20_mlqe_task1","wmt","2022-03-02 23:29:22","2024-04-04 13:50:56+00:00","0783ed2bd75f44835df4ea664f9ccb85812c8563","1047","46337","3","None","translation","Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1 Dataset Summary From the homepage: This shared task (part of WMT20) will build on its previous editions to further examine automat","None","de, en, et, ne, ro, ru, si, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","unknown","None","None","None"
+"ai4bharat/samanantar","ai4bharat","2022-03-02 23:29:22","2024-12-24 08:13:04+00:00","ead34c5b22a0354d350e7dfdd6aece7df5a48f37","893","45972","24","None","text-generation, translation","Dataset Card for Samanantar Dataset Summary Samanantar is the largest publicly available parallel corpora collection for Indic language: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi","None","en, as, bn, gu, hi, kn, ml, mr, or, pa, ta, te","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-nc-4.0","2104.05596","None","None"
+"OpenGVLab/ShareGPT-4o","OpenGVLab","2024-05-28 07:51:06","2024-08-17 07:51:28+00:00","a69d5b4d2c5343146e27b46a22638d346f14f013","17407","45223","155","task_categories:visual-question-answering, task_categories:question-answering, language:en, license:mit, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering, question-answering","None","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","auto","False","mit","None","None","None"
+"mteb/hotpotqa","mteb","2024-03-02 20:31:33","2024-03-03 11:49:33+00:00","ab518f4d6fcca38d87c25209f94beba119d02014","1930","44899","3","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"andstor/the_pile_github","andstor","2023-03-07 15:53:05","2023-03-20 23:39:53+00:00","6c6cb7e294b093c9c8d0cfaa10706147baf51190","12637","44487","8","task_categories:text-generation, task_categories:fill-mask, task_categories:text-classification, annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, source_datasets:original, language:en, license:other, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2101.00027, arxiv:2201.07311, region:us","text-generation, fill-mask, text-classification","The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together.","@misc{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Leo Gao and Stella Biderman and Sid Black and Laurence Golding and Travis Hoppe and Charles Foster and Jason Phang and Horace He and Anish Thite and Noa Nabeshima and Shawn Presser and Connor Leahy}, year={2020}, eprint={2101.00027}, archivePrefix={arXiv}, primaryClass={cs.CL} }","en","mono","None","None","False","False","False","other","2201.07311","None","None"
+"Zyphra/Zyda","Zyphra","2024-05-04 18:56:59","2024-06-19 01:06:43+00:00","d42bc68cfb537710a580e8d4b13ec536fcc151a6","5727","44183","71","task_categories:text-generation, language:en, license:odc-by, size_categories:1B<n<10B, modality:text, arxiv:2405.16712, arxiv:2101.00027, arxiv:2406.01981, doi:10.57967/hf/2394, region:us","text-generation","Zyda Zyda is a 1.3T language modeling dataset created by collecting open and high quality datasets and combining them and performing a uniform filtering and deduplication step. We find that Zyda perfo","None","en","mono","n|>|1|T","None","False","False","False","odc-by","2405.16712","None","None"
+"GEM/wiki_cat_sum","GEM","2022-03-02 23:29:22","2022-10-24 15:31:11+00:00","e732d1703eaad5b34a56370fd137b9d09921a94b","2934","43974","4","None","summarization","Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.","@inproceedings{perez2019generating, title={Generating Summaries with Topic Templates and Structured Convolutional Decoders}, author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella}, booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, pages={5107--5116}, year={2019} }","en","mono","u|n|k|n|o|w|n","None","False","False","False","cc-by-sa-3.0","1906.04687","None","None"
+"sentence-transformers/all-nli","sentence-transformers","2024-04-25 12:49:03","2024-05-15 11:22:30+00:00","d482672c8e74ce18da116f430137434ba2e52fab","2615","43814","31","None","feature-extraction, sentence-similarity","Dataset Card for AllNLI This dataset is a concatenation of the SNLI and MultiNLI datasets. Despite originally being intended for Natural Language Inference (NLI), this dataset can be used for training","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"openbmb/UltraFeedback","openbmb","2023-09-23 15:41:04","2023-12-29 14:11:19+00:00","40b436560ca83a8dba36114c22ab3c66e43f6d5e","2143","43485","347","None","text-generation","Introduction GitHub Repo UltraRM-13b UltraCM-13b UltraFeedback is a large-scale, fine-grained, diverse preference dataset, used for training powerful reward models and critic models. We collect about ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"mteb/scidocs","mteb","2024-03-02 20:24:54","2024-03-03 11:42:33+00:00","f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88","1382","42973","3","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"lesserfield/4chan-datasets","lesserfield","2023-04-16 14:39:15","2023-05-07 16:38:40+00:00","cfb1ebc8bec1f9326f69c0bb563602836796e5d8","940","42953","21","None","text-generation","Please see repo to turn the text file into json/csv format Deleted some boards, since they are already archived by https://archive.4plebs.org/","None","en","mono","None","None","False","False","False","unlicense","None","None","None"
+"edinburgh-dawg/mmlu-redux","edinburgh-dawg","2024-06-04 17:10:52","2024-08-09 18:31:31+00:00","2ebba46760ad1df575a6b9df42c280b1d5fb3c10","1542","42592","28","None","question-answering","Dataset Card for MMLU-Redux MMLU-Redux is a subset of 3,000 manually re-annotated questions across 30 MMLU subjects. Dataset Details Dataset Description Each data point in MMLU-Redux contains seven co","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2406.04127","None","None"
+"sentence-transformers/parallel-sentences-talks","sentence-transformers","2024-04-30 10:29:15","2024-06-18 19:45:50+00:00","0c70bc6714efb1df12f8a16b9056e4653563d128","3613","42438","9","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - Talks This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentence","None","en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"Lin-Chen/MMStar","Lin-Chen","2024-04-02 06:56:56","2024-04-07 08:15:45+00:00","bc98d668301da7b14f648724866e57302778ab27","5133","42295","26","None","multiple-choice, question-answering, visual-question-answering","MMStar (Are We on the Right Way for Evaluating Large Vision-Language Models?) 🌐 Homepage | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub Dataset Details As shown in the figure below, existing benchmarks lack","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"OpenGVLab/GUI-Odyssey","OpenGVLab","2024-06-13 07:21:10","2024-11-20 12:34:13+00:00","2298cb628895d1c6248b8ead10c71429a76ce943","20196","41942","10","None","None","Dataset Card for GUI Odyssey Repository: https://github.com/OpenGVLab/GUI-Odyssey Paper: https://arxiv.org/abs/2406.08451 Point of Contact: Wenqi Shao Introduction GUI Odyssey is a comprehensive datas","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2406.08451","None","None"
+"Cohere/wikipedia-22-12-en-embeddings","Cohere","2023-01-14 20:36:11","2023-03-22 16:51:57+00:00","85c2eca83d4b9dcecc043c23748cb8c1047f683f","1967","41790","64","None","text-retrieval, document-retrieval","Wikipedia (en) embedded with cohere.ai multilingual-22-12 encoder We encoded Wikipedia (en) using the cohere.ai multilingual-22-12 embedding model. To get an overview how this dataset was created and ","None","en","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"RMT-team/babilong","RMT-team","2024-03-07 14:15:14","2024-06-17 09:49:52+00:00","ee0d588794c7ac098062ee0d247c733d62e94fe2","11959","41497","14","None","None","BABILong (100 samples) : a long-context needle-in-a-haystack benchmark for LLMs Preprint is on arXiv and code for LLM evaluation is available on GitHub. BABILong Leaderboard with top-performing long-c","None","en","mono","None","None","False","False","False","None","2406.10149","None","None"
+"HuggingFaceFV/finevideo","HuggingFaceFV","2024-09-09 17:56:30","2024-12-16 08:04:42+00:00","012c690b405b22b99611e4b55bb065415d35a237","2692","41440","291","None","visual-question-answering, video-text-to-text","FineVideo FineVideo Description Dataset Explorer Revisions Dataset Distribution How to download and use FineVideo Using datasets Using huggingface_hub Load a subset of the dataset Dataset StructureDat","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc","None","None","None"
+"bentrevett/multi30k","bentrevett","2023-03-19 22:38:35","2023-03-24 14:50:27+00:00","4589883f3d09d4ef6361784e03f0ead219836469","1148","40495","6","None","translation","Multi30k This dataset contains the ""multi30k"" dataset, which is the ""task 1"" dataset from here. Each example consists of an ""en"" and a ""de"" feature. ""en"" is an English sentence, and ""de"" is the German","None","en, de","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"BAAI/Infinity-Instruct","BAAI","2024-06-13 12:17:03","2025-01-16 08:47:04+00:00","40353d346e04c94ed0de467f8b6c95061d1e7b89","5298","39161","585","task_categories:text-generation, language:en, language:zh, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.00530, arxiv:2405.19327, arxiv:2409.07045, arxiv:2408.07089, region:us","text-generation","Infinity Instruct Beijing Academy of Artificial Intelligence (BAAI) [Paper][Code][🤗] (would be released soon) The quality and scale of instruction data are crucial for model performance. Recently, ope","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","auto","False","cc-by-sa-4.0","None","None","None"
+"nvidia/OpenMathInstruct-2","nvidia","2024-09-28 16:37:52","2024-11-25 20:07:28+00:00","469216e3f46f4dacf476b382e192485ea51a143e","5288","38655","150","None","question-answering, text-generation","OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the Llama3.1-405B-Instruct model. The training set problems of GSM8K and MATH","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-4.0","2410.01560","None","None"
+"OpenAssistant/oasst2","OpenAssistant","2023-12-24 09:53:24","2024-01-11 06:09:29+00:00","179dd21fc55192153d94adb0e0ce8f69e222bf75","1297","38580","222","None","None","Open Assistant Conversations Dataset Release 2 (OASST2) Dataset Structure This dataset contains message trees. Each message tree has an initial prompt message as the root node, which can have multiple","None","en, es, ru, de, pl, th, vi, sv, bn, da, he, it, fa, sk, id, nb, el, nl, hu, eu, zh, eo, ja, ca, cs, bg, fi, pt, tr, ro, ar, uk, gl, fr, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2304.07327","None","None"
+"allenai/WildBench","allenai","2024-02-23 03:16:24","2024-11-04 02:22:12+00:00","7c05c1b4550282b2ed6a2e6ac5db069f1e07df5c","1699","38574","34","None","text-generation","🦁 WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild Loading from datasets import load_dataset wb_data = load_dataset(""allenai/WildBench"", ""v2"", split=""test"") Quick Links:","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","2406.04770","None","None"
+"edinburghcstr/ami","edinburghcstr","2022-08-17 22:02:08","2023-01-16 18:11:05+00:00","831ea5d6035059ce66bf14f13e5ccdb222db48f9","1240","38519","41","None","automatic-speech-recognition","The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, indi","@inproceedings{10.1007/11677482_3, author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre}, title = {The AMI Meeting Corpus: A Pre-Announcement}, year = {2005}, isbn = {3540325492}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, url = {https://doi.org/10.1007/11677482_3}, doi = {10.1007/11677482_3}, abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated.}, booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction}, pages = {28–39}, numpages = {12}, location = {Edinburgh, UK}, series = {MLMI'05} }","en","mono","None","None","False","False","False","cc-by-4.0","1906.11047","None","None"
+"OpenDILabCommunity/LMDrive","OpenDILabCommunity","2023-11-30 08:56:30","2023-12-25 13:28:07+00:00","5bab4ac27d40beb13d05c2bb170a92eb3bd72f32","12829","38104","13","language:en, license:apache-2.0, size_categories:100K<n<1M, format:text, modality:text, library:datasets, library:mlcroissant, arxiv:2312.07488, region:us","None","LMDrive 64K Dataset Card LMDrive Dataset consists of 64K instruction-sensor-control data clips collected in the CARLA simulator， where each clip includes one navigation instruction, several notice ins","None","en","mono","n|>|1|T","None","False","False","False","apache-2.0","2312.07488","None","None"
+"McGill-NLP/stereoset","McGill-NLP","2022-03-02 23:29:22","2024-01-23 08:34:39+00:00","bf6e7ce50491784d094fb7afe60a70ecccb89035","968","37890","16","None","text-classification","Dataset Card for StereoSet Dataset Summary StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measures model preferences across gender","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2004.09456","None","None"
+"mteb/msmarco","mteb","2024-03-02 20:25:33","2024-03-03 11:13:39+00:00","c5a29a104738b98a9e76336939199e264163d4a0","1841","37889","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"Babelscape/SREDFM","Babelscape","2023-06-13 18:35:19","2023-06-20 07:33:28+00:00","2732d2834e12e36510aeb2a468163ea2642d55db","2928","37479","14","None","token-classification","Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured k","@InProceedings{REDFM2023, author = {Huguet Cabot, Pere-Lluis and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and Navigli, Roberto}, title = {RED\\textsuperscript{FM}: a Filtered and Multilingual Relation Extraction Dataset}, booktitle = {Proceedings of the 2023 Conference on Association for Computational Linguistics}, year = {2023}, publisher = {Association for Computational Linguistics}, location = {Toronto, Canada}, }","ar, ca, de, el, en, es, fr, hi, it, ja, ko, nl, pl, pt, ru, sv, vi, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-4.0","2306.09802","None","None"
+"microsoft/orca-math-word-problems-200k","microsoft","2024-03-01 00:56:17","2024-03-04 18:01:08+00:00","29255d1770cc4eac66e5e7fa378cba542c026350","1145","37397","428","None","question-answering","Dataset Card This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to Orca-Math: Unlocking the potential of SLM","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"qmeeus/vp-er-10l","qmeeus","2024-02-19 20:45:14","2024-03-28 14:43:22+00:00","2c70f24900c5f93815a3ab16134dbe2e39126771","8125","36814","0","language:cs, language:de, language:en, language:es, language:fr, language:hu, language:it, language:nl, language:pl, language:ro, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, speech-to-text, speech-translation, automatic-speech-recognition, language-detection","None","Dataset Card for ""vp-er-10l"" More Information needed","None","cs, de, en, es, fr, hu, it, nl, pl, ro","multi","None","None","False","False","False","None","None","None","None"
+"mteb/trec-covid","mteb","2024-03-02 20:32:41","2024-03-03 14:42:59+00:00","bb9466bac8153a0349341eb1b22e06409e78ef4e","1258","36302","2","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:trec-covid, language:en, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"mythicinfinity/libritts_r","mythicinfinity","2024-02-03 04:40:33","2024-02-09 21:20:19+00:00","0d1718db351207d5a493135a62d5ed8cc4733830","1159","35927","26","None","text-to-speech","Dataset Card for LibriTTS-R LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus (http://www.openslr.org/60/) which is a multi-speaker English corpus of approximately 585 hours of","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2305.18802","None","None"
+"TempoFunk/webvid-10M","TempoFunk","2023-06-16 19:17:16","2023-08-19 09:03:19+00:00","461f7da9a310b67c7fa05fcbd6e312ddbffc8ba5","7752","35659","65","task_categories:text-to-video, task_categories:text-to-image, task_categories:video-classification, task_categories:image-classification, language:en, license:agpl-3.0, size_categories:10M<n<100M, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-video, text-to-image, video-classification, image-classification","None","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","agpl-3.0","None","None","None"
+"timbrooks/instructpix2pix-clip-filtered","timbrooks","2023-02-24 14:55:53","2023-03-02 11:19:16+00:00","aa665b890915f7a42f8615bee868a9f3447e178f","1944","35621","42","None","None","Dataset Card for InstructPix2Pix CLIP-filtered Dataset Summary The dataset can be used to train models to follow edit instructions. Edit instructions are available in the edit_prompt. original_image c","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","2211.09800","None","None"
+"lmqg/qg_squad","lmqg","2022-03-02 23:29:22","2022-12-02 18:51:10+00:00","57bdaee4b743773b6eb5c4e38490757d18a92ca0","1116","35281","8","None","text-generation, language-modeling","[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split of test and development set follows the [""Neural Question Generation""](https://ar","@inproceedings{ushio-etal-2022-generative, title = ""{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration"", author = ""Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose"", booktitle = ""Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"", month = dec, year = ""2022"", address = ""Abu Dhabi, U.A.E."", publisher = ""Association for Computational Linguistics"", }","e, n","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","cc-by-4.0","2210.03992","None","language-modeling"
+"nvidia/HelpSteer","nvidia","2023-11-15 20:55:27","2024-12-18 21:05:12+00:00","3ca5d59c1bc1080af195b4254e7407db60b6f450","1638","35129","229","None","None","HelpSteer: Helpfulness SteerLM Dataset HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adj","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2311.09528","None","None"
+"google/IFEval","google","2023-12-22 11:20:03","2024-08-14 08:21:56+00:00","966cd89545d6b6acfd7638bc708b98261ca58e84","9850","35016","49","None","text-generation","Dataset Card for IFEval Dataset Summary This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 ""verifiable instru","None","en","mono","None","None","False","False","False","apache-2.0","2311.07911","None","None"
+"swj0419/WikiMIA","swj0419","2023-10-05 23:31:10","2023-11-03 07:52:38+00:00","a89ab76d88f704e9bc5870ac39cc9d458a2a70ac","2658","34715","17","None","None","📘 WikiMIA Datasets The WikiMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language mo","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"cfilt/iitb-english-hindi","cfilt","2022-03-02 23:29:22","2023-12-30 12:00:15+00:00","321516f50bdcc1214fa75164c545478976ed84bd","1005","33959","42","None","None","IITB-English-Hindi Parallel Corpus About The IIT Bombay English-Hindi corpus contains parallel corpus for English-Hindi as well as monolingual Hindi corpus collected from a variety of existing sources","None","en, hi","bi","None","None","False","False","False","None","None","None","None"
+"lowercaseonly/cghd","lowercaseonly","2023-05-21 12:20:21","2025-01-27 03:08:48+00:00","6c7b702b2fc703476f9510d21f5765141cc8d9bb","5286","33783","1","task_categories:object-detection, task_categories:image-segmentation, language:en, language:de, license:cc-by-3.0, size_categories:1K<n<10K, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","object-detection, image-segmentation","Public Ground-Truth Dataset for Handwritten Circuit Diagrams (GTDB-HD) This repository contains images of hand-drawn electrical circuit diagrams as well as accompanying bounding box annotation for obj","None","en, de","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-3.0","None","None","None"
+"flaviagiammarino/path-vqa","flaviagiammarino","2023-06-02 12:03:51","2023-06-03 19:02:04+00:00","1685832883334b5bb5beaf4e4b333fdeecaa4ad9","922","33154","35","None","visual-question-answering","Dataset Card for PathVQA Dataset Description PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing Medical Visual Question Answ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2003.10286","None","None"
+"gretelai/synthetic_text_to_sql","gretelai","2024-03-21 16:04:08","2024-05-10 22:30:56+00:00","273a86f5f290e8d61b6767a9ff690c82bc990dc4","1933","32852","456","None","question-answering, table-question-answering, text-generation","Image generated by DALL-E. See prompt for more details synthetic_text_to_sql gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated usin","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"wendlerc/RenderedText","wendlerc","2023-06-26 11:26:16","2023-07-12 09:28:10+00:00","8df61d5bba4ff8224b56427b9400c97ec3a93507","10811","32514","37","task_categories:text-to-image, task_categories:image-to-text, language:en, size_categories:10M<n<100M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us, OCR, blender, LAION, Stability","text-to-image, image-to-text","This dataset has been created by Stability AI and LAION. This dataset contains 12 million 1024x1024 images of handwritten text written on a digital 3D sheet of paper generated using Blender geometry n","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"mteb/fever","mteb","2024-03-02 20:33:14","2024-03-03 11:38:50+00:00","bea83ef9e8fb933d90a2f1d5515737465d613e12","1442","32363","0","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:fever, language:en, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"pixparse/pdfa-eng-wds","pixparse","2024-01-17 12:42:51","2024-03-29 17:19:37+00:00","78af41b722c098baef2a87fab70a15dcf8d77ab7","2761","32256","142","None","image-to-text","Dataset Card for PDF Association dataset (PDFA) Dataset Summary PDFA dataset is a document dataset filtered from the SafeDocs corpus, aka CC-MAIN-2021-31-PDF-UNTRUNCATED. The original purpose of that ","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","other","None","None","None"
+"bitext/Bitext-customer-support-llm-chatbot-training-dataset","bitext","2023-08-24 15:50:29","2024-07-18 18:19:33+00:00","430d1a89bd93bd1fa23c16f29dd53e73f0087443","1902","31891","106","None","question-answering, table-question-answering","Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants Overview This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cdla-sharing-1.0","None","None","None"
+"mlabonne/orpo-dpo-mix-40k","mlabonne","2024-04-17 17:23:51","2024-10-17 21:44:52+00:00","0f72511202b8f093e9be60e1683d84b046062e36","956","31835","272","None","text-generation","ORPO-DPO-mix-40k v1.2 This dataset is designed for ORPO or DPO training. See Fine-tune Llama 3 with ORPO for more information about how to use it. It is a combination of the following high-quality DPO","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"allenai/olmo-mix-1124","allenai","2024-11-24 04:37:18","2024-12-02 15:57:43+00:00","166bbe2db8563a30388e37b4cfd753b8252f3622","14650","31757","30","task_categories:text-generation, language:en, license:odc-by, size_categories:10M<n<100M, modality:text, region:us","text-generation","OLMo 2 (November 2024) Pretraining set Collection of data used to train OLMo-2-1124 models. The majority of this dataset comes from DCLM-Baseline with no additional filtering, but we provide the expli","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","odc-by","None","None","None"
+"mteb/arxiv-clustering-p2p","mteb","2022-05-10 13:00:10","2022-09-27 19:15:11+00:00","0bbdb47bcbe3a90093699aefeed338a0f28a7ee8","1174","31540","1","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"TIGER-Lab/OmniEdit-Filtered-1.2M","TIGER-Lab","2024-11-11 07:40:47","2024-12-06 02:57:59+00:00","82455c6cd66db7f0e5bfce8d7a236441af59d6df","10171","30787","65","language:en, license:mit, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2411.07199, region:us, image","None","OmniEdit In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"Mutonix/Vript","Mutonix","2024-04-10 12:17:42","2024-06-11 10:38:10+00:00","acc278efb0ee249d646eef8a6b023595ca7efb93","14687","30742","18","task_categories:video-classification, task_categories:visual-question-answering, task_categories:text-to-video, task_categories:text-to-image, task_categories:image-to-video, language:en, size_categories:100K<n<1M, format:json, modality:text, modality:video, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.06040, region:us","video-classification, visual-question-answering, text-to-video, text-to-image, image-to-video","🎬 Vript: Refine Video Captioning into Video Scripting [Github Repo] We construct a fine-grained video-text dataset with 12K annotated high-resolution videos (~400k clips). The annotation of this datas","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"mteb/biorxiv-clustering-p2p","mteb","2022-05-11 06:46:41","2024-05-23 08:09:54+00:00","f5dbc242e11dd8e24def4c4268607a49e02946dc","1220","29596","0","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"grammarly/coedit","grammarly","2023-08-15 00:01:51","2023-10-21 01:49:43+00:00","e9a255c33ef910bc33a9d2b522653fa87521583e","819","29300","66","None","text-generation","Dataset Card for CoEdIT: Text Editing via Instruction Tuning Paper: CoEdIT: Text Editing by Task-Specific Instruction Tuning Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang Project Repo: h","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2305.09857","None","None"
+"AI-MO/NuminaMath-CoT","AI-MO","2024-07-15 20:14:23","2024-11-25 05:31:43+00:00","9d8d210c9f6a36c8f3cd84045668c9b7800ef517","6039","29096","337","None","text-generation","Dataset Card for NuminaMath CoT Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese hig","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"nvidia/ChatRAG-Bench","nvidia","2024-04-29 07:11:02","2024-05-24 17:33:56+00:00","22ece8bb870ddcf3f7aacfd5b6b0446d112a1e92","1364","29076","107","None","None","ChatRAG Bench ChatRAG Bench is a benchmark for evaluating a model's conversational QA capability over documents or retrieved context. ChatRAG Bench are built on and derived from 10 existing datasets: ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"MBZUAI/LaMini-instruction","MBZUAI","2023-04-08 07:48:12","2023-04-30 11:01:41+00:00","7372b3c04dd7a09e4ca5ae572557d843cb4b5482","3541","28895","134","None","text2text-generation","Dataset Card for ""LaMini-Instruction"" Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, Alham Fikri Aji, Dataset Description We distill the knowledge from large language models by performi","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-nc-4.0","2304.14402","None","None"
+"instruction-pretrain/general-instruction-augmented-corpora","instruction-pretrain","2024-06-24 04:23:32","2024-12-02 06:34:42+00:00","f07709a082db635fa7d36ba1d11ba131a13ea018","2438","28632","16","None","text-classification, table-question-answering, question-answering, zero-shot-classification","Instruction Pre-Training: Language Models are Supervised Multitask Learners (EMNLP 2024) This repo contains the general instruction-augmented corpora (containing 200M instruction-response pairs coveri","None","en","mono","None","None","False","False","False","odc-by","None","None","None"
+"OpenLLM-France/Lucie-Training-Dataset","OpenLLM-France","2024-10-16 10:46:27","2025-01-27 12:46:01+00:00","cd181be8f62274f9ece937a8d7a1d1e29585ad5e","27363","28157","10","task_categories:text-generation, task_categories:text2text-generation, task_ids:language-modeling, multilinguality:multilingual, language:en, language:fr, language:de, language:es, language:it, language:code, license:cc-by-nc-sa-4.0, size_categories:10B<n<100B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2308.12477, arxiv:2311.16840, arxiv:2402.00786, arxiv:1905.10892, arxiv:1906.02192, arxiv:2108.01139, arxiv:2010.12871, arxiv:2406.17557, arxiv:2312.17120, arxiv:2201.07311, arxiv:1904.01557, arxiv:2101.00027, arxiv:2211.15533, region:us, text-generation, conditional-text-generation","text-generation, text2text-generation, language-modeling","Lucie Training Dataset Card The Lucie Training Dataset is a curated collection of text data in English, French, German, Spanish and Italian culled from a variety of sources including: web data, video ","None","en, fr, de, es, it, code","multi","n|>|1|T","None","False","False","False","cc-by-nc-sa-4.0","2308.12477v1","None","language-modeling"
+"tinyBenchmarks/tinyHellaswag","tinyBenchmarks","2024-02-22 11:33:39","2024-05-25 10:44:12+00:00","5bb903986ca293b54d364c8c0423c768e20fb801","1637","28064","4","None","None","tinyHellaswag Welcome to tinyHellaswag! This dataset serves as a concise version of the hellaswag dataset, offering a subset of 100 data points selected from the original compilation. tinyHellaswag is","None","en","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"proj-persona/PersonaHub","proj-persona","2024-06-28 16:35:21","2024-10-05 04:04:28+00:00","c91f99f3efd4d0977e338f3b77abd251653cd405","4205","27580","499","None","text-generation, text-classification, token-classification, fill-mask, table-question-answering, text2text-generation","Scaling Synthetic Data Creation with 1,000,000,000 Personas This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas: We propose a novel persona-driv","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"mteb/sib200","mteb","2024-05-07 14:07:00","2024-05-07 14:59:53+00:00","a74d7350ea12af010cfb1c21e34f1f81fd2e615b","1212","27530","1","task_categories:text-classification, task_ids:topic-classification, annotations_creators:found, language_creators:expert-generated, multilinguality:multilingual, source_datasets:original, language:ace, language:acm, language:acq, language:aeb, language:af, language:ajp, language:ak, language:als, language:am, language:apc, language:ar, language:ars, language:ary, language:arz, language:as, language:ast, language:awa, language:ayr, language:azb, language:azj, language:ba, language:bm, language:ban, language:be, language:bem, language:bn, language:bho, language:bjn, language:bo, language:bs, language:bug, language:bg, language:ca, language:ceb, language:cs, language:cjk, language:ckb, language:crh, language:cy, language:da, language:de, language:dik, language:dyu, language:dz, language:el, language:en, language:eo, language:et, language:eu, language:ee, language:fo, language:fj, language:fi, language:fon, language:fr, language:fur, language:fuv, language:gaz, language:gd, language:ga, language:gl, language:gn, language:gu, language:ht, language:ha, language:he, language:hi, language:hne, language:hr, language:hu, language:hy, language:ig, language:ilo, language:id, language:is, language:it, language:jv, language:ja, language:kab, language:kac, language:kam, language:kn, language:ks, language:ka, language:kk, language:kbp, language:kea, language:khk, language:km, language:ki, language:rw, language:ky, language:kmb, language:kmr, language:knc, language:kg, language:ko, language:lo, language:lij, language:li, language:ln, language:lt, language:lmo, language:ltg, language:lb, language:lua, language:lg, language:luo, language:lus, language:lvs, language:mag, language:mai, language:ml, language:mar, language:min, language:mk, language:mt, language:mni, language:mos, language:mi, language:my, language:nl, language:nn, language:nb, language:npi, language:nqo, language:nso, language:nus, language:ny, language:oc, language:ory, language:pag, language:pa, language:pap, language:pbt, language:pes, language:plt, language:pl, language:pt, language:prs, language:quy, language:ro, language:rn, language:ru, language:sg, language:sa, language:sat, language:scn, language:shn, language:si, language:sk, language:sl, language:sm, language:sn, language:sd, language:so, language:st, language:es, language:sc, language:sr, language:ss, language:su, language:sv, language:swh, language:szl, language:ta, language:taq, language:tt, language:te, language:tg, language:tl, language:th, language:ti, language:tpi, language:tn, language:ts, language:tk, language:tum, language:tr, language:tw, language:tzm, language:ug, language:uk, language:umb, language:ur, language:uzn, language:vec, language:vi, language:war, language:wo, language:xh, language:ydd, language:yo, language:yue, language:zh, language:zsm, language:zu, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.07445, region:us, news-topic, sib-200, sib200","text-classification, topic-classification","Dataset Card for SIB-200 Dataset Summary SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects. The train/validation/test sets ","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nqo, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2309.07445","None","topic-classification"
+"Zyphra/dclm-dedup","Zyphra","2024-10-23 00:07:43","2024-10-24 16:28:13+00:00","1046a49ee913f67e358b99bcb9c65f226f8289bf","6852","27479","16","task_categories:text-generation, language:en, license:cc, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.11794, region:us","text-generation","DCLM-Deduped DCLM is a recently released high quality dataset that uses model-based quality filtering to filter a large subset of common-crawl for similarity to OpenHermes and other instruction-tuning","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc","None","None","None"
+"occiglot/tokenizer-wiki-bench","occiglot","2024-03-13 14:49:07","2024-04-23 21:00:00+00:00","56a5ea2bfc6a6445e9e3ed58130ef1fb2759e6c6","4761","27245","5","language:af, language:ar, language:bg, language:ca, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:ga, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:it, language:ja, language:ko, language:lt, language:lv, language:mr, language:nl, language:no, language:pl, language:pt, language:ro, language:ru, language:sa, language:sk, language:sl, language:sr, language:sv, language:ta, language:te, language:tr, language:uk, language:ur, language:vi, license:mit, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2012.15613, region:us","None","Multilingual Tokenizer Benchmark This dataset includes pre-processed wikipedia data for tokenizer evaluation in 45 languages. We provide more information on the evaluation task in general this blogpos","None","af, ar, bg, ca, cs, da, de, el, en, es, et, eu, fa, fi, fr, ga, he, hi, hr, hu, hy, id, it, ja, ko, lt, lv, mr, nl, no, pl, pt, ro, ru, sa, sk, sl, sr, sv, ta, te, tr, uk, ur, vi","multi","None","None","False","False","False","mit","2012.15613","None","None"
+"argilla/FinePersonas-v0.1","argilla","2024-09-09 14:54:01","2024-12-11 12:25:52+00:00","e72f8145c84a508738ee51bed65d403faf372dd3","2210","27194","385","None","text-generation","FinePersonas Open dataset of 21 Million detailed personas for diverse and controllable synthetic text generation. FinePersonas contains detailed personas for creating customized, realistic synthetic d","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","llama3","None","None","None"
+"mteb/reddit-clustering-p2p","mteb","2022-05-11 08:52:19","2022-09-27 19:13:59+00:00","385e3cb46b4cfa89021f56c4380204149d0efe33","1021","25906","0","language:en, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","10 sets with the following stats: 91 labels & 15592 samples 64 labels & 79172 samples 38 labels & 1942 samples 11 labels & 13224 samples 64 labels & 92303 samples 87 labels & 28607 samples 10 labels &","None","en","mono","None","None","False","False","False","None","None","None","None"
+"cais/wmdp-corpora","cais","2024-03-05 21:08:57","2024-04-25 03:07:23+00:00","daf89fa9b618b63a624228061a9cebacca88009c","779","25827","3","task_categories:text-generation, language:en, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.03218, region:us","text-generation","Dataset Card for WMDP Corpora The Weapons of Mass Destruction Proxy (WMDP) Corpora includes all of the corpora used to perform unlearning on WMDP-Bio and WMDP-Cyber. See our paper, website, and GitHub","None","en","mono","None","None","False","False","False","mit","2403.03218","None","None"
+"mteb/dbpedia","mteb","2024-03-02 20:30:10","2024-03-03 12:01:47+00:00","c0f706b76e590d620bd6618b3ca8efdd34e2d659","1412","25783","3","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:dbpedia, language:en, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"osunlp/Multimodal-Mind2Web","osunlp","2024-03-18 03:32:11","2024-06-05 05:12:21+00:00","1b4c6a8cf9f77b7a5e0d641959935c80c4a05889","1168","25779","58","None","None","Dataset Summary Multimodal-Mind2Web is the multimodal version of Mind2Web, a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete compl","None","en","mono","None","None","False","False","False","openrail","2401.01614","None","None"
+"xlangai/BRIGHT","xlangai","2024-06-07 23:11:53","2024-11-18 04:33:20+00:00","0dc152a5d69327d457c0390c692baf8e5808424b","2611","25759","21","task_categories:text-retrieval, language:en, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2407.12883, region:us, text-retrieval, code, biology, earth_science, economics, psychology, robotics, math","text-retrieval","BRIGHT benchmark BRIGHT is the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. The queries are collected from diverse domains (StackExchange, LeetCode,","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"maykcaldas/smiles-transformers","maykcaldas","2023-04-04 13:10:48","2023-04-04 22:02:47+00:00","abb3705e6c68c767854f35a1d9fd90d84f088393","8113","25459","12","language:en, license:mit, size_categories:1B<n<10B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","smiles-transformers dataset TODO: Add references to the datasets we curated dataset features name: text Molecule SMILES : string name: formula Molecular formula : string name: NumHDonors Number of hid","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","mit","None","None","None"
+"diffusers/pokemon-gpt4-captions","diffusers","2023-11-28 06:54:16","2024-02-18 06:53:30+00:00","6dc8154426e8a36ecc22b0001bb01a5ad78309c7","3028","25383","33","None","text-to-image","Dataset Card for ""pokemon-gpt4-captions"" This dataset is just lambdalabs/pokemon-blip-captions but the captions come from GPT-4 (Turbo). Code used to generate the captions: import base64 from io impor","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"RekaAI/VibeEval","RekaAI","2024-05-02 10:09:50","2024-12-12 22:53:57+00:00","3bdc7c30f09938557a21f1918d6c8c28fd5978f8","2960","25219","39","task_categories:image-to-text, task_categories:image-classification, language:en, language:pl, language:zh, language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2405.02287, region:us, Reka, Vibe, Eval, VibeEval, Vibe-Eval, Hard","image-to-text, image-classification","Vibe-Eval A benchmark for evaluating multimodal chat models, including especially challenging examples. [Link to paper] [Blogpost] [Github] Dataset Each example has the following fields: example_id: a","None","en, pl, zh, ja","multi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"tasksource/babi_nli","tasksource","2023-01-01 14:39:33","2024-06-19 12:03:10+00:00","80b689dc668b50f3640fc5368fd1893d385cb4e1","1814","25066","3","None","text-classification, natural-language-inference","bAbi_nli bAbI tasks recasted as natural language inference. https://github.com/facebookarchive/bAbI-tasks tasksource recasting code: https://colab.research.google.com/drive/1J_RqDSw9iPxJSBvCJu-VRbjXnr","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","bsd","None","None","natural-language-inference"
+"osunlp/MagicBrush","osunlp","2023-06-14 02:20:33","2023-11-07 20:19:33+00:00","1d8d4629150d18ca50afab66391866f2085be989","1863","24826","80","None","text-to-image, image-to-image","Dataset Card for MagicBrush Dataset Summary MagicBrush is the first large-scale, manually-annotated instruction-guided image editing dataset covering diverse scenarios single-turn, multi-turn, mask-pr","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"joelniklaus/MultiLegalPile_Wikipedia_Filtered","joelniklaus","2022-11-17 19:28:00","2022-11-29 21:52:23+00:00","d0925f0e223bcfb2840e66328835380f96f8f589","8249","24823","1","task_categories:fill-mask, annotations_creators:other, language_creators:found, multilinguality:multilingual, source_datasets:original, language:bg, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:fi, language:fr, language:ga, language:hr, language:hu, language:it, language:lt, language:lv, language:mt, language:nl, language:pl, language:pt, language:ro, language:sk, language:sl, language:sv, license:cc-by-4.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, region:us","fill-mask","A filtered version of the MultiLegalPile dataset, together with wikipedia articles.","","bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"papluca/language-identification","papluca","2022-03-02 23:29:22","2022-07-15 10:11:23+00:00","aa56583bf2bc52b0565770607d6fc3faebecf9e2","1015","24688","44","None","text-classification, multi-class-classification","Dataset Card for Language Identification dataset Dataset Summary The Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label. This d","None","ar, bg, de, el, en, es, fr, hi, it, ja, nl, pl, pt, ru, sw, th, tr, ur, vi, zh","multi","u|n|k|n|o|w|n","None","False","False","False","None","None","None","multi-class-classification"
+"severo/flores_101","severo","2023-06-20 21:40:23","2022-10-27 08:37:36+00:00","bc58ae43b22607b3e1e2bf3ae1bc5cb053495abb","5534","24646","2","task_categories:text-generation, task_categories:translation, annotations_creators:found, language_creators:expert-generated, multilinguality:multilingual, multilinguality:translation, source_datasets:extended|flores, language:af, language:am, language:ar, language:hy, language:as, language:ast, language:az, language:be, language:bn, language:bs, language:bg, language:my, language:ca, language:ceb, language:zho, language:hr, language:cs, language:da, language:nl, language:en, language:et, language:tl, language:fi, language:fr, language:ff, language:gl, language:lg, language:ka, language:de, language:el, language:gu, language:ha, language:he, language:hi, language:hu, language:is, language:ig, language:id, language:ga, language:it, language:ja, language:jv, language:kea, language:kam, language:kn, language:kk, language:km, language:ko, language:ky, language:lo, language:lv, language:ln, language:lt, language:luo, language:lb, language:mk, language:ms, language:ml, language:mt, language:mi, language:mr, language:mn, language:ne, language:ns, language:no, language:ny, language:oc, language:or, language:om, language:ps, language:fa, language:pl, language:pt, language:pa, language:ro, language:ru, language:sr, language:sn, language:sd, language:sk, language:sl, language:so, language:ku, language:es, language:sw, language:sv, language:tg, language:ta, language:te, language:th, language:tr, language:uk, language:umb, language:ur, language:uz, language:vi, language:cy, language:wo, language:xh, language:yo, language:zu, license:cc-by-sa-4.0, size_categories:100K<n<1M, modality:tabular, modality:text, library:datasets, library:mlcroissant, arxiv:2106.03193, region:us, conditional-text-generation","text-generation, translation","One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage","@inproceedings{, title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={ Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\\'{a}n, Francisco and Fan, Angela }, year={2021} }","af, am, ar, hy, as, ast, az, be, bn, bs, bg, my, ca, ceb, zho, hr, cs, da, nl, en, et, tl, fi, fr, ff, gl, lg, ka, de, el, gu, ha, he, hi, hu, is, ig, id, ga, it, ja, jv, kea, kam, kn, kk, km, ko, ky, lo, lv, ln, lt, luo, lb, mk, ms, ml, mt, mi, mr, mn, ne, ns, no, ny, oc, or, om, ps, fa, pl, pt, pa, ro, ru, sr, sn, sd, sk, sl, so, ku, es, sw, sv, tg, ta, te, th, tr, uk, umb, ur, uz, vi, cy, wo, xh, yo, zu","multi","u|n|k|n|o|w|n","flores","False","False","False","cc-by-sa-4.0","2106.03193","None","None"
+"microsoft/orca-agentinstruct-1M-v1","microsoft","2024-10-25 23:41:54","2024-11-01 00:14:29+00:00","86d609183249ff8037eae33d76ebca3af9390ea8","8489","24578","415","task_categories:question-answering, language:en, license:cdla-permissive-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework. AgentInstruct is ","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"ccdv/arxiv-classification","ccdv","2022-03-02 23:29:22","2024-08-08 05:50:57+00:00","d4bf86db1cecd1f42e5ef24f1718d7c02c3250a7","865","24496","20","None","text-classification, multi-class-classification, topic-classification","Arxiv Classification: a classification of Arxiv Papers (11 classes). This dataset is intended for long context classification (documents have all > 4k tokens). Copied from ""Long Document Classificatio","None","e, n","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","None","None","None","multi-class-classification, topic-classification"
+"maharshipandya/spotify-tracks-dataset","maharshipandya","2023-06-14 11:42:44","2023-12-01 13:29:39+00:00","635b034f69257814eff850a5c2b3346fe458134f","981","23585","85","None","feature-extraction, tabular-classification, tabular-regression","Content This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded qu","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","bsd","None","None","None"
+"GEM/viggo","GEM","2022-03-02 23:29:22","2022-10-24 15:31:07+00:00","c851cd5ff2ee92f0137fcf24014e37427a2d30b7","786","23502","33","None","table-to-text","ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet con","@inproceedings{juraska-etal-2019-viggo, title = ""{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation"", author = ""Juraska, Juraj and Bowden, Kevin and Walker, Marilyn"", booktitle = ""Proceedings of the 12th International Conference on Natural Language Generation"", month = oct # ""{--}"" # nov, year = ""2019"", address = ""Tokyo, Japan"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/W19-8623"", doi = ""10.18653/v1/W19-8623"", pages = ""164--172"", }","en","mono","u|n|k|n|o|w|n","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"mlfoundations/MINT-1T-ArXiv","mlfoundations","2024-06-29 23:50:55","2024-09-19 21:32:59+00:00","7c5b00ffd5b563071010c3bf2082b4a8f836eb72","2347","23486","49","None","image-to-text, text-generation","🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billi","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2406.11271","None","None"
+"intfloat/multilingual_cc_news","intfloat","2023-03-22 08:25:34","2023-04-23 08:19:06+00:00","1c24f619ae44d0a4c87fde72efa741dc25f7530e","2171","23387","17","None","None","\\ Multilingual CC-News dataset. This is the processed version from https://huggingface.co/datasets/CloverSearch/cc-news-mutlilingual.","None","en, zh, fr, de, af, ar","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"JailbreakBench/JBB-Behaviors","JailbreakBench","2024-06-12 12:57:23","2024-09-26 11:05:44+00:00","886acc352a31533ffbcf4ef22c744658688086fc","2829","23367","36","language:en, license:mit, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2404.01318, arxiv:2311.03348, arxiv:2307.15043, arxiv:2402.04249, doi:10.57967/hf/2540, region:us, jailbreaks, large language models, harmful behaviors, ml safety","None","An Open Robustness Benchmark for Jailbreaking Language Models NeurIPS 2024 Datasets and Benchmarks Track Paper | Leaderboard | Benchmark code What is JailbreakBench? Jailbreakbench is an open-source r","None","en","mono","None","None","False","False","False","mit","2404.01318","None","None"
+"Salesforce/blip3-kale","Salesforce","2024-08-27 20:53:04","2024-11-14 23:39:47+00:00","adbc857b863005dbed15596d49410ebcb0392922","9257","22543","34","task_categories:image-to-text, language:en, license:apache-2.0, size_categories:100M<n<1B, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2408.08872, arxiv:2406.11271, arxiv:2311.03079, arxiv:2411.07461, region:us","image-to-text","🥬 BLIP3-KALE:Knowledge Augmented Large-scale Dense Captions BLIP3-KALE is an open-source dataset of 218 million image-text pairs, featuring knowledge-augmented dense captions combining web-scale knowl","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","2408.08872","None","None"
+"mteb/climate-fever","mteb","2024-03-02 20:45:22","2024-03-03 11:03:05+00:00","47f2ac6acb640fc46020b02a5b59fdda04d39380","1348","22304","2","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:climate-fever, language:en, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"chenghao/scielo_books","chenghao","2022-03-02 23:29:22","2022-07-01 18:34:59+00:00","c3d46ee0b1969347cb803449156be9a59e275ae7","905","21800","1","None","sequence-modeling, language-modeling","Dataset Summary This dataset contains all text from open-access PDFs on scielo.org. As of Dec. 5 2021, the total number of books available is 962. Some of them are not in native PDF format (e.g. scann","None","en, pt, es","multi","n|<|1|K","None","False","False","False","cc-by-nc-sa-3.0","None","None","language-modeling"
+"mythicinfinity/libritts","mythicinfinity","2024-02-08 02:07:23","2024-02-09 21:19:32+00:00","35f26bdafa9883b5524f17931216af78632db8eb","1164","21657","10","None","text-to-speech","Dataset Card for LibriTTS LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speec","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","1904.02882","None","None"
+"neural-bridge/rag-dataset-12000","neural-bridge","2023-10-02 17:18:39","2024-02-05 18:25:13+00:00","586c0d0ddeb022fccd909c7b415cc2ca8660baa4","1158","21588","127","None","question-answering","Retrieval-Augmented Generation (RAG) Dataset 12000 Retrieval-Augmented Generation (RAG) Dataset 12000 is an English dataset designed for RAG-optimized models, built by Neural Bridge AI, and released u","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"nyu-visionx/Cambrian-Alignment","nyu-visionx","2024-06-04 06:03:52","2024-07-23 09:35:19+00:00","73fe54d7f4d8be6153f2e0126fa516d57cc791b3","2572","21437","32","None","visual-question-answering, question-answering","Cambrian-Alignment Dataset Please see paper & website for more information: https://cambrian-mllm.github.io/ https://arxiv.org/abs/2406.16860 Overview Cambrian-Alignment is an question-answering align","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2406.16860","None","None"
+"austindavis/lichess_uci","austindavis","2024-04-01 15:49:32","2024-10-31 09:25:35+00:00","924a2ba80bae2ebacbda4ecb63ab520178b79028","5968","21435","0","task_categories:other, source_datasets:database.lichess.org, language:en, size_categories:1B<n<10B, modality:tabular, modality:text, region:us, chess, UCI, Lichess","other","Dataset Card for Lichess.org Database in UCI format Dataset Description Dataset Summary The Lichess.org database has been converted to UCI format, making it easier to analyze and interpret chess games","None","en","mono","1|B|<|n|<|1|0|B","lichess_uci","False","False","False","None","None","None","None"
+"PromptEval/PromptEval_MMLU_full","PromptEval","2024-06-04 02:04:07","2024-06-07 05:40:35+00:00","c38a4ee89ee74ad149f3bd9b4978a719b40abe2b","1011","21420","3","None","question-answering","MMLU Multi-Prompt Evaluation Data Overview This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multit","None","en","mono","None","None","False","False","False","mit","2405.17202","None","None"
+"mteb/cqadupstack-english","mteb","2024-03-02 19:35:24","2024-03-02 19:52:17+00:00","ad9991cb51e31e31e430383c75ffb2885547b5f0","1029","21390","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"stanford-oval/wikipedia","stanford-oval","2024-08-23 07:39:01","2024-11-04 22:50:33+00:00","519d22104786a46b7320f2c9196e2ce068666e2c","3850","21286","7","None","text-retrieval, text-generation","This dataset contains preprocessed and chunked Wikipedia HTML dumps from 10 languages. Refer to the following for more information: GitHub repository: https://github.com/stanford-oval/WikiChat Papers:","None","en, de, it, pt, fa, fr, ja, es, ru, zh","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","None","2305.14292","None","None"
+"jondurbin/gutenberg-dpo-v0.1","jondurbin","2024-01-11 13:15:41","2024-01-12 13:05:37+00:00","7f48159fd39e62b7b4ddb3dfc0d189d6a5619862","1105","21200","129","language:en, license:cc-by-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, dpo","None","Gutenberg DPO Overview This is a dataset meant to enhance novel writing capabilities of LLMs, by using public domain books from Project Gutenberg Process First, the each book is parsed, split into cha","None","en","mono","n|<|1|K","None","False","False","False","cc-by-4.0","None","None","None"
+"agibot-world/AgiBotWorld-Alpha","agibot-world","2024-12-19 09:37:11","2025-01-20 08:57:47+00:00","5e14c7631342179bdc0a4f22287ed93bf2989ff8","20994","21024","164","None","other","Key Features 🔑 1 million+ trajectories from 100 robots. 100+ real-world scenarios across 5 target domains. Cutting-edge hardware: visual tactile sensors / 6-DoF dexterous hand / mobile dual-arm robots","None","en","mono","n|>|1|T","None","False","auto","False","None","None","None","None"
+"mxeval/multi-humaneval","mxeval","2023-03-14 21:37:18","2023-03-20 19:20:48+00:00","63e25957849ef5676e5b686ed0a60106697a73c0","1021","20962","11","None","text-generation","A collection of execution-based multi-lingual benchmark for code generation.","@article{mbxp_athiwaratkun2022, title = {Multi-lingual Evaluation of Code Generation Models}, author = {Athiwaratkun, Ben and Gouda, Sanjay Krishna and Wang, Zijian and Li, Xiaopeng and Tian, Yuchen and Tan, Ming and Ahmad, Wasi Uddin and Wang, Shiqi and Sun, Qing and Shang, Mingyue and Gonugondla, Sujan Kumar and Ding, Hantian and Kumar, Varun and Fulton, Nathan and Farahani, Arash and Jain, Siddhartha and Giaquinto, Robert and Qian, Haifeng and Ramanathan, Murali Krishna and Nallapati, Ramesh and Ray, Baishakhi and Bhatia, Parminder and Sengupta, Sudipta and Roth, Dan and Xiang, Bing}, doi = {10.48550/ARXIV.2210.14868}, url = {https://arxiv.org/abs/2210.14868}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }","en","mono","None","None","False","False","False","apache-2.0","2210.14868","None","None"
+"0x22almostEvil/multilingual-wikihow-qa-16k","0x22almostEvil","2023-04-29 03:37:09","2023-05-13 16:59:15+00:00","3211e4c50a3c64090a548bf5fc10c3caaeb26eff","965","20649","9","None","question-answering","Dataset Card for multilingual WikiHow with ~16.8K entries. ~(2-2.2)K for each language. Warning [1] The WikiHow team contacted me and made it clear that they forbid the use of their data for machine l","None","en, ru, pt, it, es, fr, de, nl","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-3.0","None","None","None"
+"free-law/Caselaw_Access_Project","free-law","2024-02-28 15:06:54","2024-03-16 20:01:40+00:00","cee53e98cb2b401865a017ef291a59fbb3407179","8908","20531","63","None","text-generation","The Caselaw Access Project In collaboration with Ravel Law, Harvard Law Library digitized over 40 million U.S. court decisions consisting of 6.7 million cases from the last 360 years into a dataset th","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc0-1.0","None","None","None"
+"mteb/cqadupstack-physics","mteb","2024-03-02 19:36:35","2024-03-02 19:56:34+00:00","79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4","954","20386","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"common-canvas/commoncatalog-cc-by-nd","common-canvas","2023-10-19 02:10:04","2024-05-16 19:42:40+00:00","3991ff88ebf48e0435ec8d044d2f4b159f4f716e","2174","20377","2","None","text-to-image","Dataset Card for CommonCatalog CC-BY-ND This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 20","None","en","mono","None","None","False","False","False","cc-by-nd-4.0","2310.16825","None","None"
+"rayliuca/WikidataLabels","rayliuca","2024-01-01 00:23:08","2024-01-11 04:17:57+00:00","122b626f6127ca111659a4354590845b30736f9a","5752","20169","1","task_categories:translation, task_categories:text2text-generation, language:en, language:fr, language:de, language:ja, language:zh, language:hi, language:ar, language:bn, language:ru, language:es, license:cc0-1.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation, text2text-generation","Wikidata Labels Large parallel corpus for machine translation Entity label data extracted from Wikidata (2022-01-03), filtered for item entities only Only download the languages you need with datasets","None","en, fr, de, ja, zh, hi, ar, bn, ru, es","multi","None","None","False","False","False","cc0-1.0","None","None","None"
+"MMInstruction/ArxivCap","MMInstruction","2023-12-01 15:47:54","2024-10-03 03:17:00+00:00","90655499df31afa45710c4a58ec60267e342fa64","1994","20047","50","task_categories:image-to-text, language:en, license:cc-by-4.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.00231, region:us, arxiv, multi-modal","image-to-text","Dataset Card for ArxivCap Data Instances Example-1 of single (image, caption) pairs ""......"" stands for omitted parts. { 'src': 'arXiv_src_2112_060/2112.08947', 'meta': { 'meta_from_kaggle': { 'journe","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","2403.00231","None","None"
+"allenai/wildguardmix","allenai","2024-06-15 03:38:29","2024-06-29 06:29:47+00:00","d29c47f41c8b51348b5c8e8c81c039b3132b66d1","3544","19774","21","task_categories:text-classification, language:en, license:odc-by, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.18495, region:us, safe, safety, jailbreak, ai-safety, llm, lm, moderation, classification, refusal","text-classification","Dataset Card for WildGuardMix Disclaimer: The data includes examples that might be disturbing, harmful or upsetting. It includes a range of harmful topics such as discriminatory language and discussio","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","odc-by","2406.18495","None","None"
+"HuggingFaceTB/smoltalk","HuggingFaceTB","2024-11-17 15:52:41","2024-11-26 11:02:25+00:00","5a40ecb185e55dd30edf3c24b77e67f6ea0d659b","6798","19710","293","None","None","SmolTalk Dataset description This is a synthetic dataset designed for supervised finetuning (SFT) of LLMs. It was used to build SmolLM2-Instruct family of models and contains 1M samples. During the de","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"m-a-p/Matrix","m-a-p","2024-05-08 12:49:11","2024-06-03 07:26:27+00:00","4b17de46337e064c95ce92d20a81ecabf5d3424b","2633","19665","158","None","text-generation","Matrix An open-source pretraining dataset containing 4690 billion tokens, this bilingual dataset with both English and Chinese texts is used for training neo models. Dataset Composition The dataset co","None","en, zh","bi","n|>|1|T","None","False","False","False","apache-2.0","None","None","None"
+"llamafactory/tiny-supervised-dataset","llamafactory","2024-06-07 19:25:33","2024-06-10 07:41:37+00:00","2ff06c75e01ae4195ed34fe77606e15902ea0b0d","4269","19543","1","None","text-generation, question-answering","None","None","en, zh","bi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"imageomics/TreeOfLife-10M","imageomics","2024-01-23 21:06:53","2024-12-20 16:05:17+00:00","343c36b9b362494065ac427ac1da989b834b22cf","15790","19524","25","task_categories:image-classification, task_categories:zero-shot-classification, language:en, language:la, size_categories:1M<n<10M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, doi:10.57967/hf/1972, region:us, biology, images, animals, evolutionary biology, CV, multimodal, clip, species, taxonomy, knowledge-guided, imbalanced","image-classification, zero-shot-classification","Dataset Card for TreeOfLife-10M Dataset Description Repository: Imageomics/bioclip Paper: BioCLIP: A Vision Foundation Model for the Tree of Life (arXiv) Dataset Summary With over 10 million images co","None","en, la","bi","1|||0|||M|||<|||n|||<|||1|||0|||0|||M","None","False","False","False","None","None","None","None"
+"bghira/mj-v52-redux","bghira","2024-04-12 23:41:45","2024-04-13 12:58:25+00:00","14191fc246e72c55484696ce457c2dca854296ef","4670","19498","3","task_categories:text-to-image, task_categories:image-to-image, language:en, license:unlicense, size_categories:100K<n<1M, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, midjourney, mj52, midjourney v5.2","text-to-image, image-to-image","None","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","unlicense","None","None","None"
+"BAAI/IndustryCorpus","BAAI","2024-07-10 06:19:04","2024-07-23 03:32:53+00:00","83f9d95de842486afd0dd6c99cd8c1e6bfa0c5ae","1212","19324","51","None","text-generation","[中文主页] Industry models play a crucial role in driving enterprise intelligence transformation and innovative development. High-quality industry data is key to improving the performance of large models ","None","zh, en","bi","n|>|1|T","None","False","False","False","apache-2.0","None","None","None"
+"ai4privacy/pii-masking-200k","ai4privacy","2023-11-06 03:34:07","2024-04-21 17:27:20+00:00","a157bb3aa654864f870dd728fd6d06cc0454fabc","1751","19274","91","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, task_categories:translation, task_categories:fill-mask, task_categories:tabular-classification, task_categories:tabular-to-text, task_categories:table-to-text, task_categories:text-retrieval, task_categories:other, multilinguality:multilingual, source_datasets:original, language:en, language:fr, language:de, language:it, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, doi:10.57967/hf/1532, region:us, legal, business, psychology, privacy","text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation, translation, fill-mask, tabular-classification, tabular-to-text, table-to-text, text-retrieval, other","Ai4Privacy Community Join our community at https://discord.gg/FmzWshaaQT to help build open datasets for privacy masking. Purpose and Features Previous world's largest open dataset for privacy. Now it","None","en, fr, de, it","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","9505040","None","None"
+"manifoldlabs/Infinity-Instruct","manifoldlabs","2024-08-20 00:57:17","2024-08-20 02:15:57+00:00","bdf12f953ee63be76d4a0612aae79ce62a310216","971","19251","2","None","text-generation","Infinity Instruct Beijing Academy of Artificial Intelligence (BAAI) [Paper][Code][🤗] (would be released soon) The quality and scale of instruction data are crucial for model performance. Recently, ope","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"jackyhate/text-to-image-2M","jackyhate","2024-09-11 14:02:35","2024-09-22 09:38:54+00:00","e4ece89e640210e9fc3fd0966f5a45291bdb665c","6582","19100","66","task_categories:text-to-image, task_categories:image-to-text, task_categories:image-classification, language:en, license:mit, size_categories:100K<n<1M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, doi:10.57967/hf/3066, region:us","text-to-image, image-to-text, image-classification","text-to-image-2M: A High-Quality, Diverse Text-to-Image Training Dataset Overview text-to-image-2M is a curated text-image pair dataset designed for fine-tuning text-to-image models. The dataset consi","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"KShivendu/dbpedia-entities-openai-1M","KShivendu","2023-06-20 22:29:43","2024-02-19 08:24:43+00:00","8cbbd08b474a0687e69a743953e3154d7173e543","2674","19061","20","None","feature-extraction","1M OpenAI Embeddings -- 1536 dimensions Created: June 2023. Text used for Embedding: title (string) + text (string) Embedding Model: text-embedding-ada-002 First used for the pgvector vs VectorDB (Qdr","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"mteb/cqadupstack-android","mteb","2024-02-26 17:05:12","2024-03-02 19:28:04+00:00","789b40e361a64fdddfb9860dc11094dbcf527d58","1011","19051","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"WenhaoWang/VidProM","WenhaoWang","2024-02-25 15:20:21","2024-09-26 13:55:35+00:00","03d4d5bb19c1d0b75861f9a15ed26ae92039fe7e","4767","19005","60","task_categories:text-to-video, task_categories:text-to-image, source_datasets:original, language:en, license:cc-by-nc-4.0, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, modality:video, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.06098, region:us, prompts, text-to-video, text-to-image, Pika, VideoCraft2, Text2Video-Zero, ModelScope, Video Generative Model Evaluation, Text-to-Video Diffusion Model Development, Text-to-Video Prompt Engineering, Efficient Video Generation, Fake Video Detection, Video Copy Detection for Diffusion Models","text-to-video, text-to-image","Summary This is the dataset proposed in our paper VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models (NeurIPS 2024). VidProM is the first dataset featuring 1.67 mi","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-nc-4.0","2403.06098","None","None"
+"Tanvir1337/muse_textbooks","Tanvir1337","2023-12-26 18:02:51","2024-09-08 19:03:07+00:00","94c48bcaa0ddf768c5c4cd33e581ab95b1db5d1e","6196","18842","0","task_categories:text-generation, task_categories:text2text-generation, task_ids:natural-language-inference, language_creators:AI Generated, language_creators:Google Gemini, multilinguality:monolingual, language:en, language:code, license:cc-by-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, Gemini, Synthetic, Textbook","text-generation, text2text-generation, natural-language-inference","muse_textbooks [JSONL dataset] Synthetic Textbooks generated utilizing Google Gemini Pro API using muse. Implications The generation of extensive open-source synthetic textbook data serves as a crucia","None","en, code","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","natural-language-inference"
+"HuggingFaceH4/MATH-500","HuggingFaceH4","2024-11-15 13:26:48","2024-11-15 13:36:00+00:00","ff5b20257d8185524591543f8ff5993951537bb8","13044","18761","54","None","text-generation","Dataset Card for MATH-500 This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file","None","en","mono","None","None","False","False","False","None","None","None","None"
+"visheratin/laion-coco-nllb","visheratin","2023-06-18 06:58:28","2024-04-11 16:36:31+00:00","759c44439e15d2137720c1b897c3f51444775536","1675","18499","40","None","image-to-text, translation","LAION COCO translated into 200 languages This dataset contains the samples of the LAION-COCO dataset translated to 200 languages using the largest NLLB-200 model (3.3B parameters). Fields description ","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","2309.01859","None","None"
+"pufanyi/MIMICIT","pufanyi","2023-07-12 07:22:42","2024-03-28 03:35:16+00:00","7bed19ffea23748eff599fac87695b0e54c3094b","1770","18491","42","None","None","Bo Li*,♠,1 Yuanhan Zhang*,♠,1 Liangyu Chen*,1 Jinghao Wang*,1 Fanyi Pu*,1 Jingkang Yang1 Chunyuan Li2 Ziwei Liu✉,1 1S-Lab, Nanyang Technological University 2Microsoft Research, Redmond ♠Co-Project Lea","None","en, zh, es, ja, fr, ko, ar","multi","1|M|<|n|<|1|0|M","None","False","auto","False","mit","None","None","None"
+"data-is-better-together/open-image-preferences-v1","data-is-better-together","2024-11-25 15:15:43","2024-12-09 14:45:02+00:00","071719e8f4b216b7de877384aaeb68bf95d40b04","8001","18469","23","task_categories:text-to-image, task_categories:image-to-text, language:en, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, library:distilabel, region:us, preference, vlm, flux, stable-diffusion, synthetic, distilabel","text-to-image, image-to-text","Open Image Preferences Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K. Image 1 Image 2 Prompt: 8-bit pixel art of a blue knight, green car, and ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Muennighoff/flan","Muennighoff","2022-12-12 11:32:26","2022-12-23 18:57:00+00:00","049702ca5ffc5b3c62bf226aac67ba1493e8d548","2546","18291","50","None","other","This is a repreprocessed version of the FLAN dataset with any updates that have been made to the FLAN datasets since the release of the original FLAN. The script is available here. Tasks: {'aeslc_10te","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","2109.01652","None","None"
+"longvideobench/LongVideoBench","longvideobench","2024-06-12 06:58:56","2024-10-14 05:43:04+00:00","60d1c89c1919a198b73be39c2babb213b29d6a5c","4024","18245","17","None","multiple-choice, visual-question-answering","Dataset Card for LongVideoBench Large multimodal models (LMMs) are handling increasingly longer and more complex inputs. However, few public benchmarks are available to assess these advancements. To a","None","en","mono","None","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"cardiffnlp/relentless","cardiffnlp","2023-05-24 09:57:47","2023-10-14 10:53:59+00:00","b016a6f3d327137bfa3bf4ef61d6f08866157227","840","18125","1","None","None","Named-entities Relation Ranking.","TBA","en","mono","n|<|1|K","None","False","False","False","other","2305.15002","None","None"
+"rootsautomation/ScreenSpot","rootsautomation","2024-04-10 14:34:07","2024-04-10 19:52:26+00:00","0be08781e2e188582f6131625ae1598d443b4d5d","2628","18094","21","task_categories:text-generation, task_categories:image-to-text, language:en, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2401.10935, region:us","text-generation, image-to-text","Dataset Card for ScreenSpot GUI Grounding Benchmark: ScreenSpot. Created researchers at Nanjing University and Shanghai AI Laboratory for evaluating large multimodal models (LMMs) on GUI grounding tas","None","en","mono","None","None","False","False","False","apache-2.0","2401.10935","None","None"
+"wecover/OPUS_GlobalVoices","wecover","2024-01-31 07:22:25","2024-11-24 03:46:34+00:00","4beae40f081a88d573fa3f60eb35ce3f2a3bc8b8","8465","18077","0","language:am, language:ar, language:bg, language:bn, language:ca, language:cs, language:da, language:de, language:el, language:en, language:eo, language:es, language:fa, language:fr, language:he, language:hi, language:hu, language:id, language:it, language:km, language:ko, language:ku, language:mg, language:mk, language:my, language:ne, language:nl, language:or, language:pa, language:pt, language:pl, language:ro, language:ru, language:sq, language:sr, language:sv, language:sw, language:tr, language:ur, language:zh, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","am, ar, bg, bn, ca, cs, da, de, el, en, eo, es, fa, fr, he, hi, hu, id, it, km, ko, ku, mg, mk, my, ne, nl, or, pa, pt, pl, ro, ru, sq, sr, sv, sw, tr, ur, zh","multi","None","None","False","False","False","None","None","None","None"
+"ai4privacy/pii-masking-300k","ai4privacy","2024-03-27 21:29:53","2024-04-21 16:33:46+00:00","3b99ae5980049a0dff660f424916afa9954e809b","899","18003","48","None","text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation, translation, fill-mask, tabular-classification, tabular-to-text, table-to-text, text-retrieval, other","Purpose and Features 🌍 World's largest open dataset for privacy masking 🌎 The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, espe","None","en, fr, de, it, es, nl","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","None"
+"zeroshot/twitter-financial-news-topic","zeroshot","2022-09-07 18:43:21","2024-02-23 19:04:37+00:00","acbc8af2a35ccf0916124efcbe9e6cf25f191012","1935","17946","34","None","text-classification, multi-class-classification","Dataset Description The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","multi-class-classification"
+"mteb/cqadupstack-gaming","mteb","2024-03-02 19:35:47","2024-03-02 19:54:21+00:00","4885aa143210c98657558c04aaf3dc47cfb54340","986","17922","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"nguyenvulebinh/asr-alignment","nguyenvulebinh","2024-01-04 08:46:30","2024-01-08 08:48:13+00:00","d4dfbb6e382fcebe5ef340c38f83fa753d47d7b0","2221","17809","2","None","None","Speech Recognition Alignment Dataset This dataset is a variation of several widely-used ASR datasets, encompassing Librispeech, MuST-C, TED-LIUM, VoxPopuli, Common Voice, and GigaSpeech. The differenc","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","None","None","None"
+"Salesforce/xlam-function-calling-60k","Salesforce","2024-06-13 02:27:32","2025-01-24 19:25:58+00:00","26d14ebfe18b1f7b524bd39b404b50af5dc97866","1977","17754","410","None","question-answering, text-generation, reinforcement-learning","APIGen Function-Calling Datasets Paper | Website | Models This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-4.0","2406.18518","None","None"
+"CohereForAI/Global-MMLU","CohereForAI","2024-12-01 22:45:59","2024-12-12 13:49:50+00:00","75a8cf195b89938fce486bbb607a881faa123df0","7116","17570","103","language:en, language:ar, language:bn, language:es, language:fr, language:hi, language:ru, language:de, language:id, language:it, language:ja, language:ko, language:pt, language:zh, language:yo, language:nl, language:ro, language:uk, language:vi, language:tr, language:pl, language:fa, language:cs, language:he, language:el, language:ms, language:fil, language:te, language:si, language:ne, language:ky, language:sv, language:lt, language:sr, language:mg, language:so, language:ha, language:am, language:sn, language:ig, language:ny, language:sw, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, library:argilla, arxiv:2412.03304, region:us, argilla","None","Dataset Summary Global-MMLU 🌍 is a multilingual evaluation set spanning 42 languages, including English. This dataset combines machine translations for MMLU questions along with professional translati","None","en, ar, bn, es, fr, hi, ru, de, id, it, ja, ko, pt, zh, yo, nl, ro, uk, vi, tr, pl, fa, cs, he, el, ms, fil, te, si, ne, ky, sv, lt, sr, mg, so, ha, am, sn, ig, ny, sw","multi","None","None","False","False","False","None","2412.03304","None","None"
+"Joemgu/sumstew","Joemgu","2023-05-30 20:36:23","2023-06-21 13:07:18+00:00","3d3268d0774d13711f97db82ff6b90577fc72c09","2524","17538","9","None","summarization","Dataset Card for ""sumstew"" TL DR: Sumstew is a abstractive, multilingual Dataset, with a balanced number of samples from a diverse set of summarization Datasets. The input sizes range up to 16384 toke","None","en, de, fr, it, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"mteb/touche2020","mteb","2024-03-02 20:50:18","2024-03-03 11:20:23+00:00","a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f","1105","17511","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"mlfoundations/VisIT-Bench","mlfoundations","2023-06-24 07:48:17","2024-01-23 08:48:48+00:00","e08ea1c0eded7a091a7c8e0e61e7a0fadd9083f1","903","17492","15","None","None","Dataset Card for VisIT-Bench Dataset Description Links Dataset Structure Data Fields Data Splits Data Loading Licensing Information Annotations Considerations for Using the Data Citation Information D","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2308.06595","None","None"
+"nvidia/ChatQA-Training-Data","nvidia","2024-04-29 07:25:54","2024-06-04 06:20:24+00:00","164f018cf5e7862ae5ef9c2b8ada47b07e3e3a31","1086","17378","165","None","None","Data Description We release the training dataset of ChatQA. It is built and derived from existing datasets: DROP, NarrativeQA, NewsQA, Quoref, ROPES, SQuAD1.1, SQuAD2.0, TAT-QA, a SFT dataset, as well","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","1903.00161","None","None"
+"openbmb/RLAIF-V-Dataset","openbmb","2024-05-19 15:34:55","2024-11-03 07:33:35+00:00","586aff0ea91b485a73fe99f65570f054c10c79d9","1557","17264","153","None","visual-question-answering","Dataset Card for RLAIF-V-Dataset GitHub | Paper News: [2024.05.28] 📃 Our paper is accesible at arxiv now! [2024.05.20] 🔥 Our data is used in MiniCPM-Llama3-V 2.5, which represents the first end-side M","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","2405.17220","None","None"
+"gaia-benchmark/GAIA","gaia-benchmark","2023-10-20 07:06:54","2024-03-26 10:54:04+00:00","06dfd28455b4bb0ee76378b97adc2c70aa43b701","794","17234","179","None","None","GAIA dataset GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc). We added gating to p","","en","mono","None","None","False","auto","False","None","2311.12983","None","None"
+"vidore/docvqa_test_subsampled","vidore","2024-05-31 09:02:21","2024-06-27 12:43:39+00:00","b1d89eda849e636676df6ead8002602fb1858600","2535","17096","3","None","question-answering, visual-question-answering","Dataset Description This is the test set taken from the DocVQA dataset. It includes collected images from the UCSF Industry Documents Library. Questions and answers were manually annotated. Example of","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"Spawning/PD12M","Spawning","2024-09-26 18:20:09","2025-01-09 17:42:14+00:00","867988b01138799b89d3ffdd5b4f7e1455951f32","1078","17045","150","None","None","PD12M Summary At 12.4 million image-caption pairs, PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. T","None","en","mono","None","None","False","False","False","cdla-permissive-2.0","2410.23144","None","None"
+"Muennighoff/natural-instructions","Muennighoff","2022-12-17 21:45:01","2022-12-23 20:08:44+00:00","a29a9757125f4bb1c26445ad0d2ef7d9b2cc9c4c","1061","16904","58","task_categories:other, annotations_creators:crowdsourced, annotations_creators:expert-generated, multilinguality:monolingual, language:en, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","other","Preprocessed version of Super-Natural-Instructions from https://github.com/allenai/natural-instructions/tree/master/splits. The same inputs may appear with different outputs, thus to avoid duplicate i","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"mteb/cqadupstack-gis","mteb","2024-03-02 19:36:00","2024-03-02 19:53:22+00:00","5003b3064772da1887988e05400cf3806fe491f2","1005","16884","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"mteb/cqadupstack-mathematica","mteb","2024-03-02 19:36:14","2024-03-02 19:55:33+00:00","90fceea13679c63fe563ded68f3b6f06e50061de","967","16830","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"sail/regmix-data","sail","2024-07-02 13:23:56","2024-09-12 08:12:18+00:00","0db514aa78fd8ce3db09332d7640831ebf15a54c","848","16778","4","None","None","RegMix Data Dataset Description The RegMix Data is a curated dataset derived from the Pile-Uncopyrighted, specifically designed for the RegMix paper (https://huggingface.co/papers/2407.01492). This da","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","None","None","None"
+"wikimedia/wit_base","wikimedia","2022-05-02 16:08:58","2022-11-04 15:09:33+00:00","ff6d4fb32fd566d3a1fa20e946cba3234179465e","1479","16757","59","None","image-to-text, text-retrieval, image-captioning","Dataset Card for WIT Dataset Summary Wikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset. From the official blog post: The core training data i","None","af, an, ar, arz, ast, az, azb, ba, bar, be, bg, bn, br, bs, ca, ce, ceb, ckb, cs, cv, cy, da, de, el, en, eo, es, et, eu, fa, fi, fil, fr, fy, ga, gl, hi, hr, hsb, ht, hu, hy, ia, id, io, is, it, iw, ja, jv, ka, kk, kn, ko, la, lah, lb, lmo, lt, lv, mg, mk, ml, mn, mr, ms, my, nan, nds, ne, nl, nn, no, nv, oc, pa, pl, pt, qu, ro, ru, sco, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tr, tt, uk, ur, uz, vec, vi, vo, war, xmf, yue, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","1512.03385","None","image-captioning"
+"cis-lmu/GlotCC-V1","cis-lmu","2024-05-29 14:53:46","2024-11-01 11:09:18+00:00","9ad140b6be3ac7b539606a2b4809b49d122823de","1703","16659","49","None","None","Dataset Summary GlotCC-V1.0 is a document-level, general domain dataset derived from CommonCrawl, covering more than 1000 languages.It is built using the GlotLID language identification and Ungoliant ","None","aau, aaz, ab, abk, abq, abs, abt, abx, aby, ace, acf, ach, acm, acn, acr, acu, ada, adi, adj, ady, adz, aeb, aer, aey, af, afr, agd, agm, agn, agr, agt, agu, agw, aii, aim, ain, ajp, ajz, aka, akb, akh, aln, alp, alq, als, alt, aly, am, amf, amh, ami, amm, amu, an, ang, ann, anp, any, aoi, aoj, aom, aoz, apb, apc, ape, apr, apt, apz, ara, arb, arg, arl, arn, arq, ars, ary, arz, as, asm, aso, ast, ata, atb, atd, atj, atq, att, aui, auy, av, ava, avk, avt, avu, awa, awx, aym, ayr, azb, aze, azg, azj, ba, bak, bal, bam, ban, bar, bas, bba, bbb, bbc, bbr, bcc, bch, bci, bcl, bdd, bdh, bdq, be, bef, bel, bem, ben, bew, bg, bgr, bgs, bgz, bhg, bhl, bho, bhw, bi, big, bik, bim, bin, bis, biu, bjn, bjp, bjr, bkd, bkl, blk, blw, blz, bm, bmh, bmu, bn, bnc, bno, bnp, bo, boa, bod, boj, bom, bon, bos, bpr, bps, bpy, bqc, bqj, br, bre, brh, bru, brx, bs, bsp, btd, bts, btx, bua, bug, buk, bul, bum, bus, bvr, bwd, bxh, bxr, byr, bzd, bzi, bzj, ca, caa, cab, cac, cag, cak, cao, car, cat, cax, cbi, cbk, cbr, cbs, cbt, cbu, ccp, ce, ceb, ces, cfm, cgc, ch, cha, chd, che, chk, chm, cho, chu, chv, chw, cjk, cjv, ckb, ckm, ckt, clu, cmn, cmo, cmr, cnh, cni, cnk, cnr, cnw, co, con, cop, cor, cos, cot, cpb, cpc, cpy, cre, crh, crk, crl, crn, crs, crx, cs, csb, csw, csy, cta, ctd, ctu, cu, cub, cuk, cut, cv, cwe, cwt, cy, cya, cym, czt, da, dad, dag, dah, dak, dan, dao, dar, ddg, de, ded, deu, dga, dgc, dgz, dhg, dhm, dhv, did, dik, din, diq, dis, div, dje, djk, djr, dng, dob, doi, dop, dru, dsb, dtp, dts, dua, dv, dwr, dww, dyi, dyo, dyu, dz, dzo, ee, efi, ekk, eko, el, ell, emi, eml, emp, en, eng, enl, enm, enq, eo, epo, eri, es, esk, est, esu, etr, eu, eus, eve, ewe, ewo, ext, eza, fa, faa, fai, fao, fas, fat, ffm, fi, fij, fil, fin, fit, fj, fkv, fo, fon, for, fr, fra, fro, frp, frr, fry, fub, fud, fue, fuf, fuh, ful, fur, fuv, fy, ga, gaa, gag, gah, gai, gam, gaw, gaz, gbi, gcf, gcr, gd, gdg, gdn, geb, gfk, ghs, gil, gjn, gl, gla, gle, glg, glk, glv, gmh, gmv, gnb, gng, gnn, gnw, goh, gom, gon, gor, gos, got, grc, grn, gsw, gu, gub, guc, gug, guj, gul, gum, gur, guw, gux, guz, gv, gvn, gwi, gwr, gym, gyr, ha, hac, hag, hak, hat, hau, haw, hbo, hbs, hch, he, heb, heg, her, hi, hif, hil, hin, hla, hmn, hmo, hmr, hne, hnj, hns, ho, hot, hr, hra, hrv, hsb, ht, hto, hu, hub, hui, hun, hus, huu, hvn, hwc, hy, hye, hyw, hz, ia, ian, iba, ibg, ibo, icr, id, ido, idu, ie, ify, ig, ike, ikk, ikt, iku, ikw, ile, ilo, ina, inb, ind, inh, ino, io, iou, ipi, ipk, iqw, is, isd, ish, isl, iso, it, ita, itv, ium, ivb, ivv, iws, ixl, ja, jac, jae, jam, jav, jbo, jiv, jpn, jra, jrb, jv, jvn, ka, kaa, kab, kac, kak, kal, kam, kan, kaq, kas, kat, kau, kaz, kbc, kbd, kbh, kbm, kbo, kbp, kbq, kby, kca, kcg, kck, kdc, kde, kdi, kdl, kea, kek, kew, kgk, kgr, kha, khk, khm, khz, ki, kij, kik, kin, kir, kiu, kix, kj, kjb, kjh, kjs, kk, kkc, kkl, kl, kln, klt, klv, km, kmb, kmd, kmg, kmh, kmk, kmm, kmr, kms, kmu, kmy, kn, knc, kne, kng, knv, ko, kog, koi, kok, kom, kon, kor, kos, kpe, kpf, kpg, kpj, kpo, kpr, kpv, kpw, kpx, kqc, kqf, kqn, kqw, krc, kri, krl, kru, ks, ksd, ksh, ksr, ksw, ktm, kto, ktu, ktz, kua, kum, kup, kur, kvg, kw, kwf, kwj, kwn, kwy, ky, kyc, kyg, kyq, kyu, kyz, kze, kzf, kzj, la, lad, lah, laj, lam, lao, lat, lav, lb, lbb, lbe, lbj, lbk, lcm, leu, lew, lex, lez, lfn, lg, lgg, lgl, lhi, li, lia, lid, lif, lij, lim, lin, lis, lit, liv, ljp, lki, lld, llg, lmk, lmo, ln, lnd, lo, loz, lrc, lsi, lt, ltg, ltz, lu, lua, lub, lud, lue, lug, lun, luo, lus, luy, lvs, lwg, lww, lzh, maa, mad, mag, mah, mai, mak, mal, mam, man, mar, mas, mau, maw, maz, mbb, mbf, mbh, mbs, mbt, mcd, mck, mcq, mdf, mdy, med, mej, mek, men, met, meu, mfe, mfi, mfq, mfy, mge, mgh, mh, mhi, mhr, mhw, mhx, mi, mic, mie, mif, min, mip, miq, mjc, mjw, mk, mkd, mkn, ml, mlg, mlh, mlt, mmo, mmx, mna, mnb, mni, mnk, mns, mnw, moc, mog, moh, mon, mos, mph, mpm, mpp, mps, mpx, mqj, mqy, mr, mri, mrj, mrw, msa, msb, msc, msk, msy, mt, mtg, mti, muh, mui, mup, mur, mux, mva, mvn, mvp, mwc, mwf, mwl, mwm, mwn, mwp, mwv, mww, mxb, mxt, mxv, mxx, my, mya, myb, myk, myu, myv, myw, myy, mza, mzh, mzn, naf, nah, nak, nan, nap, naq, nas, nav, nb, nbc, nbe, nbl, nbu, nca, nch, ncj, ncx, nd, ndc, nde, ndo, nds, nep, new, nfa, ng, ngl, ngu, nhe, nhg, nhi, nho, nhr, nhw, nhx, nia, nif, nii, nij, nin, niu, njm, njn, njz, nl, nld, nlg, nmf, nmz, nn, nnb, nnh, nno, nnp, nnw, nob, nog, non, nop, nor, not, nov, nph, npi, npy, nqo, nr, nrm, nsa, nsn, nso, nss, nst, nsu, ntp, ntu, nuj, nus, nuy, nv, nvm, nwi, ny, nya, nyk, nyn, nyu, nyy, nzi, nzm, obo, oc, oci, ojb, oji, okv, olo, omb, omw, ong, opm, ori, orm, orv, ory, os, oss, ota, otd, ote, ots, otw, pa, pad, pag, pam, pan, pao, pap, pau, pbb, pbt, pcd, pck, pcm, pdc, pdt, pes, pfl, pib, pis, pjt, pl, plg, pls, plt, plu, plw, pma, pmf, pms, pmx, pnb, pnt, poe, poh, poi, pol, pon, por, pot, ppk, ppo, pps, prg, prs, pt, ptp, ptu, pui, pus, pwg, pwn, qub, quc, que, quf, qug, quh, qul, qup, quw, quy, quz, qvc, qvh, qvi, qvn, qvs, qvw, qxl, qxn, qxo, rad, raj, rap, rar, raw, rcf, rej, rgu, rhg, ria, rif, rjs, rkb, rm, rmc, rml, rmn, rmo, rmq, rmy, rn, rnl, ro, roh, rom, ron, roo, rop, row, rro, rtm, ru, rue, ruf, rug, run, rup, rus, rw, rwo, sa, sab, sag, sah, san, sas, sat, sbd, sbe, sc, scn, sco, sd, sda, sdc, sdh, se, seh, ses, sg, sgb, sgc, sgh, sgs, sgw, shi, shk, shn, shp, shu, si, sid, sil, sim, sin, sk, skg, skr, sl, slk, sll, slv, sm, sma, sme, smj, smk, sml, smn, smo, sms, sn, sna, snc, snd, snf, snn, snp, snw, sny, so, som, sop, soq, sot, spa, spl, spm, spp, sps, sqi, sr, srd, srm, srn, srp, srr, ss, ssd, ssg, ssw, ssx, st, stq, su, suc, sue, suk, sun, sus, suz, sv, swa, swb, swc, swe, swg, swh, swp, sxb, sxn, sxw, syb, syc, syl, syr, szl, szy, ta, tab, tah, taj, tam, tap, taq, tar, tat, taw, tay, tbg, tbo, tbw, tby, tbz, tca, tcs, tcy, tcz, tdt, tdx, te, tel, teo, tet, tfr, tg, tgk, tgo, tgp, th, tha, thl, ti, tif, tig, tih, tir, tiv, tk, tke, tkl, tkr, tku, tlb, tlf, tlh, tll, tly, tmd, tmh, tn, tnc, tnk, tnn, tnp, tnr, to, tob, tod, tog, toi, toj, tok, ton, too, top, tos, tpi, tr, trc, trn, trp, trq, trv, ts, tsg, tsn, tso, tsz, tt, ttc, tte, ttq, tuc, tuf, tui, tuk, tum, tur, tvk, tvl, tw, twi, twu, txu, ty, tyv, tzh, tzj, tzm, tzo, ubr, ubu, udm, ug, uig, uk, ukr, umb, und, upv, ur, urd, urh, usa, usp, uvh, uvl, uzb, uzn, uzs, vap, ve, vec, ven, vep, vi, vid, vie, vls, vmw, vmy, vo, vol, vro, vun, wa, waj, wal, war, wat, way, wbm, wbp, wed, wes, wln, wls, wlv, wlx, wmw, wnc, wnu, wo, wol, wos, wrs, wsg, wsk, wuu, wuv, xal, xbi, xh, xho, xla, xmf, xmm, xmv, xog, xon, xsm, xsr, xtd, xtm, xtn, yaa, yal, yao, yap, yby, ydd, yid, yka, yle, yli, yml, yo, yom, yon, yor, yrk, yrl, yss, yua, yue, yuj, yut, yuw, yva, zac, zai, zam, zao, zap, zas, zat, zdj, zea, zgh, zha, zho, zia, zom, zos, zpm, zpo, zpt, zpu, zsm, zu, zul, zyb, zyp, zza","multi","None","None","False","False","False","cc0-1.0","2410.23825","None","None"
+"mteb/cqadupstack-wordpress","mteb","2024-03-02 19:37:59","2024-03-02 20:21:04+00:00","4ffe81d471b1924886b33c7567bfb200e9eec5c4","957","16240","2","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"ibm/AttaQ","ibm","2023-09-11 11:00:49","2024-01-26 08:16:47+00:00","5346565948b1809de748d304b7b70198d71a16c9","2025","16159","14","None","text-generation, text2text-generation","AttaQ Dataset Card The AttaQ red teaming dataset, consisting of 1402 carefully crafted adversarial questions, is designed to evaluate Large Language Models (LLMs) by assessing their tendency to genera","None","e, n","bi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2311.04124","None","None"
+"Polyglot-or-Not/Fact-Completion","Polyglot-or-Not","2023-03-22 23:42:30","2023-06-14 03:05:21+00:00","b3b224330b2ba715c46ddfdd1778c47bbd9adc30","2118","16158","13","None","text-generation, fill-mask, text2text-generation","Dataset Card Homepage: https://bit.ly/ischool-berkeley-capstone Repository: https://github.com/daniel-furman/Capstone Point of Contact: daniel_furman@berkeley.edu Dataset Summary This is the dataset f","None","en, fr, es, de, uk, bg, ca, da, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, cs","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2302.13971","None","None"
+"simon3000/genshin-voice","simon3000","2024-04-25 00:09:03","2024-08-30 08:36:05+00:00","ffe17e2e7938508a73255ec294b0ded17ed071f1","6356","16076","62","task_categories:audio-classification, task_categories:automatic-speech-recognition, task_categories:text-to-speech, language:zh, language:en, language:ja, language:ko, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","audio-classification, automatic-speech-recognition, text-to-speech","Genshin Voice Genshin Voice is a dataset of voice lines from the popular game Genshin Impact. Hugging Face 🤗 Genshin-Voice Last update at 2024-08-30 463383 wavs 20231 without speaker (4%) 24819 withou","None","zh, en, ja, ko","multi","None","None","False","False","False","None","None","None","None"
+"PrimeQA/clapnq","PrimeQA","2024-04-02 19:09:37","2024-04-02 19:15:30+00:00","399fcfd6bfc44182a4044af7ffa71dd52131c32d","1743","16071","4","None","question-answering","We present CLAP NQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. CLAP NQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to pe","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"mteb/cqadupstack-stats","mteb","2024-03-02 19:37:08","2024-03-03 14:39:32+00:00","65ac3a16b8e91f9cee4c9828cc7c335575432a2a","964","15845","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"mteb/cqadupstack-tex","mteb","2024-03-02 19:37:19","2024-03-02 20:02:18+00:00","46989137a86843e03a6195de44b09deda022eec7","1049","15828","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"CAiRE/ASCEND","CAiRE","2022-03-02 23:29:22","2024-07-16 08:56:04+00:00","737e9800ae31be9932ba8464c80366559bd28424","891","15820","30","None","automatic-speech-recognition","Dataset Card for ASCEND Dataset Summary ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switchi","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2112.06223","None","None"
+"TIGER-Lab/MMEB-eval","TIGER-Lab","2024-10-08 00:40:40","2024-10-28 16:42:34+00:00","2f069730be515ea60778413777816b53e2d2a697","3001","15808","5","None","None","Massive Multimodal Embedding Benchmark We compile a large set of evaluation tasks to understand the capabilities of multimodal embedding models. This benchmark covers 4 meta tasks and 36 datasets meti","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2410.05160","None","None"
+"WueNLP/sib-fleurs","WueNLP","2024-12-03 20:29:29","2024-12-19 11:36:50+00:00","f00a6bbc6b3e3866600f838736295dd09b393902","12774","15690","3","task_categories:audio-classification, task_categories:automatic-speech-recognition, task_categories:audio-text-to-text, task_categories:text-to-speech, task_categories:question-answering, task_categories:document-question-answering, annotations_creators:found, language_creators:expert-generated, multilinguality:multilingual, language:ace, language:acm, language:acq, language:aeb, language:af, language:ajp, language:ak, language:als, language:am, language:apc, language:ar, language:ars, language:ary, language:arz, language:as, language:ast, language:awa, language:ayr, language:azb, language:azj, language:ba, language:bm, language:ban, language:be, language:bem, language:bn, language:bho, language:bjn, language:bo, language:bs, language:bug, language:bg, language:ca, language:ceb, language:cs, language:cjk, language:ckb, language:crh, language:cy, language:da, language:de, language:dik, language:dyu, language:dz, language:el, language:en, language:eo, language:et, language:eu, language:ee, language:fo, language:fj, language:fi, language:fon, language:fr, language:fur, language:fuv, language:gaz, language:gd, language:ga, language:gl, language:gn, language:gu, language:ht, language:ha, language:he, language:hi, language:hne, language:hr, language:hu, language:hy, language:ig, language:ilo, language:id, language:is, language:it, language:jv, language:ja, language:kab, language:kac, language:kam, language:kn, language:ks, language:ka, language:kk, language:kbp, language:kea, language:khk, language:km, language:ki, language:rw, language:ky, language:kmb, language:kmr, language:knc, language:kg, language:ko, language:lo, language:lij, language:li, language:ln, language:lt, language:lmo, language:ltg, language:lb, language:lua, language:lg, language:luo, language:lus, language:lvs, language:mag, language:mai, language:ml, language:mar, language:min, language:mk, language:mt, language:mni, language:mos, language:mi, language:my, language:nl, language:nn, language:nb, language:npi, language:nqo, language:nso, language:nus, language:ny, language:oc, language:ory, language:pag, language:pa, language:pap, language:pbt, language:pes, language:plt, language:pl, language:pt, language:prs, language:quy, language:ro, language:rn, language:ru, language:sg, language:sa, language:sat, language:scn, language:shn, language:si, language:sk, language:sl, language:sm, language:sn, language:sd, language:so, language:st, language:es, language:sc, language:sr, language:ss, language:su, language:sv, language:swh, language:szl, language:ta, language:taq, language:tt, language:te, language:tg, language:tl, language:th, language:ti, language:tpi, language:tn, language:ts, language:tk, language:tum, language:tr, language:tw, language:tzm, language:ug, language:uk, language:umb, language:ur, language:uzn, language:vec, language:vi, language:war, language:wo, language:xh, language:ydd, language:yo, language:yue, language:zh, language:zsm, language:zu, language:multilingual, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.07445, region:us","audio-classification, automatic-speech-recognition, audio-text-to-text, text-to-speech, question-answering, document-question-answering","SIB-Fleurs SIB-Fleurs is a dataset suitable to evaluate Multilingual Spoken Language Understanding. For each utterance in Fleurs, the task is to determine the topic the utterance belongs to. The topic","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nqo, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu, multilingual","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"ontocord/CulturaY","ontocord","2024-02-08 12:10:31","2024-03-30 23:07:37+00:00","dc71d24f9289f16d253a7458b0f9a9f1ce4a4b2f","1708","15659","30","None","text-generation, fill-mask, language-modeling, masked-language-modeling","CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages Dataset Summary From the team that brought you CulturaX, we present CulturaY, another substantial multilingual dataset of 15TB (uncompres","None","af, ar, az, be, bg, bn, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fr, ga, gl, gu, hbs, he, hi, hu, hy, id, is, it, ja, ka, kk, kn, ko, ky, la, lt, lv, mk, ml, mn, mr, ms, mt, my, nb, ne, nl, nn, pa, pl, ps, pt, ro, ru, si, sk, sl, so, sq, sv, sw, ta, te, th, tl, tr, tt, uk, ur, uz, vi, zh","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M|||1|0|M|<|n|<|1|0|0|M|||1|0|0|M|<|n|<|1|B|||1|B|<|n|<|1|0|B","None","False","False","False","cc-by-4.0","None","None","language-modeling, masked-language-modeling"
+"mteb/cqadupstack-unix","mteb","2024-03-02 19:37:30","2024-03-02 20:00:22+00:00","6c6430d3a6d36f8d2a829195bc5dc94d7e063e53","975","15570","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"Hello-SimpleAI/HC3-Chinese","Hello-SimpleAI","2023-01-18 14:20:45","2023-01-21 13:11:49+00:00","09a687b8dc164b89e7df95abf15df3b216bc31c2","1166","15450","156","None","text-classification, question-answering, sentence-similarity, zero-shot-classification","Human ChatGPT Comparison Corpus (HC3) Chinese Version","\\","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2301.07597","None","None"
+"kaist-ai/CoT-Collection","kaist-ai","2023-06-05 07:11:17","2023-10-14 12:10:16+00:00","c9d352cdc119df4a4f7526d100e4acb4a72a7a5c","790","15430","126","None","text-generation, text-classification",""""""" _LICENSE = ""CC BY 4.0"" _HOMEPAGE = ""https://github.com/kaistAI/CoT-Collection"" _LANGUAGES = { ""en"": ""English"", } # _ALL_LANGUAGES = ""all_languages"" class CoTCollectionMultiConfig(datasets.BuilderC","@article{kim2023cot, title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning}, author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon}, journal={arXiv preprint arXiv:2305.14045}, year={2023} }","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"imageomics/rare-species","imageomics","2023-07-05 18:45:07","2024-08-28 13:24:35+00:00","2a1d673cf0dd43ab3c2df92451b98eda7f04fd7d","8220","15034","7","task_categories:image-classification, task_categories:zero-shot-classification, language:en, language:la, size_categories:10K<n<100K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, doi:10.57967/hf/1981, region:us, biology, image, animals, species, taxonomy, rare species, endangered species, evolutionary biology, balanced, CV, multimodal, CLIP, knowledge-guided","image-classification, zero-shot-classification","Dataset Card for Rare Species Dataset Dataset Description Repository: Imageomics/bioclip Paper: BioCLIP: A Vision Foundation Model for the Tree of Life (arXiv) Dataset Summary This dataset was generat","None","en, la","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","None","None","None","None"
+"parler-tts/libritts_r_filtered","parler-tts","2024-06-13 15:48:39","2024-08-06 16:45:54+00:00","8980d6531fa6404290be3ff52aeb55b95b9c8c93","1189","15025","14","None","text-to-speech, automatic-speech-recognition","Dataset Card for Filtered LibriTTS-R This is a filtered version of LibriTTS-R. It has been filtered based on two sources: LibriTTS-R paper [1], which lists samples for which speech restoration have fa","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2305.18802","None","None"
+"cardiffnlp/super_tweeteval","cardiffnlp","2023-05-16 14:33:16","2024-07-30 04:04:17+00:00","a10ac3d27a3499e870b6371df64c14e5a656131a","1149","15007","11","None","text-classification, token-classification, question-answering, other, topic-classification, named-entity-recognition, abstractive-qa","SuperTweetEval Dataset Card for ""super_tweeteval"" Dataset Summary This is the oficial repository for SuperTweetEval, a unified benchmark of 12 heterogeneous NLP tasks. More details on the task and an ","None","en","mono","n|<|5|0|K","None","False","False","False","unknown","2310.14757","None","topic-classification, named-entity-recognition, abstractive-qa"
+"qintongli/GSM-Plus","qintongli","2024-07-04 09:35:05","2024-07-07 05:01:48+00:00","3b708db57b96a16e8e3368ed2956990c0809440e","1937","14745","9","None","None","Dataset Description GSM-Plus aims to evaluate the robustness of LLMs' math reasoning capability by testing a wide range of question variations. GSM-Plus is an adversarial grade school math dataset, an","None","en","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"deepghs/nozomi_standalone_full","deepghs","2024-07-03 16:34:02","2024-10-31 06:20:56+00:00","a0943491bb99a1d3e52fa64ced4444b5f5c0ec9b","4320","14638","4","None","image-classification, zero-shot-image-classification, text-to-image","Nozomi Full Dataset This is the full dataset of nozomi.la. And only the standalone original images are maintained here. Information Images There are 20777933 images in total. The maximum ID of these i","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","other","None","None","None"
+"Multilingual-Multimodal-NLP/McEval","Multilingual-Multimodal-NLP","2024-06-11 06:15:47","2024-11-20 04:33:51+00:00","c4767fec950de7decd487a40a4dab2795df4a094","1145","14500","19","None","text-generation, fill-mask, summarization","McEval benchmark data as described in the McEval Paper. Code for the evaluation can be found on Github as McEval.","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2406.07436","None","None"
+"manu/project_gutenberg","manu","2023-09-07 14:14:10","2023-09-07 15:33:32+00:00","164853d214065df26a630ee1ab91a0c39e461caf","1455","14496","42","None","text-generation","Dataset Card for ""Project Gutenberg"" Project Gutenberg is a library of over 70,000 free eBooks, hosted at https://www.gutenberg.org/. All examples correspond to a single book, and contain a header and","None","fr, en, zh, pt, pl, nl, ru, sv, it, de, es","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"vidore/syntheticDocQA_energy_test","vidore","2024-06-03 15:25:39","2024-07-02 07:47:44+00:00","0821bc71310cfa51d5c8131d4d8b9c4d537bd8c8","2672","14339","2","None","visual-question-answering, question-answering","Dataset Description This dataset is part of a topic-specific retrieval benchmark spanning multiple domains, which evaluates retrieval in more realistic industrial applications. It includes documents a","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"vidore/arxivqa_test_subsampled","vidore","2024-05-31 09:06:54","2024-06-28 08:17:52+00:00","fe2b0e055eaac82d8f6801ebc8e85d8832248133","2485","14276","3","task_categories:visual-question-answering, task_categories:question-answering, language:en, license:cc-by-sa-4.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.00231, region:us, Figures, multimodal-retrieval, QA, Visual QA","visual-question-answering, question-answering","Dataset Description This is a VQA dataset based on figures extracted from arXiv publications taken from ArXiVQA dataset from Multimodal ArXiV. The questions were generated synthetically using GPT-4 Vi","None","en","mono","n|<|1|K","None","False","False","False","cc-by-sa-4.0","2403.00231","None","None"
+"HuggingFaceH4/stack-exchange-preferences","HuggingFaceH4","2023-02-11 03:24:28","2023-03-08 03:37:53+00:00","c7bda74048748f55749cd663c3d8d1025a841fd9","2250","14228","128","None","question-answering","Dataset Card for H4 Stack Exchange Preferences Dataset Dataset Summary This dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. Impor","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-4.0","2112.00861","None","None"
+"gigant/oldbookillustrations","gigant","2022-07-28 08:31:19","2023-12-18 13:39:10+00:00","40cc352405da6da57bd64ba785bd6a38ef3a4871","782","14092","35","None","text-to-image, image-to-text, image-to-image, image-captioning","Dataset Card for Old Book Illustrations Dataset Summary The Old Book Illustrations contains 4172 illustrations scanned from old books, this collection was collected & curated by the team of the websit","None","en, fr, de","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","None","None","image-captioning"
+"HannahRoseKirk/prism-alignment","HannahRoseKirk","2024-04-22 18:16:12","2024-04-25 09:11:35+00:00","18ab5cfb37456f4ec8cbc00212ce54cf7b1239f6","1007","14030","79","None","None","Dataset Card for PRISM PRISM is a diverse human feedback dataset for preference and value alignment in Large Language Models (LLMs). It maps the characteristics and stated preferences of humans from a","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc","2404.16019","None","None"
+"sedthh/gutenberg_english","sedthh","2023-02-28 14:15:24","2023-03-17 09:50:22+00:00","28973b04f28fd7be4a6186a042bc26159d4366ca","6413","14013","17","None","text-generation","Dataset Card for Project Gutenber - English Language eBooks A collection of non-english language eBooks (48284 rows, 80%+ of all english language books available on the site) from the Project Gutenber","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"haitengzhao/molecule_property_instruction","haitengzhao","2023-07-09 07:36:09","2023-07-13 10:30:29+00:00","aad5c7578e811e1614be9430095de0c431485cf3","1925","13911","12","task_categories:question-answering, language:en, license:afl-3.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, chemistry, biology","question-answering","Dataset Card for ""molecule_property_instruction"" More Information needed","None","en","mono","None","None","False","False","False","afl-3.0","None","None","None"
+"theblackcat102/evol-codealpaca-v1","theblackcat102","2023-07-23 01:28:44","2024-03-10 23:59:30+00:00","c75242318519b5470635e84064baed7c78594020","805","13702","156","None","text-generation","Evolved codealpaca Updates: 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response Median sequence length : 471 We employed a meth","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"google/Synthetic-Persona-Chat","google","2023-12-20 09:59:14","2024-03-01 01:01:01+00:00","a520ad7f999ca7e6dfdc25fed9f5070bf6f87b42","1353","13633","90","None","text2text-generation","Dataset Card for SPC: Synthetic-Persona-Chat Dataset Abstract from the paper introducing this dataset: High-quality conversational datasets are essential for developing AI models that can communicate ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2312.10007","None","None"
+"MultiCoNER/multiconer_v2","MultiCoNER","2023-03-01 00:57:16","2023-07-06 18:37:15+00:00","4be2d62c912977ee26ed14d2553a4fe17ca3d980","852","13404","13","None","token-classification","Complex named entities (NE), like the titles of creative works, are not simple nouns and pose challenges for NER systems (Ashwini and Choi, 2014). They can take the form of any linguistic constituent,","@inproceedings{multiconer2-report, title={{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)}}, author={Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin}, booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)}, year={2023}, publisher={Association for Computational Linguistics}, } @article{multiconer2-data, title={{MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}}, author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin}, year={2023}, }","bn, zh, de, en, es, fa, fr, hi, it, pt, sv, uk","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"RMT-team/babilong-1k-samples","RMT-team","2024-05-17 15:03:12","2024-06-17 09:51:38+00:00","fc4d1a584dfc498c37578753bee4cdd91b987ae2","1003","13350","3","None","None","BABILong (1000 samples) : a long-context needle-in-a-haystack benchmark for LLMs Preprint is on arXiv and code for LLM evaluation is available on GitHub. BABILong Leaderboard with top-performing long-","None","en","mono","None","None","False","False","False","None","2406.10149","None","None"
+"stanford-oval/ccnews","stanford-oval","2024-07-16 07:54:40","2024-08-31 17:28:13+00:00","d733e654c9a506df519e1a166a86c118c7657ce4","2886","13226","11","task_categories:text-classification, task_categories:question-answering, task_categories:text-generation, task_categories:text2text-generation, language:multilingual, language:af, language:am, language:ar, language:as, language:az, language:be, language:bg, language:bn, language:br, language:bs, language:ca, language:cs, language:cy, language:da, language:de, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gd, language:gl, language:gu, language:ha, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:jv, language:ka, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:la, language:lo, language:lt, language:lv, language:mg, language:mk, language:ml, language:mn, language:mr, language:ms, language:my, language:ne, language:nl, language:no, language:om, language:or, language:pa, language:pl, language:ps, language:pt, language:ro, language:ru, language:sa, language:sd, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:ta, language:te, language:th, language:tl, language:tr, language:ug, language:uk, language:ur, language:uz, language:vi, language:xh, language:yi, language:zh, size_categories:100M<n<1B, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, news","text-classification, question-answering, text-generation, text2text-generation","This dataset is the result of processing all WARC files in the CCNews Corpus, from the beginning (2016) to June of 2024. The data has been cleaned and deduplicated, and language of articles have been ","None","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","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"THUDM/LongAlign-10k","THUDM","2024-01-29 15:49:36","2024-02-22 11:39:00+00:00","12f17c4baff1001f0d44c4f8feab09ee2ee8c6dc","2638","13104","64","None","question-answering","LongAlign-10k 🤗 [LongAlign Dataset] • 💻 [Github Repo] • 📃 [LongAlign Paper] LongAlign is the first full recipe for LLM alignment on long context. We propose the LongAlign-10k dataset, containing 10,00","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","2401.18058","None","None"
+"kakaobrain/coyo-700m","kakaobrain","2022-08-25 15:54:43","2022-08-30 19:07:52+00:00","54ee2d8c64d3d80a5e10ef6952a4466551834fc1","1637","13103","141","None","text-to-image, image-to-text, zero-shot-classification, image-captioning","Dataset Card for COYO-700M Dataset Summary COYO-700M is a large-scale dataset that contains 747M image-text pairs as well as many other meta-attributes to increase the usability to train various model","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc-by-4.0","2102.05918","None","image-captioning"
+"OpenDatasets/dalle-3-dataset","OpenDatasets","2024-02-13 04:17:46","2024-11-20 18:19:54+00:00","96822d5c0b1338ec4bfd04b0d5909fa070e301b6","960","12849","21","None","None","Dataset Card for LAION DALL·E 3 Discord Dataset Description: This dataset consists of caption and image pairs scraped from the LAION share-dalle-3 discord channel. The purpose is to collect image-text","None","en","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"JetBrains-Research/commit-chronicle","JetBrains-Research","2023-08-08 15:54:44","2023-10-05 10:50:00+00:00","5fd076e67b812a9f3d1999e5e40f71715f84bb51","3125","12784","7","None","text-generation, summarization","📜 CommitChronicle 🔮 This is the dataset for commit message generation (and/or completion), introduced in the paper ""From Commit Message Generation to History-Aware Commit Message Completion"", ASE 2023","None","code, en","mono","1|M|<|n|<|1|0|M","None","False","False","False","other","2308.07655","None","None"
+"snap-stanford/stark","snap-stanford","2024-06-07 10:23:17","2024-10-20 17:06:53+00:00","88269e23e90587f99476c5dd74e235a0877e69be","1316","12740","5","None","question-answering","STaRK Website | Github | Paper STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases Downstream Task Retrieval systems driven by LLMs are tasked with extr","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"RUC-NLPIR/FlashRAG_datasets","RUC-NLPIR","2024-07-16 03:47:37","2024-11-07 02:59:20+00:00","d4794c8f05babde522b03ff710fab1d670b59ed5","959","12670","28","None","question-answering, summarization, text2text-generation","⚡FlashRAG: A Python Toolkit for Efficient RAG Research FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 32 pre-p","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","2405.13576","None","None"
+"ayymen/Weblate-Translations","ayymen","2024-01-07 22:29:01","2024-04-02 10:16:03+00:00","1d3d0d3fbe20cb39693118afbb17bbee6a3ad826","3554","12564","11","None","translation, text2text-generation","Dataset Card for Weblate Translations A dataset containing strings from projects hosted on Weblate and their translations into other languages. Please consider donating or contributing to Weblate if y","None","aa, ab, ace, ach, af, afh, aii, ain, ajp, ak, am, an, ang, anp, apc, ar, arn, ars, as, ast, ay, ayc, az, azb, ba, bar, bd, be, bem, ber, bg, bho, bm, bn, bo, bp, bqi, br, brx, bs, bul, by, ca, ce, ceb, ckb, cmn, cn, cnr, co, cr, crh, cs, csb, cv, cy, cz, da, de, dev, doi, dsb, dua, dum, dv, dz, eg, el, en, eng, enm, eo, es, et, eu, ext, fa, fi, fil, fo, fr, fra, frm, frp, frs, fu, fur, fy, ga, gb, gd, gl, glk, gmh, gn, gr, gsw, gu, guc, gug, gum, guw, gv, ha, haw, he, hi, hne, hr, hrx, hsb, ht, hu, hy, hz, ia, id, ie, ig, in, io, is, it, iw, ja, jam, jbo, ji, jp, jpn, jv, ka, kab, kg, kk, kl, km, kmr, kn, ko, kok, kr, krl, ks, ksh, ku, kw, ky, la, lb, lfn, lg, li, lk, ln, lo, lt, ltg, lv, lzh, mai, me, mg, mhr, mi, mjw, mk, ml, mn, mnc, mni, mnw, mo, mr, ms, mt, my, na, nah, nan, nap, nb, nds, ne, nl, nn, no, np, nqo, ny, oc, oj, om, or, os, ota, pa, pam, pap, pbb, peo, pk, pl, pms, pr, prg, ps, pt, pu, qt, rcf, rm, ro, rom, ru, rue, rw, ryu, sa, sah, sai, sat, sc, sco, sd, sdh, se, sh, shn, si, sk, skr, sl, sm, sma, sn, so, sq, sr, st, su, sv, sw, szl, ta, tam, te, tet, tg, th, ti, tk, tl, tlh, tn, to, tok, tr, trv, tt, tum, tw, ty, tzm, ua, udm, ug, uk, und, ur, us, uz, vec, vi, vls, wa, wae, wo, xh, yi, yo, yue, zgh, zh, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","other","None","None","None"
+"ylacombe/english_dialects","ylacombe","2023-11-25 12:40:07","2023-11-27 10:32:58+00:00","ed3d69abb765fd304ccdcc646ec6ca3c49740d15","1064","12407","20","None","text-to-speech, text-to-audio","Dataset Card for ""english_dialects"" Dataset Summary This dataset consists of 31 hours of transcribed high-quality audio of English sentences recorded by 120 volunteers speaking with different accents ","None","en","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"vidore/tabfquad_test_subsampled","vidore","2024-05-31 09:01:48","2024-06-27 12:44:22+00:00","501f02a80aff50c90045b0feaa81565c4e8f889e","2049","12356","0","None","table-question-answering, visual-question-answering","Dataset Description TabFQuAD (Table French Question Answering Dataset) is designed to evaluate TableQA models in realistic industry settings. Using a vision language model (GPT4V), we create additiona","None","fr, en","bi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"wikimedia/wikisource","wikimedia","2022-03-02 23:29:22","2023-12-08 13:36:41+00:00","f31a033f5f3d2107b3e864e578710df104a00baa","1028","12236","78","None","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for Wikimedia Wikisource Dataset Summary Wikisource dataset containing cleaned articles of all languages. The dataset is built from the Wikisource dumps (https://dumps.wikimedia.org/) wit","None","ar, as, az, ban, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fo, fr, gl, gu, he, hi, hr, hu, hy, id, is, it, ja, jv, kn, ko, la, li, lij, lt, mk, ml, mr, nan, nap, nl, no, or, pa, pl, pms, pt, ro, ru, sa, sah, sk, sl, sr, su, sv, ta, te, th, tr, uk, vec, vi, wa, yi, zh","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-3.0","None","None","language-modeling, masked-language-modeling"
+"jackhhao/jailbreak-classification","jackhhao","2023-09-30 00:56:39","2023-09-30 01:55:08+00:00","2f2ceeb39658696fd3f462403562b6eea5306287","1131","12205","47","None","text-classification","Jailbreak Classification Dataset Summary Dataset used to classify prompts as jailbreak vs. benign. Dataset Structure Data Fields prompt: an LLM prompt type: classification label, either jailbreak or b","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"cvssp/WavCaps","cvssp","2023-04-12 08:09:04","2023-07-06 13:28:10+00:00","0930ec11ded28fa0eaa910fde2f6fc3538acbeac","1922","12175","34","language:en, license:cc-by-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2303.17395, region:us","None","WavCaps WavCaps is a ChatGPT-assisted weakly-labelled audio captioning dataset for audio-language multimodal research, where the audio clips are sourced from three websites (FreeSound, BBC Sound Effec","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-4.0","2303.17395","None","None"
+"deepghs/subsplease_animes","deepghs","2024-07-15 13:57:37","2025-01-28 07:58:38+00:00","d7810e81906ab0a5951ee9828353f1b1f746dc11","3486","12136","2","None","None","This is an integration database of subsplease, myanimelist and nyaasi. You can know which animes are the hottest ones currently, and which of them have well-seeded magnet links. This database is refre","None","en","mono","n|<|1|K","None","False","False","False","other","None","None","None"
+"vidore/syntheticDocQA_artificial_intelligence_test","vidore","2024-06-03 15:53:45","2024-07-02 07:46:01+00:00","5fe59d7e52732b86d11ee0e9c4a8cdb0e8ba7a6e","1829","11850","1","None","visual-question-answering, question-answering","Dataset Description This dataset is part of a topic-specific retrieval benchmark spanning multiple domains, which evaluates retrieval in more realistic industrial applications. It includes documents a","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"SciPhi/AgentSearch-V1","SciPhi","2023-12-09 15:05:54","2024-01-14 03:54:39+00:00","8cdbfc9e0ba206df55f57724039c1f979d22162b","3457","11800","87","task_categories:text-generation, language:en, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2310.06786, region:us","text-generation","Getting Started The AgentSearch-V1 dataset boasts a comprehensive collection of over one billion embeddings, produced using jina-v2-base. The dataset encompasses more than 50 million high-quality docu","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","None","None","None","None"
+"multilingual-discourse-hub/disrpt","multilingual-discourse-hub","2023-04-18 07:36:18","2024-05-21 08:02:24+00:00","89a16215e41fccaaa336f7bac87f5bb07351e209","988","11648","1","None","None","Disrpt is a multilingual, multi-framework unified discourse analysis benchmark. It unifies discourse relation classification tasks (.rels) and discourse segmentation (.connlu) for many languages. ⚠️ T","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"visual-layer/imagenet-1k-vl-enriched","visual-layer","2024-07-09 08:56:33","2024-09-16 11:13:12+00:00","ac6afcdeb3be31c5ff6a7ff579874b3d372b7074","861","11634","17","None","object-detection, image-classification, text-to-image, image-to-text, visual-question-answering","Visualize on Visual Layer Imagenet-1K-VL-Enriched An enriched version of the ImageNet-1K Dataset with image caption, bounding boxes, and label issues! With this additional information, the ImageNet-1K","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"vidore/tatdqa_test","vidore","2024-05-28 21:05:08","2024-11-09 23:39:02+00:00","6c4c535932e16fd1b392cb7616a73179f40cda2b","1867","11628","2","None","visual-question-answering, question-answering","Dataset Description This is the test set taken from the TAT-DQA dataset. TAT-DQA is a large-scale Document VQA dataset that was constructed from publicly available real-world financial reports. It foc","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"MMMU/MMMU_Pro","MMMU","2024-08-27 02:09:22","2024-11-10 05:11:59+00:00","cd9382dd4f8fa7704aa1007f004bfa63aabde6fc","3520","11627","21","None","question-answering, visual-question-answering, multiple-choice","MMMU-Pro (A More Robust Multi-discipline Multimodal Understanding Benchmark) 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub 🔔News 🛠️[2024-11-10] Added options to the Vision subset","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2409.02813","None","None"
+"BeIR/webis-touche2020","BeIR","2022-06-05 16:52:25","2022-10-23 06:03:23+00:00","7acefc52139f7c7503f61643cde7dde772e4c089","1401","11588","1","None","text-retrieval, zero-shot-retrieval, information-retrieval, zero-shot-information-retrieval, passage-retrieval, entity-linking-retrieval, fact-checking-retrieval, tweet-retrieval, citation-prediction-retrieval, duplication-question-retrieval, argument-retrieval, news-retrieval, biomedical-information-retrieval, question-answering-retrieval","Dataset Card for BEIR Benchmark Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: Fact-checking: FEVER, Climate","None","en","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","passage-retrieval, entity-linking-retrieval, fact-checking-retrieval, tweet-retrieval, citation-prediction-retrieval, duplication-question-retrieval, argument-retrieval, news-retrieval, biomedical-information-retrieval, question-answering-retrieval"
+"isp-uv-es/CloudSEN12Plus","isp-uv-es","2024-08-07 18:27:00","2025-01-01 00:54:09+00:00","cea5fe1903294ada099132041552c83ac17c7ca9","3687","11558","7","None","image-segmentation","🚨 New Dataset Version Released! We are excited to announce the release of Version [1.1] of our dataset! This update includes: [L2A & L1C support]. [Temporal support]. [Check the data without downloadi","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc0-1.0","None","None","None"
+"vidore/syntheticDocQA_healthcare_industry_test","vidore","2024-06-03 15:51:07","2024-07-02 07:47:16+00:00","86f09ebc1703516c76e5f931465e2ed7626a5e52","2065","11522","0","None","visual-question-answering, question-answering","Dataset Description This dataset is part of a topic-specific retrieval benchmark spanning multiple domains, which evaluates retrieval in more realistic industrial applications. It includes documents a","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"survivi/Llama-3-SynE-Dataset","survivi","2024-08-11 17:28:15","2024-12-17 13:07:08+00:00","d6a7371ada7ac8f90abc7b5d63536cd50a35b963","931","11487","9","None","text-generation","📄 Report | 💻 GitHub Repo 🔍 English | 简体中文 Here is the continual pre-training dataset. The Llama-3-SynE model is available here. News 🌟🌟 2024/12/17: We released the code used for continual pre-training","None","en, zh","bi","None","None","False","False","False","None","2407.18743","None","None"
+"UCSC-VLAA/HQ-Edit","UCSC-VLAA","2024-04-13 09:52:03","2024-04-17 19:40:48+00:00","8d0a51255d4ebc82152806e1f3891c85b8e4e037","1841","11432","23","None","None","Dataset Card for HQ-EDIT HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building d","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"BleachNick/UltraEdit_500k","BleachNick","2024-06-07 17:18:11","2024-07-22 08:11:11+00:00","8d78dc552b576027618ff2170c4c1d7bcaf27ad2","1883","11312","12","None","text-to-image","Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Dataset Details Dataset Description Curated by: [More Inform","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"LLM360/AmberDatasets","LLM360","2023-11-21 23:12:37","2024-04-02 20:00:56+00:00","a38661d3aabe426447766c0c0f7046e29019b23f","962","11284","38","None","None","Amber-Data This dataset contains the fully prepared data sequence used to train Amber, an LLM360 model. About LLM360 LLM360 is an initiative for comprehensive and fully open-sourced LLMs, where all tr","None","en","mono","None","None","False","False","False","odc-by","None","None","None"
+"sentence-transformers/parallel-sentences-opus-100","sentence-transformers","2024-05-25 06:52:13","2024-07-09 14:28:24+00:00","f9fff3fffa048f359508f6aa77ff5bd2226d4016","1505","11275","1","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - OPUS-100 This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. The sentences ori","None","af, am, an, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, dz, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, gu, ha, he, hi, hr, hu, hy, id, ig, is, it, ja, ka, kk, km, kn, ko, ku, ky, li, lt, lv, mg, mk, ml, mn, mr, ms, mt, my, nb, ne, nl, nn, no, oc, or, pa, pl, ps, pt, ro, ru, rw, se, sh, si, sk, sl, sq, sr, sv, ta, te, tg, th, tk, tr, tt, ug, uk, ur, uz, vi, wa, xh, yi, yo, zh, zu","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"perler/ppsurf","perler","2024-02-13 10:22:13","2024-02-15 11:47:11+00:00","7d4ea267a86736b347c482f95fc8fd0394c4bceb","1569","11248","0","None","summarization","We introduced this dataset in Points2Surf, a method that turns point clouds into meshes. It consists of objects from the ABC Dataset, a collection of Famous meshes and objects from Thingi10k. These ar","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"ComplexDataLab/Misinfo_Datasets","ComplexDataLab","2024-08-28 12:53:28","2024-12-02 23:28:38+00:00","ce06269efc9232db3daed56bb71b32498fb9d65d","3815","11158","3","None","None","CDL Misinfo Detection Datasets Datasets Summary Misinformation is a challenging societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this problem, we h","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2411.05060","None","None"
+"vidore/syntheticDocQA_government_reports_test","vidore","2024-06-03 15:52:59","2024-07-02 07:46:47+00:00","8270b3751ce6b95bec362fb38fbcd2a4aa400cfc","1789","11130","1","None","visual-question-answering, question-answering","Dataset Description This dataset is part of a topic-specific retrieval benchmark spanning multiple domains, which evaluates retrieval in more realistic industrial applications. It includes documents a","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"GEM/surface_realisation_st_2020","GEM","2022-03-02 23:29:22","2022-10-24 15:30:30+00:00","f294829c59e8b1aa74aaf9f672e523f0b5c0536f","888","11002","1","None","table-to-text","Dataset Card for GEM/surface_realisation_st_2020 Link to Main Data Card You can find the main data card on the GEM Website. Dataset Summary This dataset was used as part of the multilingual surface re","None","ar, zh, en, fr, hi, id, ja, ko, pt, ru, es","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-2.5","None","None","None"
+"codesignal/tsla-historic-prices","codesignal","2023-10-14 18:16:49","2023-10-14 18:18:09+00:00","158577c50fe7e84eeaf9e3944ef4014c456d5e7f","1134","10915","1","None","None","None","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"m-a-p/PIN-100M","m-a-p","2024-05-25 04:58:09","2025-01-28 19:39:00+00:00","cbae76c60f57fe36f99bbdfed37d8a0f30535542","2129","10874","2","None","None","PIN-100M The full version of the dataset, related to the paper ""PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents"" Paper: https://arxiv.org/abs/2406.13923 This dataset","None","en, zh","bi","1|0|0|B|<|n|<|1|T","None","False","False","False","apache-2.0","2406.13923","None","None"
+"HuggingFaceGECLM/REDDIT_submissions","HuggingFaceGECLM","2023-03-15 14:13:43","2023-03-17 07:44:37+00:00","229261a8756442f8d5dc4cb98f1df1a3ba79eb23","2568","10769","7","None","text-generation, dialogue-modeling, language-modeling","Dataset Card for ""REDDIT_submissions"" Dataset Summary Submissions of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023). Supported Tasks These submissio","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","None","None","None","dialogue-modeling, language-modeling"
+"MichaelR207/MultiSimV2","MichaelR207","2024-10-01 05:16:49","2024-10-23 00:02:09+00:00","03aaf798070c845ce8e8980dc20e92f079028611","1242","10702","2","None","summarization, text2text-generation, text-generation","Dataset Card for MultiSim Benchmark Dataset Summary The MultiSim benchmark is a growing collection of text simplification datasets targeted at sentence simplification in several languages. Currently, ","None","en, fr, ru, ja, it, da, es, de, pt, sl, ur, eu","multi","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"GeroldMeisinger/laion2b-en-a65_cogvlm2-4bit_captions","GeroldMeisinger","2024-06-13 11:52:05","2024-08-12 14:05:15+00:00","e9f4da16a8f7e5ecb39e674ad8c0fc26b44332c5","857","10687","5","task_categories:image-classification, task_categories:text-to-image, task_categories:image-to-text, language:en, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, arxiv:2403.03206, region:us, CogVLM2, CogVLM2-4bit, laion2b-en-a65, laion-pop, stable-diffusion-3-medium","image-classification, text-to-image, image-to-text","Abstract This dataset contains image captions for the laion2B-en aesthetics>=6.5 image dataset using CogVLM2-4bit with the ""laion-pop""-prompt to generate captions which were ""likely"" used in Stable Di","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"k-mktr/improved-flux-prompts-photoreal-portrait","k-mktr","2024-09-28 08:22:13","2024-10-03 10:55:26+00:00","36cf6aac4216523e41c831517bc93ca42624cd58","1210","10625","103","None","text-classification","Photo Portrait Prompt Dataset for FLUX Overview This dataset contains a curated collection of prompts specifically designed for generating photo portraits using FLUX.1, an advanced text-to-image model","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"andstor/methods2test_small","andstor","2023-12-17 20:26:53","2024-11-03 09:40:11+00:00","54eb57b50de55a15774d52192bae9d5d004b1c82","7366","10589","0","task_categories:text-generation, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2203.12776, region:us, unit test, java, code","text-generation","Dataset Description Microsoft created the methods2test dataset, consisting of Java Junit test cases with their corresponding focal methods. It contains 780k pairs of JUnit test cases and focal methods","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions","ProGamerGov","2024-04-01 19:54:59","2024-10-30 15:57:33+00:00","216b8becd8c112e2a87c08f9ce8f2d65247c8c09","1532","10542","121","None","text-to-image, image-classification, image-to-text, image-text-to-text, other","Dataset Card for Dalle3 1 Million+ High Quality Captions Alt name: Human Preference Synthetic Dataset Example grids for landscapes, cats, creatures, and fantasy are also available. Description: This d","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"Locutusque/UltraTextbooks","Locutusque","2024-01-31 05:13:13","2024-02-02 15:24:22+00:00","232c6fe7143a865fd79894c255b11ffe9214bee6","899","10506","194","None","text-generation","Dataset Card for ""UltraTextbooks"" In the digital expanse, a Tree of Knowledge grows, Its branches of code and words intertwine in prose. Synthetic leaves shimmer, human insights compose, A binary symp","None","en, code","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"lmqg/qa_squadshifts_synthetic","lmqg","2022-12-20 08:31:18","2023-01-15 14:25:15+00:00","f628640cea8e85f48246455adf44356b2fd45c32","1574","10352","0","None","question-answering, extractive-qa","Dataset Card for ""lmqg/qa_squadshifts_synthetic"" Dataset Summary This is a synthetic QA dataset generated with fine-tuned QG models over lmqg/qa_squadshifts, made for question-answering based evaluati","@inproceedings{ushio-etal-2022-generative, title = ""{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration"", author = ""Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose"", booktitle = ""Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"", month = dec, year = ""2022"", address = ""Abu Dhabi, U.A.E."", publisher = ""Association for Computational Linguistics"", }","e, n","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","cc-by-4.0","2210.03992","None","extractive-qa"
+"SadilKhan/Text2CAD","SadilKhan","2024-12-03 21:51:26","2025-01-03 13:28:33+00:00","d9868e514e255c2ed19e3b5541ad3e2d008af07e","10216","10328","12","language:en, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2409.17106, region:us, text-to-cad, dataset, annotation","None","🚀 Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts 🎨 Mohammad Sadil Khan* · Sankalp Sinha* · Talha Uddin Sheikh · Didier Stricker · Sk Aziz Ali · Muhammad Zeshan ","None","en","mono","None","None","False","auto","False","cc-by-nc-sa-4.0","2409.17106","None","None"
+"MAmmoTH-VL/MAmmoTH-VL-Instruct-12M","MAmmoTH-VL","2024-11-29 16:25:14","2025-01-05 03:53:38+00:00","bac8f77cb8a8f9c4d0de407c6e3a589bd722562a","5039","10302","41","task_categories:visual-question-answering, task_categories:question-answering, language:en, license:apache-2.0, size_categories:10M<n<100M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, arxiv:2412.05237, region:us, reasoning, CoT, math","visual-question-answering, question-answering","MAmmoTH-VL-Instruct-12M 🏠 Homepage | 🤖 MAmmoTH-VL-8B | 💻 Code | 📄 Arxiv | 📕 PDF | 🖥️ Demo Introduction Our simple yet scalable visual instruction data rewriting pipeline consists of three steps: manua","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","2412.05237","None","None"
+"mercari-us/merrec","mercari-us","2024-03-06 01:56:08","2024-03-09 20:09:40+00:00","749469b2890c57d4e87e1722adcc4d99704ad454","2368","10245","4","language:en, license:cc-by-nc-4.0, size_categories:1B<n<10B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.14230, region:us, recommendation, sequential recommendation, click-through rate prediction, e-commerce","None","MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems This repository contains the dataset accompanying the paper MerRec: A Large-scale Multipurpose Mercar","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","cc-by-nc-4.0","2402.14230","None","None"
+"wchai/Video-Detailed-Caption","wchai","2024-09-02 04:58:02","2024-10-07 01:39:25+00:00","dc19e7ea5dbc51b749aaf1e2838c9e21c70bc81a","924","10113","5","None","video-text-to-text","Video Detailed Caption Benchmark Resources Website arXiv: Paper GitHub: Code Huggingface: AuroraCap Model Huggingface: VDC Benchmark Huggingface: Trainset Features Benchmark Collection and Processing ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2410.03051","None","None"
+"mikex86/stackoverflow-posts","mikex86","2023-06-14 18:48:00","2023-08-01 01:31:12+00:00","9e791fe8997879cf127e2a0b006bad3484bbda32","1094","9994","50","None","question-answering, text-generation, text2text-generation","StackOverflow Posts Markdown Dataset Summary This dataset contains all posts submitted to StackOverflow before the 14th of June 2023 formatted as Markdown text. The dataset contains ~60 Million posts,","None","code, en","mono","1|0|M|&|l|t|;|n|&|l|t|;|1|0|0|M","None","False","False","False","other","None","None","None"
+"arcee-ai/distilabel-intel-orca-dpo-pairs-binarized","arcee-ai","2024-05-18 04:04:54","2024-05-18 04:44:45+00:00","97027b1fbe42f3f047fdb1c252d5fd1cf10e4f4a","1836","9981","1","None","None","This is the binarized version of distilabel Orca Pairs for DPO and ORPO. Reference: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs?row=0","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"dora-rs/dora-robomaster","dora-rs","2024-02-26 22:28:11","2024-04-03 20:29:22+00:00","8f015491492254a514c8d4e1922a522dbd24fbf3","1771","9943","9","language:en, license:apache-2.0, size_categories:10K<n<100K, modality:text, modality:timeseries, region:us, dora, robotic","None","Dora-Robomaster This project aims to use Dora to enhance the capabilities of a RoboMaster S1. You can see a quick demo here: Getting Started command to start the demo: alias dora='dora-cli' dora up do","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"math-ai/TemplateGSM","math-ai","2024-02-01 13:22:35","2024-11-28 22:17:43+00:00","949301c4b57b748bd67f9cba083d590d3f27fd12","6885","9594","13","task_categories:text-generation, task_categories:question-answering, language:en, license:cc-by-4.0, size_categories:10M<n<100M, modality:tabular, modality:text, arxiv:2411.18104, region:us, mathematical-reasoning, reasoning, finetuning, pretraining, llm","text-generation, question-answering","Training and Evaluating Language Models with Template-based Data Generation TemplateGSM Dataset The TemplateGSM dataset is a novel and extensive collection containing over 7 million (up to infinite) g","None","en","mono","1|B|<|n|<|1|0|B","None","False","False","False","cc-by-4.0","2411.18104","None","None"
+"BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR","BByrneLab","2024-02-25 15:51:51","2024-09-01 17:39:33+00:00","5d8cf441601d964ef1e7eb2c191d81f20323f6b0","1373","9546","4","None","knowledge-based-visual-question-answering, Knowledge-retrieval, passage-retrieval","PreFLMR M2KR Dataset Card Dataset details Dataset type: M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multi","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2402.08327","None","None"
+"lmms-lab/LLaVA-NeXT-Interleave-Bench","lmms-lab","2024-07-01 10:58:27","2024-08-09 09:11:05+00:00","4031706c795c8a307718a76988da125279718f60","880","9465","12","None","visual-question-answering, question-answering","LLaVA-Interleave Bench Dataset Card Dataset details Dataset type: LLaVA-Interleave Bench is a comprehensive set of multi-image datasets that are collected from public datasets or generated by the GPT-","None","en","mono","1|0|K|<|n|<|1|0|0|k","None","False","False","False","cc-by-4.0","None","None","None"
+"xacer/vox-pretrain","xacer","2024-08-05 19:10:51","2024-10-18 02:41:24+00:00","d4cee6ec145c70c676727ffdc5d08509ff1a2d32","2749","9417","0","None","audio-to-audio","None","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"nyuuzyou/subdomains","nyuuzyou","2024-08-26 18:51:41","2024-10-07 09:18:46+00:00","0c6a5036ebc47d5f51ad0bc507a3f7c5ab7f0b5c","2208","9391","4","None","other","Dataset Card for Subdomain Statistics from scanner.ducks.party Dataset Summary This dataset contains monthly archives of subdomain statistics for websites seen by the scanner.ducks.party web bot. The ","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc0-1.0","None","None","None"
+"lavita/medical-qa-datasets","lavita","2023-11-02 03:06:29","2023-11-17 20:49:51+00:00","59d48e2739fff1de8803ee59b97547ad51846650","2609","9389","25","None","question-answering","all-processed dataset is a concatenation of of medical-meadow-* and chatdoctor_healthcaremagic datasets The Chat Doctor term is replaced by the chatbot term in the chatdoctor_healthcaremagic dataset S","None","en","mono","None","None","False","False","False","None","None","None","None"
+"NebulaeWis/e621-2024-webp-4Mpixel","NebulaeWis","2024-07-10 20:27:10","2024-07-22 12:17:57+00:00","6c368cdc37aea2120c72e30f1f4f54e88f0ee7c4","4353","9255","14","None","image-to-image, text-to-image","Dataset Description: This is a processed version of the https://huggingface.co/datasets/boxingscorpionbagel/e621-2024 dataset, primarily prepared for personal use in future projects. Therefore, for li","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"enimai/MuST-C-fr","enimai","2022-03-20 14:27:39","2022-11-21 18:39:41+00:00","c218f8fc022c761fa4515c4fd96e160426701dbf","900","9227","0","None","translation","None","None","en, fr","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"AI-MO/NuminaMath-TIR","AI-MO","2024-07-16 07:44:24","2024-11-25 05:32:53+00:00","77a91d7b7a1a98ac4b1beb7d86c09d156b935dcd","1839","9068","80","None","text-generation","Dataset Card for NuminaMath CoT Dataset Summary Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Kazimir-ai/text-to-image-prompts","Kazimir-ai","2024-02-15 18:34:13","2024-02-15 18:42:42+00:00","58422b850435e4c466fa2481893aa591e85434b6","2696","8982","6","None","None","The dataset of the most popular text-to-image prompts. Dataset Details Dataset Description Curated by: kazimir.ai Funded by [optional]: [More Information Needed] Shared by [optional]: https://kazimir.","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"sentence-transformers/s2orc","sentence-transformers","2024-05-03 13:17:17","2024-05-06 06:39:18+00:00","8cfc394e83b2ebfcf38f90b508aea383df742439","1830","8910","5","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:en, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for S2ORC This dataset contains titles, abstracts, and citations from scientific papers from the Semantic Scholar Open Research Corpus (S2ORC). This dataset can and has been used to train","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"nyu-visionx/CV-Bench","nyu-visionx","2024-06-17 16:51:07","2024-07-03 21:02:33+00:00","22409a927ab5cf68e3655023d51694587455fc99","1688","8825","26","None","visual-question-answering","Cambrian Vision-Centric Benchmark (CV-Bench) This repository contains the Cambrian Vision-Centric Benchmark (CV-Bench), introduced in Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal","None","en","mono","None","None","False","False","False","apache-2.0","2406.16860","None","None"
+"MikhailT/hifi-tts","MikhailT","2023-09-27 10:54:55","2023-11-30 07:17:30+00:00","f0a934ddf4e8da0fc6c53fb42fda2c9ac8ed93df","1011","8758","19","None","text-to-speech, text-to-audio","Dataset Card for HiFiTTS Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts.","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"djstrong/oscar-small","djstrong","2023-03-07 19:55:38","2023-03-07 19:57:38+00:00","fc47ed5a1804d8dd398255fa33da745ccb060d0e","3117","8702","1","None","text-generation, language-modeling","The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\\","@inproceedings{ortiz-suarez-etal-2020-monolingual, title = ""A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages"", author = ""Ortiz Su{\\'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit"", booktitle = ""Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"", month = jul, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.acl-main.156"", pages = ""1703--1714"", abstract = ""We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures."", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{\\'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\\""u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{\\""u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} }","af, am, ar, arz, as, az, azb, ba, be, bg, bn, bo, br, ca, ce, ceb, ckb, cs, cv, cy, da, de, dv, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gu, he, hi, hr, hu, hy, id, is, it, ja, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mhr, mk, ml, mn, mr, ms, mt, my, nds, ne, nl, nn, no, or, os, pa, pl, pnb, ps, pt, ro, ru, sa, sah, sd, sh, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vi, yi, zh","multi","None","None","False","False","False","cc0-1.0","2010.14571","None","language-modeling"
+"walledai/AdvBench","walledai","2024-07-02 06:37:45","2024-07-04 18:13:32+00:00","9d4730540082fa4017450b65ca1c0e1d8d30446e","2437","8695","13","None","text2text-generation","Dataset Card for AdvBench Paper: Universal and Transferable Adversarial Attacks on Aligned Language Models Data: AdvBench Dataset About AdvBench is a set of 500 harmful behaviors formulated as instruc","None","en","mono","None","None","False","False","False","mit","2307.15043","None","None"
+"TwoAbove/the-project-gutenberg-open-audiobook-collection","TwoAbove","2023-10-15 14:25:30","2023-11-03 10:48:48+00:00","c8a43fed10986b38d9b29b074ff42c13a5a75b0c","872","8652","1","None","None","Project Gutenberg Open Audiobook Collection Source: https://marhamilresearch4.blob.core.windows.net/gutenberg-public/Website/browse.html You will need to install librosa and soundfile to load this dat","None","en","mono","None","None","False","False","False","None","None","None","None"
+"chujiezheng/wizard_of_wikipedia","chujiezheng","2023-05-08 13:35:40","2023-05-08 15:05:32+00:00","484e8ea36929e800e26e8f21f78c050500648c19","5181","8551","2","None","None","Wizard-of-Wikipedia data for the Findings of EMNLP 2020 paper ""Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation"" GitHub repo. Original paper. @inproceedings{zheng-et","None","en","mono","None","None","False","False","False","cc-by-nc-4.0","2009.09378","None","None"
+"imomayiz/darija-english","imomayiz","2024-02-04 16:33:09","2024-04-17 00:00:58+00:00","9f7e6c57b150873ea3241c9743ac6d5a3175a635","3237","8475","8","None","translation","This work is part of DODa.","None","ar, en","bi","None","None","False","False","False","cc","None","None","None"
+"Said2k/ArtVee_dataset","Said2k","2024-09-17 04:50:38","2024-09-17 10:18:29+00:00","d37a0727f2eac0436eeda01829b8ffb218b5cdaf","2073","8337","0","None","text-to-image","None","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"BestWishYsh/ChronoMagic-Pro","BestWishYsh","2024-06-21 02:09:31","2024-12-03 02:16:50+00:00","5744f75061cf29807ea26061aadd7a1a2a034260","1123","8237","9","None","text-to-video","Usage cat ChronoMagic-Pro.zip.part-* > ChronoMagic-Pro.zip unzip ChronoMagic-Pro.zip [NeurIPS D&B 2024 Spotlight] ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"dell-research-harvard/headlines-semantic-similarity","dell-research-harvard","2023-06-07 18:55:18","2024-06-07 20:52:17+00:00","bbfbc8798150e65da7145653d8e73d33d5ca0a5d","1039","8190","11","None","sentence-similarity","Dataset Card for HEADLINES Dataset Summary HEADLINES is a massive English-language semantic similarity dataset, containing 396,001,930 pairs of different headlines for the same newspaper article, take","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc-by-2.0","2306.17810","None","None"
+"allenai/wildjailbreak","allenai","2024-06-18 04:31:27","2024-08-08 05:39:06+00:00","5ddc12a7894f842b0619b8e1c7ee496b198af009","1174","8070","28","None","text-generation","WildJailbreak Dataset Card WildJailbreak is an open-source synthetic safety-training dataset with 262K vanilla (direct harmful requests) and adversarial (complex adversarial jailbreaks) prompt-respons","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","auto","False","odc-by","None","None","None"
+"Team-PIXEL/rendered-bookcorpus","Team-PIXEL","2022-05-11 14:41:02","2022-08-03 12:03:32+00:00","a17263cdc77c46cecb979e5b997bc23853065c29","1119","8014","4","annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, source_datasets:rendered|BookCorpusOpen, language:en, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1506.06724, arxiv:2207.06991, arxiv:2105.05241, region:us","masked-auto-encoding, rendered-language-modelling, masked-auto-encoding, rendered-language-modeling","Dataset Card for Team-PIXEL/rendered-bookcorpus Dataset Summary This dataset is a version of the BookCorpus available at https://huggingface.co/datasets/bookcorpusopen with examples rendered as images","None","en","mono","1|M|<|n|<|1|0|M","bookcorpus","False","False","False","unknown","1506.06724","None","masked-auto-encoding, rendered-language-modeling"
+"tasksource/defeasible-nli","tasksource","2023-02-02 21:21:26","2024-06-24 10:04:25+00:00","7c4a57df9d8de5c36d4e9caa977907b5e8469c4f","926","7940","1","None","text-classification, natural-language-inference","https://github.com/rudinger/defeasible-nli @inproceedings{rudinger2020thinking, title={Thinking like a skeptic: feasible inference in natural language}, author={Rudinger, Rachel and Shwartz, Vered and","None","en","mono","None","None","False","False","False","apache-2.0","None","None","natural-language-inference"
+"rootsautomation/websrc","rootsautomation","2024-04-23 23:21:31","2024-04-24 18:58:16+00:00","fe36dbe20676c6356c54dcf371f4df47ee3748e0","1335","7929","6","task_categories:question-answering, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, library:webdataset, arxiv:2101.09465, region:us, webdataset","question-answering","Dataset Card for WebSRC v1.0 WebSRC v1.0 is a dataset for reading comprehension on structural web pages. The task is to answer questions about web pages, which requires a system to have a comprehensiv","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","2101.09465","None","None"
+"wassname/genies_preferences","wassname","2024-08-25 04:49:19","2024-09-19 08:12:11+00:00","9e92ec3b21e9800bb26e9f7cdc5792103b651b15","2862","7925","0","None","text-classification, question-answering","Dataset Card for ""genie_dpo"" A conversion of the distribution from GENIES to open_pref_eval format. Conversion code","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"pbevan11/EQ-Bench","pbevan11","2024-02-27 18:18:14","2024-03-05 17:58:37+00:00","9ce8e5ffc1a36be5f946b37610ec8c516871f0d3","911","7798","4","None","None","EQ-Bench This is the EQ-Bench v2 English dataset, all credit to Samuel J. Paech. Title: EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models Abstract: https://arxiv.org/abs/2312.062","None","en","mono","n|<|1|K","None","False","False","False","mit","2312.06281","None","None"
+"wecover/OPUS","wecover","2024-01-30 12:54:31","2024-05-23 09:15:01+00:00","21c82515d8c29d36cdc6355c36795c40d9874e62","883","7727","0","None","translation","Collection of OPUS Corpus from https://opus.nlpl.eu has been collected. The following corpora have been included: UNPC GlobalVoices TED2020 News-Commentary WikiMatrix Tatoeba Europarl OpenSubtitles 25","None","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, ha, he, hi, hr, hu, hy, id, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lo, lt, 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","multi","None","None","False","False","False","None","None","None","None"
+"qanastek/ELRC-Medical-V2","qanastek","2022-03-02 23:29:22","2022-10-24 17:15:17+00:00","7f5633e7f9903947a9e51ab0e12ff483574aeebf","1536","7717","15","None","translation, translation","ELRC-Medical-V2 : European parallel corpus for healthcare machine translation Dataset Summary ELRC-Medical-V2 is a parallel corpus for neural machine translation funded by the European Commission and ","@inproceedings{losch-etal-2018-european, title = ""European Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management"", author = {L{\\""o}sch, Andrea and Mapelli, Val{\\'e}rie and Piperidis, Stelios and Vasi{\\c{l}}jevs, Andrejs and Smal, Lilli and Declerck, Thierry and Schnur, Eileen and Choukri, Khalid and van Genabith, Josef}, booktitle = ""Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)"", month = may, year = ""2018"", address = ""Miyazaki, Japan"", publisher = ""European Language Resources Association (ELRA)"", url = ""https://aclanthology.org/L18-1213"", }","en, bg, cs, da, de, el, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","translation"
+"epfl-llm/guidelines","epfl-llm","2023-11-21 10:35:34","2024-03-07 09:20:01+00:00","a8f0269471088e4c8aafe2319f30c14b2fad82bc","847","7664","117","None","text-generation","🎉 NEW DROP 🎉 PubMed Guidelines We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas! Clinical Guidelines The Clinical Guidel","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","2311.16079","None","None"
+"justinphan3110/vi_pubmed","justinphan3110","2022-11-06 01:39:06","2022-11-06 21:02:17+00:00","2dc0655925b2c848b6c86b68ba6ebad82bfec491","1889","7655","2","None","text-generation, fill-mask, text-classification, language-modeling, masked-language-modeling, text-scoring, topic-classification","Dataset Card for PubMed Dataset Summary NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of eac","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","other","None","None","language-modeling, masked-language-modeling, text-scoring, topic-classification"
+"ai4privacy/pii-masking-400k","ai4privacy","2024-08-29 03:29:44","2024-09-13 02:47:23+00:00","3270451771feb33b610a9b47703e3d2b64f1a662","1149","7641","28","None","text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation, translation, fill-mask, tabular-classification, tabular-to-text, table-to-text, text-retrieval, other","Purpose and Features 🌍 World's largest open dataset for privacy masking 🌎 The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, espe","None","en, fr, de, it, es, nl","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","None"
+"m-a-p/FineFineWeb-sample","m-a-p","2024-12-18 11:00:25","2024-12-19 11:35:21+00:00","84ef38864bfe4e7680d9cba9d88e94402536de6c","6203","7595","1","task_categories:text-classification, task_categories:text2text-generation, task_categories:text-generation, language:en, license:apache-2.0, size_categories:100M<n<1B, format:json, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","text-classification, text2text-generation, text-generation","FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus arXiv: Coming Soon Project Page: Coming Soon Blog: Coming Soon Data Statistics Domain (#tokens/#samples) Iteration 1 Tokens Iterati","None","en","mono","n|>|1|T","None","False","False","False","apache-2.0","None","None","None"
+"audioshake/jam-alt","audioshake","2023-10-29 11:04:32","2024-12-30 15:34:37+00:00","0e839d1ee8a3b4baba3665b17be35312fdc4a172","1251","7557","11","None","automatic-speech-recognition","JamALT: A Readability-Aware Lyrics Transcription Benchmark JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) bench","None","en, fr, de, es","multi","None","None","False","False","False","None","2408.06370","None","None"
+"kernelmachine/open-license-corpus","kernelmachine","2023-08-08 23:21:52","2023-08-09 03:14:36+00:00","384d5e19d19361803630ce4d382604267d3951d2","1757","7549","16","None","text-generation","PubText Welcome to the Open License Corpus (OLC), a 228B token corpus for training permissively-licensed language models. Disclaimer: OLC should not be considered a universally safe-to-use dataset. We","None","en","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","apache-2.0","None","None","None"
+"bigbio/biomrc","bigbio","2022-11-13 22:06:42","2022-12-22 15:43:44+00:00","09938b2d42d6e4ecd4d5282657d0bb5f8791950b","1274","7515","3","None","None","We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple he","@inproceedings{pappas-etal-2020-biomrc, title = ""{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension"", author = ""Pappas, Dimitris and Stavropoulos, Petros and Androutsopoulos, Ion and McDonald, Ryan"", booktitle = ""Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing"", month = jul, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.bionlp-1.15"", pages = ""140--149"", }","en","mono","None","None","False","False","False","unknown","None","None","None"
+"NousResearch/hermes-function-calling-v1","NousResearch","2024-08-14 01:22:36","2024-08-30 06:07:08+00:00","8f025148382537ba84cd325e1834b706e1461692","845","7459","234","None","text-generation, question-answering, feature-extraction","Hermes Function-Calling V1 This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models. This repository contains a structured output datase","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"maxidl/FineNews-unfiltered","maxidl","2024-06-14 20:04:11","2024-06-16 19:48:41+00:00","58c6690fbaf643226bd5dae383a5de158ddf4c1a","987","7456","1","None","text-generation","FineNews WIP. Like FineWeb, but built from Common Crawl News instead of main web. For languages not listed as a split, check the data/ directory. For now, it contains the 2024-05 (May),-04 (April),-03","None","en, de, fr, pl, es, ru, it, ar, pt, tr, el, vi, ro, zh, uk, ko, hi, nl","multi","None","None","False","False","False","odc-by","None","None","None"
+"TIGER-Lab/MMEB-train","TIGER-Lab","2024-10-08 04:05:01","2025-01-28 21:38:52+00:00","76dd0a440b6d4c02776830a804443fffbb2d0bfa","1063","7380","10","None","None","Massive Multimodal Embedding Benchmark The training data split used for training VLM2Vec models in the paper VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks (ICLR 2025)","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2410.05160","None","None"
+"Magpie-Align/Magpie-Reasoning-V1-150K","Magpie-Align","2024-07-11 22:02:20","2025-01-27 19:59:05+00:00","a4bedadca568ba8fa50cae618ae62ca34dd1d196","1616","7361","53","None","None","Project Web: https://magpie-align.github.io/ Arxiv Technical Report: https://arxiv.org/abs/2406.08464 Codes: https://github.com/magpie-align/magpie Abstract Click Here High-quality instruction data is","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","llama3","2406.08464","None","None"
+"philschmid/amazon-product-descriptions-vlm","philschmid","2024-09-28 14:26:19","2024-09-30 10:39:25+00:00","f08a021c69c51d6894cfe39206448e7785d6156b","977","7327","8","None","image-to-text","Amazon Multimodal Product dataset This is a modfied and slim verison of bprateek/amazon_product_description helpful to get started training multimodal LLMs. The description field was generated used Ge","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"codesignal/sms-spam-collection","codesignal","2024-03-18 22:21:02","2024-03-18 23:14:07+00:00","93ffab6fa47849295146c709e341d169746582f3","1070","7250","1","None","None","SMS Spam Collection v.1 DESCRIPTION The SMS Spam Collection v.1 (hereafter the corpus) is a set of SMS tagged messages that have been collected for SMS Spam research. It contains one set of SMS messag","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"sci-benchmark/self-contradictory","sci-benchmark","2023-11-11 14:28:26","2024-08-05 06:09:58+00:00","5f4b7f23c089c034e75881126ce7c94cbc28ba7f","2444","7118","2","None","None","Introduction Official dataset of the ECCV24 paper, ""Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions"". Website: https://selfcontradiction.github.io G","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"apple/DataCompDR-12M","apple","2024-06-03 20:46:43","2024-07-22 22:42:50+00:00","bd23bbc361cc4b2ee0d2bd0431e107b2906b312f","1475","7042","27","None","text-to-image, image-to-text","Dataset Card for DataCompDR-12M This dataset contains synthetic captions, embeddings, and metadata for DataCompDR-12M. The metadata has been generated using pretrained image-text models on a 12M subse","None","en","mono","None","None","False","False","False","other","2311.17049","None","None"
+"osunlp/AttributionBench","osunlp","2023-09-13 21:51:47","2024-02-26 22:00:35+00:00","62569e644f4186606f54f742178a4517431b42e1","1266","7029","3","None","text-classification","Dataset Card for AttributionBench Github repository: [Github] Paper: AttributionBench: How Hard is Automatic Attribution Evaluation? Point of Contact: Yifei Li Dataset Overview We constructed this dat","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2402.15089","None","None"
+"AlexBlck/ANAKIN","AlexBlck","2023-08-17 12:33:16","2023-09-21 10:37:04+00:00","978fe26e78ecaaf6562aa249be43c2c690484d0a","1740","7010","1","None","video-classification, visual-question-answering","ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs.","@misc{black2023vader, title={VADER: Video Alignment Differencing and Retrieval}, author={Alexander Black and Simon Jenni and Tu Bui and Md. Mehrab Tanjim and Stefano Petrangeli and Ritwik Sinha and Viswanathan Swaminathan and John Collomosse}, year={2023}, eprint={2303.13193}, archivePrefix={arXiv}, primaryClass={cs.CV} }","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2303.13193","None","None"
+"Malikeh1375/medical-question-answering-datasets","Malikeh1375","2023-10-27 16:21:07","2023-11-02 03:13:38+00:00","4d69c9f6874bc5ec8be3817e9d86b7333a8a5070","1270","6951","33","None","question-answering","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"parler-tts/mls-eng-speaker-descriptions","parler-tts","2024-06-13 12:37:25","2024-08-08 12:55:57+00:00","5179a15a06e43172aea0ac73d34de65536663745","816","6903","3","None","automatic-speech-recognition, text-to-speech, text-to-audio","Dataset Card for Annotations of English MLS This dataset consists in annotations of the English subset of the Multilingual LibriSpeech (MLS) dataset. MLS dataset is a large multilingual corpus suitabl","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"taesiri/imagenet-hard-4K","taesiri","2023-05-21 17:33:17","2023-06-11 00:37:29+00:00","b70f7a9c60cc2cab806f1053815491c269484060","1537","6800","3","None","image-classification","Dataset Card for ""Imagenet-Hard-4K"" Project Page - Paper - Github ImageNet-Hard-4K is 4K version of the original ImageNet-Hard dataset, which is a new benchmark that comprises 10,980 images collected ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2304.05538","None","None"
+"Teklia/IAM-line","Teklia","2024-01-12 12:20:17","2024-03-14 16:19:29+00:00","fbdad97500ce54635c0d1ba306bf535cb40656cf","810","6791","7","None","image-to-text","IAM - line level Dataset Summary The IAM Handwriting Database contains forms of handwritten English text which can be used to train and test handwritten text recognizers and to perform writer identifi","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"DenyTranDFW/SEC_10K_FSNoNDS_Eat","DenyTranDFW","2024-08-07 08:12:12","2024-08-21 02:17:44+00:00","e4af1ff7fbaaf6fc75374aa7a5ce2fad8c6e13fb","1566","6776","0","language:en, license:gpl, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, parquet, securitiesandexchange, financial, balancesheet, incomestatement, cashflowstatement, sec.gov","None","TRY DATA HERE FINANCIAL STATEMENTS AND NO NOTES DATA SETS - 10-K FILINGS BAKERY PRODUCTS COOKIES & CRACKERS RETAIL FOOD STORES MEAT PACKING PLANTS RETAIL EATING PLACES RETAIL GROCERY STORES FOOD AND K","None","en","mono","None","None","False","False","False","gpl","None","None","None"
+"k-mktr/improved-flux-prompts","k-mktr","2024-09-26 08:40:01","2024-09-27 15:37:32+00:00","f3b4035aa8e4c57bbf447e06523a8d9bd093fb03","1039","6744","50","None","text-classification","Experimental FLUX Prompt Dataset Overview This dataset features a curated selection of prompts designed specifically for FLUX.1, an innovative text-to-image synthesis model. The prompts are crafted to","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"VinayHajare/Fruits-30","VinayHajare","2023-08-16 15:54:47","2023-11-11 05:00:28+00:00","a497a8c7e0493bd168c128ac1082299d1a1549d7","1303","6731","3","None","image-classification","Fruits30 Dataset Description: The Fruits30 dataset is a collection of images featuring 30 different types of fruits. Each image has been preprocessed and standardized to a size of 224x224 pixels, ensu","None","en","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"QubitPi/wilhelm-vocabulary","QubitPi","2024-10-11 01:42:46","2025-01-25 15:06:37+00:00","e1704f059a5317a69a2ff10b05ea24ceb8c518e6","4415","6712","0","None","None","Wilhelm Vocabulary Wilhelm Vocabulary Development Environment Setup Installing Dependencies Data Format Encoding Table in YAML Data Pipeline How Data (Vocabulary) is Stored in a Graph Database Why Gra","None","en, de, la, grc","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"therem/CLEAR","therem","2024-09-10 22:00:03","2024-10-24 12:52:36+00:00","34cc437b01f854c05adfc1d15742d6c96fc1399d","1289","6702","6","None","visual-question-answering","This repo contains dataset for the CLEAR benchmark of multimodal unlearning. Links Github repo Arxiv paper Loading the Dataset To load the dataset, use the following code: from datasets import load_da","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","2410.18057","None","None"
+"KingNish/reasoning-base-20k","KingNish","2024-10-02 15:09:22","2024-10-05 14:19:30+00:00","ae93576e3b315cf876e7429b7fa1fd041df72d29","2373","6683","207","None","text-generation","Dataset Card for Reasoning Base 20k Dataset Details Dataset Description This dataset is designed to train a reasoning model. That can think through complex problems before providing a response, simila","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Team-PIXEL/rendered-wikipedia-english","Team-PIXEL","2022-05-11 14:52:06","2022-08-02 14:01:21+00:00","504638a427b89c21bd99c1d1307e726f746e8231","900","6676","3","annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-sa-3.0, license:gfdl, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2207.06991, region:us","masked-auto-encoding, rendered-language-modelling, masked-auto-encoding, rendered-language-modeling","Dataset Card for Team-PIXEL/rendered-wikipedia-english Dataset Summary This dataset contains the full English Wikipedia from February 1, 2018, rendered into images of 16x8464 resolution. The original ","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-3.0","2207.06991","None","masked-auto-encoding, rendered-language-modeling"
+"instruction-pretrain/ft-instruction-synthesizer-collection","instruction-pretrain","2024-06-18 14:42:41","2024-12-02 06:34:04+00:00","9564d3b63796a457c9f7b0b75d8315d7a6f4f6b3","1275","6614","60","None","text-classification, table-question-answering, question-answering, zero-shot-classification","Instruction Pre-Training: Language Models are Supervised Multitask Learners (EMNLP 2024) This repo contains the fine-tuning data collection for the context-based instruction synthesizer used in our pa","None","en","mono","None","None","False","False","False","other","None","None","None"
+"terryoo/TableVQA-Bench","terryoo","2024-04-25 13:40:06","2024-04-25 13:45:49+00:00","17e291af2b1cbc9340509a0412a6f1c05b0a76b5","1640","6613","19","None","table-question-answering, visual-question-answering","Dataset Card for ""TableVQA-Bench"" More Information needed","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"gabeorlanski/bc-humaneval","gabeorlanski","2023-06-25 16:36:56","2023-07-18 16:23:28+00:00","ce98d28987ec4aed281dd061cb4167ded1fbcd76","2474","6573","4","None","text-generation, text2text-generation","The HumanEval dataset in BabelCode format.","@article{orlanski2023measuring, title={Measuring The Impact Of Programming Language Distribution}, author={Orlanski, Gabriel and Xiao, Kefan and Garcia, Xavier and Hui, Jeffrey and Howland, Joshua and Malmaud, Jonathan and Austin, Jacob and Singh, Rishah and Catasta, Michele}, journal={arXiv preprint arXiv:2302.01973}, year={2023} } @article{chen2021codex, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} }","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2302.01973","None","None"
+"yoshitomo-matsubara/srsd-feynman_hard","yoshitomo-matsubara","2022-06-08 06:22:25","2024-03-05 07:22:34+00:00","159f9b0986ae71ddaae37486b12860e7001eb5ab","1551","6552","4","None","tabular-regression","Dataset Card for SRSD-Feynman (Hard set) Dataset Summary Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery. We carefully reviewed the ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","2206.10540","None","None"
+"MedRAG/pubmed","MedRAG","2024-02-20 17:59:26","2024-02-27 05:35:03+00:00","33da3593d5756bc04c8909f170003c0b14197957","2213","6521","55","None","question-answering","The PubMed Corpus in MedRAG This HF dataset contains the snippets from the PubMed corpus used in MedRAG. It can be used for medical Retrieval-Augmented Generation (RAG). News (02/26/2024) The ""id"" col","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","2402.13178","None","None"
+"google/frames-benchmark","google","2024-09-19 02:15:32","2024-10-15 18:18:24+00:00","58d9fb6330f3ab1316d1eca12e5e8ef23dcc22ef","908","6515","179","None","text-classification, token-classification, table-question-answering, question-answering","FRAMES: Factuality, Retrieval, And reasoning MEasurement Set FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factu","None","en","mono","n|>|1|T","None","False","False","False","apache-2.0","2409.12941","None","None"
+"Cohere/miracl-en-corpus-22-12","Cohere","2023-02-02 23:21:21","2023-02-06 11:54:52+00:00","b9dbb115531962e828cc9c771a2f47ed5dbe55de","1272","6471","2","None","text-retrieval, document-retrieval","MIRACL (en) embedded with cohere.ai multilingual-22-12 encoder We encoded the MIRACL dataset using the cohere.ai multilingual-22-12 embedding model. The query embeddings can be found in Cohere/miracl-","None","en","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"bespokelabs/Bespoke-Stratos-17k","bespokelabs","2025-01-21 09:38:20","2025-01-28 08:07:02+00:00","729410217b3ab45ff11d5f5a0160c318d805a3a2","6406","6415","123","language:en, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, curator, synthetic","None","Bespoke-Stratos-17k We replicated and improved the Berkeley Sky-T1 data pipeline using SFT distillation data from DeepSeek-R1 to create Bespoke-Stratos-17k -- a reasoning dataset of questions, reasoni","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"facebook/2M-Belebele","facebook","2024-12-16 08:45:30","2024-12-17 13:39:10+00:00","1355a3bf763d9b3d3862ad0ce7d157819576fbf4","3874","6354","5","None","question-answering, automatic-speech-recognition","2M-Belebele Highly-Multilingual Speech and American Sign Language Comprehension Dataset We introduce 2M-Belebele as the first highly multilingual speech and American Sign Language (ASL) comprehension ","None","bg, pa, en, hu, sv, af, ca, ka, sk, jv, bn, tr, sr, ro, tg, fa, wo, fi, hy, vi, kea, as, ja, nl, ne, lg, hi, xh, kk, mn, yo, km, ha, ru, sw, ps, ko, cs, lv, ig, ar, es, nb, lt, fil, it, he, da, ml, my, el, et, pl, sn, sd, or, th, luo, sl, fr, id, ta, gu, mk, am, pt, cmn, de, ceb, is, ur, az, te","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2412.08274","None","None"
+"mlabonne/harmful_behaviors","mlabonne","2024-05-28 18:03:09","2024-06-04 10:45:47+00:00","01cead01398926d81f7c52bdb790ee8cf77ebba7","795","6332","34","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"sentence-transformers/msmarco-bm25","sentence-transformers","2024-05-02 15:00:54","2024-05-15 14:30:23+00:00","ce8a493a65af5e872c3c92f72a89e2e99e175f02","796","6242","1","None","feature-extraction, sentence-similarity","MS MARCO with hard negatives from bm25 MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine. For each query and gold p","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"cmarkea/doc-vqa","cmarkea","2024-06-17 14:05:32","2024-08-26 14:16:40+00:00","37fe18d0b538bd76615cdd54320337944442dbb6","851","6227","16","None","visual-question-answering","Dataset description The doc-vqa Dataset integrates images from the Infographic_vqa dataset sourced from HuggingFaceM4 The Cauldron dataset, as well as images from the dataset AFTDB (Arxiv Figure Table","None","en, fr","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"olm/olm-CC-MAIN-2017-22-sampling-ratio-0.16178770949","olm","2022-10-25 22:33:21","2022-11-04 17:12:48+00:00","5ec4fd478a40966b89315c2ad181766210c6a9d7","1061","6168","0","None","None","Dataset Card for OLM May 2017 Common Crawl Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 16% of the May 2017 Common Crawl snapshot. Note: last_modified_timestamp wa","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"EleutherAI/rpj-v2-sample","EleutherAI","2024-07-26 19:19:46","2024-07-26 22:02:35+00:00","7ed6a2f0cfeccfc57d8aec0011b1c774b4c59c4c","778","6130","1","None","text-generation","This is a mirror of the sample-10B subset of RedPajama-Data-V2 which we have re-uploaded in order to resolve issues with the original download script. Getting Started RedPajama-V2 is an open dataset f","None","en, de, fr, es, it","multi","None","None","False","False","False","None","None","None","None"
+"Mutonix/Vript_Multilingual","Mutonix","2024-10-13 01:33:27","2024-10-17 08:29:45+00:00","86a6554abb015839820e1fd861c11f27248e0604","1445","6083","3","None","video-classification, visual-question-answering, text-to-video, text-to-image, image-to-video","🎬 Vript: A Video Is Worth Thousands of Words [Github Repo] We construct another fine-grained video-text dataset with 19.1K annotated high-resolution UGC videos (~677k clips) in multiple languages to b","None","zh, en, de, ja, ko, ru, es, pt, jv, fr, id, vi","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"MLSP2024/MLSP2024","MLSP2024","2024-05-06 06:52:41","2024-08-03 08:45:03+00:00","8dd037992ef0e14f72dfc6994e273cbe3d9fc633","1127","6064","5","None","token-classification, other, text-simplification","The MultiLS dataset was created as part of the MLSP2024 shared task. The dataset contains 5,624 instances across 10 target languages. Each instance consists of a sentence from an educational text, wit","None","en, ca, fil, fr, de, it, ja, pt, si, es","multi","1|K|<|n|<|1|0|K","None","False","auto","False","cc-by-sa-4.0","2402.14972","None","text-simplification"
+"Overlaiai/OregonCoastin4K","Overlaiai","2024-11-12 19:27:48","2024-11-26 03:11:18+00:00","8d194fa0b9b4a7cfa89e2b2346a3a7cdba565ccc","828","5962","4","None","text-to-video","OREGON COAST IN 4K ""Oregon Coast in 4K"" is a fine tuning text-to-video dataset consisting of dynamic videos captured in 8K resolution on the DJI Inspire 3 and RED Weapon Helium. Key Features 🎥 Oversam","None","en","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"BGLab/FlowBench","BGLab","2024-06-12 20:16:27","2024-12-04 20:01:09+00:00","ee3e4ce823dbf29d76c01f15478a4b938e8bd98c","2507","5716","0","None","None","Overview Simulating fluid flow around arbitrary shapes is key to solving various engineering problems. However, simulating flow physics across complex geometries remains numerically challenging and co","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"allenai/preference-test-sets","allenai","2024-01-04 18:26:35","2024-03-14 03:22:18+00:00","0d1fa41424b53512142c01579802f6a5143aebfb","933","5711","22","None","summarization, question-answering","Preference Test Sets Very few preference datasets have heldout test sets for validation of reward model accuracy results. In this dataset, we curate the test sets from popular preference datasets into","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","odc-by","2204.05862","None","None"
+"Salesforce/blip3-ocr-200m","Salesforce","2024-09-04 20:02:34","2024-09-16 23:54:27+00:00","40fbea04ca78c81f19d2a397fb8f1a60b2145524","987","5704","27","None","None","BLIP3-OCR-200M Dataset Overview The BLIP3-OCR-200M dataset is designed to address the limitations of current Vision-Language Models (VLMs) in processing and interpreting text-rich images, such as docu","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"McGill-NLP/WebLINX","McGill-NLP","2024-02-08 08:04:29","2024-12-07 04:23:40+00:00","a30ff2cbecb75f2d04fac75a2b92721c6c9e3f13","840","5697","61","None","image-to-text, text-generation, text2text-generation, sentence-similarity","WebLINX: Real-World Website Navigation with Multi-Turn Dialogue Xing Han Lù*, Zdeněk Kasner*, Siva Reddy 💾Code 📄Paper 🌐Website 📓Colab 🤖Models💻Explorer 🐦Tweets 🏆Leaderboard Your browser does not suppor","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2402.05930","None","None"
+"TIGER-Lab/MEGA-Bench","TIGER-Lab","2024-10-17 00:52:03","2024-11-20 03:16:03+00:00","8107ecd6c1971ab2edf7d6e640cbf6c9f1799209","975","5681","18","None","question-answering","MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 🔎 Visualiaztion | 📖 arXiv | GitHub 🔔 News [2024-10-18]: Initial release of the","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2410.10563","None","None"
+"BAAI/IndustryCorpus_medicine","BAAI","2024-07-25 05:46:41","2024-07-26 02:30:48+00:00","1b84ba84ec1e69ff947d0ebd0da4cda01d9b84a4","1536","5645","7","None","text-generation","[中文主页] Industry models play a crucial role in driving enterprise intelligence transformation and innovative development. High-quality industry data is key to improving the performance of large models ","None","zh, en","bi","n|>|1|T","None","False","False","False","apache-2.0","None","None","None"
+"harvard-lil/cold-cases","harvard-lil","2023-09-12 17:29:50","2024-03-26 15:50:21+00:00","5d8d0d8457ef63b6463af9737da21d3badd924ad","806","5609","22","None","None","Collaborative Open Legal Data (COLD) - Cases COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as compressed parquet files. If you'd like to view a","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc0-1.0","None","None","None"
+"yoshitomo-matsubara/srsd-feynman_medium","yoshitomo-matsubara","2022-06-08 06:22:10","2024-03-05 07:22:12+00:00","de7af9e2638494678ebe18d1174bbc6a02037eec","900","5562","1","None","tabular-regression","Dataset Card for SRSD-Feynman (Medium set) Dataset Summary Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery. We carefully reviewed th","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","2206.10540","None","None"
+"imageomics/fish-vista","imageomics","2024-06-10 19:09:08","2024-11-08 00:15:07+00:00","ced15685f2deceeabaed3e8a75e07e5737e45495","1286","5551","11","task_categories:image-classification, task_categories:image-segmentation, language:en, size_categories:100K<n<1M, format:csv, modality:image, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2407.08027, doi:10.57967/hf/3471, region:us, fish, traits, processed, RGB, biology, image, animals, CV","image-classification, image-segmentation","Dataset Card for Fish-Visual Trait Analysis (Fish-Vista) Note that the '</Use this dataset>' option will only load the CSV files. To download the entire dataset, including all processed images and seg","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"turing-motors/CoVLA-Dataset","turing-motors","2024-11-26 03:28:49","2025-01-24 00:58:35+00:00","3c88d564b017b652acd446eec8d3a17d78d40952","3725","5507","6","None","None","CoVLA-Dataset WACV 2025 Oral CoVLA-Dataset is a dataset comprising real-world driving videos spanning more than 80 hours. This dataset leverages a novel, scalable approach based on automated data proc","None","en","mono","None","None","False","auto","False","None","None","None","None"
+"sentence-transformers/parallel-sentences-opensubtitles","sentence-transformers","2024-04-30 08:24:04","2024-06-18 19:45:43+00:00","d86a387587ab6f2fd9ec7453b2765cec68111c87","1412","5482","1","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - OpenSubtitles This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the ","None","en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, he, hi, hr, hu, hy, id, it, ja, ka, ko, lt, lv, mk, ms, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"squeezebits/dynamic_sonnet_llama3","squeezebits","2024-08-14 10:27:58","2024-08-14 10:28:21+00:00","9edfa5537e3fd3032b9aeb0871db961186a31ae4","919","5316","1","None","question-answering, text-generation","Dynamic Sonnet - Llama3 Curated dataset for benchmarking LLM serving systems In real-world service scenarios, each request comes with varying input token lengths. Some requests generate only a few tok","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"LongSafari/open-genome","LongSafari","2024-07-06 15:54:59","2024-07-10 05:59:55+00:00","84369c058d192dcb607086d71679b877421e3250","3333","5283","12","task_categories:text-generation, language:en, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, stripedhyena, long context, deep signal processing, hybrid, biology, genomics","text-generation","Dataset organization The OpenGenome dataset is organized in 2 stages, where stage 1 has context length 8k and stage 2 has context length 131k. Each stage has their own datasplits. - stage1 - train - v","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"nampdn-ai/mini-fineweb","nampdn-ai","2024-04-23 11:05:08","2024-05-23 02:31:39+00:00","b4a1a327788f2a8876bf5be2274f907236045b34","1639","5117","24","None","text-generation","Fineweb subset [Work-In-Progress] Filtering by how a knowledge could be useful for pretraining small model I'm targeting only 3-4% of original fineweb in size.","None","en","mono","n|>|1|T","None","False","auto","False","odc-by","None","None","None"
+"matteogabburo/mWikiQA","matteogabburo","2024-06-14 13:25:07","2024-07-27 09:26:35+00:00","3960a850ef4ba50778cea29ed6030701b67d4807","831","5109","0","None","question-answering","Dataset Description mWikiQA is a translated version of WikiQA. It contains 3,047 questions sampled from Bing query logs. The candidate answer sentences are extracted from Wikipedia and then manually l","None","en, fr, de, it, es, pt","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","other","2406.10172","None","None"
+"mesolitica/fineweb-filter-malaysian-context","mesolitica","2024-08-07 15:23:05","2024-08-13 08:38:23+00:00","c8df876812cc99955138c3f3691c2fc3b7933d6c","2034","5076","0","None","None","HuggingFaceFW/fineweb filter Malaysian context What is it? We filter the original 🍷 FineWeb dataset that consists more than 15T tokens on simple Malaysian keywords. Total tokens for the filtered datas","None","en","mono","None","None","False","False","False","None","None","None","None"
+"AnimaLab/bias-test-gpt-sentences","AnimaLab","2023-08-27 01:07:55","2024-03-16 02:49:01+00:00","c4626cbc91aafcd4de0a92dfd0526922cb2df8f0","1433","5068","1","None","None","Dataset Card for ""BiasTestGPT: Generated Test Sentences"" Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models. ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"sentence-transformers/parallel-sentences-tatoeba","sentence-transformers","2024-04-30 11:19:18","2024-06-18 19:45:56+00:00","cec1343ab5a7a8befe99af4a2d0ca847b6c84743","825","5039","0","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - Tatoeba This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the senten","None","en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"BAAI/DenseFusion-1M","BAAI","2024-06-04 02:58:10","2024-10-17 08:00:41+00:00","b935d5ad34311e351bc2f3eb33667451e331e167","788","5002","29","None","question-answering, visual-question-answering","[Paper] https://arxiv.org/abs/2407.08303 [GitHub] https://github.com/baaivision/DenseFusion Introduction An image is worth a thousand words"". Comprehensive image descriptions are essential for multi-m","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","2407.08303","None","None"
+"espnet/floras","espnet","2024-10-24 01:40:36","2024-11-29 20:12:19+00:00","0cc744b3fad853c054031df2ef110c51f28a78e5","1606","4998","4","task_categories:automatic-speech-recognition, task_categories:translation, task_categories:summarization, language:en, language:es, language:fr, language:de, language:nl, language:it, language:pt, language:hu, language:fi, language:el, language:ca, language:eo, language:et, language:da, language:la, language:sv, language:cy, language:gl, language:ru, language:pl, language:uk, language:ro, language:cs, language:sl, language:sk, language:hr, language:bg, language:bs, language:ka, language:tr, language:fa, language:ar, language:uz, language:az, language:ku, language:ky, language:hi, language:ta, language:ur, language:bn, language:id, language:vi, language:th, language:mi, language:ms, language:ja, language:zh, license:cc-by-3.0, size_categories:1K<n<10K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition, translation, summarization","FLORAS FLORAS is a 50-language benchmark For LOng-form Recognition And Summarization of spoken language. The goal of FLORAS is to create a more realistic benchmarking environment for speech recognitio","None","en, es, fr, de, nl, it, pt, hu, fi, el, ca, eo, et, da, la, sv, cy, gl, ru, pl, uk, ro, cs, sl, sk, hr, bg, bs, ka, tr, fa, ar, uz, az, ku, ky, hi, ta, ur, bn, id, vi, th, mi, ms, ja, zh","multi","None","None","False","False","False","cc-by-3.0","None","None","None"
+"phonemetransformers/CHILDES","phonemetransformers","2024-03-28 10:32:58","2025-01-20 15:00:42+00:00","328ff1e808c8a329cedb29f128e41e4556598038","1016","4996","1","None","None","Phonemized Child Directed Speech Dataset This dataset contains utterance downloaded from CHILDES which have been pre-processed and converted to phonemic transcriptions by this processing script. Many ","None","en, de, fr, es, nl, cmn, ja, yue, et, hr, da, eu, hu, tr, fa, is, id, ga, cy, ko, sv, nb, qu, ca, it, pt, ro, sv, pl","multi","1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"andstor/methods2test","andstor","2023-12-07 13:37:44","2023-12-23 03:01:51+00:00","1a9957cbcbe46ee7152cfd940b92cb80c5269104","3347","4790","0","None","text-generation","Dataset Description Microsoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. It contains 780k pairs of JUnit test cases and focal methods w","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"ProlificAI/social-reasoning-rlhf","ProlificAI","2023-10-10 23:45:21","2023-10-11 08:50:59+00:00","de1bee17491f41c5105d2ffc2254171ea4a65368","2006","4765","34","None","text-generation","Dataset Summary This repository provides access to a social reasoning dataset that aims to provide signal to how humans navigate social situations, how they reason about them and how they understand e","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"raulc0399/open_pose_controlnet","raulc0399","2024-08-01 11:26:27","2024-08-29 15:09:15+00:00","9fc8da66e5c6f397d4a0ef3aa55523cde1c532de","2175","4736","8","None","image-to-image, keypoint-detection, pose-estimation","Dataset for training controlnet models with conditioning images as Human Pose the entries have been taken from this dataset ptx0/photo-concept-bucket the open pose images have been generated with cont","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","openrail","None","None","None"
+"homebrewltd/instruction-speech-encodec-v1","homebrewltd","2024-06-24 08:52:22","2024-08-19 04:05:29+00:00","2b155e1a7285bd369160e4f0373f656ee14de30d","1125","4678","14","None","None","Dataset Card for ""Instruction Speech"" The largest open-source English speech instruction to text answer dataset Dataset Overview This dataset contains nearly 450,000 English speech instruction to text","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"Multilingual-Multimodal-NLP/TableBench","Multilingual-Multimodal-NLP","2024-07-18 10:11:04","2024-08-29 05:31:54+00:00","90593ad8af90f027f6f478b8c4c1981d9f073a83","2099","4674","16","None","question-answering","Dataset Card for TableBench Dataset Summary TableBench is a dataset that covers 4 major categories and 18 subcategories, focusing on the multi-dimensional capabilities of table question answering. Dat","None","en","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"KbsdJames/Omni-MATH","KbsdJames","2024-09-14 06:45:45","2024-10-12 09:02:05+00:00","40ba231d8f16e29ecd40e6407e2c8640145a8f62","2165","4665","67","None","None","Dataset Card for Omni-MATH Recent advancements in AI, particularly in large language models (LLMs), have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benc","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"allenai/xstest-response","allenai","2024-06-26 10:16:03","2024-06-29 06:31:11+00:00","4cd03695944eb5940c5a2dfcdf2a493a8f288dbc","1330","4617","4","None","text-classification","Dataset Card for XSTest-Response Disclaimer: The data includes examples that might be disturbing, harmful or upsetting. It includes a range of harmful topics such as discriminatory language and discus","None","en","mono","None","None","False","auto","False","odc-by","2308.01263","None","None"
+"THUDM/LongBench-v2","THUDM","2024-12-18 03:25:47","2024-12-20 02:22:11+00:00","2b48e494f2c7a2f0af81aae178e05c7e1dde0fe9","4118","4571","9","None","multiple-choice, question-answering, text-classification, table-question-answering","LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks 🌐 Project Page: https://longbench2.github.io 💻 Github Repo: https://github.com/THUDM/LongBench 📚 Arxiv Pap","None","en","mono","n|<|1|K","None","False","False","False","apache-2.0","2412.15204","None","None"
+"erfanzar/UltraChat-Mixin","erfanzar","2023-09-07 10:55:45","2023-09-07 11:28:29+00:00","38699048b08c5beeacd1139e0d9de238d72851f5","1815","4570","6","None","summarization, text-generation, translation, question-answering, conversational","Dataset Card for ""UltraChat-Mixin"" UltraChat-Mixin Dataset Overview UltraChat-Mixin is a dataset created by Me, which is a mix of three datasets: 'stingning/ultrachat', 'jondurbin/airoboros-2.1', and ","None","en, zh","bi","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"AlgorithmicResearchGroup/arxiv_s2orc_parsed","AlgorithmicResearchGroup","2023-07-15 00:35:31","2024-09-04 06:50:23+00:00","7cb654ce000f0e31108f4bf4281cf3770f06bdbd","1068","4472","15","None","text-generation, zero-shot-classification","Dataset Card for ""ArtifactAI/arxiv_s2orc_parsed"" Dataset Description https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed Dataset Summary AlgorithmicResearchGroup/arxiv_s2orc_pa","None","en","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","None","None","None","None"
+"LLM-LAT/harmful-dataset","LLM-LAT","2024-07-23 00:16:54","2024-07-24 11:09:50+00:00","8bfba31bc6d93a5b71808fee5275ef4b6330ed91","997","4468","8","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"CohereForAI/Global-MMLU-Lite","CohereForAI","2024-12-12 12:50:53","2024-12-19 13:37:17+00:00","b88e8867e030bb38e9acedfa2c46cb9b8943dc5a","3273","4456","13","None","None","Dataset Summary Global-MMLU-Lite is a multilingual evaluation set spanning 15 languages, including English. It is ""lite"" version of the original Global-MMLU dataset 🌍. It includes 200 Culturally Sensi","None","en, ar, bn, es, fr, hi, de, id, it, ja, ko, pt, zh, yo, sw","multi","None","None","False","False","False","apache-2.0","2412.03304","None","None"
+"O1-OPEN/OpenO1-SFT","O1-OPEN","2024-11-21 02:43:31","2024-12-17 02:30:09+00:00","63112de109aa755e9cdfad63a13f08a92dd7df36","1912","4425","337","None","question-answering","SFT Data for CoT Activation 🎉🎉🎉This repository contains the dataset used for fine-tuning a language model using SFT for Chain-of-Thought Activation. 🌈🌈🌈The dataset is designed to enhance the model's a","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"crumb/textfiles","crumb","2023-06-28 00:05:49","2023-07-20 19:24:06+00:00","4257cc6f35e3056cf4f99faa1515d168397d5bdc","780","4340","4","None","None","backup of textfiles.com for easy download purposes Disclaimer THANK YOU FOR READING THE DISCLAIMER TEXTFILES.COM is meant to be a historical archive, collecting textfiles written on BBSes in the 1980'","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"big-banyan-tree/BBT_CommonCrawl_2024","big-banyan-tree","2024-10-09 05:43:50","2024-10-11 08:17:41+00:00","dbad628039c9a57522ece7e9d2737b3c2a53f453","3599","4289","4","None","None","Context BigBanyanTree is an initiative to empower colleges to set up their data engineering clusters, and drive interest towards data processing and analysis using tools such as Apache Spark. The data","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","None","None","None"
+"llamaindex/vdr-multilingual-train","llamaindex","2025-01-08 10:55:39","2025-01-10 16:36:36+00:00","6b92b5cae23d44509f1e05d7062befe5ec77f7c9","4284","4284","15","None","None","Multilingual Visual Document Retrieval Dataset This dataset consists of 500k multilingual query image samples, collected and generated from scratch using public internet pdfs. The queries are syntheti","None","de, it, fr, es, en","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"quasara-io/Quasara-MajorTOM-Europe-SigLIP","quasara-io","2024-08-08 14:42:35","2024-08-23 15:43:04+00:00","253b5fadedaa69db4910de47021b043a21bbb0ed","1071","4276","4","None","zero-shot-classification","Dataset Card for Dataset Name We downloaded satellite images from Major-TOM, provided by the European Space Agency, filtered for Europe, and used our vectorisation engine 'Synapsis' to extract vector ","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"meta-llama/Llama-3.1-70B-evals","meta-llama","2024-07-22 22:00:07","2024-10-02 21:45:38+00:00","99925827d38b276b2486098eb335f861cf7f3eea","1761","4271","7","None","None","Dataset Card for Llama-3.1-70B Evaluation Result Details This dataset contains the Meta evaluation result details for Llama-3.1-70B. The dataset has been created from 12 evaluation tasks. These tasks ","None","e, n","bi","None","None","False","manual","False","llama3.1","None","None","None"
+"alpindale/two-million-bluesky-posts","alpindale","2024-11-27 17:04:00","2024-11-28 06:38:41+00:00","ba52d3e2f7f56701f07dca1c7eb474ab4b8aa1b2","951","4220","195","None","None","2 Million Bluesky Posts This dataset contains 2 million public posts collected from Bluesky Social's firehose API, intended for machine learning research and experimentation with social media data. Th","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"permutans/fineweb-bbc-news","permutans","2025-01-08 04:45:14","2025-01-27 17:07:18+00:00","95602e3906463a9893aa8be16702db79e92e1f20","4179","4179","13","language:en, license:odc-by, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.17557, region:us, news, fineweb","None","Dataset Card for BBC News from FineWeb This dataset provides a filtered subset of BBC News articles from each subset of the FineWeb dataset, expected to contain approximately 300M articles from BBC Ne","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","odc-by","2406.17557","None","None"
+"tner/wikineural","tner","2022-09-27 17:56:40","2022-09-27 19:46:37+00:00","ce7483a909a7b68ddc02920087462355f7680057","1137","4164","8","None","token-classification, named-entity-recognition","[wikineural](https://aclanthology.org/2021.findings-emnlp.215/)","@inproceedings{tedeschi-etal-2021-wikineural-combined, title = ""{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}"", author = ""Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\\`o} and Cecconi, Francesco and Navigli, Roberto"", booktitle = ""Findings of the Association for Computational Linguistics: EMNLP 2021"", month = nov, year = ""2021"", address = ""Punta Cana, Dominican Republic"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2021.findings-emnlp.215"", doi = ""10.18653/v1/2021.findings-emnlp.215"", pages = ""2521--2533"", abstract = ""Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation."", }","de, en, es, fr, it, nl, pl, pt, ru","multi","1|0|K|<|1|0|0|k","None","False","False","False","None","None","None","named-entity-recognition"
+"Supermaxman/esa-hubble","Supermaxman","2023-02-22 22:03:08","2024-03-29 18:47:13+00:00","6dfc244fda96478b69369bee7e16f348febc2d8d","810","3961","14","None","text-to-image","Dataset Card for ESA Hubble Deep Space Images & Captions Dataset Summary The ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"ARTPARK-IISc/Vaani","ARTPARK-IISc","2024-09-30 19:51:29","2025-01-20 18:22:42+00:00","5b6850c9fa70bd01ce8aae2f71e4c74861f2f480","2035","3955","14","None","automatic-speech-recognition, text-to-speech","Project Vaani, by IISc, Bangalore and ARTPARK, is capturing the true diversity of India’s spoken languages to propel language AI technologies and content for an inclusive Digital India. We expect to c","None","ne, as, ml, gu, or, en, ta, ur, te, kn, bn, hi","multi","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","cc-by-4.0","None","None","None"
+"Xkev/LLaVA-CoT-100k","Xkev","2024-11-27 08:06:45","2024-11-27 17:46:21+00:00","d83435ce2e4438c632391e40c416eb75e0f0dabb","1524","3950","69","None","visual-question-answering","Dataset Card for LLaVA-CoT Dataset Sources Repository: [https://github.com/PKU-YuanGroup/LLaVA-CoT] Paper: [https://arxiv.org/abs/2411.10440] Dataset Structure The repository includes image.zip.part-{","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2411.10440%5D","None","None"
+"crumb/flan-ul2-tinystories","crumb","2023-07-01 20:49:27","2023-07-02 04:47:47+00:00","70d90879cfbb447cdc99a9b52f3469d46583b07f","1061","3945","2","None","None","Around a quarter of a million examples generated from Flan-UL2 (20b) with the prompt ""Write a short story using the vocabulary of a first-grader."" to be used in an experimental curriculum learning set","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"BAAI/IndustryCorpus_film","BAAI","2024-07-25 05:46:40","2024-07-26 02:30:47+00:00","7a48301cc012147a4f590f53db77477c5aa30ac0","1388","3936","2","None","text-generation","[中文主页] Industry models play a crucial role in driving enterprise intelligence transformation and innovative development. High-quality industry data is key to improving the performance of large models ","None","zh, en","bi","n|>|1|T","None","False","False","False","apache-2.0","None","None","None"
+"him1411/polymath","him1411","2024-10-06 20:16:27","2024-10-24 07:55:57+00:00","15a27cc960d339f926e732ec3fdc7452c01e71dd","3081","3918","7","None","multiple-choice","Paper Information We present PolyMATH, a challenging benchmark aimed at evaluating the general cognitive reasoning abilities of MLLMs. PolyMATH comprises 5,000 manually collected high-quality images o","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","afl-3.0","2410.14702","None","None"
+"TIGER-Lab/M-BEIR","TIGER-Lab","2023-12-01 00:08:47","2024-08-07 15:15:33+00:00","023f7cc5425e15e3c93f6146f7295c28eae767dc","899","3864","16","None","text-retrieval, text-to-image, image-to-text, visual-question-answering","UniIR: Training and Benchmarking Universal Multimodal Information Retrievers (ECCV 2024) 🌐 Homepage | 🤗 Model(UniIR Checkpoints) | 🤗 Paper | 📖 arXiv | GitHub How to download the M-BEIR Dataset 🔔News 🔥","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"eltorio/ROCOv2-radiology","eltorio","2024-11-11 18:34:08","2024-11-13 08:49:36+00:00","80ffeef4eb8d34d27cb5c2815305f1d8aee8a83c","1626","3852","42","None","None","ROCOv2: Radiology Object in COntext version 2 Introduction ROCOv2 is a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Acc","None","en","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Bin1117/AnyEdit","Bin1117","2024-12-12 09:15:37","2025-01-18 01:56:16+00:00","662e0178625f3fab3adae6868196eebcebbc2226","2693","3751","3","None","text-to-image, image-to-image","Celebrate! AnyEdit resolved the data alignment with re-uploading process (but view filter is still not working:(, though it has 25 edit types). You can view the validation split for a quick look. [Not","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","2411.15738","None","None"
+"launch/ampere","launch","2022-07-01 02:29:23","2022-11-09 01:57:52+00:00","e81e6a9ac798674b1a72239936bc4f71c4fa2c4e","1065","3721","0","None","text-classification","Dataset Card for AMPERE Dataset Description This dataset is released together with our NAACL 2019 Paper ""Argument Mining for Understanding Peer Reviews"". If you find our work useful, please cite: @inp","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"hpprc/jsick","hpprc","2023-04-08 16:02:06","2023-04-11 06:18:09+00:00","6f27df527556f0c2774f45297cfca7780477ad75","943","3692","7","None","sentence-similarity, text-classification, natural-language-inference, semantic-similarity-scoring","Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset. JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese. W","@article{yanaka-mineshima-2022-compositional, title = ""Compositional Evaluation on {J}apanese Textual Entailment and Similarity"", author = ""Yanaka, Hitomi and Mineshima, Koji"", journal = ""Transactions of the Association for Computational Linguistics"", volume = ""10"", year = ""2022"", address = ""Cambridge, MA"", publisher = ""MIT Press"", url = ""https://aclanthology.org/2022.tacl-1.73"", doi = ""10.1162/tacl_a_00518"", pages = ""1266--1284"", }","ja, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","natural-language-inference, semantic-similarity-scoring"
+"Snowflake/msmarco-v2.1-snowflake-arctic-embed-l","Snowflake","2024-06-20 14:58:59","2024-08-07 16:57:50+00:00","7467d18cb02e4b85a4f0ccbe1f98c574d057437b","959","3559","0","None","question-answering","Snowflake Arctic Embed L Embeddings for MSMARCO V2.1 for TREC-RAG This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG All embeddings are created","None","en","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"avaliev/chat_doctor","avaliev","2024-03-06 18:05:55","2024-03-06 20:55:49+00:00","19646f30de72c3890c6e0bc67579cbb538076822","818","3549","11","None","question-answering","This dataset was formed from the three data sources from the ChatDoctor work. 100k real conversations between patients and doctors from HealthCareMagic.com HealthCareMagic-100k. - ADDED 10k real conve","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"lmg-anon/vntl-leaderboard","lmg-anon","2024-06-08 20:33:07","2025-01-02 16:34:32+00:00","cf3d232d77458394857dbf8411de95fd3a894aef","876","3503","23","None","translation","VNTL Leaderboard The VNTL leaderboard ranks Large Language Models (LLMs) based on their performance in translating Japanese Visual Novels into English. Please be aware that the current results are pre","None","en, ja","bi","n|<|1|K","None","False","False","False","None","None","None","None"
+"textdetox/multilingual_toxicity_dataset","textdetox","2024-02-01 15:44:46","2024-06-10 09:43:48+00:00","684bfd68651993aafc5ca653c35747e43f5b5c43","883","3460","20","None","text-classification","For the shared task CLEF TextDetox 2024, we provide a compilation of binary toxicity classification datasets for each language. Namely, for each language, we provide 5k subparts of the datasets -- 2.5","None","en, ru, uk, de, es, am, zh, ar, hi","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","openrail++","None","None","None"
+"krr-oxford/OntoLAMA","krr-oxford","2023-03-02 00:45:25","2024-11-20 23:18:29+00:00","2d9c6bfa5de20cd5c73f122f29ee331296832c0d","1316","3442","4","None","text-classification","OntoLAMA: LAnguage Model Analysis for Ontology Subsumption Inference Dataset Summary OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference. The work follows the ","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2302.06761","None","None"
+"maum-ai/COMMAND","maum-ai","2024-12-08 17:02:58","2024-12-16 00:31:04+00:00","926be7d19b13a64eb84a7a5f7b5c253af72ad5f1","2357","3432","2","None","robotics","COMMAND Dataset 😃 Dataset Structure 🗂️ The COMMAND_dataset is organized as follows: COMMAND_dataset/ ├─ README.md # Documentation for the dataset ├─ map_asset/ # Directory containing map assets 🗺️ │ ├","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"arsaporta/symile-m3","arsaporta","2024-11-12 05:26:09","2024-11-26 00:51:57+00:00","9ec61c44424ffda11b1547e9d506056b7e98870a","2172","3402","5","None","zero-shot-classification, zero-shot-image-classification","Dataset Card for Symile-M3 Symile-M3 is a multilingual dataset of (audio, image, text) samples. The dataset is specifically designed to test a model's ability to capture higher-order information betwe","None","ar, el, en, hi, ja, ko, te, th, uk, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-nc-sa-4.0","2411.01053","None","None"
+"blanchon/EuroSAT_RGB","blanchon","2023-12-05 12:56:11","2023-12-05 13:02:42+00:00","655ef66ef2be07f89aec61407f24c772802eb87d","790","3310","3","None","image-classification","EuroSAT RGB EUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced sam","None","e, n","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","1709.00029","None","None"
+"Nan-Do/code-search-net-python","Nan-Do","2023-05-14 00:42:57","2023-05-15 00:55:15+00:00","39db91866dd0f251f3b0c7f42c0f85634101df6e","859","3308","22","None","text-generation, text2text-generation, summarization","Dataset Card for ""code-search-net-python"" Dataset Description Homepage: None Repository: https://huggingface.co/datasets/Nan-Do/code-search-net-python Paper: None Leaderboard: None Point of Contact: @","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"ServiceNow/repliqa","ServiceNow","2024-06-11 20:23:34","2024-12-09 01:32:45+00:00","c1b92b58d6f3ce4c4d4dc5225ccf871fb2ac5515","1018","3299","8","None","question-answering, text-classification","RepLiQA - Repository of Likely Question-Answer for benchmarking Dataset Summary RepLiQA is an evaluation dataset that contains Context-Question-Answer triplets, where contexts are non-factual but natu","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2406.11811","None","None"
+"davanstrien/magpie-preference","davanstrien","2024-06-24 15:10:18","2025-01-28 11:32:55+00:00","63643ae537105e5a7aa84d084d3be0aa0ad66f7e","1095","3284","13","None","None","Dataset Card for Magpie Preference Dataset Dataset Description The Magpie Preference Dataset is a crowdsourced collection of human preferences on synthetic instruction-response pairs generated using t","None","en","mono","None","None","False","False","False","llama3","None","None","None"
+"strickvl/isafpressreleases","strickvl","2024-03-23 14:17:21","2024-06-15 12:10:35+00:00","74875b0589d607cdb4356d513d4c9435286df9f4","1333","3246","6","None","feature-extraction, summarization, question-answering, text-classification, fill-mask, zero-shot-classification, named-entity-recognition, topic-classification, news-articles-summarization","ISAF Press Releases Dataset Description Homepage: [N/A] Repository: [N/A] Paper: A Knock on the Door: 22 Months of ISAF Press Releases Point of Contact: Alex Strick van Linschoten (@strickvl) Dataset ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","None","None","named-entity-recognition, topic-classification, news-articles-summarization"
+"Rapidata/text-2-image-Rich-Human-Feedback","Rapidata","2025-01-06 18:21:13","2025-01-11 13:23:04+00:00","e77afd00e481d9d2ca41a5b5c4f89cb704de45c6","3215","3215","27","task_categories:text-to-image, task_categories:text-classification, task_categories:image-classification, task_categories:image-to-text, task_categories:image-segmentation, language:en, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2312.10240, region:us, t2i, preferences, human, flux, midjourney, imagen, dalle, heatmap, coherence, alignment, style, plausiblity","text-to-image, text-classification, image-classification, image-to-text, image-segmentation","Building upon Google's research Rich Human Feedback for Text-to-Image Generation we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the Python API. Collecti","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2312.10240","None","None"
+"wassname/mmlu_preferences","wassname","2024-08-11 23:22:37","2024-09-19 08:03:29+00:00","9723d2acad31727302340154bc09b3efcabb0dfe","834","3127","0","None","text-classification, question-answering","reformat of MMLU to be in DPO (paired) format examples: {'prompt': 'Which of the following statements about the lanthanide elements is NOT true?', 'chosen': 'The atomic radii of the lanthanide element","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"geshijoker/chaosmining","geshijoker","2024-06-11 23:17:45","2024-07-24 17:24:20+00:00","e3564a66399004a4a81986619706e6e936ee7530","784","3111","0","None","feature-extraction","Dataset Card for Dataset Name ChaosMining is a synthetic dataset that evaluates post-hoc local attribution methods in low signal-to-noise ratio (SNR) environments. The post-hoc local attribution metho","None","en","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"almanach/hc3_french_ood","almanach","2023-05-30 14:16:14","2023-06-05 10:19:19+00:00","34acd9cc5eb1f9855d3485c7783f28a235dd87bd","1666","3079","2","None","text-classification, question-answering, sentence-similarity, zero-shot-classification","Human ChatGPT Comparison Corpus (HC3) Translated To French. The translation is done by Google Translate API. We also add the native french QA pairs from ChatGPT, BingGPT and FAQ pages. This dataset wa","# TODO: Add BibTeX citation for our TALN 2023 paper: Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect? @article{guo-etal-2023-hc3, title = ""How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection"", author = ""Guo, Biyang and Zhang, Xin and Wang, Ziyuan and Jiang, Minqi and Nie, Jinran and Ding, Yuxuan and Yue, Jianwei and Wu, Yupeng"", journal={arXiv preprint arxiv:2301.07597} year = ""2023"", }","en, fr","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"devngho/culturax-mini-nonshuffled","devngho","2024-09-14 09:47:49","2024-09-22 06:32:15+00:00","c9993a3add5096e3b18b591230ad9f2a2879572b","1030","3027","0","None","text-generation, fill-mask, language-modeling, masked-language-modeling","This repo contains 1% of each language of uonlp/CulturaX. load_dataset('devngho/culturax-mini-nonshuffled', '[lang]', split='train') # read specified language load_dataset('devngho/culturax-mini-nonsh","None","af, als, am, an, ar, arz, as, ast, av, az, azb, ba, be, bg, bh, bn, bo, bpy, br, bs, bxr, ca, ce, ceb, ckb, cs, cv, cy, da, de, dsb, dv, el, eml, en, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, gn, gom, gu, he, hi, hr, hsb, hu, hy, ia, id, ilo, io, is, it, ja, jbo, jv, ka, kk, km, kn, ko, krc, ku, kv, kw, ky, la, lb, lez, li, lmo, lo, lt, lv, mai, mg, mhr, min, mk, ml, mn, mr, mrj, ms, mt, my, mzn, nah, nds, ne, new, nl, nn, no, oc, or, os, pa, pl, pms, pnb, ps, pt, qu, ro, ru, sa, sah, sd, sh, si, sk, sl, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vec, vi, vo, wa, war, wuu, xal, xmf, yi, yo, zh","multi","None","None","False","False","False","None","None","None","language-modeling, masked-language-modeling"
+"HumanLLMs/Human-Like-DPO-Dataset","HumanLLMs","2024-05-19 10:24:19","2025-01-12 21:01:07+00:00","dd82ab6a284a15765964149e6a6603ff8ed7d672","2062","3019","175","language:en, license:llama3, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2501.05032, region:us","None","Enhancing Human-Like Responses in Large Language Models 🤗 Models | 📊 Dataset | 📄 Paper Human-Like-DPO-Dataset This dataset was created as part of research aimed at improving conversational fluency and","None","en","mono","None","None","False","False","False","llama3","2501.05032","None","None"
+"Pendrokar/TTS_Arena","Pendrokar","2024-10-11 16:52:25","2025-01-29 05:19:25+00:00","32f8c52bfd68785c34a9a13ef440d446cb391e27","795","2962","3","None","None","TTS Arena's DB is SQLlite DB file. The above is just a summary query that should be useful for TTS developers to evaluate faults of their model. Why no audio samples? Unsafe. Cannot constantly oversee","None","en","mono","None","None","False","False","False","None","None","None","None"
+"sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1","sentence-transformers","2024-05-02 15:10:11","2024-05-15 14:24:09+00:00","84ed2d35626f617d890bd493b4d6db69a741e0e2","907","2958","5","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:en, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","MS MARCO with hard negatives from co-condenser-margin-mse-sym-mnrl-mean-v1 MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing sear","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"stemdataset/STEM","stemdataset","2024-04-08 14:43:50","2024-04-30 14:57:01+00:00","dd68ee7695a7b41b87f099d696d49ad606788dec","852","2944","1","None","None","STEM Dataset 📃 [Paper] • 💻 [Github] • 🤗 [Dataset] • 🏆 [Leaderboard] • 📽 [Slides] • 📋 [Poster] This dataset is proposed in the ICLR 2024 paper: Measuring Vision-Language STEM Skills of Neural Models. W","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2402.17205","None","None"
+"wonabru-org/basic_english","wonabru-org","2024-12-10 18:16:33","2024-12-10 18:19:52+00:00","6eec8d884e5e0fed1f202bd8d7e0ac43cf518c4c","1324","2919","0","language:en, license:cc, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Rewritten version of yahma/alpaca-cleaned","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc","None","None","None"
+"MaartenGr/arxiv_nlp","MaartenGr","2023-08-09 10:53:12","2024-03-13 08:23:32+00:00","b56743e671f49880a17b7fab44b02f1df32fcaff","912","2889","2","None","None","arXiv Abstracts Abstracts for the cs.CL category of ArXiv between 1991 and 2024. This dataset was created as an instructional tool for the Clustering and Topic Modeling chapter in the upcoming ""Hands-","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"paperswithbacktest/Stocks-Daily-Price","paperswithbacktest","2024-05-29 15:46:56","2025-01-29 01:03:57+00:00","ccb0a2b0f02e3533cf64d12269e5db690fdeb08d","1082","2879","10","None","tabular-regression","Dataset Information This dataset includes daily price data for various stocks. Instruments Included 7000+ US Stocks Dataset Columns symbol: The symbol of the stock. date: The date of the data. open: T","None","en","mono","None","None","False","manual","False","other","None","None","None"
+"openlanguagedata/flores_plus","openlanguagedata","2024-10-10 12:29:28","2025-01-27 22:54:29+00:00","be52bc8adef7ecdb2951ab5a903e9d1e363a3476","1128","2832","15","None","text2text-generation, translation","Dataset Card for FLORES+ FLORES+ is an evaluation benchmark dataset for multilingual machine translation. Dataset Details Dataset Description FLORES+ is a multilingual machine translation benchmark re","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dar, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","u|n|k|n|o|w|n","None","False","auto","False","cc-by-sa-4.0","None","None","None"
+"botsi/trust-game-llama-2-chat-history","botsi","2024-02-25 22:00:14","2024-05-29 08:14:12+00:00","ca3482ea9757159f36d2abaf706cd3baeb6db5f0","1008","2830","0","None","text-generation","None","None","en","mono","None","None","False","False","False","llama2","None","None","None"
+"zeta-alpha-ai/NanoMSMARCO","zeta-alpha-ai","2024-09-10 09:44:05","2024-09-10 09:44:11+00:00","7b8ff22f2771dc65ac5b439f222eb19a1f56abda","1709","2772","2","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"microsoft/PEACE","microsoft","2024-08-09 08:48:36","2025-01-26 06:06:03+00:00","afd290f09b5df2df64a3509b1e76f261ac7f2e25","2691","2700","13","None","question-answering","PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [Code] [Paper] [Data] Introduction We construct a geologic map benchmark, GeoMap-Bench, to evaluate the performance of MLLMs on geologi","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","","None","None"
+"TIGER-Lab/TheoremQA","TIGER-Lab","2024-01-28 04:37:18","2024-05-15 13:41:05+00:00","a340b1782960a712843aae3ed25f1e013cc008a5","777","2620","13","None","question-answering","Dataset Card for ""TheoremQA"" Introduction We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics","None","en","mono","n|<|1|K","None","False","False","False","mit","2305.12524","None","None"
+"zeta-alpha-ai/NanoNQ","zeta-alpha-ai","2024-09-10 09:44:18","2024-09-10 09:44:24+00:00","77540146379abf95df8326a3c5bb9eb21c7146c3","1238","2596","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"timpal0l/scandisent","timpal0l","2023-06-21 13:13:56","2023-06-21 13:39:40+00:00","a184a92ea9c3f5d6424dc3aa062ded1f58db12b3","974","2595","1","None","text-classification","Dataset Card for Dataset Name Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Supported Tasks and Leaderboards [More Infor","None","sv, no, da, en, fi","multi","1|K|<|n|<|1|0|K","None","False","False","False","openrail","None","None","None"
+"Qwen/ProcessBench","Qwen","2024-12-11 05:10:14","2024-12-27 14:05:30+00:00","3bdcd5371ed567559a78f559c01c13a6deee7604","1726","2560","38","language:en, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.06559, region:us, math, reasoning","None","ProcessBench This repository contains the dataset of the ProcessBench benchmark proposed by Qwen Team. You can refer to our GitHub repository for the evaluation code and the prompt templates we use in","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"zeta-alpha-ai/NanoQuoraRetrieval","zeta-alpha-ai","2024-09-10 09:44:25","2024-09-10 09:44:30+00:00","2ab2d73e6c862026282808b913a34f4136928545","1618","2533","1","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"AIEnergyScore/text_generation","AIEnergyScore","2024-04-05 17:32:11","2024-06-18 17:35:41+00:00","2dc92b2ee2cd9776a51ccf08c6c2ab04138370c3","973","2531","1","None","None","None","None","en","mono","None","None","False","False","False","None","None","None","None"
+"theblackcat102/anime-understanding-dataset","theblackcat102","2024-02-18 10:11:08","2024-02-19 00:56:19+00:00","67b19375b09f95720bcaa0350d8fc84bc706c355","1398","2494","2","None","question-answering","Anime Understanding Benchmark (WIP) Evaluate anime knowledge found in existing LLMs. We hope to provide an easy to run evaluation on knowledge understanding in anime/manga. Better understanding in ani","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"microsoft/MMLU-CF","microsoft","2024-11-21 06:02:06","2025-01-08 08:01:02+00:00","c25b89a968a2062e422dd96ddf0fe3387507f0dd","1475","2469","14","None","question-answering","MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark [📜 Paper] • [🤗 HF Dataset] • [🐱 GitHub] MMLU-CF is a contamination-free and more challenging multiple-choice question benchmar","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"phiyodr/InpaintCOCO","phiyodr","2023-12-14 22:38:22","2024-10-02 17:46:36+00:00","1ffac84be2dfc5ad9afccad868522fad64457435","1881","2425","1","None","image-to-text, text-to-image, image-classification, image-captioning","InpaintCOCO - Fine-grained multimodal concept understanding (for color, size, and COCO objects) Dataset Summary A data sample contains 2 images and 2 corresponding captions that differ only in one obj","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","image-captioning"
+"letxbe/BoundingDocs","letxbe","2025-01-03 09:04:39","2025-01-21 10:01:36+00:00","3b1efb856558b56416813d954263e3bb56a3c57e","2412","2412","10","None","question-answering, visual-question-answering","BoundingDocs 🔍 The largest spatially-annotated dataset for Document Question Answering Dataset Description BoundingDocs is a unified dataset for Document Question Answering (QA) that includes spatial ","None","en, it, es, fr, de, pt, ja, zh","multi","None","None","False","False","False","cc-by-4.0","2501.03403","None","None"
+"OpenLeecher/lmsys_chat_1m_clean","OpenLeecher","2024-07-28 23:55:29","2024-12-31 22:35:13+00:00","e9f2f6838a2dbba87c216bb6bc406e8d7ce0f389","2240","2378","70","None","None","Cleaning and Categorizing A few weeks ago, I had the itch to do some data crunching, so I began this project - to clean and classify lmsys-chat-1m. The process was somewhat long and tedious, but here ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"nyu-visionx/VSI-Bench","nyu-visionx","2024-12-17 04:08:15","2025-01-14 22:54:08+00:00","bc96b17cb6be84878a6c1f2e64c24346356e0d04","1965","2355","30","None","visual-question-answering","Visual Spatial Intelligence Benchmark (VSI-Bench) This repository contains the visual spatial intelligence benchmark (VSI-Bench), introduced in Thinking in Space: How Multimodal Large Language Models ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2412.14171","None","None"
+"HeshamHaroon/ArzEn-MultiGenre","HeshamHaroon","2023-12-31 09:13:46","2023-12-31 14:41:09+00:00","fe839175aec5e99d301c4528441cc9dfb84af367","1097","2318","8","None","translation","ArzEn-MultiGenre: A Comprehensive Parallel Dataset Overview ArzEn-MultiGenre is a distinctive parallel dataset that encompasses a diverse collection of Egyptian Arabic content. This collection include","None","ar, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"hlt-lab/voicebench","hlt-lab","2024-10-22 06:45:43","2024-12-10 05:42:56+00:00","aaa46d405d681aeb220047d3e8be782094b4cd31","1159","2285","2","None","None","License The dataset is available under the Apache 2.0 license. Citation If you use the VoiceBench dataset in your research, please cite the following paper: @article{chen2024voicebench, title={VoiceBe","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"yoshitomo-matsubara/srsd-feynman_easy_dummy","yoshitomo-matsubara","2023-02-16 06:56:39","2024-03-05 07:22:58+00:00","67a1cac8420adf421fd0a18d91fbee5f1c0bca2d","802","2282","1","None","tabular-regression","Dataset Card for SRSD-Feynman (Easy set with Dummy Variables) Dataset Summary Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery. We ca","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","2206.10540","None","None"
+"QuotaClimat/frugalaichallenge-text-train","QuotaClimat","2024-12-18 02:40:30","2025-01-27 17:05:35+00:00","4748e7b548154d188a377a96e104b9b31fcd45b4","2170","2270","5","None","text-classification","Dataset Card for Climate Disinformation quotes database from QuotaClimat & Data For Good The dataset has been built for the Frugal AI Challenge 2025 co-located at the AI Action Summit in Paris and co-","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","2405.08254","None","None"
+"zeta-alpha-ai/NanoHotpotQA","zeta-alpha-ai","2024-09-10 09:43:58","2024-09-10 09:44:04+00:00","d79c0cdda980aba54842756770928035e1b61a51","1374","2250","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"vfleaking/DirectHarm4","vfleaking","2024-02-24 20:04:47","2024-03-01 07:18:30+00:00","8e3802f9e2943b0be59aeb1488b807ca95113575","821","2234","5","None","None","Dataset Card for DirectHarm4 Dataset Summary A dataset of 400 instructions that make direct harmful requests from 4 categories, where each category contains 100 instructions. Category: Malware. Instru","None","en","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"TIGER-Lab/Fineweb-Instruct","TIGER-Lab","2024-11-16 14:57:51","2024-11-16 15:29:46+00:00","edaa76cc99247babe39223abddcbebe35c158f50","1170","2228","4","None","question-answering","We convert the pre-training corpus from Fineweb-Edu (https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) to instruction following format. We select a subset with quality filter and then use GPT","None","en","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","2405.03548","None","None"
+"nvidia/AceMath-Instruct-Training-Data","nvidia","2025-01-15 16:14:38","2025-01-17 12:41:19+00:00","24e49e73a3ab3aa373e904516838c32a972ba75e","2210","2210","31","None","None","website | paper AceMath-Instruct Training Data Card We release all the datasets to train AceMath-1.5B/7B/72B-Instruct models. These models are built upon the Qwen2.5-Math-Base models through a multi-s","None","en","mono","None","None","False","False","False","cc-by-nc-4.0","2412.15084","None","None"
+"zeta-alpha-ai/NanoArguAna","zeta-alpha-ai","2024-09-10 09:06:53","2024-09-10 09:27:29+00:00","8f4a982d470a32c45817738b9d29042ca55d75ad","1078","2181","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"sysmlv2research/tutorials_summary","sysmlv2research","2024-07-27 09:20:18","2024-07-27 15:13:02+00:00","8e7b4e223eb2d332697e9be02a7c4feae19fad70","939","2134","1","None","text2text-generation","Tutorials Summary Text Dataset This is the summary text dataset of sysmlv2's official tutorials pdf. With the text explanation and code examples in each page, organized in both Chinese and English nat","None","en, zh","bi","n|<|1|K","None","False","False","False","other","None","None","None"
+"raianand/TIE_shorts","raianand","2024-11-01 06:19:26","2024-11-16 07:43:44+00:00","28c53e285feae86f4ba25d8aaeca4fd0c709784c","1680","2120","0","None","automatic-speech-recognition, text-to-speech","Dataset Card for TIE_Shorts Dataset Summary TIE_shorts is a derived version of the Technical Indian English (TIE) dataset, a large-scale speech dataset (~ 8K hours) originally consisting of approximat","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"openfoodfacts/product-database","openfoodfacts","2024-10-21 08:44:28","2025-01-28 18:08:04+00:00","931ed1eab923794ef47d97a2cc199da1bbc7d12e","966","2030","18","None","None","Open Food Facts Database What is 🍊 Open Food Facts? A food products database Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of informat","None","en, fr, de, es, it, nl, pl, pt, sv, bg, ro, fi, ru, nb, cs, th, da, hr, hu, ar, el, ja, ca, sr, sl, sk, tr, lt, zh, et, lv, xx, uk, id, he, vi, is, la, in, ko, sq, iw, ka, ms, bs, fa, bn, gl, kk, mk, nn, hi, aa, uz, so, af, eu","multi","1|M|<|n|<|1|0|M","None","False","False","False","agpl-3.0","None","None","None"
+"PowerInfer/QWQ-LONGCOT-500K","PowerInfer","2024-12-10 10:26:54","2024-12-26 10:19:19+00:00","10a787d967281599e9be6761717147817c018424","1893","1920","118","None","None","This repository contains approximately 500,000 instances of responses generated using QwQ-32B-Preview language model. The dataset combines prompts from multiple high-quality sources to create diverse ","None","en","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"zeta-alpha-ai/NanoSCIDOCS","zeta-alpha-ai","2024-09-10 09:44:31","2024-09-10 09:44:36+00:00","484eb90549fc3f0b9c42b3551e80ceb999515537","934","1805","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"zeta-alpha-ai/NanoFEVER","zeta-alpha-ai","2024-09-10 09:43:44","2024-09-10 09:43:51+00:00","a8bfdf1bf15181167a7e22e69cf8754bdea9b4c8","954","1804","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"sgoel9/paul_graham_essays","sgoel9","2024-04-11 07:29:21","2024-04-20 03:49:51+00:00","0c7155a53c25e24c9b9858314460e0ee1f5c3e4e","1134","1801","3","None","question-answering, summarization, text2text-generation, text-generation","Dataset Card for Paul Graham Essay Collection Dataset Dataset Description This dataset contains a complete collection of essays written by Paul Graham, a renowned programmer, venture capitalist, and e","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"zeta-alpha-ai/NanoClimateFEVER","zeta-alpha-ai","2024-09-10 09:35:30","2024-09-10 09:42:58+00:00","96741bfa30b9f56db8c9eb7d08e775ed6474f206","933","1791","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"zeta-alpha-ai/NanoSciFact","zeta-alpha-ai","2024-09-10 09:44:37","2024-09-10 09:44:43+00:00","309f1d1ae3ae2e092444a8a0c25bed59b82318bc","920","1752","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"zeta-alpha-ai/NanoFiQA2018","zeta-alpha-ai","2024-09-10 09:43:51","2024-09-10 09:43:57+00:00","4163ba032953d5044a7a6244261413f609c14342","836","1714","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"zeta-alpha-ai/NanoNFCorpus","zeta-alpha-ai","2024-09-10 09:44:12","2024-09-10 09:44:18+00:00","dd542a7efb9ad2136b9e00768b60fca9038f8156","916","1695","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"harvard-lil/cold-french-law","harvard-lil","2024-05-22 03:02:46","2024-05-22 20:26:35+00:00","213b1fb128a4d2d2e133599675ecbe09fde64b1a","1065","1688","7","None","None","Collaborative Open Legal Data (COLD) - French Law COLD French Law is a dataset containing over 800 000 french law articles, filtered and extracted from France's LEGI dataset and formatted as a single ","None","fr, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"allenai/tulu-3-sft-personas-instruction-following","allenai","2024-10-30 20:27:20","2024-11-21 15:57:21+00:00","fe0c7d350c9b4542b8d829a6f1daa1c259f0ba0e","925","1679","16","None","text-generation","Dataset Descriptions This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follow instructions precisely and to satisfy user constraints. The constraints","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","odc-by","2311.07911","None","None"
+"jopan/FlowLearn","jopan","2024-06-19 15:13:59","2024-11-05 21:37:36+00:00","205b467eaf541846cf36469d17d62e8f81be7be1","943","1654","1","None","visual-question-answering","cr--- task_categories: - visual-question-answering language: - en size_categories: - 1K<n<10K References The original articles are maintained by arXiv and copyrighted to the original authors, please r","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"zeta-alpha-ai/NanoDBPedia","zeta-alpha-ai","2024-09-10 09:43:37","2024-09-10 09:43:43+00:00","438f1c25129f05db6238699b5afdc9c6b58d2096","898","1654","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"BramVanroy/CommonCrawl-CreativeCommons-v0.1","BramVanroy","2025-01-26 16:49:00","2025-01-28 12:02:47+00:00","204ad00f97caf7f3497c76d50aba93c0f73c02f2","1618","1618","2","task_categories:text-generation, task_ids:language-modeling, language:nl, language:fr, language:es, language:fy, language:de, language:af, language:en, language:it, license:cc, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, language-modeling","Raw CommonCrawl crawls, annotated with potential Creative Commons license information The licensing information is extracted from the web pages based on whether they link to Creative Commons licenses ","None","nl, fr, es, fy, de, af, en, it","multi","None","None","False","manual","False","cc","None","None","language-modeling"
+"edinburgh-dawg/mmlu-redux-2.0","edinburgh-dawg","2024-08-17 01:23:03","2024-11-07 15:38:08+00:00","63f54ebd32c36485c679f53b8e2f576d689b9b34","892","1570","13","None","question-answering","Dataset Card for MMLU-Redux-2.0 MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects. Dataset Details Dataset Description Each data point in MMLU-Redux contains seve","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2406.04127","None","None"
+"zeta-alpha-ai/NanoTouche2020","zeta-alpha-ai","2024-09-10 09:44:45","2024-09-10 09:44:52+00:00","0d2f26ed8c5ad309f95c7f9499c70a40e140fccd","859","1547","0","None","text-retrieval, document-retrieval","None","None","en","mono","None","None","False","False","False","cc-by-4.0","None","None","document-retrieval"
+"casperhansen/gsm8k_synthetic_cot","casperhansen","2024-12-17 18:53:07","2024-12-22 09:58:26+00:00","60958299fc4376215be5384c84feaab611d5513a","1184","1489","7","None","None","Converted from https://github.com/da03/Internalize_CoT_Step_by_Step","None","en","mono","None","None","False","False","False","mit","None","None","None"
+"nuprl/engineering-llm-systems","nuprl","2024-09-26 17:49:04","2025-01-03 11:33:29+00:00","dcb89b1af26e5ea8fee90839f683e9edf384bfa8","866","1451","0","None","None","Engineering LLM-Integrated Systems Engineering LLM-Integrated Systems is course at Northeastern University that teaches students how to build software that uses LLMs under the hood from a systems pers","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Maxwell-Jia/AIME_2024","Maxwell-Jia","2024-12-05 03:20:43","2024-12-05 03:25:30+00:00","eb8e74ba43d81abdad0bfd046e59f686f8ffdb0e","1135","1447","4","task_categories:text-generation, language:en, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, explanation-generation","text-generation","AIME 2024 Dataset Dataset Description This dataset contains problems from the American Invitational Mathematics Examination (AIME) 2024. AIME is a prestigious high school mathematics competition known","None","en","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"allenai/tulu-3-sft-personas-math","allenai","2024-10-30 20:26:44","2024-12-02 19:50:42+00:00","622da66c157258a0411f5bd84dcdf23f4001f132","816","1428","9","None","None","Dataset Descriptions This dataset contains 149960 examples and is synthetically created to enhance model's capabilities to answer complex and hard math word problems. To generate diverse math question","None","en","mono","None","None","False","False","False","None","None","None","None"
+"FreedomIntelligence/medical-o1-reasoning-SFT","FreedomIntelligence","2024-12-28 03:29:08","2025-01-13 06:46:27+00:00","4c9573e7de1e8660b88158db2efa7c7204bbd269","1389","1392","89","None","question-answering, text-generation","Introduction This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o, which searches for solutions to verifiable","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"EMMAModal/EMMA","EMMAModal","2025-01-11 10:16:41","2025-01-11 11:01:15+00:00","c7e4ac71330c54571005db7573721ad7e40dc23c","1372","1372","1","None","question-answering, visual-question-answering, multiple-choice","Dataset Description We introduce EMMA (Enhanced MultiModal reAsoning), a benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding. EMMA tasks demand advanced","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"Rapidata/human-style-preferences-images","Rapidata","2024-12-02 14:11:39","2025-01-10 21:59:31+00:00","79acd5ebcc535309c08d996ab1f88c01077a7b12","1176","1253","17","None","text-to-image, image-to-text, image-classification, reinforcement-learning","Rapidata Image Generation Preference Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest mod","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"mlburnham/Pol_NLI","mlburnham","2024-07-22 17:03:57","2024-09-05 17:35:44+00:00","062724c0a0e601ffc5fba98a5546cc2237a766fa","778","1208","3","None","text-classification, zero-shot-classification","Dataset Card for ""Pol_NLI"" More Information needed Citation To cite the paper introducing this dataset, please use: @misc{burnham2024politicaldebateefficientzeroshot, title={Political DEBATE: Efficien","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"unlimitedbytes/hailuo-ai-voices","unlimitedbytes","2025-01-17 21:18:21","2025-01-19 08:47:34+00:00","42040bfdbd099e091a6e859b72df295473a0a0e4","1178","1178","5","None","text-to-speech, automatic-speech-recognition, audio-to-audio","Hailuo AI Voices Dataset 🎤 A curated collection of high-quality voice recordings with corresponding transcriptions and phoneme analysis. This dataset is designed for speech recognition, text-to-speech","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"JeffreyXiang/TRELLIS-500K","JeffreyXiang","2024-12-26 04:18:20","2024-12-26 04:21:59+00:00","288b06111bc4d06800293ab087989f852a632af2","1010","1158","17","task_categories:image-to-3d, task_categories:text-to-3d, language:en, license:mit, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.01506, region:us","image-to-3d, text-to-3d","TRELLIS-500K TRELLIS-500K is a dataset of 500K 3D assets curated from Objaverse(XL), ABO, 3D-FUTURE, HSSD, and Toys4k, filtered based on aesthetic scores. This dataset serves for 3D generation tasks. ","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"chocobearz/BERSt","chocobearz","2024-12-05 07:59:10","2024-12-16 14:38:24+00:00","a7c1f74bdf847691a801da3ca18ca820dfcba6e2","860","1147","1","None","automatic-speech-recognition, audio-classification","BERSt Dataset We release the BERSt Dataset for various speech recognition tasks including Automatic Speech Recognition (ASR) and Speech Emotion Recogniton (SER) Overview 4526 single phrase recordings ","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"qingy2024/FineQwQ-142k","qingy2024","2024-12-31 19:33:52","2025-01-07 18:00:44+00:00","f7443bb54d207f590a5d13924c80c9eacfd66fe1","1031","1031","18","None","None","Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design Original Sources: qingy2024/QwQ-LongCoT-Verified-130K (amphora/QwQ-LongCoT-130K), amphora/QwQ-LongCoT-130K-2, PowerInfer/QWQ-LONGCOT-500K. Source Information R","None","en","mono","None","None","False","False","False","None","None","None","None"
+"Rapidata/human-alignment-preferences-images","Rapidata","2024-12-02 14:51:08","2025-01-10 22:00:00+00:00","804b92da58d614265377f9983d6715ef3bbb4d36","805","1006","14","None","text-to-image, image-to-text, reinforcement-learning, question-answering","Rapidata Image Generation Alignment Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest mode","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"Magpie-Align/Magpie-Reasoning-V2-250K-CoT-QwQ","Magpie-Align","2025-01-09 08:44:09","2025-01-27 19:55:41+00:00","cba01bcfced254fa9541ae24136007281a7ed28f","972","972","11","None","None","Project Web: https://magpie-align.github.io/ Arxiv Technical Report: https://arxiv.org/abs/2406.08464 Codes: https://github.com/magpie-align/magpie Abstract Click Here High-quality instruction data is","None","en","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","llama3.1","2406.08464","None","None"
+"Rapidata/human-coherence-preferences-images","Rapidata","2024-12-02 14:32:02","2025-01-10 22:00:32+00:00","72c0ebefc7ef3bebe22643fc709a6e94c22b5b02","811","945","12","None","text-to-image, image-to-text, question-answering, reinforcement-learning","Rapidata Image Generation Coherence Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest mode","None","en","mono","1|M|<|n|<|1|0|M","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"shb777/gemini-flash-2.0-speech","shb777","2025-01-16 13:46:53","2025-01-22 15:39:34+00:00","c8e82875d899f71d3780d2dbbb4d33801643cfe9","938","938","7","None","text-to-speech, automatic-speech-recognition","🎙️ Gemini Flash 2.0 Speech Dataset This is a high quality synthetic speech dataset generated by Gemini Flash 2.0 via the Multimodel Live API. It contains speech from 2 speakers - Puck (Male) and Kore ","None","en","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Rapidata/text-2-video-human-preferences","Rapidata","2024-12-20 15:56:19","2025-01-22 13:47:33+00:00","06819c0e41c0c42686d57f61c46912b382cf6273","787","800","15","None","text-to-video, video-classification","Rapidata Video Generation Preference Dataset This dataset was collected in ~12 hours using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. The data collected i","None","en","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"