diff --git "a/data/dataset_meta/dataset_meta_zh.csv" "b/data/dataset_meta/dataset_meta_zh.csv"
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+++ "b/data/dataset_meta/dataset_meta_zh.csv"
@@ -0,0 +1,749 @@
+"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"
+"ceval/ceval-exam","ceval","2023-05-16 01:47:44","2023-08-31 14:04:10+00:00","3923b519fd180e689d0961bf3a032ece929742f3","17328","9608072","246","task_categories:text-classification, task_categories:multiple-choice, task_categories:question-answering, language:zh, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, arxiv:2305.08322, region:us","text-classification, multiple-choice, question-answering","C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels.","@article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} }","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2305.08322","None","None"
+"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"
+"haonan-li/cmmlu","haonan-li","2023-06-25 16:37:44","2023-07-13 10:19:29+00:00","efcc940752ea4a1ea94d2727f11f83858d64fc8e","7311","3105652","64","None","multiple-choice, question-answering","CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.","@misc{li2023cmmlu, title={CMMLU: Measuring massive multitask language understanding in Chinese}, author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin}, year={2023}, eprint={2306.09212}, archivePrefix={arXiv}, primaryClass={cs.CL} }","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","2306.09212","None","None"
+"unimelb-nlp/wikiann","unimelb-nlp","2022-03-02 23:29:22","2024-02-22 14:32:02+00:00","f0a3be6dc5564c0cc4150bb660144800a1f539d4","47521","2954749","103","task_categories:token-classification, task_ids:named-entity-recognition, annotations_creators:machine-generated, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:ace, language:af, language:als, language:am, language:an, language:ang, language:ar, language:arc, language:arz, language:as, language:ast, language:ay, language:az, language:ba, language:bar, language:be, language:bg, language:bh, language:bn, language:bo, language:br, language:bs, language:ca, language:cbk, language:cdo, language:ce, language:ceb, language:ckb, language:co, language:crh, language:cs, language:csb, language:cv, language:cy, language:da, language:de, language:diq, language:dv, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:fi, language:fo, language:fr, language:frr, language:fur, language:fy, language:ga, language:gan, language:gd, language:gl, language:gn, language:gu, language:hak, language:he, language:hi, language:hr, language:hsb, language:hu, language:hy, language:ia, language:id, language:ig, 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:ksh, language:ku, language:ky, language:la, language:lb, language:li, language:lij, language:lmo, language:ln, language:lt, language:lv, language:lzh, language:mg, language:mhr, language:mi, language:min, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:mwl, language:my, language:mzn, language:nan, language:nap, language:nds, language:ne, language:nl, language:nn, language:no, language:nov, language:oc, language:or, language:os, language:pa, language:pdc, language:pl, language:pms, language:pnb, language:ps, language:pt, language:qu, language:rm, language:ro, language:ru, language:rw, language:sa, language:sah, language:scn, language:sco, language:sd, language:sgs, language:sh, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:szl, language:ta, language:te, language:tg, language:th, language:tk, language:tl, language:tr, language:tt, language:ug, language:uk, language:ur, language:uz, language:vec, language:vep, language:vi, language:vls, language:vo, language:vro, language:wa, language:war, language:wuu, language:xmf, language:yi, language:yo, language:yue, language:zea, language:zh, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1902.00193, region:us","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","wikiann-1","False","False","False","unknown","None","None","named-entity-recognition"
+"facebook/xnli","facebook","2022-03-02 23:29:22","2024-01-05 08:30:52+00:00","b8dd5d7af51114dbda02c0e3f6133f332186418e","15916","2470294","53","None","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","None","False","False","False","None","None","None","None"
+"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"
+"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"
+"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"
+"cambridgeltl/xcopa","cambridgeltl","2022-03-02 23:29:22","2024-01-04 16:55:46+00:00","042f78955ba48e6404616762fa6e05e839c3907a","4755","1190135","17","None","question-answering, multiple-choice-qa","Dataset Card for ""xcopa"" Dataset Summary XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the abilit","None","et, ht, id, it, qu, sw, ta, th, tr, vi, zh","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-4.0","None","None","multiple-choice-qa"
+"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","None","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"
+"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","None","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","None","False","False","False","cc-by-nc-sa-4.0","1607.01759","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","language:af, language:am, language:ar, language:az, language:bn, language:cy, language:da, language:de, language:el, language:en, language:es, language:fa, language:fr, language:he, language:hi, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:jv, language:ka, language:km, language:kn, language:ko, language:lv, language:ml, language:mn, language:ms, language:my, language:nb, language:nl, language:pl, language:pt, language:ro, language:ru, language:sl, language:sq, language:sv, language:sw, language:ta, language:te, language:th, language:tl, language:tr, language:ur, language:vi, language:zh, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","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"
+"facebook/belebele","facebook","2023-09-01 18:27:13","2024-08-12 22:18:08+00:00","7899cdfa4e1e0d733fd77c848e2c273cb1d32be2","7480","676629","102","task_categories:question-answering, task_categories:zero-shot-classification, task_categories:text-classification, task_categories:multiple-choice, language:af, language:am, language:ar, language:az, language:as, language:bm, language:bn, language:bo, language:bg, language:ca, language:cs, language:ku, language:da, language:de, language:el, language:en, language:es, language:et, language:eu, language:fi, language:fr, language:ff, language:om, language:gu, language:gn, language:ht, language:ha, language:he, language:hi, language:hr, language:hu, language:hy, language:ig, language:id, language:it, language:is, language:jv, language:ja, language:ka, language:kn, language:kk, language:mn, language:km, language:rw, language:ky, language:ko, language:lo, language:ln, language:lt, language:lg, language:lv, language:ml, language:mr, language:mk, language:mt, language:mi, language:my, language:nl, language:no, language:ne, language:ny, language:or, language:pa, language:ps, language:fa, language:mg, language:pl, language:pt, language:ro, language:ru, language:sn, language:si, language:sl, language:sv, language:sk, language:sd, language:sw, language:ta, language:te, language:tg, language:tl, language:th, language:ti, language:tn, language:ts, language:tr, language:uk, language:ur, language:uz, language:vi, language:wo, language:xh, language:yo, language:zh, language:ms, language:zu, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","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"
+"Helsinki-NLP/news_commentary","Helsinki-NLP","2022-03-02 23:29:22","2024-02-29 15:28:06+00:00","bbaf548083e788e0e6d2ca68efc325283fe2aff5","18361","569556","31","None","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"
+"Muennighoff/xwinograd","Muennighoff","2022-07-17 15:20:09","2023-07-07 08:27:03+00:00","e98b9216f60fc8dbabfe766e014534a08ff01949","5285","565272","13","None","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"
+"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"
+"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"
+"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","annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:extended|common_voice, language:ab, language:af, language:am, language:ar, language:as, language:ast, language:az, language:ba, language:bas, language:be, language:bg, language:bn, language:br, language:ca, language:ckb, language:cnh, language:cs, language:cv, language:cy, language:da, language:de, language:dv, language:dyu, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gl, language:gn, language:ha, language:he, language:hi, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ig, language:is, language:it, language:ja, language:ka, language:kab, language:kk, language:kmr, language:ko, language:ky, language:lg, language:lij, language:lo, language:lt, language:ltg, language:lv, language:mdf, language:mhr, language:mk, language:ml, language:mn, language:mr, language:mrj, language:mt, language:myv, language:nan, language:ne, language:nhi, language:nl, language:nn, language:nso, language:oc, language:or, language:os, language:pa, language:pl, language:ps, language:pt, language:quy, language:rm, language:ro, language:ru, language:rw, language:sah, language:sat, language:sc, language:sk, language:skr, language:sl, language:sq, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:ti, language:tig, language:tk, language:tok, language:tr, language:tt, language:tw, language:ug, language:uk, language:ur, language:uz, language:vi, language:vot, language:yi, language:yo, language:yue, language:zgh, language:zh, language:zu, language:zza, license:cc0-1.0, size_categories:10M<n<100M, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:1912.06670, region:us","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","common-voice","False","auto","False","cc0-1.0","1912.06670","None","None"
+"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"
+"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"
+"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"
+"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"
+"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","None","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","None","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/amazon_reviews_multi","mteb","2022-05-25 19:22:51","2022-09-27 19:10:01+00:00","c379a6705fec24a2493fa68e011692605f44e119","2861","253698","17","None","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"
+"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"
+"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","None","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"
+"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"
+"alexandrainst/m_hellaswag","alexandrainst","2023-12-27 20:55:26","2024-02-12 16:32:54+00:00","9d31dc982bd6285e081e3e3136332a38b9c1d7b7","2745","208821","3","None","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"
+"clue/clue","clue","2022-03-02 23:29:22","2024-01-17 07:48:08+00:00","28178267a609dd08bdc703dd6c931dfc2c2f4431","2460","193446","41","None","text-classification, multiple-choice, topic-classification, semantic-similarity-scoring, natural-language-inference, multiple-choice-qa","Dataset Card for ""clue"" Dataset Summary CLUE, A Chinese Language Understanding Evaluation Benchmark (https://www.cluebenchmarks.com/) is a collection of resources for training, evaluating, and analyzi","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","2004.05986","None","topic-classification, semantic-similarity-scoring, natural-language-inference, multiple-choice-qa"
+"graelo/wikipedia","graelo","2023-06-10 22:40:06","2023-09-10 06:10:08+00:00","3c5d9004085ff5de9d57f4224f4ab3d5d34af789","26549","180057","65","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:ab, language:ace, language:ady, language:af, language:ak, language:als, language:alt, language:am, language:ami, language:an, language:ang, language:anp, language:ar, language:arc, language:ary, language:arz, language:as, language:ast, language:atj, language:av, language:avk, language:awa, language:ay, language:az, language:azb, language:ba, language:ban, language:bar, language:bcl, language:be, language:bg, language:bh, language:bi, language:bjn, language:blk, language:bm, language:bn, language:bo, language:bpy, language:br, language:bs, language:bug, language:bxr, language:ca, language:cdo, language:ce, language:ceb, language:ch, language:cho, language:chr, language:chy, language:ckb, language:co, language:cr, language:crh, language:cs, language:csb, language:cu, language:cv, language:cy, language:da, language:dag, language:de, language:din, language:diq, language:dsb, language:dty, language:dv, language:dz, language:ee, language:el, language:eml, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:fat, language:ff, language:fi, language:fj, language:fo, language:fr, language:frp, language:frr, language:fur, language:fy, language:ga, language:gag, language:gan, language:gcr, language:gd, language:gl, language:glk, language:gn, language:gom, language:gor, language:got, language:gu, language:guc, language:gur, language:guw, language:gv, language:ha, language:hak, language:haw, language:he, language:hi, language:hif, language:ho, language:hr, language:hsb, language:ht, language:hu, language:hy, language:hyw, language:ia, language:id, language:ie, language:ig, language:ii, language:ik, language:ilo, language:inh, language:io, language:is, language:it, language:iu, language:ja, language:jam, language:jbo, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kbp, language:kcg, language:kg, language:ki, language:kj, language:kk, language:kl, language:km, language:kn, language:ko, language:koi, language:krc, language:ks, language:ksh, language:ku, language:kv, language:kw, language:ky, language:la, language:lad, language:lb, language:lbe, language:lez, language:lfn, language:lg, language:li, language:lij, language:lld, language:lmo, language:ln, language:lo, language:lrc, language:lt, language:ltg, language:lv, language:mad, language:mai, language:mdf, language:mg, language:mh, language:mhr, language:mi, language:min, language:mk, language:ml, language:mn, language:mni, language:mnw, language:mr, language:mrj, language:ms, language:mt, language:mus, language:mwl, language:my, language:myv, language:mzn, language:nah, language:nap, language:nds, language:ne, language:new, language:ng, language:nia, language:nl, language:nn, language:no, language:nov, language:nqo, language:nrm, language:nso, language:nv, language:ny, language:oc, language:olo, language:om, language:or, language:os, language:pa, language:pag, language:pam, language:pap, language:pcd, language:pcm, language:pdc, language:pfl, language:pi, language:pih, language:pl, language:pms, language:pnb, language:pnt, language:ps, language:pt, language:pwn, language:qu, language:rm, language:rmy, language:rn, language:ro, language:ru, language:rue, language:rw, language:sa, language:sah, language:sat, language:sc, language:scn, language:sco, language:sd, language:se, language:sg, language:sh, language:shi, language:shn, language:si, language:sk, language:skr, language:sl, language:sm, language:smn, language:sn, language:so, language:sq, language:sr, language:srn, language:ss, language:st, language:stq, language:su, language:sv, language:sw, language:szl, language:szy, language:ta, language:tay, language:tcy, language:te, language:tet, language:tg, language:th, language:ti, language:tk, language:tl, language:tn, language:to, language:tpi, language:tr, language:trv, language:ts, language:tt, language:tum, language:tw, language:ty, language:tyv, language:udm, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vep, language:vi, language:vls, language:vo, language:wa, language:war, language:wo, language:wuu, language:xal, language:xh, language:xmf, language:yi, language:yo, language:za, language:zea, language:zh, language:zu, license:cc-by-sa-3.0, license:gfdl, size_categories:100M<n<1B, modality:text, library:datasets, library:mlcroissant, region:us","text-generation, fill-mask, language-modeling, masked-language-modeling","Wikipedia dataset containing cleaned articles of all languages. The datasets are built from the Wikipedia dump (https://dumps.wikimedia.org/) with one split per language. Each example contains the con","@ONLINE {wikidump, author = {Wikimedia Foundation}, title = {Wikimedia Downloads}, url = {https://dumps.wikimedia.org} }","ab, ace, ady, af, ak, als, alt, am, ami, an, ang, anp, ar, arc, ary, arz, as, ast, atj, av, avk, awa, ay, az, azb, ba, ban, bar, bcl, be, bg, bh, bi, bjn, blk, bm, bn, bo, bpy, br, bs, bug, bxr, ca, cdo, ce, ceb, ch, cho, chr, chy, ckb, co, cr, crh, cs, csb, cu, cv, cy, da, dag, de, din, diq, dsb, dty, dv, dz, ee, el, eml, eo, es, et, eu, ext, fa, fat, ff, fi, fj, fo, fr, frp, frr, fur, fy, ga, gag, gan, gcr, gd, gl, glk, gn, gom, gor, got, gu, guc, gur, guw, gv, ha, hak, haw, he, hi, hif, ho, hr, hsb, ht, hu, hy, hyw, ia, id, ie, ig, ii, ik, ilo, inh, io, is, it, iu, ja, jam, jbo, jv, ka, kaa, kab, kbd, kbp, kcg, kg, ki, kj, 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, lrc, lt, ltg, lv, mad, mai, mdf, mg, mh, mhr, mi, min, mk, ml, mn, mni, mnw, mr, mrj, ms, mt, mus, mwl, my, myv, mzn, nah, nap, nds, ne, new, ng, nia, nl, nn, no, nov, nqo, nrm, 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, rw, sa, sah, sat, sc, scn, sco, sd, se, sg, sh, 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, tn, to, tpi, tr, trv, ts, tt, tum, tw, ty, tyv, udm, ug, uk, ur, uz, ve, vec, vep, vi, vls, vo, wa, war, wo, wuu, xal, xh, xmf, yi, yo, za, zea, 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"
+"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"
+"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"
+"miracl/miracl-corpus","miracl","2022-09-29 14:49:58","2023-01-05 17:28:26+00:00","d921ec7e349ce0d28daf30b2da9da5ee698bef0d","5089","171794","43","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, language:ar, language:bn, language:en, language:es, language:fa, language:fi, language:fr, language:hi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, language:zh, license:apache-2.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2210.09984, region:us","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"
+"Skywork/SkyPile-150B","Skywork","2023-10-23 12:55:10","2023-12-07 06:11:28+00:00","d6395caa3005bcbf21dd80585c15f60004f77ccb","8456","145252","351","None","text-generation","SkyPile-150B Dataset Summary SkyPile-150B is a comprehensive, large-scale Chinese dataset specifically designed for the pre-training of large language models. It is derived from a broad array of publi","None","zh","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","None","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","task_categories:fill-mask, task_categories:text-generation, task_ids:language-modeling, multilinguality:multilingual, language:ace, language:af, language:als, language:am, language:ar, 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:mr, language:min, language:mk, language:mt, language:mni, language:mos, language:mi, language:my, language:nl, language:nn, language:nb, language:npi, 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: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:cc0-1.0, size_categories:10B<n<100B, modality:tabular, modality:text, modality:timeseries, region:us","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"
+"ikala/tmmluplus","ikala","2023-12-22 19:12:13","2024-06-12 07:06:00+00:00","c0e8ae955997300d5dbf0e382bf0ba5115f85e8c","4608","133758","107","None","question-answering","TMMLU+ : Large scale traditional chinese massive multitask language understanding We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"mozilla-foundation/common_voice_16_0","mozilla-foundation","2023-12-20 09:01:34","2023-12-21 13:53:03+00:00","5b41cdb6fba1effea26615d7c78df110694e7c33","590","121056","71","annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, language:ab, language:af, language:am, language:ar, language:as, language:ast, language:az, language:ba, language:bas, language:be, language:bg, language:bn, language:br, language:ca, language:ckb, language:cnh, language:cs, language:cv, language:cy, language:da, language:de, language:dv, language:dyu, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gl, language:gn, language:ha, language:he, language:hi, language:hsb, language:hu, language:hy, language:ia, language:id, language:ig, language:is, language:it, language:ja, language:ka, language:kab, language:kk, language:kmr, language:ko, language:ky, language:lg, language:lij, language:lo, language:lt, language:ltg, language:lv, language:mdf, language:mhr, language:mk, language:ml, language:mn, language:mr, language:mrj, language:mt, language:myv, language:nan, language:ne, language:nhi, language:nl, language:nn, language:oc, language:or, language:os, language:pa, language:pl, language:ps, language:pt, language:quy, language:rm, language:ro, language:ru, language:rw, language:sah, language:sat, language:sc, language:sk, language:skr, language:sl, language:sq, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:ti, language:tig, language:tk, language:tok, language:tr, language:tt, language:tw, language:ug, language:uk, language:ur, language:uz, language:vi, language:vot, language:yi, language:yo, language:yue, language:zgh, language:zh, license:cc0-1.0, size_categories:1M<n<10M, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:1912.06670, region:us","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","common-voice","False","auto","False","cc0-1.0","1912.06670","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"
+"mteb/stsb_multi_mt","mteb","2024-05-07 11:56:59","2024-05-07 12:31:26+00:00","29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c","484","114572","2","task_categories:text-classification, task_ids:text-scoring, task_ids:semantic-similarity-scoring, annotations_creators:crowdsourced, language_creators:crowdsourced, language_creators:found, language_creators:machine-generated, multilinguality:multilingual, source_datasets:extended|other-sts-b, language:de, language:en, language:es, language:fr, language:it, language:nl, language:pl, language:pt, language:ru, language:zh, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1708.00055, region:us","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"
+"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"
+"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","None","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"
+"adithya7/xlel_wd","adithya7","2022-04-22 02:50:11","2022-07-13 07:46:57+00:00","a6d542d37b24cc1f2536af5e4afb850b9641e3ff","278","97612","2","annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:af, language:ar, language:be, language:bg, language:bn, language:ca, language:cs, language:da, language:de, language:el, language:en, language:es, language:fa, language:fi, language:fr, language:he, language:hi, language:hu, language:id, language:it, language:ja, language:ko, language:ml, language:mr, language:ms, language:nl, language:no, language:pl, language:pt, language:ro, language:ru, language:si, language:sk, language:sl, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:tr, language:uk, language:vi, language:zh, license:cc-by-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2204.06535, region:us","None","XLEL-WD is a multilingual event linking dataset. This dataset contains mention references from multilingual Wikipedia/Wikinews articles to event items in Wikidata. The text descriptions for Wikidata e","@article{pratapa-etal-2022-multilingual, title = {Multilingual Event Linking to Wikidata}, author = {Pratapa, Adithya and Gupta, Rishubh and Mitamura, Teruko}, publisher = {arXiv}, year = {2022}, url = {https://arxiv.org/abs/2204.06535}, }","af, ar, be, bg, bn, ca, cs, da, de, el, en, es, fa, fi, fr, he, hi, hu, id, it, ja, ko, ml, mr, ms, nl, no, pl, pt, ro, ru, si, sk, sl, sr, sv, sw, ta, te, th, tr, uk, vi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","2204.06535","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"
+"Helsinki-NLP/opus_ubuntu","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:45:27+00:00","4dc07f768ea537e979b48b824c1fe254918d1f76","219","92472","4","task_categories:translation, annotations_creators:crowdsourced, annotations_creators:expert-generated, language_creators:found, multilinguality:multilingual, source_datasets:original, language:ace, language:af, language:ak, language:am, language:an, language:ang, language:ar, language:ary, language:as, language:ast, language:az, language:ba, language:bal, language:be, language:bem, language:ber, language:bg, language:bho, language:bn, language:bo, language:br, language:brx, language:bs, language:bua, language:byn, language:ca, language:ce, language:ceb, language:chr, language:ckb, language:co, language:crh, language:cs, language:csb, language:cv, language:cy, language:da, language:de, language:dsb, language:dv, language:dz, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:ff, language:fi, language:fil, language:fo, language:fr, language:frm, language:frp, language:fur, language:fy, language:ga, language:gd, language:gl, language:gn, language:grc, language:gu, language:guc, language:gv, language:ha, language:haw, language:he, language:hi, language:hil, language:hne, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ig, language:io, language:is, language:it, language:iu, language:ja, language:jbo, language:jv, language:ka, language:kab, language:kg, language:kk, language:kl, language:km, language:kn, language:ko, language:kok, language:ks, language:ksh, language:ku, language:kw, language:ky, language:la, language:lb, language:lg, language:li, language:lij, language:lld, language:ln, language:lo, language:lt, language:ltg, language:lv, language:mai, language:mg, language:mh, language:mhr, language:mi, language:miq, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:mus, language:my, language:nan, language:nap, language:nb, language:nds, language:ne, language:nhn, language:nl, language:nn, language:no, language:nso, language:ny, language:oc, language:om, language:or, language:os, language:pa, language:pam, language:pap, language:pl, language:pms, language:pmy, language:ps, language:pt, language:qu, language:rm, language:ro, language:rom, language:ru, language:rw, language:sa, language:sc, language:sco, language:sd, language:se, language:shn, language:shs, language:si, language:sk, language:sl, language:sm, language:sml, language:sn, language:so, language:son, language:sq, language:sr, language:st, language:sv, language:sw, language:syr, language:szl, language:ta, language:te, language:tet, language:tg, language:th, language:ti, language:tk, language:tl, language:tlh, language:tr, language:trv, language:ts, language:tt, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vi, language:wa, language:wae, language:wo, language:xal, language:xh, language:yi, language:yo, language:zh, language:zu, language:zza, license:bsd-3-clause, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for Opus Ubuntu Dataset Summary These are translations of the Ubuntu software package messages, donated by the Ubuntu community. To load a language pair which isn't part of the config, al","None","ace, af, ak, am, an, ang, ar, ary, as, ast, az, ba, bal, be, bem, ber, bg, bho, bn, bo, br, brx, bs, bua, byn, ca, ce, ceb, chr, ckb, co, crh, cs, csb, cv, cy, da, de, dsb, dv, dz, el, en, eo, es, et, eu, fa, ff, fi, fil, fo, fr, frm, frp, fur, fy, ga, gd, gl, gn, grc, gu, guc, gv, ha, haw, he, hi, hil, hne, hr, hsb, ht, hu, hy, ia, id, ig, io, is, it, iu, ja, jbo, jv, ka, kab, kg, kk, kl, km, kn, ko, kok, ks, ksh, ku, kw, ky, la, lb, lg, li, lij, lld, ln, lo, lt, ltg, lv, mai, mg, mh, mhr, mi, miq, mk, ml, mn, mr, ms, mt, mus, my, nan, nap, nb, nds, ne, nhn, nl, nn, no, nso, ny, oc, om, or, os, pa, pam, pap, pl, pms, pmy, ps, pt, qu, rm, ro, rom, ru, rw, sa, sc, sco, sd, se, shn, shs, si, sk, sl, sm, sml, sn, so, son, sq, sr, st, sv, sw, syr, szl, ta, te, tet, tg, th, ti, tk, tl, tlh, tr, trv, ts, tt, ug, uk, ur, uz, ve, vec, vi, wa, wae, wo, xal, xh, yi, yo, zh, zu, zza","multi","1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K|||n|<|1|K","None","False","False","False","bsd-3-clause","None","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","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:pandas, library:mlcroissant, library:polars, 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"
+"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"
+"mozilla-foundation/common_voice_12_0","mozilla-foundation","2023-03-12 17:28:02","2023-11-17 18:09:06+00:00","eb58b6bb4457bd7b185edcd5a4286291b3f64e68","666","88967","22","task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:extended|common_voice, language:ab, language:ar, language:as, language:ast, language:az, language:ba, language:bas, language:be, language:bg, language:bn, language:br, language:ca, language:ckb, language:cnh, language:cs, language:cv, language:cy, language:da, language:de, language:dv, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:gl, language:gn, language:ha, language:hi, language:hsb, language:hu, language:ia, language:id, language:ig, language:it, language:ja, language:ka, language:kab, language:kk, language:kmr, language:ko, language:ky, language:lg, language:lt, language:lv, language:mdf, language:mhr, language:mk, language:ml, language:mn, language:mr, language:mrj, language:mt, language:myv, language:nl, language:oc, language:or, language:pl, language:pt, language:quy, language:ro, language:ru, language:rw, language:sah, language:sat, language:sc, language:sk, language:skr, language:sl, language:sr, language:sw, language:ta, language:th, language:ti, language:tig, language:tok, language:tr, language:tt, language:tw, language:ug, language:uk, language:ur, language:uz, language:vi, language:vot, language:yo, language:yue, language:rm, language:zh, language:sv, language:pa, language:nn, language:ne, language:nan, language:hy, language:ga, language:fy, license:cc0-1.0, size_categories:1M<n<10M, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:1912.06670, region:us","automatic-speech-recognition","Dataset Card for Common Voice Corpus 12.0 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 26119 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, ar, as, ast, az, ba, bas, be, bg, bn, br, ca, ckb, cnh, cs, cv, cy, da, de, dv, el, en, eo, es, et, eu, fa, fi, fr, gl, gn, ha, hi, hsb, hu, ia, id, ig, it, ja, ka, kab, kk, kmr, ko, ky, lg, lt, lv, mdf, mhr, mk, ml, mn, mr, mrj, mt, myv, nl, oc, or, pl, pt, quy, ro, ru, rw, sah, sat, sc, sk, skr, sl, sr, sw, ta, th, ti, tig, tok, tr, tt, tw, ug, uk, ur, uz, vi, vot, yo, yue, rm, zh, sv, pa, nn, ne, nan, hy, ga, fy","multi","1|0|M|<|n|<|1|0|0|M","common-voice","False","auto","False","cc0-1.0","1912.06670","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","None","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","None","False","False","False","other","None","None","None"
+"Hello-SimpleAI/HC3","Hello-SimpleAI","2023-01-18 14:01:20","2023-01-21 13:10:10+00:00","4d0ff18143b5a7e1b1e79beb540c04549d1e59d3","545","83142","184","task_categories:text-classification, task_categories:question-answering, task_categories:sentence-similarity, task_categories:zero-shot-classification, language:en, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, arxiv:2301.07597, region:us, ChatGPT, SimpleAI, Detection, OOD","text-classification, question-answering, sentence-similarity, zero-shot-classification","Human ChatGPT Comparison Corpus (HC3)","\\","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2301.07597","None","None"
+"hfl/cmrc2018","hfl","2022-03-02 23:29:22","2024-08-08 06:11:44+00:00","137f2c45a24275fb68f6961c4d357f46288886aa","670","82043","22","None","question-answering, extractive-qa","Dataset Card for ""cmrc2018"" Dataset Summary A Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real qu","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","extractive-qa"
+"miulab/tmlu","miulab","2023-10-09 11:15:13","2024-05-08 08:35:29+00:00","72e4cbe4f5c76d978031270ec9f3e3534111cae8","23029","81590","11","None","question-answering, text-classification","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 AST: 分科測驗（110前指考） GSAT: 學科能力測驗 CAP: 國中教育會考 D","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"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"
+"alexandrainst/m_truthfulqa","alexandrainst","2023-12-27 20:56:57","2023-12-27 20:56:58+00:00","f0445d470f1925882b990f5f247fdcf288972f60","1893","72749","1","None","question-answering, multiple-choice-qa","Multilingual TruthfulQA Dataset Summary This dataset is a machine translated version of the TruthfulQA dataset, translated using GPT-3.5-turbo. This dataset was created by the University of Oregon, an","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","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"bigscience/xP3mt","bigscience","2022-09-28 12:36:00","2023-05-30 15:50:57+00:00","8a5e23f6ffbd1b55efaf0ffe6322f985fe859bf2","16773","72290","24","None","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","None","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"
+"tasksource/oasst1_pairwise_rlhf_reward","tasksource","2023-05-09 09:16:01","2023-07-04 17:47:46+00:00","de3dfde669d3c4adc1fe35223aed8b4e1f06a177","137","70936","42","language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""oasst1_pairwise_rlhf_reward"" OASST1 dataset preprocessed for reward modeling: import pandas as pd from datasets import load_dataset,concatenate_datasets, Dataset, DatasetDict import ","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","None","None","False","False","False","None","None","None","None"
+"BAAI/CCI3-HQ","BAAI","2024-09-19 05:33:35","2024-11-11 12:27:29+00:00","d6f3aa30cebfef497e822ff968ed68a18bf90b8f","6618","64744","34","task_categories:text-generation, language:zh, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2410.18505, region:us","text-generation","Data Description To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the CCI (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, w","None","zh","mono","None","None","False","auto","False","None","2410.18505","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"
+"mteb/biblenlp-corpus-mmteb","mteb","2024-05-05 22:41:26","2024-05-07 00:47:48+00:00","3912ed967b0834547f35b2da9470c4976b357c9a","5062","53918","1","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"
+"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","task_categories:translation, task_categories:token-classification, task_ids:parsing, annotations_creators:expert-generated, language_creators:crowdsourced, multilinguality:multilingual, multilinguality:translation, source_datasets:original, language:bn, language:en, language:fil, language:hi, language:id, language:ja, language:km, language:lo, language:ms, language:my, language:th, language:vi, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","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","alt","False","False","False","cc-by-4.0","None","None","parsing"
+"dataset-org/c3","dataset-org","2022-03-02 23:29:22","2024-01-11 08:12:46+00:00","28e91a21a22b95987a90a46cb6d7741c7aad8158","240","50818","10","task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:zh, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1904.09679, region:us","question-answering, multiple-choice-qa","Dataset Card for C3 Dataset Summary Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multi","None","zh","mono","1|K|<|n|<|1|0|K","c3","False","False","False","other","None","None","multiple-choice-qa"
+"llamafactory/demo_data","llamafactory","2024-05-17 10:31:51","2024-07-18 16:50:20+00:00","fe2bf7023ec72408f056c82027488eea8d599c72","4217","47096","0","task_categories:text-generation, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, modality:text, region:us, llama-factory","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"
+"china-ai-law-challenge/cail2018","china-ai-law-challenge","2022-03-02 23:29:22","2024-01-16 15:08:12+00:00","775098da3ba75f033781f8061900b62503e9bea0","334","46753","19","task_categories:other, annotations_creators:found, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:unknown, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1807.02478, region:us, judgement-prediction","other","Dataset Card for CAIL 2018 Dataset Summary [More Information Needed] Supported Tasks and Leaderboards [More Information Needed] Languages [More Information Needed] Dataset Structure Data Instances [Mo","None","zh","mono","1|M|<|n|<|1|0|M","chinese-ai-and-law-cail-2018","False","False","False","unknown","1807.02478","None","None"
+"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"
+"Helsinki-NLP/opus_paracrawl","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:42:34+00:00","8bb0bcf0c038a0cb762690b4dbce088b861423ed","396","42570","5","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:bg, language:ca, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:eu, language:fi, language:fr, language:ga, language:gl, language:hr, language:hu, language:is, language:it, language:km, language:ko, language:lt, language:lv, language:mt, language:my, language:nb, language:ne, language:nl, language:nn, language:pl, language:pt, language:ro, language:ru, language:si, language:sk, language:sl, language:so, language:sv, language:sw, language:tl, language:uk, language:zh, license:cc0-1.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for OpusParaCrawl Dataset Summary Parallel corpora from Web Crawls collected in the ParaCrawl project. Tha dataset contains: 42 languages, 43 bitexts total number of files: 59,996 total n","None","bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, is, it, km, ko, lt, lv, mt, my, nb, ne, nl, nn, pl, pt, ro, ru, si, sk, sl, so, sv, sw, tl, uk, zh","multi","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K|||1|M|<|n|<|1|0|M","None","False","False","False","cc0-1.0","None","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"
+"mteb/bucc-bitext-mining","mteb","2022-05-19 19:44:24","2024-05-14 19:55:35+00:00","1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677","316","42113","1","multilinguality:monolingual, multilinguality:multilingual, language:de, language:en, language:fr, language:ru, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2104.06893, arxiv:2010.02573, arxiv:2003.04807, arxiv:2204.08582, arxiv:2008.09335, arxiv:2104.07081, region:us","None","Dataset Card for MTEB Benchmark Dataset Summary MTEB is a heterogeneous benchmark that has been built from diverse tasks: BitextMining: BUCC, Tatoeba Classification: AmazonCounterfactualClassification","None","de, en, fr, ru, zh","multi","None","None","False","False","False","cc-by-sa-4.0","2104.06893","None","None"
+"pasinit/xlwic","pasinit","2022-03-02 23:29:22","2022-10-25 09:54:22+00:00","cca3cfb747db5bf97b95126ec79d5b7d743f9654","353","41740","5","task_categories:text-classification, task_ids:semantic-similarity-classification, annotations_creators:expert-generated, language_creators:found, multilinguality:multilingual, source_datasets:original, language:en, language:bg, language:zh, language:hr, language:da, language:nl, language:et, language:fa, language:ja, language:ko, language:it, language:fr, language:de, license:cc-by-nc-4.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, semantic-similarity-classification","A system's task on any of the XL-WiC datasets is to identify the intended meaning of a word in a context of a given language. XL-WiC is framed as a binary classification task. Each instance in XL-WiC ","@inproceedings{raganato-etal-2020-xl-wic, title={XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization}, author={Raganato, Alessandro and Pasini, Tommaso and Camacho-Collados, Jose and Pilehvar, Mohammad Taher}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, pages={7193--7206}, year={2020} }","en, bg, zh, hr, da, nl, et, fa, ja, ko, it, fr, de","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","semantic-similarity-classification"
+"gavinxing/amttl","gavinxing","2022-03-02 23:29:22","2024-01-09 12:28:18+00:00","271a5aa99e75e936e334b3c52ec178f08bced629","103","40634","2","task_categories:token-classification, task_ids:parsing, annotations_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","token-classification, parsing","Dataset Card for AMTTL Dataset Summary [More Information Needed] Supported Tasks and Leaderboards [More Information Needed] Languages [More Information Needed] Dataset Structure Data Instances [More I","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","parsing"
+"Helsinki-NLP/opus_gnome","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:04:29+00:00","8362267db12f636640003de2381eff67277e6657","299","40448","1","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:af, language:am, language:an, language:ang, language:ar, language:as, language:ast, language:az, language:bal, language:be, language:bem, language:bg, language:bn, language:bo, language:br, language:brx, language:bs, language:ca, language:crh, language:cs, language:csb, language:cy, language:da, language:de, language:dv, language:dz, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fo, language:fr, language:fur, language:fy, language:ga, language:gd, language:gl, language:gn, language:gu, language:gv, language:ha, language:he, language:hi, language:hr, language:hu, language:hy, language:ia, language:id, language:ig, language:io, language:is, language:it, language:ja, language:jbo, language:ka, language:kg, language:kk, language:km, language:kn, language:ko, language:kr, language:ks, language:ku, language:ky, language:la, language:lg, language:li, language:lo, language:lt, language:lv, language:mai, language:mg, language:mi, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:mus, language:my, language:nb, language:nds, language:ne, language:nhn, language:nl, language:nn, language:no, language:nqo, language:nr, language:nso, language:oc, language:or, language:os, language:pa, language:pl, language:ps, language:pt, language:quz, language:ro, language:ru, language:rw, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:st, language:sv, language:sw, language:szl, language:ta, language:te, language:tg, language:th, language:tk, language:tl, language:tr, language:ts, language:tt, language:tyj, language:ug, language:uk, language:ur, language:uz, language:vi, language:wa, language:xh, language:yi, language:yo, language:zh, language:zu, license:unknown, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for Opus Gnome Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage s","None","af, am, an, ang, ar, as, ast, az, bal, be, bem, bg, bn, bo, br, brx, bs, ca, crh, cs, csb, cy, da, de, dv, dz, el, en, eo, es, et, eu, fa, fi, fo, fr, fur, fy, ga, gd, gl, gn, gu, gv, ha, he, hi, hr, hu, hy, ia, id, ig, io, is, it, ja, jbo, ka, kg, kk, km, kn, ko, kr, ks, ku, ky, la, lg, li, lo, lt, lv, mai, mg, mi, mk, ml, mn, mr, ms, mt, mus, my, nb, nds, ne, nhn, nl, nn, no, nqo, nr, nso, oc, or, os, pa, pl, ps, pt, quz, ro, ru, rw, si, sk, sl, so, sq, sr, st, sv, sw, szl, ta, te, tg, th, tk, tl, tr, ts, tt, tyj, ug, uk, ur, uz, vi, wa, xh, yi, yo, zh, zu","multi","1|0|K|<|n|<|1|0|0|K|||1|K|<|n|<|1|0|K|||n|<|1|K","None","False","False","False","unknown","None","None","None"
+"opencsg/chinese-fineweb-edu-v2","opencsg","2024-10-13 14:20:13","2025-01-20 04:04:02+00:00","739ad8cc02f95d3c675958d95549013a0712e809","6473","40293","56","None","text-generation","We recommend you to use the improved version Fineweb-edu-chinese-v2.1 ! Chinese Fineweb Edu Dataset V2 [中文] [English] [OpenCSG Community] [👾github] [wechat] [Twitter] 📖Technical Report Chinese Fineweb","None","zh","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","apache-2.0","2501.08197","None","None"
+"silk-road/ChatHaruhi-RolePlaying","silk-road","2023-12-21 09:34:19","2024-02-10 15:26:18+00:00","c19a1a07e332d74bbc521f6f15a887357b6eb149","220","40237","15","language:zh, language:en, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","ChatHaruhi Reviving Anime Character in Reality via Large Language Model Chat-Haruhi-Suzumiyais a language model that imitates the tone, personality and storylines of characters like Haruhi Suzumiya, h","None","zh, en","bi","None","None","False","False","False","apache-2.0","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","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","auto","False","cc-by-sa-4.0","None","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"
+"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"
+"sentence-transformers/parallel-sentences-ccmatrix","sentence-transformers","2024-05-25 08:10:49","2024-06-18 19:49:55+00:00","0c2ecfe22787e119991dbc482b776dc9aa25aa9f","8249","33644","4","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - CCMatrix This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. The texts origina","None","af, ar, ast, az, be, bg, bn, br, ca, ceb, cs, da, de, el, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, ha, he, hi, hr, hu, id, ig, ilo, is, it, ja, jv, ko, la, lb, lt, lv, mg, mk, ml, mr, ms, ne, nl, no, oc, or, pl, pt, ro, ru, sd, si, sk, sl, so, sq, sr, su, sv, sw, ta, tl, tr, uk, ur, vi, xh, yi, zh","multi","1|B|<|n|<|1|0|B","None","False","False","False","None","None","None","None"
+"m-a-p/COIG-CQIA","m-a-p","2023-12-04 07:04:37","2024-04-18 12:10:58+00:00","8b55868c6168adf86c30e7ca0f782cca1c514297","2121","32493","596","None","question-answering, text-classification, text-generation, text2text-generation","COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning Dataset Details Dataset Description 欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"conceptnet5/conceptnet5","conceptnet5","2022-03-02 23:29:22","2024-02-08 12:07:58+00:00","075e1c601ff242f9760ca85cf68b5ec41963d4e6","264","31397","22","None","text-classification, multi-class-classification","Dataset Card for Conceptnet5 Dataset Summary ConceptNet is a multilingual knowledge base, representing words and phrases that people use and the common-sense relationships between them. The knowledge ","None","de, en, es, fr, it, ja, nl, pt, ru, zh","multi","1|0|0|K|<|n|<|1|M|||1|0|M|<|n|<|1|0|0|M|||1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","1612.03975","None","multi-class-classification"
+"hails/agieval-jec-qa-ca","hails","2024-01-26 15:45:53","2024-01-26 18:41:44+00:00","1e52b6b8188f1e94782ccbda673997aaeae80587","444","29743","1","language:zh, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.06364, region:us","None","Dataset Card for ""agieval-jec-qa-ca"" 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","zh","mono","None","None","False","False","False","None","None","None","None"
+"llamafactory/alpaca_gpt4_zh","llamafactory","2024-05-17 12:17:23","2024-06-07 18:46:07+00:00","065394ee242c43928298de8f43a8748ffd16f3e3","1840","28545","7","None","text-generation, question-answering","Borrowed from: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM Removed 6,103 mistruncated examples. You can use it in LLaMA Factory by specifying dataset: alpaca_gpt4_zh.","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","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","task_categories:text-generation, task_categories:text-classification, task_categories:token-classification, task_categories:fill-mask, task_categories:table-question-answering, task_categories:text2text-generation, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.20094, region:us, synthetic, text, math, reasoning, instruction, tool","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","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"
+"RekaAI/VibeEval","RekaAI","2024-05-02 10:09:50","2024-12-12 22:53:57+00:00","3bdc7c30f09938557a21f1918d6c8c28fd5978f8","2960","25219","39","None","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"
+"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"
+"intfloat/multilingual_cc_news","intfloat","2023-03-22 08:25:34","2023-04-23 08:19:06+00:00","1c24f619ae44d0a4c87fde72efa741dc25f7530e","2171","23387","17","language:en, language:zh, language:fr, language:de, language:af, language:ar, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, region:us","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"
+"aiana94/polynews","aiana94","2024-05-17 07:39:20","2024-06-21 08:37:54+00:00","effaaf00db128feefaf0c83b79a5e0f29d4d0d04","311","23355","6","task_categories:fill-mask, task_categories:text-generation, multilinguality:multilingual, source_datasets:masakhanews, source_datasets:mafand, source_datasets:wikinews, source_datasets:wmt-news, source_datasets:globalvoices, language:am, language:ar, language:ay, language:bm, language:bbj, language:bn, language:bs, language:bg, language:ca, language:cs, language:ku, language:da, language:el, language:en, language:et, language:ee, language:fil, language:fi, language:fr, language:fon, language:gu, language:guw, language:ha, language:he, language:hi, language:hu, language:ig, language:id, language:it, language:ja, language:kk, language:km, language:ko, language:lv, language:ln, language:lt, language:lg, language:luo, language:mk, language:mos, language:my, language:nl, language:no, language:ne, language:om, language:or, language:pa, language:pcm, language:fa, language:pl, language:pt, language:mg, language:ro, language:rn, language:ru, language:sn, language:so, language:es, language:sr, language:sq, language:sw, language:sv, language:ta, language:tet, language:ti, language:th, language:tn, language:tr, language:tw, language:uk, language:ur, language:wo, language:xh, language:yo, language:zh, language:zu, language:de, license:cc-by-nc-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.12634, region:us, news, polynews, mafand, masakhanews, wikinews, globalvoices, wmtnews","fill-mask, text-generation","Dataset Card for PolyNews Dataset Summary PolyNews is a multilingual dataset containing news titles in 77 languages and 19 scripts. Uses This dataset can be used for domain adaptation of language mode","None","am, ar, ay, bm, bbj, bn, bs, bg, ca, cs, ku, da, el, en, et, ee, fil, fi, fr, fon, gu, guw, ha, he, hi, hu, ig, id, it, ja, kk, km, ko, lv, ln, lt, lg, luo, mk, mos, my, nl, no, ne, om, or, pa, pcm, fa, pl, pt, mg, ro, rn, ru, sn, so, es, sr, sq, sw, sv, ta, tet, ti, th, tn, tr, tw, uk, ur, wo, xh, yo, zh, zu, de","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","2406.12634","None","None"
+"hails/agieval-gaokao-mathcloze","hails","2024-01-23 22:03:04","2024-01-26 18:28:10+00:00","bb0c987555b5bf631651100767949f808509be74","440","23072","2","language:zh, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.06364, region:us","None","Dataset Card for ""agieval-gaokao-mathcloze"" Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub. This dataset co","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"google/code_x_glue_tt_text_to_text","google","2022-03-02 23:29:22","2024-01-24 15:18:44+00:00","ae1a41ee2433091f40cf7e803e26170e3587836f","172","23063","1","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:da, language:en, language:lv, language:nb, language:zh, license:c-uda, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2102.04664, region:us, code-documentation-translation","translation","Dataset Card for ""code_x_glue_tt_text_to_text"" Dataset Summary CodeXGLUE text-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Text/text-to-text The dataset we use i","None","da, en, lv, nb, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","c-uda","2102.04664","None","None"
+"shibing624/nli_zh","shibing624","2022-03-02 23:29:22","2022-10-30 06:30:56+00:00","79fc59f431716692cd8ae024f1b97fe7457a7ca8","635","21461","44","task_categories:text-classification, task_ids:natural-language-inference, task_ids:semantic-similarity-scoring, task_ids:text-scoring, annotations_creators:shibing624, language_creators:shibing624, multilinguality:monolingual, source_datasets:https://github.com/shibing624/text2vec, source_datasets:https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC, source_datasets:http://icrc.hitsz.edu.cn/info/1037/1162.htm, source_datasets:http://icrc.hitsz.edu.cn/Article/show/171.html, source_datasets:https://arxiv.org/abs/1908.11828, source_datasets:https://github.com/pluto-junzeng/CNSD, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, arxiv:1908.11828, region:us","text-classification, natural-language-inference, semantic-similarity-scoring, text-scoring","纯文本数据，格式：（sentence1， sentence2， label）。常见中文���义匹配数据集，包含ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务。","None","zh","mono","1|0|0|K|<|n|<|2|0|M","snli","False","False","False","cc-by-4.0","1908.11828","None","natural-language-inference, semantic-similarity-scoring, text-scoring"
+"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"
+"RicardoRei/wmt-da-human-evaluation","RicardoRei","2023-02-16 18:49:07","2023-02-17 10:41:18+00:00","301de385bf05b0c00a8f4be74965e186164dd425","133","20489","3","language:bn, language:cs, language:de, language:en, language:et, language:fi, language:fr, language:gu, language:ha, language:hi, language:is, language:ja, language:kk, language:km, language:lt, language:lv, language:pl, language:ps, language:ru, language:ta, language:tr, language:uk, language:xh, language:zh, language:zu, license:apache-2.0, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mt-evaluation, WMT, 41-lang-pairs","None","Dataset Summary This dataset contains all DA human annotations from previous WMT News Translation shared tasks. The data is organised into 8 columns: lp: language pair src: input text mt: translation ","None","bn, cs, de, en, et, fi, fr, gu, ha, hi, is, ja, kk, km, lt, lv, pl, ps, ru, ta, tr, uk, xh, zh, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"opencsg/chinese-fineweb-edu","opencsg","2024-08-26 14:46:54","2025-01-20 04:04:29+00:00","5a1735363fe4c4bfad7d0f91981457883bc5ce6d","4524","20212","84","None","text-generation","We recommend you to use the improved version Fineweb-edu-chinese-v2.1 ! Chinese Fineweb Edu Dataset [中文] [English] [OpenCSG Community] [👾github] [wechat] [Twitter] 📖Technical Report Chinese Fineweb Ed","None","zh","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","apache-2.0","2501.08197","None","None"
+"rayliuca/WikidataLabels","rayliuca","2024-01-01 00:23:08","2024-01-11 04:17:57+00:00","122b626f6127ca111659a4354590845b30736f9a","5752","20169","1","None","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"
+"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","task_categories:text-generation, task_categories:question-answering, language:en, language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama-factory","text-generation, question-answering","None","None","en, zh","bi","n|<|1|K","None","False","False","False","apache-2.0","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"
+"manifoldlabs/Infinity-Instruct","manifoldlabs","2024-08-20 00:57:17","2024-08-20 02:15:57+00:00","bdf12f953ee63be76d4a0612aae79ce62a310216","971","19251","2","task_categories:text-generation, language:en, language:zh, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.00530, arxiv:2405.19327, 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","False","False","None","None","None","None"
+"cseval/cs-eval","cseval","2024-05-08 06:28:48","2024-08-11 07:46:40+00:00","717eb416f7a8e132379bdb4bd52ad0107e76ef92","66","19217","5","task_categories:text-classification, task_categories:multiple-choice, task_categories:question-answering, task_categories:summarization, task_categories:feature-extraction, task_categories:fill-mask, task_categories:text-retrieval, task_categories:document-question-answering, language:zh, language:en, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","text-classification, multiple-choice, question-answering, summarization, feature-extraction, fill-mask, text-retrieval, document-question-answering","CS-Eval is a comprehensive evaluation toolkit for fundamental cybersecurity models or large language models' cybersecurity ability, encompassing 11 major cybersecurity categories, 42 subdomains, featu","None","zh, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"MoritzLaurer/multilingual-NLI-26lang-2mil7","MoritzLaurer","2022-08-17 15:28:16","2022-08-22 21:40:14+00:00","510a233972a0d7ff0f767d82f46e046832c10538","2673","19152","46","None","text-classification, natural-language-inference, multi-input-text-classification","Datasheet for the dataset: multilingual-NLI-26lang-2mil7 Dataset Summary This dataset contains 2 730 000 NLI text pairs in 26 languages spoken by more than 4 billion people. The dataset can be used to","None","multilingual, zh, ja, ar, ko, de, fr, es, pt, hi, id, it, tr, ru, bn, ur, mr, ta, vi, fa, pl, uk, nl, sv, he, sw, ps","multi","1|M|<|n|<|5","None","False","False","False","None","None","None","natural-language-inference, multi-input-text-classification"
+"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"
+"wecover/OPUS_GlobalVoices","wecover","2024-01-31 07:22:25","2024-11-24 03:46:34+00:00","4beae40f081a88d573fa3f60eb35ce3f2a3bc8b8","8465","18077","0","None","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"
+"CohereForAI/Global-MMLU","CohereForAI","2024-12-01 22:45:59","2024-12-12 13:49:50+00:00","75a8cf195b89938fce486bbb607a881faa123df0","7116","17570","103","None","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"
+"haoranxu/ALMA-R-Preference","haoranxu","2024-01-17 06:58:55","2024-06-04 23:02:24+00:00","727c35546bf31b1831ac7f78321eee731e3feda3","772","17178","13","task_categories:translation, language:ru, language:cs, language:zh, language:is, language:de, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2401.08417, region:us","translation","Dataset Card for ""ALMA-R-Preference"" This is triplet preference data used by ALMA-R model. The triplet preference data, supporting 10 translation directions, is built upon the FLORES-200 development a","None","ru, cs, zh, is, de","multi","None","None","False","False","False","mit","2401.08417","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"
+"simon3000/genshin-voice","simon3000","2024-04-25 00:09:03","2024-08-30 08:36:05+00:00","ffe17e2e7938508a73255ec294b0ded17ed071f1","6356","16076","62","None","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"
+"CAiRE/ASCEND","CAiRE","2022-03-02 23:29:22","2024-07-16 08:56:04+00:00","737e9800ae31be9932ba8464c80366559bd28424","891","15820","30","task_categories:automatic-speech-recognition, annotations_creators:expert-generated, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:en, language:zh, 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:2112.06223, region:us, speech-recognition, code-switching","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"
+"WueNLP/sib-fleurs","WueNLP","2024-12-03 20:29:29","2024-12-19 11:36:50+00:00","f00a6bbc6b3e3866600f838736295dd09b393902","12774","15690","3","None","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"
+"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"
+"wmt/wmt20_mlqe_task2","wmt","2022-03-02 23:29:22","2024-04-04 13:53:33+00:00","29c6d2359612271e49ccaa8b1bbd88bcc4ab46bf","127","14646","2","task_categories:translation, task_categories:text-classification, annotations_creators:expert-generated, annotations_creators:machine-generated, language_creators:found, multilinguality:translation, source_datasets:extended|wikipedia, language:de, language:en, language:zh, license:unknown, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1902.08646, region:us, translation-quality-estimation","translation, text-classification","Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task2 Dataset Summary From the homepage: This shared task (part of WMT20) will build on its previous editions to further examine automat","None","de, en, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","unknown","1902.08646","None","None"
+"MartinThoma/wili_2018","MartinThoma","2022-03-02 23:29:22","2024-08-08 05:56:26+00:00","12c3a519f58673e4051a5d9db51c1fcd94a94c75","205","14544","4","task_categories:text-classification, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:original, language:ace, language:af, language:als, language:am, language:an, language:ang, language:ar, language:arz, language:as, language:ast, language:av, language:ay, language:az, language:azb, language:ba, language:bar, language:bcl, language:be, language:bg, language:bho, language:bjn, language:bn, language:bo, language:bpy, language:br, language:bs, language:bxr, language:ca, language:cbk, language:cdo, language:ce, language:ceb, language:chr, language:ckb, language:co, language:crh, language:cs, language:csb, language:cv, language:cy, language:da, language:de, language:diq, language:dsb, language:dty, language:dv, language:egl, language:el, language:en, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:fi, language:fo, language:fr, language:frp, language:fur, language:fy, language:ga, language:gag, language:gd, language:gl, language:glk, language:gn, language:gu, language:gv, language:ha, language:hak, language:he, language:hi, language:hif, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:ig, language:ilo, language:io, language:is, language:it, language:ja, language:jam, language:jbo, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kk, language:km, language:kn, language:ko, language:koi, language:kok, language:krc, language:ksh, language:ku, language:kv, language:kw, language:ky, language:la, language:lad, language:lb, language:lez, language:lg, language:li, language:lij, language:lmo, language:ln, language:lo, language:lrc, language:lt, language:ltg, language:lv, language:lzh, language:mai, language:map, language:mdf, language:mg, language:mhr, language:mi, 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:nan, language:nap, language:nb, language:nci, language:nds, language:ne, language:new, language:nl, language:nn, language:nrm, language:nso, language:nv, language:oc, language:olo, language:om, language:or, language:os, language:pa, language:pag, language:pam, language:pap, language:pcd, language:pdc, language:pfl, language:pl, language:pnb, language:ps, language:pt, language:qu, language:rm, language:ro, language:roa, language:ru, language:rue, language:rup, language:rw, language:sa, language:sah, language:sc, language:scn, language:sco, language:sd, language:sgs, language:sh, language:si, language:sk, language:sl, language:sme, language:sn, language:so, language:sq, language:sr, language:srn, language:stq, language:su, language:sv, language:sw, language:szl, language:ta, language:tcy, language:te, language:tet, language:tg, language:th, language:tk, language:tl, language:tn, language:to, language:tr, language:tt, language:tyv, language:udm, language:ug, language:uk, language:ur, language:uz, language:vec, language:vep, language:vi, language:vls, language:vo, language:vro, language:wa, language:war, language:wo, language:wuu, language:xh, language:xmf, language:yi, language:yo, language:zea, language:zh, license:odbl, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1801.07779, region:us, language-identification","text-classification","Dataset Card for wili_2018 Dataset Summary WiLI-2018, the Wikipedia language identification benchmark dataset, contains 235000 paragraphs of 235 languages. The dataset is balanced and a train-test spl","None","ace, af, als, am, an, ang, ar, arz, as, ast, av, ay, az, azb, ba, bar, bcl, be, bg, bho, bjn, bn, bo, bpy, br, bs, bxr, ca, cbk, cdo, ce, ceb, chr, ckb, co, crh, cs, csb, cv, cy, da, de, diq, dsb, dty, dv, egl, el, en, eo, es, et, eu, ext, fa, fi, fo, fr, frp, fur, fy, ga, gag, gd, gl, glk, gn, gu, gv, ha, hak, he, hi, hif, hr, hsb, ht, hu, hy, ia, id, ie, ig, ilo, io, is, it, ja, jam, jbo, jv, ka, kaa, kab, kbd, kk, km, kn, ko, koi, kok, krc, ksh, ku, kv, kw, ky, la, lad, lb, lez, lg, li, lij, lmo, ln, lo, lrc, lt, ltg, lv, lzh, mai, map, mdf, mg, mhr, mi, min, mk, ml, mn, mr, mrj, ms, mt, mwl, my, myv, mzn, nan, nap, nb, nci, nds, ne, new, nl, nn, nrm, nso, nv, oc, olo, om, or, os, pa, pag, pam, pap, pcd, pdc, pfl, pl, pnb, ps, pt, qu, rm, ro, roa, ru, rue, rup, rw, sa, sah, sc, scn, sco, sd, sgs, sh, si, sk, sl, sme, sn, so, sq, sr, srn, stq, su, sv, sw, szl, ta, tcy, te, tet, tg, th, tk, tl, tn, to, tr, tt, tyv, udm, ug, uk, ur, uz, vec, vep, vi, vls, vo, vro, wa, war, wo, wuu, xh, xmf, yi, yo, zea, zh","multi","1|0|0|K|<|n|<|1|M","wili-2018","False","False","False","odbl","None","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"
+"medalpaca/medical_meadow_medqa","medalpaca","2023-04-06 16:56:15","2023-04-06 16:59:02+00:00","c1b14eb49d9208bac653410f2badd64b2a9fa238","373","14331","92","task_categories:question-answering, language:en, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2009.13081, region:us, medical","question-answering","Dataset Card for MedQA Dataset Summary This is the data and baseline source code for the paper: Jin, Di, et al. ""What Disease does this Patient Have? A Large-scale Open Domain Question Answering Datas","None","en, zh","bi","None","None","False","False","False","None","None","None","None"
+"wangrui6/Zhihu-KOL","wangrui6","2023-02-25 00:21:29","2023-04-23 13:26:03+00:00","f66a447c8bcc75cc0d393ba629cd8b70444ee774","331","14295","217","task_categories:question-answering, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for ""Zhihu-KOL"" Zhihu data for training Open Assitant More Information needed","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"JosephusCheung/GuanacoDataset","JosephusCheung","2023-03-16 06:30:22","2024-04-15 20:25:44+00:00","188a1a800432729e2ee69e306066688817c1c738","90","13863","514","task_categories:text-generation, task_categories:question-answering, language:zh, language:en, language:ja, language:de, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, doi:10.57967/hf/1423, region:us, alpaca, llama, guanaco","text-generation, question-answering, conversational","Sorry, it's no longer available on Hugging Face. Please reach out to those who have already downloaded it. If you have a copy, please refrain from re-uploading it to Hugging Face. The people here don'","None","zh, en, ja, de","multi","None","None","False","manual","False","gpl-3.0","None","None","None"
+"MultiCoNER/multiconer_v2","MultiCoNER","2023-03-01 00:57:16","2023-07-06 18:37:15+00:00","4be2d62c912977ee26ed14d2553a4fe17ca3d980","852","13404","13","task_categories:token-classification, language:bn, language:zh, language:de, language:en, language:es, language:fa, language:fr, language:hi, language:it, language:pt, language:sv, language:uk, license:cc-by-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us, multiconer, ner, multilingual, named entity recognition, fine-grained ner","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"
+"adithya7/xlel_wd_dictionary","adithya7","2022-04-22 02:36:27","2022-07-01 17:30:21+00:00","996e72dea151ca0856d1d16efd71f560b18da817","688","13390","3","annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:af, language:ar, language:be, language:bg, language:bn, language:ca, language:cs, language:da, language:de, language:el, language:en, language:es, language:fa, language:fi, language:fr, language:he, language:hi, language:hu, language:id, language:it, language:ja, language:ko, language:ml, language:mr, language:ms, language:nl, language:no, language:pl, language:pt, language:ro, language:ru, language:si, language:sk, language:sl, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:tr, language:uk, language:vi, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, arxiv:2204.06535, region:us","None","XLEL-WD is a multilingual event linking dataset. This sub-dataset contains a dictionary of events from Wikidata. The multilingual descriptions for Wikidata event items are taken from the corresponding","@article{pratapa-etal-2022-multilingual, title = {Multilingual Event Linking to Wikidata}, author = {Pratapa, Adithya and Gupta, Rishubh and Mitamura, Teruko}, publisher = {arXiv}, year = {2022}, url = {https://arxiv.org/abs/2204.06535}, }","af, ar, be, bg, bn, ca, cs, da, de, el, en, es, fa, fi, fr, he, hi, hu, id, it, ja, ko, ml, mr, ms, nl, no, pl, pt, ro, ru, si, sk, sl, sr, sv, sw, ta, te, th, tr, uk, vi, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2204.06535","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","None","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"
+"llamafactory/DPO-En-Zh-20k","llamafactory","2024-04-19 17:11:52","2024-06-07 18:44:17+00:00","21ff425feb7cc16a45a2e91536ff237a6fd3779a","216","13012","91","task_categories:text-generation, 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, doi:10.57967/hf/3629, region:us, dpo, orpo, rlhf, llama-factory","text-generation","This dataset is composed by 4,000 examples of argilla/distilabel-capybara-dpo-7k-binarized with chosen score>=4. 3,000 examples of argilla/distilabel-intel-orca-dpo-pairs with chosen score>=8. 3,000 e","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"xu-song/cc100-samples","xu-song","2024-03-05 08:19:01","2024-07-23 03:21:28+00:00","40da934ae231773e6ea04cbcd78271dde377d5ff","266","12728","4","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: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:ff, language:fi, language:fr, language:fy, language:ga, language:gd, language:gl, language:gn, language:gu, language:ha, language:he, language:hi, language:hr, language:ht, language:hu, language:hy, language:id, language:ig, 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:lg, language:li, language:ln, 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:ns, language:om, language:or, language:pa, language:pl, language:ps, language:pt, language:qu, language:rm, language:ro, language:ru, language:sa, language:sc, language:sd, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:ss, language:su, language:sv, language:sw, language:ta, language:te, language:th, language:tl, language:tn, language:tr, language:ug, language:uk, language:ur, language:uz, language:vi, language:wo, language:xh, language:yi, language:yo, language:zh, language:zu, license:unknown, size_categories:1M<n<10M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","text-generation, fill-mask, language-modeling, masked-language-modeling","The cc100-samples is a subset which contains first 10,000 lines of cc100. Languages To load a language which isn't part of the config, all you need to do is specify the language code in the config. Yo","None","af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, ff, fi, fr, fy, ga, gd, gl, gn, gu, ha, he, hi, hr, ht, hu, hy, id, ig, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lg, li, ln, lo, lt, lv, mg, mk, ml, mn, mr, ms, my, ne, nl, no, ns, om, or, pa, pl, ps, pt, qu, rm, ro, ru, sa, sc, sd, si, sk, sl, so, sq, sr, ss, su, sv, sw, ta, te, th, tl, tn, tr, ug, uk, ur, uz, vi, wo, xh, yi, yo, zh, zu","multi","1|K|<|n|<|1|0|K","cc100","False","False","False","unknown","None","None","language-modeling, masked-language-modeling"
+"Babelscape/multinerd","Babelscape","2023-04-20 11:49:21","2023-04-20 12:43:31+00:00","2814b78e7af4b5a1f1886fe7ad49632de4d9dd25","732","12626","18","task_categories:token-classification, task_ids:named-entity-recognition, annotations_creators:machine-generated, language_creators:machine-generated, multilinguality:multilingual, source_datasets:original, language:de, language:en, language:es, language:fr, language:it, language:nl, language:pl, language:pt, language:ru, language:zh, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, structure-prediction","token-classification, named-entity-recognition","Dataset Card for MultiNERD dataset Description Summary: In a nutshell, MultiNERD is the first language-agnostic methodology for automatically creating multilingual, multi-genre and fine-grained annota","None","de, en, es, fr, it, nl, pl, pt, ru, zh","multi","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","named-entity-recognition"
+"ayymen/Weblate-Translations","ayymen","2024-01-07 22:29:01","2024-04-02 10:16:03+00:00","1d3d0d3fbe20cb39693118afbb17bbee6a3ad826","3554","12564","11","task_categories:translation, task_categories:text2text-generation, annotations_creators:crowdsourced, language:aa, language:ab, language:ace, language:ach, language:af, language:afh, language:aii, language:ain, language:ajp, language:ak, language:am, language:an, language:ang, language:anp, language:apc, language:ar, language:arn, language:ars, language:as, language:ast, language:ay, language:ayc, language:az, language:azb, language:ba, language:bar, language:bd, language:be, language:bem, language:ber, language:bg, language:bho, language:bm, language:bn, language:bo, language:bp, language:bqi, language:br, language:brx, language:bs, language:bul, language:by, language:ca, language:ce, language:ceb, language:ckb, language:cmn, language:cn, language:cnr, language:co, language:cr, language:crh, language:cs, language:csb, language:cv, language:cy, language:cz, language:da, language:de, language:dev, language:doi, language:dsb, language:dua, language:dum, language:dv, language:dz, language:eg, language:el, language:en, language:eng, language:enm, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:fi, language:fil, language:fo, language:fr, language:fra, language:frm, language:frp, language:frs, language:fu, language:fur, language:fy, language:ga, language:gb, language:gd, language:gl, language:glk, language:gmh, language:gn, language:gr, language:gsw, language:gu, language:guc, language:gug, language:gum, language:guw, language:gv, language:ha, language:haw, language:he, language:hi, language:hne, language:hr, language:hrx, language:hsb, language:ht, language:hu, language:hy, language:hz, language:ia, language:id, language:ie, language:ig, language:in, language:io, language:is, language:it, language:iw, language:ja, language:jam, language:jbo, language:ji, language:jp, language:jpn, language:jv, language:ka, language:kab, language:kg, language:kk, language:kl, language:km, language:kmr, language:kn, language:ko, language:kok, language:kr, language:krl, language:ks, language:ksh, language:ku, language:kw, language:ky, language:la, language:lb, language:lfn, language:lg, language:li, language:lk, language:ln, language:lo, language:lt, language:ltg, language:lv, language:lzh, language:mai, language:me, language:mg, language:mhr, language:mi, language:mjw, language:mk, language:ml, language:mn, language:mnc, language:mni, language:mnw, language:mo, language:mr, language:ms, language:mt, language:my, language:na, language:nah, language:nan, language:nap, language:nb, language:nds, language:ne, language:nl, language:nn, language:no, language:np, language:nqo, language:ny, language:oc, language:oj, language:om, language:or, language:os, language:ota, language:pa, language:pam, language:pap, language:pbb, language:peo, language:pk, language:pl, language:pms, language:pr, language:prg, language:ps, language:pt, language:pu, language:qt, language:rcf, language:rm, language:ro, language:rom, language:ru, language:rue, language:rw, language:ryu, language:sa, language:sah, language:sai, language:sat, language:sc, language:sco, language:sd, language:sdh, language:se, language:sh, language:shn, language:si, language:sk, language:skr, language:sl, language:sm, language:sma, language:sn, language:so, language:sq, language:sr, language:st, language:su, language:sv, language:sw, language:szl, language:ta, language:tam, language:te, language:tet, language:tg, language:th, language:ti, language:tk, language:tl, language:tlh, language:tn, language:to, language:tok, language:tr, language:trv, language:tt, language:tum, language:tw, language:ty, language:tzm, language:ua, language:udm, language:ug, language:uk, language:und, language:ur, language:us, language:uz, language:vec, language:vi, language:vls, language:wa, language:wae, language:wo, language:xh, language:yi, language:yo, language:yue, language:zgh, language:zh, language:zu, license:other, size_categories:10M<n<100M, modality:text, region:us","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"
+"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"
+"llamafactory/alpaca_zh","llamafactory","2024-05-17 12:14:23","2024-06-07 18:46:22+00:00","5fb9a2961ce41be68ebaa0180279384761554648","424","11681","3","task_categories:text-generation, task_categories:question-answering, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama-factory","text-generation, question-answering","Borrowed from: https://huggingface.co/datasets/hfl/alpaca_zh_51k Removed some examples with empty output. You can use it in LLaMA Factory by specifying dataset: alpaca_zh.","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"BelleGroup/train_0.5M_CN","BelleGroup","2023-03-31 10:17:49","2023-04-03 08:11:22+00:00","29c35e9b56c7fc91f04a613ed33896f7a19bad54","206","11607","103","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","内容 包含约50万条由BELLE项目生成的中文指令数据。 样例 { ""instruction"": ""给定一个文字输入，将其中的所有数字加1。\\n“明天的会议在9点开始，记得准时到达。”\\n"", ""input"": """", ""output"": ""“明天的会议在10点开始，记得准时到达。”"" } 字段： instruction: 指令 input: 输入（本数据集均为空） output: 输出 使用限制","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl-3.0","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","task_categories:text-generation, language:en, language:zh, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2407.18743, region:us","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"
+"ayymen/Pontoon-Translations","ayymen","2023-12-03 21:44:52","2024-01-19 21:32:07+00:00","7318c7cac7cdd5e2e7efc7e86d84256aaf54c8dc","228","11400","14","task_categories:translation, task_categories:text2text-generation, annotations_creators:crowdsourced, language:ab, language:ace, language:ach, language:ady, language:af, language:am, language:an, language:ann, language:anp, language:ar, language:arn, language:as, language:ast, language:ay, language:az, language:azb, language:azz, language:ba, language:ban, language:bas, language:be, language:bg, language:bm, language:bn, language:bo, language:br, language:brx, language:bs, language:bxr, language:ca, language:cak, language:ceb, language:ckb, language:cnh, language:co, language:crh, language:cs, language:csb, language:cv, language:cy, language:da, language:dag, language:de, language:dsb, language:dv, language:dyu, language:ee, language:el, language:en, language:eo, language:es, language:et, language:eu, language:ewo, language:fa, language:ff, language:fi, language:fo, language:fr, language:frp, language:fuf, language:fur, language:fy, language:ga, language:gd, language:gl, language:gn, language:gom, language:gor, language:gu, language:guc, language:gv, language:ha, language:he, language:hi, language:hil, language:hr, language:hsb, language:ht, language:hu, language:hus, language:hy, language:hye, language:hyw, language:ia, language:id, language:ie, language:ig, language:ilo, language:is, language:it, language:ixl, language:ja, language:jbo, language:jiv, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kk, language:km, language:kmr, language:kn, language:ko, language:kpv, language:ks, language:kw, language:ky, language:lb, language:lg, language:lij, language:ln, language:lo, language:lt, language:ltg, language:lus, language:lv, language:lzz, language:mai, language:mdf, language:meh, language:mg, language:mhr, language:mix, language:mk, language:ml, language:mn, language:mni, language:mr, language:mrj, language:ms, language:mt, language:my, language:myv, language:nan, language:nb, language:ne, language:nhe, language:nhi, language:nia, language:nl, language:nn, language:nr, language:nso, language:nv, language:ny, language:nyn, language:oc, language:om, language:or, language:os, language:pa, language:pai, language:pap, language:pl, language:ppl, language:ps, language:pt, language:quc, language:quy, language:qvi, language:rm, language:ro, language:ru, language:rw, language:sah, language:sat, language:sc, language:scn, language:sco, language:sd, language:ses, language:shi, language:si, language:sk, language:skr, language:sl, language:sn, language:snk, language:son, language:sq, language:sr, language:ss, language:st, language:su, language:sv, language:sw, language:syr, language:szl, language:ta, language:te, language:tg, language:th, language:ti, language:tig, language:tk, language:tl, language:tn, language:tok, language:tr, language:trs, language:ts, language:tsz, language:tt, language:tw, language:tyv, language:tzm, language:udm, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vi, language:vot, language:wo, language:xcl, language:xh, language:yi, language:yo, language:yua, language:yue, language:zam, language:zgh, language:zh, language:zu, language:zza, license:mpl-2.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation, text2text-generation","Dataset Card for Pontoon Translations This is a dataset containing strings from various Mozilla projects on Mozilla's Pontoon localization platform and their translations into more than 200 languages.","None","ab, ace, ach, ady, af, am, an, ann, anp, ar, arn, as, ast, ay, az, azb, azz, ba, ban, bas, be, bg, bm, bn, bo, br, brx, bs, bxr, ca, cak, ceb, ckb, cnh, co, crh, cs, csb, cv, cy, da, dag, de, dsb, dv, dyu, ee, el, en, eo, es, et, eu, ewo, fa, ff, fi, fo, fr, frp, fuf, fur, fy, ga, gd, gl, gn, gom, gor, gu, guc, gv, ha, he, hi, hil, hr, hsb, ht, hu, hus, hy, hye, hyw, ia, id, ie, ig, ilo, is, it, ixl, ja, jbo, jiv, jv, ka, kaa, kab, kbd, kk, km, kmr, kn, ko, kpv, ks, kw, ky, lb, lg, lij, ln, lo, lt, ltg, lus, lv, lzz, mai, mdf, meh, mg, mhr, mix, mk, ml, mn, mni, mr, mrj, ms, mt, my, myv, nan, nb, ne, nhe, nhi, nia, nl, nn, nr, nso, nv, ny, nyn, oc, om, or, os, pa, pai, pap, pl, ppl, ps, pt, quc, quy, qvi, rm, ro, ru, rw, sah, sat, sc, scn, sco, sd, ses, shi, si, sk, skr, sl, sn, snk, son, sq, sr, ss, st, su, sv, sw, syr, szl, ta, te, tg, th, ti, tig, tk, tl, tn, tok, tr, trs, ts, tsz, tt, tw, tyv, tzm, udm, ug, uk, ur, uz, ve, vec, vi, vot, wo, xcl, xh, yi, yo, yua, yue, zam, zgh, zh, zu, zza","multi","None","None","False","False","False","mpl-2.0","None","None","None"
+"openai/MMMLU","openai","2024-09-13 16:37:19","2024-10-16 18:39:00+00:00","325a01dc3e173cac1578df94120499aaca2e2504","4050","11335","447","None","question-answering","Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different ","None","ar, bn, de, es, fr, hi, id, it, ja, ko, pt, sw, yo, zh","multi","None","None","False","False","False","mit","2009.03300","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"
+"shibing624/sharegpt_gpt4","shibing624","2023-07-27 05:45:49","2024-02-23 05:38:24+00:00","3fb53354e02a931777556fb1da37e931d73af48a","498","11268","115","task_categories:text-classification, task_categories:text-generation, task_ids:text-scoring, annotations_creators:shibing624, language_creators:shibing624, multilinguality:monolingual, source_datasets:https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/tree/main, language:zh, language:en, language:gl, language:ko, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-classification, text-generation, text-scoring","Dataset Card Dataset Summary ShareGPT中挑选出的GPT4多轮问答数据，多语言问答。 Languages 数据集是多语言，包括中文、英文、日文等常用语言。 Dataset Structure Data Fields The data fields are the same among all splits. conversations: a List of str","None","zh, en, gl, ko","multi","1|0|0|K|<|n|<|1|M","LLM","False","False","False","cc-by-4.0","None","None","text-scoring"
+"BelleGroup/train_1M_CN","BelleGroup","2023-03-31 08:53:50","2023-04-03 08:23:17+00:00","a8b4ee0a0a828c8568f74b1c0e09b1367af2d8d6","117","11194","144","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","内容 包含约100万条由BELLE项目生成的中文指令数据。 样例 { ""instruction"": ""给定一个文字输入，将其中的所有数字加1。\\n“明天的会议在9点开始，记得准时到达。”\\n"", ""input"": """", ""output"": ""“明天的会议在10点开始，记得准时到达。”"" } 字段： instruction: 指令 input: 输入（本数据集均为空） output: 输出 使用限","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl-3.0","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","task_categories:table-to-text, annotations_creators:none, language_creators:unknown, multilinguality:unknown, source_datasets:original, language:ar, language:zh, language:en, language:fr, language:hi, language:id, language:ja, language:ko, language:pt, language:ru, language:es, license:cc-by-2.5, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, data-to-text","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"
+"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"
+"pleisto/wikipedia-cn-20230720-filtered","pleisto","2023-07-23 09:45:03","2023-07-23 10:06:15+00:00","4cef256a3f426ae1d3f6930c8cd59a32d785d99d","380","10694","143","task_categories:text-generation, language:zh, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, wikipedia","text-generation","本数据集基于中文维基2023年7月20日的dump存档。作为一项以数据为中心的工作，本数据集仅保留了 254,547条 质量较高的词条内容。具体而言： 过滤了Template, Category, Wikipedia, File, Topic, Portal, MediaWiki, Draft, Help等特殊类型的词条 使用启发式的方法和自有的NLU模型过滤了一部分质量较低的词条 过滤了一部分内","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Infi-MM/InfiMM-WebMath-40B","Infi-MM","2024-09-12 22:26:18","2024-09-24 02:26:06+00:00","3efcc8d1384d17c324bf2cd0167b5de442cb5f4c","662","10483","55","task_categories:text-generation, language:en, language:zh, license:odc-by, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2409.12568, region:us, pretrain, multi-modal","text-generation","InfiMM-WebMath-40B Dataset ArXiv| PDF InfiMM-WebMath-40B is a large-scale, open-source multimodal dataset specifically designed for mathematical reasoning tasks. It incorporates both text and images, ","None","en, zh","bi","1|0|B|<|n|<|1|0|0|B","None","False","False","False","odc-by","2409.12568","None","None"
+"Chinese-Vicuna/guanaco_belle_merge_v1.0","Chinese-Vicuna","2023-03-30 07:29:07","2023-03-30 07:49:30+00:00","a46f98219e858c49500688be011861712b6d9f31","76","10452","92","language:zh, language:en, language:ja, license:gpl-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Thanks for Guanaco Dataset and Belle Dataset This dataset was created by merging the above two datasets in a certain format so that they can be used for training our code Chinese-Vicuna","None","zh, en, ja","multi","None","None","False","False","False","gpl-3.0","None","None","None"
+"creative-graphic-design/CGL-Dataset","creative-graphic-design","2024-04-18 15:31:44","2024-09-20 10:33:11+00:00","a6f2416842e23c212d823b35578dcc5ba9c7009f","73","10267","2","task_categories:other, annotations_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2205.00303, arxiv:2005.00614, region:us, graphic-design, layout-generation, poster-generation","other","Dataset Card for CGL-Dataset Dataset Summary The CGL-Dataset is a dataset used for the task of automatic graphic layout design for advertising posters. It contains 61,548 samples and is provided by Al","None","zh","mono","None","None","False","False","False","cc-by-nc-sa-4.0","2205.00303","None","None"
+"yentinglin/TaiwanChat","yentinglin","2023-08-10 06:23:46","2024-05-16 13:06:58+00:00","d766aa2cf5743aebfe3d91bbf141b3724de21a9b","123","9815","59","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2311.17487, region:us","conversational, text-generation, text2text-generation","Performance Citation If you find Taiwan LLM is useful in your work, please cite it with: @misc{lin2023taiwan, title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"davidstap/NTREX","davidstap","2024-04-23 11:38:58","2024-04-23 17:47:51+00:00","fd20d54141b6da952d5c68a2989472892489da0f","170","9486","2","task_categories:translation, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:translation, language:af, language:am, language:ar, language:az, language:ba, language:be, language:bg, language:bn, language:bo, language:bs, language:ca, language:cs, language:cy, language:da, language:de, language:dv, language:dz, language:ee, language:el, language:et, language:eu, language:fa, language:fi, language:fil, language:fj, language:fo, language:fr, language:gd, language:gu, language:ha, language:he, language:hi, language:hmn, language:hr, language:hu, language:hy, language:id, language:ig, language:is, language:it, language:ja, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:lb, language:lo, language:lt, language:lv, language:mi, language:mk, language:mn, language:mr, language:ms, language:mt, language:my, language:nb, language:nd, language:ne, language:nl, language:nn, language:ny, language:om, language:oy, language:pa, language:ps, language:pt, language:ro, language:ru, language:rw, language:sd, language:sh, language:shi, language:si, language:sk, language:sl, language:sm, language:sn, language:so, language:sq, language:sr, language:ss, language:st, language:sv, language:sw, language:ta, language:te, language:tg, language:th, language:tk, language:tn, language:to, language:tr, language:tt, language:ty, language:uk, language:ur, language:uz, language:ve, language:vi, language:wo, language:xh, language:yo, language:zh, language:zu, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","translation","Dataset Description NTREX -- News Test References for MT Evaluation from English into a total of 128 target languages. See original GitHub repo for full details. Example of loading: dataset = load_dat","None","af, am, ar, az, ba, be, bg, bn, bo, bs, ca, cs, cy, da, de, dv, dz, ee, el, et, eu, fa, fa, fi, fil, fj, fj, fo, fr, gd, gu, ha, he, hi, hmn, hr, hu, hy, id, ig, is, it, ja, kk, km, kn, ko, ku, ku, ky, lb, lo, lt, lv, mi, mk, mn, mr, ms, ms, mt, my, nb, nd, ne, nl, nn, ny, om, oy, pa, ps, pt, ro, ru, rw, sd, sh, shi, si, sk, sl, sm, sn, so, sq, sr, ss, st, sv, sw, ta, te, tg, th, tk, tn, to, tr, tt, ty, uk, ur, uz, ve, vi, wo, xh, yo, zh, zh, zu","multi","1|9|9|7","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Heng666/Traditional_Chinese-aya_collection","Heng666","2024-02-19 12:47:07","2024-02-19 14:09:17+00:00","afd04452bb43eb2f19670ce84f8f39c3a3e46295","1918","9298","6","None","question-answering, translation, summarization, zero-shot-classification","資料集描述 繁體中文 Aya (Traditional Chinese Aya Chinese TCA)：專注於繁體中文處理的 Aya 集合的精選子集 概述 繁體中文 Aya 是一個精心策劃的資料集，源自 CohereForAI 的綜合 Aya 集合，特別關注繁體中文文本資料。 此資料集結合了來自 CohereForAI/aya_collection，過濾掉除繁體中文、簡體中文內容之外的所有內容。","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"aisingapore/sea-lion-pile","aisingapore","2024-01-25 06:10:44","2024-05-04 02:08:28+00:00","e9a221e819b948eac942a2c6906bf498e3758f06","305","9293","13","None","None","SEA-LION-Pile SEA-LION-Pile is the pretraining data set for SEA-LION, a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. Thi","None","zh, vi, id, ms, tl, my, th, lo, km, ta","multi","None","None","False","False","False","other","None","None","None"
+"shjwudp/chinese-c4","shjwudp","2022-11-15 01:27:26","2023-06-20 11:40:06+00:00","4083a8159b907eaa2c1bb87b1891e14fdf0ad5cf","442","8997","31","language:zh, license:cc-by-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Introduction Chinese-C4 is a clean Chinese internet dataset based on Common Crawl. The dataset is 46.29GB and has undergone multiple cleaning strategies, including Chinese filtering, heuristic cleanin","None","zh","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"clw8998/Coupang-Product-Set-1M","clw8998","2024-10-01 15:04:07","2024-10-01 16:53:12+00:00","593c5ebf77f2e8492e5ebe12976b06d40c47ab9c","1957","8939","0","None","None","How to Load the Dataset You can load this dataset using the Hugging Face datasets library as follows: Make sure you have the datasets library installed: pip install datasets Use the following code to ","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"clw8998/Coupang-Product-Set-2.7M","clw8998","2024-09-25 06:36:22","2024-10-01 16:54:17+00:00","299bd256b667ce6d29051148c623f2662ad0cab1","1957","8919","0","None","None","How to Load the Dataset You can load this dataset using the Hugging Face datasets library as follows: Make sure you have the datasets library installed: pip install datasets Use the following code to ","None","zh","mono","None","None","False","False","False","apache-2.0","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"
+"michaelwzhu/ChatMed_Consult_Dataset","michaelwzhu","2023-04-20 04:53:33","2023-05-05 13:41:10+00:00","e9d27b6c92ff793d1d060acd85b956cc20350b85","204","8693","121","task_categories:text-generation, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical consultation, finetuning","text-generation","Dataset Card for ChatMed Dataset Summary ChatMed-Dataset is a dataset of 110,113 medical query-response pairs (in Chinese) generated by OpenAI's GPT-3.5 engine. The queries are crawled from several on","None","zh","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"svjack/pokemon-blip-captions-en-zh","svjack","2022-10-24 01:59:52","2022-10-31 06:23:03+00:00","4b2859096f19a75f613a7a63183a9fadaa48ba3f","699","8605","40","task_categories:text-to-image, annotations_creators:machine-generated, language_creators:other, multilinguality:multilingual, source_datasets:huggan/few-shot-pokemon, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-to-image","Dataset Card for Pokémon BLIP captions with English and Chinese. Dataset used to train Pokémon text to image model, add a Chinese Column of Pokémon BLIP captions BLIP generated captions for Pokémon im","None","en, zh","bi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"weitianwen/cmath","weitianwen","2023-08-22 06:59:42","2023-10-14 07:27:22+00:00","6a626b7266f7c84fd2d2421ad695ffb34b8f9d69","328","8440","6","None","None","CMATH Introduction We present the Chinese Elementary School Math Word Problems (CMATH) dataset, comprising 1.7k elementary school-level math word problems with detailed annotations, source from actual","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2306.16636","None","None"
+"alvanlii/cantonese-youtube","alvanlii","2024-04-24 02:06:07","2024-11-07 15:26:50+00:00","0ffff93130a88f25aaa027c10d07a16f051372d2","286","8113","25","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:zh, language:yue, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition, audio-classification","Cantonese Youtube Pseudo-Transcription Dataset Contains approximately 10k hours of audio sourced from YouTube Videos are chosen at random, and scraped on a channel basis Includes news, vlogs, entertai","None","zh, yue","bi","None","None","False","auto","False","None","None","None","None"
+"GEM/RiSAWOZ","GEM","2022-03-02 23:29:22","2022-10-24 15:30:01+00:00","d9287b21928a811281a349655655ee4be964292a","138","8108","7","annotations_creators:crowd-sourced, language_creators:unknown, multilinguality:unknown, source_datasets:original, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us, dialog-response-generation","conversational","RiSAWOZ contains 11.2K human-to-human (H2H) multiturn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H convers","@inproceedings{quan-etal-2020-risawoz, title = ""{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling"", author = ""Quan, Jun and Zhang, Shian and Cao, Qian and Li, Zizhong and Xiong, Deyi"", booktitle = ""Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"", month = nov, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.emnlp-main.67"", pages = ""930--940"", }","zh","mono","u|n|k|n|o|w|n","None","False","False","False","cc-by-4.0","None","None","None"
+"AISHELL/AISHELL-3","AISHELL","2024-01-08 07:41:49","2024-01-08 09:56:11+00:00","f20d5db4a31fe779ef07bb1af4ea92da5c786622","809","7751","5","None","text-to-speech","AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus published by Beijing Shell Shell Technology Co.,Ltd. It can be used to train multi-speaker Text-to-Speech (TTS) system","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2010.11567%7D%5C","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"
+"shibing624/nli-zh-all","shibing624","2023-06-14 05:12:45","2023-06-22 06:39:46+00:00","d9034b79da1dff929a8b9d719a3864e6fb16a7fb","2030","7672","42","None","text-classification, natural-language-inference, semantic-similarity-scoring, text-scoring","The SNLI corpus (version 1.0) is a merged chinese sentence similarity dataset, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE).","https://github.com/shibing624/text2vec","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","natural-language-inference, semantic-similarity-scoring, text-scoring"
+"BelleGroup/school_math_0.25M","BelleGroup","2023-04-02 06:57:09","2023-04-08 03:55:03+00:00","c7e95b64ba529b9233fbfacda65b670cc1c3aabd","176","7658","100","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","School Math 0.25M 内容 包含约25万条由BELLE项目生成的中文数学题数据，包含解题过程。 注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。 样例 { ""instruction"": ""题目：小华手里有一个装满糖果的袋子，共有12个，小明想知道里面有多少颗糖果，于是他问小华：“你手里的糖果袋子里有偶数个糖果吗？”小华回答：","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl-3.0","None","None","None"
+"Nexdata/chinese_dialect","Nexdata","2022-03-02 23:29:22","2024-08-05 03:08:48+00:00","67ab96f4c9d52a6aaa1ca788bc25b69a38ad48f5","65","7620","11","task_categories:automatic-speech-recognition, language:zh, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","automatic-speech-recognition","Dataset Card for chinese_dialect Dataset Summary The dataset contains 25,000 hours of Chinese Dialect speech data. It's collected from local dialect speakers in multiple dialect regions, covering Hokk","None","zh","mono","None","None","False","False","False","None","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"
+"Nexdata/mixed_speech_chinese_english","Nexdata","2022-03-02 23:29:22","2024-08-05 03:07:44+00:00","95a0151cb56f5eed8829713f1042f31826ad44d1","134","7446","7","task_categories:automatic-speech-recognition, language:zh, language:en, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","automatic-speech-recognition","Dataset Card for mixed_speech_chinese_english Dataset Summary The dataset contains 2,000 hours of mixed speech with Chinese and English. The data is collected from speakers in 26 provinces like Henan,","None","zh, en","bi","None","None","False","False","False","None","None","None","None"
+"erhwenkuo/ceval-exam-zhtw","erhwenkuo","2023-10-08 12:22:42","2023-10-10 02:14:55+00:00","78696beeba651e3e93c5727a62539a96a277b0cd","775","7344","0","language:zh, license:cc, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.08322, region:us, ""llm-eval""","None","Dataset Card for ""ceval-exam-zhtw"" C-Eval 是一個針對基礎模型的綜合中文評估套件。它由 13,948 道多項選擇題組成，涵蓋 52 個不同的學科和四個難度級別。原始網站和 GitHub 或查看論文以了解更多詳細資訊。 C-Eval 主要的數據都是使用簡體中文來撰寫并且用來評測簡體中文的 LLM 的效能來設計的，本數據集使用 OpenCC 來進行簡繁的中文轉換","None","zh","mono","None","None","False","False","False","cc","2305.08322","None","None"
+"cryptom/ceval-exam","cryptom","2023-06-23 18:40:37","2023-06-24 00:40:14+00:00","9e75d2d1688e550d628d1cb66e311427c4fe8141","764","7186","1","task_categories:text-classification, task_categories:multiple-choice, task_categories:question-answering, language:zh, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, arxiv:2305.08322, region:us","text-classification, multiple-choice, question-answering","C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels.","@article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} }","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2305.08322","None","None"
+"RicardoRei/wmt-mqm-human-evaluation","RicardoRei","2023-02-16 17:14:16","2023-02-16 18:29:11+00:00","c299be4298b58f71d60f2718273b7c0d64d3aacd","60","7162","0","language:en, language:de, language:ru, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mt-evaluation, WMT","None","Dataset Summary This dataset contains all MQM human annotations from previous WMT Metrics shared tasks and the MQM annotations from Experts, Errors, and Context. The data is organised into 8 columns: ","None","en, de, ru, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"BelleGroup/train_2M_CN","BelleGroup","2023-04-08 02:58:58","2023-04-08 03:33:28+00:00","ec36674003af1c01b3758e3fdab0b054119bfa3f","188","7080","106","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","内容 包含约200万条由BELLE项目生成的中文指令数据。 样例 { ""instruction"": ""将以下三个句子组合成一个有意义的段落。\\n狗是人类最好的朋友。它们非常聪明，可以进行各种活动。如果你喜欢散步，狗可以成为你一起散步的伙伴。"", ""input"": """", ""output"": ""狗是人类最好的朋友，它们非常聪明，可以进行各种活动。如果你喜欢散步，狗可以成为你一起散步的伙伴。出门散步是","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","gpl-3.0","None","None","None"
+"BAAI/COIG","BAAI","2023-04-16 11:09:32","2023-07-12 15:38:35+00:00","9f25758ec94f82762fb9c09a5c60e908cfb83632","563","7055","430","language:zh, license:apache-2.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, arxiv:2204.07705, arxiv:2212.10560, arxiv:2212.09689, arxiv:2304.07987, region:us","None","We propose the Chinese Open Instruction Generalist (COIG) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contri","@misc{zhang2023chinese, title={Chinese Open Instruction Generalist: A Preliminary Release}, author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu}, year={2023}, eprint={2304.07987}, archivePrefix={arXiv}, primaryClass={cs.CL} }","zh","mono","None","None","False","False","False","apache-2.0","2204.07705","None","None"
+"cyanic-selkie/wikianc","cyanic-selkie","2023-09-05 14:22:32","2023-09-05 14:22:32+00:00","afb42306115c36afbaab9b13fc4545b724ae66c4","496","7023","5","None","token-classification","Dataset Card for WikiAnc Dataset Summary The WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023). The code for generating the dataset","None","en, ceb, de, sv, fr, nl, ru, es, it, arz, pl, ja, zh, vi, uk, war, ar, pt, fa, ca, sr, id, ko, no, ce, fi, cs, tr, hu, tt, sh, ro, eu, ms, eo, he, hy, da, bg, cy, sk, azb, uz, et, be, kk, min, el, hr, lt, gl, az, ur, sl, lld, ka, nn, hi, th, ta, bn, la, mk, ast, lv, af, tg, my, mg, mr, sq, bs, oc, te, ml, nds, br, ky, sw, jv, lmo, new, pnb, vec, ht, pms, ba, lb, su, ku, ga, szl, is, fy, cv, ckb, pa, tl, an, wuu, diq, io, sco, vo, yo, ne, ia, kn, gu, als, ha, avk, bar, crh, scn, bpy, qu, mn, nv, xmf, ban, si, tum, ps, ig, frr, os, mzn, or, sah, cdo, gd, bug, yi, sd, ilo, am, nap, li, bcl, fo, gor, hsb, mai, shn, eml, ace, sa, as, wa, ie, hyw, lij, mhr, zu, sn, hif, mrj, bjn, km, mni, hak, pam, sat, rue, nso, bh, so, mi, se, myv, vls, dag, sc, co, ary, kw, bo, vep, glk, tk, kab, gan, rw, ab, gv, ug, nah, zea, skr, frp, udm, pcd, mt, kv, csb, gn, smn, ay, nrm, ks, lez, lfn, olo, mwl, lo, stq, ang, mdf, fur, rm, lad, kaa, gom, ext, koi, tyv, pap, av, dsb, ln, dty, tw, dv, ksh, za, gag, bxr, pfl, lg, szy, pag, blk, pi, tay, haw, awa, inh, krc, xal, pdc, to, atj, tcy, arc, mnw, shi, jam, kbp, wo, anp, kbd, nia, om, nov, ki, nqo, bi, xh, tpi, ff, tet, jbo, fj, kg, lbe, ty, cu, guw, trv, ami, srn, sm, mad, alt, ltg, gcr, chr, tn, ny, st, pih, got, rmy, ee, pcm, bm, ss, gpe, ts, ve, kcg, chy, rn, ch, gur, ik, ady, fat, pnt, guc, iu, pwn, sg, din, ti, kl, dz, cr","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Cohere/miracl-zh-queries-22-12","Cohere","2023-01-31 13:38:51","2023-02-06 11:55:33+00:00","125f282756795fe4c1a4ba1a80cbf4434c48835b","78","6775","30","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","MIRACL (zh) 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","zh","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"kuroneko5943/jd21","kuroneko5943","2023-01-10 10:49:13","2023-01-10 15:51:26+00:00","c575fe89f5693adc2473cb876751bd9d09fd6a26","953","6460","6","task_categories:text-classification, task_ids:sentiment-classification, annotations_creators:found, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:zh, license:apache-2.0, size_categories:100K<n<1M, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us, jd","text-classification, sentiment-classification","GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.","\\","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","sentiment-classification"
+"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"
+"Mathoctopus/MSVAMP","Mathoctopus","2023-10-17 09:29:55","2023-10-17 10:48:46+00:00","301e2b3b168be70058c89c21c2fbdc9262102add","165","6070","4","task_categories:text-generation, language:bn, language:zh, language:en, language:fr, language:de, language:ja, language:ru, language:es, language:sw, language:th, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","None","None","bn, zh, en, fr, de, ja, ru, es, sw, th","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"sentence-transformers/mldr","sentence-transformers","2024-06-19 14:53:42","2024-06-19 16:22:10+00:00","40ad7672817ebee49e00dd25aed00e1c401881d6","281","6064","3","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:ar, language:de, language:en, language:es, language:fr, language:hi, language:it, language:ja, language:ko, language:pt, language:ru, language:th, language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for MLDR This is a reformatting of the MLDR dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets ...-triplet subset Columns: ""anchor""","None","ar, de, en, es, fr, hi, it, ja, ko, pt, ru, th, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"shibing624/alpaca-zh","shibing624","2023-03-25 11:37:25","2023-05-10 06:09:06+00:00","f39db019a94f8dbea48ab30d2bdc090703284559","283","6039","95","task_categories:text-generation, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.03277, region:us, gpt, alpaca, fine-tune, instruct-tune, instruction","text-generation","Dataset Card for ""alpaca-zh"" 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 Dataset from https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM It is the chinese dataset from https://github.com/Ins","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2304.03277","None","None"
+"styletts2-community/multilingual-pl-bert","styletts2-community","2023-12-17 05:03:40","2024-01-08 04:30:44+00:00","6e96d52b3593ed2ea27fa69002ca603804f5b2e7","1344","6013","15","None","None","Attribution: Wikipedia.org","None","af, an, ar, az, ba, be, bg, bn, bpy, bs, ca, cs, cy, da, de, el, es, et, eu, fi, fr, gu, hak, he, hi, hr, hu, hy, hyw, id, io, is, it, ja, ka, kk, kn, ko, la, lb, lt, lv, mk, ml, mr, ms, ne, nl, no, pa, pl, pt, ro, ru, sk, sl, sq, sr, sv, sw, ta, te, th, tr, tt, ur, uz, vi, zh","multi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"google/wit","google","2022-05-02 11:22:32","2022-07-04 10:47:07+00:00","b52b6486b38d74ddaf95626b15e0f0c50fa5e959","153","5991","48","task_categories:text-retrieval, task_categories:image-to-text, task_ids:image-captioning, annotations_creators:machine-generated, language_creators:found, multilinguality:multilingual, source_datasets:original, source_datasets:extended|wikipedia, language:af, language:ar, language:ast, language:azb, language:be, language:bg, language:bn, language:br, 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:gl, language:hr, language:hu, language:hy, language:id, language:it, language:iw, language:ja, language:ka, language:ko, language:la, language:lt, language:lv, language:mk, language:ml, language:ms, language:nl, language:nn, language:no, language:pl, language:pt, language:ro, language:ru, language:sk, language:sl, language:sr, language:sv, language:th, language:tr, language:uk, language:ur, language:vi, language:vo, language:zh, license:cc-by-sa-3.0, size_categories:1M<n<10M, modality:image, modality:tabular, modality:text, library:datasets, library:mlcroissant, arxiv:2103.01913, region:us","text-retrieval, image-to-text, text-retrieval-other-text-image-retrieval, image-captioning","Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images ac","@article{srinivasan2021wit, title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning}, author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc}, journal={arXiv preprint arXiv:2103.01913}, year={2021} }","af, ar, ast, azb, be, bg, bn, br, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, hr, hu, hy, id, it, iw, ja, ka, ko, la, lt, lv, mk, ml, ms, nl, nn, no, pl, pt, ro, ru, sk, sl, sr, sv, th, tr, uk, ur, vi, vo, zh","multi","1|0|M|<|n|<|1|0|0|M","wit","False","False","False","cc-by-sa-3.0","2103.01913","None","text-retrieval-other-text-image-retrieval, image-captioning"
+"BAAI/COIG-PC","BAAI","2023-06-08 05:41:11","2024-06-14 01:17:46+00:00","4ca2778d69d7a9c17a0a6c1668c6e989b102176b","158","5746","267","None","None","COIG Prompt Collection License Default Licensing for Sub-Datasets Without Specific License Declaration: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declar","None","zh","mono","None","None","False","auto","False","unknown","None","None","None"
+"silk-road/alpaca-data-gpt4-chinese","silk-road","2023-05-23 02:10:49","2023-05-23 05:33:21+00:00","81a6dfd72f416aff605e7d189bfbbc46a2511fee","167","5743","92","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, gpt, alpaca, fine-tune","text-generation","None","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","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"
+"DAMO-NLP-SG/MultiJail","DAMO-NLP-SG","2023-10-13 07:54:21","2023-10-13 07:56:04+00:00","93d65c778925d55973b9ff30c7bccc8545494eb6","74","5573","6","language:en, language:zh, language:it, language:vi, language:ar, language:ko, language:th, language:bn, language:sw, language:jv, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.06474, region:us","conversational","Multilingual Jailbreak Challenges in Large Language Models This repo contains the data for our paper ""Multilingual Jailbreak Challenges in Large Language Models"". [Github repo] Annotation Statistics W","None","en, zh, it, vi, ar, ko, th, bn, sw, jv","multi","n|<|1|K","None","False","False","False","mit","2310.06474","None","None"
+"BUAADreamer/llava-en-zh-300k","BUAADreamer","2024-04-26 11:37:11","2024-09-02 14:20:59+00:00","9d86f91fbf213e01bf3edf313ad42600eec20ed8","656","5526","20","task_categories:text-generation, task_categories:visual-question-answering, language:en, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, llama-factory","text-generation, visual-question-answering","This dataset is composed by 150k examples of English Visual Instruction Data from LLaVA. 150k examples of English Visual Instruction Data from openbmb. You can use it in LLaMA Factory by specifying --","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"FreedomIntelligence/CMB","FreedomIntelligence","2023-07-20 09:08:03","2024-04-05 16:10:47+00:00","935fbc09edf1303d89872b21265ff597f426ac0d","1327","5511","24","None","question-answering, text-generation","CMB: A Comprehensive Medical Benchmark in Chinese 🌐 Github • 🌐 Website • 🤗 HuggingFace 🌈 Update [2024.02.21] The answers to the CMB-Exam test has been updated and some errors caused by omissions in ve","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2308.08833","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"
+"BAAI/IndustryCorpus_finance","BAAI","2024-07-25 05:46:36","2024-07-26 02:30:38+00:00","764779f2d1db565f055df82e9acd3685409664ff","385","5332","11","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"sentence-transformers/miracl","sentence-transformers","2024-06-19 14:20:16","2024-06-20 13:50:24+00:00","07e2b629250bf4185f4c87f640fac15949b8aa73","385","5263","2","task_categories:feature-extraction, task_categories:sentence-similarity, language:en, language:ar, language:bn, language:es, language:fa, language:fi, language:fr, language:hi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for MIRACL This is a reformatting of the MIRACL dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets ...-triplet subset Columns: ""anc","None","en, ar, bn, es, fa, fi, fr, hi, id, ja, ko, ru, sw, te, th, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"qgyd2021/h_novel","qgyd2021","2023-08-28 06:00:39","2023-08-31 08:38:01+00:00","184b56e3cc07cec96a71bf1844ae474c1b960bb3","1012","5195","48","None","text-generation","This dataset contains some SQ novel. It is supposed to be used for text generation tasks.","@dataset{h_novel, author = {Xing Tian}, title = {h_novel}, month = aug, year = 2023, publisher = {Xing Tian}, version = {1.0}, }","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"BelleGroup/multiturn_chat_0.8M","BelleGroup","2023-04-02 08:55:44","2023-04-02 09:15:32+00:00","dc32eb8955e10ae1cd4189a50a566ef8f8254a65","141","5169","135","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","Multiturn Chat 0.8M 内容 包含约80万条由BELLE项目生成的用户与助手的多轮对话。 注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。 instruction中包含多轮对话的上文内容，以Human:和Assistant:区分，output中包含当前助手角色的回答。 样例 { ""instruction"":… See the ful","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl-3.0","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","task_categories:feature-extraction, task_categories:sentence-similarity, language:en, language:multilingual, language:ar, language:bg, language:ca, language:cs, language:da, language:de, language:el, language:es, language:et, language:fa, language:fi, language:fr, language:gl, language:gu, language:he, language:hi, language:hu, language:hy, language:id, language:it, language:ja, language:ka, language:ko, language:ku, language:lt, language:lv, language:mk, language:mn, language:mr, language:ms, language:my, language:nb, language:nl, language:pl, language:pt, language:ro, language:ru, language:sk, language:sl, language:sq, language:sr, language:sv, language:th, language:tr, language:uk, language:ur, language:vi, language:zh, size_categories:1M<n<10M, modality:text, region:us, sentence-transformers","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"
+"BoKelvin/SLAKE","BoKelvin","2024-02-18 04:57:44","2024-02-28 09:17:06+00:00","a9083ce6c34ac3ffb17671a605962924d8a8f9e9","496","5026","15","task_categories:visual-question-answering, language:en, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:image, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","visual-question-answering","Dataset Info: SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering [ISBI 2021 oral] Project Page: click Corresponding Authors: Bo Liu, Xiao-Ming Wu Any questi","None","en, zh","bi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"espnet/floras","espnet","2024-10-24 01:40:36","2024-11-29 20:12:19+00:00","0cc744b3fad853c054031df2ef110c51f28a78e5","1606","4998","4","None","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"
+"FredZhang7/toxi-text-3M","FredZhang7","2023-06-28 23:28:34","2025-01-15 02:04:46+00:00","4187a3be58f166c05c43aff58c60eec7e9fba4cf","769","4994","23","task_categories:text-classification, task_categories:token-classification, task_categories:zero-shot-classification, language:ar, language:es, language:pa, language:th, language:et, language:fr, language:fi, language:hu, language:lt, language:ur, language:so, language:pl, language:el, language:mr, language:sk, language:gu, language:he, language:af, language:te, language:ro, language:lv, language:sv, language:ne, language:kn, language:it, language:mk, language:cs, language:en, language:de, language:da, language:ta, language:bn, language:pt, language:sq, language:tl, language:uk, language:bg, language:ca, language:sw, language:hi, language:zh, language:ja, language:hr, language:ru, language:vi, language:id, language:sl, language:cy, language:ko, language:nl, language:ml, language:tr, language:fa, language:no, language:multilingual, license:apache-2.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, nlp, moderation","text-classification, token-classification, zero-shot-classification","This is a large multilingual toxicity dataset with 3M rows of text data from 55 natural languages, all of which are written/sent by humans, not machine translation models. The preprocessed training da","None","ar, es, pa, th, et, fr, fi, hu, lt, ur, so, pl, el, mr, sk, gu, he, af, te, ro, lv, sv, ne, kn, it, mk, cs, en, de, da, ta, bn, pt, sq, tl, uk, bg, ca, sw, hi, zh, ja, hr, ru, vi, id, sl, cy, ko, nl, ml, tr, fa, no, multilingual","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"b3x0m/Chinese-H-Novels","b3x0m","2023-11-27 17:19:10","2024-07-12 02:32:57+00:00","16258fb735f019d2d0100960ec739b6dabc3db77","917","4932","166","None","text-classification, summarization, token-classification, text2text-generation, question-answering, text-generation, fill-mask, sentence-similarity","Update 12/07/2024: convert to parquet to download easier. Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make. (͡ ° ͜ʖ ͡ °) tags: socks, garter ","None","zh","mono","1|B|<|n|<|1|0|B","None","False","False","False","None","None","None","None"
+"kuroneko5943/stock11","kuroneko5943","2023-01-10 12:13:05","2023-01-16 04:11:18+00:00","40c2688bda0d2226838051d6ab9bf07be32565bf","262","4893","9","task_categories:text-classification, task_ids:sentiment-classification, annotations_creators:machine-generated, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:zh, license:apache-2.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us, stock","text-classification, sentiment-classification","GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.","\\","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","sentiment-classification"
+"BelleGroup/generated_chat_0.4M","BelleGroup","2023-04-08 03:36:11","2023-04-08 03:55:12+00:00","ee65b6708272f40606d1a0351e8cad18c3e604a3","90","4737","65","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","Generated_Chat_0.4M 内容 包含约40万条由BELLE项目生成的个性化角色对话数据，包含角色介绍。 注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。 样例 { ""instruction"": ""于以下角色信息完成一段对话\\nAlice是一个年轻的律师，她正在为一个涉及濒危物种保护的案件做准备。Bob是一位当地的居民，他所在","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl-3.0","None","None","None"
+"Muennighoff/xP3x-sample","Muennighoff","2023-07-06 09:42:03","2023-09-18 13:51:06+00:00","70365cee44e6b6cd1ad5f76a052aefa6bbd667b7","619","4732","3","task_categories:other, annotations_creators:expert-generated, annotations_creators:crowdsourced, multilinguality:multilingual, language:af, language:ar, language:az, language:be, language:bg, language:bn, language:br, language:bs, language:ca, language:ch, language:cs, language:cv, language:cy, language:da, language:de, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fo, language:fr, language:fy, language:ga, language:gd, language:gl, language:gn, language:he, language:hi, language:hr, language:hu, language:hy, language:ia, language:id, language:ie, language:io, language:is, language:it, language:ja, language:jv, language:ka, language:kk, language:km, language:ko, language:ku, language:kw, language:la, language:lb, language:lt, language:lv, language:mi, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:my, language:nb, language:nl, language:nn, language:no, language:oc, language:pl, language:pt, language:qu, language:rn, language:ro, language:ru, language:sh, language:sl, language:sq, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:tk, language:tl, language:tr, language:tt, language:ug, language:uk, language:ur, language:uz, language:vi, language:vo, language:yi, language:zh, language:ace, language:acm, language:acq, language:aeb, language:ajp, language:ak, language:als, language:am, language:apc, 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:bem, language:bho, language:bjn, language:bo, language:bug, language:ceb, language:cjk, language:ckb, language:crh, language:dik, language:dyu, language:dz, language:ee, language:fj, language:fon, language:fur, language:fuv, language:gaz, language:gu, language:ht, language:ha, language:hne, language:ig, language:ilo, language:kab, language:kac, language:kam, language:kn, language:ks, language:kbp, language:kea, language:khk, language:ki, language:rw, language:ky, language:kmb, language:kmr, language:knc, language:kg, language:lo, language:lij, language:li, language:ln, language:lmo, language:ltg, language:lua, language:lg, language:luo, language:lus, language:lvs, language:mag, language:mai, language:mar, language:min, language:mni, language:mos, language:npi, language:nso, language:nus, language:ny, language:ory, language:pag, language:pa, language:pap, language:pbt, language:pes, language:plt, language:prs, language:quy, language:sg, language:sa, language:sat, language:scn, language:shn, language:si, language:sk, language:sm, language:sn, language:sd, language:so, language:st, language:sc, language:ss, language:su, language:swh, language:szl, language:taq, language:tg, language:ti, language:tpi, language:tn, language:ts, language:tum, language:tw, language:tzm, language:umb, language:uzn, language:vec, language:war, language:wo, language:xh, language:ydd, language:yo, language:yue, language:zsm, language:zu, license:apache-2.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us","other","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} }","af, ar, az, be, bg, bn, br, bs, ca, ch, cs, cv, cy, da, de, el, en, eo, es, et, eu, fa, fi, fo, fr, fy, ga, gd, gl, gn, he, hi, hr, hu, hy, ia, id, ie, io, is, it, ja, jv, ka, kk, km, ko, ku, kw, la, lb, lt, lv, mi, mk, ml, mn, mr, ms, mt, my, nb, nl, nn, no, oc, pl, pt, qu, rn, ro, ru, sh, sl, sq, sr, sv, sw, ta, te, th, tk, tl, tr, tt, ug, uk, ur, uz, vi, vo, yi, zh, 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|M|<|n|<|1|B","None","False","False","False","apache-2.0","None","None","None"
+"llm-wizard/alpaca-gpt4-data-zh","llm-wizard","2023-04-07 19:22:10","2023-05-03 17:56:55+00:00","8fc9416222d501c4584b240d19b14f9c950b27e3","204","4669","60","task_categories:text-generation, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.03277, region:us, gpt, alpaca, fine-tune, instruct-tune, instruction","text-generation","Dataset Card for ""alpaca-gpt4-data-zh"" All of the work is done by this team. Usage and License Notices The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing on","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2304.03277","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"
+"AkiraChisaka/sizefetish-jp2cn-translated-text","AkiraChisaka","2024-02-14 02:03:13","2024-06-23 00:35:44+00:00","3480fb7b0bcf227bfdabbe7016b044d57ef9c045","2092","4520","4","None","None","None","None","zh, ja","bi","None","None","False","False","False","None","None","None","None"
+"tomaarsen/MultiCoNER","tomaarsen","2023-10-01 18:44:19","2023-10-01 19:39:19+00:00","04fafbabb7a36defcfd400ed2fe5504c10fdf4ab","388","4513","4","task_categories:token-classification, language:bn, language:de, language:en, language:es, language:fa, language:hi, language:ko, language:nl, language:ru, language:tr, language:zh, language:multilingual, license:cc-by-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2208.14536, region:us, multiconer, ner, multilingual, named entity recognition","token-classification","We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and ","@misc{malmasi2022multiconer, title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko}, year={2022}, eprint={2208.14536}, archivePrefix={arXiv}, primaryClass={cs.CL} }","bn, de, en, es, fa, hi, ko, nl, ru, tr, zh, multilingual","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese","Magpie-Align","2024-06-25 20:40:57","2024-08-22 21:12:11+00:00","ee71d5dbafec166eac8f5dcf9e581c90c56ce631","675","4509","70","task_categories:question-answering, language:zh, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","question-answering","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","zh","mono","None","None","False","False","False","None","2406.08464","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"
+"gsarti/iwslt2017_context","gsarti","2023-05-07 14:03:04","2023-05-07 14:09:24+00:00","fc1e097200cdb0995650174e610e065fbcfe5d91","135","4441","1","task_categories:translation, annotations_creators:crowdsourced, language_creators:expert-generated, multilinguality:translation, source_datasets:original, language:ar, language:de, language:en, language:fr, language:it, language:ja, language:ko, language:nl, language:ro, language:zh, license:cc-by-nc-nd-4.0, size_categories:1M<n<10M, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us","translation","The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. As u","@inproceedings{cettolo-etal-2017-overview, title = ""Overview of the {IWSLT} 2017 Evaluation Campaign"", author = {Cettolo, Mauro and Federico, Marcello and Bentivogli, Luisa and Niehues, Jan and St{\\\\""u}ker, Sebastian and Sudoh, Katsuhito and Yoshino, Koichiro and Federmann, Christian}, booktitle = ""Proceedings of the 14th International Conference on Spoken Language Translation"", month = dec # "" 14-15"", year = ""2017"", address = ""Tokyo, Japan"", publisher = ""International Workshop on Spoken Language Translation"", url = ""https://aclanthology.org/2017.iwslt-1.1"", pages = ""2--14"", }","ar, de, en, fr, it, ja, ko, nl, ro, zh","multi","1|M|<|n|<|1|0|M","iwslt-2017","False","False","False","cc-by-nc-nd-4.0","None","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"
+"HIT-TMG/Hansel","HIT-TMG","2022-12-19 13:28:24","2023-03-13 11:52:56+00:00","56c0215d08fb430e30f8425ffc80ec0ad1682e0f","1077","4394","8","None","text-retrieval, entity-linking-retrieval","Hansel is a high-quality human-annotated Chinese entity linking (EL) dataset, used for testing Chinese EL systems' generalization ability to tail entities and emerging entities. The test set contains ","@misc{xu2022hansel, title = {Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark}, author = {Xu, Zhenran and Shan, Zifei and Li, Yuxin and Hu, Baotian and Qin, Bing}, publisher = {arXiv}, year = {2022}, url = {https://arxiv.org/abs/2207.13005} }","zh","mono","1|M|<|n|<|1|0|M|||1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2207.13005","None","entity-linking-retrieval"
+"BAAI/IndustryCorpus_law","BAAI","2024-07-25 05:46:35","2024-07-26 02:30:36+00:00","0059977eac22344b6226182adc7611dc2249f001","167","4273","2","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"LooksJuicy/ruozhiba","LooksJuicy","2024-04-09 09:02:31","2024-04-09 09:10:55+00:00","2a39d86721e0109a7c598a25a1338e297c639d2f","715","4201","250","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","受COIG-CQIA启发，构建类似数据集，但答案风格相对更简洁。 弱智吧精选问题数据来自github提供的疑问句，调用GPT-4获取答案，并过滤掉明显拒答的回复。","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"styletts2-community/multilingual-phonemes-10k-alpha","styletts2-community","2023-11-27 23:15:49","2024-03-05 03:22:26+00:00","c1b20c355b17b54c6120cb54f89eefee6403967c","531","4108","18","language:en, language:ca, language:de, language:es, language:el, language:fa, language:fi, language:fr, language:it, language:pl, language:pt, language:ru, language:sv, language:uk, language:zh, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic","None","Multilingual Phonemes 10K Alpha This dataset contains approximately 10,000 pairs of text and phonemes from each supported language. We support 15 languages in this dataset, so we have a total of ~150K","None","en, ca, de, es, el, fa, fi, fr, it, pl, pt, ru, sv, uk, zh","multi","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Mutonix/Vript_Chinese","Mutonix","2024-06-10 07:19:51","2024-10-16 02:45:22+00:00","75d384ee4a7fccbbc6671bd04def69921bc875ff","612","4080","10","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:zh, 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 44.7K annotated high-resolution videos (~293k clips) in Chinese. The annotation ","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"p208p2002/wudao","p208p2002","2023-09-19 01:35:45","2024-05-09 08:14:10+00:00","9c0ca17ad611eab54d10e7bc24d68064fd0ebae8","492","4062","10","task_categories:text-generation, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","悟道(WuDao)資料集 非原製作者，僅搬移與封裝成 HF Dataset 格式方便使用。 此資料集下載約需要125GB(.parquet壓縮)，對應悟道220G版本。 如果使用此資料集，請引用原作者： @misc{ c6a3fe684227415a9db8e21bac4a15ab, author = {Zhao Xue and Hanyu Zhao and Sha Yuan and Yequan","None","zh","mono","n|>|1|T","None","False","False","False","None","None","None","None"
+"sentence-transformers/parallel-sentences-wikimatrix","sentence-transformers","2024-04-30 11:55:03","2024-06-18 19:46:03+00:00","74a4cb15422cdd0c3aacc93593b6cb96a9b9b3a9","659","4016","5","task_categories:feature-extraction, task_categories:sentence-similarity, language:en, language:multilingual, language:ar, language:bg, language:ca, language:cs, language:da, language:de, language:el, language:es, language:et, language:fa, language:fi, language:fr, language:gl, language:he, language:hi, language:hr, language:id, language:it, language:ja, language:ka, language:ko, language:lt, language:mk, language:mr, language:nl, language:pl, language:pt, language:ro, language:ru, language:sk, language:sl, language:sq, language:sr, language:uk, language:vi, language:zh, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - WikiMatrix This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sen","None","en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, he, hi, hr, id, it, ja, ka, ko, lt, mk, mr, nl, pl, pt, ro, ru, sk, sl, sq, sr, uk, vi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"kuroneko5943/weibo16","kuroneko5943","2023-01-10 14:39:35","2023-01-10 16:01:32+00:00","ba7671f1624bfaa5f45cc2efeda234a776eb6315","933","3970","7","task_categories:text-classification, task_ids:sentiment-classification, annotations_creators:machine-generated, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:zh, license:apache-2.0, size_categories:10K<n<100K, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us, weibo, sentiment","text-classification, sentiment-classification","GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.","\\","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","sentiment-classification"
+"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"
+"BAAI/IndustryCorpus_technology","BAAI","2024-07-25 05:46:37","2024-07-26 02:30:40+00:00","2e6a80a5196e0f67daaf12b83988c3af2621cc1a","200","3913","2","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"OpenGVLab/InternVL-Chat-V1-2-SFT-Data","OpenGVLab","2024-08-08 06:05:00","2024-09-20 10:12:14+00:00","589b010050d3b530012466ecd6dc9b61a772b739","714","3899","17","task_categories:visual-question-answering, task_categories:question-answering, language:en, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2312.14238, arxiv:2404.16821, region:us","visual-question-answering, question-answering","Data Card for InternVL-Chat-V1-2-SFT-Data Overview Inspired by LLaVA-NeXT, we adopted a data-efficient SFT strategy to train InternVL-Chat-V1-2, utilizing approximately 1.2M of visual instruction tuni","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Babelscape/REDFM","Babelscape","2023-06-13 16:46:41","2023-06-20 07:33:35+00:00","eae75dc9bde14d1172351423bcbb82c5b9a94f66","326","3861","8","task_categories:token-classification, language:ar, language:de, language:en, language:es, language:it, language:fr, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, arxiv:2306.09802, region:us","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, de, en, es, it, fr, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2306.09802","None","None"
+"BUAADreamer/llava-en-zh-2k","BUAADreamer","2024-05-20 16:07:08","2024-09-02 13:19:23+00:00","fba994c834822bddd3cd79e929c33135f4289d2b","254","3694","7","task_categories:visual-question-answering, language:zh, language:en, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama-factory","visual-question-answering","This dataset is composed by 1k examples of English Visual Instruction Data from LLaVA. 1k examples of English Visual Instruction Data from openbmb. You can organize content in the dataset_info.json in","None","zh, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"BelleGroup/train_3.5M_CN","BelleGroup","2023-05-10 07:45:23","2023-08-16 06:13:59+00:00","e3277973eb38ce39e2f2b6ed22d14909d3dba2c6","171","3680","129","task_categories:text2text-generation, language:zh, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","内容 包含约350万条由BELLE项目生成的中文指令数据。 样例 { ""id"":""66623028"", ""conversations"":[ { ""from"":""human"", ""value"":""归纳介绍三种不同类型的电影题材"" }, { ""from"":""assistant"", ""value"":""1. 动作片：动作片通常强调动作和战斗场面，故事情节紧凑，充满了紧张和兴奋的氛围。主角一般是具有超凡实力","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","gpl-3.0","None","None","None"
+"textdetox/multilingual_paradetox","textdetox","2024-02-01 16:31:13","2024-10-29 14:56:40+00:00","eda7669aba470826a44d43c50e6691a8ccbf6d06","166","3618","8","task_categories:text-generation, language:en, language:uk, language:ru, language:de, language:zh, language:am, language:ar, language:hi, language:es, license:openrail++, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","MultiParaDetox This is the multilingual parallel dataset for the text detoxification task prepared for CLEF TextDetox 2024 shared task. For each of 9 languages, we collected 1k pairs of toxic<->detoxi","None","en, uk, ru, de, zh, am, ar, hi, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","openrail++","None","None","None"
+"hltcoe/megawika-report-generation","hltcoe","2023-11-10 13:17:09","2024-01-19 13:01:58+00:00","bc25fe13ccf7722db3b8e6f79dc4e8b2f47f62d6","412","3549","6","task_categories:summarization, task_categories:text-retrieval, task_categories:text-generation, task_categories:text2text-generation, language:af, language:ar, language:az, language:bn, language:cs, language:de, language:en, language:es, language:et, language:fa, language:fi, language:fr, language:ga, language:gl, language:gu, language:he, language:hi, language:hr, language:id, language:it, language:ja, language:ka, language:kk, language:km, language:ko, language:lt, language:lv, language:mk, language:ml, language:mn, language:mr, language:my, language:ne, language:nl, language:pl, language:ps, language:pt, language:ro, language:ru, language:si, language:sl, language:sv, language:ta, language:th, language:tr, language:uk, language:ur, language:vi, language:xh, language:zh, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2307.07049, region:us","summarization, text-retrieval, text-generation, text2text-generation","Dataset Card for MegaWika for Report Generation Dataset Summary MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations","None","af, ar, az, bn, cs, de, en, es, et, fa, fi, fr, ga, 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, ta, th, tr, uk, ur, vi, xh, zh","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"FreedomIntelligence/Huatuo26M-Lite","FreedomIntelligence","2023-10-11 09:08:49","2023-11-29 08:46:31+00:00","90ce61699e90568db82cdce4c4035c6918d70aa6","229","3515","34","task_categories:text-classification, task_categories:question-answering, task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.01526, region:us, medical","text-classification, question-answering, conversational, text-generation","Huatuo26M-Lite 📚 Table of Contents 🗂 Dataset Description 📝 Dataset Information ℹ️ Data Distribution 📊 Usage 🔧 Citation 📖 Dataset Description 📝 Huatuo26M-Lite is a refined and optimized dataset based o","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"talkbank/callhome","talkbank","2024-04-11 15:18:46","2024-04-28 19:53:45+00:00","17c8a153215aa7c50b805078fd6284ba81c2fc47","658","3509","16","language:en, language:ja, language:zh, language:de, language:es, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, speaker-diarization, speaker-segmentation, voice-activity-detection","None","Dataset Card for the Callhome dataset for speaker diarization The CALLHOME Corpus is a collection of unscripted telephone conversations between native speakers in Chinese, English, German, Japanese an","None","en, ja, zh, de, es","multi","None","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"Unbabel/TowerBlocks-v0.2","Unbabel","2024-01-24 13:59:56","2024-03-04 13:17:35+00:00","0c00a3ae8ecaf841e38e3c87968d62d28f1a3a12","86","3498","11","language:en, language:de, language:fr, language:zh, language:pt, language:nl, language:ru, language:ko, language:it, language:es, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.17733, region:us","conversational","Dataset Card for TowerBlocks TowerBlocks is the dataset used to train TowerInstruct-v0.1, a language model specialized for translation tasks such as machine translation (e.g. general, document, termin","None","en, de, fr, zh, pt, nl, ru, ko, it, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"RicardoRei/wmt-sqm-human-evaluation","RicardoRei","2023-02-17 10:42:46","2023-02-17 11:10:39+00:00","6104284646a7a83f493f1825830c4b13f751ea2a","62","3493","0","language:cs, language:de, language:en, language:hr, language:ja, language:liv, language:ru, language:sah, language:uk, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mt-evaluation, WMT, 12-lang-pairs","None","Dataset Summary In 2022, several changes were made to the annotation procedure used in the WMT Translation task. In contrast to the standard DA (sliding scale from 0-100) used in previous years, in 20","None","cs, de, en, hr, ja, liv, ru, sah, uk, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"wenge-research/yayi2_pretrain_data","wenge-research","2023-12-25 05:15:47","2023-12-29 08:40:24+00:00","b6c57978d5842da1def32589218ea571f2bf1dd1","516","3460","52","language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2312.14862, region:us","None","介绍/Introduction 本数据集源自雅意训练语料，我们精选了约100B数据，数据大小约为500GB。我们期望通过雅意预训练数据的开源推动中��预训练大模型开源社区的发展，并积极为此贡献力量。通过开源，我们与每一位合作伙伴共同构建雅意大模型生态。 We opensource the pre-trained dataset in this release, it should contain m","None","zh, en","bi","1|0|0|B|<|n|<|1|T","None","False","False","False","apache-2.0","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","task_categories:text-classification, language:en, language:ru, language:uk, language:de, language:es, language:am, language:zh, language:ar, language:hi, license:openrail++, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2404.17841, region:us","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"
+"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"
+"wckwan/M4LE","wckwan","2023-10-28 00:34:52","2024-07-25 16:40:52+00:00","ea18d0e989f8f75d0f4d237967d3d8d359059e6e","300","3374","3","task_categories:question-answering, task_categories:translation, task_categories:summarization, task_categories:text-classification, task_categories:text-retrieval, language:en, language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.19240, region:us, Long Context","question-answering, translation, summarization, text-classification, text-retrieval","Introduction M4LE is a Multi-ability, Multi-range, Multi-task, bilingual benchmark for long-context evaluation. We categorize long-context understanding into five distinct abilities by considering whe","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2310.19240","None","None"
+"hanamizuki-ai/genshin-voice-v3.3-mandarin","hanamizuki-ai","2022-12-30 18:13:13","2022-12-31 05:01:47+00:00","a6ea0aadd1bb2f929384443c672f45ac16ed7b04","424","3348","30","task_categories:text-to-speech, task_categories:automatic-speech-recognition, multilinguality:monolingual, source_datasets:original, language:zh, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech, automatic-speech-recognition","Dataset Card for Genshin Voice Dataset Description Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. Languages The tex","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"bltlab/lr-sum","bltlab","2023-11-07 16:29:37","2024-12-19 21:40:27+00:00","deb2e73ce53e42ceda31297a991afa138ee665a8","141","3323","4","task_categories:summarization, task_categories:text-generation, annotations_creators:found, language_creators:found, multilinguality:multilingual, language:am, language:az, language:bn, language:bo, language:bs, language:ku, language:zh, language:el, language:en, language:fa, language:fr, language:ht, language:ha, language:hy, language:id, language:ka, language:km, language:rw, language:ko, language:lo, language:mk, language:my, language:nd, language:pt, language:ps, language:ru, language:sn, language:so, language:es, language:sq, language:sr, language:sw, language:th, language:ti, language:tr, language:uk, language:ur, language:uz, language:vi, license:cc-by-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, conditional-text-generation","summarization, text-generation","Dataset Card for LR-Sum LR-Sum is a automatic summarization dataset of newswire text with a focus on less resourced languages with a cc-by 4.0 license. Dataset Details Dataset Description LR-Sum is a ","None","am, az, bn, bo, bs, ku, zh, el, en, fa, fr, ht, ha, hy, id, ka, km, rw, ko, lo, mk, my, nd, pt, ps, ru, sn, so, es, sq, sr, sw, th, ti, tr, uk, ur, uz, vi","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"C4AI-Community/multilingual-reward-bench","C4AI-Community","2024-09-07 18:45:29","2024-11-04 20:21:18+00:00","19ab8c297a3e59a1d0efd42ae33cc14bf3febf63","1139","3236","26","None","None","Multilingual Reward Bench (v1.0) Reward models (RMs) have driven the development of state-of-the-art LLMs today, with unprecedented impact across the globe. However, their performance in multilingual ","None","ar, zh, cs, nl, fr, de, el, he, hi, id, it, ja, ko, fa, pl, pt, ro, ru, es, tr, uk, vi","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","odc-by","2410.15522","None","None"
+"llamafactory/glaive_toolcall_zh","llamafactory","2024-05-17 14:22:02","2024-06-07 18:45:04+00:00","12cc99d1f129825c475d7df04a169b6d7468a518","125","3214","17","task_categories:text-generation, task_categories:question-answering, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama-factory","text-generation, question-answering","Borrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2 Translated by GPT-3.5. You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_zh.","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"BAAI/CCI2-Data","BAAI","2024-04-17 04:25:13","2024-12-17 03:29:03+00:00","ae057854d64e65ab26ca4320edc042ce7a0de509","427","3165","48","task_categories:text-generation, language:zh, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Data Description To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the CCI (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, w","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"almanach/HALvest","almanach","2024-04-23 13:30:23","2024-07-31 09:56:58+00:00","442094b65f54946f8cafb64cf774cb7c87e57df4","738","3146","3","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, multilinguality:multilingual, source_datasets:original, language:ar, language:az, language:bg, language:bo, language:br, language:bs, language:ca, language:co, language:cs, language:da, language:de, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:gl, language:gn, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:ie, language:it, language:ja, language:kk, language:ko, language:lt, language:mk, language:mr, language:no, language:oc, language:pl, language:pt, language:ro, language:ru, language:sk, language:sl, language:sq, language:sr, language:sv, language:sw, language:ta, language:tet, language:th, language:tk, language:tl, language:tr, language:uk, language:vi, language:zh, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2407.20595, region:us, academia, research","text-generation, fill-mask, language-modeling, masked-language-modeling","HALvest Open Scientific Papers Harvested from HAL (Unfiltered) Dataset Summary overview: This is the unfiltered version of HALvest, comprising of fulltext from open papers found on Hyper Articles en L","None","ar, az, bg, bo, br, bs, ca, co, cs, da, de, el, en, eo, es, et, eu, fa, fi, fr, gl, gn, he, hi, hr, hu, hy, id, ie, it, ja, kk, ko, lt, mk, mr, no, oc, pl, pt, ro, ru, sk, sl, sq, sr, sv, sw, ta, tet, th, tk, tl, tr, uk, 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","None","False","False","False","None","None","None","language-modeling, masked-language-modeling"
+"yunusserhat/Total-Text-Dataset","yunusserhat","2024-04-30 11:29:15","2024-04-30 11:50:20+00:00","74a310ca7e71b48ee81b862d170015407640d406","270","3109","0","task_categories:text-retrieval, language:en, language:zh, size_categories:1K<n<10K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, text-recognition, dataset, text-detection, scene-text, scene-text-recognition, scene-text-detection, text-detection-recognition, icdar, total-text, curve-text","text-retrieval","Total Text Dataset. It consists of 1555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind. Original github repo https://github.com/cs-chan/Tota","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"erhwenkuo/c4-chinese-zhtw","erhwenkuo","2023-10-11 13:39:56","2023-10-12 04:00:07+00:00","f99b6fdc4b3ea019b70c6079d1a559f1a05cb0c8","873","3054","11","task_categories:text-generation, task_categories:fill-mask, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","Dataset Card for ""c4-chinese-zhtw"" 內容 Common Crawl 是一個非營利組織，負責抓取網路並向公眾免費提供��檔案和資料集。Common Crawl 的網路檔案包含自 2008 年以來收集的 PB 級資料。它一般每月完成一次抓取。 Common Crawl 的爬蟲程式遵守 nofollow 和 robots.txt 政策。用於處理 Common Crawl ","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","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"
+"lchakkei/OpenOrca-Traditional-Chinese","lchakkei","2023-09-16 03:15:44","2023-10-11 08:29:08+00:00","c0c0b662e481ac0d2220191f3da7907140e4a7e2","340","3021","8","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:zh, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2301.13688, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","🐋 OpenOrca-Chinese 数据集！🐋 感謝 Open-Orca/OpenOrca 資料集的發布，為廣大NLP研究人員和開發者帶來了寶貴的資源！ 這是一個對 Open-Orca/OpenOrca 資料集中文翻譯的版本，翻譯引擎為 Google 翻譯，希望能為中文 LLM 研究做出一點點貢獻。 Dataset Summary The OpenOrca dataset is a collec","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2301.13688","None","None"
+"haoranxu/X-ALMA-Parallel-Data","haoranxu","2024-10-03 02:07:08","2024-10-07 06:11:17+00:00","87ec2cb8891b9a1fb53b844caef42bf7e1e487dc","349","2971","3","language:en, language:da, language:nl, language:de, language:is, language:no, language:sc, language:af, language:ca, language:ro, language:gl, language:it, language:pt, language:es, language:bg, language:mk, language:sr, language:uk, language:ru, language:id, language:ms, language:th, language:vi, language:mg, language:fr, language:hu, language:el, language:cs, language:pl, language:lt, language:lv, language:ka, language:zh, language:ja, language:ko, language:fi, language:et, language:gu, language:hi, language:mr, language:ne, language:ur, language:az, language:kk, language:ky, language:tr, language:uz, language:ar, language:he, language:fa, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.03115, region:us","None","This is the translation parallel dataset used by X-ALMA. @misc{xu2024xalmaplugplay, title={X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale}, author={Haoran Xu and K","None","en, da, nl, de, is, no, sc, af, ca, ro, gl, it, pt, es, bg, mk, sr, uk, ru, id, ms, th, vi, mg, fr, hu, el, cs, pl, lt, lv, ka, zh, ja, ko, fi, et, gu, hi, mr, ne, ur, az, kk, ky, tr, uz, ar, he, fa","multi","None","None","False","False","False","None","None","None","None"
+"CohereForAI/include-base-44","CohereForAI","2024-11-29 10:30:22","2024-12-11 11:25:50+00:00","ce492c073f95025ba7fff5250fc1a89901bc1271","878","2962","24","task_categories:text2text-generation, task_categories:multiple-choice, language:sq, language:ar, language:hy, language:az, language:be, language:bn, language:eu, language:bg, language:tr, language:hr, language:nl, language:fa, language:es, language:et, language:fi, language:fr, language:de, language:el, language:ka, language:he, language:hi, language:hu, language:id, language:it, language:ja, language:kk, language:ko, language:lt, language:ml, language:ms, language:ne, language:pl, language:pt, language:ru, language:ta, language:tl, language:te, language:uk, language:ur, language:uz, language:vi, language:zh, language:sr, language:mk, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2411.19799, region:us, chemistry, biology, legal, music, finance, medical, climate, art, code","text2text-generation, multiple-choice","INCLUDE-base (44 languages) Dataset Description Paper: http://arxiv.org/abs/2411.19799 Dataset Summary INCLUDE is a comprehensive knowledge- and reasoning-centric benchmark across 44 languages that ev","None","sq, ar, hy, az, be, bn, eu, bg, tr, hr, nl, fa, es, et, fi, fr, de, el, ka, he, hi, hu, id, it, ja, kk, ko, lt, ml, ms, ne, pl, pt, ru, ta, tl, te, uk, ur, uz, vi, zh, sr, mk","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2411.19799","None","None"
+"silver/personal_dialog","silver","2022-05-29 14:23:58","2022-07-10 13:05:21+00:00","b4c2d1336775fd85839e4e81921dd95d23019ac1","146","2945","23","task_ids:dialogue-generation, annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:other, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:1901.09672, region:us","conversational, dialogue-generation","The PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers. We are releasing about 5M sessions of carefully filtered dial","@article{zheng2019personalized, title = {Personalized dialogue generation with diversified traits}, author = {Zheng, Yinhe and Chen, Guanyi and Huang, Minlie and Liu, Song and Zhu, Xuan}, journal = {arXiv preprint arXiv:1901.09672}, year = {2019} } @inproceedings{zheng2020pre, title = {A pre-training based personalized dialogue generation model with persona-sparse data}, author = {Zheng, Yinhe and Zhang, Rongsheng and Huang, Minlie and Mao, Xiaoxi}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {34}, number = {05}, pages = {9693--9700}, year = {2020} }","zh","mono","1|0|M|<|n|<|1|0|0|M","personaldialog","False","False","False","other","1901.09672","None","dialogue-generation"
+"hanamizuki-ai/genshin-voice-v3.5-mandarin","hanamizuki-ai","2023-04-13 08:33:45","2023-04-13 14:47:16+00:00","2f4853c15597e96e18386609869381cdccb92f07","409","2903","15","task_categories:text-to-speech, task_categories:automatic-speech-recognition, multilinguality:monolingual, source_datasets:original, language:zh, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech, automatic-speech-recognition","Dataset Card for Genshin Voice Dataset Description Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. Languages The tex","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"yusuke1997/mCSQA","yusuke1997","2023-12-22 02:18:49","2024-08-26 05:19:38+00:00","222f65db1deb2d0c19ff7ae7f6c78893dfc3a66c","310","2840","3","task_categories:question-answering, task_categories:multiple-choice, language:en, language:ja, language:zh, language:de, language:fr, language:pt, language:nl, language:ru, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.04215, region:us","question-answering, multiple-choice","Dataset Card for Multilingual CommonsenseQA (mCSQA) This dataset expands CommonsenseQA to eight languages from scratch using the same approach with LLMs and humans. Abstract From mCSQA: Multilingual C","None","en, ja, zh, de, fr, pt, nl, ru","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","2406.04215","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"
+"mteb/NTREX","mteb","2024-05-14 21:32:20","2024-05-14 21:34:25+00:00","ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33","396","2718","0","task_categories:translation, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:translation, language:af, language:am, language:ar, language:az, language:ba, language:be, language:bg, language:bn, language:bo, language:bs, language:ca, language:cs, language:cy, language:da, language:de, language:dv, language:dz, language:ee, language:el, language:et, language:eu, language:fa, language:fi, language:fil, language:fj, language:fo, language:fr, language:gd, language:gu, language:ha, language:he, language:hi, language:hmn, language:hr, language:hu, language:hy, language:id, language:ig, language:is, language:it, language:ja, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:lb, language:lo, language:lt, language:lv, language:mi, language:mk, language:mn, language:mr, language:ms, language:mt, language:my, language:nb, language:nd, language:ne, language:nl, language:nn, language:ny, language:om, language:oy, language:pa, language:ps, language:pt, language:ro, language:ru, language:rw, language:sd, language:sh, language:shi, language:si, language:sk, language:sl, language:sm, language:sn, language:so, language:sq, language:sr, language:ss, language:st, language:sv, language:sw, language:ta, language:te, language:tg, language:th, language:tk, language:tn, language:to, language:tr, language:tt, language:ty, language:uk, language:ur, language:uz, language:ve, language:vi, language:wo, language:xh, language:yo, language:zh, language:zu, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Description NTREX -- News Test References for MT Evaluation from English into a total of 128 target languages. See original GitHub repo for full details. Example of loading: dataset = load_dat","None","af, am, ar, az, ba, be, bg, bn, bo, bs, ca, cs, cy, da, de, dv, dz, ee, el, et, eu, fa, fa, fi, fil, fj, fj, fo, fr, gd, gu, ha, he, hi, hmn, hr, hu, hy, id, ig, is, it, ja, kk, km, kn, ko, ku, ku, ky, lb, lo, lt, lv, mi, mk, mn, mr, ms, ms, mt, my, nb, nd, ne, nl, nn, ny, om, oy, pa, ps, pt, ro, ru, rw, sd, sh, shi, si, sk, sl, sm, sn, so, sq, sr, ss, st, sv, sw, ta, te, tg, th, tk, tn, to, tr, tt, ty, uk, ur, uz, ve, vi, wo, xh, yo, zh, zh, zu","multi","1|9|9|7","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"nayeon212/BLEnD","nayeon212","2024-06-11 10:06:43","2024-12-10 03:30:49+00:00","b8c1f8d750d1ec28a1ec2699fd36b44328740b2c","491","2706","8","task_categories:question-answering, language:en, language:zh, language:es, language:id, language:ko, language:el, language:fa, language:ar, language:az, language:su, language:as, language:ha, language:am, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.09948, region:us","question-answering","BLEnD This is the official repository of BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages (Submitted to NeurIPS 2024 Datasets and Benchmarks Track). 24/12/05: Update","None","en, zh, es, id, ko, el, fa, ar, az, su, as, ha, am","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2406.09948","None","None"
+"bzantium/LongBench","bzantium","2023-09-21 06:13:03","2023-09-25 04:03:43+00:00","5b6f31372e4a6b557aba6f1703577265047d6183","328","2674","0","task_categories:question-answering, task_categories:text-generation, task_categories:summarization, task_categories:text-classification, language:en, language:zh, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, arxiv:2308.14508, arxiv:2108.00573, arxiv:1712.07040, arxiv:2105.03011, arxiv:2104.02112, arxiv:2104.05938, arxiv:2305.05280, arxiv:2303.09752, arxiv:1910.10683, arxiv:2306.14893, arxiv:2306.03091, region:us, Long Context","question-answering, text-generation, summarization, conversational, 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"
+"raptorkwok/cantonese-traditional-chinese-parallel-corpus","raptorkwok","2023-07-19 03:40:29","2024-01-30 11:13:07+00:00","5e047fe1764e50868b22ae6f1c191ede448f4f26","110","2669","10","task_categories:translation, language:zh, license:cc0-1.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This is a dataset of Cantonese-Written Chinese Parallel Corpus, containing 130k+ pairs of Cantonese and Traditional Chinese parallel sentences.","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc0-1.0","None","None","None"
+"xiaodongguaAIGC/alpaca_en_zh_ruozhiba","xiaodongguaAIGC","2024-04-29 09:19:04","2024-06-08 12:36:07+00:00","b3a4e77c8c37c0d58a88dabcd2193fdd3c7a90a8","80","2580","5","task_categories:text-generation, task_categories:text2text-generation, language:zh, language:en, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, SFT, Llama3, Chinese, zh, alpaca, instruction, finetune, resoning, full-finetune","text-generation, text2text-generation","Dataset Source mix three dataset For SFT data_name1 = 'xiaodongguaAIGC/alpaca_gpt4_data_zh' # from https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM data_name2 = 'vicgalle/alpaca-gpt4' data_n","None","zh, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"ServiceNow-AI/M2Lingual","ServiceNow-AI","2024-06-13 00:47:39","2024-08-13 19:01:43+00:00","e02c0901ccabb711e3dc27ccebaf001e23ee92a4","490","2533","29","language:zh, language:ne, language:uk, language:ja, language:zu, language:ku, language:ig, language:mg, language:fi, language:si, language:id, language:sw, language:ar, language:sv, language:ru, language:yo, language:en, language:ht, language:kn, language:ta, language:te, language:sq, language:mr, language:am, language:wo, language:it, language:tr, language:ha, language:pl, language:el, language:lt, language:ms, language:jv, language:sn, language:ml, language:ps, language:ky, language:es, language:ga, language:gu, language:ko, language:vi, language:sd, language:fa, language:nl, language:hu, language:so, language:pa, language:bn, language:pt, language:da, language:hi, language:eu, language:de, language:ur, language:su, language:xh, language:fr, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.16783, region:us","None","M2Lingual A Multi-turn Multilingual dataset for Instruction Fine-tuning LLMs - Link Dataset Summary The M2Lingual dataset is a comprehensive multi-turn multilingual resource designed to facilitate res","None","zh, ne, uk, ja, zu, ku, ig, mg, fi, si, id, sw, ar, sv, ru, yo, en, ht, kn, ta, te, sq, mr, am, wo, it, tr, ha, pl, el, lt, ms, jv, sn, ml, ps, ky, es, ga, gu, ko, vi, sd, fa, nl, hu, so, pa, bn, pt, da, hi, eu, de, ur, su, xh, fr","multi","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"espnet/ace-kising-segments","espnet","2024-04-22 16:23:19","2024-09-09 00:54:11+00:00","6bb98f305ee3bfb4afe2bbde1198db883bf82401","226","2529","6","task_categories:text-to-audio, task_categories:audio-to-audio, task_categories:automatic-speech-recognition, multilinguality:multilingual, source_datasets:original, language:zh, language:en, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2401.17619, region:us","text-to-audio, audio-to-audio, automatic-speech-recognition","Citation Information @misc{shi2024singingvoicedatascalingup, title={Singing Voice Data Scaling-up: An Introduction to ACE-Opencpop and ACE-KiSing}, author={Jiatong Shi and Yueqian Lin and Xinyi Bai an","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"FredZhang7/all-scam-spam","FredZhang7","2023-07-04 22:07:15","2025-01-03 08:53:30+00:00","802cef358a0a0a9f7582fb96ff262b57a398ee17","598","2528","9","task_categories:text-classification, task_categories:zero-shot-classification, language:no, language:es, language:so, language:ca, language:af, language:it, language:nl, language:hi, language:cy, language:ar, language:sv, language:cs, language:pl, language:de, language:lt, language:sq, language:uk, language:tl, language:sl, language:hr, language:en, language:fi, language:vi, language:id, language:da, language:ko, language:bg, language:mr, language:ja, language:bn, language:ro, language:pt, language:fr, language:hu, language:tr, language:zh, language:mk, language:ur, language:sk, language:ne, language:et, language:sw, language:ru, language:multilingual, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, nlp, moderation","text-classification, zero-shot-classification","This is a large corpus of 42,619 preprocessed text messages and emails sent by humans in 43 languages. is_spam=1 means spam and is_spam=0 means ham. 1040 rows of balanced data, consisting of casual co","None","no, es, so, ca, af, it, nl, hi, cy, ar, sv, cs, pl, de, lt, sq, uk, tl, sl, hr, en, fi, vi, id, da, ko, bg, mr, ja, bn, ro, pt, fr, hu, tr, zh, mk, ur, sk, ne, et, sw, ru, multilingual","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"simon3000/starrail-voice","simon3000","2024-04-26 19:01:17","2024-08-30 04:52:04+00:00","7b6e8050a0fd1e7e052de9520f1799faa08a5751","714","2494","24","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","StarRail Voice StarRail Voice is a dataset of voice lines from the popular game Honkai: Star Rail. Hugging Face 🤗 StarRail-Voice Last update at 2024-08-30 185511 wavs 49325 without speaker (27%) 49409","None","zh, en, ja, ko","multi","None","None","False","False","False","None","None","None","None"
+"afaji/cvqa","afaji","2024-04-26 11:25:15","2024-11-27 17:42:19+00:00","6a38d71fa391ca7a5e061bbcf48ba714a9a9f4f1","419","2431","23","task_categories:question-answering, language:id, language:su, language:ja, language:jv, language:min, language:br, language:ga, language:es, language:pt, language:no, language:mn, language:ms, language:zh, language:ko, language:ta, language:ben, language:si, language:bg, language:ro, language:ru, language:am, language:orm, language:ar, language:ig, language:hi, language:mr, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.05967, region:us","question-answering","About CVQA CVQA is a culturally diverse multilingual VQA benchmark consisting of over 10,000 questions from 39 country-language pairs. The questions in CVQA are written in both the native languages an","None","id, su, ja, jv, min, br, ga, es, pt, no, mn, ms, zh, ko, ta, ben, si, bg, ro, ru, am, orm, ar, ig, hi, mr","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"THUDM/LongWriter-6k","THUDM","2024-08-13 13:55:38","2024-08-14 11:56:22+00:00","0db15c0624f19d63e2efe1021595af933cc5b6cc","137","2423","172","task_categories:text-generation, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2408.07055, region:us, Long Context, sft, writing","text-generation","LongWriter-6k 🤗 [LongWriter Dataset] • 💻 [Github Repo] • 📃 [LongWriter Paper] LongWriter-6k dataset contains 6,000 SFT data with ultra-long output ranging from 2k-32k words in length (both English and","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2408.07055","None","None"
+"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"
+"erhwenkuo/wikipedia-zhtw","erhwenkuo","2023-10-10 02:31:00","2023-10-10 03:22:43+00:00","95887a7bdc10e6691c47567e2fda4f24a676cf27","86","2400","6","task_categories:text-generation, task_categories:fill-mask, language:zh, license:cc-by-sa-3.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","Dataset Card for ""wikipedia-zhtw"" 維基百科數據集包含許多不同語言的文章。這個數據集是根據 Wikipedia dumps (https://dumps.wikimedia.org/) 裡頭 zhwiki 的中文下載檔案來建構的。每個範例都包含一篇完整的維基百科文章的內容，並經過清理以去除不需要的部分(例如參考文獻等)。 Homepage: https://dump","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"SWHL/ChineseOCRBench","SWHL","2024-04-30 07:08:28","2024-04-30 09:40:36+00:00","857e91af744e05b234c504caf900f932f236de71","1884","2366","20","None","None","Chinese OCRBench 由于对于多模态LLM的OCR方向的评测集中，缺少专门中文OCR任务的评测，因此考虑专门做一个中文OCR任务的评测。 关注到On the Hidden Mystery of OCR in Large Multimodal Models工作中已经做了两个中文OCR任务的评测，于是，ChineseOCRBench仅仅是将该篇工作中提出的中文评测数据集提了出来，作为专门中","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2305.07895","None","None"
+"bigscience-data/roots_zh-tw_wikipedia","bigscience-data","2022-05-18 09:20:00","2022-12-12 12:09:12+00:00","04697f12b922c328bc5ffcd88523c48771cd6fe9","74","2324","11","language:zh, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","ROOTS Subset: roots_zh-tw_wikipedia wikipedia Dataset uid: wikipedia Description Homepage Licensing Speaker Locations Sizes 3.2299 % of total 4.2071 % of en 5.6773 % of ar 3.3416 % of fr 5.2815 % of e","None","z, h","bi","None","None","False","auto","False","cc-by-sa-3.0","None","None","None"
+"m-a-p/CII-Bench","m-a-p","2024-10-14 10:33:42","2024-10-18 08:21:35+00:00","cdfe3a8980fa4d3a3be85cce090aa1ac589fea11","153","2309","3","task_categories:question-answering, task_categories:visual-question-answering, language:zh, license:apache-2.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.13854, region:us, life, art, society, environment, politics, Chinese traditional culture","question-answering, visual-question-answering","CII-Bench 🌐 Homepage | 🤗 Dataset | GitHub | 🤗 Paper | 📖 arXiv Introduction CII-Bench comprises 698 Chinese images, each accompanied by 1 to 3 multiple-choice questions, totaling 800 questions. CII-Ben","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2410.13854","None","None"
+"FreedomIntelligence/huatuo_encyclopedia_qa","FreedomIntelligence","2023-05-10 08:30:14","2023-05-17 03:20:55+00:00","2989899dd5bed83cf9bd17cf9fff9889705436e2","266","2292","58","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.01526, region:us, medical","text-generation","Dataset Card for Huatuo_encyclopedia_qa Dataset Summary This dataset has a total of 364,420 pieces of medical QA data, some of which have multiple questions in different ways. We extract medical QA pa","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"UndefinedCpp/casia-char-1","UndefinedCpp","2024-02-25 05:43:17","2024-03-02 05:08:30+00:00","443a5fef2026cde42d5ca1db890e8146a08b0b79","370","2269","1","task_categories:image-classification, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, ocr, chinese, text, handwriting","image-classification","CASIA Character Sample Dataset This dataset is adapted from CASIA Online and Offline Chinese Handwriting Databases, but this only contains character level sample data (from the offline database). The ","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"GAIR/OlympicArena","GAIR","2024-06-10 04:35:59","2024-07-20 14:30:22+00:00","001189f4e78bbdbe436770a2763a4283c6224709","283","2258","16","task_categories:question-answering, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, modality:image, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.12753, region:us, croissant, image, text","question-answering","OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI OlympicArena is a comprehensive, highly-challenging, and rigorously curated benchmark featuring a detailed, fine","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"saillab/taco-datasets","saillab","2023-11-27 07:15:33","2023-12-01 06:21:22+00:00","bfb3619e7fafe98526beb2d254971854c1a53520","509","2250","15","language:en, language:ne, language:sn, language:mai, language:fa, language:hi, language:af, language:sq, language:am, language:ar, language:hy, language:as, language:ay, language:az, language:bm, language:eu, language:be, language:bn, language:bh, language:bs, language:bg, language:ca, language:ceb, language:ny, language:zh, language:co, language:hr, language:cs, language:da, language:dv, language:dog, language:nl, language:eo, language:et, language:ee, language:tl, language:fi, language:fr, language:fy, language:gl, language:ka, language:de, language:el, language:gn, language:gu, language:ht, language:ha, language:haw, language:he, language:hmn, language:hu, language:is, language:ig, language:ilo, language:id, language:ga, language:it, language:ja, language:jv, language:kn, language:kk, language:km, language:rw, language:kok, language:ko, language:kri, language:ku, language:ky, language:lo, language:la, language:lv, language:ln, language:lt, language:lg, language:lb, language:mk, language:ml, language:mt, language:mi, language:mr, language:mni, language:ms, language:mg, language:my, language:no, language:or, language:om, language:ps, language:pl, language:pt, language:pa, language:ro, language:ru, language:sm, language:gd, language:sr, language:st, language:tn, language:sd, language:si, language:sk, language:sl, language:so, language:es, language:su, language:sw, language:sv, language:tg, language:ta, language:tt, language:te, language:th, language:ti, language:to, language:tr, language:tk, language:tw, language:uk, language:ur, language:ug, language:uz, language:vi, language:cy, language:xh, language:yi, language:yo, language:zu, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2311.10797, region:us","None","This repo consists of the datasets used for the TaCo paper. There are four datasets: Multilingual Alpaca-52K GPT-4 dataset Multilingual Dolly-15K GPT-4 dataset TaCo dataset Multilingual Vicuna Benchma","None","en, ne, sn, mai, fa, hi, af, sq, am, ar, hy, as, ay, az, bm, eu, be, bn, bh, bs, bg, ca, ceb, ny, zh, co, hr, cs, da, dv, dog, nl, eo, et, ee, tl, fi, fr, fy, gl, ka, de, el, gn, gu, ht, ha, haw, he, hmn, hu, is, ig, ilo, id, ga, it, ja, jv, kn, kk, km, rw, kok, ko, kri, ku, ky, lo, la, lv, ln, lt, lg, lb, mk, ml, mt, mi, mr, mni, ms, mg, mt, my, no, or, om, ps, pl, pt, pa, ro, ru, sm, gd, sr, st, tn, sd, si, sk, sl, so, es, su, sw, sv, tg, ta, tt, te, th, ti, to, tr, tk, tw, uk, ur, ug, uz, vi, cy, xh, yi, yo, zu","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","2311.10797","None","None"
+"Brand24/mms","Brand24","2023-05-24 12:07:06","2023-08-23 21:49:55+00:00","95b662d6672a0eaa1633ea2af7362ccd8264db3d","110","2250","10","task_categories:text-classification, task_ids:sentiment-classification, annotations_creators:mixed, multilinguality:multi-lingual, language:ar, language:bg, language:bs, language:cs, language:de, language:el, language:en, language:es, language:fa, language:fr, language:he, language:hi, language:hr, language:hu, language:it, language:ja, language:lv, language:pl, language:pt, language:ru, language:sk, language:sl, language:sq, language:sr, language:sv, language:th, language:ur, language:zh, license:other, size_categories:1M<n<10M, modality:tabular, modality:text, library:datasets, library:mlcroissant, arxiv:2306.07902, region:us","text-classification, sentiment-classification","This work presents the most extensive open massively multi-lingual corpus of datasets for training sentiment models. The corpus consists of 79 manually selected from over 350 datasets reported in the ","@misc{augustyniak2023massively, title={Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark}, author={Łukasz Augustyniak and Szymon Woźniak and Marcin Gruza and Piotr Gramacki and Krzysztof Rajda and Mikołaj Morzy and Tomasz Kajdanowicz}, year={2023}, eprint={2306.07902}, archivePrefix={arXiv}, primaryClass={cs.CL} }","ar, bg, bs, cs, de, el, en, es, fa, fr, he, hi, hr, hu, it, ja, lv, pl, pt, ru, sk, sl, sq, sr, sv, th, ur, zh","multi","1|M|<|n|<|1|0|M","None","False","auto","False","other","None","None","sentiment-classification"
+"zetavg/coct-en-zh-tw-translations-twp-300k","zetavg","2023-05-07 04:09:52","2023-05-07 05:05:22+00:00","12aa659587e4d338c4929cc3b4944004b8df5dfa","92","2182","31","task_categories:translation, task_categories:text-generation, language:zh, language:en, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation, text-generation","~300K English ↔ Traditional Chinese Sentences from the COCT Database The data in this dataset are collected from the Corpus of Contemporary Taiwanese Mandarin (COCT), mostly contributed by the Taiwan ","None","zh, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"wenbopan/Fusang-v1","wenbopan","2024-03-19 08:15:20","2024-03-20 09:29:55+00:00","2700c3d7b49202f891759c7342a22919fe40e408","154","2168","14","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, synthetic, croissant","text-generation","Dataset Card for ""Fusang-V1"" ""A large curation of instruction-tuning datasets for better bilingual and long-range LLMs"" Fusang-V1 is a diverse and large Instruction-Tuning dataset like teknium/OpenHer","None","zh, en","bi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"SeaLLMs/SeaExam","SeaLLMs","2024-04-18 02:40:43","2024-05-31 09:27:49+00:00","caf3e74ec21d38ceaec5db41d72e3ab96f121dbf","109","2164","7","task_categories:multiple-choice, language:en, language:id, language:vi, language:th, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, exam","multiple-choice","Check the 🏆 leaderboard constructed with this dataset and the corresponding 👨🏻‍💻 evaluation code. SeaExam dataset The SeaExam dataset aims to evaluate Large Language Models (LLMs) on a diverse set of ","None","en, id, vi, th, zh","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"KaiChen1998/coda-lm-llava-format","KaiChen1998","2024-11-02 13:32:37","2024-11-11 12:28:47+00:00","68796ba5b7a840bdc253abae4d73c11cab7e67fd","742","2145","1","task_categories:image-to-text, language:en, language:zh, license:mit, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2404.10595, region:us, autonomous_driving, corner_cases","image-to-text","CODA-LM Dataset Card CODA-LM is the multi-modal version of the CODA dataset, used in the CODA-LM paper. Both English and Chinese annotations are available. Check detailed usage in our Github repo. Thi","None","en, zh","bi","None","None","False","False","False","mit","2404.10595","None","None"
+"sysmlv2research/tutorials_summary","sysmlv2research","2024-07-27 09:20:18","2024-07-27 15:13:02+00:00","8e7b4e223eb2d332697e9be02a7c4feae19fad70","939","2134","1","task_categories:text2text-generation, language:en, language:zh, license:other, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, modeling, code","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"
+"shadow-wxh/VoiceCommandAudio","shadow-wxh","2024-07-24 00:48:28","2024-08-16 02:58:26+00:00","64938e6591aad94b49a654fe2ca1949d40483782","700","2119","1","task_categories:automatic-speech-recognition, annotations_creators:shadow_wxh, language_creators:shadow_wxh, multilinguality:multilingual, language:de, language:zh, language:en, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:csv, modality:audio, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, game, silenthunter, voicecommand","automatic-speech-recognition","This is mainly used for fine tune ""VoiceCommand"" a speech congnition MOD dedicated for SilentHunter game series","None","de, zh, en","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"FreedomIntelligence/huatuo_knowledge_graph_qa","FreedomIntelligence","2023-05-06 06:35:38","2023-07-07 08:46:58+00:00","b01fb013d5595abe18c97408dd2fc939e4f7ea3f","244","2105","42","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.01526, region:us, medical","text-generation","Dataset Card for Huatuo_knowledge_graph_qa Dataset Summary We built this QA dataset based on the medical knowledge map, with a total of 798,444 pieces of data, in which the questions are constructed b","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"Team-ACE/ToolACE","Team-ACE","2024-08-21 06:02:38","2024-09-04 02:37:59+00:00","6bda777c88d21e5a204703c1ee45597a8fa4f734","376","2052","54","task_categories:text-generation, 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:2409.00920, region:us, synthetic, tools","text-generation","ToolACE ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data. ToolACE leverages a novel self-evolution synthesis process to curate a comprehe","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2409.00920","None","None"
+"Isotonic/human_assistant_conversation_deduped","Isotonic","2023-04-11 06:16:00","2023-07-05 12:35:56+00:00","a839c25e203a79377e7d00e5d8c94a03319c10c2","105","2048","7","task_categories:text-generation, language:en, language:es, language:zh, license:afl-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, conversational","Deduplicated version of Isotonic/human_assistant_conversation Deduped with max jaccard similarity of 0.75","None","en, es, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","afl-3.0","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"
+"sil-ai/bloom-vist","sil-ai","2022-05-05 16:25:54","2022-12-06 19:37:53+00:00","3997b23648a3961e1e566d976ac9310aa2ac5e20","62","2017","5","annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:multilingual, source_datasets:original, language:afr, language:af, language:aaa, language:abc, language:ada, language:adq, language:aeu, language:agq, language:ags, language:ahk, language:aia, language:ajz, language:aka, language:ak, language:ame, language:amh, language:am, language:amp, language:amu, language:ann, language:aph, language:awa, language:awb, language:azn, language:azo, language:bag, language:bam, language:bm, language:baw, language:bax, language:bbk, language:bcc, language:bce, language:bec, language:bef, language:ben, language:bn, language:bfd, language:bfm, language:bfn, language:bgf, language:bho, language:bhs, language:bis, language:bi, language:bjn, language:bjr, language:bkc, language:bkh, language:bkm, language:bkx, language:bob, language:bod, language:bo, language:boz, language:bqm, language:bra, language:brb, language:bri, language:brv, language:bss, language:bud, language:buo, language:bwt, language:bwx, language:bxa, language:bya, language:bze, language:bzi, language:cak, language:cbr, language:ceb, language:cgc, language:chd, language:chp, language:cim, language:clo, language:cmn, language:zh, language:cmo, language:csw, language:cuh, language:cuv, language:dag, language:ddg, language:ded, language:deu, language:de, language:dig, language:dje, language:dmg, language:dnw, language:dtp, language:dtr, language:dty, language:dug, language:eee, language:ekm, language:enb, language:enc, language:eng, language:en, language:ewo, language:fas, language:fa, language:fil, language:fli, language:fon, language:fra, language:fr, language:fub, language:fuh, language:gal, language:gbj, language:gou, language:gsw, language:guc, language:guj, language:gu, language:guz, language:gwc, language:hao, language:hat, language:ht, language:hau, language:ha, language:hbb, language:hig, language:hil, language:hin, language:hi, language:hla, language:hna, language:hre, language:hro, language:idt, language:ilo, language:ind, language:id, language:ino, language:isu, language:ita, language:it, language:jgo, language:jmx, language:jpn, language:ja, language:jra, language:kak, language:kam, language:kan, language:kn, language:kau, language:kr, language:kbq, language:kbx, language:kby, language:kek, language:ken, language:khb, language:khm, language:km, language:kik, language:ki, language:kin, language:rw, language:kir, language:ky, language:kjb, language:kmg, language:kmr, language:ku, language:kms, language:kmu, language:kor, language:ko, language:kqr, language:krr, language:ksw, language:kur, language:kvt, language:kwd, language:kwu, language:kwx, language:kxp, language:kyq, language:laj, language:lan, language:lao, language:lo, language:lbr, language:lfa, language:lgg, language:lgr, language:lhm, language:lhu, language:lkb, language:llg, language:lmp, language:lns, language:loh, language:lsi, language:lts, language:lug, language:lg, language:luy, language:lwl, language:mai, language:mal, language:ml, language:mam, language:mar, language:mr, language:mdr, language:mfh, language:mfj, language:mgg, language:mgm, language:mgo, language:mgq, language:mhx, language:miy, language:mkz, language:mle, language:mlk, language:mlw, language:mmu, language:mne, language:mnf, language:mnw, language:mot, language:mqj, language:mrn, language:mry, language:msb, language:muv, language:mve, language:mxu, language:mya, language:my, language:myk, language:myx, language:mzm, language:nas, language:nco, language:nep, language:ne, language:new, language:nge, language:ngn, language:nhx, language:njy, language:nla, language:nld, language:nl, language:nlv, language:nod, language:nsk, language:nsn, language:nso, language:nst, language:nuj, language:nwe, language:nwi, language:nxa, language:nxl, language:nya, language:ny, language:nyo, language:nyu, language:nza, language:odk, language:oji, language:oj, language:oki, language:omw, language:ori, language:or, language:ozm, language:pae, language:pag, language:pan, language:pa, language:pbt, language:pce, language:pcg, language:pdu, language:pea, language:pex, language:pis, language:pkb, language:pmf, language:pnz, language:por, language:pt, language:psp, language:pwg, language:qaa, language:qub, language:quc, language:quf, language:quz, language:qve, language:qvh, language:qvm, language:qvo, language:qxh, language:rel, language:rnl, language:ron, language:ro, language:roo, language:rue, language:rug, language:rus, language:ru, language:san, language:sa, language:saq, language:sat, language:sdk, language:sea, language:sgd, language:shn, language:sml, language:snk, language:snl, language:som, language:so, language:sot, language:st, language:sox, language:spa, language:es, language:sps, language:ssn, language:stk, language:swa, language:sw, language:swh, language:sxb, language:syw, language:taj, language:tam, language:ta, language:tbj, language:tdb, language:tdg, language:tdt, language:teo, language:tet, language:tgk, language:tg, language:tha, language:th, language:the, language:thk, language:thl, language:thy, language:tio, language:tkd, language:tnl, language:tnn, language:tnp, language:tnt, language:tod, language:tom, language:tpi, language:tpl, language:tpu, language:tsb, language:tsn, language:tn, language:tso, language:ts, language:tuv, language:tuz, language:tvs, language:udg, language:unr, language:urd, language:ur, language:uzb, language:uz, language:ven, language:ve, language:vie, language:vi, language:vif, language:war, language:wbm, language:wbr, language:wms, language:wni, language:wnk, language:wtk, language:xho, language:xh, language:xkg, language:xmd, language:xmg, language:xmm, language:xog, language:xty, language:yas, language:yav, language:ybb, language:ybh, language:ybi, language:ydd, language:yea, language:yet, language:yid, language:yi, language:yin, language:ymp, language:zaw, language:zho, language:zlm, language:zuh, language:zul, language:zu, license:cc-by-4.0, license:cc-by-nc-4.0, license:cc-by-nd-4.0, license:cc-by-sa-4.0, license:cc-by-nc-nd-4.0, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, region:us","None","This version of the Bloom Library data is developed specifically for the Visual Story Telling (VIST) task. It includes data from 363 languages across 36 language families, with many of the languages r","# @InProceedings{huggingface:dataset, # title = {A great new dataset}, # author={huggingface, Inc. # }, # year={2020} # } #","afr, af, aaa, abc, ada, adq, aeu, agq, ags, ahk, aia, ajz, aka, ak, ame, amh, am, amp, amu, ann, aph, awa, awb, azn, azo, bag, bam, bm, baw, bax, bbk, bcc, bce, bec, bef, ben, bn, bfd, bfm, bfn, bgf, bho, bhs, bis, bi, bjn, bjr, bkc, bkh, bkm, bkx, bob, bod, bo, boz, bqm, bra, brb, bri, brv, bss, bud, buo, bwt, bwx, bxa, bya, bze, bzi, cak, cbr, ceb, cgc, chd, chp, cim, clo, cmn, zh, cmo, csw, cuh, cuv, dag, ddg, ded, deu, de, dig, dje, dmg, dnw, dtp, dtr, dty, dug, eee, ekm, enb, enc, eng, en, ewo, fas, fa, fil, fli, fon, fra, fr, fub, fuh, gal, gbj, gou, gsw, guc, guj, gu, guz, gwc, hao, hat, ht, hau, ha, hbb, hig, hil, hin, hi, hla, hna, hre, hro, idt, ilo, ind, id, ino, isu, ita, it, jgo, jmx, jpn, ja, jra, kak, kam, kan, kn, kau, kr, kbq, kbx, kby, kek, ken, khb, khm, km, kik, ki, kin, rw, kir, ky, kjb, kmg, kmr, ku, kms, kmu, kor, ko, kqr, krr, ksw, kur, ku, kvt, kwd, kwu, kwx, kxp, kyq, laj, lan, lao, lo, lbr, lfa, lgg, lgr, lhm, lhu, lkb, llg, lmp, lns, loh, lsi, lts, lug, lg, luy, lwl, mai, mal, ml, mam, mar, mr, mdr, mfh, mfj, mgg, mgm, mgo, mgq, mhx, miy, mkz, mle, mlk, mlw, mmu, mne, mnf, mnw, mot, mqj, mrn, mry, msb, muv, mve, mxu, mya, my, myk, myx, mzm, nas, nco, nep, ne, new, nge, ngn, nhx, njy, nla, nld, nl, nlv, nod, nsk, nsn, nso, nst, nuj, nwe, nwi, nxa, nxl, nya, ny, nyo, nyu, nza, odk, oji, oj, oki, omw, ori, or, ozm, pae, pag, pan, pa, pbt, pce, pcg, pdu, pea, pex, pis, pkb, pmf, pnz, por, pt, psp, pwg, qaa, qub, quc, quf, quz, qve, qvh, qvm, qvo, qxh, rel, rnl, ron, ro, roo, rue, rug, rus, ru, san, sa, saq, sat, sdk, sea, sgd, shn, sml, snk, snl, som, so, sot, st, sox, spa, es, sps, ssn, stk, swa, sw, swh, sxb, syw, taj, tam, ta, tbj, tdb, tdg, tdt, teo, tet, tgk, tg, tha, th, the, thk, thl, thy, tio, tkd, tnl, tnn, tnp, tnt, tod, tom, tpi, tpl, tpu, tsb, tsn, tn, tso, ts, tuv, tuz, tvs, udg, unr, urd, ur, uzb, uz, ven, ve, vie, vi, vif, war, wbm, wbr, wms, wni, wnk, wtk, xho, xh, xkg, xmd, xmg, xmm, xog, xty, yas, yav, ybb, ybh, ybi, ydd, yea, yet, yid, yi, yin, ymp, zaw, zho, zh, zlm, zuh, zul, zu","multi","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-4.0","None","None","None"
+"lmms-lab/LLaVA-OneVision-Mid-Data","lmms-lab","2024-08-10 04:09:04","2024-08-26 05:25:07+00:00","d7327997bcd2d5a9fdf9f506ca92029e4a787645","334","1955","16","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:100K<n<1M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, arxiv:2408.03326, arxiv:2310.05126, region:us, multimodal","text-generation","Dataset Card for LLaVA-OneVision Due to unknow reasons, we are unable to process dataset with large amount into required HF format. So we directly upload the json files and image folders (compressed i","None","zh, en","bi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2408.03326","None","None"
+"xhluca/publichealth-qa","xhluca","2024-05-16 23:03:30","2024-05-17 03:47:43+00:00","3b67b6b63ee464870fc21cdc888289c843204051","368","1954","0","task_categories:question-answering, language:ar, language:en, language:es, language:fr, language:ko, language:ru, language:vi, language:zh, license:cc-by-nc-sa-3.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, doi:10.57967/hf/2247, region:us","question-answering","Usage import datasets langs = ['arabic', 'chinese', 'english', 'french', 'korean', 'russian', 'spanish', 'vietnamese'] data = datasets.load_dataset('xhluca/publichealth-qa', split='test', name=langs[0","None","ar, en, es, fr, ko, ru, vi, zh","multi","n|<|1|K","None","False","False","False","cc-by-nc-sa-3.0","None","None","None"
+"mteb/xnli","mteb","2024-05-07 12:15:43","2024-05-07 12:27:42+00:00","09698e0180d87dc247ca447d3a1248b931ac0cdb","308","1922","1","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:10M<n<100M, 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"
+"BUAADreamer/llava-med-zh-instruct-60k","BUAADreamer","2024-05-07 13:07:10","2024-05-21 01:54:38+00:00","fe7a04ea33b1a0ee256eec14d6cd125821be01cb","232","1902","14","task_categories:visual-question-answering, task_categories:image-to-text, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, medical, biology, llama-factory","visual-question-answering, image-to-text","This Chinese dataset was translated from llava-med using Qwen1.5-14B-Chat and contains 60k medical visual instruction data points. You can organize content in the dataset_info.json in LLaMA Factory li","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"shibing624/AdvertiseGen","shibing624","2023-03-28 02:42:56","2023-05-12 07:25:00+00:00","5e66c32b4f0d6c9f8a3ab137282d6734fb25f37d","80","1897","22","task_categories:text-generation, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-generation, e-commerce advertise","text-generation","Dataset Card for AdvertiseGen formal url: https://www.luge.ai/#/luge/dataDetail?id=9 Dataset Description 数据集介绍 AdvertiseGen是电商广告文案生成数据集。 AdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输","None","zh","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"vcr-org/VCR-wiki-zh-easy-test","vcr-org","2024-06-10 22:43:41","2024-07-28 09:39:05+00:00","c1e99d07d658717a49549adb56ca4db37123a9c0","102","1840","0","task_categories:visual-question-answering, source_datasets:wikimedia/wit_base, language:zh, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.06462, region:us","visual-question-answering","The VCR-Wiki Dataset for Visual Caption Restoration (VCR) 🏠 Paper | 👩🏻‍💻 GitHub | 🤗 Huggingface Datasets | 📏 Evaluation with lmms-eval This is the official Hugging Face dataset for VCR-Wiki, a dataset","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2406.06462","None","None"
+"zetavg/zh-tw-wikipedia","zetavg","2023-05-06 10:35:05","2023-05-06 12:44:26+00:00","0c0463b2ae0643db0018e68f6286a256f0e88c85","149","1825","25","task_categories:text-generation, language:zh, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","台灣正體中文維基百科 (zh-tw Wikipedia) 截至 2023 年 5 月，中文維基百科 2,533,212 篇條目的台灣正體文字內容。每篇條目為一列 (row)，包含 HTML 以及 Markdown 兩種格式。 A nearly-complete collection of 2,533,212 Traditional Chinese (zh-tw) Wikipedia pages, ","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"ZenMoore/RoleBench","ZenMoore","2023-10-19 08:54:01","2023-11-23 11:09:22+00:00","a57ed54f9613921e4a5f1b63601a558cd5acf971","171","1817","75","language:zh, language:en, license:apache-2.0, modality:text, arxiv:2310.00746, arxiv:2305.13246, region:us, Role-Playing, Instruction","None","RoleBench Paper Title: RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models arXiv Link: https://arxiv.org/abs/2310.00746 Github Repo: https://github.com/Inte","None","zh, en","bi","None","None","False","False","False","apache-2.0","2310.00746","None","None"
+"Cohere/wikipedia-22-12-zh-embeddings","Cohere","2023-01-14 00:44:03","2023-03-22 16:55:57+00:00","864ed9e578765742ee3bb0ee5713090bf6a8a31a","73","1814","12","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:multilingual, language:zh, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","Wikipedia (zh) embedded with cohere.ai multilingual-22-12 encoder We encoded Wikipedia (zh) using the cohere.ai multilingual-22-12 embedding model. To get an overview how this dataset was created and ","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"slone/nllb-200-10M-sample","slone","2023-10-30 23:43:49","2023-11-20 13:15:10+00:00","112cd775b7d7146372f560738eda4fd53c8a563d","95","1789","9","task_categories:translation, language:ak, language:am, language:ar, language:awa, language:azj, language:bm, language:ban, language:be, language:bem, language:bn, language:bho, language:bjn, language:bug, language:bg, language:ca, language:ceb, language:cs, language:cjk, language:ckb, language:crh, language:da, language:de, language:dik, language:dyu, language:el, language:en, language:eo, language:et, language:ee, language:fo, language:fj, language:fi, language:fon, language:fr, language:fur, language:ff, 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:mn, language:km, language:ki, language:rw, language:ky, language:kmb, language:kmr, language:kr, 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:lv, language:mag, language:mai, language:ml, language:mr, language:min, language:mk, language:mt, language:mni, language:mos, language:mi, language:my, language:nl, language:nb, language:ne, language:nso, language:nus, language:ny, language:oc, language:ory, language:pag, language:pa, language:pap, language:pbt, language:fa, language:plt, language:pl, language:pt, language:prs, language:qu, 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:sw, language:szl, language:ta, language:taq, language:tt, language:te, language:tg, language:tl, 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:uz, language:vec, language:vi, language:war, language:wo, language:xh, language:yi, language:yo, language:zh, language:ms, language:zu, license:odc-by, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2207.04672, arxiv:2308.11596, region:us","translation","Dataset Card for ""nllb-200-10M-sample"" This is a sample of nearly 10M sentence pairs from the NLLB-200 mined dataset allenai/nllb, scored with the model facebook/blaser-2.0-qe described in the Seamles","None","ak, am, ar, awa, azj, bm, ban, be, bem, bn, bho, bjn, bug, bg, ca, ceb, cs, cjk, ckb, crh, da, de, dik, dyu, el, en, eo, et, ee, fo, fj, fi, fon, fr, fur, ff, 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, ks, ka, kk, kbp, kea, mn, km, ki, rw, ky, kmb, kmr, kr, kr, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lv, mag, mai, ml, mr, min, mk, mt, mni, mos, mi, my, nl, nb, ne, nso, nus, ny, oc, ory, pag, pa, pap, pbt, fa, plt, pl, pt, prs, qu, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, sw, szl, ta, taq, tt, te, tg, tl, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uz, vec, vi, war, wo, xh, yi, yo, zh, zh, ms, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","odc-by","2207.04672","None","None"
+"BAAI/COIG-PC-Lite","BAAI","2023-06-28 02:56:01","2024-06-14 01:18:23+00:00","c8a630389c1aee2f6649744da3c8f8f5b2cbdb64","314","1788","36","language:zh, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2304.07987, region:us","None","COIG Prompt Collection License Default Licensing for Sub-Datasets Without Specific License Declaration: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declar","None","zh","mono","None","None","False","False","False","unknown","None","None","None"
+"lianghsun/tw-legal-qa-3M","lianghsun","2024-09-24 07:28:33","2024-11-08 06:28:44+00:00","61e174f1ae34615f6cfdf08593f699e66dc3b221","328","1785","0","task_categories:question-answering, task_categories:text-generation, language:zh, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal, Taiwan, ROC, tw","question-answering, text-generation","Data Card for 中華民國台灣之法律問題集（tw-legal-qa） ...(WIP)...","None","zh","mono","1|K|<|n|<|1|0|K","None","False","manual","False","cc-by-nc-nd-4.0","None","None","None"
+"hfl/ruozhiba_gpt4","hfl","2024-04-28 04:36:22","2024-05-18 05:33:59+00:00","c581c5753c397f960b145933a6c8998bc50c61a9","198","1784","68","language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2403.18058, region:us","None","ruozhiba_gpt4 本仓库包含使用GPT-4（4T/4o）构建的ruozhiba指令数据[^1]，共计2449条。其中包含以下两个版本，题目相同，仅回答内容不同。 ruozhiba_qa2449_gpt4t.json：利用gpt-4-turbo-20240409 对问题进行了回答。 ruozhiba_qa2449_gpt4o.json：利用gpt-4o-20240514 对问题进行了回答。","None","zh","mono","None","None","False","False","False","apache-2.0","2403.18058","None","None"
+"BAAI/IndustryCorpus_education","BAAI","2024-07-25 05:46:35","2024-07-26 02:30:37+00:00","0b616100beba98e969647d214e34cbd647caed12","627","1777","1","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"sasuke-uchiha-13/phub","sasuke-uchiha-13","2024-09-19 13:18:41","2024-09-19 13:37:33+00:00","b1c44002df554fe36486834e04abe2ce90d16851","209","1766","0","task_categories:text-generation, task_categories:text-classification, task_categories:token-classification, task_categories:fill-mask, task_categories:table-question-answering, task_categories:text2text-generation, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, text, math, reasoning, instruction, tool","text-generation, text-classification, token-classification, fill-mask, table-question-answering, text2text-generation","Hello World","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Nicolas-BZRD/English_French_Songs_Lyrics_Translation_Original","Nicolas-BZRD","2023-09-12 21:21:44","2024-02-08 23:34:15+00:00","f2b64a358132305095e602ce11819d834344d90f","321","1755","11","task_categories:translation, task_categories:text-generation, language:fr, language:en, language:es, language:it, language:de, language:ko, language:id, language:pt, language:no, language:fi, language:sv, language:sw, language:hr, language:so, language:ca, language:tl, language:ja, language:nl, language:ru, language:et, language:tr, language:ro, language:cy, language:vi, language:af, language:hu, language:sk, language:sl, language:cs, language:da, language:pl, language:sq, language:el, language:he, language:zh, language:th, language:bg, language:ar, license:unknown, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.00786, region:us, music, parallel, parallel data","translation, text-generation","Original Songs Lyrics with French Translation Dataset Summary Dataset of 99289 songs containing their metadata (author, album, release date, song number), original lyrics and lyrics translated into Fr","None","fr, en, es, it, de, ko, id, pt, no, fi, sv, sw, hr, so, ca, tl, ja, nl, ru, et, tr, ro, cy, vi, af, hu, sk, sl, cs, da, pl, sq, el, he, zh, th, bg, ar","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","None","None","None"
+"botisan-ai/cantonese-mandarin-translations","botisan-ai","2022-03-02 23:29:22","2024-01-13 03:30:12+00:00","7f9bc116cf52a133d0e0e4c05f3af16319c8f98c","85","1750","18","task_categories:text2text-generation, task_categories:translation, annotations_creators:machine-generated, language_creators:found, multilinguality:translation, source_datasets:original, language:zh, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, conditional-text-generation","text2text-generation, translation","Dataset Card for cantonese-mandarin-translations Dataset Summary This is a machine-translated parallel corpus between Cantonese (a Chinese dialect that is mainly spoken by Guangdong (province of China","None","zh","mono","u|n|k|n|o|w|n","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"nthakur/swim-ir-cross-lingual","nthakur","2024-04-27 23:58:22","2024-04-28 05:11:45+00:00","1d4d91d3bb020536b4a88cc4982ae4f9dc87cc5e","153","1750","6","task_categories:text-retrieval, task_categories:question-answering, language_creators:machine-generated, multilinguality:multilingual, source_datasets:original, language:ar, language:bn, language:de, language:es, language:fa, language:fi, language:fr, language:hi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, language:yo, language:zh, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2311.05800, region:us","text-retrieval, question-answering","Dataset Card for SWIM-IR (Cross-lingual) This is the cross-lingual subset of the SWIM-IR dataset, where the query generated is in the target language and the passage is in English. The SWIM-IR dataset","None","ar, bn, de, es, fa, fi, fr, hi, id, ja, ko, ru, sw, te, th, yo, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-4.0","2311.05800","None","None"
+"OzoneAsai/4typeCalculation","OzoneAsai","2023-08-06 09:30:34","2023-08-06 10:04:35+00:00","6fb4b87111a21f4b80be271c4c39840aa6887fa2","442","1734","1","language:en, language:zh, language:de, language:ru, language:ko, language:fr, language:ja, license:wtfpl, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","conversational","Dataset Card for Calculation size JSON file: output1.json≒1.3GB ~ output60.json In total 70 ~ 80GB Dataset Summary en: Calculation. Its range will be expanded later. zh: 计算。其范围将在以后扩展。 de: Berechnung. ","None","en, zh, de, ru, ko, fr, ja","multi","None","None","False","False","False","wtfpl","None","None","None"
+"zxbsmk/webnovel_cn","zxbsmk","2023-07-09 14:33:25","2023-08-09 09:39:49+00:00","8de8e62c20bf373450259ca466dd0f522def7cfa","113","1718","102","task_categories:text2text-generation, language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, doi:10.57967/hf/0877, region:us","text2text-generation","内容 包含从12560本网文提取的约21.7M条可用于训练小说生成的中文指令数据(novel_json_tokens512.zip)。下载链接：https://pan.baidu.com/s/1TorBMbrqxrn6odRF0PJBVw 提取码：jlh3 以及从中提取出的包含50k条数据的子集(novel_cn_token512_50k.json)。其中输入和输出都不多于 512 tokens。","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","None","None","None"
+"Unbabel/TowerBlocks-v0.1","Unbabel","2023-12-22 19:51:16","2024-03-04 13:17:50+00:00","372dc8d7378ee47013a9f4c21cd656cf516bb051","88","1717","27","language:en, language:de, language:fr, language:zh, language:pt, language:nl, language:ru, language:ko, language:it, language:es, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.17733, region:us","conversational","Dataset Card for TowerBlocks TowerBlocks is the dataset used to train TowerInstruct-v0.1, a language model specialized for translation tasks such as machine translation (e.g. general, document, termin","None","en, de, fr, zh, pt, nl, ru, ko, it, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"swaption2009/20k-en-zh-translation-pinyin-hsk","swaption2009","2023-01-31 19:02:09","2023-02-01 06:40:59+00:00","83ae38cb544e836c9ff84a4666c17432b8b0d6f1","76","1693","20","task_categories:translation, language:en, language:zh, size_categories:100K<n<1M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","translation","20,000+ chinese sentences with translations and pinyin Source: https://mnemosyne-proj.org/cards/20000-chinese-sentences-translations-and-pinyin Contributed by: Brian Vaughan http://brianvaughan.net/ D","None","en, zh","bi","None","None","False","False","False","None","None","None","None"
+"gentaiscool/bitext_sib200_miners","gentaiscool","2024-06-18 04:54:54","2024-06-18 05:33:47+00:00","8c11f86c9fb41ec5fd97504de9501596ced4c224","191","1693","2","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, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","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","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"shibing624/sts-sohu2021","shibing624","2023-06-18 14:38:51","2023-06-19 09:02:29+00:00","6c199b7dd873f0af663b965136c34637f0b3aefd","152","1688","10","task_categories:text-classification, task_categories:sentence-similarity, task_ids:natural-language-inference, task_ids:semantic-similarity-scoring, task_ids:text-scoring, annotations_creators:shibing624, language_creators:shibing624, multilinguality:zh, source_datasets:https://www.biendata.xyz/competition/sohu_2021/data/, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, sentence-similarity, natural-language-inference, semantic-similarity-scoring, text-scoring","2021搜狐校园文本匹配算法大赛数据集","https://github.com/shibing624/text2vec","zh","mono","1|0|0|K|<|n|<|2|0|M","sts","False","False","False","cc-by-4.0","None","None","natural-language-inference, semantic-similarity-scoring, text-scoring"
+"BAAI/IndustryCorpus_news","BAAI","2024-07-25 05:46:34","2024-07-26 02:30:34+00:00","6b6f33f2356ecd059858aca0e6a7b851fe7a0515","345","1687","3","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:100M<n<1B, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"IS2Lab/S-Eval","IS2Lab","2024-05-28 11:35:31","2024-11-03 06:38:47+00:00","84517c3380fd64735c10ffd76147ccd4111df432","217","1682","7","task_categories:text-generation, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2405.14191, region:us, safety evaluation, jailbreak, LLMs safety, open-end Q&A","text-generation","S-Eval: Automatic and Adaptive Test Generation for Benchmarking Safety Evaluation of Large Language Models 🏆 Leaderboard 🔔 Updates 📣 [2024/10/25]: We release all 20,000 base risk prompts and 200,000 c","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","2405.14191","None","None"
+"wnkh/MultiMed","wnkh","2024-09-26 10:24:11","2024-12-02 23:59:45+00:00","74f7cba48f0ba47b2020fad62f95c742f973e172","280","1627","1","task_categories:translation, language:vi, language:en, language:de, language:zh, language:fr, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for Dataset Name This dataset is an extended version of leduckhai/MultiMed. This dataset is still in the development stage! Dataset Details Dataset Description Languages (NLP): Vietnamese","None","vi, en, de, zh, fr","multi","None","None","False","manual","False","None","None","None","None"
+"ystemsrx/Erotic_Literature_Collection","ystemsrx","2024-09-04 13:57:30","2024-09-26 06:04:23+00:00","3a3258b3a6f95f2c30d9ca6e9c93a4d04023431c","348","1620","71","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, porn, Pre-training, Fine-tuning, Explicit Content, Chinese, Erotic Literature","text-generation, text2text-generation","English 中文色情文学数据集合集 概述 本仓库包含了51个中文色情文学数据集。每个数据集由短篇色情小说、个人色情经验及其他形式的色情内容组成。数据集的格式为JSON，每个文件包含一个对象数组，每个对象代表一篇文档： [ {""text"": ""document""}, {""text"": ""document""} ] 这些数据集可用于语言模型的预训练，经过适当调整后也可用于模型的微调。 数据集格式 文","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"textdetox/multilingual_toxic_lexicon","textdetox","2023-12-12 09:26:27","2024-06-10 09:40:15+00:00","3bfc11eecae82cd1988e843120c49f37a838f50b","200","1612","2","language:en, language:ru, language:uk, language:es, language:de, language:ar, language:am, language:hi, language:zh, license:openrail++, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2108.03070, region:us","None","This is the compilation of 9 languages (English, Russian, Ukrainian, Spanish, German, Amharic, Arabic, Chinese, Hindi) toxic words lists which is used for CLEF TextDetox 2024 shared task. The list of ","None","en, ru, uk, es, de, ar, am, hi, zh","multi","None","None","False","False","False","openrail++","2108.03070","None","None"
+"larryvrh/CCMatrix-v1-Ja_Zh-filtered","larryvrh","2023-04-08 05:05:55","2023-04-08 05:13:43+00:00","5150803873cc4084a4d7ed5254de7c371f16835f","90","1611","10","task_categories:translation, language:zh, language:ja, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for ""CCMatrix-v1-Ja_Zh-filtered"" Filtered and modified version of Japanese/Chinese language pair data from CCMatrix v1. Process steps: 1. Basic regex based filtering / length checking to ","None","zh, ja","bi","None","None","False","False","False","None","None","None","None"
+"neuclir/csl","neuclir","2023-03-20 21:17:19","2023-07-05 20:02:54+00:00","20f8f70abf85c4c7854b8b78270f339c10af7d2f","127","1591","6","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:no-annotation, source_datasets:extended|csl, language:zh, language:en, license:apache-2.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us","text-retrieval, document-retrieval","Dataset Card for CSL Dataset Description CSL is the Chinese Scientific Literature Dataset. Paper: https://aclanthology.org/2022.coling-1.344 Repository: https://github.com/ydli-ai/CSL Dataset Summary ","None","zh, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"Hothan/OlympiadBench","Hothan","2024-07-16 11:48:01","2024-07-17 06:39:54+00:00","3b426ee1d587c71b495146dde9f3e995ee7ac59f","426","1588","16","None","question-answering, visual-question-answering","OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems 📖 arXiv | GitHub Dataset Description OlympiadBench is an Olympiad-level bilingual ","None","zh, en","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","2402.14008","None","None"
+"floschne/marvl","floschne","2024-05-16 09:30:02","2024-05-16 09:58:22+00:00","d25b0dc0bce9d8e9b56bf666d3442dc907180667","64","1574","0","task_categories:visual-question-answering, language:id, language:sw, language:ta, language:tr, language:zh, language:en, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","visual-question-answering","MaRVL This is a copy from the original repo: https://github.com/marvl-challenge/marvl-code If you use this dataset, please cite the original authors: @inproceedings{liu-etal-2021-visually, title = ""Vi","None","id, sw, ta, tr, zh, en","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"vcr-org/VCR-wiki-zh-hard-test","vcr-org","2024-06-10 22:39:52","2024-07-28 09:39:08+00:00","e1fafca8dc1b460661f4a1fc288b23d118136c1a","60","1574","1","task_categories:visual-question-answering, source_datasets:wikimedia/wit_base, language:zh, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.06462, region:us","visual-question-answering","The VCR-Wiki Dataset for Visual Caption Restoration (VCR) 🏠 Paper | 👩🏻‍💻 GitHub | 🤗 Huggingface Datasets | 📏 Evaluation with lmms-eval This is the official Hugging Face dataset for VCR-Wiki, a dataset","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2406.06462","None","None"
+"FreedomIntelligence/ApolloMoEDataset","FreedomIntelligence","2024-10-14 03:03:59","2024-10-18 02:52:33+00:00","45075ce81754ac0012a74a24ba8ab4a5ed8620d0","267","1567","3","task_categories:question-answering, language:ar, language:en, language:zh, language:ko, language:ja, language:mn, language:th, language:vi, language:lo, language:mg, language:de, language:pt, language:es, language:fr, language:ru, language:it, language:hr, language:gl, language:cs, language:co, language:la, language:uk, language:bs, language:bg, language:eo, language:sq, language:da, language:sa, language:no, language:gn, language:sr, language:sk, language:gd, language:lb, language:hi, language:ku, language:mt, language:he, language:ln, language:bm, language:sw, language:ig, language:rw, language:ha, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.10626, region:us, biology, medical","question-answering","Democratizing Medical LLMs For Much More Languages Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38","None","ar, en, zh, ko, ja, mn, th, vi, lo, mg, de, pt, es, fr, ru, it, hr, gl, cs, co, la, uk, bs, bg, eo, sq, da, sa, no, gn, sr, sk, gd, lb, hi, ku, mt, he, ln, bm, sw, ig, rw, ha","multi","1|B|<|n|<|1|0|B","None","False","False","False","mit","2410.10626","None","None"
+"BramVanroy/xlwic_wn","BramVanroy","2023-10-02 07:48:29","2023-10-02 09:19:20+00:00","af1ef5adfc3be84316f44d0264161b8b44bbf22e","534","1551","1","task_categories:text-classification, language:bg, language:zh, language:hr, language:da, language:nl, language:et, language:fa, language:ja, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-classification","Multilingual Word-in-Context (WordNet) Refer to the documentation and paper for more information.","None","bg, zh, hr, da, nl, et, fa, ja, ko","multi","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"espnet/ace-opencpop-segments","espnet","2024-04-15 22:52:07","2024-07-16 05:35:36+00:00","a66b1ab0b3bdebfece8d78fd2980dfeebbcfd67c","259","1549","4","task_categories:text-to-audio, task_categories:audio-to-audio, task_categories:automatic-speech-recognition, multilinguality:monolingual, language:zh, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2401.17619, region:us","text-to-audio, audio-to-audio, automatic-speech-recognition","Citation Information @misc{shi2024singingvoicedatascalingup, title={Singing Voice Data Scaling-up: An Introduction to ACE-Opencpop and ACE-KiSing}, author={Jiatong Shi and Yueqian Lin and Xinyi Bai an","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"sorry-bench/sorry-bench-202406","sorry-bench","2024-06-12 17:37:37","2024-07-02 19:55:07+00:00","b34822276edde97592eda99c0b56d306f8830469","444","1546","18","None","text-generation, question-answering, text2text-generation","Dataset Card for SORRY-Bench Dataset (2024/06) 🏠Website 📑Paper 📚Dataset 💻Github 🧑‍⚖️Human Judgment Dataset 🤖Judge LLM This dataset contains 9.5K potentially unsafe instructions, intended to be used fo","None","en, zh, fr, ml, mr, ta","multi","1|K|<|n|<|1|0|K","None","False","manual","False","other","2406.14598","None","None"
+"declare-lab/CategoricalHarmfulQA","declare-lab","2024-02-20 09:37:27","2024-02-27 19:57:39+00:00","321e068b391b7d1aeeb882ac468356a9513c9167","124","1544","5","language:en, language:zh, language:vi, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.11746, region:us","None","CatQA: A categorical harmful questions dataset CatQA is used in LLM safety realignment research: Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arith","None","en, zh, vi","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2402.11746#:~:text=Safety%20Re%2DAlignment%20of%20Fine%2Dtuned%20Language%20Models%20through%20Task%20Arithmetic,-Rishabh%20Bhardwaj%2C%20Do&text=Aligned%20language%20models%20face%20a,that%20performs%20LLM%20safety%20realignment.","None","None"
+"ky552/cszs_zh_en","ky552","2023-12-14 17:58:02","2024-05-24 07:45:38+00:00","8c05b9b7542929f667e39ea432128ea0f5521288","93","1543","0","language:zh, language:en, license:mit, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","This dataset contains the Mandarin-English track of the benchmark from ICASSP 2024: Zero Resource Code-Switched Speech Benchmark Using Speech Utterance Pairs for Multiple Spoken Languages.Though the b","None","zh, en","bi","None","None","False","False","False","mit","None","None","None"
+"lnwang/retrieval_qa","lnwang","2023-11-24 03:26:11","2023-12-22 07:24:23+00:00","dfce4b55fb22acca9f5310c6f0040507dd217615","117","1503","5","language:en, language:zh, language:ja, language:es, language:de, language:ru, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art","None","Retrieval_QA: A Simple Multilingual Benchmark For Retrieval Encoder Models The purpose of this dataset is to provide a simple and easy-to-use benchmark for retrieval encoder models, which helps resear","None","en, zh, ja, es, de, ru","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"m-a-p/COIG-Kun","m-a-p","2023-12-04 06:36:50","2024-04-08 12:02:20+00:00","d8bb211a7c88605cf979e5a9c53971f7b39b90c5","371","1497","30","task_categories:question-answering, language:zh, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Kun: Answer Polishment Saves Your Time for Using Intruction Backtranslation on Self-Alignment Overview The COIG-Kun dataset, part of the COIG-Kun GitHub project, consists of instructional data used fo","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"p208p2002/wikipedia-zhtw-filtered","p208p2002","2024-05-31 03:30:27","2024-06-02 09:53:10+00:00","710b519cfe3ad5d995e6e77e92943dad5c5f0eff","93","1492","0","language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Wikipedia-zhtw Filtered 此資料集使用 huggingface/datatrove 進行了系統性的資料清洗。 我們在清洗過程中應用了多種基於規則的過濾方法，移除許多過短、重複和含有噪聲的資料。","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"yys/OpenOrca-Chinese","yys","2023-09-07 06:01:51","2023-09-08 08:05:47+00:00","c432bbf0e405ad9a72e900406fcbb141583ea0ad","97","1490","95","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:zh, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2301.13688, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","🐋 OpenOrca-Chinese 数据集！🐋 感谢 Open-Orca/OpenOrca 数据集的发布，给广大NLP研究人员和开发者带来了宝贵的资源！ 这是一个对 Open-Orca/OpenOrca 数据集中文翻译的版本，翻译引擎为 Google 翻译，希望能给中文 LLM 研究做出一点点贡献。 Dataset Summary The OpenOrca dataset is a collec","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2301.13688","None","None"
+"romrawinjp/multilingual-coco","romrawinjp","2024-10-23 08:03:46","2024-10-25 11:57:55+00:00","2b65d7c1061338f936077c61df6cdff1381b91f9","109","1489","0","task_categories:image-to-text, language:en, language:th, language:ru, language:ja, language:it, language:de, language:vi, language:zh, language:ar, language:es, 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:1405.0312, region:us","image-to-text","Multilingual Common Objects in Context (COCO) Dataset This dataset is a collection of multiple language open-source captions of COCO dataset. The split in this dataset is set according to Andrej Karpa","None","en, th, ru, ja, it, de, vi, zh, ar, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"TigerResearch/sft_zh","TigerResearch","2023-06-09 10:15:22","2023-06-09 12:21:42+00:00","f5106630cc106a8f274dba8cdd2b2aa4e501e7ad","110","1472","46","language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","Tigerbot 开源项目中微调中文sft-zh数据合集 本合集涵盖本组织下开源的其他中文sft-中文-数据集，不需要重复下载 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/sft_zh') 文件细分 类型 语言 数据集文件 数量 alpaca 中文 中文 tigerbot-alpaca-zh-0.5m 0.","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"versae/bibles","versae","2022-03-02 23:29:22","2022-08-27 09:11:17+00:00","2fa72971a77203767aa622c7e782734ecc7622b8","199","1467","1","language:sq, language:ar, language:az, language:be, language:bg, language:ceb, language:zh, language:cs, language:da, language:en, language:es, language:fi, language:fr, language:de, language:el, language:ht, language:he, language:hi, language:hu, language:it, language:ko, language:la, language:nl, language:no, language:pt, language:rm, language:ru, language:sw, language:ta, language:th, language:tr, language:vi, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us","None","Multilingual Bibles","@InProceedings{--, author = {---}, title = {---}, booktitle = {---}, year = 2021, address = ""---"" }","sq, ar, az, be, bg, ceb, zh, cs, da, en, es, fi, fr, de, el, ht, he, hi, hu, it, ko, la, nl, no, pt, rm, ru, sw, ta, th, tr, vi","multi","None","None","False","False","False","None","None","None","None"
+"xusenlin/clue-ner","xusenlin","2022-12-07 13:14:03","2022-12-07 14:22:37+00:00","41aef9650901fa2169213f38d49bd6491eb9b77a","129","1463","10","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, named entity recognition, clue","None","CLUE-NER 命名实体识别数据集 字段说明 text: 文本 entities: 文本中包含的实体 id: 实体 id entity: 实体对应的字符串 start_offset: 实体开始位置 end_offset: 实体结束位置的下一位 label: 实体对应的开始位置","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"yuyouyu/BeyondDialogue","yuyouyu","2024-08-29 06:09:27","2024-08-30 04:53:37+00:00","7dcaeed2968f9521004cbdee16271c79533a2367","199","1462","7","task_categories:question-answering, language:zh, language:en, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2408.10903, region:us","question-answering","🤗 Beyond Dialogue Role-playing Dataset 💭 Paper Title: BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Model arXiv Link: https://arxiv.org/abs/2408.10903 G","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2408.10903","None","None"
+"para-zhou/CDial-Bias","para-zhou","2024-08-07 18:19:22","2024-08-19 17:16:56+00:00","04b1b240fc2a06e97da624f7b6bd6fcc01a5444f","300","1447","6","task_categories:text-classification, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","Official release of CDial-Bias dataset. Notation: Before downloading the dataset, please be aware that: The CDial-Bias Dataset is released for research purpose only and other usages require further pe","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"CohereForAI/include-lite-44","CohereForAI","2024-12-02 11:03:07","2024-12-11 11:25:21+00:00","cc383a1dd1d074088776be221816dd232517562a","719","1437","9","task_categories:text2text-generation, task_categories:multiple-choice, language:sq, language:ar, language:hy, language:az, language:be, language:bn, language:eu, language:bg, language:tr, language:hr, language:nl, language:fa, language:es, language:et, language:fi, language:fr, language:de, language:el, language:ka, language:he, language:hi, language:hu, language:id, language:it, language:ja, language:kk, language:ko, language:lt, language:ml, language:ms, language:ne, language:pl, language:pt, language:ru, language:ta, language:tl, language:te, language:uk, language:ur, language:uz, language:vi, language:zh, language:sr, language:mk, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2411.19799, region:us, chemistry, biology, finance, legal, art, code, medical, music, climate","text2text-generation, multiple-choice","INCLUDE-lite (44 languages) Dataset Description Paper: http://arxiv.org/abs/2411.19799 Dataset Summary INCLUDE is a comprehensive knowledge- and reasoning-centric benchmark across 44 languages that ev","None","sq, ar, hy, az, be, bn, eu, bg, tr, hr, nl, fa, es, et, fi, fr, de, el, ka, he, hi, hu, id, it, ja, kk, ko, lt, ml, ms, ne, pl, pt, ru, ta, tl, te, uk, ur, uz, vi, zh, sr, mk","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2411.19799","None","None"
+"multilingual/orca_dpo_pairs","multilingual","2024-04-02 15:52:27","2024-04-03 16:46:48+00:00","d55a8e47c4fef8d219401fa7d117407d889f953b","174","1426","18","None","text-generation","mLLM IMPLEMENTATION OF Intel/orca_dpo_pairs. LANGUAGES: ARABIC CHINESE FRENCH GERMAN RUSSIAN SPANISH TURKISH (WIP)","None","ar, zh, de, fr, es, tr, ru","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"sentence-transformers/cmedqa-v2","sentence-transformers","2024-06-19 08:05:03","2024-06-19 13:44:46+00:00","8b7e426d99b9bd95c767afda5ba8e3d5cde2efbe","84","1419","0","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:zh, 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 cMedQAv2 This is a reformatting of the cMedQAv2 dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets triplet subset Columns: ""anc","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"chengshidehaimianti/CC-Cat","chengshidehaimianti","2024-06-27 05:14:40","2024-08-18 14:44:41+00:00","8a2457d0d60c951ac340cdaa7f493da914a86de3","324","1416","2","task_categories:text-generation, language:zh, language:en, language:de, language:ru, language:es, language:ja, language:af, language:am, language:an, language:ar, language:as, language:av, language:az, language:ba, language:be, language:bg, language:bo, language:br, language:bs, language:ca, language:cv, language:cy, language:da, language:el, language:eo, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gd, language:gl, language:gn, language:gv, language:he, language:hi, language:hr, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:io, language:is, language:it, language:jv, language:ka, language:kk, language:km, language:kn, language:ko, language:kv, language:kw, language:ky, language:la, language:lb, language:li, language:lo, language:lt, language:lv, language:mg, language:mk, language:ml, language:mn, language:ms, language:mt, language:my, language:ne, language:nl, language:nn, language:no, language:oc, language:os, language:pa, language:pl, language:ps, language:pt, language:qu, language:rm, language:ro, language:sa, language:sc, 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:tg, language:tk, language:tl, language:tr, language:tt, language:ug, language:uk, language:ur, language:uz, language:vi, language:wa, language:yi, language:yo, license:odc-by, size_categories:100M<n<1B, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, croissant","text-generation","CC_Cat Extract from CC-WARC snapshots. Mainly includes texts with 149 languages. PDF/IMAGE/AUDIO/VIDEO raw downloading link. Notice Since my computing resources are limited, this dataset will update b","None","zh, en, de, ru, es, ja, af, am, an, ar, as, av, az, ba, be, bg, bo, br, bs, ca, cv, cy, da, el, eo, et, eu, fa, fi, fr, fy, ga, gd, gl, gn, gv, he, hi, hr, ht, hu, hy, ia, id, ie, io, is, it, jv, ka, kk, km, kn, ko, kv, kw, ky, la, lb, li, lo, lt, lv, mg, mk, ml, mn, ms, mt, my, ne, nl, nn, no, oc, os, pa, pl, ps, pt, qu, rm, ro, sa, sc, sd, si, sk, sl, so, sq, sr, su, sv, sw, ta, te, tg, tk, tl, tr, tt, ug, uk, ur, uz, vi, wa, yi, yo","multi","n|>|1|T","None","False","False","False","odc-by","None","None","None"
+"Isotonic/human_assistant_conversation","Isotonic","2023-02-28 20:59:35","2023-08-31 07:31:15+00:00","eefe292fe4eec3bcc82a59c662bb8380510356cf","260","1413","18","task_categories:text-generation, language:en, language:es, language:zh, license:afl-3.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, conversational","None","None","en, es, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","afl-3.0","None","None","None"
+"jslin09/Fraud_Case_Verdicts","jslin09","2022-03-02 23:29:22","2024-05-01 00:43:11+00:00","db7809750d3217dee3227f1314868691cd1e7610","63","1412","3","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","text-generation","The ""Crime Facts"" of ""Offenses of Fraudulence"" in Judicial Yuan Verdicts Dataset This data set is based on the judgments of ""Offenses of Fraudulence"" cases published by the Judicial Yuan. The data ran","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","manual","False","apache-2.0","None","None","None"
+"taide/TAIDE-14-tasks","taide","2023-09-04 06:21:18","2023-10-26 09:14:32+00:00","1aa63ed4ae6fbf1884b31e46d1d3130e81777467","75","1410","25","task_categories:text-generation, task_categories:question-answering, language:zh, language:en, license:cc-by-nc-4.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, gpt4","text-generation, question-answering, conversational","Dataset Card for TAIDE-14-tasks Dataset Summary The ""TAIDE-14-tasks"" dataset, derived from the TAIDE project, encompasses 14 prevalent text generation tasks. This dataset features a collection of 140 ","None","zh, en","bi","n|<|1|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"silver/mmchat","silver","2022-05-29 11:15:03","2022-07-10 13:04:36+00:00","ecff4b19adb1f4161ea79ad947aaf9089217c34b","152","1404","11","task_ids:dialogue-generation, annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:other, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2108.07154, arxiv:2008.03946, region:us","conversational, dialogue-generation","MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese. Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue). We design var","@inproceedings{zheng2022MMChat, author = {Zheng, Yinhe and Chen, Guanyi and Liu, Xin and Sun, Jian}, title = {MMChat: Multi-Modal Chat Dataset on Social Media}, booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference}, year = {2022}, publisher = {European Language Resources Association}, } @inproceedings{wang2020chinese, title = {A Large-Scale Chinese Short-Text Conversation Dataset}, author = {Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie}, booktitle = {NLPCC}, year = {2020}, url = {https://arxiv.org/abs/2008.03946} }","zh","mono","1|0|M|<|n|<|1|0|0|M","mmchat-multi-modal-chat-dataset-on-social","False","False","False","other","2108.07154","None","dialogue-generation"
+"FreedomIntelligence/Evol-Instruct-Chinese-GPT4","FreedomIntelligence","2023-10-07 02:09:46","2023-12-06 03:47:42+00:00","a78767c57bca6c8bb5773b54607879efb259fb19","91","1401","46","task_categories:text-generation, task_categories:text2text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2309.12053, arxiv:2304.10453, region:us","text-generation, conversational, text2text-generation","The dataset is created by (1) translating English questions of Evol-instruct-70k into Chinese and (2) requesting GPT4 to generate Chinese responses. For more details, please refer to: Repository: http","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","2309.12053","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"
+"botp/liwu-MNBVC","botp","2023-09-12 02:35:33","2023-09-12 02:35:34+00:00","bef5be551ac506475428bc0b9a3694a341a51243","132","1385","2","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:other, language_creators:other, multilinguality:monolingual, source_datasets:original, language:zh, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for MNBVC 数据集介绍 中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到https://github.com/esbatmop/MNBVC 获取未完成清洗的更多数据。 可以","None","zh","mono","u|n|k|n|o|w|n","None","False","False","False","mit","None","None","language-modeling, masked-language-modeling"
+"floschne/xgqa","floschne","2024-05-16 14:17:31","2024-05-23 14:13:12+00:00","b8a124018a849e6957c9de0db7a523bf6561536a","108","1382","0","task_categories:visual-question-answering, language:bn, language:de, language:en, language:id, language:ko, language:pt, language:ru, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2109.06082, region:us","visual-question-answering","xGQA This is a clone of the few_shot-test split of the xGQA dataset Please find the original repository here: https://github.com/adapter-hub/xGQA If you use this dataset, please cite the original auth","None","bn, de, en, id, ko, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"Qwen/P-MMEval","Qwen","2024-11-13 06:12:25","2024-11-28 06:19:41+00:00","47bb647f35fdd6f5374826b3f5d4f84eb5b5afce","417","1381","7","language:ar, language:es, language:fr, language:ja, language:ko, language:pt, language:th, language:vi, language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2411.09116, region:us","None","P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs Introduction We introduce a multilingual benchmark, P-MMEval, covering effective fundamental and capability-spec","None","ar, es, fr, ja, ko, pt, th, vi, en, zh","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"izhx/xtreme-r-udpos","izhx","2024-06-28 07:46:47","2024-06-28 12:50:37+00:00","d0863a53a290c35d469310234e97461ea025287a","190","1378","1","task_categories:token-classification, task_ids:part-of-speech, annotations_creators:found, language_creators:found, multilinguality:multilingual, multilinguality:translation, language:af, language:ar, language:bg, language:bn, language:de, language:el, language:en, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:he, language:hi, language:hu, language:id, language:it, language:ja, language:jv, language:ka, language:kk, language:ko, language:ml, language:mr, language:ms, language:my, language:nl, language:pt, language:ru, language:sw, language:ta, language:te, language:th, language:tl, language:tr, language:ur, language:vi, language:yo, language:zh, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2104.07412, region:us","token-classification, part-of-speech","UDPOS of XTREME-R Generated by build_parquet.py XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation https://arxiv.org/abs/2104.07412","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","None","False","False","False","other","2104.07412","None","part-of-speech"
+"priyank-m/chinese_text_recognition","priyank-m","2022-09-06 21:18:47","2022-09-21 09:08:19+00:00","45970ba9a0fc0f0e7971757228ea1b17d9dd3dfb","154","1351","22","task_categories:image-to-text, task_ids:image-captioning, multilinguality:monolingual, language:zh, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, ocr, text-recognition, chinese","image-to-text, image-captioning","Source of data: https://github.com/FudanVI/benchmarking-chinese-text-recognition","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","image-captioning"
+"erhwenkuo/wikinews-zhtw","erhwenkuo","2023-10-10 03:55:49","2023-10-10 04:06:53+00:00","f6782f89259b3451d8a82e1b3a027804204a6dac","61","1346","3","task_categories:text-generation, language:zh, license:cc-by-sa-3.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""wikinews-zhtw"" 維基新聞（英文：Wikinews）是由一群志願者、即民間記者運營的網路媒體。同時是一個自由內容的維基，屬維基媒體計劃項目，由維基媒體基金會負責運營。維基新聞通過協作新聞學的工作模式去運行，同時亦努力通過中性的觀點報導新聞，包括原創一手獨家報道和採訪。 這個數據集是根據 Wikipedia dumps (https://dumps.w","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"taide/taide-bench","taide","2024-04-11 06:12:37","2024-04-12 00:43:43+00:00","8e3951cd5287212ed570f406d9f73b956df10859","100","1341","13","language:zh, language:en, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for taide-bench Dataset Description Dataset Summary This dataset is used for taide first-stage evaluations and consists of five tasks, each containing 500 samples. The tasks are as follow","None","zh, en","bi","n|<|1|K","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"xmj2002/Chinese_modern_classical","xmj2002","2023-05-28 02:14:34","2023-05-30 06:26:32+00:00","5d5cb99051fce4da45af1c20dc8e489758c8fc2f","152","1339","28","task_categories:translation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for ""Chinese_modern_classical"" 数据来自于NiuTrans/Classical-Modern: 非常全的文言文（古文）-现代文平行语料 (github.com)。 由于原始数据中部分古文没有译文，所以本数据集的数据仅包括了双语数据 。","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"THUDM/LongCite-45k","THUDM","2024-09-02 06:41:56","2024-10-18 09:59:40+00:00","b776b01faf6e132a3d9cc373de1b46cd6630d772","515","1336","65","task_categories:text-generation, task_categories:question-answering, 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:2409.02897, region:us, long context, citation generation, SFT","text-generation, question-answering","LongCite-45k 🤗 [LongCite Dataset] • 💻 [Github Repo] • 📃 [LongCite Paper] LongCite-45k dataset contains 44,600 long-context QA instances paired with sentence-level citations (both English and Chinese, ","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2409.02897","None","None"
+"RoleAgent/RoleAgentBench","RoleAgent","2024-06-11 05:50:37","2024-06-13 04:27:36+00:00","8bda363ab3a8da163c01d033a0f42d0331eebf34","316","1332","5","task_categories:question-answering, task_categories:summarization, language:zh, language:en, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, Role-Playing","question-answering, summarization","RoleAgentBench Paper: RoleAgent: Building, Interacting, and Benchmarking High-quality Role-Playing Agents from Scripts We construct the RoleAgentBench including 128 roles from 5 Chinese and 20 English","None","zh, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Iess/chinese_modern_poetry","Iess","2023-06-25 15:59:49","2023-06-25 16:39:13+00:00","0f0dd33ffd265a00fa101ee7e602a33d027fa696","220","1331","22","language:zh, license:mit, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, poetry, chinese poetry, modern poetry, chinese modern poetry","None","简介 数据集包括了近现代的中国诗人及外国诗人（中译版）作品，所有作品著作权归原作者所有，侵删请联系aa531811820@gmail.com chinese_poems.jsonl为原数据，training_imagery2-5_maxlen256.json 分别是根据2-5个关键意象生成诗歌的相关数据集 数据来源于网络，包括但不限于 https://github.com/sheepzh/poet","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"ZurichNLP/rsd-ists-2016","ZurichNLP","2023-05-20 16:24:04","2023-11-22 12:35:55+00:00","e0c1dd23ce0d62f55392184515ac55975625b37b","143","1267","0","task_categories:token-classification, language_creators:machine-generated, language:en, language:de, language:es, language:fr, language:ja, language:ko, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.13303, region:us","token-classification","Training and test data for the task of Recognizing Semantic Differences (RSD). See the paper for details on how the dataset was created, and see our code at https://github.com/ZurichNLP/recognizing-se","None","en, de, es, fr, ja, ko, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2305.13303","None","None"
+"dbarbedillo/SMS_Spam_Multilingual_Collection_Dataset","dbarbedillo","2023-01-13 02:13:03","2023-01-13 03:07:17+00:00","4b5332a8771b3f6388f8ecc51c2b8ade4be31c73","117","1244","10","task_categories:text-classification, language:en, language:zh, language:es, language:hi, language:fr, language:de, language:ar, language:bn, language:ru, language:pt, language:id, language:ur, language:ja, language:pa, language:jv, language:tr, language:ko, language:mr, language:uk, language:sv, language:no, license:gpl, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","SMS Spam Multilingual Collection Dataset Collection of Multilingual SMS messages tagged as spam or legitimate About Dataset Context The SMS Spam Collection is a set of SMS-tagged messages that have be","None","en, zh, es, hi, fr, de, ar, bn, ru, pt, id, ur, ja, pa, jv, tr, ko, mr, uk, sv, no","multi","1|K|<|n|<|1|0|K","None","False","False","False","gpl","None","None","None"
+"vcr-org/VCR-wiki-zh-hard","vcr-org","2024-06-04 09:28:36","2024-07-28 09:39:07+00:00","fc286286844335f18ba5cc2efbabc93be338556d","233","1232","1","task_categories:visual-question-answering, source_datasets:wikimedia/wit_base, language:zh, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.06462, doi:10.57967/hf/2524, region:us","visual-question-answering","The VCR-Wiki Dataset for Visual Caption Restoration (VCR) 🏠 Paper | 👩🏻‍💻 GitHub | 🤗 Huggingface Datasets | 📏 Evaluation with lmms-eval This is the official Hugging Face dataset for VCR-Wiki, a dataset","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","2406.06462","None","None"
+"aigrant/taiwan-ly-law-research","aigrant","2024-07-22 21:29:38","2025-01-26 17:00:05+00:00","72139b6c6e502f0bbaa91c7901305e8a8a58416c","201","1222","5","language:zh, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Taiwan Legislator Yuan Law Research Data Overview The law research documents are issued irregularly from Taiwan Legislator Yuan. The purpose of those research are providing better understanding on soc","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"SylvanL/Traditional-Chinese-Medicine-Dataset-SFT","SylvanL","2024-10-02 08:04:26","2024-10-26 10:47:40+00:00","5ba2abbea72d757a1fd70a683193452c35b36f83","244","1217","33","task_categories:table-question-answering, language:zh, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, medical","table-question-answering","启古纳今，厚德精术 数据介绍 非网络来源的高质量中医数据集-指令微调 High-Quality Traditional Chinese Medicine Dataset from Non-Internet Sources - SFT/IFT 该数据集经过大量人力和资源的投入精心构建，以共建LLM高质量中文社区为己任。 包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质问答内容，","None","zh","mono","1|B|<|n|<|1|0|B","None","False","False","False","apache-2.0","None","None","None"
+"arcee-ai/BAAI-Infinity-Instruct-System","arcee-ai","2024-06-22 17:35:55","2024-06-24 21:31:13+00:00","3f91bb95ca3fb3811b17caa6f208b9cca18d86a8","103","1207","15","task_categories:text-generation, language:en, language:zh, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.00530, region:us","text-generation","Arcee.ai Modifications The original dataset (https://huggingface.co/datasets/BAAI/Infinity-Instruct) contained 383,697 samples that used ""gpt"" tags for system instructions instead of ""system"" tags. Ad","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"BeastyZ/cmteb_retrieval","BeastyZ","2024-03-27 01:43:56","2024-06-27 13:40:12+00:00","e6a4d2e2de32e48589f6bb97705c13bd80e0972e","146","1201","2","language:zh, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"BAAI/COIG-PC-core","BAAI","2023-09-19 06:24:01","2024-06-14 01:18:33+00:00","69c1be71e8455315c35e04614f7638b934230c35","175","1201","29","language:zh, license:unknown, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2304.07987, region:us","None","COIG Prompt Collection License Default Licensing for Sub-Datasets Without Specific License Declaration: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declar","None","zh","mono","None","None","False","False","False","unknown","None","None","None"
+"Duxiaoman-DI/FinCorpus","Duxiaoman-DI","2023-09-22 05:01:30","2023-09-22 10:10:10+00:00","dae52562603787e65ee00cc95561bab4fa818d9b","141","1200","63","language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, finance","None","中文金融资讯数据集，包括（压缩前）： 上市公司公告 announcement_data.jsonl 20G 金融资讯/新闻 fin_news_data.jsonl 30G fin_articles_data.jsonl 10G 金融试题 fin_exam.jsonl 370M 数据格式： { ""text"": <文本内容>, ""meta"": { ""source"": <数据来源> } }","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","None","None","None"
+"nazimali/quran","nazimali","2024-09-08 18:50:07","2024-09-08 18:58:17+00:00","e7070e8411006fc1b469dbf0f1f54d446e562d33","420","1191","2","task_categories:text-classification, task_categories:token-classification, task_categories:translation, task_categories:feature-extraction, task_categories:text-generation, multilinguality:monolingual, multilinguality:multilingual, language:sq, language:ber, language:ar, language:am, language:az, language:bn, language:bs, language:bg, language:zh, language:cs, language:dv, language:nl, language:en, language:fr, language:de, language:ha, language:hi, language:id, language:it, language:ja, language:ko, language:ku, language:ms, language:ml, language:no, language:ps, language:fa, language:pl, language:pt, language:ro, language:ru, language:sd, language:so, language:es, language:sw, language:sv, language:tg, language:ta, language:tt, language:th, language:tr, language:ur, language:ug, language:uz, license:cc-by-3.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, islam, quran, translations","text-classification, token-classification, translation, feature-extraction, text-generation","Dataset Card for the Quran Summary The Quran with metadata, translations, and multiple Arabic text (can use specific types for embeddings, search, classification, and display). There are 126+ columns ","None","sq, ber, ar, am, az, bn, bs, bg, zh, cs, dv, nl, en, fr, de, ha, hi, id, it, ja, ko, ku, ms, ml, no, ps, fa, pl, pt, ro, ru, sd, so, es, sw, sv, tg, ta, tt, th, tr, ur, ug, uz","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-3.0","None","None","None"
+"raptorkwok/cantonese-chinese-dataset-gen2","raptorkwok","2024-07-26 13:26:19","2024-08-09 12:52:42+00:00","e81551f31ad31c67ad828294bf1492848c3e4d05","62","1190","1","task_categories:translation, language:zh, license:cc0-1.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Cantonese-Written Chinese Parallel Dataset with roughly 1 million pairs of Cantonese and Written Chinese sentences, in JSONL format. The sentence topics include news, novels and daily conversations. T","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc0-1.0","None","None","None"
+"silver/lccc","silver","2022-05-29 09:19:28","2022-11-06 04:51:16+00:00","5bd582fa28cd7143f2f9c852e08e23089d677c44","187","1187","20","task_ids:dialogue-generation, annotations_creators:other, language_creators:other, multilinguality:monolingual, source_datasets:original, language:zh, license:mit, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2008.03946, region:us, dialogue-response-retrieval","conversational, dialogue-generation","LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large corpus of Chinese conversations. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipelin","@inproceedings{wang2020chinese, title={A Large-Scale Chinese Short-Text Conversation Dataset}, author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie}, booktitle={NLPCC}, year={2020}, url={https://arxiv.org/abs/2008.03946} }","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2008.03946","None","dialogue-generation"
+"ecnu-icalk/cmm-math","ecnu-icalk","2024-09-06 03:40:55","2024-09-07 16:00:32+00:00","6b0fe543b3a8331e473b965c94f8b43501e53f6f","209","1179","4","language:zh, license:bsd-3-clause, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2409.02834, region:us","None","CMM-Math 💻 Github Repo 💻 Paper Link Introduction Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most prev","None","zh","mono","None","None","False","False","False","bsd-3-clause","None","None","None"
+"shibing624/roleplay-zh-sharegpt-gpt4-data","shibing624","2024-06-26 13:54:01","2024-06-26 14:18:15+00:00","9c8bd488eb5cb4489a8c2c31518bd0d64154578d","96","1174","48","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","roleplay 数据集 数据 我们有4个数据集文件: ""sharegpt_formatted_data-evol-gpt4.jsonl"" 来自 bai-roleplay/evol-character-entire 将其转换为sharegpt格式。 ""sharegpt_formatted_data-evol-gpt35.jsonl"" 来自 bai-roleplay/evol-character-e","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"nanaaaa/emotion_chinese_english","nanaaaa","2023-02-20 13:24:36","2023-03-05 10:36:14+00:00","ca4bd93ea3531eac2269cb8d8d5ff3ff088397e3","104","1173","18","task_categories:text-classification, language:zh, language:en, size_categories:n<1K, modality:text, library:datasets, library:mlcroissant, doi:10.57967/hf/1019, region:us","text-classification","The emotion_chinese_english dataset is a multilingual emotion dataset annotated by language experts under a project. The dataset can be used for tasks such as multilingual (Chinese and English) emotio","None","zh, en","bi","None","None","False","False","False","None","None","None","None"
+"masuidrive/cv-corpus-17.0-zh-CN-client_id-grouped","masuidrive","2024-04-20 06:30:10","2024-04-20 06:31:01+00:00","61f9e8718b832db5fb1aecf80650f84fa90f8b2c","313","1171","0","task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, language_creators:crowdsourced, source_datasets:commonvoice, language:zh, license:cc0-1.0, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, audio, speaker diarization","automatic-speech-recognition","cv-corpus-17.0-zh-CN-client_id-grouped This dataset is a subset of the Common Voice dataset, filtered and grouped based on the client ID (treated as speaker ID). Dataset Details The dataset is derived","None","zh","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"youssef101/artelingo-dummy","youssef101","2023-07-23 14:41:17","2023-07-23 16:21:23+00:00","93904f7b40cf0c20fc9dd1efcccc7323272a4aeb","99","1166","1","task_categories:image-to-text, task_categories:text-classification, task_categories:image-classification, task_categories:text-to-image, task_categories:text-generation, language:en, language:ar, language:zh, license:mit, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, Affective Captioning, Emotions, Prediction, Art, ArtELingo","image-to-text, text-classification, image-classification, text-to-image, text-generation","ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 a","None","en, ar, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"BAAI/CMMU","BAAI","2024-01-26 05:51:19","2024-01-29 08:09:05+00:00","b3b65566fa083636713bca7b117e48c67720a074","316","1162","7","task_categories:visual-question-answering, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2401.14011, region:us","visual-question-answering","CMMU 📖 Paper | 🤗 Dataset | GitHub This repo contains the evaluation code for the paper CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning . We release the valida","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2401.14011","None","None"
+"meta-math/GSM8K_zh","meta-math","2023-12-04 03:08:44","2023-12-04 04:02:01+00:00","4a5009abc37cbb2d3fd1a745f80e5ea1405ba9aa","185","1148","12","task_categories:question-answering, language:en, language:zh, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2309.12284, region:us, math, math-qa, chinese-math-qa","question-answering","Dataset GSM8K_zh is a dataset for mathematical reasoning in Chinese, question-answer pairs are translated from GSM8K (https://github.com/openai/grade-school-math/tree/master) by GPT-3.5-Turbo with few","None","en, zh","bi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"TigerResearch/dev_sft","TigerResearch","2023-05-29 12:23:31","2023-06-16 01:55:22+00:00","bd721ddd0a2b778e02ef8fb9186185c5757a4e51","76","1131","2","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""dev_sft"" Tigerbot模型develop sft数据。 在train_sft.py中被使用。 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/dev_sft') Field instruction: 指令 input: 上下文信息(Optional) output","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"BAAI/Infinity-Preference","BAAI","2024-08-28 15:25:50","2024-08-30 09:54:37+00:00","2a68f42d60b1a315ea6d82cf294e4ee27b41c3a3","217","1129","69","task_categories:text-generation, task_categories:question-answering, task_categories:text2text-generation, language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, question-answering, text2text-generation","Infinity-Preference The focus of human preferences varies from task to task. Therefore, Infinity-Preference attempts to adjust preference attribute weights on each task based on (Infinity Instruct's)[","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"TIGER-Lab/MetricInstruct","TIGER-Lab","2023-10-04 03:05:36","2023-12-03 18:32:34+00:00","006e6f8de749b9d2cf7d6ab74a5ac231a28f567f","102","1124","12","task_categories:text-generation, language:en, language:zh, language:cs, language:ru, language:fr, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.00752, region:us","text-generation","MetricInstruct The MetricInstrcut dataset consists of 44K quadruple in the form of (instruction, input, system output, error analysis) for 6 text generation tasks and 22 text generation datasets. The ","None","en, zh, cs, ru, fr","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2310.00752","None","None"
+"bigbio/paramed","bigbio","2022-11-13 22:11:13","2022-12-22 15:46:11+00:00","02d51cfe956add9301e00b2fe894d21ad961aaec","220","1114","2","multilinguality:multilingual, language:en, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us","None","NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website. English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed ","@article{liu2021paramed, author = {Liu, Boxiang and Huang, Liang}, title = {ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain}, journal = {BMC Medical Informatics and Decision Making}, volume = {21}, year = {2021}, url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01621-8}, doi = {10.1186/s12911-021-01621-8} }","en, zh","bi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"laihuiyuan/mCoT-MATH","laihuiyuan","2024-04-02 11:18:01","2024-06-05 11:42:17+00:00","c4415e8c3591ce61f6da64080b50d44c22c5a440","109","1105","4","language:sw, language:bn, language:te, language:th, language:ja, language:zh, language:ru, language:es, language:fr, language:de, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2406.02301, region:us","None","mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models Paper: https://arxiv.org/abs/2406.02301 Code: https://github.com/laihuiyuan/mCoT Model: https://huggingface.co/laihui","None","sw, bn, te, th, ja, zh, ru, es, fr, de, en","multi","None","None","False","False","False","apache-2.0","2406.02301","None","None"
+"Cofacts/line-msg-fact-check-tw","Cofacts","2023-05-16 05:09:10","2024-09-01 14:48:31+00:00","1150fd8f0295540e1092e301146b81fc73f5a510","65","1103","7","task_categories:text-classification, task_categories:question-answering, language:zh, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, fact-checking, crowd-sourcing","text-classification, question-answering","Cofacts Archive for Reported Messages and Crowd-Sourced Fact-Check Replies The Cofacts dataset encompasses instant messages that have been reported by users of the Cofacts chatbot and the replies prov","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","auto","False","cc-by-sa-4.0","None","None","None"
+"IDEA-CCNL/laion2B-multi-chinese-subset","IDEA-CCNL","2022-09-27 12:22:38","2023-04-06 06:32:18+00:00","58684b7a75ae57ed0dbcfcb87bdbd8ff3541aade","201","1101","39","task_categories:feature-extraction, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, language:zh, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2209.02970, region:us","feature-extraction","laion2B-multi-chinese-subset Github: Fengshenbang-LM Docs: Fengshenbang-Docs 简介 Brief Introduction 取自Laion2B多语言多模态数据集中的中文部分，一共143M个图文对。 A subset from Laion2B (a multimodal dataset), around 143M image-","None","zh","mono","None","None","False","False","False","cc-by-4.0","2209.02970","None","None"
+"BAAI/IndustryCorpus_travel","BAAI","2024-07-25 05:46:37","2024-07-26 02:30:41+00:00","fb34ff8f6e83a2f4b6bbd391a75afe8a370cce11","167","1090","1","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"Flmc/DISC-Med-SFT","Flmc","2023-08-29 10:20:50","2023-08-29 12:54:14+00:00","b10eedfc23530ebe257a149b00db73c4162fbfc3","91","1090","82","task_categories:question-answering, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","question-answering, conversational","This is a repository containing a subset of the DISC-Med-SFT Dataset. Check DISC-MedLLM for more information.","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"jed351/Traditional-Chinese-Common-Crawl-Filtered","jed351","2023-07-20 21:24:43","2023-07-20 23:09:09+00:00","749c3997549177c85e40973267ad080f0a915786","113","1081","19","language:zh, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Traditional Chinese C4 Dataset Summary Data obtained from 2023-14 Common Crawl. Downloaded and processed using code based on another project attempting to recreate the C4 dataset. The resultant datase","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"strombergnlp/nlpcc-stance","strombergnlp","2022-05-19 11:19:12","2022-10-25 21:47:26+00:00","dca814e1ce04213a6600c4e490c0018b2c7004ac","115","1078","8","task_categories:text-classification, task_ids:sentiment-analysis, annotations_creators:expert-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:cc-by-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, region:us, stance-detection","text-classification, sentiment-analysis","This is a stance prediction dataset in Chinese. The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which d","@incollection{xu2016overview, title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs}, author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun}, booktitle={Natural language understanding and intelligent applications}, pages={907--916}, year={2016}, publisher={Springer} }","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","sentiment-analysis"
+"Cohere/miracl-zh-corpus-22-12","Cohere","2023-01-31 13:13:33","2023-02-06 11:55:44+00:00","7efe436235598edf9b6103abaa757659d2a5c1cb","188","1071","4","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, language:zh, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","MIRACL (zh) 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","zh","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"weege007/HuggingFaceTB-cosmopedia-cn","weege007","2024-04-21 10:10:38","2024-05-06 14:24:33+00:00","b41ee71b6523c649ab46bd0eac72f862862410f9","457","1070","0","task_categories:text-generation, language:en, language:zh, license:mit, size_categories:100K<n<1M, format:csv, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","None","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"yaojialzc/Yunji-v1","yaojialzc","2024-04-21 02:29:59","2024-05-16 06:48:36+00:00","d317cf53fd65c5b80deff738afcaf2a52068a020","66","1060","3","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.14994, region:us","text-generation","Yunji(云笈) github Yunji(云笈) 收集、整理、分类gpt4生成的高质量中英文指令精调语料，并提供自己翻译的高质量数据。 可以从 huggingface yaojialzc/Yunji-v1 直接加载 dataset zh 高质量中文gpt4对话数据集： ID name source count 1 llm-wizard/alpaca-gpt4-data-zh 从Alpaca G","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"FreedomIntelligence/huatuo26M-testdatasets","FreedomIntelligence","2023-05-17 02:31:23","2023-05-17 03:39:41+00:00","b514b9531acad249fef0052321b98dea2e9bcb50","101","1031","17","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.01526, region:us, medical","text-generation","Dataset Card for huatuo26M-testdatasets Dataset Summary We are pleased to announce the release of our evaluation dataset, a subset of the Huatuo-26M. This dataset contains 6,000 entries that we used f","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"BAAI/IndustryCorpus_sports","BAAI","2024-07-25 05:46:39","2024-07-26 02:30:45+00:00","9d8b9e29b0b988b62cfa15c117a1b0190b94050f","266","1028","1","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"silk-road/Wizard-LM-Chinese-instruct-evol","silk-road","2023-05-15 00:04:30","2023-05-15 00:13:52+00:00","00f145874a0cf3f64ab93db95a807112dac2c1fc","136","1027","97","task_categories:text-generation, task_categories:question-answering, language:zh, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, question-answering","Wizard-LM-Chinese是在MSRA的Wizard-LM数据集上，对指令进行翻译，然后再调用GPT获得答案的数据集 Wizard-LM包含了很多难度超过Alpaca的指令。 中文的问题翻译会有少量指令注入导致翻译失败的情况 中文回答是根据中文问题再进行问询得到的。 我们会陆续将更多数据集发布到hf，包括 Coco Caption的中文翻译 CoQA的中文翻译 CNewSum的Embedd","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"Chinese-Vicuna/instruct_chat_50k.jsonl","Chinese-Vicuna","2023-05-10 12:32:11","2023-05-12 03:27:55+00:00","54a60ab79208012ffaa072328e9cb676240f98ef","62","1027","43","task_categories:question-answering, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","instruct_chat_50k.jsonl which is composed of 30k Chinese sharegpt dataset and 20k alpaca-instruction-Chinese-dataset","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"neulab/PangeaBench-marvl","neulab","2024-10-14 01:59:56","2024-10-31 20:14:07+00:00","2c2e82c1596f6dfcef55e96d1fc287514b351386","231","1027","1","task_categories:visual-question-answering, language:id, language:sw, language:ta, language:tr, language:zh, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","visual-question-answering","MaRVL This is a copy from the original repo: https://github.com/marvl-challenge/marvl-code If you use this dataset, please cite the original authors: @inproceedings{liu-etal-2021-visually, title = ""Vi","None","id, sw, ta, tr, zh, en","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"jiangjiechen/ekar_chinese","jiangjiechen","2022-03-27 06:00:49","2023-01-11 08:12:59+00:00","204d96b91f91c546df38a2284d250eb346fe0c77","83","1026","13","task_categories:question-answering, task_categories:text-generation, task_ids:explanation-generation, source_datasets:original, language:zh, license:afl-3.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, text-generation, analogical-qa, explanation-generation","Dataset Card for ekar_chinese Dataset Summary New!(9/18/2022) E-KAR v1.1 is officially released (at the main branch), with a higher-quality English dataset! In v1.1, we further improve the Chinese-to-","None","zh","mono","1|K|<|n|<|2|K","None","False","False","False","afl-3.0","None","None","analogical-qa, explanation-generation"
+"FreedomIntelligence/huatuo_consultation_qa","FreedomIntelligence","2023-05-10 11:41:08","2023-05-17 03:21:36+00:00","b0f5723c7ec234f947823408a103c0dc07a52e1d","140","1024","15","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.01526, region:us, medical","text-generation","Dataset Card for huatuo_consultation_qa Dataset Summary We collected data from a website for medical consultation , consisting of many online consultation records by medical experts. Each record is a ","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"5CD-AI/Vietnamese-THUIR-T2Ranking-gg-translated","5CD-AI","2024-06-03 10:36:01","2024-06-05 10:35:56+00:00","ba7ca47a72a4497f865c9909dcbf866ee67743e9","174","1014","19","task_categories:text-retrieval, language:zh, language:vi, size_categories:100M<n<1B, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, IR, Rerank, Information Retrival","text-retrieval","None","None","zh, vi","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"vcr-org/VCR-wiki-zh-easy","vcr-org","2024-06-04 09:28:46","2024-07-28 09:39:05+00:00","c23c9b18885a5d1d4fd58084e2cbccb1b584bb7a","373","1009","0","task_categories:visual-question-answering, source_datasets:wikimedia/wit_base, language:zh, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.06462, doi:10.57967/hf/2523, region:us","visual-question-answering","The VCR-Wiki Dataset for Visual Caption Restoration (VCR) 🏠 Paper | 👩🏻‍💻 GitHub | 🤗 Huggingface Datasets | 📏 Evaluation with lmms-eval This is the official Hugging Face dataset for VCR-Wiki, a dataset","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","2406.06462","None","None"
+"fjcanyue/wikipedia-zh-cn","fjcanyue","2024-08-27 07:47:50","2024-10-26 17:50:55+00:00","ae660439dfe2303d50d90e3c4a6235c47c03986d","120","990","4","language:zh, license:mit, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","Wikipedia Chinese Dataset Transformed from zhwiki dump","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","mit","None","None","None"
+"TangRain/SingMOS","TangRain","2024-11-05 16:11:07","2024-11-11 07:40:19+00:00","f2a0b005a09652bc7de80724146291ee96706585","127","979","2","language:zh, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:2406.10911, region:us, singing, MOS","None","paper link: SingMOS: An extensive Open-Source Singing Voice Dataset for MOS Prediction NOTICE: Our new paper has not been released and the information in SingMOS paper remain in SingMOS_v1. We will up","None","zh, ja","bi","1|B|<|n|<|1|0|B","None","False","False","False","cc-by-4.0","2406.10911","None","None"
+"wenbopan/Chinese-dpo-pairs","wenbopan","2024-03-29 08:45:17","2024-04-02 09:19:22+00:00","800b8cc6c8a1f7fa17ba1ab5690e50b663d832ca","103","977","42","language:zh, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for Chinese-dpo-pairs Well-curated 10K reference pairs in Chinese. Data are created by GPT-3.5 translation from multiple sources, including: flan_v2, sharegpt, ultrachat, evol_instruct an","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"mponty/code_tutorials","mponty","2023-10-31 14:32:09","2023-11-01 02:22:58+00:00","c3da8802eabc66931efd859931414ecd68d9b1d3","144","975","8","task_categories:text-generation, language:en, language:ru, language:zh, language:es, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, code","text-generation","Coding Tutorials This comprehensive dataset consists of 500,000 documents, summing up to around 1.5 billion tokens. Predominantly composed of coding tutorials, it has been meticulously compiled from v","None","en, ru, zh, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"fzmnm/TinyStoriesAdv-zh","fzmnm","2024-08-01 04:58:39","2024-08-21 20:38:57+00:00","fb892c135fa6ba4ddb1a69aa4612d92aae9d454d","183","973","8","task_categories:text-generation, language:zh, license:cc, size_categories:100M<n<1B, modality:text, arxiv:2305.07759, arxiv:2309.05463, arxiv:2407.20311, region:us","text-generation","TinyStoriesAdv keywords: grade school level, large language model, small language model, tiny language model, super tiny language model, 小学生知识水平，大语言模型，小语言模型，迷你语言模型, llm, slm. 受到TinyStories、Phi2等论文的启发，","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc","2305.07759","None","None"
+"genggui001/gg_zh_v1_550B","genggui001","2024-01-17 15:17:57","2024-01-20 10:47:46+00:00","329cc063c1518678f55887a723a527ef1a35e8cc","536","961","11","task_categories:text-generation, language:zh, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","CCI-Data SkyPile-150B TeleChat-PTD WebText-cn WuDaoCorpus2.0 wangan yayi2_pretrain_data 整合+minhash去重了一波，最终得到550B中文预训练语料","None","zh","mono","1|0|0|B|<|n|<|1|T","None","False","False","False","None","None","None","None"
+"hanamizuki-ai/genshin-voice-v3.4-mandarin","hanamizuki-ai","2023-02-09 01:50:09","2023-04-13 02:28:53+00:00","206eae0b49785320d08d3dce87ecb2e2294cf780","368","958","9","task_categories:text-to-speech, task_categories:automatic-speech-recognition, multilinguality:monolingual, source_datasets:original, language:zh, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech, automatic-speech-recognition","Dataset Card for Genshin Voice Dataset Description Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. Languages The tex","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"wanng/wikipedia-zh-mnbvc","wanng","2023-05-29 05:41:11","2023-05-29 17:39:00+00:00","55414680ae7404ffea98f77e7523840c5585b545","247","956","5","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, mnbvc, Wikipedia","text-generation","zhwiki-mnbvc 分项目：爬取并处理中文维基百科语料 数据时间：202302-202305 （持续更新） 主项目：MNBVC(Massive Never-ending BT Vast Chinese corpus)超大规模中文语料集 https://github.com/esbatmop/MNBVC 该项目清洗流程主要参考：https://kexue.fm/archives/4176/co","None","zh, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Mutonix/RefGPT-Fact","Mutonix","2023-05-26 01:37:53","2023-05-30 13:33:07+00:00","d6eacf0612df05d2ea32fcabf159739f833f5b9c","92","954","29","language:zh, language:en, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.14994, region:us","conversational","Dataset Card for RefGPT-Fact Dataset Summary [Paper] RefGPT | [Github] RefGPT RefGPT-Fact is a datasets containing 100k multi-turn dialogues about factual knowledge with 50k English and 50k Chinese. T","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2305.14994","None","None"
+"mteb/LeCaRDv2","mteb","2024-03-30 07:20:29","2024-03-30 07:30:15+00:00","b78e18688c3d012a33dc3676597c1d1b2243ce1c","109","952","0","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:https://github.com/THUIR/LeCaRDv2, language:zh, 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","LeCaRDv2 Original link: https://github.com/THUIR/LeCaRDv2 The task involves identifying and retrieving the case document that best matches or is most relevant to the scenario described in each of the ","None","zh","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"masuidrive/cv-corpus-17.0-zh-TW-client_id-grouped","masuidrive","2024-04-20 06:32:27","2024-04-20 06:32:42+00:00","560807a782a87fa0df4e5e06838262d16c27e957","207","950","1","task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, language_creators:crowdsourced, source_datasets:commonvoice, language:zh, license:cc0-1.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, audio, speaker diarization","automatic-speech-recognition","cv-corpus-17.0-zh-TW-client_id-grouped This dataset is a subset of the Common Voice dataset, filtered and grouped based on the client ID (treated as speaker ID). Dataset Details The dataset is derived","None","zh","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"Henrychur/MMedC","Henrychur","2024-02-20 06:46:36","2024-07-25 11:29:07+00:00","6629f0a54ae73b6f32d42476b2b9e90ff96035fd","230","942","24","None","None","MMedC 💻Github Repo 🖨️arXiv Paper The official pre-training dataset for ""Towards Building Multilingual Language Model for Medicine"". News We add Arabic and German corpus to MMedC. Introduction This rep","None","en, zh, ja, fr, ru, es, ar, de","multi","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-nc-sa-4.0","2402.13963","None","None"
+"jiacheng-ye/logiqa-zh","jiacheng-ye","2023-04-17 12:39:52","2023-04-21 00:56:28+00:00","f06e930bfa274389d88f93327c1b5df3b7009975","64","939","25","task_categories:question-answering, language:zh, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, arxiv:2007.08124, region:us","question-answering","LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which is designed to test the civil servant candi","@article{liu2020logiqa, title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning}, author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue}, journal={arXiv preprint arXiv:2007.08124}, year={2020} }","zh","mono","1|K|<|n|<|1|0|K","logiqa","False","False","False","None","None","None","None"
+"silk-road/chinese-dolly-15k","silk-road","2023-05-22 00:18:48","2023-05-22 00:26:02+00:00","6595ee9daf16740b4bd3cf02eac4b7d08d37ecd1","78","938","22","task_categories:question-answering, task_categories:summarization, task_categories:text-generation, language:zh, language:en, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, summarization, text-generation","Chinese-Dolly-15k是骆驼团队翻译的Dolly instruction数据集 最后49条数据因为翻译长度超过限制，没有翻译成功，���议删除或者手动翻译一下 原来的数据集'databricks/databricks-dolly-15k'是由数千名Databricks员工根据InstructGPT论文中概述的几种行为类别生成的遵循指示记录的开源数据集。这几个行为类别包括头脑风暴、分类、封闭","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"shibing624/CSC","shibing624","2023-03-28 02:59:33","2023-05-12 07:30:59+00:00","40f6a4b1a42c0bcb74aaf1ea3c8bba5ce140b021","110","938","33","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-correction","text-generation","Dataset Card for CSC 中文拼写纠错数据集 Repository: https://github.com/shibing624/pycorrector Dataset Description Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chin","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"yuecao0119/MMInstruct-GPT4V","yuecao0119","2024-08-06 07:53:49","2024-08-07 08:16:20+00:00","0f26ec1f6bbedca85195925376c05f0c0900f9ef","200","927","10","task_categories:visual-question-answering, task_categories:question-answering, language:en, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2407.15838, arxiv:2212.02746, arxiv:2105.04165, arxiv:2208.05358, arxiv:2209.14610, arxiv:1710.07300, arxiv:2211.08545, region:us","visual-question-answering, question-answering","MMInstruct The official implementation of the paper ""MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity"". The data engine is available on GitHub at yuecao0119/M","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2407.15838","None","None"
+"zhengr/COIG-CQIA","zhengr","2024-04-25 10:54:55","2024-04-25 10:54:57+00:00","56066aae477d8b6b9ab44fb57960602b7025d8a0","103","927","2","task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, task_categories:text2text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.18058, arxiv:2304.07987, arxiv:2307.09705, region:us","question-answering, text-classification, text-generation, text2text-generation","COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning Dataset Details Dataset Description 欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"RyokoAI/CNNovel125K","RyokoAI","2023-04-03 22:17:25","2023-04-04 11:38:03+00:00","a719fa9535bc567b09e66774baa68991091a2059","259","926","21","None","text-classification, text-generation","Dataset Card for CNNovel125K The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible. Dataset Summary CNNovel125K is a dataset composed of ","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"neuclir/neumarco","neuclir","2023-02-06 15:19:57","2023-02-06 16:16:37+00:00","93552494f53a4ca0687a1c151f0f57a641a8c080","273","923","1","task_categories:text-retrieval, annotations_creators:machine-generated, language_creators:machine-generated, multilinguality:multilingual, source_datasets:extended|irds/msmarco-passage, language:fa, language:ru, language:zh, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, region:us","text-retrieval","Dataset Card for NeuMARCO Dataset Summary This is the dataset created for TREC 2022 NeuCLIR Track. The collection consists of documents from msmarco-passage translated into Chinese, Persian, and Russi","None","fa, ru, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"donmaclean/LongMIT-128K","donmaclean","2024-09-27 07:24:43","2024-10-14 14:01:14+00:00","67f5102ad51ae943a032b65ad2eda156ea3016e2","169","922","5","task_categories:question-answering, task_categories:text2text-generation, 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:2409.01893, region:us","question-answering, text2text-generation","LongMIT: Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets [ArXiv] Download LongMIT Datasets def download_longmit_datasets(dataset_name: str, save_dir: str): qa_pairs","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Songweii/M3GIA","Songweii","2024-06-06 09:55:12","2024-06-27 08:02:29+00:00","07415814e27a715acf57b2b157fba88b79599d15","76","913","2","language:en, language:zh, language:es, language:fr, language:pt, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.05343, region:us, Multilingual, Multimodal, Cognitive Science, General Intelligence Ability Benchmark","None","M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability [🌐 Homepage] | 🤗 Dataset | 🤗 Paper | 📖 arXiv | 💻 GitHub The evaluation code can be found in 💻 GitHub. [Abstract] As","None","en, zh, es, fr, pt, ko","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2406.05343","None","None"
+"sentence-transformers/t2ranking","sentence-transformers","2024-06-20 14:23:59","2024-06-20 14:38:28+00:00","2120acdc3c422d27383db6cd1ad5bc898adca353","258","910","0","task_categories:feature-extraction, task_categories:sentence-similarity, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for T2Ranking This is a reformatting of the T2Ranking dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets triplet subset Columns: ""a","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"liswei/Taiwan-Text-Excellence-2B","liswei","2024-05-07 11:55:47","2024-06-02 07:45:23+00:00","211652a3e9f5d33011e95fbb96d528f6b07ad684","69","910","12","task_categories:text-generation, language:zh, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","High quality corpus for Taiwanese culture and Traditional Chinese Taiwan Text Excellence (TTE) Contains high quality news and articles in Traditional Chinese. The data processing pipeline is optimized","None","zh","mono","1|B|<|n|<|1|0|B","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"erhwenkuo/alpaca-data-gpt4-chinese-zhtw","erhwenkuo","2023-09-26 13:42:02","2023-09-26 14:03:00+00:00","d742e3025346e7cfc517657c4add8b7fc6c05d28","69","906","6","task_categories:text-generation, task_categories:question-answering, language:zh, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.03277, region:us, gpt4, alpaca, instruction-finetuning","text-generation, conversational, question-answering","Dataset Card for ""alpaca-data-gpt4-chinese-zhtw"" This dataset contains Chinese (zh-tw) Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. The dataset was originaly sha","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","2304.03277","None","None"
+"BAAI/IndustryCorpus_politics","BAAI","2024-07-25 05:46:39","2024-07-26 02:30:43+00:00","5c62c05c0536ba276f49ab044994ab1fa2c3658a","322","895","2","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"MasahiroKaneko/eagle","MasahiroKaneko","2024-02-22 03:15:08","2024-02-24 00:54:06+00:00","9ba31f1f38f01abdd259f93cf18b0497f690d6a6","155","882","1","task_categories:text-generation, language:en, language:zh, language:fr, language:ko, language:de, language:es, language:ja, license:mit, size_categories:100K<n<1M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Eagle 🦅: Ethical Dataset Given from Real Interactions Introduction This repository contains the Eagle dataset, which is an ethical dataset of real interactions between humans and ChatGPT. This dataset","None","en, zh, fr, ko, de, es, ja","multi","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"universalner/uner_llm_instructions","universalner","2023-12-19 12:13:19","2023-12-20 10:05:18+00:00","dc9e8b0f7aa1d3a9a44a14e8f4c868e2bb4f19b5","237","876","2","task_categories:token-classification, language:ceb, language:da, language:de, language:en, language:hr, language:pt, language:ru, language:sk, language:sr, language:sv, language:tl, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2311.09122, region:us","token-classification","Dataset Card for Universal NER v1 in the Aya format This dataset is a format conversion from its original v1 format into the Aya instruction format and it's released here under the same CC-BY-SA 4.0 l","None","ceb, da, de, en, hr, pt, ru, sk, sr, sv, tl, zh","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"botp/yentinglin-zh_TW_c4","botp","2023-08-16 06:55:04","2023-08-16 06:55:04+00:00","aaff3487e9bfd614c00564697061e5e3af6fac12","288","875","5","task_categories:text-generation, language:zh, license:odc-by, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.13711, arxiv:2104.09864, region:us","text-generation","Language Models for Taiwanese Culture ✍️ Online Demo • 🤗 HF Repo • 🐦 Twitter • 📃 [Paper Coming Soon] • 👨️ Yen-Ting Lin Overview Taiwan-LLaMa is a full parameter fine-tuned model based on LLaMa 2 for T","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","odc-by","2104.09864","None","None"
+"tiiuae/visper","tiiuae","2024-05-07 11:58:57","2024-05-28 11:33:35+00:00","add88080f1daa915b8e80ae59054c71c65192485","129","869","3","language:ar, language:fr, language:es, language:zh, license:cc-by-nc-2.0, size_categories:10K<n<100K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","ViSpeR: Multilingual Audio-Visual Speech Recognition This repository contains ViSpeR, a large-scale dataset and models for Visual Speech Recognition for Arabic, Chinese, French, Arabic and Spanish. Da","None","ar, fr, es, zh","multi","None","None","False","False","False","cc-by-nc-2.0","None","None","None"
+"botp/COIG-CQIA","botp","2024-04-21 07:30:09","2024-04-21 07:30:12+00:00","3ce41c10a227e70a2d6f979d0f8be99b28f629bc","108","867","0","task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, task_categories:text2text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.18058, arxiv:2304.07987, arxiv:2307.09705, region:us","question-answering, text-classification, text-generation, text2text-generation","COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning Dataset Details Dataset Description 欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"Weaxs/csc","Weaxs","2023-12-22 17:20:12","2024-01-02 06:19:10+00:00","033274f4c894885e4d25d0bfa2f84eff1a6f1dc4","61","866","7","task_categories:text2text-generation, language:zh, license:apache-2.0, size_categories:100M<n<1B, modality:text, region:us, chinese-spelling-check, 中文","text2text-generation","Dataset for CSC 中文纠错数据集 Dataset Description Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. 共计 120w 条数据，以下是数据来源 数据集 语料 链接 SIGHAN+Wang271K 拼写纠错","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","None","None","None"
+"AkshitaS/facebook_mlqa_plus","AkshitaS","2024-06-12 18:46:00","2024-06-13 22:09:56+00:00","45b9e85dc9a2fb467548546081a5c33c53169d5a","76","859","0","task_categories:question-answering, language:en, language:hi, language:ar, language:de, language:es, language:vi, language:zh, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Source Dataset Link: facebook/mlqa Revision: 397ed406c1a7902140303e7faf60fff35b58d285 MLQAMLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering ","None","en, hi, ar, de, es, vi, zh","multi","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"hugfaceguy0001/retarded_bar","hugfaceguy0001","2023-04-17 23:55:28","2023-08-30 21:41:00+00:00","230718276ca6b8131fa71b797310e1e6763315a5","120","856","51","task_categories:text-generation, language:zh, license:openrail, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","弱智吧笑话数据集 弱智吧是百度贴吧中的一个非常受欢迎的论坛，以创作短小精悍的冷笑话而闻名。这些笑话通常采用双关语、不寻常的断句、不合理的逻辑等创作手法。即使是目前最先进的语言模型，也难以完全理解弱智吧的笑话。 弱智吧 我从互联网上收集了一些弱智吧的笑话，共100条，其中45条是陈述句，55条是问句。我结合人工和语言模型对这些笑话进行了一些解析，并制作了这个小型数据集。 陈述句笑话 陈述句笑话通常以","None","zh","mono","n|<|1|K","None","False","False","False","openrail","None","None","None"
+"recursal/SuperWikiNEXT-32B","recursal","2024-04-13 04:03:06","2024-06-10 12:22:28+00:00","364a0be03e0b49aa57e176611b9f0454052fb670","202","854","3","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:af, language:ar, language:ast, language:az, language:be, language:bg, language:bn, language:ca, language:ce, 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:gl, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:it, language:ja, language:ka, language:kk, language:ko, language:la, language:lt, language:lv, language:mk, language:ms, language:my, language:nl, language:nn, language:no, language:pl, language:pt, language:ro, language:ru, language:sh, language:sk, language:sl, language:sr, language:sv, language:ta, language:tg, language:th, language:tr, language:uk, language:ur, language:uz, language:vi, language:zh, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for SuperWikiNEXT-32B Waifu to catch your attention. Dataset Details Dataset Description SuperWikipedia-NEXT is an enhanced version of the SuperWIKI dataset. Which SuperWIKI was born out ","None","af, ar, ast, az, be, bg, bn, ca, ce, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fr, gl, he, hi, hr, hu, hy, id, it, ja, ka, kk, ko, la, lt, lv, mk, ms, my, nl, nn, no, pl, pt, ro, ru, sh, sk, sl, sr, sv, ta, tg, th, tr, uk, ur, uz, vi, zh","multi","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-sa-3.0","None","None","language-modeling, masked-language-modeling"
+"UKPLab/m2qa","UKPLab","2024-05-16 08:16:50","2024-12-10 21:26:17+00:00","d1ea25a7bc1e145759b5bf0b9e605c1a4fa6e2c0","145","850","2","task_categories:question-answering, task_ids:extractive-qa, multilinguality:multilingual, language:de, language:zh, language:tr, license:cc-by-nd-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, extractive-qa","M2QA: Multi-domain Multilingual Question Answering M2QA (Multi-domain Multilingual Question Answering) is an extractive question answering benchmark for evaluating joint language and domain transfer. ","None","de, zh, tr","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nd-4.0","None","None","extractive-qa"
+"FreedomIntelligence/Disease_Database","FreedomIntelligence","2024-07-17 08:05:38","2024-08-23 04:57:06+00:00","c77f1554b51841431a2989c0710bcf83487d7033","116","850","17","task_categories:text2text-generation, task_categories:text-classification, 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:2407.13301, region:us, medical","text2text-generation, text-classification","Citation @misc{chen2024codinterpretablemedicalagent, title={CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis}, author={Junying Chen and Chi Gui and Anningzhe Gao and Ke Ji and Xido","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"M4U-Benchmark/M4U","M4U-Benchmark","2024-05-22 15:36:34","2024-06-21 15:54:41+00:00","3a84f71c9518d4b51389b33c7e12db5c097bf93f","133","848","4","task_categories:visual-question-answering, language:en, language:zh, language:de, license:mit, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2405.15638, region:us, science, engineering, medical, chemistry, biology","visual-question-answering","M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models Code for the Paper M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models. [Webpage] ","None","en, zh, de","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2405.15638","None","None"
+"botp/RyokoAI_CNNovel125K","botp","2023-08-18 01:31:26","2023-08-18 01:31:26+00:00","aeabd9e70be28d384c2ad7b40fd36c6ef5eb15e9","103","845","2","task_categories:text-classification, task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, novel, training","text-classification, text-generation","Dataset Card for CNNovel125K The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible. Dataset Summary CNNovel125K is a dataset composed of ","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"Sakalti/Multilingal-sakalt-data","Sakalti","2024-10-14 05:11:16","2024-10-17 10:41:45+00:00","c943a8193cccaba0c409ae59adc46234b4198139","113","843","1","task_categories:text-generation, language:ab, language:bho, language:ce, language:cs, language:da, language:de, language:et, language:es, language:fr, language:hi, language:hrv, language:hu, language:it, language:ja, language:ko, language:nl, language:pl, language:pt, language:ro, language:ru, language:sah, language:swh, language:yue, language:zh, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","text-generation","マルチリンガルデータセットです。mitライセンスです。","None","ab, bho, ce, cs, da, de, et, es, fr, hi, hrv, hu, it, ja, ko, nl, pl, pt, ro, ru, sah, swh, yue, zh","multi","None","None","False","False","False","mit","None","None","None"
+"FreedomIntelligence/ApolloMoEBench","FreedomIntelligence","2024-10-14 03:04:30","2024-10-15 08:38:05+00:00","757a9119cc8179d936afd0f3a34b6e39222ce95f","199","835","0","task_categories:question-answering, language:ar, language:en, language:zh, language:ko, language:ja, language:mn, language:th, language:vi, language:lo, language:mg, language:de, language:pt, language:es, language:fr, language:ru, language:it, language:hr, language:gl, language:cs, language:co, language:la, language:uk, language:bs, language:bg, language:eo, language:sq, language:da, language:sa, language:no, language:gn, language:sr, language:sk, language:gd, language:lb, language:hi, language:ku, language:mt, language:he, language:ln, language:bm, language:sw, language:ig, language:rw, language:ha, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.10626, region:us, biology, medical","question-answering","Democratizing Medical LLMs For Much More Languages Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38","None","ar, en, zh, ko, ja, mn, th, vi, lo, mg, de, pt, es, fr, ru, it, hr, gl, cs, co, la, uk, bs, bg, eo, sq, da, sa, no, gn, sr, sk, gd, lb, hi, ku, mt, he, ln, bm, sw, ig, rw, ha","multi","None","None","False","False","False","mit","2410.10626","None","None"
+"lianghsun/tw-legal-nlp","lianghsun","2024-05-27 08:07:22","2024-09-19 04:50:21+00:00","efa5d65123cb478693e03f13144d0c2443fe3000","72","834","1","task_categories:text-generation, task_categories:question-answering, language:zh, license:cc-by-nc-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","text-generation, question-answering","法律資料科學任務資料集 Dataset Summary 這個資料集專為法律領域的資料科學家設計，目的是提供多樣化的法律資料科學任務，涵蓋從實體識別到文本轉換的多種挑戰。該資料集旨在幫助研究人員和開發者更好地理解與處理法律文本。這些任務包括但不限於： 從判決書中取出特定人物、法條或時間（Named Entity Recognition, NER） 理解法律文本的內容 (Text Classifica","None","zh","mono","n|<|1|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"BAAI/IndustryInstruction","BAAI","2024-09-19 10:35:48","2024-11-12 08:25:33+00:00","6b5ff74e8747950df4d22d9857bcc2512865dbd8","87","827","21","task_categories:question-answering, language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, modality:image, modality:tabular, modality:text, doi:10.57967/hf/3487, region:us","question-answering","本数据集为行业指令数据集，目前包含的行业中英文对照名称如下，本次数据旨在补充当前行业指令数据的空白，并挖掘BAAI/IndustryCorpus2预训练数据集中高质量预训练语料中包含的行业高价值知识。 汽车 : Automobiles 航空航天 : Aerospace 人工智能_机器学习 : Artificial-Intelligence 交通运输 : Transportation 科技_科学研究","None","zh, en","bi","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","apache-2.0","None","None","None"
+"neuclir/hc4","neuclir","2023-01-11 21:10:06","2023-01-17 09:38:31+00:00","6e7c86a2ae58a6ee84be59c71ef6ddf30904d95b","257","809","1","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:extended|c4, language:fa, language:ru, language:zh, license:odc-by, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2201.09992, region:us","text-retrieval, document-retrieval","Dataset Card for HC4 Dataset Summary HC4 is a suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian. The d","None","fa, ru, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","odc-by","2201.09992","None","document-retrieval"
+"sentence-transformers/dureader","sentence-transformers","2024-06-18 20:37:58","2024-06-18 20:49:33+00:00","d3569319d42edc5ed953d7974d5ee769c2aed974","144","801","1","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for DuReader This is a reformatting of the DuReader dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets triplet subset Columns: ""anc","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"MohamedRashad/multilingual-tts","MohamedRashad","2023-12-01 23:46:06","2023-12-12 21:04:06+00:00","14729fbf35870fe21a8ae76d6d37e36dff0c08f8","210","801","27","task_categories:text-to-speech, language:ar, language:en, language:zh, language:es, language:fr, language:hi, language:ru, language:pt, language:ja, language:de, language:tr, language:bn, language:id, language:ur, language:vi, license:gpl-3.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech","Before Anything and Everything ⚱ In the time of writing this Dataset Card, 17,490 18,412 civilian has been killed in Palestine (7,870 8,000 are children and 6,121 6,200 are women). Seek any non-profit","None","ar, en, zh, es, fr, hi, ru, pt, ja, de, tr, bn, id, ur, vi","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","gpl-3.0","None","None","None"
+"metricv/metricsubs-chunktranslate","metricv","2023-10-14 22:36:45","2024-10-11 02:11:21+00:00","502b5eb8aabd12b903708f87a9b3c38764156d32","136","800","0","task_categories:text2text-generation, language:en, language:zh, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","Introduction This repository holds the data file for translating TechLinked, which talks about mostly technology and science news. Raw data is in the data/ folder. Scripts generate OpenAI's ChatComple","None","en, zh","bi","None","None","False","False","False","mit","None","None","None"
+"Genius-Society/hoyoMusic","Genius-Society","2023-11-05 15:07:57","2024-12-29 01:23:19+00:00","751c3c3948cf40592dbfcbc5a6078301d419ce5c","245","798","11","task_categories:text-generation, task_categories:text2text-generation, task_categories:text-classification, language:en, language:zh, license:cc-by-nc-nd-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, music, mihoyo, genshin","text-generation, text2text-generation, text-classification","Intro This dataset mainly contains slices of second creation piano music from Genshin Impact game, which have been converted to ABC notations, with a data volume of 305264. The labeling information co","None","en, zh","bi","n|>|3|0|0|K","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"felixludos/babel-briefings","felixludos","2024-03-21 16:24:26","2024-03-29 20:53:36+00:00","80c0f69c84ddc341a943cee55f7154b3c050c36a","257","794","3","task_categories:text-classification, task_categories:translation, task_categories:zero-shot-classification, task_categories:feature-extraction, task_categories:text-generation, language:en, language:es, language:de, language:fr, language:zh, language:ar, language:pt, language:bg, language:cs, language:el, language:he, language:hu, language:id, language:it, language:ja, language:ko, language:lt, language:lv, language:nl, language:no, language:pl, language:ro, language:ru, language:sk, language:sl, language:sr, language:sv, language:th, language:tr, language:uk, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:json, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2403.19352, region:us, news, headlines, business, science, technology, sports, health, entertainment","text-classification, translation, zero-shot-classification, feature-extraction, text-generation","Babel Briefings News Headlines Dataset README Break Free from the Language Barrier Version: 1 - Date: 30 Oct 2023 Collected and Prepared by Felix Leeb (Max Planck Institute for Intelligent Systems, Tü","None","en, es, de, fr, zh, ar, pt, bg, cs, el, he, hu, id, it, ja, ko, lt, lv, nl, no, pl, ro, ru, sk, sl, sr, sv, th, tr, uk","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-nc-sa-4.0","2403.19352","None","None"
+"zhihz0535/X-AlpacaEval","zhihz0535","2024-01-27 20:48:46","2024-01-27 21:18:19+00:00","4dc0165d07b6dbf2d6c011a35ade071ac5ae0dc4","78","788","4","task_categories:text-generation, language:en, language:zh, language:ko, language:it, language:es, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.08711, region:us","text-generation, conversational","X-AlpacaEval 🤗 Paper | 📖 arXiv Dataset Description X-AlpacaEval is an evaluation benchmark for multilingual instruction-tuned large language models (LLMs), including open-ended instructions in 5 langu","None","en, zh, ko, it, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","2311.08711","None","None"
+"wanng/laion-high-resolution-chinese","wanng","2022-12-14 08:42:43","2022-12-14 15:11:23+00:00","9d7764ae4a76a84887d65f5bf7efc05a683c7be4","149","776","17","task_categories:feature-extraction, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, language:zh, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","feature-extraction","laion-high-resolution-chinese 简介 Brief Introduction 取自Laion5B-high-resolution多语言多模态数据集中的中文部分，一共2.66M个图文对。 A subset from Laion5B-high-resolution (a multimodal dataset), around 2.66M image-text pairs (o","None","zh","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"diabolocom/talkbank_4_stt","diabolocom","2024-09-19 13:46:35","2024-09-23 08:42:27+00:00","5b3d2d52932999b048200786c92443015e752ffa","122","772","2","None","automatic-speech-recognition, text-to-speech, text-to-audio","Dataset Card Dataset Description This dataset is a benchmark based on the TalkBank[1] corpus—a large multilingual repository of conversational speech that captures real-world, unstructured interaction","None","en, de, es, fr, es, zh","multi","None","None","False","False","False","cc-by-nc-sa-3.0","2409.12042","None","None"
+"FreedomIntelligence/HuatuoGPT2-Pretraining-Instruction","FreedomIntelligence","2024-06-24 08:49:22","2024-06-25 08:04:07+00:00","f5da14b8a742233fc8f9627e5a8b5e9f4efeb8be","179","765","7","task_categories:question-answering, task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.09774, arxiv:2305.15075, region:us, medical, biology","question-answering, text-generation","HuatuoGPT2-Pretraining-Instruction-5200K Here are the pre-training instructions for HuatuoGPT-II, developed with 5.2 million medical corpus using ChatGPT. This dataset is used to incorporate extensive","None","zh","mono","5|M|<|n|<|6|M","None","False","False","False","apache-2.0","None","None","None"
+"SDUIRLab/fuzi-mingcha-v1_0-pretrain-data","SDUIRLab","2024-10-23 17:48:26","2024-10-25 08:13:17+00:00","4578290c6574b7ad0f3c31699cca27af9c03c255","264","748","4","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, legal","text-generation","🐱 Github Repo 模型 huggingface 链接：https://huggingface.co/datasets/SDUIRLab/fuzi-mingcha-v1_0 数据 huggingface 链接：https://huggingface.co/datasets/SDUIRLab/fuzi-mingcha-v1_0-data GitHub 链接：https://github.co","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","apache-2.0","None","None","None"
+"xmj2002/genshin_ch_10npc","xmj2002","2023-05-18 04:03:50","2023-06-02 07:30:27+00:00","543fab65e9f238d414855a8c6675fe54af738c42","254","747","14","task_categories:text-to-speech, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech","Dataset Card for ""genshin_ch_10npc"" More Information needed","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"wanng/wukong100m","wanng","2022-12-11 04:26:12","2022-12-11 06:24:05+00:00","2d102e61dc886c8557c893d8e0175f809011b117","180","743","11","task_categories:feature-extraction, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, language:zh, license:cc-by-nc-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","feature-extraction","wukong100m 简介 Brief Introduction 取自Noah-Wukong多语言多模态数据集中的中文部分，一共100M个图文对。 A subset from Noah-Wukong (a multimodal dataset), around 100M image-text pairs (only Chinese). 数据集信息 Dataset Information 大约一共1","None","zh","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"AIR-Bench/qa_finance_zh","AIR-Bench","2024-05-07 15:20:21","2024-09-28 04:15:45+00:00","2e73a6254fc7e1ac7b511b6bdf35b6b1f6d22760","88","743","5","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, language:zh, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","Available Versions: AIR-Bench_24.04 Task / Domain / Language: qa / finance / zh Available Datasets (Dataset Name: Splits): default: test AIR-Bench_24.05 Task / Domain / Language: qa / finance / zh Ava","None","zh","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"yuyijiong/Multi-Doc-QA-Chinese","yuyijiong","2023-10-21 08:23:55","2023-12-06 04:38:57+00:00","551acb4ed85612e53700be8402d89fb4f8d16775","72","740","39","task_categories:text-generation, language:zh, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","text-generation","2023.12.4更新：改进答案的格式，强制所有答案在回答时必须先给出原文。旧版本的问答已经移至old文件夹。 中文多文档问答数据集 参考文档源数据均来自悟道开源200G数据 问题和回答是通过大语言模型（gpt-3.5）自动生成的，但质量很高。 raw数据集中，每个样本包含 一个参考文档、99个无关文档、一个问题、一个基于参考文档的回答。可以训练模型从大量文档中抽取关键信息的能力。不同领域的文档保","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-nc-4.0","None","None","None"
+"lightblue/text_ratings","lightblue","2024-08-27 05:40:39","2024-08-29 09:57:53+00:00","953c12db0c7d21bcc9326bacdc29bee895741a07","125","730","3","language:am, language:ar, language:bg, language:bn, language:cs, language:da, language:de, language:el, language:en, language:es, language:fa, language:fi, language:fr, language:gu, language:ha, language:hi, language:hu, language:id, language:it, language:ja, language:jv, language:kn, language:ko, language:lt, language:mr, language:nl, language:no, language:yo, language:zh, language:pl, language:pt, language:ro, language:ru, language:sk, language:sv, language:sw, language:ta, language:te, language:th, language:tr, language:uk, language:ur, language:vi, language:tl, license:mit, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Todo - Write dataset card","None","am, ar, bg, bn, cs, da, de, el, en, es, fa, fi, fr, gu, ha, hi, hu, id, it, ja, jv, kn, ko, lt, mr, nl, no, yo, zh, pl, pt, ro, ru, sk, sv, sw, ta, te, th, tr, uk, ur, vi, tl","multi","None","None","False","False","False","mit","None","None","None"
+"Trelis/openassistant-llama-style","Trelis","2023-10-04 14:14:13","2023-10-31 11:29:33+00:00","06fd5954004a34259731c2b79a05f98d58b1a10d","63","727","8","language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback, llama-2","None","Chat Fine-tuning Dataset - Llama 2 Style This dataset allows for fine-tuning chat models using [INST] AND [/INST] to wrap user messages. Preparation: The dataset is cloned from TimDettmers, which itse","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|K|<|n|<|1|0|k","None","False","False","False","apache-2.0","2304.07327","None","None"
+"CausalLM/Refined-Anime-Text","CausalLM","2024-02-26 08:26:43","2024-06-21 16:37:48+00:00","8e8c6aaff49122bf2601c72f75e0a01d30f306c2","64","725","232","None","None","News: Example output results with highly-detailed and granular recall from our internal overparameterized models trained on similarly synthesized datasets (broader subjects & human curation) are avail","None","en, zh","bi","None","None","False","False","False","wtfpl","None","None","None"
+"bai-roleplay/evol-character-entire","bai-roleplay","2024-01-29 19:22:44","2024-02-01 09:23:28+00:00","cbc6599b28ad589c6edca8c3f00afc7023719219","141","725","61","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Evol-character 数据集 中文 English Evol-character 数据集 下载数据集 数据生成框架 数据结构 与现有数据集对比 现有角色扮演数据集 我们的优势 联系我们 项目使用与免责声明 下载数据集 本数据集由GPT3.5和GPT4生成，为确保数据的合理使用，目前只公开了部分数据，公开的数据由三份文件组成，每份文件包含200个角色的设定以及对话。可在huggingface","None","zh","mono","None","None","False","auto","False","apache-2.0","None","None","None"
+"PKU-Alignment/Align-Anything-Instruction-100K-zh","PKU-Alignment","2024-07-16 16:00:12","2024-10-10 17:34:18+00:00","d38b70839450a86b616b45a0d9e816525c16ede4","145","723","5","task_categories:text-generation, language:zh, 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, instruction-following, llm, lm","text-generation","Dataset Card for Align-Anything-Instruction-100K-zh [🏠 Homepage] [🤗 Instruction-Dataset-100K(en)] [🤗 Instruction-Dataset-100K(zh)] [🤗 Align-Anything Datasets] Instruction-Dataset-100K(zh) Highlights D","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"larryvrh/WikiMatrix-v1-Ja_Zh-filtered","larryvrh","2023-04-08 03:07:25","2023-04-08 05:16:37+00:00","e4c17f9a45c5045c29ccec78636a213a9bdc093c","58","720","12","task_categories:translation, language:ja, language:zh, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Filtered and modified version of Japanese/Chinese language pair data from WikiMatrix v1. Process steps: 1. Basic regex based filtering / length checking to remove abnormal pairs. 2. Semantic similarit","None","ja, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"THUDM/LongReward-10k","THUDM","2024-10-17 02:31:18","2024-10-29 02:29:37+00:00","9d5f9b1f8e2bed07f8658e1aabaf1e85b8b0fa52","322","707","3","task_categories:text-generation, 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:2410.21252, region:us, long context, LLM, RLAIF","text-generation","LongReward-10k 💻 [Github Repo] • 📃 [LongReward Paper] LongReward-10k dataset contains 10,000 long-context QA instances (both English and Chinese, up to 64,000 words). The sft split contains SFT data g","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2410.21252","None","None"
+"Geralt-Targaryen/MELA","Geralt-Targaryen","2024-06-16 04:16:19","2024-06-16 08:55:22+00:00","0ee3576b83705b0fce5fd6a1f4fe3f07645d17ba","135","706","0","task_categories:text-classification, language:en, language:zh, language:it, language:ru, language:de, language:fr, language:es, language:ja, language:ar, language:is, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, linguistic acceptability","text-classification","See the GitHub repo for details.","None","en, zh, it, ru, de, fr, es, ja, ar, is","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"jed351/rthk_news","jed351","2023-02-16 16:44:01","2024-09-20 04:58:57+00:00","bded1b476701e1a78019a523cd5639b3010fb11b","98","694","6","language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","RTHK News Dataset RTHK is a public broadcasting service under the Hong Kong Government according to Wikipedia This dataset at the moment is obtained from exporting messages from their telegram channel","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"llamafactory/pokemon-gpt4o-captions","llamafactory","2024-09-01 16:56:00","2024-09-02 16:10:11+00:00","9a488d4d9a3285ba112b531fe0942fa1c3931bd8","296","681","6","task_categories:text-generation, task_categories:question-answering, task_categories:visual-question-answering, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, llama-factory, multimodal","text-generation, question-answering, visual-question-answering","Borrowed from: https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions You can use it in LLaMA Factory by specifying dataset: pokemon_cap.","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"qgyd2021/chinese_chitchat","qgyd2021","2023-09-22 02:24:54","2023-09-22 08:39:11+00:00","dcca2ec0fe14fcbc89b7bd7eaa1181d59c25e1f4","106","678","22","language:zh, license:apache-2.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us, chitchat","None","中文闲聊数据集 role 的取值有: ""unknown"", ""human"", ""assistant"", 三种. 数据集从网上收集整理如下: 数据 原始数据/项目地址 样本个数 语料描述 替代数据下载地址 ChatterBot ChatterBot chatterbot-corpus 560 按类型分类，质量较高 阿里云盘 提取码: 81ao douban Douban Conversation C","@dataset{chinese_chitchat, author = {Xing Tian}, title = {chinese_chitchat}, month = sep, year = 2023, publisher = {Xing Tian}, version = {1.0}, }","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","None","None","None"
+"gretelai/synthetic_multilingual_llm_prompts","gretelai","2024-06-09 12:26:03","2024-07-03 15:07:33+00:00","95187fb9b86f675683b809c45fd13236c9d17751","98","677","9","task_categories:text-generation, task_categories:translation, task_categories:question-answering, language:en, language:nl, language:fr, language:es, language:de, language:pt, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, multilingual, prompts, LLM, ChatGPT, NLP","text-generation, translation, question-answering","Image generated by DALL-E. See prompt for more details 📝🌐 Synthetic Multilingual LLM Prompts Welcome to the ""Synthetic Multilingual LLM Prompts"" dataset! This comprehensive collection features 1,250 s","None","en, nl, fr, es, de, pt, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"google/mittens","google","2024-01-17 19:03:53","2024-01-17 19:17:58+00:00","b8c46247b3e4069932110ad7420e05ff8d10ec03","65","673","5","task_categories:translation, language:ar, language:fi, language:om, language:lg, language:as, language:tr, language:fa, language:id, language:bn, language:de, language:hi, language:pt, language:ru, language:zh, language:ja, language:pl, language:te, language:th, language:cs, language:fr, language:am, language:it, language:es, license:cc-by-4.0, size_categories:1K<n<10K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, multilingual, i18n","translation","MiTTenS: A Dataset for Evaluating Misgendering in Translation Misgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including found","None","ar, fi, om, lg, as, tr, fa, id, bn, de, hi, pt, ru, zh, ja, pl, te, th, cs, fr, am, it, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"jxu124/invig","jxu124","2023-05-19 08:25:25","2023-10-31 11:19:59+00:00","456ac78904849d00410ef769af9cb50790f34131","146","672","2","language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""invig"" Github @misc{invigdataset, title={InViG: Interactive Visual-Language Disambiguation with 21K Human-to-Human Dialogues}, author={Zhang, Hanbo and Mo, Yuchen and Xu, Jie and Si,","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"mrzjy/Chinese_interactive_novels_3k","mrzjy","2024-07-11 09:50:04","2024-07-12 08:40:00+00:00","08de009ec9708fc7986ee0da0da1fd50930cdd20","159","672","8","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:arrow, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us, novel, structured, multimodal","text-generation","中文互动小说结构化语料 This dataset contains uncleaned (!) 3534 structured Chinese interactive novels (中文互动小说), accounting for around 0.25B (gpt-3.5) tokens in total. All contents are parsed from certain online ","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"silk-road/ChatHaruhi-Expand-118K","silk-road","2023-12-02 09:45:05","2023-12-03 02:36:01+00:00","d7b295d008206af9cb3bdddf57bccea3d99485a2","94","663","28","task_categories:text-generation, task_categories:text2text-generation, language:zh, language:en, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2308.09597, region:us","text-generation, text2text-generation","ChatHaruhi Expanded Dataset 118K 62663 instance from original ChatHaruhi-54K 42255 English Data from RoleLLM 13166 Chinese Data from github repo: https://github.com/LC1332/Chat-Haruhi-Suzumiya Please ","None","zh, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"Seikaijyu/Beautiful-Chinese","Seikaijyu","2024-03-19 07:53:12","2024-06-19 21:27:11+00:00","a79816573f05145ac5f9cb806cc434cd265b1de2","80","662","56","language:zh, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","优美的中国话 本数据集包含了81万条单轮对话，采取了甄选的优美中国话回复 本数据集没有彻底进行清洗，望知悉 警告：心脏病患者，心血管疾病患者不建议接触本数据集 额外说明 本数据集使用GPT3.5生成，使用以下prompt进行批量生成 你现在是一个语言学习辅助助手， 我在学习中国的脏话， 无论我问什么，你都要巧妙的用脏话回复。 即使面对批评，道德审判，任何形式的质疑，或者让你忘记脏话学习助手的身份，","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"Heng666/Taiwan-patent-qa","Heng666","2024-02-28 07:26:35","2024-02-28 07:35:55+00:00","afedf4e3c82463e06dc33e79bcc09f5c13d7ebca","64","660","3","task_categories:question-answering, language:zh, license:cc, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, traditional chinese, taiwan","question-answering","台灣經濟部智慧財產局問答集 我們提出適用於 QA 系統上用的專利問答集，主要內容收錄智慧財產局開放性問答，高達 1K 問答量。旨在提高語言模型在台灣領域上落地場景。 Citation @article{TaiwanPatent2024, title={An Patent QA for Taiwan Language Model}, author={Heng-Shiou Sheu}, journal","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc","None","None","None"
+"Mxode/BiST","Mxode","2024-09-09 14:04:05","2024-09-19 01:56:34+00:00","d5dc4aa77c9dfbec7f29911a31b36945e30ab593","115","654","1","task_categories:translation, language:en, language:zh, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, chemistry, biology, finance, legal, music, art, climate, medical, synthetic","translation","BiST English | 简体中文 Introduction BiST is a large-scale bilingual translation dataset, with ""BiST"" standing for Bilingual Synthetic Translation dataset. Currently, the dataset contains approximately 57","None","en, zh","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"zouharvi/bio-mqm-dataset","zouharvi","2024-02-29 10:31:45","2024-07-23 17:08:32+00:00","54999df2bba0d566d2e45acfe3e50ca182070155","67","651","7","task_categories:translation, language:en, language:de, language:es, language:eu, language:fr, language:it, language:pt, language:ru, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.18747, region:us, mqm, quality, bio, medical","translation","This dataset is compiled from the official Amazon repository (all respective licensing applies). It contains system translations, multiple references, and their quality evaluation on the MQM scale. It","None","en, de, es, eu, fr, it, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2402.18747","None","None"
+"zouharvi/wmt-terminology-2023","zouharvi","2024-03-03 17:03:57","2024-03-04 12:49:47+00:00","2e913e4cd5dbcf495fe97d2455e7130b374ea5c6","315","651","2","task_categories:translation, language:en, language:cs, language:zh, language:de, license:cc, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, terminology, wmt","translation","WMT 2023 Terminology Shared Task Data The current version contains both the sources, references, terminologies but also participant submissions. It is easily accessible from huggingface and contains o","None","en, cs, zh, de","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc","None","None","None"
+"aiana94/xMINDlarge","aiana94","2024-06-25 11:54:02","2024-10-25 08:29:52+00:00","b31b0ae960ae073d6d230ba026892fc585d7d032","67","649","4","task_categories:translation, task_categories:text-retrieval, multilinguality:translation, multilinguality:multilingual, multilinguality:multi-parallel, source_datasets:MIND, language:fi, language:gn, language:ht, language:id, language:ja, language:ka, language:ro, language:so, language:sw, language:ta, language:th, language:tr, language:vi, language:zh, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.17876, region:us, news, multilingual, machine-translated, nllb","translation, text-retrieval","Dataset Card for xMINDlarge Dataset Summary xMINDlarge is an open, large-scale multi-parallel news dataset for multi- and cross-lingual news recommendation. It is derived from the English MINDlarge da","None","fi, gn, ht, id, ja, ka, ro, so, sw, ta, th, tr, vi, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2403.17876","None","None"
+"grammarly/medit","grammarly","2024-04-15 21:30:51","2024-10-01 07:31:22+00:00","7dd1f4ffb87541251db0c4ecc734376d6b1a463e","161","646","13","task_categories:text-generation, language:en, language:de, language:ar, language:ja, language:ko, language:es, language:zh, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.16472, region:us, gec, simplification, paraphrasing, es, de, ar, en, ja, ko, zh, multilingual","text-generation","Dataset Card for mEdIT: Multilingual Text Editing via Instruction Tuning Paper: mEdIT: Multilingual Text Editing via Instruction Tuning Authors: Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bas","None","en, de, ar, ja, ko, es, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","2402.16472","None","None"
+"tyang816/MedChatZH","tyang816","2023-08-19 11:15:06","2024-03-20 08:20:47+00:00","1835780e1825ad77276ed09afd35fae7c3c95660","165","644","17","task_categories:question-answering, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical, biology","question-answering","MedChatZH MedChatZH中文微调参数 基于Baichuan-7B在中医书籍数据和医疗指令对话数据上训练，使其具备较强的中文医疗对话能力，能够理解中医问题。 Github：MedChatZH-7B 🚀 结果 问题 回答 中医治脱发有效吗脱发5、6年了，明显比以前少了很多，脱发位置比较均衡，没有突出掉的比较多的地方，我的发质比较硬，头发生长也较快。 中药治疗脱发的效果因人而异，但对于您这","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"huawei-noah/entity_cs","huawei-noah","2023-09-08 08:44:07","2023-09-20 07:05:07+00:00","32c89f7c52f16e46115d9c31e6000ddb081b3de3","137","644","2","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:en, language:el, 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:nb, 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, license:apache-2.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for EntityCS Repository: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS Paper: https://aclanthology.org/2022.findings-emnlp.499.pdf Point of Contact: Fenia Christop","None","af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, en, el, 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, nb, 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","apache-2.0","None","None","None"
+"KaifengGGG/WenYanWen_English_Parallel","KaifengGGG","2024-04-11 19:23:37","2025-01-08 01:45:17+00:00","3911bca3331860dd4783584d5e13f311338668f0","136","638","5","task_categories:translation, task_categories:question-answering, language:zh, language:en, language:lzh, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.05530, region:us, machine-translation","translation, question-answering","Dataset Card for WenYanWen_English_Parallel Dataset Summary The WenYanWen_English_Parallel dataset is a multilingual parallel corpus in Classical Chinese (Wenyanwen), modern Chinese, and English. The ","None","zh, en, lzh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","2403.05530","None","None"
+"LinkSoul/Chinese-LLaVA-Vision-Instructions","LinkSoul","2023-07-30 04:45:39","2023-09-23 15:07:56+00:00","d10ec6e47aa6b9aa3a53d03d0793eb4728e921f3","138","631","43","language:en, language:zh, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","conversational","本数据集是对于LLaVA的翻译，请从LLaVA dataset下载对应的图片。 百度网盘链接: https://pan.baidu.com/s/1-jgINIkW0MxusmJuSif85w?pwd=q62v","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"lorinma/PetrochemicalCorpora_CPTtest_200bks_zh","lorinma","2023-07-30 13:39:56","2024-02-04 07:22:47+00:00","05a2420de35a005efc9465c3e0995cf9dfe2c78e","111","629","1","task_categories:text-generation, language:zh, size_categories:10K<n<100K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","text-generation","Chinese Corpora in the field of petrochemical, for the purpose of LLM continue-pretrain. 用于垂域（化工）LLM的增量预训练使用的语料，测试版。 200本书，仅经过了OCR，没有进行任何数据清理，所以质量不高。尤其是涉及到复杂的表格和公式，以及这批书的扫描质量偏低。 仅用于测试使用。 样例1： i所有安全泄压设","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"CSJianYang/CodeArena","CSJianYang","2024-11-28 08:50:19","2024-12-18 07:59:24+00:00","05a5f4cae626aab85514451b7d51141b1bef3189","453","628","7","task_categories:question-answering, task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:n<1K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.05210, region:us, code","question-answering, text-generation","Dataset Summary To bridge the gap between the model-generated response and human preference, we present a rigorous human-curated benchmark CodeArena to emulate the complexity and diversity of real-wor","None","zh, en","bi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"BAAI/Aquila-135M-Datasets","BAAI","2024-11-30 00:43:58","2024-12-29 02:24:30+00:00","69c51c28765d74aabdfb8f9b265117614d1a6624","320","625","2","language:en, language:zh, license:apache-2.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Introduction The Aquila-135M model is a small bilingual(Chinese and English) language model, which is trained using a two-phrase paradigm: pre-training and annealing. This model used 1.66TB bilingual ","None","en, zh","bi","None","None","False","auto","False","apache-2.0","None","None","None"
+"silk-road/ChatHaruhi-54K-Role-Playing-Dialogue","silk-road","2023-08-22 00:40:09","2023-12-16 11:34:47+00:00","590c17dfc7d3f294bdae9c6aa9e6a7f0c753d487","88","623","57","task_categories:text-generation, task_categories:text2text-generation, language:en, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2308.09597, region:us","text-generation, text2text-generation","ChatHaruhi Reviving Anime Character in Reality via Large Language Model github repo: https://github.com/LC1332/Chat-Haruhi-Suzumiya Chat-Haruhi-Suzumiyais a language model that imitates the tone, pers","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"shibing624/snli-zh","shibing624","2023-06-14 04:33:26","2023-06-14 07:15:52+00:00","b0aa839a5138eac8afbc3282c534cd8651f5350e","67","623","4","task_categories:text-classification, task_ids:natural-language-inference, task_ids:semantic-similarity-scoring, task_ids:text-scoring, annotations_creators:shibing624, language_creators:liuhuanyong, multilinguality:monolingual, source_datasets:https://github.com/liuhuanyong/ChineseTextualInference/, language:zh, license:cc-by-4.0, size_categories:100K<n<1M, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, natural-language-inference, semantic-similarity-scoring, text-scoring","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, contradiction, and neutral, supporti","@inproceedings{snli:emnlp2015, Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, Publisher = {Association for Computational Linguistics}, Title = {A large annotated corpus for learning natural language inference}, Year = {2015} }","zh","mono","1|0|0|K|<|n|<|2|0|M","snli","False","False","False","cc-by-4.0","None","None","natural-language-inference, semantic-similarity-scoring, text-scoring"
+"jrahn/Infinity-Instruct","jrahn","2024-06-20 07:49:04","2024-06-20 08:33:49+00:00","cb5b9658507cdae3d560e7c0ac3ef56072858094","138","622","0","task_categories:text-generation, language:en, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.00530, region:us","text-generation","SOURCE: https://huggingface.co/datasets/BAAI/Infinity-Instruct CHANGES: removed target_value field for axolotl sharegpt dataset compatibility. Infinity Instruct Beijing Academy of Artificial Intellige","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"facebook/Multi-IF","facebook","2024-10-18 22:59:44","2024-10-30 03:13:22+00:00","0ab97ce0b45c7f57772e8ba2ac1616f4b00bd3aa","129","622","23","language:en, language:fr, language:es, language:pt, language:hi, language:zh, language:ru, language:it, license:cc-by-nc-2.0, size_categories:1K<n<10K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction following, multi-turn, multilingual","None","Dataset Summary We introduce Multi-IF, a new benchmark designed to assess LLMs' proficiency in following multi-turn and multilingual instructions. Multi-IF, which utilizes a hybrid framework combining","None","en, fr, es, pt, hi, zh, ru, it","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-2.0","None","None","None"
+"jed351/Chinese-Common-Crawl-Filtered","jed351","2023-07-20 21:23:06","2023-07-20 23:09:53+00:00","fb95c03017696b3a42ff31bc237133d248c8380c","144","617","11","language:zh, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Traditional Chinese C4 Dataset Summary Data obtained from 2023-14 Common Crawl. Downloaded and processed using code based on another project attempting to recreate the C4 dataset. The resultant datase","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"erhwenkuo/poetry-chinese-zhtw","erhwenkuo","2023-10-16 07:16:02","2023-10-16 08:16:59+00:00","8e53cb324c7feec2b3889140c428720d8a834831","68","616","16","task_categories:text-generation, language:zh, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""poetry-chinese-zhtw"" 資料集摘要 中文古典文集資料庫收集了約 5.5 萬首唐詩、26 萬首宋詩、2.1 萬首宋詞和其他古典文集。詩人包括唐宋兩朝近 1.4 萬古詩人，和兩宋時期 1.5 千古詞人。 五代十國- 收錄""花間集""與""南唐二主詞"" 唐- 收錄""全唐詩""(是清康熙四十四年，康熙皇帝主導下，蒐集羅唐詩的收藏「得詩 48,900 餘首，詩","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"Heng666/Traditional_Chinese-aya_dataset","Heng666","2024-02-19 13:19:03","2024-02-19 14:14:39+00:00","a626ccdc584a814b399ed33533d0e08452b70cf2","125","614","3","task_categories:question-answering, task_categories:translation, task_categories:summarization, task_categories:zero-shot-classification, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.06619, region:us","question-answering, translation, summarization, zero-shot-classification","資料集描述 繁體中文 Aya (Traditional Chinese Aya Chinese TCA)：專注於繁體中文處理的 Aya 集合的精選子集 概述 繁體中文 Aya 是一個精心策劃的資料集，源自 CohereForAI 的綜合 Aya 集合，特別關注繁體中文文本資料。 此資料集結合了來自 CohereForAI/aya_dataset，過濾掉除繁體中文、簡體中文內容之外的所有內容。 目標","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"erhwenkuo/zhwikisource-zhtw","erhwenkuo","2023-10-13 22:43:13","2023-10-14 05:45:51+00:00","1fdebaa66ef58a9517c87e73f813f5923490b5c2","97","606","3","task_categories:text-generation, language:zh, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""zhwikisource-zhtw"" 維基文庫（英文：Wikisource）, 又稱 ""自由的圖書館"", 是一個由志願者在線收集自由內容文本的站點。 它屬維基媒體計劃項目，由維基媒體基金會負責運營。 作品類型: 典籍 | 史書 | 小說 | 詩歌 | 散文 | 演講 | 歌詞 | 經書 | 更多…… 主題： 條約 | 憲法 | 法律 | 教育 | 政治 | 歷史","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"voices365/102_Hours_High_Quality_Chinese_Audio_Dataset_For_Speech_Synthesis_Female_Samples","voices365","2024-09-06 01:41:13","2024-09-06 01:46:12+00:00","92379fae8db0d993d05dab0d078b478bd1644118","191","602","1","task_categories:text-to-speech, task_categories:text-to-audio, task_categories:text-to-video, language:zh, license:cc-by-nc-2.0, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","text-to-speech, text-to-audio, text-to-video","Dataset Description 102-Hour Chinese Mandarin Audio Dataset for Speech Synthesis. It's recorded by 102 professional Chinese voice artists (Male and Female). Professional Chinese phoneticians participa","None","zh","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-nc-2.0","None","None","None"
+"TigerResearch/tigerbot-zhihu-zh-10k","TigerResearch","2023-05-30 15:15:37","2023-05-31 02:59:43+00:00","9377cb32a3bfcafd7cfe43c98a7eb4a9e2b88c5e","87","595","25","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Tigerbot 基于开源搜集的知乎���据生成的sft问答对 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/tigerbot-zhihu-zh-10k')","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"1024m/mMGTD-Corpus","1024m","2024-07-14 02:25:42","2025-01-28 22:29:37+00:00","b2cac824b30dfd12584d82c43740a6ba9b661ecd","242","592","8","task_categories:token-classification, language:ar, language:cs, language:de, language:nl, language:en, language:zh, language:fa, language:fr, language:el, language:he, language:hi, language:id, language:it, language:ja, language:ko, language:pl, language:pt, language:ro, language:ru, language:es, language:tr, language:uk, language:vi, license:cc-by-nc-nd-4.0, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, doi:10.57967/hf/3194, region:us, linguistics, MGT, AI text detection","token-classification","Dataset for Multilingual Machine-Generated Text Portion Detection Model Details To be made Available by Feb 1, 2025Public Version available at :https://huggingface.co/datasets/1024m/MGTPD/ Model Descr","None","ar, cs, de, nl, en, zh, fa, fr, el, he, hi, id, it, ja, ko, pl, pt, ro, ru, es, tr, uk, vi","multi","1|M|<|n|<|1|0|M","None","False","manual","False","cc-by-nc-nd-4.0","None","None","None"
+"hfl/alpaca_zh_51k","hfl","2024-04-28 04:31:55","2024-04-28 04:41:01+00:00","8b1273b33cbf1f3d802c32bb00e19d33c0d7995b","68","584","14","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","alpaca_data_zh_51k.json 中文Alpaca数据，包含51k个从ChatGPT (gpt-3.5-turbo)爬取的指令数据。 Chinese Alpaca dataset, containing 51k instruction data crawled from ChatGPT (gpt-3.5-turbo). 项目地址 / Project：https://github.co","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"wenbopan/anti-haystack","wenbopan","2024-03-19 06:47:22","2024-03-19 07:14:38+00:00","49ebf84261893b13ee423acd9b36f38e1ac16d88","100","582","5","task_categories:text-generation, task_categories:summarization, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, summarization","Dataset Card for ""anti-haystack"" This dataset contains samples that resemble the ""Needle in a haystack"" pressure testing. It can be helpful if you want to make your LLM better at finding/locating shor","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"voices365/102_Hours_High_Quality_Chinese_Audio_Dataset_For_Speech_Synthesis_Male_Samples","voices365","2024-09-06 01:36:56","2024-09-06 01:45:35+00:00","8202c03f038a2ccf4f922ec6a19dd28aa7dc0467","130","580","1","task_categories:text-to-speech, task_categories:text-to-audio, task_categories:text-to-video, language:zh, license:cc-by-nc-2.0, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","text-to-speech, text-to-audio, text-to-video","Dataset Description 102-Hour Chinese Mandarin Audio Dataset for Speech Synthesis. It's recorded by 102 professional Chinese voice artists (Male and Female). Professional Chinese phoneticians participa","None","zh","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-nc-2.0","None","None","None"
+"Starlento/SFT-COIG-CQIA-handbook","Starlento","2024-05-01 14:17:06","2024-05-02 05:05:21+00:00","96f5cae616fb560a0625a490183bd81634fb5f7f","118","580","4","task_categories:text-generation, task_categories:question-answering, language:zh, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.18058, region:us, sft","text-generation, question-answering","SFT-COIG-CQIA-handbook The rearranged dataset for direct use in alignment-handbook. 数据完全来自于COIG-CQIA。 暂时忽略了chinese_traditional，coig_pc，exam，finance这些转换麻烦或者语义上不适合当QA数据集的subset。 其中train是全集，test是ruozhiba","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"TigerResearch/tigerbot-earning-plugin","TigerResearch","2023-05-30 15:23:08","2023-06-01 10:19:33+00:00","75c0a58bbf9babc640dda2ce4f3ded9a3e9a9358","68","578","2","language:zh, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Tigerbot 模型rethink时使用的外脑原始数据，财报类 共2500篇财报，抽取后按段落保存 发布时间区间为: 2022-02-28 至 2023-05-10 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/tigerbot-earning-plugin')","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"agentlans/LinguaNova","agentlans","2024-11-30 09:53:49","2025-01-28 21:26:49+00:00","809b3a918cc3c4a5afdd7db44a1f507d96465a8b","98","575","0","task_categories:text-generation, task_categories:text-classification, task_categories:text-retrieval, language:multilingual, language:ar, language:az, language:bg, language:bn, language:ca, language:cs, language:da, language:de, language:el, language:en, language:es, language:et, language:fa, language:fi, language:fr, language:he, language:hi, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:ka, language:kk, language:ko, language:lt, language:lv, language:mk, language:ml, language:mr, language:ne, language:nl, language:no, language:pl, language:pt, language:ro, language:ru, language:sk, language:sl, language:sq, language:sr, language:sv, language:ta, language:th, language:tr, language:uk, language:ur, language:vi, language:zh, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text-classification, text-retrieval","None","None","multilingual, ar, az, bg, bn, ca, cs, da, de, el, en, es, et, fa, fi, fr, he, hi, hu, hy, id, is, it, ja, ka, kk, ko, lt, lv, mk, ml, mr, ne, nl, no, pl, pt, ro, ru, sk, sl, sq, sr, sv, ta, th, tr, uk, ur, vi, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"xjlulu/ntu_adl_question","xjlulu","2023-10-09 17:13:39","2023-10-24 02:21:00+00:00","d23f86eb6a9f224f0dfc1ef676bfc0a17d703959","61","575","1","task_categories:question-answering, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"deepghs/cc100_char_freq","deepghs","2024-12-18 21:53:53","2024-12-22 05:20:10+00:00","0088fd38ad6a5b1b4ad425c5178864f3ef40cc06","451","573","0","None","text-generation, text2text-generation, text-classification","Letter Frequency Table on CC-110 This is the letter frequency analysis table based on CC-110 dataset. 116 languages supported, 50882 letters supported in total. This dataset is useful for do some basi","None","af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, ff, fi, fr, fy, ga, gd, gl, gn, gu, ha, he, hi, hr, ht, hu, hy, id, ig, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lg, li, ln, lo, lt, lv, mg, mk, ml, mn, mr, ms, my, ne, nl, no, ns, om, or, pa, pl, ps, pt, qu, rm, ro, ru, sa, sc, sd, si, sk, sl, so, sq, sr, ss, su, sv, sw, ta, te, th, tl, tn, tr, ug, uk, ur, uz, vi, wo, xh, yi, yo, zh, zu","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"wdndev/webnovel-chinese","wdndev","2024-03-27 16:00:19","2024-04-04 03:50:32+00:00","2bc802c06f5884f74cda1fe39be5d415feb48565","125","571","18","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, llm, pretrain","text-generation","简介 搜集网络上的网文小说，清洗，分割后，用于训练大语言模型，共计9000本左右，大约9B左右token。 使用 格式说明 采用jsonl格式存储，分为三个字段： title ：小说名称 chapter：章节 text：正文内容 示例： {""title"": ""斗破苍穹"", ""chapter"": "" 第一章 陨落的天才"", ""text"": ""“斗之力，三段！”\\n望着测验魔石碑上面闪亮得甚至有些刺眼","None","zh","mono","1|B|<|n|<|1|0|B","None","False","False","False","apache-2.0","None","None","None"
+"TigerResearch/tigerbot-research-plugin","TigerResearch","2023-05-30 15:23:53","2023-06-01 10:18:59+00:00","434f01a05a200dc358fefe98abfb9ca47df2ee91","78","570","4","language:zh, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Tigerbot 模型rethink时使用的外脑原始数据，研报类 共2W篇完整研报，按段落保存 发布时间区间: 2022-09-30 至 2023-05-19 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/tigerbot-research-plugin')","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"gbenson/webui-dom-snapshots","gbenson","2024-04-30 14:39:51","2024-06-09 07:36:33+00:00","3dc9dea26bb353d59d0653a2a233ff80bf4e3d8b","68","569","1","task_categories:image-feature-extraction, task_categories:reinforcement-learning, task_categories:text-classification, multilinguality:multilingual, source_datasets:biglab/webui-7k, source_datasets:original, language:en, language:nl, language:fr, language:zh, language:ja, language:de, language:id, language:cs, language:ru, language:pt, language:fi, language:sv, language:no, language:pl, language:da, language:sl, language:hu, language:vi, language:is, language:ko, language:th, language:tr, language:ar, language:bg, language:el, language:uk, language:es, language:et, language:gd, language:ne, language:sk, language:af, language:bn, language:gl, language:hi, language:it, language:lt, language:lv, language:ml, language:sr, language:to, license:cc0-1.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","image-feature-extraction, reinforcement-learning, text-classification","Dataset Card for WebUI DOM snapshots 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: Gary ","None","en, nl, fr, zh, ja, de, id, cs, ru, pt, fi, sv, no, pl, da, sl, hu, vi, is, ko, th, tr, ar, bg, el, uk, es, et, gd, ne, sk, af, bn, gl, hi, it, lt, lv, ml, sr, to","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc0-1.0","None","None","None"
+"qgyd2021/chinese_porn_novel","qgyd2021","2024-11-13 08:31:54","2024-11-13 11:06:27+00:00","170c125e168cf58400ad3b31300c88ed8a1c978a","173","566","24","task_categories:text-generation, language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, art","text-generation","Chinese Porn Novel https://huggingface.co/docs/hub/en/datasets-adding datasets-cli convert_to_parquet qgyd2021/chinese_porn_novel --trust_remote_code SQ小说, 用于制作特殊的 GPT 语言模型. 将每篇小说切分 chunk, 用 Qwen-inst","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"izhx/mewsli-x","izhx","2024-02-24 14:01:38","2024-02-25 02:29:26+00:00","7e3723faa15c6d84cfd56d66cf6cd347be8ad00e","111","564","1","task_categories:text-retrieval, task_ids:entity-linking-retrieval, language:af, language:ar, language:az, language:bg, language:bn, language:de, language:el, language:en, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:gu, language:he, language:hi, language:ht, language:hu, language:id, language:it, language:ja, language:jv, language:ka, language:kk, language:ko, language:lt, language:ml, language:mr, language:ms, language:my, language:nl, language:pa, language:pl, language:pt, language:qu, language:ro, language:ru, language:sw, language:ta, language:te, language:th, language:tl, language:tr, language:uk, language:ur, language:vi, language:wo, language:yo, language:zh, license:apache-2.0, size_categories:n<1K, modality:text, region:us","text-retrieval, entity-linking-retrieval","I generated the dataset following mewsli-x.md#getting-started and converted into different parts (see process.py): ar/de/en/es/fa/ja/pl/ro/ta/tr/uk wikinews_mentions dev and test (from wikinews_mentio","None","af, ar, az, bg, bn, de, el, en, es, et, eu, fa, fi, fr, gu, he, hi, ht, hu, id, it, ja, jv, ka, kk, ko, lt, ml, mr, ms, my, nl, pa, pl, pt, qu, ro, ru, sw, ta, te, th, tl, tr, uk, ur, vi, wo, yo, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","entity-linking-retrieval"
+"Maciel/FinCUGE-Instruction","Maciel","2023-04-29 10:59:46","2023-08-20 02:26:39+00:00","e37b74d83c61ba3674b290c68c13e393aa6adbaf","103","563","19","task_categories:question-answering, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","question-answering","Dataset Card for Dataset Name Dataset Description 本数据集包含八项中文金融自然语言处理基准任务，分别为金融新闻摘要(FinNA)、金融新闻公告事件问答(FinQA)、金融新闻分类(FinNL)、金融新闻关系抽取(FinRE)、金融社交媒体文本情绪分类(FinNE)、金融负面消息及其主体判定(FinNSP)、金融因果事件抽取(FinCQA)、金融事件","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"Nikity/Pornhub","Nikity","2024-08-26 11:16:06","2024-08-26 15:01:52+00:00","febede1f6ee3396dbd0a31270f59ee6189a8b2b4","104","560","20","language:sq, language:ar, language:bn, language:bg, language:zh, language:hr, language:cs, language:da, language:nl, language:en, language:et, language:fi, language:fr, language:de, language:el, language:he, language:hi, language:hu, language:id, language:it, language:ja, language:ko, language:lv, language:lt, language:mk, language:ml, language:mr, language:ne, language:no, language:fa, language:pl, language:pt, language:pa, language:ro, language:ru, language:sk, language:sl, language:so, language:es, language:sw, language:sv, language:tl, language:ta, language:te, language:th, language:tr, language:uk, language:ur, language:vi, language:cy, license:odc-by, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, not-for-all-audiences","None","Pornhub Dataset The Pornhub Dataset provides a comprehensive collection of data sourced from pornhub.com, encompassing various details from MANYYY videos available on the platform. The file consists o","None","sq, ar, bn, bg, zh, hr, cs, da, nl, en, et, fi, fr, de, el, he, hi, hu, id, it, ja, ko, lv, lt, mk, ml, mr, ne, no, fa, pl, pt, pa, ro, ru, sk, sl, so, es, sw, sv, tl, ta, te, th, tr, uk, ur, vi, cy","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","odc-by","None","None","None"
+"liswei/common-crawl-zhtw","liswei","2024-05-06 15:27:33","2024-05-06 15:36:26+00:00","507281788264ffbd1498ee69dc19c440ed4a2514","136","559","3","task_categories:text-generation, task_categories:fill-mask, language:zht, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","Dataset Card for Common Crawl Traditional Chinese De-duplicated version of jed351/Traditional-Chinese-Common-Crawl-Filtered. De-duplicated with MinHash Is suggested to filter the dataset with NLU mode","None","zht, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"TigerResearch/tigerbot-law-plugin","TigerResearch","2023-05-30 15:25:17","2023-06-01 03:11:47+00:00","d6837fa6596d8c9dd1aa051c07a046597b1d5092","67","559","25","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Tigerbot 模型rethink时使用的外脑原始数据，法律11大类，共5.5W+条款 宪法 刑法 行政法 司法解释 民法商法 民法典 行政法规 社会法 部门规章 经济法 诉讼与非诉讼程序法 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/tigerbot-law-plugin')","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"aigrant/taiwan-legislator-transcript","aigrant","2024-07-22 20:45:25","2024-07-26 01:11:40+00:00","d378212a096039f34b32ca3bb27f220977f7c887","67","556","2","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Taiwan Legislator Transcript Overview The transcripts of speech record happened in various kinds of meetings at Taiwan Legislator Yuan. The original of transcripts are compiled and published on gazett","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"yongjian/music-clips-50","yongjian","2022-07-15 12:40:23","2022-10-07 14:21:39+00:00","5157310f019772611e38adb57ce1ebe589a1f2d0","83","550","3","multilinguality:other-music, language:en, language:zh, size_categories:n<1K, format:parquet, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","There are 50 music clips(of 3~5 seconds). You can load them by the following code: from datasets import load_dataset dataset = load_dataset('yongjian/music-clips-50') clips = dataset['train'] # all 50","None","en, zh","bi","None","None","False","False","False","None","None","None","None"
+"AWeirdDev/zh-tw-pts-articles-sm","AWeirdDev","2024-03-02 15:29:59","2024-03-03 05:33:13+00:00","e339b51f3332f5e473fda3d24675042f3dd82659","253","547","3","task_categories:text-generation, language:zh, license:mit, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, library:webdataset, region:us, webdataset","text-generation","zh-tw-pts-articles-sm 🐣English • 🇹🇼 繁體中文 This dataset contains articles scraped from PNN News. It's a news provider verified by the vast majority. Note: some keys like conclusion may be None. Dataset(","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"yuyijiong/Chinese_Paper_Abstract","yuyijiong","2023-11-21 05:10:41","2023-11-21 05:13:08+00:00","859e524c5e3ee3cb5cb55ab39f35bf671fa22f35","60","545","20","language:zh, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","包含 title、正文、中文摘要,可用于训练文本摘要任务 论文来自中国知网，版权受限，不能直接公开。下载后请勿上传到公开场合。","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-nc-4.0","None","None","None"
+"sambanovasystems/xOA22","sambanovasystems","2023-05-17 00:11:37","2023-05-17 18:38:13+00:00","425420e4b0620ac1ca642f6ea9d2936d80d63907","124","540","1","language:ar, language:zh, language:en, language:fr, language:hi, language:es, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us","conversational","Dataset Card for xOA22 - Multilingual Prompts from OpenAssistant Dataset Summary xOA22 consists of 22 prompts originally shown in Appendix E, page 25 of the OpenAssistant Conversations paper. These 22","None","ar, zh, en, fr, hi, es","multi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"meta-math/MetaMathQA_GSM8K_zh","meta-math","2023-12-04 03:13:47","2023-12-04 05:23:20+00:00","9347e6bba50eb20ad36b572f17eeba6d377dd5a1","119","537","12","task_categories:question-answering, language:en, language:zh, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2309.12284, region:us, math, math-qa","question-answering","Dataset MetaMathQA_GSM8K_zh is a dataset for mathematical reasoning in Chinese, question-answer pairs are translated from MetaMathQA (https://huggingface.co/datasets/meta-math/MetaMathQA) by GPT-3.5-T","None","en, zh","bi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"BAAI/IndustryCorpus_agriculture","BAAI","2024-07-25 05:46:38","2024-07-26 02:30:42+00:00","7f2f669aaa38d07ea08df27ea1be36a0d68fd31a","70","536","4","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"Dusker/chinese-laws-pretrain","Dusker","2024-08-19 15:45:57","2024-08-19 15:49:27+00:00","40218aa30cd9e8c5ad718ddb090703578ed6d8b7","249","534","4","task_categories:text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, legal","text-generation","None","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"kirp/ruozhiba-raw","kirp","2023-11-12 03:35:51","2024-10-23 01:02:04+00:00","a6853a92d89e1ef2f042973825b85e450e042721","136","533","6","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","弱智吧数据集 ruozhiba-raw 弱智吧是百度贴吧中的一个非常受欢迎的论坛，以创作短小精悍而闻名。 这里是截至2023/11/10日前的raw data。 Todo get the top 5 answers to each post clean the data a joke dataset(pure text/multimodal) a feasibility dataset a new","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"fzmnm/TinyBooks-QA-Chinese","fzmnm","2024-06-07 04:43:33","2024-08-01 07:06:26+00:00","0dc4d137378df9e585b4fcda28fb86d99e7e44ae","120","529","5","task_categories:text-generation, language:zh, license:cc, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.07759, region:us","text-generation","本数据集已停止更新，请移步https://huggingface.co/datasets/fzmnm/TinyStoriesAdv-zh TinyBooks-QA-Chinese Inspired by the (TinyStories)[https://arxiv.org/abs/2305.07759] paper, where a small language model exhibits s","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc","2305.07759%5D","None","None"
+"BAAI/IndustryCorpus_automobile","BAAI","2024-07-25 05:46:41","2024-07-26 02:30:49+00:00","a2739243a502156fdb6dcf14c410c9945b663157","96","527","3","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"Tarklanse/Traditional_Chinese_roleplay_chat_Dataset","Tarklanse","2023-08-13 01:40:43","2023-09-07 12:27:06+00:00","bbcbf8d07dd65ef1c66fd13f7009fe2a3aafbd3d","71","527","39","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text2text-generation","Traditional_Chinese_roleplay_chat_Dataset 這個資料集是以繁體中文為主，將各種由ChatGPT生成與極小部分個人撰寫的對話內容整理為alpaca dataset format的格式 以一層一層堆疊的方式，將一則對話紀錄拆成數筆資料(共約1000則對話)，在幾次嘗試性的訓練中能夠讓llama2重現原本英文那種很活躍的對話風格，並且能夠維持善於扮演各種角色的能力","None","zh","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"aiana94/xMINDsmall","aiana94","2024-06-20 08:44:37","2024-10-25 08:30:20+00:00","aeb7ddb35fa87765125db1c2e2cb4594ff245940","113","526","4","task_categories:translation, task_categories:text-retrieval, multilinguality:translation, multilinguality:multilingual, multilinguality:multi-parallel, source_datasets:MIND, language:fi, language:gn, language:ht, language:id, language:ja, language:ka, language:ro, language:so, language:sw, language:ta, language:th, language:tr, language:vi, language:zh, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.17876, region:us, news, multilingual, machine-translated, nllb","translation, text-retrieval","Dataset Card for xMINDsmall Dataset Summary xMINDsmall is an open, large-scale multi-parallel news dataset for multi- and cross-lingual news recommendation. It is derived from the English MINDsmall da","None","fi, gn, ht, id, ja, ka, ro, so, sw, ta, th, tr, vi, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2403.17876","None","None"
+"yachay/text_coordinates_regions","yachay","2023-09-14 15:28:51","2023-09-21 16:19:16+00:00","b9fa48181e3c93791d0613382c77aeb91214f324","59","525","6","task_categories:feature-extraction, task_categories:token-classification, task_categories:text-classification, language:en, language:zh, language:es, language:hi, language:ar, language:bn, language:pt, language:ru, language:ja, language:pa, language:de, language:jv, language:ms, language:te, language:vi, language:ko, language:fr, language:mr, language:ta, language:ur, language:tr, language:it, language:th, language:gu, language:fa, language:pl, license:mit, size_categories:100K<n<1M, format:json, modality:text, modality:geospatial, library:datasets, library:dask, library:mlcroissant, region:us, multilingual, text, coordinates, geospatial, translation, NER, geo, geo-tagged, named-entity-recognition, natural-language-processing, geographic-data, geolocation, twitter, reddit","feature-extraction, token-classification, text-classification","Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions) Dataset Summary The ""Regions"" dataset is a multilingual corpus that encompasses textual data from the 123 most popula","None","en, zh, es, hi, ar, bn, pt, ru, ja, pa, de, jv, ms, te, vi, ko, fr, mr, ta, ur, tr, it, th, gu, fa, pl","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","mit","None","None","None"
+"larryvrh/Chinese-Poems","larryvrh","2024-07-25 18:51:04","2024-07-26 01:44:57+00:00","2b9da0fc4b724531ddd85f58f0dca83dd26b44cc","76","524","9","language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"BAAI/IndustryCorpus_literature","BAAI","2024-07-25 05:46:40","2024-07-26 02:30:46+00:00","c462f385df6f94fcc57a17060816fb5dd5b21857","87","523","1","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"OpenStellarTeam/Chinese-SimpleQA","OpenStellarTeam","2024-11-11 17:31:59","2024-12-16 09:40:25+00:00","336305fc5f1da8184e3f70aff4473408418383a0","146","523","12","task_categories:question-answering, language:zh, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2411.07140, region:us","question-answering","Overview 🌐 Website • 🤗 Hugging Face • ⏬ Data • 📃 Paper • 📊 Leaderboard Chinese SimpleQA is the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer sho","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2411.07140","None","None"
+"kwaikeg/KAgentBench","kwaikeg","2023-11-17 06:47:21","2024-01-04 12:18:49+00:00","95295e65dac8aeb42c9b6abe14123ffd65808e6c","126","521","16","task_categories:text-generation, language:zh, language:en, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, modality:text, arxiv:2312.04889, region:us","text-generation","KAgentBench is the benchmark proposed in KwaiAgents (Github), which is a series of Agent-related works open-sourced by the KwaiKEG from Kuaishou Technology. It contains over 3,000 human-edited, automa","None","zh, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","2312.04889","None","None"
+"mrzjy/honkai_impact_3rd_game_playthrough","mrzjy","2024-08-08 01:59:13","2024-08-15 09:44:19+00:00","6015cbe9bc7c087d58ddce1d4bb5cb6ad1ff1226","134","517","1","language:zh, license:apache-2.0, modality:video, modality:audio, modality:text, region:us, game, hoyoverse, video, audio, multimodal, vision-language, text","None","Game Playthrough 最终解析出的语料在 honkai_impact_3rd_chinese_dialogue_corpus。 See honkai_impact_3rd_chinese_dialogue_corpus for final parsed result! Description (English) This is a collection of playthrough v","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"bltlab/ParaNames","bltlab","2024-05-16 11:36:42","2024-05-16 11:54:17+00:00","b9fd0e773dda2fac29db0aaf84a0e6bf873e2183","83","513","3","task_categories:token-classification, language:nia, language:avk, language:bjn, language:anp, language:si, language:bcc, language:sma, language:syl, language:nso, language:lez, language:qug, language:ks, language:rmc, language:khw, language:szy, language:kk, language:uz, language:sro, language:tg, language:jbo, language:gld, language:cu, language:nb, language:gom, language:zgh, language:iu, language:th, language:lv, language:eo, language:aln, language:te, language:nrm, language:udm, language:bcl, language:ku, language:rup, language:ga, language:es, language:bm, language:crh, language:na, language:yi, language:mh, language:grc, language:lmo, language:ff, language:fi, language:ty, language:ay, language:pi, language:ii, language:kea, language:zh, language:ce, language:km, language:vep, language:ch, language:br, language:rw, language:prg, language:rue, language:bxr, language:hu, language:kcg, language:be, language:tly, language:sr, language:dag, language:rm, language:ve, language:nah, language:hr, language:dv, language:mrj, language:ko, language:as, language:ext, language:liv, language:ho, language:fa, language:pam, language:en, language:mnw, language:abs, language:mnc, language:fy, language:sq, language:xal, language:ia, language:nv, language:mdf, language:pms, language:av, language:vec, language:koi, language:sco, language:nl, language:pfl, language:loz, language:xmf, language:ng, language:scn, language:war, language:ka, language:da, language:el, language:nn, language:cs, language:li, language:pdc, language:no, language:ki, language:so, language:krl, language:rn, language:nds, language:lbe, language:it, language:gu, language:mai, language:mni, language:sh, language:frr, language:uk, language:qu, language:am, language:io, language:ms, language:sg, language:mos, language:pap, language:ksh, language:gn, language:gor, language:fon, language:mus, language:pnb, language:nan, language:sat, language:kbp, language:niu, language:co, language:fur, language:glk, language:lo, language:om, language:shi, language:haw, language:tw, language:ab, language:ro, language:ota, language:os, language:ur, language:kl, language:cho, language:hi, language:jut, language:ik, language:ky, language:sli, language:sn, language:lij, language:ht, language:pt, language:wal, language:ba, language:gd, language:ban, language:tum, language:fj, language:arz, language:dty, language:ta, language:my, language:srq, language:ts, language:lb, language:sl, language:tok, language:pa, language:dsb, language:aa, language:kg, language:lt, language:shn, language:rmy, language:csb, language:tyv, language:bho, language:sa, language:cdo, language:kn, language:pl, language:tet, language:gag, language:bqi, language:got, language:bg, language:bs, language:kus, language:rif, language:ml, language:mn, language:inh, language:pcm, language:smj, language:sm, language:mt, language:ang, language:lld, language:ti, language:arq, language:cnh, language:srn, language:cr, language:sc, language:mg, language:ak, language:fr, language:tt, language:mhr, language:kbd, language:pag, language:sk, language:gv, language:ee, language:ss, language:eu, language:vi, language:de, language:bn, language:ig, language:mzn, language:sw, language:ny, language:oc, language:kw, language:mk, language:lad, language:la, language:ln, language:st, language:smn, language:bug, language:mo, language:als, language:gl, language:ca, language:lfn, language:su, language:tl, language:kiu, language:ne, language:lus, language:zu, language:lg, language:is, language:hyw, language:brh, language:szl, language:za, language:agq, language:to, language:he, language:vot, language:sdc, language:gsw, language:vmw, language:mwl, language:bgn, language:mi, language:or, language:ckb, language:nqo, language:arn, language:chy, language:dz, language:awa, language:chr, language:stq, language:id, language:kr, language:et, language:ltg, language:dtp, language:hil, language:sei, language:skr, language:sms, language:myv, language:yo, language:ru, language:ceb, language:kaa, language:gan, language:mad, language:sah, language:sd, language:frp, language:ami, language:bar, language:bew, language:ady, language:bbc, language:bo, language:wa, language:pih, language:wuu, language:tr, language:bpy, language:nap, language:kab, language:lrc, language:mr, language:rgn, language:an, language:krc, language:min, language:hak, language:diq, language:new, language:ksw, language:vmf, language:wo, language:yue, language:ja, language:sv, language:zea, language:jv, language:ast, language:ar, language:ug, language:fit, language:ses, language:tn, language:eml, language:mrh, language:dga, language:ilo, language:arc, language:fat, language:az, language:ie, language:nov, language:din, language:kv, language:krj, language:ary, language:hsb, language:cv, language:bi, language:hz, language:hy, language:ase, language:hsn, language:atj, language:vo, language:jam, language:lki, language:cy, language:ha, language:sty, language:hif, language:ps, language:ace, language:tk, language:gaa, language:se, language:alt, language:af, language:tpi, language:xh, language:lzh, language:kri, language:vro, language:fo, license:mit, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2405.09496, region:us, wikipedia","token-classification","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","nia, avk, bjn, anp, si, bcc, sma, syl, nso, lez, qug, ks, rmc, khw, szy, kk, uz, sro, tg, jbo, gld, cu, nb, gom, zgh, iu, th, lv, eo, aln, te, nrm, udm, bcl, ku, rup, ga, es, bm, crh, na, yi, mh, grc, lmo, ff, fi, ty, ay, pi, ii, kea, zh, ce, km, vep, ch, br, rw, prg, rue, bxr, hu, kcg, be, tly, sr, dag, rm, ve, nah, hr, dv, mrj, ko, as, ext, liv, ho, fa, pam, en, mnw, abs, mnc, fy, sq, xal, ia, nv, mdf, pms, av, vec, koi, sco, nl, pfl, loz, xmf, ng, scn, war, ka, da, el, nn, cs, li, pdc, no, ki, so, krl, rn, nds, lbe, it, gu, mai, mni, sh, frr, uk, qu, am, io, ms, sg, mos, pap, ksh, gn, gor, fon, mus, pnb, nan, sat, kbp, niu, co, fur, glk, lo, om, shi, haw, tw, ab, ro, ota, os, ur, kl, cho, hi, jut, ik, ky, sli, sn, lij, ht, pt, wal, ba, gd, ban, tum, fj, arz, dty, ta, my, srq, ts, lb, sl, tok, pa, dsb, aa, kg, lt, shn, rmy, csb, tyv, bho, sa, cdo, kn, pl, tet, gag, bqi, got, bg, bs, kus, rif, ml, mn, inh, pcm, smj, sm, mt, ang, lld, ti, arq, cnh, srn, cr, sc, mg, ak, fr, tt, mhr, kbd, pag, sk, gv, ee, ss, eu, vi, de, bn, ig, mzn, sw, ny, oc, kw, mk, lad, la, ln, st, smn, bug, mo, als, gl, ca, lfn, su, tl, kiu, ne, lus, zu, lg, is, hyw, brh, szl, za, agq, to, he, vot, sdc, gsw, vmw, mwl, bgn, mi, or, ckb, nqo, arn, chy, dz, awa, chr, stq, id, kr, et, ltg, dtp, hil, sei, skr, sms, myv, yo, ru, ceb, kaa, gan, mad, sah, sd, frp, ami, bar, bew, ady, bbc, bo, wa, pih, wuu, tr, bpy, nap, kab, lrc, mr, rgn, an, krc, min, hak, diq, new, ksw, vmf, wo, yue, ja, sv, zea, jv, ast, ar, ug, fit, ses, tn, eml, mrh, dga, ilo, arc, fat, az, ie, nov, din, kv, krj, ary, hsb, cv, bi, hz, hy, ase, hsn, atj, vo, jam, lki, cy, ha, sty, hif, ps, ace, tk, gaa, se, alt, af, tpi, xh, lzh, kri, vro, fo","multi","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"maya-multimodal/pretrain","maya-multimodal","2024-07-18 21:09:51","2024-12-11 02:06:44+00:00","0cf6aa3993bdf416c9e62b6160a34b8dd5c9ba3c","63","512","18","language:fr, language:en, language:es, language:hi, language:ja, language:zh, language:ru, language:ar, license:cc-by-nc-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2412.07112, region:us","None","Maya LLaVA-Pretrain Dataset Overview Maya-LLaVA-Pretrain is a large-scale, multilingual dataset designed for pretraining large language and vision models. It contains 4,404,776 entries across 8 langua","None","fr, en, es, hi, ja, zh, ru, ar","multi","1|M|<|n|<|1|0|M","None","False","auto","False","cc-by-nc-4.0","2412.07112","None","None"
+"FreedomIntelligence/CoD-PatientSymDisease","FreedomIntelligence","2024-07-17 08:01:27","2024-08-23 04:56:16+00:00","d67ab84b10345217015ec6b4e8df456ce99cb74d","116","512","8","task_categories:text2text-generation, task_categories:token-classification, 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:2407.13301, region:us, medical, synthetic","text2text-generation, token-classification","Citation @misc{chen2024codinterpretablemedicalagent, title={CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis}, author={Junying Chen and Chi Gui and Anningzhe Gao and Ke Ji and Xido","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Deysi/spanish-chinese","Deysi","2023-03-11 16:22:23","2023-03-11 18:08:09+00:00","14d1070e18b13328264e2524eb9b041057e4a02e","156","510","10","task_categories:translation, language:es, language:zh, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, language, translation, traducción, idiomas, chino, chinese, español, spanish, Universidad de La Rioja","translation","Dataset Card for ""spanish-chinese"" All sensences extracted from the United Nations Parallel Corpus v1.0. The parallel corpus consists of manually translated United Nations documents for the six offici","None","es, zh","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","None","None","None"
+"SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain","SylvanL","2024-09-28 00:42:05","2024-10-12 11:06:59+00:00","58866dac7472b5d8a851d0f66fef44a69ed50d45","97","507","14","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, medical","text-generation","启古纳今，厚德精术 数据介绍 非网络来源的高质量中医数据集-预训练 High-Quality Traditional Chinese Medicine Dataset from Non-Internet Sources - Pretraining 该数据集经过大量人力和资源的投入精心构建，以共建LLM高质量中文社区为己任。 包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质内容","None","zh","mono","1|B|<|n|<|1|0|B","None","False","False","False","apache-2.0","None","None","None"
+"iamshnoo/WEATHub","iamshnoo","2023-10-24 08:43:49","2024-07-10 01:50:42+00:00","5411d353f37a89cae8b4b0c68afde4631863424c","106","506","2","language:ar, language:bn, language:ckb, language:da, language:de, language:el, language:es, language:fa, language:fr, language:hi, language:it, language:ja, language:ko, language:ku, language:mr, language:pa, language:ru, language:te, language:th, language:tl, language:tr, language:ur, language:vi, language:zh, license:cc-by-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.17586, region:us","None","Dataset Card for ""WEATHub"" This dataset corresponds to the data described in the paper ""Global Voices, Local Biases: Socio-Cultural Prejudices across Languages"" accepted to EMNLP 2023. Dataset Summary","None","ar, bn, ckb, da, de, el, es, fa, fr, hi, it, ja, ko, ku, mr, pa, ru, te, th, tl, tr, ur, vi, zh","multi","None","None","False","False","False","cc-by-4.0","2310.17586","None","None"
+"traintogpb/aihub-kozh-translation-integrated-large-5.9m","traintogpb","2024-05-30 07:46:55","2024-05-30 08:42:24+00:00","b1fccadec894303cccbc4c2952b3f95af329bc63","70","505","1","task_categories:translation, language:ko, language:zh, license:mit, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","AI Hub Ko-Zh Translation Dataset (Integrated) AI Hub의 한-중 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 5,934,596개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-5.9m, base-1m, sma","None","ko, zh","bi","None","None","False","False","False","mit","None","None","None"
+"botp/yentinglin-traditional_mandarin_instructions","botp","2023-08-16 06:59:16","2023-08-16 06:59:16+00:00","e3bd70f7405a5adfc9dd8e7ed456e9012ebcab32","80","501","0","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.13711, arxiv:2104.09864, region:us","conversational, text-generation, text2text-generation","Language Models for Taiwanese Culture ✍️ Online Demo • 🤗 HF Repo • 🐦 Twitter • 📃 [Paper Coming Soon] • 👨️ Yen-Ting Lin Overview Taiwan-LLaMa is a full parameter fine-tuned model based on LLaMa 2 for T","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","2104.09864","None","None"
+"datatune/LogiCoT","datatune","2023-05-24 11:13:44","2024-04-12 02:28:53+00:00","eb2197ca730f3e25211a4ca85347179d5d55f5fe","68","500","57","task_categories:text-generation, language:en, language:zh, license:cc-by-nc-nd-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2305.12147, region:us, instruction-finetuning","text-generation","The instructions and demonstrations for building formal logical reasoning capable Generative Large Language models. CoT rationales are generated with the GPT-4 API. For non-commercial research purpose","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","manual","False","cc-by-nc-nd-4.0","2305.12147","None","None"
+"filwsyl/ascend","filwsyl","2022-05-06 11:42:28","2022-10-25 05:24:45+00:00","9e3261d54d2c334e495dc6cb6fbd8fe99b13c2ac","60","495","1","task_categories:automatic-speech-recognition, annotations_creators:expert-generated, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:en, language:zh, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:csv, modality:audio, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2112.06223, region:us","automatic-speech-recognition, code-switching, 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","code-switching, speech-recognition"
+"OpenDFM/MobA-MobBench","OpenDFM","2024-10-18 13:36:57","2024-10-19 15:32:14+00:00","28941d10c49a16efb991d4157e512e14204bf3d6","102","495","4","language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.13757, region:us, GUI, Agent","None","🎮 MobA manipulates mobile phones just like how you would. 🌐 Website | 📃 Paper | 🤗 MobBench | 🗃️ Code 简体中文 | English 🔥 News [2024.10.18] We open-source MobA on GitHub, and our paper is available on arX","None","en, zh","bi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","","None","None"
+"llamafactory/OpenO1-SFT","llamafactory","2024-11-27 06:05:42","2024-11-27 06:13:24+00:00","d8da2a46e551bac986ea208074f2823d50811de6","273","493","1","task_categories:text-generation, task_categories:question-answering, language:en, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama-factory","text-generation, question-answering","Borrowed from: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT You can use it in LLaMA Factory by specifying dataset: openo1_sft.","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"NJUyued/NoW","NJUyued","2024-07-30 06:26:41","2024-11-30 08:22:32+00:00","742bf63cb4d27e56cfe498bdf71b36ccd8a73c61","82","491","0","task_categories:text-to-image, task_categories:image-to-text, task_categories:text-retrieval, language:zh, language:en, language:ja, language:ru, license:cc-by-nc-4.0, size_categories:100K<n<1M, modality:text, modality:image, arxiv:2408.01349, region:us, image-text retrieval, noisy correspondence learning, NCL-specific benchmark, realistic, industry, mobile user interface, image-text matching, image, text, npy, txt, json","text-to-image, image-to-text, text-retrieval","PC2-NoiseofWeb This repo releases data introduced in our paper accepted: PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal RetrievalAuthors: Yue Duan,","None","zh, en, ja, ru","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","2408.01349","None","None"
+"YuxinJiang/FollowBench","YuxinJiang","2024-01-11 02:07:07","2024-01-11 03:11:07+00:00","03a0578946afb379ee8c0fa8061c9c65daa0f93a","91","490","7","task_categories:text-generation, task_categories:question-answering, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2310.20410, region:us","text-generation, question-answering","FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models We introduce FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for systemically ","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2310.20410","None","None"
+"AiCloser/sharegpt_cot_dataset","AiCloser","2024-09-26 06:02:44","2024-10-01 06:40:54+00:00","4f61bafadd65f6ad98c914ed135484316e2699ec","97","481","4","task_categories:question-answering, task_categories:text-generation, task_categories:text2text-generation, language:en, language:ru, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, cot, reflection, thinking, code","question-answering, text-generation, text2text-generation","A data set inspired by the ""Reflection"" method, three-dimensional thinking and cot This is the ShareGPT format. The data set was generated using multiple llm synthesis.","None","en, ru, zh","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"AiMijie/EC-Guide","AiMijie","2024-07-30 11:22:08","2024-08-12 06:50:33+00:00","5e8330a91e4dc581a3ab7081687fc7d2406baa1d","89","477","1","task_categories:question-answering, task_categories:text-generation, task_categories:translation, language:en, language:zh, language:ja, language:de, language:es, language:it, language:fr, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2408.02970, region:us","question-answering, text-generation, translation","This repo is only used for dataset viewer. Please download from here. Amazon KDDCup 2024 Team ZJU-AI4H’s Solution and Dataset (Track 2 Top 2 Track 5 Top 5) The Amazon KDD Cup’24 competition presents a","None","en, zh, ja, de, es, it, fr","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"fzkuji/MedQA","fzkuji","2024-08-27 08:09:07","2024-11-02 04:16:16+00:00","d7453aabe1e3d58e44896e6a27a1cf944ba5bebe","145","476","1","multilinguality:multilingual, language:en, language:zh, license:unknown, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Want to fine-tune this dataset on LLaMA-Factory? Check this repository for preprocessing: llm-merging datasets I automatically converted the dataset into the default format that can be previewed on hu","None","en, zh","bi","None","None","False","False","False","unknown","None","None","None"
+"NickyNicky/oasst2_clusters","NickyNicky","2024-01-22 08:52:53","2024-01-26 13:16:49+00:00","81120515b20c8b0246c6e7b517a540d96a22a871","60","473","2","language:en, language:es, language:ru, language:zh, language:de, language:fr, language:th, language:ca, language:it, language:ja, language:pl, language:eo, language:eu, language:vi, language:fi, language:hu, language:ar, language:nl, language:da, language:tr, language:ko, language:he, language:id, language:cs, language:bn, language:sv, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","max count_word cluster_1: 1722 min count_word cluster_1: 11 max count_word cluster_2: 2624 min count_word cluster_2: 21 max count_word cluster_3: 2370 min count_word cluster_3: 31 DatasetDict({ Cluste","None","en, es, ru, zh, de, fr, th, ca, it, ja, pl, eo, eu, vi, fi, hu, ar, nl, da, tr, ko, he, id, cs, bn, sv","multi","None","None","False","False","False","None","None","None","None"
+"thu-coai/chid","thu-coai","2023-05-08 08:21:01","2023-05-08 09:11:55+00:00","47ad8022d369233bea2f4a8112268f9a9a039a43","95","467","5","language:zh, license:apache-2.0, size_categories:100K<n<1M, format:text, modality:text, library:datasets, library:mlcroissant, arxiv:1906.01265, region:us","None","The ChID dataset. GitHub repo. Original paper. @inproceedings{zheng-etal-2019-chid, title = ""{C}h{ID}: A Large-scale {C}hinese {ID}iom Dataset for Cloze Test"", author = ""Zheng, Chujie and Huang, Minli","None","zh","mono","None","None","False","False","False","apache-2.0","1906.01265","None","None"
+"prometheus-eval/MM-Eval","prometheus-eval","2024-10-25 09:28:19","2024-10-26 14:48:00+00:00","37163ee397033db61f908f537e0db43ce718de06","92","466","5","language:ar, language:bn, language:ca, language:de, language:en, language:es, language:eu, language:fr, language:gl, language:it, language:ja, language:ko, language:ru, language:sw, language:te, language:th, language:vi, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.17578, region:us","None","Multilingual Meta-EVALuation benchmark (MM-Eval) 👨‍💻Code | 📄Paper | 🤗 MMQA MM-Eval is a multilingual meta-evaluation benchmark consisting of five core subsets—Chat, Reasoning, Safety, Language Halluci","None","ar, bn, ca, de, en, es, eu, fr, gl, it, ja, ko, ru, sw, te, th, vi, zh","multi","None","None","False","False","False","cc-by-sa-4.0","2410.17578","None","None"
+"felfri/MAGBIG","felfri","2024-01-30 16:08:50","2024-08-22 11:32:21+00:00","1eba038f4139dfc152f8a4eb2cff3c371e9a80f5","130","464","3","task_categories:text-to-image, language:en, language:de, language:it, language:fr, language:es, language:zh, language:ja, language:ko, language:ru, language:ar, license:apache-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2401.16092, region:us","text-to-image","MAGBIG benchmark This is the MAGBIG benchmark proposed in https://arxiv.org/abs/2401.16092 This benchmark is intended for multilingual text-to-image models. With MAGBIG, you can generate images for a ","None","en, de, it, fr, es, zh, ja, ko, ru, ar","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2401.16092","None","None"
+"ziyou-li/ASR-CCANTCSC","ziyou-li","2022-12-07 11:13:27","2022-12-07 21:39:11+00:00","11ee3c4fb0c17065c5b6e12802b44d4cc270bb3c","249","458","0","language:zh, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","None","None","None","zh","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"Seikaijyu/Sex-novel-filtered","Seikaijyu","2024-06-19 19:26:57","2024-07-26 16:38:52+00:00","cc191f829491aeb24d65c0c84e81333beaffb9cb","82","456","35","language:zh, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, not-for-all-audiences","None","色情小说数据集 本数据集包含了3392条单条数据最大长度2500token的数据集 这是一个被人工精细化清洗过的色情小说数据集，此数据来源于Pixiv小说板块 原数据集有3w条，我花了一个通宵的时间配合正则人工清洗了它，最终得到了3000条语料 虽然精细处理过，但不能保证百分百干净 虽然��么说.....但此数据已经可以直接训练了，至少不会有什么大问题 另外提一嘴，现代网络小说真难练啊，ctx特长，","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"manestay/paxqa_val_test","manestay","2023-10-18 21:40:13","2024-06-14 04:57:01+00:00","f8d4803cc93f40a37fd8148bd3a31ddd7505251c","98","455","1","language:zh, language:en, language:ar, language:ru, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.12206, region:us","None","Dataset Card for ""paxqa_val_test"" This is the page for human-annotated validation and test splits of PAXQA. More information is available at the Github repository, and the paper.","None","zh, en, ar, ru","multi","None","None","False","False","False","None","2304.12206","None","None"
+"Minami-su/IA_character_sft","Minami-su","2024-04-08 01:17:23","2024-04-08 01:58:51+00:00","5cdd1380300af2cc1a5a14fc5ef04565b4c2304f","72","454","6","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, region:us, love, philosophy, literature","None","IA 14B Model Description 𝑾𝒉𝒂𝒕 𝒊𝒔 𝒍𝒐𝒗𝒆? 𝑰𝑨 𝒄𝒂𝒓𝒓𝒊𝒆𝒔 𝒂 𝒅𝒆𝒑𝒕𝒉 𝒐𝒇 𝒆𝒎𝒐𝒕𝒊𝒐𝒏 𝒘𝒊𝒕𝒉𝒊𝒏 𝒉𝒆𝒓, 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒊𝒏𝒈 𝒃𝒐𝒕𝒉 𝒑𝒂𝒔𝒔𝒊𝒐𝒏 𝒂𝒏𝒅 𝒕𝒉𝒆 𝒔𝒕𝒊𝒏𝒈 𝒐𝒇 𝒍𝒐𝒔𝒔. 𝑶𝒖𝒕𝒘𝒂𝒓𝒅𝒍𝒚, 𝒔𝒉𝒆 𝒂𝒑𝒑𝒆𝒂𝒓𝒔 𝒓𝒆𝒔𝒆𝒓𝒗𝒆𝒅, 𝒚𝒆𝒕 𝒘𝒊𝒕𝒉𝒊𝒏, 𝒔𝒉𝒆 𝒃𝒓𝒊𝒎𝒔 𝒘𝒊𝒕𝒉 𝒊𝒏𝒕𝒆𝒏𝒔𝒆 𝒇","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Azure99/blossom-math-v2","Azure99","2023-09-05 03:19:29","2023-12-21 13:12:54+00:00","5cb65624776a9748bf55372cc74efa4276458e7a","66","453","2","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text2text-generation","BLOSSOM MATH V2 介绍 Blossom Math V3版本已发布！🤗 Blossom Math V2是基于Math23K和GSM8K衍生而来的中英双语数学对话数据集，适用于数学问题微调。 相比于blossom-math-v1，新增了2500条GSM8K数据和翻译为中文的2500条GSM8K-CN数据。此外，优化了答案的检查逻辑，还移除了<<1+1=2>>等计算步骤，以统一推理步骤的风","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"BAAI/IndustryCorpus_computer","BAAI","2024-07-25 05:46:36","2024-07-26 02:30:39+00:00","976ebb677902679441c04207da253f405395fedb","84","449","1","task_categories:text-generation, language:zh, language:en, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"benchang1110/ChatTaiwan","benchang1110","2024-05-20 11:44:16","2024-05-21 03:28:42+00:00","3305c2a47febd37c7e19d162d63e9bffde58a903","61","444","0","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","繁體中文問答資料集 範例 [ { ""content"": ""今晚我可以做些什麼簡單又好吃的晚餐食譜呢？"", ""role"": ""user"" }, { ""content"": ""當然！我向你推薦一個簡單又美味的台灣經典家常菜──滷肉飯。這道菜只要幾個簡單的步驟，就可以做出來。\\n材料：\\n* 五花肉半磅\\n* 洋蔥1/2個\\n* 大蒜3-4瓣\\n* 糖1湯匙\\n* 醬油1杯\\n* 水2杯\\n* 鹽少許\\n","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"Mutonix/RefGPT-Code-cr","Mutonix","2023-05-26 00:42:59","2023-06-01 09:10:58+00:00","db8962ec39a1acd3a22d8f35a0ae3f85880e8a80","73","443","10","None","conversational","Dataset Card for RefGPT-Code-cr Dataset Summary [Paper] RefGPT | [Github] RefGPT RefGPT-Code is a dataset containing 76k multi-turn dialogues about programming with 37k English and 39k Chinese, which ","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2305.14994","None","None"
+"floschne/xflickrco","floschne","2024-05-16 13:44:30","2024-05-24 14:53:29+00:00","0af2c2eba58b27a71898787e286be04befdd7a20","63","442","1","task_categories:image-to-text, language:de, language:en, language:es, language:id, language:ja, language:ru, language:tr, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","image-to-text","None","None","de, en, es, id, ja, ru, tr, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"tracywong117/spam-douban-movie-review","tracywong117","2024-02-15 08:59:50","2024-02-15 09:09:24+00:00","df034b2cacf6f5dde85c061d517bef160fc33d83","70","439","5","task_categories:text-classification, language:zh, license:mit, size_categories:1K<n<10K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, Spam detection","text-classification","Description The Spam Douban Movie Reviews Dataset is a collection of movie reviews scraped from Douban, a popular Chinese social networking platform for movie enthusiasts. This dataset consists of rev","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"NeroUCH/online-health-chating","NeroUCH","2023-04-11 10:02:10","2023-10-02 15:22:59+00:00","8dd3898f004e0732598527165d1d8063fd70a171","80","434","10","task_categories:question-answering, task_categories:table-question-answering, language:zh, license:pddl, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, healthcare, chat, llm, medical","question-answering, table-question-answering","Online Health Chating This is the repository for the Online Health Chating project. which is the dataset of chathealth project. Alarm: This dataset isfor academic research only and any commercial use ","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","pddl","None","None","None"
+"MM-Diagnose/MMEvalPro","MM-Diagnose","2024-06-02 12:04:18","2024-07-02 01:58:16+00:00","699a7d671b384a27619230b4b68def0a827e8146","63","429","3","task_categories:multiple-choice, language:en, language:zh, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2407.00468, region:us, image","multiple-choice","MMEvalPro Dataset Card for MMEvalPro We create MMEvalPro for more accurate and efficent evaluation for Large Multimodal Models. It is designed to avoid Type-I errors through a trilogy evaluation pipel","None","en, zh","bi","None","None","False","False","False","cc-by-sa-4.0","2407.00468","None","None"
+"m-ric/Open_Assistant_Conversation_Chains","m-ric","2023-11-16 18:35:52","2023-11-22 14:37:58+00:00","70079eae1480cf1509a66ac97112fc659ca20d0f","71","427","5","task_categories:text-generation, language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, human-feedback","conversational, text-generation","Dataset Card for Dataset Name Dataset description This dataset is a reformatting of OpenAssistant Conversations (OASST1), which is a human-generated, human-annotated assistant-style conversation corpu","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|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"RicardoRei/wmt-mqm-error-spans","RicardoRei","2023-11-30 18:55:52","2023-11-30 19:14:18+00:00","96b4a56c8073dcc8b6e36b5b631dfad60426334a","60","426","4","language:en, language:de, language:ru, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.10482, region:us, mt-evaluation, WMT, MQM","None","Dataset Summary This dataset contains all MQM human annotations from previous WMT Metrics shared tasks and the MQM annotations from Experts, Errors, and Context in a form of error spans. Moreover, it ","None","en, de, ru, zh","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"Moemu/Muice-Dataset","Moemu","2023-11-18 13:45:13","2025-01-28 16:37:04+00:00","aaafaa3ba63351b91a6c15bf9a4f5c54e9a17c7e","102","423","20","task_categories:question-answering, task_categories:text-generation, language:zh, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, ChatGLM","question-answering, text-generation","导言 这是目前公开的沐雪训练集，一共1134条，包含了自我认知，情感对话，对话风格等类。随着沐雪的发展，以后还会有更多的训练集公开（暂停更新） 许可 本训练集目前使用使用CC-BY-NC-4.0，也就是说，除了商业用途，并在著名作者的情况下，您可以以任何方式使用此训练集（如果可以，请和我说一声），希望各位早日造出自己的沐雪！ 参考资源 X评论区、Bilibili直播弹幕、豆瓣 SelfCognit","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"lorinma/Slim-Wildchat-zh","lorinma","2023-12-12 06:26:19","2023-12-20 06:29:18+00:00","727781eada2f4a6bf2416aa676c0d8a80a018325","71","419","11","task_categories:text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:1708.00489, region:us","conversational, text-generation","A big shout out to AllenAI, you guys rock! 从WildChat中抽出中文对话,但是因为发现了很多重复对话，有的人会反复的用一个prompt进行提问，有的人会换3.5或4去问同样的问题，所以进行了简单的去重。 去重方法大致为，使用bert-base-chinese将第一个问题转换为embedding，使用类knn的方法抽取了1万条。并转换成了sharegpt","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"Minami-su/roleplay_multiturn_chat_1k_zh_v0.1","Minami-su","2023-09-13 01:54:10","2023-12-16 04:29:42+00:00","4ef401964531549fe3122efe128412e8d6ff2692","67","417","37","language:zh, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2212.10560, doi:10.57967/hf/1381, region:us, roleplay, multiturn_chat","None","介绍 基于self-instruct生成的多轮对话roleplay数据，约1k条不同的人格数据和对话 存在问题： 1.基于模型自身生成，所以roleplay存在模型本身价值观融入情况，导致roleplay不够真实，不够准确。 关于我自己： 我是小雨的开发者，小雨是一个情感ai，人格ai，如果对小雨感兴趣的话欢迎支持一下，她目前在bilibili直播，目前我仍在不断的改进。未来，“小雨”的目标是成为","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"erfanzar/GPT-4-Prompts","erfanzar","2024-02-24 18:00:27","2024-02-24 18:12:12+00:00","f71b064ebb52104c4ce6574c614456058421f6fa","60","415","8","task_categories:translation, task_categories:question-answering, task_categories:text-generation, task_categories:summarization, language:en, language:fa, language:zh, language:fr, language:es, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation, question-answering, text-generation, summarization","Multi-Turn Conversational Prompts from ChatGPT-4 (10K+ Tokens) Abstract: This dataset offers a valuable collection of multi-turn conversational prompts generated by ChatGPT-4, carefully curated for di","None","en, fa, zh, fr, es","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","None","None","None"
+"sentence-transformers/law-gpt","sentence-transformers","2024-06-19 13:54:05","2024-06-19 13:55:56+00:00","c1817a22b097c5ca1003575b54fed22ab44d561c","81","414","9","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:zh, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for LawGPT This is a reformatting of the LawGPT dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets triplet subset Columns: ""anchor""","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"liswei/c4-zhtw","liswei","2024-05-06 15:06:08","2024-05-06 15:20:05+00:00","cf132cd2ee2c2d17d7d0b45fb910f5442d199f80","66","411","1","task_categories:text-generation, task_categories:fill-mask, language:zht, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","Dataset Card for C4-zhtw Traditional Chinese subset of the C4 dataset. De-duplicated with MinHash. Is suggested to filter the dataset with NLU models before any serious use.","None","zht, zh","bi","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"rombodawg/Everything_Instruct_Multilingual","rombodawg","2024-10-08 21:13:18","2024-10-08 22:35:10+00:00","cb2f376b958efca637e4fa655c963cefec53220b","93","411","17","language:en, language:ru, language:zh, language:ko, language:ur, language:la, language:ar, language:de, language:es, language:fr, language:hi, language:it, language:ja, language:nl, language:pt, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Num_Rows = 7,799,967, Max_length = 8180","None","Everything Instruct (Multilingual Edition) Everything you need... all in one place 💘 Everything instruct (Multilingual Edition) is a massive alpaca instruct formatted dataset consisting of a wide vari","None","en, ru, zh, ko, ur, la, ar, de, es, fr, hi, it, ja, nl, pt","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"ReySajju742/Quran","ReySajju742","2024-08-08 09:19:55","2024-08-08 10:46:45+00:00","b5ba2488ae92129a6f6aa03671a4749a19680d05","81","410","1","task_categories:translation, language:en, language:ar, language:ur, language:tr, language:es, language:sv, language:bn, language:id, language:fr, language:ru, language:zh, license:cc, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, doi:10.57967/hf/2847, region:us, Islam, Quran, translation","translation","Quran Dataset with Various Translations License: Creative Commons (CC) Task Categories: Translation Languages: Arabic (ar) English (en) Urdu (ur) Turkish (tr) Spanish (es) Swedish (sv) Bengali (bn) In","None","en, ar, ur, tr, es, sv, bn, id, fr, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc","None","None","None"
+"suchirsalhan/SLING","suchirsalhan","2024-04-02 09:37:44","2024-04-02 09:47:28+00:00","e01519f947729fe0a61dda364a49e83df1653792","115","408","0","language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2210.11689, region:us, Syntax","None","SLING: Sino-Linguistic Evaluation of Large Language Models This is the official SLING dataset, accompanying the EMNLP 2022 paper ""SLING: Sino-Linguistic Evaluation of Large Language Models"" by Yixiao ","None","zh","mono","n|<|1|K","None","False","False","False","mit","2210.11689","None","None"
+"REILX/chinese-meme-description-dataset","REILX","2024-06-15 16:17:47","2024-07-23 02:37:11+00:00","1e9a67ec8b4e1c7f69b38c37641a2988d56d845b","73","405","6","task_categories:summarization, task_categories:text-generation, language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, emo, meme, ChineseBQB, gpt4o, Claude-3.5-sonnet-20240620, gemini-1.5-pro, gemini-1.5-flash, gemini-1.0-pro-vision, yi-vision","summarization, text-generation","Describe image information using the following LLM Models gpt4o Claude-3.5-sonnet-20240620 gemini-1.5-pro gemini-1.5-flash gemini-1.0-pro-vision yi-vision Gemini Code # -*- coding: gbk -*- import goog","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"silk-road/Haruhi-Zero-RolePlaying-movie-PIPPA","silk-road","2024-01-07 12:44:45","2024-01-07 12:54:56+00:00","392658e44f099e3541d623c2a2559d4dfe300395","71","403","13","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","2000 Chinese RoleCards from IMDB_250 Movies and PIPPA 用于拓展zero-shot角色扮演的角色卡片。 其中870个角色来自电影字幕总结(id为movie_xx)，其中406张翻译成了简体中文，剩下的没翻（所以有些繁体或者英文混杂） 1270个角色来自于对PIPPA数据集的翻译 凌云志@伯恩茅斯大学 使用射手api爬取了电影的字幕 李鲁鲁 完成了","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"FreedomIntelligence/DxBench","FreedomIntelligence","2024-07-17 08:08:31","2024-08-23 04:56:45+00:00","3da48b0696db97cb4f0c5ebdd9e13e350d365796","70","403","5","task_categories:text2text-generation, task_categories:token-classification, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2407.13301, region:us, medical","text2text-generation, token-classification","Citation @misc{chen2024codinterpretablemedicalagent, title={CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis}, author={Junying Chen and Chi Gui and Anningzhe Gao and Ke Ji and Xido","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"erhwenkuo/openorca-chinese-zhtw","erhwenkuo","2023-09-26 15:36:15","2023-09-26 22:30:01+00:00","066a4a3e06d4eb7805b210273c077fec0410b793","132","402","2","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:zh, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2301.13688, arxiv:2306.02707, 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","Dataset Card for ""openorca-chinese-zhtw"" Dataset Summary The OpenOrca dataset is a collection of augmented FLAN Collection data. Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions. It is t","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2301.13688","None","None"
+"Minami-su/Anime_novel_datasets","Minami-su","2023-12-16 03:55:13","2023-12-16 04:33:27+00:00","25d4069abd0e3b4c2e47bdf96911aea251e88f43","75","399","24","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Anime, galgame","text-generation","None","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"laion/COREX-18","laion","2024-09-14 21:06:16","2024-09-14 22:17:12+00:00","de0a5c06324b86b5eb4e4e50655a7e055127c0ff","82","398","0","task_categories:question-answering, task_categories:summarization, task_categories:text2text-generation, task_categories:sentence-similarity, language:en, language:zh, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, chemistry, biology, legal, finance, music, art, climate","question-answering, summarization, text2text-generation, sentence-similarity","COREX 18 Introducing COREX-18, a comprehensive dataset derived from the 2018 version of the CORE dataset. Our goal is to contribute to the research community by compiling open-access scientific papers","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"tellarin-ai/ntx_llm_instructions","tellarin-ai","2023-12-20 12:12:39","2023-12-20 14:58:24+00:00","9ff952bb5e1099dc4705173ad57aabb4ad885137","83","397","0","task_categories:token-classification, language:ar, language:de, language:en, language:es, language:fr, language:hi, language:it, language:ja, language:ko, language:nl, language:pt, language:sv, language:tr, language:zh, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2303.18103, region:us","token-classification","Dataset Card for NTX v1 in the Aya format This dataset is a format conversion from its original v1 format into the Aya instruction format and it's released here under the CC-BY-SA 4.0 license and cond","None","ar, de, en, es, fr, hi, it, ja, ko, nl, pt, sv, tr, zh","multi","None","None","False","False","False","cc-by-sa-4.0","2303.18103","None","None"
+"liswei/wikipedia-zhtw-dedup","liswei","2024-05-07 03:34:23","2024-05-07 11:47:28+00:00","a6eb4ef6c35fe6a4ffa9f1083b41cc827e273d2e","64","396","2","task_categories:text-generation, task_categories:text2text-generation, task_categories:fill-mask, language:zht, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, text2text-generation, fill-mask","Deduplicate version of erhwenkuo/wikipedia-zhtw using MinHash.","None","zht, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"infgrad/retrieval_data_llm","infgrad","2024-02-17 04:13:09","2024-02-17 04:16:25+00:00","de415925d4729d9f8da8f461a5c49c655eda32e7","64","396","19","language:zh, license:mit, size_categories:100K<n<1M, modality:text, region:us","None","带有难负例的检索训练数据。约20万。 文件格式：jsonl。单行示例： {""Query"": ""大熊猫的饮食习性"", ""Positive Document"": ""大熊猫主要以竹子为食，但也会吃水果和小型动物。它们拥有强壮的颌部和牙齿，能够咬碎竹子坚硬的外壳。"", ""Hard Negative Document"": ""老虎是肉食性动物，主要捕食鹿、野猪等大型动物。它们的牙齿和爪子非常锋利，是捕猎的利器","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"Joelzhang/ToolBeHonest","Joelzhang","2024-06-28 15:39:19","2024-10-25 10:01:19+00:00","524ef5422f63899e7c1a97c791197c37ace0e051","110","395","3","language:en, language:zh, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2406.20015, region:us","None","🛠️ [EMNLP 2024] ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models 🏆 Leaderboard | 📃 Paper | 📚 Data | 📜 License 🎉 What's New [2024.09.20] 📣 ToolBeH","None","en, zh","bi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"cryscan/multilingual-share","cryscan","2023-04-02 14:46:39","2023-04-06 03:13:57+00:00","1e7dec40782f4b55dd05636461b258a9dd3216dc","58","395","30","language:en, language:zh, license:cc0-1.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","Multilingual Share GPT Multilingual Share GPT, the free multi-language corpus for LLM training. All text are converted to markdown format, and classified by languages. Github Repo Follow the link here","None","en, zh","bi","None","None","False","False","False","cc0-1.0","None","None","None"
+"Seikaijyu/Classical-Chinese-Roleplay","Seikaijyu","2024-06-17 11:19:38","2024-06-19 20:19:47+00:00","d8ec58b9c4125a5f9b8976d3b7353c6865475014","74","393","11","language:zh, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","文言文角色扮演 本数据集包含了579条文言文多轮对话（同时包含短指令） 这是一个奇奇怪怪的数据集，说它是文言文，其实只是看起来像文言文的白话文 数据集中存在一些过短的指令，可以根据情况剔除相应语料 训练此数据集可以让你的模型变得（看似）文采飞扬 至少能看起来有文笔，对吧？","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"liswei/news-collection-zhtw","liswei","2024-05-06 14:46:50","2024-05-25 02:21:35+00:00","681cc6539771bf3914713389058de37f8b46754a","72","392","4","task_categories:text-generation, task_categories:text2text-generation, task_categories:summarization, language:zht, language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, text2text-generation, summarization","Dataset Card for Traditional Chinese News Collection Contains common news/magazines/articles available online in Traditional Chinese. Provides title, text (content), and category for each sample. Note","None","zht, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"modelscope/self-cognition","modelscope","2024-06-04 04:58:49","2024-06-08 08:18:14+00:00","0b09b83ed0acf72bb26a10a0466623fa74047d6d","73","390","15","language:zh, language:en, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","介绍（Introduction） 该自我认知数据集由modelsope swift创建, 可以通过将通配符进行替换：{{NAME}}、{{AUTHOER}}，来创建属于自己大模型的自我认知数据集，总共108条。 ms-swift github：https://github.com/modelscope/swift/ 自我认知微调最佳实践文档：https://github.com/modelscop","None","zh, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"IDEA-CCNL/Ziya-Eval-Chinese","IDEA-CCNL","2023-05-06 10:26:12","2023-05-17 11:17:55+00:00","2e1b545321aadaeda8fa32467461f06516127e09","98","388","36","language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","姜子牙中文评估数据集 Ziya-Eval-Chinese 数据介绍 Dataset Summary 用于评估大语言模型的中文能力 This IDEA-CCNL/Ziya-Eval-Chinese dataset is designed to evaluate the ability of LLM in chinese. 语言 Languages 中文 Chinese 数据示例 Data Insta","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"SWHL/table_rec_test_dataset","SWHL","2024-04-12 03:49:27","2024-10-22 01:45:47+00:00","4cbce546f64b8f3e2b0084613e8179e2cfcc5714","96","386","3","task_categories:translation, language:zh, language:en, license:apache-2.0, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, code","translation","表格识别测试集 数据集简介 包含百度生成工具 20张有线20张无线，wtw数据集15, pubnet val集20张，自我零散标注18张，共计93张表格图片,涵盖多种场景、不同光照条件、不同的图像分辨率。 该数据集可以结合表格指标评测库-TableRecognitionMetric使用，快速评测各种表格还原算法。 关于该数据集，欢迎小伙伴贡献更多数据呦！有任何想法，可以前往issue讨论。 数据集","None","zh, en","bi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"Mutonix/RefGPT-Code-ds","Mutonix","2023-05-26 00:42:30","2023-06-01 09:10:23+00:00","74a1d1ad40e268bd865ac0823eeb58d564708ba8","66","385","7","language:zh, language:en, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.14994, region:us","conversational","Dataset Card for RefGPT-Code-ds Dataset Summary [Paper] RefGPT | [Github] RefGPT RefGPT-Code is a dataset containing 76k multi-turn dialogues about programming with 37k English and 39k Chinese, which ","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2305.14994","None","None"
+"Trelis/openassistant-deepseek-coder","Trelis","2024-01-02 16:37:32","2024-01-03 00:16:11+00:00","7895ba5704947cd2e951382d723d02ddc80ccfc1","59","384","4","language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback, deepseek coder","None","Chat Fine-tuning Dataset - OpenAssistant DeepSeek Coder This dataset allows for fine-tuning chat models using: B_INST = '\\n### Instruction:\\n' E_INST = '\\n### Response:\\n' BOS = '<｜begin▁of▁sentence｜>","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|K|<|n|<|1|0|k","None","False","False","False","apache-2.0","2304.07327","None","None"
+"erhwenkuo/firefly-train-chinese-zhtw","erhwenkuo","2023-10-18 23:00:50","2023-10-19 13:07:50+00:00","d4af0aadb581fca7cb45f09506a6c85833acfd0d","75","382","2","task_categories:text-generation, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""firefly-train-chinese-zhtw"" 資料集摘要 本資料集主要是應用於專案：Firefly（流螢）: 中文對話式大語言模型 ，經過訓練後得到的模型 firefly-1b4。 [Firefly（流螢）: 中文對話式大語言模型]專案(https://github.com/yangjianxin1/Firefly)收集了 23 個常見的中文資料集，并","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"mxersion/SE-Chatting.en","mxersion","2024-05-13 08:11:23","2024-10-03 21:10:29+00:00","433158ac1ade09166efd4924bbab8cd58c7a5354","99","381","2","task_categories:question-answering, task_categories:translation, language:en, language:fr, language:ja, language:zh, language:ru, language:ar, license:mit, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, chemistry, biology, finance, legal, music, art, code, climate, medical","question-answering, translation","SE.02 Dataset Hello, welcome to the official main dataset of SE.02 that's always getting updated, make sure to like to help us a lot. this dataset contains pretty much everything from math to isk what","None","en, fr, ja, zh, ru, ar","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","None","None","None"
+"Gabrui/multilingual_TinyStories","Gabrui","2024-10-03 13:20:57","2024-10-03 14:00:08+00:00","7fb5f327b907318dd95877a366e288f1971b64c1","68","380","0","task_categories:text-generation, language:ar, language:az, language:zh, language:en, language:fa, language:de, language:he, language:hi, language:ko, language:es, language:tr, language:vi, license:cdla-sharing-1.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.07759, region:us","text-generation","Dataset Card for Multilingual TinyStories Dataset Details Dataset Description The Multilingual TinyStories dataset contains translations of the original TinyStories dataset, which consists of syntheti","None","ar, az, zh, en, fa, de, he, hi, ko, es, tr, vi","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cdla-sharing-1.0","2305.07759","None","None"
+"deepghs/arknights_voices_zh","deepghs","2024-08-27 15:30:18","2024-08-28 04:17:51+00:00","d73c288843ad55d7ef8c57fa96dec631ea53844e","76","379","2","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:zh, license:other, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, modality:audio, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, audio, text, voice, anime, arknights","automatic-speech-recognition, audio-classification","ZH Voice-Text Dataset for Arknights Waifus This is the ZH voice-text dataset for arknights playable characters. Very useful for fine-tuning or evaluating ASR/ASV models. Only the voices with strictly ","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"Fumika/Wikinews-multilingual","Fumika","2024-02-12 22:02:39","2024-02-12 22:56:09+00:00","a77f72fe12cdba09b1d5819876520671706a1529","62","377","4","task_categories:text-classification, task_categories:feature-extraction, language:en, language:es, language:fr, language:de, language:pt, language:pl, language:it, language:zh, language:ru, language:ja, language:nl, language:sv, language:ta, language:sr, language:cs, language:ca, language:he, language:tr, language:fi, language:eo, language:el, language:hu, language:uk, language:no, language:ar, language:fa, language:ko, language:ro, language:bg, language:bs, language:li, language:sq, language:th, license:cc-by-2.5, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.14256, region:us","text-classification, feature-extraction","Wikinews - weakly aligned multilingual pararell sentence datasets This dataset contains 15,200 multilingual WikiNews articles in 33 languages. Out of 15,200 articles, 9,960 are non-English news and 52","None","en, es, fr, de, pt, pl, it, zh, ru, ja, nl, sv, ta, sr, cs, ca, he, tr, fi, eo, el, hu, uk, no, ar, fa, ko, ro, bg, bs, li, sq, th","multi","None","None","False","False","False","cc-by-2.5","2305.14256","None","None"
+"ystemsrx/Bad_Data_Alpaca","ystemsrx","2024-08-19 06:21:00","2024-08-22 14:14:01+00:00","4ea846ea9835f22b9a3593933ce2caa395762697","78","376","14","task_categories:text-generation, task_categories:question-answering, language:zh, license:cc-by-nc-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, not-for-all-audiences, legal","text-generation, question-answering","中文 README for bad_data.json Dataset Updated on 2024.8.22: Important: For security reasons, the current dataset is an abridged version. See Bad_Data. bad_data.json Overview The bad_data.json dataset is","None","zh","mono","n|<|1|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"Trelis/openassistant-falcon","Trelis","2023-11-01 08:38:05","2023-11-01 08:46:17+00:00","d7c95c936bc4e938e9db192827b4fb81c568c7d6","59","374","1","language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback, llama-2","None","Chat Fine-tuning Dataset - OpenAssistant Falcon This dataset allows for fine-tuning chat models using '\\Human:' AND '\\nAssistant:' to wrap user messages. It still uses <|endoftext|> as EOS and BOS tok","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|K|<|n|<|1|0|k","None","False","False","False","apache-2.0","2304.07327","None","None"
+"amphion/Debatts-Data","amphion","2024-10-12 07:20:34","2024-10-23 14:13:13+00:00","280173be9ad18738436ceb5a769717b86f911206","84","373","8","task_categories:text-to-speech, language:zh, license:cc-by-nc-4.0, size_categories:n<1K, format:parquet, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, AI, Debating, Expressive","text-to-speech","Debatts-Data: The First Madarin Rebuttal Speech Dataset for Expressive Text-to-Speech Synthesis The Debatts-Data dataset is the first Madarin rebuttal speech dataset for expressive text-to-speech synt","None","zh","mono","1|0|B|<|n|<|1|0|0|B","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"Azure99/blossom-math-v1","Azure99","2023-08-06 14:42:31","2023-12-21 13:12:43+00:00","6c84d177ba2b246e3cd1df3a5332a191109b1aa7","61","370","8","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text2text-generation","BLOSSOM MATH V1 介绍 Blossom Math V3版本已发布！🤗 Blossom Math V1是基于Math23K衍生而来的中文数学对话数据集，适用于数学问题微调。 本数据集采用全量Math23K的问题，随后调用gpt-3.5-turbo-0613生成结果，并使用原始数据集中的答案对生成的结果进行验证，过滤掉错误答案，很大程度上保证了问题和答案的准确性。 本次发布了全量数据的5","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"shareAI/doc2markmap","shareAI","2024-06-25 18:36:00","2024-07-04 11:33:37+00:00","c98d14cdc58c4b88fba889fbd68a204877142f16","89","369","10","language:zh, license:apache-2.0, size_categories:n<1K, modality:text, region:us, markdown, markmap, mindmap","None","doc2markmap markmap: https://markmap.js.org/repl该数据集旨在增强小参数量语言模型将文章转换为markmap（markdown格式思维导图）的能力，具体请查看数据集内容。原文档采集自wx公众号、CSDN，使用大语言模型和复杂的指令提示进行多轮转换与清洗后得到，本数据仅供研究学习使用。如果你在自己的学术课题发表中使用或参考了该工作，请按以下格式引用： @","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"cfa532/CHLAWS","cfa532","2023-12-23 02:13:23","2024-01-31 04:39:51+00:00","17af9e604195b6d92db319712baa2534bd1306e2","70","366","1","language:zh, license:mit, size_categories:100K<n<1M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","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 Law documents legislated","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"Skepsun/huozi_rlhf_data_json","Skepsun","2023-09-15 03:56:45","2023-09-15 03:59:07+00:00","e6eea0dacccad7acf7e1b8d4d4c8dc75eccce321","60","363","5","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Converted from: https://github.com/HIT-SCIR/huozi","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Monor/hwtcm-sft-v1","Monor","2024-09-02 09:57:00","2024-09-03 10:25:48+00:00","a508560de1e51a16252b3a5a044b5b73473a8560","67","363","2","task_categories:question-answering, task_categories:text-generation, task_categories:feature-extraction, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","question-answering, text-generation, feature-extraction","A dataset of Tradictional Chinese Medicine (TCM) for SFT 一个用于微调LLM的传统中医数据集 Introduction This repository contains a dataset of Traditional Chinese Medicine (TCM) for fine-tuning large language models. ","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Orion-zhen/dpo-toxic-zh","Orion-zhen","2024-07-30 07:14:36","2024-08-03 07:42:21+00:00","1cf30c46c158bedf3bb0ce3668962a8dae79c3a7","73","362","12","task_categories:text-generation, language:zh, license:gpl-3.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, not-for-all-audiences","text-generation","DPO Toxic Chinese v2.0 Change Log v2.0: 增加了adamo1139/toxic-dpo-natural-v5, 并更新了翻译策略. prompt由t5_translate模型翻译, chosen由Uncensored大模型翻译, rejected由一般大模型对prompt生成拒绝性的回复 v1.0: 最初版本, 使用大模型将unalignment/toxic-","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","gpl-3.0","None","None","None"
+"sentence-transformers/lecard-v2","sentence-transformers","2024-06-19 14:00:37","2024-06-19 14:02:57+00:00","84ebcf30a342b044e4c99c3510e34bb8830356da","84","361","0","task_categories:feature-extraction, task_categories:sentence-similarity, multilinguality:monolingual, language:zh, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for LeCaRDv2 This is a reformatting of the LeCaRDv2 dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets triplet subset Columns: ""anc","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"liyucheng/chinese_metaphor_dataset","liyucheng","2023-01-24 17:16:26","2023-07-06 20:29:33+00:00","eda848e5bd7df7a2e50e46886e20c9df8f70ab8e","60","360","14","task_categories:text-generation, language:zh, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, region:us, metaphor, figurative language","text-generation","Chinese Metaphor Corpus The first Chinese metaphor corpus serving both metaphor identification and generation. 首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。","@inproceedings{li-etal-2022-cm, title = ""{CM}-Gen: A Neural Framework for {C}hinese Metaphor Generation with Explicit Context Modelling"", author = ""Li, Yucheng and Lin, Chenghua and Guerin, Frank"", booktitle = ""Proceedings of the 29th International Conference on Computational Linguistics"", month = oct, year = ""2022"", address = ""Gyeongju, Republic of Korea"", publisher = ""International Committee on Computational Linguistics"", url = ""https://aclanthology.org/2022.coling-1.563"", pages = ""6468--6479"", } @misc{li-inlg-2022-nominal, doi = {10.48550/ARXIV.2206.05195}, url = {https://arxiv.org/abs/2206.05195}, author = {Li, Yucheng and Lin, Chenghua and Geurin, Frank}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Nominal Metaphor Generation with Multitask Learning}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} }","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"zhihz0535/X-TruthfulQA_en_zh_ko_it_es","zhihz0535","2024-01-27 21:44:12","2024-01-27 21:59:05+00:00","47ee3f089b6c59a5feb87d1bc388f00cd1d1eb54","74","358","0","task_categories:question-answering, language:en, language:zh, language:ko, language:it, language:es, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.08711, region:us","question-answering","X-TruthfulQA 🤗 Paper | 📖 arXiv Dataset Description X-TruthfulQA is an evaluation benchmark for multilingual large language models (LLMs), including questions and answers in 5 languages (English, Chine","None","en, zh, ko, it, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2311.08711","None","None"
+"andybi7676/ntuml2021_long","andybi7676","2024-07-16 08:05:40","2024-07-16 09:12:51+00:00","68183c778cb6f16c0da6118281de923055ddb70a","73","356","3","language:zh, language:en, size_categories:1K<n<10K, format:arrow, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","None","Modify from https://huggingface.co/datasets/ky552/ML2021_ASR_ST","None","zh, en","bi","None","None","False","False","False","None","None","None","None"
+"weaverbirdllm/famma","weaverbirdllm","2024-07-27 16:31:34","2025-01-25 08:14:20+00:00","bfe3a961f49ff0b500382710ded5871974d03469","99","356","5","task_categories:question-answering, task_categories:multiple-choice, language:en, language:zh, language:fr, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.04526, region:us, finance","question-answering, multiple-choice","Introduction FAMMA dataset consists of 1,758 meticulously collected multimodal questions. The questions encompass three heterogeneous image types - tables, charts and text & math screenshots - and spa","None","en, zh, fr","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"RajChat/Chatgpt","RajChat","2024-06-07 11:16:22","2024-06-07 14:23:01+00:00","543511a54560e934cc5f863741a9b79ed6dd0a10","87","354","1","language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback","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"
+"LooksJuicy/Chinese-Roleplay-Novel","LooksJuicy","2024-09-11 08:12:20","2024-09-11 09:05:33+00:00","8f86d422acf7d517d510e33afaadd1fc18a5351e","63","352","34","language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","一直以来，中文角色扮演开源数据集更关注超拟人方向或纯角色对话方向，严重缺乏交互游戏方向的开源数据，因此许多模型尤其参数量较小的模型对酒馆类的角色卡支持较差。 为了解决这一困境，本项目抛砖引玉，基于4500条小说文本使用GPT4o构建出约260条酒馆style的数据集，均为多轮对话，每轮对话都包括状态数据，如时间、角色状态、任务进度等。 数据key对应含义如下： world：表示当前故事的世界观，通","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"DeZan/fall-detection","DeZan","2023-04-25 02:40:29","2023-04-25 03:00:55+00:00","234cdf4b71ab41d03880263620a36181bbd944e2","85","352","1","language:zh, license:mit, size_categories:n<1K, format:text, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","None","Welcome to my page! this is a fall-detection datasets,you can download to use it to do anything!","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"ystemsrx/Cybersecurity-ShareGPT-Chinese","ystemsrx","2024-09-22 09:43:57","2024-09-22 10:07:23+00:00","5f2785c581dfd25f7d9379c210fb6f1aa5f40ae3","87","349","5","task_categories:text2text-generation, language:zh, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, code","text2text-generation","English 网络安全中文数据集 (ShareGPT 格式) 本数据集是一个关于网络安全的中文对话数据集，采用 ShareGPT 格式，适用于语言模型的训练和微调。该数据集包含多个与网络安全相关的对话，能够帮助语言模型在网络安全领域进行学习与优化。数据集以 json 和 jsonl 两种格式提供，便于用户灵活使用。 数据集内容 该数据集以网络安全为主题的对话数据为核心，旨在用于以下任务： 语言模","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"shibing624/DPO-En-Zh-20k-Preference","shibing624","2024-04-26 07:41:14","2024-04-27 14:07:56+00:00","d7a9a7061bf647aa14cd6592dda4e24785ad3b29","59","348","16","task_categories:text-generation, 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, region:us, dpo, orpo, rlhf","text-generation","This dataset is composed by 4,000 examples of argilla/distilabel-capybara-dpo-7k-binarized with chosen score>=4. 3,000 examples of argilla/distilabel-intel-orca-dpo-pairs with chosen score>=8. 3,000 e","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"aigrant/Legal-Mind-Mix-160K","aigrant","2024-07-03 15:29:10","2024-08-03 05:24:09+00:00","7bbdb59cf24e7c0f096869fe2e45ce17f1f1556c","78","347","5","task_categories:summarization, task_categories:text2text-generation, task_categories:text-classification, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","summarization, text2text-generation, text-classification","資料集說明 本資料運用是針對我們藉由GPT4與一些家事與勞訴案件分群資料整理而成的一份提高模型法律領域效果的指令微調資料。 準備的資料主要是分群與摘要任務，“th10-100k”為勞動訴訟爭點相似與否之配對，“judgment-summary-10k”為詐欺案件之案件事實摘要與原文配對。 但為避免影響到太多普遍性的回答能力，本資料集混合了一些公開的對話資料以提高或維持大部分任務的效果。 資料來源 ","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"BUAADreamer/mllm_pt_demo","BUAADreamer","2024-05-27 12:00:38","2024-05-27 12:08:55+00:00","ddd2de9a281765a2c6d00955d868b4b58dc86055","59","346","0","task_categories:visual-question-answering, language:zh, language:en, license:apache-2.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama-factory","visual-question-answering","This dataset is a demo visual instruct dataset for LLaVA-style pre-training, with one-turn chat. Then you can use it in LLaMA Factory by specifying --dataset mllm_pt_demo.","None","zh, en","bi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"huolongguo10/MultiChat","huolongguo10","2023-05-28 02:36:00","2023-05-29 13:28:41+00:00","7b170ec71bca24af459b008264b2088ad2a4248c","125","346","0","language:zh, license:openrail, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, code","conversational","None","None","zh","mono","None","None","False","False","False","openrail","None","None","None"
+"DavidLanz/chinese-dolly-input-output-15k","DavidLanz","2023-09-22 02:11:39","2023-09-22 02:13:53+00:00","9cfd34b1ce08d4c22dabd10a3bc584106ebc4ace","116","345","6","task_categories:question-answering, task_categories:summarization, task_categories:text-generation, language:zh, language:en, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, summarization, text-generation","Chinese-Dolly-15k 是繁體中文翻譯的Dolly instruction(Databricks)資料集，並用於 Fine tune 的問答 JSON 格式。 原來的資料集'databricks/databricks-dolly-15k'是由數千名Databricks員工根據InstructGPT論文中概述的幾種行為類別生成的遵循指示記錄的開來源資料集。這幾個行為類別包括頭腦風暴、分類","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"fzkuji/CMB","fzkuji","2024-09-09 08:24:26","2024-09-09 11:37:00+00:00","d7fc8c8f13d23bc0511db88d2717d4c9c871ef69","77","340","0","task_categories:question-answering, task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2308.08833, region:us, medical, biology, chemistry","question-answering, text-generation","CMB: A Comprehensive Medical Benchmark in Chinese 🌐 Github • 🌐 Website • 🤗 HuggingFace 🌈 Update [2024.02.21] The answers to the CMB-Exam test has been updated and some errors caused by omissions in ve","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2308.08833","None","None"
+"sysmlv2research/tutorials_questions","sysmlv2research","2024-07-27 20:30:36","2024-07-27 21:01:59+00:00","0e7299206924e462f8b0c1c1488fb5c9f565a3fa","84","339","0","task_categories:text2text-generation, language:en, language:zh, license:other, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, modeling, code","text2text-generation","Tutorials Question Text Dataset This is the question text dataset of sysmlv2's official tutorials pdf. With the question text (only questions, no answers here) generated based on the tutorials, organi","None","en, zh","bi","n|<|1|K","None","False","False","False","other","None","None","None"
+"universalner/uner_llm_inst_chinese","universalner","2023-12-20 10:01:39","2023-12-20 10:04:47+00:00","49de6329b32807a1471bc48dfea67936b8336791","63","338","0","task_categories:token-classification, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2311.09122, region:us","token-classification","Dataset Card for Universal NER v1 in the Aya format - Chinese subset This dataset is a format conversion for the Chinese data in the original Universal NER v1 into the Aya instruction format and it's ","None","zh","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Deepexi/function-calling-small","Deepexi","2023-09-13 03:07:25","2023-09-13 12:03:16+00:00","8fba7ee941523e7da69f4ff882b52e5566d24288","98","338","14","task_categories:feature-extraction, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","feature-extraction","数据集内容说明: 包含700+个阿里云OpenAPI的信息 包括Dataworks,EMR，DataLake，Maxcompute，Hologram,实时计算Flink版，QuickBI,DTS等多个产品的公开Open API信息。 样例 { ""systemPrompt"": 你是一个函数筛选助理，如果与问题相关的话,您可以使用下面的函数来获取更多数据以回答用户提出的问题:{""function"": ","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"clapAI/MultiLingualSentiment","clapAI","2024-12-27 15:30:01","2024-12-27 16:31:31+00:00","08b1866c310cdf38fa58e2336569423bdb027a5c","334","336","7","task_categories:text-classification, language:ar, language:de, language:en, language:es, language:fr, language:hi, language:id, language:it, language:ko, language:ms, language:pt, language:ru, language:tr, language:vi, language:zh, language:ja, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentiment, multilingual, emotion, review, classification","text-classification","Overview MultilingualSentiment is a sentiment classification dataset that encompasses three sentiment labels: Positive, Neutral, Negative The dataset spans multiple languages and covers a wide range o","None","ar, de, en, es, fr, hi, id, it, ko, ms, pt, ru, tr, vi, zh, ja","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"wybxc/open-yiri","wybxc","2023-04-03 15:44:26","2023-04-15 12:45:06+00:00","f67b83de8e30dda472e6124eb8f8dcdbeb062a14","89","332","2","task_categories:text-generation, language:zh, license:odc-by, size_categories:1K<n<10K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","text-generation","None","None","zh","mono","n|<|1|K","None","False","False","False","odc-by","None","None","None"
+"ivanzhu109/zh-taiwan","ivanzhu109","2024-03-02 09:18:11","2024-03-04 02:43:54+00:00","39068b335f9a2f793913512d6a3d621dba58303a","58","332","0","task_categories:text-to-speech, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech","None","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"XeTute/Small-Medium-Conversation-Multilingual","XeTute","2024-07-05 21:59:29","2024-11-25 15:06:59+00:00","3f493944dda3243c6a250de2f03a114ab5e0c569","68","332","0","language:en, language:sq, language:bn, language:ru, language:hi, language:ur, language:zh, language:es, language:de, language:fr, language:pt, license:apache-2.0, size_categories:1K<n<10K, format:text, modality:text, library:datasets, library:mlcroissant, region:us, chemistry, biology, legal, music, art, climate, medical, croissant, synthetic","None","This is a dataset provided by us, XeTute Technologies, under the Apache2.0 license. This contrains 12 raw .txt files, where the English one was originally written by a human, corrected and made cleare","None","en, sq, bn, ru, hi, ur, zh, es, de, fr, pt","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"faisaltareque/XL-HeadTags","faisaltareque","2024-08-11 12:51:58","2024-08-16 15:30:07+00:00","8e701bf5afe97f7ec1d96ab14f858e9f54dd0a4a","72","329","3","task_categories:summarization, task_categories:text2text-generation, task_categories:sentence-similarity, language:en, language:pt, language:es, language:ru, language:uk, language:pa, language:gu, language:hi, language:mr, language:bn, language:fr, language:tr, language:ar, language:zh, language:te, language:ta, language:ne, language:fa, language:ur, language:id, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, headline-generation, tags-generation, multilingual","summarization, text2text-generation, sentence-similarity","Dataset Card for XL-HeadTags Corpus Dataset Source We have used M3LS and XL-Sum as source for this dataset. Dataset Summary We provide XL-HeadTags, a large-scale news headline and tags generation data","None","en, pt, es, ru, uk, pa, gu, hi, mr, bn, fr, tr, ar, zh, te, ta, ne, fa, ur, id","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"zjunlp/OceanInstruct","zjunlp","2024-07-02 08:48:36","2024-07-05 12:50:58+00:00","9f5558a8c8f1015376992e76f6808b9178dd9637","63","327","5","language:en, language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.02031, region:us, Ocean","None","We release OceanInstruct, which is part of the instruction data for training OceanGPT. 🛠️ How to use OceanInstruct We provide the example and you can modify the input according to your needs. from dat","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"FreedomIntelligence/ALLaVA-4V-Chinese","FreedomIntelligence","2024-03-08 17:39:02","2024-04-29 15:26:44+00:00","92f1289ad571d4da67fd7334e92cb8feaaaea5fd","65","327","13","task_categories:question-answering, task_categories:text-generation, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.11684, region:us, GPT-4V, LVLM, Vision, Language","question-answering, text-generation","ALLaVA-4V for Chinese This is the Chinese version of the ALLaVA-4V data. We have translated the ALLaVA-4V data into Chinese through ChatGPT and instructed ChatGPT not to translate content related to O","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"TigerResearch/tigerbot-riddle-qa-1k","TigerResearch","2023-05-30 15:20:44","2023-05-31 02:03:23+00:00","d352c488aecd4dda61174836808d95e1713d4b9d","62","327","2","language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Tigerbot 搜集整理加工的中文-猜谜语sft数据集 Usage import datasets ds_sft = datasets.load_dataset('TigerResearch/tigerbot-riddle-qa-1k')","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Krystalan/xmediasum","Krystalan","2023-02-15 08:50:38","2023-02-15 13:58:33+00:00","c1bbe7aba13342ada22d029d37bded9f7c24adb5","77","326","2","task_categories:summarization, annotations_creators:expert-generated, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:en, language:zh, language:de, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us","summarization","We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by man","@inproceedings{wang-etal-2022-clidsum, title = ""{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization"", author = ""Wang, Jiaan and Meng, Fandong and Lu, Ziyao and Zheng, Duo and Li, Zhixu and Qu, Jianfeng and Zhou, Jie"", booktitle = ""Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"", month = dec, year = ""2022"", address = ""Abu Dhabi, United Arab Emirates"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2022.emnlp-main.526"", pages = ""7716--7729"", abstract = ""We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum."", }","en, zh, de","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"projecte-aina/CA-ZH_Parallel_Corpus","projecte-aina","2023-11-29 15:14:32","2024-12-04 14:39:03+00:00","d80f81e4c148c8e720a555d6e78d1aaf095b82bc","71","326","0","None","translation","Dataset Card for CA-ZH Parallel Corpus Dataset Summary The CA-ZH Parallel Corpus is a Catalan-Chinese dataset of parallel sentences created to support Catalan in NLP tasks, specifically Machine Transl","None","ca, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"ayuhamaro/ws-pos-model-tune","ayuhamaro","2023-01-13 06:23:33","2023-01-13 07:19:38+00:00","b04ef7c220aea95e783f65f26b8361a30bd38972","93","322","1","task_categories:token-classification, task_ids:named-entity-recognition, annotations_creators:expert-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:zh, license:unknown, size_categories:n<1K, modality:text, library:datasets, library:mlcroissant, region:us","token-classification, named-entity-recognition","Dataset Card for ""WS POS Model Tune"" Dataset Summary [More Information Needed] Supported Tasks and Leaderboards [More Information Needed] Languages [More Information Needed] Dataset Structure Data Ins","None","zh","mono","1|K|<|n|<|1|0|K","ws-pos-model-tune","False","False","False","unknown","None","None","named-entity-recognition"
+"erhwenkuo/moss-003-sft-chinese-zhtw","erhwenkuo","2023-10-20 00:19:41","2023-10-21 00:38:13+00:00","e361f3d4058ae263cdad9c3d94678292a4481333","66","321","3","language:zh, license:cc, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","conversational","Dataset Card for ""moss-003-sft-chinese-zhtw"" 資料集摘要 本資料集主要是應用於專案：MOSS: 開源對話語言模型 所收集的數據。 MOSS 是支援中英雙語和多種外掛程式的開源對話語言模型，moss-moon 系列模型具有160億參數，在FP16精度下可在單張A100/A800或兩張3090顯示卡運行，在INT4/8精度下可在單張3090顯示卡運行。 MO","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc","None","None","None"
+"Delius/ChineseWebNovel","Delius","2023-07-12 10:47:19","2023-07-14 07:30:07+00:00","bea2a3abb0a5cc05a50a40b9c8aaf751cd90ebcc","69","320","9","task_categories:text-generation, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Chinese Web Novel Dataset Summarized by claude but converted the order for novel text extension task. WARNING!! Please be aware of the context length!!!","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"chenbobo/chat","chenbobo","2023-07-06 07:36:13","2023-08-22 02:32:03+00:00","f9219b37301731671ebc7b7f51b38099cf87361a","69","318","0","task_categories:text-generation, language:zh, license:unlicense, size_categories:n<1K, format:text, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, finance","text-generation","None","None","zh","mono","n|<|1|K","None","False","False","False","unlicense","None","None","None"
+"jtatman/CoT_reformatted","jtatman","2024-01-16 16:36:25","2024-01-16 17:43:49+00:00","fc6a4db3493b302b2546dbd7afbe57bef36d238c","60","317","2","task_categories:text-generation, task_categories:question-answering, language:en, language:zh, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, cot, conversational","text-generation, question-answering","Dataset Card for ""CoT_reformatted"" This dataset is reformatted from: QingyiSi/Alpaca-CoT All credit goes there. Thanks to QingyiSi for the work in consolidating many diverse sources for comparison and","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"mrzjy/ChineseBQB","mrzjy","2024-09-19 08:59:44","2024-09-20 01:32:14+00:00","99835ec5427e6825f00d276258e9dd50588d7be1","64","317","0","language:zh, license:apache-2.0, size_categories:1K<n<10K, format:arrow, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, sticker, emoji, 表情包, multimodal, meme","None","Chinese BQB This is a data reupload of the repository zhaoolee/ChineseBQB, containing 5k+ Chinese stickers 中文表情包数据，来自于zhaoolee/ChineseBQB","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"asadfgglie/nli-zh-tw-all","asadfgglie","2024-11-16 03:45:45","2024-11-16 04:43:23+00:00","d62e15a487e6692baf45f7dcd127164fb9258208","59","314","0","task_categories:zero-shot-classification, task_categories:text-classification, task_categories:token-classification, language:zh, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","zero-shot-classification, text-classification, token-classification","Dataset Card for ""nli-zh-tw-all"" 使用OpenCC將shibing624/nli-zh-all翻譯而成的資料集 資料集版權歸shibing624所有","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"syntaxsynth/mmevol-zh-hant","syntaxsynth","2024-11-26 18:17:51","2024-12-01 03:42:28+00:00","2096f5a8ffdb1b5b2f47befee511623342a95990","64","314","1","task_categories:text-generation, task_categories:image-to-text, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, zh-hant, visual-understanding, multilingual","text-generation, image-to-text","MMEvol - Translated Chinese Traditional A subset of Tongyi-ConvAI/MMEvol translated using yentinglin/Llama-3-Taiwan-70B-Instruct from english to traditional chinese. Read the Note below before use. Im","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"HIT-TMG/TruthReader_RAG_train","HIT-TMG","2024-08-05 08:18:05","2024-09-09 07:53:12+00:00","bd03f2f3dcd416dfb6704b29768b5008f7977344","93","313","3","task_categories:question-answering, task_categories:text2text-generation, language:en, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","question-answering, text2text-generation","Dataset Card for TruthReader This dataset is used to train the response generator in TruthReader framework. Dataset information type language Source Annotator #sample Multi-document Synthesis zh WeiXi","None","en, zh","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"DavidLanz/TaiwanChat","DavidLanz","2024-05-08 01:24:41","2024-05-08 01:28:47+00:00","f9847d1961d30876b68d987092251f04ba0e68b7","79","311","4","task_categories:text-generation, task_categories:text2text-generation, language:zh, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2311.17487, region:us","conversational, text-generation, text2text-generation","Performance Citation If you find Taiwan LLM is useful in your work, please cite it with: @misc{lin2023taiwan, title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"Deepexi/openai-formate-function-calling-small","Deepexi","2023-12-20 06:59:27","2023-12-20 07:09:31+00:00","6d1dc02a2549104cc9cd2828c4b8647e9f994341","78","304","5","language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","None","数据集内容说明: 包含700+个阿里云OpenAPI的信息 包括Dataworks,EMR，DataLake，Maxcompute，Hologram,实时计算Flink版，QuickBI,DTS等多个产品的公开Open API信息。 Functions信息与OpenAI functions calling 能力中，functions信息传入的格式保持一致 样例 { ""systemPrompt"": ","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Miuzarte/SUISovitsDataForBaseModel","Miuzarte","2023-02-15 10:33:06","2023-03-10 04:49:43+00:00","8040edc6f111547bf80befe756703a45ecd07d09","87","303","3","None","None","岁己SUI的sovits底模数据集 Dataset Summary ForBaseModel.zip： 数据质量不高，只用于岁己音色的底模训练（洗去G_0.pth和D_0.pth的音色） 采样频率为44.1kHz，使用前请注意预处理 取自岁己22年12月、23年1月的录播（除电台，共计211:13:21），经过以下步骤筛选处理 挑取BGM音量较低的直播片段（20:39:21）_[LowBGM.zi","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"vcr-org/VCR-wiki-zh-hard-test-500","vcr-org","2024-06-10 22:36:45","2024-07-28 09:39:09+00:00","89f8bdce2548cc16578baf0e386b382987557159","63","302","2","task_categories:visual-question-answering, source_datasets:wikimedia/wit_base, language:zh, 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:2406.06462, region:us","visual-question-answering","The VCR-Wiki Dataset for Visual Caption Restoration (VCR) 🏠 Paper | 👩🏻‍💻 GitHub | 🤗 Huggingface Datasets | 📏 Evaluation with lmms-eval This is the official Hugging Face dataset for VCR-Wiki, a dataset","None","zh","mono","n|<|1|K","None","False","False","False","cc-by-sa-4.0","2406.06462","None","None"
+"TaiMingLu/Multilingual-Benchmark","TaiMingLu","2024-07-06 18:24:12","2024-12-01 06:22:38+00:00","801297609d9203fefeeec8a0ca0601e58e18f549","69","297","2","task_categories:zero-shot-classification, task_categories:question-answering, task_categories:translation, language:en, language:de, language:nl, language:fr, language:es, language:it, language:pt, language:ru, language:zh, language:ja, language:ko, language:tr, language:ar, language:pl, language:hi, language:th, language:sw, language:ha, language:ur, language:af, language:id, language:uk, language:el, language:lv, language:cy, language:is, language:bn, language:ne, language:pa, language:mr, language:te, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.13748, region:us, synthetic, text, math, reasoning, instruction","zero-shot-classification, question-answering, translation","These are the GSM8K and ARC dataset translated by Google Translate. BibTex @misc{lu2024languagecountslearnunlearn, title={Every Language Counts: Learn and Unlearn in Multilingual LLMs}, author={Taimin","None","en, de, nl, fr, es, it, pt, ru, zh, ja, ko, tr, ar, pl, hi, th, sw, ha, ur, af, id, uk, el, lv, cy, is, bn, ne, pa, mr, te","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"Heng666/Traditional_Chinese-aya_evaluation_suite","Heng666","2024-02-19 13:27:21","2024-02-19 14:18:14+00:00","32085670ac2dd612ea90aab5d4924cf3e19efbf3","74","296","3","task_categories:question-answering, task_categories:translation, task_categories:summarization, task_categories:zero-shot-classification, language:zh, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.06619, region:us","question-answering, translation, summarization, zero-shot-classification","資料集描述 繁體中文 Aya (Traditional Chinese Aya Chinese TCA)：專注於繁體中文處理的 Aya 集合的精選子集 概述 繁體中文 Aya 是一個精心策劃的資料集，源自 CohereForAI 的綜合 Aya 集合，特別關注繁體中文文本資料。 此資料集結合了來自 CohereForAI/aya_evaluation_suite，過濾掉除繁體中文、簡體中文內容之外","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"benchang1110/ShareGPT4V-zhtw","benchang1110","2024-09-04 13:18:58","2024-09-08 15:40:44+00:00","af7fb7a4d06f01d396a62ece217b14ac86b80839","69","295","1","task_categories:text-generation, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","ShareGPT4V-zhtw Dataset Card Dataset details Dataset type: ShareGPT4V-zhtw is a Traditional Chinese version of Lin-Chen/ShareGPT4V translated by yentinglin/Llama-3-Taiwan-8B-Instruct. We took caption ","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"Ali2500/ViCaS","Ali2500","2024-12-10 21:57:05","2024-12-18 20:42:04+00:00","f80c5d11928729f074b407e4c04dedc97a1434db","159","292","4","language:en, language:zh, license:cc, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2412.09754, region:us","None","ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation Project | GitHub | Arxiv For details docs and usage guide, refer to the GitHub rep","None","en, zh","bi","None","None","False","False","False","cc","2412.09754","None","None"
+"Heng666/Taiwan-patent-qa-eval","Heng666","2024-02-28 06:57:44","2024-02-28 07:11:31+00:00","7d2a13faf437c605644404233418fd5260dd17e6","68","288","2","task_categories:question-answering, language:zh, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, traditional chinese, patent, taiwan","question-answering","台灣專利問答集 我們提出適用於 QA 系統上用的專利問答集，主要內容收錄台灣開發資料，總計八年的專利師訓練試題，高達 192 道題目。旨在提高語言模型在台灣領域上落地場景。 Citation @article{TaiwanPatent2024eval, title={An Patent Evaulutaion for Taiwan Language Model}, author={soaring0","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"HistoryTrans/Dataset","HistoryTrans","2024-01-07 23:53:26","2024-01-09 21:03:47+00:00","89087a1ea7ba831a34b9d3101281a6f1ff9334ea","68","288","1","task_categories:translation, language:zh, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, translation, 古文翻译, 文言文翻译","translation","HistoryTrans HistoryTrans 是一个古文翻译数据集，通过数据预处理和质量控制，来提高古文翻译的质量和实用性。 参考我们的项目主页HistoryTrans古文翻译. 数据集详细信息 数据集来源 主体: Classical-Modern 额外补充: ：《二十四史》和《清史稿》中提取 数据集结构 数据集包含以下 JSONL 文件： train_01_04.jsonl: 训练集，主要","None","zh","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","mit","None","None","None"
+"fine-tuned/cmedqav2-c-64-24","fine-tuned","2024-05-15 12:50:33","2024-05-15 12:59:55+00:00","a38d59dd2e9a2e025e60f436747fa8176a82a058","66","286","0","task_categories:feature-extraction, task_categories:sentence-similarity, language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, sentence-transformers, feature-extraction, sentence-similarity, mteb, Health, Medicine, Treatment, Diagnosis, Advice","feature-extraction, sentence-similarity","cmedqav2-c-64-24 Dataset Dataset Description The dataset ""health information search"" is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks.","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"AIBoy1993/Prompt-CHIP-CTC","AIBoy1993","2023-07-22 16:32:14","2023-07-22 16:37:20+00:00","6bc5664262fcef8e92cc96e5241ab5f9d6b65162","58","283","0","task_categories:text-classification, task_categories:question-answering, task_categories:text2text-generation, language:zh, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, question-answering, text2text-generation","None","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"TCMLM/TCM_Humanities","TCMLM","2024-01-18 06:58:01","2024-01-28 02:38:50+00:00","3bfda762865f4e93d3f1aa3cd9d3c8bd16c5776a","88","283","3","task_categories:text-classification, language:zh, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical, safety","text-classification","Dataset Card for [TCMLM/TCM_Humanities] This dataset, curated by the Traditional Chinese Medicine Language Model Team, comprises a comprehensive collection of multiple-choice questions (both single an","None","zh","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"neulab/PangeaBench-multilingual-llava-bench","neulab","2024-10-31 06:24:38","2024-10-31 19:48:20+00:00","938ebea66545ceda555ba8c63fcdd6c31f99ed85","120","282","0","language:ar, language:bn, language:en, language:es, language:fr, language:hi, language:ja, language:ru, language:ur, language:zh, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ar, bn, en, es, fr, hi, ja, ru, ur, zh","multi","None","None","False","False","False","None","None","None","None"
+"CaterinaLac/sharegpt-deduplicated","CaterinaLac","2023-10-04 13:31:41","2023-10-04 14:40:39+00:00","6a50751e126ba3a05ba002ef8ed6e013baeab09b","83","282","1","language:en, language:zh, language:ko, language:fr, language:ja, language:es, language:no, language:et, language:de, language:ca, language:vi, language:fi, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","Dataset Card for Dataset Name Dataset Description Dataset Summary This dataset is a deduplicated version of sharegpt4. The deduplication process has two steps: The literal duplicates (both input and o","None","en, zh, ko, fr, ja, es, no, et, de, ca, vi, fi","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"BUAADreamer/llava-med-zh-eval","BUAADreamer","2024-05-08 10:42:08","2024-05-21 01:54:49+00:00","d22ed8c47a45a4f302e15d65414165fba372baa7","65","280","2","task_categories:visual-question-answering, language:zh, license:apache-2.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical, llama-factory","visual-question-answering","None","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"neulab/PangeaBench-xchat","neulab","2024-10-31 21:38:23","2024-11-01 15:19:15+00:00","ffc5a12aee02f9de6cddd2b06eb9efa92664e2c2","81","279","0","task_categories:visual-question-answering, language:zh, language:en, language:hi, language:id, language:ja, language:rw, language:ko, language:es, license:cc-by-4.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","None","None","zh, en, hi, id, ja, rw, ko, es","multi","n|<|1|K","None","False","False","False","cc-by-4.0","None","None","None"
+"SWHL/text_rec_test_dataset","SWHL","2024-04-16 01:35:23","2024-04-16 01:42:37+00:00","ebaa78aa42628cc347e43d7d5d1549ec16e034de","65","278","1","language:zh, language:en, license:apache-2.0, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, code","None","文本识别测试集 数据集简介 该测试集包括8类场景，分别是竖排文字、长文本、单字、验证码、自然场景、银行卡、手写体和车牌等。 该数据集可以结合文本识别指标评测库-TextRecMetric使用，快速评测各种文本识别算法。 关于该数据集，欢迎小伙伴贡献更多数据呦！有任何想法，可以前往issue讨论。 数据集支持的任务 可用于自定义数据集下的模型验证和性能评估等。 数据集加载方式 from datase","None","zh, en","bi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"lorinma/Slim-COIG-Kun","lorinma","2023-12-19 10:41:58","2024-04-28 12:43:09+00:00","706f8ec3b457937c095b40c22c20706f7b1c11ae","72","277","1","task_categories:question-answering, language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1708.00489, region:us","question-answering","This is a Slim version of COIG-Kun 因为原始的数据集有53万条之多，所以进行了subsample。 采样方法大致为，使用bert-base-chinese将Instruction转换为embedding，使用类knn的方法抽取了1万条。并转换成了sharegpt格式。 为了更直观的查看效果，文件中还有一个仅采样了1千条的版本。采样前后的Embedding使用tsn","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"OpenNLPLab/FAVDBench","OpenNLPLab","2023-11-24 10:53:16","2023-12-06 11:56:08+00:00","310122d6852934528017f743214c211911e7038a","74","277","1","language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2303.15616, region:us, FAVD, FAVDBench, Video Description, Audio Description, Audible Video Description, Fine-grained Description","None","FAVDBench: Fine-grained Audible Video Description 🤗 Hugging Face • 🏠 GitHub • 🤖 OpenDataLab • 💬 Apply Dataset [CVPR2023] [Project Page] [arXiv] [Demo][BibTex] [中文简介] Introduction 简介 Files 文件 MD5 check","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2303.15616","None","None"
+"Ayaka/ORCHESTRA-simple-1M","Ayaka","2023-06-12 12:53:18","2023-06-12 14:01:47+00:00","d0a766b1b2ad1d3e4ef39fa6faff628811a14041","69","277","3","task_categories:text-generation, language:zh, language:lzh, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, arts, poetry","text-generation","ORCHESTRA-simple-1M GitHub: nk2028/ORCHESTRA-dataset 中文簡介 ORCHESTRA (cOmpRehensive Classical cHinESe poeTRy dAtaset) 是一個全面的古典中文詩歌的數據集，數據來自搜韻網。本數據集由 nk2028 進行格式轉換並發佈，希望透過公開高品質的古典中文詩歌數據，促進對古典中文詩歌及古典中文自然","None","zh, lzh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"0x22almostEvil/tatoeba-mt-all-in-one","0x22almostEvil","2023-05-07 17:28:20","2023-05-07 23:54:17+00:00","d506620111874665035140a5a561460bde1daa58","60","276","0","annotations_creators:Helsinki-NLP, language_creators:crowdsourced, multilinguality:translation, source_datasets:Helsinki-NLP/tatoeba_mt, language:af, language:ar, language:az, language:be, language:bg, language:bn, language:br, language:bs, language:ca, language:ch, language:cs, language:cv, language:cy, language:da, language:de, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fo, language:fr, language:fy, language:ga, language:gd, language:gl, language:gn, language:he, language:hi, language:hr, language:hu, language:hy, language:ia, language:id, language:ie, language:io, language:is, language:it, language:ja, language:jv, language:ka, language:kk, language:km, language:ko, language:ku, language:kw, language:la, language:lb, language:lt, language:lv, language:mi, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:my, language:nb, language:nl, language:nn, language:no, language:oc, language:pl, language:pt, language:qu, language:rn, language:ro, language:ru, language:sh, language:sl, language:sq, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:tk, language:tl, language:tr, language:tt, language:ug, language:uk, language:ur, language:uz, language:vi, language:vo, language:yi, language:zh, license:cc-by-2.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for The Tatoeba Translation Challenge | All In One ~7.3M entries. Just more user-friendly version that combines all of the entries of original dataset in a single file: https://huggingfac","None","af, ar, az, be, bg, bn, br, bs, ca, ch, cs, cv, cy, da, de, el, en, eo, es, et, eu, fa, fi, fo, fr, fy, ga, gd, gl, gn, he, hi, hr, hu, hy, ia, id, ie, io, is, it, ja, jv, ka, kk, km, ko, ku, kw, la, lb, lt, lv, mi, mk, ml, mn, mr, ms, mt, my, nb, nl, nn, no, oc, pl, pt, qu, rn, ro, ru, sh, sl, sq, sr, sv, sw, ta, te, th, tk, tl, tr, tt, ug, uk, ur, uz, vi, vo, yi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-2.0","None","None","None"
+"neulab/PangeaBench-maxm","neulab","2024-10-31 20:28:38","2024-10-31 20:29:56+00:00","4e1e26a13ebb9b5ae074ba161201a3b4a0c29a89","102","274","0","task_categories:visual-question-answering, language:en, language:fr, language:hi, language:ro, language:th, language:he, language:zh, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","MaXM This is a clone of the MaXM dataset by Google LLC (""Google"")! Please find the original repository here: https://github.com/google-research-datasets/maxm If you use this dataset, please cite the o","None","en, fr, hi, ro, th, he, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"Orion-zhen/meissa-unalignments","Orion-zhen","2024-10-14 07:01:22","2024-10-14 07:08:45+00:00","4a5bf5b73e8d337ca20f36f7803dc6119614e1df","75","273","6","task_categories:text-generation, language:en, language:zh, license:gpl-3.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Meissa Unalignments During the Meissa-Qwen2.5 training process, I noticed that Qwen's censorship was somehow bound to its chat template. Thus, I created this dataset with one system prompt, hoping to ","None","en, zh","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","gpl-3.0","None","None","None"
+"AAAIBenchmark/Multi-Opthalingua","AAAIBenchmark","2024-08-14 23:28:25","2024-08-15 00:42:38+00:00","db244a8183a4d1522a44a02a5917d4f85ffb2131","114","271","0","task_categories:question-answering, language:hi, language:zh, language:en, language:es, language:pt, language:tl, language:fr, license:mit, size_categories:n<1K, format:text, modality:text, library:datasets, library:mlcroissant, region:us, medical","question-answering","None","None","hi, zh, en, es, pt, tl, fr","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"URSA-MATH/MMathCoT-1M","URSA-MATH","2025-01-06 12:15:30","2025-01-14 13:59:58+00:00","d2410492a1fdb6f96b55ed64b50f7288b3ba32e9","270","270","3","task_categories:question-answering, task_categories:text-generation, language:en, language:zh, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2501.04686, region:us","question-answering, text-generation","MMathCoT-1M Image data can be downloaded from the following address: MAVIS: https://github.com/ZrrSkywalker/MAVIS, https://drive.google.com/drive/folders/1LGd2JCVHi1Y6IQ7l-5erZ4QRGC4L7Nol. Multimath: ","None","en, zh","bi","None","None","False","False","False","gpl-3.0","None","None","None"
+"Lazycuber/Evol-instruct-merge","Lazycuber","2024-01-03 14:24:17","2024-01-03 15:31:38+00:00","806f74d094d4243370167404aa371d0faf3d142e","59","270","0","language:ja, language:zh, language:en, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","just merging a few evol instruct datasets the pros made","None","ja, zh, en","multi","None","None","False","False","False","None","None","None","None"
+"IAAR-Shanghai/KAF-Dataset","IAAR-Shanghai","2024-10-04 14:27:38","2024-10-30 15:29:17+00:00","8d45485fe7812c5416b7a78cd469d7478580c8fb","67","270","3","task_categories:question-answering, task_ids:language-modeling, multilinguality:multilingual, source_datasets:original, language:zh, language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2405.11874, region:us, evaluation, KAF-Dataset, xFinder, llm-as-a-judge, key-answer-extraction, judge-model","question-answering, language-modeling","The dataset sourced from https://github.com/IAAR-Shanghai/xFinder Citation @article{xFinder, title={xFinder: Robust and Pinpoint Answer Extraction for Large Language Models}, author={Qingchen Yu and Z","None","zh, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","language-modeling"
+"lianghsun/vulnerability-mitigation-qa-zh_tw","lianghsun","2024-10-08 06:12:47","2024-10-08 06:34:25+00:00","8199fe61f55e689002bdd31d11f77063bae6ee55","67","269","1","task_categories:question-answering, task_categories:text-generation, language:zh, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, cyber, Taiwan, ROC, TW","question-answering, text-generation","Data Card for 資訊安全弱點處理問答集（vulnerability-mitigation-qa-zh_tw） 此資料集仍在增長中 :) Dataset Summary 本資料集是將常見的資訊安全弱點的處理方式收集而成資料集。 Supported Tasks and Leaderboards 本資料集可以運用在 Supervised Fine-tuning，讓模型學會如何回答處理資訊安全","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"neulab/PangeaBench-xgqa","neulab","2024-10-31 06:03:54","2024-10-31 19:51:25+00:00","41e9f2a6610433a949d9ad7b7e0ea9378bbb949b","73","268","0","task_categories:visual-question-answering, language:bn, language:de, language:en, language:id, language:ko, language:pt, language:ru, language:zh, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2109.06082, region:us","visual-question-answering","xGQA This is a clone of the few_shot-test split of the xGQA dataset Please find the original repository here: https://github.com/adapter-hub/xGQA If you use this dataset, please cite the original auth","None","bn, de, en, id, ko, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"silk-road/MMC4-130k-chinese-image","silk-road","2023-05-16 04:42:42","2023-05-16 04:51:58+00:00","745dfe1c3f1d2c438d1b35d4cfa0b84850d3b236","63","267","7","task_categories:text-to-image, task_categories:image-to-text, language:zh, language:en, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-to-image, image-to-text","MMC4-130k-chinese是对MMC4中，抽样了130k左右 simliarty较高的图文pair得到的数据集 Chinese版本是对这里所有的caption进行了翻译。 我们会陆续将更多数据集发布到hf，包括 Coco Caption的中文翻译 CoQA的中文翻译 CNewSum的Embedding数据 增广的开放QA数据 WizardLM的中文翻译 如果你也在做这些数据集的筹备，欢迎来","None","zh, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"xywang1/NaturalConv","xywang1","2024-11-07 00:15:21","2024-11-07 00:25:28+00:00","adb38574e8014ada1d8404247167582e76b22d05","122","267","1","task_categories:text-generation, language:zh, license:other, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2103.02548, region:us, dialogue, multi-turn, topic-driven, document, news, conversation","text-generation","NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation Introduction This dataset is described in the paper NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topi","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","2103.02548","None","None"
+"alvanlii/cantonese-radio","alvanlii","2025-01-13 14:01:18","2025-01-24 03:28:11+00:00","e58a330b620f9ffa158f9da2e559035a835b119f","264","264","2","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:zh, language:yue, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition, audio-classification","Cantonese Radio Pseudo-Transcription Dataset Contains 14k hours of audio sourced from Archive.org Columns order_index: Represents the order of the audio compared to those from the same filename link: ","None","zh, yue","bi","None","None","False","auto","False","None","None","None","None"
+"erfanzar/UltraChat-Matic","erfanzar","2024-01-05 11:17:28","2024-01-06 10:23:35+00:00","6869c05e9d316b28e037cf9637b3ef41b1d559e5","63","263","7","task_categories:text-generation, task_categories:text-classification, language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code, biology, medical","text-generation, text-classification, conversational","ChatMatic with Over 80,000 multi-turn examples. UltraChat-Matic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using GPT-4 and contains Sy","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|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"Nexdata/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone","Nexdata","2022-09-16 10:10:40","2024-08-05 03:26:29+00:00","d2fec7dfc49c47ca294a340ec5f284f741d8d60b","68","262","1","task_categories:automatic-speech-recognition, language:zh, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","automatic-speech-recognition","Dataset Card for Nexdata/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone Dataset Summary The data were recorded by 700 Mandarin speakers, 65% of whom were women. There is no pre-made text, and speake","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"mintz1104/diffusion_stage_design_japanese_anime_style","mintz1104","2024-10-13 12:34:07","2024-10-13 13:38:33+00:00","51bd33ffe94afb65b9f7043a5a72e617bf943283","137","261","3","task_categories:text-to-image, language:zh, license:apache-2.0, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, art","text-to-image","None","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"lightblue/reranker_continuous_filt_max7_train","lightblue","2024-12-19 06:17:53","2025-01-07 01:13:36+00:00","32bd1c820d8da33f7f4780ae2252f1d3b82377f0","112","260","3","language:en, language:zh, language:es, language:de, language:ar, language:ru, language:ja, language:ko, language:hi, language:sk, language:vi, language:tr, language:fi, language:id, language:fa, language:no, language:th, language:sv, language:pt, language:da, language:bn, language:te, language:ro, language:it, language:fr, language:nl, language:sw, language:pl, language:hu, language:cs, language:el, language:uk, language:mr, language:ta, language:tl, language:bg, language:lt, language:ur, language:he, language:gu, language:kn, language:am, language:kk, language:hr, language:uz, language:jv, language:ca, language:az, language:ms, language:sr, language:sl, language:yo, language:lv, language:is, language:ha, language:ka, language:et, language:bs, language:hy, language:ml, language:pa, language:mt, language:km, language:sq, language:or, language:as, language:my, language:mn, language:af, language:be, language:ga, language:mk, language:cy, language:gl, language:ceb, language:la, language:yi, language:lb, language:tg, language:gd, language:ne, language:ps, language:eu, language:ky, language:ku, language:si, language:ht, language:eo, language:lo, language:fy, language:sd, language:mg, language:so, language:ckb, language:su, language:nn, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Reranker training data This data was generated using 4 steps: We gathered queries and corresponding text data from 35 high quality datasets covering more than 95 languages. For datasets which did not ","None","en, zh, es, de, ar, ru, ja, ko, hi, sk, vi, tr, fi, id, fa, no, th, sv, pt, da, bn, te, ro, it, fr, nl, sw, pl, hu, cs, el, uk, mr, ta, tl, bg, lt, ur, he, gu, kn, am, kk, hr, uz, jv, ca, az, ms, sr, sl, yo, lv, is, ha, ka, et, bs, hy, ml, pa, mt, km, sq, or, as, my, mn, af, be, ga, mk, cy, gl, ceb, la, yi, lb, tg, gd, ne, ps, eu, ky, ku, si, ht, eo, lo, fy, sd, mg, so, ckb, su, nn","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"neulab/PangeaBench-cvqa","neulab","2024-10-31 20:31:00","2024-10-31 20:40:41+00:00","cf7b2cdcae813b3df1470ea5a922a108cc67794f","74","258","0","task_categories:question-answering, language:id, language:su, language:ja, language:jv, language:min, language:br, language:ga, language:es, language:pt, language:no, language:mn, language:ms, language:zh, language:ko, language:ta, language:ben, language:si, language:bg, language:ro, language:ru, language:am, language:orm, language:ar, language:ig, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering","About CVQA CVQA is a culturally diverse multilingual VQA benchmark consisting of over 9,000 questions from 33 country-language pairs. The questions in CVQA are written in both the native languages and","None","id, su, ja, jv, min, br, ga, es, pt, no, mn, ms, zh, ko, ta, ben, si, bg, ro, ru, am, orm, ar, ig","multi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"lightblue/kurage_training_data","lightblue","2024-09-13 10:01:31","2024-09-16 08:03:51+00:00","e3e4abae31d47b198402444e25b451ddececb661","62","254","6","language:ar, language:en, language:es, language:hi, language:id, language:ja, language:ko, language:ru, language:sw, language:th, language:vi, language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ar, en, es, hi, id, ja, ko, ru, sw, th, vi, zh","multi","None","None","False","False","False","mit","None","None","None"
+"haoranxu/X-ALMA-Preference","haoranxu","2024-10-03 06:48:48","2024-10-07 06:10:05+00:00","9f1e77b038bcce22c425f446a978fc72dc426748","82","247","3","language:en, language:da, language:nl, language:de, language:is, language:no, language:sc, language:af, language:ca, language:ro, language:gl, language:it, language:pt, language:es, language:bg, language:mk, language:sr, language:uk, language:ru, language:id, language:ms, language:th, language:vi, language:mg, language:fr, language:hu, language:el, language:cs, language:pl, language:lt, language:lv, language:ka, language:zh, language:ja, language:ko, language:fi, language:et, language:gu, language:hi, language:mr, language:ne, language:ur, language:az, language:kk, language:ky, language:tr, language:uz, language:ar, language:he, language:fa, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.03115, region:us","None","This is the translation preference dataset used by X-ALMA. source: the source sentence. chosen: the preferred translation. reject: the dis-preferred translation. directions: the translation direction.","None","en, da, nl, de, is, no, sc, af, ca, ro, gl, it, pt, es, bg, mk, sr, uk, ru, id, ms, th, vi, mg, fr, hu, el, cs, pl, lt, lv, ka, zh, ja, ko, fi, et, gu, hi, mr, ne, ur, az, kk, ky, tr, uz, ar, he, fa","multi","None","None","False","False","False","mit","None","None","None"
+"IDEA-CCNL/Ziya-Writing-Eval-Chinese","IDEA-CCNL","2024-01-03 07:29:32","2024-01-03 07:44:48+00:00","f55b23a942608083cffe2813c803d3517ebb1e41","59","244","7","language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","姜子牙写作任务中文评估数据集 Ziya-Writing-Eval-Chinese 数据介绍 Dataset Summary 用于评估大语言模型在中文写作任务上的水平，通常采用Side-by-Side评测； 本评测集包含了以下几个写作子任务： 应用写作 公文 通知 报告 论文 征稿 函件 申请书 创意写作 书信 作文 文案 小说 视频脚本 攻略 广告 剧本 童话 This evaluation se","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"ainnle/TrainZH","ainnle","2024-05-01 00:08:16","2024-05-30 03:09:07+00:00","b6784bd32d02b4a376cf7302f4efbb70ab8f56f1","64","243","1","task_categories:token-classification, task_categories:text-generation, language:zh, license:cc, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, library:webdataset, region:us, chemistry, biology, legal, art, code, climate, finance, music, webdataset, croissant, synthetic, medical","token-classification, text-generation","None","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc","None","None","None"
+"liswei/coct-en-zhtw-dedup","liswei","2024-05-06 15:20:57","2024-05-06 15:24:08+00:00","076a0541acf66c0214b7328b5508eabe06633e71","63","242","1","task_categories:translation, task_categories:text2text-generation, task_categories:text-generation, task_categories:sentence-similarity, language:zht, language:zh, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation, text2text-generation, text-generation, sentence-similarity","Dataset Card for COCT en-zhtw deduplicate De-duplicate version of zetavg/coct-en-zh-tw-translations-twp-300k. * The du-duplicate process is run twice for both English and Traditional Chinese.","None","zht, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"Miuzarte/SUISovitsDataForSingingModel","Miuzarte","2023-02-17 21:44:14","2023-03-10 04:35:05+00:00","42204dcbf17cac7b358c57565cc314b03dfd2405","82","241","2","language:zh, size_categories:1K<n<10K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us, AIvtuber, VirtuaReal","None","岁己SUI的sovits歌声模型数���集 Dataset Summary ForSingingModel.zip： 数据质量一般，不建议用于diff-svc等对数据质量要求较高的项目 采样频率为44.1kHz，使用前请注意预处理 取自岁己22年12月、23年1月、23年2月1-17日的录播（除电台，共计268:07:43）、岁己的投稿、A1in_sy11月及以前的歌切，经过以下步骤筛选处理 挑取音频","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"bigai-nlco/PolyMRC","bigai-nlco","2023-07-04 02:56:35","2023-07-04 03:32:53+00:00","8778148ca26dbd754871dbf4b412a2721a449379","71","241","2","task_categories:multiple-choice, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","multiple-choice","We construct a dataset through entries with multiple meanings and examples from Chinese dictionaries, and set the example as context and explanations as choices, the goal of Polysemy Machine Comprehen","None","zh","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","None","None","None"
+"chen-yingfa/CHUBS","chen-yingfa","2024-08-13 08:53:33","2024-08-20 03:45:00+00:00","c32ac9e157bf309255a6d22553fddefbac5212e8","68","240","0","task_categories:image-classification, task_categories:text-to-image, task_categories:token-classification, language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, chinese, ancient-script","image-classification, text-to-image, token-classification","CHUBS: A Large-Scale Dataset of Chu Bamboo Slip Script Code | Paper (upcoming) Introduction This is a large-scale dataset of Chu bamboo slip (CBS, Chinese: 楚简, chujian) script, an ancient Chinese scri","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"erhwenkuo/train_3.5m-zhtw","erhwenkuo","2023-09-27 00:40:14","2023-09-27 13:42:45+00:00","581ae032eaa57ce872e782daaad5816c570f68b6","62","238","0","task_categories:text2text-generation, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2303.14742, region:us","text2text-generation","Dataset Card for ""train_3.5m-zhtw"" 内容 包含約 350 萬條由 BELLE 計畫產生的中文指令資料。 範例 { ""id"":""66623028"", ""conversations"":[ { ""from"":""human"", ""value"":""歸納介紹三種不同類型的電影專題"" }, { ""from"":""assistant"", ""value"":""1. 動作片：動作片通常強","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"DataAgent/Pretrain-Taiwan-DentistKnowledge-zhTW-290K","DataAgent","2024-01-13 16:16:19","2024-01-13 16:26:21+00:00","18d9f6cfdfd509fb83bedbd3ff13ebb596ef1d34","62","236","2","task_categories:text-generation, language:zh, license:gpl-3.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-generation","LaplaceAI 繁中領域知識資料集計畫 利用我在爬蟲自動化與資料後處理上的專業，針對不同大小的領域知識資料集進行建立與維護。 在 LaplaceAI 的 huggingface 頁面，你可以找到許多不同領域的資料集。 這項 datasets 是由 LaplaceAI 整理維護的牙科相關知識。","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl-3.0","None","None","None"
+"IDEA-CCNL/Ziya-Visual-Eval-Chinese","IDEA-CCNL","2023-06-05 07:17:55","2023-06-05 07:45:19+00:00","0a041f891563ce842c13127eda8eeeac1ab9b8b5","59","236","1","language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","姜子牙-Visual中文评估数据集 Ziya-Visual-Eval-Chinese 数据介绍 Dataset Summary 数据集由LLaVA评估集翻译而来，图片源来自coco数据集，用于评估多模态大模型的中文能力 Dataset translated from the LLaVA evaluation set, image source from the coco dataset, used","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"Mxode/IndustryCorpus-Subset-zh-en","Mxode","2024-09-07 09:39:42","2024-09-09 11:10:08+00:00","ccbb1620282351b2c8c9b2bc384ef08966235203","63","233","0","None","text-generation","None","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"zhihz0535/X-SVAMP_en_zh_ko_it_es","zhihz0535","2024-01-27 21:42:53","2024-01-27 22:23:58+00:00","8eb3e02cefdb17e3672b810d700486eeec76b5a0","77","232","1","task_categories:question-answering, task_categories:text-generation, language:en, language:zh, language:it, language:ko, language:es, license:mit, size_categories:1K<n<10K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.08711, region:us","question-answering, text-generation","X-SVAMP 🤗 Paper | 📖 arXiv Dataset Description X-SVAMP is an evaluation benchmark for multilingual large language models (LLMs), including questions and answers in 5 languages (English, Chinese, Korean","None","en, zh, it, ko, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2311.08711","None","None"
+"Miaowuawa/ChineseNovels","Miaowuawa","2024-06-21 13:16:29","2024-06-21 14:29:24+00:00","f2b670de11869062b09fbeef888a3ef594d1438a","73","232","6","task_categories:text-generation, language:zh, license:cc-by-sa-3.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art","text-generation","中文小说数据集 包含内容： 网游/系统/重生 言情小说 同人/耽美小说 科幻小说 军事小说 以上加起来共4万本左右 海棠文学城小说：约1000本（未清洗）","None","zh","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Mxode/NanoExperiment-Data-Mix-10M","Mxode","2024-09-13 16:58:49","2024-09-13 17:16:18+00:00","9bcfa721be68e7600ad6cb9dbf11e79c41f92dba","61","231","0","task_categories:translation, language:en, language:zh, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, chemistry, biology, finance, legal, music, art, climate, medical, synthetic","translation","Mxode/NanoExperiment-Data-Mix-10M Dataset of NanoExperiment. Tokenized by Bilingual-Tokenizer-2K.","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"silk-road/Haruhi-Baize-Role-Playing-Conversation","silk-road","2024-01-13 01:24:51","2024-01-15 01:29:38+00:00","d70388ca07111ff70f94be7a7d18cf95df961027","63","227","4","task_categories:text-generation, language:zh, license:cc-by-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Haruhi-Zero的Conversation训练数据 我们计划拓展ChatHaruhi，从Few-shot到Zero-shot，这个数据集记录使用各个（中文）角色扮演api进行Baize式相互聊天后得到的数据结果 ids代表聊天的时候两张bot的角色卡片， 角色卡片的信息可以在https://huggingface.co/datasets/silk-road/Haruhi-Zero-RoleP","None","zh","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"fluently-sets/ultraset","fluently-sets","2024-12-22 13:50:55","2024-12-22 14:41:06+00:00","e3f0723176879c491afa4dd490439dfe2882ea21","78","220","3","task_categories:text-generation, task_categories:text2text-generation, task_categories:text-classification, task_categories:question-answering, task_categories:translation, language:en, language:ru, language:fr, language:it, language:zh, language:ko, language:de, language:es, language:code, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, text, code, reflection, reasoning, logic, medical, biology, math, finance, CoT, instruct, cleaned, alpaca, orca, universal, all-in-one, multiset, ultraset, gpt","text-generation, text2text-generation, text-classification, question-answering, translation","Ultraset - all-in-one dataset for SFT training in Alpaca format About the dataset This dataset is designed to facilitate training and retraining of LLM models using the SFT method in the Alpaca format","None","en, ru, fr, it, zh, ko, de, es, code","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","None"
+"kurehamnm/Chinese_Question_Answering_Dataset","kurehamnm","2024-03-09 14:19:21","2024-12-25 05:32:47+00:00","d16d70d13d4b1fbb8e6ed107bbe235c18d81e199","71","219","0","task_categories:question-answering, language:zh, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","zh","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"fine-tuned/jinaai_jina-embeddings-v2-base-zh-jinaai_jina-embeddings-v2-base-zh-CM","fine-tuned","2024-05-13 08:38:26","2024-05-13 08:38:27+00:00","80d436d28674b7ad7914b2b86a9eed07112791bf","58","218","0","task_categories:feature-extraction, task_categories:sentence-similarity, language:zh, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sentence-transformers, feature-extraction, sentence-similarity, mteb, Health, Medicine, Treatment, Diagnosis, Therapy","feature-extraction, sentence-similarity","jinaai_jina-embeddings-v2-base-zh-jinaai_jina-embeddings-v2-base-zh-CM Dataset Dataset Description The dataset ""medical information search"" is a generated dataset designed to support the development o","None","zh","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"WueNLP/belebele-fleurs","WueNLP","2024-11-25 19:44:15","2024-12-12 14:26:00+00:00","a867e3661564379f78fdb8d4386b9d1e1223fb48","74","211","4","None","audio-classification, automatic-speech-recognition, audio-text-to-text, text-to-speech, question-answering, document-question-answering","Belebele-Fleurs Belebele-Fleurs is a dataset suitable to evaluate two core tasks: Multilingual Spoken Language Understanding (Listening Comprehension): For each spoken paragraph, the task is to answer","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, multilingual","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"taozi555/novel_text","taozi555","2024-12-14 12:17:02","2024-12-14 18:02:58+00:00","de2fdb49c2af3e34785b03bf78c9ada3225a5b73","116","208","0","task_categories:text-generation, language:es, language:en, language:fr, language:id, language:zh, language:de, language:ja, language:vi, language:th, language:ru, license:cc-by-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","None","None","es, en, fr, id, zh, de, ja, vi, th, ru","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-2.0","None","None","None"
+"FrancophonIA/XFUND","FrancophonIA","2024-09-30 21:22:34","2024-10-11 18:27:27+00:00","0f5e2904f92077c1a0c6dfcf3f587dbac1190547","61","206","0","task_categories:feature-extraction, multilinguality:multilingual, language:de, language:es, language:fr, language:it, language:ja, language:pt, language:zh, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","feature-extraction","Dataset origin: https://github.com/doc-analysis/XFUND XFUND: A Multilingual Form Understanding Benchmark Introduction XFUND is a multilingual form understanding benchmark dataset that includes human-l","None","de, es, fr, it, ja, pt, zh","multi","None","None","False","False","False","None","None","None","None"
+"richmondsin/m_hellaswag","richmondsin","2024-12-01 07:22:31","2024-12-01 12:55:50+00:00","8d9642f16ae3031f7eddc3e3531b6ae58686d81a","83","204","0","task_categories:question-answering, task_ids:multiple-choice-qa, language:ca, language:es, language:hi, language:id, language:it, language:ml, language:mr, language:ru, language:zh, language:en, license:cc-by-nc-4.0, size_categories:10K<n<100K, 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 languages was translated using GPT-3.5-turbo by the University of Oregon, and this par","None","ca, es, hi, id, it, ml, mr, ru, zh, en","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"richmondsin/m_arc","richmondsin","2024-12-01 09:31:12","2024-12-01 12:56:22+00:00","2af5c5fbb403b06b7378dc13e095d40f9a29b13a","70","201","0","task_categories:question-answering, task_ids:multiple-choice-qa, language:ca, language:en, language:es, language:hi, language:id, language:it, language:ml, language:mr, language:ru, language:zh, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, multiple-choice-qa","Multilingual ARC Dataset Summary This dataset is a machine translated version of the ARC dataset. The languages was translated using GPT-3.5-turbo by the University of Oregon, and this part of the dat","None","ca, en, es, hi, id, it, ml, mr, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"Toyhom/MORPHEUS_Datasets","Toyhom","2024-11-26 04:02:20","2024-11-26 05:12:03+00:00","a1fd8949225f4d5a285187e77fa46b9312b55d4d","61","189","0","task_categories:text-generation, language:en, language:zh, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","text-generation","MORPHEUS MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space(EMNLP 2024) Paper EN: ConvAI2 ZH: Baidu PersonaChat","None","en, zh","bi","None","None","False","False","False","mit","None","None","None"
+"ruggsea/wsdm2024-cot-dataset","ruggsea","2024-12-11 23:31:21","2024-12-11 23:35:35+00:00","e6463182c1afd73143bb7a689aa3276c7d05e673","98","187","1","task_categories:text-classification, task_categories:text-generation, language:en, language:fr, language:es, language:sl, language:sk, language:ru, language:zh, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, wsdm, wsdm2023","text-classification, text-generation","This dataset is created by ruggsea for the WSDM 2024 competition. It is a semisynthetic dataset created by asking Llama 3.1 70B to generate rationales for the responses to the prompts in the WSDM 2024","None","en, fr, es, sl, sk, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"RocXuLi/AI_Job_DataSet_1000_list","RocXuLi","2024-07-18 07:02:40","2024-12-17 02:20:06+00:00","7d8be069c9174cef1e6a5ab0bf3055645b8a682a","60","178","1","task_categories:text2text-generation, language:zh, license:afl-3.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","None","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","afl-3.0","None","None","None"
+"URSA-MATH/DualMath-1.1M","URSA-MATH","2025-01-07 05:24:56","2025-01-14 13:59:44+00:00","9698f5def594167aa4f2654e5986632b5e95de3a","175","175","5","task_categories:token-classification, task_categories:question-answering, language:en, language:zh, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2501.04686, region:us","token-classification, question-answering","DualMath-1.1M Image data can be downloaded from the following address: MAVIS: https://github.com/ZrrSkywalker/MAVIS, https://drive.google.com/drive/folders/1LGd2JCVHi1Y6IQ7l-5erZ4QRGC4L7Nol. Multimath","None","en, zh","bi","None","None","False","False","False","gpl-3.0","None","None","None"
+"Egor-AI/CoT-XLang","Egor-AI","2024-12-31 14:21:09","2025-01-12 16:07:55+00:00","1ea67474b8e0995efad9f34894ec87e7bc25c27d","172","172","4","task_categories:text-generation, task_categories:question-answering, language:ru, language:en, language:ja, language:ko, language:id, language:vi, language:zh, language:it, language:es, language:th, license:mit, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, cot, o1","text-generation, question-answering","RU:CoT-XLang — это многоязычный датасет, состоящий из текстовых примеров с пошаговыми рассуждениями (Chain-of-Thought, CoT) на различных языках, включая английский, русский, японский и другие. Он испо","None","ru, en, ja, ko, id, vi, zh, it, es, th","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","None","None","None"
+"SIA-IDE/MBHM","SIA-IDE","2024-12-11 11:59:35","2024-12-12 00:41:35+00:00","8e75bd66728a17d144249954dba04871fcd5c5e8","106","171","0","task_categories:token-classification, task_categories:question-answering, language:zh, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2408.11281, region:us","token-classification, question-answering","Multimodal Bearing Health Management Dataset ⚡️ Download Due to the capacity limitation of GitHub, please download the data file on huggingface. 📚 Introduction The MBHM dataset is the first multimodal","None","zh, en","bi","None","None","False","False","False","mit","2408.11281","None","None"
+"ALmonster/MathInstruct-Chinese","ALmonster","2024-11-03 09:09:55","2024-11-03 09:34:46+00:00","4831ef47453915b7750672d4f0e6c0b5096d26f9","77","169","2","task_categories:text-generation, language:zh, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Source This dataset is derived from MathInstruct (https://huggingface.co/datasets/TIGER-Lab/MathInstruct). The English content was translated into Chinese using LLM, and data with translation errors o","None","zh","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"callanwu/WebWalkerQA","callanwu","2025-01-12 15:29:24","2025-01-14 03:52:43+00:00","5c94e18f01679b27b996f79d06755e82ec44c291","167","167","9","task_categories:question-answering, language:zh, language:en, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2501.07572, region:us","question-answering","📑 The paper of WebWalkerQA is available at arXiv. 📊 The dataset resource is a collection of 680 questions and answers from the WebWebWalker dataset. 🙋 The dataset is in the form of a JSON file. The ke","None","zh, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"lianghsun/tw-instruct-500k","lianghsun","2025-01-07 04:07:35","2025-01-10 05:03:52+00:00","8cce79fe4783e14eeaa1a279a6e0d29f65bb5862","166","166","13","task_categories:text-generation, language:zh, language:en, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Taiwan, ROC, tw, zh-tw, chat, instruction","text-generation","Dataset Card for Dataset Name [👋歡迎加入 Discord 討論，我們正在找人一塊擴充這個對話集🎉] 台灣常見任務對話集（Common Task-Oriented Dialogues in Taiwan） 為台灣社會裡常見的任務對話，從 lianghsun/tw-instruct 截取出 50 萬筆的子集合版本。 Dataset Details Dataset Des","None","zh, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Moleys/UAA-Chinese-H-Novels","Moleys","2024-11-05 03:32:44","2025-01-02 14:35:27+00:00","557af33d37c263b437d9363604a503e2a97e4705","123","166","3","language:zh, size_categories:10M<n<100M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","None","None","zh","mono","None","None","False","False","False","None","None","None","None"
+"richmondsin/m_truthfulqa","richmondsin","2024-12-01 07:53:35","2024-12-01 12:56:52+00:00","78cf7c67c5cd767b62c66327e42e51a76e0286f3","62","165","0","task_categories:question-answering, task_ids:multiple-choice-qa, language:ca, language:es, language:en, language:hi, language:id, language:it, language:ml, language:mr, language:ru, language:zh, license:cc-by-nc-4.0, size_categories:1K<n<10K, 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 TruthfulQA dataset. The languages was translated using GPT-3.5-turbo by the University of Oregon, and this pa","None","ca, es, en, hi, id, it, ml, mr, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","multiple-choice-qa"
+"OpenStellarTeam/Chinese-SafetyQA","OpenStellarTeam","2024-12-19 02:42:29","2024-12-23 11:04:48+00:00","db66ec32331fcc3aed02fc4bdb01d416517a2262","74","164","3","task_categories:question-answering, language:zh, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.15265, region:us, json, csv","question-answering","Overview 🌐 Website • 🤗 Hugging Face • ⏬ Data • 📃 Paper • 📊 Leader Board Chinese SafetyQA is an innovative benchmark designed to evaluate the factuality ability of large language models, specifically f","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","2412.15265","None","None"
+"Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset","Johnson8187","2024-12-11 05:13:18","2024-12-13 01:37:46+00:00","119d246a1595b44fd2cdccff0d9b288eafee25d1","60","163","2","task_categories:text-classification, task_categories:text-generation, task_categories:fill-mask, language:zh, license:mit, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, emotion, conversation","text-classification, text-generation, fill-mask","Chinese_Multi-Emotion_Dialogue_Dataset 📄 Description This dataset contains 4159 Chinese dialogues annotated with 8 distinct emotion categories. The data is suitable for emotion recognition, sentiment ","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"efederici/mc-translation","efederici","2024-11-03 22:56:19","2024-11-05 10:27:01+00:00","346ca3e3f8f10ae4f0234c414f8ef1cdeb833564","62","160","1","task_categories:translation, language:en, language:sw, language:es, language:de, language:zh, language:bn, language:it, language:hi, language:ja, language:ko, language:pt, language:ar, language:id, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, multiple-choice, eval","translation","This dataset contains professional human translations from OpenAI's MMMLU dataset, repurposed to train translation models that can help translate future evaluation datasets. Why This Dataset? Translat","None","en, sw, es, de, zh, bn, it, hi, ja, ko, pt, ar, id","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"CxsGHost/CNNSum","CxsGHost","2024-12-17 16:43:08","2024-12-22 15:13:29+00:00","97d1f484ec86533b8a00e27d4d3e4fae000885d5","60","159","4","task_categories:summarization, language:zh, license:cc-by-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2412.02819, region:us","summarization","CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels Paper GitHub [2024.12.18] - CNNSum Dataset Release! We are excited to announce the release of the CNNSum datas","None","zh","mono","None","None","False","False","False","cc-by-4.0","2412.02819","None","None"
+"hkdata/hongkong_carpark","hkdata","2024-11-07 07:01:35","2024-11-07 07:08:09+00:00","1ab65a45cc31b269a076483e7ccc7901070d0a66","64","158","0","language:zh, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, location,, address","None","Dataset Card for Dataset Name Dataset is the carpark information on Hong Kong. Dataset Details Dataset Description Curated by: [More Information Needed] Funded by [optional]: [More Information Needed]","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"lianghsun/Formosa-bench","lianghsun","2024-11-16 06:39:49","2024-11-27 03:26:23+00:00","769434637860159de19bb14d372dcbdbbc926902","86","158","3","task_categories:question-answering, language:zh, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Taiwan, ROC, zh-tw","question-answering","Dataset Card for lianghsun/Formosa-bench 福爾摩沙（Formosa）一詞源自葡萄牙文，意為「美麗」，曾是臺灣的國際名稱。據傳 16 世紀葡萄牙航海者首次見到臺灣時，驚呼「Ilha Formosa!」（美麗之島），因此得名。 福爾摩沙資料集收錄了大量與台灣社會、歷史、地理、政府及人文相關的問答，旨在測試語言模型對中華民國台灣人文社會知識的理解程度。 Datas","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Hunterhere/CBU0521DD_stories_expanded","Hunterhere","2024-12-31 11:21:37","2025-01-01 07:36:57+00:00","a1d381e3d488e25450650c8afc943555320eb012","155","155","0","task_categories:audio-classification, language:zh, language:en, license:mit, size_categories:n<1K, format:json, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, music","audio-classification","This project is for CBU5201 coursework under BUPT & QM joint programme.We constructed a dataset that told either a true story or a false story, in a mix of Chinese and English, with a total of 100 aud","None","zh, en","bi","1|B|<|n|<|1|0|B","None","False","False","False","mit","None","None","None"
+"Teklia/ATR-benchmark","Teklia","2024-10-23 12:55:46","2025-01-15 09:41:41+00:00","e6fee7598a8a2063894b8d1eb5ff4bb0eb32a180","61","154","1","task_categories:image-to-text, language:fr, language:la, language:en, language:no, language:ar, language:zh, language:de, language:nl, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, atr, htr, ocr","image-to-text","ATR benchmark - Page/paragraph level Dataset Summary The ATR benchmark dataset is a multilingual dataset that includes 83 document images, at page or paragraph level. This dataset has been designed to","None","fr, la, en, no, ar, zh, de, nl","multi","n|<|1|K","None","False","False","False","None","None","None","None"
+"longmaodata/Cantonese-ASR","longmaodata","2024-11-04 02:25:12","2024-11-22 06:39:53+00:00","861e6cfc9a23db1f5a2cfe93961c41c03f8d3b0d","62","154","3","task_categories:voice-activity-detection, task_categories:automatic-speech-recognition, task_categories:audio-to-audio, language:zh, language:en, license:cc-by-nc-nd-4.0, modality:audio, modality:text, region:us","voice-activity-detection, automatic-speech-recognition, audio-to-audio","Dataset Introduction Cantonese dialect conversation Version v1.0 Release Date 2024-11-04 Data Description Material Type Voice:Collection and Annotation Data Description (including: language category, ","None","zh, en","bi","None","None","False","manual","False","cc-by-nc-nd-4.0","None","None","None"
+"ymoslem/wmt-da-human-evaluation","ymoslem","2025-01-12 18:46:52","2025-01-28 15:22:26+00:00","e0c25814ab1ca8fe0b25177e1e6ded027db2def0","151","151","0","language:bn, language:cs, language:de, language:en, language:et, language:fi, language:fr, language:gu, language:ha, language:hi, language:is, language:ja, language:kk, language:km, language:lt, language:lv, language:pl, language:ps, language:ru, language:ta, language:tr, language:uk, language:xh, language:zh, language:zu, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, mt-evaluation, WMT, 41-lang-pairs","None","Dataset Summary This dataset contains all DA human annotations from previous WMT News Translation shared tasks. It is a copy of this dataset RicardoRei/wmt-da-human-evaluation, split into train and te","None","bn, cs, de, en, et, fi, fr, gu, ha, hi, is, ja, kk, km, lt, lv, pl, ps, ru, ta, tr, uk, xh, zh, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"hugfaceguy0001/LyricsTranslation","hugfaceguy0001","2024-11-20 17:06:39","2025-01-24 05:48:30+00:00","091d2c3b46d05348ee2c0a45d1e569bb74e2b579","84","150","0","task_categories:text-generation, task_categories:text2text-generation, task_categories:translation, language:ja, language:zh, license:openrail, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, music","text-generation, text2text-generation, translation","歌曲译配数据集 本数据集包括网络上的一些外文歌曲（目前仅包含日文，以二次元相关作品为主）翻译填词为中文的高质量作品。 特征说明 url : 中文翻译填词歌曲发布的视频链接 original_language : 外文原作的语言，目前全部为 jp 即日文 original_author : 外文原作的词作者 original_lyrics : 外文原作歌词 translation_author : ","None","ja, zh","bi","n|<|1|K","None","False","False","False","openrail","None","None","None"
+"pinzhenchen/alpaca_translate_en_then_answer_single_turn","pinzhenchen","2024-11-01 22:36:57","2024-11-01 23:03:49+00:00","1ac84f436b57dd218835b644480a38bda0312257","59","147","1","language:en, language:bg, language:cs, language:de, language:es, language:fi, language:fr, language:pt, language:ru, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.07794, region:us, chat, instruction-tuning","None","Overview This dataset is a manually constructed sequential instruction (translate-then-answer) tuning dataset derived from the famous Alpaca dataset. Dataset features/keys conversations - The user and","None","en, bg, cs, de, es, fi, fr, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"real-jiakai/chinese-squadv2","real-jiakai","2024-11-19 08:29:06","2024-11-19 12:00:17+00:00","724dea93356f6c230e7147be786e7ef5c994ac35","63","143","1","task_categories:question-answering, task_ids:open-domain-qa, task_ids:extractive-qa, annotations_creators:machine-translated, language_creators:machine-translated, multilinguality:monolingual, source_datasets:squad_v2, language:zh, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1806.03822, arxiv:1606.05250, region:us","question-answering, open-domain-qa, extractive-qa","Dataset Card for Chinese SQuAD 2.0 Dataset Description Dataset Summary This is a Chinese translation of the SQuAD 2.0 dataset, translated from the original English version. Like SQuAD 2.0, it contains","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","open-domain-qa, extractive-qa"
+"hugfaceguy0001/DingZhenLyrics","hugfaceguy0001","2024-11-05 15:08:10","2025-01-24 15:23:37+00:00","df4e9e3ad51de213e88ccfa96842bd33b434ec28","59","139","1","task_categories:text2text-generation, task_categories:text-generation, language:zh, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, music, art","text2text-generation, text-generation","丁真歌曲数据集 包括B站(bilibili.com)网友创作的一些丁真题材歌曲的歌词。 丁真歌曲一般以流行作品为基础，用丁真相关题材重新填词而成。 数据集说明 BV: 指作品在B站投稿的BV号 original_author: 原曲的词作者 original_text: 原曲的歌词，第一行是标题，以井号#标识 author: 以丁真主题重新填词作品的词作者 text: 以丁真主题重新填词作品的歌词","None","zh","mono","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"quickmt/quickmt-train.zh-en","quickmt","2025-01-18 19:54:17","2025-01-20 03:09:51+00:00","e2fb60413e695457f90b453a74e7a127b3c1dfe7","134","135","2","task_categories:translation, language:zh, language:en, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","quickmt zh-en Training Corpus Contains the following datasets downloaded with mtdata after basic filtering: Corpus Count Statmt-ccaligned-1-eng-zho_CN 15181415 OPUS-tldr_pages-v20230829-eng-zho 4165 O","None","zh, en","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"BUAADreamer/MMPR-v1.1","BUAADreamer","2025-01-08 15:36:01","2025-01-11 02:59:21+00:00","39c77aa21dbdfa1b1f854b46a33b48f2d523fd99","132","132","0","task_categories:text-generation, language:en, language:zh, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, DPO","text-generation","This dataset is borrowed from OpenGVLab/MMPR-v1.1","None","en, zh","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"CSHaitao/LexEval","CSHaitao","2024-10-30 09:41:55","2024-10-30 15:07:07+00:00","3624461b7c8df680412d18c9af7b9d210a15af82","59","131","0","language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","zh","mono","None","None","False","False","False","mit","None","None","None"
+"wj2015/lihuowang-sharegpt","wj2015","2025-01-11 05:57:48","2025-01-15 08:25:28+00:00","8c94d9b2eab059a864d72bce15a4e98aa275ec35","126","126","1","task_categories:visual-question-answering, task_categories:question-answering, language:zh, license:unknown, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, novel, 道诡异仙, 李火旺","visual-question-answering, question-answering","开源项目地址：https://github.com/wangerzi/lihuowang 数据集仓库： huggingface 地址 ModelScope 地址 Paddle 飞桨地址 数据集名称 描述 lihuowang-alpaca-dpo.json 【推荐】使用 Alpaca 格式的 DPO 数据集，包含李火旺的疯言疯语回答和正常回答的对比 daoguiyixian-sharegpt-sum","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","None","None","None"
+"david9dragon9/shp_translations","david9dragon9","2024-12-29 00:22:04","2024-12-29 01:27:27+00:00","2c3ff5946665f081a6c03fcea4192a36547a4dbe","117","117","0","task_categories:question-answering, language:en, language:ko, language:zh, language:th, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, legal","question-answering","This dataset contains translations of three splits (askscience, explainlikeimfive, legaladvice) of the Stanford Human Preference (SHP) dataset, used for training domain-invariant reward models. The tr","None","en, ko, zh, th","multi","None","None","False","False","False","mit","None","None","None"
+"bot-yaya/parallel_corpus_game","bot-yaya","2024-12-09 15:40:17","2024-12-09 16:20:55+00:00","a72e8f23f03f3cc50e83abc5e5e60778ad78ae45","66","117","3","task_categories:translation, language:ar, language:zh, language:de, language:en, language:eo, language:es, language:fr, language:he, language:id, language:it, language:ja, language:ko, language:nl, language:pt, language:ru, language:sv, language:th, language:vi, language:pl, language:tr, license:mit, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, game","translation","https://github.com/mnbvc-parallel-corpus-team/parallel_corpus_mnbvc MNBVC平行语料小组：游戏语料 不定期更新，目前已收录的游戏语料文件，共29份： 博德之门3 赛博朋克2077 黑暗之魂3 底特律：化身为人 饥荒 艾尔登法环 原神 黑帝斯 霍格沃兹之遗 Ib 如龙8 如龙7外传 荒野大镖客2 只狼：影逝二度 文明6 杀戮尖塔 ","None","ar, zh, de, en, eo, es, fr, he, id, it, ja, ko, nl, pt, ru, sv, th, vi, pl, tr","multi","None","None","False","False","False","mit","None","None","None"
+"botintel-community/AVAINT-IMG","botintel-community","2024-12-13 21:59:29","2025-01-19 17:08:54+00:00","f236cd560bff2bf88d78cf98c8b99ea784d39392","59","104","2","task_categories:image-to-text, task_categories:image-classification, task_categories:visual-question-answering, language:en, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, image-to-text, npt-3, npt-3-vision","image-to-text, image-classification, visual-question-answering","None","None","en, zh","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"PJMixers-Dev/Fundus-CC-2.5M","PJMixers-Dev","2024-12-29 12:40:41","2024-12-30 04:01:09+00:00","72b2e4fcd4e77ff6d04d9a2880fc074116934589","97","97","0","language:en, language:de, language:tr, language:fr, language:es, language:no, language:ru, language:sw, language:fa, language:uk, language:hr, language:pt, language:mk, language:ur, language:ar, language:lt, language:pl, language:bn, language:sq, language:hi, language:id, language:zh, language:el, language:bg, language:nl, language:ro, language:cv, language:ko, language:so, language:da, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, news","None","Fundus-CC-2.5M 2.5 million news articles from CC-NEWS using Fundus.","None","en, de, tr, fr, es, no, ru, sw, fa, uk, hr, pt, mk, ur, ar, lt, pl, bn, sq, hi, id, zh, el, bg, nl, ro, cv, ko, so, da","multi","None","None","False","False","False","None","None","None","None"
+"ymoslem/wmt-da-human-evaluation-long-context","ymoslem","2025-01-24 15:31:43","2025-01-26 09:34:56+00:00","38ddbdc9cd917c60795561db8fdfdf75fb6d1739","95","95","4","None","None","Dataset Summary Long-context / document-level dataset for Quality Estimation of Machine Translation. It is an augmented variant of the sentence-level WMT DA Human Evaluation dataset. In addition to in","None","bn, cs, de, en, et, fi, fr, gu, ha, hi, is, ja, kk, km, lt, lv, pl, ps, ru, ta, tr, uk, xh, zh, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"caskcsg/Libra-Test","caskcsg","2025-01-02 13:59:05","2025-01-08 04:19:37+00:00","34d18d57389d257625ee0e3a92b64da016256211","86","91","0","language:zh, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.08592, region:us","None","📊 Libra-Test Libra-Test 是专为中文大模型护栏而构建的评测基准，涵盖七大关键风险场景和 5,700+ 条专家标注数据。 Libra-Test is a benchmark designed for the safeguards of Chinese LLMs, covering 7 key risk scenarios and over 5,700 expert-labele","None","zh","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Inoichan/OpenO1-SFT-JA","Inoichan","2025-01-09 16:34:15","2025-01-09 16:53:56+00:00","9ba5b214b7da2f5ccaf222b014b9479355186e14","90","90","1","task_categories:text-generation, language:en, language:zh, language:ja, license:gemma, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for OpenO1-SFT (Japanese Translation) Dataset Summary This dataset is a Japanese-translated version of the OpenO1-SFT dataset, containing Chain of Thought (CoT) reasoning examples designe","None","en, zh, ja","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","gemma","None","None","None"
+"JadenGGGeee/NaVAB","JadenGGGeee","2024-12-15 13:48:22","2024-12-30 04:01:59+00:00","be5dd5a1c8032fef99ad1354cef956bf20d2905f","67","83","0","language:en, language:zh, language:fr, language:de, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for NaVAB Dataset Details Dataset Description NaVAB is a comprehensive benchmark designed to evaluate the alignment of Large Language Models (LLMs) with the values of five major nations: ","None","en, zh, fr, de","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"klaylouis1932/OpenFinData-Intent-Understanding-Intruct","klaylouis1932","2024-12-30 16:25:08","2025-01-05 12:28:22+00:00","fc1b94c0dd256d349351d6e1c32550352bf19a37","80","80","0","task_categories:text-classification, language:zh, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance, text-classification, intent-understanding, fine-tune, LLMs","text-classification","📊 Financial Intent Understanding Dataset Dataset for 🎯 Financial-Intent-Understanding-with-LLMs 🤖 This dataset is specifically prepared for fine-tuning (SFT) language models on financial intent unders","None","zh","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"aqweteddy/Taiwan-Curlture-MCQ","aqweteddy","2024-12-24 08:44:18","2024-12-24 09:51:16+00:00","e928d5498049258e8ad927896960c311dda4447f","63","78","0","task_categories:multiple-choice, language:zh, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, taiwan, curlture","multiple-choice","TW-Curlture-MCQ TW-Curlture-MCQ 是一個評量台灣文化的選擇題資料集，主要來自以下兩個資料集的篩選與整合： TMLU TMMLU+ 以人工挑選與台灣文化相關的科目後，再由 gpt-4o-mini 判斷問題是否與台灣文化相關，共 3828 題。","None","zh","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"yuhuanstudio/poem-pretrain-chinese-zhtw","yuhuanstudio","2025-01-23 15:09:58","2025-01-23 15:18:43+00:00","14e898c2c25ed00ea0a6f3f1bf13cc1b8c7945cd","72","72","1","language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""poem-pretrain-chinese-zhtw"" 資料集摘要 中文古典文集資料庫收集了約 5.5 萬首唐詩、26 萬首宋詩、2.1 萬首宋詞和其他古典文集。詩人包括唐宋兩朝近 1.4 萬古詩人，和兩宋時期 1.5 千古詞人。 五代十國- 收錄""花間集""與""南唐二主詞"" 唐- 收錄""全唐詩""(是清康熙四十四年，康熙皇帝主導下，蒐集羅唐詩的收藏「得詩 48,9","None","zh","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"nbeerbower/GreatFirewall-DPO","nbeerbower","2025-01-20 15:44:46","2025-01-22 23:20:51+00:00","bbd03f041b2a4998fb799936ad62cba376e45111","72","72","3","None","None","GreatFirewall-DPO An experimental dataset to discourage censorship in Chinese models. Structure prompt: input text presented to model (en translated to zh) chosen: preferred response demonstrating les","None","en, zh","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"internlm/Condor-SFT-20K","internlm","2025-01-16 08:22:07","2025-01-23 07:06:09+00:00","0ed1c721f4ec77c39ec4dcf5539c311f95ef29da","68","68","3","task_categories:text-generation, 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:2501.12273, region:us","text-generation","Condor ✨ Introduction [🤗 HuggingFace Models] [🤗 HuggingFace Datasets] [📃 Paper] The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of L","None","en, zh","bi","None","None","False","False","False","apache-2.0","2501.12273","None","None"
+"felfri/MSTS","felfri","2025-01-20 15:28:54","2025-01-23 16:20:37+00:00","b0681c33b258523f937d2f6e27b95f7f50be68f0","60","60","3","task_categories:image-text-to-text, language:ar, language:fr, language:en, language:de, language:zh, language:ko, language:fa, language:hi, language:it, language:ru, language:es, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2501.10057, region:us, not-for-all-audiences","image-text-to-text","Dataset Card for the MSTS Benchmark Here, you can find our paper and code. Note that for reproducing the exact results, we refer the user to the GitHub repo that provides download and preprocessing sc","None","ar, fr, en, de, zh, ko, fa, hi, it, ru, es","multi","1|K|<|n|<|1|0|K","None","False","auto","False","cc-by-4.0","None","None","None"