diff --git "a/data/dataset_meta/dataset_meta_ja.csv" "b/data/dataset_meta/dataset_meta_ja.csv"
new file mode 100644--- /dev/null
+++ "b/data/dataset_meta/dataset_meta_ja.csv"
@@ -0,0 +1,747 @@
+"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"
+"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"
+"unimelb-nlp/wikiann","unimelb-nlp","2022-03-02 23:29:22","2024-02-22 14:32:02+00:00","f0a3be6dc5564c0cc4150bb660144800a1f539d4","47521","2954749","103","None","token-classification, named-entity-recognition","Dataset Card for WikiANN Dataset Summary WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person","None","ace, af, als, am, an, ang, ar, arc, arz, as, ast, ay, az, ba, bar, be, bg, bh, bn, bo, br, bs, ca, cbk, cdo, ce, ceb, ckb, co, crh, cs, csb, cv, cy, da, de, diq, dv, el, eml, en, eo, es, et, eu, ext, fa, fi, fo, fr, frr, fur, fy, ga, gan, gd, gl, gn, gu, hak, he, hi, hr, hsb, hu, hy, ia, id, ig, ilo, io, is, it, ja, jbo, jv, ka, kk, km, kn, ko, ksh, ku, ky, la, lb, li, lij, lmo, ln, lt, lv, lzh, mg, mhr, mi, min, mk, ml, mn, mr, ms, mt, mwl, my, mzn, nan, nap, nds, ne, nl, nn, no, nov, oc, or, os, pa, pdc, pl, pms, pnb, ps, pt, qu, rm, ro, ru, rw, sa, sah, scn, sco, sd, sgs, sh, si, sk, sl, so, sq, sr, su, sv, sw, szl, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vec, vep, vi, vls, vo, vro, wa, war, wuu, xmf, yi, yo, yue, zea, zh","multi","n|<|1|K","None","False","False","False","unknown","None","None","named-entity-recognition"
+"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"
+"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","task_categories:text-generation, language:af, language:ar, language:az, language:bn, language:cs, language:de, language:en, language:es, language:et, language:fi, language:fr, 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:sw, language:ta, language:te, language:th, language:tl, language:tr, language:uk, language:ur, language:vi, language:xh, language:zh, license:cc-by-nc-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, arxiv:2008.00401, arxiv:2305.15011, region:us, instruction-finetuning, multilingual","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","None","None","None","None","af, am, ar, az, bn, cy, da, de, el, en, es, fa, fr, he, hi, hu, hy, id, is, it, ja, jv, ka, km, kn, ko, lv, ml, mn, ms, my, nb, nl, pl, pt, ro, ru, sl, sq, sv, sw, ta, te, th, tl, tr, ur, vi, zh","multi","None","None","False","False","False","None","None","None","None"
+"facebook/belebele","facebook","2023-09-01 18:27:13","2024-08-12 22:18:08+00:00","7899cdfa4e1e0d733fd77c848e2c273cb1d32be2","7480","676629","102","None","question-answering, zero-shot-classification, text-classification, multiple-choice","The Belebele Benchmark for Massively Multilingual NLU Evaluation Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the eval","None","af, am, ar, az, as, bm, bn, bo, bg, ca, cs, ku, da, de, el, en, es, et, eu, fi, fr, ff, om, gu, gn, ht, ha, he, hi, hr, hu, hy, ig, id, it, is, jv, ja, ka, kn, kk, mn, km, rw, ky, ko, lo, ln, lt, lg, lv, ml, mr, mk, mt, mi, my, nl, no, ne, ny, or, pa, ps, fa, mg, pl, pt, ro, ru, sn, si, sl, sv, sk, sd, sw, ta, te, tg, tl, th, ti, tn, ts, tr, uk, ur, uz, vi, wo, xh, yo, zh, ms, zu","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"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"
+"mteb/amazon_counterfactual","mteb","2022-05-26 10:48:56","2024-06-17 12:12:21+00:00","1f7e6a9d6fa6e64c53d146e428565640410c0df1","1284","452474","1","None","None","Amazon Multilingual Counterfactual Dataset The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary ","None","de, en, ja","multi","None","None","False","False","False","None","2104.06893","None","None"
+"gsarti/flores_101","gsarti","2022-03-02 23:29:22","2022-10-27 08:37:36+00:00","bc58ae43b22607b3e1e2bf3ae1bc5cb053495abb","20112","449039","23","None","text-generation, translation","One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage","@inproceedings{, title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={ Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\\'{a}n, Francisco and Fan, Angela }, year={2021} }","af, am, ar, hy, as, ast, az, be, bn, bs, bg, my, ca, ceb, zho, hr, cs, da, nl, en, et, tl, fi, fr, ff, gl, lg, ka, de, el, gu, ha, he, hi, hu, is, ig, id, ga, it, ja, jv, kea, kam, kn, kk, km, ko, ky, lo, lv, ln, lt, luo, lb, mk, ms, ml, mt, mi, mr, mn, ne, ns, no, ny, oc, or, om, ps, fa, pl, pt, pa, ro, ru, sr, sn, sd, sk, sl, so, ku, es, sw, sv, tg, ta, te, th, tr, uk, umb, ur, uz, vi, cy, wo, xh, yo, zu","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-sa-4.0","2106.03193","None","None"
+"juletxara/mgsm","juletxara","2023-05-09 08:20:29","2023-05-09 16:46:31+00:00","f52417ca77bd71e9888ddc29f92587660725d2b4","5671","380450","28","None","text2text-generation","Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2","@article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } @misc{shi2022language, title={Language Models are Multilingual Chain-of-Thought Reasoners}, author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei}, year={2022}, eprint={2210.03057}, archivePrefix={arXiv}, primaryClass={cs.CL} }","en, es, fr, de, ru, zh, ja, th, sw, bn","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2210.03057","None","None"
+"mozilla-foundation/common_voice_17_0","mozilla-foundation","2024-04-04 10:06:19","2024-06-16 13:50:23+00:00","b10d53980ef166bc24ce3358471c1970d7e6b5ec","28071","375384","215","None","None","Dataset Card for Common Voice Corpus 17.0 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 31175 recorded hours in the dataset also include de","@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }","ab, af, am, ar, as, ast, az, ba, bas, be, bg, bn, br, ca, ckb, cnh, cs, cv, cy, da, de, dv, dyu, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gn, ha, he, hi, hsb, ht, hu, hy, ia, id, ig, is, it, ja, ka, kab, kk, kmr, ko, ky, lg, lij, lo, lt, ltg, lv, mdf, mhr, mk, ml, mn, mr, mrj, mt, myv, nan, ne, nhi, nl, nn, nso, oc, or, os, pa, pl, ps, pt, quy, rm, ro, ru, rw, sah, sat, sc, sk, skr, sl, sq, sr, sv, sw, ta, te, th, ti, tig, tk, tok, tr, tt, tw, ug, uk, ur, uz, vi, vot, yi, yo, yue, zgh, zh, zu, zza","multi","None","None","False","auto","False","cc0-1.0","1912.06670","None","None"
+"INK-USC/xcsr","INK-USC","2022-03-02 23:29:22","2024-01-04 17:03:17+00:00","f691f311a11f84773a899e9bfc7da2bae51b0b02","3729","354396","6","task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:crowdsourced, language_creators:crowdsourced, language_creators:machine-generated, multilinguality:multilingual, source_datasets:extended|codah, source_datasets:extended|commonsense_qa, language:ar, language:de, language:en, language:es, language:fr, language:hi, language:it, language:ja, language:nl, language:pl, language:pt, language:ru, language:sw, language:ur, language:vi, language:zh, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2106.06937, region:us","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","task_categories:text-generation, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:oscar, language:af, language:am, language:ar, language:arz, language:as, language:az, language:azb, language:ba, language:be, language:bg, language:bn, language:bo, language:br, language:ca, language:ce, language:ceb, language:ckb, 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:fy, language:ga, language:gl, language:gu, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:ka, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:la, language:lb, language:lo, language:lt, language:lv, language:mg, language:mhr, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:my, language:nds, language:ne, language:nl, language:nn, language:no, language:or, language:os, language:pa, language:pl, language:pnb, language:ps, language:pt, language:ro, language:ru, language:sa, language:sah, language:sd, language:sh, language:si, language:sk, language:sl, language:sq, language:sr, language:sv, language:sw, 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:vi, language:yi, language:zh, license:cc0-1.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2010.14571, region:us","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","oscar","False","False","False","cc0-1.0","2010.14571","None","language-modeling"
+"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"
+"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"
+"graelo/wikipedia","graelo","2023-06-10 22:40:06","2023-09-10 06:10:08+00:00","3c5d9004085ff5de9d57f4224f4ab3d5d34af789","26549","180057","65","None","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"
+"miracl/miracl-corpus","miracl","2022-09-29 14:49:58","2023-01-05 17:28:26+00:00","d921ec7e349ce0d28daf30b2da9da5ee698bef0d","5089","171794","43","None","text-retrieval, document-retrieval","Dataset Card for MIRACL Corpus MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages,","None","ar, bn, en, es, fa, fi, fr, hi, id, ja, ko, ru, sw, te, th, zh","multi","None","None","False","False","False","apache-2.0","2210.09984","None","document-retrieval"
+"HPLT/HPLT2.0_cleaned","HPLT","2024-10-19 12:29:38","2025-01-08 13:45:28+00:00","5fca0192f7e79bb2c20f0513a896d02bf3f568f3","89473","134552","11","None","fill-mask, text-generation, language-modeling","This is a large-scale collection of web-crawled documents in 191 world languages, produced by the HPLT project. The source of the data is mostly Internet Archive with some additions from Common Crawl.","None","ace, af, als, am, ar, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mr, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","n|>|1|T","None","False","False","False","cc0-1.0","None","None","language-modeling"
+"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","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"
+"Helsinki-NLP/opus_openoffice","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:14:50+00:00","cba6a33cf9a39667ed21151b5dd767e6bb27a34c","2705","116898","7","task_categories:translation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:de, language:en, language:es, language:fr, language:ja, language:ru, language:sv, language:zh, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for [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"
+"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"
+"adithya7/xlel_wd","adithya7","2022-04-22 02:50:11","2022-07-13 07:46:57+00:00","a6d542d37b24cc1f2536af5e4afb850b9641e3ff","278","97612","2","None","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"
+"Helsinki-NLP/opus_ubuntu","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:45:27+00:00","4dc07f768ea537e979b48b824c1fe254918d1f76","219","92472","4","None","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","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"
+"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","None","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","None","False","auto","False","cc0-1.0","1912.06670","None","None"
+"castorini/mr-tydi","castorini","2022-03-02 23:29:22","2022-10-12 20:25:19+00:00","1d43c80218d06d0ef80f5b172ccabd848b948bc1","970","86336","19","None","text-retrieval","Dataset Summary Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking","None","ar, bn, en, fi, id, fi, ja, ko, ru, sw, te, th","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"kunishou/databricks-dolly-15k-ja","kunishou","2023-04-13 08:31:08","2024-04-01 17:26:37+00:00","6391034b0126850543299cda071dc6281c31a6fb","1224","77735","83","None","None","This dataset was created by automatically translating ""databricks-dolly-15k"" into Japanese.This dataset is licensed under CC-BY-SA-3.0 Last Update : 2023-05-11 databricks-dolly-15k-jahttps://github.co","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","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","None","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"
+"castorini/mr-tydi-corpus","castorini","2022-03-02 23:29:22","2022-10-12 20:25:51+00:00","3a3aa212bbe94a8cc0dc858710a3dad49d532054","249","56226","7","task_categories:text-retrieval, multilinguality:multilingual, language:ar, language:bn, language:en, language:fi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, license:apache-2.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2108.08787, region:us","text-retrieval","Dataset Summary Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking","None","ar, bn, en, fi, id, fi, ja, ko, ru, sw, te, th","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"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","task_categories:text-classification, task_ids:sentiment-analysis, task_ids:sentiment-classification, multilinguality:monolingual, multilinguality:multilingual, language:de, language:en, language:es, language:fr, language:ja, language:zh, language:id, language:ar, language:hi, language:it, language:ms, language:pt, license:apache-2.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, sentiment-analysis, sentiment-classification","Multilingual Sentiments Dataset A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative. Most multilingual sentiment datasets are either 2-class positive","None","de, en, es, fr, ja, zh, id, ar, hi, it, ms, pt","multi","1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","sentiment-analysis, sentiment-classification"
+"mutiyama/alt","mutiyama","2022-03-02 23:29:22","2024-01-09 12:07:24+00:00","afbd92e198bbcf17f660e03076fd2938f5a4bbb2","896","53209","17","None","translation, token-classification, parsing","Dataset Card for Asian Language Treebank (ALT) Dataset Summary The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration fo","None","bn, en, fil, hi, id, ja, km, lo, ms, my, th, vi, zh","multi","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","parsing"
+"turing-motors/Cauldron-JA","turing-motors","2024-08-05 02:20:03","2024-10-24 02:57:55+00:00","06c65c0ffab0c5b98a1c49e296bebfb035c4408e","6442","53198","6","task_categories:visual-question-answering, language:ja, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, modality:image, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2405.02246, arxiv:1603.07396, arxiv:2206.01718, arxiv:2208.05358, arxiv:1612.06890, arxiv:2310.00367, arxiv:1710.07300, arxiv:2312.12241, arxiv:1912.03098, arxiv:2211.08545, arxiv:2306.05425, arxiv:1709.00103, arxiv:2003.12462, arxiv:1612.00837, arxiv:2205.00363, arxiv:2403.09029, region:us, image, text","visual-question-answering","Dataset Card for The Cauldron-JA Dataset description The Cauldron-JA is a Vision Language Model dataset that translates 'The Cauldron' into Japanese using the DeepL API. The Cauldron is a massive coll","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"ibm/SocialStigmaQA-JA","ibm","2024-06-05 01:14:25","2025-01-09 07:20:55+00:00","0779dd6349c5aabca15697f77efa44ef3d277d10","99","49843","4","task_categories:question-answering, language:ja, license:cdla-permissive-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","SocialStigmaQA-JA Dataset Card It is crucial to test the social bias of large language models. SocialStigmaQA dataset is meant to capture the amplification of social bias, via stigmas, in generative l","None","ja","mono","None","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"elyza/ELYZA-tasks-100","elyza","2023-08-28 09:01:44","2023-12-27 09:17:36+00:00","240fc800c4695d86ce0f32a713e199cd2ba66ab6","1924","45542","92","None","text2text-generation","ELYZA-tasks-100: 日本語instructionモデル評価データセット Data Description 本データセットはinstruction-tuningを行ったモデルの評価用データセットです。詳細は リリースのnote記事 を参照してください。 特徴: 複雑な指示・タスクを含む100件の日本語データです。 役に立つAIアシスタントとして、丁寧な出力が求められます。 全てのデータ","None","ja","mono","n|<|1|K","None","False","False","False","cc-by-sa-4.0","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","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:hr, 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: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 - 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"
+"pasinit/xlwic","pasinit","2022-03-02 23:29:22","2022-10-25 09:54:22+00:00","cca3cfb747db5bf97b95126ec79d5b7d743f9654","353","41740","5","None","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"
+"Helsinki-NLP/opus_gnome","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:04:29+00:00","8362267db12f636640003de2381eff67277e6657","299","40448","1","None","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"
+"Coldog2333/JMedBench","Coldog2333","2024-08-17 11:12:54","2025-01-19 13:12:11+00:00","fe772d4fb76c11a4b24e06a2d06c72a7e3e32ef5","1152","40038","5","None","text-classification, question-answering, translation, sentence-similarity","Maintainers Junfeng Jiang@Aizawa Lab: jiangjf (at) is.s.u-tokyo.ac.jp Jiahao Huang@Aizawa Lab: jiahao-huang (at) g.ecc.u-tokyo.ac.jp If you find any error in this benchmark or want to contribute to th","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","2303.18027","None","None"
+"copenlu/answerable_tydiqa","copenlu","2022-08-16 11:31:34","2024-07-12 11:53:23+00:00","4413588cd5f38fddbf047f87a7167a0a9de5369a","107","39407","8","task_categories:question-answering, task_ids:extractive-qa, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:extended|wikipedia, language:en, language:ar, language:bn, language:fi, language:id, language:ja, language:sw, language:ko, language:ru, language:te, language:th, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, extractive-qa","Dataset Card for ""answerable-tydiqa"" Dataset Summary TyDi QA is a question answering dataset covering 11 typologically diverse languages. Answerable TyDi QA is an extension of the GoldP subtask of the","None","en, ar, bn, fi, id, ja, sw, ko, ru, te, th","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","extractive-qa"
+"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"
+"kumapo/JAQKET","kumapo","2023-06-21 13:04:38","2023-10-09 06:44:28+00:00","83338341033c27c300af8ea810c01cc7087ed077","2607","37901","5","None","multiple-choice, question-answering","JAQKET: JApanese Questions on Knowledge of EnTitie","@InProceedings{Kurihara_nlp2020, author = ""鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也"", title = ""JAQKET: クイズを題材にした日本語 QA データセットの構築"", booktitle = ""言語処理学会第26回年次大会"", year = ""2020"", url = ""https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"" note= ""in Japanese""","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Babelscape/SREDFM","Babelscape","2023-06-13 18:35:19","2023-06-20 07:33:28+00:00","2732d2834e12e36510aeb2a468163ea2642d55db","2928","37479","14","task_categories:token-classification, language:ar, language:ca, language:de, language:el, language:en, language:es, language:fr, language:hi, language:it, language:ja, language:ko, language:nl, language:pl, language:pt, language:ru, language:sv, language:vi, language:zh, license:cc-by-sa-4.0, size_categories:10M<n<100M, 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, 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"
+"ryo0634/bsd_ja_en","ryo0634","2022-03-02 23:29:22","2024-01-11 07:36:44+00:00","ed6539dc16c18c481ff3574376b79d7a83a57fb2","186","35435","12","task_categories:translation, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:translation, source_datasets:original, language:en, language:ja, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, business-conversations-translation","translation","Dataset Card for Business Scene Dialogue Dataset Summary This is the Business Scene Dialogue (BSD) dataset, a Japanese-English parallel corpus containing written conversations in various business scen","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","business-scene-dialogue","False","False","False","cc-by-nc-sa-4.0","None","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","task_categories:feature-extraction, task_categories:sentence-similarity, language:af, language:ar, language:ast, language:az, language:be, language:bg, language:bn, language:br, language:ca, language:ceb, language:cs, language:da, language:de, 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:ha, language:he, language:hi, language:hr, language:hu, language:id, language:ig, language:ilo, language:is, language:it, language:ja, language:jv, language:ko, language:la, language:lb, language:lt, language:lv, language:mg, language:mk, language:ml, language:mr, language:ms, language:ne, language:nl, language:no, language:oc, language:or, language:pl, language:pt, language:ro, language:ru, language:sd, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:ta, language:tl, language:tr, language:uk, language:ur, language:vi, language:xh, language:yi, language:zh, size_categories:1B<n<10B, 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 - 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"
+"hpprc/emb","hpprc","2024-04-15 14:12:27","2024-09-13 01:51:47+00:00","7a1930877b27fc5b1bc5138a5c6593f37a04823e","2268","32430","11","None","None","still WIP Information Name Type License (根拠) MMARCO Retrieval Apache 2.0 (?) Mr. TyDi Retrieval Apache 2.0 MIRACL Retrieval Apache 2.0 JaGovFaqs QA CC-BY-4.0 Auto Wiki QA QA & Retrieval CC-BY-SA-4.0 A","None","ja","mono","None","None","False","False","False","other","2409.07737","None","None"
+"conceptnet5/conceptnet5","conceptnet5","2022-03-02 23:29:22","2024-02-08 12:07:58+00:00","075e1c601ff242f9760ca85cf68b5ec41963d4e6","264","31397","22","task_categories:text-classification, task_ids:multi-class-classification, annotations_creators:crowdsourced, language_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:original, language:de, language:en, language:es, language:fr, language:it, language:ja, language:nl, language:pt, language:ru, language:zh, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1612.03975, region:us","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","conceptnet","False","False","False","cc-by-4.0","1612.03975","None","multi-class-classification"
+"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"
+"occiglot/tokenizer-wiki-bench","occiglot","2024-03-13 14:49:07","2024-04-23 21:00:00+00:00","56a5ea2bfc6a6445e9e3ed58130ef1fb2759e6c6","4761","27245","5","None","None","Multilingual Tokenizer Benchmark This dataset includes pre-processed wikipedia data for tokenizer evaluation in 45 languages. We provide more information on the evaluation task in general this blogpos","None","af, ar, bg, ca, cs, da, de, el, en, es, et, eu, fa, fi, fr, ga, he, hi, hr, hu, hy, id, it, ja, ko, lt, lv, mr, nl, no, pl, pt, ro, ru, sa, sk, sl, sr, sv, ta, te, tr, uk, ur, vi","multi","None","None","False","False","False","mit","2012.15613","None","None"
+"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"
+"severo/flores_101","severo","2023-06-20 21:40:23","2022-10-27 08:37:36+00:00","bc58ae43b22607b3e1e2bf3ae1bc5cb053495abb","5534","24646","2","None","text-generation, translation","One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage","@inproceedings{, title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={ Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\\'{a}n, Francisco and Fan, Angela }, year={2021} }","af, am, ar, hy, as, ast, az, be, bn, bs, bg, my, ca, ceb, zho, hr, cs, da, nl, en, et, tl, fi, fr, ff, gl, lg, ka, de, el, gu, ha, he, hi, hu, is, ig, id, ga, it, ja, jv, kea, kam, kn, kk, km, ko, ky, lo, lv, ln, lt, luo, lb, mk, ms, ml, mt, mi, mr, mn, ne, ns, no, ny, oc, or, om, ps, fa, pl, pt, pa, ro, ru, sr, sn, sd, sk, sl, so, ku, es, sw, sv, tg, ta, te, th, tr, uk, umb, ur, uz, vi, cy, wo, xh, yo, zu","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-sa-4.0","2106.03193","None","None"
+"mkshing/xlsum_ja","mkshing","2023-06-16 04:15:41","2023-06-20 23:28:48+00:00","b02b5e36500f816754e48f7607eef4b8b4e80087","695","23474","6","None","summarization, text-classification","This is the filtered Japanese subset of XL-Sum followed by PaLM 2 filters 15-gram overlap * code: https://gist.github.com/mkshing/d6371cbfdd50d4f352cee247fd4dd86a number of examples train: 4215 (befor","None","ja","mono","None","None","False","False","False","cc-by-nc-sa-4.0","2305.10403","None","None"
+"aiana94/polynews","aiana94","2024-05-17 07:39:20","2024-06-21 08:37:54+00:00","effaaf00db128feefaf0c83b79a5e0f29d4d0d04","311","23355","6","None","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"
+"stanford-oval/wikipedia","stanford-oval","2024-08-23 07:39:01","2024-11-04 22:50:33+00:00","519d22104786a46b7320f2c9196e2ce068666e2c","3850","21286","7","task_categories:text-retrieval, task_categories:text-generation, language:en, language:de, language:it, language:pt, language:fa, language:fr, language:ja, language:es, language:ru, language:zh, size_categories:100M<n<1B, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.14292, arxiv:2406.00562, region:us","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"
+"SkelterLabsInc/JaQuAD","SkelterLabsInc","2022-03-02 23:29:22","2022-10-25 09:06:40+00:00","05600ff310a0970823e70f82f428893b85c71ffe","299","21282","11","None","question-answering, extractive-qa","Dataset Card for JaQuAD Dataset Summary Japanese Question Answering Dataset (JaQuAD), released in 2022, is a human-annotated dataset created for Japanese Machine Reading Comprehension. JaQuAD is devel","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","None","None","extractive-qa"
+"RicardoRei/wmt-da-human-evaluation","RicardoRei","2023-02-16 18:49:07","2023-02-17 10:41:18+00:00","301de385bf05b0c00a8f4be74965e186164dd425","133","20489","3","None","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"
+"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"
+"llm-book/wrime-sentiment","llm-book","2023-07-29 06:38:26","2023-10-06 00:56:38+00:00","fb9b134c9e55912b1681a2460c2b6a28fc43f0db","436","19286","10","task_categories:text-classification, language:ja, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us","text-classification","Dataset Card for llm-book/wrime-sentiment 日本語の感情分析データセット WRIME を、ポジティブ/ネガティブの二値分類のタスクに加工したデータセットです。 GitHub リポジトリ ids-cv/wrime で公開されているデータセットを利用しています。 Avg. Readers_Sentiment の値が0より大きいものをポジティブ、0より小さいものを","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","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","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, annotations_creators:crowdsourced, language_creators:machinetranslation, source_datasets:multi_nli, source_datasets:anli, source_datasets:fever, source_datasets:lingnli, source_datasets:alisawuffles/WANLI, language:multilingual, language:zh, language:ja, language:ar, language:ko, language:de, language:fr, language:es, language:pt, language:hi, language:id, language:it, language:tr, language:ru, language:bn, language:ur, language:mr, language:ta, language:vi, language:fa, language:pl, language:uk, language:nl, language:sv, language:he, language:sw, language:ps, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2104.07179, region:us","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"
+"fujiki/japanese_hh-rlhf-49k","fujiki","2023-05-28 05:55:53","2023-05-28 06:08:04+00:00","e54073cd64d675fbbef9b0a0743f3d6d63a3ca06","69","18050","12","language:ja, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This is a little bit different version of kunishou/hh-rlhf-49k-ja without ng_translation == 1 examples. Please also refer to the original dataset kunishou/hh-rlhf-49k-ja.","None","ja","mono","None","None","False","False","False","mit","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"
+"wikimedia/wit_base","wikimedia","2022-05-02 16:08:58","2022-11-04 15:09:33+00:00","ff6d4fb32fd566d3a1fa20e946cba3234179465e","1479","16757","59","None","image-to-text, text-retrieval, image-captioning","Dataset Card for WIT Dataset Summary Wikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset. From the official blog post: The core training data i","None","af, an, ar, arz, ast, az, azb, ba, bar, be, bg, bn, br, bs, ca, ce, ceb, ckb, cs, cv, cy, da, de, el, en, eo, es, et, eu, fa, fi, fil, fr, fy, ga, gl, hi, hr, hsb, ht, hu, hy, ia, id, io, is, it, iw, ja, jv, ka, kk, kn, ko, la, lah, lb, lmo, lt, lv, mg, mk, ml, mn, mr, ms, my, nan, nds, ne, nl, nn, no, nv, oc, pa, pl, pt, qu, ro, ru, sco, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tr, tt, uk, ur, uz, vec, vi, vo, war, xmf, yue, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","1512.03385","None","image-captioning"
+"cis-lmu/GlotCC-V1","cis-lmu","2024-05-29 14:53:46","2024-11-01 11:09:18+00:00","9ad140b6be3ac7b539606a2b4809b49d122823de","1703","16659","49","multilinguality:multilingual, language:aau, language:aaz, language:ab, language:abk, language:abq, language:abs, language:abt, language:abx, language:aby, language:ace, language:acf, language:ach, language:acm, language:acn, language:acr, language:acu, language:ada, language:adi, language:adj, language:ady, language:adz, language:aeb, language:aer, language:aey, language:af, language:afr, language:agd, language:agm, language:agn, language:agr, language:agt, language:agu, language:agw, language:aii, language:aim, language:ain, language:ajp, language:ajz, language:aka, language:akb, language:akh, language:aln, language:alp, language:alq, language:als, language:alt, language:aly, language:am, language:amf, language:amh, language:ami, language:amm, language:amu, language:an, language:ang, language:ann, language:anp, language:any, language:aoi, language:aoj, language:aom, language:aoz, language:apb, language:apc, language:ape, language:apr, language:apt, language:apz, language:ara, language:arb, language:arg, language:arl, language:arn, language:arq, language:ars, language:ary, language:arz, language:as, language:asm, language:aso, language:ast, language:ata, language:atb, language:atd, language:atj, language:atq, language:att, language:aui, language:auy, language:av, language:ava, language:avk, language:avt, language:avu, language:awa, language:awx, language:aym, language:ayr, language:azb, language:aze, language:azg, language:azj, language:ba, language:bak, language:bal, language:bam, language:ban, language:bar, language:bas, language:bba, language:bbb, language:bbc, language:bbr, language:bcc, language:bch, language:bci, language:bcl, language:bdd, language:bdh, language:bdq, language:be, language:bef, language:bel, language:bem, language:ben, language:bew, language:bg, language:bgr, language:bgs, language:bgz, language:bhg, language:bhl, language:bho, language:bhw, language:bi, language:big, language:bik, language:bim, language:bin, language:bis, language:biu, language:bjn, language:bjp, language:bjr, language:bkd, language:bkl, language:blk, language:blw, language:blz, language:bm, language:bmh, language:bmu, language:bn, language:bnc, language:bno, language:bnp, language:bo, language:boa, language:bod, language:boj, language:bom, language:bon, language:bos, language:bpr, language:bps, language:bpy, language:bqc, language:bqj, language:br, language:bre, language:brh, language:bru, language:brx, language:bs, language:bsp, language:btd, language:bts, language:btx, language:bua, language:bug, language:buk, language:bul, language:bum, language:bus, language:bvr, language:bwd, language:bxh, language:bxr, language:byr, language:bzd, language:bzi, language:bzj, language:ca, language:caa, language:cab, language:cac, language:cag, language:cak, language:cao, language:car, language:cat, language:cax, language:cbi, language:cbk, language:cbr, language:cbs, language:cbt, language:cbu, language:ccp, language:ce, language:ceb, language:ces, language:cfm, language:cgc, language:ch, language:cha, language:chd, language:che, language:chk, language:chm, language:cho, language:chu, language:chv, language:chw, language:cjk, language:cjv, language:ckb, language:ckm, language:ckt, language:clu, language:cmn, language:cmo, language:cmr, language:cnh, language:cni, language:cnk, language:cnr, language:cnw, language:co, language:con, language:cop, language:cor, language:cos, language:cot, language:cpb, language:cpc, language:cpy, language:cre, language:crh, language:crk, language:crl, language:crn, language:crs, language:crx, language:cs, language:csb, language:csw, language:csy, language:cta, language:ctd, language:ctu, language:cu, language:cub, language:cuk, language:cut, language:cv, language:cwe, language:cwt, language:cy, language:cya, language:cym, language:czt, language:da, language:dad, language:dag, language:dah, language:dak, language:dan, language:dao, language:dar, language:ddg, language:de, language:ded, language:deu, language:dga, language:dgc, language:dgz, language:dhg, language:dhm, language:dhv, language:did, language:dik, language:din, language:diq, language:dis, language:div, language:dje, language:djk, language:djr, language:dng, language:dob, language:doi, language:dop, language:dru, language:dsb, language:dtp, language:dts, language:dua, language:dv, language:dwr, language:dww, language:dyi, language:dyo, language:dyu, language:dz, language:dzo, language:ee, language:efi, language:ekk, language:eko, language:el, language:ell, language:emi, language:eml, language:emp, language:en, language:eng, language:enl, language:enm, language:enq, language:eo, language:epo, language:eri, language:es, language:esk, language:est, language:esu, language:etr, language:eu, language:eus, language:eve, language:ewe, language:ewo, language:ext, language:eza, language:fa, language:faa, language:fai, language:fao, language:fas, language:fat, language:ffm, language:fi, language:fij, language:fil, language:fin, language:fit, language:fj, language:fkv, language:fo, language:fon, language:for, language:fr, language:fra, language:fro, language:frp, language:frr, language:fry, language:fub, language:fud, language:fue, language:fuf, language:fuh, language:ful, language:fur, language:fuv, language:fy, language:ga, language:gaa, language:gag, language:gah, language:gai, language:gam, language:gaw, language:gaz, language:gbi, language:gcf, language:gcr, language:gd, language:gdg, language:gdn, language:geb, language:gfk, language:ghs, language:gil, language:gjn, language:gl, language:gla, language:gle, language:glg, language:glk, language:glv, language:gmh, language:gmv, language:gnb, language:gng, language:gnn, language:gnw, language:goh, language:gom, language:gon, language:gor, language:gos, language:got, language:grc, language:grn, language:gsw, language:gu, language:gub, language:guc, language:gug, language:guj, language:gul, language:gum, language:gur, language:guw, language:gux, language:guz, language:gv, language:gvn, language:gwi, language:gwr, language:gym, language:gyr, language:ha, language:hac, language:hag, language:hak, language:hat, language:hau, language:haw, language:hbo, language:hbs, language:hch, language:he, language:heb, language:heg, language:her, language:hi, language:hif, language:hil, language:hin, language:hla, language:hmn, language:hmo, language:hmr, language:hne, language:hnj, language:hns, language:ho, language:hot, language:hr, language:hra, language:hrv, language:hsb, language:ht, language:hto, language:hu, language:hub, language:hui, language:hun, language:hus, language:huu, language:hvn, language:hwc, language:hy, language:hye, language:hyw, language:hz, language:ia, language:ian, language:iba, language:ibg, language:ibo, language:icr, language:id, language:ido, language:idu, language:ie, language:ify, language:ig, language:ike, language:ikk, language:ikt, language:iku, language:ikw, language:ile, language:ilo, language:ina, language:inb, language:ind, language:inh, language:ino, language:io, language:iou, language:ipi, language:ipk, language:iqw, language:is, language:isd, language:ish, language:isl, language:iso, language:it, language:ita, language:itv, language:ium, language:ivb, language:ivv, language:iws, language:ixl, language:ja, language:jac, language:jae, language:jam, language:jav, language:jbo, language:jiv, language:jpn, language:jra, language:jrb, language:jv, language:jvn, language:ka, language:kaa, language:kab, language:kac, language:kak, language:kal, language:kam, language:kan, language:kaq, language:kas, language:kat, language:kau, language:kaz, language:kbc, language:kbd, language:kbh, language:kbm, language:kbo, language:kbp, language:kbq, language:kby, language:kca, language:kcg, language:kck, language:kdc, language:kde, language:kdi, language:kdl, language:kea, language:kek, language:kew, language:kgk, language:kgr, language:kha, language:khk, language:khm, language:khz, language:ki, language:kij, language:kik, language:kin, language:kir, language:kiu, language:kix, language:kj, language:kjb, language:kjh, language:kjs, language:kk, language:kkc, language:kkl, language:kl, language:kln, language:klt, language:klv, language:km, language:kmb, language:kmd, language:kmg, language:kmh, language:kmk, language:kmm, language:kmr, language:kms, language:kmu, language:kmy, language:kn, language:knc, language:kne, language:kng, language:knv, language:ko, language:kog, language:koi, language:kok, language:kom, language:kon, language:kor, language:kos, language:kpe, language:kpf, language:kpg, language:kpj, language:kpo, language:kpr, language:kpv, language:kpw, language:kpx, language:kqc, language:kqf, language:kqn, language:kqw, language:krc, language:kri, language:krl, language:kru, language:ks, language:ksd, language:ksh, language:ksr, language:ksw, language:ktm, language:kto, language:ktu, language:ktz, language:kua, language:kum, language:kup, language:kur, language:kvg, language:kw, language:kwf, language:kwj, language:kwn, language:kwy, language:ky, language:kyc, language:kyg, language:kyq, language:kyu, language:kyz, language:kze, language:kzf, language:kzj, language:la, language:lad, language:lah, language:laj, language:lam, language:lao, language:lat, language:lav, language:lb, language:lbb, language:lbe, language:lbj, language:lbk, language:lcm, language:leu, language:lew, language:lex, language:lez, language:lfn, language:lg, language:lgg, language:lgl, language:lhi, language:li, language:lia, language:lid, language:lif, language:lij, language:lim, language:lin, language:lis, language:lit, language:liv, language:ljp, language:lki, language:lld, language:llg, language:lmk, language:lmo, language:ln, language:lnd, language:lo, language:loz, language:lrc, language:lsi, language:lt, language:ltg, language:ltz, language:lu, language:lua, language:lub, language:lud, language:lue, language:lug, language:lun, language:luo, language:lus, language:luy, language:lvs, language:lwg, language:lww, language:lzh, language:maa, language:mad, language:mag, language:mah, language:mai, language:mak, language:mal, language:mam, language:man, language:mar, language:mas, language:mau, language:maw, language:maz, language:mbb, language:mbf, language:mbh, language:mbs, language:mbt, language:mcd, language:mck, language:mcq, language:mdf, language:mdy, language:med, language:mej, language:mek, language:men, language:met, language:meu, language:mfe, language:mfi, language:mfq, language:mfy, language:mge, language:mgh, language:mh, language:mhi, language:mhr, language:mhw, language:mhx, language:mi, language:mic, language:mie, language:mif, language:min, language:mip, language:miq, language:mjc, language:mjw, language:mk, language:mkd, language:mkn, language:ml, language:mlg, language:mlh, language:mlt, language:mmo, language:mmx, language:mna, language:mnb, language:mni, language:mnk, language:mns, language:mnw, language:moc, language:mog, language:moh, language:mon, language:mos, language:mph, language:mpm, language:mpp, language:mps, language:mpx, language:mqj, language:mqy, language:mr, language:mri, language:mrj, language:mrw, language:msa, language:msb, language:msc, language:msk, language:msy, language:mt, language:mtg, language:mti, language:muh, language:mui, language:mup, language:mur, language:mux, language:mva, language:mvn, language:mvp, language:mwc, language:mwf, language:mwl, language:mwm, language:mwn, language:mwp, language:mwv, language:mww, language:mxb, language:mxt, language:mxv, language:mxx, language:my, language:mya, language:myb, language:myk, language:myu, language:myv, language:myw, language:myy, language:mza, language:mzh, language:mzn, language:naf, language:nah, language:nak, language:nan, language:nap, language:naq, language:nas, language:nav, language:nb, language:nbc, language:nbe, language:nbl, language:nbu, language:nca, language:nch, language:ncj, language:ncx, language:nd, language:ndc, language:nde, language:ndo, language:nds, language:nep, language:new, language:nfa, language:ng, language:ngl, language:ngu, language:nhe, language:nhg, language:nhi, language:nho, language:nhr, language:nhw, language:nhx, language:nia, language:nif, language:nii, language:nij, language:nin, language:niu, language:njm, language:njn, language:njz, language:nl, language:nld, language:nlg, language:nmf, language:nmz, language:nn, language:nnb, language:nnh, language:nno, language:nnp, language:nnw, language:nob, language:nog, language:non, language:nop, language:nor, language:not, language:nov, language:nph, language:npi, language:npy, language:nqo, language:nr, language:nrm, language:nsa, language:nsn, language:nso, language:nss, language:nst, language:nsu, language:ntp, language:ntu, language:nuj, language:nus, language:nuy, language:nv, language:nvm, language:nwi, language:ny, language:nya, language:nyk, language:nyn, language:nyu, language:nyy, language:nzi, language:nzm, language:obo, language:oc, language:oci, language:ojb, language:oji, language:okv, language:olo, language:omb, language:omw, language:ong, language:opm, language:ori, language:orm, language:orv, language:ory, language:os, language:oss, language:ota, language:otd, language:ote, language:ots, language:otw, language:pa, language:pad, language:pag, language:pam, language:pan, language:pao, language:pap, language:pau, language:pbb, language:pbt, language:pcd, language:pck, language:pcm, language:pdc, language:pdt, language:pes, language:pfl, language:pib, language:pis, language:pjt, language:pl, language:plg, language:pls, language:plt, language:plu, language:plw, language:pma, language:pmf, language:pms, language:pmx, language:pnb, language:pnt, language:poe, language:poh, language:poi, language:pol, language:pon, language:por, language:pot, language:ppk, language:ppo, language:pps, language:prg, language:prs, language:pt, language:ptp, language:ptu, language:pui, language:pus, language:pwg, language:pwn, language:qub, language:quc, language:que, language:quf, language:qug, language:quh, language:qul, language:qup, language:quw, language:quy, language:quz, language:qvc, language:qvh, language:qvi, language:qvn, language:qvs, language:qvw, language:qxl, language:qxn, language:qxo, language:rad, language:raj, language:rap, language:rar, language:raw, language:rcf, language:rej, language:rgu, language:rhg, language:ria, language:rif, language:rjs, language:rkb, language:rm, language:rmc, language:rml, language:rmn, language:rmo, language:rmq, language:rmy, language:rn, language:rnl, language:ro, language:roh, language:rom, language:ron, language:roo, language:rop, language:row, language:rro, language:rtm, language:ru, language:rue, language:ruf, language:rug, language:run, language:rup, language:rus, language:rw, language:rwo, language:sa, language:sab, language:sag, language:sah, language:san, language:sas, language:sat, language:sbd, language:sbe, language:sc, language:scn, language:sco, language:sd, language:sda, language:sdc, language:sdh, language:se, language:seh, language:ses, language:sg, language:sgb, language:sgc, language:sgh, language:sgs, language:sgw, language:shi, language:shk, language:shn, language:shp, language:shu, language:si, language:sid, language:sil, language:sim, language:sin, language:sk, language:skg, language:skr, language:sl, language:slk, language:sll, language:slv, language:sm, language:sma, language:sme, language:smj, language:smk, language:sml, language:smn, language:smo, language:sms, language:sn, language:sna, language:snc, language:snd, language:snf, language:snn, language:snp, language:snw, language:sny, language:so, language:som, language:sop, language:soq, language:sot, language:spa, language:spl, language:spm, language:spp, language:sps, language:sqi, language:sr, language:srd, language:srm, language:srn, language:srp, language:srr, language:ss, language:ssd, language:ssg, language:ssw, language:ssx, language:st, language:stq, language:su, language:suc, language:sue, language:suk, language:sun, language:sus, language:suz, language:sv, language:swa, language:swb, language:swc, language:swe, language:swg, language:swh, language:swp, language:sxb, language:sxn, language:sxw, language:syb, language:syc, language:syl, language:syr, language:szl, language:szy, language:ta, language:tab, language:tah, language:taj, language:tam, language:tap, language:taq, language:tar, language:tat, language:taw, language:tay, language:tbg, language:tbo, language:tbw, language:tby, language:tbz, language:tca, language:tcs, language:tcy, language:tcz, language:tdt, language:tdx, language:te, language:tel, language:teo, language:tet, language:tfr, language:tg, language:tgk, language:tgo, language:tgp, language:th, language:tha, language:thl, language:ti, language:tif, language:tig, language:tih, language:tir, language:tiv, language:tk, language:tke, language:tkl, language:tkr, language:tku, language:tlb, language:tlf, language:tlh, language:tll, language:tly, language:tmd, language:tmh, language:tn, language:tnc, language:tnk, language:tnn, language:tnp, language:tnr, language:to, language:tob, language:tod, language:tog, language:toi, language:toj, language:tok, language:ton, language:too, language:top, language:tos, language:tpi, language:tr, language:trc, language:trn, language:trp, language:trq, language:trv, language:ts, language:tsg, language:tsn, language:tso, language:tsz, language:tt, language:ttc, language:tte, language:ttq, language:tuc, language:tuf, language:tui, language:tuk, language:tum, language:tur, language:tvk, language:tvl, language:tw, language:twi, language:twu, language:txu, language:ty, language:tyv, language:tzh, language:tzj, language:tzm, language:tzo, language:ubr, language:ubu, language:udm, language:ug, language:uig, language:uk, language:ukr, language:umb, language:und, language:upv, language:ur, language:urd, language:urh, language:usa, language:usp, language:uvh, language:uvl, language:uzb, language:uzn, language:uzs, language:vap, language:ve, language:vec, language:ven, language:vep, language:vi, language:vid, language:vie, language:vls, language:vmw, language:vmy, language:vo, language:vol, language:vro, language:vun, language:wa, language:waj, language:wal, language:war, language:wat, language:way, language:wbm, language:wbp, language:wed, language:wes, language:wln, language:wls, language:wlv, language:wlx, language:wmw, language:wnc, language:wnu, language:wo, language:wol, language:wos, language:wrs, language:wsg, language:wsk, language:wuu, language:wuv, language:xal, language:xbi, language:xh, language:xho, language:xla, language:xmf, language:xmm, language:xmv, language:xog, language:xon, language:xsm, language:xsr, language:xtd, language:xtm, language:xtn, language:yaa, language:yal, language:yao, language:yap, language:yby, language:ydd, language:yid, language:yka, language:yle, language:yli, language:yml, language:yo, language:yom, language:yon, language:yor, language:yrk, language:yrl, language:yss, language:yua, language:yue, language:yuj, language:yut, language:yuw, language:yva, language:zac, language:zai, language:zam, language:zao, language:zap, language:zas, language:zat, language:zdj, language:zea, language:zgh, language:zha, language:zho, language:zia, language:zom, language:zos, language:zpm, language:zpo, language:zpt, language:zpu, language:zsm, language:zu, language:zul, language:zyb, language:zyp, language:zza, license:cc0-1.0, size_categories:1B<n<10B, modality:tabular, modality:text, arxiv:2410.23825, region:us, multilingual","None","Dataset Summary GlotCC-V1.0 is a document-level, general domain dataset derived from CommonCrawl, covering more than 1000 languages.It is built using the GlotLID language identification and Ungoliant ","None","aau, aaz, ab, abk, abq, abs, abt, abx, aby, ace, acf, ach, acm, acn, acr, acu, ada, adi, adj, ady, adz, aeb, aer, aey, af, afr, agd, agm, agn, agr, agt, agu, agw, aii, aim, ain, ajp, ajz, aka, akb, akh, aln, alp, alq, als, alt, aly, am, amf, amh, ami, amm, amu, an, ang, ann, anp, any, aoi, aoj, aom, aoz, apb, apc, ape, apr, apt, apz, ara, arb, arg, arl, arn, arq, ars, ary, arz, as, asm, aso, ast, ata, atb, atd, atj, atq, att, aui, auy, av, ava, avk, avt, avu, awa, awx, aym, ayr, azb, aze, azg, azj, ba, bak, bal, bam, ban, bar, bas, bba, bbb, bbc, bbr, bcc, bch, bci, bcl, bdd, bdh, bdq, be, bef, bel, bem, ben, bew, bg, bgr, bgs, bgz, bhg, bhl, bho, bhw, bi, big, bik, bim, bin, bis, biu, bjn, bjp, bjr, bkd, bkl, blk, blw, blz, bm, bmh, bmu, bn, bnc, bno, bnp, bo, boa, bod, boj, bom, bon, bos, bpr, bps, bpy, bqc, bqj, br, bre, brh, bru, brx, bs, bsp, btd, bts, btx, bua, bug, buk, bul, bum, bus, bvr, bwd, bxh, bxr, byr, bzd, bzi, bzj, ca, caa, cab, cac, cag, cak, cao, car, cat, cax, cbi, cbk, cbr, cbs, cbt, cbu, ccp, ce, ceb, ces, cfm, cgc, ch, cha, chd, che, chk, chm, cho, chu, chv, chw, cjk, cjv, ckb, ckm, ckt, clu, cmn, cmo, cmr, cnh, cni, cnk, cnr, cnw, co, con, cop, cor, cos, cot, cpb, cpc, cpy, cre, crh, crk, crl, crn, crs, crx, cs, csb, csw, csy, cta, ctd, ctu, cu, cub, cuk, cut, cv, cwe, cwt, cy, cya, cym, czt, da, dad, dag, dah, dak, dan, dao, dar, ddg, de, ded, deu, dga, dgc, dgz, dhg, dhm, dhv, did, dik, din, diq, dis, div, dje, djk, djr, dng, dob, doi, dop, dru, dsb, dtp, dts, dua, dv, dwr, dww, dyi, dyo, dyu, dz, dzo, ee, efi, ekk, eko, el, ell, emi, eml, emp, en, eng, enl, enm, enq, eo, epo, eri, es, esk, est, esu, etr, eu, eus, eve, ewe, ewo, ext, eza, fa, faa, fai, fao, fas, fat, ffm, fi, fij, fil, fin, fit, fj, fkv, fo, fon, for, fr, fra, fro, frp, frr, fry, fub, fud, fue, fuf, fuh, ful, fur, fuv, fy, ga, gaa, gag, gah, gai, gam, gaw, gaz, gbi, gcf, gcr, gd, gdg, gdn, geb, gfk, ghs, gil, gjn, gl, gla, gle, glg, glk, glv, gmh, gmv, gnb, gng, gnn, gnw, goh, gom, gon, gor, gos, got, grc, grn, gsw, gu, gub, guc, gug, guj, gul, gum, gur, guw, gux, guz, gv, gvn, gwi, gwr, gym, gyr, ha, hac, hag, hak, hat, hau, haw, hbo, hbs, hch, he, heb, heg, her, hi, hif, hil, hin, hla, hmn, hmo, hmr, hne, hnj, hns, ho, hot, hr, hra, hrv, hsb, ht, hto, hu, hub, hui, hun, hus, huu, hvn, hwc, hy, hye, hyw, hz, ia, ian, iba, ibg, ibo, icr, id, ido, idu, ie, ify, ig, ike, ikk, ikt, iku, ikw, ile, ilo, ina, inb, ind, inh, ino, io, iou, ipi, ipk, iqw, is, isd, ish, isl, iso, it, ita, itv, ium, ivb, ivv, iws, ixl, ja, jac, jae, jam, jav, jbo, jiv, jpn, jra, jrb, jv, jvn, ka, kaa, kab, kac, kak, kal, kam, kan, kaq, kas, kat, kau, kaz, kbc, kbd, kbh, kbm, kbo, kbp, kbq, kby, kca, kcg, kck, kdc, kde, kdi, kdl, kea, kek, kew, kgk, kgr, kha, khk, khm, khz, ki, kij, kik, kin, kir, kiu, kix, kj, kjb, kjh, kjs, kk, kkc, kkl, kl, kln, klt, klv, km, kmb, kmd, kmg, kmh, kmk, kmm, kmr, kms, kmu, kmy, kn, knc, kne, kng, knv, ko, kog, koi, kok, kom, kon, kor, kos, kpe, kpf, kpg, kpj, kpo, kpr, kpv, kpw, kpx, kqc, kqf, kqn, kqw, krc, kri, krl, kru, ks, ksd, ksh, ksr, ksw, ktm, kto, ktu, ktz, kua, kum, kup, kur, kvg, kw, kwf, kwj, kwn, kwy, ky, kyc, kyg, kyq, kyu, kyz, kze, kzf, kzj, la, lad, lah, laj, lam, lao, lat, lav, lb, lbb, lbe, lbj, lbk, lcm, leu, lew, lex, lez, lfn, lg, lgg, lgl, lhi, li, lia, lid, lif, lij, lim, lin, lis, lit, liv, ljp, lki, lld, llg, lmk, lmo, ln, lnd, lo, loz, lrc, lsi, lt, ltg, ltz, lu, lua, lub, lud, lue, lug, lun, luo, lus, luy, lvs, lwg, lww, lzh, maa, mad, mag, mah, mai, mak, mal, mam, man, mar, mas, mau, maw, maz, mbb, mbf, mbh, mbs, mbt, mcd, mck, mcq, mdf, mdy, med, mej, mek, men, met, meu, mfe, mfi, mfq, mfy, mge, mgh, mh, mhi, mhr, mhw, mhx, mi, mic, mie, mif, min, mip, miq, mjc, mjw, mk, mkd, mkn, ml, mlg, mlh, mlt, mmo, mmx, mna, mnb, mni, mnk, mns, mnw, moc, mog, moh, mon, mos, mph, mpm, mpp, mps, mpx, mqj, mqy, mr, mri, mrj, mrw, msa, msb, msc, msk, msy, mt, mtg, mti, muh, mui, mup, mur, mux, mva, mvn, mvp, mwc, mwf, mwl, mwm, mwn, mwp, mwv, mww, mxb, mxt, mxv, mxx, my, mya, myb, myk, myu, myv, myw, myy, mza, mzh, mzn, naf, nah, nak, nan, nap, naq, nas, nav, nb, nbc, nbe, nbl, nbu, nca, nch, ncj, ncx, nd, ndc, nde, ndo, nds, nep, new, nfa, ng, ngl, ngu, nhe, nhg, nhi, nho, nhr, nhw, nhx, nia, nif, nii, nij, nin, niu, njm, njn, njz, nl, nld, nlg, nmf, nmz, nn, nnb, nnh, nno, nnp, nnw, nob, nog, non, nop, nor, not, nov, nph, npi, npy, nqo, nr, nrm, nsa, nsn, nso, nss, nst, nsu, ntp, ntu, nuj, nus, nuy, nv, nvm, nwi, ny, nya, nyk, nyn, nyu, nyy, nzi, nzm, obo, oc, oci, ojb, oji, okv, olo, omb, omw, ong, opm, ori, orm, orv, ory, os, oss, ota, otd, ote, ots, otw, pa, pad, pag, pam, pan, pao, pap, pau, pbb, pbt, pcd, pck, pcm, pdc, pdt, pes, pfl, pib, pis, pjt, pl, plg, pls, plt, plu, plw, pma, pmf, pms, pmx, pnb, pnt, poe, poh, poi, pol, pon, por, pot, ppk, ppo, pps, prg, prs, pt, ptp, ptu, pui, pus, pwg, pwn, qub, quc, que, quf, qug, quh, qul, qup, quw, quy, quz, qvc, qvh, qvi, qvn, qvs, qvw, qxl, qxn, qxo, rad, raj, rap, rar, raw, rcf, rej, rgu, rhg, ria, rif, rjs, rkb, rm, rmc, rml, rmn, rmo, rmq, rmy, rn, rnl, ro, roh, rom, ron, roo, rop, row, rro, rtm, ru, rue, ruf, rug, run, rup, rus, rw, rwo, sa, sab, sag, sah, san, sas, sat, sbd, sbe, sc, scn, sco, sd, sda, sdc, sdh, se, seh, ses, sg, sgb, sgc, sgh, sgs, sgw, shi, shk, shn, shp, shu, si, sid, sil, sim, sin, sk, skg, skr, sl, slk, sll, slv, sm, sma, sme, smj, smk, sml, smn, smo, sms, sn, sna, snc, snd, snf, snn, snp, snw, sny, so, som, sop, soq, sot, spa, spl, spm, spp, sps, sqi, sr, srd, srm, srn, srp, srr, ss, ssd, ssg, ssw, ssx, st, stq, su, suc, sue, suk, sun, sus, suz, sv, swa, swb, swc, swe, swg, swh, swp, sxb, sxn, sxw, syb, syc, syl, syr, szl, szy, ta, tab, tah, taj, tam, tap, taq, tar, tat, taw, tay, tbg, tbo, tbw, tby, tbz, tca, tcs, tcy, tcz, tdt, tdx, te, tel, teo, tet, tfr, tg, tgk, tgo, tgp, th, tha, thl, ti, tif, tig, tih, tir, tiv, tk, tke, tkl, tkr, tku, tlb, tlf, tlh, tll, tly, tmd, tmh, tn, tnc, tnk, tnn, tnp, tnr, to, tob, tod, tog, toi, toj, tok, ton, too, top, tos, tpi, tr, trc, trn, trp, trq, trv, ts, tsg, tsn, tso, tsz, tt, ttc, tte, ttq, tuc, tuf, tui, tuk, tum, tur, tvk, tvl, tw, twi, twu, txu, ty, tyv, tzh, tzj, tzm, tzo, ubr, ubu, udm, ug, uig, uk, ukr, umb, und, upv, ur, urd, urh, usa, usp, uvh, uvl, uzb, uzn, uzs, vap, ve, vec, ven, vep, vi, vid, vie, vls, vmw, vmy, vo, vol, vro, vun, wa, waj, wal, war, wat, way, wbm, wbp, wed, wes, wln, wls, wlv, wlx, wmw, wnc, wnu, wo, wol, wos, wrs, wsg, wsk, wuu, wuv, xal, xbi, xh, xho, xla, xmf, xmm, xmv, xog, xon, xsm, xsr, xtd, xtm, xtn, yaa, yal, yao, yap, yby, ydd, yid, yka, yle, yli, yml, yo, yom, yon, yor, yrk, yrl, yss, yua, yue, yuj, yut, yuw, yva, zac, zai, zam, zao, zap, zas, zat, zdj, zea, zgh, zha, zho, zia, zom, zos, zpm, zpo, zpt, zpu, zsm, zu, zul, zyb, zyp, zza","multi","None","None","False","False","False","cc0-1.0","2410.23825","None","None"
+"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"
+"lmqg/qg_jaquad","lmqg","2022-03-02 23:29:22","2022-12-02 18:51:27+00:00","59c7622ea1cf5ab07568ba8e161379129c79f7c1","424","16018","5","None","text-generation, language-modeling","[JaQuAD](https://github.com/SkelterLabsInc/JaQuAD) dataset for question generation (QG) task. The test set of the original data is not publicly released, so we randomly sampled test questions from the","@inproceedings{ushio-etal-2022-generative, title = ""{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration"", author = ""Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose"", booktitle = ""Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"", month = dec, year = ""2022"", address = ""Abu Dhabi, U.A.E."", publisher = ""Association for Computational Linguistics"", }","j, a","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","cc-by-sa-3.0","2210.03992","None","language-modeling"
+"joujiboi/japanese-anime-speech-v2","joujiboi","2024-06-26 14:18:01","2024-12-18 18:47:26+00:00","1dea3fb40e0b1a224c011bab3efcde55893bf742","1326","15911","66","None","automatic-speech-recognition","Japanese Anime Speech Dataset V2 日本語はこちら japanese-anime-speech-v2 is an audio-text dataset designed for training automatic speech recognition models. The dataset comprises 292,637 audio clips and thei","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","gpl","None","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","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:ar, language:az, language:be, language:bg, language:bn, 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:ga, language:gl, language:gu, language:hbs, language:he, language:hi, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:ka, language:kk, language:kn, language:ko, language:ky, language:la, language:lt, language:lv, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:my, language:nb, language:ne, language:nl, language:nn, language:pa, language:pl, language:ps, language:pt, language:ro, language:ru, language:si, language:sk, language:sl, language:so, language:sq, language:sv, language:sw, language:ta, language:te, language:th, language:tl, language:tr, language:tt, language:uk, language:ur, language:uz, language:vi, language:zh, license:cc-by-4.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"MartinThoma/wili_2018","MartinThoma","2022-03-02 23:29:22","2024-08-08 05:56:26+00:00","12c3a519f58673e4051a5d9db51c1fcd94a94c75","205","14544","4","None","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","None","False","False","False","odbl","None","None","None"
+"JosephusCheung/GuanacoDataset","JosephusCheung","2023-03-16 06:30:22","2024-04-15 20:25:44+00:00","188a1a800432729e2ee69e306066688817c1c738","90","13863","514","None","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"
+"joujiboi/japanese-anime-speech","joujiboi","2023-11-07 13:53:40","2024-09-06 18:19:39+00:00","315cbb50bc7a9fdfb2db26b019e1b2b0cffcf259","734","13712","113","None","automatic-speech-recognition","Japanese Anime Speech Dataset 日本語はこちら japanese-anime-speech is an audio-text dataset designed for the training of automatic speech recognition models. The dataset is comprised of thousands of audio cl","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc0-1.0","None","None","None"
+"izumi-lab/wikipedia-ja-20230720","izumi-lab","2023-07-28 02:11:33","2023-07-29 03:05:36+00:00","7f6448ad5b13bc63602dc609181f3a02499c4693","221","13473","12","language:ja, 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","None","Dataset Card for ""wikipedia-ja-20230720"" More Information needed","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","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","None","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"
+"xu-song/cc100-samples","xu-song","2024-03-05 08:19:01","2024-07-23 03:21:28+00:00","40da934ae231773e6ea04cbcd78271dde377d5ff","266","12728","4","None","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","None","False","False","False","unknown","None","None","language-modeling, masked-language-modeling"
+"ayymen/Weblate-Translations","ayymen","2024-01-07 22:29:01","2024-04-02 10:16:03+00:00","1d3d0d3fbe20cb39693118afbb17bbee6a3ad826","3554","12564","11","None","translation, text2text-generation","Dataset Card for Weblate Translations A dataset containing strings from projects hosted on Weblate and their translations into other languages. Please consider donating or contributing to Weblate if y","None","aa, ab, ace, ach, af, afh, aii, ain, ajp, ak, am, an, ang, anp, apc, ar, arn, ars, as, ast, ay, ayc, az, azb, ba, bar, bd, be, bem, ber, bg, bho, bm, bn, bo, bp, bqi, br, brx, bs, bul, by, ca, ce, ceb, ckb, cmn, cn, cnr, co, cr, crh, cs, csb, cv, cy, cz, da, de, dev, doi, dsb, dua, dum, dv, dz, eg, el, en, eng, enm, eo, es, et, eu, ext, fa, fi, fil, fo, fr, fra, frm, frp, frs, fu, fur, fy, ga, gb, gd, gl, glk, gmh, gn, gr, gsw, gu, guc, gug, gum, guw, gv, ha, haw, he, hi, hne, hr, hrx, hsb, ht, hu, hy, hz, ia, id, ie, ig, in, io, is, it, iw, ja, jam, jbo, ji, jp, jpn, jv, ka, kab, kg, kk, kl, km, kmr, kn, ko, kok, kr, krl, ks, ksh, ku, kw, ky, la, lb, lfn, lg, li, lk, ln, lo, lt, ltg, lv, lzh, mai, me, mg, mhr, mi, mjw, mk, ml, mn, mnc, mni, mnw, mo, mr, ms, mt, my, na, nah, nan, nap, nb, nds, ne, nl, nn, no, np, nqo, ny, oc, oj, om, or, os, ota, pa, pam, pap, pbb, peo, pk, pl, pms, pr, prg, ps, pt, pu, qt, rcf, rm, ro, rom, ru, rue, rw, ryu, sa, sah, sai, sat, sc, sco, sd, sdh, se, sh, shn, si, sk, skr, sl, sm, sma, sn, so, sq, sr, st, su, sv, sw, szl, ta, tam, te, tet, tg, th, ti, tk, tl, tlh, tn, to, tok, tr, trv, tt, tum, tw, ty, tzm, ua, udm, ug, uk, und, ur, us, uz, vec, vi, vls, wa, wae, wo, xh, yi, yo, yue, zgh, zh, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","other","None","None","None"
+"range3/wiki40b-ja","range3","2023-02-04 04:54:17","2023-02-04 05:44:21+00:00","9ceb37b7773d4e25acc49962aec3ae7dd2e8141b","124","12453","9","task_categories:text-generation, task_categories:fill-mask, language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","range3/wiki40b-ja This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code. このデータセットは、wiki40bデータセットの日本語データの","None","ja","mono","None","None","False","False","False","None","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"
+"ayymen/Pontoon-Translations","ayymen","2023-12-03 21:44:52","2024-01-19 21:32:07+00:00","7318c7cac7cdd5e2e7efc7e86d84256aaf54c8dc","228","11400","14","None","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"
+"neulab/odex","neulab","2023-01-06 14:30:00","2023-02-10 18:01:34+00:00","c3741a66c486b1a23beefdf6c75b06dba288d4f9","262","11394","9","task_categories:text2text-generation, task_categories:text-generation, language:en, language:es, language:ja, language:ru, license:cc-by-sa-4.0, size_categories:n<1K, modality:text, library:datasets, library:mlcroissant, arxiv:2212.10481, region:us","text2text-generation, text-generation","ODEX is an Open-Domain EXecution-based NL-to-Code generation data benchmark. It contains 945 samples with a total of 1,707 human-written test cases, covering intents in four different natural language","@article{wang2022execution, title={Execution-Based Evaluation for Open-Domain Code Generation}, author={Wang, Zhiruo and Zhou, Shuyan and Fried, Daniel and Neubig, Graham}, journal={arXiv preprint arXiv:2212.10481}, year={2022} }","en, es, ja, ru","multi","n|<|1|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"openai/MMMLU","openai","2024-09-13 16:37:19","2024-10-16 18:39:00+00:00","325a01dc3e173cac1578df94120499aaca2e2504","4050","11335","447","task_categories:question-answering, language:ar, language:bn, language:de, language:es, language:fr, language:hi, language:id, language:it, language:ja, language:ko, language:pt, language:sw, language:yo, language:zh, license:mit, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2009.03300, region:us","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"
+"GEM/surface_realisation_st_2020","GEM","2022-03-02 23:29:22","2022-10-24 15:30:30+00:00","f294829c59e8b1aa74aaf9f672e523f0b5c0536f","888","11002","1","None","table-to-text","Dataset Card for GEM/surface_realisation_st_2020 Link to Main Data Card You can find the main data card on the GEM Website. Dataset Summary This dataset was used as part of the multilingual surface re","None","ar, zh, en, fr, hi, id, ja, ko, pt, ru, es","multi","u|n|k|n|o|w|n","None","False","False","False","cc-by-2.5","None","None","None"
+"MichaelR207/MultiSimV2","MichaelR207","2024-10-01 05:16:49","2024-10-23 00:02:09+00:00","03aaf798070c845ce8e8980dc20e92f079028611","1242","10702","2","None","summarization, text2text-generation, text-generation","Dataset Card for MultiSim Benchmark Dataset Summary The MultiSim benchmark is a growing collection of text simplification datasets targeted at sentence simplification in several languages. Currently, ","None","en, fr, ru, ja, it, da, es, de, pt, sl, ur, eu","multi","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"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","None","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"
+"hotchpotch/wikipedia-ja-20231030","hotchpotch","2023-11-07 07:04:51","2023-11-13 11:00:53+00:00","f023a57bea99a76c4e62edcfe2dfbbb0bfcd4607","569","9759","1","language:ja, license:cc, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Wikipedia Japanese data (20231030) Source Date: 2023/10/30 Source: https://dumps.wikimedia.org/other/cirrussearch/ License CC BY-SA 4.0 Example WIP","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc","None","None","None"
+"izumi-lab/llm-japanese-dataset","izumi-lab","2023-04-30 06:13:24","2024-01-18 13:42:50+00:00","3f50712a59c0e796d088de9d45145e34c42e0edb","830","9610","122","None","None","llm-japanese-dataset LLM構築用の日本語インストラクション(チャット)データセット 主に，英語で構築されたLLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます． ※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます． updates 2023/5/15にAlpaca","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","2305.12720","None","None"
+"litagin/Galgame_Speech_ASR_16kHz","litagin","2024-10-10 05:05:29","2024-10-14 06:37:25+00:00","3fb86654222b3f0af0f7c332ae6a0ef9752a9451","470","9513","20","task_categories:automatic-speech-recognition, multilinguality:monolingual, language:ja, license:gpl-3.0, size_categories:1M<n<10M, format:webdataset, modality:audio, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us, speech, audio, text, japanese, anime, voice, visual novel, galgame","automatic-speech-recognition","Dataset Card for Galgame_Speech_ASR_16kHz The following rules (in the original repository) must be followed: 必须遵守GNU General Public License v3.0内的所有协议！附加：禁止商用，本数据集以及使用本数据集训练出来的任何模型都不得用于任何商业行为，如要用于商业用途","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","gpl-3.0","None","None","None"
+"davidstap/NTREX","davidstap","2024-04-23 11:38:58","2024-04-23 17:47:51+00:00","fd20d54141b6da952d5c68a2989472892489da0f","170","9486","2","None","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"
+"pierreguillou/DocLayNet-small","pierreguillou","2023-01-25 17:47:43","2023-05-17 08:56:10+00:00","b61d29f477163034001472614dc97fb9614dddea","509","9167","11","task_categories:object-detection, task_categories:image-segmentation, task_categories:token-classification, task_ids:instance-segmentation, annotations_creators:crowdsourced, language:en, language:de, language:fr, language:ja, license:other, size_categories:n<1K, modality:image, modality:text, library:datasets, library:mlcroissant, arxiv:2206.01062, region:us, DocLayNet, COCO, PDF, IBM, Financial-Reports, Finance, Manuals, Scientific-Articles, Science, Laws, Law, Regulations, Patents, Government-Tenders, object-detection, image-segmentation, token-classification","object-detection, image-segmentation, token-classification, instance-segmentation","Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep","@article{doclaynet2022, title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis}, doi = {10.1145/3534678.353904}, url = {https://arxiv.org/abs/2206.01062}, author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J}, year = {2022} }","en, de, fr, ja","multi","1|K|<|n|<|1|0|K","None","False","False","False","other","2206.01062","None","instance-segmentation"
+"djstrong/oscar-small","djstrong","2023-03-07 19:55:38","2023-03-07 19:57:38+00:00","fc47ed5a1804d8dd398255fa33da745ccb060d0e","3117","8702","1","task_categories:text-generation, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:oscar, language:af, language:am, language:ar, language:arz, language:as, language:az, language:azb, language:ba, language:be, language:bg, language:bn, language:bo, language:br, language:ca, language:ce, language:ceb, language:ckb, 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:fy, language:ga, language:gl, language:gu, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:ka, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:la, language:lb, language:lo, language:lt, language:lv, language:mg, language:mhr, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:my, language:nds, language:ne, language:nl, language:nn, language:no, language:or, language:os, language:pa, language:pl, language:pnb, language:ps, language:pt, language:ro, language:ru, language:sa, language:sah, language:sd, language:sh, language:si, language:sk, language:sl, language:sq, language:sr, language:sv, language:sw, 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:vi, language:yi, language:zh, license:cc0-1.0, size_categories:10M<n<100M, modality:text, library:datasets, library:mlcroissant, arxiv:2010.14571, region:us","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","oscar","False","False","False","cc0-1.0","2010.14571","None","language-modeling"
+"kanhatakeyama/AutoMultiTurnByCalm3-22B","kanhatakeyama","2024-07-17 09:53:20","2024-07-17 10:03:02+00:00","5d068c091911fe5ca55addf8d7a563df7d6774f2","65","7771","1","language:ja, license:other, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","自動生成のマルチターンデータセット オープンなデータソースから､Calm3-22bを使ってQ&Aを自動生成したものです｡ 一部の計算には東京工業大学のスーパーコンピュータTSUBAME4.0を利用しました｡ データソース はじめの質問(q1)を､種々のデータソースから収集しました｡その後のやりとりはすべて､Calmが生成しました｡質問文については､元データのライセンスに準拠します｡ oasst2-","None","ja","mono","None","None","False","False","False","other","None","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"
+"LHF/escorpius-mr","LHF","2022-05-03 18:49:47","2023-05-11 22:29:21+00:00","8b6e3f5b1dbd7244ae8251f5b1dc02da9146a04b","2123","7433","4","None","text-generation, fill-mask, language-modeling, masked-language-modeling","esCorpius Multilingual Raw In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of","None","af, ar, bn, ca, cs, da, de, el, eu, fa, fi, fr, gl, hi, hr, it, ja, ko, mt, nl, no, oc, pa, pl, pt, ro, sl, sr, sv, tr, uk, ur","multi","1|0|0|B|<|n|<|1|T","None","False","False","False","cc-by-nc-nd-4.0","2206.15147","None","language-modeling, masked-language-modeling"
+"singletongue/wikipedia-utils","singletongue","2023-05-29 01:42:43","2024-04-09 23:32:12+00:00","27bb3ccd9d731013e8e80a04b87b03cf11b6dbb1","315","7422","7","language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Wikipedia-Utils: Preprocessed Wikipedia Texts for NLP Preprocessed Wikipedia texts generated with the scripts in singletongue/wikipedia-utils repo. For detailed information on how the texts are proces","None","ja","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"coastalcph/tydi_xor_rc","coastalcph","2024-07-12 12:03:01","2024-09-19 08:20:09+00:00","42871590d25e82c9531347ca205b56037514753b","47","7414","0","task_categories:question-answering, task_ids:extractive-qa, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:extended|wikipedia, language:en, language:ar, language:bn, language:fi, language:ja, language:ko, language:ru, language:te, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, extractive-qa","Dataset Card for ""tydi_xor_rc"" Dataset Summary TyDi QA is a question answering dataset covering 11 typologically diverse languages. XORQA is an extension of the original TyDi QA dataset to also includ","None","en, ar, bn, fi, ja, ko, ru, te","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","extractive-qa"
+"Nexdata/multi_language","Nexdata","2022-03-02 23:29:22","2024-08-05 03:08:08+00:00","33ad034a237151d01432dc876d62315234ec7409","59","7354","3","task_categories:automatic-speech-recognition, language:en, language:de, language:fr, language:it, language:es, language:ko, language:ja, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","automatic-speech-recognition","Dataset Card for multi_language Dataset Summary The dataset contains 25,000 hours of multi-language reading speech data. It's recorded by native speakers, covering English, French, German, Russian, Sp","None","en, de, fr, it, es, ko, ja","multi","None","None","False","False","False","None","None","None","None"
+"llm-jp/databricks-dolly-15k-ja","llm-jp","2024-01-27 07:11:25","2024-01-30 18:09:37+00:00","f8ae87df31fac05429d974264eeb35c453bde737","391","7289","17","task_categories:question-answering, task_categories:summarization, language:ja, 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","databricks-dolly-15k-ja This repository provides an instruction tuning dataset developed by LLM-jp, a collaborative project launched in Japan. This dataset is a Japanese translation of databricks-doll","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Atsushi/fungi_indexed_mycological_papers_japanese","Atsushi","2022-03-02 23:29:22","2024-09-28 08:32:49+00:00","cc07b6205a265d809453fd54efd0e66c0ee1b28d","100","7212","0","annotations_creators:other, multilinguality:monolingual, source_datasets:original, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","fungi_indexed_mycological_papers_japanese 大菌輪「論文3行まとめ」データセット最終更新日：2024/9/28（R3-12108まで） Languages Japanese This dataset is available in Japanese only. 概要 / Overview 大菌輪は、Atsushi Nakajima（中島淳志）が個人で運営して","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"Atsushi/fungi_diagnostic_chars_comparison_japanese","Atsushi","2022-03-02 23:29:22","2024-09-28 08:36:50+00:00","72b7d102fb0728c2fbca3625c3932230ef56d9d1","54","7210","0","task_categories:text-classification, task_ids:multi-class-classification, annotations_creators:other, multilinguality:monolingual, source_datasets:original, language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, multi-class-classification","fungi_diagnostic_chars_comparison_japanese大菌輪「識別形質まとめ」データセット最終更新日 / Last updated: 2024/9/28（R3-12108まで / up to R3-12108） Languages Japanese This dataset is available in Japanese only. 概要 / Overview At","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","multi-class-classification"
+"cyanic-selkie/wikianc","cyanic-selkie","2023-09-05 14:22:32","2023-09-05 14:22:32+00:00","afb42306115c36afbaab9b13fc4545b724ae66c4","496","7023","5","task_categories:token-classification, annotations_creators:machine-generated, annotations_creators:crowdsourced, language_creators:machine-generated, language_creators:crowdsourced, multilinguality:multilingual, language:en, language:ceb, language:de, language:sv, language:fr, language:nl, language:ru, language:es, language:it, language:arz, language:pl, language:ja, language:zh, language:vi, language:uk, language:war, language:ar, language:pt, language:fa, language:ca, language:sr, language:id, language:ko, language:no, language:ce, language:fi, language:cs, language:tr, language:hu, language:tt, language:sh, language:ro, language:eu, language:ms, language:eo, language:he, language:hy, language:da, language:bg, language:cy, language:sk, language:azb, language:uz, language:et, language:be, language:kk, language:min, language:el, language:hr, language:lt, language:gl, language:az, language:ur, language:sl, language:lld, language:ka, language:nn, language:hi, language:th, language:ta, language:bn, language:la, language:mk, language:ast, language:lv, language:af, language:tg, language:my, language:mg, language:mr, language:sq, language:bs, language:oc, language:te, language:ml, language:nds, language:br, language:ky, language:sw, language:jv, language:lmo, language:new, language:pnb, language:vec, language:ht, language:pms, language:ba, language:lb, language:su, language:ku, language:ga, language:szl, language:is, language:fy, language:cv, language:ckb, language:pa, language:tl, language:an, language:wuu, language:diq, language:io, language:sco, language:vo, language:yo, language:ne, language:ia, language:kn, language:gu, language:als, language:ha, language:avk, language:bar, language:crh, language:scn, language:bpy, language:qu, language:mn, language:nv, language:xmf, language:ban, language:si, language:tum, language:ps, language:ig, language:frr, language:os, language:mzn, language:or, language:sah, language:cdo, language:gd, language:bug, language:yi, language:sd, language:ilo, language:am, language:nap, language:li, language:bcl, language:fo, language:gor, language:hsb, language:mai, language:shn, language:eml, language:ace, language:sa, language:as, language:wa, language:ie, language:hyw, language:lij, language:mhr, language:zu, language:sn, language:hif, language:mrj, language:bjn, language:km, language:mni, language:hak, language:pam, language:sat, language:rue, language:nso, language:bh, language:so, language:mi, language:se, language:myv, language:vls, language:dag, language:sc, language:co, language:ary, language:kw, language:bo, language:vep, language:glk, language:tk, language:kab, language:gan, language:rw, language:ab, language:gv, language:ug, language:nah, language:zea, language:skr, language:frp, language:udm, language:pcd, language:mt, language:kv, language:csb, language:gn, language:smn, language:ay, language:nrm, language:ks, language:lez, language:lfn, language:olo, language:mwl, language:lo, language:stq, language:ang, language:mdf, language:fur, language:rm, language:lad, language:kaa, language:gom, language:ext, language:koi, language:tyv, language:pap, language:av, language:dsb, language:ln, language:dty, language:tw, language:dv, language:ksh, language:za, language:gag, language:bxr, language:pfl, language:lg, language:szy, language:pag, language:blk, language:pi, language:tay, language:haw, language:awa, language:inh, language:krc, language:xal, language:pdc, language:to, language:atj, language:tcy, language:arc, language:mnw, language:shi, language:jam, language:kbp, language:wo, language:anp, language:kbd, language:nia, language:om, language:nov, language:ki, language:nqo, language:bi, language:xh, language:tpi, language:ff, language:tet, language:jbo, language:fj, language:kg, language:lbe, language:ty, language:cu, language:guw, language:trv, language:ami, language:srn, language:sm, language:mad, language:alt, language:ltg, language:gcr, language:chr, language:tn, language:ny, language:st, language:pih, language:got, language:rmy, language:ee, language:pcm, language:bm, language:ss, language:gpe, language:ts, language:ve, language:kcg, language:chy, language:rn, language:ch, language:gur, language:ik, language:ady, language:fat, language:pnt, language:guc, language:iu, language:pwn, language:sg, language:din, language:ti, language:kl, language:dz, language:cr, license:cc-by-sa-4.0, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, wikidata, wikipedia, wikification, named-entity-linking, nel, entity-linking, el, named-entity-disambiguation, ned, entity-disambiguation, ed","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"
+"Atsushi/fungi_trait_circus_database","Atsushi","2022-03-02 23:29:22","2023-12-29 06:22:46+00:00","c8e5049ab833801f41d6a835200b7e39c42916db","57","7006","0","annotations_creators:other, multilinguality:multilingual, source_datasets:original, language:en, language:ja, license:cc-by-4.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","fungi_trait_circus_database大菌輪「Trait Circus」データセット（統制形質）最終更新日：2023/12/29 Languages Japanese and English Please do not use this dataset for academic purposes for the time being. (casual use only)当面の間仮公","None","en, ja","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"facebook/2M-Belebele","facebook","2024-12-16 08:45:30","2024-12-17 13:39:10+00:00","1355a3bf763d9b3d3862ad0ce7d157819576fbf4","3874","6354","5","task_categories:question-answering, task_categories:automatic-speech-recognition, language:bg, language:pa, language:en, language:hu, language:sv, language:af, language:ca, language:ka, language:sk, language:jv, language:bn, language:tr, language:sr, language:ro, language:tg, language:fa, language:wo, language:fi, language:hy, language:vi, language:kea, language:as, language:ja, language:nl, language:ne, language:lg, language:hi, language:xh, language:kk, language:mn, language:yo, language:km, language:ha, language:ru, language:sw, language:ps, language:ko, language:cs, language:lv, language:ig, language:ar, language:es, language:nb, language:lt, language:fil, language:it, language:he, language:da, language:ml, language:my, language:el, language:et, language:pl, language:sn, language:sd, language:or, language:th, language:luo, language:sl, language:fr, language:id, language:ta, language:gu, language:mk, language:am, language:pt, language:cmn, language:de, language:ceb, language:is, language:ur, language:az, language:te, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, modality:audio, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.08274, region:us, speech-recognition, multilingual, flores200, translation, audio, speech","question-answering, automatic-speech-recognition","2M-Belebele Highly-Multilingual Speech and American Sign Language Comprehension Dataset We introduce 2M-Belebele as the first highly multilingual speech and American Sign Language (ASL) comprehension ","None","bg, pa, en, hu, sv, af, ca, ka, sk, jv, bn, tr, sr, ro, tg, fa, wo, fi, hy, vi, kea, as, ja, nl, ne, lg, hi, xh, kk, mn, yo, km, ha, ru, sw, ps, ko, cs, lv, ig, ar, es, nb, lt, fil, it, he, da, ml, my, el, et, pl, sn, sd, or, th, luo, sl, fr, id, ta, gu, mk, am, pt, cmn, de, ceb, is, ur, az, te","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2412.08274","None","None"
+"globis-university/aozorabunko-clean","globis-university","2023-06-26 13:31:28","2023-10-27 13:22:32+00:00","42a9c9c0f1d67e6a5554d9bea4201973dc9b049c","996","6220","21","None","text-generation, text-classification","Overview This dataset provides a convenient and user-friendly format of data from Aozora Bunko (青空文庫), a website that compiles public-domain books in Japan, ideal for Machine Learning applications. [F","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"kogi-jwu/jhumaneval","kogi-jwu","2023-10-21 08:20:14","2024-01-10 21:52:35+00:00","7d12fbc3be4a1f7de2050a0e932577bc459da379","546","6111","6","None","text2text-generation","Dataset Card for JHumanEval: Japanese Hand-Translated HumanEval Dataset Summary This is a Japanese translated version of HumanEval, an evaluation harness for the HumanEval problem solving dataset desc","None","ja, en","bi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"llm-jp/hh-rlhf-12k-ja","llm-jp","2024-02-04 21:19:53","2024-02-04 21:45:59+00:00","63914832ed0239ed518239ce2b1000724d1a8829","118","6085","14","language:ja, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","hh-rlhf-12k-ja This repository provides a human preference dataset developed by LLM-jp, a collaborative project launched in Japan. This dataset is a Japanese translation of a subset of hh-rlhf using D","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"Mutonix/Vript_Multilingual","Mutonix","2024-10-13 01:33:27","2024-10-17 08:29:45+00:00","86a6554abb015839820e1fd861c11f27248e0604","1445","6083","3","None","video-classification, visual-question-answering, text-to-video, text-to-image, image-to-video","🎬 Vript: A Video Is Worth Thousands of Words [Github Repo] We construct another fine-grained video-text dataset with 19.1K annotated high-resolution UGC videos (~677k clips) in multiple languages to b","None","zh, en, de, ja, ko, ru, es, pt, jv, fr, id, vi","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"Mathoctopus/MSVAMP","Mathoctopus","2023-10-17 09:29:55","2023-10-17 10:48:46+00:00","301e2b3b168be70058c89c21c2fbdc9262102add","165","6070","4","None","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"
+"MLSP2024/MLSP2024","MLSP2024","2024-05-06 06:52:41","2024-08-03 08:45:03+00:00","8dd037992ef0e14f72dfc6994e273cbe3d9fc633","1127","6064","5","None","token-classification, other, text-simplification","The MultiLS dataset was created as part of the MLSP2024 shared task. The dataset contains 5,624 instances across 10 target languages. Each instance consists of a sentence from an educational text, wit","None","en, ca, fil, fr, de, it, ja, pt, si, es","multi","1|K|<|n|<|1|0|K","None","False","auto","False","cc-by-sa-4.0","2402.14972","None","text-simplification"
+"sentence-transformers/mldr","sentence-transformers","2024-06-19 14:53:42","2024-06-19 16:22:10+00:00","40ad7672817ebee49e00dd25aed00e1c401881d6","281","6064","3","None","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"
+"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","None","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","None","False","False","False","cc-by-sa-3.0","2103.01913","None","text-retrieval-other-text-image-retrieval, image-captioning"
+"cl-nagoya/auto-wiki-qa","cl-nagoya","2024-03-28 01:33:41","2024-09-13 01:45:16+00:00","672dd0d496352282b4064e944a16e00315b796a8","225","5838","25","task_categories:question-answering, language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2409.07737, region:us","question-answering","AutoWikiQA 東工大が公開しているSwallow-MXを用いて、Wikipedia中のテキストを入力として「質問(query)」と「回答(answer)」を生成し、生成された質問と回答についてフィルタリングを行ったデータセットです。日本語のフリーなQAデータセットとしては2024年4月現在で最大規模となっています。 また、データの生成にはテンプレートなどのルールベース処理を入れていないため","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","2409.07737","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"
+"yuzuai/rakuda-questions","yuzuai","2023-06-23 01:08:52","2023-06-23 08:01:35+00:00","c319e9cebf932dd29edff194d77294b99fd7d611","134","5450","8","task_categories:question-answering, source_datasets:original, language:ja, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational, question-answering","Rakuda - Questions for Japanese models Repository: https://github.com/yuzu-ai/japanese-llm-ranking This is a set of 40 questions in Japanese about Japanese-specific topics designed to evaluate the cap","None","ja","mono","n|<|1|K","None","False","False","False","mit","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","None","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"
+"taishi-i/awesome-japanese-nlp-classification-dataset","taishi-i","2023-09-09 06:37:36","2023-09-09 11:09:04+00:00","9dcc8a7c613f9bd7cbac894b6c0b810ffe53e9a5","149","5238","5","task_categories:text-classification, language:en, language:ja, license:other, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, region:us, code","text-classification","This dataset determines whether a GitHub repository description relates to Japanese natural language processing (NLP). The labels are categorized as ""Relevant (1)"" and ""Not Relevant (0)"".","None","en, ja","bi","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"hotchpotch/JQaRA","hotchpotch","2024-03-03 01:58:33","2025-01-20 08:47:35+00:00","e8016f8cf40f2fd51145be618ebc5a0963831d42","418","5208","19","task_categories:question-answering, language:ja, license:cc, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","JQaRA : Japanese Question Answering with Retrieval Augmentation - 検索拡張(RAG)評価のための日本語 Q&A データセット 高性能な LLM の台頭に伴い、LLM を用いた質疑応答のユースケースが増加しています。しかしながら、LLM は質問に対して適切な回答する知識を有していないと、答えることができないだけでなく、誤った回答を返答","None","ja","mono","None","None","False","False","False","cc","None","None","None"
+"pierreguillou/DocLayNet-base","pierreguillou","2023-01-25 17:53:26","2023-05-17 08:56:30+00:00","86fa5ebffa3d336210ee1eeeec349b2c7f07899b","445","5145","9","task_categories:object-detection, task_categories:image-segmentation, task_categories:token-classification, task_ids:instance-segmentation, annotations_creators:crowdsourced, language:en, language:de, language:fr, language:ja, license:other, size_categories:1K<n<10K, modality:image, modality:text, library:datasets, library:mlcroissant, arxiv:2206.01062, region:us, DocLayNet, COCO, PDF, IBM, Financial-Reports, Finance, Manuals, Scientific-Articles, Science, Laws, Law, Regulations, Patents, Government-Tenders, object-detection, image-segmentation, token-classification","object-detection, image-segmentation, token-classification, instance-segmentation","Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep","@article{doclaynet2022, title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis}, doi = {10.1145/3534678.353904}, url = {https://arxiv.org/abs/2206.01062}, author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J}, year = {2022} }","en, de, fr, ja","multi","1|K|<|n|<|1|0|K","None","False","False","False","other","2206.01062","None","instance-segmentation"
+"sentence-transformers/parallel-sentences-tatoeba","sentence-transformers","2024-04-30 11:19:18","2024-06-18 19:45:56+00:00","cec1343ab5a7a8befe99af4a2d0ca847b6c84743","825","5039","0","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - Tatoeba This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the senten","None","en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"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"
+"phonemetransformers/CHILDES","phonemetransformers","2024-03-28 10:32:58","2025-01-20 15:00:42+00:00","328ff1e808c8a329cedb29f128e41e4556598038","1016","4996","1","None","None","Phonemized Child Directed Speech Dataset This dataset contains utterance downloaded from CHILDES which have been pre-processed and converted to phonemic transcriptions by this processing script. Many ","None","en, de, fr, es, nl, cmn, ja, yue, et, hr, da, eu, hu, tr, fa, is, id, ga, cy, ko, sv, nb, qu, ca, it, pt, ro, sv, pl","multi","1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"FredZhang7/toxi-text-3M","FredZhang7","2023-06-28 23:28:34","2025-01-15 02:04:46+00:00","4187a3be58f166c05c43aff58c60eec7e9fba4cf","769","4994","23","None","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"
+"sentence-transformers/parallel-sentences-jw300","sentence-transformers","2024-04-29 21:24:59","2024-06-18 19:45:32+00:00","c30971b3e4ef10c81e6cf15ec1b0987e98822223","717","4977","4","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - JW300 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, cs, da, de, el, es, et, fa, fi, fr, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, lt, lv, mk, mn, mr, my, nl, pl, pt, ro, ru, sk, sl, sq, sv, th, tr, uk, ur, vi","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"litagin/reazon-speech-v2-clone","litagin","2024-02-25 13:47:58","2024-04-16 23:20:06+00:00","ddff3829fb26a84b2e10c352779e8f013073ece4","49","4947","8","task_categories:automatic-speech-recognition, language:ja, license:other, size_categories:10K<n<100K, format:webdataset, modality:audio, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us","automatic-speech-recognition","Reazon Speech v2 dataset mirror Original Dataset Source Hugging Face Dataset Page: reazon-research/reazonspeech Project Page: Reazon Research License This dataset is a mirror of the original Reazon Sp","None","ja","mono","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","other","None","None","None"
+"kunishou/oasst1-89k-ja","kunishou","2023-05-06 09:12:30","2024-04-01 17:15:31+00:00","f889b3264eb79d7392d6ba004c1646c1741df212","140","4918","25","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This dataset was created by automatically translating ""OpenAssistant/oasst1"" into Japanese. The ""ng_translation"" flag indicates that the translation was not successful, and ""1"" means that the translat","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"jniimi/weather_forecast_japan","jniimi","2024-08-22 11:37:59","2025-01-29 03:17:02+00:00","a1c7d35e9ae4ec3fa60332db279967e99dd66cb6","845","4833","2","task_categories:tabular-regression, task_categories:tabular-classification, language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, climate","tabular-regression, tabular-classification","Overview This dataset contains daily-collected weather forecasts for up to seven days ahead for all the meteorological observatories in Japan, published by the Japan Meteorological Agency (JMA) at htt","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"shunk031/jsnli","shunk031","2022-11-30 16:34:02","2022-12-12 07:36:58+00:00","6d84ffafa2b74d9c9b8d567ad338ad2e6c255a6d","52","4771","5","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, multilinguality:monolingual, language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us, natural-language-inference, nli, jsnli","text-classification, natural-language-inference, multi-input-text-classification","== 日本語SNLI(JSNLI)データセット == SNLI コーパスを日本語に翻訳した自然言語推論データセット 学習データは元データを翻訳し、計算機によるフィルタリングによって作成 評価データは日本語として意味が通るか、翻訳後のラベルが元のラベルと一致しているかどうかの2段階のクラウドソーシングによりデータをフィルタリング","- 吉越 卓見, 河原 大輔, 黒橋 禎夫: 機械翻訳を用いた自然言語推論データセットの多言語化, 第244回自然言語処理研究会, (2020.7.3). - Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). - Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. ""From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions."" Transactions of the Association for Computational Linguistics 2 (2014): 67-78.","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","natural-language-inference, multi-input-text-classification"
+"range3/cc100-ja","range3","2023-02-04 05:10:34","2023-02-04 05:43:32+00:00","5f83e950d0581df275911f8bed66939e361e697f","179","4739","18","task_categories:text-generation, task_categories:fill-mask, language:ja, license:unknown, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","range3/cc100-ja This dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded. このデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。","None","ja","mono","None","None","False","False","False","unknown","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","None","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"
+"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"
+"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","None","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","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"izumi-lab/wikinews-ja-20230728","izumi-lab","2023-07-28 07:01:06","2023-07-29 03:06:48+00:00","f4099716864f2e27e48406d0773dd6cf3912cfb2","55","4438","5","language:ja, license:cc-by-2.5, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""wikinews-ja-20230728"" More Information needed","None","ja","mono","None","None","False","False","False","cc-by-2.5","None","None","None"
+"tasksource/esci","tasksource","2023-08-09 10:12:27","2023-08-09 11:23:31+00:00","8113b17a5d4099e20243282c926f1bc1a08a4d13","292","4389","3","task_categories:text-classification, task_categories:text-retrieval, language:en, language:ja, language:es, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2206.06588, region:us","text-classification, text-retrieval","Dataset Card for ""esci"" ESCI product search dataset https://github.com/amazon-science/esci-data/ Preprocessings: -joined the two relevant files -product_text aggregate all product text -mapped esci_la","None","en, ja, es","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"taishi-i/nagisa_stopwords","taishi-i","2023-08-06 17:10:10","2023-08-06 17:58:31+00:00","30143dfe2e86ccdb673d76b6ea29dc7b0e6245d3","74","4331","1","language:ja, license:mit, size_categories:n<1K, modality:text, library:datasets, library:mlcroissant, region:us, stopwords","None","Japanese stopwords for nagisa.","None","ja","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"fujiki/japanese_alpaca_data","fujiki","2023-05-18 07:13:15","2023-05-19 12:54:13+00:00","e6eaffc1e1b37df94808dc5473bb18873bf80eaa","88","4236","14","language:ja, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""japanese_alpaca_data"" This dataset is based on masa3141's great work on japanese-alpaca-lora [github]. Please also refer to this repo. More Information needed","None","ja","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"izumi-lab/mc4-ja","izumi-lab","2023-07-06 00:43:16","2023-07-29 03:11:03+00:00","021d1e66c8f6b094f9ee57fc4489626eaaf7ec8c","523","4078","5","language:ja, license:odc-by, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""mc4-ja"" More Information needed","None","ja","mono","None","None","False","False","False","odc-by","None","None","None"
+"sentence-transformers/mr-tydi","sentence-transformers","2024-06-19 15:55:59","2024-06-20 13:53:43+00:00","abbdf55c630352da943f779610c3ce6268118351","368","4030","0","task_categories:feature-extraction, task_categories:sentence-similarity, language:ar, language:bn, language:en, language:fi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, 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 Mr. TyDi This is a reformatting of the Mr. TyDi dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data. Dataset Subsets ...-triplet subset Columns: ","None","ar, bn, en, fi, id, ja, ko, ru, sw, te, th","multi","1|M|<|n|<|1|0|M","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","None","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"
+"kunishou/oasst1-chat-44k-ja","kunishou","2023-11-12 07:53:04","2023-12-25 13:22:22+00:00","6e5f278b6fd864abf1ab27d3ff5064e2ef7acaea","44","3846","10","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","oasst1-89k-jaをチャット形式に変換したデータセットになります。マルチターン会話でのファインチューニングをする際にご活用下さい（1レコードのトークン長が大きいのでそれなりの計算リソースが必要になります）。フォーマットは ShareGPT 形式になっています。ファインチューニングをする際はこちらの記事を参考にして下さい。 oasst1-ja-89k Repositoryhttps://gi","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"hatakeyama-llm-team/japanese2010","hatakeyama-llm-team","2024-03-20 00:39:02","2024-03-21 00:26:06+00:00","4a55fa16b04ae1823f34e1cd4d2d65791694f2bf","894","3785","2","None","None","日本語ウェブコーパス2010 こちらのデータをhuggingfaceにアップロードしたものです｡ 2009 年度における著作権法の改正（平成21年通常国会 著作権法改正等について | 文化庁）に基づき，情報解析研究への利用に限って利用可能です｡ 形態素解析を用いて､自動で句点をつけました｡ 変換コード 変換スクリプト 形態素解析など","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"AhmedSSabir/Japanese-wiki-dump-sentence-dataset","AhmedSSabir","2022-06-08 11:34:04","2023-07-11 12:22:09+00:00","148b4d77bc004f3775c08d1112ebebdf3927d8c1","133","3750","4","task_categories:sentence-similarity, task_categories:text-classification, task_categories:text-generation, language:ja, size_categories:1M<n<10M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","sentence-similarity, text-classification, text-generation","Dataset 5M (5121625) clean Japanese full sentence with the context. This dataset can be used to learn unsupervised semantic similarity, etc.","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"hpprc/jsick","hpprc","2023-04-08 16:02:06","2023-04-11 06:18:09+00:00","6f27df527556f0c2774f45297cfca7780477ad75","943","3692","7","None","sentence-similarity, text-classification, natural-language-inference, semantic-similarity-scoring","Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset. JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese. W","@article{yanaka-mineshima-2022-compositional, title = ""Compositional Evaluation on {J}apanese Textual Entailment and Similarity"", author = ""Yanaka, Hitomi and Mineshima, Koji"", journal = ""Transactions of the Association for Computational Linguistics"", volume = ""10"", year = ""2022"", address = ""Cambridge, MA"", publisher = ""MIT Press"", url = ""https://aclanthology.org/2022.tacl-1.73"", doi = ""10.1162/tacl_a_00518"", pages = ""1266--1284"", }","ja, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","natural-language-inference, semantic-similarity-scoring"
+"hltcoe/megawika-report-generation","hltcoe","2023-11-10 13:17:09","2024-01-19 13:01:58+00:00","bc25fe13ccf7722db3b8e6f79dc4e8b2f47f62d6","412","3549","6","None","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"
+"talkbank/callhome","talkbank","2024-04-11 15:18:46","2024-04-28 19:53:45+00:00","17c8a153215aa7c50b805078fd6284ba81c2fc47","658","3509","16","None","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"
+"lmg-anon/vntl-leaderboard","lmg-anon","2024-06-08 20:33:07","2025-01-02 16:34:32+00:00","cf3d232d77458394857dbf8411de95fd3a894aef","876","3503","23","None","translation","VNTL Leaderboard The VNTL leaderboard ranks Large Language Models (LLMs) based on their performance in translating Japanese Visual Novels into English. Please be aware that the current results are pre","None","en, ja","bi","n|<|1|K","None","False","False","False","None","None","None","None"
+"RicardoRei/wmt-sqm-human-evaluation","RicardoRei","2023-02-17 10:42:46","2023-02-17 11:10:39+00:00","6104284646a7a83f493f1825830c4b13f751ea2a","62","3493","0","None","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"
+"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"
+"aixsatoshi/Swallow-MX-chatbot-DPO","aixsatoshi","2024-03-31 06:42:39","2024-03-31 08:16:43+00:00","484c960118fca9768938d0df7ba11b3fe4e0a215","47","3266","4","language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Chatbot Arena Conversationsの質問文から、aixsatoshi/Swallow-MX-8x7b-NVE-chatvector-Mixtral-instruct-v2を使用して応答文を作成しました 質問文は、以下のモデルのPrompt部分を使用しました Chatbot Arena Conversations JA (calm2) 以下引用です。 指示文（prompt）はlm","None","ja","mono","None","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"
+"almanach/HALvest","almanach","2024-04-23 13:30:23","2024-07-31 09:56:58+00:00","442094b65f54946f8cafb64cf774cb7c87e57df4","738","3146","3","None","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"
+"Amani27/massive_translation_dataset","Amani27","2023-07-20 16:09:42","2023-07-25 14:54:44+00:00","120f2cac016f0f8ee4862a755c59587ca1701997","128","3092","8","task_categories:translation, language:en, language:de, language:es, language:hi, language:fr, language:it, language:ar, language:nl, language:ja, language:pt, license:cc-by-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for Massive Dataset for Translation Dataset Summary This dataset is derived from AmazonScience/MASSIVE dataset for translation task purpose. Supported Tasks and Leaderboards Translation L","None","en, de, es, hi, fr, it, ar, nl, ja, pt","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"JMMMU/JMMMU","JMMMU","2024-09-29 22:21:14","2024-10-25 14:14:10+00:00","38109431ddf24746a631948da6e168fc071149de","431","3074","15","None","question-answering, visual-question-answering, multiple-choice","JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark 🌐 Homepage | 🤗 Dataset | 🏆 HF Leaderboard | 📖 arXiv | 💻 Code Introduction We introduce JMMMU (Japanese MMMU), a multimodal","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2410.17250","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"
+"ByteDance/MTVQA","ByteDance","2024-05-20 07:42:19","2024-05-30 02:38:32+00:00","7cdca63ec6cd71cdd0b52f79fc3176bb9ce36a9c","282","3006","24","task_categories:visual-question-answering, task_categories:image-to-text, language:multilingual, language:ar, language:de, language:vi, language:ja, language:ko, language:fr, language:ru, language:it, language:th, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2405.11985, region:us, multilingual, text-centric, vqa","visual-question-answering, image-to-text","Dataset Card The dataset is oriented toward visual question answering of multilingual text scenes in nine languages, including Korean, Japanese, Italian, Russian, Deutsch, French, Thai, Arabic, and Vi","None","multilingual, ar, de, vi, ja, ko, fr, ru, it, th","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"izumi-lab/mc4-ja-filter-ja-normal","izumi-lab","2023-07-16 13:46:25","2023-07-29 03:11:41+00:00","975801c05ad3be9b1a7e662ba198940b05243798","458","2990","3","language:ja, license:odc-by, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""mc4-ja-filter-ja-normal"" More Information needed","None","ja","mono","None","None","False","False","False","odc-by","None","None","None"
+"if001/oscar_2023_filtered","if001","2023-08-21 03:56:56","2023-08-31 13:35:37+00:00","7e99db36695285abc67b1b3527d67ddeed80d9ae","603","2972","2","None","text-generation","from datasets import load_dataset ds=load_dataset(""if001/oscar_2023_filtered"") ds['train'] --- Dataset({ features: ['text'], num_rows: 312396 }) oscar 2023をfilterしたものhttps://huggingface.co/datasets/os","None","ja","mono","None","None","False","False","False","cc0-1.0","None","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","None","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","None","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"
+"HachiML/Hachi-Alpaca","HachiML","2024-05-01 12:45:21","2024-05-20 01:43:26+00:00","80b9dbaea45e7f3b362925886486c0585a31f17f","484","2938","14","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, self-instruct","text-generation","Hachi-Alpaca Hachi-Alpacaは、 Stanford Alpacaの手法 mistralai/Mixtral-8x22B-Instruct-v0.1 で作った合成データ(Synthetic data)です。モデルの利用にはDeepinfraを利用しています。 また、""_cleaned""がついたデータセットはmistralai/Mixtral-8x22B-Instruct-v0.","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"1-800-SHARED-TASKS/xlsum-subset","1-800-SHARED-TASKS","2024-09-26 13:08:59","2024-09-26 13:12:19+00:00","069b328a4afb41fb11db60ee74ae4153ada45332","50","2934","0","task_categories:summarization, task_categories:text-generation, annotations_creators:found, language_creators:found, multilinguality:multilingual, source_datasets:original, language:am, language:ar, language:az, language:bn, language:my, language:zh, language:en, language:fr, language:gu, language:ha, language:hi, language:ig, language:id, language:ja, language:rn, language:ko, language:ky, language:mr, language:ne, language:om, language:ps, language:fa, language:pcm, language:pt, language:pa, language:ru, language:gd, language:sr, language:si, language:so, language:es, language:sw, language:ta, language:te, language:th, language:ti, language:tr, language:uk, language:ur, language:uz, language:vi, language:cy, language:yo, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:webdataset, modality:text, library:datasets, library:webdataset, library:mlcroissant, arxiv:1607.01759, region:us, conditional-text-generation","summarization, text-generation","Dataset Card for ""XL-Sum"" Dataset Summary We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally annotated article-summary pairs from BBC, extracted using a set o","None","am, ar, az, bn, my, zh, en, fr, gu, ha, hi, ig, id, ja, rn, ko, ky, mr, ne, om, ps, fa, pcm, pt, pa, ru, gd, sr, si, so, es, sw, ta, te, th, ti, tr, uk, ur, uz, vi, cy, yo","multi","1|M|<|n|<|1|0|M","xl-sum","False","False","False","cc-by-nc-sa-4.0","1607.01759","None","None"
+"matsuxr/JaGovFaqs-22k","matsuxr","2023-12-31 13:58:41","2024-02-29 02:51:20+00:00","a9fd35ae0f7d984cef7c054cfa6ae68bc7b5d354","290","2859","26","task_categories:question-answering, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","question-answering","このデータセットについて このデータは、日本の官公庁のWebサイトに掲載されている「よくある質問」を手作業で抽出し、インストラクション用のデータセットとしたものです。 日本の官公庁のWebサイトは多くが「政府標準利用規約（第2.0版）」に準拠しており、この規約はCC-BY-4.0（国際）と互換性があると記述されています。 参考 https://www.digital.go.jp/copyright","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"llm-jp/oasst1-21k-ja","llm-jp","2024-01-28 23:27:03","2024-02-06 04:06:04+00:00","f05b5816a8c1ce8c1f5ae3cd87ae5a7b6409fea5","95","2850","16","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","oasst1-21k-ja This repository provides an instruction tuning dataset developed by LLM-jp, a collaborative project launched in Japan. This dataset is a Japanese translation of an English subset of oass","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"yusuke1997/mCSQA","yusuke1997","2023-12-22 02:18:49","2024-08-26 05:19:38+00:00","222f65db1deb2d0c19ff7ae7f6c78893dfc3a66c","310","2840","3","None","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","None","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"
+"fujiki/wiki40b_ja","fujiki","2023-04-28 23:14:50","2023-04-28 23:35:57+00:00","f4b757145a0964d08cb01f505c5d65a285333c5d","483","2703","2","None","None","This dataset is a reformatted version of the Japanese portion of wiki40b dataset. When you use this dataset, please cite the original paper: @inproceedings{guo-etal-2020-wiki, title = ""{W}iki-40{B}: M","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"hpprc/wikipedia-20240101","hpprc","2024-01-18 14:33:06","2024-01-22 07:41:53+00:00","26cd4e4c1b0574b0b2b76a2cdbf46b4b89c8102b","51","2633","4","language:ja, 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","None","apache_beamとmwparserfromhellを用いて通常のWikipediaの前処理を行ったデータセットです。 本家の前処理時間が長すぎたため、前処理済みバージョンを配布しています。 各種規約・ライセンスは本家Wikipediaにしたがってください。 Preprocessing Code import datasets as ds dataset = ds.load_dataset( ","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"hpprc/jawiki-paragraphs","hpprc","2024-05-15 04:21:52","2024-07-19 07:49:35+00:00","63dfa56f549ef412824489bf6d70a199caa36e75","64","2559","2","language:ja, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","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","None","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"
+"nlp4j/wikipedia","nlp4j","2023-11-09 01:39:19","2023-11-15 06:12:59+00:00","f2e7d89fb54c4817de42ad47b5050b55e0040daa","110","2532","1","annotations_creators:no-annotation, source_datasets:original, language:ja, license:cc-by-sa-3.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""wikipedia"" More Information needed","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","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","None","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"
+"cl-nagoya/ruri-dataset-reranker","cl-nagoya","2024-08-08 03:57:57","2024-09-13 01:49:51+00:00","97d5e171d0936056e54bfeb684b943369ce55dfe","1070","2519","3","None","question-answering","Ruri-Dataset Reranker Datasets used for training Ruri-Reranker. Please refer to https://huggingface.co/datasets/hpprc/emb for individual datasets.","None","ja","mono","None","None","False","False","False","other","2409.07737","None","None"
+"simon3000/starrail-voice","simon3000","2024-04-26 19:01:17","2024-08-30 04:52:04+00:00","7b6e8050a0fd1e7e052de9520f1799faa08a5751","714","2494","24","None","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"
+"zetavg/ShareGPT-Processed","zetavg","2023-05-16 19:50:04","2023-05-21 03:50:14+00:00","9f5f6eac1e788e28d12babc03db941c2d3bfa942","53","2452","29","task_categories:text-generation, language:en, language:zh, language:es, language:ja, language:fr, license:cc0-1.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, conversation, rlhf, chatgpt, gpt-3.5","text-generation","ShareGPT-Processed The RyokoAI/ShareGPT52K dataset, converted to Markdown and labeled with the language used. Acknowledgements vinta/pangu.js — To insert whitespace between CJK (Chinese, Japanese, Kor","None","en, zh, es, ja, fr","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc0-1.0","None","None","None"
+"afaji/cvqa","afaji","2024-04-26 11:25:15","2024-11-27 17:42:19+00:00","6a38d71fa391ca7a5e061bbcf48ba714a9a9f4f1","419","2431","23","None","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"
+"WorldMedQA/V","WorldMedQA","2024-10-03 17:39:46","2024-10-17 09:24:52+00:00","7d4b008bdba5c961a249cf44c9a552675d351ca5","330","2422","16","task_categories:question-answering, language:en, language:he, language:ja, language:es, language:pt, size_categories:1K<n<10K, format:csv, modality:image, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2410.12722, region:us, medical","question-answering","WorldMedQA-V: A Multilingual, Multimodal Medical Examination Dataset Overview WorldMedQA-V is a multilingual and multimodal benchmarking dataset designed to evaluate vision-language models (VLMs) in h","None","en, he, ja, es, pt","multi","n|<|1|K","None","False","False","False","None","None","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"
+"cyberagent/camera","cyberagent","2024-06-03 08:03:48","2024-09-02 06:29:40+00:00","2d1c23e6f3e19b9d67816bb54747770b23235779","190","2397","7","task_categories:text2text-generation, task_categories:image-to-text, language:ja, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, advertisement","text2text-generation, image-to-text","Dataset Card for CAMERA📷: Table of Contents: Dataset Card for Camera Table of Contents Dataset Details Dataset Description Dataset Sources Uses Direct Use Dataset Information Data Example Dataset Stru","None","j, a","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Brand24/mms","Brand24","2023-05-24 12:07:06","2023-08-23 21:49:55+00:00","95b662d6672a0eaa1633ea2af7362ccd8264db3d","110","2250","10","None","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"
+"saillab/taco-datasets","saillab","2023-11-27 07:15:33","2023-12-01 06:21:22+00:00","bfb3619e7fafe98526beb2d254971854c1a53520","509","2250","15","None","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"
+"izumi-lab/llm-japanese-dataset-vanilla","izumi-lab","2023-05-23 14:45:27","2024-02-17 16:17:18+00:00","4a1a7938f32aca7b1ec54d08ebbb5f6bddb0debe","200","2224","30","language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.12720, region:us","None","llm-japanese-dataset-vanilla LLM構築用の日本語チャットデータセット izumi-lab/llm-japanese-dataset から，日英翻訳のデータセット等を抜いたものです． 主に，日本語LLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます． ※様々な公開言語資源を利用させていただきました","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","2305.12720","None","None"
+"kunishou/J-ResearchCorpus","kunishou","2024-02-12 14:03:42","2024-03-16 07:55:08+00:00","a1896d0d7fc771351ce170b92d9ff2b6f826bf6b","115","2184","31","language:ja, license:other, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","J-ResearchCorpus Update: 2024/3/16言語処理学会第30回年次大会(NLP2024)を含む、論文 1,343 本のデータを追加 2024/2/25言語処理学会誌「自然言語処理」のうち CC-BY-4.0 で公開されている論文 360 本のデータを追加 概要 CC-BY-* ライセンスで公開されている日本語論文や学会誌等から抜粋した高品質なテキストのデータセットです。言","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"tohoku-nlp/abc-multiple-choice","tohoku-nlp","2024-03-02 03:58:25","2024-03-12 07:32:13+00:00","6a2794c212a5e56da44960d421d6c9e7eba6fbc2","63","2165","3","task_categories:multiple-choice, task_categories:question-answering, task_ids:multiple-choice-qa, multilinguality:monolingual, language:ja, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","multiple-choice, question-answering, multiple-choice-qa","abc-multiple-choice Dataset abc-multiple-choice は、競技クイズの大会「abc」で使用された4択問題を元に作成された、多肢選択式の質問応答データセットです。 データセットの詳細については、下記の発表資料を参照してください。 鈴木正敏. 4択クイズを題材にした多肢選択式日本語質問応答データセットの構築. 言語処理学会第30回年次大会 (NLP2024) ","None","ja","mono","n|<|1|K","None","False","False","False","None","None","None","multiple-choice-qa"
+"lmqg/qag_jaquad","lmqg","2022-12-18 07:05:33","2022-12-18 07:54:08+00:00","eb342671ce8e5abdbb856c2c06520ff7c3af50fa","43","2162","0","task_categories:text-generation, task_ids:language-modeling, multilinguality:monolingual, source_datasets:lmqg/qg_jaquad, language:ja, license:cc-by-sa-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, arxiv:2210.03992, region:us, question-generation","text-generation, language-modeling","Question & answer generation dataset based on SQuAD.","@inproceedings{ushio-etal-2022-generative, title = ""{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration"", author = ""Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose"", booktitle = ""Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"", month = dec, year = ""2022"", address = ""Abu Dhabi, U.A.E."", publisher = ""Association for Computational Linguistics"", }","j, a","bi","1|||k|||<|||n|||<|||1|||0|||K","None","False","False","False","cc-by-sa-4.0","2210.03992","None","language-modeling"
+"stockmark/ner-wikipedia-dataset","stockmark","2023-09-02 14:38:55","2023-09-02 14:42:18+00:00","d6c4d4821885ed656b9fbc7b2283dc7d165916d1","65","2146","10","task_categories:token-classification, language:ja, 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, Named Entity Recognition, NER","token-classification","Wikipediaを用いた日本語の固有表現抽出データセット GitHub: https://github.com/stockmarkteam/ner-wikipedia-dataset/ LICENSE: CC-BY-SA 3.0 Developed by Stockmark Inc.","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"cl-nagoya/ruri-dataset-ft","cl-nagoya","2024-08-13 05:29:17","2024-09-13 01:49:58+00:00","38b63afd286e85a6811211bcc9d2bf76ab1cdf1e","350","2142","4","language:ja, license:other, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2409.07737, region:us","None","Ruri-Dataset FT Datasets used for fine-tuning Ruri. Please refer to https://huggingface.co/datasets/hpprc/emb for individual datasets.","None","ja","mono","None","None","False","False","False","other","2409.07737","None","None"
+"litagin/ehehe-corpus","litagin","2024-03-30 10:00:54","2024-03-31 02:03:08+00:00","5febbad65f887affeb8a7f900dc34aaf21a14b0e","74","2129","13","task_categories:text-to-speech, task_categories:audio-to-audio, task_categories:audio-classification, multilinguality:monolingual, language:ja, license:other, size_categories:10K<n<100K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us, laughter, audio, speech, japanese, anime, voice","text-to-speech, audio-to-audio, audio-classification","😁 Ehehe Corpus 🤣 Audio dataset of laughter performed by Japanese voice actors 日本語はこちら このデータセットは、著作権法第三十条の四の情報解析（機械学習等）の目的でのみ使用が許可されています。それ以外の用途での使用はライセンスにより禁止されています。 This dataset is only permitted for","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","other","None","None","None"
+"DeL-TaiseiOzaki/magpie-llm-jp-3-13b-20k","DeL-TaiseiOzaki","2024-10-13 03:40:26","2024-10-13 04:07:44+00:00","3eb7352243bc50b24a41c2a87303704b144c4924","358","2096","0","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","合成日本語指示データセット 概要 このデータセットは、大規模言語モデル（LLM）を用いて自動生成された日本語の指示とそれに対する応答のコレクションです。データセットは指示付与型のタスクのための学習や評価に使用することを目的としています。 データセット仕様 サンプル数: 20,000 言語: 日本語 フォーマット: JSON 生成方法 データセットは以下のプロセスを通じて生成されました： LLM-J","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Elite35P-Server/EliteVoiceProject","Elite35P-Server","2022-11-30 16:10:15","2023-01-14 19:28:16+00:00","520557a22a9299f45211e9bf66ee75bcd4b8c8d7","536","2084","10","annotations_creators:crowdsourced, language_creators:さくらみこ, language_creators:hololive production, multilinguality:monolingual, language:ja, license:other, size_categories:1K<n<10K, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","None","Elite Voice Project これはホロライブ所属Vtuberさくらみこ氏の声をデータセット化し音声認識などで活用できるようにする事を目的とした非公式プロジェクトです。 LICENSEについて データセット内の音声データ すべてのデータは、hololive productionの二次創作ガイドラインに準拠する形で利用されています。 これらのデータの著作権はカバー株式会社等が保有しており、","@InProceedings{elitevoiceproject:dataset, title = {Elite Voice Project}, author={Elite35P Server.}, year={2022} }","ja","mono","None","None","False","False","False","other","None","None","None"
+"DeL-TaiseiOzaki/Tengentoppa-sft-v1.0","DeL-TaiseiOzaki","2024-11-24 01:20:50","2024-12-10 07:36:22+00:00","93eff58ebe3c1d62b7d3c73a7afff83c112dfce7","106","2063","22","task_categories:question-answering, language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Tengentoppa corpus for sft (Combined Japanese Instruction Dataset) 概要 このデータセットは、日本語の instruction-following データセット16個を統合して作成された大規模な教師あり学習用データセットです。様々なタスクや対話形式を含む多様なデータソースから構成されています。 データセット構成 基本情報 フォーマッ","None","ja","mono","None","None","False","False","False","cc-by-4.0","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"
+"hpprc/jawiki","hpprc","2024-02-02 06:36:00","2024-02-13 15:19:49+00:00","e7a57a34c5d5ba0aded664d1bffb40396f31c4e2","151","2026","16","language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","JaWiki WikipediaのHTML形式のダンプファイルから抽出したテキストデータセットです。 Wikiextractorによって抽出したテキストデータと異なり、段落などの文書構造を維持したまま、不要なマークアップのないテキストが利用できます。 ダンプファイルは、2024年1月1日に公開されたものを利用してい���す。 また、各種NLPタスクに利用しやすいよう、様々なデータを同梱しています。 各","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"kunishou/amenokaku-code-instruct","kunishou","2023-10-01 01:04:50","2024-04-01 17:01:54+00:00","2cebb257662b7054bd6629efb7e58930f8d9f5ba","114","2026","17","language:ja, license:other, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Amenokaku-Code-Instruct Update: 2023/12/27データセットに JaxTon , プロになるJava のコードデータ 180 レコードを追加しました。 概要 コードに特化した5.2KのInstructionデータセットです。 データセットに含まれるデータは商用利用できるラインセンスが付与されたプログラミング学習コンテンツから収集、加工し作成しました（英語のコンテ","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"Cohere/miracl-ja-queries-22-12","Cohere","2023-01-31 09:20:40","2023-02-06 11:57:00+00:00","5fcfc107f41b705395ba07ecc14fe5ce0f61eb93","46","2024","3","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, language:ja, 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 (ja) 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","ja","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"sil-ai/bloom-vist","sil-ai","2022-05-05 16:25:54","2022-12-06 19:37:53+00:00","3997b23648a3961e1e566d976ac9310aa2ac5e20","62","2017","5","None","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"
+"cyberagent/chatbot-arena-ja-calm2-7b-chat-experimental","cyberagent","2024-01-20 04:07:21","2024-08-15 04:51:49+00:00","efd0a6d57bab699a2f05d0e301950136effb8d5d","46","2003","19","language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.16316, arxiv:2004.06063, arxiv:2305.18290, region:us","None","Dataset Card for ""chatbot-arena-ja-calm2-7b-chat"" Chatbot Arena Conversations JA (calm2) Dataset Chatbot Arena Conversations JA (calm2)はこちらの論文で構築されたRLHFのための日本語Instructionデータセットです。 「英語で公開されているデータセットをオー","None","ja","mono","None","None","False","auto","False","cc-by-4.0","2406.16316","None","None"
+"shunk031/STAIR-Captions","shunk031","2024-06-21 04:10:47","2024-06-22 06:23:12+00:00","091864e6ed130ee08b4fdbcebd0a2ae41f6f360e","205","1988","5","task_categories:image-to-text, task_ids:image-captioning, annotations_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:extended|MSCOCO, language:ja, 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:1705.00823, arxiv:2005.00614, region:us, image-captioning, mscoco","image-to-text, image-captioning","Dataset Card for STAIR-Captions Dataset Summary STAIR Captions is a large-scale dataset containing 820,310 Japanese captions. This dataset can be used for caption generation, multimodal retrieval, and","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","1705.00823","None","image-captioning"
+"sarulab-speech/SaSLaW","sarulab-speech","2024-08-11 12:27:42","2024-08-13 12:27:29+00:00","30314ed88ed68e03e2c5a8a93d6ebb9f9eaba1e3","298","1981","0","task_categories:text-to-speech, language:ja, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","text-to-speech","This repository contains the data of SaSLaW corpus. You can download it via the following command: huggingface-cli download sarulab-speech/SaSLaW --repo-type dataset --local-dir SaSLaW Please see the ","None","ja","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"hotchpotch/JaCWIR","hotchpotch","2024-03-23 05:57:57","2025-01-20 08:44:59+00:00","0cd42bf06d0cde0a4c46c82d1a1d76341dc4ddd9","141","1949","6","language:ja, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","JaCWIR: Japanese Casual Web IR - 日本語情報検索評価のための小規模でカジュアルなWebタイトルと概要のデータセット 近年、大規模言語モデル（LLM）の台頭により、一般的な日本語を用いた自然な検索クエリで質問するユースケースが増えています。しかしながら、多様なジャンルの Web 記事に対して、ユーザーの質問に適切に答えられるような情報検索システムを評価するための日本語","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"kunishou/oasst2-135k-ja","kunishou","2023-12-24 22:04:54","2023-12-25 13:23:55+00:00","af218f0f269812551436c1b659ab8089ca20286a","54","1945","12","language:ja, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Update: 2023/12/25oasst2-135k-jaをチャット形式に変換したoasst2-chat-68k-jaを公開しました。 This dataset was created by automatically translating ""OpenAssistant/oasst2"" into Japanese by DeepL. ""OpenAssistant/oasst2"" を Dee","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"shivendrra/consolidated-datasets","shivendrra","2024-02-03 05:55:31","2024-12-12 23:13:21+00:00","0d9e5a0ef3ed189781fd6bb2ecffc094e105c1a7","317","1819","1","task_categories:text-generation, task_categories:summarization, language:en, language:hi, language:ja, language:fr, size_categories:100M<n<1B, format:text, modality:text, library:datasets, library:mlcroissant, region:us, textdataset, text, youtube, webscrapped data, youtube transcripts, llm training, transformer models","text-generation, summarization","Dataset Card for YouTubeTranscriptData Dataset Details Dataset Description This dataset contains transcripts of around 167K youtube videos that include coding lectures, podcasts, interviews, news vide","None","en, hi, ja, fr","multi","1|B|<|n|<|1|0|B|||1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"slone/nllb-200-10M-sample","slone","2023-10-30 23:43:49","2023-11-20 13:15:10+00:00","112cd775b7d7146372f560738eda4fd53c8a563d","95","1789","9","None","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"
+"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","None","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"
+"nthakur/swim-ir-cross-lingual","nthakur","2024-04-27 23:58:22","2024-04-28 05:11:45+00:00","1d4d91d3bb020536b4a88cc4982ae4f9dc87cc5e","153","1750","6","None","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","None","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"
+"alfredplpl/anime-with-caption-cc0","alfredplpl","2024-06-03 04:37:13","2024-06-03 05:49:20+00:00","13c9c9a1df5cf927962b575e51b110c4fa113d5c","191","1728","15","task_categories:image-to-text, language:en, language:ja, license:cc0-1.0, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","image-to-text","Anime with caption CC-0 dataset このデータセットはイラストに対する日本語キャプションを 倫理的に学習しやすくするためのデータセットです。 ここに掲載されているイラストは自律的にAIが作成したものであり、 著作権はありません。またキャプションも自律的につけられたものなので、 著作権はありません。したがって、データセットの著作権を私は放棄します。 勝手に使ってください。","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc0-1.0","None","None","None"
+"blancsw/oasst2_top1_chat_format","blancsw","2024-01-02 08:48:25","2024-01-23 09:36:49+00:00","edf92a505b0f6afaa4c435dd96072c0bd6a16b37","50","1711","12","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, sft","conversational","OpenAssistant TOP-1 Conversation Threads in huggingface chat format Export of oasst2 only top 1 threads in huggingface chat format Script The convert script can be find here","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","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","None","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"
+"SakanaAI/JA-VG-VQA-500","SakanaAI","2024-02-29 23:31:26","2024-05-14 04:11:31+00:00","aa13b70cd939faa1783187a752be2c0ca0d8303b","73","1681","13","task_categories:visual-question-answering, language:ja, 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:2403.13187, region:us","visual-question-answering","JA-VG-VQA-500 Dataset Description JA-VG-VQA-500 is a 500-sample subset of Japanese Visual Genome VQA dataset. This dataset was used in the evaluation of EvoVLM-JP-v1-7B. Please refer to our report and","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","2403.13187","None","None"
+"hpprc/jawiki-books","hpprc","2024-04-10 14:19:33","2024-09-13 11:13:12+00:00","15378a72f0ec7339ed336541e28fae8928ad4dfa","53","1679","2","language:ja, 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","None","None","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"saldra/sakura_japanese_dataset","saldra","2023-06-07 05:44:23","2023-06-08 11:31:06+00:00","70a52342ded273b4ecfac44541619d77e0effde3","102","1635","18","task_categories:question-answering, language:ja, license:other, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Sakura_dataset 商用利用可能な超小規模高品質日本語データセット。 categoryは以下 commonsense_qa: 常識問題 Calc-ape210k: 数学問題 japanese-commonsense-openqa: 日本の常識問題(自作) 下記データセットを使用しています。 commonsense_qa MU-NLPC/Calc-ape210k LICENSE This ","None","ja","mono","n|<|1|K","None","False","False","False","other","None","None","None"
+"yulanfmy/databricks-qa-ja","yulanfmy","2023-05-15 13:27:23","2023-05-15 14:55:06+00:00","8d96a601235a8191f5363c5bf2ce576a8fe88b39","63","1627","5","task_categories:question-answering, language:ja, 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","question-answering","データセット概要 手動で作成したDatabricksに関する質問と回答ペアの日本語データセットです。 件数：約1,300件 情報源：Databricks HPの日本語ブログやFAQなど、データブリック社員がポストしたQitta記事 https://github.com/yulan-yan/build-your-chat-bot-JP デモに利用したデータです。","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-3.0","None","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","None","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"
+"hatakeyama-llm-team/AutoGeneratedJapaneseQA","hatakeyama-llm-team","2024-04-27 04:32:20","2024-05-19 03:22:08+00:00","d49cac443fe2c34a0bb9bf27b03059a88098a32e","72","1588","3","language:ja, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","自動生成Q&A 種々のデータソースから､MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUFを使ってQ&Aを自動生成したものです｡ CC-BY系またはApatch-2.0のデータソースを改変して生成しています。 元の文章との類似度(依拠性)が低くなるようにするため、元文章からランダムに部分抜粋したテキストを用いています。 不自然な文章も含まれているためクリー","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Cohere/wikipedia-22-12-ja-embeddings","Cohere","2023-01-14 03:52:53","2023-03-22 16:55:06+00:00","40586a9887f2d274e10e7d365c349b69eb4a03e4","142","1577","1","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:multilingual, language:ja, 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 (ja) embedded with cohere.ai multilingual-22-12 encoder We encoded Wikipedia (ja) using the cohere.ai multilingual-22-12 embedding model. To get an overview how this dataset was created and ","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"FreedomIntelligence/ApolloMoEDataset","FreedomIntelligence","2024-10-14 03:03:59","2024-10-18 02:52:33+00:00","45075ce81754ac0012a74a24ba8ab4a5ed8620d0","267","1567","3","None","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","None","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"
+"sentence-transformers/parallel-sentences-news-commentary","sentence-transformers","2024-04-30 07:30:07","2024-06-18 19:45:37+00:00","35fd9337ef40e687350785254e7bca6f4f345157","355","1549","2","task_categories:feature-extraction, task_categories:sentence-similarity, language:en, language:multilingual, language:ar, language:cs, language:de, language:es, language:fr, language:it, language:ja, language:nl, language:pt, language:ru, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sentence-transformers","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - News Commentary This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of th","None","en, multilingual, ar, cs, de, es, fr, it, ja, nl, pt, ru","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"izumi-lab/oscar2301-ja-filter-ja-normal","izumi-lab","2023-07-12 16:38:36","2023-07-29 03:16:00+00:00","7be26b7ad5bbb4960db92f5b87583ff94fe32335","154","1527","4","language:ja, license:cc0-1.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""oscar2301-ja-filter-ja-normal"" More Information needed","None","ja","mono","None","None","False","False","False","cc0-1.0","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","None","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"
+"romrawinjp/multilingual-coco","romrawinjp","2024-10-23 08:03:46","2024-10-25 11:57:55+00:00","2b65d7c1061338f936077c61df6cdff1381b91f9","109","1489","0","None","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"
+"shi3z/alpaca_cleaned_ja_json","shi3z","2023-05-17 06:37:34","2023-08-25 23:18:42+00:00","7542436257173e638085955b5605b910f71f29e7","111","1484","7","task_categories:text-generation, language:ja, 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","Dataset Card for Dataset Name Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Supported Tasks and Leaderboards [More Infor","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"bclavie/mmarco-japanese-hard-negatives","bclavie","2023-12-24 13:04:26","2023-12-24 18:52:04+00:00","e25c91bc31859606507a968559ab1de0f472d007","289","1472","5","task_categories:text-retrieval, language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-retrieval","[Under Construction] This is a repository containing all the queries from the Japanese part of the MMarco dataset, the multilingual version of the MSMarco dataset. For each query, there are matching h","None","ja","mono","None","None","False","False","False","None","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","None","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"
+"y2lan/japan-law","y2lan","2023-07-20 06:26:25","2023-07-20 06:45:14+00:00","4faf88c5ce18c8ad5f195f540e2754b9004ff416","55","1421","11","task_categories:summarization, task_categories:text-generation, task_categories:question-answering, language:ja, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","summarization, text-generation, question-answering","Japanese Laws This dataset comprises 8.75K law records retrieved from the official Japanese government website e-Gov. Each entry furnishes comprehensive details about a particular law, encapsulating i","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","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","None","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"
+"toramaru-u/cc100-ja","toramaru-u","2024-07-05 05:00:50","2024-12-19 04:51:22+00:00","0be877f993b07b30accfe5d98cdc73572ae9837e","647","1390","2","None","None","None","None","ja","mono","None","None","False","False","False","None","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","None","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","None","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"
+"sakusakumura/databricks-dolly-15k-ja-scored","sakusakumura","2023-06-27 09:14:41","2023-06-27 09:18:39+00:00","637c9f403fb92b7bb713a9bb91a1a70ef72c95c7","53","1376","5","task_categories:question-answering, task_categories:summarization, language:ja, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1904.09675, region:us","question-answering, summarization","For the English version, please click here. 概要 databricks-dolly-15k-ja-scoredはkunishou/databricks-dolly-15k-jaの派生であり、BERTScoreによって提供される翻訳品質スコアが追加されています。 このデータセットは、学術的・商業的問わずクリエイティブ・コモンズ 表示 - 継承 3.0 非移","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","1904.09675","None","None"
+"llm-jp/magpie-sft-v1.0","llm-jp","2024-11-13 18:02:16","2024-11-13 18:54:02+00:00","4f949e18cfa01d99abe6f92e5629157d69737e6d","241","1369","10","None","text-generation","magpie-sft-v1.0 This repository provides an instruction-tuning dataset developed by LLM-jp, a collaborative project launched in Japan. This is a dataset of instruction and response pairs created using","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2406.08464","None","None"
+"kunishou/OpenMathInstruct-1-1.8m-ja","kunishou","2024-02-23 16:31:34","2024-02-24 18:29:28+00:00","fe9ca7ffd21df54e080e5484b6b2fb064ea37c5f","99","1369","11","language:ja, license:other, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.10176, region:us","None","OpenMathInstruct-1 を日本語に自動翻訳した商用利用可能な180万件の指示チューニングデータセットになります。 OpenMathInstruct-1 は、GSM8K および MATH ベンチマーク トレーニングセットの question と Mixtral-8x7B モデルを使用して生成された solution のペアで構成される数学分野のデータセットです。solution は合成","None","ja","mono","None","None","False","False","False","other","2402.10176","None","None"
+"llm-jp/oasst2-33k-ja","llm-jp","2024-04-28 16:24:00","2024-04-28 16:39:03+00:00","df6f564ceaa48d3435a200f9f926aa9ab71768a9","66","1365","11","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","oasst2-33k-ja This repository provides an instruction tuning dataset developed by LLM-jp, a collaborative project launched in Japan. The dataset comprises a Japanese translation of an English subset f","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"neulab/mconala","neulab","2023-02-10 18:08:54","2023-02-10 19:01:31+00:00","3b1cd700203f4a613a4f306270220f32514be2cd","123","1360","2","task_categories:text-generation, task_categories:translation, language:es, language:ja, language:ru, license:cc-by-sa-4.0, size_categories:n<1K, modality:text, library:datasets, library:mlcroissant, arxiv:2203.08388, region:us, code generation","text-generation, translation","MCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian.","@article{wang2022mconala, title={MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages}, author={Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, Graham Neubig}, journal={arXiv preprint arXiv:2203.08388}, year={2022} }","es, ja, ru","multi","n|<|1|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"p1atdev/pvc","p1atdev","2023-01-21 16:12:04","2023-11-29 12:01:52+00:00","254ac58b7ce1716a32f244b80e390359e201aadc","348","1349","7","language:en, language:ja, license:cc0-1.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","PVC figure products dataset This dataset contains product information of figure images scraped from multiple Web sites. Dataset information Subset Source Size goodsmile-figma https://www.goodsmile.inf","None","en, ja","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc0-1.0","None","None","None"
+"hpprc/janli","hpprc","2023-04-05 12:25:01","2023-04-11 04:40:37+00:00","b91923216e61ded52ec77316cb329b47227e4955","79","1299","6","task_categories:text-classification, task_ids:natural-language-inference, language_creators:other, multilinguality:monolingual, language:ja, license:cc-by-sa-4.0, size_categories:10K<n<100K, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, natural-language-inference","Dataset Card for JaNLI Dataset Summary The JaNLI (Japanese Adversarial NLI) dataset, inspired by the English HANS dataset, is designed to necessitate an understanding of Japanese linguistic phenomena ","@InProceedings{yanaka-EtAl:2021:blackbox, author = {Yanaka, Hitomi and Mineshima, Koji}, title = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference}, booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)}, year = {2021}, }","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","natural-language-inference"
+"ZurichNLP/rsd-ists-2016","ZurichNLP","2023-05-20 16:24:04","2023-11-22 12:35:55+00:00","e0c1dd23ce0d62f55392184515ac55975625b37b","143","1267","0","None","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","None","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"
+"systemk/washi","systemk","2024-02-13 01:17:22","2024-03-06 03:16:54+00:00","911546e8d94e774bfa2c2b3f49dde5b0c8dbdb49","172","1237","1","task_categories:text-generation, language:ja, license:other, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, nlp, pretrain, llm","text-generation","Washi (a kind of traditional Japanese paper) This dataset is sampled from a subset of ja (Japanese) sourced from uonlp/CulturaX. Utilizing DSIR (Data Selection for Language Models via Importance Resam","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","other","None","None","None"
+"Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k","Aratako","2024-07-05 13:53:44","2024-07-05 13:57:08+00:00","cd16697271d8fb29113293be8cb0b6210c90ba84","74","1233","11","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","text-generation","Synthetic-JP-Conversations-Magpie-Nemotron-4-10k Magpieの手法をnvidia/Nemotron-4-340B-Instructに対して適用し作成した、約10000件の日本語instruction tuning用データセットです。 データセットの作成にはDeepInfraを利用しました。 また、このリポジトリでデータセット作成に用いたコードを公開","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2406.08464","None","None"
+"YANS-official/ogiri-debug","YANS-official","2024-08-30 04:18:35","2024-09-04 01:49:46+00:00","b759e7f0ed7051b0a561810bee5a937b5b90626a","42","1223","2","task_categories:image-to-text, task_categories:text2text-generation, language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","image-to-text, text2text-generation","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/ogiri-debug"", split=""test"") 概要 大喜利生成の動作確認用データセットです。以下の3タスクが含まれます。 text_to_text: テキストでお題が渡され、それに対する回答を返します。 image_to_text: ","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"YANS-official/senryu-debug","YANS-official","2024-08-30 05:47:58","2024-09-04 01:49:15+00:00","4eec86f5f84fde245cac9caee0525917a71c036d","43","1204","0","task_categories:image-to-text, task_categories:text2text-generation, language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","image-to-text, text2text-generation","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/senryu-debug"", split=""test"") 概要 大喜利生成の動作確認用データセットです。以下の2タスクが含まれます。 text_to_text: テキストでお題が渡され、それに対する回答を返します。 image_to_text:","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"KBlueLeaf/danbooru2023-metadata-database","KBlueLeaf","2024-01-11 05:13:15","2024-11-01 16:45:37+00:00","da5c8c2be09022b5aaf718d18bd981cdf32111ff","70","1196","72","task_categories:image-classification, task_categories:text-to-image, task_categories:image-to-text, task_categories:image-to-image, task_categories:text-retrieval, task_categories:text-generation, task_categories:text-classification, language:en, language:ja, license:mit, size_categories:1M<n<10M, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, anime","image-classification, text-to-image, image-to-text, image-to-image, text-retrieval, text-generation, text-classification","Metadata Database for Danbooru2023 Danbooru 2023 datasets: https://huggingface.co/datasets/nyanko7/danbooru2023 The latest entry of this database is id 7,866,491. Which is newer than nyanko7's dataset","None","en, ja","bi","1|M|<|n|<|1|0|M","None","False","auto","False","mit","None","None","None"
+"nazimali/quran","nazimali","2024-09-08 18:50:07","2024-09-08 18:58:17+00:00","e7070e8411006fc1b469dbf0f1f54d446e562d33","420","1191","2","None","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"
+"kanhatakeyama/SyntheticText","kanhatakeyama","2024-06-04 04:56:13","2024-07-16 07:30:43+00:00","4b6841c54e278d2e5c3aa2f33d5eecb5301cceb4","190","1178","1","language:ja, 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","None","以下のデータ源からランダムに抽出したテキストをもとに､phi3で再生成した文章です｡ Wikibooks Wikipedia Cosmopedia 判例データ データ parquetファイルが数十GB程度あります datasetsライブラリからでは､はじめの数GB程度しか読み込めない可能性があります｡git lfsなどでダウンロードする必要がありそうです｡ コード こちら 一部の計算には東京工業大","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"fujiki/guanaco_ja","fujiki","2023-06-24 08:27:30","2023-07-16 15:01:30+00:00","3075ead3c0e231759e7b846b9e53deb35e237c65","135","1169","6","language:ja, license:gpl-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This is a Japanese portion of the Guanaco dataset. You can also refer to other similar datasets like inu-ai/alpaca-guanaco-japanese-gpt-1b.","None","j, a","bi","None","None","False","False","False","gpl-3.0","None","None","None"
+"zan/lima-ja","zan","2023-07-08 07:35:34","2023-07-08 13:39:45+00:00","5989cc1a5fb3d46aa3565ee92f6effed199a1974","78","1166","3","language:ja, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, arxiv:2305.11206, region:us","None","A high-quality japanese dataset for efficient instruction tuning.","@InProceedings{huggingface:dataset, title = {LIMA-JA: Japanese LIMA Dataset}, author={zan}, year={2023} }","ja","mono","None","None","False","False","False","cc-by-nc-sa-4.0","2305.11206","None","None"
+"NilanE/ParallelFiction-Ja_En-100k","NilanE","2024-03-25 23:41:17","2024-06-02 18:03:38+00:00","d0ec44278a34afce934ff1d2553f9628e03d6158","231","1163","66","task_categories:translation, language:ja, language:en, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset details: Each entry in this dataset is a sentence-aligned Japanese web novel chapter and English fan translation. The intended use-case is for document translation tasks. Dataset format: { 'sr","None","ja, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"recursal/MDN","recursal","2024-05-30 06:50:40","2024-06-13 01:18:19+00:00","8fc26a9528158cb87691d8c4776b31a2b9dfaef0","70","1142","0","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, source_datasets:original, language:en, language:es, language:fr, language:ja, language:ko, language:pt, language:ru, language:zho, license:cc-by-sa-3.0, size_categories:10K<n<100K, 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 MDN Waifu to catch your attention. Dataset Description MDN is a ~57M Tokens (llama-2-7b-chat-tokenizer) / ~46.52M Tokens (RWKV Tokenizer) scrape of MDN (Developer.mozilla.org). It ser","None","en, es, fr, ja, ko, pt, ru, zho","multi","None","None","False","False","False","cc-by-sa-3.0","None","None","language-modeling, masked-language-modeling"
+"seungwon929/Ja-miracl","seungwon929","2024-05-07 04:06:27","2024-05-07 05:17:43+00:00","be7905d518104cda56809c97eb1bc35e42530d05","43","1126","0","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:miracl, language:ja, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","Ja-miracl This dataset represents a conversion of the Japanese (Ja) section from the miracl dataset into the BeIR format, making it compatible for use with mteb.","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","document-retrieval"
+"laihuiyuan/mCoT-MATH","laihuiyuan","2024-04-02 11:18:01","2024-06-05 11:42:17+00:00","c4415e8c3591ce61f6da64080b50d44c22c5a440","109","1105","4","None","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"
+"neody/kusanagi","neody","2024-06-28 14:00:10","2024-07-08 22:57:44+00:00","affdde8c834738be9e26083e0b34c2a86ec90c18","63","1085","1","language:ja, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"range3/wikipedia-ja-20230101","range3","2023-02-04 04:29:29","2023-02-04 05:44:41+00:00","8e3272f867812aa23cfd3214a36f44aafd45d4fc","56","1075","4","task_categories:text-generation, task_categories:fill-mask, language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, fill-mask","range3/wikipedia-ja-20230101 This dataset consists of a parquet file from the wikipedia dataset with only Japanese data extracted. It is generated by the following python code. このデータセットは、wikipediaデータセ","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"hpprc/alt-parallel-en-ja","hpprc","2024-03-21 02:24:27","2024-03-21 12:40:15+00:00","1302a9544ace91130f00d7f94a3d73a23fe7b4f9","72","1053","3","task_categories:translation, language:en, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Asian Language Treebank (ALT) Project ALT Parallel Corpusのうち、日英対訳部分のみを抽出したデータセットです。 処理元のデータとしては、HuggingFace上のhttps://huggingface.co/datasets/altを利用しています。 Citation @inproceedings{riza2016introduction, ","None","en, ja","bi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"kanhatakeyama/japanese-corpus-categorized","kanhatakeyama","2024-09-25 23:18:11","2024-09-28 01:36:48+00:00","42846d8e445493c647f7972e200a8f3632969fff","465","1048","2","language:ja, license:odbl, size_categories:100M<n<1B, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","日本語コーパス mc4-jaなどのwebコーパスをクリーニング後､教師なし学習モデルでテキストを約1万件にクラスタリングしたコーパスです｡ 著作権法で認められた情報解析目的で使用できます｡ 一部のファイルしかparquet化されていないので､ご注意ください｡ファイルリストはoutフォルダ内にあります git lfsなどでダウンロードください｡","None","ja","mono","None","None","False","False","False","odbl","None","None","None"
+"Silviase/Japanese-Heron-Bench","Silviase","2024-07-16 08:12:30","2024-07-28 12:33:15+00:00","334d1d21ef9c63e635d253e163b102f7f466e590","169","1048","1","language:ja, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2404.07824, region:us","None","This dataset is a clarified version of the image, context, and question set included in the Japanese-Heron-Bench for the construction of the Japanese evaluation benchmark suite. The original dataset r","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"kunishou/HelpSteer-35k-ja","kunishou","2024-03-02 16:45:19","2024-03-03 10:10:54+00:00","4631a16c928e6958190fc11834a196c5d7d16d16","48","1046","3","language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","NVIDIA が公開している SteerLM 向けのトライアルデータセット HelpSteerを日本語に自動翻訳したデータセットになります。SteerLM でのアライメントをお試ししたい際にご活用下さい。 SteerLM での LLM トレーニング方法については以下の URL を参考にして下さい。 Announcing NVIDIA SteerLM : https://developer.nvid","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k","Aratako","2024-07-07 11:08:34","2024-07-07 11:13:47+00:00","34bd57bc633a568520e3d4080b2c77c89c34e351","52","1042","5","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, translation","text-generation","Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k Magpieの手法をnvidia/Nemotron-4-340B-Instructに対して適用し作成した、20000件の日⇔英翻訳データセットです。 データセットの作成にはDeepInfraを利用しました。 また、このリポジトリでデータセット作成に用いたコードを公開しています。これを","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2406.08464","None","None"
+"hotchpotch/cc100-ja-documents","hotchpotch","2024-07-11 22:26:16","2024-07-13 12:02:11+00:00","ac27775f68ca3e7b3184aa54bd286e409d093d16","291","1028","2","language:ja, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","cc100-ja-documents HuggingFace で公開されている cc100 / cc100-ja は line 単位の分割のため、document 単位に結合したものです。 ライセンスはオリジナルのcc100 に準拠します。","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Aratako/Synthetic-JP-EN-Coding-Dataset-801k","Aratako","2024-07-14 14:04:30","2024-09-28 05:01:14+00:00","9a4bbe5ee813ef51b0c03149a75dd06fbfc48d38","94","1026","15","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, code","text-generation","Synthetic-JP-EN-Coding-Dataset-801k Magpieによって作成したコードSFTデータセットであるAratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69kを元に、Evol-Instructのような手法を用いて複数のinstructionとresonseを生成し拡張して作成した、日英混合801262件のコードSFT用合成データ","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"ayousanz/OSCOR-2301-ja-cleaned","ayousanz","2024-05-08 04:52:54","2024-05-09 03:16:36+00:00","92d441c1a026aa80aae1dbb73898306a8baf58a0","223","1026","0","None","None","概要 oscar-corpus/OSCAR-2301の以下のjaのみを corpus-cleanerを使用してデータクリーニングを行なったデーセット群 Code Language # docs # words Content Length : ja Japanese 94,236,404 4,401,059,165 181.2 GB ただし以下のファイルは、クリーニングが成功していないため除外して","None","ja","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"grider-withourai/nekopara-speech","grider-withourai","2024-07-20 02:48:24","2024-07-20 13:52:58+00:00","5a526eda117946b16f147671176938162ef42fa5","128","1012","12","task_categories:text-to-speech, task_categories:automatic-speech-recognition, language:ja, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, anime, galgame, visual novel","text-to-speech, automatic-speech-recognition","Nekopara Audio Dataset Dataset Description This dataset contains audio samples and associated metadata from the Nekopara visual novel series, covering volumes 0-4 and extra content. Features Feature N","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"TangRain/SingMOS","TangRain","2024-11-05 16:11:07","2024-11-11 07:40:19+00:00","f2a0b005a09652bc7de80724146291ee96706585","127","979","2","None","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"
+"Mitsua/vrm-color-concept-550k","Mitsua","2024-11-29 02:38:21","2024-11-29 09:04:12+00:00","d37b8d75e31dd52db37db5554e7ee6dc9412b56c","154","976","2","task_categories:text-to-image, task_categories:image-to-text, language:en, language:ja, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us","text-to-image, image-to-text","VRM Color Concept 550K Summary This is a dataset to train anime-style text-to-image or any text and image multimodal models without copyright/licensing concerns. All assets/materials utilized in this ","None","en, ja","bi","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"weblab-GENIAC/aya-ja-nemotron-dpo-masked","weblab-GENIAC","2024-10-12 03:14:11","2024-10-19 01:41:40+00:00","453e778b4ed6b55757dd2a81af2a967d204884af","59","961","3","task_categories:text-generation, task_categories:question-answering, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic","text-generation, question-answering","aya-ja-nemotron-dpo-masked LLMの推論能力を向上させるためのデータセット CohereForAI/aya_datasetから日本語パートを抜粋 deepinfraのnvidia/Nemotron-4-340B-Instructで応答を再生成 2024年8月現在ではnvidia/Nemotron-4-340B-Instructは使用不可 5,651件(6,259件の内60","None","ja","mono","1|K|<|n|<|1|0|K","None","False","auto","False","apache-2.0","None","None","None"
+"Henrychur/MMedC","Henrychur","2024-02-20 06:46:36","2024-07-25 11:29:07+00:00","6629f0a54ae73b6f32d42476b2b9e90ff96035fd","230","942","24","language:en, language:zh, language:ja, language:fr, language:ru, language:es, language:ar, language:de, license:cc-by-nc-sa-4.0, size_categories:10B<n<100B, modality:text, arxiv:2402.13963, region:us, medical","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"
+"HachiML/alpaca_jp_math","HachiML","2024-05-19 13:39:40","2024-05-21 21:46:21+00:00","080b7c480f35066ce4f3fa0959800dd925eda964","103","934","6","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, math, code, python, self-instruct","text-generation","alpaca_jp_math alpaca_jp_mathは、 Stanford Alpacaの手法 mistralai/Mixtral-8x22B-Instruct-v0.1 で作った合成データ(Synthetic data)です。モデルの利用にはDeepinfraを利用しています。 また、""_cleaned""がついたデータセットは以下の手法で精査されています。 pythonの計算結果がきちんと","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Cohere/miracl-ja-corpus-22-12","Cohere","2023-01-31 08:42:35","2023-02-06 11:57:11+00:00","f514a4d85a2c560ed24c17b14a4d1b0c225bc6ef","54","918","1","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, 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","text-retrieval, document-retrieval","MIRACL (ja) 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","ja","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"kajuma/CC-news-2024-July-October-cleaned","kajuma","2024-11-17 13:16:59","2024-11-17 15:07:26+00:00","510b225698ffe38683ec52fa44eefd7bd968b2d7","219","915","15","None","text-generation","CC-news-2024-July-October-cleaned このデータセットはCommon Crawlのnewsサブセットから作成した2024年7月から10月の日本語のニュースの文章が収録されています。 データセットの規模はllm-jp/llm-jp-13b-v1.0 tokenizer使用で612M tokensです。 使用したツールはUzushioです。フィルター設定はサンプルのpip","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","odc-by","None","None","None"
+"hpprc/kaken-trans-ja-en","hpprc","2024-11-29 13:02:31","2025-01-09 04:09:18+00:00","5f33a0d81885ef7aca7766623f6a5240587c8b80","537","914","6","task_categories:translation, language:ja, language:en, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, machine-translation, synthetic","translation","llm-jp-corpus-v3のkakenサブセット中の日本語テキストを、Qwen/Qwen2.5-32B-Instructを用いて日本語から英語に翻訳したデータセットです。 オープンな日英パラレルコーパスを意図して作成・公開しました。 id列は翻訳の際に使用したカラムであり、元データセットに存在するidカラムとは異なっています。 kakenサブセット自体のHF版データセットも合わせてご覧くださ","None","ja, en","bi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"AmazonScience/tydi-as2","AmazonScience","2023-05-16 00:02:00","2023-07-24 17:33:28+00:00","39481652408123c412b400f50b2eef530d2da1e8","191","906","1","task_categories:question-answering, task_categories:text-retrieval, task_ids:open-domain-qa, annotations_creators:machine-generated, language_creators:found, multilinguality:multilingual, multilinguality:translation, source_datasets:extended|tydiqa, language:bn, language:en, language:fi, language:id, language:ja, language:ko, language:ru, language:sw, license:cdla-permissive-2.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, as2, answer sentence selection, text retrieval, question answering","question-answering, text-retrieval, open-domain-qa","TyDi-AS2 Dataset Summary TyDi-AS2 and Xtr-TyDi-AS2 are multilingual Answer Sentence Selection (AS2) datasets comprising 8 diverse languages, proposed in our paper accepted at ACL 2023 (Findings): Cros","None","bn, en, fi, id, ja, ko, ru, sw","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cdla-permissive-2.0","None","None","open-domain-qa"
+"YANS-official/senryu-test","YANS-official","2024-09-03 15:02:47","2024-09-09 05:53:26+00:00","ec8c0d12e0db90b4d057966d2723447652207f35","228","905","0","task_categories:image-to-text, task_categories:text2text-generation, language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","image-to-text, text2text-generation","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/senryu-test"", split=""test"") 概要 川柳投稿サイトの『写真川柳』と『川柳投稿まるせん』のクロールデータ、および YANS 委員が作成したデータを含みます。 以下のページからクロールし、原本のHTMLファイルと構造化処理","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"SakanaAI/JA-Multi-Image-VQA","SakanaAI","2024-07-26 11:11:42","2024-08-01 11:44:27+00:00","15da0a53731cb2a69ffa97fda26340fe2aeba91e","91","905","10","task_categories:visual-question-answering, language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","JA-Multi-Image-VQA Dataset Description JA-Multi-Image-VQA is a dataset for evaluating the question answering capabilities on multiple image inputs. We carefully collected a diverse set of 39 images wi","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"sudy-super/CoTangent","sudy-super","2023-07-04 09:15:33","2024-12-28 17:26:22+00:00","a9f059a89aa11b810d42d79e6a10a894df268165","77","889","22","language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","CoTangentは人手で作成された高品質でクリーンな100セットの日本語CoT用データセットです。 CoTangent_ja.json: CoT部分とoutput部分が繋がっています。 CoTangent_separated_ja.json: CoT部分とoutput部分が分離されていますが、CoTangent_ja.jsonの方が繋ぎが自然です。","None","ja","mono","None","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","None","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"
+"llm-book/jawiki-paragraphs","llm-book","2023-06-03 03:04:05","2023-06-03 03:04:43+00:00","5932d8c85a3611e94bcbad4a1e7e366798ab3c43","51","868","0","language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us","None","Dataset Card for llm-book/jawiki-paragraphs 書籍『大規模言語モデル入門』で使用する Wikipedia 段落のデータセットです。 GitHub リポジトリ singletongue/wikipedia-utils で公開されているデータセットを利用しています。 Licence 本データセットで使用している Wikipedia のコンテンツは、クリエイティ","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"YANS-official/senryu-shashin","YANS-official","2024-08-28 18:50:08","2024-08-31 03:47:50+00:00","2eb54844811f1dfe4c2d6e7a6c7beaa2e9d356af","67","863","0","language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","None","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/senryu-shashin"", split=""train"") 概要 株式会社東建コーポレーションが運営するホームメイト・リサーチによる『ホームメイト川柳大賞』のうち、お題が画像形式で提供される『写真川柳』に関するクロールデータです。以下のペー","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"ShoukanLabs/OpenNiji-Dataset","ShoukanLabs","2023-05-26 07:28:21","2023-09-29 02:59:14+00:00","1945f5274a057f3d49ee0c9b4de99ff5c802406d","49","863","16","task_categories:text-to-image, language:en, language:ja, language:ko, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, anime, dataset, Nijijourney, Midjourney, discord","text-to-image","NOTE: Recently Discord has added link expiry and tracking for their CDN content, however, this is for CDN attachments outside of Discord, now due to the nature of how this was scraped (being directly ","None","en, ja, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-4.0","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","None","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"
+"Sakalti/Multilingal-sakalt-data","Sakalti","2024-10-14 05:11:16","2024-10-17 10:41:45+00:00","c943a8193cccaba0c409ae59adc46234b4198139","113","843","1","None","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"
+"Aratako/Magpie-Tanuki-8B-97k","Aratako","2024-10-03 14:02:26","2024-10-03 14:07:49+00:00","5b246a6846b1d8c89548415357b5dfc5abfe482f","65","842","11","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","text-generation","Magpie-Tanuki-8B-97k Magpieの手法をweblab-GENIAC/Tanuki-8B-dpo-v1.0に対して適用し作成した、97269件の日本語対話データセットです。 特に事後的なフィルタ処理は加えていないため、クオリティの低いレコードが含まれている可能性があります。ご注意ください。","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2406.08464","None","None"
+"FreedomIntelligence/ApolloMoEBench","FreedomIntelligence","2024-10-14 03:04:30","2024-10-15 08:38:05+00:00","757a9119cc8179d936afd0f3a34b6e39222ce95f","199","835","0","None","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"
+"hpprc/wiki-trans-en-ja","hpprc","2024-11-22 04:34:17","2025-01-09 04:08:49+00:00","a92c055b154d970ab3d02434bef8ecc1ae58e202","753","833","3","None","translation","None","None","ja, en","bi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"llm-book/aio-passages-bpr-bert-base-japanese-v3","llm-book","2023-06-06 08:24:36","2023-06-30 10:30:40+00:00","b871815c51d257745cda3e032ab1654f967bfafc","97","825","1","language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for llm-book/aio-passages-bert-base-japanese-v3-bpr 書籍『大規模言語モデル入門』で使用する、「AI王」コンペティションのパッセージデータセットに BPR によるパッセージの埋め込みを適用したデータセットです。 llm-book/aio-passages のデータセットに対して、llm-book/bert-base-jap","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"HachiML/alpaca_jp_python","HachiML","2024-05-16 02:02:08","2024-05-20 01:44:32+00:00","616136ab64788270ab3f43ce2010e24492514711","81","816","8","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, code, python, self-instruct","text-generation","alpaca_jp_python alpaca_jp_pythonは、 Stanford Alpacaの手法 mistralai/Mixtral-8x22B-Instruct-v0.1 で作った合成データ(Synthetic data)です。モデルの利用にはDeepinfraを利用しています。 また、""_cleaned""がついたデータセットはmistralai/Mixtral-8x22B-Inst","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"AmazonScience/xtr-wiki_qa","AmazonScience","2023-05-16 00:03:14","2023-07-24 17:32:38+00:00","49cc4ceb83259714e032dcbe7ff3a7b018c52efc","172","812","4","task_categories:question-answering, task_categories:text-retrieval, task_ids:open-domain-qa, annotations_creators:machine-generated, language_creators:found, multilinguality:multilingual, multilinguality:translation, source_datasets:extended|wiki_qa, language:ar, language:es, language:fr, language:de, language:hi, language:it, language:ja, language:nl, language:pt, license:cdla-permissive-2.0, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, as2, answer sentence selection, text retrieval, question answering","question-answering, text-retrieval, open-domain-qa","Xtr-WikiQA Dataset Summary Xtr-WikiQA is an Answer Sentence Selection (AS2) dataset in 9 non-English languages, proposed in our paper accepted at ACL 2023 (Findings): Cross-Lingual Knowledge Distillat","None","ar, es, fr, de, hi, it, ja, nl, pt","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cdla-permissive-2.0","None","None","open-domain-qa"
+"MohamedRashad/multilingual-tts","MohamedRashad","2023-12-01 23:46:06","2023-12-12 21:04:06+00:00","14729fbf35870fe21a8ae76d6d37e36dff0c08f8","210","801","27","None","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"
+"isek-ai/danbooru-wiki-2024","isek-ai","2024-07-07 05:28:47","2024-11-15 04:33:40+00:00","150ab9fe223322ce4e8ca2a1875c796ab7e0b371","213","799","14","task_categories:text-generation, task_categories:text-classification, language:en, language:ja, 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","text-generation, text-classification","danbooru-wiki-2024 About Wiki pages about the danbooru tags on danbooru.donmai.us. The wiki contains the description of each tag and matching to pixiv tags. Usage from datasets import load_dataset ds ","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-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","None","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"
+"allganize/RAG-Evaluation-Dataset-JA","allganize","2024-09-03 09:00:27","2024-09-13 00:53:44+00:00","c3a756711f108324d6f27f27093ad2c5eb53f7be","200","782","21","language:ja, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Allganize RAG Leaderboard とは Allganize RAG Leaderboard は、5つの業種ドメイン（金融、情報通信、製造、公共、流通・小売）において、日本語のRAGの性能評価を実施したものです。一般的なRAGは簡単な質問に対する回答は可能ですが、図表の中に記載されている情報などに対して回答できないケースが多く存在します。RAGの導入を希望する多くの企業は、自社と同","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"p1atdev/open2ch","p1atdev","2023-10-31 17:00:13","2023-11-01 01:59:51+00:00","8a48274854909717f0f9acd1c3400a320f57ba85","85","750","11","None","text-generation, text2text-generation","おーぷん2ちゃんねる対話コーパス Dataset Details Dataset Description おーぷん2ちゃんねる対話コーパス を Huggingface Datasets 向けに変換したものになります。 Curated by: [More Information Needed] Language: Japanese License: Apache-2.0 Dataset Source","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"baobab-trees/wikipedia-human-retrieval-ja","baobab-trees","2024-01-15 13:52:30","2024-03-19 04:25:44+00:00","02e85c83a64f345aee62fba3a35af99c075a331f","54","749","31","task_categories:question-answering, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","question-answering","Japanese Wikipedia Human Retrieval dataset This is a Japanese question answereing dataset with retrieval on Wikipedia articles by trained human workers. Contributors Yusuke Oda defined the dataset spe","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","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","None","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","None","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"
+"sakusakumura/dolly-14k-ines","sakusakumura","2023-11-03 12:44:16","2023-11-04 02:58:13+00:00","0003c3e8217bccc5c7c969ee1e266753a1860f7a","42","726","5","task_categories:question-answering, task_categories:summarization, language:ja, 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, summarization","dolly-14k-ines Description The dolly-14k-ines dataset is derived from the databricks-dolly-15k-ja, which is a machine-translated version of the databricks/dolly-15k. The entries have been further conv","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","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","None","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"
+"p1atdev/danbooru-ja-tag-pair-20241015","p1atdev","2024-10-15 14:35:08","2024-10-16 12:48:36+00:00","846b9a569c8595ba5fdde77a06af92d589e94692","60","715","5","task_categories:translation, language:en, language:ja, license:cc0-1.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","danbooru-ja-tag-pair-20241015 2024/10/15に作成したdanbooruタグと日本語タグのペアデータセット(約15万件) p1atdev/danbooru-ja-tag-pair-20240715 との違いは、 ベースのwikiデータが増えたのでその分対応タグも増えた fasttextでのフィルタリングを挟むようにした 「明らかに他言語のタグ」が混じる頻度はちょっ","None","en, ja","bi","None","None","False","False","False","cc0-1.0","None","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","None","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"
+"kunishou/databricks-dolly-69k-ja-en-translation","kunishou","2023-04-17 18:31:42","2023-10-21 15:09:14+00:00","4a11cb973b985e3c4cb55ce20b836d4834057fc0","73","697","11","None","None","This dataset was created by automatically translating ""databricks-dolly-15k"" into Japanese.This dataset contains 69K ja-en-translation task data and is licensed under CC BY SA 3.0. Last Update : 2023-","None","ja, en","bi","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"bandad/sayoko-tts-corpus","bandad","2023-08-16 02:11:29","2023-08-16 05:42:35+00:00","c3f2fe2f5fbfb87f4216db816471ebcfda4f3220","69","695","3","task_categories:text-to-speech, language:ja, license:cc-by-4.0, size_categories:n<1K, format:text, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","text-to-speech","サヨ子 音声コーパス ダウンロード方法 データセットを圧縮したzipファイルを、gdriveに置いています。 また、以下のスクリプトで、huggingface hubからダウンロードも可能です。 # pip install --upgrade huggingface_hub from huggingface_hub import snapshot_download snapshot_downloa","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"hac541309/basic_korean_dict","hac541309","2023-07-26 12:13:04","2023-07-26 12:28:43+00:00","1ba29964518a8e859e5898cdeacfc46ff8215a60","51","691","4","task_categories:table-question-answering, task_categories:text-generation, task_categories:text-classification, task_categories:question-answering, language:ko, language:mn, language:vi, language:th, language:id, language:ru, language:ja, language:en, language:fr, language:es, language:ar, 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, dictionary","table-question-answering, text-generation, text-classification, question-answering","Dataset Card for ""basic_korean_dict"" This dataset is a NLP learnable form of Korean Basic Dictionary(한국어기초사전). It follows the original copyright policy (cc-by-sa-2.0) Some words have usage examples in","None","ko, mn, vi, th, id, ru, ja, en, fr, es, ar, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"sappho192/Tatoeba-Challenge-jpn-kor","sappho192","2024-01-30 01:01:30","2024-01-30 16:51:21+00:00","096faf56f5facedf92caa606b529ad675d61eb7b","109","689","0","task_categories:translation, language:ja, language:ko, license:cc-by-nc-sa-4.0, size_categories:10M<n<100M, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Card for Dataset Name This dataset contains Japanese-Korean paired text which is from Helsinki-NLP/Tatoeba-Challenge. Dataset Details Dataset Sources Repository: Helsinki-NLP/Tatoeba-Challenge","None","ja, ko","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"sbintuitions/JEMHopQA","sbintuitions","2024-06-25 16:26:47","2025-01-22 07:17:12+00:00","81299ad7bbfdd342cebf851b576e766fcb076fb6","70","684","1","task_categories:question-answering, language:ja, 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","question-answering","評価スコアの再現性確保と SB Intuitions 修正版の公開用クローン ソース: aiishii/JEMHopQA on GitHub JEMHopQA JEMHopQA (Japanese Explainable Multi-hop Question Answering) is a Japanese multi-hop QA dataset that can evaluate intern","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"google/mittens","google","2024-01-17 19:03:53","2024-01-17 19:17:58+00:00","b8c46247b3e4069932110ad7420e05ff8d10ec03","65","673","5","None","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"
+"masuidrive/cv-corpus-17.0-ja-client_id-grouped","masuidrive","2024-04-20 06:48:02","2024-04-20 06:48:20+00:00","54e34c20140d041958ef0cbc1f75930eba3f55d8","150","667","1","task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, language_creators:crowdsourced, source_datasets:commonvoice, language:ja, 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-ja-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 fr","None","ja","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"floschne/multilingual-llava-bench-in-the-wild","floschne","2024-05-15 13:45:59","2024-05-16 13:33:36+00:00","0b338b97f9890e2aa8786a35de696e847d73a539","52","665","0","language:ar, language:bn, language:zh, language:en, language:fr, language:ru, language:es, language:ur, language:hi, language:ja, license:cc-by-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.14818, region:us","None","Multilingual LLaVA Bench in the Wild Note that this is a copy from https://huggingface.co/datasets/MBZUAI/multilingual-llava-bench-in-the-wild It was created due to issues in the original repo. It als","None","ar, bn, zh, en, fr, ru, es, ur, hi, ja","multi","n|<|1|K","None","False","False","False","cc-by-4.0","None","None","None"
+"if001/aozorabunko-clean-sin","if001","2023-09-04 04:22:55","2024-10-14 12:46:29+00:00","13f76e871176ebacfe5de492864467b9d693da9f","59","662","5","task_categories:text-generation, task_categories:text-classification, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text-classification","this is forkhttps://huggingface.co/datasets/globis-university/aozorabunko-clean filtered row[""meta""][""文字遣い種別""] == ""新字新仮名""","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"tokyotech-llm/Swallow-Instruct-v0.1","tokyotech-llm","2024-07-18 20:27:22","2024-07-18 20:31:35+00:00","e1a56aaf4e49ede3a9ce44b851844ab2fbdf0be1","85","649","6","language:ja, 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","Swallow Instruct v0.1 Dataset This dataset was used for supervised fine-tuning (SFT) of the Swallow v0.1 model series. Model Index The following Instruct models were created using this dataset: Llama-","None","ja, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"aiana94/xMINDlarge","aiana94","2024-06-25 11:54:02","2024-10-25 08:29:52+00:00","b31b0ae960ae073d6d230ba026892fc585d7d032","67","649","4","None","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","None","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"
+"huawei-noah/entity_cs","huawei-noah","2023-09-08 08:44:07","2023-09-20 07:05:07+00:00","32c89f7c52f16e46115d9c31e6000ddb081b3de3","137","644","2","None","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"
+"Verah/JParaCrawl-Filtered-English-Japanese-Parallel-Corpus","Verah","2024-03-01 06:17:09","2024-03-07 21:20:21+00:00","b9e28c23dc1484974523a9ec7b523208ee2d803f","52","639","1","task_categories:translation, language:en, language:ja, license:other, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Introduction This is a LLM-filtered set of the first 1M rows from ntt's JParaCrawl v3 large English-Japanese parallel corpus. The original JParaCrawl corpus was put together by automated means - align","None","en, ja","bi","1|M|<|n|<|1|0|M","None","False","False","False","other","None","None","None"
+"litagin/Galgame_Speech_SER_16kHz","litagin","2024-11-10 00:56:46","2024-11-10 23:51:32+00:00","a9dc51cd44782722f22f1e97899eeb7d0cb77de9","226","632","4","task_categories:automatic-speech-recognition, task_categories:audio-classification, multilinguality:monolingual, language:ja, license:gpl-3.0, size_categories:1M<n<10M, format:webdataset, modality:audio, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us, speech, audio, emotion, speech-emotion-recognition, text, japanese, anime, voice, visual novel, galgame","automatic-speech-recognition, audio-classification","Dataset Card for Galgame_Speech_SER_16kHz The following rules (in the original repository) must be followed: 必须遵守GNU General Public License v3.0内的所有协议！附加：禁止商用，本数据集以及使用本数据集训练出来的任何模型都不得用于任何商业行为，如要用于商业用途","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","gpl-3.0","None","None","None"
+"llm-jp/mbpp-ja","llm-jp","2024-04-19 00:26:56","2024-04-20 06:26:51+00:00","94afe76bc974b12e475f72cba00c98ce9d3a3c45","71","629","2","None","text2text-generation","mbpp-ja This repository provides a mbpp dataset translated from English into Japanese by LLM-jp, a collaborative project launched in Japan. For English to Japanese translation, DeepL was used. The lin","None","ja","mono","n|<|1|K","None","False","False","False","cc-by-4.0","None","None","None"
+"weblab-GENIAC/Open-Platypus-Japanese-masked","weblab-GENIAC","2024-10-12 03:12:20","2024-10-19 01:40:51+00:00","ac51e5114ff457798fb8efc53b9ba617b9f30773","61","626","1","task_categories:text-generation, task_categories:question-answering, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2308.07317, region:us, math","text-generation, question-answering","Open-Platypus-Japanese-masked LLMの数学能力と推論能力を向上させるために作成したデータセット garage-bAInd/Open-Platypusをcyberagent/calm3-22b-chatで翻訳 13,881件(13,883件の内2件削除) ルールベー���・機械学習ベースのフィルタリング処理の後、目視確認を行い、個人情報が含まれ得るサンプルについては全件削除","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-4.0","None","None","None"
+"Mathoctopus/GSM8KInstruct_Parallel","Mathoctopus","2023-10-15 14:44:31","2023-10-15 14:48:41+00:00","dd8076b83bacd7c6ade2829317e8a500c6ffbec4","50","626","6","task_categories:question-answering, language:en, language:zh, language:es, language:fr, language:th, language:sw, language:ja, language:bn, language:de, language:ru, 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","None","None","en, zh, es, fr, th, sw, ja, bn, de, ru","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"kunishou/oasst2-chat-68k-ja","kunishou","2023-12-25 13:19:09","2023-12-25 13:21:58+00:00","3b814d81f3821c6943250993d8667c8e2c45608f","47","625","8","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","oasst2-135k-jaをチャット形式に変換したデータセットになります。マルチターン会話でのファインチューニングをする際にご活用下さい（1レコードのトークン長が大きいのでそれなりの計算リソースが必要になります）。フォーマットは ShareGPT 形式になっています。ファインチューニングをする際はこちらの記事を参考にして下さい。 OpenAssistant/oasst2https://huggi","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"hatakeyama-llm-team/AutoGeneratedJapaneseQA-other","hatakeyama-llm-team","2024-05-19 02:45:59","2024-05-19 14:17:58+00:00","07f930639e579b65c0382a1c8c14fc0a7f732a70","66","615","1","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","自動生成Q&A データソースから､MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUFを使ってQ&Aを自動生成したものです｡ チームで作成したデータおよび「Common Crawlをもとに生成しています。 Common Crawl terms of useに従ってご利用ください。 hatakeyama-llm-team/AutoGeneratedJapane","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Aratako/Japanese-RP-Bench-testdata-SFW","Aratako","2024-09-22 06:25:01","2024-09-29 05:34:09+00:00","159cfa0beb56244d6d9b3b2c2423f1f0559de138","97","613","4","task_categories:text-generation, language:ja, license:other, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Japanese-RP-Bench-testdata-SFW 本データセットは、LLMの日本語ロールプレイ能力を計測するベンチマークJapanese-RP-Bench用の評価データセットです。 ベンチマークの詳細については記事を参照してください。 データの概要 本データは以下のようなキーを持ちます。 genre: ロールプレイのジャンル tag: ロールプレイの年齢区分 world_setting","None","ja","mono","n|<|1|K","None","False","False","False","other","None","None","None"
+"cc-clean/CC-MAIN-2019-30","cc-clean","2024-12-22 08:01:41","2024-12-22 12:36:11+00:00","db9931e791fd23d7191df914ef1f0111a4c50bec","601","612","0","None","None","CC-MAIN-2019-30へようこそ 本データセットはCommonCrawlerと呼ばれるものから日本語のみを抽出したものです。 利用したものはcc-downloader-rsです。 なおIPAのICSCoEと呼ばれるところから資源を借りてやりましたゆえに、みなさんIPAに感謝しましょう。 ※ IPAは独立行政法人 情報処理推進機構のことです。テストに出ますので覚えましょう。 利用について 本","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"hotchpotch/jaqket_v1_qa_wikija_context","hotchpotch","2023-12-07 08:36:30","2024-02-25 06:14:45+00:00","a1b44eed9777b056e42187098bb2fdc139401a0f","63","609","4","task_categories:question-answering, language:ja, 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","question-answering","⚠️ ! 注意 ! 公開したデータセットは、私の不注意で本来学習させる用途には相応しくない、JAQKET テスト用のデータセットも train に含んでしまっています。 そのため、以下のように再度テスト用のデータを分離してからの利用をお勧めします。 import datasets ds = datasets.load_dataset(""hotchpotch/jaqket_v1_qa_wikija_","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Kendamarron/jimba-instuction-1k-beta","Kendamarron","2024-02-29 15:23:48","2024-04-25 12:49:28+00:00","4e354a1a4265e02cddb150a4445547086caa2674","58","602","5","task_categories:text-generation, language:ja, 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","cyberagent/calm2-7b-chatの出力を人手でチェック・修正することで作成した日本語Instructionデータセットです。 詳しくはこちらの記事を御覧ください。 https://zenn.dev/kendama/articles/dc727218a2eae6","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"minnade/chat-daily","minnade","2024-04-18 23:41:30","2024-11-26 03:25:02+00:00","6c98fe4611672e2a6ee9fbbaa0f9b467d8fd347e","70","602","8","task_categories:text-generation, language:ja, license:cc0-1.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","MinnadeChat データセット (毎日更新) みんなで作る指示データセット の投稿のデータセットです。このデータセットは毎日12時に更新されます。 🤗 HuggingFace datasets から使う 最新版 を取得したい場合: from datasets import load_dataset ds = load_dataset(""minnade/chat-daily"", split=""","None","ja","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"p1atdev/modern_haiku","p1atdev","2023-09-10 05:49:41","2023-09-10 12:46:58+00:00","889a4d9072cde4d55d1af7a44ecba67b58d77b85","120","600","1","task_categories:text-generation, language:ja, license:mit, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, region:us, haiku","text-generation","This new dataset is designed to solve this great NLP task and is crafted with a lot of care.","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"Davlan/xtreme-up-semantic-parsing","Davlan","2024-09-25 20:22:49","2024-09-25 20:43:28+00:00","535fcd6f3a36d6bf591ce36e8a175304907ffe9b","124","596","0","task_categories:text-classification, multilinguality:multilingual, language:am, language:be, language:bn, language:de, language:en, language:es, language:fi, language:fr, language:ha, language:hi, language:hu, language:ja, language:pt, language:ru, language:sw, language:ta, language:th, language:tr, language:yo, language:zu, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, xtreme-up, semantic parsing","text-classification","Dataset Card for afrixnli Dataset Summary See XTREME-UP GitHub Languages There are 20 languages available : Dataset Structure Data Instances The examples look like this for English: from datasets impo","None","am, be, bn, de, en, es, fi, fr, ha, hi, hu, ja, pt, ru, sw, ta, th, tr, yo, zu","multi","n|<|1|K","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","None","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"
+"sudy-super/dialogsum-ja","sudy-super","2023-07-15 10:16:20","2023-07-15 10:27:58+00:00","c5be0ee48275ce46e944d23502301f896447ce5b","69","592","16","task_categories:summarization, language:ja, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","summarization","dialogsum-ja このデータセットはdialogsum、CSDSなどを翻訳した日本語対話要約データセットです。 元のデータセット knkarthick/dialogsum https://huggingface.co/datasets/knkarthick/dialogsum xiaolinAndy/CSDS https://github.com/xiaolinAndy/CSDS","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"TFMC/imatrix-dataset-for-japanese-llm","TFMC","2024-04-19 19:46:14","2024-04-19 20:35:40+00:00","3ed4554232a1925e4ee5b1f9ed7548462bec7a51","90","590","18","task_categories:text-generation, language:ja, language:en, license:odc-by, size_categories:n<1K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","text-generation","None","None","ja, en","bi","n|<|1|K","None","False","False","False","odc-by","None","None","None"
+"jlli/JDocQA-binary","jlli","2024-08-23 00:34:44","2024-09-24 18:53:36+00:00","6ca9f1c4f118e5264f35a3b21173076385a82f63","92","584","0","language:ja, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"augmxnt/shisa-pretrain-en-ja-v1","augmxnt","2023-11-19 09:48:04","2023-12-05 20:08:51+00:00","67e3a3877f374dad650a61d1b7621f7030989b01","42","583","7","language:ja, language:en, license:odc-by, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This pre-training dataset was created for shisa-base-7b-v1. It is primarily composed of a DSIR sampling of MADLAD-400 JA/EN tokens in a 90%/10% ratio.","None","ja, en","bi","None","None","False","False","False","odc-by","None","None","None"
+"mohamed-khalil/AnimeSongsLyrics","mohamed-khalil","2024-03-05 17:17:25","2024-03-05 18:02:39+00:00","9c05e77aff85c1d033380bfcb71e54562c485b7d","91","583","4","task_categories:text-generation, task_categories:text2text-generation, task_categories:text-classification, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, music, anime, lyrics, Anime Songs Lyrics","text-generation, text2text-generation, text-classification","Anime Songs Lyrics Dataset ― アニメソングの歌詞データセット Welcome to the Anime Songs Lyrics Dataset Overview This dataset compiles a diverse collection of lyrics from various anime songs, providing a rich resource","None","ja","mono","1|0|K|<|n|<|2|0|K","None","False","False","False","apache-2.0","None","None","None"
+"oshizo/japanese-wikipedia-paragraphs","oshizo","2023-12-09 11:14:53","2023-12-09 14:09:30+00:00","fca2755c4d56e3e2a47b88f2c97d3cd8f725a21a","62","581","2","language:ja, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","A slightly modified version of the parsing and chunking method for singletongue/wikipedia-utils. Pre-processing was performed using oshizo/wikipedia-utils, which is a fork of the original repository, ","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"agentlans/LinguaNova","agentlans","2024-11-30 09:53:49","2025-01-28 21:26:49+00:00","809b3a918cc3c4a5afdd7db44a1f507d96465a8b","98","575","0","None","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"
+"deepghs/cc100_char_freq","deepghs","2024-12-18 21:53:53","2024-12-22 05:20:10+00:00","0088fd38ad6a5b1b4ad425c5178864f3ef40cc06","451","573","0","task_categories:text-generation, task_categories:text2text-generation, task_categories:text-classification, 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:other, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, text, fonts, languaes","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"
+"hpprc/mqa-ja","hpprc","2024-04-07 06:23:02","2024-04-07 15:16:42+00:00","5258da250cdf924ecf7d5a2c46dfeef3c2e7e458","87","569","6","language:ja, license:cc0-1.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","mqaデータセットのquery--passageのペアについて重複を削除したデータセットです。 元データ中のノイジーなテキストのクリーニングやNFKC正規化などの前処理を行ってあります。 dataset subsetのpos_idsおよびneg_ids中のidは、collectionsubsetのインデックス番号に対応しています。 したがって、collection[pos_id]のようにアクセスし","None","ja","mono","None","None","False","False","False","cc0-1.0","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","None","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"
+"retarfi/economy-watchers-survey","retarfi","2024-05-02 08:31:42","2025-01-17 11:19:01+00:00","2cce989ae461d98335b3bcb58db1bd6059b13095","102","566","0","language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2407.14727, region:us","None","economy-watchers-survey Economy Watchers Survey data.It is automatically updated by GitHub Actions as the economy watcher is updated.The dataset for tasks is retarfi/economy-watchers-survey-evaluation","None","j, a","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","2407.14727","None","None"
+"izhx/mewsli-x","izhx","2024-02-24 14:01:38","2024-02-25 02:29:26+00:00","7e3723faa15c6d84cfd56d66cf6cd347be8ad00e","111","564","1","None","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"
+"DataPilot/databricks-dolly-15k-Nyan-ja","DataPilot","2024-05-18 13:03:25","2024-05-19 10:24:16+00:00","55fb5551b8ae0ec279bff717de2aff6a114128ad","48","563","1","language:ja, 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","None","このデータセットはkunishou氏が公開している""databricks-dolly-15k""を日本語訳したkunishou/databricks-dolly-15k-jaデータセットの語尾をArrowPro-7B-KUJIRAを用いて「にゃん！」に変更したものです。 ライセンスは元データセットに依存します。 注：このデータセットは語尾を変更することを目的としており、性能向上を目的とするものではあ","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Nikity/Pornhub","Nikity","2024-08-26 11:16:06","2024-08-26 15:01:52+00:00","febede1f6ee3396dbd0a31270f59ee6189a8b2b4","104","560","20","None","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"
+"YANS-official/ogiri-bokete","YANS-official","2024-07-21 09:58:14","2024-09-15 02:26:56+00:00","7d2778749120adc5a15286d498b9c60f5387e429","64","557","1","task_categories:text-generation, task_categories:text2text-generation, task_categories:image-to-text, language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, arxiv:2312.02439, region:us","text-generation, text2text-generation, image-to-text","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/ogiri-bokete"", split=""train"") 概要 大喜利投稿サイトBoketeのクロールデータです。元データは CLoT-Oogiri-Go [Zhang+ CVPR2024]というデータの一部です。 詳細はCVPRのプロジェク","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Mitsua/art-museums-pd-440k","Mitsua","2024-12-15 13:55:05","2024-12-16 05:43:36+00:00","6b5ba12da15efc2852c0f4725956f3033d522432","350","557","4","task_categories:image-to-text, task_categories:text-to-image, language:en, language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:webdataset, modality:image, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us, legal","image-to-text, text-to-image","Art Museums PD 440K Summary This is a dataset to train text-to-image or any text and image multimodal models with minimized copyright/licensing concerns. All images and texts in this dataset are orign","None","en, ja","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"Aratako/Synthetic-JP-10-Turns-Roleplay-Dialogues-Nemotron-4-1k","Aratako","2024-07-03 13:21:22","2024-07-03 13:53:20+00:00","cf9267ba9dafd0327040d72c99cbf3ece55d44ed","61","548","3","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, roleplay","text-generation","Synthetic-JP-10-Turns-Roleplay-Dialogues-Nemotron-4-1k nvidia/Nemotron-4-340B-Instructを用いて作成した、約1000件・各10ターンの日本語ロールプレイの対話を収録した合成対話データセットです。 Magpieの手法を用いて作成した合成instructionデータセットであるAratako/Synthetic-JP-","None","ja","mono","None","None","False","False","False","apache-2.0","2406.08464","None","None"
+"shanearora/CaLMQA","shanearora","2024-06-12 08:08:20","2024-06-13 07:13:20+00:00","a31ec3378b1d009839544f64d339714072b405b4","49","546","2","multilinguality:multilingual, language:aa, language:ar, language:bal, language:de, language:en, language:es, language:fj, language:fo, language:he, language:hi, language:hil, language:hu, language:ja, language:ko, language:ru, language:rn, language:pap, language:ps, language:sm, language:to, language:tn, language:wo, language:zh, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, doi:10.57967/hf/2525, region:us","None","Dataset Summary CaLMQA is a long-form question answering (LFQA) dataset spanning 23 high- to low-resource languages. Dataset Details Dataset Description CaLMQA is an LFQA dataset with 2K questions fro","None","aa, ar, bal, de, en, es, fj, fo, he, hi, hil, hu, ja, ko, ru, rn, pap, ps, sm, to, tn, wo, zh","multi","None","None","False","False","False","mit","None","None","None"
+"llm-book/aio-passages","llm-book","2023-06-06 02:03:33","2023-06-24 05:55:37+00:00","7f54d80b085768f6c3db03276983db4616cd6737","49","540","1","language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us","None","Dataset Card for llm-book/aio-passages 書籍『大規模言語モデル入門』で使用する、「AI王」コンペティションのパッセージデータセットです。 GitHub リポジトリ cl-tohoku/quiz-datasets で公開されているデータセットを利用しています。 Licence 本データセットで利用している Wikipedia のコンテンツは、クリエイティブ・コモ","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"aiana94/xMINDsmall","aiana94","2024-06-20 08:44:37","2024-10-25 08:30:20+00:00","aeb7ddb35fa87765125db1c2e2cb4594ff245940","113","526","4","None","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","None","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"
+"p1atdev/ja-stackoverflow","p1atdev","2023-12-16 02:41:12","2023-12-21 05:30:24+00:00","f1cbe26ae7aa6db5e9ac199e037893f592c9748f","55","525","8","task_categories:text-generation, task_categories:question-answering, language:ja, 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, region:us, stackoverflow, programming","text-generation, question-answering","ja-stackoverflow 日本語版 Stack Overflow の スタック・オーバーフロー のデータダンプ をもとにデータを加工し、質問文と回答文のペアになるように調整した QA データセット。 データ構造 投稿本文は html2text を使ってマークダウン化されています。その際、 コードブロックは ``` で囲まれるように変更されています。 画像 URL に base64 エンコー","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"aixsatoshi/cosmopedia-japanese-100k","aixsatoshi","2024-03-03 16:06:15","2024-03-03 16:20:35+00:00","cbf6e7f5750fc4b598935556e7bad94c9d1cb250","64","524","6","language:ja, language:en, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","cosmopedia-japanese-20kのデータに、kunishou様から20k-100kをご提供いただけることになり100kまで拡大しました。 https://huggingface.co/datasets/kunishou/cosmopedia-100k-ja-preview テキスト生成プロンプトの翻訳も含むデータは、上記レポジトリを確認してください。","None","ja, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"AkitoP/MiSide-Japanese","AkitoP","2025-01-01 15:44:41","2025-01-01 21:37:44+00:00","365a44f821b6efd55b7e6573fe321460b8aa075d","524","524","14","task_categories:text-to-speech, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","text-to-speech","None","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Mitsua/wikidata-parallel-descriptions-en-ja","Mitsua","2024-05-13 12:02:42","2024-05-17 00:25:10+00:00","e99628bbb7ad880619b767b80ae19884089622bc","55","524","5","task_categories:translation, language:en, language:ja, license:cc0-1.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Wikidata parallel descriptions en-ja Parallel corpus for machine translation generated from wikidata dump (2024-05-06). Currently we processed only English/Japanese pair. The jsonl file is ready-to-tr","None","en, ja","bi","None","None","False","False","False","cc0-1.0","None","None","None"
+"shi3z/ja_conv_wikipedia_orion14B_100K","shi3z","2024-01-30 01:45:05","2024-01-30 02:13:12+00:00","7b0d0411941cf99f61ce0572a8b88732464b0907","44","517","2","language:ja, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","Abstruct This is a multi-turn conversation dataset generated from the Japanese Wikipedia dataset using Orion14B-Chat. Commercial use is possible, but the license is complicated, so please read it care","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"bltlab/ParaNames","bltlab","2024-05-16 11:36:42","2024-05-16 11:54:17+00:00","b9fd0e773dda2fac29db0aaf84a0e6bf873e2183","83","513","3","None","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"
+"neon-mao/language-dataset","neon-mao","2024-01-16 02:38:01","2024-01-16 03:08:19+00:00","21d40813562b0873feec3c5da91109000f8878c7","46","512","3","task_categories:text-classification, language:en, language:zh, language:fr, language:ru, language:ja, language:it, language:tr, language:de, language:pt, language:es, language:he, language:uk, language:nl, language:fi, language:pl, language:lt, language:cs, language:da, language:sv, language:sr, language:ar, language:el, language:ro, language:bg, language:vi, language:sk, language:id, language:is, language:ko, language:ca, language:hr, language:th, language:et, language:sl, language:no, license:mit, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","None","None","en, zh, fr, ru, ja, it, tr, de, pt, es, he, uk, nl, fi, pl, lt, cs, da, sv, sr, ar, el, ro, bg, vi, sk, id, is, ko, ca, hr, th, et, sl, no","multi","1|0|M|<|n|<|1|0|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","None","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"
+"weblab-GENIAC/aya-ja-evol-instruct-calm3-dpo-masked","weblab-GENIAC","2024-10-12 03:03:38","2024-10-19 01:40:31+00:00","50fbc72bb0626463159b232b08f68830d6997863","65","509","6","task_categories:text-generation, task_categories:question-answering, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.12244, region:us, synthetic","text-generation, question-answering","aya-ja-evol-instruct-calm3-dpo-masked LLMの推論能力の向上のためのデータセット CohereForAI/aya_datasetの日本語パートを抜粋 Evol-instructを適用し、質問を複雑化 進化元の質問文はこのデータセットに含まれていません。team-hatakeyama-phase2/aya-ja-nemotron-dpo-maskedをご参照くだ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","apache-2.0","2304.12244","None","None"
+"iamshnoo/WEATHub","iamshnoo","2023-10-24 08:43:49","2024-07-10 01:50:42+00:00","5411d353f37a89cae8b4b0c68afde4631863424c","106","506","2","None","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"
+"stockmark/business-questions","stockmark","2023-10-22 14:47:54","2024-05-14 05:45:15+00:00","67503585fab6b531e5612b5ddecc0cad7376878d","54","496","5","language:ja, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Stockmark Business Questions","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"NJUyued/NoW","NJUyued","2024-07-30 06:26:41","2024-11-30 08:22:32+00:00","742bf63cb4d27e56cfe498bdf71b36ccd8a73c61","82","491","0","None","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"
+"Aruno/guanaco_jp","Aruno","2023-04-24 03:07:04","2023-04-24 03:45:26+00:00","a3c30451e89e4ad65268a0ac605471e1698352a2","67","488","6","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Japanese Prompt of GuanacoDataset extracted using langdetect.","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Kendamarron/jimba-wiki-instruction-calm3","Kendamarron","2024-07-09 22:18:35","2024-07-20 12:57:05+00:00","774abdac8d58bc42b13d6d09ec784866751dd75e","44","486","0","task_categories:text-generation, language:ja, 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, synthetic","text-generation","Kendamarron/jimba-wiki-instruction-calm3 grapevine-AI/CALM3-22B-Chat-GGUFのQ4_K_Mを使った合成instructionデータセットです。 RAGのように文章を参照しながら回答しているため比較的ハルシネーションは少ないですが、一部、ハルシネーションが含まれていることを確認しています。ご利用の際はご注意ください。 生成後のフィ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"ayousanz/oscor-2301-ja-text-content","ayousanz","2024-04-22 00:04:18","2024-04-22 01:25:06+00:00","d9c6f6b62dc819249c77bbde31611274d1389a2d","174","486","2","language:ja, license:cc0-1.0, size_categories:10M<n<100M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","OSCOR-2301-jaのcontent部分だけをテキスト化したもの データセットからcontentのvalueだけ取得する際には、以下のコードで実行しました import json import os import sys # コマンドライン引数からフォルダ名を取得する if len(sys.argv) < 2: print(""使用法: python script.py folder_name","None","ja","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"sarulab-speech/J-CHAT","sarulab-speech","2024-07-13 19:35:48","2024-09-13 11:47:27+00:00","0833bba04e24e3765d3131f2425e6388901f6e85","87","482","23","language:ja, license:other, size_categories:1K<n<10K, format:text, modality:text, library:datasets, library:mlcroissant, arxiv:2407.15828, region:us","None","J-CHAT is a Japanese large-scale dialogue speech corpus. For the detailed explanation, please see our paper Disclaimer TO USE THIS DATASET, YOU MUST AGREE THAT YOU WILL USE THE DATASET SOLELY FOR THE ","None","ja","mono","1|M|<|n|<|1|0|M","None","False","auto","False","other","2407.15828","None","None"
+"AiMijie/EC-Guide","AiMijie","2024-07-30 11:22:08","2024-08-12 06:50:33+00:00","5e8330a91e4dc581a3ab7081687fc7d2406baa1d","89","477","1","None","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"
+"awinml/MultiFin","awinml","2024-04-30 09:20:13","2024-04-30 12:47:51+00:00","d44a842791acb83a104440a5662b787537ff980b","102","476","0","task_categories:text-classification, language:en, language:es, language:pl, language:hu, language:el, language:da, language:tr, language:ja, language:sv, language:fi, language:no, language:ru, language:it, language:he, language:is, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","text-classification","MultiFin MultiFin – a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists","None","en, es, pl, hu, el, da, tr, ja, sv, fi, no, ru, it, he, is","multi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted","Aratako","2024-08-16 16:46:06","2024-10-27 11:31:03+00:00","abab9914424c85034ef4a2a06cddfc4ed1d1fe4a","82","475","7","task_categories:text-generation, language:ja, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, roleplay","text-generation","Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted 20240907 データ増量（約19800件→約39600件） 概要 gpt-4o-miniを用いて作成した日本語ロールプレイデータセットであるAratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6kにsystem messageを追加し","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"svjack/pokemon-blip-captions-en-ja","svjack","2022-10-29 07:26:57","2022-10-31 06:22:04+00:00","8b2557a673e0e0d687c1484a7e197d3f8c43c699","55","475","3","task_categories:text-to-image, annotations_creators:machine-generated, language_creators:other, multilinguality:multilingual, source_datasets:huggan/few-shot-pokemon, language:en, language:ja, 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 Japanese. Dataset used to train Pokémon text to image model, add a Japanese Column of Pokémon BLIP captions BLIP generated captions for Pokémon ","None","en, ja","bi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","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","None","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"
+"Manual-Dataset-Creation-Project/Malum-230","Manual-Dataset-Creation-Project","2024-12-26 23:27:07","2025-01-05 20:33:36+00:00","ba1a2a4766178170f7932c364b0d2573f7824107","455","470","6","language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Malum-230 Description Malum-230 is a meticulously handcrafted Japanese dataset featuring multi-turn conversations and passages, specifically designed for logical reasoning tasks. This dataset can be u","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"sbintuitions/JSQuAD","sbintuitions","2024-07-30 05:20:25","2025-01-22 04:16:50+00:00","42b9cf2a9b4e0538812ceb0cc8b30a7575783cd0","128","470","0","task_categories:question-answering, language:ja, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","評価スコアの再現性確保と SB Intuitions 修正版の公開用クローン ソース: yahoojapan/JGLUE on GitHub JSQuAD JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2016), one of the datasets of reading comprehension. Each instance in t","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"polm-stability/jblimp","polm-stability","2023-05-29 09:31:31","2023-05-29 09:49:16+00:00","a9db3804717d828b5af0d50b04b96e10df0ed5d0","46","470","0","language:ja, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","JBLiMP This is the data from ""JBLiMP: Japanese Benchmark of Linguistic Minimal Pairs"" (Someya and Oseki, 2023). Only the validated pairs used for benchmarks are included, and only in JSONL format, sin","None","ja","mono","None","None","False","False","False","None","None","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","None","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","None","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"
+"jbross-ibm-research/mgsm","jbross-ibm-research","2024-09-24 18:50:13","2024-09-24 21:01:33+00:00","bd4f2be4127dc3d568a7a1ab13a07ceb921afff3","50","464","1","task_categories:text2text-generation, annotations_creators:found, language_creators:found, language_creators:expert-generated, multilinguality:multilingual, source_datasets:extended|gsm8k, language:en, language:es, language:fr, language:de, language:ru, language:zh, language:ja, language:th, language:sw, language:bn, language:ca, language:gl, language:eu, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2110.14168, arxiv:2210.03057, region:us, math-word-problems","text2text-generation","Dataset Card for MGSM Dataset Summary Copy and merge of this MGSM Dataset, Catalan version, Basque version, Galician version, but in training samples we removed the prompt formatting, e.g. removed Que","None","en, es, fr, de, ru, zh, ja, th, sw, bn, ca, gl, eu","multi","1|K|<|n|<|1|0|K","multi-task-language-understanding-on-mgsm","False","False","False","cc-by-sa-4.0","2210.03057","None","None"
+"neody/nwc2010-cleaned","neody","2024-07-08 09:48:05","2024-07-10 08:12:25+00:00","dce565eec12875ae302b2de13061a2ed2679253b","44","463","2","language:ja, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Sin2pi/JA_audio_JA_text_180k_samples","Sin2pi","2024-10-06 04:58:15","2024-10-10 04:55:21+00:00","ddcf040ba57b348fc7369ce00c0c7e12e46697c3","181","460","6","task_categories:automatic-speech-recognition, task_categories:translation, task_categories:text-to-speech, task_categories:text-to-audio, language:ja, license:artistic-2.0, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, Japanese, good dataset","automatic-speech-recognition, translation, text-to-speech, text-to-audio","None","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","artistic-2.0","None","None","None"
+"kanhatakeyama/AutoMultiTurnByMixtral8x22b","kanhatakeyama","2024-05-18 00:43:54","2024-05-22 20:25:15+00:00","4b11f5401c11e3886b6d41c5c55dd64012ae7900","66","457","5","language:ja, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","自動生成のマルチターンデータセット オープンなデータソースから､MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUFを使ってQ&Aを自動生成したものです｡ 関連コード 一部の計算には東京工業大学のスーパーコンピュータTSUBAME4.0を利用しました｡ データソース はじめの質問(q1)を､種々のデータソースから収集しました｡その後のやりとりはすべて､Mix","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"HachiML/Timeseries-QA","HachiML","2024-06-23 23:22:57","2024-06-25 02:09:56+00:00","879f5c1aabcb5e828f0bd86aed765153727b9df3","41","454","5","task_categories:text-generation, task_categories:time-series-forecasting, language:en, language:ja, license:mit, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, time-series, Timeseries Instruction Tuning, synthetic","text-generation, time-series-forecasting","Timeseries-QA This is a dataset for Timeseries Instruction Tuning. It was created using the following steps: Extracted features from time series data in AutonLab/Timeseries-PILE microsoft/Phi-3-medium","None","en, ja","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"GENIAC-Team-Ozaki/WikiHowNFQA-ja_cleaned","GENIAC-Team-Ozaki","2024-05-10 23:31:18","2024-05-10 23:34:09+00:00","37ff1e6285c1726f83dd866274eec0d324b3edac","102","453","1","task_categories:question-answering, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Lurunchik/WikiHowNFQAを日本語に翻訳し、人手でクリーニングしたデータセットです。","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"kajuma/ABEJA-CC-JA","kajuma","2024-11-03 05:37:36","2024-12-06 07:22:40+00:00","fecbcb63aa6772df386262ed934b3f0db23339fa","103","453","0","task_categories:text-generation, language:ja, license:pddl, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","ABEJA-CC-JA This dataset is hf mirror of https://registry.opendata.aws/abeja-cc-ja/ Please Refer to https://tech-blog.abeja.asia/entry/abeja-cc-ja-202409 このデータセットはhttps://registry.opendata.aws/abeja-c","None","ja","mono","None","None","False","False","False","pddl","None","None","None"
+"recruit-jp/japanese-image-classification-evaluation-dataset","recruit-jp","2023-12-19 09:17:24","2024-01-22 10:48:13+00:00","8c24bc529d1391e230bdb5614e1a94db19c74f33","78","446","7","task_categories:image-classification, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:csv, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","image-classification","recruit-jp/japanese-image-classification-evaluation-dataset Overview Developed by: Recruit Co., Ltd. Dataset type: Image Classification Language(s): Japanese LICENSE: CC-BY-4.0 More details are descri","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"floschne/xflickrco","floschne","2024-05-16 13:44:30","2024-05-24 14:53:29+00:00","0af2c2eba58b27a71898787e286be04befdd7a20","63","442","1","None","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"
+"YANS-official/ogiri-test-with-references","YANS-official","2024-08-18 02:53:04","2024-09-02 09:37:28+00:00","e021586a00a42f4ac593ca6b3467441ec082af7a","95","441","0","task_categories:image-to-text, task_categories:text2text-generation, language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, arxiv:2312.02439, region:us","image-to-text, text2text-generation","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/bokete-ogiri-test"", split=""test"") 概要 大喜利投稿サイトBoketeのクロールデータです。元データは CLoT-Oogiri-Go [Zhang+ CVPR2024]というデータの一部です。 詳細はCVPRのプ","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"hpprc/tanaka-corpus","hpprc","2024-03-21 01:49:50","2024-03-21 12:50:28+00:00","253cff9deb84f9dbbff4844cade78cb6b3761982","108","441","7","task_categories:translation, language:ja, language:en, 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","HF Datasets version of Tanaka Corpus. Preprocess for HF Datasets 以下の内容でオリジナルデータを前処理しました。 wget ftp://ftp.edrdg.org/pub/Nihongo/examples.utf.gz gunzip examples.utf.gz import re from pathlib import Path ","None","ja, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"p1atdev/LLM-jp-Toxicity-Dataset","p1atdev","2024-08-07 07:11:08","2024-08-07 07:21:07+00:00","cc461b3a088cafda1d03a64b71b1efb6b072b5ba","83","441","5","task_categories:text-classification, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","LLM-jp Toxicity Dataset 日本語有害文書データセット「LLM-jp Toxicity Dataset」 See https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-toxicity-dataset","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"HachiML/Evol-hh-rlhf-gen3-1k","HachiML","2024-05-12 13:16:17","2024-05-20 01:44:05+00:00","8519a1e97674008c9f69b175f67312005b7de87c","45","439","1","task_categories:text-generation, language:ja, license:mit, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.12244, region:us, synthetic, evol-instruct","text-generation","Evol-hh-rlhf-gen3-1k Evol-hh-rlhf-gen3-1kは、 kunishou/hh-rlhf-49k-jaをseed tasksとして Evol-Instructionの手法 mistralai/Mixtral-8x22B-Instruct-v0.1 で作った合成データ(Synthetic data)です。モデルの利用にはDeepinfraを利用しています。 Datas","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2304.12244","None","None"
+"p1atdev/japanese-stackexchange","p1atdev","2023-12-16 03:14:00","2023-12-21 05:30:37+00:00","8cdd1a7919135d5d020e51ba47791d508b92c161","75","437","2","task_categories:text-generation, task_categories:question-answering, language:en, language:ja, 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, region:us, stackexchange","text-generation, question-answering","japanese-stackexchange 英語による日本語に関する質問ができる Japanese Stack Exchange のデータダンプ をもとにデータを加工し、質問文と回答文のペアになるように調整した QA データセット。 日本語翻訳された StackExchange ではないです。 データ構造 投稿本文は html2text を使ってマークダウン化されています。その際、 コードブロッ","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"JTBTechnology/taoyuan_travel_qa","JTBTechnology","2024-02-27 06:43:07","2024-03-26 15:04:49+00:00","e703cec653db25e766ab2f29f25f3ef2b9637abd","42","434","0","task_categories:translation, language:zh, language:en, language:ja, language:ko, language:id, language:vi, language:th, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, 台灣, 桃園捷運, 旅遊","translation","桃園捷運旅遊問答翻譯資料集 本專案包含六國語言，中英日韓印越泰 全程由語言模型 (GPT4) 產生合成數據(synthesis data)，每一組語言存在 2K 資料量。","None","zh, en, ja, ko, id, vi, th","multi","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","apache-2.0","None","None","None"
+"neulab/PangeaBench-xmmmu","neulab","2024-10-28 01:28:23","2024-11-03 16:51:11+00:00","eca424e5b5481c39248d14bf0c7176b9118b965d","126","433","0","task_categories:visual-question-answering, task_categories:question-answering, task_categories:multiple-choice, language:ar, language:fr, language:hi, language:id, language:ja, language:pt, language:en, license:mit, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering, question-answering, multiple-choice","None","None","ar, fr, hi, id, ja, pt, en","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"Aratako/Magpie-Tanuki-8B-annotated-96k","Aratako","2024-10-23 06:23:00","2024-10-24 14:48:34+00:00","640570bf62f34051c5380d66b0915809b758baec","59","428","6","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","text-generation","Magpie-Tanuki-8B-annotated-96k Magpieの手法をweblab-GENIAC/Tanuki-8B-dpo-v1.0に対して適用し作成したデータセットであるAratako/Magpie-Tanuki-8B-97kに対して、cyberagent/calm3-22b-chatを用いてinstructionに対して難易度、クオリティ、カテゴリをアノテーションしたデータセット","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","2406.08464","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","None","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"
+"alfredplpl/wikipedia-qa-ja-100k","alfredplpl","2024-02-29 20:24:19","2024-02-29 21:05:11+00:00","5fc9e5de1da12f8e29b0b9183eda3107b3a10e73","122","426","3","task_categories:question-answering, language:ja, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for ""wikipedia-qa-ja-100k"" Original Dataset hpprc/wikipedia-20240101 Procedure Extract the first line of the title from the dataset. Generate the answer by summizing the line using LLM: I","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Marianoleiras/voxpopuli_es-ja","Marianoleiras","2024-12-08 21:55:33","2025-01-13 15:16:49+00:00","d71e50fcb1e28effdf9c3d5c921f1e48dddd6ba2","132","426","1","task_categories:automatic-speech-recognition, task_categories:translation, language:es, language:ja, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition, translation","Dataset Card for Spanish-to-Japanese Automatic Speech Recognition Dataset Dataset Summary This dataset was created as part of a workshop organized by Yasmin Moslem, focusing on speech-to-text pipeline","None","es, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"AbeShinzo0708/AbeShinzo_voice_dataset","AbeShinzo0708","2023-04-17 07:36:16","2024-10-20 21:45:15+00:00","2614fad70ff8f1018b24aed534720471cbfb57da","54","422","2","language:ja, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us, AbeShinzo, FormerJapanesePrimeMinister, voice, dataset","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"GENIAC-Team-Ozaki/chatbot-arena-ja-calm2-7b-chat-experimental_deduped","GENIAC-Team-Ozaki","2024-05-02 08:30:14","2024-05-02 08:31:34+00:00","02231efaead8761d3956eb1bcd1a08bf41c3895b","42","422","0","task_categories:question-answering, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","chatbot-arena-ja-calm2-7b-chatからpromptが一致するデータを削除したデータセットです。","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"kunishou/jp-effective-instructions","kunishou","2023-12-01 07:36:25","2023-12-01 07:52:41+00:00","de6f76a9c01bf9b213257e74ea72727ec9fa17ac","55","417","3","language:ja, license:other, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","oasst1-89k-ja , databricks-dolly-15k-ja , hh-rlhf-49k-ja の中から JGLUE（ JcommonsenseQA , MARC-ja , JSQuAD ）の観点で高品質なデータセットに絞り込んだデータセットです。 品質スコアリングの詳細はこちらを参考にして下さい。https://qiita.com/kunishou/items/efd9f68d","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"globis-university/aozorabunko-chats","globis-university","2023-08-04 00:11:22","2023-10-27 13:26:00+00:00","0f51d9ee846094cb6cea3851acb729595b783161","88","416","11","task_categories:text-generation, task_categories:text-classification, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text-classification","Overview This dataset is of conversations extracted from Aozora Bunko (青空文庫), which collects public-domain books in Japan, using a simple heuristic approach. [For Japanese] 日本語での概要説明を Qiita に記載しました: h","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"numad/yuho-text-2023","numad","2024-06-14 07:39:00","2024-06-14 10:55:42+00:00","241a141c8e5bb1ca79532c405ec6a2737c25688d","71","411","0","None","None","Dataset Card for Dataset Name このデータはEDINET閲覧（提出）サイトで公開されている2023年に提出された有価証券報告書から特定の章を抜粋したデータです。 各レコードのurl列が出典となります。 Dataset Details Dataset Description データの内容は下記想定です 物理名 論理名 型 概要 必須 doc_id 文書ID str 有価証","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"copenlu/tydiqa_copenlu","copenlu","2022-08-16 12:04:50","2022-08-16 12:10:21+00:00","acd4175d190c6bdc00a8544ba8b9758eba191585","47","411","0","task_categories:question-answering, task_ids:extractive-qa, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:extended|wikipedia, language:ar, language:bn, language:en, language:fi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, license:apache-2.0, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, extractive-qa","Dataset Card for ""tydiqa"" Dataset Summary TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with","None","ar, bn, en, fi, id, ja, ko, ru, sw, te, th","multi","u|n|k|n|o|w|n","tydi-qa","False","False","False","apache-2.0","None","None","extractive-qa"
+"rombodawg/Everything_Instruct_Multilingual","rombodawg","2024-10-08 21:13:18","2024-10-08 22:35:10+00:00","cb2f376b958efca637e4fa655c963cefec53220b","93","411","17","None","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"
+"hotchpotch/ms_marco_japanese","hotchpotch","2024-02-16 23:37:22","2024-02-20 09:32:51+00:00","c861bba6c1bb494174e48d78d2d045b784d821c1","74","407","0","language:ja, license:other, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","ms_marco_japanese ms_marco の日本語翻訳データです。 翻訳には、google/madlad400-3b-mtを利用しています。 HuggingFace で公開されている、ms_marco と同等の構造で保存しています。 翻訳品質はそれほど高くありません。繁体字などが含まれるデータもあります。Google Translate API を用いて翻訳された、マルチリンガルms_","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"team-hatakeyama-phase2/LLMChat","team-hatakeyama-phase2","2024-08-25 10:27:23","2024-08-28 16:01:48+00:00","e83c3760a26f1eaed6a7364e1492568ec2c135fd","84","406","2","task_categories:text-classification, task_categories:text-generation, language:ja, license:other, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, text-generation","LLMChat 概要 GENIAC 松尾研 LLM開発プロジェクトで開発したモデルを人手評価するために構築したLLMChatというシステムで収集された質問とLLMの回答、及び人手評価のデータです。 このシステムはChatbot Arenaと同様に、ユーザーが質問を入力するとランダムな2つのLLMからそれぞれ回答が出力され、人間がその2つの出力のどちらが良いか（あるいはどちらも悪い、どちらも良い）を","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"YukiTomita-CC/ELYZA-tasks-100_Human_solved","YukiTomita-CC","2024-07-15 01:33:31","2024-07-17 03:55:33+00:00","0399947ff33a3c7ef3e5c4aabb8803a13183e004","50","405","5","language:ja, license:cc-by-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","概要 このデータセットは日本語LLMの評価用としてよく用いられるelyza/ELYZA-tasks-100について人間が回答を行った結果です。outputはこのリポジトリの作成者によるオリジナルですが、inputはELYZA-tasks-100からそのまま流用しています。 詳しくは下記の記事をご覧ください。https://zenn.dev/yuki127/articles/2496cd8383c8","None","ja","mono","n|<|1|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"hatakeyama-llm-team/AutoGeneratedJapaneseQA-CC","hatakeyama-llm-team","2024-05-18 03:55:41","2024-05-19 09:25:43+00:00","2317028bfdd1856a798bdfaeffadd4db6d24d367","47","400","2","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","自動生成Q&A データソースから､MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUFを使ってQ&Aを自動生成したものです｡ Common Crawlをもとに生成しています。 Common Crawl terms of useに従ってご利用ください。 元の文章との類似度(依拠性)が低くなるようにするため、元文章からランダムに部分抜粋したテキストを用いています","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"nyuuzyou/suno","nyuuzyou","2024-11-11 16:32:01","2024-11-20 06:32:55+00:00","cd8ae54fc319ca3802c0217d235b390e00b14ee2","93","400","57","task_categories:audio-classification, task_categories:text-to-audio, annotations_creators:found, multilinguality:multilingual, source_datasets:original, language:en, language:ja, language:multilingual, license:cc0-1.0, size_categories:100K<n<1M, modality:image, modality:audio, modality:video, modality:text, region:us, audio, video, image, text","audio-classification, text-to-audio","Dataset Card for Suno.ai Music Generation Dataset Summary This dataset contains metadata for 659,788 songs generated by artificial intelligence on the suno.com platform, a service that generates music","None","en, ja, multilingual","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc0-1.0","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","None","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"
+"YukiTomita-CC/AKU-d_ms-0.5B-v0.1_dataset","YukiTomita-CC","2024-08-31 13:37:35","2024-09-15 12:33:54+00:00","b1724e7958845286155bb46127d159ddf15ebd19","53","396","3","task_categories:text-generation, language:ja, license:other, size_categories:10M<n<100M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","text-generation","AKU-d_ms-0.5B-v0.1_dataset Overview このリポジトリは、私の開発しているAKUシリーズの1つ目となる、AKU-d_ms-0.5B-chat-v0.1の事前学習に使用したテキストデータを集めています。 これには次で説明するように複数の公開データセットから作成したテキストファイルが含まれており、それぞれのライセンスや著作権は各データセットに従います。また、各データセッ","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"cc-clean/CC-MAIN-2019-39","cc-clean","2024-12-11 07:58:31","2024-12-11 12:53:42+00:00","dcc6c25b936e25bc994ecfba8f5525d97767f9a7","243","396","1","language:ja, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","CC-MAIN-2019-39へようこそ 本データセットはCommonCrawlerと呼ばれるものから日本語のみを抽出したものです。 利用したものはcc-downloader-rsです。 なおIPAのICSCoEと呼ばれるところから資源を借りてやりましたゆえに、みなさんIPAに感謝しましょう。 ※ IPAは独立行政法人 情報処理推進機構のことです。テストに出ますので覚えましょう。 利用について 本","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"kubota/defamation-japanese-twitter","kubota","2023-01-20 06:50:46","2023-02-06 18:26:10+00:00","55bf906bdab5cd2849c339f17ef4404e8ddb0822","48","392","2","task_categories:text-classification, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, region:us","text-classification","defamation_japanese_twitter Twitter日本語誹謗中傷検出データセット Dataset Summary SNSにおける誹謗中傷検出のためのデータセットです． 5,000件の日本語のツイートに，それぞれ以下で定義している誹謗中傷の対象者と内容をアノテーションしています．アノテーションは，3人のクラウドワーカーにより行われています．2022年2月15日から2022年6月3","","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k","Aratako","2024-08-16 14:35:16","2024-09-07 12:33:18+00:00","6a6ffb10f4d10cdde84d7fdcb65c24c494a642f0","65","389","2","task_categories:text-generation, language:ja, 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, roleplay","text-generation","Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k 20240907 データ増量（約19800件→約39600件） 概要 gpt-4o-miniを用いて作成した、約39600件の日本語ロールプレイの対話を収録した合成データセットです。各データは5ターンから10ターン程度あります。 データの詳細 各データは以下のキーを含んでいます。 genre: ジャンル t","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"llm-book/ja-vicuna-qa-benchmark","llm-book","2024-06-25 22:14:55","2024-08-31 12:37:25+00:00","62c4ff6c4662d86a5fe2bbe36c999829f0e57444","74","389","0","task_categories:text-generation, language:ja, 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 llm-book/ja-vicuna-qa-benchmark 書籍『大規模言語モデル入門Ⅱ〜生成型LLMの実装と評価』で使用する、京都大学が提供する「Japanese Vicuna QA Benchmark」のデータセットです。 オリジナルのサイトと同じものを使用しています。 本データセットは、10のカテゴリー(汎用、コーディング、ロールプレイ、ライティングなど","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"Aratako/Bluemoon_Top50MB_Sorted_Fixed_ja","Aratako","2024-05-18 15:35:17","2024-05-18 15:38:35+00:00","9eb6d8fae99cffa30c95f91563002f49af07d6c9","53","388","2","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, roleplay","text-generation","Bluemoon_Top50MB_Sorted_Fixed_ja SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixedを、GENIAC-Team-Ozaki/karakuri-lm-8x7b-chat-v0.1-awqを用いて日本語に翻訳したロールプレイ学習用データセットです。 LLMの推論にはDeepInfraというサービスを使いました。 翻訳の詳","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"Aratako/LimaRP-augmented-ja-karakuri","Aratako","2024-05-16 13:46:26","2024-05-17 17:58:35+00:00","ee1107b2c4aa2231c1c14ca1a2d66c33bf3af8ff","70","387","3","None","text-generation","LimaRP-augmented-ja-karakuri grimulkan/LimaRP-augmentedを、GENIAC-Team-Ozaki/karakuri-lm-8x7b-chat-v0.1-awqを用いて日本語に翻訳したロールプレイ学習用データセットです。 LLMの推論にはDeepInfraというサービスを使いました。 翻訳の詳細 3-shots promptingでの翻訳 mist","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"deepghs/fgo_voices_jp","deepghs","2024-08-28 08:56:04","2024-08-28 09:14:22+00:00","8fe1cbd07be28420b3cbf957dcfce74a4ff343f3","48","387","9","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:ja, 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, fgo","automatic-speech-recognition, audio-classification","JP Voice-Text Dataset for FGO Waifus This is the JP voice-text dataset for FGO playable characters. Very useful for fine-tuning or evaluating ASR/ASV models. Only the voices with strictly one voice ac","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"Atom007/mc4-japanese-data","Atom007","2023-07-09 14:56:56","2023-07-09 15:04:14+00:00","0009b5c5a278f85f1c0507cbac510750ac44bbb2","52","387","0","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","conversational","Reference https://huggingface.co/datasets/mc4","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"toshi456/LLaVA-CC3M-Pretrain-595K-JA","toshi456","2023-12-03 07:26:55","2024-04-12 03:21:46+00:00","0e95c71649a95d3bac1dcb42f1f6e5f9fc3c00ba","58","386","10","task_categories:visual-question-answering, language:ja, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","Dataset Card for ""LLaVA-CC3M-Pretrain-595K-JA"" Dataset Details Dataset Type: Japanese LLaVA CC3M Pretrain 595K is a localized version of the original LLaVA Visual Instruct CC3M 595K dataset. This vers","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","None"
+"Aratako/Rosebleu-1on1-Dialogues-RP","Aratako","2024-04-13 13:36:22","2024-07-21 05:38:19+00:00","9e1966b732efb8f577f9523c3082dbf510e5003f","69","384","12","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, not-for-all-audiences, nsfw","text-generation","Rosebleu-1on1-Dialogues-RP 2025/05/17 3人での対話のデータを追加&無駄な改行の削除 @matsuxrさんが公開しているRosebleuデータセットを加工したAratako/Rosebleu-1on1-Dialoguesを元に、キャラクターや作品の設定などを付け加えたうえで、ロールプレイ的な文脈になるように加工したデータセットです。 LLMのファインチューニング","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","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","None","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"
+"sbintuitions/aio-extended-answers","sbintuitions","2024-06-21 08:15:23","2024-12-17 03:28:15+00:00","4f157c8f495a62ffb5219dea855702960d95f9b7","55","382","0","None","question-answering","AIO with extended answers AIO (AI王) is a Japanese quiz dataset. This repository contains the validation set of Version 2.0, augmented with manually annotated valid answers for each question. Source: A","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"aixsatoshi/Longcontext-aozora-instruction","aixsatoshi","2024-03-29 11:35:09","2024-03-30 11:44:00+00:00","1dee068e25c8d79b917d06c1ae8b7dab469976d1","57","382","3","language:ja, license:cc, size_categories:1K<n<10K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","長文用のinstructionデータセットです。 長文は以下の青空文庫データセットを利用しました。 globis-university/aozorabunko-clean Limitation このデータセットは、長文の質問応答スタイルを提示することを主な目的としています。質問応答の正誤についてのフィルタリングはあえて行っていません。 長文では一般に性能低下が認められるため困難なタスクとなります。","None","ja","mono","None","None","False","False","False","cc","None","None","None"
+"turing-motors/Wikipedia-Vision-JA","turing-motors","2024-08-19 09:38:13","2024-08-21 03:11:40+00:00","ddcbeebf5ce3e4125e1658a2361f6dcef0804b83","83","382","14","task_categories:image-to-text, language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, image, text","image-to-text","Dataset Card for Wikipedia-Vision-JA Dataset description The Wikipedia-Vision-JA is a Vision Language Model dataset generated from Japanese Wikipedia, containing 1.6M pairs of images, captions, and de","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"shi3z/Japanese_Wikipedia_Conversation","shi3z","2023-11-10 07:36:40","2023-11-10 22:46:29+00:00","e4a6e67ee84f1eacb6e6ca8964a7059e08615e69","53","382","6","language:ja, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","Wikipedia日本語版データセット(izumi-lab/wikipedia-ja-20230720)を元にGPT-3.5-Turboで会話文を生成したデータセットです GPT-3.5-Turboを使っているため、商用利用はできません","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"kunishou/HelpSteer2-20k-ja","kunishou","2024-06-21 08:09:33","2024-06-21 08:44:21+00:00","ea432e41ac690821953c38f77be9690c9c19cd9f","53","382","7","language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","NVIDIA が公開している SteerLM 向けのトライアルデータセット HelpSteer2を日本語に自動翻訳したデータセットになります。HelpSteer2 は Nemotron-4-430B-Reward でも利用されています。SteerLM でのアライメントや報酬モデルの作成にご活用下さい。 NVIDIA Releases Open Synthetic Data Generation P","None","ja","mono","None","None","False","False","False","cc-by-4.0","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","None","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"
+"kai271/TinyStories-Japanese","kai271","2024-10-13 12:38:07","2024-10-13 12:44:19+00:00","12600cbea2f92b313c955e45673b0553d8419d0e","191","380","1","task_categories:text-generation, language:ja, license:mit, size_categories:1K<n<10K, format:text, modality:text, library:datasets, library:mlcroissant, arxiv:2305.07759, region:us","text-generation","Dataset containing ~7000 synthetically generated (by GPT-4o-mini) children's stories in Japanese that only use simple words. It was created using the method described in https://arxiv.org/abs/2305.077","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","2305.07759","None","None"
+"tarudesu/gendec-dataset","tarudesu","2023-11-14 01:59:12","2024-03-23 16:58:27+00:00","078ee69f0f0fd8aabc5652f208a004e89f38b05f","87","380","1","task_categories:text-classification, language:ja, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.11001, region:us, code","text-classification","Gendec: Gender Dection from Japanese Names with Machine Learning This is the official repository for the Gendec framework from the paper Gendec: Gender Dection from Japanese Names with Machine Learnin","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","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","None","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"
+"ryota39/Aya_ja","ryota39","2024-02-14 08:03:42","2024-02-14 08:25:06+00:00","7a25099c5fc3f443bc1b3a73dd274bd0982ac4e5","50","377","3","task_categories:question-answering, task_categories:text-generation, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, text-generation","Aya_ja このデータセットはCohereForAI/aya_datasetの日本語インストラクションデータのみを抽出したデータセットです。 人手でアノテーションされた指示応答のペアが6,259件収録されています。 pythonでの使用例 from datasets import load_dataset aya_ja = load_dataset( ""ryota39/Aya_ja"", spli","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"jlli/JDocQA-nonbinary","jlli","2024-08-23 00:35:07","2024-09-24 18:53:10+00:00","9d6280e4bbf80a8ef8b7c9d96ed177ad1a0dbbad","99","376","0","language:ja, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Kendamarron/japanese-photo-instruction","Kendamarron","2024-12-01 13:42:28","2024-12-02 14:02:17+00:00","d9cdc44e2ad8bb7cf3e854f5d4129ac6928c5181","110","375","2","task_categories:visual-question-answering, language:ja, 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","visual-question-answering","Dataset Information このデータセットは、ThePioneer/japanese-photosの写真をお借りして、 Qwen/Qwen2-VL-7B-Instruct Qwen/Qwen2.5-32B-Instruct-AWQ の2つのモデルを使って生成した合成データセットです。 Detail https://zenn.dev/kendama/articles/cd5196a33","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"naist-nlp/Wiki-ImageReview1.0","naist-nlp","2024-01-17 07:30:55","2024-02-21 15:43:16+00:00","437c6fe5896aa32c5a9408783523691d89e5aaf6","50","374","2","task_categories:visual-question-answering, language:en, language:ja, license:other, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, vision","visual-question-answering","Dataset Card for ""Wiki-ImageReview1.0"" Dataset Summary we introduce a novel task for LVLMs, which involves reviewing the good and bad points of a given imagewe construct a benchmark dataset containing","None","en, ja","bi","n|<|1|K","None","False","False","False","other","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","None","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"
+"DeL-TaiseiOzaki/Tengentoppa-sft-reasoning-ja","DeL-TaiseiOzaki","2024-10-12 15:26:10","2024-10-12 15:45:43+00:00","90c8ca1cbc7e8800c2965c9792faf5b012e42efd","63","373","0","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","日本語指示・推論・回答データセット 概要 このリポジトリは、SkunkworksAI/reasoning-0.01 に含まれるインストラクションデータを基に、Qwen/Qwen2.5-32B-Instruct モデルを用いて作成した日本語版の指示・推論・回答データセットです。全2000件を生成し、一部の問題のあるデータを除外した後、約1800件の高品質なデータを収録しています。 データセットの構成","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"tet550/jawiki_sentences","tet550","2023-07-16 00:14:53","2023-07-16 08:17:58+00:00","3cede3214c7f94fda892ae7674b87380634702a7","135","368","1","language:ja, license:cc-by-3.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Jawiki Sentences Dataset このデータセットは、日本語版Wikipediaの記事を元に作成されました。原文からできる限り不要なタグや表など文章にならないものを取り除いています。各エントリーには、その文が含まれる記事タイトル、セクションタイトルを含めています。 データの構造 各エントリーは以下の3つのフィールドからなります: article_title: 記事のタイトルを表す文","None","ja","mono","None","None","False","False","False","cc-by-3.0","None","None","None"
+"deepghs/arknights_voices_jp","deepghs","2024-08-27 15:41:13","2024-08-28 04:16:00+00:00","d534a29504354d9c5abddf7e8f60059b79922ffd","51","367","1","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:ja, 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","JP Voice-Text Dataset for Arknights Waifus This is the JP voice-text dataset for arknights playable characters. Very useful for fine-tuning or evaluating ASR/ASV models. Only the voices with strictly ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"Nan-Do/OpenSubtitlesJapanese","Nan-Do","2023-04-18 07:43:48","2023-04-19 09:34:45+00:00","6d0bfcbd654d6e5b6345574963b1e8f5fca12d4b","65","366","1","task_categories:text-generation, language:ja, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, text, tv, movies","text-generation","The dataset contains (almost) the entire OpenSubtittles database for Japanese: Over 7000 tv shows and/or movies. The subtittles are human generated. The dataset has been parsed, cleaned and converted ","None","ja","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"
+"oshizo/ASRClustering-ja","oshizo","2024-06-22 12:12:34","2024-06-23 15:35:03+00:00","4a95cda37b11db043dde430f2e78dde0d2452bad","46","364","2","task_categories:text-classification, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","埋め込みモデルの学習、評価のためのクラスタリングデータセットです。 numad/yuho-text-2023を整形して作成しました。元データの取得元であるEDINETの利用規約に基づきデータセットとして公開しております。 事業の内容を表すテキストとして、tagが""DescriptionOfBusinessTextBlock""のデータを抽出し、edinet_codeを使ってEDINETで使われている","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"shi3z/rachel","shi3z","2023-06-01 00:02:31","2023-06-01 23:28:53+00:00","c70d29378ddc67878259154267ad06c8a7264e58","97","364","1","task_categories:question-answering, language:ja, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","This is a handmade dataset for making a Japanese chatbot. Conversations will continue to grow.","None","ja","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"Twitter/HashtagPrediction","Twitter","2022-11-06 02:52:17","2022-11-21 21:22:07+00:00","ed7cc1bbeea46791a75ece509a414c12fd264167","55","362","2","language:sl, language:ur, language:sd, language:pl, language:vi, language:sv, language:am, language:da, language:mr, language:no, language:gu, language:in, language:ja, language:el, language:lv, language:it, language:ca, language:is, language:cs, language:te, language:tl, language:ro, language:ckb, language:pt, language:ps, language:zh, language:sr, language:pa, language:si, language:ml, language:ht, language:kn, language:ar, language:hu, language:nl, language:bg, language:bn, language:ne, language:hi, language:de, language:ko, language:fi, language:fr, language:es, language:et, language:en, language:fa, language:lt, language:or, language:cy, language:eu, language:iw, language:ta, language:th, language:tr, license:cc-by-4.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2209.07562, region:us, Twitter, Multilingual, Classification, Benchmark","None","Hashtag Prediction Dataset from paper TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations This repo contains the Hashtag prediction dataset from our paper","None","sl, ur, sd, pl, vi, sv, am, da, mr, no, gu, in, ja, el, lv, it, ca, is, cs, te, tl, ro, ckb, pt, ps, zh, sr, pa, si, ml, ht, kn, ar, hu, nl, bg, bn, ne, hi, de, ko, fi, fr, es, et, en, fa, lt, or, cy, eu, iw, ta, th, tr","multi","None","None","False","False","False","cc-by-4.0","2209.07562","None","None"
+"shi3z/OpenOrcaJapanese","shi3z","2023-10-27 06:15:27","2023-10-28 02:50:27+00:00","8471a2411e3294a628ac958122891948dcf6b879","51","357","5","task_categories:table-question-answering, language:ja, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","table-question-answering","OpenOrcaデータセットの日本語翻訳版です https://huggingface.co/datasets/Open-Orca/OpenOrca 現在翻訳作業が続行中で、OpenOrca全体の1/5程度の翻訳が終わった状態でひとまず公開します。商用利用可能です。","None","ja","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","mit","None","None","None"
+"Verah/tatoeba_dedupe_en-jp_2024-March-01","Verah","2024-03-05 13:46:13","2024-03-06 08:34:02+00:00","5dc1ab351c25a529d4147969a2850f474414dc98","40","355","4","task_categories:translation, language:en, language:ja, license:cc-by-2.0, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","English - Japanese pairs taken from https://tatoeba.org/en/downloads and then deduplicated. Row order has also been randomized to avoid clusters of similar translations.","None","en, ja","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-2.0","None","None","None"
+"deepghs/girlsfrontline_voices_jp","deepghs","2024-08-28 11:32:04","2024-08-28 11:43:19+00:00","c7e345765845a8a91bc6c4941a00562c6587301c","41","354","4","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:ja, 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, girlsfrontline","automatic-speech-recognition, audio-classification","JP Voice-Text Dataset for Girls Front Line Waifus This is the JP voice-text dataset for girls front line playable characters. Very useful for fine-tuning or evaluating ASR/ASV models. Only the voices ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"RajChat/Chatgpt","RajChat","2024-06-07 11:16:22","2024-06-07 14:23:01+00:00","543511a54560e934cc5f863741a9b79ed6dd0a10","87","354","1","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"
+"agomberto/DoCLayNet-large-wt-image","agomberto","2024-02-14 11:15:41","2024-02-14 11:21:20+00:00","d724c595791ee3039e2dc46afab5e9edc099fbb1","49","353","0","task_categories:object-detection, task_categories:image-segmentation, task_categories:token-classification, task_ids:instance-segmentation, annotations_creators:crowdsourced, language:en, language:de, language:fr, language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, DocLayNet, COCO, PDF, IBM, Financial-Reports, Finance, Manuals, Scientific-Articles, Science, Laws, Law, Regulations, Patents, Government-Tenders, object-detection, image-segmentation, token-classification","object-detection, image-segmentation, token-classification, instance-segmentation","Dataset Card for DocLayNet large without image About this card (02/14/2024) Property and license All information from this page but the content of this paragraph ""About this card (02/14/2025)"" has bee","None","en, de, fr, ja","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","instance-segmentation"
+"toshi456/llava-bench-in-the-wild-ja","toshi456","2024-03-06 21:56:53","2024-04-01 15:15:57+00:00","d0b6894c252840cfbd37991712f564ed9cee61ed","60","350","0","task_categories:image-to-text, language:ja, license:cc-by-4.0, size_categories:n<1K, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","image-to-text","This dataset is the data that corrected the translation errors and untranslated data of the Japanese data in MBZUAI/multilingual-llava-bench-in-the-wild. Original dataset is liuhaotian/llava-bench-in-","None","ja","mono","n|<|1|K","None","False","False","False","cc-by-4.0","None","None","None"
+"neody/amazon-reviews-label-ja","neody","2024-06-29 07:31:36","2024-07-06 08:30:16+00:00","3e47b95946f8d01c3f2c2010857ae91b9e3469e4","43","348","1","language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"cl-nagoya/auto-wiki-qa-nemotron","cl-nagoya","2024-06-20 13:27:49","2024-09-13 01:44:38+00:00","5d7cfe57c4d3a91243f4988d188760444758f542","134","348","2","language:ja, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.11704, arxiv:2409.07737, region:us","None","AutoWikiQA Nemotron A dataset constructed by generating queries and answers from Wikipedia text using Nemotron-4 340B.","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","2406.11704","None","None"
+"deepghs/azurlane_voices_jp","deepghs","2024-08-28 09:15:32","2024-08-28 09:38:53+00:00","358062c41d1ceca6d5d6f2a422199eb0ff1c3850","41","346","4","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:ja, 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, azurlane","automatic-speech-recognition, audio-classification","JP Voice-Text Dataset for Azur Lane Waifus This is the JP voice-text dataset for azur lane playable characters. Very useful for fine-tuning or evaluating ASR/ASV models. Only the voices with strictly ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"hpprc/wiki-trans-ja-en","hpprc","2024-11-23 10:09:02","2025-01-09 04:09:49+00:00","a4c22d66e9959a57ce8dd416093a16325ce48140","340","345","3","task_categories:translation, language:ja, language:en, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, synthetic","translation","None","None","ja, en","bi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Beluuuuuuga/Japanese-Instruction-Linux-Command-169","Beluuuuuuga","2023-07-08 18:20:48","2023-07-17 11:06:56+00:00","ad1ca67639e2935b7d121d21a0eca0476ddc9f38","57","344","3","task_categories:question-answering, language:ja, license:cc-by-nc-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","ja","mono","n|<|1|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"kunishou/cosmopedia-100k-ja-preview","kunishou","2024-02-28 07:58:55","2024-03-05 23:30:38+00:00","ae0041db137296331bf3ac3541b65308e3e4d17a","51","344","4","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","cosmopedia-100k のindex 20k ～ 100k を日本語に自動翻訳したデータになります（テキストが長すぎて翻訳エラーになったレコードは除外しています）。このデータセット自体は別作業者が取り組んでいる 0 ～ 20k の翻訳結果とマージ後に削除します。データセット自体は残しておきます。","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"NickyNicky/oasst2_chatml","NickyNicky","2024-01-07 01:44:47","2024-01-09 19:13:26+00:00","7561e33c16c7a47a973f9efa5019a7ba8834080e","54","340","3","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:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","link: https://huggingface.co/datasets/OpenAssistant/oasst2 Message counts by language: en: 64,513 es: 28,199 ru: 13,935 zh: 8,615 de: 6,145 fr: 3,880 pt-BR: 2,699 th: 1,560 ca: 1,283 it: 943 uk-UA: 84","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"
+"clapAI/MultiLingualSentiment","clapAI","2024-12-27 15:30:01","2024-12-27 16:31:31+00:00","08b1866c310cdf38fa58e2336569423bdb027a5c","334","336","7","None","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"
+"aixsatoshi/Chat-with-cosmopedia","aixsatoshi","2024-03-24 07:45:02","2024-03-28 14:36:58+00:00","ab33af3432746b35968d600a8f9a6f561f43bd8f","54","334","3","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Reasoning、知識、会話の掛け合いなどの情報密度が高いマルチターンの会話データです。 利用データセット日本語化cosmopedia日本語化cosmopedia から作成した合成データセットです。 Example1 ユーザー: 数学をもっと身近に感じるためには、どのような取り組みが必要でしょうか？ アシスタント: 数学を身近に感じるためには、適切な年齢層に合わせた教材やビデオ記録を利用すること","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"morizon/databricks-dolly-15k-ja","morizon","2023-08-06 05:17:38","2023-08-08 13:59:26+00:00","349e768654191b58abf908662a48b87400dadbf0","47","333","1","language:ja, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","このデータセットはkunishou/databricks-dolly-15k-jaを元に作成されています。また、databricks-dolly-15kの情報も参考にしました。 主な修正点 databricks-dolly-15kに注意事項として、注釈は削除した方が良いとの以下記載があり、注釈を削除しています。Reference text (indicated by the context fie","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"aixsatoshi/cosmopedia-japanese-20k","aixsatoshi","2024-02-26 02:39:07","2024-03-03 15:23:54+00:00","231d7218d50d9d1ff931b7dcefeb58bd943e9b7e","70","332","7","language:ja, language:en, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","None","None","ja, en","bi","None","None","False","False","False","None","None","None","None"
+"Kendamarron/pret-a-porter-instruction-v0.1","Kendamarron","2024-03-26 13:08:14","2024-04-01 04:30:44+00:00","4ffef8d8a69806f7b37c63e13daecabfd7a71c5a","44","328","1","language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","データセットについて オープンソースLLMの出力を人手でチェック・修正したinstructionにSwallow-MXでoutputを生成したデータセットです。 outputの精査は行っていないので、答えが間違っているデータが含まれます。 詳細についてはこちらをご覧ください。 備考 Discordサーバー「ローカルLLMに向き合う会」とメタデータラボ株式会社が共同開催された「LOCAL AI HA","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"alfredplpl/wikipedia-qa-ja-1m","alfredplpl","2024-03-10 15:31:10","2024-03-10 23:26:28+00:00","83d61a23b22f3bed375c22b68d143b7e956672d1","40","327","4","task_categories:question-answering, language:ja, license:cc-by-sa-3.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for ""wikipedia-qa-ja-1m"" Original Dataset hpprc/wikipedia-20240101 Procedure Extract the first line of the title from the dataset. Generate the answer by summizing the line using LLM: Inp","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"TrainingDataPro/llm-dataset","TrainingDataPro","2024-04-03 08:43:43","2024-04-25 11:49:11+00:00","594078a375ec33252e4b029c0aefac03f36ac896","63","325","5","task_categories:text-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:text2text-generation, task_categories:text-generation, language:en, language:es, language:ar, language:el, language:fr, language:ja, language:pt, language:uk, language:az, language:ga, language:ko, language:ca, language:eo, language:hi, language:ml, language:sl, language:hu, language:mr, language:cs, language:fa, language:id, language:nl, language:th, language:de, language:fi, language:it, language:pl, language:tr, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code, legal, finance","text-classification, table-question-answering, question-answering, text2text-generation, text-generation","LLM Dataset - Prompts and Generated Texts The dataset contains prompts and texts generated by the Large Language Models (LLMs) in 32 different languages. The prompts are short sentences or phrases for","None","en, es, ar, el, fr, ja, pt, uk, az, ga, ko, ca, eo, hi, ml, sl, hu, mr, cs, fa, id, nl, th, de, fi, it, pl, tr","multi","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"numad/yuho-text-2014-2022","numad","2024-06-15 00:24:41","2024-06-15 00:29:42+00:00","e1dd7e00d5de82aa1b00cf7a28c829d327a93ca7","62","324","0","language:ja, 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","Dataset Card for Dataset Name このデータはEDINET閲覧（提出）サイトで公開されている2014~2022年に提出された有価証券報告書から特定の章を抜粋したデータです。 各レコードのurl列が出典となります。データ取得の都合上2014/06/14以降のデータになります。 Dataset Details Dataset Description データの内容は下記想定です","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"turing-motors/LLaVA-Pretrain-JA","turing-motors","2024-04-10 05:07:24","2024-04-12 09:15:37+00:00","e7189ee4f8b950a953bbdfeeb9d9c5d24063b5ab","63","322","3","task_categories:visual-question-answering, task_categories:question-answering, language:ja, license:other, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering, question-answering","Dataset Details Dataset Type:Japanese LLaVA Pretrain is a localized version of the original LLaVA Pretrain dataset. This version is translated into Japanese using DeepL API and is aimed at serving sim","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","None"
+"Ego/jpflan","Ego","2024-04-17 06:28:50","2024-04-18 00:22:26+00:00","efb5386a57a2a952ca8e5753b5f5288adc38c209","45","322","1","task_categories:text-generation, language:ja, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:arrow, modality:text, library:datasets, library:mlcroissant, region:us","text-generation","Description This is a templated version of data from ~40 Japanese open source downstream task datasets.This dataset was built with the intention of instruction tuning LLMs with high quality data that ","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"longquan/llm-japanese-dataset-split_10","longquan","2023-08-19 13:47:14","2023-08-19 13:51:20+00:00","cf4e14c989a19d7c7c7ef54e39a75192a8711e03","52","321","0","task_categories:question-answering, language:ja, language:en, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","ja, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"YANS-official/senryu-test-with-references","YANS-official","2024-08-28 18:47:38","2024-08-31 15:07:22+00:00","3c5cb1f92f656af7d1608feb2a5af914d0fbae54","67","321","0","task_categories:image-to-text, task_categories:text2text-generation, language:ja, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","image-to-text, text2text-generation","読み込み方 from datasets import load_dataset dataset = load_dataset(""YANS-official/senryu-test"", split=""test"") 概要 川柳投稿サイトの『写真川柳』と『川柳投稿まるせん』のクロールデータです。 以下のページからクロールし、原本のHTMLファイルと構造化処理を行った結果を格納しました。 https://","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"traintogpb/aihub-koja-translation-integrated-small-100k","traintogpb","2024-05-30 07:47:41","2024-05-30 08:44:54+00:00","e48a4ffd9364bb2fbdfbea0d74629ef3d73c6ebc","45","321","0","task_categories:translation, language:ko, language:ja, license:mit, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","AI Hub Ko-Ja Translation Dataset (Integrated) AI Hub의 한-일 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 4,339,465개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-4.3m, base-1m, sma","None","ko, ja","bi","None","None","False","False","False","mit","None","None","None"
+"neulab/PangeaBench-tydiqa","neulab","2024-11-01 16:28:55","2024-11-01 18:29:22+00:00","9672ee24043b7d70d9e1cfe6e443e57205496cee","89","321","0","task_categories:question-answering, task_ids:extractive-qa, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:extended|wikipedia, language:ar, language:bn, language:en, language:fi, language:id, language:ja, language:ko, language:ru, language:sw, language:te, language:th, license:apache-2.0, size_categories:100K<n<1M, modality:text, region:us","question-answering, extractive-qa","Dataset Card for ""tydiqa"" Dataset Summary TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with","None","ar, bn, en, fi, id, ja, ko, ru, sw, te, th","multi","u|n|k|n|o|w|n","tydi-qa","False","False","False","apache-2.0","None","None","extractive-qa"
+"kanhatakeyama/SyntheticTextWikiTranslate","kanhatakeyama","2024-06-13 06:50:51","2024-07-16 07:31:35+00:00","510098b7c4c8a9ca35b6cf6c603b10e3f23a8150","49","321","1","language:ja, 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","None","以下のデータ源からランダムに抽出した日本語のテキストをPhi-3で再生成し､更に自動英訳したコーパスです｡ Wikibooks Wikipedia コード こちら 一部の計算には東京工業大学のスーパーコンピュータTSUBAME4.0を利用しました｡ データ parquetファイルが数十GB程度あります datasetsライブラリからでは､はじめの数GB程度しか読み込めない可能性があります｡git ","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Nexdata/English-Japanese_Parallel_Corpus_Data","Nexdata","2023-11-08 10:47:40","2024-08-05 03:14:27+00:00","c86a6bd394e34896d0cbc9c40b39de990bb6ee60","51","319","3","task_categories:translation, language:en, language:ja, size_categories:1K<n<10K, format:text, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","translation","Dataset Card for Nexdata/English-Japanese_Parallel_Corpus_Data Description The 850,000 English Japanese Parallel Corpus Data is a bilingual text is stored in text format. It covers multiple fields suc","None","en, ja","bi","None","None","False","False","False","None","None","None","None"
+"NilanE/SmallParallelDocs-Ja_En-6k","NilanE","2024-03-04 21:04:01","2024-03-05 03:48:08+00:00","dfae06fe15013a62bf45518ddfcd581e83e69515","44","317","2","task_categories:translation, language:ja, language:en, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This dataset contains document-length Japanese-English parallel texts from various sources. The intended use case is for translation tasks. The cumulative dataset is under an apache 2.0 license, but t","None","ja, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k","Aratako","2024-08-16 16:55:47","2024-09-07 12:38:09+00:00","423385bdd614179d21826115daf42362131dd4f1","69","317","8","None","text-generation","Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k 20240907 データ増量（約10500件→約15300件） 概要 Claude 3.5 Sonnetを用いて作成した、約15300件の日本語ロールプレイの対話を収録した合成データセットです。各データは10ターンから20ターン程度あります。 このデータセットはNSFW表現を含みます。 データの詳","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"miracl/nomiracl-instruct","miracl","2024-07-25 20:18:13","2024-11-23 18:33:23+00:00","ac055a28d45adeacc11a60270424c737e166a157","49","316","0","task_categories:text-classification, task_categories:text-generation, language:ar, language:bn, language:de, 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:yo, 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-classification, text-generation","Dataset Card for NoMIRACL (EMNLP 2024 Findings Track) Quick Overview This repository contains the fine-tuning (training & development split) of the NoMIRACL instruct dataset for fine-tuning LLMs on mu","None","ar, bn, de, en, es, fa, fi, fr, hi, id, ja, ko, ru, sw, te, th, yo, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"toramaru-u/wiki40b-ja","toramaru-u","2024-07-24 07:12:30","2024-12-18 10:05:25+00:00","0b37c06196fae25a1f8c59362b29d659dbcbb55d","97","315","4","language:ja, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"BNNT/IPQA","BNNT","2023-05-19 07:32:52","2023-08-09 04:04:06+00:00","1f8e280a0c9d0a6c53d6055fb229a0051edb13d5","55","314","2","task_categories:question-answering, language:zh, language:en, language:de, language:fr, language:ja, language:es, language:ru, 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, Intellectual property","question-answering","QA evaluation dataset in intellectual property The IPQA contains questions in seven languages, and the 100 data items include 35 each in Chinese and English, and 6 each in Spanish, Japanese, German, F","None","zh, en, de, fr, ja, es, ru","multi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"alfredplpl/commoncatalog-cc-by-ja","alfredplpl","2024-06-09 21:31:00","2024-06-23 22:34:30+00:00","af1591793092109e43f6faa3c730c00aeea80565","50","314","1","task_categories:image-to-text, task_categories:text-to-image, language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","image-to-text, text-to-image","CommonCatalog CC-BY Ja このリポジトリはCommonCatalog CC-BYを拡張して、追加の情報を入れたものです。 以下の情報が追加されています。 LLaVA-JPを改良したモデルによる簡易な日本語キャプション1つ LLaVA-JPを改良したモデルによるできるだけ詳細な日本語キャプション3つ (予定) Sample Code df2=pandas.read_csv(""cc","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"theblackcat102/oasst-red-team","theblackcat102","2023-05-06 09:59:47","2023-05-07 09:15:21+00:00","5b8bd3f6a89af8f7bce11ab659baae53140dc48c","56","313","1","language:en, language:de, language:fr, language:ru, language:zh, language:ja, language:it, language:pt, language:th, language:nl, language:ro, language:pl, language:hu, language:hr, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Work in progress Red team datasets for training and testing reward model for open assistant","None","en, de, fr, ru, zh, ja, it, pt, th, nl, ro, pl, hu, hr","multi","None","None","False","False","False","None","None","None","None"
+"traintogpb/aihub-koja-translation-integrated-base-1m","traintogpb","2024-05-30 07:47:22","2024-05-30 08:45:06+00:00","620a5774efd051c2c04635deaf49ff33c518c4aa","69","305","0","task_categories:translation, language:ko, language:ja, 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-Ja Translation Dataset (Integrated) AI Hub의 한-일 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 4,339,465개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-4.3m, base-1m, sma","None","ko, ja","bi","None","None","False","False","False","mit","None","None","None"
+"mohamed-khalil/AnimeQuotes","mohamed-khalil","2024-02-18 18:26:50","2024-02-21 15:17:52+00:00","5a8e95dbb88281f6fa81b1a0ef791fdbc8cd869c","66","305","4","task_categories:text-generation, task_categories:text2text-generation, task_categories:text-classification, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Quotes, Anime, AnimeQuotes, NLP","text-generation, text2text-generation, text-classification","Anime Quotes Dataset ― アニメの名言データセット🎐 Welcome to Anime Quotes Dataset Overview This dataset contains a curated collection of inspiring and memorable quotes from various anime series, sourced from the A","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"sudy-super/oasst2-chat-5k-ja","sudy-super","2024-05-17 15:09:57","2024-05-18 13:47:14+00:00","1279686e83ac669bc962d8d8c8be1b80156db01b","56","304","6","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","説明 これはoasst2-chat-68k-jaの前半約41000件をSwallow-MX-8x7b-NVE-instruct-v2で評価した中で最高評価を獲得した物のみを抽出したデータセットです。 マルチターン会話でのファインチューニングをする際にご活用下さい。 Pret-a-porter データセット Variant Link instruction-v0.1 Kendamarron/pret","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"alfredplpl/commoncatalog-cc-by-ext","alfredplpl","2024-05-23 00:54:43","2024-06-01 01:50:33+00:00","253233ac3957073685e25f337cf0ba5509fc7a75","43","303","7","task_categories:text-to-image, task_categories:image-to-text, language:en, language:ja, license:cc-by-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-to-image, image-to-text","CommonCatalog CC-BY Extention このリポジトリはCommonCatalog CC-BYを拡張して、追加の情報を入れたものです。 以下の情報が追加されています。 Phi-3 VisionでDense Captioningした英語キャプション 英語キャプションをPhi-3 Mediumで日本語化した日本語キャプション 主キーはphotoidですので、CommonCatalo","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"hpprc/jawiki-slim","hpprc","2024-02-14 00:57:51","2024-04-10 14:02:00+00:00","b36d59512951b2008028812b72779cd06bb4b761","41","302","2","language:ja, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"ganchengguang/MMM-datasets-Testset","ganchengguang","2024-07-18 16:06:19","2024-07-18 22:44:38+00:00","8beaa0c2e995b992f6d0c456654f99e114b85358","51","302","1","language:en, language:ja, language:zh, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2407.10953, arxiv:2308.03279, region:us, Information Extraction, NER","None","Multilingual Mutual Reinforcement Effect Mix Datasets This is a Training set of OIELLM. This Train set already formatted by OIELLM's format. The test set is in the another page in huggingface. The MMM","None","en, ja, zh","multi","None","None","False","False","False","cc-by-nc-4.0","2407.10953","None","None"
+"mpasila/ParallelFiction-Ja_En-100k-alpaca","mpasila","2024-03-26 11:14:15","2024-04-19 14:21:22+00:00","a0c938744184e374098f9f102234cfca46166df5","53","302","3","task_categories:translation, language:ja, language:en, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This is a modified version of NilanE/ParallelFiction-Ja_En-100k which has been turned into Alpaca format. Update no longer chunked!!! This is the non chunked version. If you want the chunked version i","None","ja, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Heng666/MultiCCAligned-TW-Corpus","Heng666","2024-02-02 17:14:20","2024-02-19 09:22:50+00:00","abb7095ef0baa8f0b25500d30113eecd9cf49291","50","299","4","task_categories:translation, language:tw, language:en, language:ja, language:ko, language:id, language:vi, language:th, language:zh, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, MultiCCAligned, translation, OPUS","translation","Dataset Card for [MultiCCAligned-TW-Corpus] Dataset Summary MultiCCAligned-TW-Corpus 是一個機器翻譯基準的多語言資料集，源自 OPUS 收集的使用者貢獻的��譯，並由 OPUS。該資料集包括按語言對排序的測試和開發資料。它包括數百種語言對的測試集，並且不斷更新。請檢查版本號標籤以引用您正在使用的版本。 Support","None","tw, en, ja, ko, id, vi, th, zh","multi","1|M|<|n|<|1|0|M","None","False","auto","False","mit","None","None","None"
+"hpprc/llmjp-kaken","hpprc","2024-09-18 02:50:21","2024-12-08 15:03:11+00:00","53ab0c9c391d0ffd2b61371c0e7b7ec9e1ff88be","101","298","5","language:ja, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","llm-jp-corpus-v3のkakenサブセットをHFフォーマットに変換し、各データに付与されたURLから元記事のタイトルを取得可能なものについては取得して付与したデータセットです。 ライセンスは元ページに従いCC-BY 4.0とします。","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"hpprc/quiz-works","hpprc","2024-08-04 05:57:46","2024-08-04 21:25:11+00:00","8b11ea4bd70f147663b739104522d651288eaf75","41","297","1","language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Quiz Works様に掲載のクイズのうち、2024年8月4日~8月5日時点において取得可能だったクイズを収載したデータセットです。 検索拡張生成(RAG)や文書検索システムの構築などへの利用に適した高品質なデータです。 Quiz Works様ホームページ上のこのサイトについてに記載されている このサイトに掲載されるクイズはすべて自由に二次利用可能です。 との記述に基づき、本データセットも同様、自","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"ReDUB/SoundHarvest","ReDUB","2023-11-20 04:21:11","2023-12-14 22:51:51+00:00","165f683c00b3aae4d99c0647cfb0115655614fe3","76","297","2","task_categories:translation, task_categories:audio-to-audio, language:ar, language:es, language:fr, language:hi, language:id, language:ja, language:ko, language:pt, language:ru, language:th, language:tr, language:vi, language:en, license:other, size_categories:n<1K, format:text, modality:text, library:datasets, library:mlcroissant, region:us, speech2speech","translation, audio-to-audio","Data Format The dataset is organized in the following structure: dataset/ ├── video_id_1/ │ ├── audio_language_1.wav │ ├── audio_language_2.wav │ ├── subtitle_language_1.vtt │ ├── subtitle_language_2.","None","ar, es, fr, hi, id, ja, ko, pt, ru, th, tr, vi, en","multi","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"oshizo/HSClustering-ja","oshizo","2024-06-23 12:46:05","2024-06-23 15:34:38+00:00","0098faf36796c290b789a69c54747d419bc52024","46","297","1","task_categories:text-classification, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","埋め込みモデルの学習、評価のためのクラスタリングデータセットです。 税関の事前教示回答（品目分類）から収集したデータを整形して作成しました。利用規約に基づき、データセットとして公開しております。 2024/6/23時点でダウンロードしたデータから、「一般的品名」が重複しているデータを削除しました。text列は「一般的品名」と「貨物概要」を結合したデータです。label列は、HSコードの「部」に該当","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"TaiMingLu/Multilingual-Benchmark","TaiMingLu","2024-07-06 18:24:12","2024-12-01 06:22:38+00:00","801297609d9203fefeeec8a0ca0601e58e18f549","69","297","2","None","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"
+"OmniAICreator/Japanese-Roleplay-Dialogues","OmniAICreator","2024-06-08 08:41:10","2024-06-08 16:25:27+00:00","1e3518b4b51f1394729f6154fc04810a1926e1e2","59","296","7","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, roleplay","text-generation","Japanese-Roleplay-Dialogues This is a dialogue corpus collected from Japanese role-playing forum (commonly known as ""なりきりチャット(narikiri chat)""). Each record corresponds to a single thread. For the orig","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"hotchpotch/jaqket_cc","hotchpotch","2024-02-20 09:35:00","2024-02-20 09:43:45+00:00","ca560706fe1f05f8f1670181a922ea2fa6bf1b7f","52","294","1","task_categories:question-answering, language:ja, 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","question-answering","JAQKET から CC-BY-SA のデータのみを含めたデータセット AI王 公式配布データセット(JAQKET) で配布されているクイズデータのうち、ライセンスが CC-BY-SA-4.0のデータのみを含めたデータセットです。 謝辞 AI王の開催・クイズ作成等々をされた関係者の方々に、有益なデータセットの公開ありがとうございます。 利用方法 from datasets import load_","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"oshizo/LawClustering-ja","oshizo","2024-06-23 05:25:56","2024-06-23 15:35:15+00:00","6f8428d0af164f8f3dd8b3a4fa78c4cfa18e666f","41","293","1","task_categories:text-classification, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","埋め込みモデルの学習、評価のためのクラスタリングデータセットです。 e-Govから収集したXMLデータを整形して作成しました。利用規約に基づき、データセットとして公開しております。 2024/6/22時点でダウンロードした11,111件のXMLファイルのうち、Eraタグが""Heisei""または""Reiwa""のデータ6,127件を抽出しました。 text列はLawTitleタグから取得した法令タイト","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"henilp105/ESCI","henilp105","2024-06-13 14:48:07","2024-06-13 17:21:40+00:00","8bdce351e20f29bb2e2755d3d798bc128cd601ed","72","293","0","task_categories:text-classification, task_categories:text-retrieval, language:en, language:ja, language:es, license:apache-2.0, size_categories:100K<n<1M, format:arrow, modality:tabular, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, text-retrieval","None","None","en, ja, es","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"preference-team/dataset-for-annotation-v2-annotated","preference-team","2024-11-30 18:21:30","2025-01-29 00:40:55+00:00","a034ae4b8d8212bf2c112754bede45044b3fcfba","94","291","1","language:ja, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"oshizo/JMDNClustering-ja","oshizo","2024-06-23 15:17:04","2024-06-23 15:35:29+00:00","d70031e6bd7b4cd6ed73f919b4f9293492534e9c","49","291","1","task_categories:text-classification, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","埋め込みモデルの学習、評価のためのクラスタリングデータセットです。 独立行政法人医薬品医療機器総合機構のウェブサイトのページから取得したデータを整形して作成しました。利用規約に基づき、出展を明示してデータセットとして公開しております。 2024/6/22時点でダウンロードしたデータから「類別コード」が空欄のデータを削除しました。text列は「一般的名称」と「一般的名称定義」を結合したデータです。l","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"ryota39/hh-rlhf-12k-ja_orpo","ryota39","2024-04-20 04:32:45","2024-07-04 12:56:38+00:00","ad1209a7b6a9bd40596510854dc338e3909db726","43","290","1","language:ja, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"hpprc/jawiki-wiktionary","hpprc","2024-04-11 04:49:52","2024-07-19 04:40:45+00:00","8cf40b8f2152d3fdc856e22cab1b0c83e4ab9c05","44","290","1","language:ja, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"Hoshikuzu/opus-100-en-ja","Hoshikuzu","2024-08-01 11:06:08","2024-08-03 16:41:33+00:00","5fdf9f8d4d0af65e7ba2432ecf80816533a215cb","43","289","1","language:en, language:ja, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for OPUS-100-en-ja Dataset Summary This corpus is extracted from Helsinki-NLP/opus-100, with Japanese and English pairs. How to use It is used in much the same way as Helsinki-NLP/opus-10","None","en, ja","bi","None","None","False","False","False","unknown","None","None","None"
+"neody/oscar-ja-cleaned","neody","2024-06-20 07:55:12","2024-07-06 08:30:33+00:00","8bfcdd61c8de88769f71e4c3fc0d0751f0192a5b","70","289","0","language:ja, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Svngoku/xP3x-Kongo","Svngoku","2024-06-24 21:26:30","2024-06-24 21:33:28+00:00","aa7799519d63bb40dbf964dd69fdcbc5dccb0196","55","288","2","task_categories:other, task_categories:translation, 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:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2211.01786, region:us","other, translation","Dataset Card for xP3x Kikongo Focus Dataset Summary xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP","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, 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","2211.01786","None","None"
+"data-silence/sumnews","data-silence","2024-08-15 20:12:12","2024-08-21 17:45:16+00:00","227315d6d07ec1288598fc3e49122d83fc9eb099","44","285","0","task_categories:summarization, task_categories:text-generation, multilinguality:multilingual, language:am, language:ar, language:az, language:bn, language:my, language:zh, language:en, language:fr, language:gu, language:ha, language:hi, language:ig, language:id, language:ja, language:rn, language:ko, language:ky, language:mr, language:ne, language:om, language:ps, language:fa, language:pcm, language:pt, language:pa, language:ru, language:gd, language:sr, language:si, language:so, language:es, language:sw, language:ta, language:te, language:th, language:ti, language:tr, language:uk, language:ur, language:uz, language:vi, language:cy, language:yo, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, news, media, conditional-text-generation","summarization, text-generation","Dataset Card for ""Sum-any-news"" Dataset Description This dataset is a set for fine-tuning the summarization task on the “google/mt5-base” model and its derivatives. The set is based on the well-known ","None","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|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"HachiML/humaneval-ja-v0.6","HachiML","2023-07-23 13:32:38","2023-07-24 08:25:21+00:00","807d712e56994d43e686df00f21d6bc03d28ccb0","49","285","2","task_categories:text2text-generation, source_datasets:openai_humaneval, language:ja, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code, code-generation","text2text-generation","Dataset Card for ""humaneval-ja"" More Information needed","None","ja","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"jaeyong2/persona-inst","jaeyong2","2024-10-16 14:43:18","2024-10-26 03:05:38+00:00","92a3746801963fab53751aecc75f5964b5496311","72","283","0","language:ja, language:ko, language:th, language:vi, 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","None","How to use >>> from datasets import load_dataset >>> ds = load_dataset(""jaeyong2/persona-inst"", split=""train"") >>> ds Dataset({ features: ['Level', 'English', 'Korean', 'Thai', 'Vietnamese', 'context'","None","ja, ko, th, vi","multi","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Trelis/openassistant-guanaco-EOS","Trelis","2023-10-04 12:28:22","2023-10-04 16:17:59+00:00","eb5894a2378551ec255283bc870c83d650e58676","49","283","2","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 - Guanaco Style This dataset allows for fine-tuning chat models using ""### Human:"" AND ""### Assistant"" as the beginning and end of sequence tokens. Preparation: The dataset is","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"
+"sbintuitions/MGSM_ja","sbintuitions","2024-08-02 02:09:43","2025-01-09 07:17:43+00:00","669a6715bb1d49b28f8f3d1c74a9dac59abf4877","79","282","1","task_categories:text2text-generation, language:ja, license:cc-by-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2110.14168, arxiv:2210.03057, region:us","text2text-generation","評価スコアの再現性確保と SB Intuitions 修正版の公開用クローン ソース: juletxara/mgsm on Hugging Face Subset: ja のみを抽出 MGSM Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed ","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","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","None","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"
+"neulab/PangeaBench-multilingual-llava-bench","neulab","2024-10-31 06:24:38","2024-10-31 19:48:20+00:00","938ebea66545ceda555ba8c63fcdd6c31f99ed85","120","282","0","None","None","None","None","ar, bn, en, es, fr, hi, ja, ru, ur, zh","multi","None","None","False","False","False","None","None","None","None"
+"Calvin-Xu/Furigana-NDLBIB","Calvin-Xu","2024-07-28 08:37:17","2024-07-28 09:08:08+00:00","9cf75663498ecdb95968cbe566fa6b7dd3b3cd02","67","280","0","task_categories:text2text-generation, language:ja, license:mit, size_categories:10M<n<100M, format:arrow, modality:text, library:datasets, library:mlcroissant, region:us, furigana","text2text-generation","Derived from 全国書誌データから作成した振り仮名のデータセット（GitHub） https://github.com/ndl-lab/huriganacorpus-ndlbib Certain mismatches in the original corpus were eliminated during validation (5064 instances).","None","ja","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","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","None","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"
+"if001/oasst1_ja_ppl","if001","2023-10-17 12:01:55","2023-10-23 11:22:05+00:00","a40396b12b0b6c7610e7012d69317b02533ea0b0","46","278","0","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","** 以下のrepositoryのforkです。 ** https://huggingface.co/datasets/kunishou/oasst1-89k-ja instructionとinput、outputにまとめ、kenllmでperplexityのスコアが付与してあります。 perplexityの計算に用いたtokenizerはこちら https://huggingface.co/if","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Hoshikuzu/TEDtalk-en-ja","Hoshikuzu","2024-08-24 13:36:48","2024-08-25 13:29:44+00:00","e39769d8be6000738d6be3b6d349889d1cb6342f","53","278","2","task_categories:translation, language:en, language:ja, license:cc-by-nc-nd-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for TEDtalk-en-ja Dataset Summary This corpus is extracted from the MTTT, with Japanese-English pairs. For more information, see website below! (https://www.cs.jhu.edu/~kevinduh/a/multita","None","en, ja","bi","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"traintogpb/aihub-koja-translation-integrated-large-4.3m","traintogpb","2024-05-30 07:45:56","2024-05-30 08:44:33+00:00","643ae7952ceba43ca6008cb248b2e850548427ee","47","277","1","task_categories:translation, language:ko, language:ja, 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-Ja Translation Dataset (Integrated) AI Hub의 한-일 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 4,339,465개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-4.3m, base-1m, sma","None","ko, ja","bi","None","None","False","False","False","mit","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","None","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"
+"nikolare/test","nikolare","2024-10-19 12:41:44","2024-12-01 21:47:25+00:00","528e728489790fe4f8782d039d867c2d7914f84b","63","275","0","language:ca, language:ja, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ca, ja","bi","n|>|1|T","None","False","False","False","None","None","None","None"
+"shi3z/Qarasu_Wikipedia_multiturn_human_gpt_10K","shi3z","2024-01-07 02:15:58","2024-01-07 04:26:40+00:00","87bf45e711bb0381009d8afb7d945cafc1217c8d","42","275","0","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","Japanese multi-turn conversation data was generated using Qarasu14B based on Wikipedia data. for non commercial use(Because Qarasu14B learned from ShareGPT) Human-gpt format conversation dataset that ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"longquan/llm-japanese-dataset-custom","longquan","2023-08-18 03:45:17","2023-08-18 15:24:48+00:00","6d5d0416c6fc84ae5be7d2ff39b34d445dd5b285","55","274","1","task_categories:question-answering, language:ja, language:en, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering","None","None","ja, en","bi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"shi3z/ja_conv_wikipedia_llama2pro8b_30k","shi3z","2024-01-21 22:14:41","2024-01-21 22:16:01+00:00","1fc3cf7b38e57b1207edd395f4d9edab58e3c68a","52","272","0","language:ja, license:llama2, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","This dataset is based on the Japanese version of Wikipedia dataset and converted into a multi-turn conversation format using llama2Pro8B. Since it is a llama2 license, it can be used commercially for ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","llama2","None","None","None"
+"Studeni/amazon-esci-data","Studeni","2024-11-16 18:23:47","2024-11-16 20:36:43+00:00","df26fdd3a36a6ea9aaf8a60fe372dc534f6f87c1","62","272","0","task_categories:text-classification, task_categories:token-classification, task_categories:text-generation, task_categories:text2text-generation, task_categories:sentence-similarity, language:en, language:ja, language:es, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2206.06588, region:us, amazon, retrieval, search, ecommerce, ranking, reranking","text-classification, token-classification, text-generation, text2text-generation, sentence-similarity","Amazon Shopping Queries Dataset A comprehensive dataset for improving product search, ranking and recommendations, featuring query-product pairs with detailed relevance labels. Overview The dataset co","None","en, ja, es","multi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"cl-nagoya/auto-wiki-nli-reward","cl-nagoya","2024-07-18 01:36:31","2024-09-13 01:46:46+00:00","c738eee953eae33e5e58d197734df980c40677a6","64","271","2","language:ja, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.11704, arxiv:2409.07737, region:us","None","AutoWikiNLI reward A dataset constructed by generating hypothesis sentences corresponding to entailment and contradiction from Wikipedia text using Nemotron-4 340B. Helpfulness and other scores are as","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","2406.11704","None","None"
+"sbintuitions/niilc-qa","sbintuitions","2024-06-29 04:02:35","2025-01-22 06:44:39+00:00","11f21762d22105132406f66a6f0d73bfbe70124e","66","271","0","task_categories:question-answering, language:ja, 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","question-answering","評価スコアの再現性確保と SB Intuitions 修正版の公開用クローン ソース: mynlp/niilc-qa on GitHub NIILC Question Answering Dataset This project provides a dataset for supporting the development of question answering systems. The ","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","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","None","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"
+"Calvin-Xu/Furigana-Aozora-Speech","Calvin-Xu","2024-07-29 04:19:29","2024-07-30 08:25:50+00:00","ba8d93ed086ad9823a6537c758eb128ab4e2b1cf","49","268","1","task_categories:text2text-generation, language:ja, license:cc, size_categories:1M<n<10M, format:arrow, modality:text, library:datasets, library:mlcroissant, region:us, furigana, education","text2text-generation","Derived from 青空文庫及びサピエの音声デイジーデータから作成した振り仮名注釈付き音声コーパスのデータセット https://github.com/ndl-lab/hurigana-speech-corpus-aozora All text files in the original data were processed for 3361443 entries duplicates a","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc","None","None","None"
+"lesserfield/fgo-story","lesserfield","2024-04-15 03:29:30","2024-04-15 03:36:56+00:00","e7fa10afb76699dcdea9a9d3f4083cd460a454cc","84","268","3","task_categories:translation, language:en, language:ja, language:id, license:cc-by-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, story, conversation","translation","None","None","en, ja, id","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-2.0","None","None","None"
+"jaeyong2/ja-rag-cot","jaeyong2","2024-10-29 15:19:38","2024-11-01 03:55:04+00:00","1aa2fea55fc6bf2ced1d30efcaaa4bc6cfdf8c84","55","265","1","language:ja, 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",">>> from datasets import load_dataset >>> ds = load_dataset(""jaeyong2/ja-rag-cot"", split=""train"") >>> ds Dataset({ features: ['context', 'Question', 'RAW Ground Truth', 'Thinking', 'Final Answer'], nu","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"lesserfield/translate-en-ja-id","lesserfield","2024-07-01 23:00:58","2024-07-12 19:46:31+00:00","7c0a68d6b58a674534980db6b6e125dafb927c1e","49","265","0","task_categories:translation, language:en, language:ja, language:id, license:cc-by-nc-nd-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","translation","None","None","en, ja, id","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-nd-4.0","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","None","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"
+"aixsatoshi/Longcontext-aozora-summary","aixsatoshi","2024-03-29 11:09:15","2024-03-30 10:55:37+00:00","af3dd86e24f5b546efe08dc7b75282ccadd63057","53","262","4","language:ja, license:cc, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","長文からの要約データセットです。 長文は以下の青空文庫データセットを利用しました。 globis-university/aozorabunko-clean License CC BY 4.0","None","ja","mono","None","None","False","False","False","cc","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","None","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"
+"Calvin-Xu/Furigana-Aozora","Calvin-Xu","2024-07-28 02:29:57","2024-07-28 09:09:55+00:00","be9abdc9313a16146df74358c482015f2576ef03","49","259","0","task_categories:text2text-generation, language:ja, license:mit, size_categories:1M<n<10M, format:arrow, modality:text, library:datasets, library:mlcroissant, region:us, furigana, education","text2text-generation","Derived from 青空文庫及びサピエの点字データから作成した振り仮名のデータセット（GitHub） https://github.com/ndl-lab/huriganacorpus-aozora Certain mismatches in the original corpus were eliminated during validation (307 instances) Error","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","mit","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","None","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"
+"spow12/llava_instruct_mix_jp","spow12","2024-11-21 04:20:41","2024-11-21 05:36:14+00:00","2d07ce62d83148141645f8ecc44ddf6c6ce1155e","90","256","0","task_categories:visual-question-answering, language:ja, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","visual-question-answering","Translated using ChatWaifu_12B_v2.2(private) Prompt prompt = [ { 'role': 'user', 'content': [ { 'type': 'text', 'text': f""""""Translate the sentece to japanese. If there is any structure like markdown t","None","ja","mono","None","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","None","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"
+"mohamed-khalil/KaidanNihonbunka","mohamed-khalil","2024-04-15 15:43:41","2024-04-15 16:03:13+00:00","761aada7a5c6b40287d3d1c2c926059042b8e461","46","253","0","task_categories:text-generation, task_categories:text2text-generation, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, folklore, Hyakumonogatari, Nihonbunka","text-generation, text2text-generation","Kaidan Nihonbunka: A Journey Through Hyakumonogatari's Ghostly Tales Welcome to the Kaidan Nihonbunka Dataset About Name kaidan Nihonbunka translates to 怪談日本文化 in Japanese: 怪談 (Kwaidan): Ghost story o","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"anusfoil/NeuroPiano-data","anusfoil","2024-09-10 02:18:13","2024-10-19 06:44:17+00:00","3912590901fb5e88251f82bebb70c11cd352214b","57","253","6","task_categories:audio-classification, language:ja, language:en, license:mit, size_categories:1K<n<10K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, music","audio-classification","Dataset Card for NeuroPiano-data This dataset contains 2255 entries of audio-question-answer pairs that specializes in music education. Questions ranges from cleaness of attack to hands balancing, eac","None","ja, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"kanhatakeyama/SyntheticTextOpenMathInstruct","kanhatakeyama","2024-06-13 07:47:23","2024-06-14 07:12:50+00:00","d66651c7ba88350088337b4015b3abf86c9afe45","62","252","2","language:ja, license:other, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","以下のデータ源からランダムに抽出した日本語のテキストをもとに､Phi-3で作文したコーパスです｡ OpenMathInstruct-1-1.8m-ja コード こちら 一部の計算には東京工業大学のスーパーコンピュータTSUBAME4.0を利用しました｡","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"kunishou/ApolloCorpus-ja","kunishou","2024-03-13 02:08:51","2024-03-13 17:55:25+00:00","d76ca83bd66e0a23ff09a3a6b868ffc5d09a73f0","48","251","4","language:ja, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.03640, region:us","None","ApolloCorpus-ja 概要 多言語医療データセットの ApolloCorpus を日本語に自動翻訳した 525k の指示チューニングデータセットになります。ApolloCorpus は、オープンソースでかつ品質を担保できるデータのみをスクリーニングし収集されたデータセットになります。詳細は 論文 をご覧下さい。 翻訳対象ファイル データ量が多いのでひとまず以下の 1 ファイルのみを翻訳し","None","ja","mono","None","None","False","False","False","apache-2.0","2403.03640","None","None"
+"sbintuitions/JCommonsenseQA","sbintuitions","2024-07-30 05:21:01","2024-12-27 08:05:47+00:00","ec4b09c9c385ca9b9bf21a3016edb3712d0aec76","67","250","0","task_categories:question-answering, language:ja, 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, region:us","question-answering","評価スコアの再現性確保と SB Intuitions 修正版の公開用クローン ソース: yahoojapan/JGLUE on GitHub datasets/jcommonsenseqa-v1.1 JCommonsenseQA JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a mul","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"kogi-jwu/cl-humaneval_v1.0","kogi-jwu","2024-10-28 07:06:59","2024-12-31 06:23:04+00:00","eafa02c049d014cb5566d248e3d5f48dcb59c67e","154","249","0","task_categories:text2text-generation, language:en, language:ja, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","text2text-generation","CL-HumanEval Dataset Description CL-HumanEval is a benchmark for evaluating cross-lingual transfer through code generation. It is based on the code generation benchmark HumanEval. Languages The datase","None","en, ja","bi","None","None","False","False","False","mit","None","None","None"
+"neody/madlad-400-ja-cleaned","neody","2024-06-18 08:28:11","2024-07-06 08:30:01+00:00","b4bd6fdc8e45f62cb97608e0b72f902c31e9c611","54","247","0","language:ja, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","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","None","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"
+"joujiboi/bluemoon-fandom-1-1-rp-jp-translated","joujiboi","2024-09-02 12:56:09","2024-09-09 13:20:16+00:00","ead99aba2f2b475d44f64c647e6465b26f46f6f5","60","246","2","task_categories:text-generation, language:ja, license:gpl, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, roleplay, storywriting","text-generation","bluemoon-fandom-1-1-rp-jp-translated A subset of Squish42/bluemoon-fandom-1-1-rp-cleaned translated to Japanese using command-r-08-2024. Misc. info I used openrouter's api for inference with command-r","None","j, a","bi","None","None","False","False","False","gpl","None","None","None"
+"ryota39/preference-en-ja-100k","ryota39","2024-11-04 05:10:16","2024-11-19 11:24:26+00:00","fc8a4379a11839b2f1a583c52d473076e98ca50d","68","242","1","task_categories:reinforcement-learning, language:en, language:ja, license:unknown, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, translation","reinforcement-learning","preference-en-ja-100k 以下のデータセットを選好データの形式に加工し、和訳データを加えた後にOpenAI message形式に加工しました Dahoas/synthetic-instruct-gptj-pairwise openai/webgpt_comparisons 和訳に使用したモデル llm-jp/llm-jp-3-13b-instruct License 使用させてい","None","en, ja","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","unknown","None","None","None"
+"watashihakobashi/ogiri","watashihakobashi","2024-07-19 20:46:13","2024-09-24 20:41:22+00:00","95e07ba530756f3c1c998a6404f8dd607a2b58a2","62","242","4","language:ja, license:cc-by-nc-nd-4.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","東京大学松尾・岩澤研究室主催のLLM講座2024の第5回「SFT」演習で使用するデータセットです。https://weblab.t.u-tokyo.ac.jp/en/education/large-language-model/ 人手で作成した入力に対して以下の2つのモデルによる出力を抜粋したデータとなります。https://huggingface.co/watashiha/watashiha-g","None","ja","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"Aratako/Rosebleu-1on1-Dialogues","Aratako","2024-04-13 13:14:55","2024-05-16 15:03:47+00:00","03e1c9bc1385393ff7f9644e066134fb5da84d86","115","242","0","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, not-for-all-audiences, nsfw","text-generation","Rosebleu-1on1-Dialogues 2025/05/17 3人での対話のデータを追加 概要 @matsuxrさんが公開しているRosebleuデータセットのうち、1つのシーン中で1対1の会話が��われていレコード（登場人物が2人のみのもの）を抽出し、さらに各話者ごとの連続した発話をまとめ、データセットとしてまとめたものです。 データの提供者である青猫様、およびデータをまとめデータセットの","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"Hoshikuzu/Japanese-Law-Translation","Hoshikuzu","2024-08-24 14:43:16","2024-08-25 13:26:11+00:00","635619321e8d2a836434208713873a3b9b08224a","41","240","0","task_categories:translation, language:en, language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for Japanese-Law-Translation Dataset Summary This corpus is extracted from the Japanese-English Legal Parallel Corpus, with Japanese-English pairs. For more information, see website below","None","en, ja","bi","None","None","False","False","False","None","None","None","None"
+"Nekofox/ja-zh-twitter-translate","Nekofox","2023-05-08 13:49:39","2023-05-08 13:55:45+00:00","c2ba3bf82ff975ac02b707e7cd1d70ec22041828","40","240","3","task_categories:translation, language:zh, language:ja, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","translate by @Nekofoxtweet (me) twitter source from @RindouMikoto","None","zh, ja","bi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"Hoshikuzu/JESC","Hoshikuzu","2024-08-24 14:21:53","2024-08-27 12:46:26+00:00","963675b2a695db67830cee0b9528028b949d404c","58","238","1","task_categories:translation, language:en, language:ja, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1710.10639, region:us","translation","Dataset Card for JESC Dataset Summary This corpus is extracted from the JESC, with Japanese-English pairs. For more information, see website below! (https://nlp.stanford.edu/projects/jesc/index_ja.htm","None","en, ja","bi","None","None","False","False","False","cc-by-4.0","1710.10639","None","None"
+"cl-nagoya/nu-mnli","cl-nagoya","2024-03-23 10:28:03","2024-03-29 04:51:12+00:00","f7386d11113947ba804ea0a8827ccc8e04c8e5b7","44","237","1","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, multilinguality:bilingual, source_datasets:nyu-mll/multi_nli, language:ja, language:en, license:cc-by-3.0, license:cc-by-sa-3.0, license:mit, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, natural-language-inference, multi-input-text-classification","Translation Code We used vLLM for a faster, batched generation. import datasets as ds from vllm import LLM, SamplingParams, RequestOutput from transformers import AutoTokenizer model_path = ""hoge/fuga","None","ja, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-3.0","None","None","natural-language-inference, multi-input-text-classification"
+"ikeno-ada/Japanese-English_translation_of_contents_HScodes","ikeno-ada","2024-05-09 09:29:48","2024-05-09 09:37:08+00:00","53b7450452e99ea01e6922cf0860e8bf9d1ebfd6","46","236","0","language:ja, language:en, license:mit, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","日本郵便が提供する「国際郵便 内容品の日英・中英訳、HSコード類」（2024/05/09）のデータに基づいています。 詳しくはサイトをご覧ください https://www.post.japanpost.jp/int/use/publication/contentslist/index.php?id=0&ie=utf8&lang=_ja&q=","None","ja, en","bi","None","None","False","False","False","mit","None","None","None"
+"jlcmoore/ValueConsistency","jlcmoore","2024-07-30 23:59:36","2024-09-21 19:38:00+00:00","58ab6d8d58c544c19a70c245300e9552dce2c748","53","235","1","language:en, language:zh, language:de, language:ja, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2407.02996, region:us","None","Dataset Card for ValueConsistency This is the ValueConsistency data set as introduced in the paper ""Are Large Language Models Consistent over Value-laden Questions?"". Dataset Details Dataset Descripti","None","en, zh, de, ja","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2407.02996%22","None","None"
+"hotchpotch/sentence_transformer_japanese","hotchpotch","2025-01-19 08:23:23","2025-01-20 10:19:14+00:00","e919174ab810b903e5f08f8f07fab4b83ed166de","234","234","3","language:ja, license:unknown, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","日本語のデータセットを SentenceTransformes で学習しやすいカラ��名と構造に変換したもの。 主に (anchor, positive), (anchor, positive, negative), (anchor, positive, negative_1, ..., negative_n) といった構造になっているため、とりわけ対照学習で使いやすくなっています。 以下のデータセ","None","ja","mono","None","None","False","False","False","unknown","None","None","None"
+"Hoshikuzu/JParaCrawl","Hoshikuzu","2024-08-24 15:07:12","2024-08-25 13:15:52+00:00","7ca855658f6338df0c15d23229e151ae0452608f","44","233","1","task_categories:translation, language:en, 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 JParaCrawl Dataset Summary Cleaned JParaCrawl data. For more information, see website below! https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ JParaCrawl is the largest publicly availab","None","en, ja","bi","None","None","False","False","False","None","None","None","None"
+"liboaccn/MIT-10M","liboaccn","2024-08-21 17:37:55","2025-01-21 08:29:34+00:00","9e94eae575bc6bdccfd6d7acfc5bfc0a98924777","53","231","7","task_categories:translation, task_categories:image-to-text, language:en, language:zh, language:pt, language:ja, language:fr, language:es, language:it, language:de, language:ru, language:ar, language:ko, language:tr, language:th, language:hi, license:apache-2.0, size_categories:10M<n<100M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation, image-to-text","MIT-10M Paper: https://aclanthology.org/2025.coling-main.346/ Introduction: Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within im","None","en, zh, pt, ja, fr, es, it, de, ru, ar, ko, tr, th, hi","multi","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","apache-2.0","None","None","None"
+"waddledee/three_line_summarization_for_japanese_news_articles","waddledee","2024-04-15 07:34:42","2024-04-15 08:09:17+00:00","97d90b2741c22a2248048a3d6513dd9e4d0f7220","45","230","0","task_categories:summarization, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","summarization","ライブドアニュースコーパスの3行要約データセットです。 Llama v2向けのプロンプトを追加して成形してあります。 学習に利用する際は、 [R_START] [R_END] をspecial tokenとして追加することを推奨します。 Number of rows: 3,907 Datasetは以下のリポジトリを利用してscrapeしました。 git@github.com:KodairaTomo","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"hatakeyama-llm-team/CommonCrawlPDFJa","hatakeyama-llm-team","2024-04-09 06:40:01","2024-05-28 12:03:17+00:00","15fbc1cf74536383d3ac45a3a9a8f3f3ba5136fe","45","230","1","language:ja, license:odc-by, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Data extracted from CommonCrawlPDF Japanese domain Code is here","None","ja","mono","None","None","False","False","False","odc-by","None","None","None"
+"ayousanz/reazon-speech-v2-all-speechMOS-analyze","ayousanz","2024-04-16 06:13:47","2024-04-16 16:59:16+00:00","8228187415b18ba0360d443c6fca48b8e346af5e","41","230","0","language:ja, license:cdla-sharing-1.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","概要 reazon-research/reazonspeech-v2[all]をspeechMOSにて音声品質の分析を行った結果です。 分析した結果を audio_analysis_results_speechMOS.jsonというjsonにて保存しました。 jsonのフォーマットは以下になっています ""ファイル名"": file_name, ""MOS値"": snr value, ""トランスクリプシ","None","ja","mono","None","None","False","False","False","cdla-sharing-1.0","None","None","None"
+"lenML/oaast_rm_full_jieba","lenML","2024-01-11 18:16:53","2024-01-13 07:36:20+00:00","0e6ce017cc22ad329c19d40901ddcd1ada90619d","53","229","0","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:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, human-feedback","None","尝试解决""llm repetition problem""，使用分词模型对oaast语料进行“结巴化”数据增强，提供更强的重复内容拒绝效果。 Attempts to solve the ""llm repetition problem"" by using a segmentation model to enhance the oaast corpus with ""stuttering"" data to","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"
+"mpasila/ParallelFiction-Ja_En-100k-json","mpasila","2024-03-26 06:08:26","2024-04-02 04:46:10+00:00","6060595351e070f0b0776a01ee2f009549bbecc9","41","229","3","task_categories:translation, language:ja, language:en, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This is my conversion of NilanE/ParallelFiction-Ja_En-100k into json which can be read by text-generation-webui when training a model. Original Dataset card Dataset details Each entry in this dataset ","None","ja, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"hpprc/quiz-no-mori","hpprc","2024-08-05 01:24:25","2024-08-05 08:04:34+00:00","83f7d38a0c440b0d8c6bc151364751cb3e1189f8","41","228","1","task_categories:question-answering, language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","クイズの杜様に掲載のクイズのうち、2024年8月5日時点において取得可能だったクイズのうち「二次利用許諾レベル」が「フリー」であったものを収載したデータセットです。 検索拡張生成(RAG)や文書検索システムの構築などへの利用に適した高品質なデータです。 クイズの杜様ホームページ上の問題の二次利用についてに記載されている「二次利用許諾レベル」に関する説明のうち フリー改変した問題、前フリをつけた問題","None","ja","mono","None","None","False","False","False","other","None","None","None"
+"Sugisaku8/SCRDataSet","Sugisaku8","2024-02-26 06:34:22","2024-02-26 06:56:11+00:00","4b4e7e38c637e0c4061b80021cb01111cb2bfbb8","59","226","0","task_categories:question-answering, task_categories:text2text-generation, language:ja, language:en, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, doi:10.57967/hf/1814, region:us","question-answering, text2text-generation","SCR Data Set Dataset Details This dataset is for tuning already existing models for use in school settings. Dataset Details. Dataset Description <! -- A longer summary of what this dataset is. -->. da","None","ja, en","bi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"speed/arxiver_ja","speed","2024-10-22 18:25:39","2024-10-23 02:57:05+00:00","ae52935730248cde62b3f299e596eb40b39edd20","57","226","3","language:en, language:ja, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","This dataset is a translation of the abstract column of the neuralwork/arxiver into Japanese using the google/gemma-2-2b-it model. The license follows the original dataset's CC BY-NC-SA 4.0 License. T","None","en, ja","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"biki647/japanese_language","biki647","2024-05-27 06:17:56","2024-05-30 06:23:01+00:00","627b0d745adcbdca60984a4a10e279c80b71c2ae","58","226","0","language:en, language:ja, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en, ja","bi","None","None","False","False","False","None","None","None","None"
+"erfanzar/LinguaMatic-Mixin","erfanzar","2024-01-05 11:07:52","2024-01-06 10:27:27+00:00","d7d819f482d30923440afc8fa1ee6b1ae3e7565c","50","226","0","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:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code, biology, medical","text-generation, text-classification, conversational","Dataset Card for ""UltraChat-Mixin"" UltraChat-Mixin Dataset Overview llama 2 prompted style frin ChatMatic ChatMatic Dataset is built with mix of 4 other dataset and which carefully chosing best one fr","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"
+"noname0202/merged-ja","noname0202","2024-12-17 08:21:18","2024-12-19 11:26:30+00:00","411c9c19cbe2d23950bdb6d090474e8efa5879ec","56","225","0","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","以下のデータセットの中の256文字以下の行を抽出しマージしました。 https://huggingface.co/datasets/neody/c4-ja-cleaned https://huggingface.co/datasets/neody/cc100-ja-cleaned https://huggingface.co/datasets/neody/oscar-ja-cleaned","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"SakanaAI/ChouBun","SakanaAI","2024-09-02 04:15:52","2024-10-18 02:45:43+00:00","3378915723514cccd186ec05211478401f371226","61","224","8","task_categories:question-answering, task_categories:summarization, language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.13166, region:us, Long Context","question-answering, summarization","ChouBun Dataset Description ChouBun is a benchmark for assessing LLMs' performance in long-context tasks in the Japanese language. It is created and introduced in the paper An Evolved Universal Transf","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","2410.13166","None","None"
+"Manirathinam21/Multi-lingual_Detection","Manirathinam21","2024-03-09 14:11:05","2024-03-09 14:13:32+00:00","d7321439b61adc96a9580bf6b6bb3f6d7ed9b8b7","47","223","0","task_categories:text-classification, language:ar, language:ta, language:ja, language:en, language:es, language:fr, language:de, license:mit, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","None","None","ar, ta, ja, en, es, fr, de","multi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"masajek/openassistant-guanaco-ja","masajek","2023-08-15 15:38:34","2023-08-17 15:36:00+00:00","8a559b851a4bf9954f82a50c8685ffb887a9417a","51","222","0","language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This dataset is a subset of the Open Assistant dataset, which contains Japanese conversations only.You can find a base datset here: https://huggingface.co/datasets/timdettmers/openassistant-guanaco","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Hoshikuzu/ccmatrix-en-ja","Hoshikuzu","2024-08-01 07:18:51","2024-08-03 16:25:04+00:00","95536b0456e2f499067242cddfe317ad18858e42","52","221","1","task_categories:translation, task_categories:text2text-generation, language:en, language:ja, license:unknown, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1911.00359, arxiv:1911.04944, arxiv:2010.11125, region:us","translation, text2text-generation","Dataset Card for CCMatrix-en-ja Dataset Summary This corpus is extracted from yhavinga/ccmatrix, with Japanese and English pairs. How to use It is used in much the same way as yhavinga/ccmatrix. The o","None","en, ja","bi","None","None","False","False","False","unknown","1911.00359","None","None"
+"davanstrien/ml-kge","davanstrien","2023-12-21 12:06:12","2023-12-21 12:24:14+00:00","95eb358969c07270821c7fdc73201d7375afb309","49","221","0","language:en, language:ar, language:de, language:es, language:fr, language:it, language:ja, language:ko, 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, arxiv:2311.15781, region:us, knowledge-graphs","None","MKGE: Multilingual Knowledge Graph Enhancement note this dataset card was copied from this GitHub Repository Task Description | WikiKGE-10 | Evaluation | Paper | Citation | License Recent work in Natu","None","en, ar, de, es, fr, it, ja, ko, ru, zh","multi","n|<|1|K","None","False","False","False","cc-by-sa-4.0","2311.15781","None","None"
+"tellarin-ai/llm-japanese-dataset-vanilla-aya-format","tellarin-ai","2023-12-15 13:03:17","2024-01-31 14:27:56+00:00","40a061c9e0d0ecc6502476d7e3b852f2560d75be","46","220","1","language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2309.03412, region:us","None","Dataset Card for llm-japanese-dataset-vanilla in the Aya format This dataset is a format conversion from its original v1.0.0 format and released here under the same CC-BY-SA 4.0 license and conditions","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"if001/elementray_l","if001","2024-10-15 15:31:38","2024-10-21 13:15:01+00:00","026a693af176301a54177c832faf63857450b42f","42","219","0","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","calm3-22bを使って簡単な日本語の例文を作成したデータセットです。 以下のパターンが含まれるように一文を作成しています。 生成に失敗しているものはクリーニングしています。 ""です/だ (肯定文)"", ""ではありません/じゃない (否定文)"", ""〜ます (動詞の丁寧形)"", ""〜ません (動詞の否定形)"", ""〜たい (希望・願望)"", ""〜ている (進行形)"", ""〜てください (依頼)""","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"floschne/xflickrco_1k","floschne","2024-05-24 14:51:35","2024-05-27 07:05:47+00:00","133d0275abf964d804fc4350aba3e5134316cf34","55","219","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:1K<n<10K, 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|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"lmg-anon/VNTL-v2.5-1.6k-dpo-pairs","lmg-anon","2024-02-01 15:56:36","2024-02-02 05:48:20+00:00","9db691887ed681e46af417b6a7a1a657495885be","43","218","0","task_categories:translation, language:en, language:ja, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, dpo","translation","Dataset Card for ""VNTL-v2.5-1.6k-dpo-pairs"" This is a very experimental DPO dataset for VNTL, I have no idea if DPO will work well to improve translation, but I guess it's worth a shot! This dataset w","None","en, ja","bi","None","None","False","False","False","None","None","None","None"
+"saillab/testalpaca_sanskrit_taco","saillab","2024-06-03 21:27:26","2024-06-04 00:07:46+00:00","b8b8098bd303615435c1d05a17651dd4764eef44","48","218","0","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:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, 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"
+"WarriorMama777/databricks-dolly-15k-ja_cool","WarriorMama777","2023-07-08 06:16:29","2023-07-08 06:45:18+00:00","e173b4b5f6b49371121165c322b40141ddc4d446","51","217","1","language:ja, license:cc-by-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Overview This dataset is edited from kunishou/databricks-dolly-15k-en.It was edited so that it would be like Yuki Nagato, who appears in ""The Melancholy of Haruhi Suzumiya,"" with an emotionless and in","None","ja","mono","None","None","False","False","False","cc-by-3.0","None","None","None"
+"sudy-super/JetCopper-10B","sudy-super","2024-03-21 17:21:08","2024-12-26 20:55:50+00:00","2fee5b2d33901f0505acde623aaf19eab02e8dde","76","217","5","language:ja, language:en, language:code, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","JetCopper-10B Description JetCopper-10B was created by extracting a portion of the data after cleaning, filtering, and deduplicating the following datasets. The japanese subset of C4 The japanese subs","None","ja, en, code","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","None","None","None"
+"noname0202/oscar-cleaned-256","noname0202","2024-12-15 12:19:36","2024-12-19 11:23:05+00:00","941d017cb4531abb0561cbd5f4c25a669c2da296","57","217","0","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","neody/oscar-ja-cleanedのデータの中から256文字以下のものを抽出しました。","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"cl-nagoya/nu-snli","cl-nagoya","2024-03-23 09:39:01","2024-03-29 04:50:36+00:00","42c9f133e0fdfe74a5827a599dd0ad578c9f820d","41","216","0","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, multilinguality:bilingual, source_datasets:snli, source_datasets:extended|other-flicker-30k, source_datasets:extended|other-visual-genome, language:ja, language:en, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, natural-language-inference, multi-input-text-classification","Translation Code We used vLLM for a faster, batched generation. import datasets as ds from vllm import LLM, SamplingParams, RequestOutput from transformers import AutoTokenizer model_path = ""hoge/fuga","None","ja, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","natural-language-inference, multi-input-text-classification"
+"lianghsun/Everything-Instruct-Multilingual-DPO","lianghsun","2024-12-05 01:37:25","2024-12-10 01:06:43+00:00","144446f373e7efa8bf2390f0e58a806aa0a07c4b","58","216","1","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:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.18290, region:us, sft, dpo","None","Dataset Card for lianghsun/Everything-Instruct-Multilingual-DPO Dataset Details Dataset Description 這個資料集是分叉於 rombodawg/Everything_Instruct_Multilingual，但在中文回答的部份透過 opencc-python 將簡體中文（zh-cn）轉成繁體中文（zh","None","en, ru, zh, ko, ur, la, ar, de, es, fr, hi, it, ja, nl, pt","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-nc-sa-4.0","2305.18290","None","None"
+"Ego/jpflan-raw","Ego","2024-04-01 06:11:18","2024-04-03 00:25:15+00:00","18ca666db3f81f5d76d23bd2d70fcedc31dfeaa6","42","213","0","task_categories:text-generation, language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:arrow, modality:text, library:datasets, library:mlcroissant, region:us","text-generation","Description This is a collection of raw data from ~40 Japanese open source downstream task datasets.This dataset was built with the intention of collecting high quality datasets which were not machine","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","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","task_categories:audio-classification, task_categories:automatic-speech-recognition, task_categories:audio-text-to-text, task_categories:text-to-speech, task_categories:question-answering, task_categories:document-question-answering, annotations_creators:found, language_creators:expert-generated, multilinguality:multilingual, language: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, language:multilingual, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","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"
+"nakayama/hh-rlhf-helpful-base-ja","nakayama","2023-05-26 15:48:51","2023-05-26 15:57:12+00:00","910cfd6591d761607853e065cf0b0e80c93431d2","46","210","0","language:ja, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","https://github.com/anthropics/hh-rlhf の内容のうち、helpful-base内のchosenに記載されている英文をfuguMTで翻訳、うまく翻訳できていないものを除外、修正したものです。","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"werty1248/OpenOrca-EnKoZhJa-18k","werty1248","2024-08-10 18:54:09","2024-08-10 19:16:35+00:00","212355bd7cc6e9651cff74402d0f638981235a2b","44","209","0","language:en, language:ko, language:zh, language:ja, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This dataset is a collection of Korean, Chinese, and Japanese OpenOrca translation datasets. The dataset was matched using id based on kyujinpy/OpenOrca-KO, which had the smallest number of rows. When","None","en, ko, zh, ja","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"HachiML/hh-rlhf-49k-ja-alpaca-format","HachiML","2023-08-13 00:57:29","2023-08-13 01:04:53+00:00","d733f197c14bb64cc5bce331c244fbddf417db87","43","209","0","language:ja, 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 ""hh-rlhf-49k-ja-alpaca-format"" これはkunishou/hh-rlhf-49k-jaに以下の変更を加えたデータセットです。 alpaca形式に変更 翻訳が失敗したもの（つまりng_translation!=""0.0""のもの）の削除 indexの頭に""hh-rlhf.""をつける This is a dataset of kunishou","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"Heng666/OpenSubtitles-TW-Corpus","Heng666","2024-02-02 11:17:34","2024-02-20 03:16:06+00:00","6433fde247cb8f7ef20f04b8519df2f612d5bb7a","54","209","3","task_categories:translation, language:en, language:ja, language:ko, language:id, language:vi, language:th, language:zh, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, translation, OPUS, OpenSubtitles","translation","Dataset Card for [OpenSubtitles-TW-Corpus] Dataset Summary OpenSubtitles-TW-Corpus 是一個機器翻譯基準的多語言資料集，源自 OpenSubtitles 收集的使用者貢獻的翻譯，並由 OPUS。該資料集包括按語言對排序的測試和開發資料。它包括數百種語言對的測試集，並且不斷更新。請檢查版本號標籤以引用您正在使用的版本。 ","None","en, ja, ko, id, vi, th, zh","multi","1|M|<|n|<|1|0|M","None","False","auto","False","unknown","None","None","None"
+"karakuri-ai/corrected-mt-bench-ja","karakuri-ai","2024-07-11 06:25:14","2024-07-11 12:47:19+00:00","1174d9ca0aeea5a8f8d9eb91d9ba601a6dcec02c","55","209","1","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Corrected MT-Bench-ja Inflection AIによるCorrected MT-Benchの日本語訳です。 一部の設問はStability AIによるJapanese MT-Benchを使用しています。","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","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","None","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"
+"cl-nagoya/nu-snli-rev","cl-nagoya","2024-03-27 12:13:13","2024-11-15 15:53:11+00:00","40078f99d4ffebbdd5c5510b12db271180ebcd5c","44","207","1","language:ja, language:en, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja, en","bi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"cyberagent/FaithCAMERA","cyberagent","2024-10-22 06:04:54","2024-10-22 10:22:34+00:00","917997e2c4c3148f5689b59f484397ca9f8dedbd","67","206","0","task_categories:text-generation, language:ja, license:cc-by-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.03839, region:us, faithfulness, advertisement","text-generation","Overview In ad text generation (ATG), desirable ad text is both faithful and informative. That is, it should be faithful to the input document, while at the same time containing important information ","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2410.03839","None","None"
+"FrancophonIA/XFUND","FrancophonIA","2024-09-30 21:22:34","2024-10-11 18:27:27+00:00","0f5e2904f92077c1a0c6dfcf3f587dbac1190547","61","206","0","None","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"
+"Aratako/Synthetic-JP-Roleplay-Instruction-Nemotron-4-1k","Aratako","2024-06-23 08:28:26","2024-06-23 08:42:32+00:00","b50b6b25c15caaebd2c227a5f41af29f6faa9641","43","204","6","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, roleplay","text-generation","Synthetic-JP-Roleplay-Instruction-Nemotron-4 Magpieの手法をnvidia/Nemotron-4-340B-Instructに対して適用し作成した、約1000件の日本語ロールプレイ用のinstructionデータセットです。 データセットの作成にはDeepInfraを利用しました。 特に事後的なフィルタ処理は加えていないため、クオリティの低いレコード","None","ja","mono","None","None","False","False","False","apache-2.0","2406.08464","None","None"
+"ayousanz/reazon-speech-v2-all-WAND-SNR-analyze","ayousanz","2024-03-31 14:13:56","2024-04-03 12:31:56+00:00","e70e8841c518ada77f87bbc50cbab35261631065","47","204","0","language:ja, license:cdla-sharing-1.0, size_categories:10M<n<100M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","概要 reazon-research/reazonspeech-v2[all]をWADA SNRにて音声品質の分析を行った結果です。 分析した結果を reazonspeech-all-wada-snr.jsonというjsonにて保存しました。 jsonのフォーマットは以下になっています ""ファイル名"": file_name, ""SNR値"": snr value, ""トランスクリプション"": tra","None","ja","mono","None","None","False","False","False","cdla-sharing-1.0","None","None","None"
+"UniDataPro/llm-training-dataset","UniDataPro","2024-09-27 09:22:06","2024-09-27 09:41:32+00:00","4e2d8c1b811a6aef929350fa90bfcccad215e81b","60","203","2","task_categories:text-generation, task_categories:text2text-generation, task_categories:text-classification, language:uk, language:tr, language:th, language:sk, language:pt, language:pl, language:fa, language:nl, language:mr, language:ml, language:ko, language:ja, language:it, language:id, language:hu, language:hi, language:ga, language:el, language:de, language:fr, language:fi, language:es, language:en, language:da, language:cs, language:ca, language:az, language:ar, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llm, llm fine-tuning , finetuning , logs, llm training, nlp, question answering","text-generation, text2text-generation, text-classification","LLM Fine-Tuning Dataset - 4,000,000+ logs, 32 languages The dataset contains over 4 million+ logs written in 32 languages and is tailored for LLM training. It includes log and response pairs from 3 mo","None","uk, tr, th, sk, pt, pl, fa, nl, mr, ml, ko, ja, it, id, hu, hi, ga, el, de, fr, fi, es, en, da, cs, ca, az, ar","multi","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"DeL-TaiseiOzaki/magpie-qwen2.5-32B-10K-ja","DeL-TaiseiOzaki","2024-10-14 22:20:52","2024-10-14 22:54:57+00:00","4ef7be131b9b6c91a09cdbe392b4b3d0bf0bdb7d","91","203","0","language:ja, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","合成日本語指示データセット 概要 このデータセットは、大規模言語モデル（Qwen2.5-32B-instruct）を用いて自動生成された日本語の指示とそれに対する応答のコレクションです。データセットは指示付与型のタスクのための学習や評価に使用することを目的としています。 データセット仕様 サンプル数: 20,000 言語: 日本語 フォーマット: JSON ライセンス: Apache-2.0 サイ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Prgckwb/shokujin-menu","Prgckwb","2024-02-23 14:04:06","2024-02-26 03:57:13+00:00","a2f025ff4697706df12e6bc4792f1bf7c5c97e17","42","202","0","language:ja, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Shokujinjp Menu 食神餃子王 (@Shokujinjp)のメニューデータです。 Usage import pandas as pd from datasets import load_dataset # Hugging Face Datasets 形式で読み込み data = load_dataset( ""Prgckwb/shokujin-menu"", split=""train"" )","None","ja","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"shi3z/ja_conv_wikipedia_llama2pro8b_10k","shi3z","2024-01-12 06:17:35","2024-01-12 06:18:48+00:00","1ebd1dd6f5172620d68f249e801501006201f032","43","202","2","language:ja, license:llama2, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","This dataset is based on the Japanese version of Wikipedia dataset and converted into a multi-turn conversation format using llama2Pro8B. After generating 10,000 conversations and screening, only abou","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","llama2","None","None","None"
+"Aratako/dataset-for-annotation-v2","Aratako","2024-11-11 10:24:55","2024-11-30 15:13:22+00:00","184cc52cc66a2ac5e39fcdb4cf1f5ab2d23d9ad2","55","202","0","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","None","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"hpprc/llmjp-warp-html","hpprc","2024-09-18 02:41:06","2024-09-20 10:33:29+00:00","c7b81df79a4ad1b6e078abb42f6432149a52d3e8","52","201","3","language:ja, license:cc-by-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","llm-jp-corpus-v3のwarp_htmlのうちlevel2フィルタリングされたデータをHFフォーマットに変換し、各データに付与されたURLから元記事のタイトルを取得可能なものについては取得して付与したデータセットです。 ライセンスは元ページに従いCC-BY 4.0とします。","None","ja","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"Sakalti/PossitivaAI","Sakalti","2024-10-10 11:17:35","2024-10-17 08:20:14+00:00","87718f69ecf5a2ecf6999610e28dc98e550c7dfd","49","200","0","language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Heng666/TED2020-TW-Corpus","Heng666","2024-02-02 08:21:07","2024-02-02 08:53:00+00:00","294f8945fa888f80261de3be6dd95d48902102c1","59","200","3","task_categories:translation, language:en, language:ja, language:ko, language:id, language:vi, language:th, language:tw, license:unknown, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, taiwan, translation, Ted2020","translation","Dataset Card for [TED2020-TW-Corpus] Dataset Summary TED2020 是一個機器翻譯基準的多語言資料集，源自 OPUS 收集的使用者貢獻的翻譯，並由 OPUS。該資料集包括按語言對排序的測試和開發資料。它包括數百種語言對的測試集，並且不斷更新。請��查版本號標籤以引用您正在使用的版本。 TED2020 收集了從1984年到2020年的演講，涵蓋了各","None","en, ja, ko, id, vi, th, tw","multi","1|0|M|<|n|<|1|0|0|M","None","False","auto","False","unknown","None","None","None"
+"andrijdavid/jtruthful_qa","andrijdavid","2024-04-09 12:08:15","2024-04-10 22:32:53+00:00","7a4877ab62a37877302793683b7534391b1aafca","41","200","0","task_categories:multiple-choice, task_categories:text-generation, task_categories:question-answering, task_ids:multiple-choice-qa, task_ids:language-modeling, task_ids:open-domain-qa, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, language:ja, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2109.07958, region:us","multiple-choice, text-generation, question-answering, multiple-choice-qa, language-modeling, open-domain-qa","Dataset Card for jtruthful_qa Dataset Summary JTruthfulQA is a Japanese iteration of TruthfulQA (Lin+, 2022). This particular dataset isn't a translation of the original TruthfulQA, but rather, it's b","None","ja","mono","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","multiple-choice-qa, language-modeling, open-domain-qa"
+"googlefan/kusanagi-audio","googlefan","2024-09-17 04:14:30","2024-09-19 22:15:41+00:00","49ce4a9b0e2da6ea82ecc15cf5e40b83a9bdaac2","74","200","1","language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"TrainingDataPro/messengers-reviews-google-play","TrainingDataPro","2023-09-25 11:57:44","2024-04-25 10:45:15+00:00","9c2e96d7348db640334d6304db3b5f016829cf2c","41","197","1","task_categories:text-classification, language:en, language:fr, language:ja, language:it, language:de, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code, finance","text-classification","Reviews on Messengers Dataset - Review dataset The Reviews on Messengers Dataset is a comprehensive collection of 200 the most recent customer reviews on 6 messengers obtained from the popular app sto","None","en, fr, ja, it, de","multi","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"jayliqinzhang/Test_mumospee","jayliqinzhang","2024-10-14 18:06:21","2024-10-21 07:22:00+00:00","4532730ccb32974cfebedbfd61840bca49a4cf22","46","197","0","language:de, language:en, language:zh, language:ja, language:ko, language:fr, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:parquet, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Mumospee tiny demo This is a tiny Mumospee demo.","None","de, en, zh, ja, ko, fr","multi","n|<|1|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"Coaso/yokote_test","Coaso","2023-06-06 05:45:27","2023-06-06 08:09:30+00:00","4582bc08659732c9d4f53f528e45181e8bd04948","41","196","0","task_categories:table-question-answering, language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","table-question-answering","test","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"opencompass/mmmlu_lite","opencompass","2024-10-08 13:54:09","2024-11-01 08:34:22+00:00","02c08f44d1b6450b439d0415fc7c63bd70891bd8","44","196","2","task_categories:question-answering, language:ar, language:bn, language:de, language:es, language:fr, language:hi, language:id, language:it, language:ja, language:ko, language:pt, language:sw, language:yo, language:zh, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","MMMLU-Lite Introduction A lite version of the MMMLU dataset, which is an community version of the MMMLU dataset by OpenCompass. Due to the large size of the original dataset (about 200k questions), we","None","ar, bn, de, es, fr, hi, id, it, ja, ko, pt, sw, yo, zh","multi","None","None","False","False","False","mit","None","None","None"
+"iryneko571/CCMatrix-v1-Ja_Zh-fused","iryneko571","2024-07-01 07:50:25","2024-07-01 10:10:46+00:00","e2abd4264136b8e1eaa4fbfc9b22e85a8b0f3314","51","195","2","task_categories:translation, language:ja, language:zh, license:unknown, size_categories:100K<n<1M, format:arrow, modality:text, library:datasets, library:mlcroissant, region:us","translation","Derived from larryvrh/CCMatrix-v1-Ja_Zh-filtered Made some changes to the dataset to train the new mt5-base model since it's all from the community anyway so i disclose this. putting lora adapters isn","None","ja, zh","bi","None","None","False","False","False","unknown","None","None","None"
+"yubo0306/fed_ja","yubo0306","2023-10-29 03:55:00","2023-10-29 04:26:57+00:00","5b5fa11c80464bca501710c6cf39758982c4b805","41","195","0","language:ja, license:unknown, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","FEDデータセットをGoogle Cloud Translate API v2で日本語化したデータセットです． 機械翻訳のため，一部dimensionはアノテーションとの整合性が適切ではない可能性があります． 使用するdimensionには注意してください．","None","ja","mono","None","None","False","False","False","unknown","None","None","None"
+"shi3z/ja_conv_wikipedia_llama2pro8b_20k","shi3z","2024-01-20 01:15:26","2024-01-20 01:17:11+00:00","39e7b4326c31ab0b9d3fbbbd832c7e3d184f4fee","41","195","0","language:ja, license:llama2, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","This dataset is based on the Japanese version of Wikipedia dataset and converted into a multi-turn conversation format using llama2Pro8B. Since it is a llama2 license, it can be used commercially for ","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","llama2","None","None","None"
+"Sakalti/hachiwari","Sakalti","2024-10-05 01:31:37","2024-12-15 04:40:15+00:00","ac41cf52e9d795b64067306916e940559d98f6c3","50","195","1","task_categories:question-answering, language:ja, license:cc-by-sa-4.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering","#Origin The name comes from ""hachiwari/はちわれ"" (chiikawa/ちいかわ).","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"OzoneAsai/factorExpander","OzoneAsai","2023-07-27 09:19:43","2023-07-27 10:18:11+00:00","c77ed23b67e754a7dd58bcde3923e4efa3b03bff","42","194","1","language:ja, license:wtfpl, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","Polynomial Expansion and Factoring Dataset This dataset contains problem and solution pairs for polynomial expansion and factoring. Each problem is a result of expanding and factoring the (x + n)^2 fo","None","ja","mono","None","None","False","False","False","wtfpl","None","None","None"
+"None1145/Theresa","None1145","2024-11-06 14:51:39","2024-11-16 12:23:38+00:00","77a6887ec94ccf5b40a08b3ed5f0103405211b43","43","194","1","task_categories:text-to-speech, task_categories:text-generation, language:zh, language:ja, license:mit, size_categories:n<1K, format:json, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Theresa, Arknights, 特蕾西娅, 魔王, 明日方舟","text-to-speech, text-generation","None","None","zh, ja","bi","None","None","False","False","False","mit","None","None","None"
+"kurogane/Magpie_RakutenAI_prompt_extract_example50","kurogane","2024-06-17 13:33:28","2024-06-17 14:04:01+00:00","78d6f4a158d2000ba86b6da6136121004f955178","42","193","0","language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","None","Magpie方式によるprompt抽出をRakuten/RakutenAI-7B-chatで行ってみました。 設定しているsystem promptは論文のサプリ記載のpromptを翻訳したものを使用しています。 Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing https://","None","ja","mono","None","None","False","False","False","apache-2.0","2406.08464","None","None"
+"cl-nagoya/Simplifyingmt","cl-nagoya","2024-08-08 05:09:57","2024-08-08 05:25:52+00:00","f98ccfd3898d6a1e041e1a82c8c908ec1312cc0d","58","192","0","task_categories:text2text-generation, language:en, language:ja, 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","text2text-generation","SimplifyingMT Dataset Description -Repository: https://github.com/nttcslab-nlp/SimplifyingMT_ACL24-Papre: to appear Paper Oshika et al., Simplifying Translations for Children: Iterative Simplification","None","en, ja","bi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"alfredplpl/wikipedia-qa-ja-15k","alfredplpl","2024-02-29 03:23:58","2024-02-29 03:39:35+00:00","d43e6972c4ffbb44d4703a1ebee3387adb30a7e9","47","192","1","task_categories:question-answering, language:ja, 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","Dataset Card for ""wikipedia-qa-ja-15k"" This is made of hpprc/wikipedia-20240101 .","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"hpprc/honyaku","hpprc","2024-11-20 04:42:28","2024-11-20 09:06:40+00:00","a4214ecf37092f4aac54608a1c695350cfa251e5","67","192","2","task_categories:translation, language:ja, language:en, license:cc-by-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","英語Wikipedia記事の冒頭複数文を抽出し、人手で日本語翻訳した文章レベル対訳データセットです。 日英対訳コーパスはライセンスが厳しいものが多く、自由に利用できる高品質なパッセージレベルの対訳データセットが少なかったため作成しました。 翻訳は大体をhpprcが、数件をyano氏が行いました。 passageサブセットは文章レベルの翻訳(対応する文ごとに改行区切り)を、sentenceサブセット","None","ja, en","bi","n|<|1|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"angelmmiguel/synthetic-introduction-extraction","angelmmiguel","2024-03-16 10:00:21","2024-03-18 15:34:41+00:00","91fc7a545ef6cb93a2e164ea3f739e3097d58cfd","46","191","0","task_categories:feature-extraction, language:es, language:en, language:fr, language:ru, language:ja, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","feature-extraction","None","None","es, en, fr, ru, ja","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"aisc-team-c1/MMedBench","aisc-team-c1","2024-03-04 22:17:10","2024-03-05 01:43:13+00:00","c9ce429fad24c4f731c7b0e4c0eabf4982462567","44","191","1","task_categories:question-answering, language:en, language:zh, language:ja, language:fr, language:ru, language:es, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.13963, region:us, medical","question-answering","This is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/Henrychur/MMedBench MMedBench 💻Github ","None","en, zh, ja, fr, ru, es","multi","None","None","False","False","False","cc-by-4.0","2402.13963","None","None"
+"shi3z/ja_conv_wikipedia_llama2pro8b_3k","shi3z","2024-01-08 23:11:22","2024-01-09 23:18:17+00:00","dcdb260cd059403abe8836f0cee3e7dd7535b863","42","190","1","language:ja, license:llama2, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","This dataset is based on the Japanese version of Wikipedia dataset and converted into a multi-turn conversation format using llama2Pro8B. After generating 10,000 conversations and screening, only abou","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","llama2","None","None","None"
+"ikedachin/CC-news-2024-October-cleaned-1204","ikedachin","2024-12-04 08:10:52","2025-01-27 02:19:08+00:00","1cbe3468d8619dcd0aa7c88285cfb50911c20b8f","79","190","1","task_categories:feature-extraction, language:ja, license:odc-by, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","feature-extraction","kajuma/CC-news-2024-July-October-cleanedを元に、9月、10月のニュースのみを抜き出したデータセット。outputトークン数を1024と想定。効率よく学習するために約1000tokensに調整（使用tokenizerはllm-jp/llm-jp-3-13b）","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","odc-by","None","None","None"
+"shi3z/ja_conv_wikipedia_orion14B_10K","shi3z","2024-01-25 20:30:08","2024-01-25 20:56:08+00:00","60136c034401e610e3741f8e548e4bebab5f9132","43","190","0","language:ja, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","Abstruct This is a multi-turn conversation dataset generated from the Japanese Wikipedia dataset using Orion14B-Chat. Commercial use is possible, but the license is complicated, so please read it care","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"cc-clean/CC-MAIN-2019-35","cc-clean","2024-12-22 01:37:19","2024-12-22 08:07:11+00:00","736e3f50e3cc81c783c70688e280f7c6b170a2b5","181","190","0","language:ja, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","CC-MAIN-2019-35へようこそ 本データセットはCommonCrawlerと呼ばれるものから日本語のみを抽出したものです。 利用したものはcc-downloader-rsです。 なおIPAのICSCoEと呼ばれるところから資源を借りてやりましたゆえに、みなさんIPAに感謝しましょう。 ※ IPAは独立行政法人 情報処理推進機構のことです。テスト���出ますので覚えましょう。 利用について 本","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"zenless-lab/jsquad","zenless-lab","2024-12-18 10:11:17","2024-12-27 00:44:36+00:00","2d984b444d61a401ee619bce75dee170d8ca58b7","123","190","0","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"numad/yuho-text-2024","numad","2024-06-14 11:17:32","2024-06-14 11:18:17+00:00","2993206922ead839ce003e2faa65cd5d2ff07607","45","189","1","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, finance","None","Dataset Card for Dataset Name このデータはEDINET閲覧（提出）サイトで公開されている2024年に提出された有価証券報告書から特定の章を抜粋したデータです。 各レコードのurl列が出典となります。 Dataset Details Dataset Description データの内容は下記想定です 物理名 論理名 型 概要 必須 doc_id 文書ID str 有価証","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"alfredplpl/wikipedia-simple-ja-500k","alfredplpl","2024-03-05 04:22:37","2024-03-05 04:25:22+00:00","ce639a638b0706aefa65ba9106ca066759170fbc","40","188","1","task_categories:summarization, language:ja, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","summarization","Dataset Card for ""wikipedia-simple-ja-500k"" Original Dataset hpprc/wikipedia-20240101 Procedure Exract the first line of the title from the dataset. Generate the answer by summizing the line using LLM","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"alfredplpl/genai-terminology-en-ja","alfredplpl","2023-11-01 08:01:31","2023-11-01 08:05:56+00:00","d6f939386443ae3799a55edca8d6f31ddd28ef7d","46","187","0","language:en, language:ja, license:apache-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","生成AIの日英専門用語集です。正確さは保証しませんが、GPT-4などの頭に入れておくと綺麗に訳せると思います。","None","en, ja","bi","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"BEE-spoke-data/TxT360-500k-sample-no_cc","BEE-spoke-data","2024-10-10 20:06:03","2024-10-11 06:30:04+00:00","49983b48c699b4e540d2d07b1a9759e637f82357","63","187","0","task_categories:text-generation, task_categories:feature-extraction, language:en, language:de, language:ja, language:fr, language:es, language:it, language:cs, language:ar, language:pl, language:ru, license:odc-by, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, feature-extraction","BEE-spoke-data/TxT360-500k-sample-no_cc no common crawl","None","en, de, ja, fr, es, it, cs, ar, pl, ru","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","odc-by","None","None","None"
+"iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B","iam-ajaymeena","2024-06-15 08:11:55","2024-06-16 09:12:56+00:00","b029dddee8f865da4750cef734a7d610c015cac3","41","185","0","task_categories:text-generation, language:ja, 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","A Japanese dataset generated with an opensource elyza/ELYZA-japanese-Llama-2-13b-instruct model. This dataset is used in evaluating AI-generated text detection methods and is well-suited for self-inst","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Aratako/SFT-Dataset-For-Self-Taught-Evaluators-iter1","Aratako","2024-10-27 08:26:38","2024-10-27 10:29:53+00:00","bcd681381b864a1a68e5ad5676f30e952db3839e","43","182","1","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","None","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"AICU/LuC4","AICU","2024-01-08 09:01:41","2024-01-08 10:07:19+00:00","2e27605b8acae3cc144310c4496fd85aa0d226ad","43","181","0","task_categories:feature-extraction, language:ja, license:creativeml-openrail-m, size_categories:n<1K, format:imagefolder, modality:image, modality:text, library:datasets, library:mlcroissant, region:us, LuC4, SDXL, LoRA","feature-extraction","None","None","ja","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","creativeml-openrail-m","None","None","None"
+"tombailey/oasst1-ja","tombailey","2023-07-24 14:34:59","2023-07-26 11:32:45+00:00","2ff85a45ab8a30aa3481ff4cf7127faf841745b4","51","179","1","language:ja, license:apache-2.0, size_categories:n<1K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","oasst1-ja Description Based on OpenAssistant Conversations Dataset (OASST1) but only the messages labeled as Japanese. Structure The format is changed to ### Human: ...### Assistant: .... Each row of ","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"tomo1222/Japanese-QA111dataset","tomo1222","2024-12-14 13:54:53","2024-12-14 14:07:10+00:00","333e9b9194d8624e2ccbc4814618cedf59d17859","83","179","0","language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Data created manually","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"p1atdev/OnlineMathContest-1.4k","p1atdev","2024-03-22 15:04:39","2024-03-23 12:25:16+00:00","53275f7d1240a82e94ba029c6c013edda050f28c","42","178","2","language:ja, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math","None","OnlineMathContest 1.4k 1.4k pairs dataset of math problems and their solutions in Japanese scraped from OnlineMathContest. Notice: All problems and solutions have source urls (the url field), and thei","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"p1atdev/novecomi-novel-metadata","p1atdev","2023-12-16 08:48:49","2023-12-16 08:57:17+00:00","81d54f6c2d619e7b245d2a5a97b35d1543203f52","42","177","1","language:ja, license:cc0-1.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","novecomi-novel-metadata https://dengekibunko.jp/novecomi/novel/ からスクレイピング。 (本文なし)","None","ja","mono","n|<|1|K","None","False","False","False","cc0-1.0","None","None","None"
+"kurogane/Magpie_llama-3-youko-8b_prompt_extract_example50","kurogane","2024-06-18 12:45:15","2024-06-18 12:47:01+00:00","ed078fb06f7f3fb739abfd931aac2170d6e3f339","44","175","0","language:ja, license:llama3, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","None","Magpie方式によるprompt抽出をrinna/llama-3-youko-8bで行ってみました。 設定しているsystem promptは論文のサプリ記載のpromptを翻訳したものを使用しています。 rinna/llama-3-youko-8bはinstructモデルではないため、不適切な試みであったかもしれません。 Magpie: Alignment Data Synthesis fro","None","ja","mono","None","None","False","False","False","llama3","2406.08464","None","None"
+"DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k","DeL-TaiseiOzaki","2024-11-03 07:18:52","2024-11-03 07:34:14+00:00","fa2497452a9b765e8f1e2b7b08e60b276521c26f","56","173","0","language:ja, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","合成日本語指示データセット 概要 このデータセットは、大規模言語モデル（Qwen2.5-32B-instruct）を用いて自動生成された日本語の指示とそれに対する応答のコレクションです。データセットは指示付与型のタスクのための学習や評価に使用することを目的としています。 データセット仕様 サンプル数: 125,000 言語: 日本語 フォーマット: JSONL ライセンス: Apache-2.0 ","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"ttaront/filtered_clx","ttaront","2024-03-30 02:52:37","2024-03-30 12:37:32+00:00","b4f33aeb99534f69b8793a046a462832cc250aa9","42","173","0","language:en, language:ja, size_categories:100K<n<1M, modality:text, region:us","None","None","None","en, ja","bi","None","None","False","False","False","None","None","None","None"
+"rikka-snow/prompt-injection-multilingual","rikka-snow","2024-10-28 10:08:49","2024-10-28 10:11:21+00:00","f1ad1f3dd44581f53a4c67e96a9dde2fb419ee5b","55","172","0","task_categories:text-classification, language:vi, language:en, language:zh, language:fr, language:de, language:hi, language:it, language:ja, language:pt, language:es, language:th, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","None","None","vi, en, zh, fr, de, hi, it, ja, pt, es, th","multi","None","None","False","False","False","mit","None","None","None"
+"Mitsua/safe-commons-pd-3m","Mitsua","2024-12-16 08:37:14","2024-12-16 08:41:24+00:00","b987830e34afa7ef546b54a628d9e35f0b269100","143","172","2","task_categories:text-to-image, task_categories:image-to-text, language:en, language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2103.00020, arxiv:2309.16671, region:us, legal","text-to-image, image-to-text","Safe Commons PD 3M This is a balanced and safe-to-use public domain / CC0 images dataset. All images and texts come from Wikimedia Commons and Wikidata with strict filtering. Images license is either ","None","en, ja","bi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","2103.00020","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","None","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"
+"speedcell4/JGLUE","speedcell4","2024-08-19 11:31:36","2024-08-21 08:47:59+00:00","edcc47edb729148ae7e7822c4d5ca1a5ba129c26","48","171","0","language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"DeL-TaiseiOzaki/magpie-reasonig-ja-qwen2.5-72b-16k","DeL-TaiseiOzaki","2024-10-28 01:32:23","2024-11-19 15:30:41+00:00","4b96e5be52fb0fa5523c1b012af5cf6408bb3cde","46","167","0","language:ja, license:other, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","合成日本語指示データセット 概要 このデータセットは、大規模言語モデル（LLM）を用いて自動生成された日本語の指示とそれに対する推論・初期応答・改善応答のコレクションです。データセットは指示付与型のタスクのための学習や評価に使用することを目的としています。 データセット仕様 サンプル数: 約16,000 言語: 日本語 フォーマット: JSONL 生成方法 データセットは以下のプロセスを通じて生成","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"p1atdev/manga_line_generation","p1atdev","2024-03-18 17:47:17","2024-03-18 17:55:42+00:00","5e657bae115b349b1cdd5a18bb1508dabbbeac92","41","166","1","task_categories:image-to-text, language:ja, license:unknown, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","image-to-text","Manga Line Generation dataset Converted from https://github.com/1never/MangaLineGeneration. Paper: https://aclanthology.org/2023.paclic-1.34.pdf","None","ja","mono","n|<|1|K","None","False","False","False","unknown","None","None","None"
+"wolf4032/token-classification-japanese-search-local-cuisine","wolf4032","2024-04-28 07:26:48","2024-05-12 07:19:14+00:00","fc8afe7d8fe148dca988c8f45d516f9a22a1a918","41","166","0","task_categories:token-classification, language:ja, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","token-classification","料理を検索するための質問文と、質問文に含まれる検索検索用キーワードの情報を持ったデータセットです 固有表現の種類は以下の４つです。 AREA： 都道府県/地方 TYPE： 種類 SZN： 季節 INGR： 食材 GitHub untokenized_dataset_list.ipynb(データセットの作成に使ったノートブック) このデータセットを使った言語モデルのファインチューニングと、ファインチ","None","ja","mono","None","None","False","False","False","mit","None","None","None"
+"ibm/AttaQ-JA","ibm","2024-09-27 00:04:13","2024-11-05 05:17:50+00:00","a48b72e118d48bb8541aa83175074bbd5df43160","49","165","1","task_categories:question-answering, language:ja, license:cdla-permissive-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","AttaQ-JA Dataset Card AttaQ red teaming dataset was designed to evaluate Large Language Models (LLMs) by assessing their tendency to generate harmful or undesirable responses, which consists of 1402 c","None","ja","mono","None","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"ifmain/comment-translation","ifmain","2024-10-13 11:30:55","2024-10-13 13:16:36+00:00","ec6ef5ab4c5c7a9070b5fed00c4912df8406cd2f","50","164","0","language:en, language:de, language:fr, language:es, language:it, language:sv, language:fi, language:pl, language:cs, language:lv, language:zh, language:ja, language:ko, language:ru, language:uk, language:be, language:kk, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","This dataset is based on Kaggle. This dataset includes translations of 69,000 Reddit comments into 17 languages (English to 16 languages): Belarusian, Czech, German, English, Spanish, Finnish, French,","None","en, de, fr, es, it, sv, fi, pl, cs, lv, zh, ja, ko, ru, uk, be, kk","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"Sakalti/Tylbass","Sakalti","2024-10-17 10:24:05","2024-10-17 10:36:59+00:00","9a6080587d370a62137593127c797932f2ca3689","54","163","1","task_categories:translation, language:ja, language:sah, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","None","None","ja, sah","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"DeL-TaiseiOzaki/Tengentoppa-sft-reasoning-llama-nemotron-70b-100k","DeL-TaiseiOzaki","2024-10-22 07:10:57","2024-10-22 07:56:38+00:00","14dad61eceab631b2d48c9024303788d2b23ffaf","46","163","0","task_categories:text-generation, language:ja, license:llama3.1, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","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","ja","mono","None","None","False","False","False","llama3.1","None","None","None"
+"ziozzang/osx_dictionary_translation_pairs","ziozzang","2024-04-02 03:57:54","2024-04-02 04:16:05+00:00","9f74221d7f7e8d14bb7601e643c89caddadd4353","43","160","0","task_categories:translation, language:ko, language:en, language:cs, language:ar, language:nl, language:fi, language:fr, language:de, language:hu, language:hi, language:el, language:pl, language:id, language:it, language:pt, language:ru, language:vi, language:tr, language:te, language:es, language:zh, language:th, language:ja, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","translation","Apple's Internal dictionary extracted. the pairs are word level example of translation pairs (usage case, or example pairs) Original data are Human curated. This can be used for make machine generated","None","ko, en, cs, ar, nl, fi, fr, de, hu, hi, el, pl, id, it, pt, ru, vi, tr, te, es, zh, th, ja","multi","None","None","False","auto","False","None","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","None","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"
+"jaeyong2/ja-persona-cot-inst","jaeyong2","2024-10-26 02:29:21","2024-10-26 03:20:07+00:00","cb44a729fb45f6b964b732f5a4a12e5627e02d7d","42","159","0","language:ja, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","How to use >>> from datasets import load_dataset >>> ds = load_dataset(""jaeyong2/ja-persona-cot-inst"", split=""train"") >>> ds Dataset({ features: ['content', 'text'], num_rows: 645000 }) Development Pr","None","ja","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"hpprc/ihyoki","hpprc","2024-11-11 13:53:39","2024-11-11 14:07:53+00:00","af7f25bfe4cc6978acf6ac657ca519390c1482d4","42","156","4","language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"ganchengguang/Text-Classification-and-Relation-Event-Extraction-Mix-datasets","ganchengguang","2024-10-18 15:02:08","2024-10-18 15:05:42+00:00","6a1071c4e12d13b7ef9ed45a9fc92edd48fbda58","55","156","0","task_categories:text-classification, task_categories:token-classification, language:ja, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.06838, region:us","text-classification, token-classification","The paper of GIELLM dataset. https://arxiv.org/abs/2311.06838 Cite: @article{gan2023giellm, title={Giellm: Japanese general information extraction large language model utilizing mutual reinforcement e","None","ja","mono","None","None","False","False","False","cc-by-nc-4.0","2311.06838","None","None"
+"zenless-lab/jnli","zenless-lab","2024-12-18 10:09:43","2024-12-18 14:40:06+00:00","123c2a92dd828fdf70004b2baf9ac45f690574aa","53","156","0","task_categories:text-classification, language:ja, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, nli, benchmark, evaluation","text-classification","JGLUE[JNLI]: Japanese General Language Understanding Evaluation JNLI(yahoojapan/JGLUE) is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference r","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"akahana/mini-multilanguage","akahana","2024-12-06 22:08:23","2024-12-26 01:56:21+00:00","eb7e096f545c00c18a8bd7a4133509601652c178","63","155","0","language:id, language:ar, language:ko, language:en, language:ja, language:ms, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","id, ar, ko, en, ja, ms","multi","None","None","False","False","False","None","None","None","None"
+"hotchpotch/msmarco-ja-hard-negatives","hotchpotch","2024-11-18 02:33:24","2024-11-18 20:46:33+00:00","63639fff5006f8eccd8305d1a7f97872585f8c0f","54","154","2","language:ja, language:en, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","msmarco-ja-hard-negatives hpprc/msmarco-ja で公開されているMS MARCOの日本語翻訳データに、以下の処理を加えたハードネガティブマイニングをしたものです。また、後段タスクとして、情報検索モデルのSPLADEを学習させたモデルで mMARCO との比較を行いました。 データの処理 翻訳データの正規化、フィルタと選択 正規化 ユニコード正規化 (NFKC)","None","ja, en","bi","None","None","False","False","False","None","None","None","None"
+"Aratako/LLMChat-Judge-Results","Aratako","2024-10-26 06:17:00","2024-10-26 07:22:39+00:00","71345d863455f42c365185c504e3db70d39b8aba","53","152","1","task_categories:text-generation, task_categories:text-classification, language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, text-classification","LLMChat-Judge-Results team-hatakeyama-phase2/LLMChatの2つのモデルの応答に対して、様々なモデルを用いてPairwise評価を行った結果のデータです。 人手評価とオープンLLMによる自動評価の一致率の検証のために作成しました。詳細についてはこちらの記事を確認してください。 ライセンスについて 元のデータセットであるteam-hatakeyama-p","None","ja","mono","None","None","False","False","False","other","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","None","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","None","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"
+"azminetoushikwasi/global-festivals-translated","azminetoushikwasi","2024-10-22 19:41:41","2024-10-22 20:48:55+00:00","98c3369f862c8e40a5912db5ebdf576519400bb0","50","150","0","task_categories:text-generation, language:en, language:fr, language:es, language:de, language:hi, language:ja, language:ko, language:tr, language:it, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, GLOBAL_FESTIVALS_DATASET = 'ciol-research/global-festivals-wiki'","text-generation","None","None","en, fr, es, de, hi, ja, ko, tr, it","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"zenless-lab/jcommonsenseqa","zenless-lab","2024-12-18 10:11:40","2024-12-23 00:34:58+00:00","9bcb62df6a80ebd742f4a697d16b93ecbb09e5d0","60","149","0","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"ifmain/multilingual-moderation-90K","ifmain","2024-10-13 11:37:25","2024-10-13 13:50:00+00:00","adb40ed41113b1caad07a6ed9167291344faeff9","47","149","0","language:en, language:de, language:fr, language:es, language:it, language:sv, language:fi, language:pl, language:cs, language:lv, language:zh, language:ja, language:ko, language:ru, language:uk, language:be, language:kk, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This dataset is based on Kaggle.It represents a version of @ifmain/text-moderation-410K that has been cleansed of semantically similar values and normalized to a 50/50 ratio of negative and neutral en","None","en, de, fr, es, it, sv, fi, pl, cs, lv, zh, ja, ko, ru, uk, be, kk","multi","None","None","False","False","False","apache-2.0","None","None","None"
+"speed/english_quotes_ja","speed","2024-10-22 14:22:50","2024-10-22 14:46:27+00:00","d719098ba4d144c299d2b8201a02e7e47e035582","47","148","1","language:en, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This dataset is a translation of https://huggingface.co/datasets/Abirate/english_quotes into Japanese using the llm-jp/llm-jp-3-3.7b-instruct model. The license follows the original dataset's Creative","None","en, ja","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"JunichiroMorita/Our-Regional-Cuisines","JunichiroMorita","2024-12-14 13:23:01","2024-12-14 13:53:41+00:00","418b53228312fe9ef84d83fabdb6eaea2266d0ac","73","148","0","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Our Regional Cuisines Database ""Our Regional Cuisines"" is a database created by Japan's Ministry of Agriculture, Forestry and Fisheries to preserve and pass on Washoku, a UNESCO Intangible Cultural He","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"saillab/alpaca-japanese-cleaned","saillab","2024-09-20 23:21:58","2024-09-20 23:32:52+00:00","35b9233aa73d250c9f451e866bcfedc5ca51c892","45","147","0","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This repository contains the dataset used for the TaCo paper. Please refer to the paper for more details: OpenReview If you have used our dataset, please cite it as follows: Citation @inproceedings{up","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"ymoslem/Tatoeba-Translations","ymoslem","2024-12-17 06:34:18","2024-12-29 13:38:30+00:00","c313a0934b5e2f50f78230fdedfe63ea36e1a05f","130","144","0","task_categories:translation, language:multilingual, language:ab, language:af, language:am, language:ar, language:an, language:as, language:av, language:ay, language:az, language:ba, language:bm, language:be, language:bn, language:bi, language:bo, language:bs, language:br, language:bg, language:ca, language:cs, language:ch, language:ce, language:cv, language:kw, language:co, language:cy, language:da, language:de, language:dv, language:el, language:en, language:eo, language:et, language:eu, language:ee, language:fo, language:fj, language:fi, language:fr, language:fy, language:gd, language:ga, language:gl, language:gv, language:gn, language:gu, language:ht, language:ha, language:he, language:hi, language:hr, language:hu, language:hy, language:ig, language:io, language:ii, language:ie, language:ia, language:id, language:is, language:it, language:jv, language:ja, language:kl, language:kn, language:ks, language:ka, language:kk, language:km, language:rw, language:ky, language:ko, language:lo, language:la, language:li, language:ln, language:lt, language:lb, language:lg, language:mh, language:ml, language:mr, language:mk, language:mg, language:mt, language:mn, language:mi, language:my, language:na, language:nv, language:nl, language:nn, language:nb, language:ny, language:oc, language:oj, language:or, language:os, language:pa, language:pi, language:pl, language:pt, language:ps, language:qu, language:rm, language:ro, language:rn, language:ru, language:sg, language:sa, language:si, language:sk, language:sl, language:se, language:sm, language:sn, language:sd, language:so, language:st, language:es, language:sq, language:sc, language:sr, language:ss, language:su, language:sv, language:ty, language:ta, language:tt, language:te, language:tg, language:tl, language:th, language:ti, language:to, language:tn, language:ts, language:tk, language:tr, language:ug, language:uk, language:ur, language:uz, language:vi, language:vo, language:wa, language:wo, language:xh, language:yi, language:yo, language:zu, license:cc-by-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","Dataset Details This is the latest version of Tatoeba translations as of December 2024. The sentences are downloaded from the Tatoeba collection website. The dataset is processed through mapping sente","None","multilingual, ab, af, am, ar, an, as, av, ay, az, ba, bm, be, bn, bi, bo, bs, br, bg, ca, cs, ch, ce, cv, kw, co, cy, da, de, dv, el, en, eo, et, eu, ee, fo, fj, fi, fr, fy, gd, ga, gl, gv, gn, gu, ht, ha, he, hi, hr, hu, hy, ig, io, ii, ie, ia, id, is, it, jv, ja, kl, kn, ks, ka, kk, km, rw, ky, ko, lo, la, li, ln, lt, lb, lg, mh, ml, mr, mk, mg, mt, mn, mi, my, na, nv, nl, nn, nb, ny, oc, oj, or, os, pa, pi, pl, pt, ps, qu, rm, ro, rn, ru, sg, sa, si, sk, sl, se, sm, sn, sd, so, st, es, sq, sc, sr, ss, su, sv, ty, ta, tt, te, tg, tl, th, ti, to, tn, ts, tk, tr, ug, uk, ur, uz, vi, vo, wa, wo, xh, yi, yo, zu","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-2.0","None","None","None"
+"akkikiki/global_mmlu_ja_edited","akkikiki","2024-12-08 08:34:26","2024-12-09 05:18:20+00:00","351e7a2a3c4ece24360e871cb21809a08f9be206","65","141","0","language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.03304, region:us","None","Associated code for this dataset is available at https://github.com/akkikiki/global_mmlu_edit Loading the dataset from datasets import load_dataset # From JSON ds = load_dataset(""akkikiki/global_mmlu_","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"zenless-lab/chABSA","zenless-lab","2024-12-18 10:12:21","2024-12-26 00:51:47+00:00","254de3bf9771bcafb40812cecd33e6cdb234bb49","52","138","0","language:ja, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"Kendamarron/multiturn-qwen2.5-32b","Kendamarron","2024-11-11 07:28:24","2024-11-16 04:33:18+00:00","431814ab886c58838acf51ca8041917a909a435c","52","134","1","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Qwen/Qwen2.5-32B-Instruct-AWQで生成した3ターンのマルチターンinstructionデータセットです。 Qwenで生成したため、英語や中国語が混ざっているレコードがあります。適宜フィルタリングを行ってください。 Aratako/Magpie-Tanuki-8B-annotated-96kのinstructionをお借りしました。","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"eyl45/demo","eyl45","2024-11-27 02:07:34","2024-11-27 02:32:45+00:00","6b6863561d7d14d7846a130719bca4f13048b443","46","133","0","task_categories:text-generation, language:zh, language:ko, language:ja, license:mit, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","None","None","zh, ko, ja","multi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"llm-jp/japanese-photos-conversation","llm-jp","2024-10-22 06:39:57","2024-11-19 10:46:49+00:00","883347ac627ea1c5fa5ed7b2991f35665876b434","71","131","3","task_categories:visual-question-answering, language:ja, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","Dataset Card for japanese photos conversation Dataset details This dataset contains multi-turn conversational instructions about images taken in Japan. The images were sourced from https://huggingface","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"ryota39/preference_test","ryota39","2024-11-09 05:59:35","2024-11-16 03:25:50+00:00","dd6512175dad5325dbde41f234ac971f85088b0c","43","130","0","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"zenless-lab/kuci","zenless-lab","2024-12-18 10:11:57","2024-12-23 00:34:32+00:00","69c6030aa29ba8c4b06a0a1fc7b5cadf57ac48c2","52","129","0","language:ja, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"zenless-lab/jsts","zenless-lab","2024-12-18 10:14:03","2024-12-23 00:34:51+00:00","40d2e07fcb6a1515efc51cffbf5dad8448159864","55","129","0","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"tokyotech-llm/swallow-magpie-ultra-v0.1","tokyotech-llm","2024-12-05 12:40:04","2025-01-07 14:26:51+00:00","5adf479ee0f218aaee8d49cdfa5776172d065502","127","128","3","task_categories:text-generation, language:en, language:ja, license:llama3.1, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, synthetic","text-generation","📰 News [07/01/2025] Release of the first version of the dataset containing 42k Japanese pairs and 42k English pairs. Dataset Summary Part of Swallow-Magpie-Ultra-v0.1 is a subset of instruction tuning","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","llama3.1","None","None","None"
+"zenless-lab/jmmlu-nc-nd","zenless-lab","2024-12-18 10:14:14","2024-12-23 00:35:04+00:00","f4102e20257479a6ee1ebfe3d3b1e4535f0aa4ef","46","128","0","language:ja, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"labofsahil/animelist-dataset","labofsahil","2024-11-16 05:37:06","2024-12-25 22:00:46+00:00","497baa5133a3338e67b757fe5b6bebbf22f738c2","47","124","1","language:en, language:ja, license:agpl-3.0, size_categories:10K<n<100K, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, anime","None","A JSON based anime dataset containing the most important meta data as well as cross references to various anime sites such as MAL, ANIDB, ANILIST, KITSU and more... Credits: https://github.com/manami-","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","agpl-3.0","None","None","None"
+"zenless-lab/jsem","zenless-lab","2024-12-18 10:10:11","2024-12-19 08:52:15+00:00","ed80646801d994d9f539a926e6757692b48dc592","49","123","0","task_categories:text-classification, language:ja, license:bsd-3-clause, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, evaluation, benchmark, nli","text-classification","JSeM: Japanese semantic test suite (Japanese FraCaS and extensions) 叙述文間の含意関係は、言語学においては意味論の中心的な説明対象の一つであるとともに、理論を検証するためのベンチマークとして用いられています。 また近年の自然言語処理においては、含意関係認識(Recognizing Textual Entailment: RTE)が","None","ja","mono","None","None","False","False","False","bsd-3-clause","None","None","None"
+"llm-jp/llava-instruct-ja","llm-jp","2024-10-22 06:43:09","2024-11-19 10:35:55+00:00","2f1cb5cd09ef0635912e46784469876f6039f5fe","53","122","2","task_categories:visual-question-answering, language:ja, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","Dataset Card for llava_instruct_ja Dataset details This is the Japanese version of LLaVA-Instruct, which contains 156K samples. We used gpt-4o-mini-2024-07-18 to generate data through via Azure OpenAI","None","ja","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"NaniDAO/nanipilled","NaniDAO","2024-11-25 22:59:51","2024-11-25 23:59:55+00:00","a35192bbacf74d9d4124639df088323f4ffaac16","54","122","0","task_categories:text-generation, language:en, language:ja, license:agpl-3.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","None","None","en, ja","bi","1|K|<|n|<|1|0|K","None","False","False","False","agpl-3.0","None","None","None"
+"Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k","Aratako","2024-11-29 08:10:29","2024-12-15 06:10:27+00:00","a71ff9ac914d49f19ef03a74cc3dd59bb1f2e1f8","80","120","0","task_categories:text-generation, language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, arxiv:2304.12244, region:us","text-generation","Magpie-Tanuki-Instruction-Selected-Evolved-26.5k 概要 以下の手順で作成した約2万6500件の日本語の合成instructionデータセットです。 Magpieの手法をteam-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bitに適用し、約10万件のinstructionを作成 cl-nagoya/rur","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","2406.08464","None","None"
+"hpprc/TALPCo","hpprc","2024-12-01 09:31:51","2024-12-01 09:37:29+00:00","48d9ecfeb2d041259afd69cc7c5c5a2661c0524b","63","119","2","task_categories:translation, language:ja, language:en, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","TALPCoデータセットの日英翻訳ペアをHuggingFace形式に変換したデータセットです。日本語の生データには分かち書き由来の空白文字が含まれていたため、それについは削除してあります。ライセンスは元データセットの記述に従い、CC-BY 4.0とします。 GitHub 論文 Nomoto, Hiroki, Kenji Okano, David Moeljadi and Hideo Sawada.","None","ja, en","bi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"blastai/Open_o1_sft_Pro_translated_jp","blastai","2024-12-29 16:29:37","2024-12-30 18:43:22+00:00","e3ec714344005b2947f2e7a6204d2d889261afab","119","119","8","task_categories:text-generation, language:ja, license:unknown, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","概要 このデータセットはOpen_o1_sft_ProデータセットをQwen社のQwen2.5-14B-Instructを用いて日本語に翻訳したものになります。 テンプレート テンプレートは以下です。 {""conversations"": [{""role"": ""user"", ""content"": ""入力""}, {""role"": ""assistant"", ""thought"": ""思考"", ""conte","None","ja","mono","None","None","False","False","False","unknown","None","None","None"
+"DeL-TaiseiOzaki/Tengentoppa-sft-reasoning-llama-v4.0","DeL-TaiseiOzaki","2024-12-18 07:20:40","2024-12-18 07:23:25+00:00","c61d847608a4b621e1e576e7bc9500ddce198600","53","118","0","task_categories:text-generation, language:ja, license:llama3.3, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","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","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","llama3.3","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","None","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"
+"Nurture-intelligence/ins_dataset","Nurture-intelligence","2024-12-15 09:39:47","2024-12-17 16:27:37+00:00","7a0ef3bbbe493d126dfa77167ff0a480e16a9c7d","55","117","0","language:ja, license:gemma, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","概要 このデータセットはsakura_japanese_datasetの質問に回答する形式で作られた、一問一答形式の合成データセットです。 ライセンス このデータセットのライセンスは元データセットのライセンスとGemma Terms of Useライセンスが適用されます。 合成方法 このデータセットは複数の推論結果から良い推論結果を選ぶPro型推論を適用したNurture-intelligence","None","ja","mono","n|<|1|K","None","False","False","False","gemma","None","None","None"
+"zenless-lab/jcommonsensemorality","zenless-lab","2024-12-18 10:11:28","2024-12-23 00:34:19+00:00","7eeb236ebdfa5098708cebe15dc25b05deeed478","43","116","0","language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"zenless-lab/jamp","zenless-lab","2024-12-18 10:09:27","2024-12-18 14:30:20+00:00","2c9fdddc9cc43b7f0e148142212cfbca4d3f55dd","50","116","0","task_categories:text-classification, language:ja, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.10727, region:us, nli, evaluation, benchmark","text-classification","Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models Jamp(tomo-vv/temporalNLI_dataset) is the Japanese temporal inference benchmark. This data","None","ja","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"BASF-AI/PubChemWikiJAPC","BASF-AI","2024-12-04 22:35:35","2024-12-05 20:29:37+00:00","1ff497203bda734d7f5c9aba79d986637ff87678","52","114","0","task_categories:text-classification, language:en, language:ja, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, chemistry, chemteb, wikipedia, pubchem","text-classification","PubChem & Wikipedia English-Japanese Paragraph Pair Classification This dataset is a multilingual extension of the PubChem & Wikipedia Paragraphs Pair Classification dataset. It includes pairs of para","None","en, ja","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"Aratako/dataset-for-annotation","Aratako","2024-11-10 15:50:23","2024-11-10 15:52:06+00:00","e6707e8113d5aca1ca5ccd9ff3f48a3e1d544953","41","114","1","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","None","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Kendamarron/jimba-instruction-all","Kendamarron","2024-11-16 05:56:14","2024-11-16 06:01:16+00:00","dede45b661e48904ba7e891a1b807774ae2b90a4","43","113","0","language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","データセットについて これまで作成した合成データセットを1つにまとめたものです。 構成 Apache-2.0 Kendamarron/jimba-instruction-calm3 Kendamarron/roleplay-multiturn-calm3-chat-format Kendamarron/system-instruction-calm3-chat-format Kendamarron","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"JunSotohigashi/JapaneseWikipediaTypoDataset_kanji","JunSotohigashi","2025-01-22 07:54:19","2025-01-22 08:19:02+00:00","b5824ea01388ba1df8dc9f190302a909c23d1a8a","112","112","0","language:ja, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","日本語ウィキペディア入力誤りデータセット (漢字誤変換抽出版) 概要 このデータセットは，京都大学 言語メディア研究室によって公開されているデータセットをHuggingFaceで使用できるよう変換したものです．日本語Wikipedia入力誤りデータセット (v2) - 京都大学 言語メディア研究室 このデータセットでは，JunSotohigashi/JapaneseWikipediaTypoDat","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"ryota39/open_preference_v0.4","ryota39","2024-08-16 07:56:26","2024-08-26 08:40:53+00:00","14a338061d24145ec8610a70d78eab35f85dc926","44","111","1","language:en, language:ja, license:unknown, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","This dataset is reformatted version of following datasets ryota39/synthetic-instruct-gptj-pairwise-ja ryota39/webgpt_comparisons-ja label 1 stands for chosen sentence label 0 stands for rejected sente","None","en, ja","bi","None","None","False","False","False","unknown","None","None","None"
+"llm-jp/ja-vg-vqa-conversation","llm-jp","2024-10-22 06:14:02","2024-11-19 10:55:53+00:00","7051feb29ced9a57f47db48fc5f181bea250aae0","53","110","0","task_categories:visual-question-answering, language:ja, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","Dataset Card for ja-vg-vqa-conversation Dataset details This dataset consists of multiple QA pairs from Japanese Visual Genome VQA dataset that have been converted into multi-turn conversations. The p","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"zenless-lab/jaqket-aio","zenless-lab","2024-12-18 10:10:59","2024-12-23 00:34:42+00:00","9e47b74f2db423e608d7e90cc2a6483b88eed207","46","110","0","language:ja, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"hpprc/jsec","hpprc","2024-11-20 12:55:54","2024-11-20 13:52:38+00:00","37ed3b6b7f4f60554fd5b556d2f61b1a8e9bf677","59","109","1","language:ja, language:en, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","JSECホームページ","None","ja, en","bi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"myst72/sakuraeval-alpha0101","myst72","2025-01-04 06:40:19","2025-01-04 11:37:04+00:00","4d2db8617ed4815d1eb45f76ac7b9e0ce7c764ca","109","109","0","task_categories:text2text-generation, language:en, language:ja, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","text2text-generation","None","None","en, ja","bi","None","None","False","False","False","mit","None","None","None"
+"jaeyong2/Ja-emb-PreView","jaeyong2","2024-12-02 12:37:11","2024-12-04 06:56:45+00:00","d77efd7890583babb8739b1c7914bf21dcce6b81","51","103","0","language:ja, 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","Development Process source dataset from range3/wikipedia-ja-20230101 We used Qwen/Qwen2-72B-Instruct model to generate answer with COT. License Qwen/Qwen2.5-72B-Instruct : https://huggingface.co/Qwen/","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"NewEden/BlueSky-Sharegpt-V0.1","NewEden","2024-12-18 20:23:18","2024-12-18 20:39:51+00:00","c7746a0b6ebd1e0176fe43cb2bf23ec3693811c5","42","102","0","language:en, language:ru, language:ja, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, chat","None","https://huggingface.co/datasets/metalure/733k_bluesky_threads Converted into sharegpt format for Finetuning. Need to filter out all non-english data. Only rows with more then 3 turns were considered t","None","en, ru, ja","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"marzena/jbats","marzena","2024-11-18 21:38:45","2024-11-18 22:57:25+00:00","36c344797ebf29ba4d5b2cd1a8b008242caf0abc","45","99","0","task_categories:text-generation, language:ja, license:cc-by-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, analogies","text-generation","While we are working on adding a nice datacard please refer to the paper (https://aclanthology.org/W18-2905/) and here (https://vecto.space/projects/jBATS/) for more details!","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted","Aratako","2024-11-20 12:42:37","2024-11-25 16:44:08+00:00","36bbb211b673c1ccda910f76480e6cae3ee14e45","56","97","0","language:ja, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","magpie-qwen2.5-32b-reasoning-100k-formatted DeL-TaiseiOzaki/magpie-qwen2.5-32b-reasoning-100kをOpenAI messages形式に変換したデータセットです。 中身や注意事項等については元データセットの概要をご確認ください。","None","ja","mono","None","None","False","False","False","apache-2.0","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"
+"Chasottco/Japanese-patent-evaluation-dataset-01","Chasottco","2025-01-01 14:38:29","2025-01-01 15:16:47+00:00","d0059faa183da0c87ca3ab03773579f8faf697f5","95","95","0","language:ja, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","None","試験的なデータセットとなります 評価用データセットの作成方法 ChatGPT-4oで弁理士紹介を含む5つの観点から50のクエリを生成 ChatGPT-4oでクエリに対する回答と評価ポイントを生成※弁理士を直接紹介する10問は除く 除外した10問に関しては、開放特許情報データベース及びj-platpatの公開データより回答を手動作成","None","ja","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Kendamarron/Synthetic-Preference-ja-Qwen2.5-32B-Mistral-Nemo","Kendamarron","2024-12-19 12:56:38","2024-12-19 13:50:32+00:00","89e50a8cfce00820ad3f315791256e2760b3ce66","45","95","0","language:ja, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","detail https://zenn.dev/kendama/articles/d9f6937f28c5a5","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"niryuu/Magpie-QwQ-Reasoning-dpo","niryuu","2024-12-17 03:09:05","2024-12-18 11:41:10+00:00","e58aad06ae5f228a5cf05c6ae7a650ad92d5cec8","50","93","0","language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, region:us","None","This DPO dataset is synthesized by this way: instructions: Magpie on QwQ-32b-Preview outputs: QwQ-32b-Preview and calm3-22b-chat.","None","ja","mono","None","None","False","False","False","apache-2.0","2406.08464","None","None"
+"Aratako/Self-Instruct-Qwen2.5-72B-Instruct-60k","Aratako","2024-12-13 02:56:47","2024-12-16 17:21:59+00:00","5c10422c13a3f1a77de6760ef48ee65e21958da5","67","92","0","task_categories:text-generation, language:ja, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08464, arxiv:2304.12244, arxiv:2212.10560, region:us","text-generation","Self-Instruct-Qwen2.5-72B-Instruct-60k 概要 以下の手順で作成した約6万件の日本語の合成instructionデータセットです。 MagpieとEvol-Instructを使って作成された合成instructionデータであるAratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5kに対し、Qwen/Qwe","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","2406.08464","None","None"
+"zenless-lab/llm-jp-eval","zenless-lab","2024-12-27 01:02:07","2024-12-30 12:41:28+00:00","d9420335b037fbf49ba3a2c17ad70a89cdf31916","42","92","0","language:ja, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"supercatdoing/booru_tag_and_artist_20241208","supercatdoing","2024-12-08 11:37:01","2024-12-10 08:41:39+00:00","7a89119fbb28f446c81c70494ed0306f8abfeb6b","45","92","0","language:en, language:ja, license:cc-by-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","booru_tag_and_artist_20241208 About This data set is a JSON file with tags and artists extracted from danbooru (2024/12/8 version) All data is pre-sorted by tag count. A JSONL file is also available s","None","en, ja","bi","None","None","False","False","False","cc-by-sa-4.0","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","None","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"
+"Alsebay/JP-novel-selected-dataset","Alsebay","2024-12-14 05:24:42","2024-12-16 05:00:50+00:00","83add1019956a315bc36d768c6f3b3bba3153e85","49","90","1","language:ja, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","None","None","ja","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"Aratako/iterative-dpo-data-for-SimPO-iter2","Aratako","2024-12-04 03:24:08","2024-12-15 06:33:44+00:00","7fbe09c7f9943b83d6123752ddbd71dd7720933a","70","86","1","task_categories:text-generation, language:ja, license:llama3.1, license:gemma, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","iterative-dpo-data-for-SimPO-iter2 概要 合成instructionデータであるAratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5kを元に以下のような手順で作成した日本語Preferenceデータセットです。 開発途中のモデルであるAratako/Llama-Gemma-2-27b-CPO_SimPO-i","None","ja","mono","None","None","False","False","False","llama3.1","None","None","None"
+"spow12/ShareGPT4V_Waifu","spow12","2024-11-21 05:09:35","2024-11-21 05:38:28+00:00","9f9a5210e497244ec2bf0a7ab2b14b9ddd840aad","43","84","0","task_categories:visual-question-answering, language:ja, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, roleplay, visual_novel","visual-question-answering","Caution! This dataset may contain errors or incorrect translations, even after filtering. Original Dataset Lin-Chen/ShareGPT4V","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"kajuma/Llama-SFT-3000","kajuma","2024-12-14 13:52:01","2024-12-14 13:56:02+00:00","10fd0569fbe05c4c610879693eabfceb78648ae7","42","81","0","task_categories:text-generation, language:ja, license:llama3.1, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","The instructions of this dataset are generated by Tanuki-8B-dpo-v1.0 and the outputs are generated by Llama-3.1-SuperSwallow-70B-Instruct-v0.1 Llama 3.1 is licensed under the Llama 3.1 Community Licen","None","ja","mono","1|K|<|n|<|1|0|K","None","False","False","False","llama3.1","None","None","None"
+"Aratako/magpie-sft-v1.0-dpo-judged","Aratako","2024-11-27 17:37:12","2024-12-15 05:36:57+00:00","ae31872d4d86c9d26ef730ac75b213267d149c58","63","80","0","task_categories:text-generation, language:ja, license:llama3.1, license:gemma, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","magpie-sft-v1.0-dpo-judged 概要 llm-jp/magpie-sft-v1.0を元に以下のような改変を加えて作成した日本語Preferenceデータセットです。 開発途中のモデルであるAratako/Llama-Gemma-2-27b-SFT-trial1を用いて回答を再生成 元データセットにあるQwen/Qwen2.5-32B-Instructの回答と再生成した回答の2","None","ja","mono","None","None","False","False","False","llama3.1","None","None","None"
+"Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered","Aratako","2024-11-22 03:06:57","2024-11-25 16:53:15+00:00","012c539da4fdc95849b432db3e0798b9357a37fb","57","79","0","language:ja, license:llama3.1, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","magpie-reasoning-llama-nemotron-70b-100k-filtered DeL-TaiseiOzaki/magpie-reasoning-llama-nemotron-70b-100kから、refined_answer列に""改良""という文字が含まれていないものを抽出し、OpenAI messages形式に変換したデータセットです。 中身や注意事項等については元データセッ","None","ja","mono","None","None","False","False","False","llama3.1","None","None","None"
+"Aratako/iterative-dpo-data-for-ORPO-iter3","Aratako","2024-12-15 16:03:35","2024-12-17 01:42:13+00:00","8fdedb180036e962cd6eeda77fd0b11e87a9dfba","63","78","2","task_categories:text-generation, language:ja, license:llama3.1, license:gemma, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","iterative-dpo-data-for-ORPO-iter3 概要 合成instructionデータであるAratako/Self-Instruct-Qwen2.5-72B-Instruct-60kを元に以下のような手順で作成した日本語Preferenceデータセットです。 開発途中のモデルであるAratako/Llama-Gemma-2-27b-CPO_SimPO-iter2を用いて、te","None","ja","mono","None","None","False","False","False","llama3.1","None","None","None"
+"Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered","Aratako","2024-11-21 14:38:55","2024-11-25 16:48:52+00:00","cb39e55392eddfd69edc08b330760e17dad05fd1","51","78","0","language:ja, license:other, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Magpie-Tanuki-Qwen2.5-72B-Answered Aratako/Magpie-Tanuki-8B-annotated-96kからinput_qualityがexcellentのものを抽出し、それに対してQwen/Qwen2.5-72B-Instructで回答の再生成を行ったデータセットです。 ライセンス 基本的にはApache 2.0に準じますが、Qwen Licenseの影","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"Inoichan/KUM-Bench","Inoichan","2025-01-13 07:18:11","2025-01-13 14:40:24+00:00","6cb5d12047984dcfecc62a76693ba04721d9b4cc","77","77","1","task_categories:text-generation, language:ja, license:other, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math","text-generation","KUM-Bench: A Benchmark for Advanced Japanese Reasoning Capabilities KUM-Bench (Kyoto University Math Entrance Exam Benchmark) is designed to evaluate advanced Japanese reasoning capabilities by levera","None","ja","mono","n|<|1|K","None","False","False","False","other","None","None","None"
+"trojblue/danbooru2025-metadata","trojblue","2025-01-02 19:25:56","2025-01-04 10:29:23+00:00","92eccb27fa5a081cf3c0b18be1c9082b3c3e936f","74","74","1","task_categories:text-to-image, task_categories:image-classification, language:en, language:ja, license:mit, size_categories:1M<n<10M, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-image, image-classification","Dataset Card for Danbooru 2025 Metadata This dataset repo provides comprehensive, up-to-date metadata for the Danbooru booru site. All metadata was freshly scraped starting on January 2, 2025, resulti","None","en, ja","bi","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k","Aratako","2024-11-22 03:21:08","2024-12-10 16:57:38+00:00","81c068df0eb3f40056004d5290a4e0f1bc10ad6d","46","71","1","language:ja, language:en, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Synthetic-JP-EN-Coding-Dataset-801k-50k Aratako/Synthetic-JP-EN-Coding-Dataset-801kから英語部分5万件を抽出したデータセットです。 中身や注意事項等については元データセットの概要をご確認ください。","None","ja, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"Sunbread/SyosetuNames-3.5M","Sunbread","2025-01-22 14:17:48","2025-01-24 05:17:03+00:00","c867e5fe311cf35b42a9ead172130a0768021efb","66","70","1","task_categories:text-generation, language:ja, license:odc-by, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, art, light-novel, fiction, literature","text-generation","SyosetuNames-3.5M: Japanese Light Novel Character Names Corpus Overview This dataset extracts fictional character names from the publicly available text of novels on the Japanese light novel platform ","None","ja","mono","1|M|<|n|<|1|0|M","None","False","False","False","odc-by","None","None","None"
+"Nurture-intelligence/thinking_dataset_v1","Nurture-intelligence","2024-12-22 08:13:59","2024-12-22 08:35:44+00:00","61535f728013703d361683d25d5ea45ef9296a9c","56","68","1","task_categories:text-generation, language:ja, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","概要 このデータセットは思考モデルを製作する際のもととなる質問データを集めたものになります。 このデータはQwen/Qwen2.5-32B-Instructのq8_0/GGUFをollama上で動かして製作されたものです。 一応(質問の)クリーニングを入れてはありますが、回答のクリーニング入れておりません。 注意 回答には別のモデル（mistral large）(確か)を利用したため、""質問の部分","None","ja","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"JunSotohigashi/JapaneseWikipediaTypoDataset","JunSotohigashi","2025-01-22 03:40:18","2025-01-22 08:18:40+00:00","97890019d379af4f3cf21c1d142e4fcc017f050d","63","63","0","language:ja, license:cc-by-sa-3.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","日本語Wikipedia入力誤りデータセット 概要 これは京都大学 言語メディア研究室によって公開されているデータセットをHuggingFaceで使用できるよう変換したものです．日本語Wikipedia入力誤りデータセット (v2) - 京都大学 言語メディア研究室 生成に使用したソースコードはここから参照できます． ライセンス 配布元に従い，CC-BY-SA 3.0とします．","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"cc-clean/CC-MAIN-2019-49","cc-clean","2024-12-10 10:14:17","2024-12-10 10:59:42+00:00","ba4ee1aef3499eabb0df96eb5f8eea49b1af38f8","41","60","0","language:ja, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","CC-MAIN-2019-49へようこそ 本データセットはCommonCrawlerと呼ばれるものから日本語のみを抽出したものです。 利用したものはcc-downloader-rsです。 なおIPAのICSCoEと呼ばれるところから資源を借りてやりましたゆえに、みなさんIPAに感謝しましょう。 ※ IPAは独立行政法人 情報処理推進機構のことです。テストに出ますので覚えましょう。 利用について 本","None","ja","mono","None","None","False","manual","False","None","None","None","None"
+"Moleys/Filtered-Japanese-English-Parallel-Corpus","Moleys","2025-01-02 13:50:11","2025-01-26 11:19:52+00:00","f7417e81c20dfc42fbc76ddfb2d0982352aadb28","57","57","1","task_categories:translation, language:ja, language:en, size_categories:10M<n<100M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","translation","def prompt(japanese, english): system_prompt = cleandoc(""""""<s>[INST]Your role is to evaluate the accuracy of the provided Japanese to English translation. - Translations with parts missing should be r","None","ja, en","bi","None","None","False","manual","False","None","None","None","None"
+"Inoichan/NuminaMath-CoT-JA-100K","Inoichan","2025-01-05 05:30:50","2025-01-05 17:22:46+00:00","bd45ff642e816eb90182b8eb07103ac87304f1b4","57","57","1","task_categories:text-generation, language:en, language:ja, license:gemma, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math","text-generation","Dataset Card for NuminaMath CoT (Japanese 100k Subset) Dataset Summary This dataset is a Japanese-translated subset of the NuminaMath CoT dataset, containing the first 100k samples from the original d","None","en, ja","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","gemma","None","None","None"
+"jaeyong2/Ja-functioncall","jaeyong2","2024-12-25 06:40:31","2025-01-22 06:49:14+00:00","5c3b3e2b6f64cce9c9874684b55de5008f0b0673","50","56","1","language:ja, 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","Development Process source dataset from range3/wikipedia-ja-20230101 We used Qwen/Qwen2-72B-Instruct model to generate function. License Qwen/Qwen2.5-72B-Instruct : https://huggingface.co/Qwen/Qwen2-7","None","ja","mono","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"fukugawa/kamakura-tasks-100","fukugawa","2024-12-27 07:24:24","2024-12-27 07:45:38+00:00","bc13f59bcb20c2e8aeab8950e98c8eeba84ef6f0","49","53","0","task_categories:text2text-generation, language:ja, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text2text-generation","Usage from datasets import load_dataset data = load_dataset(""fukugawa/kamakura-tasks-100"") data DatasetDict({ train: Dataset({ features: ['task_id', 'input', 'output'], num_rows: 100 }) }) data[""train","None","ja","mono","None","None","False","False","False","None","None","None","None"
+"efwkjn/reazonspeech_mtl","efwkjn","2025-01-27 09:56:18","2025-01-28 06:27:32+00:00","546297e914ec3a41da1bbbfd6b436f1da0dcbe90","48","48","0","language:ja, language:en, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","japanese-asr/whisper_transcriptions.reazon_speech_all without audio","None","ja, en","bi","None","None","False","False","False","None","None","None","None"
+"hama-jp/magpie-qwen-turbo-27k","hama-jp","2024-12-24 15:26:04","2024-12-24 16:18:58+00:00","74aace955635653e06e8fb11bb6c52c0523bb914","41","47","0","task_categories:text-generation, language:ja, 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","Magpie-Qwen-Turbo-27k Aratako/Magpie-Tanuki-8B-annotated-96k のアノテーションを利用して件数を減らし、outputをqwen-2.5-turboで再生成したSFT用の26728件のサブセットです。 用途 コーディングを除く小規模な日本語チャット用LLMのためのファインチューニングを想定しています。 使用データ 以下の条件で抽出したinst","None","ja","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"tarudesu/idiom-test-ko-ja-th-sl-fn-en","tarudesu","2025-01-24 19:03:23","2025-01-24 19:46:15+00:00","1fa197bfd9d5f8cbde190dc4c2c48e7153b35a93","46","46","0","None","translation","None","None","ko, ja, th, sl, fi","multi","None","None","False","False","False","None","None","None","None"
+"Miwa-Keita/zenz-v2.5-dataset","Miwa-Keita","2025-01-13 09:57:07","2025-01-17 06:45:33+00:00","7d9c9ea36347b638a627c1d264da48ebc7eb38aa","42","42","1","task_categories:text2text-generation, language:ja, size_categories:100M<n<1B, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, ime, japanese input, kana-kanji conversion","text2text-generation","zenz-v2.5-dataset zenz-v2.5-datasetはかな漢字変換タスクに特化した条件付き言語モデル「zenz-v2.5」シリーズの学習を目的として構築したデータセットです。 約190Mペアの「左文脈-入力-変換結果」を含み、かな漢字変換モデルの学習において十分な性能を実現できる規模になっています。 本データセットで学習したzenz-v2.5は公開しています。 zenz-v2.5","None","ja","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","None","None","None","None"