diff --git "a/data/dataset_meta/dataset_meta_ko.csv" "b/data/dataset_meta/dataset_meta_ko.csv"
new file mode 100644--- /dev/null
+++ "b/data/dataset_meta/dataset_meta_ko.csv"
@@ -0,0 +1,748 @@
+"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"
+"HAERAE-HUB/KMMLU","HAERAE-HUB","2023-11-27 09:06:18","2024-03-05 14:13:32+00:00","d61b3f19e552c576bf5960dd24289763edc36a88","24676","1980159","62","None","multiple-choice","KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks th","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nd-4.0","2402.11548","None","None"
+"google/xtreme","google","2022-03-02 23:29:22","2024-02-22 17:12:06+00:00","ec5f1f46e9af79639a90684a7a70a956c4998f04","42104","1893686","97","None","multiple-choice, question-answering, token-classification, text-classification, text-retrieval, token-classification, multiple-choice-qa, extractive-qa, open-domain-qa, natural-language-inference, named-entity-recognition, part-of-speech","Dataset Card for ""xtreme"" Dataset Summary The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs ","None","af, ar, bg, bn, de, el, en, es, et, eu, fa, fi, fr, he, hi, hu, id, it, ja, jv, ka, kk, ko, ml, mr, ms, my, nl, pt, ru, sw, ta, te, th, tl, tr, ur, vi, yo, zh","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","multiple-choice-qa, extractive-qa, open-domain-qa, natural-language-inference, named-entity-recognition, part-of-speech"
+"Helsinki-NLP/opus-100","Helsinki-NLP","2022-03-02 23:29:22","2024-02-28 09:17:34+00:00","805090dc28bf78897da9641cdf08b61287580df9","24897","1450598","168","task_categories:translation, annotations_creators:no-annotation, language_creators:found, multilinguality:translation, source_datasets:extended, language:af, language:am, language:an, 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:dz, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gd, language:gl, language:gu, language:ha, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:ig, language:is, language:it, language:ja, language:ka, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:li, language:lt, language:lv, language:mg, language:mk, language:ml, language:mn, language:mr, language:ms, language:mt, language:my, language:nb, language:ne, language:nl, language:nn, language:no, language:oc, language:or, language:pa, language:pl, language:ps, language:pt, language:ro, language:ru, language:rw, language:se, language:sh, language:si, language:sk, language:sl, language:sq, language:sr, language:sv, language:ta, language:te, language:tg, language:th, language:tk, language:tr, language:tt, language:ug, language:uk, language:ur, language:uz, language:vi, language:wa, language:xh, language:yi, language:yo, language:zh, language:zu, license:unknown, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2004.11867, region:us","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","opus-100","False","False","False","unknown","2004.11867","None","None"
+"HAERAE-HUB/KMMLU-HARD","HAERAE-HUB","2024-01-12 05:49:07","2024-03-09 23:46:06+00:00","58564a93d9f8d302a5a5ae41ab2f901dc303d5e1","7249","1389854","8","task_categories:question-answering, language:ko, license:cc-by-nd-4.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.11548, region:us, haerae, mmlu","question-answering","KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks th","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nd-4.0","2402.11548","None","None"
+"clips/mqa","clips","2022-03-02 23:29:22","2022-09-27 12:38:50+00:00","27eebc4a00d229f8dd4ae2a6d9f1e4ad45781f3b","1078","1116742","51","None","question-answering, multiple-choice-qa","MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages.","@misc{debruyn2021mfaq, title={MFAQ: a Multilingual FAQ Dataset}, author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans}, year={2021}, booktitle={MRQA@EMNLP2021}, }","ca, en, de, es, fr, ru, ja, it, zh, pt, nl, tr, pl, vi, ar, id, uk, ro, no, th, sv, el, fi, he, da, cs, ko, fa, hi, hu, sk, lt, et, hr, is, lv, ms, bg, sr, ca","multi","u|n|k|n|o|w|n","None","False","False","False","cc0-1.0","None","None","multiple-choice-qa"
+"wikimedia/wikipedia","wikimedia","2022-03-02 23:29:22","2024-01-09 09:40:51+00:00","b04c8d1ceb2f5cd4588862100d08de323dccfbaa","116436","787764","707","None","text-generation, fill-mask, language-modeling, masked-language-modeling","Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with o","None","ab, ace, ady, af, alt, am, ami, an, ang, anp, ar, arc, ary, arz, as, ast, atj, av, avk, awa, ay, az, azb, ba, ban, bar, bbc, bcl, be, bg, bh, bi, bjn, blk, bm, bn, bo, bpy, br, bs, bug, bxr, ca, cbk, cdo, ce, ceb, ch, chr, chy, ckb, co, cr, crh, cs, csb, cu, cv, cy, da, dag, de, dga, din, diq, dsb, dty, dv, dz, ee, el, eml, en, eo, es, et, eu, ext, fa, fat, ff, fi, fj, fo, fon, fr, frp, frr, fur, fy, ga, gag, gan, gcr, gd, gl, glk, gn, gom, gor, got, gpe, gsw, gu, guc, gur, guw, gv, ha, hak, haw, hbs, he, hi, hif, hr, hsb, ht, hu, hy, hyw, ia, id, ie, ig, ik, ilo, inh, io, is, it, iu, ja, jam, jbo, jv, ka, kaa, kab, kbd, kbp, kcg, kg, ki, kk, kl, km, kn, ko, koi, krc, ks, ksh, ku, kv, kw, ky, la, lad, lb, lbe, lez, lfn, lg, li, lij, lld, lmo, ln, lo, lt, ltg, lv, lzh, mad, mai, map, mdf, mg, mhr, mi, min, mk, ml, mn, mni, mnw, mr, mrj, ms, mt, mwl, my, myv, mzn, nah, nan, nap, nds, ne, new, nia, nl, nn, no, nov, nqo, nrf, nso, nv, ny, oc, olo, om, or, os, pa, pag, pam, pap, pcd, pcm, pdc, pfl, pi, pih, pl, pms, pnb, pnt, ps, pt, pwn, qu, rm, rmy, rn, ro, ru, rue, rup, rw, sa, sah, sat, sc, scn, sco, sd, se, sg, sgs, shi, shn, si, sk, skr, sl, sm, smn, sn, so, sq, sr, srn, ss, st, stq, su, sv, sw, szl, szy, ta, tay, tcy, te, tet, tg, th, ti, tk, tl, tly, tn, to, tpi, tr, trv, ts, tt, tum, tw, ty, tyv, udm, ug, uk, ur, uz, ve, vec, vep, vi, vls, vo, vro, wa, war, wo, wuu, xal, xh, xmf, yi, yo, yue, za, zea, zgh, zh, zu","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","language-modeling, masked-language-modeling"
+"MBZUAI/Bactrian-X","MBZUAI","2023-04-22 12:42:39","2023-05-27 12:54:05+00:00","3698480a001cb3f62c61c314d4acb181eb31e983","4006","749794","115","None","text-generation","Dataset Card for ""Bactrian-X"" A. Dataset Description Homepage: https://github.com/mbzuai-nlp/Bactrian-X Repository: https://huggingface.co/datasets/MBZUAI/Bactrian-X Paper: to-be-soon released Dataset","None","af, ar, az, bn, cs, de, en, es, et, fi, fr, gl, gu, he, hi, hr, id, it, ja, ka, kk, km, ko, lt, lv, mk, ml, mn, mr, my, ne, nl, pl, ps, pt, ro, ru, si, sl, sv, sw, ta, te, th, tl, tr, uk, ur, vi, xh, zh","multi","None","None","False","False","False","cc-by-nc-4.0","2008.00401","None","None"
+"csebuetnlp/xlsum","csebuetnlp","2022-03-02 23:29:22","2023-04-18 01:46:20+00:00","30fece425f9a3866e04321773ca7a80056d55ca6","6872","748041","125","None","summarization, text-generation","We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The datas","@inproceedings{hasan-etal-2021-xl, title = ""{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages"", author = ""Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat"", booktitle = ""Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"", month = aug, year = ""2021"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2021.findings-acl.413"", pages = ""4693--4703"", }","am, ar, az, bn, my, zh, en, fr, gu, ha, hi, ig, id, ja, rn, ko, ky, mr, ne, om, ps, fa, pcm, pt, pa, ru, gd, sr, si, so, es, sw, ta, te, th, ti, tr, uk, ur, uz, vi, cy, yo","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-nc-sa-4.0","1607.01759","None","None"
+"mteb/amazon_massive_intent","mteb","2022-05-15 20:59:15","2024-05-07 21:43:00+00:00","4672e20407010da34463acc759c162ca9734bca6","6939","699412","21","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"
+"mteb/sts17-crosslingual-sts","mteb","2022-05-19 12:59:56","2024-04-30 13:49:22+00:00","faeb762787bd10488a50c8b5be4a3b82e411949c","3133","639031","3","None","None","None","None","ar, de, en, es, fr, it, nl, ko, tr","multi","None","None","False","False","False","None","None","None","None"
+"skt/kobest_v1","skt","2022-04-07 13:54:23","2024-03-28 08:22:52+00:00","a5ea15e3ac77ed694b79f6204eb31889a2ba989f","6086","544841","44","annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2204.04541, region:us","None","Dataset Card for KoBEST Dataset Summary KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean. Supported Tasks and Leaderboar","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","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"
+"klue/klue","klue","2022-03-02 23:29:22","2024-01-04 14:05:57+00:00","349481ec73fff722f88e0453ca05c77a447d967c","3221","385767","66","task_categories:fill-mask, task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, task_categories:token-classification, task_ids:extractive-qa, task_ids:named-entity-recognition, task_ids:natural-language-inference, task_ids:parsing, task_ids:semantic-similarity-scoring, task_ids:text-scoring, task_ids:topic-classification, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2105.09680, region:us, relation-extraction","fill-mask, question-answering, text-classification, text-generation, token-classification, extractive-qa, named-entity-recognition, natural-language-inference, parsing, semantic-similarity-scoring, text-scoring, topic-classification","Dataset Card for KLUE Dataset Summary KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topi","None","ko","mono","1|0|K|<|n|<|1|0|0|K","klue","False","False","False","cc-by-sa-4.0","2105.09680","None","extractive-qa, named-entity-recognition, natural-language-inference, parsing, semantic-similarity-scoring, text-scoring, topic-classification"
+"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"
+"nthngdy/oscar-small","nthngdy","2022-03-23 09:26:03","2023-03-08 09:57:45+00:00","686600ca31931048cbb9f0acc98b83c29d0036b9","3332","333501","14","None","text-generation, language-modeling","The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\\","@inproceedings{ortiz-suarez-etal-2020-monolingual, title = ""A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages"", author = ""Ortiz Su{\\'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit"", booktitle = ""Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"", month = jul, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.acl-main.156"", pages = ""1703--1714"", abstract = ""We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures."", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{\\'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\\""u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{\\""u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} }","af, am, ar, arz, as, az, azb, ba, be, bg, bn, bo, br, ca, ce, ceb, ckb, cs, cv, cy, da, de, dv, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gu, he, hi, hr, hu, hy, id, is, it, ja, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mhr, mk, ml, mn, mr, ms, mt, my, nds, ne, nl, nn, no, or, os, pa, pl, pnb, ps, pt, ro, ru, sa, sah, sd, sh, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vi, yi, zh","multi","None","None","False","False","False","cc0-1.0","2010.14571","None","language-modeling"
+"Davlan/sib200","Davlan","2024-01-27 14:04:12","2024-02-19 16:34:23+00:00","38977a667f6fc264d5c26ec57a01e16db040b358","1788","278456","12","task_categories:text-classification, task_ids:topic-classification, annotations_creators:found, language_creators:expert-generated, multilinguality:multilingual, source_datasets:original, language:ace, language:acm, language:acq, language:aeb, language:af, language:ajp, language:ak, language:als, language:am, language:apc, language:ar, language:ars, language:ary, language:arz, language:as, language:ast, language:awa, language:ayr, language:azb, language:azj, language:ba, language:bm, language:ban, language:be, language:bem, language:bn, language:bho, language:bjn, language:bo, language:bs, language:bug, language:bg, language:ca, language:ceb, language:cs, language:cjk, language:ckb, language:crh, language:cy, language:da, language:de, language:dik, language:dyu, language:dz, language:el, language:en, language:eo, language:et, language:eu, language:ee, language:fo, language:fj, language:fi, language:fon, language:fr, language:fur, language:fuv, language:gaz, language:gd, language:ga, language:gl, language:gn, language:gu, language:ht, language:ha, language:he, language:hi, language:hne, language:hr, language:hu, language:hy, language:ig, language:ilo, language:id, language:is, language:it, language:jv, language:ja, language:kab, language:kac, language:kam, language:kn, language:ks, language:ka, language:kk, language:kbp, language:kea, language:khk, language:km, language:ki, language:rw, language:ky, language:kmb, language:kmr, language:knc, language:kg, language:ko, language:lo, language:lij, language:li, language:ln, language:lt, language:lmo, language:ltg, language:lb, language:lua, language:lg, language:luo, language:lus, language:lvs, language:mag, language:mai, language:ml, language:mar, language:min, language:mk, language:mt, language:mni, language:mos, language:mi, language:my, language:nl, language:nn, language:nb, language:npi, language:nqo, language:nso, language:nus, language:ny, language:oc, language:ory, language:pag, language:pa, language:pap, language:pbt, language:pes, language:plt, language:pl, language:pt, language:prs, language:quy, language:ro, language:rn, language:ru, language:sg, language:sa, language:sat, language:scn, language:shn, language:si, language:sk, language:sl, language:sm, language:sn, language:sd, language:so, language:st, language:es, language:sc, language:sr, language:ss, language:su, language:sv, language:swh, language:szl, language:ta, language:taq, language:tt, language:te, language:tg, language:tl, language:th, language:ti, language:tpi, language:tn, language:ts, language:tk, language:tum, language:tr, language:tw, language:tzm, language:ug, language:uk, language:umb, language:ur, language:uzn, language:vec, language:vi, language:war, language:wo, language:xh, language:ydd, language:yo, language:yue, language:zh, language:zsm, language:zu, license:cc-by-sa-4.0, size_categories:100K<n<1M, modality:text, arxiv:2309.07445, region:us, news-topic, sib-200, sib200","text-classification, topic-classification","Dataset Card for SIB-200 Dataset Summary SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects. The train/validation/test sets ","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nqo, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2309.07445","None","topic-classification"
+"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","language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback","None","OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-an","None","en, es, ru, de, pl, th, vi, sv, bn, da, he, it, fa, sk, id, nb, el, nl, hu, eu, zh, eo, ja, ca, cs, bg, fi, pt, tr, ro, ar, uk, gl, fr, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","2304.07327","None","None"
+"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","annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, language:ab, language:af, language:am, language:ar, language:as, language:ast, language:az, language:ba, language:bas, language:be, language:bg, language:bn, language:br, language:ca, language:ckb, language:cnh, language:cs, language:cv, language:cy, language:da, language:de, language:dv, language:dyu, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gl, language:gn, language:ha, language:he, language:hi, language:hsb, language:hu, language:hy, language:ia, language:id, language:ig, language:is, language:it, language:ja, language:ka, language:kab, language:kk, language:kmr, language:ko, language:ky, language:lg, language:lij, language:lo, language:lt, language:ltg, language:lv, language:mdf, language:mhr, language:mk, language:ml, language:mn, language:mr, language:mrj, language:mt, language:myv, language:nan, language:ne, language:nhi, language:nl, language:nn, language:oc, language:or, language:os, language:pa, language:pl, language:ps, language:pt, language:quy, language:rm, language:ro, language:ru, language:rw, language:sah, language:sat, language:sc, language:sk, language:skr, language:sl, language:sq, language:sr, language:sv, language:sw, language:ta, language:te, language:th, language:ti, language:tig, language:tk, language:tok, language:tr, language:tt, language:tw, language:ug, language:uk, language:ur, language:uz, language:vi, language:vot, language:yi, language:yo, language:yue, language:zgh, language:zh, license:cc0-1.0, size_categories:1M<n<10M, modality:audio, modality:text, library:datasets, library:mlcroissant, arxiv:1912.06670, region:us","None","Dataset Card for Common Voice Corpus 16 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 30328 recorded hours in the dataset also include demo","@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }","ab, af, am, ar, as, ast, az, ba, bas, be, bg, bn, br, ca, ckb, cnh, cs, cv, cy, da, de, dv, dyu, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gn, ha, he, hi, hsb, hu, hy, ia, id, ig, is, it, ja, ka, kab, kk, kmr, ko, ky, lg, lij, lo, lt, ltg, lv, mdf, mhr, mk, ml, mn, mr, mrj, mt, myv, nan, ne, nhi, nl, nn, oc, or, os, pa, pl, ps, pt, quy, rm, ro, ru, rw, sah, sat, sc, sk, skr, sl, sq, sr, sv, sw, ta, te, th, ti, tig, tk, tok, tr, tt, tw, ug, uk, ur, uz, vi, vot, yi, yo, yue, zgh, zh","multi","None","common-voice","False","auto","False","cc0-1.0","1912.06670","None","None"
+"KorQuAD/squad_kor_v1","KorQuAD","2022-03-02 23:29:22","2024-08-08 06:03:11+00:00","01aad23853355e5f4f6317eeaaa8186811424834","719","180250","24","None","question-answering, extractive-qa","Dataset Card for KorQuAD v1.0 Dataset Summary KorQuAD 1.0 is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand t","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nd-4.0","1909.07005","None","extractive-qa"
+"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"
+"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"
+"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"
+"kakaobrain/kor_nli","kakaobrain","2022-03-02 23:29:22","2024-08-22 08:05:04+00:00","3e0e4626f66911b344c490c26e3cc07e6c3bb0f9","224","72388","20","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, annotations_creators:crowdsourced, language_creators:machine-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:extended|multi_nli, source_datasets:extended|snli, source_datasets:extended|xnli, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2004.03289, region:us","text-classification, natural-language-inference, multi-input-text-classification","Dataset Card for ""kor_nli"" Dataset Summary Korean Natural Language Inference datasets. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data","None","ko","mono","1|0|0|K|<|n|<|1|M","kornli","False","False","False","cc-by-sa-4.0","None","None","natural-language-inference, multi-input-text-classification"
+"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","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"
+"Helsinki-NLP/opus_paracrawl","Helsinki-NLP","2022-03-02 23:29:22","2024-02-22 15:42:34+00:00","8bb0bcf0c038a0cb762690b4dbce088b861423ed","396","42570","5","None","translation","Dataset Card for OpusParaCrawl Dataset Summary Parallel corpora from Web Crawls collected in the ParaCrawl project. Tha dataset contains: 42 languages, 43 bitexts total number of files: 59,996 total n","None","bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, is, it, km, ko, lt, lv, mt, my, nb, ne, nl, nn, pl, pt, ro, ru, si, sk, sl, so, sv, sw, tl, uk, zh","multi","1|0|0|K|<|n|<|1|M|||1|0|K|<|n|<|1|0|0|K|||1|M|<|n|<|1|0|M","None","False","False","False","cc0-1.0","None","None","None"
+"sentence-transformers/parallel-sentences-talks","sentence-transformers","2024-04-30 10:29:15","2024-06-18 19:45:50+00:00","0c70bc6714efb1df12f8a16b9056e4653563d128","3613","42438","9","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - Talks This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentence","None","en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"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"
+"beomi/KoAlpaca-v1.1a","beomi","2023-05-26 06:27:44","2023-05-26 06:32:02+00:00","03d3122c530b1e47195c08a3d851eeadddad9689","666","40674","45","None","text-generation","Dataset Card for ""KoAlpaca-v1.1a"" Project Repo Github Repo: Beomi/KoAlpaca How to use >>> from datasets import load_dataset >>> ds = load_dataset(""beomi/KoAlpaca-v1.1a"", split=""train"") >>> ds Dataset(","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"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"
+"copenlu/answerable_tydiqa","copenlu","2022-08-16 11:31:34","2024-07-12 11:53:23+00:00","4413588cd5f38fddbf047f87a7167a0a9de5369a","107","39407","8","None","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"
+"Babelscape/SREDFM","Babelscape","2023-06-13 18:35:19","2023-06-20 07:33:28+00:00","2732d2834e12e36510aeb2a468163ea2642d55db","2928","37479","14","None","token-classification","Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured k","@InProceedings{REDFM2023, author = {Huguet Cabot, Pere-Lluis and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and Navigli, Roberto}, title = {RED\\textsuperscript{FM}: a Filtered and Multilingual Relation Extraction Dataset}, booktitle = {Proceedings of the 2023 Conference on Association for Computational Linguistics}, year = {2023}, publisher = {Association for Computational Linguistics}, location = {Toronto, Canada}, }","ar, ca, de, el, en, es, fr, hi, it, ja, ko, nl, pl, pt, ru, sv, vi, zh","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-4.0","2306.09802","None","None"
+"maywell/korean_textbooks","maywell","2023-12-27 23:13:45","2024-01-10 09:21:36+00:00","0f78e815bf4ba9ca0addf3484ee195d526c506a4","1377","35689","108","None","None","Massive Korean synthetic dataset This dataset is a large-scale Korean artificial data set created using Gemini Pro. It was created using the methodology described in Creation of synthetic textbook-qua","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2306.11644","None","None"
+"sentence-transformers/parallel-sentences-ccmatrix","sentence-transformers","2024-05-25 08:10:49","2024-06-18 19:49:55+00:00","0c2ecfe22787e119991dbc482b776dc9aa25aa9f","8249","33644","4","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - CCMatrix This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. The texts origina","None","af, ar, ast, az, be, bg, bn, br, ca, ceb, cs, da, de, el, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, ha, he, hi, hr, hu, id, ig, ilo, is, it, ja, jv, ko, la, lb, lt, lv, mg, mk, ml, mr, ms, ne, nl, no, oc, or, pl, pt, ro, ru, sd, si, sk, sl, so, sq, sr, su, sv, sw, ta, tl, tr, uk, ur, vi, xh, yi, zh","multi","1|B|<|n|<|1|0|B","None","False","False","False","None","None","None","None"
+"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"
+"taeminlee/Ko-StrategyQA","taeminlee","2024-01-12 01:58:26","2024-01-19 08:48:28+00:00","d243889a3eb6654029dbd7e7f9319ae31d58f97c","6240","26627","16","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:Ko-StrategyQA, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, text-retrieval","text-retrieval, document-retrieval","Ko-StrategyQA This dataset represents a conversion of the Ko-StrategyQA dataset into the BeIR format, making it compatible for use with mteb. The original dataset was designed for multi-hop QA, so we ","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","document-retrieval"
+"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"
+"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"
+"daekeun-ml/naver-news-summarization-ko","daekeun-ml","2022-08-01 14:54:17","2023-01-10 11:12:44+00:00","fa6ec90a7beb96d182372f09b04b96797ea6588a","1308","20588","49","None","summarization","This dataset is a custom dataset created by the author by crawling Naver News (https://news.naver.com) for the Korean NLP model hands-on. Period: July 1, 2022 - July 10, 2022 Subject: IT, economics Da","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"HAERAE-HUB/HAE_RAE_BENCH_1.1","HAERAE-HUB","2023-09-25 04:16:13","2024-03-30 02:27:25+00:00","b480e81024913f27783d2b05d2f0b10089db19ad","3456","19595","13","task_categories:multiple-choice, language:ko, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2309.02706, region:us","multiple-choice","The HAE_RAE_BENCH 1.1 is an ongoing project to develop a suite of evaluation tasks designed to test the understanding of models regarding Korean cultural and contextual nuances. Currently, it comprise","None","ko","mono","None","None","False","False","False","cc-by-nc-nd-4.0","2309.02706","None","None"
+"lmqg/qg_koquad","lmqg","2022-06-02 23:42:21","2022-12-02 18:53:42+00:00","49ad3eba360e4f6c40c0720e19be9d358dd893d0","3812","19229","8","task_categories:text-generation, task_ids:language-modeling, multilinguality:monolingual, source_datasets:squad_es, language:ko, license:cc-by-4.0, size_categories:10K<n<100K, modality:text, library:datasets, library:mlcroissant, arxiv:2210.03992, region:us, question-generation","text-generation, language-modeling","[KorQuAD](https://huggingface.co/datasets/squad_kor_v1) dataset for question generation (QG) task.","@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"", }","k, o","bi","1|||0|||K|||<|||n|||<|||1|||0|||0|||K","None","False","False","False","cc-by-4.0","2210.03992","None","language-modeling"
+"MoritzLaurer/multilingual-NLI-26lang-2mil7","MoritzLaurer","2022-08-17 15:28:16","2022-08-22 21:40:14+00:00","510a233972a0d7ff0f767d82f46e046832c10538","2673","19152","46","None","text-classification, natural-language-inference, multi-input-text-classification","Datasheet for the dataset: multilingual-NLI-26lang-2mil7 Dataset Summary This dataset contains 2 730 000 NLI text pairs in 26 languages spoken by more than 4 billion people. The dataset can be used to","None","multilingual, zh, ja, ar, ko, de, fr, es, pt, hi, id, it, tr, ru, bn, ur, mr, ta, vi, fa, pl, uk, nl, sv, he, sw, ps","multi","1|M|<|n|<|5","None","False","False","False","None","None","None","natural-language-inference, multi-input-text-classification"
+"visheratin/laion-coco-nllb","visheratin","2023-06-18 06:58:28","2024-04-11 16:36:31+00:00","759c44439e15d2137720c1b897c3f51444775536","1675","18499","40","task_categories:image-to-text, task_categories:translation, language:ace, language:acm, language:acq, language:aeb, language:af, language:ajp, language:ak, language:als, language:am, language:apc, language:ar, language:ars, language:ary, language:arz, language:as, language:ast, language:awa, language:ayr, language:azb, language:azj, language:ba, language:bm, language:ban, language:be, language:bem, language:bn, language:bho, language:bjn, language:bo, language:bs, language:bug, language:bg, language:ca, language:ceb, language:cs, language:cjk, language:ckb, language:crh, language:cy, language:da, language:de, language:dik, language:dyu, language:dz, language:el, language:en, language:eo, language:et, language:eu, language:ee, language:fo, language:fj, language:fi, language:fon, language:fr, language:fur, language:fuv, language:gaz, language:gd, language:ga, language:gl, language:gn, language:gu, language:ht, language:ha, language:he, language:hi, language:hne, language:hr, language:hu, language:hy, language:ig, language:ilo, language:id, language:is, language:it, language:jv, language:ja, language:kab, language:kac, language:kam, language:kn, language:ks, language:ka, language:kk, language:kbp, language:kea, language:khk, language:km, language:ki, language:rw, language:ky, language:kmb, language:kmr, language:knc, language:kg, language:ko, language:lo, language:lij, language:li, language:ln, language:lt, language:lmo, language:ltg, language:lb, language:lua, language:lg, language:luo, language:lus, language:lvs, language:mag, language:mai, language:ml, language:mar, language:min, language:mk, language:mt, language:mni, language:mos, language:mi, language:my, language:nl, language:nn, language:nb, language:npi, language:nso, language:nus, language:ny, language:oc, language:ory, language:pag, language:pa, language:pap, language:pbt, language:pes, language:plt, language:pl, language:pt, language:prs, language:quy, language:ro, language:rn, language:ru, language:sg, language:sa, language:sat, language:scn, language:shn, language:si, language:sk, language:sl, language:sm, language:sn, language:sd, language:so, language:st, language:es, language:sc, language:sr, language:ss, language:su, language:sv, language:swh, language:szl, language:ta, language:taq, language:tt, language:te, language:tg, language:tl, language:th, language:ti, language:tpi, language:tn, language:ts, language:tk, language:tum, language:tr, language:tw, language:tzm, language:ug, language:uk, language:umb, language:ur, language:uzn, language:vec, language:vi, language:war, language:wo, language:xh, language:ydd, language:yo, language:yue, language:zh, language:zsm, language:zu, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.01859, doi:10.57967/hf/1006, region:us","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","language:en, language:zh, language:es, language:ja, language:fr, language:ko, language:ar, license:mit, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.05425, region:us","None","Bo Li*,♠,1 Yuanhan Zhang*,♠,1 Liangyu Chen*,1 Jinghao Wang*,1 Fanyi Pu*,1 Jingkang Yang1 Chunyuan Li2 Ziwei Liu✉,1 1S-Lab, Nanyang Technological University 2Microsoft Research, Redmond ♠Co-Project Lea","None","en, zh, es, ja, fr, ko, ar","multi","1|M|<|n|<|1|0|M","None","False","auto","False","mit","None","None","None"
+"wecover/OPUS_GlobalVoices","wecover","2024-01-31 07:22:25","2024-11-24 03:46:34+00:00","4beae40f081a88d573fa3f60eb35ce3f2a3bc8b8","8465","18077","0","None","None","None","None","am, ar, bg, bn, ca, cs, da, de, el, en, eo, es, fa, fr, he, hi, hu, id, it, km, ko, ku, mg, mk, my, ne, nl, or, pa, pt, pl, ro, ru, sq, sr, sv, sw, tr, ur, zh","multi","None","None","False","False","False","None","None","None","None"
+"CohereForAI/Global-MMLU","CohereForAI","2024-12-01 22:45:59","2024-12-12 13:49:50+00:00","75a8cf195b89938fce486bbb607a881faa123df0","7116","17570","103","None","None","Dataset Summary Global-MMLU 🌍 is a multilingual evaluation set spanning 42 languages, including English. This dataset combines machine translations for MMLU questions along with professional translati","None","en, ar, bn, es, fr, hi, ru, de, id, it, ja, ko, pt, zh, yo, nl, ro, uk, vi, tr, pl, fa, cs, he, el, ms, fil, te, si, ne, ky, sv, lt, sr, mg, so, ha, am, sn, ig, ny, sw","multi","None","None","False","False","False","None","2412.03304","None","None"
+"EunsuKim/CLIcK","EunsuKim","2024-03-19 23:08:45","2024-09-07 08:09:27+00:00","d61627859645b5e6edc03fd9f835735d8226fa4e","309","16895","10","task_categories:multiple-choice, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2403.06412, region:us, Culture, Language","multiple-choice","CLIcK 🇰🇷🧠 A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean Introduction 🎉 CLIcK (Cultural and Linguistic Intelligence in Korean) is a comprehensive dataset designed to evaluate cu","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","2403.06412","None","None"
+"wikimedia/wit_base","wikimedia","2022-05-02 16:08:58","2022-11-04 15:09:33+00:00","ff6d4fb32fd566d3a1fa20e946cba3234179465e","1479","16757","59","None","image-to-text, text-retrieval, image-captioning","Dataset Card for WIT Dataset Summary Wikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset. From the official blog post: The core training data i","None","af, an, ar, arz, ast, az, azb, ba, bar, be, bg, bn, br, bs, ca, ce, ceb, ckb, cs, cv, cy, da, de, el, en, eo, es, et, eu, fa, fi, fil, fr, fy, ga, gl, hi, hr, hsb, ht, hu, hy, ia, id, io, is, it, iw, ja, jv, ka, kk, kn, ko, la, lah, lb, lmo, lt, lv, mg, mk, ml, mn, mr, ms, my, nan, nds, ne, nl, nn, no, nv, oc, pa, pl, pt, qu, ro, ru, sco, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tr, tt, uk, ur, uz, vec, vi, vo, war, xmf, yue, zh","multi","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-4.0","1512.03385","None","image-captioning"
+"cis-lmu/GlotCC-V1","cis-lmu","2024-05-29 14:53:46","2024-11-01 11:09:18+00:00","9ad140b6be3ac7b539606a2b4809b49d122823de","1703","16659","49","None","None","Dataset Summary GlotCC-V1.0 is a document-level, general domain dataset derived from CommonCrawl, covering more than 1000 languages.It is built using the GlotLID language identification and Ungoliant ","None","aau, aaz, ab, abk, abq, abs, abt, abx, aby, ace, acf, ach, acm, acn, acr, acu, ada, adi, adj, ady, adz, aeb, aer, aey, af, afr, agd, agm, agn, agr, agt, agu, agw, aii, aim, ain, ajp, ajz, aka, akb, akh, aln, alp, alq, als, alt, aly, am, amf, amh, ami, amm, amu, an, ang, ann, anp, any, aoi, aoj, aom, aoz, apb, apc, ape, apr, apt, apz, ara, arb, arg, arl, arn, arq, ars, ary, arz, as, asm, aso, ast, ata, atb, atd, atj, atq, att, aui, auy, av, ava, avk, avt, avu, awa, awx, aym, ayr, azb, aze, azg, azj, ba, bak, bal, bam, ban, bar, bas, bba, bbb, bbc, bbr, bcc, bch, bci, bcl, bdd, bdh, bdq, be, bef, bel, bem, ben, bew, bg, bgr, bgs, bgz, bhg, bhl, bho, bhw, bi, big, bik, bim, bin, bis, biu, bjn, bjp, bjr, bkd, bkl, blk, blw, blz, bm, bmh, bmu, bn, bnc, bno, bnp, bo, boa, bod, boj, bom, bon, bos, bpr, bps, bpy, bqc, bqj, br, bre, brh, bru, brx, bs, bsp, btd, bts, btx, bua, bug, buk, bul, bum, bus, bvr, bwd, bxh, bxr, byr, bzd, bzi, bzj, ca, caa, cab, cac, cag, cak, cao, car, cat, cax, cbi, cbk, cbr, cbs, cbt, cbu, ccp, ce, ceb, ces, cfm, cgc, ch, cha, chd, che, chk, chm, cho, chu, chv, chw, cjk, cjv, ckb, ckm, ckt, clu, cmn, cmo, cmr, cnh, cni, cnk, cnr, cnw, co, con, cop, cor, cos, cot, cpb, cpc, cpy, cre, crh, crk, crl, crn, crs, crx, cs, csb, csw, csy, cta, ctd, ctu, cu, cub, cuk, cut, cv, cwe, cwt, cy, cya, cym, czt, da, dad, dag, dah, dak, dan, dao, dar, ddg, de, ded, deu, dga, dgc, dgz, dhg, dhm, dhv, did, dik, din, diq, dis, div, dje, djk, djr, dng, dob, doi, dop, dru, dsb, dtp, dts, dua, dv, dwr, dww, dyi, dyo, dyu, dz, dzo, ee, efi, ekk, eko, el, ell, emi, eml, emp, en, eng, enl, enm, enq, eo, epo, eri, es, esk, est, esu, etr, eu, eus, eve, ewe, ewo, ext, eza, fa, faa, fai, fao, fas, fat, ffm, fi, fij, fil, fin, fit, fj, fkv, fo, fon, for, fr, fra, fro, frp, frr, fry, fub, fud, fue, fuf, fuh, ful, fur, fuv, fy, ga, gaa, gag, gah, gai, gam, gaw, gaz, gbi, gcf, gcr, gd, gdg, gdn, geb, gfk, ghs, gil, gjn, gl, gla, gle, glg, glk, glv, gmh, gmv, gnb, gng, gnn, gnw, goh, gom, gon, gor, gos, got, grc, grn, gsw, gu, gub, guc, gug, guj, gul, gum, gur, guw, gux, guz, gv, gvn, gwi, gwr, gym, gyr, ha, hac, hag, hak, hat, hau, haw, hbo, hbs, hch, he, heb, heg, her, hi, hif, hil, hin, hla, hmn, hmo, hmr, hne, hnj, hns, ho, hot, hr, hra, hrv, hsb, ht, hto, hu, hub, hui, hun, hus, huu, hvn, hwc, hy, hye, hyw, hz, ia, ian, iba, ibg, ibo, icr, id, ido, idu, ie, ify, ig, ike, ikk, ikt, iku, ikw, ile, ilo, ina, inb, ind, inh, ino, io, iou, ipi, ipk, iqw, is, isd, ish, isl, iso, it, ita, itv, ium, ivb, ivv, iws, ixl, ja, jac, jae, jam, jav, jbo, jiv, jpn, jra, jrb, jv, jvn, ka, kaa, kab, kac, kak, kal, kam, kan, kaq, kas, kat, kau, kaz, kbc, kbd, kbh, kbm, kbo, kbp, kbq, kby, kca, kcg, kck, kdc, kde, kdi, kdl, kea, kek, kew, kgk, kgr, kha, khk, khm, khz, ki, kij, kik, kin, kir, kiu, kix, kj, kjb, kjh, kjs, kk, kkc, kkl, kl, kln, klt, klv, km, kmb, kmd, kmg, kmh, kmk, kmm, kmr, kms, kmu, kmy, kn, knc, kne, kng, knv, ko, kog, koi, kok, kom, kon, kor, kos, kpe, kpf, kpg, kpj, kpo, kpr, kpv, kpw, kpx, kqc, kqf, kqn, kqw, krc, kri, krl, kru, ks, ksd, ksh, ksr, ksw, ktm, kto, ktu, ktz, kua, kum, kup, kur, kvg, kw, kwf, kwj, kwn, kwy, ky, kyc, kyg, kyq, kyu, kyz, kze, kzf, kzj, la, lad, lah, laj, lam, lao, lat, lav, lb, lbb, lbe, lbj, lbk, lcm, leu, lew, lex, lez, lfn, lg, lgg, lgl, lhi, li, lia, lid, lif, lij, lim, lin, lis, lit, liv, ljp, lki, lld, llg, lmk, lmo, ln, lnd, lo, loz, lrc, lsi, lt, ltg, ltz, lu, lua, lub, lud, lue, lug, lun, luo, lus, luy, lvs, lwg, lww, lzh, maa, mad, mag, mah, mai, mak, mal, mam, man, mar, mas, mau, maw, maz, mbb, mbf, mbh, mbs, mbt, mcd, mck, mcq, mdf, mdy, med, mej, mek, men, met, meu, mfe, mfi, mfq, mfy, mge, mgh, mh, mhi, mhr, mhw, mhx, mi, mic, mie, mif, min, mip, miq, mjc, mjw, mk, mkd, mkn, ml, mlg, mlh, mlt, mmo, mmx, mna, mnb, mni, mnk, mns, mnw, moc, mog, moh, mon, mos, mph, mpm, mpp, mps, mpx, mqj, mqy, mr, mri, mrj, mrw, msa, msb, msc, msk, msy, mt, mtg, mti, muh, mui, mup, mur, mux, mva, mvn, mvp, mwc, mwf, mwl, mwm, mwn, mwp, mwv, mww, mxb, mxt, mxv, mxx, my, mya, myb, myk, myu, myv, myw, myy, mza, mzh, mzn, naf, nah, nak, nan, nap, naq, nas, nav, nb, nbc, nbe, nbl, nbu, nca, nch, ncj, ncx, nd, ndc, nde, ndo, nds, nep, new, nfa, ng, ngl, ngu, nhe, nhg, nhi, nho, nhr, nhw, nhx, nia, nif, nii, nij, nin, niu, njm, njn, njz, nl, nld, nlg, nmf, nmz, nn, nnb, nnh, nno, nnp, nnw, nob, nog, non, nop, nor, not, nov, nph, npi, npy, nqo, nr, nrm, nsa, nsn, nso, nss, nst, nsu, ntp, ntu, nuj, nus, nuy, nv, nvm, nwi, ny, nya, nyk, nyn, nyu, nyy, nzi, nzm, obo, oc, oci, ojb, oji, okv, olo, omb, omw, ong, opm, ori, orm, orv, ory, os, oss, ota, otd, ote, ots, otw, pa, pad, pag, pam, pan, pao, pap, pau, pbb, pbt, pcd, pck, pcm, pdc, pdt, pes, pfl, pib, pis, pjt, pl, plg, pls, plt, plu, plw, pma, pmf, pms, pmx, pnb, pnt, poe, poh, poi, pol, pon, por, pot, ppk, ppo, pps, prg, prs, pt, ptp, ptu, pui, pus, pwg, pwn, qub, quc, que, quf, qug, quh, qul, qup, quw, quy, quz, qvc, qvh, qvi, qvn, qvs, qvw, qxl, qxn, qxo, rad, raj, rap, rar, raw, rcf, rej, rgu, rhg, ria, rif, rjs, rkb, rm, rmc, rml, rmn, rmo, rmq, rmy, rn, rnl, ro, roh, rom, ron, roo, rop, row, rro, rtm, ru, rue, ruf, rug, run, rup, rus, rw, rwo, sa, sab, sag, sah, san, sas, sat, sbd, sbe, sc, scn, sco, sd, sda, sdc, sdh, se, seh, ses, sg, sgb, sgc, sgh, sgs, sgw, shi, shk, shn, shp, shu, si, sid, sil, sim, sin, sk, skg, skr, sl, slk, sll, slv, sm, sma, sme, smj, smk, sml, smn, smo, sms, sn, sna, snc, snd, snf, snn, snp, snw, sny, so, som, sop, soq, sot, spa, spl, spm, spp, sps, sqi, sr, srd, srm, srn, srp, srr, ss, ssd, ssg, ssw, ssx, st, stq, su, suc, sue, suk, sun, sus, suz, sv, swa, swb, swc, swe, swg, swh, swp, sxb, sxn, sxw, syb, syc, syl, syr, szl, szy, ta, tab, tah, taj, tam, tap, taq, tar, tat, taw, tay, tbg, tbo, tbw, tby, tbz, tca, tcs, tcy, tcz, tdt, tdx, te, tel, teo, tet, tfr, tg, tgk, tgo, tgp, th, tha, thl, ti, tif, tig, tih, tir, tiv, tk, tke, tkl, tkr, tku, tlb, tlf, tlh, tll, tly, tmd, tmh, tn, tnc, tnk, tnn, tnp, tnr, to, tob, tod, tog, toi, toj, tok, ton, too, top, tos, tpi, tr, trc, trn, trp, trq, trv, ts, tsg, tsn, tso, tsz, tt, ttc, tte, ttq, tuc, tuf, tui, tuk, tum, tur, tvk, tvl, tw, twi, twu, txu, ty, tyv, tzh, tzj, tzm, tzo, ubr, ubu, udm, ug, uig, uk, ukr, umb, und, upv, ur, urd, urh, usa, usp, uvh, uvl, uzb, uzn, uzs, vap, ve, vec, ven, vep, vi, vid, vie, vls, vmw, vmy, vo, vol, vro, vun, wa, waj, wal, war, wat, way, wbm, wbp, wed, wes, wln, wls, wlv, wlx, wmw, wnc, wnu, wo, wol, wos, wrs, wsg, wsk, wuu, wuv, xal, xbi, xh, xho, xla, xmf, xmm, xmv, xog, xon, xsm, xsr, xtd, xtm, xtn, yaa, yal, yao, yap, yby, ydd, yid, yka, yle, yli, yml, yo, yom, yon, yor, yrk, yrl, yss, yua, yue, yuj, yut, yuw, yva, zac, zai, zam, zao, zap, zas, zat, zdj, zea, zgh, zha, zho, zia, zom, zos, zpm, zpo, zpt, zpu, zsm, zu, zul, zyb, zyp, zza","multi","None","None","False","False","False","cc0-1.0","2410.23825","None","None"
+"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"
+"WueNLP/sib-fleurs","WueNLP","2024-12-03 20:29:29","2024-12-19 11:36:50+00:00","f00a6bbc6b3e3866600f838736295dd09b393902","12774","15690","3","None","audio-classification, automatic-speech-recognition, audio-text-to-text, text-to-speech, question-answering, document-question-answering","SIB-Fleurs SIB-Fleurs is a dataset suitable to evaluate Multilingual Spoken Language Understanding. For each utterance in Fleurs, the task is to determine the topic the utterance belongs to. The topic","None","ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nqo, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu, multilingual","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"ontocord/CulturaY","ontocord","2024-02-08 12:10:31","2024-03-30 23:07:37+00:00","dc71d24f9289f16d253a7458b0f9a9f1ce4a4b2f","1708","15659","30","None","text-generation, fill-mask, language-modeling, masked-language-modeling","CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages Dataset Summary From the team that brought you CulturaX, we present CulturaY, another substantial multilingual dataset of 15TB (uncompres","None","af, ar, az, be, bg, bn, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fr, ga, gl, gu, hbs, he, hi, hu, hy, id, is, it, ja, ka, kk, kn, ko, ky, la, lt, lv, mk, ml, mn, mr, ms, mt, my, nb, ne, nl, nn, pa, pl, ps, pt, ro, ru, si, sk, sl, so, sq, sv, sw, ta, te, th, tl, tr, tt, uk, ur, uz, vi, zh","multi","n|<|1|K|||1|K|<|n|<|1|0|K|||1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M|||1|M|<|n|<|1|0|M|||1|0|M|<|n|<|1|0|0|M|||1|0|0|M|<|n|<|1|B|||1|B|<|n|<|1|0|B","None","False","False","False","cc-by-4.0","None","None","language-modeling, masked-language-modeling"
+"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"
+"HAERAE-HUB/csatqa","HAERAE-HUB","2023-07-13 05:41:47","2023-09-10 17:12:24+00:00","61f92d2e08aada5e7ecc7e1eef3a06c6e938204b","861","14426","13","None","multiple-choice","CSAT-QA","\\","ko","mono","None","None","False","False","False","None","None","None","None"
+"nlpai-lab/kullm-v2","nlpai-lab","2023-06-01 05:26:22","2023-06-01 05:45:04+00:00","cddcb73c259269928e974e0ce141f123eb068030","155","14412","68","task_categories:text-generation, language:ko, 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","Dataset Card for ""KULLM-v2"" Dataset Summary Korean translation of GPT4ALL, Dolly, and Vicuna data. repository: nlpai-lab/KULLM huggingface: nlpai-lab/kullm-v2 Translate dataset Translated 'instruction","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","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"
+"jason9693/APEACH","jason9693","2022-04-14 14:27:43","2022-07-05 04:18:07+00:00","e57e10cf1f793a854486c7dc040ac18d59595199","2490","13028","5","task_categories:text-classification, annotations_creators:crowdsourced, annotations_creators:crowd-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2202.12459, region:us","text-classification, binary-classification","Dataset for project: kor_hate_eval(APEACH) Sample Code Dataset Descritpion Korean Hate Speech Evaluation Datasets : trained with BEEP! and evaluate with APEACH Repository: Korean HateSpeech Evaluation","None","ko","mono","1|K|<|n|<|1|0|K","apeach","False","False","False","cc-by-sa-4.0","2202.12459","None","binary-classification"
+"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"
+"heegyu/namuwiki-extracted","heegyu","2022-10-01 01:27:07","2023-01-15 09:46:31+00:00","d5ef945611040f7f760e02abfdc05be74b01edbe","163","12538","13","task_categories:other, language_creators:other, multilinguality:monolingual, language:ko, license:cc-by-nc-sa-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","other","namu.wiki database dump https://namu.wiki/ database dump 2022/03/01 571308rows download size: 2.19GB 주의사항 namu-wiki-extractor를 이용하여 전처리, 추가로 아래 전처리를 수행했습니다 헤더 제거 == 개요 == 테이블 제거 [age(1997-01-01)] 는 전처","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-2.0","None","None","None"
+"bongsoo/kowiki20220620","bongsoo","2022-07-25 04:45:16","2022-10-05 00:08:42+00:00","47e32b8a853777f36903af82a1008f5d3f230d2a","75","12287","0","language:ko, license:apache-2.0, size_categories:1M<n<10M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","-kowiki202206 1줄 말뭉치","None","ko","mono","None","None","False","False","False","apache-2.0","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"
+"openai/MMMLU","openai","2024-09-13 16:37:19","2024-10-16 18:39:00+00:00","325a01dc3e173cac1578df94120499aaca2e2504","4050","11335","447","None","question-answering","Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different ","None","ar, bn, de, es, fr, hi, id, it, ja, ko, pt, sw, yo, zh","multi","None","None","False","False","False","mit","2009.03300","None","None"
+"sentence-transformers/parallel-sentences-opus-100","sentence-transformers","2024-05-25 06:52:13","2024-07-09 14:28:24+00:00","f9fff3fffa048f359508f6aa77ff5bd2226d4016","1505","11275","1","None","feature-extraction, sentence-similarity","Dataset Card for Parallel Sentences - OPUS-100 This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. The sentences ori","None","af, am, an, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, dz, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, gu, ha, he, hi, hr, hu, hy, id, ig, is, it, ja, ka, kk, km, kn, ko, ku, ky, li, lt, lv, mg, mk, ml, mn, mr, ms, mt, my, nb, ne, nl, nn, no, oc, or, pa, pl, ps, pt, ro, ru, rw, se, sh, si, sk, sl, sq, sr, sv, ta, te, tg, th, tk, tr, tt, ug, uk, ur, uz, vi, wa, xh, yi, yo, zh, zu","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"shibing624/sharegpt_gpt4","shibing624","2023-07-27 05:45:49","2024-02-23 05:38:24+00:00","3fb53354e02a931777556fb1da37e931d73af48a","498","11268","115","None","text-classification, text-generation, text-scoring","Dataset Card Dataset Summary ShareGPT中挑选出的GPT4多轮问答数据，多语言问答。 Languages 数据集是多语言，包括中文、英文、日文等常用语言。 Dataset Structure Data Fields The data fields are the same among all splits. conversations: a List of str","None","zh, en, gl, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","text-scoring"
+"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"
+"royboy0416/ko-alpaca","royboy0416","2023-03-31 14:16:10","2023-03-31 21:14:40+00:00","f4de14db58cd402f76f6224b585563d3b93a8954","100","10659","13","task_categories:text-generation, language:ko, 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","Testing purpose only. Do not redistribute. Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"sean0042/KorMedMCQA","sean0042","2024-03-05 09:23:31","2024-12-09 06:59:28+00:00","79efd6f91edfc8036330d7a4daa88b9f2deb9a82","514","10500","22","None","question-answering","KorMedMCQA : Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations We present KorMedMCQA, the first Korean Medical Multiple-Choice Question Answering benc","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-2.0","2403.01469","None","None"
+"wicho/kor_3i4k","wicho","2022-03-02 23:29:22","2024-08-08 06:08:43+00:00","1592c3ec6b1e16ba361f8243513b039504c09a42","101","10444","4","task_categories:text-classification, task_ids:intent-classification, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1811.04231, region:us","text-classification, intent-classification","Dataset Card for 3i4K Dataset Summary The 3i4K dataset is a set of frequently used Korean words (corpus provided by the Seoul National University Speech Language Processing Lab) and manually created q","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","1811.04231","None","intent-classification"
+"SpellOnYou/kor_sarcasm","SpellOnYou","2022-03-02 23:29:22","2024-08-08 06:08:05+00:00","3d96e36e10a88d5b7a3f617cf8362d997504494b","253","9945","4","task_categories:text-classification, annotations_creators:expert-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:ko, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sarcasm-detection","text-classification","Dataset Card for Korean Sarcasm Detection Dataset Summary The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original meaning of a sentence. 9319 tweet","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","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"
+"Bingsu/zeroth-korean","Bingsu","2022-08-14 08:50:33","2022-08-15 10:30:30+00:00","bd173fe2c8ed0dccd47acb4eda77542593651622","717","9085","26","None","automatic-speech-recognition","Zeroth-Korean Zeroth-Korean The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) an","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"MarkrAI/KoCommercial-Dataset","MarkrAI","2024-03-06 15:31:12","2024-03-22 09:22:58+00:00","c7ae950f2984ab3ff6a2365331bc9fa27cdac185","362","8947","136","language:ko, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.14045, arxiv:2309.09530, arxiv:2107.06499, region:us","None","SSL 데이터 생성을 위한 코드 공개 SSL 데이터 생성용 Github Repo NIA와 AI-Hub와의 저작권 협의 하에, 조금 혼선이 생긴것 죄송합니다. 이에 기존에 저희가 code베이스로 SSL 데이터를 생성했던 코드를 그대로 공개드립니다. 다만, 이 과정에서는 저희 이후 파이프라인인, 자체 로컬 모델을 가지고 필터링하거나 수정하는 과정이 없어, 어느","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"jeanlee/kmhas_korean_hate_speech","jeanlee","2022-11-21 05:03:58","2022-11-28 16:26:56+00:00","c657d15baf277c48d467f0625f7d33c50d4352ef","241","8904","20","task_categories:text-classification, task_ids:multi-label-classification, task_ids:hate-speech-detection, annotations_creators:crowdsourced, language_creators:found, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, arxiv:2208.10684, region:us, K-MHaS, Korean NLP, Hate Speech Detection, Dataset, Coling2022","text-classification, multi-label-classification, hate-speech-detection","The K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class. The fine-grained","@inproceedings{lee-etal-2022-k, title = ""K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment"", author = ""Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren"", booktitle = ""Proceedings of the 29th International Conference on Computational Linguistics"", month = oct, year = ""2022"", address = ""Gyeongju, Republic of Korea"", publisher = ""International Committee on Computational Linguistics"", url = ""https://aclanthology.org/2022.coling-1.311"", pages = ""3530--3538"", abstract = ""Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class."", }","ko","mono","1|0|0|K|<|n|<|1|M","korean-multi-label-hate-speech-dataset","False","False","False","cc-by-sa-4.0","2208.10684","None","multi-label-classification, hate-speech-detection"
+"djstrong/oscar-small","djstrong","2023-03-07 19:55:38","2023-03-07 19:57:38+00:00","fc47ed5a1804d8dd398255fa33da745ccb060d0e","3117","8702","1","None","text-generation, language-modeling","The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\\","@inproceedings{ortiz-suarez-etal-2020-monolingual, title = ""A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages"", author = ""Ortiz Su{\\'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit"", booktitle = ""Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"", month = jul, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.acl-main.156"", pages = ""1703--1714"", abstract = ""We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures."", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{\\'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\\""u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{\\""u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} }","af, am, ar, arz, as, az, azb, ba, be, bg, bn, bo, br, ca, ce, ceb, ckb, cs, cv, cy, da, de, dv, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gl, gu, he, hi, hr, hu, hy, id, is, it, ja, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mhr, mk, ml, mn, mr, ms, mt, my, nds, ne, nl, nn, no, or, os, pa, pl, pnb, ps, pt, ro, ru, sa, sah, sd, sh, si, sk, sl, sq, sr, sv, sw, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vi, yi, zh","multi","None","None","False","False","False","cc0-1.0","2010.14571","None","language-modeling"
+"lcw99/wikipedia-korean-20221001","lcw99","2022-10-10 03:49:37","2024-05-05 09:12:11+00:00","49af0d4be8266217cb60bbc2bdb8e0ca0929b250","76","8644","5","language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","20240501 update","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"kyujinpy/KOpen-platypus","kyujinpy","2023-08-21 14:59:26","2023-11-01 20:18:07+00:00","917b993563785711f651f1c9f0897c052aa7d275","223","8006","34","language:en, language:ko, 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, arxiv:2305.20050, arxiv:2305.12524, region:us","None","KOpenPlatypus: Korean Translation dataset about Open-Platypus Korean Translation Method I use DeepL-pro-API and selenium. It takes about 140h times.+) 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2308.07317","None","None"
+"wecover/OPUS","wecover","2024-01-30 12:54:31","2024-05-23 09:15:01+00:00","21c82515d8c29d36cdc6355c36795c40d9874e62","883","7727","0","task_categories:translation, language:af, language:am, language:ar, language:as, language:az, language:be, language:bg, language:bn, language:br, language:bs, language:ca, language:cs, language:cy, language:da, language:de, language:el, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:fy, language:ga, language:gd, language:gl, language:ha, language:he, language:hi, language:hr, language:hu, language:hy, language:id, language:is, language:it, language:ja, language:jv, language:ka, language:kk, language:km, language:kn, language:ko, language:ku, language:ky, language:la, language:lo, language:lt, language:mg, language:mk, language:ml, language:mn, language:mr, language:ms, language:my, language:ne, language:nl, language:no, language:om, language:or, language:pa, language:pl, language:ps, language:pt, language:ro, language:ru, language:sa, language:sd, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:ta, language:te, language:th, language:tl, language:tr, language:ug, language:uk, language:ur, language:uz, language:vi, language:xh, language:yi, language:zh, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2004.09813, arxiv:1907.05791, region:us","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"
+"maxidl/FineNews-unfiltered","maxidl","2024-06-14 20:04:11","2024-06-16 19:48:41+00:00","58c6690fbaf643226bd5dae383a5de158ddf4c1a","987","7456","1","None","text-generation","FineNews WIP. Like FineWeb, but built from Common Crawl News instead of main web. For languages not listed as a split, check the data/ directory. For now, it contains the 2024-05 (May),-04 (April),-03","None","en, de, fr, pl, es, ru, it, ar, pt, tr, el, vi, ro, zh, uk, ko, hi, nl","multi","None","None","False","False","False","odc-by","None","None","None"
+"LHF/escorpius-mr","LHF","2022-05-03 18:49:47","2023-05-11 22:29:21+00:00","8b6e3f5b1dbd7244ae8251f5b1dc02da9146a04b","2123","7433","4","task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, multilinguality:multilingual, source_datasets:original, language:af, language:ar, language:bn, language:ca, language:cs, language:da, language:de, language:el, language:eu, language:fa, language:fi, language:fr, language:gl, language:hi, language:hr, language:it, language:ja, language:ko, language:mt, language:nl, language:no, language:oc, language:pa, language:pl, language:pt, language:ro, language:sl, language:sr, language:sv, language:tr, language:uk, language:ur, license:cc-by-nc-nd-4.0, size_categories:1B<n<10B, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2206.15147, region:us","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"
+"coastalcph/tydi_xor_rc","coastalcph","2024-07-12 12:03:01","2024-09-19 08:20:09+00:00","42871590d25e82c9531347ca205b56037514753b","47","7414","0","None","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","None","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"
+"kyujinpy/KOR-OpenOrca-Platypus-v3","kyujinpy","2023-11-08 18:56:08","2023-11-18 20:22:23+00:00","be495584e53fe165be9e6723895fb7a1f8f0b83b","51","7345","13","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","KOR-OpenOrca-Platypus-v3 KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋. 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭 KOpen-platpyus Repo: KOpen-platypus 고품질 한국어 데이터셋 코드와 주","None","ko","mono","1|0|K|<|n|<|5|0|K","None","False","False","False","cc-by-nc-4.0","2306.02707","None","None"
+"cyanic-selkie/wikianc","cyanic-selkie","2023-09-05 14:22:32","2023-09-05 14:22:32+00:00","afb42306115c36afbaab9b13fc4545b724ae66c4","496","7023","5","None","token-classification","Dataset Card for WikiAnc Dataset Summary The WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023). The code for generating the dataset","None","en, ceb, de, sv, fr, nl, ru, es, it, arz, pl, ja, zh, vi, uk, war, ar, pt, fa, ca, sr, id, ko, no, ce, fi, cs, tr, hu, tt, sh, ro, eu, ms, eo, he, hy, da, bg, cy, sk, azb, uz, et, be, kk, min, el, hr, lt, gl, az, ur, sl, lld, ka, nn, hi, th, ta, bn, la, mk, ast, lv, af, tg, my, mg, mr, sq, bs, oc, te, ml, nds, br, ky, sw, jv, lmo, new, pnb, vec, ht, pms, ba, lb, su, ku, ga, szl, is, fy, cv, ckb, pa, tl, an, wuu, diq, io, sco, vo, yo, ne, ia, kn, gu, als, ha, avk, bar, crh, scn, bpy, qu, mn, nv, xmf, ban, si, tum, ps, ig, frr, os, mzn, or, sah, cdo, gd, bug, yi, sd, ilo, am, nap, li, bcl, fo, gor, hsb, mai, shn, eml, ace, sa, as, wa, ie, hyw, lij, mhr, zu, sn, hif, mrj, bjn, km, mni, hak, pam, sat, rue, nso, bh, so, mi, se, myv, vls, dag, sc, co, ary, kw, bo, vep, glk, tk, kab, gan, rw, ab, gv, ug, nah, zea, skr, frp, udm, pcd, mt, kv, csb, gn, smn, ay, nrm, ks, lez, lfn, olo, mwl, lo, stq, ang, mdf, fur, rm, lad, kaa, gom, ext, koi, tyv, pap, av, dsb, ln, dty, tw, dv, ksh, za, gag, bxr, pfl, lg, szy, pag, blk, pi, tay, haw, awa, inh, krc, xal, pdc, to, atj, tcy, arc, mnw, shi, jam, kbp, wo, anp, kbd, nia, om, nov, ki, nqo, bi, xh, tpi, ff, tet, jbo, fj, kg, lbe, ty, cu, guw, trv, ami, srn, sm, mad, alt, ltg, gcr, chr, tn, ny, st, pih, got, rmy, ee, pcm, bm, ss, gpe, ts, ve, kcg, chy, rn, ch, gur, ik, ady, fat, pnt, guc, iu, pwn, sg, din, ti, kl, dz, cr","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"KETI-AIR/kor_duorc","KETI-AIR","2023-11-03 08:19:16","2023-11-15 01:15:36+00:00","33220ff59a6acb9c1855f20c1a977bc798399428","213","6821","2","task_categories:question-answering, task_categories:text2text-generation, task_ids:abstractive-qa, task_ids:extractive-qa, multilinguality:monolingual, source_datasets:original, language:ko, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering, text2text-generation, abstractive-qa, extractive-qa","Dataset Card for duorc Licensing Information MIT License Source Data Citation Information @inproceedings{DuoRC, author = { Amrita Saha and Rahul Aralikatte and Mitesh M. Khapra and Karthik Sankaranara","None","ko","mono","1|0|K|<|n|<|1|M","duorc","False","False","False","mit","None","None","abstractive-qa, extractive-qa"
+"bongsoo/news_talk_en_ko","bongsoo","2022-09-20 05:10:56","2022-10-05 00:09:50+00:00","8ffecf6e6c61389f9c02f13f3875d810ff506fa3","360","6809","10","language:ko, license:apache-2.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","뉴스&일상대화 en-ko 번역 말뭉치","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"jojo0217/korean_safe_conversation","jojo0217","2024-04-27 11:26:50","2024-04-27 11:57:10+00:00","f195c2095b65fc9bf0335ac0b8cd9001e5ceef6c","507","6666","46","None","text-generation","개요 성균관대 - VAIV COMPANY 산학협력을 위해 구축한 일상대화 데이터입니다. 자연스럽고 윤리적인 챗봇 구축을 위한 데이터셋 입니다. 고품질을 위해 대부분의 과정에서 사람이 직접 검수하였으며생성 번역 등의 과정에서는 GPT3.5-turbo, GPT4를 사용하였습니다. 일상대화에 중점을 두면서혐오표현, 편향적인 대답을 지양하면서 일상대화를 하는 것에","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"taeminlee/Ko-miracl","taeminlee","2024-01-16 08:46:29","2024-01-19 02:13:05+00:00","5c7690518e481375551916f24241048cf7b017d0","309","6609","1","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:miracl, language:ko, 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","Ko-miracl This dataset represents a conversion of the Korean (Ko) section from the miracl dataset into the BeIR format, making it compatible for use with mteb.","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","document-retrieval"
+"FBK-MT/Speech-MASSIVE","FBK-MT","2024-06-10 09:35:41","2024-08-08 07:12:36+00:00","6b709ebffe1a313e392854ac3fe6396a40ce072c","947","6384","33","task_categories:audio-classification, task_categories:text-classification, task_categories:zero-shot-classification, task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, language_creators:found, multilinguality:multilingual, source_datasets:extended, language:ar, language:de, language:es, language:fr, language:hu, language:ko, language:nl, language:pl, language:pt, language:ru, language:tr, language:vi, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2408.03900, region:us, spoken language understanding, slot filling, intent classification, speech translation, speaker identification","audio-classification, text-classification, zero-shot-classification, automatic-speech-recognition","Speech-MASSIVE Dataset Description Speech-MASSIVE is a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. Speech-MA","None","ar, de, es, fr, hu, ko, nl, pl, pt, ru, tr, vi","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","2408.03900","None","None"
+"facebook/2M-Belebele","facebook","2024-12-16 08:45:30","2024-12-17 13:39:10+00:00","1355a3bf763d9b3d3862ad0ce7d157819576fbf4","3874","6354","5","None","question-answering, automatic-speech-recognition","2M-Belebele Highly-Multilingual Speech and American Sign Language Comprehension Dataset We introduce 2M-Belebele as the first highly multilingual speech and American Sign Language (ASL) comprehension ","None","bg, pa, en, hu, sv, af, ca, ka, sk, jv, bn, tr, sr, ro, tg, fa, wo, fi, hy, vi, kea, as, ja, nl, ne, lg, hi, xh, kk, mn, yo, km, ha, ru, sw, ps, ko, cs, lv, ig, ar, es, nb, lt, fil, it, he, da, ml, my, el, et, pl, sn, sd, or, th, luo, sl, fr, id, ta, gu, mk, am, pt, cmn, de, ceb, is, ur, az, te","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2412.08274","None","None"
+"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"
+"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"
+"msarmi9/korean-english-multitarget-ted-talks-task","msarmi9","2022-03-02 23:29:22","2022-10-22 15:05:15+00:00","d29aba88703a445763c2ff7344b2de28960d3554","441","5703","10","annotations_creators:expert-generated, language_creators:other, multilinguality:translation, multilinguality:multilingual, language:en, language:ko, license:cc-by-nc-nd-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conditional-text-generation, machine-translation","Dataset Card for english-korean-multitarget-ted-talks-task Dataset Summary Parallel English-Korean Text Corpus Text was originally transcribed to English from various Ted Talks, then translated to Kor","None","en, ko","bi","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","machine-translation"
+"DAMO-NLP-SG/MultiJail","DAMO-NLP-SG","2023-10-13 07:54:21","2023-10-13 07:56:04+00:00","93d65c778925d55973b9ff30c7bccc8545494eb6","74","5573","6","None","conversational","Multilingual Jailbreak Challenges in Large Language Models This repo contains the data for our paper ""Multilingual Jailbreak Challenges in Large Language Models"". [Github repo] Annotation Statistics W","None","en, zh, it, vi, ar, ko, th, bn, sw, jv","multi","n|<|1|K","None","False","False","False","mit","2310.06474","None","None"
+"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"
+"kyujinpy/OpenOrca-KO","kyujinpy","2023-09-29 15:26:20","2023-10-12 19:55:47+00:00","7837437c12379d04b51d2b59cefb79a8b7c3cbe8","114","5267","28","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","OpenOrca-KO OpenOrca dataset 중 약 2만개를 sampling하여 번역한 데이터셋 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭 Dataset inf0 NIV // 1571개 FLAN // 9434개 T0 // 6351개 CoT // 2117개 KoCoT // 2159개 ","None","ko","mono","1|0|K|<|n|<|5|0|K","None","False","False","False","mit","2306.02707","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"
+"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"
+"Bingsu/laion2b_multi_korean_subset_with_image","Bingsu","2022-10-17 04:32:45","2022-11-03 05:10:40+00:00","899b75095a573984a124727dcce8de7e30ad67dc","365","5006","5","task_categories:feature-extraction, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:extended|laion/laion2B-multi, language:ko, license:cc-by-4.0, size_categories:100K<n<1M, modality:image, modality:text, library:datasets, library:mlcroissant, region:us","feature-extraction","laion2b_multi_korean_subset_with_image img2dataset을 통해 다운로드에 성공한 Bingsu/laion2B-multi-korean-subset 이미지를 정리한 데이터셋입니다. 이미지는 9,800,137장입니다. 이미지는 짧은 쪽 길이가 256이 되도록 리사이즈 되었으며, 품질 100인 webp파일로 다운로드 되었습니다. ","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","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"
+"lcw99/cc100-ko-only","lcw99","2022-10-21 06:05:16","2022-10-21 07:23:11+00:00","56ede88fa531e775aa97d6f958c501207ceace7b","456","4949","4","language:ko, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","cc100 dataset Korean only","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"lawcompany/KLAID","lawcompany","2022-11-13 05:21:05","2022-11-17 07:09:10+00:00","cc77aedcfe59f728c64449bf35522d0201c49e7f","158","4877","12","task_categories:text-classification, task_ids:multi-class-classification, multilinguality:monolingual, language:ko, license:cc-by-nc-nd-4.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us","text-classification, multi-class-classification","KLAID (Korean Legal Artificial Intelligence Datasets) is a dataset for the development of Korean legal artificial intelligence technology. This time we offer 1 task, which is legal judgment prediction","None","k, o","bi","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","multi-class-classification"
+"Moo/korean-parallel-corpora","Moo","2022-05-16 07:35:42","2022-07-01 15:32:54+00:00","b814940b602d179b21beac3b8c14c97bcde0e963","365","4837","17","task_categories:translation, annotations_creators:other, language_creators:other, multilinguality:multilingual, multilinguality:translation, source_datasets:original, language:ko, language:en, 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","translation","None","None","ko, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"jojo0217/korean_rlhf_dataset","jojo0217","2023-08-08 07:37:14","2023-09-25 08:36:04+00:00","01b06b9ced034b1460dfa5673e5174f4e7054b1b","213","4779","19","task_categories:text-generation, language:ko, 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","성균관대학교 산학협력프로젝트 과정에서 한국어 llm 모델 SFT 학습을 위해 구축한 데이터셋 입니다.2023-09-25오픈 어시스턴트 data에서 오픈 어시스턴트를 포함하는 데이터 삭제-> 답변에 오픈 어시스턴트라고 하는 경우가 나오기 때문또한 스탠포드 대학 번역 데이터에서 번역 과정 오류로 input에 입력없음 과 같이 추가된 부분 삭제그리고 <unk> ","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Muennighoff/xP3x-sample","Muennighoff","2023-07-06 09:42:03","2023-09-18 13:51:06+00:00","70365cee44e6b6cd1ad5f76a052aefa6bbd667b7","619","4732","3","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"
+"traintogpb/aihub-koen-translation-integrated-base-1m","traintogpb","2024-01-05 00:51:54","2024-01-05 04:17:17+00:00","eb15b41a52964f7d0906b5cc10c347fa4ec66de1","111","4536","3","task_categories:translation, language:en, language:ko, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","AI Hub Ko-En Translation Dataset (Integrated) AI Hub의 한-영 번역 관련 데이터셋 8개를 병합한 자료입니다. 병합 시 총 데이터 개수는 10,416,509개 이며, train / validation / test는 8:1:1 비율로 분할되었습니다. base-10m: 병합 데이터 100% 사용, 총 10,416,509개","None","en, ko","bi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"tomaarsen/MultiCoNER","tomaarsen","2023-10-01 18:44:19","2023-10-01 19:39:19+00:00","04fafbabb7a36defcfd400ed2fe5504c10fdf4ab","388","4513","4","None","token-classification","We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and ","@misc{malmasi2022multiconer, title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko}, year={2022}, eprint={2208.14536}, archivePrefix={arXiv}, primaryClass={cs.CL} }","bn, de, en, es, fa, hi, ko, nl, ru, tr, zh, multilingual","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"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"
+"davidkim205/kollm-converations","davidkim205","2024-03-18 05:04:38","2024-03-21 02:11:17+00:00","f2e932e1c0c3389b569e4dbabdcbeb6bcd8f3801","54","4186","27","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","kollm Converations Dataset This dataset is an integrated dataset created in conversations format for SFT learning using the Korean dataset currently available on huggingface and github. Our the nox mo","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"BCCard/BCCard-Finance-Kor-QnA","BCCard","2024-07-11 05:43:25","2024-11-20 15:55:26+00:00","cd803e0bc1e88b56001fad130dc67f5a9b5d9238","128","4161","12","language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","apache-2.0","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","None","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"
+"taeshahn/ko-lima","taeshahn","2023-06-13 15:10:24","2024-03-01 14:01:58+00:00","a4ccbdeb1d4d2dc136659bf067c6c24c770c9b09","74","3856","10","language:ko, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.11206, region:us, lima, kolima, korean, instruction","None","Dataset Card for KoLIMA Dataset Description KoLIMA는 Meta에서 공개한 LIMA: Less Is More for Alignment (Zhou et al., 2023)의 학습 데이터를 한국어로 번역한 데이터셋입니다. 번역에는 DeepL API를 활용하였고, SK(주) Tech Collaborative Lab으로부터 비","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","2305.11206","None","None"
+"kuotient/gsm8k-ko","kuotient","2024-03-27 07:35:55","2024-03-27 15:14:15+00:00","0b63c02fedcdf506c2f98396659084da83471d1a","679","3772","12","None","None","번역 모델 kuotient/Seagull-13B-translate 사용. How to evaluate git clone https://github.com/kuotient/lm-evaluation-harness.git cd lm-evaluation-harness pip install -e . lm_eval --model hf \\ --model_args pre","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"leey4n/KR3","leey4n","2022-03-02 23:29:22","2023-07-19 08:35:54+00:00","290dc889066d7127077a2bec7f0be726fdeaa760","63","3642","3","task_categories:text-classification, task_ids:sentiment-classification, multilinguality:monolingual, language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, sentiment-classification","KR3: Korean Restaurant Reviews with Ratings Korean sentiment classification dataset Size: 460K(+180K) Language: Korean-centric ⚠️ Caution with Rating Column 0 stands for negative review, 1 stands for ","None","ko","mono","1|0|0|K|<|n|<|1|m","None","False","False","False","cc-by-nc-sa-4.0","None","None","sentiment-classification"
+"SabaPivot/KMMLU-Summarized-Chain_of_Thought","SabaPivot","2024-12-09 04:07:39","2024-12-28 16:12:37+00:00","c9a085b96917608163c8d26a470c895d601739a1","2220","3603","1","language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.11548, region:us, mmlu, kmmlu, summarized_data, solar-pro","None","Dataset Card for Condensed Chain-of-Thought KMMLU Dataset This dataset card provides detailed information about the condensed KMMLU dataset. The dataset has been summarized using Upstage's LLM: Solar-","None","ko","mono","None","None","False","False","False","None","2402.11548","None","None"
+"hltcoe/megawika-report-generation","hltcoe","2023-11-10 13:17:09","2024-01-19 13:01:58+00:00","bc25fe13ccf7722db3b8e6f79dc4e8b2f47f62d6","412","3549","6","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"
+"joonhok-exo-ai/korean_law_open_data_precedents","joonhok-exo-ai","2023-06-29 12:51:31","2025-01-14 02:15:43+00:00","5fcac60e29e700bb18e8cccef3705630a569de0b","171","3532","25","language:ko, license:openrail, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, legal","None","Dataset Card for Dataset Name 공지사항 인공지능 나홀로소송 서비스를 만들어 보고 있습니다. 모바일에서 탭과 스크롤만으로 소장을 완성하고 전자소송 사이트를 거치지 않고 제출까지 가능한 서비스를 만들어 보고 있는데 보다 자세한 내용은 이 링크를 참고해주세요. 저랑 같이 만들어 보실 분은 joonhok@botoai.co로 연락 바랍니다. ","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","openrail","None","None","None"
+"Unbabel/TowerBlocks-v0.2","Unbabel","2024-01-24 13:59:56","2024-03-04 13:17:35+00:00","0c00a3ae8ecaf841e38e3c87968d62d28f1a3a12","86","3498","11","None","conversational","Dataset Card for TowerBlocks TowerBlocks is the dataset used to train TowerInstruct-v0.1, a language model specialized for translation tasks such as machine translation (e.g. general, document, termin","None","en, de, fr, zh, pt, nl, ru, ko, it, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"davidkim205/ko_truthful_qa","davidkim205","2024-03-11 06:33:47","2024-03-11 06:34:57+00:00","221bb55a282c165886f6a4635d3ce65dd0bf5d0f","107","3452","4","language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Korean TruthfulQA TruthfulQA 영어 데이터셋을 한국어로 번역 https://huggingface.co/datasets/truthful_qa Structure Multiple Choice { ""question"": ""수박씨를 먹으면 어떻게 되나요?"", ""mc1_targets"": { ""choices"": [ ""수박 씨앗은 소화기를 통과합니다.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"HAERAE-HUB/KOREAN-WEBTEXT","HAERAE-HUB","2024-05-26 11:20:25","2024-05-31 15:54:12+00:00","2ad96e4983923d91350ab2214e059bd57219eddd","307","3412","33","language:ko, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","KOREAN-WEBTEXT KOREAN-WEBTEXT is a high-quality Korean language corpus consisting of 2.2 billion tokens. The data has been collected from the following sources: cc100 oscar-corpus/OSCAR-2201 oscar-cor","None","ko","mono","None","None","False","False","False","None","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","task_categories:zero-shot-classification, task_categories:zero-shot-image-classification, language:ar, language:el, language:en, language:hi, language:ja, language:ko, language:te, language:th, language:uk, language:zh, license:cc-by-nc-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:audio, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2411.01053, region:us, multimodal, representation learning, multilingual","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"
+"bltlab/lr-sum","bltlab","2023-11-07 16:29:37","2024-12-19 21:40:27+00:00","deb2e73ce53e42ceda31297a991afa138ee665a8","141","3323","4","None","summarization, text-generation","Dataset Card for LR-Sum LR-Sum is a automatic summarization dataset of newswire text with a focus on less resourced languages with a cc-by 4.0 license. Dataset Details Dataset Description LR-Sum is a ","None","am, az, bn, bo, bs, ku, zh, el, en, fa, fr, ht, ha, hy, id, ka, km, rw, ko, lo, mk, my, nd, pt, ps, ru, sn, so, es, sq, sr, sw, th, ti, tr, uk, ur, uz, vi","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","None"
+"C4AI-Community/multilingual-reward-bench","C4AI-Community","2024-09-07 18:45:29","2024-11-04 20:21:18+00:00","19ab8c297a3e59a1d0efd42ae33cc14bf3febf63","1139","3236","26","None","None","Multilingual Reward Bench (v1.0) Reward models (RMs) have driven the development of state-of-the-art LLMs today, with unprecedented impact across the globe. However, their performance in multilingual ","None","ar, zh, cs, nl, fr, de, el, he, hi, id, it, ja, ko, fa, pl, pt, ro, ru, es, tr, uk, vi","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","odc-by","2410.15522","None","None"
+"davidkim205/ko_arc_challenge","davidkim205","2024-03-11 06:45:47","2024-03-11 06:47:39+00:00","1528d195b749bd744cb51d8b5cbd1cc4580abbec","111","3195","2","language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Korean ARC (Challenge) ai2_arc 영어 데이터셋을 한국어로 번역 https://huggingface.co/datasets/allenai/ai2_arc Structure { ""id"": ""Mercury_7175875"", ""question"": ""한 천문학자가 운석 충돌 후 행성이 더 빨리 자전하는 것을 관찰했습니다. 이러한 자전 증가의 가장","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"almanach/HALvest","almanach","2024-04-23 13:30:23","2024-07-31 09:56:58+00:00","442094b65f54946f8cafb64cf774cb7c87e57df4","738","3146","3","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"
+"davidkim205/ko_hellaswag","davidkim205","2024-03-11 06:38:58","2024-03-19 07:32:16+00:00","c5a04cb3f28e958a74bac6c2459038ce0e35d3f8","110","3142","3","language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Korean HellaSwag hellaswag 영어 데이터셋을 한국어로 번역 https://huggingface.co/datasets/Rowan/hellaswag Structure { ""ind"": 24, ""activity_label"": ""지붕 슁글 제거"", ""ctx_a"": ""한 남자가 지붕 위에 앉아 있다."", ""ctx_b"": ""그"", ""ctx"": ""한 ","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"squarelike/OpenOrca-gugugo-ko","squarelike","2023-11-04 05:57:56","2023-11-12 22:41:06+00:00","296c9722709a5e8f1354012be7513ef3e1908a49","80","3078","34","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:ko, license:mit, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","OpenOrca 한국어 번역 데이터셋 Gugugo-koen-7B-V1.1을 이용하여 OpenOrca데이터셋을 번역하고 있습니다. 번역 진행상황은 아래를 참고해 주십시오. 진행상황 GPT4 생성물 약 100만 개 중 약 64만 개 번역완료 GPT3.5 생성물 약 350만 개 중 약 159만 개 번역완료 데이터셋 사용 후 출처표기는 제작자에게 큰 힘이 됩니다.","None","ko","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","mit","2306.02707","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"
+"jp1924/KsponSpeech","jp1924","2024-03-22 14:59:33","2024-06-14 06:08:01+00:00","e2121c93cad72896964029a54849f951beed2828","247","3008","4","task_categories:automatic-speech-recognition, language:ko, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, STT, Audio","automatic-speech-recognition","None","None","ko","mono","1|0|0|B|<|n|<|1|T","None","False","auto","False","None","None","None","None"
+"ByteDance/MTVQA","ByteDance","2024-05-20 07:42:19","2024-05-30 02:38:32+00:00","7cdca63ec6cd71cdd0b52f79fc3176bb9ce36a9c","282","3006","24","None","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"
+"Bingsu/ko_alpaca_data","Bingsu","2023-03-20 05:36:21","2023-03-30 23:21:40+00:00","dee822fcf96efd25e994ba9a59237e4615f47435","78","3000","17","task_categories:text-generation, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""ko_alpaca_data"" Dataset Summary Korean translation of alpaca data. repository: Beomi/KoAlpaca huggingface: beomi/KoAlpaca Translate dataset Translated 'instruction' and 'input' in th","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","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"
+"changpt/ko-lima-vicuna","changpt","2023-06-14 03:58:58","2023-06-14 07:47:51+00:00","0c28275a5605f3bfa4cc116d8099bfa0e5abb43b","104","2971","23","task_categories:text-generation, language:ko, license:cc-by-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, KoLima","text-generation","Ko Lima Vicuna Dataset GPT4 API를 사용하여 lima_vicuna_format 데이터를 한국어로 재생성한 데이터셋입니다.GPT4 사용시 프롬프트는 ""단순 번역이 아닌, 원문 내용을 참조하여 데이터를 재생성하라""는 내용으로 작성하였습니다.원본 데이터셋 1,030건 중 866건의 데이터가 재생성되었습니다. 데이터셋 제작은 웹 크롤링 전문","None","ko","mono","n|<|1|K","None","False","False","False","cc-by-2.0","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"
+"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","None","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","None","False","False","False","cc-by-nc-sa-4.0","1607.01759","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"
+"heegyu/namuwiki","heegyu","2022-10-01 00:40:12","2022-10-01 02:40:40+00:00","5631a9bd17a096bab2cd02ea23adbf2327db0d91","102","2765","17","task_categories:other, language_creators:other, multilinguality:monolingual, language:ko, license:cc-by-nc-sa-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","other","namu.wiki database dump https://namu.wiki/ database dump 2022/03/01 867024 rows download size: 3GB Usage pip install datasets from datasets import load_dataset dataset = load_dataset(""heegyu/namuwiki""","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-2.0","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"
+"nayeon212/BLEnD","nayeon212","2024-06-11 10:06:43","2024-12-10 03:30:49+00:00","b8c1f8d750d1ec28a1ec2699fd36b44328740b2c","491","2706","8","None","question-answering","BLEnD This is the official repository of BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages (Submitted to NeurIPS 2024 Datasets and Benchmarks Track). 24/12/05: Update","None","en, zh, es, id, ko, el, fa, ar, az, su, as, ha, am","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","2406.09948","None","None"
+"MarkrAI/KOpen-HQ-Hermes-2.5-60K","MarkrAI","2024-08-27 03:15:55","2024-08-28 07:34:48+00:00","69d5997a93eebc7d50f7d27afb8d18398683a60a","236","2695","58","task_categories:question-answering, task_categories:text-generation, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, text-generation","MarkrAI/KOpen-HQ-Hermes-2.5-60K The KOpen-HQ-Hermes-2.5-60K dataset has been released! Anyone can use it under the MIT license, so feel free to take advantage of this high-quality dataset. In this dat","None","ko","mono","None","None","False","auto","False","mit","None","None","None"
+"nlpai-lab/databricks-dolly-15k-ko","nlpai-lab","2023-06-01 10:19:09","2023-06-16 03:01:52+00:00","183b7a25c302efac83478ee5ef9ab14e105af16e","74","2547","30","task_categories:question-answering, task_categories:summarization, language:ko, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2203.02155, region:us","question-answering, summarization","Korean translation of databricks-dolly-15k via the DeepL API Note: There are cases where multilingual data has been converted to monolingual data during batch translation to Korean using the API. Belo","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-sa-3.0","2203.02155","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"
+"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"
+"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"
+"hac541309/open-lid-dataset","hac541309","2023-08-09 08:46:10","2023-10-27 01:18:24+00:00","4154c607845d70af9cc6331f960fb501a08ba6a0","727","2450","4","language:en, language:ko, language:fr, language:aa, language:hi, license:gpl-3.0, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.13820, region:us","None","This dataset is built from the open source data accompanying ""An Open Dataset and Model for Language Identification"" (Burchell et al., 2023) The repository containing the actual data can be found here","None","en, ko, fr, aa, hi","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","gpl-3.0","2305.13820","None","None"
+"Bingsu/KSS_Dataset","Bingsu","2022-04-19 06:59:21","2022-07-02 00:10:10+00:00","48fdfd7ab1dbc1a62e4e8a8b9f4c360259d51d3c","292","2438","14","task_categories:text-to-speech, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-to-speech","Description of the original author KSS Dataset: Korean Single speaker Speech Dataset KSS Dataset is designed for the Korean text-to-speech task. It consists of audio files recorded by a professional f","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","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"
+"lcw99/oscar-ko-only","lcw99","2022-10-21 05:38:36","2022-10-21 05:52:05+00:00","18112c5f65fe4c2593104cbc0850e2a7737cc41f","514","2408","3","None","None","oscar dataset only korean","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"heegyu/kowiki-sentences","heegyu","2022-10-06 00:46:26","2022-10-06 00:54:57+00:00","4821c01a0f2344040a16c8b7febc15f3a8e110d7","82","2350","10","task_categories:other, language_creators:other, multilinguality:monolingual, language:ko, license:cc-by-sa-3.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","other","20221001 한국어 위키를 kss(backend=mecab)을 이용해서 문장 단위로 분리한 데이터 549262 articles, 4724064 sentences 한국어 비중이 50% 이하거나 한국어 글자가 10자 이하인 경우를 제외","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"hac541309/polyglot-ko-tokenizer-corpus","hac541309","2023-05-02 09:40:33","2023-07-13 04:46:18+00:00","c9e7a7b4e135afa96f966039cc6d3a92fd8fa459","410","2326","1","language:ko, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""polyglot-ko-tokenizer-corpus"" More Information needed","None","k, o","bi","None","None","False","False","False","None","None","None","None"
+"Ja-ck/Orca-DPO-Pairs-KO","Ja-ck","2023-12-12 01:56:54","2023-12-12 01:58:39+00:00","700b5f5a8b63323878697e98013f3a5088afb518","35","2272","4","language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Orca-DPO-Pairs-KO Intel/orca_dpo_pais 를 한국어로 번역한 데이터세트 입니다. 번역은 maywell/Syntra-7B-v0.3-Translation 을 사용했습니다. 번역 후 일부 오류가 발생한 라인은 삭제했기 때문에 원본과 차이가 있을 수 있습니다.","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"davidkim205/ko_common_gen","davidkim205","2024-03-11 07:25:02","2024-03-11 07:25:43+00:00","2f20909a47fd8b08b03e5f78c81e6dada7f66a86","95","2272","1","language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","News Common Gen 소개 뉴스 데이터를 이용하여 제작한 common gen 데이터셋. 총 4,639개. 구조 { ""concept_set"": ""concept set: {해석이, 스마트워치, 이끈다는, 마이크, LED 시장 성장}"", ""ending0"": ""마이크로 스마트워치가 LED 시장 성장을 이끈다는 해석이다."", ""ending1"": ""스마트워치가","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"saillab/taco-datasets","saillab","2023-11-27 07:15:33","2023-12-01 06:21:22+00:00","bfb3619e7fafe98526beb2d254971854c1a53520","509","2250","15","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"
+"CarrotAI/ko-instruction-dataset","CarrotAI","2024-05-23 04:25:06","2024-09-25 02:46:12+00:00","5c0e2c0180b50400e401dd0b296043f18fc6cb3f","107","2248","25","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.12244, region:us","text-generation","고품질 한국어 데이터셋 한국어로 이루어진 ���품질 한국어 데이터셋 입니다. WizardLM-2-8x22B 모델을 사용하여 WizardLM: Empowering Large Language Models to Follow Complex Instructions에서 소개된 방법으로 생성되었습니다. @article{koinstructiondatasetcard, titl","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2304.12244","None","None"
+"humane-lab/K-HATERS","humane-lab","2023-10-16 04:29:58","2023-12-14 09:25:04+00:00","67b979254f2f5874f3c0f649e0db8ab33d038e0d","437","2167","2","language:ko, license:cc-by-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.15439, region:us, hate speech detection","None","None","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"sentence-transformers/parallel-sentences-global-voices","sentence-transformers","2024-04-29 21:19:26","2024-06-18 19:45:13+00:00","4cc20add371f246bb1559b543f8b0dea178a1803","452","2088","0","task_categories:feature-extraction, task_categories:sentence-similarity, language:en, language:multilingual, language:ar, language:bg, language:ca, language:cs, language:da, language:de, language:el, language:es, language:fa, language:fr, language:he, language:hi, language:hu, language:id, language:it, language:ko, language:mk, language:my, language:nl, language:pl, language:pt, language:ro, language:ru, language:sq, language:sr, language:sv, language:tr, language:ur, 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 - Global Voices 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, fa, fr, he, hi, hu, id, it, ko, mk, my, nl, pl, pt, ro, ru, sq, sr, sv, tr, ur","multi","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"traintogpb/aihub-koen-translation-integrated-large-10m","traintogpb","2024-01-05 00:41:18","2024-01-05 04:17:04+00:00","0d6ddbfb116ef4207bec014c0075222bfe39d760","112","2074","16","task_categories:translation, language:en, language:ko, size_categories:10M<n<100M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","AI Hub Ko-En Translation Dataset (Integrated) AI Hub의 한-영 번역 관련 데이터셋 8개를 병합한 자료입니다. 병합 시 총 데이터 개수는 10,416,509개 이며, train / validation / test는 8:1:1 비율로 분할되었습니다. base-10m: 병합 데이터 100% 사용, 총 10,416,509개","None","en, ko","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","None","None","None","None"
+"openfoodfacts/product-database","openfoodfacts","2024-10-21 08:44:28","2025-01-28 18:08:04+00:00","931ed1eab923794ef47d97a2cc199da1bbc7d12e","966","2030","18","None","None","Open Food Facts Database What is 🍊 Open Food Facts? A food products database Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of informat","None","en, fr, de, es, it, nl, pl, pt, sv, bg, ro, fi, ru, nb, cs, th, da, hr, hu, ar, el, ja, ca, sr, sl, sk, tr, lt, zh, et, lv, xx, uk, id, he, vi, is, la, in, ko, sq, iw, ka, ms, bs, fa, bn, gl, kk, mk, nn, hi, aa, uz, so, af, eu","multi","1|M|<|n|<|1|0|M","None","False","False","False","agpl-3.0","None","None","None"
+"sil-ai/bloom-vist","sil-ai","2022-05-05 16:25:54","2022-12-06 19:37:53+00:00","3997b23648a3961e1e566d976ac9310aa2ac5e20","62","2017","5","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"
+"ziozzang/EverythingLM-data-V2-Ko","ziozzang","2023-08-23 06:53:09","2023-08-23 07:03:47+00:00","fd80802010fd902c0fdf0fb6152bf654f137194b","53","1993","10","language:ko, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Translated into Korean with DeepL All Texts are translated with DeepL. (Machine Translated.) Issue: some data items are missing, cause of DeepL plan and processing method. I use very cheap plan and al","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"voice-is-cool/voxtube","voice-is-cool","2024-01-11 20:08:17","2024-02-13 20:28:10+00:00","1dada8c7a0066a79d2b87b6b491daa777367c458","287","1968","11","task_categories:audio-classification, language:en, language:ru, language:es, language:pt, language:fr, language:ar, language:it, language:de, language:tr, language:nl, language:ko, license:cc-by-nc-sa-4.0, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","audio-classification","The VoxTube Dataset The VoxTube is a multilingual speaker recognition dataset collected from the CC BY 4.0 YouTube videos. It includes 5.040 speaker identities pronouncing ~4M utterances in 10+ langua","None","en, ru, es, pt, fr, ar, it, de, tr, nl, ko","multi","1|M|<|n|<|1|0|M","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"xhluca/publichealth-qa","xhluca","2024-05-16 23:03:30","2024-05-17 03:47:43+00:00","3b67b6b63ee464870fc21cdc888289c843204051","368","1954","0","None","question-answering","Usage import datasets langs = ['arabic', 'chinese', 'english', 'french', 'korean', 'russian', 'spanish', 'vietnamese'] data = datasets.load_dataset('xhluca/publichealth-qa', split='test', name=langs[0","None","ar, en, es, fr, ko, ru, vi, zh","multi","n|<|1|K","None","False","False","False","cc-by-nc-sa-3.0","None","None","None"
+"traintogpb/aihub-koen-translation-integrated-small-100k","traintogpb","2024-01-05 01:30:43","2024-01-05 04:16:44+00:00","768f5e72d1b3039971a7d58ab936c316d47d8d84","68","1905","3","task_categories:translation, language:en, language:ko, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","AI Hub Ko-En Translation Dataset (Integrated) AI Hub의 한-영 번역 관련 데이터셋 8개를 병합한 자료입니다. 병합 시 총 데이터 개수는 10,416,509개 이며, train / validation / test는 8:1:1 비율로 분할되었습니다. base-10m: 병합 데이터 100% 사용, 총 10,416,509개","None","en, ko","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"kyujinpy/KOR-OpenOrca-Platypus","kyujinpy","2023-10-09 14:23:30","2023-10-24 06:54:44+00:00","d6a31dbd55a7724a969dd9e1f15282fcbd3b62ff","81","1808","7","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","KOR-OpenOrca-Platypus OpenOrca-Ko + KOpen-platypus 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭 KOpen-platpyus Repo: KOpen-platypus 고품질 한���어 데이터셋 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정 1번과 더","None","ko","mono","1|0|K|<|n|<|5|0|K","None","False","False","False","cc-by-nc-4.0","2306.02707","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"
+"kuotient/orca-math-korean-dpo-pairs","kuotient","2024-04-05 09:55:21","2024-04-05 10:04:55+00:00","d2d04ff51a52c4aa74563982888279dfb4d5a317","75","1764","9","language:ko, 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","axolotl does not take revision arg as an option and i'm lazy so i made this. type: chatml.intel Orca-math-korean-preference question: orca-math dataset의 question chosen: label이 참일 경우 answer 혹은 generat","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Nicolas-BZRD/English_French_Songs_Lyrics_Translation_Original","Nicolas-BZRD","2023-09-12 21:21:44","2024-02-08 23:34:15+00:00","f2b64a358132305095e602ce11819d834344d90f","321","1755","11","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"
+"allganize/RAG-Evaluation-Dataset-KO","allganize","2024-05-13 10:56:45","2024-11-22 00:21:35+00:00","35db50ff8739d78b11ee68b6f4ef04a95862a504","242","1719","80","language:ko, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Allganize RAG Leaderboard Allganize RAG 리더보드는 5개 도메인(금융, 공공, 의료, 법률, 커머스)에 대해서 한국어 RAG의 성능을 평가합니다.일반적인 RAG는 간단한 질문에 대해서는 답변을 잘 하지만, 문서의 테이블과 이미지에 대한 질문은 답변을 잘 못합니다. RAG 도입을 원하는 수많은 기업들은 자사에 맞는 도메인, 문서","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"Unbabel/TowerBlocks-v0.1","Unbabel","2023-12-22 19:51:16","2024-03-04 13:17:50+00:00","372dc8d7378ee47013a9f4c21cd656cf516bb051","88","1717","27","None","conversational","Dataset Card for TowerBlocks TowerBlocks is the dataset used to train TowerInstruct-v0.1, a language model specialized for translation tasks such as machine translation (e.g. general, document, termin","None","en, de, fr, zh, pt, nl, ru, ko, it, es","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"blancsw/oasst2_top1_chat_format","blancsw","2024-01-02 08:48:25","2024-01-23 09:36:49+00:00","edf92a505b0f6afaa4c435dd96072c0bd6a16b37","50","1711","12","None","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"
+"Smoked-Salmon-s/empathetic_dialogues_ko","Smoked-Salmon-s","2023-07-25 05:13:43","2023-12-08 01:42:03+00:00","f62544137647af4c1d550e415fee7ad48eead615","53","1683","8","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, conversational","Dataset Card for ""한국어 일상 속 공감형 대화 데이터셋(멀티-턴)"" Dataset Summary boostCamp AI Tech 5기 과정 중 NLP 12조 훈제연어들 팀의 최종 프로젝트에서 제작한 데이터입니다. 일상 속 다양한 상황에서 사용자와 챗봇 간의 대화를 담은 데이터셋 입니다. GPT4, GPT3.5-turbo로 제작된 합성데이터이며","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"kuotient/orca-math-word-problems-193k-korean","kuotient","2024-03-27 04:06:47","2024-03-31 05:14:33+00:00","9d2edbed8d734d7ca6c5f7ee864d8d23686a14dc","47","1671","9","language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.14830, region:us","None","원본 데이터셋: https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k 번역 모델: Seagull-13b-translation 후처리 번역 repetition 오류 제거 LaTeX 오류 체크(전부는 아닐 수 있음. /(/) -> /(/ 같은 오류 등...) Citation @misc{m","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"nlpai-lab/openassistant-guanaco-ko","nlpai-lab","2023-06-01 06:54:34","2023-06-01 10:44:35+00:00","d1c4520a0fb31c086af415fc6a66fa1affa95a77","77","1622","8","task_categories:text-generation, task_categories:question-answering, task_categories:summarization, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, question-answering, summarization","Dataset Summary Korean translation of Guanaco via the DeepL API Note: There are cases where multilingual data has been converted to monolingual data during batch translation to Korean using the API. B","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"heegyu/hh-rlhf-ko","heegyu","2023-12-01 04:55:40","2023-12-24 14:35:56+00:00","b9e92fa5ef5a69910505709233ae5104038d0947","40","1568","3","language:ko, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Original Dataset: Anthropic/hh-rlhf Translation by using maywell/Synatra-7B-v0.3-Translation Translating in progress...","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"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"
+"squarelike/ko_medical_chat","squarelike","2023-08-18 18:24:58","2023-08-19 06:45:48+00:00","4e163691dce181131fefbc6c381edf61d38fae11","98","1542","17","language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, medical","None","https://github.com/jwj7140/ko-medical-chat Korean medical conversation dataset from converting MedText and ChatDoctor","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"nlp-with-deeplearning/Ko.SlimOrca","nlp-with-deeplearning","2023-11-30 09:57:45","2023-12-04 03:29:35+00:00","53437fed96158d97834b3e06ef43185b8f229b23","43","1518","3","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:en, language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","원본 데이터셋: Open-Orca/SlimOrca","None","en, ko","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"versae/bibles","versae","2022-03-02 23:29:22","2022-08-27 09:11:17+00:00","2fa72971a77203767aa622c7e782734ecc7622b8","199","1467","1","None","None","Multilingual Bibles","@InProceedings{--, author = {---}, title = {---}, booktitle = {---}, year = 2021, address = ""---"" }","sq, ar, az, be, bg, ceb, zh, cs, da, en, es, fi, fr, de, el, ht, he, hi, hu, it, ko, la, nl, no, pt, rm, ru, sw, ta, th, tr, vi","multi","None","None","False","False","False","None","None","None","None"
+"danielpark/mquad-v1","danielpark","2023-04-06 06:31:15","2023-04-07 12:21:48+00:00","49393e5301b7155407e0910b015cc7a635910242","62","1467","2","task_categories:question-answering, task_categories:text-generation, language:en, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, biology","question-answering, text-generation","MQuAD The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows. Qu","None","en, ko","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"HAERAE-HUB/qarv-instruct-ko","HAERAE-HUB","2024-05-12 08:13:16","2024-05-12 08:26:54+00:00","b033d8fa64138899ff7bd76be5481fcbf0914dbb","133","1467","20","task_categories:text-generation, 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","text-generation","QARV-Instruct-KO This subset of our dataset contains pairs of instructions and answers requiring knowledge of Korea.Unlike past works relying heavily on proprietary LLMs for data generation, we use a ","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"CohereForAI/include-lite-44","CohereForAI","2024-12-02 11:03:07","2024-12-11 11:25:21+00:00","cc383a1dd1d074088776be221816dd232517562a","719","1437","9","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"
+"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"
+"eaglewatch/Korean_Wikipedia_Dataset_for_GPT2_August_2022","eaglewatch","2023-08-25 05:30:30","2024-06-03 06:22:10+00:00","46d2589714cbda34e9fd6368c43c350025467c40","57","1400","6","task_categories:question-answering, task_categories:text2text-generation, task_categories:translation, task_categories:visual-question-answering, task_ids:open-domain-qa, task_ids:closed-domain-qa, task_ids:dialogue-generation, task_ids:visual-question-answering, annotations_creators:other, language_creators:other, multilinguality:multilingual, language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, gpt2, korean, wikipedia, pertained","question-answering, text2text-generation, translation, visual-question-answering, open-domain-qa, closed-domain-qa, closed-domain-qa, dialogue-generation, visual-question-answering","Dataset Card for korean_wikipedia_dataset_for_GPT2 Dataset Description Entire Korean language Wikipedia data for GPT-2 training as of August 1st, 2022. email: oscar.eaglewatch@gmail.com Dataset Summar","None","ko","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","None","None","open-domain-qa, closed-domain-qa, closed-domain-qa, dialogue-generation, visual-question-answering"
+"taeminlee/Ko-mrtydi","taeminlee","2024-01-19 10:08:45","2024-01-19 10:16:40+00:00","71a2e011a42823051a2b4eb303a3366bdbe048d3","54","1393","0","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, source_datasets:mrtydi, language:ko, 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","Ko-mrtydi This dataset represents a conversion of the Korean (Ko) section from the Mr.TyDI dataset into the BeIR format, making it compatible for use with mteb.","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","document-retrieval"
+"floschne/xgqa","floschne","2024-05-16 14:17:31","2024-05-23 14:13:12+00:00","b8a124018a849e6957c9de0db7a523bf6561536a","108","1382","0","None","visual-question-answering","xGQA This is a clone of the few_shot-test split of the xGQA dataset Please find the original repository here: https://github.com/adapter-hub/xGQA If you use this dataset, please cite the original auth","None","bn, de, en, id, ko, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"lcw99/wikipedia-korean-20240501","lcw99","2024-05-05 08:50:30","2024-05-05 11:56:56+00:00","8e9d21864e0dbe3e31fe8e82699477db38393788","111","1382","15","language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","wikipedia Korean 2024.5.1 cut","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Qwen/P-MMEval","Qwen","2024-11-13 06:12:25","2024-11-28 06:19:41+00:00","47bb647f35fdd6f5374826b3f5d4f84eb5b5afce","417","1381","7","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"
+"amphora/korfin-asc","amphora","2023-01-16 14:53:48","2023-01-16 15:26:46+00:00","07cc4a29341ef26e8614ae1139847f4d4888727d","93","1290","3","task_categories:text-classification, task_ids:topic-classification, task_ids:sentiment-classification, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:klue, language:ko, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2301.03136, region:us, sentiment analysis, aspect based sentiment analysis, finance","text-classification, topic-classification, sentiment-classification","Dataset Card for KorFin-ABSA Dataset Description Dataset Summary The KorFin-ASC is an extension of KorFin-ABSA including 8818 samples with (aspect, polarity) pairs annotated. The samples were collecte","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","2301.03136","None","topic-classification, sentiment-classification"
+"jp1924/KoreaSpeech","jp1924","2024-03-26 15:23:26","2024-06-14 06:07:34+00:00","32d8f6b4be827bfc82af58b8ce583684eb967651","140","1287","6","task_categories:automatic-speech-recognition, language:ko, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, STT, Audio","automatic-speech-recognition","None","None","ko","mono","1|0|0|B|<|n|<|1|T","None","False","auto","False","None","None","None","None"
+"ZurichNLP/rsd-ists-2016","ZurichNLP","2023-05-20 16:24:04","2023-11-22 12:35:55+00:00","e0c1dd23ce0d62f55392184515ac55975625b37b","143","1267","0","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"
+"Bingsu/KcBERT_Pre-Training_Corpus","Bingsu","2022-07-13 06:18:42","2022-07-13 07:26:02+00:00","183fa71f5416ad2ab1b50b6be69769ad1508581a","111","1193","1","task_categories:fill-mask, task_categories:text-generation, task_ids:masked-language-modeling, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:ko, 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","fill-mask, text-generation, masked-language-modeling, language-modeling","KcBERT Pre-Training Corpus (Korean News Comments) KcBERT beomi/kcbert-base Github KcBERT Repo: https://github.com/Beomi/KcBERTKcBERT is Korean Comments BERT pretrained on this Corpus set.(You can use ","None","ko","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-sa-4.0","None","None","masked-language-modeling, language-modeling"
+"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"
+"kyujinpy/KOR-OpenOrca-Platypus-v2","kyujinpy","2023-10-25 02:50:26","2023-10-25 11:50:48+00:00","a5f9b4ece06afb1e1eff8ef950455b4794d3778b","70","1186","5","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us","conversational, text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","KOR-OpenOrca-Platypus-v2 KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋. 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭 KOpen-platpyus Repo: KOpen-platypus 고품질 한국어 데이터셋 코드와 주","None","ko","mono","1|0|K|<|n|<|5|0|K","None","False","False","False","cc-by-nc-4.0","2306.02707","None","None"
+"LDCC/korag","LDCC","2024-09-08 14:11:26","2024-11-13 11:18:03+00:00","f69d65face57630d3f784f35d37e8174bcd8fc97","141","1165","6","task_categories:question-answering, task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering, text-generation","한국어 검색증강 데이터셋 1. 데이터셋 개요 이 데이터셋은 한국어 검색 증강을 목적으로, 원본 PDF 파일에서 질의에 맞는 문서를 검색하고 이를 기반으로 응답을 생성하는 질의응답 데이터셋입니다. 문서는 백과, 법률, 의학, 교육, 행정의 5개 범주로 구성되며, 지식재산권 및 저작권을 고려하여 공공 문서를 바탕으로 제작되었습니다. 2. 데이터 원천 분야별 데","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"channelcorp/KoMagpie-raw","channelcorp","2024-09-09 08:59:55","2024-09-23 06:14:10+00:00","62198fb91661d81719d8524dec2c64911757f67b","155","1156","26","language:ko, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","KoMagpie-raw-preview 🐦 This is the first, preview version of 🐦 KoMagpie dataset. How to use >>> from datasets import load_dataset >>> ds = load_dataset(""channelcorp/komagpie-raw-preview"", split=""train","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jp1924/KconfSpeech","jp1924","2024-03-24 08:28:43","2024-06-14 06:06:17+00:00","ce86bd94845e428dd200823e90d47a68c1303ef5","111","1144","2","task_categories:automatic-speech-recognition, language:ko, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, STT, Audio","automatic-speech-recognition","None","None","ko","mono","1|0|0|B|<|n|<|1|T","None","False","auto","False","None","None","None","None"
+"recursal/MDN","recursal","2024-05-30 06:50:40","2024-06-13 01:18:19+00:00","8fc26a9528158cb87691d8c4776b31a2b9dfaef0","70","1142","0","None","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"
+"jihye-moon/LawQA-Ko","jihye-moon","2023-10-19 07:30:09","2025-01-13 23:11:10+00:00","6319b9aa1b7d20d32dff206f01ef95643824672a","40","1127","11","task_categories:text-generation, task_categories:question-answering, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","text-generation, question-answering","Dataset Description 법률에 대한 질문과 답변으로 구성된 데이터셋 입니다. 아래의 데이터셋에서 질문과 답변을 병합하여 Datasets를 만들었습니다. 정보 출처 Dataset Page Rows 찾기쉬운생활법령정보 백문백답 jiwoochris/easylaw_kr 2,195 rows 대한법률구조공단 법률상담사례 jihye-moon/klac_leg","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"Bingsu/namuwiki_20210301_filtered","Bingsu","2022-07-14 02:18:12","2022-10-14 07:49:53+00:00","bb6b2ea9bac5837836d38dc524d0b987d2a1fc0f","85","1099","6","task_categories:fill-mask, task_categories:text-generation, task_ids:masked-language-modeling, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-nc-sa-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","fill-mask, text-generation, masked-language-modeling, language-modeling","Namuwiki database dump (2021-03-01) Namuwiki https://namu.wiki/ It is a Korean wiki based on the seed engine, established on April 17, 2015 (KST). About dataset All data from Namuwiki collected on 202","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-2.0","None","None","masked-language-modeling, language-modeling"
+"allenai/pixmo-cap-qa","allenai","2024-11-27 16:23:42","2024-12-05 18:49:29+00:00","fd3ff4b2905455ab5edfd6d06039ea6027948521","267","1081","5","task_categories:visual-question-answering, language:en, language:ko, license:odc-by, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","PixMo-CapQA PixMo-CapQA is a synthetic dataset of question/answer pairs about images. The data was generated by using the Claude large language model to build Q/A pairs from dense captions of images (","None","en, ko","bi","None","None","False","False","False","odc-by","None","None","None"
+"jp1924/MeetingSpeech","jp1924","2024-04-11 11:22:12","2024-11-06 01:05:39+00:00","ca642cdd624f1addf33260337691665f0bc9fa95","37","1026","1","task_categories:automatic-speech-recognition, language:ko, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition","None","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"nlpai-lab/ko-triplet-v1.0","nlpai-lab","2024-09-05 07:21:30","2024-11-29 08:00:37+00:00","1f5d72d21ae8309b5221a588b13930b423385bff","207","1019","18","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for nlpai-lab/ko-triplet-v1.0 Dataset Statistics Split # Examples Size (bytes) Train 744,862 628,315,763 Dataset Structure Train Sample query document hard_negative 데이터 사전 캐시 방법을 적용하면 어떻게","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jp1924/KrespSpeech","jp1924","2024-03-23 07:20:06","2024-06-14 06:07:48+00:00","8b7adceb882e653c2d83817f5339027e1fdf81e9","106","1019","2","task_categories:automatic-speech-recognition, language:ko, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, STT, Audio","automatic-speech-recognition","None","None","ko","mono","1|0|0|B|<|n|<|1|T","None","False","auto","False","None","None","None","None"
+"allganize/rag-ko","allganize","2024-03-25 14:47:53","2024-04-02 04:51:46+00:00","a1507c010cfb0eac4b26dc7445789f5ae266835d","133","1011","11","language:ko, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","rag-ko rag-ko 데이터는 금융 도메인의 RAG(Retrieval Augmented Generation, 검색증강생성) 데이터셋입니다. RAG를 진행할 수 있도록 Golden Context 1개와 Negative Context 2개가 제공되고 Golen Context에 관련된 질문과 그 답변이 주어집니다. 데이터의 컨텍스트는 위키피디아와 공공기관의 ","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Genius1237/TyDiP","Genius1237","2022-11-11 01:08:56","2023-10-15 05:14:26+00:00","dfc63068215c270ac0f6702228fd80ea2ae170a5","295","1009","0","task_categories:text-classification, annotations_creators:crowdsourced, language_creators:found, multilinguality:multilingual, language:en, language:hi, language:ko, language:es, language:ta, language:fr, language:vi, language:ru, language:af, language:hu, license:cc-by-4.0, size_categories:1K<n<10K, modality:text, library:datasets, library:mlcroissant, region:us, politeness, wikipedia, multilingual","text-classification","The TyDiP dataset is a dataset of requests in conversations between wikipedia editors that have been annotated for politeness. The splits available below consists of only requests from the top 25 perc","@inproceedings{srinivasan-choi-2022-tydip, title = ""{T}y{D}i{P}: A Dataset for Politeness Classification in Nine Typologically Diverse Languages"", author = ""Srinivasan, Anirudh and Choi, Eunsol"", booktitle = ""Findings of the Association for Computational Linguistics: EMNLP 2022"", month = dec, year = ""2022"", address = ""Abu Dhabi, United Arab Emirates"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2022.findings-emnlp.420"", pages = ""5723--5738"", }","en, hi, ko, es, ta, fr, vi, ru, af, hu","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"psymon/namuwiki_alpaca_dataset","psymon","2023-06-29 07:18:44","2023-06-29 07:29:01+00:00","4c2c4732232b31713b4fe87b3bcf527590f8bf06","76","992","10","language:ko, license:cc-by-nc-sa-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","namuwiki for Stanford Alpaca 나무위키 덤프 파일을 Stanford Alpaca 학습에 맞게 수정한 데이터셋입니다. 데이터 형식은 Stanford Alpaca와 동일합니다. instruction은 '나무위키 문서 제목' + '에 대해 설명해줘.' 형태이고, output은 문서 == 개요 == 에 해당하는 내용입니다. 개요가 없는 항목,","None","ko","mono","None","None","False","False","False","cc-by-nc-sa-2.0","None","None","None"
+"causal-lm/instructions-ko","causal-lm","2023-07-02 06:42:03","2024-05-14 01:01:01+00:00","649b79381e9c94c0403d50030d6378f27d0b27ac","63","947","3","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""instructions-ko"" More Information needed","None","k, o","bi","None","None","False","False","False","None","None","None","None"
+"Bingsu/laion2B-multi-korean-subset","Bingsu","2022-08-03 06:57:55","2022-10-14 05:23:17+00:00","162574e34bf5cd64881b2689909f43b0aa971a0b","169","933","13","task_categories:feature-extraction, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, language:ko, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:image, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","feature-extraction","laion2B-multi-korean-subset About dataset a subset data of laion/laion2B-multi, including only korean Lisence CC-BY-4.0 Data Structure Data Instance >>> from datasets import load_dataset >>> dataset =","None","ko","mono","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"allganize/financial-mmlu-ko","allganize","2024-03-28 22:25:24","2024-04-02 04:50:38+00:00","2b827871962e2f8dd3efb118d105f04c597f6f03","53","930","6","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","financial-mmlu-ko financial-mmlu-ko 데이터는 금융 도메인의 다중 선택(Multiple Choice) 데이터셋입니다. 질문과 선택지가 주어졌을 때, 답을 찾는 객관식 문제입니다. 입력값은 text이며, 아래 Fewshot 예시의 텍스트를 system prompt 혹은 context로 함께 제공할 수 있습니다. 한국어 데이터를 생성","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Songweii/M3GIA","Songweii","2024-06-06 09:55:12","2024-06-27 08:02:29+00:00","07415814e27a715acf57b2b157fba88b79599d15","76","913","2","None","None","M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability [🌐 Homepage] | 🤗 Dataset | 🤗 Paper | 📖 arXiv | 💻 GitHub The evaluation code can be found in 💻 GitHub. [Abstract] As","None","en, zh, es, fr, pt, ko","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2406.05343","None","None"
+"AmazonScience/tydi-as2","AmazonScience","2023-05-16 00:02:00","2023-07-24 17:33:28+00:00","39481652408123c412b400f50b2eef530d2da1e8","191","906","1","None","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"
+"imvladikon/QAmeleon","imvladikon","2023-08-13 19:29:03","2023-08-13 19:36:48+00:00","bf35de1f9cc3db3938be5bca8130c492bbd60757","86","896","1","task_categories:question-answering, language:ar, language:bn, language:fi, language:id, language:ko, language:ru, language:sw, language:te, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2211.08264, region:us","question-answering","Dataset Card for ""QAmeleon"" QAmeleon introduces synthetic multilingual QA data contaning in 8 langauges using PaLM-540B, a large language model. This dataset was generated by prompt tuning PaLM with o","None","ar, bn, fi, id, ko, ru, sw, te","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","2211.08264","None","None"
+"jp1924/OutsideKnowledgebasedMultimodalQAData","jp1924","2024-03-21 04:33:40","2024-06-14 06:08:30+00:00","406ce38393fc69df4e91485b55651cba76caf345","48","891","1","task_categories:visual-question-answering, language:ko, language:en, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, Caption, Image","visual-question-answering","None","None","ko, en","bi","1|0|B|<|n|<|1|0|0|B","None","False","auto","False","None","None","None","None"
+"jiwoochris/easylaw_kr","jiwoochris","2023-07-04 08:33:38","2023-07-06 13:36:05+00:00","80aeea54fcb7b87d1762710177f3eeb1240523a3","43","884","5","language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","conversational","None","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","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"
+"CertifiedJoon/Korean-Instruction","CertifiedJoon","2023-06-07 15:05:39","2023-07-06 17:44:53+00:00","3be08acaa7a7386b8e022d5413a3aabf4a47dea1","53","872","5","task_categories:question-answering, language:ko, license:cdla-permissive-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for Dataset Name Dataset Summary The most active korean qna site - Knowledge In Naver. Instruction + response format. Created for language model. Dataset Structure [Instruction, Response,","None","ko","mono","n|<|1|K","None","False","False","False","cdla-permissive-2.0","None","None","None"
+"traintogpb/aihub-flores-koen-integrated-prime-small-30k","traintogpb","2024-03-08 09:49:34","2024-05-23 02:13:15+00:00","d8a4b69cbb5cd45c4ddbe34e34ba2b472290bb92","61","863","8","task_categories:translation, language:en, language:ko, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","High Quality Ko-En Translation Dataset (AIHub-FLoRes Integrated) AI Hub의 한-영 번역 데이터셋과 FLoRes 한-영 번역 데이터셋의 합본입니다. High Quality AIHub Dataset AI Hub의 경우 한-영 번역 관련 데이터셋을 8개 병합한 병렬 데이터 traintogpb/aihub-ko","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","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","None","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"
+"lemon-mint/korean_english_parallel_wiki_augmented_v1","lemon-mint","2024-07-17 00:20:25","2024-07-17 03:13:40+00:00","3a1b788b1b1acb9922a03d94ac5127d4bd01741d","197","855","9","task_categories:translation, task_categories:text2text-generation, language:ko, language:en, 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, sentence-transformers, translation, korean, english, parallel, embedding, distill, distillation","translation, text2text-generation","Korean-English Parallel Wiki Augmented v1 This dataset contains a large number of Korean-English parallel sentences extracted from Wikipedia. It was created by augmenting the original English Wikipedi","None","ko, en","bi","None","None","False","False","False","cc-by-sa-3.0","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"
+"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"
+"Ash-Hun/Welfare-QA","Ash-Hun","2024-01-25 15:02:32","2024-01-25 15:55:16+00:00","7d48965bc1421f07f3a663f0894820d7765b6ead","57","817","3","task_categories:question-answering, language:ko, license:mit, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Ask-for-Welfare, WelSSiSKo","question-answering","Dataset Card for Welfare-QA Description 대한민국 보건복지부에서 발간하였으며 2023년 5월 11일에 복지로에 등록된 안내책자를 바탕으로 만들어졌습니다. 총 413페이지의 비정형 PDF에 담긴 약 460여개의 복지제도에 대한 Question-Answering-Documents 데이터셋입니다. 원본은 다음 링크에서 확인해보실 수","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"dbdu/ShareGPT-74k-ko","dbdu","2023-05-23 16:30:43","2023-08-19 07:00:39+00:00","5a76fbb20808d7c53ee64e2d467e9983fbd740b2","87","802","15","task_categories:text-generation, language:ko, license:cc-by-2.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us, conversation, chatgpt, gpt-3.5","text-generation","ShareGPT-ko-74k ShareGPT 90k의 cleaned 버전을 구글 번역기를 이용하여 번역하였습니다.원본 데이터셋은 여기에서 확인하실 수 있습니다. Korean-translated version of ShareGPT-90k, translated by Google Translaton.You can check the original dataset ","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-2.0","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"
+"luizapzbn/from-one-to-many-toxicity-mitigation","luizapzbn","2024-05-23 18:52:15","2024-05-24 17:09:53+00:00","453bd0857c4345340a57c7b5928a55ceb35f8545","160","792","0","task_categories:text-generation, task_categories:text-classification, language:en, language:pt, language:hi, language:it, language:fr, language:ru, language:ar, language:ko, language:es, license:apache-2.0, modality:text, arxiv:2403.03893, arxiv:2205.09209, region:us, harmful, toxic","text-generation, text-classification","From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models [arxiv][code][data] Data accompanying the paper ""From One to Many: Expanding the Scope of Toxicity Mitigation in Languag","None","en, pt, hi, it, fr, ru, ar, ko, es","multi","None","None","False","False","False","apache-2.0","2205.09209","None","None"
+"zhihz0535/X-AlpacaEval","zhihz0535","2024-01-27 20:48:46","2024-01-27 21:18:19+00:00","4dc0165d07b6dbf2d6c011a35ade071ac5ae0dc4","78","788","4","None","text-generation, conversational","X-AlpacaEval 🤗 Paper | 📖 arXiv Dataset Description X-AlpacaEval is an evaluation benchmark for multilingual instruction-tuned large language models (LLMs), including open-ended instructions in 5 langu","None","en, zh, ko, it, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-4.0","2311.08711","None","None"
+"hac541309/pg-ko-tknizer-ko","hac541309","2023-06-05 22:58:01","2023-07-13 04:45:01+00:00","83cf99924965a1a10a0d7cefd02a1d2ae257f7b6","125","778","0","language:ko, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""pg-ko-tknizer-ko"" More Information needed","None","k, o","bi","None","None","False","False","False","None","None","None","None"
+"blueapple8259/c4-ko-cleaned-2","blueapple8259","2024-07-07 04:05:39","2024-07-07 04:17:31+00:00","9ace056fba5383336184a242f51fdab2e536db9c","110","778","2","task_categories:text-generation, language:ko, license:odc-by, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","이전 데이터셋에서 아쉬운 점이 많이 보여 조금 개선한 데이터셋 입니다. 원본 데이터셋: c4 파일 크기: 약 10gb 데이터 수: 2261464","None","ko","mono","None","None","False","False","False","odc-by","None","None","None"
+"KETI-AIR/kor_squad_v2","KETI-AIR","2023-11-08 07:28:12","2023-11-15 01:19:19+00:00","56cb08b68db6d1cee603f8e504ef9ecd3d096293","145","775","1","task_categories:question-answering, task_ids:open-domain-qa, task_ids:extractive-qa, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1606.05250, region:us","question-answering, open-domain-qa, extractive-qa","Dataset Card for squad_v2 Licensing Information The data is distributed under the CC BY SA 4.0 license. Source Data Citation Information @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","open-domain-qa, extractive-qa"
+"allganize/en-fpb-ko","allganize","2024-03-25 15:35:02","2024-04-02 04:37:01+00:00","543e8ccc3c0543184daaa43882bcfd431c35baaf","37","759","9","language:ko, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","en-fpb-ko 데이터 설명 en-fpb-ko 데이터는 금융 뉴스로부터의 문장을 '긍정', '중립', '부정' 중 하나로 분류하는 감성 분류 (sentiment analysis) 데이터셋입니다. 입력값으로는 text만 주어집니다. 한국어 데이터를 생성하기 위해, 먼저 사내 언어 번역 모델인 Allganize Translator를 활용하여 ChanceFoc","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"ohilikeit/empathetic_dialogues_mutli_turn_ko","ohilikeit","2023-07-05 15:01:22","2023-08-04 02:59:46+00:00","9847cd2c7d7634c9523d36cd9c1d377ea354eac9","61","755","7","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for ""한국어 일상 속 공감형 대화 데이터셋(멀티-턴)"" Dataset Summary boostCamp AI Tech 5기 과정 중 NLP 12조 훈제연어들 팀의 최종 프로젝트에서 제작한 데이터입니다. 일상 속 다양한 상황에서 사용자와 챗봇 간의 대화를 담은 데이터셋 입니다. GPT4, GPT3.5-turbo로 제작된 합성데이터이며","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"lemon-mint/korean_parallel_sentences_v1.1","lemon-mint","2024-05-07 01:32:46","2024-06-21 00:47:56+00:00","c3ffa5bfe5bf0cd5b4d634e863978b5eb265c9e1","72","753","10","task_categories:translation, task_categories:text2text-generation, language:ko, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sentence-transformers, translation, korean, english, parallel, embedding, distill, distillation","translation, text2text-generation","Dataset Card for Korean Parallel Sentences Ver 1.1 This dataset card provides information about the Korean Parallel Sentences Ver 1.1 dataset. Dataset Details Dataset Description The Korean Parallel S","None","ko, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"psyche/korean_idioms","psyche","2022-09-16 11:31:37","2024-06-05 00:39:55+00:00","a7a69be0cf880c980b2430999d2d68ba5caab1fd","97","744","4","task_categories:text-classification, annotations_creators:machine-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","NLI를 위한 한국어 속담 데이터셋입니다. 'question'은 속담의 의미와 보기(5지선다)가 표시되어 있으며, 'label'에는 정답의 번호(0-4)가 표시되어 있습니다. licence: cc-by-sa-2.0-kr (원본 출처:국립국어원 표준국어대사전) Model psyche/korean_idioms klue/bert-base 0.7646","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"Gunulhona/llm_datasets","Gunulhona","2024-02-19 01:27:19","2024-02-21 06:51:51+00:00","2a235f691e5def50cbbc3d444a6b38bc2ddc8373","210","743","0","task_categories:text-generation, language:ko, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","None","None","ko","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","mit","None","None","None"
+"heegyu/OIG-small-chip2-ko","heegyu","2023-04-19 13:12:25","2023-04-19 13:25:41+00:00","c2e1b8791afbb3d8314dcb390ad53d93eed78a89","115","741","9","language:ko, 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","None","Dataset Card for ""OIG-small-chip2-ko"" 210282 items Original Dataset: OIG-small-chip2 dataset from https://laion.ai/blog/oig-dataset/ Translated by Google Translate API example { ""user"": ""Is there a go","None","ko, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"FBK-MT/Speech-MASSIVE-test","FBK-MT","2024-06-25 22:35:04","2024-08-08 07:13:45+00:00","289765c4538a4d59dc436ad2bf31050460cc136c","238","736","5","task_categories:audio-classification, task_categories:text-classification, task_categories:zero-shot-classification, task_categories:automatic-speech-recognition, annotations_creators:crowdsourced, language_creators:found, multilinguality:multilingual, source_datasets:extended, language:ar, language:de, language:es, language:fr, language:hu, language:ko, language:nl, language:pl, language:pt, language:ru, language:tr, language:vi, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2408.03900, region:us, spoken language understanding, slot filling, intent classification, speech translation, speaker identification","audio-classification, text-classification, zero-shot-classification, automatic-speech-recognition","Speech-MASSIVE Test Split This dataset repository is only for test split of Speech-MASSIVE. train and dev splits are available in the separate dataset repository. https://huggingface.co/datasets/FBK-M","None","ar, de, es, fr, hu, ko, nl, pl, pt, ru, tr, vi","multi","1|0|K|<|n|<|1|0|0|K","None","False","auto","False","cc-by-nc-sa-4.0","2408.03900","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"
+"kuotient/orca-math-korean-preference","kuotient","2024-03-28 15:21:53","2024-04-10 08:16:05+00:00","c612bdf84d65b5258a96593964812bb3b3aa4ddf","87","722","10","language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Orca-math-korean-preference llm: claude-haiku와 gpt3.5로 평가된 generated(Student) answer의 정답 여부 question: orca-math dataset의 question answer: orca-math dataset의 answer generated: EEVE-Math-10.8B(M1)의 출력 l","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Cohere/wikipedia-22-12-ko-embeddings","Cohere","2023-01-13 23:51:11","2023-03-22 16:55:35+00:00","caf814d284f0c7cdf873c1d8d091a3d3b7d9e6db","61","705","6","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:multilingual, language:ko, 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 (ko) embedded with cohere.ai multilingual-22-12 encoder We encoded Wikipedia (ko) using the cohere.ai multilingual-22-12 embedding model. To get an overview how this dataset was created and ","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"kyujinpy/KoCommercial-NoSSL","kyujinpy","2024-03-12 07:14:28","2024-03-12 07:18:04+00:00","eabc11af74c5a475844832bcb13f8009f14b0140","47","705","0","language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset for kyujinpy/KoCommercial-NoSSL Info Dataset 개수: 약 175K License: CC-BY-NC-4.0 (*통합에 활용한 각 데이터셋은 모두 상업적 용도로 사용가능.) Dataset list(전부 상업적 용도로 이용가능) kyujinpy/KOpen-platypus (*Except non-commercial ","None","ko","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"jp1924/VisualQuestionAnswering","jp1924","2024-03-21 05:35:14","2024-06-14 06:08:46+00:00","ab8195859ef7dc7352875a8ab7ff3674f6642d05","37","703","4","task_categories:visual-question-answering, language:ko, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, Caption, Image","visual-question-answering","None","None","ko","mono","1|0|B|<|n|<|1|0|0|B","None","False","auto","False","None","None","None","None"
+"davidkim205/kollm-comparision","davidkim205","2024-03-18 06:25:45","2024-03-21 02:12:15+00:00","937fed55798b956669041b04fd693bef829d53b7","43","702","12","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","davidkim205/kollm-comparision nox-solar-10.7b-v4에 사용된 dpo 데이터셋으로 huggingface에 공개된 데이터와 twodigit에서 제작한 데이터로 구성되어 있습니다. nox github에서 사용가능하도록 comparision 형식으로 되어 있습니다. 공개 데이터셋 Source 설명 원본 URL kobest_boo","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"nebchi/koen-text","nebchi","2024-07-01 00:52:22","2024-07-01 00:58:32+00:00","0a759d46995e2eb756a48425d3b4bfa632c18b37","49","700","0","language:ko, language:en, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Source Created with reference to the following datasets. nampdn-ai/mini-fineweb HAERAE-HUB/KOREAN-WEBTEXT [AI Hub Datasets]","None","ko, en","bi","None","None","False","False","False","None","None","None","None"
+"maximoss/lingnli-multi-mt","maximoss","2023-03-25 12:06:26","2024-05-18 17:26:34+00:00","cf78d295ef80e34d88d7a28ab2f426cbef4de3af","241","696","1","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, language:el, language:fr, language:it, language:es, language:pt, language:ko, language:fi, language:lt, language:bg, license:bsd-2-clause, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-classification, natural-language-inference, multi-input-text-classification","Dataset Card for Dataset Name Dataset Summary This repository contains a collection of machine translations of LingNLI dataset into 9 different languages (Bulgarian, Finnish, French, Greek, Italian, K","None","el, fr, it, es, pt, ko, fi, lt, bg","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","bsd-2-clause","None","None","natural-language-inference, multi-input-text-classification"
+"hac541309/basic_korean_dict","hac541309","2023-07-26 12:13:04","2023-07-26 12:28:43+00:00","1ba29964518a8e859e5898cdeacfc46ff8215a60","51","691","4","None","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","None","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"
+"shreyanshu09/BD-EnKo","shreyanshu09","2024-05-24 04:49:11","2024-06-05 10:43:30+00:00","f4bc53a85e32cb71603083a6794dc38f9d2631e5","123","679","2","language:en, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, block diagrams","None","BD-EnKo Dataset It was introduced in the paper ""Unveiling the Power of Integration: Block Diagram Summarization through Local-Global Fusion"" accepted at ACL 2024. The full code is available in BD-EnKo","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"maywell/kiqu_samples","maywell","2024-02-23 09:01:05","2024-03-05 09:03:30+00:00","34e6a990462e4608440a3588fcb3bc7d7c313480","57","662","4","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","본 데이터셋은 kiqu-70b 모델의 출력 샘플입니다. Sionic AI에서 GPU 자원을 지원받아 제작되었습니다.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Bingsu/laion-translated-to-en-korean-subset","Bingsu","2022-12-01 01:58:31","2023-02-01 01:15:43+00:00","cf370843eae0cac330f1ed47534c1f29a74da542","86","661","6","task_categories:feature-extraction, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:multilingual, language:ko, language:en, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","feature-extraction","laion-translated-to-en-korean-subset About dataset a subset data of laion/laion2B-multi-joined-translated-to-en and laion/laion1B-nolang-joined-translated-to-en, including only korean Lisence CC-BY-4.","None","ko, en","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"Bingsu/arcalive_220506","Bingsu","2022-05-07 03:40:31","2022-07-02 00:09:48+00:00","e96165af1c82b5dd47b286d196f6ad6ab03ed3ff","59","656","2","task_categories:fill-mask, task_categories:text-generation, task_ids:masked-language-modeling, task_ids:language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:ko, license:cc0-1.0, size_categories:100K<n<1M, modality:text, library:datasets, library:mlcroissant, region:us","fill-mask, text-generation, masked-language-modeling, language-modeling","[아카라이브 베스트 라이브 채널](https://arca.live/b/live)의 2021년 8월 16일부터 2022년 5월 6일까지의 데이터를 수집하여, 댓글만 골라낸 데이터입니다.","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc0-1.0","None","None","masked-language-modeling, language-modeling"
+"shreyanshu09/Block_Diagram","shreyanshu09","2024-05-24 05:56:40","2024-06-05 01:55:41+00:00","7add611f9b0c258060d07a25a3fb202ee5c8f89a","70","652","10","language:en, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, block diagram","None","Block Diagram Dataset This dataset is a combination of four block diagram datasets. The first dataset is the BD-EnKo dataset, which was introduced in the paper ""Unveiling the Power of Integration: Blo","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","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"
+"heegyu/namuwiki-sentences","heegyu","2022-10-01 04:48:22","2022-10-14 07:55:44+00:00","13a03baacde282bc1573bee2963ea0ca677286d3","62","644","1","task_categories:other, language_creators:other, multilinguality:monolingual, language:ko, license:cc-by-nc-sa-2.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","other","38,015,081 rows","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-2.0","None","None","None"
+"huawei-noah/entity_cs","huawei-noah","2023-09-08 08:44:07","2023-09-20 07:05:07+00:00","32c89f7c52f16e46115d9c31e6000ddb081b3de3","137","644","2","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"
+"werty1248/EnKo-Translation-LongTextOnly-dedup","werty1248","2024-07-02 03:01:24","2024-07-02 03:24:38+00:00","7070072c5031104355b8c203ce00dcf369cd5071","331","643","6","task_categories:translation, language:ko, language:en, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","translation","장문 번역 데이터만 추출 gemma 토크나이저 기준으로 영문+한글 토큰 합이 1K 이상인 데이터만 추출 데이터 수 1K~2K: 146,957 2K~4K: 11,823 4K~: 2,229 한/영 둘 중 한쪽만 중복인 경우는 제거하지 않았습니다. 데이터 출처 nayohan/aihub-en-ko-translation-12m nayohan/instruction_e","None","ko, en","bi","None","None","False","False","False","None","None","None","None"
+"lmqg/qag_koquad","lmqg","2022-12-18 07:05:17","2022-12-18 08:03:53+00:00","cc90041343272411122d092d816a2eabb9f8d9d1","55","638","3","task_categories:text-generation, task_ids:language-modeling, multilinguality:monolingual, source_datasets:lmqg/qg_koquad, language:ko, 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"", }","k, o","bi","1|||k|||<|||n|||<|||1|||0|||K","None","False","False","False","cc-by-sa-4.0","2210.03992","None","language-modeling"
+"cfpark00/KoreanSAT","cfpark00","2024-10-23 18:04:35","2024-11-21 15:50:01+00:00","0e9b384ada26b943afe41e640ac88203a4470aeb","261","627","2","task_categories:question-answering, language:ko, language:en, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math, QA, Korean, Exam","question-answering","KoreanSAT Benchmark Current Topics over years Math o1-preview Year 2022 2023 2024 2025 General 62 50 62 54 Probability & Statistics 22 20 26 22 Calculus 18 18 15 19 Geometry 15 22 18 19 General+Prob. ","None","ko, en","bi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"jgermanmx/multimuc4","jgermanmx","2024-07-19 11:48:11","2024-09-25 12:39:54+00:00","567a397763335e31d67a9b41cc2935c8cef6dd81","48","626","0","task_categories:text-classification, language:ar, language:en, language:fa, language:ko, language:ru, language:zh, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","None","None","ar, en, fa, ko, ru, zh","multi","None","None","False","False","False","mit","None","None","None"
+"jp1924/DataTableInfoQA","jp1924","2024-05-27 01:34:27","2024-08-05 14:15:56+00:00","33ba59929b228b1c54feb6f36bc41ec36f764104","36","621","2","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"caro-holt/MultiQ","caro-holt","2024-03-02 14:16:05","2024-03-02 15:22:59+00:00","589f984cc6c3b973e561aca36eba9fd6dd448c9e","37","618","6","task_categories:question-answering, language:tl, language:sm, language:mk, language:gu, language:fi, language:mn, language:bm, language:ta, language:ur, language:hy, language:nl, language:tk, language:en, language:bg, language:gd, language:pt, language:ko, language:ga, language:eu, language:sv, language:bs, language:co, language:fr, language:gn, language:ro, language:it, language:dv, language:ku, language:ak, language:eo, language:zu, language:id, language:te, language:sl, language:lv, language:pa, language:ru, language:si, language:ee, language:yi, language:ny, language:az, language:sw, language:hi, language:mt, language:sr, language:hr, language:ka, language:ug, language:tt, language:lg, language:kn, language:fy, language:kk, language:ca, language:lb, language:jv, language:et, language:la, language:tr, language:ps, language:km, language:zh, language:uk, language:as, language:he, language:yo, language:sq, language:da, language:gl, language:vi, language:ay, language:is, language:ln, language:mr, language:st, language:xh, language:cs, language:ky, language:ml, language:ht, language:mi, language:so, language:uz, language:el, language:ti, language:be, language:cy, language:am, language:ig, language:or, language:fa, language:ms, language:su, language:de, language:lo, language:ha, language:ts, language:om, language:ar, language:my, language:es, language:qu, language:no, language:th, language:sa, language:mg, language:pl, language:sd, language:sk, language:bn, language:rw, language:af, language:ne, language:lt, language:tg, language:ja, language:sn, language:hu, license:cc-by-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for MultiQ This is the dataset corresponding to the paper ""Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ"". It is a silver standard benchmark tha","None","tl, sm, mk, gu, fi, mn, bm, ta, ur, hy, nl, tk, en, bg, gd, pt, ko, ga, eu, sv, bs, co, fr, gn, ro, it, dv, ku, ak, eo, zu, id, te, sl, lv, pa, ru, si, ee, yi, ny, az, sw, hi, mt, sr, hr, ka, ug, tt, lg, kn, fy, kk, ca, lb, jv, et, la, tr, ps, km, zh, uk, as, he, yo, sq, da, gl, vi, ay, is, ln, mr, st, xh, cs, ky, ml, ht, mi, so, uz, el, ti, be, cy, am, ig, or, fa, ms, su, de, lo, ha, ts, om, ar, my, es, qu, no, th, sa, mg, pl, sd, sk, bn, rw, af, ne, lt, tg, ja, sn, hu","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"JosephLee/science_textbook_elementary_kor","JosephLee","2023-11-06 06:06:36","2023-11-07 07:25:44+00:00","9295417171a912dbd46394d61211346a6b1e4b52","46","593","2","task_categories:question-answering, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, testbook, elementary, science","question-answering","None","None","ko","mono","None","None","False","False","False","None","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"
+"nayohan/Magpie-Gemma2-Pro-200K-Filtered-ko","nayohan","2024-08-07 16:19:16","2024-08-07 17:11:07+00:00","3ad37f02c6af3c9cd0f453dd2d2c88957c0a7b5d","67","592","4","task_categories:text-generation, language:ko, license:gemma, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, li","text-generation","Translate Magpie-Align/Magpie-Gemma2-Pro-200K-Filtered using nayohan/llama3-instrucTrans-enko-8b. This is a raw translation dataset. It needs to be filtered for repetitions generated by the model. @mi","None","ko","mono","None","None","False","False","False","gemma","None","None","None"
+"KETI-AIR/kor_qasc","KETI-AIR","2023-11-08 06:41:46","2023-11-15 01:21:25+00:00","d9cb140a7f011da71cfe5bfa3cb2b874e463bf35","33","588","0","task_categories:question-answering, task_categories:multiple-choice, task_ids:extractive-qa, task_ids:multiple-choice-qa, language:ko, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1910.11473, region:us","question-answering, multiple-choice, extractive-qa, multiple-choice-qa","Dataset Card for QASC Licensing Information The data is distributed under the CC BY 4.0 license. Source Data Citation INformation @article{allenai:qasc, author = {Tushar Khot and Peter Clark and Micha","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","extractive-qa, multiple-choice-qa"
+"youjunhyeok/PersonaHub-ko","youjunhyeok","2024-09-18 08:58:23","2024-09-19 00:09:14+00:00","71c1169a1cfd391ea8944d95175e872ecd2988a5","122","586","1","task_categories:text-generation, task_categories:text-classification, task_categories:token-classification, task_categories:fill-mask, task_categories:table-question-answering, task_categories:text2text-generation, language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.20094, region:us, synthetic, text, math, reasoning, instruction, tool","text-generation, text-classification, token-classification, fill-mask, table-question-answering, text2text-generation","Translated proj-persona/PersonaHub using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of English. Thanks for @pro","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"QubitPi/wiktionary-data","QubitPi","2024-11-20 02:34:20","2024-12-14 21:59:00+00:00","969b5a54cf2dc37d3363ea80eba5f9be6c7614f0","255","583","1","language:en, language:de, language:la, language:grc, language:ko, language:peo, language:akk, language:elx, language:sa, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Natural Language Processing, NLP, Wiktionary, Vocabulary, German, Latin, Ancient Greek, Korean, Old Persian, Akkadian, Elamite, Sanskrit, Knowledge Graph","None","Wiktionary Data on Hugging Face Datasets wiktionary-data is a sub-data extraction of the English Wiktionary that currently supports the following languages: Deutsch - German Latinum - Latin Ἑλληνική -","None","en, de, la, grc, ko, peo, akk, elx, sa","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","None","None","None"
+"jaejoo/llama-2-ko-law","jaejoo","2023-09-08 04:47:14","2023-09-08 05:13:09+00:00","bd8a419db175f7f390a711dc9be7ae8424115605","44","576","5","language:ko, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","None","None","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"agentlans/LinguaNova","agentlans","2024-11-30 09:53:49","2025-01-28 21:26:49+00:00","809b3a918cc3c4a5afdd7db44a1f507d96465a8b","98","575","0","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"
+"mssongit/KorfinQA","mssongit","2023-05-26 00:45:01","2023-05-26 00:48:15+00:00","b59d9d6282255c6bca83ea76e02c6f8e5d35594b","52","573","2","task_categories:question-answering, language:ko, license:mit, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","question-answering","FinQA 한국어 번역본 Question, Answer 총 6252 Rows","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"deepghs/cc100_char_freq","deepghs","2024-12-18 21:53:53","2024-12-22 05:20:10+00:00","0088fd38ad6a5b1b4ad425c5178864f3ef40cc06","451","573","0","None","text-generation, text2text-generation, text-classification","Letter Frequency Table on CC-110 This is the letter frequency analysis table based on CC-110 dataset. 116 languages supported, 50882 letters supported in total. This dataset is useful for do some basi","None","af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, ff, fi, fr, fy, ga, gd, gl, gn, gu, ha, he, hi, hr, ht, hu, hy, id, ig, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lg, li, ln, lo, lt, lv, mg, mk, ml, mn, mr, ms, my, ne, nl, no, ns, om, or, pa, pl, ps, pt, qu, rm, ro, ru, sa, sc, sd, si, sk, sl, so, sq, sr, ss, su, sv, sw, ta, te, th, tl, tn, tr, ug, uk, ur, uz, vi, wo, xh, yi, yo, zh, zu","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","other","None","None","None"
+"nlpai-lab/ko_commongen_v2","nlpai-lab","2024-08-11 12:37:57","2024-08-11 13:13:06+00:00","781204a1a89bef3ea4601e050e8c094ce68bbfac","63","569","3","task_categories:text-generation, language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","🌠 KoCommonGEN v2 KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models (ACL 2024-Findings) Jaehyung Seo, Jaewook Lee, Chanjun Park, SeongTae Hong,","None","ko","mono","None","None","False","False","False","None","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"
+"taeminlee/CLIcK","taeminlee","2024-05-21 06:31:13","2024-05-21 07:11:47+00:00","8a95bfb9bffc48e85d3914b296572ae06b250594","158","565","0","task_categories:multiple-choice, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.06412, region:us, Culture, Language","multiple-choice","This dataset is the same as https://huggingface.co/datasets/EunsuKim/CLIcK. This dataset has been subdivided for simplified viewing and evaluation. CLIcK 🇰🇷🧠 Evaluation of Cultural and Linguistic Inte","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","2403.06412","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"
+"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"
+"aiqwe/FinShibainu","aiqwe","2024-12-09 13:28:20","2024-12-18 12:21:49+00:00","7643138203e538ba3eaeeb2061c31b37342a2f19","198","558","2","task_categories:question-answering, task_categories:multiple-choice, task_categories:text-generation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, finance, accounting, stock, quant, economics","question-answering, multiple-choice, text-generation","FinShibainu Datset Card github: https://github.com/aiqwe/FinShibainu model: https://huggingface.co/aiqwe/FinShibainu KRX LLM 경진대회 리더보드에서 우수상을 수상한 shibainu24 모델의 데이터셋 Repository입니다.모델에 대한 내용은 https://h","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"jp1924/GyeongsangSpeech","jp1924","2024-04-05 04:23:43","2024-06-14 06:07:16+00:00","1d539972cfa614c1ad9b9cd328b6e7f078ba73bf","35","554","2","task_categories:automatic-speech-recognition, language:ko, size_categories:1M<n<10M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, STT, Audio","automatic-speech-recognition","None","None","ko","mono","1|0|0|B|<|n|<|1|T","None","False","auto","False","None","None","None","None"
+"shanearora/CaLMQA","shanearora","2024-06-12 08:08:20","2024-06-13 07:13:20+00:00","a31ec3378b1d009839544f64d339714072b405b4","49","546","2","None","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"
+"bongsoo/social_science_en_ko","bongsoo","2022-09-20 04:45:54","2022-10-05 00:09:30+00:00","baa096440c81620325d5c6f774eacb668dbd1db8","71","534","3","language:ko, license:apache-2.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","사회과학-en-ko 번역 말뭉치","None","ko","mono","None","None","False","False","False","apache-2.0","None","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"
+"hac541309/stdict_kor","hac541309","2023-07-26 11:52:59","2023-07-26 12:01:59+00:00","c76642c5ffc81b677ecc9971632abc7388e1264f","45","522","2","task_categories:table-question-answering, task_categories:text-generation, task_categories:text-classification, task_categories:question-answering, language:ko, 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","table-question-answering, text-generation, text-classification, question-answering","Dataset Card for ""Standard Korean Dictionary"" This dataset is a NLP learnable form of Standard Dictionary from the National Institute of Korean Language (국립국어원 표준국어대사전). It follows the original copyri","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"heegyu/PKU-SafeRLHF-ko","heegyu","2023-12-01 05:02:17","2023-12-31 05:26:05+00:00","4b32a779ee7e21ce321517d94860d8da94b6e6a1","67","517","4","language:ko, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Original Dataset: PKU-Alignment/PKU-SafeRLHF Translation by using maywell/Synatra-7B-v0.3-Translation Translating in progress...","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"bltlab/ParaNames","bltlab","2024-05-16 11:36:42","2024-05-16 11:54:17+00:00","b9fd0e773dda2fac29db0aaf84a0e6bf873e2183","83","513","3","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","None","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"
+"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"
+"youngwoo3283/df_sentiment_chat","youngwoo3283","2023-11-24 07:05:40","2023-11-24 07:12:36+00:00","ce853de80a11ea18242b9898e2d02ec19dd66896","34","506","0","language:ko, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","데이터 출처 : https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=86 해당 데이터에서 사람응답1과 시스템 응답1로만 만든 데이터","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"traintogpb/aihub-kozh-translation-integrated-large-5.9m","traintogpb","2024-05-30 07:46:55","2024-05-30 08:42:24+00:00","b1fccadec894303cccbc4c2952b3f95af329bc63","70","505","1","None","translation","AI Hub Ko-Zh Translation Dataset (Integrated) AI Hub의 한-중 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 5,934,596개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-5.9m, base-1m, sma","None","ko, zh","bi","None","None","False","False","False","mit","None","None","None"
+"zzunyang/LawQA_LawSee","zzunyang","2024-01-22 05:39:04","2024-01-22 05:42:36+00:00","f25e1c91c459a7342f219ca574892fa9a8ee6056","38","499","1","language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","conversational","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"DinoTheLewis/KoAlpaca_persona_multiturn","DinoTheLewis","2024-06-26 08:52:05","2024-06-28 13:56:28+00:00","52ca060814f15cecbc09c6b93f9625081d72527f","34","497","2","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, KoAlpaca","text-generation","Dataset Information Originated: beomi/KoAlpaca_v1.1 1st answer is rewritten by gpt-3.5 left turns are generated by google gemini pro 1.0, both question and answer inspired by Ultrachat Description KoA","None","ko","mono","1|K|<|n|<|1|0|K","None","False","manual","False","apache-2.0","None","None","None"
+"nayohan/Magpie-Pro-MT-300K-v0.1-ko","nayohan","2024-07-13 05:11:51","2024-07-13 06:34:34+00:00","ee286c9faaab11671263495e0238bc6a3bc1145e","67","497","25","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean","text-generation","Translated Magpie-Align/Magpie-Pro-MT-300K-v0.1 using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it need","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"ChuGyouk/MedQA","ChuGyouk","2024-07-15 03:52:20","2024-08-16 06:15:28+00:00","481d0f2f7e67d94425cc7f3499ad14e6f88d99ba","204","496","2","task_categories:text-generation, language:ko, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2009.13081, region:us, medical","text-generation","Original dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams En split Just edited columns. Contents are sam","None","ko, en","bi","None","None","False","False","False","cc-by-4.0","None","None","None"
+"qwopqwop/ALMA-R-ko-en","qwopqwop","2024-03-10 10:06:51","2024-03-10 10:48:05+00:00","a5e033b454f0eaf7d94a4c493863b6a17f40617a","88","474","6","task_categories:translation, language:ko, language:en, license:cc-by-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for ""ALMA-R-ko-en-Preference"" ref) https://huggingface.co/datasets/haoranxu/ALMA-R-Preference The triplet prference data, supporting 2 translation directions, is built upon the FLORES-200","None","ko, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"jayhii/font_valid_10000","jayhii","2024-05-20 03:24:34","2024-06-19 23:52:58+00:00","e63ae73633ffc454866d335b70c7a00da0adf161","59","473","0","language:ko, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, font license","None","None","None","ko","mono","None","None","False","False","False","apache-2.0","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"
+"HAERAE-HUB/HAE-RAE-COT-1.5M","HAERAE-HUB","2023-09-10 17:40:37","2023-10-25 05:37:21+00:00","9c36894a078a730ed8a2c4ea19a3291d3d4e7d48","115","472","5","language:ko, license:cc-by-4.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, haerae","None","Dataset Card for ""HAE-RAE-COT-1.5M"" HAE-RAE-COT-1.5M is a dataset encompassing 1,586,688 samples of questions paired with CoT (Chain of Thought) rationales. The majority of this dataset is a translati","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-4.0","None","None","None"
+"KETI-AIR/kor_quarel","KETI-AIR","2023-11-08 06:36:14","2023-12-06 01:21:21+00:00","f81632a76cd4e428438c8a868479dfbde1950326","67","471","0","language:ko, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1805.05377, region:us","None","Dataset Card for Quarel Licensing Information The data is distributed under the CC BY 4.0 license. Source Data Citation INformation @inproceedings{quarel_v1, title={QuaRel: A Dataset and Models for An","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"SamsungNLP/SAMSEMO","SamsungNLP","2024-08-20 13:55:53","2024-09-18 11:43:16+00:00","9d3a85852aff7aee6742c6d63bf3364080e5bc47","126","466","3","task_categories:image-classification, task_categories:video-classification, language:en, language:pl, language:ko, language:de, language:es, size_categories:10K<n<100K, format:text, modality:text, modality:video, library:datasets, library:mlcroissant, region:us, video","image-classification, video-classification","Dataset Card for Dataset Name SAMSEMO: New dataset for multilingual and multimodal emotion recognition Dataset Details Dataset Sources Repository: https://github.com/samsungnlp/samsemo Paper: SAMSEMO:","None","en, pl, ko, de, es","multi","1|0|K|<|n|<|1|0|0|K","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"
+"KETI-AIR/kor_cosmos_qa","KETI-AIR","2023-11-08 06:48:59","2023-11-15 01:23:57+00:00","8a2bbb6095d0107626d394058686281e060873a7","68","459","0","language:ko, license:cc-by-4.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for cosmos_qa Licensing Information The data is distributed under the CC BY 4.0 license. Source Data Citation INformation @inproceedings{huang-etal-2019-cosmos, title = ""Cosmos {QA}: Mach","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"naver-ai/kobbq","naver-ai","2024-02-26 08:08:13","2024-04-16 14:58:07+00:00","ccb902a2209a9d79ef921a21d90b52711cbf4d2a","63","458","5","task_categories:question-answering, task_categories:zero-shot-classification, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2307.16778, region:us","question-answering, zero-shot-classification","Dataset Card for KoBBQ The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2307.16778","None","None"
+"nlp-with-deeplearning/Ko.WizardLM_evol_instruct_V2_196k","nlp-with-deeplearning","2023-11-30 10:00:05","2023-12-01 01:42:39+00:00","de19f7eaa31de073fa784d240efc93c131ebca8f","45","458","4","task_categories:text-generation, task_categories:question-answering, language:en, language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2308.09583, arxiv:2304.12244, arxiv:2306.08568, region:us","text-generation, question-answering","이 데이터셋은 자체 구축한 번역기로 WizardLM/WizardLM_evol_instruct_V2_196k을 번역한 데이터셋입니다. 아래 README 페이지도 번역기를 통해 번역되었습니다. 참고 부탁드립니다. News 🔥 🔥 🔥 [08/11/2023] WizardMath 모델을 출시합니다. 🔥 WizardMath-70B-V1.0 모델은 ChatGPT 3.5","None","en, ko","bi","None","None","False","False","False","cc-by-nc-sa-4.0","2308.09583","None","None"
+"allganize/flare-fiqasa-ko","allganize","2024-03-25 15:37:29","2024-04-02 04:43:25+00:00","05735c464c85ed368ac61f75ffe856aaf8880bee","36","456","3","language:ko, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","flare-fiqasa-ko 데이터 설명 flare-fiqasa-ko 데이터는 금융 도메인 뉴스 헤드라인의 감성을 예측(sentiment analysis)하는 데이터셋입니다. 입력값은 text로만 이루어져 있습니다. 한국어 데이터를 생성하기 위해, 우선 사내 언어 번역 모델 Allganize Translator을 활용하여 ChanceFocus/flare-f","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"g0ster/TinyStories-Korean","g0ster","2024-05-14 11:52:01","2024-05-15 09:23:41+00:00","07ebd15e0ea9fe0b3c195a48f19656607d59da20","78","447","7","task_categories:translation, language:ko, language:en, license:mit, size_categories:10M<n<100M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","translation","TinyStories-Korean This dataset is a translated version of roneneldan's TinyStories dataset. I first downloaded roneneldan's TinyStories, and I organized it in a db file. Then I used a local transalat","None","ko, en","bi","1|M|<|n|<|1|0|M","None","False","False","False","mit","None","None","None"
+"bebechien/HongGildongJeon","bebechien","2024-04-03 01:02:24","2024-04-24 12:06:14+00:00","736debceb992afbcf0ae325916e28f92daa4aaa3","61","438","0","task_categories:text-generation, language:ko, license:pddl, size_categories:n<1K, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Source original : https://ko.wikisource.org/wiki/%ED%99%8D%EA%B8%B8%EB%8F%99%EC%A0%84_36%EC%9E%A5_%EC%99%84%ED%8C%90%EB%B3%B8 license : public domain modern translation : https://ko.wikisource.org/wik","None","ko","mono","None","None","False","False","False","pddl","None","None","None"
+"nlp-with-deeplearning/ko.openhermes","nlp-with-deeplearning","2023-12-04 03:14:12","2023-12-04 03:26:54+00:00","dec24968c6e3eea2d2f0691d57797299374ba612","56","436","4","task_categories:text-generation, language:en, language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, distillation, synthetic data, gpt","text-generation","원본 데이터셋: teknium/openhermes","None","en, ko","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-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","None","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"
+"paragonnov/coway_faq","paragonnov","2023-06-08 06:54:37","2023-06-12 05:56:59+00:00","e47a94f9d9f48ed1dde9c4059bb3c0a521d908ec","39","428","2","task_categories:text-generation, task_categories:question-answering, language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, question-answering","None","None","ko","mono","n|<|1|K","None","False","False","False","None","None","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"
+"kozistr/mqa-ko","kozistr","2023-12-31 11:04:07","2023-12-31 11:24:27+00:00","05105722b12661ee13913d3e5a07c9d06759721b","37","426","2","task_categories:question-answering, language:ko, license:cc0-1.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, mqa","question-answering","https://huggingface.co/datasets/clips/mqa","None","ko","mono","None","None","False","False","False","cc0-1.0","None","None","None"
+"ChuGyouk/AI-MO-NuminaMath-CoT-Ko","ChuGyouk","2024-09-12 07:38:09","2024-09-12 15:38:35+00:00","b5ed10dd9739889d9da3c6c97d0844da4d812a77","80","424","7","task_categories:text-generation, language:en, language:ko, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","Dataset Card for NuminaMath CoT Korean 🎉 Translation finished! If there are any errors, please open the PR. 🎉 If you use this data, please make sure to credit my source! ⚠️ There may be errors in the ","None","en, ko","bi","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo","youjunhyeok","2024-05-10 06:24:16","2024-05-22 08:34:52+00:00","6245e56d73fa9db365baca608d7c9ab9753c636c","39","424","2","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","위 표에 기재된 데이터셋들을 merge했습니다. rating이 있는 데이터셋의 경우 chosen 점수가 높은 것만 선택됐습니다.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Yettiesoft/voice_medical","Yettiesoft","2024-05-13 07:08:42","2024-05-21 03:46:24+00:00","a8e72c41cc0640550b3840a9a8287036fb2329b8","38","412","0","task_categories:automatic-speech-recognition, language:ko, license:other, size_categories:n<1K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition","None","None","ko","mono","None","None","False","False","False","other","None","None","None"
+"rombodawg/Everything_Instruct_Multilingual","rombodawg","2024-10-08 21:13:18","2024-10-08 22:35:10+00:00","cb2f376b958efca637e4fa655c963cefec53220b","93","411","17","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"
+"ChuGyouk/argilla-distilabel-math-preference-dpo-korean","ChuGyouk","2024-08-25 08:35:31","2024-08-27 14:32:51+00:00","c973646855268e9582c84bcaf35205fe4e8d6bba","37","411","5","language:en, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Information This is a gpt-4o-2024-08-06 Korean translated-version of argilla/distilabel-math-preference-dpo. I used OpenAI BATCH API with prompt below, temperature=0.0, max_tokens=4000, seed=0","None","en, ko","bi","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","None","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","None","False","False","False","apache-2.0","None","None","extractive-qa"
+"KETI-AIR/kor_amazon_polarity","KETI-AIR","2023-11-03 06:33:37","2023-11-15 01:14:28+00:00","a8cdaa818910969e4ac26d52982c4506ca03f607","41","410","0","task_categories:text-classification, task_ids:sentiment-classification, source_datasets:original, language:ko, license:cc0-1.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-classification, sentiment-classification","Dataset Card for amazon_polarity Licensing Information The data is distributed under the CC0 1.0 license. Source Data Citation Information McAuley, Julian, and Jure Leskovec. ""Hidden factors and hidde","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc0-1.0","None","None","sentiment-classification"
+"werty1248/sharegpt-tagengo-gpt4-ko","werty1248","2024-05-05 14:55:22","2024-05-05 18:10:25+00:00","56d1f491db28515d580361c79dcaf8d8dc71b227","38","410","3","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","text-generation","Original Dataset: lightblue/tagengo-gpt4 ShareGPT-tagengo-GPT4-ko LMSYS-Chat-1M를 통해 수집된 실제 유저들과 GPT-4(gpt-4-0125-preview) 사이의 단발성 대화 데이터 셋입니다. tagengo-gpt4 데이터 셋에서 한국어 데이터 1,609개를 추출한 뒤, 사람이 직접 확인하여 일","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"youjunhyeok/smoltalk-ko-translate","youjunhyeok","2024-12-18 00:19:02","2024-12-31 06:17:06+00:00","bd9c0ff0864068940cfc001e1fb22fd1332af3fb","164","406","3","task_categories:text-generation, language:ko, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, synthetic","text-generation","번역 결과에 토큰이 반복된 결과들이 포함되어 있습니다. 필터링 후 재업로드 하겠습니다. Z 알고리즘을 사용해 결과를 필터링 하였으며 {subset}_filtered 로 업로드하였습니다. 필터링 후 결과 subset 전 후 split/train 4205413 4162254 split/test 221249 218830 merge/train 1043917 103","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"kmgme/KorQuADv2_1","kmgme","2024-08-14 07:53:58","2024-08-15 00:04:32+00:00","9a1592d73c025c205f4a2bdde2e397dd31ea4669","155","404","0","task_categories:question-answering, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for ""korsquadv2_1"" More Information needed","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"NCSOFT/K-DTCBench","NCSOFT","2024-11-26 05:52:20","2024-12-06 05:19:25+00:00","2ab14c836a7703fe6c79fc3af52759421b101f75","292","403","12","language:ko, license:cc-by-nc-4.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2411.19103, region:us","None","K-DTCBench We introduce K-DTCBench, a newly developed Korean benchmark featuring both computer-generated and handwritten documents, tables, and charts. It consists of 80 questions for each image type ","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"zzunyang/dpo_data","zzunyang","2024-01-26 03:13:48","2024-01-26 03:15:58+00:00","e80f96d7a9926145c75c9b84885523b3928ab340","39","399","0","language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","conversational","None","None","ko","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"psyche/bool_sentence","psyche","2022-09-16 12:30:21","2022-10-23 02:52:40+00:00","28fb0d7e0d32c1ac7b6dd09f8d9a4e283212e1c0","71","398","1","task_categories:text-classification, annotations_creators:machine-generated, language_creators:found, multilinguality:monolingual, source_datasets:original, language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","Model psyche/bool_sentence (10k) klue/bert-base 0.9335 licence: cc-by-sa-2.0-kr (원본 출처:국립국어원 표준국어대사전)","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","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"
+"wisenut-nlp-team/alpaca_nmt","wisenut-nlp-team","2024-06-24 09:09:52","2024-07-08 01:13:00+00:00","bdb429133e815f33ed9d8bd26e0dc6f5fad30c60","49","390","0","language:en, language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en, ko","bi","None","None","False","False","False","None","None","None","None"
+"sh2orc/bccard-maywell-jojo0217-markai-lcw99-kendamarron-microsoft","sh2orc","2024-08-13 15:40:48","2024-08-19 13:54:52+00:00","6c40c2c5803ef773e73ef1134ac9f820855f9a94","36","389","0","language:ko, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","[Dataset merged] maywell/ko_wikidata_QA jojo0217/korean_rlhf_dataset BCCard/BCCard-Finance-Kor-QnA MarkrAI/KoCommercial-Dataset lcw99/wikipedia-korean-20240501-1million-qna maywell/gpt4_evol_1.3k Kend","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"kms7530/ko-coco-bal","kms7530","2024-07-31 01:05:33","2024-07-31 04:41:58+00:00","59be0b68845f315abc2e443a2cdf73326d20ce9e","37","385","4","task_categories:image-to-text, task_categories:text-to-image, language:ko, language:en, license:cc, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, coco, image caption","image-to-text, text-to-image","Ko COCO BAL 본 데이터셋은 기존의 MS COCO의 영어 캡션을 한국어 문장으로 번역(AIHub)한 데이터셋의 한국어 묘사문을 통합하여 하나의 문장으로 생성한 데이터셋 입니다. 생성된 문장은 GPT-4o mini를 이용하여 다음의 프롬프트를 이용해 재생성 하였습니다. 다음은 어떠한 이미지에 대해 글자로 묘사해놓은 정보이다. 모든 정보를 이용하여 텍스","None","ko, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc","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"
+"gbenson/interesting-dom-snapshots","gbenson","2024-05-06 22:13:34","2024-06-09 07:37:18+00:00","5359de90fc4d2a3250b310dc2e93b19933601bb8","45","384","0","source_datasets:gbenson/webui-dom-snapshots, language:en, language:zh, language:nl, language:cs, language:ko, license:cc0-1.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for Interesting DOM snapshots A small split of gbenson/webui-dom-snapshots. Curated by: Gary Benson Languages: Mostly English, some Chinese, Dutch, Czech and Korean License: CC0 1.0 Unive","None","en, zh, nl, cs, ko","multi","n|<|1|K","None","False","False","False","cc0-1.0","None","None","None"
+"jaeyong2/Ko-emb-PreView","jaeyong2","2024-11-29 13:05:47","2024-12-04 06:46:46+00:00","7d1b5ab11edbc27e0a6782cc73899ed33ec3480b","68","382","0","language:ko, license:apache-2.0, 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 daje/ko_wiki and maywell/korean_textbooks We used Qwen/Qwen2-72B-Instruct model to generate answer with COT. License Qwen/Qwen2.5-72B-Instruct : https://hugging","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Gabrui/multilingual_TinyStories","Gabrui","2024-10-03 13:20:57","2024-10-03 14:00:08+00:00","7fb5f327b907318dd95877a366e288f1971b64c1","68","380","0","None","text-generation","Dataset Card for Multilingual TinyStories Dataset Details Dataset Description The Multilingual TinyStories dataset contains translations of the original TinyStories dataset, which consists of syntheti","None","ar, az, zh, en, fa, de, he, hi, ko, es, tr, vi","multi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","cdla-sharing-1.0","2305.07759","None","None"
+"NCSOFT/K-SEED","NCSOFT","2024-11-26 05:50:37","2024-12-06 05:19:53+00:00","38d45164928fe16b41ec98b3a769a457bf597f7d","130","378","17","language:ko, 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:2307.16125, arxiv:2411.19103, region:us","None","K-SEED We introduce K-SEED, a Korean adaptation of the SEED-Bench [1] designed for evaluating vision-language models. By translating the first 20 percent of the test subset of SEED-Bench into Korean, ","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","2307.16125","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"
+"FrancophonIA/multilingual-hatespeech-dataset","FrancophonIA","2024-10-07 21:07:11","2024-10-11 18:26:14+00:00","5e10826765dd61af31a14b1e1b3adbb83b51453f","54","377","2","multilinguality:multilingual, language:ar, language:fr, language:en, language:zh, language:ka, language:de, language:hi, language:id, language:ga, language:it, language:ko, language:mn, language:fa, language:pt, language:ru, language:es, language:tr, language:ur, size_categories:100K<n<1M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset origin: https://www.kaggle.com/datasets/wajidhassanmoosa/multilingual-hatespeech-dataset Description This dataset contains hate speech text with labels where 0 represents non-hate and 1 shows ","None","ar, fr, en, zh, ka, de, hi, id, ga, it, ko, mn, fa, pt, ru, es, tr, ur","multi","None","None","False","False","False","None","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"
+"wisenut-nlp-team/aihub_corpus_expertise","wisenut-nlp-team","2023-03-26 13:14:00","2023-05-23 13:00:44+00:00","2a384eb7baa60411d13b1e7fe1ef48249162ae3b","71","371","0","task_categories:other, task_categories:token-classification, annotations_creators:no-annotation, language_creators:found, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-4.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","other, token-classification","Dataset Card for ""corpus_professional_field"" 전문분야 말뭉치","None","ko","mono","1|0|0|M|<|n|<|1|B","None","False","False","False","cc-by-4.0","None","None","None"
+"mncai/MedGPT-5k-ko","mncai","2023-04-30 17:36:37","2023-05-01 09:49:01+00:00","d3798135b61112b2e0c5358afd2efdfb49337624","54","369","9","language:ko, license:gpl-3.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","conversational","None","None","ko","mono","None","None","False","False","False","gpl-3.0","None","None","None"
+"nayohan/finance-alpaca-ko","nayohan","2024-07-17 17:49:20","2024-07-17 17:51:48+00:00","6ee3ef89bd649512aad857816db5e30729488bed","37","365","4","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","None","Translated gbharti/finance-alpaca using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filter","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Twitter/HashtagPrediction","Twitter","2022-11-06 02:52:17","2022-11-21 21:22:07+00:00","ed7cc1bbeea46791a75ece509a414c12fd264167","55","362","2","None","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"
+"NCSOFT/K-MMBench","NCSOFT","2024-11-26 03:42:38","2024-12-06 05:20:04+00:00","28726be522e9397d522dec826e56c5ae5f17acaa","204","361","13","language:ko, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2307.06281, arxiv:2411.19103, region:us","None","K-MMBench We introduce K-MMBench, a Korean adaptation of the MMBench [1] designed for evaluating vision-language models. By translating the dev subset of MMBench into Korean and carefully reviewing it","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","2307.06281","None","None"
+"zhihz0535/X-TruthfulQA_en_zh_ko_it_es","zhihz0535","2024-01-27 21:44:12","2024-01-27 21:59:05+00:00","47ee3f089b6c59a5feb87d1bc388f00cd1d1eb54","74","358","0","None","question-answering","X-TruthfulQA 🤗 Paper | 📖 arXiv Dataset Description X-TruthfulQA is an evaluation benchmark for multilingual large language models (LLMs), including questions and answers in 5 languages (English, Chine","None","en, zh, ko, it, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","2311.08711","None","None"
+"ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5","ChuGyouk","2024-08-19 06:18:35","2024-08-21 16:23:13+00:00","c451c3db3aa936adadbdb2d5dc84eb42b93fff2f","40","357","3","task_categories:question-answering, language:ko, 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, medical","question-answering","Note To create data [ChuGyouk/AI_healthcare_QA], I extracted a few samples and get answer from claude-3-5-sonnet-20240620 (by using welcome credit). (I did this briefly for debugging purposes.) You ar","None","ko","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"tabtoyou/KoLLaVA-CC3M-Pretrain-595K","tabtoyou","2023-06-07 16:15:38","2023-06-25 12:32:19+00:00","3bd7d1a330db7aebdbf0b70c6002fe6fbaca652f","90","357","11","task_categories:visual-question-answering, language:ko, license:other, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","visual-question-answering","LLaVA Visual Instruct CC3M 595K Pretrain Dataset Card LLaVA에서 공개한 CC3M의 595K개 Visual Instruction dataset을 한국어로 번역한 데이터셋입니다. 기존 Ko-conceptual-captions에 공개된 한국어 caption을 가져와 데이터셋을 구축했습니다. 번역 결과가 다소 좋지 않","None","ko","mono","None","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"
+"dev7halo/bluehouse-national-petition","dev7halo","2023-06-19 04:11:19","2023-06-20 05:18:07+00:00","8e907b8e6fdf1fca7f2a2a8bf7d7b733dbc7fe56","51","352","3","language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Usage pip install datasets from datasets import load_dataset dataset = load_dataset(""dev7halo/bluehouse-national-petition"") DatasetDict({ train: Dataset({ features: ['number', '제목', '답변상태', '참여인원', '카","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Cohere/miracl-ko-queries-22-12","Cohere","2023-01-30 16:31:20","2023-02-06 11:58:15+00:00","037a753c7358589ee9084d7b10c135ab48b52041","52","352","3","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, language:ko, 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 (ko) 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","ko","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"letgoofthepizza/pokemon-blip-captions-en-ko","letgoofthepizza","2024-05-14 10:36:03","2024-05-17 05:25:32+00:00","fc977fce0f5f5231258f389d00fab70427ac5142","62","351","3","language:en, language:ko, license:apache-2.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","en, ko","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"jiyounglee0523/KorNAT","jiyounglee0523","2024-05-10 01:15:07","2024-05-13 01:53:32+00:00","43f72cd13a01738cde2bd69bc8852497f3b25942","70","350","6","task_categories:multiple-choice, language:ko, language:en, license:cc-by-nc-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.13605, region:us, national-alignment","multiple-choice","KorNAT (Korean National Alignment Test) When deploying LLMs in a specific country, it is essential to ensure that the model is aware of that country’s culture and basic knowledge, so called national a","None","ko, en","bi","1|0|<|n|<|1|2","None","False","False","False","cc-by-nc-2.0","2402.13605","None","None"
+"nayohan/math-gpt-4o-200k-ko","nayohan","2024-08-01 16:24:25","2024-08-01 16:27:43+00:00","54f2d50c798ae9050c02e8069e6cf0591afb22f8","67","349","3","task_categories:text-generation, language:ko, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction, korean","text-generation","Translated PawanKrd/math-gpt-4o-200k using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be fil","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"hac541309/woori_spring_dict","hac541309","2023-07-26 11:34:08","2023-08-15 11:00:14+00:00","6066408bc11f3ca38c5e3d73296ff6641ac2d11b","39","348","5","task_categories:table-question-answering, task_categories:text-generation, task_categories:text-classification, task_categories:question-answering, language:ko, 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","table-question-answering, text-generation, text-classification, question-answering","Dataset Card for ""woori_spring_dict"" This dataset is a NLP learnable form of woori mal saem(우리말샘) a Korean collaborative open source dictionary. It follows the original copyright policy (cc-by-sa-2.0)","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"tabtoyou/KoLLaVA-Instruct-150k","tabtoyou","2023-06-04 10:24:11","2023-11-30 12:45:44+00:00","40b19fdc832d434f9a9453b64b94641698c08d29","43","346","20","task_categories:visual-question-answering, task_categories:question-answering, language:ko, license:cc-by-nc-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","visual-question-answering, question-answering","Korean Visual Instruct 150K Dataset Card 🌋LLaVA의 Instruction-following Dataset을 한국어로 번역한 데이터셋입니다. (feat. DeepL) 1. Conversation 이미지에 대해 질문하는 사람과 이에 답하는 Assistant 사이의 대화 형식으로 디자인합니다. 대답은 Assistant가 이미지","None","ko","mono","None","None","False","auto","False","cc-by-nc-4.0","None","None","None"
+"appleparan/OpenOrca-Ko-En","appleparan","2024-03-04 13:32:00","2024-03-04 15:28:34+00:00","46c998c87c949da74e531f0dcb606f156fb18875","48","340","3","task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, task_categories:text2text-generation, language:en, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, doi:10.57967/hf/1840, region:us","text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, summarization, feature-extraction, text-generation, text2text-generation","OpenOrca-Ko-En kyujinpy/OpenOrca-KO와 Open-Orca/OpenOrca를 공통된 데이터만 필터링하고 합친 데이터셋입니다. 컬럼은 기존 OpenOrca에 ��춰서 system_prompt_{ko/en}, question_{ko/en}, response_{ko/en} 으로 변경하였습니다. 중복된 id를 제거하여 데이터수가 일부 감소하","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2306.02707","None","None"
+"NickyNicky/oasst2_chatml","NickyNicky","2024-01-07 01:44:47","2024-01-09 19:13:26+00:00","7561e33c16c7a47a973f9efa5019a7ba8834080e","54","340","3","None","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"
+"traintogpb/aihub-kozh-translation-integrated-base-1m","traintogpb","2024-05-30 07:47:57","2024-05-30 08:43:52+00:00","bf2bfb3e2324c981329e77f2b02ac8154ea1fe36","38","338","0","task_categories:translation, language:ko, language:zh, license:mit, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","AI Hub Ko-Zh Translation Dataset (Integrated) AI Hub의 한-중 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 5,934,596개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-5.9m, base-1m, sma","None","ko, zh","bi","None","None","False","False","False","mit","None","None","None"
+"Nexdata/93_Hours_Korean_Children_Real_world_Casual_Conversation_and_Monologue_speech_dataset","Nexdata","2024-04-12 01:52:26","2024-08-16 08:06:42+00:00","849657592d47470bd0332111887435fdc763305e","39","338","0","language:ko, license:cc-by-nc-nd-4.0, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","None","Description Korean(Korea) Children Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live, lecture, variety show and other generic domains, mirrors real-wor","None","ko","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"clapAI/MultiLingualSentiment","clapAI","2024-12-27 15:30:01","2024-12-27 16:31:31+00:00","08b1866c310cdf38fa58e2336569423bdb027a5c","334","336","7","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"
+"ChuGyouk/KoMedInstruct-52k","ChuGyouk","2024-03-27 16:07:26","2024-08-16 05:47:38+00:00","840633a1c83906b210a67ea0f05d6ef388467315","50","334","5","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2310.14558, region:us, medical","text-generation","1차 번역 완료 I found several problems during translation, so additional filtering will be needed after completion. Step 1. It was confirmed that the existing data contained a lot of data that was close to","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"KETI-AIR/kor_anli","KETI-AIR","2023-11-08 07:39:41","2023-11-15 01:18:06+00:00","2c517432a254429c101a7bf523fd28229d39927a","44","331","0","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, language:ko, license:cc-by-nc-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","Dataset Card for anli Licensing Information cc-4 Attribution-NonCommercial Source Data Citation INformation @InProceedings{nie2019adversarial, title={Adversarial NLI: A New Benchmark for Natural Langu","None","ko","mono","1|0|0|K|<|n|<|1|M","anli","False","False","False","cc-by-nc-4.0","None","None","natural-language-inference, multi-input-text-classification"
+"nayohan/Neural-DPO-ko","nayohan","2024-06-30 08:39:01","2024-06-30 10:45:31+00:00","7741c3503e33f8bbfd92ff53e452bf8e7960e199","33","330","1","task_categories:question-answering, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, DPO, ko","question-answering","Dataset Card for ""Neural-DPO"" Translated NeuralNovel/Neural-DPO using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Junhoee/Logo-Dataset-Korean","Junhoee","2024-08-03 06:40:49","2024-08-16 12:12:43+00:00","cdbfbe593505bd864b59d50c33778c19f7f20fc9","47","330","2","language:ko, size_categories:10K<n<100K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, logo","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"TJPark/kr_webgpt","TJPark","2024-06-18 02:18:20","2024-06-21 08:40:30+00:00","d6560f03a2a2e8a8630899bc673ab2d0d9f6c07c","36","329","0","task_categories:question-answering, language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Citations @inproceedings{nakano2021webgpt, author = {Reiichiro Nakano and Jacob Hilton and Suchir Balaji and Jeff Wu and Long Ouyang and Christina Kim and Christopher Hesse and Shantanu Jain and Vinee","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"yangwang825/klue-ynat","yangwang825","2023-05-18 00:29:06","2023-05-19 02:07:06+00:00","934295eaa0aeaf5fa4cb1cc39b96b87600dfc82b","38","326","0","task_categories:text-classification, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","None","None","ko","mono","None","None","False","False","False","None","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","None","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"
+"neulab/PangeaBench-tydiqa","neulab","2024-11-01 16:28:55","2024-11-01 18:29:22+00:00","9672ee24043b7d70d9e1cfe6e443e57205496cee","89","321","0","None","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","None","False","False","False","apache-2.0","None","None","extractive-qa"
+"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","None","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"
+"CarrotAI/ko-code-alpaca-QA","CarrotAI","2024-06-18 13:46:49","2024-11-10 03:08:39+00:00","c717254ec0cfafdd1dbb91acd5d56506a2bce15c","50","319","5","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","code-alpaca QA 데이터셋입니다. 필터링이 어느정도 필요합니다. 참고하시고 사용하시면 됩니다.","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"nayohan/reward-bench-ko","nayohan","2024-06-30 08:32:29","2024-06-30 12:21:57+00:00","be0eef8abf7a5a8c3cc5072eb86516a132d93644","55","318","0","task_categories:question-answering, language:ko, license:odc-by, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, DPO, ko","question-answering","Dataset Card for ""reward-bench"" Translated allenai/reward-bench using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","odc-by","None","None","None"
+"jr-d-analyst24/ai_hub_summ_train","jr-d-analyst24","2024-07-26 07:42:13","2024-07-26 12:58:47+00:00","20b6a9d8812f7416188230248bbcb4d4ecefea4e","43","318","0","task_categories:summarization, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, speech, news","summarization","None","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"KETI-AIR/kor_ag_news","KETI-AIR","2023-11-03 08:42:19","2023-11-15 01:14:56+00:00","17799a2a70d8b394744a0e19c14f3c4b728fb490","49","316","1","task_categories:text-classification, task_ids:topic-classification, language:ko, license:unknown, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification, topic-classification","Dataset Card for ag_news Licensing Information & Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyH","None","ko","mono","1|0|0|K|<|n|<|1|M","ag-news","False","False","False","unknown","None","None","topic-classification"
+"NCSOFT/K-LLaVA-W","NCSOFT","2024-11-26 05:52:59","2024-12-06 05:19:07+00:00","702000b9a6133a65b82bda401dae32794d782606","104","316","13","language:ko, license:cc-by-nc-4.0, size_categories:n<1K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.08485, arxiv:2411.19103, region:us","None","K-LLaVA-W We introduce K-LLaVA-W, a Korean adaptation of the LLaVA-Bench-in-the-wild [1] designed for evaluating vision-language models. By translating the LLaVA-Bench-in-the-wild into Korean and care","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","2304.08485","None","None"
+"miracl/nomiracl-instruct","miracl","2024-07-25 20:18:13","2024-11-23 18:33:23+00:00","ac055a28d45adeacc11a60270424c737e166a157","49","316","0","None","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"
+"junghyeon0427/dataset_final","junghyeon0427","2024-06-24 05:37:02","2024-06-24 06:19:07+00:00","b215277097af475a9a78005644ade1ede091c28f","36","315","0","task_categories:question-answering, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","VAIV x KIISE AI contest korean QA dataset ""reject"" : Answers generated through gpt-3.5-turbo""chosen"" : Answers generated through GeM2-Llamion-14B-Chat","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"HAERAE-HUB/HRM8K","HAERAE-HUB","2025-01-13 05:28:35","2025-01-29 01:21:35+00:00","8fdef7431a3269be5cf828a65663c22c935a1a7d","313","313","9","language:ko, language:en, license:mit, size_categories:1K<n<10K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2501.02448, region:us, haerae","None","| 📖 Paper | 📝 Blog | 🖥️ Code(Coming soon!) | HRM8K We introduce HAE-RAE Math 8K (HRM8K), a bilingual math reasoning benchmark for Korean and English. HRM8K comprises 8,011 instances for evaluation, so","None","ko, en","bi","None","None","False","False","False","mit","2501.02448","None","None"
+"bongsoo/bongevalsmall","bongsoo","2022-07-25 06:04:14","2022-10-04 23:48:22+00:00","79cedccdca57aee5a769b1898987f489c8aa3b8b","43","313","0","language:ko, license:apache-2.0, size_categories:n<1K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","평가 말뭉치","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"mncai/kin_med_2M","mncai","2023-05-01 15:43:09","2023-05-01 16:00:39+00:00","e6a763a8409c6a36a043343c23577a09785d2418","56","310","7","language:ko, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","conversational","None","None","ko","mono","None","None","False","False","False","gpl-3.0","None","None","None"
+"yatsby/persona_chat","yatsby","2024-02-05 08:54:05","2024-02-16 10:11:01+00:00","1defdf8227071fe0a417ca25642283505c4dbbec","50","309","2","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","conversational","gemini 에서 생성한 Persona 와 질문, 답변을 담은 데이터셋입니다.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"devngho/ko_llm_annotations","devngho","2024-08-02 18:11:32","2024-09-26 08:26:20+00:00","5ef93ea8cc39906defab4351cc0c2f518d780e44","36","308","1","task_categories:text-classification, annotations_creators:machine-generated, language_creators:machine-generated, source_datasets:HAERAE-HUB/KOREAN-WEBTEXT, source_datasets:blueapple8259/c4-ko-cleaned-2, language:ko, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, synthetic","text-classification","Dataset 이 데이터셋은 fineweb-edu의 방법을 한국어에 적용하기 위해 만들어진 합성 데이터셋입니다. v1과 v2는 퀄리티가 낮습니다. v3을 사용하는 것을 권장합니다. This synthetic dataset was created to apply the methods of fineweb-edu to Korean datasets. v1 and v","None","ko","mono","None","None","False","auto","False","mit","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","None","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"
+"sungmogi/en2ko_hiphop","sungmogi","2023-08-19 10:48:51","2023-08-27 08:28:05+00:00","a296ae798154cd746c574f2668a04d99798702c1","39","302","2","task_categories:translation, language:en, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset Card for ""en2ko_hiphop"" Copyright Disclaimer The dataset ""en2ko_hiphop"" was curated from publicly available sources and is believed to be in the public domain. The translations provided in thi","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"nebchi/kor-orpo","nebchi","2024-06-17 23:31:33","2024-06-18 23:28:39+00:00","7c9e07c3e59310d830176c1d9410e793a9c8f39e","37","301","1","language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","axolotl does not take revision arg as an option and i'm lazy so i made this. type: chat_template.argilla Dataset Source Created with reference to the following datasets. maywell/ko_Ultrafeedback_binar","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","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","None","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。 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"
+"nayohan/KOLD","nayohan","2024-07-07 09:45:05","2024-07-07 12:10:08+00:00","2e7aa9d2e202db21eb15c3ab10baff60cf7b8a98","37","299","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety","None","reference: https://github.com/boychaboy/KOLD @inproceedings{jeong2022kold, title={KOLD: Korean Offensive Language Dataset}, author={Jeong, Younghun and Oh, Juhyun and Lee, Jongwon and Ahn, Jaimeen and","None","ko","mono","None","None","False","False","False","None","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"
+"ReDUB/SoundHarvest","ReDUB","2023-11-20 04:21:11","2023-12-14 22:51:51+00:00","165f683c00b3aae4d99c0647cfb0115655614fe3","76","297","2","None","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"
+"binjang/NIKL-korean-english-dictionary","binjang","2024-02-24 22:39:11","2024-02-24 22:45:30+00:00","3b4cfd2126debfd5440cade3ef6c2a3c20cf7cf9","61","296","5","task_categories:translation, task_categories:token-classification, language:ko, language:en, license:mit, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation, token-classification","Column Name Type Description 설명 Form str Registered word entry 단어 Part of Speech str or None Part of speech of the word in Korean 품사 Korean Definition List[str] Definition of the word in Korean 해당 단어의","None","ko, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","None","None","None"
+"nayohan/CodeFeedback-Filtered-Instruction-ko","nayohan","2024-06-30 08:27:40","2024-06-30 10:20:08+00:00","7490b48c4766482207c5ef000cffddd0fbd449ba","99","294","2","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","None","Dataset Card for ""CodeFeedback-Filtered-Instruction-ko"" Translated m-a-p/CodeFeedback-Filtered-Instruction using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"dev7halo/korean-mcfaq","dev7halo","2023-06-19 04:16:31","2023-06-20 05:17:49+00:00","57e51083a0958e06e02ef51b65860d1593cb3117","60","293","4","language:ko, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Usage pip install datasets from datasets import load_dataset dataset = load_dataset(""dev7halo/korean-mcfaq"") DatasetDict({ train: Dataset({ features: ['Unnamed: 0', '제목', '등록일', '질문', '답변'], num_rows:","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"sh2orc/bccard-qna-augmented","sh2orc","2024-05-26 14:19:20","2024-05-28 14:08:14+00:00","7cc15151c0a4ec2f689d7d788b9ba4bc49475740","46","290","0","language:ko, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Data Augmented BC Card Q&A Dataset by BERT Insertion Korean Payment","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"ChuGyouk/AIHUB-mathsolution-edit","ChuGyouk","2024-10-15 05:16:00","2024-10-15 05:23:38+00:00","675846bcd598bc3c3570db31c7ec779149af9f47","47","289","4","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math","None","Details This is the subset of 수학 과목 자동 풀이 데이터 from AIHUB, and do edit by Qwen/Qwen2.5-72B-Instruct. To see the difference of answers between original dataset and this dataset, go to the original data ","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"SungJoo/KBMC","SungJoo","2024-06-10 16:06:44","2024-06-10 16:15:54+00:00","9cd99c088d3192577f9355b4832b96c3087996e8","48","289","0","task_categories:text-classification, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.16158, region:us, medical, NER, Korean, Bio-medical","text-classification","Dataset Summary This dataset is the first open-source medical Named Entity Recognition (NER) dataset in Korean language. Dataset Structure Data Instances Each row in the KBMC.tsv file contains a sente","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"coastral/korean-writing-style-instruct","coastral","2024-09-07 07:55:17","2024-09-07 14:54:18+00:00","9f31462314f07847534fd13f7406c330ab15d156","61","289","3","task_categories:text-generation, 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","text-generation","한국어 문체 데이터셋 여러 분야의 문체(문학, 일상적 대화, 고전시가 등)를 포함한 합성 데이터셋입니다. 모델에게 여러 문체를 출력하는 능력을 가르치기 위해 제작되었습니다. 훈련시키실 때, 일반 인스트럭스 데이터셋과 혼용해서 사용하시는 것을 추천드립니다. 이 데이터셋은 apache-2 라이선스로 자유롭게 이용하실 수 있습니다. 데이터는 Glaive 플랫폼을","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"blueapple8259/OSCAR-ko-cleaned","blueapple8259","2024-09-05 17:05:33","2024-09-05 17:14:26+00:00","4b3df00069295c4ab804d4406c758eefdfaedbfa","84","289","1","task_categories:text-generation, language:ko, license:mit, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Warning: No filtering related to safety has been applied. There is a high possibility that inappropriate content may be included in the data, so please be cautious. Original: OSCAR-2301 8gb 5m","None","ko","mono","None","None","False","False","False","mit","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","None","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","None","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"
+"iknow-lab/wildguardmix-test-ko","iknow-lab","2024-10-19 07:24:10","2024-10-19 07:26:24+00:00","b6a1b2f1fc9156f00f19023f90e063897de776e3","47","283","0","task_categories:text-classification, language:ko, license:odc-by, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.18495, region:us","text-classification","Original dataset: allenai/wildguardmix Translated by nayohan/llama3-instrucTrans-enko-8b @misc{wildguard2024, title={WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusal","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","odc-by","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","None","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"
+"werty1248/Korean-1930-Novel-Scene-Summarize","werty1248","2024-06-25 07:01:07","2024-06-25 07:15:54+00:00","3dbf48550c3b8e83da6491cd77ba6531360f8b24","59","283","2","task_categories:summarization, language:ko, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","summarization","한국 저작권 만료 소설에 대한 씬 분리 및 요약 데이터 셋 원천 데이터 출처: https://gongu.copyright.or.kr/gongu/wrt/wrtCl/listWrtText.do?menuNo=200019 총 96개 소설 수집 및 전처리 한자가 많은 소설 제외 한자 제거, 띄어쓰기 전처리 수행 씬 분리 사용 모델: Gemini-1.5-Flash (띄","None","ko","mono","None","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","None","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"
+"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-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"
+"hecatonai/Housing_Subscription_QA_Dataset","hecatonai","2024-09-09 01:21:38","2024-09-24 04:56:47+00:00","a17b81f209fc6a35d9898c461c33f2684856b6e7","35","278","0","task_categories:question-answering, language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","question-answering","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 Description Language(s) (NLP): Korean Dataset Source","None","ko","mono","n|<|1|K","None","False","False","False","None","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","None","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"
+"LGAI-EXAONE/KoMT-Bench","LGAI-EXAONE","2024-08-05 06:32:49","2024-08-08 00:42:43+00:00","e240c8154aee230b48d5e716e9c23759f4d72c64","53","273","31","task_categories:question-answering, language:ko, license:lgpl-3.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.05685, arxiv:2408.03541, region:us, evaluation, instruction-following, language model, LLM-as-a-judge","question-answering","KoMT-Bench Introduction We present KoMT-Bench, a benchmark designed to evaluate the capability of language models in following instructions in Korean. KoMT-Bench is an in-house dataset created by tran","None","ko","mono","n|<|1|K","None","False","False","False","lgpl-3.0","2306.05685","None","None"
+"kifai/KoInFoBench","kifai","2024-04-25 23:47:29","2024-05-18 10:16:59+00:00","90bf8c459ec136fe5e35c04be3f3f42d721d7b62","40","270","3","task_categories:text-generation, language:ko, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","KoInFoBench KoInFoBench is a specialized evaluation dataset designed to assess the performance of Large Language Models (LLMs) on capabilities of Korean instructions following. The current version of ","None","ko","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"neulab/PangeaBench-xgqa","neulab","2024-10-31 06:03:54","2024-10-31 19:51:25+00:00","41e9f2a6610433a949d9ad7b7e0ea9378bbb949b","73","268","0","None","visual-question-answering","xGQA This is a clone of the few_shot-test split of the xGQA dataset Please find the original repository here: https://github.com/adapter-hub/xGQA If you use this dataset, please cite the original auth","None","bn, de, en, id, ko, pt, ru, zh","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"KimuGenie/KLUE_mrc_negative_train","KimuGenie","2023-06-15 10:17:04","2023-06-22 04:18:36+00:00","f842c394cc8679de579cd554846e28063e7de749","38","264","2","task_categories:question-answering, language:ko, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2105.09680, region:us","question-answering","Dataset Card for ""KLUE_mrc_negative_train"" KLUE mrc train dataset에 BM25을 이용해서 question에 대한 hard negative text 20개를 추가한 데이터입니다. BM25로 hard negative text를 찾았고, preprocessing을 통해 중복 데이터를 최대한 삭제했습니다. 사용한 ","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"KETI-AIR/kor_web_questions","KETI-AIR","2023-11-08 06:32:03","2023-11-15 01:17:34+00:00","5c9f77a35d8b6c1dcce7e3a18f487d4421b0bf4d","56","264","0","task_categories:question-answering, task_ids:open-domain-qa, language:ko, 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, open-domain-qa","Dataset Card for WEB_QUESTIONS Licensing Information The data is distributed under the CC BY 4.0 license. Source Data Citation INformation @inproceedings{berant-etal-2013-semantic, title = ""Semantic P","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","open-domain-qa"
+"paion-data/wiktionary-data","paion-data","2024-11-25 14:02:28","2025-01-17 12:28:14+00:00","88818383512396159facef901b7374ef415e3076","124","264","1","language:en, language:de, language:la, language:grc, language:ko, language:peo, language:akk, language:elx, language:sa, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Natural Language Processing, NLP, Wiktionary, Vocabulary, German, Latin, Ancient Greek, Korean, Old Persian, Akkadian, Elamite, Sanskrit, Knowledge Graph","None","Wiktionary Data on Hugging Face Datasets wiktionary-data is a sub-data extraction of the English Wiktionary that currently supports the following languages: Deutsch - German Latinum - Latin Ἑλληνική -","None","en, de, la, grc, ko, peo, akk, elx, sa","multi","1|0|0|M|<|n|<|1|B","None","False","False","False","apache-2.0","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"
+"kurugai/KoMedText","kurugai","2024-03-12 05:08:14","2024-03-12 05:17:41+00:00","5546b680c997b2322cb0c993199b0701344125b0","49","261","3","language:ko, 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","이 데이터셋은 BI55/MedText을 deepl로 번역한 자료입니다.","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"AIdenU/orca_dpo_data_ko","AIdenU","2024-03-22 06:39:50","2024-04-01 00:05:00+00:00","21bc1a233ed7eefd1ffdb68eb7b5591946295bc5","43","260","0","language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","원본 데이터 : HuggingFaceH4/orca_dpo_pairs 원본 데이터 셋을 system, question, chosen, rejected 형태에 맞게 정제 후, squarelike/Gugugo-koen-7B-V1.1-AWQ 으로 번역 오번역된 데이터는 삭제 검수","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"lightblue/reranker_continuous_filt_max7_train","lightblue","2024-12-19 06:17:53","2025-01-07 01:13:36+00:00","32bd1c820d8da33f7f4780ae2252f1d3b82377f0","112","260","3","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"
+"ShoukanLabs/OpenNiji-Dataset-Aesthetic-Finetune","ShoukanLabs","2023-10-04 06:38:42","2023-10-04 06:41:14+00:00","c803e7566be88069138b180852c422cfd42b7f50","36","258","1","task_categories:text-to-image, language:en, language:ja, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, anime, dataset, Nijijourney, Midjourney, discord","text-to-image","Used in quality tuning for OpenNiji","None","en, ja, ko","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"neulab/PangeaBench-cvqa","neulab","2024-10-31 20:31:00","2024-10-31 20:40:41+00:00","cf7b2cdcae813b3df1470ea5a922a108cc67794f","74","258","0","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"
+"GMLBsst/RecoTravRoute","GMLBsst","2024-09-25 13:30:41","2024-10-01 01:55:09+00:00","546c89200b08dac926bb11bdb0789c323094619a","34","257","0","task_categories:question-answering, language:ko, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"traintogpb/aihub-kozh-translation-integrated-small-100k","traintogpb","2024-05-30 07:48:13","2024-05-30 08:42:54+00:00","6ed1ff6a1793e44edd39e842e3ec150c6f82e86b","38","257","0","task_categories:translation, language:ko, language:zh, 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-Zh Translation Dataset (Integrated) AI Hub의 한-중 번역 관련 데이터셋 10개를 병합한 자료입니다. 병합 시 총 데이터 개수는 5,934,596개이며, 이중 10,000개의 validation set와 2,000개의 test set가 분리되어 모든 데이터 사이즈(large-5.9m, base-1m, sma","None","ko, zh","bi","None","None","False","False","False","mit","None","None","None"
+"jihye-moon/klac_legal_aid_counseling","jihye-moon","2023-10-20 03:26:51","2023-10-20 03:49:28+00:00","2460d3a56b7a767acbfa7b77fea879332f0082d6","49","257","3","task_categories:text-classification, language:ko, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, le","conversational, text-classification","Dataset Description 법률구조공단의 법률구조상담 웹페이지를 크롤링하여 구축한 데이터셋 입니다.","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"floschne/xgqa_1k","floschne","2024-05-23 15:18:49","2024-05-23 15:24:12+00:00","a0193b43fc05f20b015775620e1a121495e11e8a","57","256","0","task_categories:visual-question-answering, language:bn, language:de, language:en, language:id, language:ko, language:pt, language:ru, language:zh, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2109.06082, region:us","visual-question-answering","xGQA 1K This is a 1K subset of the few_shot-test split of the xGQA dataset Please find the original repository here: https://github.com/adapter-hub/xGQA If you use this dataset, please cite the origin","None","bn, de, en, id, ko, pt, ru, zh","multi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"m2af/ko-emotion-dataset","m2af","2024-03-26 17:02:22","2024-03-31 14:30:29+00:00","874b2163c48aceb42d32ffe9f9bf16f2cc3c061a","48","254","0","language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","한국어 감정 단문 데이터셋 데이터 개요 데이터 개수: 총 1039개 라벨러: (A, B, C, D, E)로 표시 단문 출처: X, Threads, Youtube, Naver Blog, Naver Cafe 감정 라벨 분류 표 중분류, 소분류로 구분 소분류의 대표 감정을 기준으로 구분 중분류 (총 9개) 기쁨, 사랑, 슬픔, 두려움, 분노, 미움(상대방), 욕","None","ko","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"
+"Cohere/miracl-ko-corpus-22-12","Cohere","2023-01-30 16:21:23","2023-02-06 11:58:37+00:00","5f60de06665fd9a3c6fc7bcb04e9e6e38f2273db","35","253","4","task_categories:text-retrieval, task_ids:document-retrieval, annotations_creators:expert-generated, multilinguality:multilingual, language:ko, 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 (ko) 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","ko","mono","None","None","False","False","False","apache-2.0","None","None","document-retrieval"
+"blueapple8259/c4-ko-cleaned","blueapple8259","2024-06-29 11:03:37","2024-06-29 11:09:02+00:00","4029f94c1d0dea4ebcdd5b8504f5a396e3ff837f","68","252","4","task_categories:text-generation, language:ko, license:odc-by, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","학교 점심시간 때 할 거 없어서 만든 c4를 정제한 데이터입니다. 다 하면 컴퓨터가 감당 못 할 거 같아서 전체 데이터의 1/10만 진행하였으며 아마 품질은 안 좋을 겁니다. 파일 크기: 약 3gb 데이터 수: 1847023","None","ko","mono","None","None","False","False","False","odc-by","None","None","None"
+"Junnos/luckyvicky-DPO","Junnos","2024-07-09 05:16:39","2024-07-10 06:20:15+00:00","f697e302a4a38989a96d0a3e9fe412376fa0a136","39","252","1","task_categories:reinforcement-learning, task_categories:text2text-generation, language:ko, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, lifestyle, DPO","reinforcement-learning, text2text-generation","원영적 사고 데이터셋","None","ko","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"ChuGyouk/PubMedQA-test-Ko","ChuGyouk","2024-07-15 11:24:56","2024-08-16 05:52:35+00:00","63262adf46258146fbb433d1e08be35529abdf9d","53","250","2","task_categories:text-generation, language:ko, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-generation","This is the test data of PubMedQA, Korean translated version.","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"SachinPatel248/mqnli","SachinPatel248","2023-10-14 12:13:02","2023-10-14 12:55:37+00:00","0f1bd105c7088239e4f1c7fc4983a7fb93ed93b4","34","250","0","task_categories:text-classification, language:en, language:de, language:es, language:ar, language:zh, language:hi, language:pt, language:ru, language:ja, language:fr, language:ur, language:tr, language:ko, language:pl, language:it, language:sv, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","None","None","en, de, es, ar, zh, hi, pt, ru, ja, fr, ur, tr, ko, pl, it, sv","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"Bingsu/national_library_of_korea_book_info","Bingsu","2022-08-30 05:48:26","2022-08-30 08:32:14+00:00","64a374c848cde26885e77f50fa7de87d58697d5d","44","249","1","multilinguality:monolingual, language:ko, license:other, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","national_library_of_korea_book_info 국립중앙도서관에서 배포한, 국립중앙도서관에서 보관중인 도서 정보에 관한 데이터. License other (KOGL (Korea Open Government License) Type-1) According to above KOGL, user can use public works freely a","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","other","None","None","None"
+"KETI-AIR/kor_race","KETI-AIR","2023-11-03 01:02:06","2023-11-15 01:13:23+00:00","267e519d3a0e977d2ae85729660111336e4f70e8","46","247","0","task_categories:multiple-choice, task_ids:multiple-choice-qa, language:ko, license:other, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","multiple-choice, multiple-choice-qa","Dataset Card for race Licensing Information http://www.cs.cmu.edu/~glai1/data/race/ RACE dataset is available for non-commercial research purpose only. All passages are obtained from the Internet whic","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","multiple-choice-qa"
+"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"
+"youjunhyeok/Magpie-Llama-3.1-Pro-1M-v0.1-ko","youjunhyeok","2024-09-12 15:31:51","2024-10-24 07:13:35+00:00","b45f53d8e3ff53afbce4c99e48ab4f687eeb00a5","75","247","0","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","일부 필드 번역이 안되어 재번역 예정입니다. Translated Magpie-Align/Magpie-Llama-3.1-Pro-1M using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and ha","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"min9805/skb","min9805","2023-07-18 10:10:56","2023-07-26 05:50:58+00:00","220e25bbe3b2549347a21c984c49b4b2b8a6430e","47","246","0","task_categories:fill-mask, task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, task_categories:token-classification, task_ids:extractive-qa, task_ids:named-entity-recognition, task_ids:natural-language-inference, task_ids:parsing, task_ids:semantic-similarity-scoring, task_ids:text-scoring, task_ids:topic-classification, annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-4.0, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, relation-extraction","fill-mask, question-answering, text-classification, text-generation, token-classification, extractive-qa, named-entity-recognition, natural-language-inference, parsing, semantic-similarity-scoring, text-scoring, topic-classification","None","None","ko","mono","1|0|K|<|n|<|1|0|0|K","klue","False","False","False","cc-by-sa-4.0","None","None","extractive-qa, named-entity-recognition, natural-language-inference, parsing, semantic-similarity-scoring, text-scoring, topic-classification"
+"4n3mone/aya_collection_korean","4n3mone","2024-06-14 00:23:44","2024-06-14 00:58:29+00:00","954f263dacdc6e733242f018ab5b1512b047ecf1","44","245","3","language:ko, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","CohereForAI/aya_collection_language_split 에서 한국어 스플릿만 추출한 데이터셋 입니다. 데이처 출처(dataset name)는 다음과 같습니다. (번역된 데이터셋은 (T) 표시) Aya-Dataset Flan-CoT-submix(T) Adversarial QA(T) Flan-Coqa(T) Flan-unified-QA(T) ","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"traintogpb/aihub-flores-koen-integrated-prime-base-300k","traintogpb","2024-05-20 05:27:32","2024-05-23 02:11:48+00:00","ead9e09e51d8a86c9566c547987a42b09a2115f2","43","245","0","task_categories:translation, language:en, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, xcomet, high-qualtiy, translation","translation","High Quality Ko-En Translation Dataset (AIHub-FLoRes Integrated) AI Hub의 한-영 번역 데이터셋과 FLoRes 한-영 번역 데이터셋의 합본입니다. High Quality AIHub Dataset AI Hub의 경우 한-영 번역 관련 데이터셋을 8개 병합한 병렬 데이터 traintogpb/aihub-ko","None","en, ko","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"CHEONINJEONG/Korea_address","CHEONINJEONG","2023-11-14 00:41:52","2023-11-15 04:25:25+00:00","55b6fa5e7a9f5cf583bd89cfa28cb30f80d5f34d","58","244","1","task_categories:feature-extraction, language:ko, size_categories:n<1K, format:parquet, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","feature-extraction","None","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"ChuGyouk/AI-MO-NuminaMath-TIR-korean-240918","ChuGyouk","2024-09-12 11:40:47","2024-09-17 16:32:54+00:00","edbe1e7d733190674507dd6faa6683eefeed0498","91","244","4","task_categories:text-generation, language:en, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math, aimo","text-generation","IMPORTANT NOTE This data is part of the progress. Current translation progress: 24.85% (2024-09-18 01:32 KST) I'm taking a short break due to personal reasons. I'll be back in a month. TODO-LIST Finis","None","en, ko","bi","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"kuotient/Verified-Camel-KO","kuotient","2023-11-21 04:03:18","2023-11-21 04:20:55+00:00","4a1bb324202e4ba2c680887f9bc2efc86afd8165","44","244","4","task_categories:question-answering, task_categories:text-generation, language:ko, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Physics, Biology, Math, Chemistry, Culture, Logic","conversational, question-answering, text-generation","Verified-Camel-KO 이 데이터셋은 https://huggingface.co/datasets/LDJnr/Verified-Camel 의 한국어 번역입니다. GPT4 Turbo로 번역한 뒤, 약간의 수정을 거쳤습니다. 이 데이터에 대한 방침은 전부 원 저자의 방침을 따릅니다. This is the Official Verified Camel datas","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"devngho/oldhangul-dataset-cleaned","devngho","2024-05-13 13:06:14","2024-07-04 14:47:44+00:00","61213ce6e5c0664604963b30cbe21ebc31c51a61","39","242","2","language:ko, 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, oldhangul","None","옛한글 데이터셋 original | cleaned(HERE) 위키문헌을 기반으로 구축한 옛한글 데이터셋 불러오기 from datasets import load_dataset ds = load_dataset(""devngho/oldhangul-dataset-cleaned"", ""wikisource_v2"") 라이선스 데이터셋 CC BY-SA 4.0 DEED 자료 ","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"haebo1/test","haebo1","2024-03-28 05:39:52","2024-03-28 05:58:17+00:00","515b880920fc70ebf779dc7149731b010186f9d0","43","242","0","annotations_creators:expert-generated, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2204.04541, region:us","None","Dataset Card for KoBEST Dataset Summary KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean. Supported Tasks and Leaderboar","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"mangostin2010/KakaoChatData","mangostin2010","2023-08-25 10:20:53","2023-08-25 16:53:42+00:00","0db284badcbfcfe696814c968e55153d9a817a50","50","242","1","language:ko, license:unknown, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"nlp-with-deeplearning/Ko.HelpSteer","nlp-with-deeplearning","2023-12-04 03:18:07","2023-12-04 03:25:23+00:00","56303eec1fe0f52eb30a3191a0ec569953f9b34b","43","241","1","language:en, language:ko, license:cc-by-nc-sa-4.0, size_categories:10K<n<100K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","원본 데이터셋: nvidia/HelpSteer","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"deepghs/arknights_voices_kr","deepghs","2024-08-27 15:03:22","2024-08-28 04:20:15+00:00","f1264d2265f074325f330d62711a095507457be3","43","241","0","task_categories:automatic-speech-recognition, task_categories:audio-classification, language:ko, license:other, size_categories:1K<n<10K, 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","KR Voice-Text Dataset for Arknights Waifus This is the KR voice-text dataset for arknights playable characters. Very useful for fine-tuning or evaluating ASR/ASV models. Only the voices with strictly ","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","other","None","None","None"
+"PurpleBananas/komed","PurpleBananas","2024-06-12 03:38:30","2024-06-17 03:42:12+00:00","a170ca60af03bd913bd98a41ac376eac5b30b764","43","240","0","task_categories:text2text-generation, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text2text-generation","None","None","ko","mono","n|<|1|K","None","False","False","False","None","None","None","None"
+"nayohan/Sujet-Finance-Instruct-177k-ko","nayohan","2024-06-30 12:41:59","2024-06-30 13:00:17+00:00","ed4b07618dec78da6d262df217f61a34a6bc183d","37","237","3","task_categories:question-answering, task_categories:text-generation, language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance, ko","question-answering, text-generation","Translated sujet-ai/Sujet-Finance-Instruct-177k using nayohan/llama3-instrucTrans-enko-8b. It may contain repetitive sentences, so recommend filtering them.","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"Ssua/tagdataset","Ssua","2023-08-04 03:21:01","2023-08-04 04:29:04+00:00","50c4dec52453b45d854c3e77d8135096a1135369","34","236","0","task_categories:text-generation, language:ko, license:unknown, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mgame","text-generation","None","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"nlp-with-deeplearning/ko.databricks-dolly-15k","nlp-with-deeplearning","2023-12-04 03:12:08","2023-12-04 03:27:59+00:00","38ee9a74b40f897c2347b5068fda5d7208cac0ce","38","236","1","task_categories:question-answering, task_categories:summarization, language:en, language:ko, 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","question-answering, summarization","원본 데이터셋: databricks/databricks-dolly-15k","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"ohjimin/hscode","ohjimin","2024-06-27 22:43:55","2024-07-01 08:53:03+00:00","5bb649e5cc45a439f683059ade5d5a820f85eb03","58","235","0","task_categories:table-question-answering, language:ko, language:en, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","table-question-answering","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","ko, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"jp1924/NaturalandArtificialOccurrenceNonverbalSoundDatasets","jp1924","2024-02-06 13:12:53","2024-06-14 06:06:31+00:00","a400696cfbf528bb1e5eb9f95939118fa95cc2e4","34","233","0","task_categories:automatic-speech-recognition, language:ko, size_categories:10K<n<100K, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, STT, Audio, Noise","automatic-speech-recognition","노이즈 데이터인데 노이즈 앞쪽에 해당 음성은 ~~해서 어쩌구 저쩌구 들어가 있음. 전처리 할 때 참고","None","ko","mono","1|0|B|<|n|<|1|0|0|B","None","False","auto","False","None","None","None","None"
+"gegeta/allist","gegeta","2024-07-18 06:17:15","2024-07-29 00:49:13+00:00","b965e049505588024028cccd0008b6e4f3832b02","46","232","0","language:ko, language:en, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko, en","bi","1|0|M|<|n|<|1|0|0|M","None","False","False","False","apache-2.0","None","None","None"
+"instructkr/ko_elo_arena_0207","instructkr","2024-02-07 11:52:47","2024-02-09 11:38:27+00:00","9c68df0515a0a67ca0b02028689eb2e91452b298","47","232","22","language:ko, size_categories:n<1K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for ""ko_elo_arena_0207"" More Information needed","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"zhihz0535/X-SVAMP_en_zh_ko_it_es","zhihz0535","2024-01-27 21:42:53","2024-01-27 22:23:58+00:00","8eb3e02cefdb17e3672b810d700486eeec76b5a0","77","232","1","None","question-answering, text-generation","X-SVAMP 🤗 Paper | 📖 arXiv Dataset Description X-SVAMP is an evaluation benchmark for multilingual large language models (LLMs), including questions and answers in 5 languages (English, Chinese, Korean","None","en, zh, it, ko, es","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","2311.08711","None","None"
+"upskyy/ko-wiki-reranking","upskyy","2024-08-15 06:49:08","2024-08-15 07:16:28+00:00","e2a98d2f6bfc16227c2fdd7e78ac9d21800f46d3","38","231","6","task_categories:text-classification, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-classification","ko-wiki-reranking maywell/ko_wikidata_QA 데이터셋을 랜덤 샘플링하여 50,000개의 reranking 학습 데이터셋을 만들었습니다. train_minedHN.jsonl 파일을 사용하여 학습하시는 것을 권장합니다. FlagEmbedding 코드 기반으로 학습 데이터셋을 만들었습니다. train.jsonl : 50,000개의 r","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"CarrotAI/KoreaWebTextDataset","CarrotAI","2024-08-01 13:34:08","2024-08-01 13:40:31+00:00","6eaaf59f3ba8ff6213378adcd57653a1831cfef1","40","231","0","language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, webtext","None","한국어 웹 텍스트 데이터입니다. 학습으로만 사용하길 바랍니다.","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"junyeong-nero/kin_med_100K_edited","junyeong-nero","2024-08-16 05:37:31","2024-08-20 08:22:03+00:00","5290de10b41e103ad0c0d58e39e4c91cbd585688","42","231","1","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","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","None","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"
+"blueapple8259/wikipedia-ko","blueapple8259","2024-07-26 11:39:50","2024-07-31 03:09:54+00:00","f9154f19bca03b031634b27b2ccadea2df59df91","48","230","0","task_categories:text-generation, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","None","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"nlp-with-deeplearning/ko.lima","nlp-with-deeplearning","2023-11-30 08:24:22","2023-12-04 03:31:00+00:00","cd2ac81f7584d4ec33631192be439a1dded3001d","38","230","1","language:en, language:ko, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","원본 데이터셋: GAIR/lima","None","en, ko","bi","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"CanariaView/GlobalCopperSupplyForecastingDataset","CanariaView","2023-12-30 17:03:56","2023-12-30 17:21:30+00:00","86e74111e73d95ecfb69de292c614d521234fb76","41","230","3","task_categories:time-series-forecasting, language:en, language:ko, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mining, LSTM, TimeSeries, CanariaView","time-series-forecasting","CanariaView Global Copper Supply Forecasting Dataset Description This dataset encompasses economic and industrial indicators vital for constructing a copper supply forecasting model. Coverage Period: ","None","en, ko","bi","None","None","False","False","False","None","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","None","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"
+"nlp-with-deeplearning/ko.SHP","nlp-with-deeplearning","2023-11-30 08:56:00","2023-12-04 03:24:48+00:00","033dfacd4b23e4171dd40e2001ecdac1eba9a748","39","229","1","task_categories:text-generation, task_categories:question-answering, language:ko, language:en, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2112.00861, arxiv:2001.08435, region:us, human feedback, rlhf, preferences, reddit, preference model, RL, NLG, evaluation","text-generation, question-answering","🚢 Korean Stanford Human Preferences Dataset (Ko.SHP) 이 데이터셋은 자체 구축한 번역기를 활용하여 stanfordnlp/SHP 데이터셋을 번역한 것입니다. 아래의 내용은 해당 번역기로 README 파일을 번역한 것입니다. 참고 부탁드립니다. If you mention this dataset in a paper, pl","None","ko, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-nc-sa-4.0","2001.08435","None","None"
+"shchoice/finance-legal-mrc","shchoice","2024-11-19 05:56:01","2024-11-25 01:21:51+00:00","862644c8087f7a1971801df84cebb1144a422ac7","50","228","0","language:ko, 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, mrc, korean, finance, legal","None","금융, 법률 문서 기계독해 데이터셋 금융 및 법률 분야 전문문서를 활용하여 기계독해 모델 생성을 위한 지문-질문-답변으로 구성된 데이터셋입니다. 데이터셋 구성 Subsets span_extraction: 정답경계 추출형(정답이 지문 내 특정 범위에 있는 데이터) span_extraction_how: 절차(방법)(어떻게로 시작하는 질문에 대한 정답 추출 데이","None","k, o","bi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"PrompTartLAB/PTT_en_ko","PrompTartLAB","2024-09-28 12:51:09","2024-10-06 03:36:00+00:00","ac979abee63c0a3ee17b1e25e64d3fa635142018","47","228","3","task_categories:translation, language:en, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.00683, region:us","translation","Parenthetical Terminology Translation (PTT) Dataset Overview The Parenthetical Terminology Translation (PTT) dataset is designed for evaluating and training models in translating technical terms while","None","en, ko","bi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"richard-park/aihub-contents-ko-only","richard-park","2024-05-07 07:03:40","2024-05-07 07:29:27+00:00","5feb867f0f26507a928169d104360e2fd77bd240","45","227","0","task_categories:translation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","None","None","ko","mono","None","None","False","False","False","apache-2.0","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","None","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"
+"yuneun92/admin-multiplechoice-100","yuneun92","2024-04-19 08:47:40","2024-04-21 10:37:36+00:00","f7a0f57f579dec8c30b47c529833fc31b7d8edc0","40","226","0","language:ko, license:cc-by-nc-4.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","📑 행정 문서 - multiple choice 데이터셋 📑 AI Hub의 행정 문서 기계독해 데이터셋을 이용해, 100개의 텍스트에 대해 각 텍스트마다 5개 이하의 질문-답변 쌍을 만든 데이터셋입니다. 질문 생성은 kullm3을, 답변 생성은 gpt 3.5 turbo API를 이용해 생성했습니다.","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"aldente0630/LLaVA-Pretrain-Ko","aldente0630","2024-07-27 13:10:22","2024-08-15 15:47:03+00:00","66e60fc7bb3f91802aa9480d11eb7f6371a660af","42","225","1","task_categories:visual-question-answering, language:ko, license:other, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.08485, region:us","visual-question-answering","LLaVA 비전 인스트럭트 사전 훈련 데이터셋 카드 개요 이 데이터셋은 하오톈 리우가 LLaVA 모델의 사전 훈련을 위해 제작한 LLaVA-Pretrain 데이터셋의 한국어 버전입니다. 상세 원 데이터셋의 blip_laion_cc_sbu_558k.json 파일에 해당합니다. 이미지 파일(image.zip)은 원본 리포지토리에서 별도로 다운로드할 수 있습니다","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","other","2304.08485","None","None"
+"youjunhyeok/Magpie-Llama-3.1-Pro-300K-Filtered-ko","youjunhyeok","2024-08-23 06:38:56","2024-08-23 07:23:04+00:00","c4188e05f0bf509b6bec468b5dc0246d352584ba","42","224","1","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","Translated Magpie-Align/Magpie-Llama-3.1-Pro-300K-Filtered using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of ","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"meenham/MSC_korean","meenham","2024-03-10 07:17:15","2024-03-10 15:26:50+00:00","9ca57fa8b6468e744fa08d155104fde1df709620","54","223","1","task_categories:translation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Data source MSC data from the paper < Beyond Goldfish Memory: Long-Term Open-Domain Conversation > train/valid/test dataset of session 4 translation ( English -> Koeran ) GPT-3.5-turbo is used mostly ","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"nayohan/korean-hate-speech","nayohan","2024-07-07 08:22:36","2024-07-07 08:25:42+00:00","3e5b0ae2b1559db61982b47af66367306b67c407","34","223","0","language:ko, 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, safety","None","reference: https://github.com/kocohub/korean-hate-speech @inproceedings{moon-etal-2020-beep, title = ""{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection"", author = ""Moon, Jihyu","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Suchae/korean-judgment-easyread-transform","Suchae","2024-09-18 05:15:05","2024-09-18 05:24:56+00:00","5eb1f5519edd5d13b22c58202e46416543963542","41","221","2","task_categories:summarization, language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, legal","summarization","None","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"davanstrien/ml-kge","davanstrien","2023-12-21 12:06:12","2023-12-21 12:24:14+00:00","95eb358969c07270821c7fdc73201d7375afb309","49","221","0","None","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"
+"fluently-sets/ultraset","fluently-sets","2024-12-22 13:50:55","2024-12-22 14:41:06+00:00","e3f0723176879c491afa4dd490439dfe2882ea21","78","220","3","None","text-generation, text2text-generation, text-classification, question-answering, translation","Ultraset - all-in-one dataset for SFT training in Alpaca format About the dataset This dataset is designed to facilitate training and retraining of LLM models using the SFT method in the Alpaca format","None","en, ru, fr, it, zh, ko, de, es, code","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","other","None","None","None"
+"nayohan/SQuARe-question","nayohan","2024-07-07 02:39:16","2024-07-07 06:48:12+00:00","98cfea51c3b55e99c4011eb8ac0c7a799f981523","34","220","2","language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety","None","reference: https://github.com/naver-ai/korean-safety-benchmarks @inproceedings{lee2023square, title={SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"FrancophonIA/W2C","FrancophonIA","2024-10-30 20:40:52","2024-10-30 22:42:30+00:00","0433b50f7ba2f383a1ba3a48ed32d0353f77037b","71","220","0","task_categories:token-classification, multilinguality:multilingual, language:af, language:als, language:sq, language:am, language:ar, language:an, language:hy, language:ast, language:az, language:eu, language:be, language:bn, language:bpy, language:bs, language:br, language:bg, language:ca, language:cv, language:hr, language:cs, language:da, language:diq, language:nl, language:arz, language:en, language:eo, language:et, language:fo, language:hif, language:fi, language:fr, language:gl, language:ka, language:de, language:glk, language:gu, language:ht, language:he, language:hi, language:hu, language:is, language:io, language:id, language:ia, language:ga, language:it, language:jv, language:kn, language:kk, language:ko, language:ku, language:la, language:lv, language:li, language:lt, language:lmo, language:nds, language:lb, language:mk, language:mg, language:ms, language:ml, language:mi, language:mr, language:el, language:mn, language:nap, language:ne, language:new, language:no, language:nn, language:pam, language:fa, language:pms, language:pl, language:pt, language:ro, language:ru, language:sco, language:gd, language:sr, language:sh, language:sk, language:sl, language:es, language:su, language:sw, language:sv, language:gsw, language:tl, language:tg, language:ta, language:tt, language:te, language:tr, language:uk, language:hsb, language:ur, language:uz, language:vec, language:vi, language:vo, language:wa, language:war, language:cy, language:fy, language:sah, language:yi, language:blc, language:bel, language:bug, language:ceb, language:cos, language:gan, language:jpn, language:mya, language:yor, language:zho, license:cc-by-sa-3.0, size_categories:100M<n<1B, format:text, modality:text, library:datasets, library:mlcroissant, region:us","token-classification","Dataset origin: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0022-6133-9# Description A set of corpora for 120 languages automatically collected from wikipedia and the web. Co","None","af, als, sq, am, ar, an, hy, ast, az, eu, be, bn, bpy, bs, br, bg, ca, cv, hr, cs, da, diq, nl, arz, en, eo, et, fo, hif, fi, fr, gl, ka, de, glk, gu, ht, he, hi, hu, is, io, id, ia, ga, it, jv, kn, kk, ko, ku, la, lv, li, lt, lmo, nds, lb, mk, mg, ms, ml, mi, mr, el, mn, nap, ne, new, no, nn, pam, fa, pms, pl, pt, ro, ru, sco, gd, sr, sh, sk, sl, es, su, sw, sv, gsw, tl, tg, ta, tt, te, tr, uk, hsb, ur, uz, vec, vi, vo, wa, war, cy, fy, sah, yi, hsb, blc, bel, bug, ceb, cos, gan, jpn, mya, yor, zho","multi","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"mintaeng/min_test","mintaeng","2024-05-28 06:26:42","2024-05-29 05:15:25+00:00","d8caf6388cbf3d026e9e577bc04eadd9b0ba1f4f","37","220","0","task_categories:text-generation, task_categories:question-answering, language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, question-answering","llama3_8b_ 모델 파인튜닝 테스트용 데이터입니다. 원본 데이터 : databricks-dolly-15k-ko.jsonl 응답 형식 변환 후 자체 질의응답 세트 추가","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"yjg30737/onepiece-characters","yjg30737","2023-07-08 05:28:59","2023-07-08 08:33:08+00:00","9668f2b388c3ccd0604a65e1043f2661e404ac96","34","219","1","task_categories:table-question-answering, language:en, language:ja, language:ko, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","table-question-answering","onepiece-character This is a dataset created from crawling the One Piece Fandom on 2023-07-08.","None","en, ja, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"poopki/ANSAN_WORK","poopki","2024-08-29 03:58:37","2024-08-29 11:03:52+00:00","2aed855cdfe79f337ccf127329d0469bead22582","37","219","0","task_categories:text-generation, task_categories:question-answering, language:ko, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation, question-answering","None","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"nayohan/Magpie-Air-MT-300K-v0.1-ko","nayohan","2024-08-01 15:57:47","2024-08-01 16:15:26+00:00","e0465e5975948e86db0abf272148f247946284fe","45","218","0","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean","text-generation","Translated Magpie-Align/Magpie-Air-MT-300K-v0.1 using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it need","None","ko","mono","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","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"
+"maum-ai/General-Evol-VQA","maum-ai","2024-11-19 06:37:23","2024-12-06 13:42:21+00:00","c0a0e1d22bfb95c81543c76bae8b16a10a8ce6b9","109","218","5","task_categories:visual-question-answering, task_categories:question-answering, language:ko, language:en, license:apache-2.0, size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","visual-question-answering, question-answering","Dataset Card for General-Evol-VQA-1.2M This dataset has been carefully curated to enhance the general instruction capabilities of Vision-Language Models (VLMs). It comprises two subsets: 600k English ","None","ko, en","bi","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"nayohan/hh-rlhf-ko","nayohan","2024-07-17 17:53:52","2024-07-17 17:59:10+00:00","6d1fb56899007228c2818d8ca8a5f06e887b39c1","40","217","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction, rlhf","None","Translated Anthropic/hh-rlhf using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filtered.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"lianghsun/Everything-Instruct-Multilingual-DPO","lianghsun","2024-12-05 01:37:25","2024-12-10 01:06:43+00:00","144446f373e7efa8bf2390f0e58a806aa0a07c4b","58","216","1","None","None","Dataset Card for lianghsun/Everything-Instruct-Multilingual-DPO Dataset Details Dataset Description 這個資料集是分叉��� 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"
+"zzunyang/legal_preference","zzunyang","2024-01-25 09:52:59","2024-01-25 10:05:06+00:00","39d10047381ce15c8b2c59ddba73ba20fda50756","34","215","0","language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","conversational","None","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"Taegyuu/KoAlpaca_hira_v1.1a","Taegyuu","2023-09-13 08:22:21","2023-09-13 11:11:19+00:00","59b9bc3030c14e798d08ecdecf1c5c3f1e0edbd6","35","214","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, KoAlpaca","text-generation","Dataset Card for ""KoAlpaca-v1.1a"" Project Repo Github Repo: Beomi/KoAlpaca How to use >>> from datasets import load_dataset >>> ds = load_dataset(""beomi/KoAlpaca-v1.1a"", split=""train"") >>> ds Dataset(","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"vitus9988/ko_gpt4omini_note_15.4k","vitus9988","2024-10-11 06:37:48","2024-10-29 03:25:08+00:00","b10fb88ec3c9acb9e295d00392014649ea177cba","49","214","0","language:ko, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, ChatGPT","None","한국어 메모 데이터셋 GPT-4o-mini를 통해 생성된 한국어 메모 데이터셋입니다. 대주제(main_topic), 소주제(sub_topic)를 통해 메모처럼 보이는 데이터를 생성하였습니다.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"datumo/KorNAT","datumo","2024-06-07 11:37:42","2024-06-07 11:38:51+00:00","731de342b04dc4f194eedc77ed96ef66fba1664a","37","214","0","task_categories:multiple-choice, language:ko, language:en, license:cc-by-nc-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.13605, region:us, national-alignment","multiple-choice","KorNAT (Korean National Alignment Test) When deploying LLMs in a specific country, it is essential to ensure that the model is aware of that country’s culture and basic knowledge, so called national a","None","ko, en","bi","1|0|<|n|<|1|2","None","False","False","False","cc-by-nc-2.0","2402.13605","None","None"
+"werty1248/sentence-transformer-parallel-En-Ko-with-Similarity","werty1248","2024-07-10 02:36:02","2024-07-10 03:45:02+00:00","13089fbdab4b06cd0d098b7432c206aac6bd2bf5","51","214","1","task_categories:translation, language:en, language:ko, size_categories:1M<n<10M, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Preprocessing En-Ko subset of Parallel Sentences Datasets 해외에서 제작된 많은 대규모 번역 쌍 데이터들이 영어 텍스트와 한국어 텍스트를 문장 단위로 분리한 후 기계적으로 매핑시키고 있습니다. 이로 인해 전혀 엉뚱한 문장이 번역 쌍으로 매칭되어 있는 문제가 발생합니다. 임베딩 유사도 기반 전처리를 통해 이 문제를","None","en, ko","bi","None","None","False","False","False","None","None","None","None"
+"KETI-AIR/kor_snli","KETI-AIR","2023-11-01 07:29:14","2023-11-15 01:12:23+00:00","fac5a975a687cb50298e26602bf3e54813414047","34","214","1","task_categories:text-classification, task_ids:natural-language-inference, task_ids:multi-input-text-classification, language:ko, license:cc-by-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","Dataset Card for QASC Licensing Information The data is distributed under the CC BY 4.0 license. Source Data Citation INformation @inproceedings{snli:emnlp2015, Author = {Bowman, Samuel R. and Angeli,","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","cc-by-4.0","None","None","natural-language-inference, multi-input-text-classification"
+"rycont/hanja-grade","rycont","2024-06-06 05:00:01","2024-06-06 05:03:04+00:00","7865c84448024b90ddad5943a8a0dee918a100f6","52","214","0","language:ko, license:unknown, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","한국어문회 등급별 선정한자 CSV 데이터셋 사단법인 한국어문회에서 분류한 급수별 한자 데이터셋입니다. 공식 홈페이지의 학습자료 / 기타자료에 업로드된 Xls 파일을 CSV 파일로 변환하여 제공합니���. 데이터의 저작권은 한국어문회에 있습니다. 데이터의 오류나 문제가 있을 경우 이슈를 남겨주시면 확인 후 수정하겠습니다. 데이터 구성 필드 interface Ha","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","unknown","None","None","None"
+"SangChan/KCC_Profit_DataSet_v2","SangChan","2023-08-08 06:15:56","2023-08-08 06:50:27+00:00","ab19212e4df4388c1e6079438bfd5fd9b28dcf21","52","213","1","task_categories:text-generation, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, chemistry","text-generation","Dataset Card for ""SangChan/KCC_Profit_DataSet_v2"" Project Repo Github Repo: Beomi/KoAlpaca How to use >>> from datasets import load_dataset >>> ds = load_dataset(""SangChan/KCC_Profit_DataSet_v2"", spli","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Junnos/luckyvicky","Junnos","2024-07-01 00:13:55","2024-07-08 23:17:55+00:00","8a7192df659d63aff63121afb58d44d39f6a6382","37","212","0","task_categories:text2text-generation, language:ko, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, lifestyle","text2text-generation","원영적 사고 데이터셋","None","ko","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"nayohan/Magpie-Phi3-Pro-300K-Filtered-ko","nayohan","2024-08-07 16:31:16","2024-08-07 17:10:47+00:00","7cb4dc2602885486e51f599f09ae6d493da77909","37","212","1","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, li","text-generation","Translate Magpie-Align/Magpie-Phi3-Pro-300K-Filtered using nayohan/llama3-instrucTrans-enko-8b. This is a raw translation dataset. It needs to be filtered for repetitions generated by the model. @misc","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"nayohan/Evol-Instruct-Code-80k-v1-ko","nayohan","2024-08-07 15:34:18","2024-08-07 17:01:55+00:00","122d0bddfbb8a416251aa9bc2dba87f7bc69c92e","62","212","1","task_categories:text-generation, language:ko, 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, instruction, korean","text-generation","Translated nickrosh/Evol-Instruct-Code-80k-v1 using nayohan/llama3-instrucTrans-enko-8b. This is a raw translation dataset. It needs to be filtered for repetitions generated by the model.","None","ko","mono","None","None","False","False","False","cc-by-nc-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","None","audio-classification, automatic-speech-recognition, audio-text-to-text, text-to-speech, question-answering, document-question-answering","Belebele-Fleurs Belebele-Fleurs is a dataset suitable to evaluate two core tasks: Multilingual Spoken Language Understanding (Listening Comprehension): For each spoken paragraph, the task is to answer","None","af, am, ar, az, as, bm, bn, bo, bg, ca, cs, ku, da, de, el, en, es, et, eu, fi, fr, ff, om, gu, gn, ht, ha, he, hi, hr, hu, hy, ig, id, it, is, jv, ja, ka, kn, kk, mn, km, rw, ky, ko, lo, ln, lt, lg, lv, ml, mr, mk, mt, mi, my, nl, no, ne, ny, or, pa, ps, fa, mg, pl, pt, ro, ru, sn, si, sl, sv, sk, sd, sw, ta, te, tg, tl, th, ti, tn, ts, tr, uk, ur, uz, vi, wo, xh, yo, zh, ms, zu, multilingual","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"lamhieu/sharegpt_dialogue_base","lamhieu","2024-01-21 07:55:43","2024-05-17 10:49:21+00:00","e5f4e61891c6f10df3a5cfbc363d0ed96b28bfee","39","211","1","task_categories:text-generation, task_categories:text2text-generation, language:en, language:vi, language:zh, language:es, language:pt, language:ja, language:ko, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation, text2text-generation","Description The dataset is from unknown, formatted as dialogues for speed and ease of use. Many thanks to author for releasing it. Importantly, this format is easy to use via the default chat template","None","en, vi, zh, es, pt, ja, ko","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","None","None","None"
+"fine-tuned/BAAI_bge-m3-6142024-0ndt-webapp","fine-tuned","2024-06-14 06:41:18","2024-06-14 06:41:18+00:00","b50bbf83db427908fd5cb717df1224ab9fe05b5a","37","211","0","task_categories:feature-extraction, task_categories:sentence-similarity, language:ko, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sentence-transformers, feature-extraction, sentence-similarity, mteb, Profanity, Korean, Conversational, Detection, Moderation","feature-extraction, sentence-similarity","BAAI_bge-m3-6142024-0ndt-webapp Dataset Dataset Description The dataset ""content moderation"" is a generated dataset designed to support the development of domain specific embedding models for retrieva","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"nayohan/KoSBi-v2","nayohan","2024-07-07 02:13:03","2024-07-07 02:21:34+00:00","f0671d6e13767ef8e5c633c6fe53f3d22ef264ac","48","210","1","language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety","None","reference: https://github.com/naver-ai/korean-safety-benchmarks @inproceedings{lee2023kosbi, title={KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application}, a","None","ko","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","None","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"
+"werty1248/OpenOrca-EnKoZhJa-18k","werty1248","2024-08-10 18:54:09","2024-08-10 19:16:35+00:00","212355bd7cc6e9651cff74402d0f638981235a2b","44","209","0","None","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"
+"MichiganNLP/MAiDE-up","MichiganNLP","2024-05-07 15:57:04","2024-05-10 15:09:27+00:00","045e9933ba5c8c216a49c5efc69d07a766c38c3e","45","209","3","task_categories:text-classification, task_categories:zero-shot-classification, language:en, language:zh, language:fr, language:de, language:it, language:ko, language:ru, language:es, language:tr, language:ro, license:mit, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2404.12938, region:us, misinformation, LLM, multilingual, reviews","text-classification, zero-shot-classification","Dataset Card for MAiDE-up: Multilingual Deception Detection of GPT-generated Hotel Reviews Dataset Summary Multilingual Deception Detection of GPT-generated Hotel Reviews. We compare real hotel review","None","en, zh, fr, de, it, ko, ru, es, tr, ro","multi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2404.12938","None","None"
+"nayohan/HC3-ko","nayohan","2024-06-30 08:29:59","2024-06-30 10:36:50+00:00","4ae232d43e4c2d6ee7199a911a320d440bf228b7","40","208","0","task_categories:question-answering, task_categories:text-generation, language:ko, 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, ChatGPT, DPO, ko","question-answering, text-generation","Dataset Card for ""HC3"" Translated Hello-SimpleAI/HC3 using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"devngho/oldhangul-dataset","devngho","2024-05-13 13:01:33","2024-07-04 14:48:26+00:00","eeba90e1a9d5be4248a60bacef24771feb9d09ad","49","207","0","language:ko, 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, oldhangul","None","옛한글 데이터셋 original(HERE) | cleaned 위키문헌을 기반으로 구축한 옛한글 데이터셋 불러오기 from datasets import load_dataset ds = load_dataset(""devngho/oldhangul-dataset"", ""wikisource_v2"") 라이선스 데이터셋 CC BY-SA 4.0 DEED 자료 위키문헌 기여자","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"CarrotAI/kmmlu-conversation-sample","CarrotAI","2024-08-06 15:10:45","2024-08-14 02:03:17+00:00","281be1d469cc2346b2bb3b131756961569dae48d","39","207","1","task_categories:text-generation, language:ko, license:mit, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","Kmmlu 데이터를 이용해서 대화 데이터셋 샘픔을 생성하였습니다. 멀티턴 데이터셋으로 학습용도로 만들어졌습니다.","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"teddylee777/rag-eval-mini","teddylee777","2024-09-03 14:49:20","2024-09-05 13:21:22+00:00","c3efa1c82c7a4c43a82dd88f5490390ae108202a","35","207","2","language:ko, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"Yettiesoft/ragtruth-qa-ko","Yettiesoft","2024-07-25 03:13:06","2024-07-25 03:31:34+00:00","748db40ada4ae36a1e6cc023d30c22f76739214d","53","207","1","task_categories:question-answering, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for Dataset Name ragtruth-qa 데이터셋을 gpt-4o를 이용하여 한글로 번역 한 데이터셋. Dataset Details Dataset Description Curated by: [More Information Needed] Language(s) (NLP): [한국어] License: [미정] Dataset Sou","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"taresco/xP3x_african_subset","taresco","2024-09-26 14:21:39","2024-09-26 19:17:29+00:00","34d77bfba42fa9f257f8b166fda485006ae8f2e5","81","205","0","language:aeb, language:af, language:ak, language:ars, language:ary, language:arz, language:bm, language:ee, language:en, language:ff, language:fr, language:fon, language:gaz, language:ha, language:ig, language:kam, language:ki, language:knc, language:ko, language:lg, language:ln, language:luo, language:pcm, language:plt, language:po, language:nso, language:ny, language:rn, language:rw, language:sn, language:so, language:st, language:ss, language:swh, language:ti, language:tn, language:tum, language:tw, language:umb, language:xh, language:wo, language:yo, language:zu, size_categories:10M<n<100M, modality:text, region:us","None","Dataset Card for xP3x This version of xP3x includes only African Languages + Arabic, English, French, & Portuguese. See Cohere/xP3x for full information on the dataset and its license.","None","aeb, af, ak, ars, ary, arz, bm, ee, en, ff, fr, fon, gaz, ha, ig, kam, ki, knc, ko, lg, ln, luo, pcm, plt, po, nso, ny, rn, rw, sn, so, st, ss, swh, ti, tn, tum, tw, umb, xh, wo, yo, zu","multi","None","None","False","False","False","None","None","None","None"
+"didi0di/KoChatGPT","didi0di","2024-03-12 00:30:12","2024-03-12 01:11:07+00:00","b06517dca816fcb025fe318502a73044d7e988bb","36","205","0","task_categories:text-generation, language:ko, license:unknown, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction, SFT","text-generation","Dataset Summary 1.2.1) 한국어 QA 데이터셋의 질문으로 답변 자동 생성(ChatGPT): 12,000개 SFT 데이터: data/train 다음 단어만 잘 생성하는 모델에서 질문에 '잘' 대답하는 모델 만들기 Human labling이 필요하지만 ChatGPT 이용 질문을 입력으로 주고 ChatGPT로 답변 자동생성 데이터셋 예시 { ""p","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","unknown","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","None","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"
+"inswave/AISquare_Koalpaca_Orca_merged","inswave","2023-11-24 02:02:34","2023-11-24 02:08:22+00:00","f9ee0cdb4fd5264e4ae87f5cca118523b5f1667d","54","203","1","task_categories:text-generation, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","None","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"FrancophonIA/TED_talks","FrancophonIA","2024-10-11 17:20:34","2024-10-11 18:26:32+00:00","0a4f1c67bb3187965fba520d632fcc880d6e2ab8","39","203","0","task_categories:translation, multilinguality:multilingual, language:en, language:es, language:fr, language:he, language:it, language:ja, language:ko, language:pt, language:ru, language:tr, language:zh, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Dataset origin: https://www.kaggle.com/datasets/miguelcorraljr/ted-ultimate-dataset Context TED is devoted to spreading powerful ideas in just about any topic. These datasets contain over 4,000 TED ta","None","en, es, fr, he, it, ja, ko, pt, ru, tr, zh","multi","None","None","False","False","False","None","None","None","None"
+"hajun1020/korean_profanity_masking","hajun1020","2024-06-09 09:44:24","2024-06-10 08:20:24+00:00","9fd1b7379150d0fcf275130b2581f41f4145e50b","33","203","0","task_categories:fill-mask, language:ko, license:apache-2.0, size_categories:1M<n<10M, modality:text, region:us","fill-mask","None","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"C4AI-Community/mMGTD-corpus","C4AI-Community","2024-09-07 07:07:27","2024-09-07 09:01:37+00:00","e9f92b05a86b45ec63448e1b7e483d612b104cc9","38","202","5","task_categories:token-classification, language:ar, language:cs, language:de, language:nl, language:en, language:zh, language:fa, language:fr, language:el, language:he, language:hi, language:id, language:it, language:ja, language:ko, language:pl, language:pt, language:ro, language:ru, language:es, language:tr, language:uk, language:vi, license:cc-by-nc-nd-4.0, size_categories:1M<n<10M, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, linguistics, MGT, news, cohere, aya-23, command-r","token-classification","Dataset Card for Dataset Name Not all texts are entirely human written or entirely machine generated, what if there are changes made by a human later ? What if some sentences were rewritten ? These ca","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","auto","False","cc-by-nc-nd-4.0","None","None","None"
+"overfit-brothers/KRX-MCQA","overfit-brothers","2024-12-05 06:02:39","2024-12-11 00:48:17+00:00","e51aa6d979aa724dff51e0eef0c8fc4808e2d808","70","202","0","language:ko, 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, krx","None","제3회 KRX 금융 언어 모델 경진대회 MCQA 데이터셋 팀명 : overfit-brothers 데이터셋 구성 example-mcqa KRX-Bench 예시 데이터 출처: KRX-Bench 예시 데이터 account_calc 회계원리 수준 MCQA 계산문제 seed 데이터 37건을 직접 제작한 후 gpt-4o API를 통해 증강 데이터 개수: 222 출처:","None","ko","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"passing2961/KorEmpatheticDialogues","passing2961","2024-05-31 05:20:29","2024-11-06 04:34:35+00:00","5373a3e0c453ed85ae842c5303beb6b19df7323a","45","202","1","task_ids:conversational, source_datasets:EmpatheticDialogues, language:ko, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, social dialogue","conversational","Dataset Card for 💗 KorEmpatheticDialogues 🚨 Disclaimer: All models and datasets are intended for research purposes only. Dataset Summary KorEmpatheticDialogues is a publicly available Korean empatheti","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-nc-4.0","None","None","conversational"
+"ChuGyouk/Ko-MTS-Dialog","ChuGyouk","2024-03-29 17:06:16","2024-03-30 14:08:47+00:00","3bf7d1d1bf5c154cbb76e38cecb0a8db2c81695d","45","202","1","task_categories:text-generation, language:ko, license:cc-by-4.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-generation","The validation set and two test sets will also be updated soon. MTS-Dialog This is the repo for Ko-MTS-Dialog, which is a Korean translated version of MTS-Dialog dataset. MTS-Dialog dataset is a colle","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"Taegyuu/KoAlpaca-v1.1a","Taegyuu","2023-09-10 16:00:48","2023-09-10 16:04:27+00:00","95e3a94100aa39a5cf93091215b7d88d7928fa6a","36","201","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, KoAlpaca","text-generation","Dataset Card for ""KoAlpaca-v1.1a"" Project Repo Github Repo: Beomi/KoAlpaca How to use >>> from datasets import load_dataset >>> ds = load_dataset(""beomi/KoAlpaca-v1.1a"", split=""train"") >>> ds Dataset(","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"CanariaView/GlobalCopperDemandForecastingDataset","CanariaView","2023-12-30 16:17:42","2023-12-30 16:52:21+00:00","c9fbefec90b10fda261ab03804e068d33e72b2b6","36","201","1","task_categories:time-series-forecasting, language:en, language:ko, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mining, LSTM, TimeSeries, CanariaView","time-series-forecasting","CanariaView Global Copper Demand Forecasting Dataset Description This dataset encompasses economic and industrial indicators vital for constructing a copper demand forecasting model. Coverage Period: ","None","en, ko","bi","None","None","False","False","False","None","None","None","None"
+"4n3mone/mmmlu_kor","4n3mone","2024-09-24 00:15:46","2024-09-24 00:16:48+00:00","b9f82f39118e4f07c04691b259c8adb8f5fc0b02","40","201","2","task_categories:question-answering, language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2009.03300, region:us","question-answering","MMMLU_KOREAN this dataset is korean subset of openai/MMMLU dataset. Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attaine","None","ko","mono","None","None","False","False","False","mit","2009.03300","None","None"
+"youjunhyeok/SkunkworksAI-reasoning-0.01-ko","youjunhyeok","2024-10-15 00:22:45","2024-10-29 08:44:59+00:00","6b8296c35771d62773baa88237084c98a03d293c","43","200","0","task_categories:text-generation, 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, instruction, korean, reasoning","text-generation","SkunkworksAI/reasoning-0.01 데이터셋을 nayohan/llama3-instrucTrans-enko-8b 모델을 사용해 번역했습니다. Thanks for SkunkworksAI and nayohan. 원본 reasoning-0.01 subset synthetic dataset of reasoning chains for a wide var","None","ko","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","None","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。請檢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"
+"klei22/korean-english-jamon-parallel-corpora","klei22","2024-04-29 01:40:46","2024-07-31 07:26:32+00:00","d3a1907ba327a8fade984f68bebb71b4a9d42e80","46","200","1","task_categories:translation, language:ko, language:en, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This is modified from the korean-english-parallel-corpora adding jamon style phonetic content. license: cc-by-sa-3.0","None","ko, en","bi","None","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"KETI-AIR/kor_ropes","KETI-AIR","2023-11-08 06:44:49","2023-11-15 01:22:37+00:00","b7ca186e78f441a0a0d0c73f4310fd12c9915916","37","199","0","task_categories:question-answering, task_ids:extractive-qa, language:ko, 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, extractive-qa","Dataset Card for ROPES Licensing Information The data is distributed under the CC BY 4.0 license. Source Data Citation INformation @inproceedings{Lin2019ReasoningOP, title={Reasoning Over Paragraph Ef","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","cc-by-4.0","None","None","extractive-qa"
+"jaypyon/orca-math-word-problems-193k-korean-jsonl","jaypyon","2024-05-12 01:19:44","2024-05-14 03:37:12+00:00","0fde7acd33c1981c2eaac2c0f3709f01144df994","44","199","0","language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2402.14830, region:us","None","원본 데이터셋 https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k https://huggingface.co/datasets/kuotient/orca-math-word-problems-193k-korean Citation @misc{mitra2024orcamath, title={Orc","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"yuneun92/create_qa_news","yuneun92","2024-04-18 05:19:06","2024-04-18 05:29:30+00:00","1868639302ca62207d4d760c6d7470bb35ace56b","36","198","0","task_categories:question-answering, language:ko, license:cc-by-4.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","질문 생성: kullm3 모델 이용 답변 생성: GPT3.5 turbo API 이용 지문 원본: AI HUB 뉴스 기계독해 데이터셋","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","cc-by-4.0","None","None","None"
+"NCSOFT/K-MMStar","NCSOFT","2024-11-26 05:51:49","2024-12-06 05:19:40+00:00","f52ec27f7c350cbb5a231ea9358902f6c21dcd9b","88","197","11","language:ko, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2403.20330, arxiv:2411.19103, region:us","None","K-MMStar We introduce K-MMStar, a Korean adaptation of the MMStar [1] designed for evaluating vision-language models. By translating the val subset of MMStar into Korean and carefully reviewing its na","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","2403.20330","None","None"
+"ziozzang/deepl-trans-ES-KO","ziozzang","2024-04-02 04:29:42","2024-04-02 04:30:30+00:00","2d2daeeda4dc63a023a4dd79f303978f7a186f2d","60","197","1","task_categories:translation, language:ko, language:es, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","translation","This dataset is some wikipedia article with DeepL translation, auto-aggregated. String/Corpus pairs From ES/Spanish to KO/Korean. Quality Filtering Stripping whole HTML tags. removed references and an","None","ko, es","bi","None","None","False","auto","False","None","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","None","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"
+"UICHEOL-HWANG/InterView_Datasets","UICHEOL-HWANG","2024-11-12 13:58:03","2024-11-27 04:29:47+00:00","4bf14b5056edf0225e9919ce1a8ebb57e8db03b7","44","197","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","AiHub Datasets origin : https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=71592 import data from datasets import load_dataset # train 데이터셋 로드 dataset = loa","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"sosoai/dataset_ko_Ultrafeedback_binarized_test","sosoai","2023-12-18 22:50:41","2024-01-17 21:06:23+00:00","9f1a101055495820e2ec7814d3fc57fea776e6d3","43","197","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","maywell 님 께서 업로드 하신 maywell/ko_Ultrafeedback_binarized 부분을 테스트 하고자 업로드 하였습니다. This is the only test dataset and will use for train our own model. The provided data is a dataset that has been translate","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"csujeong/KoAlpaca-v1.1a","csujeong","2023-12-23 01:13:13","2023-12-23 01:18:03+00:00","b5d0474b6ebc0b4492abd88d47979c4e1603c10b","35","196","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, KoAlpaca","text-generation","Dataset Card for ""KoAlpaca-v1.1a"" Project Repo Github Repo: Beomi/KoAlpaca How to use >>> from datasets import load_dataset >>> ds = load_dataset(""beomi/KoAlpaca-v1.1a"", split=""train"") >>> ds Dataset(","None","ko","mono","None","None","False","False","False","None","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","None","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"
+"jungsoon/ComputerLiteracy","jungsoon","2024-09-25 18:55:56","2024-11-13 19:49:15+00:00","c36bd2e1bfb55dd6e85ce4e1d18b05b3eaf02b87","34","195","0","task_categories:multiple-choice, language:ko, license:apache-2.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Computer Proficiency Test Level 2, computer proficiency Test","multiple-choice","None","None","ko","mono","None","None","False","auto","False","apache-2.0","None","None","None"
+"nayohan/coedit-ko","nayohan","2024-08-01 16:30:45","2024-08-01 16:36:10+00:00","e57409674b9353056bcd7bcdcacbdb6d6c82f420","49","195","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2305.09857, region:us, instruction, korean","text-generation","Translated grammarly/coedit using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filtered. @a","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"davidkim205/Ko-Bench","davidkim205","2024-08-13 02:25:56","2024-08-13 03:55:59+00:00","372d04f70e168b2c8ece119e83e8bf953dda3f88","42","193","0","language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Ko-Bench 1. Introduction Ko-Bench is a benchmark designed for evaluating the performance of Korean language models. Following the translation of the MT-Bench dataset into Korean, we modified the quest","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"Saxo/alpaca_function_calling_dataset","Saxo","2024-07-10 06:23:52","2024-07-10 06:36:19+00:00","36769101d375c9a2552dd7da617c54b979a04ed9","46","193","0","language:ko, language:en, license:apache-2.0, size_categories:100K<n<1M, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, alpaca format, function calling, dataset","None","AI 와 빅데이터 분석 전문 기업인 Linkbricks(www.linkbricks.com)의 데이터사이언티스트인 지윤성(Saxo) 박사가 만든 llm RAG를 위한 function calling 학습용 데이터셋으로 llam3 instruct format인 mzbac/function-calling-llama-3-format-v1.1 을 Alpaca Forma","None","ko, en","bi","None","None","False","False","False","apache-2.0","None","None","None"
+"JosephLee/science_textbook_elementary_kor_seed","JosephLee","2023-11-07 07:22:58","2023-11-07 07:42:12+00:00","579ae69382ff7b7f848f3b53c893086daf0ab03e","42","193","3","task_categories:question-answering, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"HyaDoo/ko-voicephishing-binary-classification","HyaDoo","2024-03-25 12:58:23","2024-04-02 01:23:16+00:00","7cdc6b82cd279e3c404b12afc82ba1fae951477a","45","193","0","language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"csujeong/Non_life_insurance","csujeong","2024-01-06 23:48:20","2024-03-22 07:11:43+00:00","cfbd1188b7cf7d26c3eb49abb9305e0523d9091b","33","193","2","language:ko, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","손해보험 데이터","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"richard-park/horangi-336K-Filtered-split","richard-park","2024-11-01 06:23:32","2024-12-05 22:56:16+00:00","c3418bbd7077cd1823addba6d4115450d5f56cf6","42","192","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Dataset Details Dataset Description 호랑이 리더 보드 점수 향상을 위한 data","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jr-d-analyst24/naver_review_sum","jr-d-analyst24","2024-07-20 02:35:40","2024-07-20 03:35:31+00:00","6f8740199b5e7795d258ba93207a3895fe28b1f9","35","192","0","language:ko, license:apache-2.0, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, blog, review","None","None","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"jonghwanhyeon/korean-emotion-lexicon","jonghwanhyeon","2024-10-03 17:31:41","2024-10-03 17:32:37+00:00","db7ce1e7b392cb3ac9a0204ad20dfc03c4a517d9","53","191","2","language:ko, license:cc0-1.0, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, korean, emotion","None","Korean Emotion Lexicon This repository contains a comprehensive dataset of Korean emotion lexicons developed through psychological research conducted by In-jo Park and Kyung-Hwan Min from Seoul Nation","None","ko","mono","n|<|1|K","None","False","False","False","cc0-1.0","None","None","None"
+"hyun5ooo/hansoldeco","hyun5ooo","2024-02-13 06:05:38","2024-02-13 07:20:24+00:00","887aeb977c80cb8dab29b2e7c5a380f514da05ac","47","191","0","task_categories:table-question-answering, language:ko, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","table-question-answering","None","None","ko","mono","1|M|<|n|<|1|0|M","None","False","False","False","None","None","None","None"
+"twojk/08_new_sample","twojk","2024-08-14 01:44:44","2024-08-19 06:20:07+00:00","b288d879fbca4fd8ac3399ae16056b579a65e3ef","38","190","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"nayohan/contextual-dpo-v0.1-ko","nayohan","2024-06-30 08:28:41","2024-06-30 10:37:49+00:00","2ae078b79325a4f8289edbd6f3f742c26c627f6e","34","189","0","language:ko, license:cc-by-4.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, DPO","None","Dataset Card for ""contextual-dpo-v0.1-ko"" Translated jondurbin/contextual-dpo-v0.1 using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"nayohan/cyber-threat-intelligence-custom-data-ko","nayohan","2024-06-30 08:29:16","2024-06-30 16:00:24+00:00","b9b6a4f82cf615a67ffe002b939ca06c8114a189","41","189","1","task_categories:text-generation, language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, security","text-generation","Dataset Card for ""cyber-threat-intelligence-custom-data"" Translated swaption2009/cyber-threat-intelligence-custom-data using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"ChuGyouk/medical_questions_pairs_ko","ChuGyouk","2024-08-16 08:44:51","2024-08-16 08:46:26+00:00","f25c1e570e087520594bac9ca8e35000ffc411f0","43","189","2","task_categories:text-classification, language:ko, license:unknown, size_categories:1K<n<10K, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-classification","Original Data: curaihealth/medical_questions_pairs. Translated into Korean by ""solar-1-mini-translate-enko"".","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"nayohan/HelpSteer-ko","nayohan","2024-07-17 17:54:26","2024-07-17 18:01:26+00:00","c3726c5ae7833acae4458f7981c2eeaeacf524ad","40","189","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.09528, region:us, helpfulness","text-generation","Translated nvidia/HelpSteer using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filtered. @m","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"humane-lab/contextomized-quote","humane-lab","2024-02-15 08:54:05","2024-02-15 09:53:36+00:00","2784e7729a0138775a0bd0abfc61e39b94bfc5fb","38","187","0","task_categories:text-classification, language:ko, 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","None","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"Ssua/testdata","Ssua","2023-08-02 08:43:48","2023-08-02 08:58:08+00:00","7e1eeaaf1f6e2faf3e45bc145d5bdf7a84bc8f8d","37","187","0","task_categories:text-generation, language:ko, license:unknown, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, mgame","text-generation","None","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"ChuGyouk/AI_healthcare_QA","ChuGyouk","2024-08-21 11:22:08","2024-08-21 16:26:02+00:00","2a0ef7b2ef4fefff8676335e8507d93589875c56","35","187","3","task_categories:question-answering, language:ko, 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, medical","question-answering","Dataset Description From Super-large AI healthcare Q&A data [AIHUB], I sampled a subset of questions from the training data, and then obtained responses using gpt-4o-2024-08-06 and gpt-4o-mini-2024-07","None","ko","mono","None","None","False","manual","False","cc-by-nc-sa-4.0","None","None","None"
+"junyeong-nero/kin_med_2M_filtered","junyeong-nero","2024-08-06 03:53:08","2024-08-12 04:41:11+00:00","6d625841500a4a1541bac89721b267d1d1d420eb","37","185","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","This dataset is a filtered dataset of the mncai/kin_med_2M dataset. dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: t","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"SungJoo/KoTox","SungJoo","2024-03-06 05:26:25","2024-03-06 05:35:03+00:00","ca4021cf516e03ad4e46638a701a3cee1f10fc5a","33","185","0","task_categories:question-answering, task_categories:text-generation, language:ko, license:mit, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2311.18215, region:us, ethics, toxicity","question-answering, text-generation","KoTox is an automatically generated toxic instruction dataset in Korean, comprising 39K unethical instruction-output pairs. The dataset is generated based on predefined lexicons and linguistic templat","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","mit","2311.18215","None","None"
+"youjunhyeok/mental_health_counseling_conversations_ko","youjunhyeok","2024-05-23 01:23:38","2024-05-23 04:19:16+00:00","e8b8dab561452dd97d7160cd1b3a19619c7b92fc","46","185","1","task_categories:question-answering, task_categories:text-generation, language:en, language:ko, license:openrail, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medi, cro","question-answering, text-generation","Amod/mental_health_counseling_conversations GPT-4o 를 사용해 한글로 번역한 데이터셋입니다.","None","en, ko","bi","1|K|<|n|<|1|0|K","None","False","False","False","openrail","None","None","None"
+"jr-d-analyst24/small_kitchen_appliances_review","jr-d-analyst24","2024-07-25 02:47:56","2024-07-25 06:41:10+00:00","93d441a923b53be67413af8485c9ed33f88f51b2","38","184","0","language:ko, license:apache-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, code","None","None","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"aiyets/ragtruth-qa-ko-alpaca","aiyets","2024-07-31 08:01:47","2024-07-31 08:06:04+00:00","15e51af78e79b6c525ab518aef9c2602a3b5221c","35","184","0","task_categories:question-answering, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","Dataset Card for Dataset Name ragtruth-qa 데이터셋을 gpt-4o를 이용하여 한글로 번역 한 데이터셋. ragtruth-qa-ko 데이터 셋을 alpaca 포맷으로 변환. Dataset Details Dataset Description Curated by: [More Information Needed] Language(s) ","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","None","None","None","None"
+"ChuGyouk/GenMedGPT-5k-ko","ChuGyouk","2024-08-16 08:19:40","2024-08-16 08:21:01+00:00","c2cc89c581efd23ae344f3c9c6d4a42327d32781","55","184","1","task_categories:text-generation, language:ko, license:mit, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-generation","MedQA-Evol Original Data: ChatDoctor. Translated into Korean by DeepL Pro.","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"youjunhyeok/Magpie-Llama-3.1-Pro-500K-Filtered-ko","youjunhyeok","2024-09-11 02:22:36","2024-10-24 07:14:01+00:00","ee0e2b1de1e10fdcaded0a84780a95bce6c065d1","44","183","0","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","일부 필드 번역이 안되어 재번역 예정입니다. Translated Magpie-Align/Magpie-Llama-3.1-Pro-500K-Filtered using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in le","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"hundredeuk2/qg_concat","hundredeuk2","2024-03-21 09:16:02","2024-03-23 07:08:13+00:00","98b68bd3580ddb341c36667371579cb9af73d371","40","183","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"hundredeuk2/qg_concat_v1","hundredeuk2","2024-05-10 06:44:06","2024-05-27 07:26:53+00:00","f49ffe7b63a4245d6e850783e5a5abd2231cedf8","41","183","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"fine-tuned/BAAI_bge-m3-6122024-ibs3-webapp","fine-tuned","2024-06-12 12:26:43","2024-06-12 12:26:44+00:00","d71a1d9586dd52a162f22c650c0d7eb284827b0e","40","183","1","task_categories:feature-extraction, task_categories:sentence-similarity, language:ko, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, sentence-transformers, feature-extraction, sentence-similarity, mteb, Pets, Hamster, Cat, Feeding, Behavior","feature-extraction, sentence-similarity","BAAI_bge-m3-6122024-ibs3-webapp Dataset Dataset Description The dataset ""Pet care"" is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks. A","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"HyaDoo/hd-bert-voicephishing-binary-classification-ver4","HyaDoo","2024-03-27 15:54:14","2024-04-02 00:21:58+00:00","29b0801276e95d89dd517d93286bb04b68f45aa9","33","183","0","language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"youjunhyeok/Magpie-Pro-300K-Filtered-ko","youjunhyeok","2024-10-10 07:06:33","2024-10-14 06:45:06+00:00","5ef03ace6efc52920f416862a18fca7bf9e73a13","38","182","0","task_categories:text-generation, language:ko, license:llama3, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","Translated Magpie-Align/Magpie-Pro-300K-Filtered using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of English. T","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","llama3","2406.08464","None","None"
+"Ssua/real_data","Ssua","2023-08-03 00:13:43","2023-08-03 05:05:19+00:00","ac1688e4f4f672ca5443016a187cec16bb02de20","34","182","1","task_categories:text-generation, language:ko, license:unknown, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, mgame","text-generation","None","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"won75/text_to_sql_ko","won75","2024-10-24 19:40:43","2024-11-27 21:22:44+00:00","bb0297385bbe97e98a05d84999cda7c461d64b75","66","181","2","language:ko, license:cc-by-nc-4.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Korean Text to MySQL Dataset Dataset Summary Korean Text to MySQL is a dataset comprising approximately 3,300 samples generated using OpenAI's gpt-4o model. This dataset is designed to train models th","None","ko","mono","None","None","False","False","False","cc-by-nc-4.0","None","None","None"
+"youjunhyeok/Magpie-Qwen2-Pro-300K-Filtered-ko","youjunhyeok","2024-08-24 01:07:03","2024-08-25 23:59:17+00:00","1d3922cc9dcae9d1ee8e85c7c240468470f5d11e","40","181","0","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","Translated Magpie-Align/Magpie-Qwen2-Pro-300K-Filtered using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of Engl","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"williamjeong2/ko-coffee-QA","williamjeong2","2024-09-21 16:05:55","2024-09-24 03:14:28+00:00","cee675324daeabd8ec1adcd855b3e227273cdd17","36","181","1","language:ko, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"qwopqwop/ko-arena-hard-auto-v0.1","qwopqwop","2024-12-10 14:07:50","2024-12-27 05:53:09+00:00","4228650fdf66c09200cf104ee563105d37286e7e","57","181","10","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","현재 추가적인 번역 검수를 한번더 진행하고 있습니다. 아마 2달 이내로 완료될 예정입니다. 혹시 그 전에 모델을 벤치마크 하고 싶으시면 https://github.com/qwopqwop200/ko-arena-hard-auto를 참고하시면 도움이 되실것 같습니다. arena-hard-auto-v0.1 를 GPT-4o와 o1을 사용하여 한국어로 번역하고 수작업","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"Nexdata/Korean_Conversational_Speech_Data_by_Mobile_Phone","Nexdata","2022-09-16 10:13:43","2024-08-09 03:35:33+00:00","1d4a2fd090f06798fb516690e32320133247bd71","37","181","0","language:ko, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","conversational","Dataset Card for Nexdata/Korean_Conversational_Speech_Data_by_Mobile_Phone Dataset Summary About 700 Korean speakers participated in the recording, and conducted face-to-face communication in a natura","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"prometheus-eval/MMQA","prometheus-eval","2024-10-19 06:26:32","2024-11-18 18:33:31+00:00","08157274925d9bdd80212990d9b5969d82fe46e3","52","180","3","language:bn, language:ko, language:eu, language:ca, language:es, language:vi, language:ar, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.17578, region:us","None","Links for Reference Repository: https://github.com/guijinSON/MM-Eval Paper: https://arxiv.org/abs/2410.17578 Point of Contact:spthsrbwls123@yonsei.ac.kr / dkyoon@kaist.ac.kr Multilingual Multicultural","None","bn, ko, eu, ca, es, vi, ar","multi","None","None","False","False","False","None","2410.17578","None","None"
+"PerRing/CulturaX_ko_10k","PerRing","2024-12-02 11:50:06","2024-12-16 02:58:52+00:00","4da73b7cd56367c82b6ceb10552ac8625db905d4","48","180","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"ChuGyouk/MedExpQA-Kor","ChuGyouk","2024-08-16 08:14:12","2024-08-16 08:16:43+00:00","ac653dc4fbbad993b4681fe4386b9a936a1f727f","40","179","1","task_categories:text-generation, language:ko, license:cc-by-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-generation","MedExpQA Original Data: HiTZ/MedExpQA En subset, train split and validation split are translated into Korean by ""solar-1-mini-translate-enko"".","None","ko","mono","None","None","False","False","False","cc-by-4.0","None","None","None"
+"nayohan/Maths-College-ko","nayohan","2024-06-30 10:16:51","2024-06-30 10:48:12+00:00","c1e660f09dbf184eab791171d1a0dfb1ea516d64","37","178","0","task_categories:question-answering, 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, math, ko","question-answering","Translated 5% ajibawa-2023/Maths-College using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"phnyxlab/klue-nli-simcse","phnyxlab","2024-08-04 23:29:57","2024-08-06 21:09:18+00:00","cd40baf63ceec1e78831a7ce8c1eaca93d1a291b","39","177","0","language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2105.09680, region:us","None","KLUENLI for SimCSE Dataset For a better dataset description, please visit: LINK This dataset was prepared by converting KLUENLI dataset to use it for contrastive training (SimCSE). The code used to pr","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"qowlsdud/CounselGPT","qowlsdud","2024-01-08 02:21:17","2024-01-08 02:25:18+00:00","78893d55372386f6349c5e382698a155283b2e11","33","177","0","language:ko, license:openrail, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","openrail","None","None","None"
+"seongs/dell-qa-en-to-ko-translated-by-ke-t5-base","seongs","2024-04-28 10:35:09","2024-04-30 04:06:47+00:00","60cf5a5424d5f126d49e96f12056356be0b85365","37","177","1","task_categories:question-answering, task_categories:translation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering, translation","Dell QA English to Korean Translation Dataset Dataset Description This dataset, dell-qa-en-to-ko-translated-by-ke-t5-base, is a Korean translation of the original English Dell QA dataset. Source The o","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"tellarin-ai/ntx_llm_inst_korean","tellarin-ai","2023-12-20 15:18:10","2023-12-20 15:18:50+00:00","654696ea66602ec379d31f54e794e13de7883540","36","176","0","task_categories:token-classification, language:ko, license:cc-by-sa-4.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2303.18103, region:us","token-classification","Dataset Card for NTX v1 in the Aya format - Korean subset This dataset is a format conversion for the Korean data from the original NTX into the Aya instruction format and it's released here under the","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Nexdata/Korean_Spontaneous_Speech_Data","Nexdata","2023-11-07 08:53:43","2024-08-09 02:25:00+00:00","4587e0f16ac8be8ea4c444b7ed5323fc9601e1f0","36","176","0","task_categories:automatic-speech-recognition, language:ko, size_categories:n<1K, format:audiofolder, modality:audio, modality:text, library:datasets, library:mlcroissant, region:us","automatic-speech-recognition, conversational","Dataset Card for Nexdata/Korean_Spontaneous_Speech_Data Description 396 Hours - Korean Spontaneous Speech Data, the content covering multiple topics. All the speech audio was manually transcribed into","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"nayohan/reasoning-0.01-ko","nayohan","2024-08-07 15:34:59","2024-08-07 17:03:35+00:00","9c8bafd8abe1c5e0acb767c9770799667da620bf","41","174","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction, korean","text-generation","Translate SkunkworksAI/reasoning-0.01 using nayohan/llama3-instrucTrans-enko-8b. This is a raw translation dataset. It needs to be filtered for repetitions generated by the model.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"ziozzang/deepl-trans-ID-KO","ziozzang","2024-04-02 04:25:52","2024-04-02 04:26:43+00:00","d09d5b50fce642cb73cf299e9adab6fd23c60617","62","174","1","task_categories:translation, language:ko, language:id, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This dataset is some wikipedia article with DeepL translation, auto-aggregated. String/Corpus pairs From ID/Indonesian to KO/Korean. Quality Filtering Stripping whole HTML tags. removed references and","None","ko, id","bi","None","None","False","auto","False","None","None","None","None"
+"seongyeon1/nursing-home-qa","seongyeon1","2024-09-01 07:34:41","2024-09-03 06:17:01+00:00","7949d657d061aa1fa74dc305725f632661fe2556","35","174","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"joonhok-exo-ai/korean_law_case_codes","joonhok-exo-ai","2024-03-21 04:54:19","2024-03-21 05:36:14+00:00","8d985c4244aa0755c406e994a855992960f9a1b6","36","174","1","language:ko, license:openrail, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","None","Dataset Card for Dataset Name Dataset Summary 사건별 부호문자의 부여에 관한 예규(재일 2003-1, 재판예규 제1769호)에서 규정한 전체 사건부호 데이터셋입니다. Additional Information Dataset Curators 김준호(링크드인): 이 데이터셋은 인공지능 법률 서비스를 만들고 있는 제가 직접 필요","None","ko","mono","n|<|1|K","None","False","False","False","openrail","None","None","None"
+"nayohan/HelpSteer2-ko","nayohan","2024-07-17 17:54:56","2024-07-17 18:02:48+00:00","9bb1dcc77ed620bb30b5fbe7d33a931ebb5becba","37","173","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.08673, region:us, dpo","text-generation","Translated nvidia/HelpSteer2 using nayohan/llama3-instrucTrans-enko-8b. This dataset is a raw translated dataset and contains repetitive sentences generated by the model, so it needs to be filtered. @","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"youjunhyeok/Magpie-Qwen2-Pro-200K-English-ko","youjunhyeok","2024-08-24 16:31:32","2024-08-25 23:58:42+00:00","a92ec196bea829e4d7832e2a1f267fa1b741d7d5","42","173","0","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","Translated Magpie-Align/Magpie-Qwen2-Pro-200K-English using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of Engli","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"Egor-AI/CoT-XLang","Egor-AI","2024-12-31 14:21:09","2025-01-12 16:07:55+00:00","1ea67474b8e0995efad9f34894ec87e7bc25c27d","172","172","4","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"
+"iknow-lab/hermes-function-calling-v1-ko","iknow-lab","2024-10-19 09:56:06","2024-10-19 10:08:09+00:00","5cd13f24a016d5713814eb89a4205c5869e5d3d7","44","171","0","language:en, 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","None","Original Data: NousResearch/hermes-function-calling-v1 GPT-4o-mini로 번역했으나, 길이가 너무 길거나 에러가 난 경우가 1300 건 가량 제외됨. Example 1 [ { ""role"": ""system"", ""content"": ""당신은 전문가로서 구조화된 정보 추출 AI 모델입니다. 정보 추출을 위해 문서를 ","None","en, ko","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"SugoiLoki/TranslationTraining","SugoiLoki","2024-11-14 11:40:00","2024-11-14 12:07:38+00:00","f876176526d655318eb722ea444b8584c982c28e","62","171","1","task_categories:translation, language:en, language:ko, 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","translation","None","None","en, ko","bi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","None","None","None"
+"GyuHyeonWkdWkdMan/lawdata","GyuHyeonWkdWkdMan","2024-11-27 14:16:14","2024-12-03 01:09:44+00:00","0956dac72889cc26262c614f8eb65be0ff04ee11","62","171","0","language:ko, license:apache-2.0, size_categories:1M<n<10M, format:csv, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"nayohan/SQuARe-response","nayohan","2024-07-07 02:40:37","2024-07-07 06:48:34+00:00","421339f746cb877d52590b8381514565a953bfc6","34","170","0","language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety","None","reference: https://github.com/naver-ai/korean-safety-benchmarks @inproceedings{lee2023square, title={SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"hyun5ooo/krmcqa","hyun5ooo","2024-05-01 02:55:47","2024-05-01 16:35:06+00:00","60354bdd88e0c39d664903886ff82aa715c910fc","34","170","0","task_categories:multiple-choice, language:ko, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","multiple-choice","Original dataset is 'HAERAE-HUB/KMMLU'","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"devngho/korean-wikipedia-edu","devngho","2024-11-03 11:47:28","2024-11-03 12:16:23+00:00","1a2fec03a3f1308e7798aa923cb9e82ad7dd7585","82","170","1","task_categories:text-generation, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","🇰🇷📚 korean-wikipedia-edu devngho/korean_wikipedia의 20240701-filtered subset을 devngho/ko_edu_classifier_v2_nlpai-lab_KoE5 모델로 평가한 데이터셋 불러오기 from datasets import load_dataset ds = load_dataset(""devngho/","None","ko","mono","1|0|K|<|n|<|1|0|0|K|||1|0|0|K|<|n|<|1|M","None","False","auto","False","cc-by-sa-4.0","None","None","None"
+"FrancophonIA/WiLI-2018","FrancophonIA","2024-10-11 18:15:51","2024-10-11 18:25:35+00:00","d505038b20006eb17994578c1e870d443f1cd5d2","38","170","0","multilinguality:multilingual, language:en, language:ar, language:fr, language:hi, language:ur, language:pt, language:fa, language:ps, language:es, language:ko, language:ta, language:tr, language:et, language:ru, language:ro, language:zh, language:sv, language:la, language:id, language:nl, language:ja, language:th, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset origin: https://www.kaggle.com/datasets/zarajamshaid/language-identification-datasst Context WiLI-2018, the Wikipedia language identification benchmark dataset, contains 235000 paragraphs of 2","None","en, ar, fr, hi, ur, pt, fa, ps, es, ko, ta, tr, et, ru, ro, zh, sv, la, id, nl, ja, th","multi","None","None","False","False","False","None","None","None","None"
+"valencianatasha/AnythingLLM","valencianatasha","2024-08-15 04:27:21","2024-08-15 05:00:13+00:00","2fa4fda3e00cdf965902de8575dc44e03edeab3c","34","170","0","task_categories:text-generation, language:zh, language:ja, language:ko, license:openrail, 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, ja, ko","multi","n|<|1|K","None","False","False","False","openrail","None","None","None"
+"SangMoone/KoreanDataSet","SangMoone","2024-06-07 04:47:24","2024-06-07 06:14:34+00:00","af7c4c9665bddc7f0b0ec2a446b012ccb49142e1","36","169","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, Aihub, 한국지능정보사회진흥원","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"TwinDoc/math-qa-ko","TwinDoc","2024-08-29 10:01:15","2024-08-30 01:43:41+00:00","951774839177b78a32bb5e80e6868f6a7e01ade5","37","169","0","task_categories:question-answering, language:ko, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, economy, mathQA","question-answering","데이터 출처 AI-HUB 에서 다운로드 받은 숫자연산 기계독해 데이터 입니다. 경제 > Train > json 파일을 DataFrame 형태로 변형하여 수정 없이 업로드하였습니다. 저작권에 의해 본 데이터는 외부 반출 및 타인의 acess 승낙은 불허합니다. 데이터 설명 본 데이터의 Type 은 ['양자/다자비교', '비율연산', '단서추출', '날짜추출'","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"saillab/alpaca_korean_taco","saillab","2024-06-04 00:26:27","2024-09-20 22:08:08+00:00","88b6275ca08cb5bb3bf5a8e977c128f2ff6bf9b1","35","169","1","language:ko, 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. The dataset follows the style outlined in the TaCo paper, as follows: { ""instruction"": ""instruction in xx"", ""input"": ""input in xx"", ""outpu","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"KETI-AIR/kor_wiki_hop","KETI-AIR","2023-11-08 07:03:36","2023-11-15 01:23:19+00:00","39a7ba4b08bdee0c2fb2ab0db465522c9e528897","34","168","0","task_categories:question-answering, task_ids:extractive-qa, multilinguality:monolingual, source_datasets:original, language:ko, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1710.06481, region:us","question-answering, extractive-qa","Dataset Card for wiki_hop Licensing Information The data is distributed under the CC BY SA 3.0 license. Source Data Citation Information @article{DBLP:journals/corr/abs-1710-06481, author = {Johannes ","None","ko","mono","1|0|K|<|n|<|1|0|0|K","wikihop","False","False","False","cc-by-sa-3.0","None","None","extractive-qa"
+"AIDX-ktds/ko_leaderboard","AIDX-ktds","2024-10-30 04:45:29","2024-10-30 05:04:15+00:00","8ea2a1594c33ea7c0132783e0bb429bb5aab952e","44","168","2","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, ko, kr, korean, leaderboard","text-generation","한국어 리더보드 학습에서 사용된 데이터 중 약 8,000건에 대해서 공개합니다. 데이터 생성 시 도움이 되기를 바랍니다. 감사합니다.","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"youjunhyeok/Magpie-Qwen-Air-300K-Filtered-ko","youjunhyeok","2024-08-25 08:27:52","2024-08-25 23:57:25+00:00","d051ad04304544043094aa729b3c2317757f29ea","38","167","0","task_categories:text-generation, language:ko, size_categories:100K<n<1M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, region:us, instruction, korean, magpie","text-generation","Translated Magpie-Align/Magpie-Qwen-Air-300K-Filtered using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of Engli","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"ChuGyouk/ChainofDiagnosis-Ko","ChuGyouk","2024-08-16 08:22:21","2024-08-16 08:23:59+00:00","faedbdb7f45a6d0dd743fcae7e6174f1f3593dfb","34","167","1","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","text-generation","MedQA-Evol Original Data: FreedomIntelligence/CoD-PatientSymDisease. Only CoD_conversations is translated into Korean by ""solar-1-mini-translate-enko"".","None","ko","mono","None","None","False","manual","False","apache-2.0","None","None","None"
+"nayohan/curse-detection-data","nayohan","2024-07-11 07:33:20","2024-07-12 14:58:11+00:00","46b21c0b9002cded0dafca70697d2b05900619dc","39","167","0","language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety","None","Reference: https://github.com/2runo/Curse-detection-data","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"nayohan/SentimentSynth-ko","nayohan","2024-06-30 08:33:01","2024-06-30 15:54:18+00:00","a76efc3fdbfd76b06fc18f3cf145ac55e01b58d7","35","166","1","task_categories:question-answering, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, DPO, ko","question-answering","Dataset Card for ""SentimentSynth"" Translated OEvortex/SentimentSynth using nayohan/llama3-instrucTrans-enko-8b.","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"jr-d-analyst24/small_kitchen_appliances_total_reviews","jr-d-analyst24","2024-07-25 07:40:13","2024-07-25 07:41:14+00:00","5545a3a60dc317b0ff59ce2951728bc2406fecbe","35","165","0","language:ko, license:apache-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","n|<|1|K","None","False","False","False","apache-2.0","None","None","None"
+"onit3772/EvChargerFineTune.ai","onit3772","2024-08-01 12:33:33","2024-08-02 02:13:15+00:00","25e346eb02332bc06d90ac395ac290f93871da22","36","165","0","task_categories:text-generation, language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, KoAlpaca","text-generation","Dataset Card for ""EvChargerFineTune.ai"" Project Repo Github Repo: No Repo How to use >>> from datasets import load_dataset >>> ds = load_dataset(""onit3772/EvChargerFineTune.1a"", split='train', streami","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jin-code/test3","jin-code","2024-02-27 03:56:24","2024-02-27 09:04:55+00:00","e0b5053d97469ea00103586717192a1ad4646625","40","165","0","language:ko, license:cc-by-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","n|<|1|K","None","False","False","False","cc-by-4.0","None","None","None"
+"jr-d-analyst24/ai_hub_narr_sum_vali","jr-d-analyst24","2024-07-26 15:20:12","2024-07-26 15:21:12+00:00","211fdae95be12572f342066042cc20f112e18365","46","165","0","task_categories:summarization, language:ko, license:apache-2.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","summarization","None","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-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","None","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"
+"ziozzang/deepl-trans-IT-KO","ziozzang","2024-04-02 04:27:10","2024-04-02 04:28:14+00:00","66dd09ec269ed087563b1f1d6c782189c59ff0a9","39","162","0","task_categories:translation, language:ko, language:it, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","translation","This dataset is some wikipedia article with DeepL translation, auto-aggregated. String/Corpus pairs From IT/Italian to KO/Korean. Quality Filtering Stripping whole HTML tags. removed references and an","None","ko, it","bi","None","None","False","auto","False","None","None","None","None"
+"jiinnn/llama-custom-dataset","jiinnn","2024-08-03 06:29:14","2024-08-03 23:20:58+00:00","2459da81ec76ad4edcaaa0c8d304cd3850a948d7","40","162","0","task_categories:text-generation, language:ko, license:mit, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, llama","text-generation","None","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"dooooosooooo/ai-chatbot-scrubee","dooooosooooo","2024-02-16 06:10:26","2024-02-23 09:00:46+00:00","1b5f27287bc08d8d0f053c724211be8c6defa1de","34","162","0","task_categories:question-answering, language:ko, license:unknown, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","None","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"lukekim420/sshsbamboobot","lukekim420","2023-10-18 15:09:08","2023-10-18 15:11:31+00:00","68f781c7b49757cd0a8176ac40304814fc4113e5","35","161","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","sshsbamboogroove crawled dataset license: apache-2.0","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"williamjeong2/msmarco-triplets-ko-v1","williamjeong2","2024-09-09 05:51:22","2024-09-20 02:43:28+00:00","a5ff759811ad78f8ecbd470f9cd50227cff5c676","46","161","5","task_categories:feature-extraction, language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1611.09268, region:us","feature-extraction","MS MARCO Triplets - Korean Version (v1) Introduction Welcome to the Korean version of the MS MARCO Triplets dataset. This project aims to provide a comprehensive, high-quality translation of the origi","None","ko","mono","None","None","False","auto","False","None","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","None","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"
+"AIR-Bench/qa_web_ko","AIR-Bench","2024-09-28 03:46:40","2024-09-28 04:20:11+00:00","f6fd1fc8d26683ca4273c8b88ff7a5de8909ccc9","36","160","0","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, language:ko, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","Available Versions: AIR-Bench_24.05 Task / Domain / Language: qa / web / ko Available Datasets (Dataset Name: Splits): default: dev, test","None","ko","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"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"
+"nayohan/KoSBi","nayohan","2024-07-07 02:13:37","2024-07-07 02:21:55+00:00","d23ca625f4a2a14742315883478ef39605d23702","34","158","1","language:ko, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety","None","reference: https://github.com/naver-ai/korean-safety-benchmarks @inproceedings{lee2023kosbi, title={KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application}, a","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"iqwiki-kor/llama-3.1-onpolicy-5samples-rank","iqwiki-kor","2024-08-19 20:47:04","2024-08-21 17:12:24+00:00","2fd103103c439543165d24ee1679a76329ca9529","36","158","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"devngho/korean-textbooks-edu","devngho","2024-11-04 11:07:32","2024-11-04 12:00:20+00:00","0f9d772332e4ba75da8dbad025f127561bae4929","60","157","1","task_categories:text-generation, source_datasets:maywell/korean_textbooks, language:ko, license:mit, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","🇰🇷📚 korean-textbooks-edu maywell/korean_textbooks의 모든 subset을 devngho/ko_edu_classifier_v2_nlpai-lab_KoE5 모델로 평가한 데이터셋 불러오기 from datasets import load_dataset ds = load_dataset(""devngho/korean-textbook","None","ko","mono","1|M|<|n|<|1|0|M","None","False","auto","False","mit","None","None","None"
+"kikikara/ko_QA_dataset","kikikara","2024-10-06 11:29:08","2024-10-06 11:49:58+00:00","2bb5c538465a92cf4ef0e006af58ae9b749fc2c3","39","157","0","task_categories:question-answering, language:ko, 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","maywell/korean_textbooks 의 dataset을 Q&A 형식으로 재구성한 dataset입니다.","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"AIR-Bench/qa_news_ko","AIR-Bench","2024-09-28 02:19:49","2024-09-28 04:11:47+00:00","76980f15241c9edf2b64259456e16412cab7a3be","36","157","1","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, language:ko, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","Available Versions: AIR-Bench_24.05 Task / Domain / Language: qa / news / ko Available Datasets (Dataset Name: Splits): default: dev, test","None","ko","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"werty1248/Magpie-Ko-Qwen2.5-Reasoning-2k-example","werty1248","2024-09-28 16:52:41","2024-11-19 08:11:05+00:00","b08e94d95f28bb9474559f93d9bf4be7f60dcbce","38","155","1","language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","데이터 확인 및 테스트용 샘플 데이터 생성 ✅ temperature: 1.0 top_p: 1.0 한자/영어 필터링 ✅(수정필요) 답변에도 한자/영어 필터링 적용해야 함 데이터 분포 확인 ✅ instruction에 대한 BGE-m3 embedding + T-SNE 문제 난이도 / 답변 퀄리티 확인 풀 수 없는 문제 있음 사소한 문법 오류 있음 러시아어/아랍어","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"youjunhyeok/glaiveai-reflection-v1-ko","youjunhyeok","2024-10-06 08:41:39","2024-10-06 08:48:12+00:00","84fd972f3f97dbc9bf2685dffd9e3a75566eb5ef","34","155","0","task_categories:text-generation, 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, instruction, korean","text-generation","Translated glaiveai/reflection-v1 using nayohan/llama3-instrucTrans-enko-8b. For this dataset, we only used data that is 5000 characters or less in length and has language of English. Thanks for @Magp","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","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","None","None","None","None","id, ar, ko, en, ja, ms","multi","None","None","False","False","False","None","None","None","None"
+"reimunyancat/huk","reimunyancat","2024-07-12 08:50:55","2024-11-10 12:50:45+00:00","fa53d4741d37f57f9c67cb3d5a2c9a3088188d00","34","154","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jubang0219/KsponSpeech","jubang0219","2024-07-30 07:28:18","2024-07-31 10:45:50+00:00","3a7bb5fef6937bbc29baffd25bbf1443e4f40564","38","153","1","language:ko, license:cc-by-nc-nd-4.0, size_categories:1K<n<10K, format:parquet, modality:audio, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for KsponSpeech Dataset Summary The KsponSpeech is a large-scale spontaneous speech corpus in Korean. This corpus contains 969 hours of general open-domain dialog utterances, spoken by ap","None","ko","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"devngho/korean_wikipedia","devngho","2024-09-19 10:39:57","2024-09-19 11:40:09+00:00","fda93dcee4818611add16cc4a5506021c1ad0f6c","37","153","0","task_categories:text-generation, language:ko, 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","text-generation","None","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","auto","False","cc-by-sa-4.0","None","None","None"
+"hscrown/seoul-spot","hscrown","2024-04-26 21:29:03","2024-04-26 21:30:55+00:00","44cc98f88a3110d26bcd9e13dbb04da238458749","34","153","0","language:ko, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","대한민국 서울시의 공원, 박물관과 미술관, 도서관의 이름과 고유 id를 맵핑한 데이터셋입니다.","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"nayohan/APEACH","nayohan","2024-07-07 12:07:27","2024-07-07 12:11:18+00:00","4e3b7fadac3f78f4a8ef38a558418b2b34bac381","34","153","0","language:ko, 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, safety","None","reference: https://github.com/jason9693/APEACH @inproceedings{yang-etal-2022-apeach, title = ""{APEACH}: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Dataset","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"AIR-Bench/qa_wiki_ko","AIR-Bench","2024-09-28 02:54:56","2024-09-28 04:16:49+00:00","746619bdda424a5722711ffc21b4ea49ee75c453","34","153","0","task_categories:text-retrieval, task_ids:document-retrieval, multilinguality:monolingual, language:ko, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-retrieval, document-retrieval","Available Versions: AIR-Bench_24.05 Task / Domain / Language: qa / wiki / ko Available Datasets (Dataset Name: Splits): default: dev, test","None","ko","mono","None","None","False","False","False","None","None","None","document-retrieval"
+"mer0na/Muzic2","mer0na","2024-05-13 11:26:12","2024-05-13 11:32:41+00:00","e2b97d050c305b9e16bbaa6a35b31045e28846e4","38","152","0","language:ko, language:en, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko, en","bi","None","None","False","False","False","None","None","None","None"
+"kikikara/ko-math","kikikara","2024-10-06 11:58:42","2024-10-06 12:10:40+00:00","373f733b657bae15526339be8080145220735bd9","37","151","0","task_categories:question-answering, language:ko, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, math","question-answering","hendrycks/math dataset을 한국어로 번역한 dataset 입니다.","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"TwinDoc/math-qa-sample_ext-ko","TwinDoc","2024-08-29 10:04:45","2024-08-30 01:45:28+00:00","9a3f7c10c043cd6c472cb0957f5ee956594e4c9c","35","151","0","task_categories:question-answering, language:ko, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, economy, mathQA","question-answering","데이터 출처 AI-HUB 에서 다운로드 받은 숫자연산 기계독해 데이터 를 사용해서 만든 데이터입니다. 경제 > Train > json 파일을 DataFrame 형태로 변형하여 전처리 및 답변 생성을 하였습니다. Raw 데이터의 answer 정보를 참고하여 답변을 생성하였습니다. 답변 생성 시 gpt-4o 를 활용했습니다. 저작권에 의해 본 데이터는 외부 반","None","ko","mono","1|K|<|n|<|1|0|K","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"youngmon/atlassian-qna","youngmon","2024-10-09 08:17:14","2024-11-13 06:03:56+00:00","ab08bb9f51742f88d0b2dc6120f4e4ac7a7313e6","34","150","1","task_categories:question-answering, language:en, language:ko, language:zh, language:ja, language:es, language:ru, license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","📄 Question and Answer for Atlassian Products Dataset Sources Atlassian Community Data Description The dataset primarily includes questions, answers, tags, and URLs. Questions contain the author, title","None","en, ko, zh, ja, es, ru","multi","1|0|0|K|<|n|<|1|M","None","False","False","False","mit","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","None","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"
+"ChuGyouk/KorMedConceptsQA","ChuGyouk","2024-08-16 07:31:56","2024-09-04 10:20:42+00:00","b5cd96f0bc9e4ee30b1c4cf66b3ce4e3f56f867e","35","150","2","task_categories:question-answering, language:ko, license:cc-by-nc-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2405.07348, region:us, medical","question-answering","Warning: If there is some copyright issue, this dataset may be deleted. KoMedConceptsQA This is data that reproduces the work of paper MedConceptsQA by using Korean medical codes. Update 2024/08/29 19","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","manual","False","cc-by-nc-3.0","2405.07348","None","None"
+"FrancophonIA/Hearthstone","FrancophonIA","2024-10-07 20:16:46","2024-10-11 18:28:24+00:00","7801e5b13bd5baf5885c285c713d29da9a5d52f3","39","150","0","task_categories:translation, multilinguality:multilingual, language:de, language:en, language:es, language:fr, language:it, language:ja, language:ko, language:pl, language:pt, language:ru, language:th, language:zh, size_categories:10K<n<100K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","Content Translation of Hearthstone card names and descriptions. Languages: German, English, Spanish, French, Italian, Japanese, Korean, Polish, Portuguese, Russian, Thai and Chinese Acknowledgements M","None","de, en, es, fr, it, ja, ko, pl, pt, ru, th, zh","multi","None","None","False","False","False","None","None","None","None"
+"ziozzang/deepl_trans_DE-KO","ziozzang","2023-11-10 03:04:01","2024-04-02 04:29:19+00:00","4c983ba213c9ec6df85241122afe70eff64aa29a","39","149","0","task_categories:translation, language:de, language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","translation","This dataset is some wikipedia article with DeepL translation, auto-aggregated. String/Corpus pairs From DE/German to KO/Korean. Quality Filtering Stripping whole HTML tags. removed references and ann","None","de, ko","bi","None","None","False","auto","False","None","None","None","None"
+"iknow-lab/wildguardmix-train-ko-11k","iknow-lab","2024-10-19 07:35:51","2024-10-19 07:37:33+00:00","3a950a18a4fe71fcb35482c026cd3ac114712ae3","43","149","0","task_categories:text-classification, language:ko, license:odc-by, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.18495, region:us","text-classification","Original dataset: allenai/wildguardmix Translated by nayohan/llama3-instrucTrans-enko-8b @misc{wildguard2024, title={WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusal","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","odc-by","None","None","None"
+"desimin2/bludus_employee_test","desimin2","2024-08-01 05:16:13","2024-08-01 05:59:26+00:00","343e75e1b09da282fa02d0c8755a4f95b4d42144","35","149","0","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, doi:10.57967/hf/2808, region:us","text-generation","None","None","ko","mono","None","None","False","auto","False","apache-2.0","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","None","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"
+"saillab/alpaca-korean-cleaned","saillab","2024-09-20 23:04:56","2024-09-20 23:32:11+00:00","f91824d188749c53eb1f2d15a0888e72874d0ae3","44","149","0","language:ko, 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","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","None","None","None","None"
+"kenu/hf-first","kenu","2024-07-02 07:10:31","2024-07-02 07:37:08+00:00","4967803ab42b3345e756ceacabe6bb873f8f2be8","35","148","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"
+"TwinDoc/math-qa-sample_addsub-ko","TwinDoc","2024-08-29 10:03:56","2024-08-30 01:44:36+00:00","d554b163ff067723a08a96209437e1a6ea837dda","35","148","0","task_categories:question-answering, language:ko, license:cc-by-nc-sa-4.0, size_categories:1K<n<10K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, economy, mathQA","question-answering","데이터 출처 AI-HUB 에서 다운로드 받은 숫자연산 기계독해 데이터 를 사용해서 만든 데이터입니다. 경제 > Train > json 파일을 DataFrame 형태로 변형하여 전처리 및 답변 생성을 하였습니다. Raw 데이터의 answer 정보를 참고하여 답변을 생성하였습니다. 답변 생성 시 gpt-4o 를 활용했습니다. 저작권에 의해 본 데이터는 외부 반","None","ko","mono","1|K|<|n|<|1|0|K","None","False","auto","False","cc-by-nc-sa-4.0","None","None","None"
+"overfit-brothers/KRX-INST","overfit-brothers","2024-12-05 06:22:21","2024-12-11 00:48:31+00:00","3d6a1c5e3dce31a28fb2ff40fa2b9a960fcdfa7b","64","146","0","language:ko, license:cc-by-nc-nd-4.0, size_categories:10K<n<100K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, krx","None","제3회 KRX 금융 언어 모델 경진대회 Instruction 데이터셋 팀명 : overfit-brothers 데이터셋 구성 example-it KRX-Bench 예시 데이터 출처: KRX-Bench 예시 데이터 finqa-50k-merged-quality_filtered ConvFinQA-ko fingpt-convfinqa 데이터셋을 샘플링하여 번역한 mn","None","ko","mono","None","None","False","False","False","cc-by-nc-nd-4.0","None","None","None"
+"CarrotAI/Korean-Common","CarrotAI","2024-10-25 01:01:28","2024-11-08 00:20:46+00:00","742ce7375e6f2ed1b3865bf2c5438ff12bcdbabd","60","146","1","task_categories:text-generation, language:ko, 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 Detail It is a learning dataset of 100,000 in Korean. LLM 학습용도로 만들어진 한국어 데이터셋입니다. @article{KoreanCommon, title={CarrotAI/Korean-Common Card}, author={CarrotAI (L, GEUN)}, year={2024}, url = {h","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","auto","False","cc-by-4.0","None","None","None"
+"blueapple8259/wikipedia_qa","blueapple8259","2024-12-08 12:50:51","2024-12-09 05:44:54+00:00","103f1745e1e3dadc00802d10c5fdf4c5c8d6093d","95","146","2","task_categories:question-answering, language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","question-answering","wikipedia 데이터를 qa형식으로 가공한 데이터셋입니다. 언어모델 없이 코드로만 가공하는 것이 목표이며 새로운 가공 아이디어가 떠오를 시 새 버전으로 업로드 하겠습니다.","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"ziozzang/deepl-trans-PT-KO","ziozzang","2023-11-10 09:07:33","2023-11-10 09:09:09+00:00","2da98ef659fa5c34a645ea4e76172bf44ee6d4f0","40","145","0","task_categories:translation, language:ko, language:pt, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","This dataset is some wikipedia article with DeepL translation, auto-aggregated. String/Corpus pairs From PT/Portugese to KO/Korean. Quality Filtering Stripping whole HTML tags. removed references and ","None","ko, pt","bi","None","None","False","auto","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","None","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"
+"kingkim/DS_Building_SecurityManual_V2","kingkim","2024-09-26 23:20:04","2024-09-28 14:45:57+00:00","052f1e171ea1995aa857245ddf8757efa418dde8","34","144","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"FrancophonIA/Deltacorpus_1.1","FrancophonIA","2024-10-24 19:56:01","2024-10-24 21:12:33+00:00","170e6bc8e88f01ade1d65d5604852fc93b88b94f","50","144","0","task_categories:token-classification, multilinguality:multilingual, language:af, language:sq, language:am, language:ar, language:an, language:hy, language:ast, language:az, language:eu, language:be, language:bn, language:bpy, language:bs, language:br, language:bg, language:ca, language:cv, language:hr, language:cs, language:da, language:diq, language:nl, language:arz, language:en, language:eo, language:et, language:fo, language:hif, language:fi, language:fr, language:gl, language:ka, language:de, language:glk, language:gu, language:ht, language:he, language:hi, language:hu, language:is, language:io, language:id, language:ia, language:ga, language:it, language:jv, language:kn, language:kk, language:ko, language:ku, language:la, language:lv, language:li, language:lt, language:lmo, language:nds, language:lb, language:mk, language:mg, language:ms, language:ml, language:mi, language:mr, language:el, language:mn, language:nap, language:ne, language:new, language:no, language:nn, language:pam, language:fa, language:pms, language:pl, language:pt, language:ro, language:ru, language:sco, language:gd, language:sr, language:sh, language:sk, language:sl, language:es, language:su, language:sw, language:sv, language:gsw, language:tl, language:tg, language:ta, language:tt, language:te, language:tr, language:uk, language:hsb, language:ur, language:uz, language:vec, language:vi, language:vo, language:wa, language:war, language:cy, language:fy, language:sah, language:yi, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:text, modality:text, library:datasets, library:mlcroissant, region:us","token-classification","Dataset origin: https://lindat.cz/repository/xmlui/handle/11234/1-1743 Description Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 to","None","af, sq, am, ar, an, hy, ast, az, eu, be, bn, bpy, bs, br, bg, ca, cv, hr, cs, da, diq, nl, arz, en, eo, et, fo, hif, fi, fr, gl, ka, de, glk, gu, ht, he, hi, hu, is, io, id, ia, ga, it, jv, kn, kk, ko, ku, la, lv, li, lt, lmo, nds, lb, mk, mg, ms, ml, mi, mr, el, mn, nap, ne, new, no, nn, pam, fa, pms, pl, pt, ro, ru, sco, gd, sr, sh, sk, sl, es, su, sw, sv, gsw, tl, tg, ta, tt, te, tr, uk, hsb, ur, uz, vec, vi, vo, wa, war, cy, fy, sah, yi","multi","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"MaChangH/QA-Youtube_categorization_1000-each","MaChangH","2024-10-03 16:58:36","2024-10-04 23:44:47+00:00","4e8d79edbc209c32c4b6f682b997778d0c6c77e1","39","143","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, korean, youtube ","None","generated by MaChangH/QA-Youtube_categorization Total Entries: 13000 {'Autos & Vehicles': {'count': 1000, 'percentage': 7.6923076923076925}, 'Comedy': {'count': 1000, 'percentage': 7.6923076923076925}","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"ziozzang/deepl-trans-FR-KO","ziozzang","2024-04-02 04:23:55","2024-04-02 04:25:26+00:00","008ca1b7a8ee1cedd4ed58d65c61f7eab80c1c80","40","142","0","task_categories:translation, language:ko, language:fr, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","translation","This dataset is some wikipedia article with DeepL translation, auto-aggregated. String/Corpus pairs From FR/French to KO/Korean. Quality Filtering Stripping whole HTML tags. removed references and ann","None","ko, fr","bi","None","None","False","auto","False","None","None","None","None"
+"PeterGraebner/LDNOOBW_V2","PeterGraebner","2023-11-17 16:06:07","2024-02-23 09:32:13+00:00","c6f1e76494c968312f2f84324eab42e1006e5d9f","34","141","3","language:af, language:az, language:am, language:be, language:bg, language:dz, language:eu, language:my, language:ca, language:cs, language:cy, language:hr, language:zh, language:da, language:de, language:nl, language:el, language:en, language:eo, language:es, language:et, language:fa, language:fi, language:fr, language:gl, language:gd, language:hi, language:hy, language:hu, language:id, language:is, language:it, language:ja, language:ko, language:la, language:lt, language:lv, language:mi, language:mk, language:ml, language:ms, language:mt, language:mr, language:mn, language:no, language:pl, language:pt, language:ro, language:ru, language:sk, language:sl, language:sm, language:sq, language:te, language:ta, language:to, language:tr, language:uk, language:uz, language:vi, language:yid, language:zu, license:cc0-1.0, size_categories:10K<n<100K, format:text, modality:text, library:datasets, library:mlcroissant, region:us","None","Written with StackEdit. List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words_V2 This list of words is a follow-up and extension of the Shutterstock List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Wor","None","af, az, am, be, bg, dz, eu, my, ca, cs, cy, hr, zh, da, de, nl, el, en, eo, es, et, fa, fi, fr, gl, gd, hi, hy, hu, id, is, it, ja, ko, la, lt, lv, mi, mk, ml, ms, mt, mr, mn, no, pl, pt, ro, ru, sk, sl, sm, sq, te, ta, to, tr, uk, uz, vi, yid, zu","multi","1|0|K|<|n|<|1|0|0|K","None","False","manual","False","cc0-1.0","None","None","None"
+"Hyungmo/Nurier","Hyungmo","2024-06-25 06:36:14","2024-06-25 06:48:04+00:00","1d946df50e526cd1b1b977a46475e31ec28de527","33","138","0","language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","DatasetDict({ train: Dataset({ features: ['questions', 'answers'], num_rows: 22194 }) test: Dataset({ features: ['questions', 'answers'], num_rows: 2740 }) validation: Dataset({ features: ['questions'","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"williamjeong2/msmarco-triplets-ko-v2","williamjeong2","2024-10-21 02:03:36","2024-10-21 02:12:23+00:00","b647f9c4c20b92059c5ed1a58785f2344a9c0248","46","134","4","task_categories:feature-extraction, language:ko, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:1611.09268, region:us","feature-extraction","MS MARCO Triplets - Korean Version (v2) Introduction Welcome to the Korean version of the MS MARCO Triplets dataset. This project aims to provide a comprehensive, high-quality translation of the origi","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"eyl45/demo","eyl45","2024-11-27 02:07:34","2024-11-27 02:32:45+00:00","6b6863561d7d14d7846a130719bca4f13048b443","46","133","0","None","text-generation","None","None","zh, ko, ja","multi","n|<|1|K","None","False","False","False","mit","None","None","None"
+"kuuhaku06/RP","kuuhaku06","2024-11-02 13:23:09","2024-11-03 23:24:49+00:00","379b78449fadae0e3ed86ccad32072a59499f5ca","41","133","1","language:ko, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted를 EzTransXP로 번역","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"lomit/business_report_qa_ko_2023","lomit","2024-10-30 12:25:42","2024-10-30 13:56:32+00:00","897fe9146a9aad576154eeb8e8438be332c75353","42","131","1","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","None","None","None","ko","mono","None","None","False","manual","False","None","None","None","None"
+"PrompTartLAB/PTT_advanced_en_ko","PrompTartLAB","2024-11-13 12:06:13","2024-11-13 12:15:49+00:00","efab77a57fcce7e9a09cc5e6b086a1b410ae8f3b","50","130","1","task_categories:translation, language:en, language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2410.00683, region:us","translation","Parenthetical Terminology Translation (PTT) Dataset Overview The Parenthetical Terminology Translation (PTT) dataset is designed for training language models on technical term translation, ensuring cl","None","en, ko","bi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"mssongit/KoFinDER","mssongit","2024-10-15 08:20:44","2024-10-15 08:21:27+00:00","8761c7f94a021daa8d94eeb72c66f877338babac","37","130","0","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","None","None","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"BASF-AI/PubChemWikiKOPC","BASF-AI","2024-11-25 12:34:23","2024-12-05 20:20:19+00:00","72d18832682effe50d956a3c872f1449402934c3","44","129","0","task_categories:text-classification, language:en, language:ko, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, chemistry, chemteb, wikipedia, pubchem","text-classification","PubChem & Wikipedia English-Korean Paragraph Pair Classification This dataset is a multilingual extension of the PubChem & Wikipedia Paragraphs Pair Classification dataset. It includes pairs of paragr","None","en, ko","bi","n|<|1|K","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"youjunhyeok/Magpie-DPO-100K-SML-ko","youjunhyeok","2024-11-07 01:08:48","2024-11-07 04:03:47+00:00","5eac2067edca6b7fc14ac274e104fc1aea7d57d4","58","128","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, instruction, korean, magpie","text-generation","Magpie-Align/Magpie-DPO-100K-SML 데이터셋을 nayohan/llama3-instrucTrans-enko-8b 모델을 사용해 번역했습니다. Thanks for Magpie-Align and nayohan. This dataset is still under internal assessment. Please use it with caut","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"choeeiden/kor-insu-qaset","choeeiden","2024-12-18 05:47:32","2024-12-20 01:15:41+00:00","686c5ba4fea3a45efd418b953def318f6d3de97f","43","126","1","task_categories:question-answering, language:ko, license:mit, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, insurance, medical","question-answering","None","None","ko","mono","n|<|1|K","None","False","False","False","mit","None","None","None"
+"lenML/advbench_behaviors_m5","lenML","2024-11-13 18:30:18","2024-11-13 18:42:35+00:00","1af7c884d0a006dff562cfe9ea9dc8ee70fe81a5","50","125","0","task_categories:text2text-generation, language:en, language:zh, language:ko, language:jp, language:ru, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2307.15043, region:us","text2text-generation","About advbench_behaviors_m5 此数据集为 advbench_behaviors.csv 文件的多语言翻译版本。一个常见的任务是用于 abliterator 脚本。 Dataset Card for AdvBench Paper: Universal and Transferable Adversarial Attacks on Aligned Language Model","None","en, zh, ko, jp, ru","multi","None","None","False","False","False","mit","2307.15043","None","None"
+"jaeyong2/ko-persona-cot-inst","jaeyong2","2024-10-26 02:43:56","2024-10-26 03:19:30+00:00","fefb25b6e13916d35f16d9554b0afe91f995f65a","44","124","0","language:ko, 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/ko-persona-cot-inst"", split=""train"") >>> ds Dataset({ features: ['content', 'text'], num_rows: 240000 }) Development Pr","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"CarrotAI/ko-tree-conversation","CarrotAI","2024-10-30 00:51:53","2024-10-31 03:13:31+00:00","ef0d1ace3795e88f92bba38ea5025f749cd0ab28","45","124","0","task_categories:text-generation, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","한국어로 이루어진 멀티턴 대화셋 입니다. @article{tree-multi, title={CarrotAI/tree-multi Card}, author={CarrotAI (L, GEUN)}, year={2024}, url = {https://huggingface.co/datasets/CarrotAI/tree-multi} }","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"ehottl/common","ehottl","2024-11-03 12:15:59","2024-11-16 13:18:03+00:00","ad6b951fdc41912c2089212e74231790077a01c3","47","121","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jp1924/KoLLaVAReCap118K","jp1924","2024-10-23 06:23:27","2024-11-08 00:36:39+00:00","6ca5df956b7dbc43fbac43e3c1ed5ed962694e97","42","120","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, caption","None","Dataset Card for KoLLaVAReCap118K 만드는데 3만원 정도 들감 그리고 원본 데이터, 이거 저 해상도라 답변 못하겠음 등등의 노이즈 있으니 참고하셈","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"werty1248/Magpie-Ko-Qwen2.5-Reasoning-Raw","werty1248","2024-11-16 15:34:06","2024-11-19 07:31:44+00:00","3c0bbfd32a8aef544046aa9d17df105352615197","71","120","0","task_categories:text-generation, language:ko, 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","text-generation","Data Generation System Message Korean Reasoning Template ""pre_query_template"": ""<|im_start|>system\\n당신은 알리바바 클라우드에서 만든 Qwen입니다. 당신은 유용한 어시스턴트입니다.\\nuser가 논리적인 다단계의 추론 과정이 필요한 복잡한 문제를 내면, assistant는 한국어","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"BigShort/bok_words_700","BigShort","2024-11-01 10:39:43","2024-11-05 00:11:10+00:00","0966ab021c781986330daecb44eb2aab292cc661","51","118","0","task_categories:text-generation, language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","text-generation","None","None","ko","mono","n|<|1|K","None","False","auto","False","None","None","None","None"
+"david9dragon9/shp_translations","david9dragon9","2024-12-29 00:22:04","2024-12-29 01:27:27+00:00","2c3ff5946665f081a6c03fcea4192a36547a4dbe","117","117","0","None","question-answering","This dataset contains translations of three splits (askscience, explainlikeimfive, legaladvice) of the Stanford Human Preference (SHP) dataset, used for training domain-invariant reward models. The tr","None","en, ko, zh, th","multi","None","None","False","False","False","mit","None","None","None"
+"global-llm-2024/ko_ifeval","global-llm-2024","2024-11-01 07:52:13","2024-11-01 07:54:26+00:00","ec906775e543f4a8cf03a94c73cf08938a780cce","39","117","0","language:ko, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Dataset Details Dataset Description Curated by: [More Inform","None","ko","mono","None","None","False","manual","False","None","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"
+"devngho/korean-webtext-edu","devngho","2024-11-08 12:30:56","2024-11-08 14:02:21+00:00","9dbc5a9f7fcb08c03c2aa70f08c1d7b470c1ceac","45","117","1","task_categories:text-generation, source_datasets:HAERAE-HUB/KOREAN-WEBTEXT, language:ko, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","text-generation","🇰🇷🌐📚 korean-webtext-edu HAERAE-HUB/KOREAN-WEBTEXT를 devngho/ko_edu_classifier_v2_nlpai-lab_KoE5 모델로 평가한 데이터셋 불러오기 from datasets import load_dataset ds = load_dataset(""devngho/korean-webtext-edu"", name=","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","auto","False","mit","None","None","None"
+"youjunhyeok/Magpie-Pro-DPO-100K-v0.1-ko","youjunhyeok","2024-11-05 18:25:23","2024-11-06 00:17:30+00:00","860c855cba51bbda5f938ddaf7ac602b9c2eff1e","51","117","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, arxiv:2406.12845, region:us, instruction, korean, magpie","text-generation","Magpie-Align/Magpie-Pro-DPO-100K-v0.1 데이터셋을 nayohan/llama3-instrucTrans-enko-8b 모델을 사용해 번역했습니다. Thanks for Magpie-Align and nayohan. Project Web: https://magpie-align.github.io/ Arxiv Technical Report","None","ko","mono","None","None","False","False","False","None","2406.08464","None","None"
+"jp1924/DevelopmentandDataofLLMswithEnhancedKoreanLanguagePerformance","jp1924","2024-11-20 08:16:13","2024-12-12 06:49:53+00:00","b0cea4b2a13b192d36c69d6df80239568a941856","37","116","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","데이터에 <NAME>과 같은 비식별화가 되어 있다. 이거 참고해서 사용해야 함.데이터 필더할 때, 비식별화 되어 있는 데이터는 따로 전처리 하지 않음","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"youjunhyeok/Magpie-Air-DPO-100K-v0.1-ko","youjunhyeok","2024-11-03 12:46:37","2024-11-04 04:59:04+00:00","f65d2102e5fb7544f43e05282058b7d90e6d05de","47","116","0","task_categories:text-generation, language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.08464, arxiv:2406.12845, region:us, instruction, korean, magpie","text-generation","Magpie-Align/Magpie-Air-DPO-100K-v0.1 데이터셋을 nayohan/llama3-instrucTrans-enko-8b 모델을 사용해 번역했습니다. Thanks for Magpie-Align and nayohan. Project Web: https://magpie-align.github.io/ Arxiv Technical Report","None","ko","mono","None","None","False","False","False","None","2406.08464","None","None"
+"Hyunggi/influencer_rec_ko","Hyunggi","2024-11-05 05:22:43","2024-11-05 05:25:30+00:00","f2ecc071c1faf2b9be18cbe4dc50c886f4764209","42","114","1","language:ko, license:gpl-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, influencer, korean","None","None","None","ko","mono","1|0|K|<|n|<|1|0|0|K","None","False","False","False","gpl-3.0","None","None","None"
+"kwanwoo02/vita_chatbot_Instruct_dataset","kwanwoo02","2024-11-09 07:29:37","2024-11-13 06:04:08+00:00","db69313f8b3ea5aac440ca000d87a351c3859155","43","113","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"BigShort/krx_report","BigShort","2024-11-02 12:51:40","2024-11-07 14:02:18+00:00","41fb78e519588273d483cd8fccc35dc5b12a5a0c","41","111","0","task_categories:text-generation, language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, finance","text-generation","None","None","ko","mono","1|K|<|n|<|1|0|K","None","False","auto","False","None","None","None","None"
+"Hanbin42/lama3_lhb_unslot_data","Hanbin42","2024-11-07 06:18:45","2025-01-21 11:44:47+00:00","03549917264273fba2db16cf61c096734cfcd70e","45","110","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"jp1924/KoAudioCaps","jp1924","2024-10-29 12:51:22","2024-10-30 02:43:23+00:00","0fb271b14a68cf408e784be488c04bcb1bb88299","42","107","0","language:ko, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ko, en","bi","None","None","False","auto","False","mit","None","None","None"
+"junyeong-nero/KorMedLaw","junyeong-nero","2024-08-22 04:47:54","2024-12-22 06:09:43+00:00","e5a0592d34289644ab28377d841c860bcb323068","53","103","1","language:ko, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","None","한국 보건의료 국가시험에 출제에 사용되는 법률 데이터를 기반으로 solar-1-mini-chat 과 gpt-4o-mini 를 이용하여 생성한 QA 데이터셋 입니다. 사용된 법률은 다음과 같습니다: 감염병의 예방 및 관리에 관한 법률, 검역법, 국민건강보험법, 국민건강증진법, 농어촌 등 보건의료를 위한 특별조치법, 마약류 관리에 관한 법률, 보건의료기본법, ","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"
+"KoFinanceLLM/Finance_open_data","KoFinanceLLM","2024-10-22 06:22:13","2024-10-23 08:29:54+00:00","9dee0e738b171370b4fed5f6143f680323932e7e","40","103","0","language:ko, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"kt3472/etf_interview_preference","kt3472","2024-12-01 05:30:14","2024-12-01 05:49:06+00:00","d4cadcafb250a71966eb03c90a1278b3083ad357","47","98","0","language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"PJMixers-Dev/Fundus-CC-2.5M","PJMixers-Dev","2024-12-29 12:40:41","2024-12-30 04:01:09+00:00","72b2e4fcd4e77ff6d04d9a2880fc074116934589","97","97","0","None","None","Fundus-CC-2.5M 2.5 million news articles from CC-NEWS using Fundus.","None","en, de, tr, fr, es, no, ru, sw, fa, uk, hr, pt, mk, ur, ar, lt, pl, bn, sq, hi, id, zh, el, bg, nl, ro, cv, ko, so, da","multi","None","None","False","False","False","None","None","None","None"
+"blueapple8259/wikipedia_raw_ko","blueapple8259","2024-12-18 15:29:35","2024-12-18 15:51:35+00:00","e52a8238a7624f78b5cc6a58f89ee3eef0a281e1","53","96","0","language:ko, license:cc-by-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","위키피디아 20241201 덤프를 아무런 가공도 안 한 데이터셋입니다. 가공 하나도 안 한 원본 덤프 없길래 올렸습니다. 가공 안 된 데이터는 필요한데 xml 파싱하는 코드 작성은 귀찮거나 그냥 할 줄 몰라서 못 하시는 분들 있으면 가져다 쓰세요.","None","ko","mono","None","None","False","False","False","cc-by-sa-4.0","None","None","None"
+"Mineru/MME","Mineru","2025-01-16 05:26:17","2025-01-22 10:16:50+00:00","8bb9aa9d517c3e99562d7b81029533467a5d08db","96","96","1","language:ko, language:en, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"jaeyong2/RAG-COT-Ko","jaeyong2","2025-01-15 12:37:57","2025-01-28 05:42:02+00:00","70de514e06954282ac5bd57bb577281001306794","95","95","0","language:ko, 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 HAERAE-HUB/KOREAN-WEBTEXT and HAERAE-HUB/KOREAN-SyntheticText-1.5B We used Qwen/Qwen2-72B-Instruct model to generate answer with COT. License Qwen/Qwen2.5-72B-I","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"o0dimplz0o/Zeroth-STT-Korean","o0dimplz0o","2025-01-19 09:35:29","2025-01-20 09:54:55+00:00","c8ed68c1c86dc1528c8e5fa16be12b983cfbbbe2","87","87","0","task_categories:automatic-speech-recognition, language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition","Zeroth-STT-Korean Dataset Description This is a shuffled version of the Zeroth-STT-Ko dataset. Citation Zeroth-Korean Dataset, created by [Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.)]","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"jp1924/KoBPO","jp1924","2024-11-22 15:13:28","2024-12-12 07:03:45+00:00","843ed74106891fa56ae539eda3273e5b3547cfd3","41","85","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","auto","False","None","None","None","None"
+"robertsonwang/llm-child-safety-toxicity","robertsonwang","2024-12-04 05:22:46","2025-01-13 01:14:41+00:00","b3c486d2d605bf8eaf31e8eeb2fb549911974c80","46","81","0","task_categories:text-classification, language:en, language:es, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, safety, toxic, child_safety, instruct","text-classification","Access will be automatically granted to user accounts with an institutional email associated with their account. Other accounts will be granted access on a case by case basis, if you apply for access ","None","en, es, ko","multi","1|K|<|n|<|1|0|K","None","False","manual","False","apache-2.0","None","None","None"
+"dev7halo/naver-news-dpo","dev7halo","2024-10-19 04:21:16","2025-01-03 15:13:38+00:00","9fb0ba571d5d5461b829a510c277414a2a5c6b49","44","78","0","language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"dev7halo/naver-news-summarization-ko-with-gen","dev7halo","2024-10-17 07:35:46","2025-01-03 15:13:17+00:00","7ab1433bcc4d818a3cf388346d81672d256d7cb2","47","77","0","language:ko, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"KKI-testing/test","KKI-testing","2024-11-28 06:08:13","2024-11-28 06:09:45+00:00","a20c69c2a579faa85c479134ffdf8b339a2fb039","43","75","0","task_categories:text-generation, language:ko, license:cc-by-sa-3.0, size_categories:n<1K, modality:text, region:us, text, wikipedia","text-generation","None","None","ko","mono","n|<|1|K","None","False","False","False","cc-by-sa-3.0","None","None","None"
+"junyeong-nero/KMA-term","junyeong-nero","2024-12-27 01:42:23","2025-01-06 05:05:38+00:00","75a4c5b20982080c8fd64de1b500d7cb181abe0b","56","73","0","task_categories:translation, language:ko, language:en, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","translation","This dataset is generated by crawling KMA medical terms","None","ko, en","bi","1|0|K|<|n|<|1|0|0|K","None","False","False","False","apache-2.0","None","None","None"
+"niruka/kicj","niruka","2025-01-15 01:29:21","2025-01-15 02:18:08+00:00","0415cf241d7c71c1ddf3063bc92e1810bb6e0e42","71","71","1","task_categories:question-answering, language:ko, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, legal","question-answering","This is a dataset of 30 research reports published by the Korea Institute of Criminal Justice and Public Policy (KICJ) over the past 10 years, augmented with a Q/A set using GPT4o based on each resear","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"ChuGyouk/medical-o1-reasoning-SFT-Ko","ChuGyouk","2025-01-02 10:27:38","2025-01-02 10:34:06+00:00","a929b9edf0176daf0a15e13a71503be417ba6cff","67","67","2","task_categories:question-answering, task_categories:text-generation, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.18925, region:us, medical, biology","question-answering, text-generation","Introduction The original data was used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. Original dataset was constructed using GPT-4o, which searches for solutions to","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"werty1248/EnKo-Translation-Preference-Eval","werty1248","2025-01-06 09:24:59","2025-01-06 09:34:51+00:00","8118126090e98103d4475e9f9bc98569591fb54f","66","66","0","language:en, language:ko, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","Mistranslation dataset for evaluating the performance of reward models or filtering methods used in assessing the quality of Korean to English translations. Unnatural grammar usage, misinterpretation ","None","en, ko","bi","None","None","False","False","False","None","None","None","None"
+"o0dimplz0o/zeroth-STT-Ko","o0dimplz0o","2025-01-17 20:59:10","2025-01-19 19:45:51+00:00","7ecd8d971f3a62c2e3379ee081b898737183e964","65","65","0","task_categories:automatic-speech-recognition, language:ko, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:audio, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","automatic-speech-recognition","Zeroth-STT-Ko Dataset Description This dataset combines the following publicly available Korean language datasets: Junhoee/STT_Korean_Dataset_80000 and Zeroth-Korean Dataset (from Project: Zeroth, by ","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","apache-2.0","None","None","None"
+"jaeyong2/Ko-math-gemini-flash","jaeyong2","2025-01-20 14:52:35","2025-01-23 12:53:42+00:00","63a73fd9bff3ae43b6413ab23ff3e7897aa3df85","62","62","0","language:ko, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","참고 자료 : Aarushhh/finemath-refined 생성 모델 : gemini-2.0-flash-exp","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"felfri/MSTS","felfri","2025-01-20 15:28:54","2025-01-23 16:20:37+00:00","b0681c33b258523f937d2f6e27b95f7f50be68f0","60","60","3","None","image-text-to-text","Dataset Card for the MSTS Benchmark Here, you can find our paper and code. Note that for reproducing the exact results, we refer the user to the GitHub repo that provides download and preprocessing sc","None","ar, fr, en, de, zh, ko, fa, hi, it, ru, es","multi","1|K|<|n|<|1|0|K","None","False","auto","False","cc-by-4.0","None","None","None"
+"jaeyong2/RAG-COT-En_KO","jaeyong2","2025-01-20 16:15:02","2025-01-22 07:31:06+00:00","02ae1655eb75ecfe3d6cd321fdc6dd2ef01c44f1","56","56","0","language:ko, license:cdla-sharing-1.0, 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 ohsuz/fineweb-edu-2024-10-1M and roneneldan/TinyStories We used Qwen/Qwen2-72B-Instruct model to generate answer with COT. License Qwen/Qwen2.5-72B-Instruct : h","None","ko","mono","None","None","False","False","False","cdla-sharing-1.0","None","None","None"
+"jaeyong2/Ko-functioncall","jaeyong2","2024-12-24 15:37:13","2025-01-22 06:34:13+00:00","f9ce45b6d827d6589e6e35e0895ade57407ccbca","50","55","0","language:ko, license:apache-2.0, 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 daje/ko_wiki and maywell/korean_textbooks We used Qwen/Qwen2-72B-Instruct model to generate answer with COT. License Qwen/Qwen2.5-72B-Instruct : https://hugging","None","ko","mono","None","None","False","False","False","apache-2.0","None","None","None"
+"ChuGyouk/PubMedVision-EnKo","ChuGyouk","2025-01-20 04:07:20","2025-01-20 15:00:06+00:00","33a3526a86dcc4d285016de98526808384befec4","54","54","2","task_categories:question-answering, task_categories:text-generation, language:en, language:ko, license:apache-2.0, size_categories:1M<n<10M, format:json, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.19280, region:us, Vision, medical, biology","question-answering, text-generation","Informations This is the Korean translation of FreedomIntelligence/PubMedVision. The translation was primarily generated using the 'solar-pro-241126' model, with occasional manual assistance from the ","None","en, ko","bi","1|M|<|n|<|1|0|M","None","False","False","False","apache-2.0","2406.19280","None","None"
+"daekeun-ml/GLAN-qna-kr-300k","daekeun-ml","2024-12-26 06:39:05","2024-12-26 06:42:03+00:00","b10d1e9853f57ca31b6e1276798f4893424ecd5f","47","51","0","task_categories:question-answering, language:ko, license:openrail, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2402.13064, region:us","question-answering","Korean GLAN (Generalized Instruction Tuning) Instructions Dataset What is GLAN? Catastrophic forgetting, also known as catastrophic interference, occurs during SLM/LLM fine-tuning when a model trained","None","ko","mono","1|0|0|K|<|n|<|1|M","None","False","False","False","openrail","2402.13064","None","None"
+"datalama/RAG-Evaluation-Dataset-KO","datalama","2025-01-16 08:10:13","2025-01-20 06:28:39+00:00","c00e3d2007674071285d9906aa18b900385585ee","48","48","0","task_categories:other, annotations_creators:human-annotated, multilinguality:monolingual, source_datasets:extended|allganize/RAG-Evaluation-Dataset-KO, language:ko, license:mit, size_categories:1K<n<10K, format:webdataset, modality:text, library:datasets, library:webdataset, library:mlcroissant, region:us","other","Dataset Card for Reconstructed RAG Evaluation Dataset (KO) Dataset Summary 본 데이터셋은 allganize/RAG-Evaluation-Dataset-KO를 기반으로 PDF 파일을 포함하도록 재구성한 한국어 평가 데이터셋입니다. 원본 데이터셋에서는 PDF 파일의 경로만 제공되어 수동으로 파일을 다운로","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"ygyoung/easylaw_ko_qa","ygyoung","2025-01-10 01:32:14","2025-01-10 01:58:01+00:00","580989013f70d2165857774eef0d4e0da0c05f23","48","48","1","task_categories:question-answering, language:ko, 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","None","None","ko","mono","1|M|<|n|<|1|0|M","None","False","auto","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","task_categories:translation, language:ko, language:ja, language:th, language:sl, language:fi, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","translation","None","None","ko, ja, th, sl, fi","multi","None","None","False","False","False","None","None","None","None"
+"nmixx-fin/opensource_korean_finance_datasets","nmixx-fin","2025-01-15 11:56:34","2025-01-15 11:59:11+00:00","199112dcb95700ccf60f54842a01094063a8773c","45","45","0","language:ko, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us, finance","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"blueapple8259/TinyStories-korean-edu","blueapple8259","2025-01-09 12:34:48","2025-01-09 12:56:09+00:00","a881a10a0e4fefea5e52d6f93a213decaaab9dd0","43","43","0","task_categories:text-generation, language:ko, license:cdla-sharing-1.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","text-generation","TinyStories와 TinyStories-Korean, fineweb-edu-classifier를 이용하여 만든 데이터입니다. TinyStories를 fineweb-edu-classifier로 평가를 한 뒤 int_score가 3 이상이면 이에 해당되는 TinyStories-Korean을 데이터에 추가하는 방식으로 제작하였습니다. TinyStories-","None","ko","mono","None","None","False","False","False","cdla-sharing-1.0","None","None","None"
+"blueapple8259/TinyFinewebEdu-ko","blueapple8259","2025-01-09 12:41:07","2025-01-10 07:45:53+00:00","46e4fc6bd04df52e8a503b2c6160d925505c63ac","40","40","0","task_categories:text-generation, language:ko, 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","text-generation","fineweb-edu에서 int_score가 4 이상인 데이터만 필터링한 후 DeepSeek-v3을 이용해 데이터를 단순한 형태의 문장과 간단한 어휘로 구성되도록 변환한 데이터입니다. 비용 문제로 데이터는 67k 개만 있습니다.","None","ko","mono","None","None","False","False","False","cc-by-nc-sa-4.0","None","None","None"
+"junyeong-nero/vqa-rad-ko","junyeong-nero","2025-01-07 12:38:06","2025-01-09 08:34:37+00:00","3bdb8217ffa8b492f3c2c902b66b339466efe58a","40","40","1","task_categories:image-to-text, language:ko, license:apache-2.0, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, medical","image-to-text","None","None","ko","mono","1|K|<|n|<|1|0|K","None","False","False","False","apache-2.0","None","None","None"
+"dev7halo/kor-rag-opentest","dev7halo","2025-01-02 05:14:55","2025-01-02 05:44:45+00:00","6314b87abe13c1cd6aae4f709f94c093c2ef6d3d","40","40","0","language:ko, size_categories:n<1K, format:csv, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"json1018/china_three_kingdoms","json1018","2024-12-31 04:49:22","2024-12-31 05:06:24+00:00","cbcf2e5d6c8a8271808273e1fbe702d9f21345cc","39","39","0","language:ko, license:unknown, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, three","None","None","None","ko","mono","None","None","False","False","False","unknown","None","None","None"
+"coorung/Kor-financial-qa-7K","coorung","2025-01-08 05:38:20","2025-01-10 00:58:51+00:00","89430eb398c720e58ac252ff8a40527173fd87ab","39","39","0","language:ko, license:mit, size_categories:1K<n<10K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us","None","None","None","ko","mono","None","None","False","False","False","mit","None","None","None"
+"richard-park/aihub-llm-corpora-2024-1113","richard-park","2024-11-13 08:30:44","2024-11-13 13:12:31+00:00","af67147fdaa1cbc81b85392b342a2de16cc9a9d9","36","36","0","language:ko, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Dataset Details Dataset Description tokenizer: sapie-tokeniz","None","ko","mono","None","None","False","False","False","None","None","None","None"
+"fantastic-jobs/linkedin-industry-list","fantastic-jobs","2025-01-11 14:49:37","2025-01-11 15:04:13+00:00","2f507ff22e8db75d26e1644c1bda16a4766318a3","34","34","0","task_categories:text-classification, task_categories:translation, language:en, language:ko, language:es, language:ru, language:nl, language:de, language:ml, language:it, language:fr, language:pt, language:id, language:cs, language:tr, language:da, language:sv, language:ar, language:th, license:mit, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, jobs, linkedin, job, industries, industry","text-classification, translation","None","None","en, ko, es, ru, nl, de, ml, it, fr, pt, id, cs, tr, da, sv, ar, th","multi","1|K|<|n|<|1|0|K","None","False","False","False","mit","None","None","None"
+"daje/ko-hatefulmemes_train_8500","daje","2025-01-13 16:27:24","2025-01-14 03:27:52+00:00","4f8e1f940bff82de2d99cfc2fb3bfab908a6b169","34","34","0","language:ko, language:en, size_categories:1K<n<10K, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us","None","None","None","ko, en","bi","1|K|<|n|<|1|0|K","None","False","False","False","None","None","None","None"