数据集:

Muennighoff/xP3x

任务:

task_categories:other

语言:

计算机处理:

multilingual

大小:

100M<n<1B

批注创建人:

expert-generated crowdsourced

预印本库:

arxiv:2211.01786

许可:

apache-2.0

数据集介绍文件清单

英文

xP3x 数据集卡片

数据集摘要

xP3x（跨语言公共提示池扩展版）是一个覆盖277种语言和16个NLP任务的提示和数据集收集。它包含了所有xP3的内容，以及更多！它用于训练Aya项目@ C4AI 未来的mT0和BLOOMZ竞争者。

创建：可以使用提供的指令 here 以及此存储库中的文件xp3x_create.py重新创建数据集。我们提供此版本以节省处理时间。
语言：277
xP3数据集系列：

Name	Explanation	Example models
1236321	Mixture of 17 tasks in 277 languages with English prompts	WIP - Join us at Project Aya @ 1237321 to help!
1238321	Mixture of 13 training tasks in 46 languages with English prompts	1239321 & 12310321
12311321	Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)	12312321 & 12313321
12314321	xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts
12315321	12316321 processed version of xP3	1239321
12318321	Repreprocessed version of the English-only 12319321 with 8 training tasks	12320321 & 12321321

数据集结构

数据实例

示例如下：

{
  'inputs': '11月、遂にクロームはファイヤーフォックスを引き離し始めた。_はインターネットユーザーの評価が高まったのだ。\nReplace the _ in the above sentence with the correct option: \n- ファイヤーフォックス\n- クローム',
  'targets': 'クローム',
  'language': 'jpn_Jpan',
  'split': 'test',
  'template': 'Replace',
  'dataset': 'Muennighoff/xwinograd',
  'config': 'jp'
}

数据字段

数据字段在所有拆分中都相同：

输入：输入到模型的自然语言输入
目标：模型必须生成的自然语言目标
语言：语言代码。这些代码是FLORES-200代码的扩展，其中第一部分是语言代码，第二部分是脚本代码。
模板：使用的提示名称。
数据集：数据来源的Hugging Face数据集标识符。
配置：Hugging Face数据集的配置。

用法

该数据集有680GB和5.3亿个样本。根据需求进行筛选和去重。

按语言加载：

# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("Muennighoff/xP3x", "zho_Hans", streaming=True) # Use streaming to not download all at once
for x in ds["train"]:
    print(x)
    break

然后，您可以通过数据字段进行筛选，例如仅获取特定的配置或数据集。由于每个数据集-配置-模板都是一个单独的jsonl文件，因此您也可以根据您想要的数据集、配置和模板进行决定并仅下载它们。例如，要下载所有日语xwinograd样本，您可以执行：

# pip install -q datasets
from datasets import load_dataset
import multiprocessing
# pip install --upgrade huggingface-hub
from huggingface_hub import HfFileSystem, hf_hub_url

fs = HfFileSystem()
fps = fs.glob(f"datasets/Muennighoff/xP3x/data/jpn_Jpan/*xwinograd*")
resolved_paths = [fs.resolve_path(file) for file in fps]
data_files = [hf_hub_url(resolved_path.repo_id, resolved_path.path_in_repo, repo_type=resolved_path.repo_type) for resolved_path in resolved_paths]

ds = load_dataset("json", data_files=data_files, num_proc=8)["train"]

数据拆分

Language	Code	Kilobytes	%	Samples	%
Emilian	egl_Latn	104	0.0	402	0.0
Swiss German	gsw_Latn	104	0.0	408	0.0
Novial	nov_Latn	116	0.0	432	0.0
Ainu (Latin script)	ain_Latn	120	0.0	410	0.0
Chamorro	cha_Latn	120	0.0	452	0.0
Gothic	got_Goth	120	0.0	402	0.0
Prussian	prg_Latn	120	0.0	424	0.0
Picard	pcd_Latn	140	0.0	530	0.0
Northern Frisian	frr_Latn	156	0.0	554	0.0
Uzbek (Latin script)	uzb_Latn	156	0.0	600	0.0
Ottoman Turkish (Latin script)	ota_Latn	188	0.0	632	0.0
Swahili (macrolanguage)	swa_Latn	212	0.0	772	0.0
Talossan	tzl_Latn	220	0.0	836	0.0
Kven Finnish	fkv_Latn	260	0.0	910	0.0
Zaza	zza_Latn	260	0.0	1,056	0.0
Frisian	fry_Latn	268	0.0	956	0.0
Piemontese	pms_Latn	276	0.0	998	0.0
Kalmyk	xal_Cyrl	288	0.0	976	0.0
Hunsrik	hrx_Latn	352	0.0	1,380	0.0
Romany	rom_Latn	364	0.0	1,410	0.0
Ancient Greek (to 1453)	grc_Grek	392	0.0	1,226	0.0
Tase Naga	nst_Latn	424	0.0	1,608	0.0
Albanian	sqi_Latn	596	0.0	2,216	0.0
Guadeloupean Creole French	gcf_Latn	608	0.0	2,326	0.0
Yakut	sah_Cyrl	608	0.0	1,986	0.0
Ho (Latin script)	hoc_Latn	632	0.0	2,634	0.0
Khasi	kha_Latn	676	0.0	2,664	0.0
Algerian Arabic	arq_Arab	688	0.0	2,278	0.0
Lower Sorbian	dsb_Latn	692	0.0	2,596	0.0
Chuvash	chv_Cyrl	716	0.0	2,446	0.0
Old Russian	orv_Cyrl	752	0.0	2,586	0.0
Pampanga	pam_Latn	784	0.0	2,984	0.0
Kurdish (Latin script)	kur_Latn	796	0.0	3,050	0.0
Ottoman Turkish	ota_Arab	832	0.0	2,772	0.0
Kotava	avk_Latn	864	0.0	3,118	0.0
Upper Sorbian	hsb_Latn	900	0.0	3,474	0.0
Buryat	bua_Cyrl	924	0.0	3,218	0.0
Swabian	swg_Latn	996	0.0	3,366	0.0
Coastal Kadazan	kzj_Latn	1,136	0.0	3,766	0.0
Chavacano	cbk_Latn	1,352	0.0	4,994	0.0
Quechua	que_Latn	1,704	0.0	5,312	0.0
Lingua Franca Nova (Cyrillic script)	lfn_Cyrl	1,740	0.0	5,458	0.0
Gronings	gos_Latn	1,864	0.0	7,462	0.0
Volapük	vol_Latn	1,948	0.0	7,712	0.0
Yue Chinese (Simplified)	yue_Hans	2,300	0.0	7,872	0.0
Mari (Russia)	chm_Cyrl	2,540	0.0	7,496	0.0
Kadazan Dusun	dtp_Latn	2,548	0.0	8,892	0.0
Breton	bre_Latn	3,048	0.0	11,868	0.0
Ladino	lad_Latn	3,224	0.0	11,916	0.0
Cornish	cor_Latn	3,492	0.0	13,880	0.0
Interlingue	ile_Latn	3,700	0.0	14,468	0.0
Wu Chinese	wuu_Hans	3,784	0.0	13,062	0.0
Japanese (Katakana)	jpn_Kana	4,208	0.0	13,942	0.0
Ido	ido_Latn	6,180	0.0	23,742	0.0
Yiddishi	yid_Hebr	9,896	0.0	34,412	0.01
Klingon	tlh_Latn	11,716	0.0	46,010	0.01
Lingua Franca Nova	lfn_Latn	13,328	0.0	46,826	0.01
Lojban	jbo_Latn	17,468	0.0	66,694	0.01
Low German	nds_Latn	18,364	0.0	68,098	0.01
Interlingua (International Auxiliary Language Association)	ina_Latn	25,700	0.0	76,584	0.01
Java	java	25,904	0.0	13,551	0.0
Japanese (Kanji)	jpn_Hani	26,292	0.0	89,978	0.02
Norwegian	nor_Latn	26,724	0.0	93,116	0.02
Toki Pona	toki_Latn	26,808	0.0	97,170	0.02
Latin	lat_Latn	28,900	0.0	101,390	0.02
Serbo-Croatian	hbs_Latn	29,452	0.0	105,748	0.02
Nigerian Pidgin	pcm_Latn	145,872	0.02	88,992	0.02
Azerbaijani (South or North; Latin script)	aze_Latn	147,564	0.02	77,875	0.01
Serbian (Latin script)	srp_Latn	179,072	0.03	131,101	0.02
Japanese (Hiragana)	jpn_Hira	188,944	0.03	628,758	0.12
Berber (Latin script)	ber_Latn	201,464	0.03	693,602	0.13
Jupyter Notebook	jupyter_notebook	416,056	0.06	400,000	0.08
Yue Chinese	yue_Hant	613,352	0.09	1,227,429	0.23
Haitian Creole	hat_Latn	629,420	0.09	1,228,281	0.23
Mossi	mos_Latn	630,416	0.09	1,223,481	0.23
Pangasinan	pag_Latn	630,684	0.09	1,223,481	0.23
Twi	twi_Latn	631,172	0.09	1,223,481	0.23
Bosnian	bos_Latn	633,016	0.09	1,224,479	0.23
Ewe	ewe_Latn	633,292	0.09	1,223,481	0.23
Bambara	bam_Latn	634,520	0.09	1,223,481	0.23
Javanese	jav_Latn	635,248	0.09	1,224,003	0.23
Southwestern Dinka	dik_Latn	635,416	0.09	1,223,481	0.23
Kabuverdianu	kea_Latn	636,144	0.09	1,223,481	0.23
Dyula	dyu_Latn	636,464	0.09	1,223,481	0.23
Venetian	vec_Latn	637,412	0.09	1,223,481	0.23
Chokwe	cjk_Latn	637,532	0.09	1,223,481	0.23
Latgalian	ltg_Latn	637,612	0.09	1,223,481	0.23
Sundanese	sun_Latn	638,120	0.09	1,223,481	0.23
Asturian	ast_Latn	638,708	0.09	1,223,481	0.23
Akan	aka_Latn	639,648	0.09	1,223,481	0.23
Mizo	lus_Latn	639,680	0.09	1,223,481	0.23
Guarani	grn_Latn	641,540	0.09	1,225,647	0.23
Limburgish	lim_Latn	642,368	0.09	1,223,481	0.23
Faroese	fao_Latn	642,432	0.09	1,224,067	0.23
Buginese	bug_Latn	643,472	0.09	1,223,481	0.23
Sango	sag_Latn	643,596	0.09	1,223,481	0.23
Luba-Kasai	lua_Latn	643,640	0.09	1,223,481	0.23
Papiamento	pap_Latn	643,648	0.09	1,223,481	0.23
Silesian	szl_Latn	644,608	0.09	1,223,481	0.23
Sicilian	scn_Latn	645,636	0.1	1,223,481	0.23
Kimbundu	kmb_Latn	645,964	0.1	1,223,481	0.23
Basque	eus_Latn	646,084	0.1	1,246,877	0.23
Balinese	ban_Latn	646,408	0.1	1,223,481	0.23
Norwegian Nynorsk	nno_Latn	646,996	0.1	1,229,699	0.23
Central Aymara	ayr_Latn	647,236	0.1	1,223,481	0.23
Tamasheq (Latin script)	taq_Latn	648,656	0.1	1,223,481	0.23
Kikongo	kon_Latn	648,992	0.1	1,223,481	0.23
Friulian	fur_Latn	649,272	0.1	1,223,481	0.23
Ayacucho Quechua	quy_Latn	649,992	0.1	1,223,481	0.23
Maori	mri_Latn	650,336	0.1	1,224,211	0.23
Icelandic	isl_Latn	650,372	0.1	1,246,623	0.23
Galician	glg_Latn	652,088	0.1	1,233,291	0.23
Catalan	cat_Latn	652,116	0.1	1,241,381	0.23
Lombard	lmo_Latn	652,120	0.1	1,223,481	0.23
Banjar (Latin script)	bjn_Latn	652,372	0.1	1,223,481	0.23
Fijian	fij_Latn	652,796	0.1	1,223,481	0.23
Crimean Tatar	crh_Latn	653,920	0.1	1,223,895	0.23
Northern Kurdish	kmr_Latn	654,108	0.1	1,223,481	0.23
Ligurian	lij_Latn	654,432	0.1	1,223,481	0.23
Occitan	oci_Latn	655,676	0.1	1,227,945	0.23
Turkmen	tuk_Latn	658,672	0.1	1,241,205	0.23
Luxembourgish	ltz_Latn	658,768	0.1	1,225,339	0.23
Cebuano	ceb_Latn	659,124	0.1	1,226,039	0.23
Samoan	smo_Latn	659,704	0.1	1,223,481	0.23
Sardinian	srd_Latn	660,000	0.1	1,223,481	0.23
Bemba	bem_Latn	660,504	0.1	1,223,481	0.23
Minangkabau (Latin script)	min_Latn	660,672	0.1	1,223,481	0.23
Acehnese (Latin script)	ace_Latn	661,084	0.1	1,223,481	0.23
Ilocano	ilo_Latn	661,184	0.1	1,227,663	0.23
Irish	gle_Latn	661,660	0.1	1,227,357	0.23
Fon	fon_Latn	663,124	0.1	1,223,481	0.23
Waray	war_Latn	664,120	0.1	1,226,503	0.23
Norwegian Bokmål	nob_Latn	666,240	0.1	1,300,607	0.24
Tosk Albanian	als_Latn	666,692	0.1	1,223,481	0.23
Standard Malay	zsm_Latn	667,088	0.1	1,270,715	0.24
Southern Sotho	sot_Latn	667,728	0.1	1,223,481	0.23
Kabyle	kab_Latn	668,128	0.1	1,346,605	0.25
Jingpho	kac_Latn	669,464	0.1	1,223,481	0.23
Lingala	lin_Latn	670,428	0.1	1,323,481	0.25
Wolof	wol_Latn	670,568	0.1	1,373,481	0.26
Central Kanuri (Latin script)	knc_Latn	670,800	0.1	1,223,481	0.23
Kikuyu	kik_Latn	672,096	0.1	1,223,481	0.23
Tok Pisin	tpi_Latn	672,916	0.1	1,223,481	0.23
Nuer	nus_Latn	673,632	0.1	1,223,481	0.23
Tagalog	tgl_Latn	673,684	0.1	1,247,417	0.23
Tumbuka	tum_Latn	676,948	0.1	1,223,481	0.23
Plateau Malagasy	plt_Latn	677,852	0.1	1,223,481	0.23
Afrikaans	afr_Latn	679,164	0.1	1,337,091	0.25
North Azerbaijani	azj_Latn	679,820	0.1	1,223,481	0.23
Kabiyè	kbp_Latn	684,880	0.1	1,223,481	0.23
Modern Standard Arabic (Romanized)	arb_Latn	685,408	0.1	1,223,481	0.23
Scottish Gaelic	gla_Latn	708,620	0.1	1,243,627	0.23
Sindhi	snd_Arab	718,680	0.11	1,223,481	0.23
North Levantine Arabic	apc_Arab	720,048	0.11	1,223,481	0.23
Tunisian Arabic	aeb_Arab	720,360	0.11	1,223,481	0.23
South Levantine Arabic	ajp_Arab	720,488	0.11	1,223,481	0.23
Dari	prs_Arab	720,500	0.11	1,223,481	0.23
Moroccan Arabic	ary_Arab	722,904	0.11	1,223,481	0.23
Egyptian Arabic	arz_Arab	723,356	0.11	1,223,481	0.23
Najdi Arabic	ars_Arab	725,784	0.11	1,223,481	0.23
Acehnese (Arabic script)	ace_Arab	726,272	0.11	1,223,481	0.23
Mesopotamian Arabic	acm_Arab	728,472	0.11	1,223,481	0.23
Ta’izzi-Adeni Arabic	acq_Arab	734,780	0.11	1,223,481	0.23
South Azerbaijani	azb_Arab	735,728	0.11	1,223,481	0.23
Central Kanuri (Arabic script)	knc_Arab	746,936	0.11	1,223,481	0.23
Rundi	run_Latn	749,792	0.11	1,296,111	0.24
Banjar (Arabic script)	bjn_Arab	751,112	0.11	1,223,481	0.23
Central Kurdish	ckb_Arab	756,804	0.11	1,223,481	0.23
Bashkir	bak_Cyrl	758,816	0.11	1,223,481	0.23
Kashmiri (Arabic script)	kas_Arab	759,140	0.11	1,223,481	0.23
Tatar	tat_Cyrl	764,212	0.11	1,247,685	0.23
Minangkabau (Arabic script)	min_Arab	765,384	0.11	1,223,481	0.23
Kazakh	kaz_Cyrl	766,176	0.11	1,232,697	0.23
Halh Mongolian	khk_Cyrl	776,384	0.11	1,224,353	0.23
Tajik	tgk_Cyrl	780,452	0.11	1,223,481	0.23
Eastern Yiddish	ydd_Hebr	781,452	0.12	1,223,481	0.23
Uyghur	uig_Arab	785,444	0.12	1,256,999	0.24
Armenian	hye_Armn	789,952	0.12	1,228,171	0.23
Hebrew	heb_Hebr	793,144	0.12	1,604,365	0.3
Belarusian	bel_Cyrl	806,588	0.12	1,261,197	0.24
Macedonian	mkd_Cyrl	813,436	0.12	1,384,567	0.26
Welsh	cym_Latn	821,036	0.12	1,321,455	0.25
Northern Uzbek	uzn_Latn	835,560	0.12	1,273,404	0.24
Central Atlas Tamazight	tzm_Tfng	843,508	0.12	1,223,481	0.23
Tamasheq (Tifinagh script)	taq_Tfng	848,104	0.12	1,223,481	0.23
Magahi	mag_Deva	851,360	0.13	1,223,481	0.23
Bhojpuri	bho_Deva	854,848	0.13	1,223,481	0.23
Awadhi	awa_Deva	857,096	0.13	1,224,037	0.23
Chhattisgarhi	hne_Deva	859,332	0.13	1,223,481	0.23
Kyrgyz	kir_Cyrl	860,700	0.13	1,250,163	0.23
Maithili	mai_Deva	863,476	0.13	1,223,481	0.23
Assamese	asm_Beng	865,904	0.13	1,223,481	0.23
Kashmiri (Devanagari script)	kas_Deva	867,232	0.13	1,223,481	0.23
Sanskrit	san_Deva	879,236	0.13	1,223,481	0.23
Lao	lao_Laoo	888,240	0.13	1,223,481	0.23
Odia	ory_Orya	890,508	0.13	1,223,481	0.23
Santali	sat_Olck	902,300	0.13	1,223,481	0.23
Kannada	kan_Knda	909,260	0.13	1,223,481	0.23
Meitei (Bengali script)	mni_Beng	917,984	0.14	1,223,481	0.23
Georgian	kat_Geor	928,712	0.14	1,226,729	0.23
Kamba	kam_Latn	936,468	0.14	2,136,615	0.4
Tigrinya	tir_Ethi	949,608	0.14	1,276,536	0.24
Swati	ssw_Latn	950,564	0.14	2,195,002	0.41
Malayalam	mal_Mlym	953,984	0.14	1,225,083	0.23
Nigerian Fulfulde	fuv_Latn	956,328	0.14	2,126,652	0.4
Umbundu	umb_Latn	974,104	0.14	2,264,553	0.43
Ganda	lug_Latn	975,780	0.14	2,273,481	0.43
Northern Sotho	nso_Latn	978,484	0.14	2,250,971	0.42
Khmer	khm_Khmr	984,756	0.14	1,227,825	0.23
Luo	luo_Latn	993,068	0.15	2,249,242	0.42
Standard Tibetan	bod_Tibt	993,732	0.15	1,223,481	0.23
Tswana	tsn_Latn	1,009,328	0.15	2,323,481	0.44
Kinyarwanda	kin_Latn	1,010,752	0.15	2,273,481	0.43
Sinhala	sin_Sinh	1,012,012	0.15	1,256,582	0.24
Xhosa	xho_Latn	1,019,804	0.15	2,323,481	0.44
Shona	sna_Latn	1,026,320	0.15	2,273,481	0.43
Esperanto	epo_Latn	1,029,444	0.15	2,612,083	0.49
Tsonga	tso_Latn	1,031,856	0.15	2,323,481	0.44
Dzongkha	dzo_Tibt	1,033,552	0.15	1,223,481	0.23
Zulu	zul_Latn	1,039,296	0.15	2,323,481	0.44
Serbian	srp_Cyrl	1,040,024	0.15	1,362,598	0.26
Nyanja	nya_Latn	1,061,780	0.16	2,323,481	0.44
Shan	shn_Mymr	1,074,940	0.16	1,223,481	0.23
Igbo	ibo_Latn	1,095,300	0.16	2,282,301	0.43
Hausa	hau_Latn	1,112,272	0.16	2,335,738	0.44
West Central Oromo	gaz_Latn	1,115,600	0.16	2,343,260	0.44
Nepali	npi_Deva	1,144,676	0.17	1,281,430	0.24
Yoruba	yor_Latn	1,164,540	0.17	2,334,801	0.44
Southern Pashto	pbt_Arab	1,170,840	0.17	1,365,533	0.26
Somali	som_Latn	1,198,320	0.18	2,482,437	0.47
Burmese	mya_Mymr	1,228,196	0.18	1,279,882	0.24
Amharic	amh_Ethi	1,261,128	0.19	1,980,215	0.37
Eastern Panjabi	pan_Guru	1,305,636	0.19	1,307,897	0.25
Gujarati	guj_Gujr	1,331,780	0.2	1,317,314	0.25
Marathi	mar_Deva	1,494,024	0.22	1,443,950	0.27
Bengali	ben_Beng	1,650,272	0.24	1,411,514	0.27
Chinese (Traditional)	zho_Hant	1,778,736	0.26	1,956,189	0.37
Tamil	tam_Taml	1,833,328	0.27	1,394,473	0.26
Swahili	swh_Latn	1,970,784	0.29	4,185,608	0.79
Telugu	tel_Telu	2,224,480	0.33	1,573,325	0.3
Ukrainian	ukr_Cyrl	2,227,616	0.33	2,216,119	0.42
Western Persian	pes_Arab	2,389,340	0.35	1,811,121	0.34
Turkish	tur_Latn	3,106,600	0.46	4,146,153	0.78
Urdu	urd_Arab	3,553,960	0.52	3,513,218	0.66
Korean	kor_Hang	4,642,468	0.68	3,415,920	0.64
Python	python	4,728,504	0.7	3,142,962	0.59
Japanese	jpn_Jpan	5,079,788	0.75	4,193,570	0.79
Thai	tha_Thai	6,860,704	1.01	4,666,299	0.88
Chinese (Simplified)	zho_Hans	8,063,684	1.19	7,355,509	1.38
Vietnamese	vie_Latn	8,398,824	1.24	6,194,925	1.16
Indonesian	ind_Latn	9,380,144	1.38	5,301,812	1.0
Hindi	hin_Deva	9,914,328	1.46	5,612,176	1.05
Croatian	hrv_Latn	10,028,028	1.48	5,583,975	1.05
Modern Standard Arabic	arb_Arab	11,051,064	1.63	7,232,551	1.36
Romanian	ron_Latn	11,441,636	1.68	5,594,927	1.05
Maltese	mlt_Latn	11,614,488	1.71	5,513,885	1.04
Slovenian	slv_Latn	12,014,912	1.77	5,533,689	1.04
Estonian	est_Latn	12,126,212	1.79	5,584,057	1.05
Lithuanian	lit_Latn	12,253,976	1.8	5,603,047	1.05
Slovak	slk_Latn	12,286,300	1.81	5,513,481	1.04
Standard Latvian	lvs_Latn	12,298,584	1.81	5,517,287	1.04
Polish	pol_Latn	12,409,684	1.83	5,868,631	1.1
Hungarian	hun_Latn	12,607,420	1.86	6,086,621	1.14
Russian	rus_Cyrl	13,110,908	1.93	8,798,927	1.65
Czech	ces_Latn	14,316,052	2.11	6,418,462	1.21
Bulgarian	bul_Cyrl	14,615,468	2.15	7,265,885	1.37
Swedish	swe_Latn	14,646,656	2.16	5,634,363	1.06
Finnish	fin_Latn	15,011,464	2.21	6,077,501	1.14
Danish	dan_Latn	16,136,612	2.38	5,831,109	1.1
Dutch	nld_Latn	22,387,020	3.3	8,992,864	1.69
Greek	ell_Grek	23,144,296	3.41	7,224,001	1.36
Italian	ita_Latn	23,952,824	3.53	9,967,738	1.87
Portuguese	por_Latn	27,297,252	4.02	11,242,808	2.11
German	deu_Latn	27,909,808	4.11	15,806,969	2.97
French	fra_Latn	28,428,608	4.18	16,365,984	3.08
Spanish	spa_Latn	30,969,580	4.56	16,315,928	3.07
English	eng_Latn	69,530,384	10.24	53,015,690	9.96
Total	-	679,318,704	100	532,107,156	100

语言特定信息

日语：在jpn_Hira、jpn_Kana、jpn_Hani中的数据的每个样本中都包含平假名、片假名或汉字。但是，它们仍然可能包含其他样式。因此，虽然jpn_Kana中的所有样本都保证有片假名，但仍然可能包含平假名或汉字。

数据集创建

源数据

训练数据集

代码杂项
闭书问答
提取式问答
- Adversarial QA
- CMRC2018
- DRCD
- DuoRC
- MLQA
- Quoref
- ReCoRD
- ROPES
- SQuAD v2
- xQuAD
- TyDI QA
  - Primary
  - Goldp
多项选择问答
- ARC
- C3
- CoS-E
- Cosmos
- DREAM
- MultiRC
- OpenBookQA
- PiQA
- QUAIL
- QuaRel
- QuaRTz
- QASC
- RACE
- SciQ
- Social IQA
- Wiki Hop
- WiQA
释义识别
- MRPC
- PAWS
- PAWS-X
- QQP
程序综合
结构化文本
- Common Gen
- Wiki Bio
情感
- Amazon
- App Reviews
- IMDB
- Rotten Tomatoes
- Yelp
简化
- BiSECT
摘要
- CNN Daily Mail
- Gigaword
- MultiNews
- SamSum
- Wiki-Lingua
- XLSum
- XSum
主题分类
- AG News
- DBPedia
- TNEWS
- TREC
- CSL
翻译
词义消歧
- WiC
- XL-WiC
自然语言推理（NLI）
- ANLI
- CB
- RTE
- XNLI
指代消解
- Winogrande
- XWinograd
句子完成
- COPA
- Story Cloze
- XCOPA
- XStoryCloze

数据集特定信息

Flores-200：Flores有三个提示：continuation、question、command，分别代表三种常用的提示风格，即使得提示看起来像是自然的延续，转化为问题或命令模型执行某些操作。
tatoeba_mt：包含重复项。例如，它具有同时被分类为jpn_Kana和jpn_Jpan的数据，因此您可能需要去重。

其他信息

许可信息

数据集收集在Apache 2.0下发布。请注意，各个数据集可能具有不同的许可证。

引用信息

@article{muennighoff2022crosslingual,
  title={Crosslingual generalization through multitask finetuning},
  author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
  journal={arXiv preprint arXiv:2211.01786},
  year={2022}
}

贡献

感谢 promptsource 的贡献者为该数据集添加了许多提示。感谢Aya团队@ C4AI ?

作者:

Muennighoff

数据集大小:

4.34 GB