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Datasets:
CohereForAI
/
xP3x

Tasks:
Other
Languages: Afrikaans Arabic Azerbaijani
Multilinguality: multilingual
Size Categories: 100M<n<1B
Annotations Creators: expert-generated crowdsourced
ArXiv:
License:
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Dataset Card for xP3x

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 xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @C4AI 🧡

  • Creation: The dataset can be recreated using instructions available here together with the file in this repository named xp3x_create.py. We provide this version to save processing time.
  • Languages: 277
  • xP3 Dataset Family:
Name Explanation Example models
xP3x Mixture of 17 tasks in 277 languages with English prompts WIP - Join us at Project Aya @C4AI to help!
xP3 Mixture of 13 training tasks in 46 languages with English prompts bloomz & mt0-xxl
xP3mt Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English) bloomz-mt & mt0-xxl-mt
xP3all xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts
xP3megds Megatron-DeepSpeed processed version of xP3 bloomz
P3 Repreprocessed version of the English-only P3 with 8 training tasks bloomz-p3 & mt0-xxl-p3

Dataset Structure

Data Instances

An example looks as follows:

{
  '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'
}

Data Fields

The data fields are the same among all splits:

  • inputs: the natural language input fed to the model
  • targets: the natural language target that the model has to generate
  • language: The language code. The codes are an extension of the FLORES-200 codes, where the first part is the language code and the second part the script code.
  • template: The name of the prompt used.
  • dataset: The Hugging Face dataset identifier of where the data stems from.
  • config: The config of the Hugging Face dataset.

Usage

The dataset has 680 gigabytes and 530 million samples. You may want to filter it and then deduplicate depending on your needs.

Loading by language:

# 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

You can then filter down by the data fields to e.g. only get certain configs or datasets. As every dataset-config-template is its own jsonl file, you can also decide on the datasets, configs and templates you want and only download them. For example, to download all Japanese xwinograd samples, you could do:

# 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/CohereForAI/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"]

Sometimes it may be faster to clone the entire repo. To download all English files, you could do e.g.

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/CohereForAI/xP3x
cd xP3x
git lfs pull --include="data/eng_Latn/*"

Data Splits

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

Language specifics

  • Japanese: Data in jpn_Hira, jpn_Kana, jpn_Hani is guaranteed to have Hiragana, Katakana or Kanji, respectively in each sample. However, they may still include other styles. So while all samples in jpn_Kana are guaranteed to have Katakana, there may still be Hiragana or Kanji.

Dataset Creation

Source Data

Training datasets

Dataset specifics

  • Flores-200: There are three prompts for Flores: continuation, question, command, which represent three commonly used prompting styles, i.e. making a prompt seem like a natural continuation, turning it into a question or commanding the model to do something.
  • tatoeba_mt: Contains duplicates. For example, it has data that is both classified as jpn_Kana and jpn_Jpan, so you may want to deduplicate.

Additional Information

Licensing Information

The dataset collection is released under Apache 2.0. Note that individual datasets may have different licenses.

Citation Information 

@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}
}

Contributions

Thanks to the contributors of promptsource for adding many prompts used in this dataset. Thanks to the Aya team @C4AI 🧡

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Repository:
github.com
Paper:
Crosslingual Generalization through Multitask Finetuning
Point of Contact:
Niklas Muennighoff

Models trained or fine-tuned on CohereForAI/xP3x