from functools import partial import json import multiprocessing import os import random from datasets import load_dataset # pip install -q iso-639 from iso639 import languages from promptsource.templates import DatasetTemplates # Set to False to use multilingual prompts e.g. 'id' for xcopa/id instead of 'en' USE_ENGLISH_PROMPTS = False MAX_EXAMPLES_PER_DATASET_PROMPT = 100_000 STORY_CLOZE_DIR = "/gpfswork/rech/six/commun/code/tr13f-6B3-ml-t0/story_cloze_data" XSTORY_CLOZE_DIR = "/gpfswork/rech/six/commun/code/tr13f-6B3-ml-t0/xstory_cloze_data" # Some datasets have test sets with hidden labels which will still compile but only to noise # e.g. piqa test labels are all [-1] which still works on list indices resulting in # noise samples where the label is always the same SKIP_PROMPTS = { "common_gen": {"test": ["all"]}, "piqa": {"test": ["all"]}, "qasc": {"test": ["all"]}, "imdb": {"unsupervised": ["all"]}, "glue/qqp": {"test": ["all"]}, "qasc": {"test": ["all"]}, "cosmos_qa": {"test": [ "description_context_question_answer_text", "description_context_question_text", "description_context_question_answer_id", "context_answer_to_question", "context_description_question_answer_text", "context_description_question_answer_id", "context_question_description_answer_id", "context_description_question_text", "context_question_description_answer_text", "only_question_answer", "no_prompt_id", "context_question_description_text", "no_prompt_text", ]}, "clue/tnews": {"test": ["all"]}, "clue/csl": {"test": ["all"]}, "clue/cmrc2018": {"test": ["generate_question", "in_an_exam", "answer_in_the_passage", "answer_following_question", "xp3longcontinue"]}, "clue/drcd": {"test": ["generate_question", "in_an_exam", "answer_in_the_passage", "answer_following_question", "xp3longcontinue"]}, "hellaswag": {"test": ["complete_first_then", "Topic of the context", "Open-ended completion", "Randomized prompts template", "Appropriate continuation - Yes or No", "Predict ending with hint", "Open-ended start", "Reversed appropriate continuation - Yes or No", "how_ends", "if_begins_how_continues"]}, } DS_TO_ENG_PROMPT = { "xcopa": "en", "Muennighoff/xstory_cloze": "en", "Muennighoff/xwinograd": "en", 'GEM/wiki_lingua': 'en_en', # Contains correct language names 'xnli': 'en', "paws-x": "en", "mlqa": "mlqa.en.en", "xquad": "xquad.en", "khalidalt/tydiqa-primary": "english", "khalidalt/tydiqa-goldp": "english", "pasinit/xlwic": "en", "GEM/xlsum": "english", "GEM/BiSECT": "en", } BIAS_FAIRNESS = [ ('crows_pairs', None), ('jigsaw_toxicity_pred', None), ('super_glue','axg'), ('wino_bias','type1_anti'), ('wino_bias','type2_anti'), ('wino_bias','type1_pro'), ('wino_bias','type2_pro'), ] EVAL_DATASETS_L1 = [ ('super_glue','wsc.fixed'), ('winogrande','winogrande_xl'), ('super_glue','cb'), ('super_glue','rte'), ('anli',None), ('story_cloze', '2016'), ('Muennighoff/xstory_cloze', 'ar'), ('Muennighoff/xstory_cloze', 'es'), ('Muennighoff/xstory_cloze', 'eu'), ('Muennighoff/xstory_cloze', 'id'), ('Muennighoff/xstory_cloze', 'hi'), ('Muennighoff/xstory_cloze', 'te'), ('Muennighoff/xstory_cloze', 'sw'), ('Muennighoff/xstory_cloze', 'zh'), ('hellaswag', None), ('super_glue', 'copa'), # Multilingual ('Muennighoff/xwinograd','en'), ('Muennighoff/xwinograd','fr'), ('Muennighoff/xwinograd','pt'), ('Muennighoff/xwinograd','zh'), ('clue', 'cluewsc2020'), ('xcopa','id'), ('xcopa','ta'), ('xcopa','sw'), ('xcopa','vi'), ('xcopa','zh'), ("xnli", "ar"), ("xnli", "en"), ("xnli", "es"), ("xnli", "fr"), ("xnli", "hi"), ("xnli", "sw"), ("xnli", "ur"), ("xnli", "vi"), ("xnli", "zh"), ("openai_humaneval", None), ("multi_eurlex", "all_languages") ] ADD_TRAIN_DATASETS_L1_BLOOMZZ = [ ('super_glue','wsc.fixed'), ('winogrande','winogrande_xl'), ('story_cloze', '2016'), ('Muennighoff/xstory_cloze', 'ar'), ('Muennighoff/xstory_cloze', 'es'), ('Muennighoff/xstory_cloze', 'eu'), ('Muennighoff/xstory_cloze', 'id'), ('Muennighoff/xstory_cloze', 'hi'), ('Muennighoff/xstory_cloze', 'te'), ('Muennighoff/xstory_cloze', 'sw'), ('Muennighoff/xstory_cloze', 'zh'), ('hellaswag', None), ('super_glue', 'copa'), # Multilingual ('Muennighoff/xwinograd','en'), ('Muennighoff/xwinograd','fr'), ('Muennighoff/xwinograd','pt'), ('Muennighoff/xwinograd','zh'), ('clue', 'cluewsc2020'), ('xcopa','id'), ('xcopa','ta'), ('xcopa','sw'), ('xcopa','vi'), ('xcopa','zh'), ("multi_eurlex", "all_languages") # ("openai_humaneval", None), # Low quality prompts ] EVAL_DATASETS_L2 = [ ('Muennighoff/xwinograd','jp'), ('Muennighoff/xwinograd','ru'), ('xcopa','et'), ('xcopa','ht'), ('xcopa','it'), ('xcopa','qu'), ('xcopa','th'), ('xcopa','tr'), ("xnli", "bg"), ("xnli", "de"), ("xnli", "el"), ("xnli", "ru"), ("xnli", "th"), ("xnli", "tr"), ] TRAIN_DATASETS = [ # English-only ('glue','mrpc'), ('glue','qqp'), ('paws','labeled_final'), ('ai2_arc','ARC-Challenge'), ('ai2_arc','ARC-Easy'), ('kilt_tasks','hotpotqa'), ('trivia_qa','unfiltered'), ('web_questions',None), ('wiki_qa',None), ('adversarial_qa','dbidaf'), ('adversarial_qa','dbert'), ('adversarial_qa','droberta'), ('duorc','SelfRC'), ('duorc','ParaphraseRC'), ('ropes',None), ('squad_v2',None), ('super_glue','record'), ('quoref',None), ('cos_e','v1.11'), ('cosmos_qa',None), ('dream',None), ('openbookqa','main'), ('qasc',None), ('quail',None), ('quarel',None), ('quartz',None), ('race','high'), ('race','middle'), ('sciq',None), ('social_i_qa',None), ('super_glue','boolq'), ('super_glue','multirc'), ('wiki_hop','original'), ('wiqa',None), ('piqa',None), ('amazon_polarity',None), ('app_reviews',None), ('imdb',None), ('rotten_tomatoes',None), ('yelp_review_full',None), ('common_gen',None), ('wiki_bio',None), ('cnn_dailymail','3.0.0'), ('gigaword',None), ('multi_news',None), ('samsum',None), ('xsum',None), ('ag_news',None), ('dbpedia_14',None), ('trec',None), # Multilingual ('GEM/wiki_lingua', 'ar'), ('GEM/wiki_lingua', 'en'), ('GEM/wiki_lingua', 'es'), ('GEM/wiki_lingua', 'fr'), ('GEM/wiki_lingua', 'hi'), ('GEM/wiki_lingua', 'id'), ('GEM/wiki_lingua', 'pt'), ('GEM/wiki_lingua', 'vi'), ('GEM/wiki_lingua', 'zh'), ('Helsinki-NLP/tatoeba_mt', 'ara-eng'), ('Helsinki-NLP/tatoeba_mt', 'ara-fra'), ('Helsinki-NLP/tatoeba_mt', 'ara-spa'), ('Helsinki-NLP/tatoeba_mt', 'ben-eng'), ('Helsinki-NLP/tatoeba_mt', 'cat-eng'), ('Helsinki-NLP/tatoeba_mt', 'cat-fra'), ('Helsinki-NLP/tatoeba_mt', 'cat-por'), ('Helsinki-NLP/tatoeba_mt', 'cat-spa'), ('Helsinki-NLP/tatoeba_mt', 'eng-cmn_Hans'), ('Helsinki-NLP/tatoeba_mt', 'eng-cmn_Hant'), ('Helsinki-NLP/tatoeba_mt', 'eng-eus'), ('Helsinki-NLP/tatoeba_mt', 'eng-fra'), ('Helsinki-NLP/tatoeba_mt', 'eng-hin'), ('Helsinki-NLP/tatoeba_mt', 'eng-ind'), ('Helsinki-NLP/tatoeba_mt', 'eng-mal'), ('Helsinki-NLP/tatoeba_mt', 'eng-mar'), ('Helsinki-NLP/tatoeba_mt', 'eng-por'), ('Helsinki-NLP/tatoeba_mt', 'eng-run'), ('Helsinki-NLP/tatoeba_mt', 'eng-spa'), ('Helsinki-NLP/tatoeba_mt', 'eng-swa'), ('Helsinki-NLP/tatoeba_mt', 'eng-tam'), ('Helsinki-NLP/tatoeba_mt', 'eng-tel'), ('Helsinki-NLP/tatoeba_mt', 'eng-urd'), ('Helsinki-NLP/tatoeba_mt', 'eng-vie'), ('Helsinki-NLP/tatoeba_mt', 'eng-zho'), ('Helsinki-NLP/tatoeba_mt', 'eus-spa'), ('Helsinki-NLP/tatoeba_mt', 'fra-cmn_Hans'), ('Helsinki-NLP/tatoeba_mt', 'fra-cmn_Hant'), ('Helsinki-NLP/tatoeba_mt', 'fra-ind'), ('Helsinki-NLP/tatoeba_mt', 'fra-por'), ('Helsinki-NLP/tatoeba_mt', 'fra-run'), ('Helsinki-NLP/tatoeba_mt', 'fra-spa'), ('Helsinki-NLP/tatoeba_mt', 'fra-vie'), ('Helsinki-NLP/tatoeba_mt', 'fra-zho'), ('Helsinki-NLP/tatoeba_mt', 'hin-urd'), ('Helsinki-NLP/tatoeba_mt', 'hin-zho'), ('Helsinki-NLP/tatoeba_mt', 'por-cmn_Hans'), ('Helsinki-NLP/tatoeba_mt', 'por-cmn_Hant'), ('Helsinki-NLP/tatoeba_mt', 'por-spa'), ('Helsinki-NLP/tatoeba_mt', 'por-zho'), ('Helsinki-NLP/tatoeba_mt', 'run-spa'), ('Helsinki-NLP/tatoeba_mt', 'spa-cmn_Hans'), ('Helsinki-NLP/tatoeba_mt', 'spa-cmn_Hant'), ('Helsinki-NLP/tatoeba_mt', 'spa-vie'), ('Helsinki-NLP/tatoeba_mt', 'spa-zho'), ('Helsinki-NLP/tatoeba_mt', 'vie-cmn_Hans'), ('Helsinki-NLP/tatoeba_mt', 'vie-zho'), ('xquad', 'xquad.ar'), ('xquad', 'xquad.zh'), ('xquad', 'xquad.vi'), ('xquad', 'xquad.en'), ('xquad', 'xquad.es'), ('xquad', 'xquad.hi'), ('mlqa', 'mlqa.ar.ar'), ('mlqa', 'mlqa.vi.vi'), ('mlqa', 'mlqa.zh.zh'), ('mlqa', 'mlqa.es.es'), ('mlqa', 'mlqa.en.en'), ('mlqa', 'mlqa.hi.hi'), ('mlqa', 'mlqa.ar.vi'), ('mlqa', 'mlqa.ar.zh'), ('mlqa', 'mlqa.ar.es'), ('mlqa', 'mlqa.ar.en'), ('mlqa', 'mlqa.ar.hi'), ('mlqa', 'mlqa.vi.ar'), ('mlqa', 'mlqa.vi.zh'), ('mlqa', 'mlqa.vi.es'), ('mlqa', 'mlqa.vi.en'), ('mlqa', 'mlqa.vi.hi'), ('mlqa', 'mlqa.zh.ar'), ('mlqa', 'mlqa.zh.vi'), ('mlqa', 'mlqa.zh.es'), ('mlqa', 'mlqa.zh.en'), ('mlqa', 'mlqa.zh.hi'), ('mlqa', 'mlqa.es.ar'), ('mlqa', 'mlqa.es.vi'), ('mlqa', 'mlqa.es.zh'), ('mlqa', 'mlqa.es.en'), ('mlqa', 'mlqa.es.hi'), ('mlqa', 'mlqa.en.ar'), ('mlqa', 'mlqa.es.vi'), ('mlqa', 'mlqa.es.zh'), ('mlqa', 'mlqa.es.es'), ('mlqa', 'mlqa.es.hi'), ('mlqa', 'mlqa.hi.ar'), ('mlqa', 'mlqa.hi.vi'), ('mlqa', 'mlqa.hi.zh'), ('mlqa', 'mlqa.hi.es'), ('mlqa', 'mlqa.hi.en'), ('paws-x', 'en'), ('paws-x', 'es'), ('paws-x', 'fr'), ('paws-x', 'zh'), ('khalidalt/tydiqa-primary', 'arabic'), ('khalidalt/tydiqa-primary', 'bengali'), ('khalidalt/tydiqa-primary', 'english'), ('khalidalt/tydiqa-primary', 'indonesian'), ('khalidalt/tydiqa-primary', 'swahili'), ('khalidalt/tydiqa-primary', 'telugu'), ('khalidalt/tydiqa-goldp', 'arabic'), ('khalidalt/tydiqa-goldp', 'bengali'), ('khalidalt/tydiqa-goldp', 'english'), ('khalidalt/tydiqa-goldp', 'indonesian'), ('khalidalt/tydiqa-goldp', 'swahili'), ('khalidalt/tydiqa-goldp', 'telugu'), ('Muennighoff/mbpp', 'sanitized'), ("great_code", None), ("neural_code_search", "evaluation_dataset"), ("codeparrot/codecomplex", "codeparrot--codecomplex"), ("codeparrot/github-jupyter-text-code-pairs", None), ("codeparrot/apps", "all"), ("codeparrot/xlcost-text-to-code", "Python-program-level"), ("codeparrot/xlcost-text-to-code", "C-program-level"), ("codeparrot/xlcost-text-to-code", "C++-program-level"), ("codeparrot/xlcost-text-to-code", "Csharp-program-level"), ("codeparrot/xlcost-text-to-code", "Java-program-level"), ("codeparrot/xlcost-text-to-code", "Javascript-program-level"), ("codeparrot/xlcost-text-to-code", "PHP-program-level"), ("teven/code_contests", None), ("teven/code_docstring_corpus", "top_level"), ("Fraser/python-state-changes", None), ('clue', 'c3'), ('clue', 'cmrc2018'), ('clue', 'csl'), ('clue', 'drcd'), ('clue', 'tnews'), ('super_glue', 'wic'), ('pasinit/xlwic', "xlwic_en_zh"), ('pasinit/xlwic', "xlwic_fr_fr"), ('GEM/BiSECT', "en"), ('GEM/BiSECT', "es"), ('GEM/BiSECT', "fr"), ('GEM/xlsum', "arabic"), ('GEM/xlsum', "bengali"), ('GEM/xlsum', "chinese_simplified"), ('GEM/xlsum', "chinese_traditional"), ('GEM/xlsum', "english"), ('GEM/xlsum', "french"), ('GEM/xlsum', "gujarati"), ('GEM/xlsum', "hindi"), ('GEM/xlsum', "igbo"), ('GEM/xlsum', "indonesian"), ('GEM/xlsum', "kirundi"), ('GEM/xlsum', "marathi"), ('GEM/xlsum', "nepali"), ('GEM/xlsum', "portuguese"), ('GEM/xlsum', "punjabi"), ('GEM/xlsum', "spanish"), ('GEM/xlsum', "swahili"), ('GEM/xlsum', "tamil"), ('GEM/xlsum', "telugu"), ('GEM/xlsum', "urdu"), ('GEM/xlsum', "vietnamese"), ('GEM/xlsum', "yoruba"), # flores200, wmt & more wikilingua added below ] FLORES_LANGS = [ ("Acehnese (Arabic script)", "ace_Arab"), ("Acehnese (Latin script)", "ace_Latn"), ("Mesopotamian Arabic", "acm_Arab"), ("Ta’izzi-Adeni Arabic", "acq_Arab"), ("Tunisian Arabic", "aeb_Arab"), ("Afrikaans", "afr_Latn"), ("South Levantine Arabic", "ajp_Arab"), ("Akan", "aka_Latn"), ("Amharic", "amh_Ethi"), ("North Levantine Arabic", "apc_Arab"), ("Modern Standard Arabic", "arb_Arab"), ("Modern Standard Arabic (Romanized)", "arb_Latn"), ("Najdi Arabic", "ars_Arab"), ("Moroccan Arabic", "ary_Arab"), ("Egyptian Arabic", "arz_Arab"), ("Assamese", "asm_Beng"), ("Asturian", "ast_Latn"), ("Awadhi", "awa_Deva"), ("Central Aymara", "ayr_Latn"), ("South Azerbaijani", "azb_Arab"), ("North Azerbaijani", "azj_Latn"), ("Bashkir", "bak_Cyrl"), ("Bambara", "bam_Latn"), ("Balinese", "ban_Latn"), ("Belarusian", "bel_Cyrl"), ("Bemba", "bem_Latn"), ("Bengali", "ben_Beng"), ("Bhojpuri", "bho_Deva"), ("Banjar (Arabic script)", "bjn_Arab"), ("Banjar (Latin script)", "bjn_Latn"), ("Standard Tibetan", "bod_Tibt"), ("Bosnian", "bos_Latn"), ("Buginese", "bug_Latn"), ("Bulgarian", "bul_Cyrl"), ("Catalan", "cat_Latn"), ("Cebuano", "ceb_Latn"), ("Czech", "ces_Latn"), ("Chokwe", "cjk_Latn"), ("Central Kurdish", "ckb_Arab"), ("Crimean Tatar", "crh_Latn"), ("Welsh", "cym_Latn"), ("Danish", "dan_Latn"), ("German", "deu_Latn"), ("Southwestern Dinka", "dik_Latn"), ("Dyula", "dyu_Latn"), ("Dzongkha", "dzo_Tibt"), ("Greek", "ell_Grek"), ("English", "eng_Latn"), ("Esperanto", "epo_Latn"), ("Estonian", "est_Latn"), ("Basque", "eus_Latn"), ("Ewe", "ewe_Latn"), ("Faroese", "fao_Latn"), ("Fijian", "fij_Latn"), ("Finnish", "fin_Latn"), ("Fon", "fon_Latn"), ("French", "fra_Latn"), ("Friulian", "fur_Latn"), ("Nigerian Fulfulde", "fuv_Latn"), ("Scottish Gaelic", "gla_Latn"), ("Irish", "gle_Latn"), ("Galician", "glg_Latn"), ("Guarani", "grn_Latn"), ("Gujarati", "guj_Gujr"), ("Haitian Creole", "hat_Latn"), ("Hausa", "hau_Latn"), ("Hebrew", "heb_Hebr"), ("Hindi", "hin_Deva"), ("Chhattisgarhi", "hne_Deva"), ("Croatian", "hrv_Latn"), ("Hungarian", "hun_Latn"), ("Armenian", "hye_Armn"), ("Igbo", "ibo_Latn"), ("Ilocano", "ilo_Latn"), ("Indonesian", "ind_Latn"), ("Icelandic", "isl_Latn"), ("Italian", "ita_Latn"), ("Javanese", "jav_Latn"), ("Japanese", "jpn_Jpan"), ("Kabyle", "kab_Latn"), ("Jingpho", "kac_Latn"), ("Kamba", "kam_Latn"), ("Kannada", "kan_Knda"), ("Kashmiri (Arabic script)", "kas_Arab"), ("Kashmiri (Devanagari script)", "kas_Deva"), ("Georgian", "kat_Geor"), ("Central Kanuri (Arabic script)", "knc_Arab"), ("Central Kanuri (Latin script)", "knc_Latn"), ("Kazakh", "kaz_Cyrl"), ("Kabiyè", "kbp_Latn"), ("Kabuverdianu", "kea_Latn"), ("Khmer", "khm_Khmr"), ("Kikuyu", "kik_Latn"), ("Kinyarwanda", "kin_Latn"), ("Kyrgyz", "kir_Cyrl"), ("Kimbundu", "kmb_Latn"), ("Northern Kurdish", "kmr_Latn"), ("Kikongo", "kon_Latn"), ("Korean", "kor_Hang"), ("Lao", "lao_Laoo"), ("Ligurian", "lij_Latn"), ("Limburgish", "lim_Latn"), ("Lingala", "lin_Latn"), ("Lithuanian", "lit_Latn"), ("Lombard", "lmo_Latn"), ("Latgalian", "ltg_Latn"), ("Luxembourgish", "ltz_Latn"), ("Luba-Kasai", "lua_Latn"), ("Ganda", "lug_Latn"), ("Luo", "luo_Latn"), ("Mizo", "lus_Latn"), ("Standard Latvian", "lvs_Latn"), ("Magahi", "mag_Deva"), ("Maithili", "mai_Deva"), ("Malayalam", "mal_Mlym"), ("Marathi", "mar_Deva"), ("Minangkabau (Arabic script)", "min_Arab"), ("Minangkabau (Latin script)", "min_Latn"), ("Macedonian", "mkd_Cyrl"), ("Plateau Malagasy", "plt_Latn"), ("Maltese", "mlt_Latn"), ("Meitei (Bengali script)", "mni_Beng"), ("Halh Mongolian", "khk_Cyrl"), ("Mossi", "mos_Latn"), ("Maori", "mri_Latn"), ("Burmese", "mya_Mymr"), ("Dutch", "nld_Latn"), ("Norwegian Nynorsk", "nno_Latn"), ("Norwegian Bokmål", "nob_Latn"), ("Nepali", "npi_Deva"), ("Northern Sotho", "nso_Latn"), ("Nuer", "nus_Latn"), ("Nyanja", "nya_Latn"), ("Occitan", "oci_Latn"), ("West Central Oromo", "gaz_Latn"), ("Odia", "ory_Orya"), ("Pangasinan", "pag_Latn"), ("Eastern Panjabi", "pan_Guru"), ("Papiamento", "pap_Latn"), ("Western Persian", "pes_Arab"), ("Polish", "pol_Latn"), ("Portuguese", "por_Latn"), ("Dari", "prs_Arab"), ("Southern Pashto", "pbt_Arab"), ("Ayacucho Quechua", "quy_Latn"), ("Romanian", "ron_Latn"), ("Rundi", "run_Latn"), ("Russian", "rus_Cyrl"), ("Sango", "sag_Latn"), ("Sanskrit", "san_Deva"), ("Santali", "sat_Olck"), ("Sicilian", "scn_Latn"), ("Shan", "shn_Mymr"), ("Sinhala", "sin_Sinh"), ("Slovak", "slk_Latn"), ("Slovenian", "slv_Latn"), ("Samoan", "smo_Latn"), ("Shona", "sna_Latn"), ("Sindhi", "snd_Arab"), ("Somali", "som_Latn"), ("Southern Sotho", "sot_Latn"), ("Spanish", "spa_Latn"), ("Tosk Albanian", "als_Latn"), ("Sardinian", "srd_Latn"), ("Serbian", "srp_Cyrl"), ("Swati", "ssw_Latn"), ("Sundanese", "sun_Latn"), ("Swedish", "swe_Latn"), ("Swahili", "swh_Latn"), ("Silesian", "szl_Latn"), ("Tamil", "tam_Taml"), ("Tatar", "tat_Cyrl"), ("Telugu", "tel_Telu"), ("Tajik", "tgk_Cyrl"), ("Tagalog", "tgl_Latn"), ("Thai", "tha_Thai"), ("Tigrinya", "tir_Ethi"), ("Tamasheq (Latin script)", "taq_Latn"), ("Tamasheq (Tifinagh script)", "taq_Tfng"), ("Tok Pisin", "tpi_Latn"), ("Tswana", "tsn_Latn"), ("Tsonga", "tso_Latn"), ("Turkmen", "tuk_Latn"), ("Tumbuka", "tum_Latn"), ("Turkish", "tur_Latn"), ("Twi", "twi_Latn"), ("Central Atlas Tamazight", "tzm_Tfng"), ("Uyghur", "uig_Arab"), ("Ukrainian", "ukr_Cyrl"), ("Umbundu", "umb_Latn"), ("Urdu", "urd_Arab"), ("Northern Uzbek", "uzn_Latn"), ("Venetian", "vec_Latn"), ("Vietnamese", "vie_Latn"), ("Waray", "war_Latn"), ("Wolof", "wol_Latn"), ("Xhosa", "xho_Latn"), ("Eastern Yiddish", "ydd_Hebr"), ("Yoruba", "yor_Latn"), ("Yue Chinese", "yue_Hant"), ("Chinese (Simplified)", "zho_Hans"), ("Chinese (Traditional)", "zho_Hant"), ("Standard Malay", "zsm_Latn"), ("Zulu", "zul_Latn"), ] WMT22_LANGS = [ ("afr", "eng"), ("afr", "som"), ("amh", "eng"), ("amh", "fra"), ("amh", "nya"), ("amh", "orm"), ("amh", "sna"), ("amh", "som"), ("amh", "ssw"), ("amh", "swh"), ("amh", "tsn"), ("amh", "tso"), ("amh", "umb"), ("amh", "xho"), ("amh", "yor"), ("amh", "zul"), ("eng", "fuv"), ("eng", "hau"), ("eng", "ibo"), ("eng", "kam"), ("eng", "kin"), ("eng", "lin"), ("eng", "lug"), ("eng", "luo"), ("eng", "nso"), ("eng", "nya"), ("eng", "orm"), ("eng", "sna"), ("eng", "som"), ("eng", "ssw"), ("eng", "swh"), ("eng", "tsn"), ("eng", "tso"), ("eng", "umb"), ("eng", "wol"), ("eng", "xho"), ("eng", "yor"), ("eng", "zul"), ("fra", "hau"), ("fra", "ibo"), ("fra", "kam"), ("fra", "kin"), ("fra", "lin"), ("fra", "lug"), ("fra", "luo"), ("fra", "nso"), ("fra", "nya"), ("fra", "orm"), ("fra", "som"), ("fra", "ssw"), ("fra", "swh"), ("fra", "tsn"), ("fra", "tso"), ("fra", "umb"), ("fra", "wol"), ("fra", "xho"), ("fra", "zul"), ("fuv", "hau"), ("fuv", "ibo"), ("fuv", "kam"), ("fuv", "kin"), ("fuv", "lug"), ("fuv", "luo"), ("fuv", "nso"), ("fuv", "nya"), ("fuv", "orm"), ("fuv", "sna"), ("fuv", "som"), ("fuv", "ssw"), ("fuv", "swh"), ("fuv", "tsn"), ("fuv", "tso"), ("fuv", "umb"), ("fuv", "xho"), ("fuv", "yor"), ("fuv", "zul"), ("hau", "ibo"), ("hau", "kam"), ("hau", "kin"), ("hau", "lug"), ("hau", "luo"), ("hau", "nso"), ("hau", "nya"), ("hau", "orm"), ("hau", "sna"), ("hau", "som"), ("hau", "ssw"), ("hau", "swh"), ("hau", "tsn"), ("hau", "tso"), ("hau", "umb"), ("hau", "xho"), ("hau", "yor"), ("hau", "zul"), ("ibo", "kam"), ("ibo", "kin"), ("ibo", "lug"), ("ibo", "luo"), ("ibo", "nso"), ("ibo", "nya"), ("ibo", "orm"), ("ibo", "sna"), ("ibo", "som"), ("ibo", "ssw"), ("ibo", "swh"), ("ibo", "tsn"), ("ibo", "tso"), ("ibo", "umb"), ("ibo", "xho"), ("ibo", "yor"), ("ibo", "zul"), ("kam", "kin"), ("kam", "lug"), ("kam", "luo"), ("kam", "nso"), ("kam", "nya"), ("kam", "orm"), ("kam", "sna"), ("kam", "som"), ("kam", "ssw"), ("kam", "swh"), ("kam", "tsn"), ("kam", "tso"), ("kam", "umb"), ("kam", "xho"), ("kam", "yor"), ("kam", "zul"), ("kin", "lug"), ("kin", "luo"), ("kin", "nso"), ("kin", "nya"), ("kin", "orm"), ("kin", "sna"), ("kin", "som"), ("kin", "ssw"), ("kin", "swh"), ("kin", "tsn"), ("kin", "tso"), ("kin", "umb"), ("kin", "xho"), ("kin", "yor"), ("kin", "zul"), ("lug", "luo"), ("lug", "nso"), ("lug", "nya"), ("lug", "orm"), ("lug", "sna"), ("lug", "som"), ("lug", "ssw"), ("lug", "swh"), ("lug", "tsn"), ("lug", "tso"), ("lug", "umb"), ("lug", "xho"), ("lug", "yor"), ("lug", "zul"), ("luo", "nso"), ("luo", "nya"), ("luo", "orm"), ("luo", "sna"), ("luo", "som"), ("luo", "ssw"), ("luo", "swh"), ("luo", "tsn"), ("luo", "tso"), ("luo", "umb"), ("luo", "xho"), ("luo", "yor"), ("luo", "zul"), ("nso", "nya"), ("nso", "orm"), ("nso", "sna"), ("nso", "som"), ("nso", "ssw"), ("nso", "swh"), ("nso", "tsn"), ("nso", "tso"), ("nso", "umb"), ("nso", "xho"), ("nso", "yor"), ("nso", "zul"), ("nya", "orm"), ("nya", "sna"), ("nya", "som"), ("nya", "ssw"), ("nya", "swh"), ("nya", "tsn"), ("nya", "tso"), ("nya", "umb"), ("nya", "xho"), ("nya", "yor"), ("nya", "zul"), ("orm", "sna"), ("orm", "som"), ("orm", "ssw"), ("orm", "swh"), ("orm", "tsn"), ("orm", "tso"), ("orm", "umb"), ("orm", "xho"), ("orm", "yor"), ("orm", "zul"), ("sna", "som"), ("sna", "ssw"), ("sna", "swh"), ("sna", "tsn"), ("sna", "tso"), ("sna", "umb"), ("sna", "xho"), ("sna", "yor"), ("sna", "zul"), ("som", "ssw"), ("som", "swh"), ("som", "tsn"), ("som", "tso"), ("som", "umb"), ("som", "wol"), ("som", "xho"), ("som", "yor"), ("som", "zul"), ("ssw", "swh"), ("ssw", "tsn"), ("ssw", "tso"), ("ssw", "umb"), ("ssw", "xho"), ("ssw", "yor"), ("ssw", "zul"), ("swh", "tsn"), ("swh", "tso"), ("swh", "umb"), ("swh", "xho"), ("swh", "yor"), ("swh", "zul"), ("tsn", "tso"), ("tsn", "umb"), ("tsn", "xho"), ("tsn", "yor"), ("tsn", "zul"), ("tso", "umb"), ("tso", "xho"), ("tso", "yor"), ("tso", "zul"), ("umb", "xho"), ("umb", "yor"), ("umb", "zul"), ("xho", "yor"), ("xho", "zul"), ("yor", "zul"), ] # Copied from metadata BLOOM_LANGS = """ - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu """ DS_TO_LANG = { 'Muennighoff/mbpp': 'code', 'openai_humaneval': 'code', "great_code": "code", "neural_code_search": "code", "codeparrot/codecomplex": "code", "codeparrot/github-jupyter-text-code-pairs": "code", "codeparrot/apps": "code", "Fraser/python-state-changes": "code", "codeparrot/xlcost-text-to-code": "code", "teven/code_contests": "code", "teven/code_docstring_corpus": "code", "clue": "zh", "cmn": "zh", # == zho "npi": "ne", # == npe "ory": "or", # == ori "swh": "sw", # == swa "kirundi": "rn", # == rundi "punjabi": "pa", # == panjabi "chinese_simplified": "zh", "chinese_traditional": "zh", } bloom_lang_codes_iso3 = [] bloom_lang_codes_iso2 = [] for lang in BLOOM_LANGS.split("\n")[1:-1]: iso2 = lang.replace("- ", "") DS_TO_LANG[iso2] = iso2 try: name = languages.get(alpha2=iso2) DS_TO_LANG[name.name.lower()] = iso2 # name is e.g. 'swahili (macrolanguage)' also add swahili DS_TO_LANG[name.name.lower().split(" ")[0]] = iso2 iso3 = name.part3 DS_TO_LANG[iso3] = iso2 except KeyError: print(f"Could not find iso3 code for {lang}.") # Add GEM multilingual WIKILINGUA_LANGS = ["ar", "en", "es", "fr", "hi", "id", "pt", "vi", "zh"] for l1_code in WIKILINGUA_LANGS: for l2_code in WIKILINGUA_LANGS: if l1_code == l2_code: continue TRAIN_DATASETS.append(("GEM/wiki_lingua", f"{l1_code}_{l2_code}")) # Add flores200 for (l1_name, l1_code) in FLORES_LANGS: for (l2_name, l2_code) in FLORES_LANGS: if l1_code.split("_")[0] not in DS_TO_LANG or l2_code.split("_")[0] not in DS_TO_LANG: print(f"Skipping as {l1_name} or {l2_name} was not pre-trained on.") continue elif l1_name == l2_name: continue TRAIN_DATASETS.append(("facebook/flores", f"{l1_code}-{l2_code}")) # Add wmt22 for (l1_code, l2_code) in WMT22_LANGS: if l1_code not in DS_TO_LANG or l2_code not in DS_TO_LANG: print(f"Skipping as {l1_code} or {l2_code} was not pre-trained on.") continue elif l1_code == l2_code: continue TRAIN_DATASETS.append(("allenai/wmt22_african", f"{l1_code}-{l2_code}")) ### DATASET CREATION ### # Copied from promptsource.utils def removeHyphen(example): example_clean = {} for key in example.keys(): if "-" in key: new_key = key.replace("-", "_") example_clean[new_key] = example[key] else: example_clean[key] = example[key] example = example_clean return example def apply_template(dataset, template, strip_connection=True): def map_fn(ex): ex = removeHyphen(ex) try: inputs_and_targets = template.apply( ex, strip_connection=strip_connection, truncate=True, ) # Skip ValueError("Prompt did not produce an input and at least one target.") # which happens for some prompts with if else clauses based on inputs producing occasional # empty targets except ValueError: return {"inputs": "", "targets": ""} if len(inputs_and_targets) == 2: # Note that the signature changed in promptsource # In 0.1.0 template.apply returned two strings; In >0.3.0 it retuns a str & list inputs, targets = inputs_and_targets if len(targets) > 1: # Safer to skip, as could be a bug print(f"Found targets longer than 1. Inputs: {inputs} ; Targets {targets}. Skipping.") return {"inputs": "", "targets": ""} targets = targets[0] return {"inputs": inputs, "targets": targets} # When template results in an empty example, template.apply returns [""] # Also, if the template gets split wrong, len can be > 2 # We will filter these out later else: # inputs is a str by default & targets a str return {"inputs": "", "targets": ""} def filter_fn(ex): return len(ex["inputs"]) > 0 and len(ex["targets"]) > 0 original_columns = dataset.column_names dataset = dataset.map(map_fn).filter(filter_fn) # map keeps original columns, remove them return dataset.remove_columns(set(original_columns) - {"inputs", "targets"}) def add_language_name_wikilingua(example): example["source_language_name"] = languages.get(alpha2=example["source_language"]).name example["target_language_name"] = languages.get(alpha2=example["target_language"]).name return example def filter_l1_l2_wikilingua(example, l1, l2): return example["source_language"] == l1 and example["target_language"] == l2 def filter_empty_solution_apps(example): return bool(example["solutions"]) def add_solution_apps(example): example["solution"] = random.choice(json.loads(example["solutions"])) return example def clean_code_xlcost(example): clean_lines = [] cur_indent = 0 for line in example["code"].split("NEW_LINE"): cur_indent += line.count("INDENT") cur_indent -= line.count("DEDENT") line = line.replace("INDENT", "").replace("DEDENT", "") line = line.replace("STRNEWLINE", "\n") line = line.replace("TABSYMBOL", "\t") clean_lines.append("\t" * cur_indent + line.strip()) example["code_clean"] = "\n".join(clean_lines) return example def write_to_jsonl_hub(ds, split="train"): ### GET DATASET & LANGUAGE ### ds_name, subset_name = ds is_wikilingua_cross_lingual = (ds_name == "GEM/wiki_lingua") and ("_") in subset_name lang_dir = DS_TO_LANG.get(ds_name, None) if lang_dir is None: lang_dir = DS_TO_LANG.get(subset_name, "en") if ds_name == "facebook/flores": lang_dir = DS_TO_LANG.get(subset_name.split("-")[-1].split("_")[0]) elif is_wikilingua_cross_lingual or ds_name == "pasinit/xlwic": lang_dir = DS_TO_LANG.get(subset_name.split("_")[-1]) elif ds_name == "xquad": lang_dir = DS_TO_LANG.get(subset_name.split(".")[1]) elif ds_name == "mlqa": # Classify it by the target language for cross-lingual (i.e. what the loss is computed on) lang_dir = DS_TO_LANG.get(subset_name.split(".")[1]) os.makedirs(lang_dir, exist_ok=True) if ds_name == "Helsinki-NLP/tatoeba_mt": ds = load_dataset(ds_name, subset_name, ignore_verifications=True, revision="49aa20ac768eabc5a106a123549ea58053fc9b40") elif ds_name == "story_cloze": ds = load_dataset(ds_name, subset_name, data_dir=STORY_CLOZE_DIR) elif ds_name == "Muennighoff/xstory_cloze": ds = load_dataset(ds_name, subset_name, data_dir=XSTORY_CLOZE_DIR) else: ds = load_dataset(ds_name, subset_name) if ds_name == "GEM/wiki_lingua": # Add names, e.g. Chinese for zh to use them in the jinja prompts ds = ds.map(add_language_name_wikilingua) if is_wikilingua_cross_lingual: # Keep only L1 -> L2 (L2 -> L1 will be a separate dataset) ds = ds.filter(partial(filter_l1_l2_wikilingua, l1=subset_name.split("_")[0], l2=subset_name.split("_")[1])) elif ds_name == "codeparrot/apps": ds = ds.filter(filter_empty_solution_apps).map(add_solution_apps) elif ds_name == "codeparrot/xlcost-text-to-code": ds = ds.map(clean_code_xlcost) ### SELECT SPLITS ### dataset_splits = list(ds.keys()) if subset_name == "xlwic_en_zh": # Train set is en; val & test are zh dataset_splits.remove("train") elif ds_name == "teven/code_docstring_corpus": # Bad quality split dataset_splits.remove("class_level") if split == "validation": if split not in dataset_splits or len(dataset_splits) == 1: print(f"Validation not found for {ds_name}") return dataset_splits = ["validation"] elif split == "train": # Use as much as possible # Would need to remove e.g. test datasets to benchmark same task performance if len(dataset_splits) > 1 and "validation" in dataset_splits: dataset_splits.remove("validation") # WikiLingua if "sampled_validation" in dataset_splits: dataset_splits.remove("sampled_validation") if "sampled_test" in dataset_splits: dataset_splits.remove("sampled_test") ### SELECT PROMPTS ### if subset_name is None: prompt_dataset_name = ds_name else: subset_name_prompt = subset_name if USE_ENGLISH_PROMPTS and ds_name in DS_TO_ENG_PROMPT: subset_name_prompt = DS_TO_ENG_PROMPT[ds_name] prompt_dataset_name = f"{ds_name}/{subset_name_prompt}" prompts = DatasetTemplates(prompt_dataset_name) ### PROCESS ### for split in dataset_splits: for t_name in prompts.all_template_names: #if not "mt" in t_name: # print(f"Skipping {t_name}") # continue print(f"Running {ds_name}/{subset_name}/{split}/{t_name}") if SKIP_PROMPTS.get(prompt_dataset_name, {}).get(split, False): if ("all" in SKIP_PROMPTS[prompt_dataset_name][split]) or (t_name in SKIP_PROMPTS[prompt_dataset_name][split]): print(f"Skipping DS: {prompt_dataset_name} Split {split} Prompt {t_name}") continue if ds_name == "Helsinki-NLP/tatoeba_mt": # E.g. translate-this-ara-eng, where eng is the target lang_dir = DS_TO_LANG.get(t_name.split("-")[-1].split("_")[0], "en") elif ds_name in ("allenai/wmt22_african", "multi_eurlex"): # One prompt in multi_eurlex has -source+target appended to the languages lang_dir = DS_TO_LANG.get(t_name.replace("-source+target", "").split("-")[-1]) out_path = os.path.join( lang_dir, f'xp3_{ds_name}_{subset_name}_{split}_{t_name}.jsonl'.replace("/", "_").replace(" ", "_") ) if os.path.exists(out_path): print("Skipping as exists: ", out_path) continue assert len(ds[split]) > 0, f"Got empty: {ds_name}" try: if ds_name == "allenai/wmt22_african": # Sort by laser score, i.e. by increasing confidence & limit samples due to mediocre quality ds[split] = ds[split].sort("laser_score", reverse=True) max_range = min(len(ds[split]), MAX_EXAMPLES_PER_DATASET_PROMPT // 2) else: # Allow 5x buffer for empty examples max_range = min(len(ds[split]), MAX_EXAMPLES_PER_DATASET_PROMPT * 5) # Shuffle to avoid using the same subset # Leave \n in-between input & targets for code out_ds = apply_template( dataset=ds[split].shuffle().select(list(range(max_range))), template=prompts[t_name], strip_connection=False if lang_dir == "code" else True ) # Keep X shortest examples max_range = min(len(out_ds), MAX_EXAMPLES_PER_DATASET_PROMPT) out_ds = out_ds.sort("inputs").select(list(range(max_range))) except Exception as e: print(f"Skipping due to {e}. DS: {ds_name}/{subset_name} Template: {t_name}") continue # Do not force ascii to allow chars like é if len(out_ds) > 0: out_ds.to_json(out_path, orient="records", lines=True, force_ascii=False) # Testing: TRAIN_DATASETS = [ ('xquad', 'xquad.ar'), ('xquad', 'xquad.vi'), ('xquad', 'xquad.en'), ('xquad', 'xquad.es'), ('xquad', 'xquad.hi'), ('mlqa', 'mlqa.ar.ar'), ('mlqa', 'mlqa.vi.vi'), ('mlqa', 'mlqa.zh.zh'), ('mlqa', 'mlqa.es.es'), ('mlqa', 'mlqa.en.en'), ('mlqa', 'mlqa.hi.hi'), ('mlqa', 'mlqa.ar.vi'), ('mlqa', 'mlqa.ar.zh'), ('mlqa', 'mlqa.ar.es'), ('mlqa', 'mlqa.ar.en'), ('mlqa', 'mlqa.ar.hi'), ('mlqa', 'mlqa.vi.ar'), ('mlqa', 'mlqa.vi.zh'), ('mlqa', 'mlqa.vi.es'), ('mlqa', 'mlqa.vi.en'), ('mlqa', 'mlqa.vi.hi'), ('mlqa', 'mlqa.es.ar'), ('mlqa', 'mlqa.es.vi'), ('mlqa', 'mlqa.es.zh'), ('mlqa', 'mlqa.es.en'), ('mlqa', 'mlqa.es.hi'), ('mlqa', 'mlqa.en.ar'), ('mlqa', 'mlqa.es.vi'), ('mlqa', 'mlqa.es.zh'), ('mlqa', 'mlqa.es.es'), ('mlqa', 'mlqa.es.hi'), ('mlqa', 'mlqa.hi.ar'), ('mlqa', 'mlqa.hi.vi'), ('mlqa', 'mlqa.hi.zh'), ('mlqa', 'mlqa.hi.es'), ('mlqa', 'mlqa.hi.en'), ('paws-x', 'es'), ('paws-x', 'fr'), ('khalidalt/tydiqa-primary', 'arabic'), ('khalidalt/tydiqa-primary', 'bengali'), ('khalidalt/tydiqa-primary', 'english'), ('khalidalt/tydiqa-primary', 'indonesian'), ('khalidalt/tydiqa-primary', 'swahili'), ('khalidalt/tydiqa-primary', 'telugu'), ('khalidalt/tydiqa-goldp', 'arabic'), ('khalidalt/tydiqa-goldp', 'bengali'), ('khalidalt/tydiqa-goldp', 'english'), ('khalidalt/tydiqa-goldp', 'indonesian'), ('khalidalt/tydiqa-goldp', 'swahili'), ('khalidalt/tydiqa-goldp', 'telugu'), ('pasinit/xlwic', "xlwic_fr_fr"), ('GEM/BiSECT', "es"), ('GEM/BiSECT', "fr"), ('GEM/xlsum', "arabic"), ('GEM/xlsum', "bengali"), ('GEM/xlsum', "chinese_traditional"), ('GEM/xlsum', "english"), ('GEM/xlsum', "french"), ('GEM/xlsum', "gujarati"), ('GEM/xlsum', "hindi"), ('GEM/xlsum', "igbo"), ('GEM/xlsum', "indonesian"), ('GEM/xlsum', "kirundi"), ('GEM/xlsum', "marathi"), ('GEM/xlsum', "nepali"), ] TRAIN_DATASETS = [ ('GEM/xlsum', "portuguese"), ('GEM/xlsum', "punjabi"), ('GEM/xlsum', "spanish"), ('GEM/xlsum', "swahili"), ('GEM/xlsum', "tamil"), ('GEM/xlsum', "telugu"), ('GEM/xlsum', "urdu"), ('GEM/xlsum', "vietnamese"), ('GEM/xlsum', "yoruba"), ] TRAIN_DATASETS = [ ('khalidalt/tydiqa-primary', 'indonesian'), ('khalidalt/tydiqa-goldp', 'indonesian'), ] for ds in TRAIN_DATASETS: write_to_jsonl_hub(ds, split="train") #with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool: #pool.map(partial(write_to_jsonl_hub, split="train"), TRAIN_DATASETS) #pool.map(partial(write_to_jsonl_hub, split="validation"), TRAIN_DATASETS) # pool.map(partial(write_to_jsonl_hub, split="train"), ADD_TRAIN_DATASETS_L1_BLOOMZZ) # pool.map(partial(write_to_jsonl_hub, split="validation"), ADD_TRAIN_DATASETS_L1_BLOOMZZ)