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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 = True
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'), # Not used due to time constraints
('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), # Not used due to time constraints
('super_glue', 'copa'),
# Multilingual
('Muennighoff/xwinograd','en'),
('Muennighoff/xwinograd','fr'),
('Muennighoff/xwinograd','pt'),
('Muennighoff/xwinograd','zh'),
# ('clue', 'cluewsc2020'), # Included in 'Muennighoff/xwinograd','zh'
('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), # Used without prompts in evaluation
# ("multi_eurlex", "all_languages")
]
ADD_TRAIN_DATASETS_L1_XP3ALL = [
('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
]
TRAIN_DATASETS_RU = [
('GEM/xlsum', "russian"),
('khalidalt/tydiqa-primary', 'russian'),
('khalidalt/tydiqa-goldp', 'russian'),
('xquad', 'xquad.ru'),
('Helsinki-NLP/tatoeba_mt', "ara-rus"),
('Helsinki-NLP/tatoeba_mt', "eng-rus"),
('Helsinki-NLP/tatoeba_mt', "eus-rus"),
('Helsinki-NLP/tatoeba_mt', "fra-rus"),
('Helsinki-NLP/tatoeba_mt', "rus-cmn_Hans"),
('Helsinki-NLP/tatoeba_mt', "rus-cmn_Hant"),
('Helsinki-NLP/tatoeba_mt', "rus-rus"),
('Helsinki-NLP/tatoeba_mt', "rus-spa"),
('Helsinki-NLP/tatoeba_mt', "rus-uig_Arab"),
('Helsinki-NLP/tatoeba_mt', "rus-vie"),
('Helsinki-NLP/tatoeba_mt', "rus-zho"),
('GEM/wiki_lingua', 'ru'),
]
TRAIN_DATASETS_TH = [
('GEM/xlsum', "thai"),
('khalidalt/tydiqa-primary', 'thai'),
('khalidalt/tydiqa-goldp', 'thai'),
('xquad', 'xquad.th'),
]
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
SINGLE_LANGUAGE = "ru"
SINGLE_LANGUAGE_LONG = "rus"
SINGLE_LANGUAGE_NAME = "Russian"
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
- ru
"""
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"] + [SINGLE_LANGUAGE]
for l1_code in WIKILINGUA_LANGS:
for l2_code in WIKILINGUA_LANGS:
if l1_code == l2_code:
continue
if SINGLE_LANGUAGE:
if (l1_code != SINGLE_LANGUAGE) and (l2_code != SINGLE_LANGUAGE):
continue
TRAIN_DATASETS_RU.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
if (SINGLE_LANGUAGE_NAME not in l1_name) and (SINGLE_LANGUAGE_NAME not in l2_name):
print(f"Skipping {l1_name}, {l2_name}, {SINGLE_LANGUAGE_NAME}")
continue
TRAIN_DATASETS_RU.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
if SINGLE_LANGUAGE_LONG:
if (l1_code != SINGLE_LANGUAGE_LONG) and (l2_code != SINGLE_LANGUAGE_LONG):
continue
TRAIN_DATASETS_RU.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:
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'),
#]
#for ds in TRAIN_DATASETS_RU:
# write_to_jsonl_hub(ds, split="train")
with multiprocessing.Pool(processes=32) as pool:
pool.map(partial(write_to_jsonl_hub, split="train"), TRAIN_DATASETS_RU)
#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_XP3ALL)
#pool.map(partial(write_to_jsonl_hub, split="validation"), ADD_TRAIN_DATASETS_L1_XP3ALL)