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| """GEM: Generation Evaluation Metrics supporting datasets""" |
|
|
|
|
| import csv |
| import json |
| import os |
|
|
| import datasets |
|
|
|
|
| _CITATION = """\ |
| @article{gem_benchmark, |
| author = {Sebastian Gehrmann and |
| Tosin P. Adewumi and |
| Karmanya Aggarwal and |
| Pawan Sasanka Ammanamanchi and |
| Aremu Anuoluwapo and |
| Antoine Bosselut and |
| Khyathi Raghavi Chandu and |
| Miruna{-}Adriana Clinciu and |
| Dipanjan Das and |
| Kaustubh D. Dhole and |
| Wanyu Du and |
| Esin Durmus and |
| Ondrej Dusek and |
| Chris Emezue and |
| Varun Gangal and |
| Cristina Garbacea and |
| Tatsunori Hashimoto and |
| Yufang Hou and |
| Yacine Jernite and |
| Harsh Jhamtani and |
| Yangfeng Ji and |
| Shailza Jolly and |
| Dhruv Kumar and |
| Faisal Ladhak and |
| Aman Madaan and |
| Mounica Maddela and |
| Khyati Mahajan and |
| Saad Mahamood and |
| Bodhisattwa Prasad Majumder and |
| Pedro Henrique Martins and |
| Angelina McMillan{-}Major and |
| Simon Mille and |
| Emiel van Miltenburg and |
| Moin Nadeem and |
| Shashi Narayan and |
| Vitaly Nikolaev and |
| Rubungo Andre Niyongabo and |
| Salomey Osei and |
| Ankur P. Parikh and |
| Laura Perez{-}Beltrachini and |
| Niranjan Ramesh Rao and |
| Vikas Raunak and |
| Juan Diego Rodriguez and |
| Sashank Santhanam and |
| Joao Sedoc and |
| Thibault Sellam and |
| Samira Shaikh and |
| Anastasia Shimorina and |
| Marco Antonio Sobrevilla Cabezudo and |
| Hendrik Strobelt and |
| Nishant Subramani and |
| Wei Xu and |
| Diyi Yang and |
| Akhila Yerukola and |
| Jiawei Zhou}, |
| title = {The {GEM} Benchmark: Natural Language Generation, its Evaluation and |
| Metrics}, |
| journal = {CoRR}, |
| volume = {abs/2102.01672}, |
| year = {2021}, |
| url = {https://arxiv.org/abs/2102.01672}, |
| archivePrefix = {arXiv}, |
| eprint = {2102.01672} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, |
| both through human annotations and automated Metrics. |
| |
| GEM aims to: |
| - measure NLG progress across 13 datasets spanning many NLG tasks and languages. |
| - provide an in-depth analysis of data and models presented via data statements and challenge sets. |
| - develop standards for evaluation of generated text using both automated and human metrics. |
| |
| It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development |
| by extending existing data or developing datasets for additional languages. |
| """ |
|
|
| _HOMEPAGE = "https://gem-benchmark.github.io/" |
|
|
| _LICENSE = "CC-BY-SA-4.0" |
|
|
| _TASKS = { |
| "summarization": { |
| "mlsum": ["mlsum_de", "mlsum_es"], |
| "wiki_lingua": [ |
| "wiki_lingua_es_en_v0", |
| "wiki_lingua_ru_en_v0", |
| "wiki_lingua_tr_en_v0", |
| "wiki_lingua_vi_en_v0", |
| "wiki_lingua_arabic_ar", |
| "wiki_lingua_chinese_zh", |
| "wiki_lingua_czech_cs", |
| "wiki_lingua_dutch_nl", |
| "wiki_lingua_english_en", |
| "wiki_lingua_french_fr", |
| "wiki_lingua_german_de", |
| "wiki_lingua_hindi_hi", |
| "wiki_lingua_indonesian_id", |
| "wiki_lingua_italian_it", |
| "wiki_lingua_japanese_ja", |
| "wiki_lingua_korean_ko", |
| "wiki_lingua_portuguese_pt", |
| "wiki_lingua_russian_ru", |
| "wiki_lingua_spanish_es", |
| "wiki_lingua_thai_th", |
| "wiki_lingua_turkish_tr", |
| "wiki_lingua_vietnamese_vi", |
| ], |
| "xsum": ["xsum"], |
| }, |
| "struct2text": { |
| "common_gen": ["common_gen"], |
| "cs_restaurants": ["cs_restaurants"], |
| "dart": ["dart"], |
| "e2e": ["e2e_nlg"], |
| "totto": ["totto"], |
| "web_nlg": ["web_nlg_en", "web_nlg_ru"], |
| }, |
| "simplification": { |
| "wiki_auto_asset_turk": ["wiki_auto_asset_turk"], |
| }, |
| "dialog": { |
| "schema_guided_dialog": ["schema_guided_dialog"], |
| }, |
| } |
|
|
| _URLs = { |
| "common_gen": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/common_gen.zip", |
| }, |
| "cs_restaurants": { |
| "train": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/train.json", |
| "validation": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/devel.json", |
| "test": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/test.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/cs_restaurants.zip", |
| }, |
| "dart": { |
| "train": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-train.json", |
| "validation": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-dev.json", |
| "test": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-test.json", |
| }, |
| "e2e_nlg": { |
| "train": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/train-fixed.no-ol.csv", |
| "validation": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/devel-fixed.no-ol.csv", |
| "test": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/test-fixed.csv", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/e2e_nlg.zip", |
| }, |
| "mlsum_de": { |
| "train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_train.zip", |
| "validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_val.zip", |
| "test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_test.zip", |
| "bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_de.zip", |
| }, |
| "mlsum_es": { |
| "train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_train.zip", |
| "validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_val.zip", |
| "test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_test.zip", |
| "bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_es.zip", |
| }, |
| "schema_guided_dialog": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_sgd_context.zip", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/schema_guided_dialog.zip", |
| }, |
| "totto": { |
| "data": "https://storage.googleapis.com/totto-public/totto_data.zip", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip", |
| }, |
| "web_nlg_en": { |
| "train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_train.json", |
| "validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_val.json", |
| "test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_test.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_en.zip", |
| }, |
| "web_nlg_ru": { |
| "train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_train.json", |
| "validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_val.json", |
| "test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_test.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_ru.zip", |
| }, |
| "wiki_auto_asset_turk": { |
| "train": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.tsv", |
| "validation": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/valid.tsv", |
| "test_turk": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_turk_detokenized.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/wiki_auto_asset_turk_train_valid.zip", |
| }, |
| "wiki_lingua_es_en_v0": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
| }, |
| "wiki_lingua_ru_en_v0": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
| }, |
| "wiki_lingua_tr_en_v0": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
| }, |
| "wiki_lingua_vi_en_v0": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
| }, |
| "wiki_lingua_arabic_ar": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/arabic.zip", |
| }, |
| "wiki_lingua_chinese_zh": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/chinese.zip", |
| }, |
| "wiki_lingua_czech_cs": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/czech.zip", |
| }, |
| "wiki_lingua_dutch_nl": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/dutch.zip", |
| }, |
| "wiki_lingua_english_en": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/english.zip", |
| }, |
| "wiki_lingua_french_fr": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/french.zip", |
| }, |
| "wiki_lingua_german_de": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/german.zip", |
| }, |
| "wiki_lingua_hindi_hi": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/hindi.zip", |
| }, |
| "wiki_lingua_indonesian_id": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/indonesian.zip", |
| }, |
| "wiki_lingua_italian_it": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/italian.zip", |
| }, |
| "wiki_lingua_japanese_ja": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/japanese.zip", |
| }, |
| "wiki_lingua_korean_ko": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/korean.zip", |
| }, |
| "wiki_lingua_portuguese_pt": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/portuguese.zip", |
| }, |
| "wiki_lingua_russian_ru": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/russian.zip", |
| }, |
| "wiki_lingua_spanish_es": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/spanish.zip", |
| }, |
| "wiki_lingua_thai_th": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/thai.zip", |
| }, |
| "wiki_lingua_turkish_tr": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/turkish.zip", |
| }, |
| "wiki_lingua_vietnamese_vi": { |
| "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/vietnamese.zip", |
| }, |
| "xsum": { |
| "data": "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz", |
| "splits": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_xsum_confidence_0.8.json", |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/xsum.zip", |
| }, |
| } |
|
|
| |
| _URLs["wiki_auto_asset_turk"][ |
| "test_asset_orig" |
| ] = "https://github.com/facebookresearch/asset/raw/main/dataset/asset.test.orig" |
| for i in range(10): |
| _URLs["wiki_auto_asset_turk"][ |
| f"test_asset_{i}" |
| ] = f"https://github.com/facebookresearch/asset/raw/main/dataset/asset.test.simp.{i}" |
|
|
| _SGD_ACTS = [ |
| "AFFIRM", |
| "AFFIRM_INTENT", |
| "CONFIRM", |
| "GOODBYE", |
| "INFORM", |
| "INFORM_COUNT", |
| "INFORM_INTENT", |
| "NEGATE", |
| "NEGATE_INTENT", |
| "NOTIFY_FAILURE", |
| "NOTIFY_SUCCESS", |
| "OFFER", |
| "OFFER_INTENT", |
| "REQUEST", |
| "REQUEST_ALTS", |
| "REQ_MORE", |
| "SELECT", |
| "THANK_YOU", |
| ] |
|
|
| _XSUM_REMOVE_LINES = set( |
| [ |
| "Share this with\n", |
| "Email\n", |
| "Facebook\n", |
| "Messenger\n", |
| "Twitter\n", |
| "Pinterest\n", |
| "WhatsApp\n", |
| "Linkedin\n", |
| "LinkedIn\n", |
| "Copy this link\n", |
| "These are external links and will open in a new window\n", |
| ] |
| ) |
|
|
|
|
| class Gem(datasets.GeneratorBasedBuilder): |
| """GEM: datasets supporting the Generation Evaluation Metrics 2021 shared task.""" |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig( |
| name=conf, |
| version=datasets.Version("1.1.0"), |
| description=f"GEM benchmark: {task} task, {conf} subset", |
| ) |
| for task, dset_confs in _TASKS.items() |
| for conf_list in dset_confs.values() |
| for conf in conf_list |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "common_gen" |
|
|
| def _info(self): |
| if self.config.name == "common_gen": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "concept_set_id": datasets.Value("int32"), |
| "concepts": [datasets.Value("string")], |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name == "cs_restaurants": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "dialog_act": datasets.Value("string"), |
| "dialog_act_delexicalized": datasets.Value("string"), |
| "target_delexicalized": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name == "dart": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "dart_id": datasets.Value("int32"), |
| "tripleset": [[datasets.Value("string")]], |
| "subtree_was_extended": datasets.Value("bool"), |
| "target_sources": [datasets.Value("string")], |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name == "e2e_nlg": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "meaning_representation": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name.startswith("mlsum"): |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "text": datasets.Value("string"), |
| "topic": datasets.Value("string"), |
| "url": datasets.Value("string"), |
| "title": datasets.Value("string"), |
| "date": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name == "schema_guided_dialog": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "dialog_acts": [ |
| { |
| "act": datasets.ClassLabel(names=_SGD_ACTS), |
| "slot": datasets.Value("string"), |
| "values": [datasets.Value("string")], |
| } |
| ], |
| "context": [datasets.Value("string")], |
| "dialog_id": datasets.Value("string"), |
| "service": datasets.Value("string"), |
| "turn_id": datasets.Value("int32"), |
| "prompt": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name == "totto": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "totto_id": datasets.Value("int32"), |
| "table_page_title": datasets.Value("string"), |
| "table_webpage_url": datasets.Value("string"), |
| "table_section_title": datasets.Value("string"), |
| "table_section_text": datasets.Value("string"), |
| "table": [ |
| [ |
| { |
| "column_span": datasets.Value("int32"), |
| "is_header": datasets.Value("bool"), |
| "row_span": datasets.Value("int32"), |
| "value": datasets.Value("string"), |
| } |
| ] |
| ], |
| "highlighted_cells": [[datasets.Value("int32")]], |
| "example_id": datasets.Value("string"), |
| "sentence_annotations": [ |
| { |
| "original_sentence": datasets.Value("string"), |
| "sentence_after_deletion": datasets.Value("string"), |
| "sentence_after_ambiguity": datasets.Value("string"), |
| "final_sentence": datasets.Value("string"), |
| } |
| ], |
| "overlap_subset": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| }, |
| ) |
| elif self.config.name.startswith("web_nlg"): |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "input": [datasets.Value("string")], |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| "category": datasets.Value("string"), |
| "webnlg_id": datasets.Value("string"), |
| } |
| ) |
| elif self.config.name == "wiki_auto_asset_turk": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "source": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name.startswith("wiki_lingua"): |
| if "v0" in self.config.name: |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "source": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| else: |
| ln = self.config.name.split("_")[-1] |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "source_aligned": datasets.Translation(languages=[ln, "en"]), |
| "target_aligned": datasets.Translation(languages=[ln, "en"]), |
| "source": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.name == "xsum": |
| features = datasets.Features( |
| { |
| "gem_id": datasets.Value("string"), |
| "gem_parent_id": datasets.Value("string"), |
| "xsum_id": datasets.Value("string"), |
| "document": datasets.Value("string"), |
| "target": datasets.Value("string"), |
| "references": [datasets.Value("string")], |
| } |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) |
| if self.config.name == "common_gen": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_common_gen_RandomSample500.json"), |
| ("challenge_validation_sample", "validation_common_gen_RandomSample500.json"), |
| ("challenge_test_scramble", "test_common_gen_ScrambleInputStructure500.json"), |
| ] |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["data"], "commongen.train.jsonl"), |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["data"], "commongen.dev.jsonl"), |
| "split": "validation", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["data"], "commongen.test_noref.jsonl"), |
| "split": "test", |
| }, |
| ), |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name == "cs_restaurants": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_cs_restaurants_RandomSample500.json"), |
| ("challenge_validation_sample", "validation_cs_restaurants_RandomSample500.json"), |
| ("challenge_test_scramble", "test_cs_restaurants_ScrambleInputStructure500.json"), |
| ] |
| return [ |
| datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
| for spl in ["train", "validation", "test"] |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name == "dart": |
| return [ |
| datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
| for spl in ["train", "validation", "test"] |
| ] |
| elif self.config.name == "e2e_nlg": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_e2e_nlg_RandomSample500.json"), |
| ("challenge_validation_sample", "validation_e2e_nlg_RandomSample500.json"), |
| ("challenge_test_scramble", "test_e2e_nlg_ScrambleInputStructure500.json"), |
| ] |
| return [ |
| datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
| for spl in ["train", "validation", "test"] |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name.startswith("mlsum"): |
| lang = self.config.name.split("_")[1] |
| challenge_sets = [ |
| ("challenge_train_sample", f"train_mlsum_{lang}_RandomSample500.json"), |
| ("challenge_validation_sample", f"validation_mlsum_{lang}_RandomSample500.json"), |
| ("challenge_test_covid", f"{lang}_test_covid19_cleaned.jsonl"), |
| ] |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["train"], lang + "_train.jsonl"), |
| "split": "train", |
| "lang": lang, |
| "filepaths": dl_dir["bad_ids"], |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["validation"], lang + "_val.jsonl"), |
| "split": "validation", |
| "lang": lang, |
| "filepaths": dl_dir["bad_ids"], |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["test"], lang + "_test.jsonl"), |
| "split": "test", |
| "lang": lang, |
| "filepaths": dl_dir["bad_ids"], |
| }, |
| ), |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name == "schema_guided_dialog": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_schema_guided_dialog_RandomSample500_reformatted.json"), |
| ("challenge_validation_sample", "validation_schema_guided_dialog_RandomSample500_reformatted.json"), |
| ("challenge_test_backtranslation", "test_schema_guided_dialog_BackTranslation500_reformatted.json"), |
| ( |
| "challenge_test_bfp02", |
| "test_schema_guided_dialog_ButterFingersPerturbation_p=0.02_500_reformatted.json", |
| ), |
| ( |
| "challenge_test_bfp05", |
| "test_schema_guided_dialog_ButterFingersPerturbation_p=0.05_500_reformatted.json", |
| ), |
| ("challenge_test_nopunc", "test_schema_guided_dialog_WithoutPunctuation500_reformatted.json"), |
| ("challenge_test_scramble", "test_schema_guided_dialog_ScrambleInputStructure500_reformatted.json"), |
| ] |
| return [ |
| datasets.SplitGenerator( |
| name=spl, gen_kwargs={"filepath": os.path.join(dl_dir["data"], "gem_sgd.json"), "split": spl} |
| ) |
| for spl in ["train", "validation", "test"] |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name == "totto": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_totto_RandomSample500.json"), |
| ("challenge_validation_sample", "validation_totto_RandomSample500.json"), |
| ("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"), |
| ] |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["data"], "totto_data/totto_train_data.jsonl"), |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["data"], "totto_data/totto_dev_data.jsonl"), |
| "split": "validation", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"), |
| "split": "test", |
| }, |
| ), |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name.startswith("web_nlg"): |
| ln = self.config.name.split("_")[-1] |
| challenge_sets = [ |
| ("challenge_train_sample", f"train_web_nlg_{ln}_RandomSample500.json"), |
| ("challenge_validation_sample", f"validation_web_nlg_{ln}_RandomSample500.json"), |
| ("challenge_test_scramble", f"test_web_nlg_{ln}_ScrambleInputStructure500.json"), |
| ] |
| if ln == "en": |
| challenge_sets += [("challenge_test_numbers", f"test_web_nlg_{ln}_replace_numbers_500.json")] |
| return [ |
| datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
| for spl in ["train", "validation", "test"] |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name == "wiki_auto_asset_turk": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_wiki_auto_asset_turk_RandomSample500.json"), |
| ("challenge_validation_sample", "validation_wiki_auto_asset_turk_RandomSample500.json"), |
| ("challenge_test_asset_backtranslation", "test_asset_wiki_auto_asset_turk_BackTranslation.json"), |
| ( |
| "challenge_test_asset_bfp02", |
| "test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json", |
| ), |
| ( |
| "challenge_test_asset_bfp05", |
| "test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json", |
| ), |
| ("challenge_test_asset_nopunc", "test_asset_wiki_auto_asset_turk_WithoutPunctuation.json"), |
| ("challenge_test_turk_backtranslation", "detok_test_turk_wiki_auto_asset_turk_BackTranslation.json"), |
| ( |
| "challenge_test_turk_bfp02", |
| "detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json", |
| ), |
| ( |
| "challenge_test_turk_bfp05", |
| "detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json", |
| ), |
| ("challenge_test_turk_nopunc", "detok_test_turk_wiki_auto_asset_turk_WithoutPunctuation.json"), |
| ] |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": dl_dir["train"], |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": dl_dir["validation"], |
| "split": "validation", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name="test_asset", |
| gen_kwargs={ |
| "filepath": "", |
| "split": "test_asset", |
| "filepaths": [dl_dir["test_asset_orig"]] + [dl_dir[f"test_asset_{i}"] for i in range(10)], |
| }, |
| ), |
| datasets.SplitGenerator( |
| name="test_turk", |
| gen_kwargs={ |
| "filepath": dl_dir["test_turk"], |
| "split": "test_turk", |
| }, |
| ), |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], "wiki_auto_asset_turk", filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
| elif self.config.name.startswith("wiki_lingua"): |
| if "v0" in self.config.name: |
| lang = self.config.name.split("_")[-3] |
| base_dir = os.path.join(dl_dir["data"], "GEM_data_crosslingual", f"{lang}_en") |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": base_dir, |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": base_dir, |
| "split": "val", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": base_dir, |
| "split": "test", |
| }, |
| ), |
| ] |
| else: |
| lang_name = self.config.name.split("_")[-2] |
| lang = self.config.name.split("_")[-1] |
| base_dir = os.path.join(dl_dir["data"], lang_name) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": base_dir, |
| "split": "train", |
| "lang": lang, |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": base_dir, |
| "split": "val", |
| "lang": lang, |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": base_dir, |
| "split": "test", |
| "lang": lang, |
| }, |
| ), |
| ] |
| elif self.config.name == "xsum": |
| challenge_sets = [ |
| ("challenge_train_sample", "train_xsum_RandomSample500.json"), |
| ("challenge_validation_sample", "validation_xsum_RandomSample500.json"), |
| ("challenge_test_backtranslation", "test_xsum_BackTranslation500.json"), |
| ("challenge_test_bfp_02", "test_xsum_ButterFingersPerturbation_p=0.02_500.json"), |
| ("challenge_test_bfp_05", "test_xsum_ButterFingersPerturbation_p=0.05_500.json"), |
| ("challenge_test_nopunc", "test_xsum_WithoutPunctuation500.json"), |
| ("challenge_test_covid", "en_test_covid19.jsonl"), |
| ] |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": dl_dir["splits"], |
| "split": "train", |
| "filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": dl_dir["splits"], |
| "split": "validation", |
| "filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": dl_dir["splits"], |
| "split": "test", |
| "filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"), |
| }, |
| ), |
| ] + [ |
| datasets.SplitGenerator( |
| name=challenge_split, |
| gen_kwargs={ |
| "filepath": os.path.join(dl_dir["challenge_set"], "xsum", filename), |
| "split": challenge_split, |
| }, |
| ) |
| for challenge_split, filename in challenge_sets |
| ] |
|
|
| def _generate_examples(self, filepath, split, filepaths=None, lang=None): |
| """Yields examples.""" |
| if self.config.name == "common_gen": |
| if split.startswith("challenge"): |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| with open(filepath, encoding="utf-8") as f: |
| id_ = -1 |
| i = -1 |
| for row in f: |
| row = row.replace(", }", "}") |
| data = json.loads(row) |
| concepts = [word for word in data["concept_set"].split("#")] |
| if split == "train": |
| i += 1 |
| for scene in data["scene"]: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "concept_set_id": i, |
| "concepts": concepts, |
| "target": scene, |
| "references": [], |
| } |
| else: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "concept_set_id": id_, |
| "concepts": concepts, |
| "target": "" if split == "test" else data["scene"][0], |
| "references": [] if split == "test" else data["scene"], |
| } |
| elif self.config.name == "cs_restaurants": |
| if split.startswith("challenge"): |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| with open(filepath, encoding="utf8") as f: |
| data = json.load(f) |
| for id_, instance in enumerate(data): |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "dialog_act": instance["da"], |
| "dialog_act_delexicalized": instance["delex_da"], |
| "target": instance["text"], |
| "target_delexicalized": instance["delex_text"], |
| "references": [] if split == "train" else [instance["text"]], |
| } |
| elif self.config.name == "dart": |
| with open(filepath, encoding="utf-8") as f: |
| data = json.loads(f.read()) |
| id_ = -1 |
| i = -1 |
| for example in data: |
| if split == "train": |
| i += 1 |
| for annotation in example["annotations"]: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "dart_id": i, |
| "tripleset": example["tripleset"], |
| "subtree_was_extended": example.get("subtree_was_extended", None), |
| "target_sources": [annotation["source"] for annotation in example["annotations"]], |
| "target": annotation["text"], |
| "references": [], |
| } |
| else: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "dart_id": id_, |
| "tripleset": example["tripleset"], |
| "subtree_was_extended": example.get("subtree_was_extended", None), |
| "target_sources": [annotation["source"] for annotation in example["annotations"]], |
| "target": example["annotations"][0]["text"] if len(example["annotations"]) > 0 else "", |
| "references": [annotation["text"] for annotation in example["annotations"]], |
| } |
| elif self.config.name == "e2e_nlg": |
| if split.startswith("challenge"): |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| with open(filepath, encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for id_, example in enumerate(reader): |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "meaning_representation": example["mr"], |
| "target": example["ref"], |
| "references": [] if split == "train" else [example["ref"]], |
| } |
| elif self.config.name.startswith("mlsum"): |
| if split in ["train", "validation", "test", "challenge_test_covid"]: |
| if split == "challenge_test_covid": |
| bad_ids = {} |
| else: |
| bad_ids_dct = json.load(open(filepaths, encoding="utf-8")) |
| bad_ids = dict((bad_url, True) for _, bad_url in bad_ids_dct[f"{lang}-{split}"]) |
| with open(filepath, encoding="utf-8") as f: |
| id_ = -1 |
| for line in f: |
| data = json.loads(line) |
| if data["url"] in bad_ids: |
| continue |
| else: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "text": data["text"], |
| "target": data["summary"], |
| "references": [] if split == "train" else [data["summary"]], |
| "topic": data["topic"], |
| "url": data["url"], |
| "title": data["title"], |
| "date": data["date"], |
| } |
| else: |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| elif self.config.name == "schema_guided_dialog": |
| if "challenge" in split: |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| examples = json.load(open(filepath, encoding="utf-8"))[split] |
| for id_, example in enumerate(examples): |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "dialog_acts": [ |
| { |
| "act": act_id, |
| "slot": slot, |
| "values": values, |
| } |
| for act_id, slot, values in example["da"] |
| ], |
| "context": example["context"], |
| "dialog_id": example["dialog_id"], |
| "service": example["service"], |
| "turn_id": example["turn_ix"], |
| "prompt": example["prompt"], |
| "target": example["target"], |
| "references": [] if split == "train" else [example["target"]], |
| } |
| elif self.config.name == "totto": |
| if "challenge" in split: |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| with open(filepath, "r", encoding="utf-8") as json_file: |
| json_list = list(json_file) |
| id_ = -1 |
| i = -1 |
| for json_str in json_list: |
| result = json.loads(json_str) |
| if split == "train": |
| i += 1 |
| for sentence in result["sentence_annotations"]: |
| id_ += 1 |
| response = { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "totto_id": i, |
| "table_page_title": result["table_page_title"], |
| "table_webpage_url": result["table_webpage_url"], |
| "table_section_title": result["table_section_title"], |
| "table_section_text": result["table_section_text"], |
| "table": result["table"], |
| "highlighted_cells": result["highlighted_cells"], |
| "example_id": str(result["example_id"]), |
| "overlap_subset": "none", |
| "sentence_annotations": [sentence], |
| "references": [], |
| "target": sentence["final_sentence"], |
| } |
| yield id_, response |
| else: |
| id_ += 1 |
| response = { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "totto_id": id_, |
| "table_page_title": result["table_page_title"], |
| "table_webpage_url": result["table_webpage_url"], |
| "table_section_title": result["table_section_title"], |
| "table_section_text": result["table_section_text"], |
| "table": result["table"], |
| "highlighted_cells": result["highlighted_cells"], |
| "example_id": str(result["example_id"]), |
| "overlap_subset": str(result["overlap_subset"]), |
| "sentence_annotations": [] if split == "test" else result["sentence_annotations"], |
| } |
| response["references"] = [ |
| sentence["final_sentence"] for sentence in response["sentence_annotations"] |
| ] |
| response["target"] = response["references"][0] if len(response["references"]) > 0 else "" |
| yield id_, response |
| elif self.config.name.startswith("web_nlg"): |
| if "challenge" in split: |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| if len(exple) == 0: |
| continue |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| with open(filepath, encoding="utf-8") as f: |
| examples = json.load(f) |
| id_ = -1 |
| for example in examples["values"]: |
| if split == "train": |
| for target in example["target"]: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "input": example["input"], |
| "target": target, |
| "references": [] if split == "train" else example["target"], |
| "category": example["category"], |
| "webnlg_id": example["webnlg-id"], |
| } |
| else: |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "input": example["input"], |
| "target": example["target"][0] if len(example["target"]) > 0 else "", |
| "references": example["target"], |
| "category": example["category"], |
| "webnlg_id": example["webnlg-id"], |
| } |
| elif self.config.name == "wiki_auto_asset_turk": |
| if split in ["train", "validation"]: |
| keys = [ |
| "source", |
| "target", |
| ] |
| with open(filepath, encoding="utf-8") as f: |
| for id_, line in enumerate(f): |
| values = line.strip().split("\t") |
| assert len(values) == 2, f"Not enough fields in ---- {line} --- {values}" |
| example = dict([(k, val) for k, val in zip(keys, values)]) |
| example["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| example["gem_parent_id"] = example["gem_id"] |
| example["references"] = [] if split == "train" else [example["target"]] |
| yield id_, example |
| elif split == "test_turk": |
| examples = json.load(open(filepath, encoding="utf-8")) |
| for id_, example in enumerate(examples): |
| example["gem_parent_id"] = example["gem_id"] |
| for k in ["source_id", "target_id"]: |
| if k in example: |
| del example[k] |
| yield id_, example |
| elif split == "test_asset": |
| files = [open(f_name, encoding="utf-8") for f_name in filepaths] |
| for id_, lines in enumerate(zip(*files)): |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "target": lines[1].strip(), |
| "source": lines[0].strip(), |
| "references": [line.strip() for line in lines[1:]], |
| } |
| else: |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| for k in ["source_id", "target_id"]: |
| if k in exple: |
| del exple[k] |
| yield id_, exple |
| elif self.config.name.startswith("wiki_lingua"): |
| if "v0" in self.config.name: |
| with open(os.path.join(filepath, f"{split}.src"), encoding="utf-8") as f_in: |
| with open(os.path.join(filepath, f"{split}.tgt"), encoding="utf-8") as f_out: |
| for id_, (src, tgt) in enumerate(zip(f_in, f_out)): |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "source": src.strip(), |
| "target": tgt.strip(), |
| "references": [] if split == "train" else [tgt.strip()], |
| } |
| else: |
| with open(os.path.join(filepath, f"{split}.src.{lang}"), encoding="utf-8") as f_in_ln: |
| with open(os.path.join(filepath, f"{split}.src.en"), encoding="utf-8") as f_in_en: |
| with open(os.path.join(filepath, f"{split}.tgt.{lang}"), encoding="utf-8") as f_out_ln: |
| with open(os.path.join(filepath, f"{split}.tgt.en"), encoding="utf-8") as f_out_en: |
| for id_, (src_ln, src_en, tgt_ln, tgt_en) in enumerate( |
| zip(f_in_ln, f_in_en, f_out_ln, f_out_en) |
| ): |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "source_aligned": {lang: src_ln.strip(), "en": src_en.strip()}, |
| "target_aligned": {lang: tgt_ln.strip(), "en": tgt_en.strip()}, |
| "source": src_ln.strip(), |
| "target": tgt_en.strip(), |
| "references": [] if split == "train" else [tgt_en.strip()], |
| } |
| elif self.config.name == "xsum": |
| if "challenge" in split: |
| if "covid" in split: |
| with open(filepath, encoding="utf-8") as f: |
| id_ = -1 |
| for line in f: |
| data = json.loads(line) |
| id_ += 1 |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "xsum_id": data["url"], |
| "document": data["text"], |
| "target": data["summary"], |
| "references": [] if split == "train" else [data["summary"]], |
| } |
| else: |
| exples = json.load(open(filepath, encoding="utf-8")) |
| if isinstance(exples, dict): |
| assert len(exples) == 1, "multiple entries found" |
| exples = list(exples.values())[0] |
| for id_, exple in enumerate(exples): |
| exple["gem_parent_id"] = exple["gem_id"] |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
| yield id_, exple |
| else: |
| with open(filepath, "r", encoding="utf-8") as f: |
| split_ids = json.load(f) |
| for id_, i in enumerate(split_ids[split]): |
| with open(os.path.join(filepaths, i + ".summary"), "r", encoding="utf-8") as f: |
| text = "".join( |
| [line for line in f.readlines() if line not in _XSUM_REMOVE_LINES and line.strip()] |
| ) |
| segs = text.split("[SN]") |
| yield id_, { |
| "gem_id": f"{self.config.name}-{split}-{id_}", |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", |
| "xsum_id": i, |
| "document": segs[8].strip(), |
| "target": segs[6].strip(), |
| "references": [] if split == "train" else [segs[6].strip()], |
| } |
|
|
