# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """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", }, } # Add Asset files _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" # First alphabetical 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"), # single target for train "references": [datasets.Value("string")], # multiple references for validation } ) 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")]], # list of triples "subtree_was_extended": datasets.Value("bool"), "target_sources": [datasets.Value("string")], "target": datasets.Value("string"), # single target for train "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"), # single target for train "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"), # single target for train "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(", }", "}") # Fix possible JSON format error 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), # some are missing "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), # some are missing "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()], }