Datasets:
ArXiv:
License:
# 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()], | |
} | |