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# 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""" | |
from __future__ import absolute_import, division, print_function | |
import csv | |
import json | |
import os | |
import datasets | |
# TODO: Add BibTeX citation | |
_CITATION = """\ | |
@InProceedings{huggingface:dataset, | |
title = {A great new dataset}, | |
authors={huggingface, Inc. | |
}, | |
year={2020} | |
} | |
""" | |
_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", "wiki_lingua_ru_en", "wiki_lingua_tr_en", "wiki_lingua_vi_en"], | |
"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", | |
}, | |
"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", | |
}, | |
"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", | |
}, | |
"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", | |
}, | |
"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", | |
}, | |
"schema_guided_dialog": { | |
"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_sgd.json.zip", | |
}, | |
"totto": { | |
"data": "https://storage.googleapis.com/totto/totto_data.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", | |
}, | |
"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", | |
}, | |
"wiki_auto_asset_turk": { | |
"train": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv", | |
"validation": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/dev.tsv", | |
}, | |
"wiki_lingua_es_en": { | |
"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", | |
}, | |
"wiki_lingua_ru_en": { | |
"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", | |
}, | |
"wiki_lingua_tr_en": { | |
"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", | |
}, | |
"wiki_lingua_vi_en": { | |
"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.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", | |
}, | |
} | |
# Add Turk and Asset files | |
for i in range(10): | |
_URLs["wiki_auto_asset_turk"][ | |
f"test_asset_{i}" | |
] = f"https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.simp.{i}" | |
for i in range(8): | |
_URLs["wiki_auto_asset_turk"][ | |
f"test_turk_{i}" | |
] = f"https://raw.githubusercontent.com/cocoxu/simplification/master/data/turkcorpus/GEM/test.8turkers.tok.turk.{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("1.0.0"), | |
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"), | |
"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"), | |
"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"), | |
"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"), | |
"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"), | |
"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"), | |
"dialog_acts": [ | |
{ | |
"act": datasets.ClassLabel(names=_SGD_ACTS), | |
"slot": datasets.Value("string"), | |
"values": [datasets.Value("string")], | |
} | |
], | |
"dialog_id": 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"), | |
"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"), | |
"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"), | |
"source_id": datasets.Value("string"), | |
"target_id": datasets.Value("string"), | |
"source": datasets.Value("string"), | |
"target": datasets.Value("string"), | |
"references": [datasets.Value("string")], | |
} | |
) | |
elif self.config.name.startswith("wiki_lingua"): | |
features = datasets.Features( | |
{ | |
"gem_id": datasets.Value("string"), | |
"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"), | |
"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": | |
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", | |
}, | |
), | |
] | |
elif self.config.name == "cs_restaurants": | |
return [ | |
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) | |
for spl in ["train", "validation", "test"] | |
] | |
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": | |
return [ | |
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) | |
for spl in ["train", "validation", "test"] | |
] | |
elif self.config.name.startswith("mlsum"): | |
lang = self.config.name.split("_")[1] | |
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"], | |
}, | |
), | |
] | |
elif self.config.name == "schema_guided_dialog": | |
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"] | |
] | |
elif self.config.name == "totto": | |
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", | |
}, | |
), | |
] | |
elif self.config.name.startswith("web_nlg"): | |
return [ | |
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) | |
for spl in ["train", "validation", "test"] | |
] | |
elif self.config.name == "wiki_auto_asset_turk": | |
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", | |
"filepaths": [dl_dir[f"test_asset_{i}"] for i in range(10)], | |
}, | |
), | |
datasets.SplitGenerator( | |
name="test_turk", | |
gen_kwargs={ | |
"filepath": "", | |
"split": "test", | |
"filepaths": [dl_dir[f"test_turk_{i}"] for i in range(8)], | |
}, | |
), | |
] | |
elif self.config.name.startswith("wiki_lingua"): | |
lang = self.config.name.split("_")[-2] | |
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", | |
}, | |
), | |
] | |
elif self.config.name == "xsum": | |
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"), | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split, filepaths=None, lang=None): | |
""" Yields examples. """ | |
if self.config.name == "common_gen": | |
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_}", | |
"concept_set_id": i, | |
"concepts": concepts, | |
"target": scene, | |
"references": [], | |
} | |
else: | |
id_ += 1 | |
yield id_, { | |
"gem_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": | |
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_}", | |
"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_}", | |
"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_}", | |
"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": | |
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_}", | |
"meaning_representation": example["mr"], | |
"target": example["ref"], | |
"references": [] if split == "train" else [example["ref"]], | |
} | |
elif self.config.name.startswith("mlsum"): | |
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: # TODO : check | i or i-1? | |
continue | |
else: | |
id_ += 1 | |
yield id_, { | |
"gem_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"], | |
} | |
elif self.config.name == "schema_guided_dialog": | |
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_}", | |
"dialog_acts": [ | |
{ | |
"act": act_id, | |
"slot": slot, | |
"values": values, | |
} | |
for act_id, slot, values in example["da"] | |
], | |
"dialog_id": example["dialog_id"], | |
"turn_id": example["turn_ix"], | |
"prompt": example["prompt"], | |
"target": example["target"], | |
"references": [] if split == "train" else [example["target"]], | |
} | |
elif self.config.name == "totto": | |
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_}", | |
"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_}", | |
"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"]), | |
} | |
response["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"): | |
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_}", | |
"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_}", | |
"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 = [ | |
"target_id", | |
"source_id", | |
"target", | |
"source", | |
] | |
with open(filepath, encoding="utf-8") as f: | |
for id_, line in enumerate(f): | |
values = line.strip().split("\t") | |
assert len(values) == 5, f"Not enough fields in ---- {line} --- {values}" | |
example = dict([(k, val) for k, val in zip(keys, values[1:])]) | |
example["gem_id"] = f"{self.config.name}-{split}-{id_}" | |
example["references"] = [] if split == "train" else [example["target"]] | |
yield id_, example | |
elif split.startswith("test"): | |
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_}", | |
"source_id": "", | |
"target_id": "", | |
"target": lines[1].strip(), | |
"source": lines[0].strip(), | |
"references": [line.strip() for line in lines[1:]], | |
} | |
elif self.config.name.startswith("wiki_lingua"): | |
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_}", | |
"source": src.strip(), | |
"target": tgt.strip(), | |
"references": [] if split == "train" else [tgt.strip()], | |
} | |
elif self.config.name == "xsum": | |
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_}", | |
"xsum_id": i, | |
"document": segs[8].strip(), | |
"target": segs[6].strip(), | |
"references": [] if split == "train" else [segs[6].strip()], | |
} | |