Datasets:
Tasks:
Text Classification
Sub-tasks:
fact-checking
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 6,691 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""TabFact: A Large-scale Dataset for Table-based Fact Verification"""
import json
import os
import datasets
_CITATION = """\
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
"""
_DESCRIPTION = """\
The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, \
also known as fact verification, plays an important role in the study of natural language \
understanding and semantic representation. However, existing studies are restricted to \
dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), \
while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. \
TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements \
designed for fact verification with semi-structured evidence. \
The statements are labeled as either ENTAILED or REFUTED. \
TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
"""
_HOMEPAGE = "https://tabfact.github.io/"
_GIT_ARCHIVE_URL = (
"https://github.com/wenhuchen/Table-Fact-Checking/archive/948b5560e2f7f8c9139bd91c7f093346a2bb56a8.zip"
)
class TabFact(datasets.GeneratorBasedBuilder):
"""TabFact: A Large-scale Dataset for Table-based Fact Verification"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="tab_fact",
version=datasets.Version("1.0.0"),
),
datasets.BuilderConfig(
name="blind_test",
version=datasets.Version("1.0.0"),
description="Blind test dataset",
),
]
def _info(self):
features = {
"id": datasets.Value("int32"),
"table_id": datasets.Value("string"),
"table_text": datasets.Value("string"),
"table_caption": datasets.Value("string"),
"statement": datasets.Value("string"),
}
if self.config.name == "tab_fact":
features["label"] = datasets.ClassLabel(names=["refuted", "entailed"])
else:
features["test_id"] = datasets.Value("string")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
extracted_path = dl_manager.download_and_extract(_GIT_ARCHIVE_URL)
repo_path = os.path.join(extracted_path, "Table-Fact-Checking-948b5560e2f7f8c9139bd91c7f093346a2bb56a8")
all_csv_path = os.path.join(repo_path, "data", "all_csv")
if self.config.name == "blind_test":
test_file_path = os.path.join(repo_path, "challenge", "blind_test.json")
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"statements_file": test_file_path, "all_csv_path": all_csv_path},
),
]
train_statements_file = os.path.join(repo_path, "tokenized_data", "train_examples.json")
val_statements_file = os.path.join(repo_path, "tokenized_data", "val_examples.json")
test_statements_file = os.path.join(repo_path, "tokenized_data", "test_examples.json")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"statements_file": train_statements_file, "all_csv_path": all_csv_path},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"statements_file": val_statements_file, "all_csv_path": all_csv_path},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"statements_file": test_statements_file, "all_csv_path": all_csv_path},
),
]
def _generate_examples(self, statements_file, all_csv_path):
with open(statements_file, encoding="utf-8") as f:
examples = json.load(f)
if self.config.name == "blind_test":
test_examples = self._generate_blind_test_examples(examples, all_csv_path)
for idx, example in test_examples:
yield idx, example
else:
for i, (table_id, example) in enumerate(examples.items()):
table_file_path = os.path.join(all_csv_path, table_id)
with open(table_file_path, encoding="utf-8") as f:
tabel_text = f.read()
statements, labels, caption = example
for statement_idx, (statement, label) in enumerate(zip(statements, labels)):
yield f"{i}_{statement_idx}", {
"id": i,
"table_id": table_id,
"table_text": tabel_text,
"table_caption": caption,
"statement": statement,
"label": label,
}
def _generate_blind_test_examples(self, examples, all_csv_path):
for i, (test_id, example) in enumerate(examples.items()):
statement, table_id, caption = example
table_file_path = os.path.join(all_csv_path, table_id)
with open(table_file_path, encoding="utf-8") as f:
tabel_text = f.read()
yield i, {
"id": i,
"test_id": test_id,
"table_id": table_id,
"table_text": tabel_text,
"table_caption": caption,
"statement": statement,
}
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