Datasets:
tab_fact

	# 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""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,
	}