Datasets:
datacommons_factcheck

	# 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.
	"""DataCommons Fact Checked claims"""


	import json

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {Data Commons 2019 Fact Checks},
	authors={datacommons.org},
	year={2019}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	A dataset of fact checked claims by news media maintained by datacommons.org
	"""

	_HOMEPAGE = "https://datacommons.org/factcheck/faq"

	_LICENSE = "CC-BY-NC-4.0"

	_URL = "https://datacommons.org/data/factcheck/fact_checks_20190605.txt.gz"


	class DatacommonsFactcheck(datasets.GeneratorBasedBuilder):
	"""DataCommons Fact Checked claims"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="fctchk_politifact_wapo", version=VERSION, description="The 06/05/2019 version of the dataset"
	),
	datasets.BuilderConfig(
	name="weekly_standard",
	version=VERSION,
	description="Includes Weekly Standard fact checked claims. See the README for concerns about these data items.",
	),
	]

	DEFAULT_CONFIG_NAME = (
	"fctchk_politifact_wapo" # It's not mandatory to have a default configuration. Just use one if it make sense.
	)

	def _info(self):
	features = datasets.Features(
	{
	"reviewer_name": datasets.Value("string"),
	"claim_text": datasets.Value("string"),
	"review_date": datasets.Value("string"),
	"review_url": datasets.Value("string"),
	"review_rating": datasets.Value("string"),
	"claim_author_name": datasets.Value("string"),
	"claim_date": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features, # Here we define them above because they are different between the two configurations
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	file_path = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": file_path,
	},
	),
	]

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	id_ = -1
	for row in f:
	data = json.loads(row.strip()[35:-9])
	res = {
	"reviewer_name": data["author"]["name"],
	"claim_text": data["claimReviewed"],
	"review_date": data.get("datePublished", ""),
	"review_url": data["url"],
	"review_rating": data["reviewRating"]["alternateName"],
	"claim_author_name": data["itemReviewed"]["author"].get("name", ""),
	"claim_date": data["itemReviewed"].get("datePublished", ""),
	}
	if self.config.name == "weekly_standard":
	if data["author"]["name"] == "The Weekly Standard":
	id_ += 1
	yield id_, res
	else:
	if data["author"]["name"] != "The Weekly Standard":
	id_ += 1
	yield id_, res