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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.
"""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
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