Datasets:

bigbio
/

pubhealth

	# 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.
	"""
	A dataset of 11,832 claims for fact- checking, which are related a range of health topics
	including biomedical subjects (e.g., infectious diseases, stem cell research), government healthcare policy
	(e.g., abortion, mental health, women’s health), and other public health-related stories
	"""

	import csv
	import os
	from pathlib import Path

	import datasets

	from .bigbiohub import pairs_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	logger = datasets.utils.logging.get_logger(__name__)

	_LANGUAGES = ['English']
	_PUBMED = False
	_LOCAL = False
	_CITATION = """\
	@article{kotonya2020explainable,
	title={Explainable automated fact-checking for public health claims},
	author={Kotonya, Neema and Toni, Francesca},
	journal={arXiv preprint arXiv:2010.09926},
	year={2020}
	}
	"""

	_DATASETNAME = "pubhealth"
	_DISPLAYNAME = "PUBHEALTH"

	_DESCRIPTION = """\
	A dataset of 11,832 claims for fact- checking, which are related a range of health topics
	including biomedical subjects (e.g., infectious diseases, stem cell research), government healthcare policy
	(e.g., abortion, mental health, women’s health), and other public health-related stories
	"""

	_HOMEPAGE = "https://github.com/neemakot/Health-Fact-Checking/tree/master/data"

	_LICENSE = 'MIT License'

	_URLs = {
	_DATASETNAME: "https://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj"
	}

	_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
	_SOURCE_VERSION = "1.0.0"
	_BIGBIO_VERSION = "1.0.0"

	_CLASSES = ["true", "false", "unproven", "mixture"]


	class PUBHEALTHDataset(datasets.GeneratorBasedBuilder):
	"""Pubhealth text classification dataset"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="pubhealth_source",
	version=SOURCE_VERSION,
	description="PUBHEALTH source schema",
	schema="source",
	subset_id="pubhealth",
	),
	BigBioConfig(
	name="pubhealth_bigbio_pairs",
	version=BIGBIO_VERSION,
	description="PUBHEALTH BigBio schema",
	schema="bigbio_pairs",
	subset_id="pubhealth",
	),
	]

	DEFAULT_CONFIG_NAME = "pubhealth_source"

	def _info(self):

	if self.config.schema == "source":
	features = datasets.Features(
	{
	"claim_id": datasets.Value("string"),
	"claim": datasets.Value("string"),
	"date_published": datasets.Value("string"),
	"explanation": datasets.Value("string"),
	"fact_checkers": datasets.Value("string"),
	"main_text": datasets.Value("string"),
	"sources": datasets.Value("string"),
	"label": datasets.ClassLabel(names=_CLASSES),
	"subjects": datasets.Value("string"),
	}
	)

	# Using in entailment schema
	elif self.config.schema == "bigbio_pairs":
	features = pairs_features

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls = _URLs[_DATASETNAME]
	data_dir = Path(dl_manager.download_and_extract(urls))

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "PUBHEALTH/train.tsv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "PUBHEALTH/test.tsv"),
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "PUBHEALTH/dev.tsv"),
	"split": "validation",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples as (key, example) tuples."""

	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file,
	quotechar='"',
	delimiter="\t",
	quoting=csv.QUOTE_NONE,
	skipinitialspace=True,
	)
	next(csv_reader, None) # remove column headers
	for id_, row in enumerate(csv_reader):
	# train.tsv/dev.tsv only has 9 columns
	# test.tsv has an additional column at the beginning
	# Some entries are malformed, will log skipped lines
	if len(row) < 9:
	logger.info("Line %s is malformed", id_)
	continue
	(
	claim_id,
	claim,
	date_published,
	explanation,
	fact_checkers,
	main_text,
	sources,
	label,
	subjects,
	) = row[
	-9:
	] # only take last 9 columns to fix test.tsv disparity

	if label not in _CLASSES:
	logger.info("Line %s is missing label", id_)
	continue

	if self.config.schema == "source":
	yield id_, {
	"claim_id": claim_id,
	"claim": claim,
	"date_published": date_published,
	"explanation": explanation,
	"fact_checkers": fact_checkers,
	"main_text": main_text,
	"sources": sources,
	"label": label,
	"subjects": subjects,
	}

	elif self.config.schema == "bigbio_pairs":
	yield id_, {
	"id": id_, # uid is an unique identifier for every record that starts from 0
	"document_id": claim_id,
	"text_1": claim,
	"text_2": explanation,
	"label": label,
	}