Datasets:
fake_news_filipino

	# 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.
	"""Fake News Filipino Dataset"""

	import csv
	import os

	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
	"""

	_CITATION = """\
	@inproceedings{cruz2020localization,
	title={Localization of Fake News Detection via Multitask Transfer Learning},
	author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
	booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
	pages={2596--2604},
	year={2020}
	}
	"""

	_HOMEPAGE = "https://github.com/jcblaisecruz02/Tagalog-fake-news"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/fakenews/fakenews.zip"


	class FakeNewsFilipino(datasets.GeneratorBasedBuilder):
	"""Low-Resource Fake News Detection Corpora in Filipino"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{"label": datasets.features.ClassLabel(names=["0", "1"]), "article": datasets.Value("string")}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="article", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	train_path = os.path.join(data_dir, "fakenews", "full.csv")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": train_path,
	"split": "train",
	},
	)
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
	)
	next(csv_reader)
	for id_, row in enumerate(csv_reader):
	label, article = row
	yield id_, {"label": label, "article": article}