Datasets:
hate_speech_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.
	"""Hate Speech Text Classification Dataset in Filipino."""

	import csv
	import os

	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.
	"""

	_CITATION = """\
	@article{Cabasag-2019-hate-speech,
	title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.},
	author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng},
	journal={Philippine Computing Journal},
	volume={XIV},
	number={1},
	month={August},
	year={2019}
	}
	"""

	_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"

	# 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/hatenonhate/hatespeech_raw.zip"


	class HateSpeechFilipino(datasets.GeneratorBasedBuilder):
	"""Hate Speech Text Classification Dataset in Filipino."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	# Labels: 0="Non-hate Speech", 1="Hate Speech"
	features = datasets.Features(
	{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["0", "1"])}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", 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, "hatespeech", "train.csv")
	test_path = os.path.join(data_dir, "hatespeech", "train.csv")
	validation_path = os.path.join(data_dir, "hatespeech", "valid.csv")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": train_path,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": test_path,
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": validation_path,
	"split": "dev",
	},
	),
	]

	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):
	try:
	text, label = row
	yield id_, {"text": text, "label": label}
	except ValueError:
	pass