Datasets:
hate_offensive

	# 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.
	"""Automated Hate Speech Detection and the Problem of Offensive Language."""

	from __future__ import absolute_import, division, print_function

	import csv
	import os

	import datasets


	_CITATION = """
	@article{article,
	author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
	year = {2017},
	month = {03},
	pages = {},
	title = {Automated Hate Speech Detection and the Problem of Offensive Language}
	}
	"""

	_DESCRIPTION = "This dataset contains annotated tweets for automated hate-speech recognition"

	_HOMEPAGE = "https://arxiv.org/abs/1905.12516"

	_LICENSE = "MIT License"

	_URLs = "https://github.com/t-davidson/hate-speech-and-offensive-language/raw/master/data/labeled_data.csv"


	class HateOffensive(datasets.GeneratorBasedBuilder):
	"""Automated Hate Speech Detection and the Problem of Offensive Language """

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"total_annotation_count": datasets.Value("int32"),
	"hate_speech_annotations": datasets.Value("int32"),
	"offensive_language_annotations": datasets.Value("int32"),
	"neither_annotations": datasets.Value("int32"),
	"label": datasets.ClassLabel(names=["hate-speech", "offensive-language", "neither"]),
	"tweet": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=("tweet", "label"),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir)})]

	def _generate_examples(self, filepath):
	""" Yields examples. """
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file, lineterminator="\n", delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
	)
	next(csv_reader, None)
	for id_, row in enumerate(csv_reader):
	yield id_, {
	"total_annotation_count": row[1],
	"hate_speech_annotations": row[2],
	"offensive_language_annotations": row[3],
	"neither_annotations": row[4],
	"label": int(row[5]),
	"tweet": str(row[6]),
	}