Datasets:

tdavidson
/

hate_speech_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.
	"""An annotated dataset for hate speech and offensive language detection on tweets."""


	import csv

	import datasets


	_CITATION = """\
	@inproceedings{hateoffensive,
	title = {Automated Hate Speech Detection and the Problem of Offensive Language},
	author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
	booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
	series = {ICWSM '17},
	year = {2017},
	location = {Montreal, Canada},
	pages = {512-515}
	}
	"""

	_DESCRIPTION = """\
	An annotated dataset for hate speech and offensive language detection on tweets.
	"""

	_HOMEPAGE = "https://github.com/t-davidson/hate-speech-and-offensive-language"

	_LICENSE = "MIT"

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

	_CLASS_MAP = {
	"0": "hate speech",
	"1": "offensive language",
	"2": "neither",
	}


	class HateSpeechOffensive(datasets.GeneratorBasedBuilder):
	"""An annotated dataset for hate speech and offensive language detection on tweets."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"count": datasets.Value("int64"),
	"hate_speech_count": datasets.Value("int64"),
	"offensive_language_count": datasets.Value("int64"),
	"neither_count": datasets.Value("int64"),
	"class": datasets.ClassLabel(names=["hate speech", "offensive language", "neither"]),
	"tweet": datasets.Value("string"),
	}
	),
	supervised_keys=("tweet", "class"),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	data_file = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_file,
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""

	with open(filepath, encoding="utf-8") as f:
	reader = csv.reader(f)
	for id_, row in enumerate(reader):
	if id_ == 0:
	continue

	yield id_, {
	"count": row[1],
	"hate_speech_count": row[2],
	"offensive_language_count": row[3],
	"neither_count": row[4],
	"class": _CLASS_MAP[row[5]],
	"tweet": row[6],
	}