hate_offensive / hate_offensive.py
# 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."""
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]),
}