Datasets:
hatexplain

	# 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.
	"""Hatexplain: A Benchmark Dataset for Explainable Hate Speech Detection"""


	import json

	import datasets


	_CITATION = """\
	@misc{mathew2020hatexplain,
	title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
	author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
	year={2020},
	eprint={2012.10289},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. \
	Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification \
	(i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of \
	hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling \
	decision (as hate, offensive or normal) is based.
	"""

	_HOMEPAGE = ""

	_LICENSE = "cc-by-4.0"

	_URL = "https://raw.githubusercontent.com/punyajoy/HateXplain/master/Data/"
	_URLS = {
	"dataset": _URL + "dataset.json",
	"post_id_divisions": _URL + "post_id_divisions.json",
	}


	class HatexplainConfig(datasets.BuilderConfig):
	"""BuilderConfig for Hatexplain."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Hatexplain.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(HatexplainConfig, self).__init__(**kwargs)


	class Hatexplain(datasets.GeneratorBasedBuilder):
	"""Hatexplain: A Benchmark Dataset for Explainable Hate Speech Detection"""

	BUILDER_CONFIGS = [
	HatexplainConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"annotators": datasets.features.Sequence(
	{
	"label": datasets.ClassLabel(names=["hatespeech", "normal", "offensive"]),
	"annotator_id": datasets.Value("int32"),
	"target": datasets.Sequence(datasets.Value("string")),
	}
	),
	"rationales": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"))),
	"post_tokens": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	license=_LICENSE,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)

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

	def _generate_examples(self, filepath, split):
	"""This function returns the examples depending on split"""

	with open(filepath["post_id_divisions"], encoding="utf-8") as f:
	post_id_divisions = json.load(f)
	with open(filepath["dataset"], encoding="utf-8") as f:
	dataset = json.load(f)

	for id_, tweet_id in enumerate(post_id_divisions[split]):
	info = dataset[tweet_id]
	annotators, rationales, post_tokens = info["annotators"], info["rationales"], info["post_tokens"]

	yield id_, {"id": tweet_id, "annotators": annotators, "rationales": rationales, "post_tokens": post_tokens}