Datasets:
ethos

	"""Ethos dataset"""


	import pandas as pd

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """
	@misc{mollas2020ethos,
	title={ETHOS: an Online Hate Speech Detection Dataset},
	author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas},
	year={2020},
	eprint={2006.08328},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """

	ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech
	detection on social media platforms, called Ethos. There are two variations of the dataset:

	Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label
	about hate speech presence or absence. 565 of them do not contain hate speech,
	while the rest of them, 433, contain.

	Ethos_Dataset_Multi_Label: which contains 8 labels for the 433 comments with hate speech content.
	These labels are violence (if it incites (1) or not (0) violence), directed_vs_general (if it is
	directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like,
	gender, race, national_origin, disability, religion and sexual_orientation.
	"""

	_URL = "https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"


	class EthosConfig(datasets.BuilderConfig):
	"""BuilderConfig for Ethos."""

	def __init__(self, variation="binary", **kwargs):
	"""Constructs an EthosDataset.

	Args:
	variation: can be binary or multilabel
	**kwargs: keyword arguments forwarded to super.
	"""
	if variation.lower() == "binary":
	self.variation = "binary"
	elif variation.lower() == "multilabel":
	self.variation = "multilabel"
	else:
	logger.warning("Wrong variation. Could be either 'binary' or 'multilabel', using 'binary' instead.")
	self.variation = "binary"
	super(EthosConfig, self).__init__(**kwargs)


	class Ethos(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIG_CLASS = EthosConfig

	BUILDER_CONFIGS = [
	EthosConfig(
	name="binary",
	version=datasets.Version("1.0.0", ""),
	description="Ethos Binary",
	variation="binary",
	),
	EthosConfig(
	name="multilabel",
	version=datasets.Version("1.0.0", ""),
	description="Ethos Multi Label",
	variation="multilabel",
	),
	]

	def _info(self):
	if self.config.variation == "binary":
	f = datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["no_hate_speech", "hate_speech"]),
	}
	)
	else:
	f = datasets.Features(
	{
	"text": datasets.Value("string"),
	"violence": datasets.ClassLabel(names=["not_violent", "violent"]),
	"directed_vs_generalized": datasets.ClassLabel(names=["generalied", "directed"]),
	"gender": datasets.ClassLabel(names=["false", "true"]),
	"race": datasets.ClassLabel(names=["false", "true"]),
	"national_origin": datasets.ClassLabel(names=["false", "true"]),
	"disability": datasets.ClassLabel(names=["false", "true"]),
	"religion": datasets.ClassLabel(names=["false", "true"]),
	"sexual_orientation": datasets.ClassLabel(names=["false", "true"]),
	}
	)
	return datasets.DatasetInfo(
	features=f,
	supervised_keys=None,
	homepage="https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset/tree/masterethos/ethos_data",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	if self.config.variation == "binary":
	url = {
	"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
	"/master/ethos/ethos_data/Ethos_Dataset_Binary.csv"
	}
	else:
	url = {
	"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
	"/master/ethos/ethos_data/Ethos_Dataset_Multi_Label.csv"
	}
	downloaded_files = dl_manager.download_and_extract(url)

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

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

	data = pd.read_csv(filepath, delimiter=";")
	if self.config.variation == "binary":

	x = data["comment"].values
	y = [1 if i >= 0.5 else 0 for i in data["isHate"].values]
	class_names = ["no_hate_speech", "hate_speech"]
	for i in range(len(x)):
	_id = i
	yield _id, {"text": x[i], "label": class_names[y[i]]}
	else:
	x = data["comment"].values
	y_temp = data.loc[:, data.columns != "comment"].values
	y = []
	for yt in y_temp:
	yi = []
	for i in yt:
	if i >= 0.5:
	yi.append(int(1))
	else:
	yi.append(int(0))
	y.append(yi)
	for i in range(len(x)):
	_id = i
	yield _id, {
	"text": x[i],
	"violence": y[i][0],
	"directed_vs_generalized": y[i][1],
	"gender": y[i][2],
	"race": y[i][3],
	"national_origin": y[i][4],
	"disability": y[i][5],
	"religion": y[i][6],
	"sexual_orientation": y[i][7],
	}