Datasets:
told-br

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""Toxic/Abusive Tweets Multilabel Classification Dataset for Brazilian Portuguese."""


	import os

	import pandas as pd

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{DBLP:journals/corr/abs-2010-04543,
	author = {Joao Augusto Leite and
	Diego F. Silva and
	Kalina Bontcheva and
	Carolina Scarton},
	title = {Toxic Language Detection in Social Media for Brazilian Portuguese:
	New Dataset and Multilingual Analysis},
	journal = {CoRR},
	volume = {abs/2010.04543},
	year = {2020},
	url = {https://arxiv.org/abs/2010.04543},
	eprinttype = {arXiv},
	eprint = {2010.04543},
	timestamp = {Tue, 15 Dec 2020 16:10:16 +0100},
	biburl = {https://dblp.org/rec/journals/corr/abs-2010-04543.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """\
	ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced
	by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming
	to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender).
	Each tweet was labeled by three annotators in 6 possible categories:
	LGBTQ+phobia,Xenophobia, Obscene, Insult, Misogyny and Racism.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://github.com/JAugusto97/ToLD-Br"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "https://github.com/JAugusto97/ToLD-Br/blob/main/LICENSE_ToLD-Br.txt "

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)

	_URLS = {
	"multilabel": "https://raw.githubusercontent.com/JAugusto97/ToLD-Br/main/ToLD-BR.csv",
	"binary": "https://github.com/JAugusto97/ToLD-Br/raw/main/experiments/data/1annotator.zip",
	}


	class ToldBr(datasets.GeneratorBasedBuilder):
	"""Toxic/Abusive Tweets Classification Dataset for Brazilian Portuguese."""

	VERSION = datasets.Version("1.0.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="multilabel",
	version=VERSION,
	description="""
	Full multilabel dataset with target values ranging
	from 0 to 3 representing the votes from each annotator.
	""",
	),
	datasets.BuilderConfig(
	name="binary",
	version=VERSION,
	description="""
	Binary classification dataset version separated in train, dev and test test.
	A text is considered toxic if at least one of the multilabel classes were labeled
	by at least one annotator.
	""",
	),
	]

	DEFAULT_CONFIG_NAME = "binary"

	def _info(self):
	if self.config.name == "binary":
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["not-toxic", "toxic"]),
	}
	)
	else:
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"homophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
	"obscene": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
	"insult": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
	"racism": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
	"misogyny": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
	"xenophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
	)

	def _split_generators(self, dl_manager):
	urls = _URLS[self.config.name]
	data_dir = dl_manager.download_and_extract(urls)
	if self.config.name == "binary":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_train_1annotator.csv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_test_1annotator.csv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_validation_1annotator.csv")},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir),
	},
	)
	]

	def _generate_examples(self, filepath):
	df = pd.read_csv(filepath, engine="python")
	for key, row in enumerate(df.itertuples()):
	if self.config.name == "multilabel":
	yield key, {
	"text": row.text,
	"homophobia": int(float(row.homophobia)),
	"obscene": int(float(row.obscene)),
	"insult": int(float(row.insult)),
	"racism": int(float(row.racism)),
	"misogyny": int(float(row.misogyny)),
	"xenophobia": int(float(row.xenophobia)),
	}
	else:
	yield key, {"text": row.text, "label": int(row.toxic)}