Datasets:
roman_urdu_hate_speech

	# 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.
	"""roman_urdu_hate_speech dataset"""


	import csv

	import datasets
	from datasets.tasks import TextClassification


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{rizwan2020hate,
	title={Hate-speech and offensive language detection in roman Urdu},
	author={Rizwan, Hammad and Shakeel, Muhammad Haroon and Karim, Asim},
	booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
	pages={2512--2522},
	year={2020}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	The Roman Urdu Hate-Speech and Offensive Language Detection (RUHSOLD) dataset is a \
	Roman Urdu dataset of tweets annotated by experts in the relevant language. \
	The authors develop the gold-standard for two sub-tasks. \
	First sub-task is based on binary labels of Hate-Offensive content and Normal content (i.e., inoffensive language). \
	These labels are self-explanatory. \
	The authors refer to this sub-task as coarse-grained classification. \
	Second sub-task defines Hate-Offensive content with \
	four labels at a granular level. \
	These labels are the most relevant for the demographic of users who converse in RU and \
	are defined in related literature. The authors refer to this sub-task as fine-grained classification. \
	The objective behind creating two gold-standards is to enable the researchers to evaluate the hate speech detection \
	approaches on both easier (coarse-grained) and challenging (fine-grained) scenarios. \
	"""

	_HOMEPAGE = "https://github.com/haroonshakeel/roman_urdu_hate_speech"

	_LICENSE = "MIT License"

	_Download_URL = "https://raw.githubusercontent.com/haroonshakeel/roman_urdu_hate_speech/main/"

	# 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 = {
	"Coarse_Grained_train": _Download_URL + "task_1_train.tsv",
	"Coarse_Grained_validation": _Download_URL + "task_1_validation.tsv",
	"Coarse_Grained_test": _Download_URL + "task_1_test.tsv",
	"Fine_Grained_train": _Download_URL + "task_2_train.tsv",
	"Fine_Grained_validation": _Download_URL + "task_2_validation.tsv",
	"Fine_Grained_test": _Download_URL + "task_2_test.tsv",
	}


	class RomanUrduHateSpeechConfig(datasets.BuilderConfig):
	"""BuilderConfig for RomanUrduHateSpeech Config"""

	def __init__(self, **kwargs):
	"""BuilderConfig for RomanUrduHateSpeech Config.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(RomanUrduHateSpeechConfig, self).__init__(**kwargs)


	class RomanUrduHateSpeech(datasets.GeneratorBasedBuilder):
	"""Roman Urdu Hate Speech dataset"""

	VERSION = datasets.Version("1.1.0")

	# 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 = [
	RomanUrduHateSpeechConfig(
	name="Coarse_Grained",
	version=VERSION,
	description="This part of my dataset covers the Coarse Grained dataset",
	),
	RomanUrduHateSpeechConfig(
	name="Fine_Grained", version=VERSION, description="This part of my dataset covers the Fine Grained dataset"
	),
	]

	DEFAULT_CONFIG_NAME = "Coarse_Grained"
	# It's not mandatory to have a default configuration. Just use one if it makes sense.

	def _info(self):

	if self.config.name == "Coarse_Grained":
	features = datasets.Features(
	{
	"tweet": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["Abusive/Offensive", "Normal"]),
	# These are the features of your dataset like images, labels ...
	}
	)
	if self.config.name == "Fine_Grained":
	features = datasets.Features(
	{
	"tweet": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=["Abusive/Offensive", "Normal", "Religious Hate", "Sexism", "Profane/Untargeted"]
	),
	# These are the features of your dataset like images, labels ...
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	task_templates=[TextClassification(text_column="tweet", label_column="label")],
	)

	def _split_generators(self, dl_manager):

	urls_train = _URLS[self.config.name + "_train"]

	urls_validate = _URLS[self.config.name + "_validation"]

	urls_test = _URLS[self.config.name + "_test"]

	data_dir_train = dl_manager.download_and_extract(urls_train)

	data_dir_validate = dl_manager.download_and_extract(urls_validate)

	data_dir_test = dl_manager.download_and_extract(urls_test)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_train,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_test,
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_validate,
	"split": "dev",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):

	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as tsv_file:
	tsv_reader = csv.reader(tsv_file, quotechar="\|", delimiter="\t", quoting=csv.QUOTE_ALL)
	for key, row in enumerate(tsv_reader):
	if key == 0:
	continue
	if self.config.name == "Coarse_Grained":
	tweet, label = row
	label = int(label)
	yield key, {
	"tweet": tweet,
	"label": None if split == "test" else label,
	}
	if self.config.name == "Fine_Grained":
	tweet, label = row
	label = int(label)
	yield key, {
	"tweet": tweet,
	"label": None if split == "test" else label,
	}
	# Yields examples as (key, example) tuples