Datasets:
poleval2019_cyberbullying

	# coding=utf-8
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	"""Cyberbullying Classification Dataset in Polish"""


	import os

	import datasets


	_DESCRIPTION = """\
	In Task 6-1, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets
	that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and
	related phenomena.

	In Task 6-2, the participants shall distinguish between three classes of tweets: 0 (non-harmful),
	1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech,
	some of them even putting those two phenomena in the same group. The specific conditions on which we based
	our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research
	will be summarized in an introductory paper for the task, however, the main and definitive condition to 1
	distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying),
	or a public person/entity/large group (hate-speech).
	"""

	_HOMEPAGE = "http://2019.poleval.pl/index.php/tasks/task6"

	_URL_TRAIN_TASK1 = "http://2019.poleval.pl/task6/task_6-1.zip"
	_URL_TRAIN_TASK2 = "http://2019.poleval.pl/task6/task_6-2.zip"
	_URL_TEST = "http://2019.poleval.pl/task6/task6_test.zip"

	_CITATION = """\
	@proceedings{ogr:kob:19:poleval,
	editor = {Maciej Ogrodniczuk and Łukasz Kobyliński},
	title = {{Proceedings of the PolEval 2019 Workshop}},
	year = {2019},
	address = {Warsaw, Poland},
	publisher = {Institute of Computer Science, Polish Academy of Sciences},
	url = {http://2019.poleval.pl/files/poleval2019.pdf},
	isbn = "978-83-63159-28-3"}
	}
	"""


	class Poleval2019CyberBullyingConfig(datasets.BuilderConfig):
	"""BuilderConfig for Poleval2019CyberBullying."""

	def __init__(
	self,
	text_features,
	label_classes,
	**kwargs,
	):
	super(Poleval2019CyberBullyingConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
	self.text_features = text_features
	self.label_classes = label_classes


	class Poleval2019CyberBullying(datasets.GeneratorBasedBuilder):
	"""Cyberbullying Classification Dataset in Polish"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	Poleval2019CyberBullyingConfig(
	name="task01",
	text_features=["text"],
	label_classes=["0", "1"],
	),
	Poleval2019CyberBullyingConfig(
	name="task02",
	text_features=["text"],
	label_classes=["0", "1", "2"],
	),
	]

	def _info(self):

	features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
	features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=("text", "label"),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	if self.config.name == "task01":
	train_path = dl_manager.download_and_extract(_URL_TRAIN_TASK1)

	if self.config.name == "task02":
	train_path = dl_manager.download_and_extract(_URL_TRAIN_TASK2)

	data_dir_test = dl_manager.download_and_extract(_URL_TEST)

	if self.config.name == "task01":
	test_path = os.path.join(data_dir_test, "Task6", "task 01")

	if self.config.name == "task02":
	test_path = os.path.join(data_dir_test, "Task6", "task 02")

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

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

	if split == "train":
	text_path = os.path.join(filepath, "training_set_clean_only_text.txt")
	label_path = os.path.join(filepath, "training_set_clean_only_tags.txt")

	if split == "test":
	if self.config.name == "task01":
	text_path = os.path.join(filepath, "test_set_clean_only_text.txt")
	label_path = os.path.join(filepath, "test_set_clean_only_tags.txt")
	if self.config.name == "task02":
	text_path = os.path.join(filepath, "test_set_only_text.txt")
	label_path = os.path.join(filepath, "test_set_only_tags.txt")

	with open(text_path, encoding="utf-8") as text_file:
	with open(label_path, encoding="utf-8") as label_file:
	for id_, (text, label) in enumerate(zip(text_file, label_file)):
	yield id_, {"text": text.strip(), "label": int(label.strip())}