Datasets:
Tasks:
Text Classification
Sub-tasks:
intent-classification
Languages:
Polish
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
File size: 5,855 Bytes
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# 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.
"""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())}
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