Datasets:
cdt

Task Categories: text-classification
Languages: Polish
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: other
Annotations Creators: expert-generated
Source Datasets: original
cdt / cdt.py
# 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 detection task"""
import csv
import os
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@article{ptaszynski2019results,
title={Results of the PolEval 2019 Shared Task 6: First Dataset and Open Shared Task for Automatic Cyberbullying Detection in Polish Twitter},
author={Ptaszynski, Michal and Pieciukiewicz, Agata and Dybala, Pawel},
journal={Proceedings of the PolEval 2019 Workshop},
publisher={Institute of Computer Science, Polish Academy of Sciences},
pages={89},
year={2019}
}
"""
_DESCRIPTION = """\
The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.
"""
_HOMEPAGE = "https://github.com/ptaszynski/cyberbullying-Polish"
_LICENSE = "BSD 3-Clause"
_URLs = "https://klejbenchmark.com/static/data/klej_cbd.zip"
class Cdt(datasets.GeneratorBasedBuilder):
"""CyberbullyingDetectionTask"""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"target": datasets.ClassLabel(names=["0", "1"]),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[TextClassification(text_column="sentence", label_column="target")],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.tsv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(reader):
yield id_, {
"sentence": row["sentence"],
"target": -1 if split == "test" else row["target"],
}