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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """K-MHaS Korean Multi-label Hate Speech Dataset"""
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+
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+
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+ import csv
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{lee-etal-2022-k,
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+ title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
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+ author = "Lee, Jean and
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+ Lim, Taejun and
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+ Lee, Heejun and
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+ Jo, Bogeun and
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+ Kim, Yangsok and
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+ Yoon, Heegeun and
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+ Han, Soyeon Caren",
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+ booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
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+ month = oct,
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+ year = "2022",
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+ address = "Gyeongju, Republic of Korea",
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+ publisher = "International Committee on Computational Linguistics",
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+ url = "https://aclanthology.org/2022.coling-1.311",
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+ pages = "3530--3538",
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+ abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.",
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class.
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+ The fine-grained hate speech classes are politics, origin, physical, age, gender, religion, race, and profanity and these categories are selected in order to reflect the social and historical context.
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+ """
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+
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+ _HOMEPAGE = "https://github.com/adlnlp/K-MHaS"
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+
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+ _LICENSE = "cc-by-sa-4.0"
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+
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+ _TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/adlnlp/K-MHaS/main/data/kmhas_train.txt"
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+ _VALIDATION_DOWNLOAD_URL = "https://raw.githubusercontent.com/adlnlp/K-MHaS/main/data/kmhas_valid.txt"
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+ _TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/adlnlp/K-MHaS/main/data/kmhas_test.txt"
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+
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+ _CLASS_NAMES = [
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+ "origin",
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+ "physical",
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+ "politics",
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+ "profanity",
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+ "age",
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+ "gender",
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+ "race",
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+ "religion",
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+ "not_hate_speech"
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+ ]
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+
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+ class Kmhas(datasets.GeneratorBasedBuilder):
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+ """K-MHaS Korean Multi-label Hate Speech Dataset"""
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+
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+ VERSION = datasets.Version("1.0.0")
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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+ "text": datasets.Value("string"),
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+ "label": datasets.Sequence(datasets.ClassLabel(names=_CLASS_NAMES))
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+ }
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+ )
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+
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=features,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
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+ validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
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+ test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Generate K-MHaS Korean Multi-label Hate Speech examples"""
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+
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+ with open(filepath, 'r', encoding="utf-8") as f:
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+ lines = f.readlines()[1:]
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+
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+ for index, line in enumerate(lines):
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+ row = line.strip().split('\t')
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+ sentence = row[0]
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+ label = [int(ind) for ind in row[1].split(",")]
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+ yield index, {
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+ "text" : sentence,
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+ "label": label,
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+ }
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+