Delete kmhas_Korean_hatespeech.py

kmhas_Korean_hatespeech.py

@@ -1,116 +0,0 @@
-# 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.
-"""K-MHaS Korean Multi-label Hate Speech Dataset"""
-
-
-import csv
-
-import datasets
-from datasets.tasks import TextClassification
-
-_CITATION = """\
-@inproceedings{lee-etal-2022-k,
-    title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
-    author = "Lee, Jean  and
-      Lim, Taejun  and
-      Lee, Heejun  and
-      Jo, Bogeun  and
-      Kim, Yangsok  and
-      Yoon, Heegeun  and
-      Han, Soyeon Caren",
-    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
-    month = oct,
-    year = "2022",
-    address = "Gyeongju, Republic of Korea",
-    publisher = "International Committee on Computational Linguistics",
-    url = "https://aclanthology.org/2022.coling-1.311",
-    pages = "3530--3538",
-    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.",
-}
-"""
-
-_DESCRIPTION = """\
-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.
-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.
-"""
-
-_HOMEPAGE = "https://github.com/adlnlp/K-MHaS"
-
-_LICENSE = "cc-by-sa-4.0"
-
-_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/adlnlp/K-MHaS/main/data/kmhas_train.txt"
-_VALIDATION_DOWNLOAD_URL = "https://raw.githubusercontent.com/adlnlp/K-MHaS/main/data/kmhas_valid.txt"
-_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/adlnlp/K-MHaS/main/data/kmhas_test.txt"
-
-_CLASS_NAMES = [
-    "origin",
-    "physical",
-    "politics",
-    "profanity",
-    "age",
-    "gender",
-    "race",
-    "religion",
-    "not hate speech"
-]
-
-class Kmhas(datasets.GeneratorBasedBuilder):
-    """K-MHaS Korean Multi-label Hate Speech Dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "document": datasets.Value("string"),
-                "label": datasets.Sequence(datasets.ClassLabel(names=_CLASS_NAMES))
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="document", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate K-MHaS Korean Multi-label Hate Speech examples"""
-        
-        with open(filepath, 'r') as f:
-            lines = f.readlines()[1:]
-            
-            for index, line in enumerate(lines):
-                row = line.strip().split('\t')
-                sentence = row[0]
-                label = [int(ind) for ind in row[1].split(",")]
-                yield index, {
-                    "document" : sentence,
-                    "label": label,              
-                }
-