Upload kmhas_korean_hate_speech.py

kmhas_korean_hate_speech.py

@@ -0,0 +1,115 @@
+# 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,              
+                }
+