Create kote.py

kote.py

@@ -0,0 +1,126 @@
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+
+import csv
+import os
+
+import datasets
+
+_CITATION = """\
+not yet
+"""
+
+_DESCRIPTION = """\
+50k Korean online comments labeled for 44 emotion categories.
+"""
+
+_HOMEPAGE = "https://github.com/searle-j/KOTE"
+
+_LICENSE = "MIT License"
+
+_BASE_URL = "https://raw.githubusercontent.com/searle-j/KOTE/main/"
+
+_LABELS = [
+'불평/불만',
+ '환영/호의',
+ '감동/감탄',
+ '지긋지긋',
+ '고마움',
+ '슬픔',
+ '화남/분노',
+ '존경',
+ '기대감',
+ '우쭐댐/무시함',
+ '안타까움/실망',
+ '비장함',
+ '의심/불신',
+ '뿌듯함',
+ '편안/쾌적',
+ '신기함/관심',
+ '아껴주는',
+ '부끄러움',
+ '공포/무서움',
+ '절망',
+ '한심함',
+ '역겨움/징그러움',
+ '짜증',
+ '어이없음',
+ '없음',
+ '패배/자기혐오',
+ '귀찮음',
+ '힘듦/지침',
+ '즐거움/신남',
+ '깨달음',
+ '죄책감',
+ '증오/혐오',
+ '흐뭇함(귀여움/예쁨)',
+ '당황/난처',
+ '경악',
+ '부담/안_내킴',
+ '서러움',
+ '재미없음',
+ '불쌍함/연민',
+ '놀람',
+ '행복',
+ '불안/걱정',
+ '기쁨',
+ '안심/신뢰'
+]
+
+class KOTEConfig(datasets.BuilderConfig):
+    @property
+    def features(self):
+        if self.name == "dichotomized":
+            return {
+                "ID": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "labels": datasets.Sequence(datasets.ClassLabel(names=_LABELS)),
+            }
+
+class KOTE(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [KOTEConfig(name="dichotomized")]
+    BUILDER_CONFIG_CLASS = KOTEConfig
+    DEFAULT_CONFIG_NAME = "dichotomized"
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(self.config.features),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    
+    
+    def _split_generators(self, dl_manager):
+        if self.config.name=="dichotomized":
+            train_path = dl_manager.download_and_extract(os.path.join(_BASE_URL, "train.tsv"))
+            test_path = dl_manager.download_and_extract(os.path.join(_BASE_URL, "test.tsv"))
+            val_path = dl_manager.download_and_extract(os.path.join(_BASE_URL, "val.tsv"))
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": [train_path],}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": [test_path],}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": [val_path],}),
+            ]
+            
+    def _generate_examples(self, filepaths):
+        if self.config.name=="dichotomized":
+            for filepath in filepaths:
+                with open(filepath, mode="r", encoding="utf-8") as f:
+                    reader = csv.DictReader(f, delimiter="\t", fieldnames=list(self.config.features.keys()))
+                    for idx, row in enumerate(reader):
+                        row["labels"] = [int(lab) for lab in row["labels"].split(",")]
+                        yield idx, row