Upload wilde_emotion.py

wilde_emotion.py

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+# 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.
+"""emotion_chinese_english dataset: A multilingual emotion dataset of wilde's children's literature"""
+
+
+import datasets
+
+
+_DESCRIPTION = """\
+The emotion_chinese_english dataset is a multilingual emotion dataset annotated by language experts under a project. \
+The dataset can be used for tasks such as multilingual (Chinese and English) emotion classification and identification.
+"""
+
+
+_HOMEPAGE = "https://github.com/nana-lyj/emotion_chinese_english"
+
+_BASE_URL = "https://github.com/nana-lyj/emotion_chinese_english/tree/main/data/"
+_URLS = {
+    "train": f"{_BASE_URL}/train.tsv",
+    "dev": f"{_BASE_URL}/dev.tsv",
+    "test": f"{_BASE_URL}/test.tsv",
+}
+_LABEL_MAPPING = {0, 1, 2, 3, 4, 5, 6, 7, 8}
+
+
+class WildeEmotion(datasets.GeneratorBasedBuilder):
+    """emotion_chinese_english dataset: A multilingual emotion dataset of wilde's children's literature"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "sentence": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["joy", "sadness", "anger", "fear", "trust", "disgust", "surprise", "anticipation", "other"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            lines = f.readlines()
+            for line in lines:
+                fields = line.strip().split("\t")
+                idx, verse_text, label = fields
+                label = _LABEL_MAPPING[int(label)]
+                yield int(idx), {"id": int(idx), "sentence": verse_text, "label": label}