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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.
# Version 5-0-pro.
"""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"
_URLS = {
"train": f"https://raw.githubusercontent.com/nana-lyj/emotion_chinese/main/data/train.tsv",
"dev": f"https://raw.githubusercontent.com/nana-lyj/emotion_chinese/main/data/dev.tsv",
"test": f"https://raw.githubusercontent.com/nana-lyj/emotion_chinese/main/data/test.tsv",
}
_LABEL_MAPPING = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8}
class emotionchineseenglish(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, sentence, label = fields
label = _LABEL_MAPPING[int(label)]
yield int(idx), {"id": int(idx), "sentence": sentence, "label": label}
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