BilingualChildrenEmo.py

# 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 10-0-sadder.
"""BilingualChildrenEmo dataset: A multilingual emotion dataset of wilde's children's literature"""


import datasets


_DESCRIPTION = """\
The BilingualChildrenEmo 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/BilingualChildrenEmo"

_URLS = {
    "train": f"https://raw.githubusercontent.com/nana-lyj/BilingualChildrenEmo/main/data/train.tsv",
    "dev": f"https://raw.githubusercontent.com/nana-lyj/BilingualChildrenEmo/main/data/dev.tsv",
    "test": f"https://raw.githubusercontent.com/nana-lyj/BilingualChildrenEmo/main/data/test.tsv",
}

_LABEL_MAPPING = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4}


class BilingualChildrenEmo(datasets.GeneratorBasedBuilder):
    """BilingualChildrenEmo 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", "love"]),
                }
            ),
            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}