wilde_emotion.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
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"""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}