awesome-japanese-nlp-classification-dataset.py

import json

import datasets

_DESCRIPTION = """\
This dataset determines whether a GitHub repository description relates to Japanese natural language processing (NLP). The labels are categorized as "Relevant (1)" and "Not Relevant (0)".
"""

_HOMEPAGE = "https://github.com/taishi-i/awesome-japanese-nlp-resources"
_CITATION = ""
_LICENSE = "other"


class NagisaStopwordsDataset(datasets.GeneratorBasedBuilder):
    """awesome-japanese-nlp-classification-dataset."""

    VERSION = datasets.Version("0.0.1")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="awesome-japanese-nlp-classification-dataset",
            version=VERSION,
            description=_DESCRIPTION,
        ),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "label": datasets.features.ClassLabel(names=["0", "1"]),
                "text": datasets.Value("string"),
                "url": datasets.Value("string"),
                "created_at": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_url = "https://huggingface.co./datasets/taishi-i/awesome-japanese-nlp-classification-dataset/raw/main"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(
                        f"{data_url}/train.json"
                    ),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(
                        f"{data_url}/val.json"
                    ),
                    "split": "val",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(
                        f"{data_url}/test.json"
                    ),
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Generates examples."""
        with open(filepath, "r") as file:
            data = json.load(file)
            for id_, row in enumerate(data):
                yield id_, {
                    "label": row["label"],
                    "text": row["text"],
                    "url": row["url"],
                    "created_at": row["created_at"],
                }