qg_korquad.py

""" python -c "from datasets import load_dataset;load_dataset('.')" """
import json
from itertools import chain
import datasets

logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[KorQuAD](https://huggingface.co/datasets/squad_kor_v1) dataset for question generation (QG) task."""
_URL = 'https://huggingface.co/datasets/asahi417/qg_korquad/raw/main/data/processed'
_URLS = {
    str(datasets.Split.TEST): [f'{_URL}/test{i:02d}.jsonl' for i in range(4)],
    str(datasets.Split.TRAIN): [f'{_URL}/train{i:02d}.jsonl' for i in range(37)],
    str(datasets.Split.VALIDATION): [f'{_URL}/validation{i:02d}.jsonl' for i in range(4)],
}


class QGKorQuADConfig(datasets.BuilderConfig):
    """BuilderConfig for SquadQG"""

    def __init__(self, **kwargs):
        """BuilderConfig for SquadQG.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(QGKorQuADConfig, self).__init__(**kwargs)


class QGKorQuAD(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "answer": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "sentence": datasets.Value("string"),
                    "paragraph": datasets.Value("string"),
                    "sentence_answer": datasets.Value("string"),
                    "paragraph_answer": datasets.Value("string"),
                    "paragraph_sentence": datasets.Value("string"),
                    "paragraph_id": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/asahi417/lm-question-generation"
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URLS)
        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]

    def _generate_examples(self, filepaths):
        _key = 0
        for filepath in filepaths:
            logger.info("generating examples from = %s", filepath)
            with open(filepath, encoding="utf-8") as f:
                _list = f.read().split('\n')
                if _list[-1] == '':
                    _list = _list[:-1]
                for i in _list:
                    data = json.loads(i)
                    yield _key, data
                    _key += 1