qag_tweetqa.py

import json
import datasets

logger = datasets.logging.get_logger(__name__)
_VERSION = "2.0.1"
_NAME = "qag_tweetqa"
_CITATION = """
@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
"""
_DESCRIPTION = """Question & answer generation dataset based on [TweetQA](https://huggingface.co/datasets/tweet_qa)."""
_URL = "https://huggingface.co/datasets/lmqg/qag_tweetqa/resolve/main/data/processed"
_URLS = {
    'train': f'{_URL}/train.jsonl',
    'test': f'{_URL}/test.jsonl',
    'validation': f'{_URL}/validation.jsonl'
}


class QAGTweetQAConfig(datasets.BuilderConfig):
    """BuilderConfig"""

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


class QAGTweetQA(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        QAGTweetQAConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
    ]
    
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "answers": datasets.Sequence(datasets.Value("string")),
                    "questions": datasets.Sequence(datasets.Value("string")),
                    "paragraph": datasets.Value("string"),
                    "paragraph_id": datasets.Value("string"),
                    "questions_answers": 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=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file["validation"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file["test"]}),
        ]

    def _generate_examples(self, filepath):
        _key = 0
        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