qg_subjqa.py

import json
import datasets

logger = datasets.logging.get_logger(__name__)
_VERSION = "1.0.1"
_CITATION = """
TBA
"""
_DESCRIPTION = """[SubjQA](https://github.com/megagonlabs/SubjQA) dataset for question generation (QG) task."""
_URL = 'https://huggingface.co/datasets/lmqg/qg_subjqa/raw/main/data/processed'
_DOMAINS = ["books", "electronics", "grocery", "movies", "restaurants", "tripadvisor"]


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

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


class QGSubjQA(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [QGSubjQAConfig(name="all", version=datasets.Version(_VERSION), description="SubjQA from all domain of `{}`.")]
    BUILDER_CONFIGS += [QGSubjQAConfig(name=i, version=datasets.Version(_VERSION), description=f"SubjQA from domain of `{i}`.") for i in _DOMAINS]

    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"),
                    "question_subj_level": datasets.Value("int32"),
                    "answer_subj_level": datasets.Value("int32"),
                    "domain": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/asahi417/lm-question-generation"
        )

    def _split_generators(self, dl_manager):
        if self.config.name == 'all':
            downloaded_file = dl_manager.download_and_extract({
                'train': [f"{_URL}/{i}.train.jsonl" for i in _DOMAINS],
                'dev': [f"{_URL}/{i}.dev.jsonl" for i in _DOMAINS],
                'test': [f"{_URL}/{i}.test.jsonl" for i in _DOMAINS]
            })
        else:
            downloaded_file = dl_manager.download_and_extract({
                'train': [f"{_URL}/{self.config.name}.train.jsonl"],
                'dev': [f"{_URL}/{self.config.name}.dev.jsonl"],
                'test': [f"{_URL}/{self.config.name}.test.jsonl"]
            })
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_file["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": downloaded_file["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": downloaded_file["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