import json import datasets logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """[SubjQA](https://github.com/megagonlabs/SubjQA) dataset for question generation (QG) task.""" _URL = 'https://huggingface.co/datasets/asahi417/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="default", description="SubjQA from all domain of `{}`.")] BUILDER_CONFIGS += [QGSubjQAConfig(name=i, 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 == 'default': 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