import json import datasets logger = datasets.logging.get_logger(__name__) _VERSION = "1.0.0" _NAME = "qg_squad" _CITATION = """ TBA """ _DESCRIPTION = """ [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11). """ _URL = 'https://huggingface.co/datasets/lmqg/qg_squad/raw/main/data/processed' _URLS = { 'train': ['{}/train{:02d}.jsonl'.format(_URL, i) for i in range(23)], 'test': ['{}/test{:02d}.jsonl'.format(_URL, i) for i in range(4)], 'validation': ['{}/dev{:02d}.jsonl'.format(_URL, i) for i in range(4)] } class QGSquadConfig(datasets.BuilderConfig): """BuilderConfig for SquadQG""" def __init__(self, **kwargs): """BuilderConfig for SquadQG. Args: **kwargs: keyword arguments forwarded to super. """ super(QGSquadConfig, self).__init__(**kwargs) class QGSquad(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ QGSquadConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION), ] 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") } ), 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={"filepaths": downloaded_file["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": downloaded_file["validation"]}), 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