import json import datasets logger = datasets.logging.get_logger(__name__) _VERSION = "1.0.1" _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 = """[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