Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
squad
ArXiv:
License:
| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _VERSION = "5.0.1" | |
| _NAME = "qg_squad" | |
| _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 = """ | |
| [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/resolve/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"), "paragraph_question": 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 | |