Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
Italian
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
squad_es
ArXiv:
License:
| """ python -c "from datasets import load_dataset;load_dataset('.')" """ | |
| import json | |
| from itertools import chain | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _VERSION = "5.0.1" | |
| _NAME = "qg_itquad" | |
| _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-it](https://huggingface.co/datasets/squad_it) dataset for question generation (QG) task.""" | |
| _URL = 'https://huggingface.co/datasets/lmqg/qg_itquad/resolve/main/data/processed' | |
| _URLS = { | |
| str(datasets.Split.TEST): [f'{_URL}/test{i:02d}.jsonl' for i in range(6)], | |
| str(datasets.Split.TRAIN): [f'{_URL}/train{i:02d}.jsonl' for i in range(32)], | |
| str(datasets.Split.VALIDATION): [f'{_URL}/validation{i:02d}.jsonl' for i in range(6)], | |
| } | |
| class QGITQuADConfig(datasets.BuilderConfig): | |
| """BuilderConfig for SquadQG""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for SquadQG. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(QGITQuADConfig, self).__init__(**kwargs) | |
| class QGITQuAD(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| QGITQuADConfig(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"), | |
| "paragraph_id": 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=i, gen_kwargs={"filepaths": downloaded_file[str(i)]}) | |
| for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.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 | |