Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<
Source Datasets:
extended|wikipedia
ArXiv:
Tags:
License:
| import json | |
| import datasets | |
| from datasets.tasks import QuestionAnsweringExtractive | |
| logger = datasets.logging.get_logger(__name__) | |
| _VERSION = "0.0.2" | |
| _NAME = "qa_squad" | |
| _DESCRIPTION = """SQuAD with the train/validation/test split used in SQuAD QG""" | |
| _CITATION = """ | |
| @article{2016arXiv160605250R, | |
| author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, | |
| Konstantin and {Liang}, Percy}, | |
| title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", | |
| journal = {arXiv e-prints}, | |
| year = 2016, | |
| eid = {arXiv:1606.05250}, | |
| pages = {arXiv:1606.05250}, | |
| archivePrefix = {arXiv}, | |
| eprint = {1606.05250}, | |
| } | |
| """ | |
| _BASE_URL = "https://huggingface.co/datasets/lmqg/qa_squad/resolve/main/datasets" | |
| _URLS = {k: f'{_BASE_URL}/{k}.jsonl' for k in | |
| [str(datasets.Split.TEST), str(datasets.Split.TRAIN), str(datasets.Split.VALIDATION)]} | |
| class QASquadConfig(datasets.BuilderConfig): | |
| """BuilderConfig""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(QASquadConfig, self).__init__(**kwargs) | |
| class QASquad(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| QASquadConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "context": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answers": datasets.features.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| } | |
| ), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="https://github.com/asahi417/lm-question-generation", | |
| task_templates=[ | |
| QuestionAnsweringExtractive( | |
| question_column="question", context_column="context", answers_column="answers" | |
| ) | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_file = dl_manager.download_and_extract(_URLS) | |
| return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[str(i)]}) | |
| for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| _key = 0 | |
| 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 | |