Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Japanese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
| '''Dataset loading script for JaQuAD. | |
| We refer to https://huggingface.co/datasets/squad_v2/blob/main/squad_v2.py | |
| ''' | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = ''' | |
| @article{SkelterLabsInc:JaQuAD, | |
| title = {{JaQuAD}: Japanese Question Answering Dataset for Machine | |
| Reading Comprehension}, | |
| author = {Byunghoon So and | |
| Kyuhong Byun and | |
| Kyungwon Kang and | |
| Seongjin Cho}, | |
| year = {2022}, | |
| } | |
| ''' | |
| _DESCRIPTION = '''Japanese Question Answering Dataset (JaQuAD), released in | |
| 2022, is a human-annotated dataset created for Japanese Machine Reading | |
| Comprehension. JaQuAD is developed to provide a SQuAD-like QA dataset in | |
| Japanese. JaQuAD contains 39,696 question-answer pairs. Questions and answers | |
| are manually curated by human annotators. Contexts are collected from Japanese | |
| Wikipedia articles. | |
| ''' | |
| _LICENSE = 'CC BY-SA 3.0' | |
| _HOMEPAGE = 'https://skelterlabs.com/en/' | |
| _URL = 'https://huggingface.co/datasets/SkelterLabsInc/JaQuAD/raw/main/data/' | |
| class JaQuAD(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version('0.1.0') | |
| def _info(self): | |
| features = datasets.Features({ | |
| 'id': datasets.Value('string'), | |
| 'title': datasets.Value('string'), | |
| 'context': datasets.Value('string'), | |
| 'question': datasets.Value('string'), | |
| 'question_type': datasets.Value('string'), | |
| 'answers': | |
| datasets.features.Sequence({ | |
| 'text': datasets.Value('string'), | |
| 'answer_start': datasets.Value('int32'), | |
| 'answer_type': datasets.Value('string'), | |
| }), | |
| }) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| urls_to_download = { | |
| 'train': [ | |
| os.path.join(_URL, f'train/jaquad_train_{i:04d}.json') | |
| for i in range(30) | |
| ], | |
| 'dev': [ | |
| os.path.join(_URL, f'dev/jaquad_dev_{i:04d}.json') | |
| for i in range(4) | |
| ], | |
| } | |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={'filepaths': downloaded_files['train']}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={'filepaths': downloaded_files['dev']}, | |
| ), | |
| ] | |
| def _generate_examples(self, filepaths): | |
| for filename in filepaths: | |
| with open(filename, encoding='utf-8') as ifile: | |
| jaquad = json.load(ifile) | |
| for article in jaquad['data']: | |
| title = article.get('title', '').strip() | |
| for paragraph in article['paragraphs']: | |
| context = paragraph['context'].strip() | |
| for qa in paragraph['qas']: | |
| qa_id = qa['id'] | |
| question = qa['question'].strip() | |
| question_type = qa['question_type'] | |
| answer_starts = [ | |
| answer['answer_start'] | |
| for answer in qa['answers'] | |
| ] | |
| answer_texts = [ | |
| answer['text'].strip() | |
| for answer in qa['answers'] | |
| ] | |
| answer_types = [ | |
| answer['answer_type'] | |
| for answer in qa['answers'] | |
| ] | |
| yield qa_id, { | |
| 'title': title, | |
| 'context': context, | |
| 'question': question, | |
| 'question_type': question_type, | |
| 'id': qa_id, | |
| 'answers': { | |
| 'text': answer_texts, | |
| 'answer_start': answer_starts, | |
| 'answer_type': answer_types, | |
| }, | |
| } | |