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:
| 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="" | |
| _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 f: | |
| jaquad = json.load(f) | |
| 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"]] | |
| assert len(answer_starts) == len(answer_texts) == len(answer_types) == 1 | |
| 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, | |
| }, | |
| } | |