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import datasets |
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import pandas as pd |
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_DATA_URLS = { |
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"train": "https://korquad.github.io/dataset/KorQuAD_v1.0_train.json", |
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"dev": "https://korquad.github.io/dataset/KorQuAD_v1.0_dev.json", |
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} |
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_VERSION = "0.0.0" |
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_LICENSE = "CC BY-ND 2.0 KR" |
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_CITATION = """\ |
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@misc{lim2019korquad1.0, |
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title={KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension}, |
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author={Seungyoung Lim, Myungji Kim, Jooyoul Lee}, |
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year={2019}, |
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eprint={1909.07005}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_HOMEPAGE = "https://korquad.github.io/KorQuad%201.0/" |
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_DESCRIPTION = """\ |
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KorQuAD 1.0 (Korean Question Answering Dataset v1.0) |
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KorQuAD 1.0 is a dataset created for Korean Machine Reading Comprehension. |
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The answers to all your questions are made up of some subareas in the corresponding Wikipedia article paragraphs. |
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It is structured in the same way as the Stanford Question Answering Dataset (SQuAD) v1.0. |
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""" |
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class KorQuADV1Config(datasets.BuilderConfig): |
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def __init__(self, data_url, features, **kwargs): |
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super().__init__(version=datasets.Version(_VERSION, ""), **kwargs) |
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self.features = features |
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self.data_url = data_url |
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class KorQuADV1(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG_NAME = "korquad" |
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BUILDER_CONFIGS = [ |
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KorQuADV1Config( |
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name="korquad", |
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data_url=_DATA_URLS, |
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features=datasets.Features( |
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{ |
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"answers": datasets.Sequence( |
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feature={ |
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"text": datasets.Value(dtype="string"), |
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"answer_start": datasets.Value(dtype="int32"), |
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}, |
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), |
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"context": datasets.Value(dtype="string"), |
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"guid": datasets.Value(dtype="string"), |
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"question": datasets.Value(dtype="string"), |
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"title": datasets.Value(dtype="string"), |
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} |
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) |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=self.config.features, |
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description=_DESCRIPTION, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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data_path = dl_manager.download(self.config.data_url) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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|
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gen_kwargs={"data_file": data_path["train"]} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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|
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gen_kwargs={"data_file": data_path["dev"]} |
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), |
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] |
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|
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def _generate_examples(self, data_file: str): |
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idx = 0 |
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korquad = pd.read_json(data_file) |
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for example in korquad["data"].tolist(): |
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paragraphs = example["paragraphs"] |
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title = example["title"] |
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for paragraph in paragraphs: |
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qas = paragraph["qas"] |
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context = paragraph["context"] |
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for qa in qas: |
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text = [answers["text"] for answers in qa["answers"]] |
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answer_start = [answers["answer_start"] for answers in qa["answers"]] |
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features = { |
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"guid": qa["id"], |
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"question": qa["question"], |
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"answers": {"text": text, "answer_start": answer_start}, |
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"context": context, |
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"title": title, |
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} |
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yield idx, features |
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idx += 1 |
