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