Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
Japanese
Size:
10K - 100K
ArXiv:
License:
Change documentation and remove unused field
Browse files
JaQuAD.py
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import json
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import os
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import datasets
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_CITATION = """\
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@article{SkelterLabsInc:JaQuAD,
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title = {{JaQuAD}: Japanese Question Answering Dataset for Machine
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Reading Comprehension},
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author = {Byunghoon
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Kyuhong
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Kyungwon
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Seongjin
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year = {2022},
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}
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_DESCRIPTION =
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JaQuAD
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_URL = "https://huggingface.co/datasets/SkelterLabsInc/JaQuAD/raw/main/data/"
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class JaQuAD(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.1.0")
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def _info(self):
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features = datasets.Features({
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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def _split_generators(self, dl_manager):
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urls_to_download = {
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}
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downloaded_files = dl_manager.download_and_extract(urls_to_download)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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),
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]
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def _generate_examples(self, filepaths):
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for filename in filepaths:
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with open(filename, encoding='utf-8') as
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jaquad = json.load(
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for article in jaquad['data']:
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title = article.get('title', '').strip()
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for paragraph in article['paragraphs']:
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context = paragraph['context'].strip()
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for qa in paragraph['qas']:
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qa_id = qa[
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question = qa[
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question_type = qa[
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answer_starts = [answer["answer_start"] for answer in qa["answers"]]
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answer_texts = [answer["text"].strip() for answer in qa["answers"]]
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answer_types = [answer["answer_type"] for answer in qa["answers"]]
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yield qa_id, {
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},
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}
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'''Dataset loading script for JaQuAD.
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We refer to https://huggingface.co/datasets/squad_v2/blob/main/squad_v2.py
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'''
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import json
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import os
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import datasets
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_CITATION = '''
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@article{SkelterLabsInc:JaQuAD,
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title = {{JaQuAD}: Japanese Question Answering Dataset for Machine
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Reading Comprehension},
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author = {Byunghoon So and
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Kyuhong Byun and
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Kyungwon Kang and
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Seongjin Cho},
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year = {2022},
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}
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'''
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_DESCRIPTION = '''Japanese Question Answering Dataset (JaQuAD), released in
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2022, is a human-annotated dataset created for Japanese Machine Reading
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Comprehension. JaQuAD is developed to provide a SQuAD-like QA dataset in
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Japanese. JaQuAD contains 39,696 question-answer pairs. Questions and answers
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are manually curated by human annotators. Contexts are collected from Japanese
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Wikipedia articles.
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'''
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_LICENSE = 'CC BY-SA 3.0'
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_HOMEPAGE = 'https://skelterlabs.com/en/'
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_URL = 'https://huggingface.co/datasets/SkelterLabsInc/JaQuAD/raw/main/data/'
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class JaQuAD(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version('0.1.0')
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def _info(self):
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features = datasets.Features({
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'id': datasets.Value('string'),
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'title': datasets.Value('string'),
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'context': datasets.Value('string'),
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'question': datasets.Value('string'),
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'question_type': datasets.Value('string'),
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'answers':
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datasets.features.Sequence({
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'text': datasets.Value('string'),
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'answer_start': datasets.Value('int32'),
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'answer_type': datasets.Value('string'),
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}),
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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def _split_generators(self, dl_manager):
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urls_to_download = {
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'train': [
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os.path.join(_URL, f'train/jaquad_train_{i:04d}.json')
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for i in range(30)
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],
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'dev': [
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os.path.join(_URL, f'dev/jaquad_dev_{i:04d}.json')
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for i in range(4)
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],
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}
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downloaded_files = dl_manager.download_and_extract(urls_to_download)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={'filepaths': downloaded_files['train']},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={'filepaths': downloaded_files['dev']},
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),
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]
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def _generate_examples(self, filepaths):
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for filename in filepaths:
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with open(filename, encoding='utf-8') as ifile:
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jaquad = json.load(ifile)
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for article in jaquad['data']:
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title = article.get('title', '').strip()
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for paragraph in article['paragraphs']:
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context = paragraph['context'].strip()
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for qa in paragraph['qas']:
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qa_id = qa['id']
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question = qa['question'].strip()
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question_type = qa['question_type']
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answer_starts = [
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answer['answer_start']
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for answer in qa['answers']
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]
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answer_texts = [
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answer['text'].strip()
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for answer in qa['answers']
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]
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answer_types = [
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answer['answer_type']
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for answer in qa['answers']
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]
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yield qa_id, {
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'title': title,
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'context': context,
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'question': question,
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'question_type': question_type,
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'id': qa_id,
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'answers': {
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'text': answer_texts,
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'answer_start': answer_starts,
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'answer_type': answer_types,
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},
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}
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README.md
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"question_type": "Multiple sentence reasoning",
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"answers": {
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"text": "イタセンパラ",
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"
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"answer_type": "Object",
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},
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},
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- `question_type`: a `string` feature.
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- `answers`: a dictionary feature containing:
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- `text`: a `string` feature.
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- `
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- `answer_type`: a `string` feature.
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### Data Splitting
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"question_type": "Multiple sentence reasoning",
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"answers": {
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"text": "イタセンパラ",
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"answer_start": 0,
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"answer_type": "Object",
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},
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},
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- `question_type`: a `string` feature.
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- `answers`: a dictionary feature containing:
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- `text`: a `string` feature.
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- `answer_start`: a `int32` feature.
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- `answer_type`: a `string` feature.
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### Data Splitting
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