Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<
Source Datasets:
extended|wikipedia
ArXiv:
Tags:
License:
File size: 3,148 Bytes
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import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_VERSION = "0.0.2"
_NAME = "qa_squad"
_DESCRIPTION = """SQuAD with the train/validation/test split used in SQuAD QG"""
_CITATION = """
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
"""
_BASE_URL = "https://huggingface.co/datasets/lmqg/qa_squad/resolve/main/datasets"
_URLS = {k: f'{_BASE_URL}/{k}.jsonl' for k in
[str(datasets.Split.TEST), str(datasets.Split.TRAIN), str(datasets.Split.VALIDATION)]}
class QASquadConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(QASquadConfig, self).__init__(**kwargs)
class QASquad(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
QASquadConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
}
),
supervised_keys=None,
homepage="https://github.com/asahi417/lm-question-generation",
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS)
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[str(i)]})
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
_key = 0
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
_list = f.read().split('\n')
if _list[-1] == '':
_list = _list[:-1]
for i in _list:
data = json.loads(i)
yield _key, data
_key += 1
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