Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
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 | |