Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
File size: 2,769 Bytes
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import datasets
from datasets import load_dataset
from datasets.tasks import Summarization
_DESCRIPTION = """
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split
of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is
compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11).
"""
_URL = 'https://huggingface.co/datasets/asahi417/squad_qg/data/processed'
_URLS = {
'train': ['{}/train{:02d}.jsonl'.format(_URL, i) for i in range(23)],
'test': ['{}/test{:02d}.jsonl'.format(_URL, i) for i in range(4)],
'validation': ['{}/dev{:02d}.jsonl'.format(_URL, i) for i in range(4)]
}
class SquadQGConfig(datasets.BuilderConfig):
"""BuilderConfig for SquadQG"""
def __init__(self, **kwargs):
"""BuilderConfig for SquadQG.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadQGConfig, self).__init__(**kwargs)
class SquadQG(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"answer": datasets.Value("string"),
"question": datasets.Value("string"),
"sentence": datasets.Value("string"),
"passage": datasets.Value("string"),
"sentence_answer": datasets.Value("string"),
"passage_answer": datasets.Value("string"),
"passage_sentence": datasets.Value("string")
}
),
supervised_keys=None,
task_templates=[
Summarization(task='question generation', text_column="passage_answer", summary_column='question')
],
homepage="https://github.com/asahi417/lm-question-generation"
)
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file["validation"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file["test"]}),
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
for _id, i in enumerate(f.read().split('\n')):
data = json.loads(i)
yield _id, data
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