Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
Korean
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
File size: 2,844 Bytes
1cb45f8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_VERSION = "0.0.0"
_NAME = "qag_koquad"
_CITATION = """
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
"""
_DESCRIPTION = """Question & answer generation dataset based on SQuAD."""
_URL = f"https://huggingface.co/datasets/lmqg/{_NAME}/resolve/main/data/processed"
_URLS = {
'train': f'{_URL}/train.jsonl',
'test': f'{_URL}/test.jsonl',
'validation': f'{_URL}/validation.jsonl'
}
class QAGKOQuADConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(QAGKOQuADConfig, self).__init__(**kwargs)
class QAGKOQuAD(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
QAGKOQuADConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"answers": datasets.Sequence(datasets.Value("string")),
"questions": datasets.Sequence(datasets.Value("string")),
"paragraph": datasets.Value("string"),
"questions_answers": datasets.Value("string")
}
),
supervised_keys=None,
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):
_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 |