Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
German
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
deepset/germanquad
ArXiv:
License:
File size: 3,303 Bytes
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""" python -c "from datasets import load_dataset;load_dataset('.')" """
import json
from itertools import chain
import datasets
logger = datasets.logging.get_logger(__name__)
_VERSION = "5.0.1"
_NAME = "qg_dequad"
_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 = """[GermanSQuAD](https://huggingface.co/datasets/deepset/germanquad) dataset for question generation (QG) task."""
_URL = 'https://huggingface.co/datasets/lmqg/qg_dequad/resolve/main/data/processed'
_URLS = {
str(datasets.Split.TEST): [f'{_URL}/test{i:02d}.jsonl' for i in range(5)],
str(datasets.Split.TRAIN): [f'{_URL}/train{i:02d}.jsonl' for i in range(19)],
str(datasets.Split.VALIDATION): [f'{_URL}/validation{i:02d}.jsonl' for i in range(5)],
}
class QGDEQuADConfig(datasets.BuilderConfig):
"""BuilderConfig for SquadQG"""
def __init__(self, **kwargs):
"""BuilderConfig for SquadQG.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(QGDEQuADConfig, self).__init__(**kwargs)
class QGDEQuAD(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
QGDEQuADConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"answer": datasets.Value("string"), "paragraph_question": datasets.Value("string"),
"question": datasets.Value("string"),
"sentence": datasets.Value("string"),
"paragraph": datasets.Value("string"),
"sentence_answer": datasets.Value("string"),
"paragraph_answer": datasets.Value("string"),
"paragraph_sentence": datasets.Value("string"),
"paragraph_id": 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=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
def _generate_examples(self, filepaths):
_key = 0
for filepath in filepaths:
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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