Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
Japanese
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
File size: 2,927 Bytes
0444c95 6821adc 0444c95 |
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 |
import json
import datasets
from datasets import load_dataset
from datasets.tasks import Summarization
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """
[JaQuAD](https://github.com/SkelterLabsInc/JaQuAD) dataset for question generation (QG) task. The test set of the original
data is not publicly released, so we randomly sampled test questions from the training set.
"""
_URL = 'https://huggingface.co/datasets/asahi417/qg_jaquad/raw/main/data/processed'
_URLS = {
'train': ['{}/train{:02d}.jsonl'.format(_URL, i) for i in range(28)],
'test': ['{}/test{:02d}.jsonl'.format(_URL, i) for i in range(4)],
'validation': ['{}/validation{:02d}.jsonl'.format(_URL, i) for i in range(4)]
}
class QGJaquadConfig(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 QGJaquad(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={"filepaths": downloaded_file["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": downloaded_file["validation"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": downloaded_file["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
|