Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
subjqa
ArXiv:
License:
File size: 4,064 Bytes
3085290 4cf327a ccb88a4 5362bd4 ccb88a4 3085290 4cf327a 3085290 ccb88a4 3085290 a34d09d 3085290 4cf327a 3085290 22d4f68 3085290 ccb88a4 5683f74 be20096 5683f74 3085290 5683f74 3085290 |
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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_VERSION = "5.0.1"
_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 = """[SubjQA](https://github.com/megagonlabs/SubjQA) dataset for question generation (QG) task."""
_URL = 'https://huggingface.co/datasets/lmqg/qg_subjqa/resolve/main/data/processed'
_DOMAINS = ["books", "electronics", "grocery", "movies", "restaurants", "tripadvisor"]
class QGSubjQAConfig(datasets.BuilderConfig):
"""BuilderConfig for SquadQG"""
def __init__(self, **kwargs):
"""BuilderConfig for SquadQG.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(QGSubjQAConfig, self).__init__(**kwargs)
class QGSubjQA(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [QGSubjQAConfig(name="all", version=datasets.Version(_VERSION), description="SubjQA from all domain of `{}`.")]
BUILDER_CONFIGS += [QGSubjQAConfig(name=i, version=datasets.Version(_VERSION), description=f"SubjQA from domain of `{i}`.") for i in _DOMAINS]
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"),
"question_subj_level": datasets.Value("int32"),
"answer_subj_level": datasets.Value("int32"),
"domain": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/asahi417/lm-question-generation"
)
def _split_generators(self, dl_manager):
if self.config.name == 'all':
downloaded_file = dl_manager.download_and_extract({
'train': [f"{_URL}/{i}.train.jsonl" for i in _DOMAINS],
'dev': [f"{_URL}/{i}.dev.jsonl" for i in _DOMAINS],
'test': [f"{_URL}/{i}.test.jsonl" for i in _DOMAINS]
})
else:
downloaded_file = dl_manager.download_and_extract({
'train': [f"{_URL}/{self.config.name}.train.jsonl"],
'dev': [f"{_URL}/{self.config.name}.dev.jsonl"],
'test': [f"{_URL}/{self.config.name}.test.jsonl"]
})
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_file["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": downloaded_file["dev"]}),
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
|