Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
question-generation
License:
import json | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_VERSION = "2.0.1" | |
_NAME = "qag_tweetqa" | |
_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 [TweetQA](https://huggingface.co/datasets/tweet_qa).""" | |
_URL = "https://huggingface.co/datasets/lmqg/qag_tweetqa/resolve/main/data/processed" | |
_URLS = { | |
'train': f'{_URL}/train.jsonl', | |
'test': f'{_URL}/test.jsonl', | |
'validation': f'{_URL}/validation.jsonl' | |
} | |
class QAGTweetQAConfig(datasets.BuilderConfig): | |
"""BuilderConfig""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(QAGTweetQAConfig, self).__init__(**kwargs) | |
class QAGTweetQA(datasets.GeneratorBasedBuilder): | |
BUILDER_CONFIGS = [ | |
QAGTweetQAConfig(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"), | |
"paragraph_id": 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 |