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# Loading script for the SQAC dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
bibtex
@article{DBLP:journals/corr/abs-2107-07253,
author = {Asier Guti{\'{e}}rrez{-}Fandi{\~{n}}o and
Jordi Armengol{-}Estap{\'{e}} and
Marc P{\`{a}}mies and
Joan Llop{-}Palao and
Joaqu{\'{\i}}n Silveira{-}Ocampo and
Casimiro Pio Carrino and
Aitor Gonzalez{-}Agirre and
Carme Armentano{-}Oller and
Carlos Rodr{\'{\i}}guez Penagos and
Marta Villegas},
title = {Spanish Language Models},
journal = {CoRR},
volume = {abs/2107.07253},
year = {2021},
url = {https://arxiv.org/abs/2107.07253},
archivePrefix = {arXiv},
eprint = {2107.07253},
timestamp = {Wed, 21 Jul 2021 15:55:35 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-07253.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """
This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode).
This dataset can be used to build extractive-QA.
"""
_HOMEPAGE = """"""
_URL = "https://huggingface.co/datasets/PlanTL-GOB-ES/SQAC/resolve/main/"
_TRAINING_FILE = "train.json"
_DEV_FILE = "dev.json"
_TEST_FILE = "test.json"
class SQACConfig(datasets.BuilderConfig):
""" Builder config for the SQAC dataset """
def __init__(self, **kwargs):
"""BuilderConfig for SQAC.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SQACConfig, self).__init__(**kwargs)
class SQAC(datasets.GeneratorBasedBuilder):
"""SQAC Dataset."""
BUILDER_CONFIGS = [
SQACConfig(
name="SQAC",
#version=datasets.Version("1.0.1"),
description="SQAC dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
sqac_data = json.load(f)
for article in sqac_data["data"]:
title = article.get("title", "").strip()
for paragraph in article["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
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