Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
File size: 4,368 Bytes
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"""ViquiQuAD Dataset."""
# Loading script for the ViquiQuAD dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
ViquiQuAD: an extractive QA dataset from Catalan Wikipedia (Version ViquiQuad_v.1.0.1)
[Data set]. Zenodo. http://doi.org/10.5281/zenodo.4761412
"""
_DESCRIPTION = """\
ViquiQuAD: an extractive QA dataset from Catalan Wikipedia.
This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations)
articles in the Catalan Wikipedia "Viquipèdia" (ca.wikipedia.org), and 1 to 5 questions with their
answer for each fragment. Viquipedia articles are used under CC-by-sa licence.
This dataset can be used to build extractive-QA and Language Models.
Funded by the Generalitat de Catalunya, Departament de Polítiques Digitals i Administració Pública (AINA),
MT4ALL and Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
"""
_HOMEPAGE = "https://zenodo.org/record/4562345#.YK41aqGxWUk"
_URL = "https://huggingface.co/datasets/projecte-aina/viquiquad/resolve/main/"
_TRAINING_FILE = "train.json"
_DEV_FILE = "dev.json"
_TEST_FILE = "test.json"
class ViquiQuAD(datasets.GeneratorBasedBuilder):
"""ViquiQuAD Dataset."""
VERSION = datasets.Version("1.0.1")
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": [
{
"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(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:
viquiquad = json.load(f)
for article in viquiquad["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"]]
text = qa["answers"][0]["text"]
answer_start = qa["answers"][0]["answer_start"]
# 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": [{"text": text, "answer_start": answer_start}],
}
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