Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
json
Sub-tasks:
extractive-qa
Languages:
Catalan
Size:
1K - 10K
ArXiv:
License:
File size: 4,280 Bytes
4effc5f 5806f45 4effc5f 5806f45 4effc5f 5806f45 4effc5f 5806f45 4effc5f 5806f45 4effc5f 5806f45 4effc5f 5806f45 4effc5f 560559b 4effc5f 5806f45 4effc5f 478d9f6 4effc5f 32c9625 4effc5f 8050dad 4effc5f 5806f45 4effc5f 5806f45 4effc5f |
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 94 95 96 97 98 99 100 101 102 103 104 105 106 |
# Loading script for the VilaQuAD dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
VilaQuAD: an extractive QA dataset for catalan, from Vilaweb newswire text
[Data set]. Zenodo. https://doi.org/10.5281/zenodo.4562337
"""
_DESCRIPTION = """\
This dataset contains 2095 of Catalan language news articles along with 1 to 5 questions referring to each fragment (or context).
VilaQuad articles are extracted from the daily Vilaweb (www.vilaweb.cat) and used under CC-by-nc-sa-nd (https://creativecommons.org/licenses/by-nc-nd/3.0/deed.ca) 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://doi.org/10.5281/zenodo.4562337"
_URL = "https://huggingface.co/datasets/projecte-aina/vilaquad/resolve/main/"
_TRAINING_FILE = "train.json"
_DEV_FILE = "dev.json"
_TEST_FILE = "test.json"
class VilaQuAD(datasets.GeneratorBasedBuilder):
"""VilaQuAD 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:
vilaquad = json.load(f)
for article in vilaquad["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.
text = qa["answers"][0]["text"]
answer_start = qa["answers"][0]["answer_start"]
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": [{"text": text, "answer_start": answer_start}],
}
|