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from datasets.utils import version
"""TODO(squad_es): Add a description here."""
import json
import datasets
# TODO(squad_es): BibTeX citation
_CITATION = """\
@article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.05200v1},
pages = {arXiv:1912.05200v1},
archivePrefix = {arXiv},
eprint = {1912.05200v2},
}
"""
# TODO(squad_es_v1):
_DESCRIPTION = """\
automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish
"""
_URL = "https://raw.githubusercontent.com/EvelynQuevedo/becas/main/"
print(_URL)
_URLS_V1 = {
"train": _URL + "datos/train2.json",
"validation": _URL + "datos/validation2.json",
}
print(_URLS_V1)
#_URLS_V2 = {
# "train": _URL + "SQuAD-es-v2.0/train-v2.0-es.json",
#"dev": _URL + "SQuAD-es-v2.0/dev-v2.0-es.json",
#}
class SquadEsConfig(datasets.BuilderConfig):
"""BuilderConfig for SQUADEsV2."""
def __init__(self, **kwargs):
"""BuilderConfig for SQUADEsV2.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadEsConfig, self).__init__(**kwargs)
class SquadEs(datasets.GeneratorBasedBuilder):
"""TODO(squad_es): Short description of my dataset."""
# TODO(squad_es): Set up version.
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
SquadEsConfig(
name="v1.1.0",
version=datasets.Version("1.1.0", ""),
description="Plain text Spanish squad version 1",
),
#SquadEsConfig(
# name="v2.0.0",
# version=datasets.Version("2.0.0", ""),
# description="Plain text Spanish squad version 2",
#),
]
def _info(self):
# TODO(squad_es): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
# These are the features of your dataset like images, labels ...
"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"),
"answer_end": datasets.Value("int32"),
}
),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/EvelynQuevedo/becas",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_es): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
if self.config.name == "v1.1.0":
dl_dir = dl_manager.download_and_extract(_URLS_V1)
print(dl_dir)
# elif self.config.name == "v2.0.0":
# dl_dir = dl_manager.download_and_extract(_URLS_V2)
else:
raise Exception("version does not match any existing one")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["validation"]},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(squad_es): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for example in data["data"]:
title = example.get("title", "").strip()
for paragraph in example["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"]]
answer_ends = [answer["answer_end"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"answer_end": answer_ends,
"text": answers,
},
}
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