Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Spanish
Size:
10K<n<100K
ArXiv:
License:
"""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://huggingface.co/datasets/TheTung/squad_es_v2/resolve/main/" | |
_URLS_FULL = { | |
"train": _URL + "train-v2.0-es.json", | |
"dev": _URL + "dev-v2.0-es.json", | |
} | |
_URLS_SMALL = { | |
"train": _URL + "train-v2.0-es_small.json", | |
"dev": _URL + "dev-v2.0-es_small.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="full", | |
version=datasets.Version("2.0.0", ""), | |
description="Plain text Spanish squad version 1", | |
), | |
SquadEsConfig( | |
name="small", | |
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"), | |
} | |
), | |
} | |
), | |
# 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, | |
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 == "full": | |
dl_dir = dl_manager.download_and_extract(_URLS_FULL) | |
elif self.config.name == "small": | |
dl_dir = dl_manager.download_and_extract(_URLS_SMALL) | |
else: | |
raise Exception("version or config name 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["dev"]}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""Yields examples.""" | |
# TODO(squad_es): Yields (key, example) tuples from the dataset | |
print(f"opening data from {filepath}") | |
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"]] | |
answers = [answer["text"].strip() for answer in qa["answers"]] | |
yield id_, { | |
"title": title, | |
"context": context, | |
"question": question, | |
"id": id_, | |
"answers": { | |
"answer_start": answer_starts, | |
"text": answers, | |
}, | |
} | |