Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Multilinguality:
multilingual
Size Categories:
unknown
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
extended|squad
ArXiv:
Tags:
License:
File size: 5,316 Bytes
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"""TODO(xquad): Add a description here."""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
_CITATION = """\
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
"""
_DESCRIPTION = """\
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel
across 12 languages.
"""
_URL = "https://github.com/deepmind/xquad/raw/master/"
_LANG = ["ar", "de", "zh", "vi", "en", "es", "hi", "el", "th", "tr", "ru", "ro"]
class XquadConfig(datasets.BuilderConfig):
"""BuilderConfig for Xquad"""
def __init__(self, lang, **kwargs):
"""
Args:
lang: string, language for the input text
**kwargs: keyword arguments forwarded to super.
"""
super(XquadConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.lang = lang
class Xquad(datasets.GeneratorBasedBuilder):
"""TODO(xquad): Short description of my dataset."""
# TODO(xquad): Set up version.
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [XquadConfig(name=f"xquad.{lang}", description=_DESCRIPTION, lang=lang) for lang in _LANG]
def _info(self):
# TODO(xquad): 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(
{
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
# These are the features of your dataset like images, labels ...
}
),
# 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/deepmind/xquad",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(xquad): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = {lang: _URL + f"xquad.{lang}.json" for lang in _LANG}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": downloaded_files[self.config.lang]},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(xquad): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
xquad = json.load(f)
id_ = 0
for article in xquad["data"]:
for paragraph in article["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
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_, {
"context": context,
"question": question,
"id": qa["id"],
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
id_ += 1
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