Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
ArXiv:
Tags:
License:
File size: 5,923 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""
import json
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{roy2020lareqa,
title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
journal={arXiv preprint arXiv:2004.05484},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/google-research-datasets/lareqa"
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://github.com/google-research-datasets/lareqa/raw/master/xquad-r/"
_LANG = ["ar", "de", "zh", "vi", "en", "es", "hi", "el", "th", "tr", "ru"]
class XquadRConfig(datasets.BuilderConfig):
"""BuilderConfig for XquadR"""
def __init__(self, lang, **kwargs):
"""
Args:
lang: string, language for the input text
**kwargs: keyword arguments forwarded to super.
"""
super(XquadRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.lang = lang
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class XquadR(datasets.GeneratorBasedBuilder):
"""TODO(xquad-r): Short description of my dataset."""
# TODO(xquad-r): Set up version.
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [XquadRConfig(name=f"{lang}", description=_DESCRIPTION, lang=lang) for lang in _LANG]
def _info(self):
# TODO(xquad-r): 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"),
}
),
}
),
# 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=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(xquad-r): 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"{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-r): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for article in data["data"]:
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.
yield id_, {
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
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