Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
ArXiv:
Tags:
License:
# 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. | |
"""MKQA: Multilingual Knowledge Questions & Answers""" | |
import json | |
import datasets | |
_CITATION = """\ | |
@misc{mkqa, | |
title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering}, | |
author = {Shayne Longpre and Yi Lu and Joachim Daiber}, | |
year = {2020}, | |
URL = {https://arxiv.org/pdf/2007.15207.pdf} | |
} | |
""" | |
_DESCRIPTION = """\ | |
We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries and answers were then human translated into 25 Non-English languages. | |
""" | |
_HOMEPAGE = "https://github.com/apple/ml-mkqa" | |
_LICENSE = "CC BY-SA 3.0" | |
_URLS = {"train": "https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz"} | |
class Mkqa(datasets.GeneratorBasedBuilder): | |
"""MKQA dataset""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="mkqa", | |
version=VERSION, | |
description=_DESCRIPTION, | |
), | |
] | |
def _info(self): | |
langs = [ | |
"ar", | |
"da", | |
"de", | |
"en", | |
"es", | |
"fi", | |
"fr", | |
"he", | |
"hu", | |
"it", | |
"ja", | |
"ko", | |
"km", | |
"ms", | |
"nl", | |
"no", | |
"pl", | |
"pt", | |
"ru", | |
"sv", | |
"th", | |
"tr", | |
"vi", | |
"zh_cn", | |
"zh_hk", | |
"zh_tw", | |
] | |
# Preferring list type instead of datasets.Sequence | |
queries_features = {lan: datasets.Value("string") for lan in langs} | |
answer_feature = [ | |
{ | |
"type": datasets.ClassLabel( | |
names=[ | |
"entity", | |
"long_answer", | |
"unanswerable", | |
"date", | |
"number", | |
"number_with_unit", | |
"short_phrase", | |
"binary", | |
] | |
), | |
"entity": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
"aliases": [datasets.Value("string")], | |
} | |
] | |
answer_features = {lan: answer_feature for lan in langs} | |
features = datasets.Features( | |
{ | |
"example_id": datasets.Value("string"), | |
"queries": queries_features, | |
"query": datasets.Value("string"), | |
"answers": answer_features, | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# download and extract URLs | |
urls_to_download = _URLS | |
downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] | |
def _generate_examples(self, filepath): | |
"""Yields examples.""" | |
with open(filepath, encoding="utf-8") as f: | |
for row in f: | |
data = json.loads(row) | |
data["example_id"] = str(data["example_id"]) | |
id_ = data["example_id"] | |
for language in data["answers"].keys(): | |
# Add default values for possible missing keys | |
for a in data["answers"][language]: | |
if "aliases" not in a: | |
a["aliases"] = [] | |
if "entity" not in a: | |
a["entity"] = "" | |
yield id_, data | |