Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
ArXiv:
Tags:
License:
File size: 4,784 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.
"""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/main/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
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