Datasets:
File size: 4,851 Bytes
bc0ee5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
# 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"""
from __future__ import absolute_import, division, print_function
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
|