Datasets:
File size: 6,743 Bytes
2a822d8 8fcdce5 2a822d8 3404ff9 2a822d8 5761450 2a822d8 72259ac 2a822d8 5761450 2a822d8 5761450 2a822d8 5761450 2a822d8 5761450 2a822d8 7c489e1 2a822d8 c21dc67 2a822d8 76b7219 2a822d8 7c489e1 2a822d8 fc8402c 2a822d8 7c489e1 2a822d8 76b7219 2a822d8 5761450 2a822d8 5761450 2a822d8 5761450 2a822d8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
# ok
import os
import json
import datasets
_DESCRIPTION = """MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages."""
_HOMEPAGE_URL = "https://huggingface.co/datasets/clips/mqa"
_CITATION = """
@misc{debruyn2021mfaq,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
booktitle={MRQA@EMNLP2021},
}
"""
_VERSION = "0.1"
_BASE_NAME = ""
_BASE_URL = "data/data.{}.{}.json.gz"
_LANGUAGES = [
"ca", "en", "de", "es", "fr",
"ru", "ja", "it", "zh", "pt",
"nl", "tr", "pl", "vi", "ar",
"id", "uk", "ro", "no", "th",
"sv", "el", "fi", "he", "da",
"cs", "ko", "fa", "hi", "hu",
"sk", "lt", "et", "hr", "is",
"lv", "ms", "bg", "sr",
]
_SCOPES = ["faq", "cqa"]
_LEVELS = ["domain", "page", "question"]
class MQAConfig(datasets.BuilderConfig):
def __init__(self, *args, language="en", scope="all", level="question", **kwargs):
super().__init__(
*args,
name=f"{language}-{scope}-{level}",
**kwargs,
)
self.language = language
self.scope = scope
self.level = level
class MQA(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = []
for language in _LANGUAGES:
for scope in _SCOPES:
for level in _LEVELS:
BUILDER_CONFIGS.append(MQAConfig(language=language, scope=scope, level=level))
for language in _LANGUAGES:
BUILDER_CONFIGS.append(MQAConfig(language=language, scope="all", level=level))
for scope in _SCOPES:
BUILDER_CONFIGS.append(MQAConfig(language="all", scope=scope, level=level))
BUILDER_CONFIG_CLASS = MQAConfig
def _info(self):
question = {
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"name": datasets.Value("string"),
"domain": datasets.Value("string"),
"bucket": datasets.Value("string"),
"answers": [{
"text": datasets.Value("string"),
"name": datasets.Value("string"),
"is_accepted": datasets.Value("bool"),
}]
}
page = {
"id": datasets.Value("string"),
"bucket": datasets.Value("string"),
"domain": datasets.Value("string"),
# "description": datasets.Value("string"),
# "title": datasets.Value("string"),
"questions": [question]
}
domain = {
"domain": datasets.Value("string"),
"pages": [page]
}
if self.config.level == "question":
features = question
elif self.config.level == "page":
features = page
elif self.config.level == "domain":
features = domain
else:
raise NotImplementedError()
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
filenames = []
languages = _LANGUAGES if self.config.language == "all" else [self.config.language]
scopes = _SCOPES if self.config.scope == "all" else [self.config.scope]
for language in languages:
for scope in scopes:
path = dl_manager.download_and_extract(_BASE_URL.format(language, scope))
filenames.append(path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filenames": filenames},
)
]
def _generate_examples(self, filenames):
def default(e, key, default_value=""):
if e[key] is None:
return default_value
return e[key]
for filename in filenames:
with open(filename, "r") as f:
domain = []
previous_domain = ''
for line in f:
page = json.loads(line)
questions = [{
"text": default(question, "text"),
"name": default(question, "name"),
"domain": page["domain"],
"bucket": page["bucket"],
"id": question["hash"],
"answers": [{
"text": default(answer, "text"),
"name": default(answer, "name"),
"is_accepted": answer["is_accepted"]
} for answer in question["answers"]]
} for question in page["questions"]]
page = {
"id": page["page_hash"],
"domain": page["domain"],
"bucket": page["bucket"],
# "title": default(page, "title"),
# "description": default(page, "description"),
"questions": questions
}
if self.config.level == "question":
for question in questions:
yield question["id"], question
if self.config.level == "page":
yield page["id"], page
if self.config.level == "domain":
if page["domain"] == previous_domain or previous_domain == "":
domain.append(page)
else:
yield previous_domain, {
"domain": previous_domain,
"pages": domain
}
domain = []
previous_domain = page["domain"] |