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# 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 = "0.4"
_BASE_NAME = ""
_TRAIN_BASE_URL = "data/{}.json"
_TRAIN_NEG_URL = "data/{}.neg.json"
_LANGUAGES = [
"en", "de", "es", "fr",
"ru", "ja", "it", "zh", "pt",
"nl", "tr", "pl", "vi", "ar",
"id", "uk", "ro", "no",
"sv", "el", "fi", "da",
"cs", "ko", "fa", "hi", "hu",
"sk", "lt", "et", "hr", "is",
"lv", "ms", "bg", "sr", "ca"
]
class MFAQLightConfig(datasets.BuilderConfig):
def __init__(self, *args, language="en", negatives=True, **kwargs):
super().__init__(
*args,
name=f"{language}-{negatives}",
**kwargs,
)
self.language = language
self.negatives = negatives
class MFAQLight(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = []
for language in _LANGUAGES:
BUILDER_CONFIGS.append(MFAQLightConfig(language=language, negatives=True))
BUILDER_CONFIGS.append(MFAQLightConfig(language=language, negatives=False))
BUILDER_CONFIGS.append(MFAQLightConfig(language="all", negatives=True))
BUILDER_CONFIGS.append(MFAQLightConfig(language="all", negatives=False))
BUILDER_CONFIG_CLASS = MFAQLightConfig
def _info(self):
features = {
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"id": datasets.Value("string"),
"negatives": [datasets.Value("string")]
}
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_filenames = []
languages = _LANGUAGES if self.config.language == "all" else [self.config.language]
base_path = _TRAIN_NEG_URL if self.config.negatives else _TRAIN_BASE_URL
for language in languages:
path = dl_manager.download_and_extract(base_path.format(language))
train_filenames.append(path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filenames": train_filenames},
)
]
def _generate_examples(self, filenames):
for filename in filenames:
with open(filename, "r") as f:
for _, line in enumerate(f):
question = json.loads(line)
yield question["id"], {
"question": question["question"],
"answer": question["answer"],
"id": question["id"],
"negatives": question.get("negative", [])
}
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