Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
Portuguese
Size:
< 1K
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the 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. | |
# | |
# Adapted from the SQuAD script. | |
# | |
# Lint as: python3 | |
"""FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education.""" | |
import json | |
import datasets | |
from datasets.tasks import QuestionAnsweringExtractive | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """\ | |
@INPROCEEDINGS{ | |
8923668, | |
author={Sayama, Hélio Fonseca and Araujo, Anderson Viçoso and Fernandes, Eraldo Rezende}, | |
booktitle={2019 8th Brazilian Conference on Intelligent Systems (BRACIS)}, | |
title={FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education}, | |
year={2019}, | |
volume={}, | |
number={}, | |
pages={443-448}, | |
doi={10.1109/BRACIS.2019.00084} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Academic secretaries and faculty members of higher education institutions face a common problem: | |
the abundance of questions sent by academics | |
whose answers are found in available institutional documents. | |
The official documents produced by Brazilian public universities are vast and disperse, | |
which discourage students to further search for answers in such sources. | |
In order to lessen this problem, we present FaQuAD: | |
a novel machine reading comprehension dataset | |
in the domain of Brazilian higher education institutions. | |
FaQuAD follows the format of SQuAD (Stanford Question Answering Dataset) [Rajpurkar et al. 2016]. | |
It comprises 900 questions about 249 reading passages (paragraphs), | |
which were taken from 18 official documents of a computer science college | |
from a Brazilian federal university | |
and 21 Wikipedia articles related to Brazilian higher education system. | |
As far as we know, this is the first Portuguese reading comprehension dataset in this format. | |
""" | |
_URL = "https://raw.githubusercontent.com/liafacom/faquad/master/data/" | |
_URLS = { | |
"train": _URL + "train.json", | |
"dev": _URL + "dev.json", | |
} | |
class FaquadConfig(datasets.BuilderConfig): | |
"""BuilderConfig for FaQuAD.""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for FaQuAD. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(FaquadConfig, self).__init__(**kwargs) | |
class Faquad(datasets.GeneratorBasedBuilder): | |
"""FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education. Version 1.0.""" | |
BUILDER_CONFIGS = [ | |
FaquadConfig( | |
name="plain_text", | |
version=datasets.Version("1.0.0", ""), | |
description="Plain text", | |
), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"context": datasets.Value("string"), | |
"question": datasets.Value("string"), | |
"answers": datasets.features.Sequence( | |
{ | |
"text": datasets.Value("string"), | |
"answer_start": datasets.Value("int32"), | |
} | |
), | |
} | |
), | |
# No default supervised_keys (as we have to pass both question | |
# and context as input). | |
supervised_keys=None, | |
homepage="https://github.com/liafacom/faquad", | |
citation=_CITATION, | |
task_templates=[ | |
QuestionAnsweringExtractive( | |
question_column="question", context_column="context", answers_column="answers" | |
) | |
], | |
) | |
def _split_generators(self, dl_manager): | |
downloaded_files = dl_manager.download_and_extract(_URLS) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), | |
] | |
def _generate_examples(self, filepath): | |
"""This function returns the examples in the raw (text) form.""" | |
logger.info("generating examples from = %s", filepath) | |
key = 0 | |
with open(filepath, encoding="utf-8") as f: | |
faquad = json.load(f) | |
for article in faquad["data"]: | |
title = article.get("title", "") | |
for paragraph in article["paragraphs"]: | |
context = paragraph["context"] # do not strip leading blank spaces GH-2585 | |
for qa in paragraph["qas"]: | |
answer_starts = [answer["answer_start"] for answer in qa["answers"]] | |
answers = [answer["text"] for answer in qa["answers"]] | |
# Features currently used are "context", "question", and "answers". | |
# Others are extracted here for the ease of future expansions. | |
yield key, { | |
"title": title, | |
"context": context, | |
"question": qa["question"], | |
"id": qa["id"], | |
"answers": { | |
"answer_start": answer_starts, | |
"text": answers, | |
}, | |
} | |
key += 1 | |