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  2. faquad.py +155 -0
README.md ADDED
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+ ---
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+ pretty_name: FaQuAD
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+ annotations_creators:
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+ - expert-generated
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+ language_creators:
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+ - found
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+ language:
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+ - br
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+ license:
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+ - cc-by-4.0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - n<1K
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+ source_datasets:
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+ - extended|wikipedia
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+ task_categories:
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+ - question-answering
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+ task_ids:
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+ - extractive-qa
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+ # paperswithcode_id: faquad
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+ train-eval-index:
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+ - config: plain_text
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+ task: question-answering
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+ task_id: extractive_question_answering
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+ splits:
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+ train_split: train
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+ eval_split: validation
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+ col_mapping:
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+ question: question
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+ context: context
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+ answers:
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+ text: text
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+ answer_start: answer_start
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+ metrics:
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+ - type: squad
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+ name: SQuAD
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+ ---
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+
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+ # Dataset Card for [Dataset Name]
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+
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+ ## Table of Contents
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+ - [Table of Contents](#table-of-contents)
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+ - [Contributions](#contributions)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://github.com/liafacom/faquad
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+ - **Repository:** https://github.com/liafacom/faquad
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+ - **Paper:** https://ieeexplore.ieee.org/document/8923668/
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+ <!-- - **Leaderboard:** -->
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+ - **Point of Contact:** Eraldo R. Fernandes <eraldoluis@gmail.com>
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+
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+ ### Dataset Summary
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+
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+ Academic secretaries and faculty members of higher education institutions face a common problem:
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+ the abundance of questions sent by academics
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+ whose answers are found in available institutional documents.
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+ The official documents produced by Brazilian public universities are vast and disperse,
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+ which discourage students to further search for answers in such sources.
82
+ In order to lessen this problem, we present FaQuAD:
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+ a novel machine reading comprehension dataset
84
+ in the domain of Brazilian higher education institutions.
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+ FaQuAD follows the format of SQuAD (Stanford Question Answering Dataset) [Rajpurkar et al. 2016].
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+ It comprises 900 questions about 249 reading passages (paragraphs),
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+ which were taken from 18 official documents of a computer science college
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+ from a Brazilian federal university
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+ and 21 Wikipedia articles related to Brazilian higher education system.
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+ As far as we know, this is the first Portuguese reading comprehension dataset in this format.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ [More Information Needed]
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+
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+ ### Languages
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+
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+ [More Information Needed]
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+
100
+ ## Dataset Structure
101
+
102
+ ### Data Instances
103
+
104
+ [More Information Needed]
105
+
106
+ ### Data Fields
107
+
108
+ [More Information Needed]
109
+
110
+ ### Data Splits
111
+
112
+ [More Information Needed]
113
+
114
+ ## Dataset Creation
115
+
116
+ ### Curation Rationale
117
+
118
+ [More Information Needed]
119
+
120
+ ### Source Data
121
+
122
+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
132
+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
141
+
142
+ [More Information Needed]
143
+
144
+ ## Considerations for Using the Data
145
+
146
+ ### Social Impact of Dataset
147
+
148
+ [More Information Needed]
149
+
150
+ ### Discussion of Biases
151
+
152
+ [More Information Needed]
153
+
154
+ ### Other Known Limitations
155
+
156
+ [More Information Needed]
157
+
158
+ ## Additional Information
159
+
160
+ ### Dataset Curators
161
+
162
+ [More Information Needed]
163
+
164
+ ### Licensing Information
165
+
166
+ [More Information Needed]
167
+
168
+ ### Citation Information
169
+
170
+ [More Information Needed]
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+
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+ ### Contributions
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+
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+ Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
faquad.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ #
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+ # Adapted from the SQuAD script.
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+ #
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+
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+ # Lint as: python3
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+ """FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education."""
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+
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+
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+ import json
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+
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+ import datasets
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+ from datasets.tasks import QuestionAnsweringExtractive
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+
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+
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+ logger = datasets.logging.get_logger(__name__)
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+
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+
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+ _CITATION = """\
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+ @INPROCEEDINGS{
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+ 8923668,
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+ author={Sayama, Hélio Fonseca and Araujo, Anderson Viçoso and Fernandes, Eraldo Rezende},
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+ booktitle={2019 8th Brazilian Conference on Intelligent Systems (BRACIS)},
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+ title={FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education},
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+ year={2019},
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+ volume={},
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+ number={},
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+ pages={443-448},
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+ doi={10.1109/BRACIS.2019.00084}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ Academic secretaries and faculty members of higher education institutions face a common problem:
48
+ the abundance of questions sent by academics
49
+ whose answers are found in available institutional documents.
50
+ The official documents produced by Brazilian public universities are vast and disperse,
51
+ which discourage students to further search for answers in such sources.
52
+ In order to lessen this problem, we present FaQuAD:
53
+ a novel machine reading comprehension dataset
54
+ in the domain of Brazilian higher education institutions.
55
+ FaQuAD follows the format of SQuAD (Stanford Question Answering Dataset) [Rajpurkar et al. 2016].
56
+ It comprises 900 questions about 249 reading passages (paragraphs),
57
+ which were taken from 18 official documents of a computer science college
58
+ from a Brazilian federal university
59
+ and 21 Wikipedia articles related to Brazilian higher education system.
60
+ As far as we know, this is the first Portuguese reading comprehension dataset in this format.
61
+ """
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+
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+ _URL = "https://raw.githubusercontent.com/liafacom/faquad/master/data/"
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+ _URLS = {
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+ "train": _URL + "train.json",
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+ "dev": _URL + "dev.json",
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+ }
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+
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+
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+ class FaquadConfig(datasets.BuilderConfig):
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+ """BuilderConfig for FaQuAD."""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for FaQuAD.
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+
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(FaquadConfig, self).__init__(**kwargs)
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+
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+
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+ class Faquad(datasets.GeneratorBasedBuilder):
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+ """FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education. Version 1.0."""
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+
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+ BUILDER_CONFIGS = [
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+ FaquadConfig(
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+ name="plain_text",
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+ version=datasets.Version("1.0.0", ""),
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+ description="Plain text",
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+ ),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "id": datasets.Value("string"),
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+ "title": datasets.Value("string"),
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+ "context": datasets.Value("string"),
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+ "question": datasets.Value("string"),
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+ "answers": datasets.features.Sequence(
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+ {
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+ "text": datasets.Value("string"),
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+ "answer_start": datasets.Value("int32"),
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+ }
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+ ),
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+ }
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+ ),
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+ # No default supervised_keys (as we have to pass both question
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+ # and context as input).
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+ supervised_keys=None,
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+ homepage="https://github.com/liafacom/faquad",
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+ citation=_CITATION,
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+ task_templates=[
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+ QuestionAnsweringExtractive(
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+ question_column="question", context_column="context", answers_column="answers"
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+ )
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+ ],
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ downloaded_files = dl_manager.download_and_extract(_URLS)
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """This function returns the examples in the raw (text) form."""
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+ logger.info("generating examples from = %s", filepath)
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+ key = 0
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+ with open(filepath, encoding="utf-8") as f:
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+ faquad = json.load(f)
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+ for article in faquad["data"]:
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+ title = article.get("title", "")
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+ for paragraph in article["paragraphs"]:
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+ context = paragraph["context"] # do not strip leading blank spaces GH-2585
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+ for qa in paragraph["qas"]:
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+ answer_starts = [answer["answer_start"] for answer in qa["answers"]]
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+ answers = [answer["text"] for answer in qa["answers"]]
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+ # Features currently used are "context", "question", and "answers".
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+ # Others are extracted here for the ease of future expansions.
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+ yield key, {
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+ "title": title,
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+ "context": context,
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+ "question": qa["question"],
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+ "id": qa["id"],
150
+ "answers": {
151
+ "answer_start": answer_starts,
152
+ "text": answers,
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+ },
154
+ }
155
+ key += 1