README and load script

README.md

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+---
+pretty_name: FaQuAD
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- br
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- extended|wikipedia
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+# paperswithcode_id: faquad
+train-eval-index:
+- config: plain_text
+  task: question-answering
+  task_id: extractive_question_answering
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    question: question
+    context: context
+    answers:
+      text: text
+      answer_start: answer_start
+  metrics:
+    - type: squad
+      name: SQuAD
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/liafacom/faquad
+- **Repository:** https://github.com/liafacom/faquad
+- **Paper:** https://ieeexplore.ieee.org/document/8923668/
+<!-- - **Leaderboard:** -->
+- **Point of Contact:** Eraldo R. Fernandes <eraldoluis@gmail.com>
+
+### Dataset Summary
+
+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. 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

faquad.py

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+# 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