---
pretty_name: FaQuAD-NLI
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
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 FaQuAD-NLI

## 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

FaQuAD is a Portuguese reading comprehension dataset that follows the format of the Stanford Question Answering Dataset (SQuAD). It is a pioneer Portuguese reading comprehension dataset using the challenging format of SQuAD. The dataset aims to address the problem of abundant questions sent by academics whose answers are found in available institutional documents in the Brazilian higher education system. It consists of 900 questions about 249 reading passages taken from 18 official documents of a computer science college from a Brazilian federal university and 21 Wikipedia articles related to the Brazilian higher education system.

FaQuAD-NLI is a modified version of the [FaQuAD dataset](https://huggingface.co/datasets/eraldoluis/faquad) that repurposes the question answering task as a textual entailment task between a question and its possible answers.

### Supported Tasks and Leaderboards

- `question_answering`: The dataset can be used to train a model for question-answering tasks in the domain of Brazilian higher education institutions.
- `textual_entailment`: FaQuAD-NLI can be used to train a model for textual entailment tasks, where answers in Q&A pairs are classified as either suitable or unsuitable.

### Languages

This dataset is in Brazilian Portuguese.

## Dataset Structure

### Data Fields

- `document_index`: an integer representing the index of the document.
- `document_title`: a string containing the title of the document.
- `paragraph_index`: an integer representing the index of the paragraph within the document.
- `question`: a string containing the question related to the paragraph.
- `answer`: a string containing the answer related to the question.
- `label`: an integer (0 or 1) representing if the answer is suitable (1) or unsuitable (0) for the question.

### Data Splits

The dataset is split into three subsets: train, validation, and test. 
The splits were made carefully to avoid question and answer pairs belonging to the same document appearing in more than one split.

|            | Train | Validation | Test |
|------------|-------|------------|------|
| Instances  | 3128  | 731        | 650  |

### Contributions

Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset.