Hugging Face's logo

Datasets:
liaad
/
PtBrVId

Modalities:
Text
Formats:
parquet
Size:
1M - 10M
Libraries:
Datasets
Dask
Dataset card Data Studio Files
xet
Community
2
PtBrVId / README.md
hugosousa's picture
hugosousa
Update README.md
910745e verified
preview code
|
raw
history blame contribute delete
8.88 kB
 ---
dataset_info:
- config_name: journalistic
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: valid
num_bytes: 571631
num_examples: 1000
- name: train
num_bytes: 1165703801.742406
num_examples: 1776290
- name: test
num_bytes: 27508.055555555555
num_examples: 35
download_size: 7801787253
dataset_size: 1166302940.7979615
- config_name: legal
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 10385
num_examples: 37
- name: valid
num_bytes: 282724.0
num_examples: 1000
- name: train
num_bytes: 859653342.1992471
num_examples: 2961596
download_size: 3051546595
dataset_size: 859946451.1992471
- config_name: literature
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 12767
num_examples: 36
- name: valid
num_bytes: 373696.9233584274
num_examples: 1000
- name: train
num_bytes: 28191249.0
num_examples: 75512
download_size: 174029597
dataset_size: 28577712.92335843
- config_name: politics
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 64499
num_examples: 48
- name: valid
num_bytes: 1469255.532624226
num_examples: 1000
- name: train
num_bytes: 44787070.0
num_examples: 30495
download_size: 154407264
dataset_size: 46320824.53262423
- config_name: social_media
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 6146
num_examples: 28
- name: valid
num_bytes: 110535.71291367509
num_examples: 1000
- name: train
num_bytes: 261685711.908761
num_examples: 2367418
download_size: 1689212607
dataset_size: 261802393.62167466
- config_name: web
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 64024
num_examples: 34
- name: valid
num_bytes: 2216516.5075847963
num_examples: 1000
- name: train
num_bytes: 256894009.0
num_examples: 86909
download_size: 1377291469
dataset_size: 259174549.5075848
configs:
- config_name: journalistic
data_files:
- split: train
path: journalistic/train-*
- split: valid
path: journalistic/valid-*
- split: test
path: journalistic/test-*
- config_name: legal
data_files:
- split: train
path: legal/train-*
- split: valid
path: legal/valid-*
- split: test
path: legal/test-*
- config_name: literature
data_files:
- split: train
path: literature/train-*
- split: valid
path: literature/valid-*
- split: test
path: literature/test-*
- config_name: politics
data_files:
- split: train
path: politics/train-*
- split: valid
path: politics/valid-*
- split: test
path: politics/test-*
- config_name: social_media
data_files:
- split: train
path: social_media/train-*
- split: valid
path: social_media/valid-*
- split: test
path: social_media/test-*
- config_name: web
data_files:
- split: train
path: web/train-*
- split: valid
path: web/valid-*
- split: test
path: web/test-*
---
# PtBrVId
**PtBrVId** is a Portuguese Variety Identification corpus, built by combining pre-existing datasets originally created for different NLP tasks and released under permissive licenses.
Our goal is to provide a large, diverse, and multi-domain resource for studying and improving automatic identification of **European Portuguese (PT-PT)** and **Brazilian Portuguese (PT-BR)**.
---
## 📚 Data Sources
The corpus is composed of datasets from various domains, each selected to ensure (as much as possible) mono-variety content. The current release is silver-labeled and unsupervised, meaning that we cannot fully guarantee that all documents are strictly mono-variety. A future version will include a refined annotation schema with both automatic and manual verification.
<p align="center">
<table>
<tr>
<th>Domain</th>
<th>Variety</th>
<th>Dataset</th>
<th>Original Task</th>
<th># Docs</th>
<th>License</th>
<th>Silver Labeled</th>
</tr>
<tr>
<td rowspan="5">Literature</td>
<td rowspan="3">PT-PT</td>
<td><a href="http://arquivopessoa.net/">Arquivo Pessoa</a></td>
<td>-</td>
<td>~4k</td>
<td>CC</td>
<td>✔</td>
</tr>
<tr>
<td><a href="https://www.gutenberg.org/ebooks/bookshelf/99">Gutenberg Project</a></td>
<td>-</td>
<td>6</td>
<td>CC</td>
<td>✔</td>
</tr>
<tr>
<td><a href="https://www.clul.ulisboa.pt/recurso/corpus-de-textos-literarios">LT-Corpus</a></td>
<td>-</td>
<td>56</td>
<td>ELRA END USER</td>
<td>✘</td>
</tr>
<tr>
<td rowspan="2">PT-BR</td>
<td><a href="https://www.kaggle.com/datasets/rtatman/brazilian-portuguese-literature-corpus">Brazilian Literature</a></td>
<td>Author Identification</td>
<td>81</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td>LT-Corpus</td>
<td>-</td>
<td>8</td>
<td>ELRA END USER</td>
<td>✘</td>
</tr>
<tr>
<td rowspan="2">Politics</td>
<td>PT-PT</td>
<td><a href="http://www.statmt.org/europarl/">Koehn (2005) Europarl</a></td>
<td>Machine Translation</td>
<td>~10k</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td>PT-BR</td>
<td>Brazilian Senate Speeches<sup>1</sup></td>
<td>-</td>
<td>~5k</td>
<td>CC</td>
<td>✔</td>
</tr>
<tr>
<td rowspan="2">Journalistic</td>
<td>PT-PT</td>
<td><a href="https://www.linguateca.pt/CETEMPublico/">CETEM Público</a></td>
<td>-</td>
<td>1M</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td>PT-BR</td>
<td><a href="https://www.linguateca.pt/CETEMFolha/">CETEM Folha</a></td>
<td>-</td>
<td>272k</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td rowspan="3">Social Media</td>
<td>PT-PT</td>
<td><a href="https://www.aclweb.org/anthology/2021.ranlp-1.37/">Ramalho (2021)</a></td>
<td>Fake News Detection</td>
<td>2M</td>
<td>MIT</td>
<td>✔</td>
</tr>
<tr>
<td rowspan="2">PT-BR</td>
<td><a href="https://www.aclweb.org/anthology/2022.lrec-1.322/">Vargas (2022)</a></td>
<td>Hate Speech Detection</td>
<td>5k</td>
<td>CC-BY-NC-4.0</td>
<td>✘</td>
</tr>
<tr>
<td><a href="https://www.aclweb.org/anthology/2021.wlp-1.72/">Cunha (2021)</a></td>
<td>Fake News Detection</td>
<td>2k</td>
<td>GPL-3.0</td>
<td>✔</td>
</tr>
<tr>
<td>Web</td>
<td>BOTH</td>
<td><a href="https://www.aclweb.org/anthology/2020.lrec-1.451/">Ortiz-Suarez (2020)</a></td>
<td>-</td>
<td>10k</td>
<td>CC</td>
<td>✔</td>
</tr>
</table>
</p>
<p align="center"><em>Table 1: PtBrVId data sources and metadata.</em></p>
<sup>1</sup> The **Brazilian Senate Speeches** dataset was created by the authors through web crawling of the Brazilian Senate website and is available on [Hugging Face](https://huggingface.co/).
A raw version of the dataset is available [here](https://huggingface.co/datasets/liaad/PtBrVId-Raw).
---
## 🛠 Annotation & Preprocessing
### Annotation
We selected data sources known to contain primarily mono-variety Portuguese texts. While this approach helps ensure quality, this **first release** is entirely unsupervised. A planned **v2** will introduce a hybrid annotation strategy combining automated labeling and manual review.
### Preprocessing Pipeline
To standardize and clean the data, we applied the following steps:
1. **Remove NaN values**.
2. **Remove empty documents**.
3. **Remove duplicate documents**.
4. Apply the [`clean-text`](https://github.com/jfilter/clean-text) library to strip non-relevant content for variety identification.
5. Remove outlier documents with lengths below `Q1 - 1.5 × IQR` or above `Q3 + 1.5 × IQR`, where `Q1` and `Q3` are the first and third quartiles, and `IQR` is the interquartile range.
---
## 📖 Citation
If you use this corpus, please cite:
```bibtex
@article{Sousa_Almeida_Silvano_Cantante_Campos_Jorge_2025,
title={Enhancing Portuguese Variety Identification with Cross-Domain Approaches},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={39},
number={24},
pages={25192--25200},
year={2025},
doi={10.1609/aaai.v39i24.34705},
author={Sousa, Hugo and Almeida, Rúben and Silvano, Purificação and Cantante, Inês and Campos, Ricardo and Jorge, Alípio}
}