README.md

---
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: 164254.33938245106
    num_examples: 1000
  - name: train
    num_bytes: 141194788.7749479
    num_examples: 859638
  download_size: 1314542963
  dataset_size: 141365189.11433035
- 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

The developed corpus is a composition of pre-existing datasets initially created for other NLP tasks that provide permissive licenses. The first release of the corpus is available on [Huggingface](https://huggingface.co/datasets/Random-Mary-Smith/port_data_random).

#### Data Sources

The corpus consists of the following datasets:

<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</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 license</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: Data Sources</em>
</p>

#####

 Note: The dataset "Brazilian Senate Speeches" was created by the authors of this paper, using web crawling of the Brazilian Senate website and is available in the Huggingface repository.

#### Annotation Schema & Data Preprocessing Pipeline

We leveraged our knowledge of the Portuguese language to identify data sources that guaranteed mono-variety documents. However, this first release lacks any kind of supervision, so we cannot guarantee that all documents are mono-variety. In the future, we plan to release a second version of the corpus with a more robust annotation schema, combining automatic and manual annotation.

To improve the quality of the corpus, we applied a preprocessing pipeline to all documents. The pipeline consists of the following steps:

1. Remove all NaN values.
2. Remove all empty documents.
3. Remove all duplicated documents.
4. Apply the [clean_text](https://github.com/jfilter/clean-text) library to remove non-relevant information for language identification from the documents.
5. Remove all documents with a length significantly more than two standard deviations from the mean length of the documents in the corpus.

The pipeline is illustrated in Figure 1.

<p align="center">
  <img src="assets/pipeline_lid.jpg" alt="Image Description">
</p>
<p align="center">
  <em>Figure 1: Data Pre-Processing Pipeline</em>
</p>

#### Class Distribution

The class distribution of the corpus is presented in Table 2. The corpus is highly imbalanced, with the majority of the documents being from the journalistic domain. In the future, we plan to release a second version of the corpus with a more balanced distribution across the six domains. Depending on the imbalance of the textual domain, we used different strategies to perform train-validation-test splits. For the heavily imbalanced domains, we ensured a minimum of 100 documents for validation and 400 for testing. In the other domains, we applied a stratified split.

<p align="center">
  <table>
    <tr>
      <th>Domain</th>
      <th># PT-PT</th>
      <th># PT-BR</th>
      <th>Stratified</th>
    </tr>
    <tr>
      <td>Politics</td>
      <td>6500</td>
      <td>4894</td>
      <td>&#10003;</td>
    </tr>
    <tr>
      <td>Web</td>
      <td>7960</td>
      <td>21592</td>
      <td>&#10003;</td>
    </tr>
    <tr>
      <td>Literature</td>
      <td>18282</td>
      <td>2772</td>
      <td>&#10003;</td>
    </tr>
    <tr>
      <td>Law</td>
      <td>392839</td>
      <td>5766</td>
      <td>&#10005;</td>
    </tr>
    <tr>
      <td>Journalistic</td>
      <td>1494494</td>
      <td>354180</td>
      <td>&#10003;</td>
    </tr>
    <tr>
      <td>Social Media</td>
      <td>2013951</td>
      <td>6222</td>
      <td>&#10005;</td>
    </tr>
  </table>
</p>

<p align="center">
  <em>Table 2: Class Balance across the six textual domains in both varieties of Portuguese.</em>
</p>

#### Future Releases & How to Contribute

We plan to release a second version of this corpus considering more textual domains and extending the scope to other Portuguese varieties. If you want to contribute to this corpus, please [contact us]().