--- dataset_info: - config_name: journalistic features: - name: text dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 28294 num_examples: 36 - name: train num_bytes: 1165704458 num_examples: 1776291 - name: valid num_bytes: 571631 num_examples: 1000 download_size: 7016689676 dataset_size: 1166304383.0 - config_name: legal features: - name: text dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 10385 num_examples: 37 - name: train num_bytes: 859678956.7275112 num_examples: 2961599 - name: valid num_bytes: 290275.27248878434 num_examples: 1000 download_size: 1978266272 dataset_size: 859979617.0 - config_name: literature features: - name: text dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 12767 num_examples: 36 - name: train num_bytes: 28218602.07664157 num_examples: 75512 - name: valid num_bytes: 373696.9233584274 num_examples: 1000 download_size: 155640296 dataset_size: 28605066.0 - config_name: politics features: - name: text dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 64499 num_examples: 48 - name: train num_bytes: 44804947.46737577 num_examples: 30495 - name: valid num_bytes: 1469255.532624226 num_examples: 1000 download_size: 127842443 dataset_size: 46338702.0 - config_name: social_media features: - name: text dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 6146 num_examples: 28 - name: train num_bytes: 141250847.66061756 num_examples: 859952 - name: valid num_bytes: 164254.33938245106 num_examples: 1000 download_size: 1213579168 dataset_size: 141421248.0 - config_name: web features: - name: text dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 64024 num_examples: 34 - name: train num_bytes: 192635233.15768707 num_examples: 86909 - name: valid num_bytes: 2216516.5075847963 num_examples: 1000 download_size: 1225981669 dataset_size: 194915773.66527188 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:

Domain Variety Dataset Original Task # Docs License Silver Labeled
Literature PT-PT Arquivo Pessoa - ~4k CC ✔
Gutenberg Project - 6 CC ✔
LT-Corpus - 56 ELRA END USER ✘
PT-BR Brazilian Literature Author Identification 81 CC ✘
LT-Corpus - 8 ELRA END USER ✘
Politics PT-PT Koehn (2005) Europarl Machine Translation ~10k CC ✘
PT-BR Brazilian Senate Speeches - ~5k CC ✔
Journalistic PT-PT CETEM Público - 1M CC ✘
PT-BR CETEM Folha - 272k CC ✘
Social Media PT-PT Ramalho (2021) Fake News Detection 2M MIT ✔
PT-BR Vargas (2022) Hate Speech Detection 5k CC-BY-NC-4.0 ✘
Cunha (2021) Fake News Detection 2k GPL-3.0 license ✔
Web BOTH Ortiz-Suarez (2020) - 10k CC ✔

Table 1: Data Sources

##### 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.

Image Description

Figure 1: Data Pre-Processing Pipeline

#### 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.

Domain # PT-PT # PT-BR Stratified
Politics 6500 4894 ✓
Web 7960 21592 ✓
Literature 18282 2772 ✓
Law 392839 5766 ✕
Journalistic 1494494 354180 ✓
Social Media 2013951 6222 ✕

Table 2: Class Balance across the six textual domains in both varieties of Portuguese.

#### 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]().