task_categories:text-generation task_categories:fill-mask task_ids:language-modeling task_ids:masked-language-modeling annotations_creators:no-annotation multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:pt license:other parlamentopt parlamento parlamento-pt albertina-pt* albertina-ptpt albertina-ptbr fill-mask bert deberta portuguese encoder foundation model arxiv:2305.06721 region:us
---
annotations_creators:
- no-annotation
language:
- pt
license:
- other
multilinguality:
- monolingual
pretty_name: ParlamentoPT
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
tags:
- parlamentopt
- parlamento
- parlamento-pt
- albertina-pt*
- albertina-ptpt
- albertina-ptbr
- fill-mask
- bert
- deberta
- portuguese
- encoder
- foundation model
---

# Dataset Card for ParlamentoPT

### Dataset Summary

The ParlamentoPT is a **Portuguese** language data set obtained by collecting publicly available documents containing transcriptions of debates in the Portuguese Parliament.
The data was collected from the Portuguese Parliament portal in accordance with its [open data policy](https://www.parlamento.pt/Cidadania/Paginas/DadosAbertos.aspx).


This dataset was collected with the purpose of creating the [Albertina-PT*](https://huggingface.co/PORTULAN/albertina-ptpt) language model, and it serves as training data for model development. 
The development of the model is a collaborative effort between the University of Lisbon and the University of Porto in Portugal

</br>

# Citation

When using or citing this data set, kindly cite the following [publication](https://arxiv.org/abs/2305.06721):

``` latex
@misc{albertina-pt,
      title={Advancing Neural Encoding of Portuguese
             with Transformer Albertina PT-*}, 
      author={João Rodrigues and Luís Gomes and João Silva and
              António Branco and Rodrigo Santos and
              Henrique Lopes Cardoso and Tomás Osório},
      year={2023},
      eprint={2305.06721},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

<br>

# Acknowledgments

The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language,
funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the
grant PINFRA/22117/2016; research project ALBERTINA - Foundation Encoder Model for Portuguese and AI, funded by FCT—Fundação para a Ciência e Tecnologia under the
grant CPCA-IAC/AV/478394/2022; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização; and LIACC - Laboratory for AI and Computer Science, funded by FCT—Fundação para a Ciência e Tecnologia under the grant FCT/UID/CEC/0027/2020.