
# LegalBERTPT-br

LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased). 


# Corpora

– From [this site](https://www2.camara.leg.br/transparencia/servicos-ao-cidadao/participacao-popular), we used the column `Conteudo` with 215,713 comments. We removed the comments from PL 3723/2019, PEC 471/2005, and Hashtag Corpus, in order to avoid bias.

– From [this site](https://www2.camara.leg.br/transparencia/servicos-ao-cidadao/participacao-popular), we also used 147,008 bills. From these projects, we used the summary field named `txtEmenta` and the project core text named `txtExplicacaoEmenta`.

– From Political Speeches, we used 462,831 texts, specifically, we used the columns: `sumario`, `textodiscurso`, and `indexacao`.

These corpora were segmented into sentences and concatenated, producing 2,307,426 sentences.


# Citing and Authors

If you find this model helpful, feel free to cite our publication [Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_8):
```bibtex 
@inproceedings{bracis,
 author = {Nádia Silva and Marília Silva and Fabíola Pereira and João Tarrega and João Beinotti and Márcio Fonseca and Francisco Andrade and André Carvalho},
 title = {Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies},
 booktitle = {Anais da X Brazilian Conference on Intelligent Systems},
 location = {Online},
 year = {2021},
 keywords = {},
 issn = {0000-0000},
 publisher = {SBC},
 address = {Porto Alegre, RS, Brasil},
 url = {https://sol.sbc.org.br/index.php/bracis/article/view/19061}
}
```
{"language": "pt", "license": "mit", "tags": ["sentence-transformers"]}
sentence-transformers pt license:mit region:us
TAGS
#sentence-transformers #pt #license-mit #region-us 


# LegalBERTPT-br

LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau. 


# Corpora

– From this site, we used the column 'Conteudo' with 215,713 comments. We removed the comments from PL 3723/2019, PEC 471/2005, and Hashtag Corpus, in order to avoid bias.

– From this site, we also used 147,008 bills. From these projects, we used the summary field named 'txtEmenta' and the project core text named 'txtExplicacaoEmenta'.

– From Political Speeches, we used 462,831 texts, specifically, we used the columns: 'sumario', 'textodiscurso', and 'indexacao'.

These corpora were segmented into sentences and concatenated, producing 2,307,426 sentences.


# Citing and Authors

If you find this model helpful, feel free to cite our publication Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies:

# LegalBERTPT-br

LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau. # Corpora

– From this site, we used the column 'Conteudo' with 215,713 comments. We removed the comments from PL 3723/2019, PEC 471/2005, and Hashtag Corpus, in order to avoid bias.

– From this site, we also used 147,008 bills. From these projects, we used the summary field named 'txtEmenta' and the project core text named 'txtExplicacaoEmenta'.

– From Political Speeches, we used 462,831 texts, specifically, we used the columns: 'sumario', 'textodiscurso', and 'indexacao'.

These corpora were segmented into sentences and concatenated, producing 2,307,426 sentences. # Citing and Authors

If you find this model helpful, feel free to cite our publication Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies:
TAGS
#sentence-transformers #pt #license-mit #region-us 
 # LegalBERTPT-br

LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau. # Corpora

– From this site, we used the column 'Conteudo' with 215,713 comments. We removed the comments from PL 3723/2019, PEC 471/2005, and Hashtag Corpus, in order to avoid bias.

– From this site, we also used 147,008 bills. From these projects, we used the summary field named 'txtEmenta' and the project core text named 'txtExplicacaoEmenta'.

– From Political Speeches, we used 462,831 texts, specifically, we used the columns: 'sumario', 'textodiscurso', and 'indexacao'.

These corpora were segmented into sentences and concatenated, producing 2,307,426 sentences. # Citing and Authors

If you find this model helpful, feel free to cite our publication Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies:
passage: TAGS
#sentence-transformers #pt #license-mit #region-us 
# LegalBERTPT-br

LegalBERTPT-br is a trained sentence embedding using SimCSE, a contrastive learning framework, coupled with the Portuguese pre-trained language model named BERTimbau.# Corpora

– From this site, we used the column 'Conteudo' with 215,713 comments. We removed the comments from PL 3723/2019, PEC 471/2005, and Hashtag Corpus, in order to avoid bias.

– From this site, we also used 147,008 bills. From these projects, we used the summary field named 'txtEmenta' and the project core text named 'txtExplicacaoEmenta'.

– From Political Speeches, we used 462,831 texts, specifically, we used the columns: 'sumario', 'textodiscurso', and 'indexacao'.

These corpora were segmented into sentences and concatenated, producing 2,307,426 sentences.# Citing and Authors

If you find this model helpful, feel free to cite our publication Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies: