---
language: "pt"
widget:
- text: "O paciente recebeu no hospital e falou com a médica"
- text: "COMO ESQUEMA DE MEDICAÇÃO PARA ICC PRESCRITO NO ALTA, RECEBE FUROSEMIDA 40 BID, ISOSSORBIDA 40 TID, DIGOXINA 0,25 /D, CAPTOPRIL 50 TID E ESPIRONOLACTONA 25 /D."
- text: "ESTAVA EM USO DE FUROSEMIDA 40 BID, DIGOXINA 0,25 /D, SINVASTATINA 40 /NOITE, CAPTOPRIL 50 TID, ISOSSORBIDA 20 TID, AAS 100 /D E ESPIRONOLACTONA 25 /D."
datasets: 
- MacMorpho
---

# POS-Tagger Bio Portuguese

We fine-tuned the BioBERTpt(all) model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9818.

Metrics:

```
              Precision  Recall  F1    Suport
accuracy                         0.98  38320
macro avg     0.95       0.94    0.94  38320
weighted avg  0.98       0.98    0.98  38320

F1:  0.9818 Accuracy:  0.9818
```

Parameters:

```
nclasses = 27
nepochs_total = 30
nepochs_stop = 12 (stop in 12th because early stop)
batch_size = 32
batch_status = 32
learning_rate = 1e-5
early_stop = 3
max_length = 200
```

## Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

## Citation
```
coming soon
```

## Questions?

Please, post a Github issue on the [NLP Portuguese Chunking](https://github.com/HAILab-PUCPR/nlp-portuguese-chunking).