readme: ---
  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).
  
  