readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_base_tcm_no_objeto_0.8
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # bert_base_tcm_no_objeto_0.8
  
  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0076
  - Criterio Julgamento Precision: 0.7444
  - Criterio Julgamento Recall: 0.8684
  - Criterio Julgamento F1: 0.8016
  - Criterio Julgamento Number: 114
  - Data Sessao Precision: 0.7297
  - Data Sessao Recall: 0.9153
  - Data Sessao F1: 0.8120
  - Data Sessao Number: 59
  - Modalidade Licitacao Precision: 0.9412
  - Modalidade Licitacao Recall: 0.9697
  - Modalidade Licitacao F1: 0.9552
  - Modalidade Licitacao Number: 462
  - Numero Exercicio Precision: 0.9018
  - Numero Exercicio Recall: 0.9619
  - Numero Exercicio F1: 0.9309
  - Numero Exercicio Number: 210
  - Valor Objeto Precision: 0.7778
  - Valor Objeto Recall: 0.8537
  - Valor Objeto F1: 0.8140
  - Valor Objeto Number: 41
  - Overall Precision: 0.8803
  - Overall Recall: 0.9458
  - Overall F1: 0.9119
  - Overall Accuracy: 0.9983
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.012         | 1.0   | 2863  | 0.0099          | 0.7059                        | 0.8421                     | 0.7680                 | 114                        | 0.7013                | 0.9153             | 0.7941         | 59                 | 0.9366                         | 0.9589                      | 0.9476                  | 462                         | 0.9136                     | 0.9571                  | 0.9349              | 210                     | 0.5902                 | 0.8780              | 0.7059          | 41                  | 0.8583            | 0.9368         | 0.8958     | 0.9974           |
  | 0.0095        | 2.0   | 5726  | 0.0076          | 0.8095                        | 0.8947                     | 0.8500                 | 114                        | 0.6935                | 0.7288             | 0.7107         | 59                 | 0.9346                         | 0.9589                      | 0.9466                  | 462                         | 0.9054                     | 0.9571                  | 0.9306              | 210                     | 0.8409                 | 0.9024              | 0.8706          | 41                  | 0.8901            | 0.9323         | 0.9107     | 0.9981           |
  | 0.005         | 3.0   | 8589  | 0.0091          | 0.7574                        | 0.9035                     | 0.8240                 | 114                        | 0.6471                | 0.9322             | 0.7639         | 59                 | 0.9371                         | 0.9675                      | 0.9521                  | 462                         | 0.9091                     | 0.9524                  | 0.9302              | 210                     | 0.7660                 | 0.8780              | 0.8182          | 41                  | 0.8715            | 0.9492         | 0.9087     | 0.9978           |
  | 0.0042        | 4.0   | 11452 | 0.0076          | 0.7444  