readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_base_tcm_teste
    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_teste
  
  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: 0.0192
  - Criterio Julgamento Precision: 0.7209
  - Criterio Julgamento Recall: 0.8942
  - Criterio Julgamento F1: 0.7983
  - Criterio Julgamento Number: 104
  - Data Sessao Precision: 0.6351
  - Data Sessao Recall: 0.8545
  - Data Sessao F1: 0.7287
  - Data Sessao Number: 55
  - Modalidade Licitacao Precision: 0.9224
  - Modalidade Licitacao Recall: 0.9596
  - Modalidade Licitacao F1: 0.9406
  - Modalidade Licitacao Number: 421
  - Numero Exercicio Precision: 0.8872
  - Numero Exercicio Recall: 0.9351
  - Numero Exercicio F1: 0.9105
  - Numero Exercicio Number: 185
  - Objeto Licitacao Precision: 0.2348
  - Objeto Licitacao Recall: 0.4576
  - Objeto Licitacao F1: 0.3103
  - Objeto Licitacao Number: 59
  - Valor Objeto Precision: 0.5424
  - Valor Objeto Recall: 0.7805
  - Valor Objeto F1: 0.64
  - Valor Objeto Number: 41
  - Overall Precision: 0.7683
  - Overall Recall: 0.8971
  - Overall F1: 0.8277
  - Overall Accuracy: 0.9948
  
  ## 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: 0.0001
  - 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: 50.0
  
  ### 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 | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0346        | 0.96  | 2750  | 0.0329          | 0.6154                        | 0.8462                     | 0.7126                 | 104                        | 0.5495                | 0.9091             | 0.6849         | 55                 | 0.8482                         | 0.9287                      | 0.8866                  | 421                         | 0.7438                     | 0.9730                  | 0.8431              | 185                     | 0.0525                     | 0.3220                  | 0.0903              | 59                      | 0.4762                 | 0.7317              | 0.5769          | 41                  | 0.5565            | 0.8763         | 0.6807     | 0.9880           |
  | 0.0309        | 1.92  | 5500  | 0.0322          | 0.6694                        | 0.7788                     | 0.72                   | 104                        | 0.5976                | 0.8909             | 0.7153         | 55                 | 0.9178                         | 0.9549                      | 0.9360                  | 421                         | 0.8211                     | 0.8432                  | 0.8320              | 185                     | 0.15                       | 0.2034                  | 0.1727              | 59                      | 0.2203                 | 0.3171              | 0.26            | 41                  | 0.7351            | 0.8243         | 0.7771     | 0.9934           |
  | 0.0179        | 2.88  | 8250  | 0.0192          | 0.7209                        | 0.8942                     | 0.7983                 | 104                     