readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_base_tcm_0.9_10_epochs
    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_0.9_10_epochs
  
  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: 0.0190
  - Criterio Julgamento Precision: 0.8170
  - Criterio Julgamento Recall: 0.8803
  - Criterio Julgamento F1: 0.8475
  - Criterio Julgamento Number: 142
  - Data Sessao Precision: 0.7798
  - Data Sessao Recall: 0.9444
  - Data Sessao F1: 0.8543
  - Data Sessao Number: 90
  - Modalidade Licitacao Precision: 0.9549
  - Modalidade Licitacao Recall: 0.9799
  - Modalidade Licitacao F1: 0.9673
  - Modalidade Licitacao Number: 648
  - Numero Exercicio Precision: 0.9559
  - Numero Exercicio Recall: 0.9848
  - Numero Exercicio F1: 0.9701
  - Numero Exercicio Number: 330
  - Objeto Licitacao Precision: 0.5496
  - Objeto Licitacao Recall: 0.6792
  - Objeto Licitacao F1: 0.6076
  - Objeto Licitacao Number: 106
  - Valor Objeto Precision: 0.8182
  - Valor Objeto Recall: 0.8438
  - Valor Objeto F1: 0.8308
  - Valor Objeto Number: 32
  - Overall Precision: 0.8868
  - Overall Recall: 0.9414
  - Overall F1: 0.9133
  - Overall Accuracy: 0.9957
  
  ## 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: 1e-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 | 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0187        | 1.0   | 3497  | 0.0201          | 0.776                         | 0.6831                     | 0.7266                 | 142                        | 0.7565                | 0.9667             | 0.8488         | 90                 | 0.9548                         | 0.9784                      | 0.9665                  | 648                         | 0.9329                     | 0.9697                  | 0.9510              | 330                     | 0.375                      | 0.5377                  | 0.4419              | 106                     | 0.7045                 | 0.9688              | 0.8158          | 32                  | 0.8496            | 0.9095         | 0.8785     | 0.9946           |
  | 0.0173        | 2.0   | 6994  | 0.0190          | 0.8170                        | 0.8803                     | 0.8475                 | 142                        | 0.7798                | 0.9444             | 0.8543         | 90                 | 0.9549                         | 0.9799                      | 0.9673                  | 648                         | 0.9559                     | 0.9848                  | 0.9701              | 330                     | 0.5496                     | 0.6792                  | 0.6076              | 106                     | 0.8182                 | 0.8438              | 0.8308          | 32                  | 0.8868            | 0.9414         | 0.9133     | 0.9957           |
  | 0.0106        | 3.0   | 10491 | 0.0306          | 0.8187                        | 0.9225                     | 0.8675                 | 142   