readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_base_tcm_0.5
    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_0.5
  
  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: 0.0149
  - Criterio Julgamento Precision: 0.8409
  - Criterio Julgamento Recall: 0.8740
  - Criterio Julgamento F1: 0.8571
  - Criterio Julgamento Number: 127
  - Data Sessao Precision: 0.7901
  - Data Sessao Recall: 0.9143
  - Data Sessao F1: 0.8477
  - Data Sessao Number: 70
  - Modalidade Licitacao Precision: 0.8976
  - Modalidade Licitacao Recall: 0.9581
  - Modalidade Licitacao F1: 0.9269
  - Modalidade Licitacao Number: 430
  - Numero Exercicio Precision: 0.9676
  - Numero Exercicio Recall: 0.9721
  - Numero Exercicio F1: 0.9698
  - Numero Exercicio Number: 215
  - Objeto Licitacao Precision: 0.4375
  - Objeto Licitacao Recall: 0.5976
  - Objeto Licitacao F1: 0.5052
  - Objeto Licitacao Number: 82
  - Valor Objeto Precision: 0.76
  - Valor Objeto Recall: 0.8444
  - Valor Objeto F1: 0.8
  - Valor Objeto Number: 45
  - Overall Precision: 0.8410
  - Overall Recall: 0.9112
  - Overall F1: 0.8747
  - Overall Accuracy: 0.9963
  
  ## 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: 3.0
  
  ### 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0212        | 1.0   | 3996  | 0.0203          | 0.7483                        | 0.8425                     | 0.7926                 | 127                        | 0.5739                | 0.9429             | 0.7135         | 70                 | 0.9033                         | 0.9558                      | 0.9288                  | 430                         | 0.8805                     | 0.9256                  | 0.9025              | 215                     | 0.3445                     | 0.5                     | 0.4080              | 82                      | 0.5846                 | 0.8444              | 0.6909          | 45                  | 0.7676            | 0.8896         | 0.8241     | 0.9950           |
  | 0.012         | 2.0   | 7992  | 0.0158          | 0.8201                        | 0.8976                     | 0.8571                 | 127                        | 0.7174                | 0.9429             | 0.8148         | 70                 | 0.8686                         | 0.9535                      | 0.9091                  | 430                         | 0.9591                     | 0.9814                  | 0.9701              | 215                     | 0.2987                     | 0.5610                  | 0.3898              | 82                      | 0.6364                 | 0.7778              | 0.7000          | 45                  | 0.7792            | 0.9102         | 0.8396     | 0.9954           |
  | 0.0062        | 3.0   | 11988 | 0.0149          | 0.8409                        | 0.8740                     | 0.8571                 | 127                        | 0.79