readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_base_tcm_0.6
    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_0.6
  
  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: 0.0193
  - Criterio Julgamento Precision: 0.8875
  - Criterio Julgamento Recall: 0.8659
  - Criterio Julgamento F1: 0.8765
  - Criterio Julgamento Number: 82
  - Data Sessao Precision: 0.7571
  - Data Sessao Recall: 0.9636
  - Data Sessao F1: 0.848
  - Data Sessao Number: 55
  - Modalidade Licitacao Precision: 0.9394
  - Modalidade Licitacao Recall: 0.9718
  - Modalidade Licitacao F1: 0.9553
  - Modalidade Licitacao Number: 319
  - Numero Exercicio Precision: 0.9172
  - Numero Exercicio Recall: 0.9688
  - Numero Exercicio F1: 0.9422
  - Numero Exercicio Number: 160
  - Objeto Licitacao Precision: 0.4659
  - Objeto Licitacao Recall: 0.7069
  - Objeto Licitacao F1: 0.5616
  - Objeto Licitacao Number: 58
  - Valor Objeto Precision: 0.8333
  - Valor Objeto Recall: 0.9211
  - Valor Objeto F1: 0.875
  - Valor Objeto Number: 38
  - Overall Precision: 0.8537
  - Overall Recall: 0.9340
  - Overall F1: 0.8920
  - Overall Accuracy: 0.9951
  
  ## 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 | 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0252        | 1.0   | 1963  | 0.0202          | 0.8022                        | 0.8902                     | 0.8439                 | 82                         | 0.7391                | 0.9273             | 0.8226         | 55                 | 0.9233                         | 0.9812                      | 0.9514                  | 319                         | 0.8966                     | 0.975                   | 0.9341              | 160                     | 0.4730                     | 0.6034                  | 0.5303              | 58                      | 0.7083                 | 0.8947              | 0.7907          | 38                  | 0.8327            | 0.9298         | 0.8786     | 0.9948           |
  | 0.0191        | 2.0   | 3926  | 0.0226          | 0.8554                        | 0.8659                     | 0.8606                 | 82                         | 0.5641                | 0.4                | 0.4681         | 55                 | 0.9572                         | 0.9812                      | 0.9690                  | 319                         | 0.9273                     | 0.9563                  | 0.9415              | 160                     | 0.3770                     | 0.3966                  | 0.3866              | 58                      | 0.8571                 | 0.7895              | 0.8219          | 38                  | 0.8620            | 0.8596         | 0.8608     | 0.9951           |
  | 0.0137        | 3.0   | 5889  | 0.0193          | 0.8875                        | 0.8659                     | 0.8765                 | 82                         | 0