readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: distilbert_tcm_0.9
    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. -->
  
  # distilbert_tcm_0.9
  
  This model is a fine-tuned version of [adalbertojunior/distilbert-portuguese-cased](https://huggingface.co/adalbertojunior/distilbert-portuguese-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0297
  - Criterio Julgamento Precision: 0.7771
  - Criterio Julgamento Recall: 0.8592
  - Criterio Julgamento F1: 0.8161
  - Criterio Julgamento Number: 142
  - Data Sessao Precision: 0.7027
  - Data Sessao Recall: 0.8667
  - Data Sessao F1: 0.7761
  - Data Sessao Number: 90
  - Modalidade Licitacao Precision: 0.9520
  - Modalidade Licitacao Recall: 0.9799
  - Modalidade Licitacao F1: 0.9658
  - Modalidade Licitacao Number: 648
  - Numero Exercicio Precision: 0.9386
  - Numero Exercicio Recall: 0.9727
  - Numero Exercicio F1: 0.9554
  - Numero Exercicio Number: 330
  - Objeto Licitacao Precision: 0.4340
  - Objeto Licitacao Recall: 0.6509
  - Objeto Licitacao F1: 0.5208
  - Objeto Licitacao Number: 106
  - Valor Objeto Precision: 0.8824
  - Valor Objeto Recall: 0.9375
  - Valor Objeto F1: 0.9091
  - Valor Objeto Number: 32
  - Overall Precision: 0.8537
  - Overall Recall: 0.9310
  - Overall F1: 0.8907
  - 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: 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0257        | 1.0   | 3497  | 0.0241          | 0.7662                        | 0.8310                     | 0.7973                 | 142                        | 0.6444                | 0.9667             | 0.7733         | 90                 | 0.9407                         | 0.9784                      | 0.9592                  | 648                         | 0.8703                     | 0.9758                  | 0.92                | 330                     | 0.1255                     | 0.2925                  | 0.1756              | 106                     | 0.5556                 | 0.9375              | 0.6977          | 32                  | 0.7479            | 0.9065         | 0.8196     | 0.9921           |
  | 0.0222        | 2.0   | 6994  | 0.0195          | 0.8182                        | 0.8873                     | 0.8514                 | 142                        | 0.6667                | 0.9111             | 0.7700         | 90                 | 0.9407                         | 0.9799                      | 0.9599                  | 648                         | 0.8981                     | 0.9879                  | 0.9408              | 330                     | 0.2806                     | 0.5189                  | 0.3642              | 106                     | 0.7105                 | 0.8438              | 0.7714          | 32                  | 0.8076            | 0.9280         | 0.8637     | 0.9944           |
  | 0.0131        | 3.0   | 10491 | 0.0225          | 0.8025                        | 0.9155                     | 0.8553                 | 142                      