readme: ---
  language:
  - pt
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - brazilian_court_decisions
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: lener_br
        type: lener_br
        config: lener_br
        split: train
        args: lener_br
      metrics:
      - name: Precision
        type: precision
        value: 0
      - name: Recall
        type: recall
        value: 0
      - name: F1
        type: f1
        value: 0
      - name: Accuracy
        type: accuracy
        value: 0
  ---
  
  <!-- 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-multilingual-cased-finetuned-lener_br-finetuned-lener-br
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the lener_br dataset.
  It achieves the following results on the evaluation set:
  - Loss: nan  (To update)
  - Precision: 0.9122  (To update)
  - Recall: 0.9163  (To update)
  - F1: 0.9142  (To update)
  - Accuracy: 0.9826  (To update)
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters (To update) 
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results (To update)
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.068         | 1.0   | 3914  | nan             | 0.6196    | 0.8604 | 0.7204 | 0.9568   |
  | 0.0767        | 2.0   | 7828  | nan             | 0.8270    | 0.8710 | 0.8484 | 0.9693   |
  | 0.0257        | 3.0   | 11742 | nan             | 0.7243    | 0.9005 | 0.8029 | 0.9639   |
  | 0.0193        | 4.0   | 15656 | nan             | 0.9010    | 0.8984 | 0.8997 | 0.9821   |
  | 0.0156        | 5.0   | 19570 | nan             | 0.7150    | 0.9121 | 0.8016 | 0.9641   |
  | 0.0165        | 6.0   | 23484 | nan             | 0.7640    | 0.8796 | 0.8177 | 0.9691   |
  | 0.0225        | 7.0   | 27398 | nan             | 0.8851    | 0.9098 | 0.8973 | 0.9803   |
  | 0.016         | 8.0   | 31312 | nan             | 0.9081    | 0.9015 | 0.9048 | 0.9792   |
  | 0.0078        | 9.0   | 35226 | nan             | 0.8941    | 0.8863 | 0.8902 | 0.9788   |
  | 0.0061        | 10.0  | 39140 | nan             | 0.9026    | 0.9002 | 0.9014 | 0.9804   |
  | 0.0057        | 11.0  | 43054 | nan             | 0.8793    | 0.9018 | 0.8904 | 0.9769   |
  | 0.0044        | 12.0  | 46968 | nan             | 0.8790    | 0.9033 | 0.8910 | 0.9785   |
  | 0.0043        | 13.0  | 50882 | nan             | 0.9122    | 0.9163 | 0.9142 | 0.9826   |
  | 0.0003        | 14.0  | 54796 | nan             | 0.9032    | 0.9070 | 0.9051 | 0.9807   |
  | 0.0025        | 15.0  | 58710 | nan             | 0.8903    | 0.9085 | 0.8993 | 0.9798   |
  
  
  ### Framework versions (To update)
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  