readme: ---
  language:
  - pt
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - lener_br
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-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: 0.844312854675549
      - name: Recall
        type: recall
        value: 0.8844662703540966
      - name: F1
        type: f1
        value: 0.8639232517041151
      - name: Accuracy
        type: accuracy
        value: 0.97516697297055
  ---
  
  <!-- 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. -->
  
  # xlm-roberta-base-finetuned-lener-br
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lener_br dataset.
  It achieves the following results on the evaluation set:
  - Loss: nan
  - Precision: 0.8443
  - Recall: 0.8845
  - F1: 0.8639
  - Accuracy: 0.9752
  
  ## 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: 2e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - 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
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0832        | 1.0   | 1957  | nan             | 0.6752    | 0.8625 | 0.7575 | 0.9578   |
  | 0.0477        | 2.0   | 3914  | nan             | 0.8391    | 0.8839 | 0.8609 | 0.9704   |
  | 0.029         | 3.0   | 5871  | nan             | 0.7530    | 0.9059 | 0.8224 | 0.9648   |
  | 0.0223        | 4.0   | 7828  | nan             | 0.7488    | 0.8744 | 0.8067 | 0.9659   |
  | 0.0234        | 5.0   | 9785  | nan             | 0.7216    | 0.8783 | 0.7923 | 0.9644   |
  | 0.0171        | 6.0   | 11742 | nan             | 0.7072    | 0.8969 | 0.7908 | 0.9642   |
  | 0.0121        | 7.0   | 13699 | nan             | 0.7769    | 0.8775 | 0.8241 | 0.9681   |
  | 0.0093        | 8.0   | 15656 | nan             | 0.7218    | 0.8772 | 0.7920 | 0.9621   |
  | 0.0074        | 9.0   | 17613 | nan             | 0.8241    | 0.8767 | 0.8496 | 0.9739   |
  | 0.0055        | 10.0  | 19570 | nan             | 0.7369    | 0.8801 | 0.8021 | 0.9638   |
  | 0.0055        | 11.0  | 21527 | nan             | 0.8443    | 0.8845 | 0.8639 | 0.9752   |
  | 0.0029        | 12.0  | 23484 | nan             | 0.8338    | 0.8935 | 0.8626 | 0.9753   |
  | 0.0026        | 13.0  | 25441 | nan             | 0.7721    | 0.8992 | 0.8308 | 0.9694   |
  | 0.004         | 14.0  | 27398 | nan             | 0.7466    | 0.8886 | 0.8114 | 0.9672   |
  | 0.0006        | 15.0  | 29355 | nan             | 0.7518    | 0.8995 | 0.8190 | 0.9686   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  