readme: ---
  language:
  - pt
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - lener_br
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-large-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: 0.8762313715584744
      - name: Recall
        type: recall
        value: 0.8966141121736882
      - name: F1
        type: f1
        value: 0.8863055697496168
      - name: Accuracy
        type: accuracy
        value: 0.979500052295785
  ---
  
  <!-- 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-large-finetuned-lener-br
  
  This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lener_br dataset.
  It achieves the following results on the evaluation set:
  - Loss: nan
  - Precision: 0.8762
  - Recall: 0.8966
  - F1: 0.8863
  - Accuracy: 0.9795
  
  ## 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: 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
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0785        | 1.0   | 3914  | nan             | 0.7119    | 0.8410 | 0.7711 | 0.9658   |
  | 0.076         | 2.0   | 7828  | nan             | 0.8397    | 0.8679 | 0.8536 | 0.9740   |
  | 0.0434        | 3.0   | 11742 | nan             | 0.8545    | 0.8666 | 0.8605 | 0.9693   |
  | 0.022         | 4.0   | 15656 | nan             | 0.8293    | 0.8573 | 0.8431 | 0.9652   |
  | 0.0284        | 5.0   | 19570 | nan             | 0.8789    | 0.8571 | 0.8678 | 0.9776   |
  | 0.029         | 6.0   | 23484 | nan             | 0.8521    | 0.8788 | 0.8653 | 0.9771   |
  | 0.0227        | 7.0   | 27398 | nan             | 0.7648    | 0.8873 | 0.8215 | 0.9686   |
  | 0.0219        | 8.0   | 31312 | nan             | 0.8609    | 0.9026 | 0.8813 | 0.9780   |
  | 0.0121        | 9.0   | 35226 | nan             | 0.8746    | 0.8979 | 0.8861 | 0.9812   |
  | 0.0087        | 10.0  | 39140 | nan             | 0.8829    | 0.8827 | 0.8828 | 0.9808   |
  | 0.0081        | 11.0  | 43054 | nan             | 0.8740    | 0.8749 | 0.8745 | 0.9765   |
  | 0.0058        | 12.0  | 46968 | nan             | 0.8838    | 0.8842 | 0.8840 | 0.9788   |
  | 0.0044        | 13.0  | 50882 | nan             | 0.869     | 0.8984 | 0.8835 | 0.9788   |
  | 0.002         | 14.0  | 54796 | nan             | 0.8762    | 0.8966 | 0.8863 | 0.9795   |
  | 0.0017        | 15.0  | 58710 | nan             | 0.8729    | 0.8982 | 0.8854 | 0.9791   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  