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bertimbau-base-lener_br

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metadata
language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: bertimbau-base-lener_br
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          args: lener_br
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9692504609383333
model-index:
  - name: Luciano/bertimbau-base-lener_br
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: test
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          - type: loss
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      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
        metrics:
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            name: Accuracy
            verified: true
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          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: train
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            name: Accuracy
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bertimbau-base-lener_br

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the lener_br dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2298
  • Precision: 0.8501
  • Recall: 0.9138
  • F1: 0.8808
  • Accuracy: 0.9693

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

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0686 1.0 1957 0.1399 0.7759 0.8669 0.8189 0.9641
0.0437 2.0 3914 0.1457 0.7997 0.8938 0.8441 0.9623
0.0313 3.0 5871 0.1675 0.8466 0.8744 0.8603 0.9651
0.0201 4.0 7828 0.1621 0.8713 0.8839 0.8775 0.9718
0.0137 5.0 9785 0.1811 0.7783 0.9159 0.8415 0.9645
0.0105 6.0 11742 0.1836 0.8568 0.9009 0.8783 0.9692
0.0105 7.0 13699 0.1649 0.8339 0.9125 0.8714 0.9725
0.0059 8.0 15656 0.2298 0.8501 0.9138 0.8808 0.9693
0.0051 9.0 17613 0.2210 0.8437 0.9045 0.8731 0.9693
0.0061 10.0 19570 0.2499 0.8627 0.8946 0.8784 0.9681
0.0041 11.0 21527 0.1985 0.8560 0.9052 0.8799 0.9720
0.003 12.0 23484 0.2204 0.8498 0.9065 0.8772 0.9699
0.0014 13.0 25441 0.2152 0.8425 0.9067 0.8734 0.9709
0.0005 14.0 27398 0.2317 0.8553 0.8987 0.8765 0.9705
0.0015 15.0 29355 0.2436 0.8543 0.8989 0.8760 0.9700

Framework versions

  • Transformers 4.8.2
  • Pytorch 1.9.0+cu102
  • Datasets 1.9.0
  • Tokenizers 0.10.3