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bertimbau-large-lener_br

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bertimbau-large-lener_br / README.md
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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-large-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: 0.9801301293674859
model-index:
  - name: Luciano/bertimbau-large-lener_br
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: test
        metrics:
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            name: Accuracy
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            name: Precision
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          - type: recall
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            name: 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:
          - type: accuracy
            value: 0.9801301293674859
            name: Accuracy
            verified: true
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          - type: recall
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      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: train
        metrics:
          - type: accuracy
            value: 0.9989004979420315
            name: Accuracy
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            name: Recall
            verified: true
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            name: loss
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bertimbau-large-lener_br

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

  • Loss: 0.1271
  • Precision: 0.8965
  • Recall: 0.9198
  • F1: 0.9080
  • Accuracy: 0.9801

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
0.0674 1.0 1957 0.1349 0.7617 0.8710 0.8127 0.9594
0.0443 2.0 3914 0.1867 0.6862 0.9194 0.7858 0.9575
0.0283 3.0 5871 0.1185 0.8206 0.8766 0.8477 0.9678
0.0226 4.0 7828 0.1405 0.8072 0.8978 0.8501 0.9708
0.0141 5.0 9785 0.1898 0.7224 0.9194 0.8090 0.9629
0.01 6.0 11742 0.1655 0.9062 0.8856 0.8958 0.9741
0.012 7.0 13699 0.1271 0.8965 0.9198 0.9080 0.9801
0.0091 8.0 15656 0.1919 0.8890 0.8886 0.8888 0.9719
0.0042 9.0 17613 0.1725 0.8977 0.8985 0.8981 0.9744
0.0043 10.0 19570 0.1530 0.8878 0.9034 0.8955 0.9761
0.0042 11.0 21527 0.1635 0.8792 0.9108 0.8947 0.9774
0.0033 12.0 23484 0.2009 0.8155 0.9138 0.8619 0.9719
0.0008 13.0 25441 0.1766 0.8737 0.9135 0.8932 0.9755
0.0005 14.0 27398 0.1868 0.8616 0.9129 0.8865 0.9743
0.0014 15.0 29355 0.1910 0.8694 0.9101 0.8893 0.9746

Framework versions

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