README.md

metadata

language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bertimbau-base-lener-br-finetuned-lener-br
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: train
          args: lener_br
        metrics:
          - type: precision
            value: 0.8942967409948542
            name: Precision
          - type: recall
            value: 0.8969892473118279
            name: Recall
          - type: f1
            value: 0.8956409705819198
            name: F1
          - type: accuracy
            value: 0.9696009264479559
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: test
        metrics:
          - type: accuracy
            value: 0.981178408105048
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.98709417546121
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9862996055703132
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.986696730552424
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.144147127866745
            name: loss
            verified: true
            verifyToken: >-
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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: 0.9696009264479559
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.974166236103935
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9847359110437199
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9794225581044623
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.26272761821746826
            name: loss
            verified: true
            verifyToken: >-
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bertimbau-base-lener-br-finetuned-lener-br

This model is a fine-tuned version of Luciano/bertimbau-base-finetuned-lener-br on the lener_br dataset. It achieves the following results on the evaluation set:

• Loss: nan
• Precision: 0.8943
• Recall: 0.8970
• F1: 0.8956
• Accuracy: 0.9696

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
0.0678	1.0	1957	nan	0.8148	0.8882	0.8499	0.9689
0.0371	2.0	3914	nan	0.8347	0.9022	0.8671	0.9671
0.0242	3.0	5871	nan	0.8491	0.8905	0.8693	0.9716
0.0197	4.0	7828	nan	0.9014	0.8772	0.8892	0.9780
0.0135	5.0	9785	nan	0.8651	0.9060	0.8851	0.9765
0.013	6.0	11742	nan	0.8882	0.9054	0.8967	0.9767
0.0084	7.0	13699	nan	0.8559	0.9097	0.8820	0.9751
0.0069	8.0	15656	nan	0.8916	0.8828	0.8872	0.9696
0.0047	9.0	17613	nan	0.8964	0.8931	0.8948	0.9716
0.0028	10.0	19570	nan	0.8864	0.9047	0.8955	0.9691
0.0023	11.0	21527	nan	0.8860	0.9011	0.8935	0.9693
0.0009	12.0	23484	nan	0.8952	0.8987	0.8970	0.9686
0.0014	13.0	25441	nan	0.8929	0.8985	0.8957	0.9699
0.0025	14.0	27398	nan	0.8914	0.8981	0.8947	0.9700
0.001	15.0	29355	nan	0.8943	0.8970	0.8956	0.9696

Framework versions

• Transformers 4.21.2
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1