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Add evaluation results on the lener_br config and validation split of 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: xlm-roberta-base-finetuned-lener_br-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: 0.9206349206349206
          - name: Recall
            type: recall
            value: 0.9294391315585423
          - name: F1
            type: f1
            value: 0.925016077170418
          - name: Accuracy
            type: accuracy
            value: 0.9832504071600401
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9832802904657313
            verified: true
          - name: Precision
            type: precision
            value: 0.986258771429967
            verified: true
          - name: Recall
            type: recall
            value: 0.9897717432152019
            verified: true
          - name: F1
            type: f1
            value: 0.9880121346555324
            verified: true
          - name: loss
            type: loss
            value: 0.1050868034362793
            verified: true

xlm-roberta-base-finetuned-lener_br-finetuned-lener-br

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

  • Loss: nan
  • Precision: 0.9206
  • Recall: 0.9294
  • F1: 0.9250
  • Accuracy: 0.9833

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
0.0657 1.0 1957 nan 0.7780 0.8687 0.8209 0.9718
0.0321 2.0 3914 nan 0.8755 0.8708 0.8731 0.9793
0.0274 3.0 5871 nan 0.8096 0.9124 0.8579 0.9735
0.0216 4.0 7828 nan 0.7913 0.8842 0.8352 0.9718
0.0175 5.0 9785 nan 0.7735 0.9248 0.8424 0.9721
0.0117 6.0 11742 nan 0.9206 0.9294 0.9250 0.9833
0.0121 7.0 13699 nan 0.8988 0.9318 0.9150 0.9819
0.0086 8.0 15656 nan 0.8922 0.9175 0.9047 0.9801
0.007 9.0 17613 nan 0.8482 0.8997 0.8732 0.9769
0.0051 10.0 19570 nan 0.8730 0.9274 0.8994 0.9798
0.0045 11.0 21527 nan 0.9172 0.9051 0.9111 0.9819
0.0014 12.0 23484 nan 0.9138 0.9155 0.9147 0.9823
0.0029 13.0 25441 nan 0.9099 0.9287 0.9192 0.9834
0.0035 14.0 27398 nan 0.9019 0.9294 0.9155 0.9831
0.0005 15.0 29355 nan 0.8886 0.9343 0.9109 0.9825

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.1