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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-large-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: 0.9122490993309316
          - name: Recall
            type: recall
            value: 0.9162574308606876
          - name: F1
            type: f1
            value: 0.9142488716956804
          - name: Accuracy
            type: accuracy
            value: 0.982592974434832
      - 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: 0.982592974434832
            verified: true
          - name: Precision
            type: precision
            value: 0.9882345251323615
            verified: true
          - name: Recall
            type: recall
            value: 0.9881214973122232
            verified: true
          - name: F1
            type: f1
            value: 0.9881780079902612
            verified: true
          - name: loss
            type: loss
            value: 0.16145998239517212
            verified: true

xlm-roberta-large-finetuned-lener_br-finetuned-lener-br

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

  • Loss: nan
  • Precision: 0.9122
  • Recall: 0.9163
  • F1: 0.9142
  • Accuracy: 0.9826

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
0.068 1.0 3914 nan 0.6196 0.8604 0.7204 0.9568
0.0767 2.0 7828 nan 0.8270 0.8710 0.8484 0.9693
0.0257 3.0 11742 nan 0.7243 0.9005 0.8029 0.9639
0.0193 4.0 15656 nan 0.9010 0.8984 0.8997 0.9821
0.0156 5.0 19570 nan 0.7150 0.9121 0.8016 0.9641
0.0165 6.0 23484 nan 0.7640 0.8796 0.8177 0.9691
0.0225 7.0 27398 nan 0.8851 0.9098 0.8973 0.9803
0.016 8.0 31312 nan 0.9081 0.9015 0.9048 0.9792
0.0078 9.0 35226 nan 0.8941 0.8863 0.8902 0.9788
0.0061 10.0 39140 nan 0.9026 0.9002 0.9014 0.9804
0.0057 11.0 43054 nan 0.8793 0.9018 0.8904 0.9769
0.0044 12.0 46968 nan 0.8790 0.9033 0.8910 0.9785
0.0043 13.0 50882 nan 0.9122 0.9163 0.9142 0.9826
0.0003 14.0 54796 nan 0.9032 0.9070 0.9051 0.9807
0.0025 15.0 58710 nan 0.8903 0.9085 0.8993 0.9798

Framework versions

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