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Librarian Bot: Add base_model information to model (#5)
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metadata
language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: xlm-roberta-large
model-index:
  - name: xlm-roberta-large-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: 0.8762313715584744
            name: Precision
          - type: recall
            value: 0.8966141121736882
            name: Recall
          - type: f1
            value: 0.8863055697496168
            name: F1
          - type: accuracy
            value: 0.979500052295785
            name: Accuracy

xlm-roberta-large-finetuned-lener-br

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

  • Loss: nan
  • Precision: 0.8762
  • Recall: 0.8966
  • F1: 0.8863
  • Accuracy: 0.9795

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
0.0785 1.0 3914 nan 0.7119 0.8410 0.7711 0.9658
0.076 2.0 7828 nan 0.8397 0.8679 0.8536 0.9740
0.0434 3.0 11742 nan 0.8545 0.8666 0.8605 0.9693
0.022 4.0 15656 nan 0.8293 0.8573 0.8431 0.9652
0.0284 5.0 19570 nan 0.8789 0.8571 0.8678 0.9776
0.029 6.0 23484 nan 0.8521 0.8788 0.8653 0.9771
0.0227 7.0 27398 nan 0.7648 0.8873 0.8215 0.9686
0.0219 8.0 31312 nan 0.8609 0.9026 0.8813 0.9780
0.0121 9.0 35226 nan 0.8746 0.8979 0.8861 0.9812
0.0087 10.0 39140 nan 0.8829 0.8827 0.8828 0.9808
0.0081 11.0 43054 nan 0.8740 0.8749 0.8745 0.9765
0.0058 12.0 46968 nan 0.8838 0.8842 0.8840 0.9788
0.0044 13.0 50882 nan 0.869 0.8984 0.8835 0.9788
0.002 14.0 54796 nan 0.8762 0.8966 0.8863 0.9795
0.0017 15.0 58710 nan 0.8729 0.8982 0.8854 0.9791

Framework versions

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