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This model is a fine-tuned version of belisards/congretimbau on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5239
  • Accuracy: 0.8254
  • F1: 0.7442
  • Recall: 0.7267
  • Precision: 0.7727

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 5151
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Recall Precision
0.5681 1.0323 32 0.5508 0.75 0.4286 0.5 0.375
0.5233 2.0645 64 0.5138 0.7381 0.5146 0.5317 0.5897
0.4339 3.0968 96 0.4529 0.7917 0.6875 0.6706 0.7240
0.3907 4.1290 128 0.4087 0.8393 0.7683 0.75 0.7970
0.2166 5.1613 160 0.4054 0.8452 0.7867 0.7778 0.7976
0.14 6.1935 192 0.4474 0.8274 0.7716 0.7738 0.7696
0.0673 7.2258 224 0.5118 0.8393 0.7726 0.7579 0.7932

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.1
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