test-trainer
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0804
- Accuracy: 0.9849
- F1: 0.9788
- Precision: 0.9743
- Recall: 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: 5e-05
- train_batch_size: 600
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.0271 | 1.0 | 703 | 0.0577 | 0.9845 | 0.9782 | 0.9760 | 0.9803 |
0.0247 | 2.0 | 1406 | 0.0736 | 0.9850 | 0.9791 | 0.9719 | 0.9864 |
0.012 | 3.0 | 2109 | 0.0804 | 0.9849 | 0.9788 | 0.9743 | 0.9833 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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