bert-base-uncased_fold_5
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0360
- Accuracy: 0.9944
- F1: 0.9896
- Precision: 0.9964
- Recall: 0.9828
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: 38
- eval_batch_size: 38
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0283 | 1.0 | 3983 | 0.0268 | 0.9937 | 0.9883 | 0.9946 | 0.9819 |
| 0.0286 | 2.0 | 7966 | 0.0354 | 0.9931 | 0.9871 | 0.9946 | 0.9797 |
| 0.0119 | 3.0 | 11949 | 0.0360 | 0.9944 | 0.9896 | 0.9964 | 0.9828 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2