bert-large-uncased_fold_9
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0323
- Accuracy: 0.9955
- F1: 0.9917
- Precision: 0.9975
- Recall: 0.9859
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: 15
- eval_batch_size: 15
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0355 | 1.0 | 10089 | 0.0322 | 0.9933 | 0.9875 | 0.9892 | 0.9859 |
| 0.0285 | 2.0 | 20178 | 0.0282 | 0.9950 | 0.9907 | 0.9964 | 0.9850 |
| 0.0149 | 3.0 | 30267 | 0.0323 | 0.9955 | 0.9917 | 0.9975 | 0.9859 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2