bert-base-uncased_train_v1
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: 0.0359
- Accuracy: 0.9941
- F1: 0.9891
- Precision: 0.9942
- Recall: 0.9841
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: 32
- eval_batch_size: 32
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0243 | 1.0 | 4087 | 0.0318 | 0.9928 | 0.9866 | 0.9972 | 0.9762 |
| 0.0388 | 2.0 | 8174 | 0.0292 | 0.9936 | 0.9882 | 0.9980 | 0.9786 |
| 0.0066 | 3.0 | 12261 | 0.0359 | 0.9941 | 0.9891 | 0.9942 | 0.9841 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2