bert-finetuned-balanced
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: 0.7493
- Accuracy: 0.6556
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0791 | 0.6667 | 30 | 0.9192 | 0.4889 |
| 0.8247 | 1.3333 | 60 | 0.7806 | 0.6667 |
| 0.78 | 2.0 | 90 | 0.7564 | 0.6667 |
| 0.7218 | 2.6667 | 120 | 0.7519 | 0.7222 |
| 0.6821 | 3.3333 | 150 | 0.7527 | 0.6333 |
| 0.6921 | 4.0 | 180 | 0.7548 | 0.6111 |
| 0.6177 | 4.6667 | 210 | 0.7493 | 0.6556 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1