bert-base-uncased-test
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: 3.3027
- Accuracy: 0.725
- Precision: 0.7153
- Recall: 0.7372
- F1: 0.7210
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 30 | 3.2153 | 0.7333 | 0.7226 | 0.7478 | 0.7299 |
| 0.0 | 2.0 | 60 | 3.2642 | 0.725 | 0.7153 | 0.7372 | 0.7210 |
| 0.0 | 3.0 | 90 | 3.2930 | 0.725 | 0.7153 | 0.7372 | 0.7210 |
| 0.0 | 4.0 | 120 | 3.3027 | 0.725 | 0.7153 | 0.7372 | 0.7210 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.2.0
- Tokenizers 0.22.1