ModernBERT-base-distilled-Optuna
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1815
- Accuracy: 0.8878
- F1: 0.8837
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: 48
- eval_batch_size: 48
- 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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 5.2624 | 1.0 | 318 | 2.4553 | 0.9029 | 0.9001 |
| 1.7423 | 2.0 | 636 | 1.3311 | 0.9397 | 0.9387 |
| 1.0175 | 3.0 | 954 | 1.0368 | 0.9513 | 0.9505 |
| 0.7953 | 4.0 | 1272 | 0.9595 | 0.9519 | 0.9513 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1