distilbert-base-uncased-nvidia-aegis-v1-augmented
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1704
- Accuracy: 0.9453
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use 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_steps: 50
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6501 | 1.0 | 3024 | 0.2429 | 0.9094 |
| 0.3271 | 2.0 | 6048 | 0.1835 | 0.9347 |
| 0.1831 | 3.0 | 9072 | 0.1703 | 0.9454 |
| 0.3713 | 4.0 | 12096 | 0.2546 | 0.9353 |
| 0.1770 | 5.0 | 15120 | 0.2046 | 0.9483 |
| 0.0570 | 6.0 | 18144 | 0.2254 | 0.9485 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2