attack_detection_fine_tuned_bert
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: 0.4901
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch 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: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3936 | 1.0 | 88 | 0.4908 |
| 0.6087 | 2.0 | 176 | 0.5245 |
| 0.5628 | 3.0 | 264 | 0.4868 |
| 0.5004 | 4.0 | 352 | 0.4955 |
| 0.5573 | 5.0 | 440 | 0.4901 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for osei1819/attack_detection_fine_tuned_bert
Base model
google-bert/bert-base-uncased