Vulnerability_binary
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0736
- Accuracy: 0.6712
- Precision: 0.6927
- Recall: 0.6169
- F1: 0.6526
- Auc: 0.7228
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: 32
- eval_batch_size: 32
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 118 | 0.6261 | 0.6687 | 0.7024 | 0.5871 | 0.6396 | 0.7236 |
| No log | 2.0 | 236 | 0.5943 | 0.6936 | 0.7549 | 0.5746 | 0.6525 | 0.7619 |
| No log | 3.0 | 354 | 0.6206 | 0.6936 | 0.7042 | 0.6692 | 0.6862 | 0.7537 |
| No log | 4.0 | 472 | 0.6992 | 0.6899 | 0.7192 | 0.6244 | 0.6684 | 0.7458 |
| 0.5353 | 5.0 | 590 | 0.8100 | 0.6849 | 0.6822 | 0.6940 | 0.6880 | 0.7303 |
| 0.5353 | 6.0 | 708 | 0.9372 | 0.6712 | 0.6835 | 0.6393 | 0.6607 | 0.7217 |
| 0.5353 | 7.0 | 826 | 1.0736 | 0.6712 | 0.6927 | 0.6169 | 0.6526 | 0.7228 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1