VLAI for Severity
Collection
A collection of papers, models, and datasets supporting the AI and NLP components of the Vulnerability-Lookup project.
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7 items
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Updated
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1
vulnerability-severity-classification-roberta-base
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5053
- Accuracy: 0.8195
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: 32
- eval_batch_size: 32
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6458 | 1.0 | 14962 | 0.6352 | 0.7394 |
| 0.4643 | 2.0 | 29924 | 0.5741 | 0.7702 |
| 0.5519 | 3.0 | 44886 | 0.5261 | 0.7922 |
| 0.3822 | 4.0 | 59848 | 0.5054 | 0.8111 |
| 0.344 | 5.0 | 74810 | 0.5053 | 0.8195 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
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Model tree for CIRCL/vulnerability-severity-classification-roberta-base
Base model
FacebookAI/roberta-base