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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vulnerability-severity-classification-chinese-macbert-base
This model is a fine-tuned version of hfl/chinese-macbert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6059
- Accuracy: 0.7771
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|
| 0.5764 | 1.0 | 1772 | 0.6157 | 0.7462 |
| 0.5644 | 2.0 | 3544 | 0.5618 | 0.7663 |
| 0.4589 | 3.0 | 5316 | 0.5615 | 0.7781 |
| 0.3881 | 4.0 | 7088 | 0.5791 | 0.7823 |
| 0.3433 | 5.0 | 8860 | 0.6059 | 0.7771 |
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-chinese-macbert-base
Base model
hfl/chinese-macbert-base