vulnerability-severity-classification-russian-ruRoberta-large

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VLAI: Automated Vulnerability Severity Classification (Russian Text)

A fine-tuned ai-forever/ruRoberta-large model for classifying Russian vulnerability descriptions from the FSTEC.

Trained on the CIRCL/Vulnerability-FSTEC dataset as part of the VulnTrain project.

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

It achieves the following results on the evaluation set:

• Loss: 2.6495
• Accuracy: 0.7417
• F1 Macro: 0.6650
• Low Precision: 0.6154
• Low Recall: 0.3380
• Low F1: 0.4364
• Medium Precision: 0.7619
• Medium Recall: 0.8312
• Medium F1: 0.7951
• High Precision: 0.6869
• High Recall: 0.6080
• High F1: 0.6450
• Critical Precision: 0.7678
• Critical Recall: 0.7996
• Critical F1: 0.7834

Training results

Framework versions

• Transformers 5.5.0
• Pytorch 2.11.0+cu130
• Datasets 4.8.4
• Tokenizers 0.22.2