CIRCL/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
• —trainbatchsize: 64
• —evalbatchsize: 64
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: 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