---
language:
- ru
license: cc-by-4.0
library_name: transformers
tags:
- text-classification
- vulnerability
- severity
- cybersecurity
- fstec
- generated_from_trainer
datasets:
- CIRCL/Vulnerability-FSTEC
base_model: ai-forever/ruRoberta-large
pipeline_tag: text-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# VLAI: Automated Vulnerability Severity Classification (Russian Text)

A fine-tuned [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) model for classifying Russian vulnerability descriptions from the [FSTEC](https://vulnerability.circl.lu/recent#fstec).

Trained on the [CIRCL/Vulnerability-FSTEC](https://huggingface.co/datasets/CIRCL/Vulnerability-FSTEC) dataset as part of the [VulnTrain](https://github.com/vulnerability-lookup/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

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Low Precision | Low Recall | Low F1 | Medium Precision | Medium Recall | Medium F1 | High Precision | High Recall | High F1 | Critical Precision | Critical Recall | Critical F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
| 3.0373        | 1.0   | 1167 | 3.0503          | 0.6895   | 0.5626   | 0.7959        | 0.1099     | 0.1931 | 0.7233           | 0.7958        | 0.7578    | 0.6083         | 0.5152      | 0.5579  | 0.6947             | 0.7954          | 0.7416      |
| 2.9084        | 2.0   | 2334 | 2.8601          | 0.7142   | 0.6048   | 0.8           | 0.1803     | 0.2943 | 0.7523           | 0.8001        | 0.7754    | 0.6923         | 0.5156      | 0.5910  | 0.6660             | 0.8807          | 0.7584      |
| 2.5937        | 3.0   | 3501 | 2.6529          | 0.7335   | 0.6349   | 0.6967        | 0.2394     | 0.3564 | 0.7565           | 0.8379        | 0.7952    | 0.7126         | 0.5411      | 0.6152  | 0.7092             | 0.8488          | 0.7727      |
| 2.5230        | 4.0   | 4668 | 2.6348          | 0.7365   | 0.6549   | 0.6170        | 0.3268     | 0.4273 | 0.7403           | 0.8568        | 0.7943    | 0.7208         | 0.5451      | 0.6207  | 0.7526             | 0.8038          | 0.7773      |
| 2.0599        | 5.0   | 5835 | 2.6495          | 0.7417   | 0.6650   | 0.6154        | 0.3380     | 0.4364 | 0.7619           | 0.8312        | 0.7951    | 0.6869         | 0.6080      | 0.6450  | 0.7678             | 0.7996          | 0.7834      |


### Framework versions

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