---
library_name: transformers
license: cc-by-4.0
base_model: roberta-base
metrics:
- accuracy
tags:
- generated_from_trainer
- text-classification
- classification
- nlp
- vulnerability
model-index:
- name: vulnerability-severity-classification-roberta-base
  results: []
datasets:
- CIRCL/vulnerability-scores
---


# VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification

# Severity classification

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).

The model was presented in the paper [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://huggingface.co/papers/2507.03607) [[arXiv](https://arxiv.org/abs/2507.03607)].

**Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.

You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information.


## Model description

It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions.

## How to get started with the model

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

labels = ["low", "medium", "high", "critical"]

model_name = "CIRCL/vulnerability-severity-classification-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

print("Model revision:", model.config._commit_hash)

test_description = "SAP NetWeaver Visual Composer Metadata Uploader is not protected with a proper authorization, allowing unauthenticated agent to upload potentially malicious executable binaries \
that could severely harm the host system. This could significantly affect the confidentiality, integrity, and availability of the targeted system."
inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

# Print results
print("Predictions:", predictions)
predicted_class = torch.argmax(predictions, dim=-1).item()
print("Predicted severity:", labels[predicted_class])
```

## 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

It achieves the following results on the evaluation set:
- Loss: 2.0378
- Accuracy: 0.8152
- F1 Macro: 0.7487
- Low Precision: 0.6544
- Low Recall: 0.5105
- Low F1: 0.5736
- Medium Precision: 0.8440
- Medium Recall: 0.8653
- Medium F1: 0.8545
- High Precision: 0.8110
- High Recall: 0.8111
- High F1: 0.8111
- Critical Precision: 0.7619
- Critical Recall: 0.7493
- Critical F1: 0.7556

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
| 2.3504        | 1.0   | 17926 | 2.6006          | 0.7357   | 0.6372   | 0.5440        | 0.3233     | 0.4056 | 0.7788           | 0.8171        | 0.7975    | 0.7123         | 0.7369      | 0.7244  | 0.6738             | 0.5764          | 0.6213      |
| 2.4658        | 2.0   | 35852 | 2.3303          | 0.7636   | 0.6543   | 0.7435        | 0.2598     | 0.3850 | 0.7912           | 0.8500        | 0.8195    | 0.7377         | 0.7716      | 0.7543  | 0.7336             | 0.5973          | 0.6585      |
| 2.0186        | 3.0   | 53778 | 2.1500          | 0.7856   | 0.7041   | 0.6568        | 0.3937     | 0.4923 | 0.8148           | 0.8526        | 0.8333    | 0.7805         | 0.7728      | 0.7767  | 0.7146             | 0.7138          | 0.7142      |
| 1.7673        | 4.0   | 71704 | 2.0545          | 0.8046   | 0.7352   | 0.5998        | 0.5178     | 0.5558 | 0.8334           | 0.8602        | 0.8466    | 0.8018         | 0.7995      | 0.8007  | 0.7623             | 0.7150          | 0.7379      |
| 1.2162        | 5.0   | 89630 | 2.0378          | 0.8152   | 0.7487   | 0.6544        | 0.5105     | 0.5736 | 0.8440           | 0.8653        | 0.8545    | 0.8110         | 0.8111      | 0.8111  | 0.7619             | 0.7493          | 0.7556      |


### Framework versions

- Transformers 5.14.1
- Pytorch 2.13.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2