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

<!-- 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. -->

# vulnerability-severity-classification-roberta-base

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).

It achieves the following results on the evaluation set:
- Loss: 0.5087
- Accuracy: 0.8286

## 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-distilbert-base-uncased"
... tokenizer = AutoTokenizer.from_pretrained(model_name)
... model = AutoModelForSequenceClassification.from_pretrained(model_name)
... model.eval()
... 
... 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])
... 
Predictions: tensor([[4.9335e-04, 3.4782e-02, 2.6257e-01, 7.0215e-01]])
Predicted severity: critical
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.6379        | 1.0   | 26871  | 0.6473          | 0.7292   |
| 0.4942        | 2.0   | 53742  | 0.5829          | 0.7669   |
| 0.4624        | 3.0   | 80613  | 0.5428          | 0.7982   |
| 0.3467        | 4.0   | 107484 | 0.5104          | 0.8187   |
| 0.4102        | 5.0   | 134355 | 0.5087          | 0.8286   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1