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---
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).
You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information.
This model is cited in arxiv.org/abs/2507.03607.
It achieves the following results on the evaluation set:
- Loss: 0.5060
- Accuracy: 0.8229
## 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])
```
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.8163 | 1.0 | 27850 | 0.6419 | 0.7376 |
| 0.5531 | 2.0 | 55700 | 0.5674 | 0.7754 |
| 0.5027 | 3.0 | 83550 | 0.5263 | 0.7974 |
| 0.4098 | 4.0 | 111400 | 0.5043 | 0.8148 |
| 0.3837 | 5.0 | 139250 | 0.5060 | 0.8229 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1 |