|
--- |
|
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: 1.1168 |
|
- Accuracy: 0.4499 |
|
|
|
## 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-scores" |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
model = AutoModelForSequenceClassification.from_pretrained(model_name) |
|
model.eval() |
|
|
|
test_description = "langchain_experimental 0.0.14 allows an attacker to bypass the CVE-2023-36258 fix and execute arbitrary code via the PALChain in the python exec method." |
|
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 | |
|
|:-------------:|:-----:|:------:|:---------------:|:--------:| |
|
| 1.1123 | 1.0 | 26248 | 1.1174 | 0.4499 | |
|
| 1.076 | 2.0 | 52496 | 1.1196 | 0.4499 | |
|
| 1.0503 | 3.0 | 78744 | 1.1173 | 0.4499 | |
|
| 1.1249 | 4.0 | 104992 | 1.1166 | 0.4499 | |
|
| 1.0597 | 5.0 | 131240 | 1.1168 | 0.4499 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.49.0 |
|
- Pytorch 2.6.0+cu124 |
|
- Datasets 3.4.0 |
|
- Tokenizers 0.21.1 |
