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--- |
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library_name: transformers |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vulnerability-severity-classification-roberta-base |
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results: [] |
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datasets: |
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- CIRCL/vulnerability-scores |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vulnerability-severity-classification-roberta-base |
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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). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5058 |
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- Accuracy: 0.8269 |
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## Model description |
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It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions. |
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## How to get started with the model |
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```python |
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>>> from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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... import torch |
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... |
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... labels = ["low", "medium", "high", "critical"] |
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... |
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... model_name = "CIRCL/vulnerability-severity-classification-distilbert-base-uncased" |
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... tokenizer = AutoTokenizer.from_pretrained(model_name) |
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... model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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... model.eval() |
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... |
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... test_description = "SAP NetWeaver Visual Composer Metadata Uploader is not protected with a proper authorization, allowing unauthenticated agent to upload potentially malicious executable binaries \ |
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that could severely harm the host system. This could significantly affect the confidentiality, integrity, and availability of the targeted system." |
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... inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True) |
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... |
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... # Run inference |
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... with torch.no_grad(): |
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... outputs = model(**inputs) |
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... predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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... |
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... # Print results |
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... print("Predictions:", predictions) |
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... predicted_class = torch.argmax(predictions, dim=-1).item() |
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... print("Predicted severity:", labels[predicted_class]) |
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... |
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Predictions: tensor([[4.9335e-04, 3.4782e-02, 2.6257e-01, 7.0215e-01]]) |
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Predicted severity: critical |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 0.6291 | 1.0 | 27084 | 0.6327 | 0.7463 | |
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| 0.6025 | 2.0 | 54168 | 0.5640 | 0.7770 | |
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| 0.5139 | 3.0 | 81252 | 0.5181 | 0.8016 | |
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| 0.3072 | 4.0 | 108336 | 0.4975 | 0.8182 | |
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| 0.2318 | 5.0 | 135420 | 0.5058 | 0.8269 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
