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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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You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4963 |
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- Accuracy: 0.8298 |
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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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labels = ["low", "medium", "high", "critical"] |
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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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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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# 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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# 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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## Training procedure |
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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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| 0.5857 | 1.0 | 27531 | 0.6245 | 0.7464 | |
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| 0.6164 | 2.0 | 55062 | 0.5566 | 0.7777 | |
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| 0.467 | 3.0 | 82593 | 0.5368 | 0.8013 | |
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| 0.4208 | 4.0 | 110124 | 0.4849 | 0.8209 | |
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| 0.2856 | 5.0 | 137655 | 0.4963 | 0.8298 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.1+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
