Update README.md

README.md

@@ -16,24 +16,48 @@ 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 an unknown dataset.
+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: 0.5058
 - Accuracy: 0.8269
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+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 hyperparameters