Update README.md

README.md

@@ -32,29 +32,32 @@ It is a classification model and is aimed to assist in classifying vulnerabiliti
 ## 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])
+>>> 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 procedure