Update README.md

README.md

@@ -16,22 +16,47 @@ 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).
+
+You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.5141
 - Accuracy: 0.8267
 
 ## Model description
 
-More information needed
+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()
 
-## Intended uses & limitations
+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)
 
-More information needed
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
 
-## Training and evaluation data
+# Print results
+print("Predictions:", predictions)
+predicted_class = torch.argmax(predictions, dim=-1).item()
+print("Predicted severity:", labels[predicted_class])
+```
 
-More information needed
 
 ## Training procedure