Update README.md

README.md

@@ -16,18 +16,45 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vulnerability-severity-classification-distilbert-base-uncased
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on a the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).
+
 It achieves the following results on the evaluation set:
 - Loss: 0.6447
 - Accuracy: 0.7595
 
 ## Model description
 
-More information needed
+It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions.
 
-## Intended uses & limitations
 
-More information needed
+## How to get started with the model 
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+labels = ["low", "medium", "high", "critical"]
+
+model_name = "vulnerability-severity-classification-distilbert-base-uncased"
+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])
+```
+
 
 ## Training and evaluation data