Update README.md

README.md

@@ -19,7 +19,7 @@ Load the pretrained AITSecNER model directly from Hugging Face:
 ```python
 from gliner import GLiNER
 
-model = GLiNER.from_pretrained("selfconstruct3d/AITSecNER", load_tokenizer=True)
+model = GLiNER.from_pretrained(\"selfconstruct3d/AITSecNER\", load_tokenizer=True)
 ```
 
 ### Predict Entities
@@ -28,14 +28,14 @@ Define the input text and entity labels you wish to extract:
 
 ```python
 # Example input text
-text = """
-Upon opening Emotet maldocs, victims are greeted with fake Microsoft 365 prompt that states
+text = \"\"\"
+Upon opening Emotet maldocs, victims are greeted with fake Microsoft 365 prompt that states 
 “THIS DOCUMENT IS PROTECTED,” and instructs victims on how to enable macros.
-"""
+\"\"\"
 
 # Entity labels
 labels = [
-    'CLICommand/CodeSnippet', 'CON', 'DATE', 'GROUP', 'LOC',
+    'CLICommand/CodeSnippet', 'CON', 'DATE', 'GROUP', 'LOC', 
     'MALWARE', 'ORG', 'SECTOR', 'TACTIC', 'TECHNIQUE', 'TOOL'
 ]
 
@@ -44,7 +44,7 @@ entities = model.predict_entities(text, labels, threshold=0.5)
 
 # Display results
 for entity in entities:
-    print(f"{entity['text']} => {entity['label']}")
+    print(f\"{entity['text']} => {entity['label']}\")
 ```
 
 ### Sample Output
@@ -54,6 +54,10 @@ Emotet => MALWARE
 Microsoft => ORG
 ```
 
+## Model Details
+
+The **AITSecNER** model was fine-tuned using the [urchade/gliner_small](https://huggingface.co/urchade/gliner_small) model from Hugging Face on the [priamai/AnnoCTR dataset](https://huggingface.co/datasets/priamai/AnnoCTR). For more details about the dataset, see the paper ["AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports"](https://arxiv.org/abs/2305.10472).
+
 ## About
 
 **AITSecNER** leverages GLiNER to quickly and accurately extract cybersecurity-specific entities, making it highly suitable for tasks such as: