update markdown

app.py

@@ -64,9 +64,22 @@ default_text, default_labels, default_threshold, default_nested = common_example
 # Build the combined Gradio app with three tabs
 with gr.Blocks(title="GLiNER NER Testbed") as demo:
     gr.Markdown("# GLiNER NER Testbed")
-    with gr.Accordion("Detailed information on the approach", open=True):
+    with gr.Accordion("This interface allows you to compare different zero-shot Named Entity Recognition models...", open=True):
         gr.Markdown(
-            """
+            """        
+            ## Models Available:
+            - **GLiNER Medium v2.1**: The original GLiNER medium model
+            - **GLiNER Multi PII**: Fine-tuned for detecting personally identifiable information across multiple languages
+            - **NuNER Zero**: A specialized token-based NER model
+            
+            ## Features:
+            - Select different models
+            - Select examples based on different use cases
+            - Toggle nested entity recognition
+            - Entity merging is currently enabled for NuNER Zero only
+            
+            ## About GLiNER:
+
             **GLiNER** is a state-of-the-art Named Entity Recognition (NER) system that leverages a BERT-like bidirectional transformer encoder to identify a wide range of entity types in text. Unlike conventional NER models that are restricted to fixed entity categories, GLiNER supports flexible, zero-shot extraction, making it ideal for diverse real-world applications. It also provides a resource-efficient alternative to large language models (LLMs) for scenarios where cost and speed are critical. Distributed under the Apache 2.0 license, GLiNER is commercially friendly and readily deployable.
 
             **Useful Links**