Update app.py

app.py

@@ -23,8 +23,9 @@ def get_relevance_score_and_excerpt(query, paragraph, threshold_weight):
     logit = output.logits.squeeze().item()
     base_relevance_score = torch.sigmoid(torch.tensor(logit)).item()
 
-    # Dynamically adjust the attention threshold based on user weight
-    dynamic_threshold = max(0.02, threshold_weight)
+    # Calculate dynamic threshold using sigmoid-based formula
+    sigmoid_factor = 1 / (1 + torch.exp(-5 * (base_relevance_score - 0.5))).item()
+    dynamic_threshold = max(0.02, threshold_weight * sigmoid_factor)
 
     # Extract attention scores (last layer)
     attention = output.attentions[-1]
@@ -66,7 +67,7 @@ interface = gr.Interface(
     inputs=[
         gr.Textbox(label="Query", placeholder="Enter your search query..."),
         gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match..."),
-        gr.Slider(minimum=0.02, maximum=0.5, value=0.1, step=0.01, label="Attention Threshold")
+        gr.Slider(minimum=0.02, maximum=0.5, value=0.1, step=0.01, label="Threshold Weight")
     ],
     outputs=[
         gr.Textbox(label="Relevance Score"),
@@ -74,7 +75,7 @@ interface = gr.Interface(
         gr.HTML(label="Highlighted Document Paragraph")
     ],
     title="Cross-Encoder Attention Highlighting",
-    description="Adjust the attention threshold to control token highlighting sensitivity.",
+    description="Adjust the threshold weight to influence dynamic token highlighting based on relevance.",
     allow_flagging="never",
     live=True
 )