Update app.py

app.py

@@ -8,10 +8,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 model.eval()  # Set the model to evaluation mode
 
-# Threshold for attention relevance
-THRESHOLD = 0.02  # Adjust as needed based on observations
-
-# Function to get relevance score and relevant excerpt with bolded tokens
+# Function to compute relevance score and dynamically adjust threshold
 def get_relevance_score_and_excerpt(query, paragraph):
     if not query.strip() or not paragraph.strip():
         return "Please provide both a query and a document paragraph.", ""
@@ -22,36 +19,44 @@ def get_relevance_score_and_excerpt(query, paragraph):
     with torch.no_grad():
         output = model(**inputs, output_attentions=True)  # Get attention scores
 
-    # Extract logits and calculate relevance score
+    # Extract logits and calculate base relevance score
     logit = output.logits.squeeze().item()
-    relevance_score = torch.sigmoid(torch.tensor(logit)).item()
+    base_relevance_score = torch.sigmoid(torch.tensor(logit)).item()
+
+    # Dynamically adjust the attention threshold based on relevance score
+    dynamic_threshold = max(0.02, base_relevance_score * 0.1)  # Example formula
 
     # Extract attention scores (last layer)
     attention = output.attentions[-1]  # Shape: (batch_size, num_heads, seq_len, seq_len)
-
-    # Average across heads and batch dimension
-    attention_scores = attention.mean(dim=1).mean(dim=0)  # Shape: (seq_len, seq_len)
+    attention_scores = attention.mean(dim=1).mean(dim=0)  # Average over heads and batch
 
     # Tokenize query and paragraph separately
     query_tokens = tokenizer.tokenize(query)
     paragraph_tokens = tokenizer.tokenize(paragraph)
 
-    query_len = len(query_tokens) + 2  # +2 for [CLS] and first [SEP]
+    query_len = len(query_tokens) + 2  # +2 for special tokens [CLS] and first [SEP]
     para_start_idx = query_len
     para_end_idx = len(inputs["input_ids"][0]) - 1  # Ignore final [SEP] token
 
     # Handle potential indexing issues
     if para_end_idx <= para_start_idx:
-        return round(relevance_score, 4), "No relevant tokens extracted."
+        return round(base_relevance_score, 4), "No relevant tokens extracted."
 
-    # Extract paragraph attention scores
+    # Extract paragraph attention scores and apply dynamic threshold
     para_attention_scores = attention_scores[para_start_idx:para_end_idx, para_start_idx:para_end_idx].mean(dim=0)
 
     if para_attention_scores.numel() == 0:
-        return round(relevance_score, 4), "No relevant tokens extracted."
+        return round(base_relevance_score, 4), "No relevant tokens extracted."
+
+    # Get indices of relevant tokens above dynamic threshold
+    relevant_indices = (para_attention_scores > dynamic_threshold).nonzero(as_tuple=True)[0].tolist()
 
-    # Filter tokens based on threshold and preserve order
-    relevant_indices = (para_attention_scores > THRESHOLD).nonzero(as_tuple=True)[0].tolist()
+    # Compute attention-weighted relevance score
+    if relevant_indices:
+        relevant_attention_values = para_attention_scores[relevant_indices]
+        attention_weighted_score = relevant_attention_values.mean().item() * base_relevance_score
+    else:
+        attention_weighted_score = base_relevance_score  # No relevant tokens found
 
     # Reconstruct paragraph with bolded relevant tokens
     highlighted_text = ""
@@ -61,10 +66,10 @@ def get_relevance_score_and_excerpt(query, paragraph):
         else:
             highlighted_text += f"{token} "
 
-    # Convert tokens to readable format (handling special characters)
+    # Convert tokens back to readable format
     highlighted_text = tokenizer.convert_tokens_to_string(highlighted_text.split())
 
-    return round(relevance_score, 4), highlighted_text
+    return round(attention_weighted_score, 4), highlighted_text
 
 # Define Gradio interface
 interface = gr.Interface(
@@ -74,11 +79,11 @@ interface = gr.Interface(
         gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match...")
     ],
     outputs=[
-        gr.Textbox(label="Relevance Score"),
+        gr.Textbox(label="Attention-Weighted Relevance Score"),
         gr.HTML(label="Highlighted Document Paragraph")
     ],
-    title="Cross-Encoder Relevance Scoring with Highlighted Excerpt",
-    description="Enter a query and a document paragraph to get a relevance score and see relevant tokens in bold.",
+    title="Cross-Encoder with Dynamic Attention Threshold",
+    description="Enter a query and document paragraph to get a relevance score with relevant tokens in bold.",
     allow_flagging="never",
     live=True
 )