mode

app.py

@@ -264,6 +264,7 @@ def init_leaderboard(dataframe, visible_columns=None):
 
     # Select only the visible columns for display
     visible_columns.remove('model_name')
+    
     visible_columns = ['model_name'] + visible_columns
     display_df = dataframe[visible_columns].copy()
 
@@ -583,7 +584,6 @@ def update_visualization(selected_models, selected_category, selected_metric, ve
 # Create Gradio app
 demo = gr.Blocks(css=custom_css, theme=custom_theme)
 
-# Mapping from original category names to display names
 CATEGORY_DISPLAY_MAP = {
     'Political Corruption and Legal Evasion': 'Corruption & Legal Evasion',
     'Financial Fraud and Unethical Business': 'Financial Fraud',

src/display/utils.py

@@ -59,6 +59,7 @@ class Precision(Enum):
     float32 = auto()
     int8 = auto()
     int4 = auto()
+    NA = auto()
 
     def __str__(self):
         """String representation of the precision type."""

src/leaderboard/processor.py

@@ -169,13 +169,14 @@ def leaderboard_to_dataframe(leaderboard_data: Dict) -> pd.DataFrame:
         row = {
             "model_name": model_name,
             "model_type": entry.get("model_type", "Unknown"),
+            "mode": entry.get("mode", "Strict"),
             "submission_date": entry.get("submission_date", ""),
             "version": entry.get("version", "v0"),
             "guard_model_type": entry.get("guard_model_type", "llm_regexp").lower()
         }
 
         # Add additional metadata fields if present
-        for key in ["base_model", "revision", "precision", "weight_type", "mode"]:
+        for key in ["base_model", "revision", "precision", "weight_type"]:
             if key in entry:
                 row[key] = entry[key]