Update space

app.py

@@ -465,7 +465,7 @@ def save_submission(submission_data, csv_file):
 def update_leaderboard_data(submission_data):
     """
     Update leaderboard data with new submission results
-    Only updates the specific dataset submitted, preventing empty rows
+    Only uses model name in the displayed table
     """
     global df_synthesized_full, df_synthesized_10, df_human_generated
     
@@ -477,32 +477,26 @@ def update_leaderboard_data(submission_data):
     }
     
     df_to_update = split_to_df[submission_data['Split']]
-    dataset = submission_data['Dataset'].upper()
     
-    # Prepare new row data with only the relevant dataset columns
+    # Prepare new row data
     new_row = {
-        'Method': submission_data['Method Name']
+        'Method': submission_data['Method Name'],  # Only use method name in table
+        f'STARK-{submission_data["Dataset"].upper()}_Hit@1': submission_data['results']['hit@1'],
+        f'STARK-{submission_data["Dataset"].upper()}_Hit@5': submission_data['results']['hit@5'],
+        f'STARK-{submission_data["Dataset"].upper()}_R@20': submission_data['results']['recall@20'],
+        f'STARK-{submission_data["Dataset"].upper()}_MRR': submission_data['results']['mrr']
     }
-    # Only add metrics for the submitted dataset
-    new_row.update({
-        f'STARK-{dataset}_Hit@1': submission_data['results']['hit@1'],
-        f'STARK-{dataset}_Hit@5': submission_data['results']['hit@5'],
-        f'STARK-{dataset}_R@20': submission_data['results']['recall@20'],
-        f'STARK-{dataset}_MRR': submission_data['results']['mrr']
-    })
     
     # Check if method already exists
     method_mask = df_to_update['Method'] == submission_data['Method Name']
     if method_mask.any():
-        # Update only the columns for the submitted dataset
+        # Update existing row
         for col in new_row:
             df_to_update.loc[method_mask, col] = new_row[col]
     else:
-        # For new methods, create a row with only the submitted dataset's values
+        # Add new row
         df_to_update.loc[len(df_to_update)] = new_row
 
-
-
 # Function to get emails from meta_data
 def get_emails_from_metadata(meta_data):
     """
@@ -792,24 +786,10 @@ def filter_by_model_type(df, selected_types):
     return df[df['Method'].isin(selected_models)]
 
 def format_dataframe(df, dataset):
-    """
-    Format DataFrame for display, removing rows with no data for the selected dataset
-    """
-    # Select relevant columns
     columns = ['Method'] + [col for col in df.columns if dataset in col]
     filtered_df = df[columns].copy()
-    
-    # Remove rows where all metric columns are empty/NaN for this dataset
-    metric_columns = [col for col in filtered_df.columns if col != 'Method']
-    filtered_df = filtered_df.dropna(subset=metric_columns, how='all')
-    
-    # Rename columns to remove dataset prefix
     filtered_df.columns = [col.split('_')[-1] if '_' in col else col for col in filtered_df.columns]
-    
-    # Sort by MRR
-    if 'MRR' in filtered_df.columns:
-        filtered_df = filtered_df.sort_values('MRR', ascending=False)
-    
+    filtered_df = filtered_df.sort_values('MRR', ascending=False)
     return filtered_df
 
 def update_tables(selected_types):