Update utils.py

utils.py

@@ -62,9 +62,9 @@ SUBMIT_INTRODUCTION = """# Submit on MMEB Leaderboard Introduction
 [
     {
         "Model": "<Model Name>",
-        <Optional>"URL": "<Model URL>",
-        "Model Size(B)": 1000,
-        "Data Source": Self-Reported,
+        "URL": "<Model URL>" or null,
+        "Model Size(B)": 1000 or null,
+        "Data Source": "Self-Reported",
         "V1-Overall": 50.0,
         "I-CLS": 50.0,
         "I-QA": 50.0,
@@ -79,9 +79,9 @@ SUBMIT_INTRODUCTION = """# Submit on MMEB Leaderboard Introduction
 [
     {
         "Model": "<Model Name>",
-        <Optional>"URL": "<Model URL>",
-        "Model Size(B)": 1000,
-        "Data Source": Self-Reported,
+        "URL": "<Model URL>" or null,
+        "Model Size(B)": 1000 or null,
+        "Data Source": "Self-Reported",
         "V2-Overall": 50.0,
         "V-CLS": 50.0,
         "V-QA": 50.0,
@@ -124,7 +124,7 @@ def get_df(file="results.jsonl"):
     df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
     for task in TASKS_V1 + TASKS_V2:
         if df[task].isnull().any():
-            df[task] = df[task].apply(process_unknown_scores)
+            df[task] = df[task].apply(lambda score: '-' if pd.isna(score) else score)
     df = df.sort_values(by=['V1-Overall'], ascending=False)
     df = create_hyperlinked_names(df)
     df['Rank'] = range(1, len(df) + 1)
@@ -170,9 +170,6 @@ def process_model_size(size):
     except (ValueError, TypeError):
         return 'unknown'
 
-def process_unknown_scores(score):
-    return '-' if pd.isna(score) else score
-
 def filter_columns_by_tasks(df, selected_tasks=None):
     if selected_tasks is None or len(selected_tasks) == 0:
         return df[COLUMN_NAMES]