Update utils.py

utils.py

@@ -39,7 +39,7 @@ This comprehensive suite enables robust evaluation of multimodal embedding model
 | [**🤗Hugging Face**](https://huggingface.co/datasets/TIGER-Lab/MMEB-V2) |
 """
 
-TABLE_INTRODUCTION = """"""
+TABLE_INTRODUCTION = """Models are ranked based on V1-Overall."""
 
 LEADERBOARD_INFO = """
 ## Dataset Summary
@@ -122,6 +122,9 @@ def create_hyperlinked_names(df):
 def get_df(file="results.jsonl"):
     df = pd.read_json(file, orient='records', lines=True)
     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 = df.sort_values(by=['V1-Overall'], ascending=False)
     df = create_hyperlinked_names(df)
     df['Rank'] = range(1, len(df) + 1)
@@ -167,6 +170,8 @@ 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: