update scripts

src/leaderboard/load_results.py

@@ -6,7 +6,6 @@ def load_data(data_path):
     columns = ['Model', 'type', 'open?', 'shot', 'en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']
     columns_sorted = ['rank','type', 'Model', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi']
 
-    
     # Splitting into three separate DataFrames based on the groups M3Exam and MMLU and average
     df_m3exam = df.iloc[:, :11]  # M3Exam columns
     df_mmlu = df.iloc[:, [0, 1, 2, 3, 11, 12, 13, 14, 15, 16, 17]]  # MMLU columns
@@ -18,8 +17,16 @@ def load_data(data_path):
     for df_tmp in [df_m3exam, df_mmlu, df_avg]:
         df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] *= 100
         df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] = df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']].round(2)
-        df_tmp['rank'] = df_tmp['avg_sea'].rank(ascending=False).astype(int)
-        df_tmp = df_tmp[columns_sorted]
+
+    # rank the DataFrames by the 'avg_sea' column
+    df_m3exam['rank'] = df_m3exam['avg_sea'].rank(ascending=False).astype(int)
+    df_mmlu['rank'] = df_mmlu['avg_sea'].rank(ascending=False).astype(int)
+    df_avg['rank'] = df_avg['avg_sea'].rank(ascending=False).astype(int)
+
+    # reorder the columns
+    df_m3exam = df_m3exam[columns_sorted]
+    df_mmlu = df_mmlu[columns_sorted]
+    df_avg = df_avg[columns_sorted]
 
     # sort the DataFrames by the 'avg_sea' column in descending order
     df_m3exam = df_m3exam.sort_values(by='avg_sea', ascending=False)