Update app.py

app.py

@@ -265,43 +265,43 @@ elif page == "LeaderBoard":
         "Spearman (Non-Factoid QA)": [],
     }
 
-    TeamId = ["baseline", "baseline", "baseline", "baseline", 'ISLab', 'ISLab', 'ISLab', 'ISLab']
-    Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", 'gpt4o-mini-baseline', 'gpt4o-mini-baseline2', 'llama3-1-baseline', 'llama3-1-baseline2']
+    TeamId = ["baseline", "baseline", "baseline", "baseline", 'ISLab', 'ISLab', 'ISLab', 'ISLab', 'ISLab']
+    Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", 'gpt4o-mini-baseline', 'gpt4o-mini-baseline2', 'llama3-1-baseline', 'llama3-1-baseline2', 'short -test']
 
     # teamId 唯一标识码
     DG = {
         "TeamId": TeamId,
         "Methods": Methods,
-        "Accuracy": [0.5806, 0.5483, 0.6001, 0.6472, 0, 0, 0, 0],
-        "Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 0, 0, 0, 0],
-        "Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 0, 0, 0, 0]
+        "Accuracy": [0.5806, 0.5483, 0.6001, 0.6472, 0, 0, 0, 0, 0],
+        "Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 0, 0, 0, 0, 0],
+        "Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 0, 0, 0, 0, 0]
     }
     df1 = pd.DataFrame(DG)
 
     TE = {
         "TeamId": TeamId,
         "Methods": Methods,
-        "Accuracy": [0.5107, 0.5050, 0.5461, 0.5581, 0, 0, 0, 0],
-        "Kendall's Tau": [0.1281, 0.0635, 0.2716, 0.3864, 0, 0, 0, 0],
-        "Spearman": [0.1352, 0.0667, 0.2867, 0.4157, 0, 0, 0, 0]
+        "Accuracy": [0.5107, 0.5050, 0.5461, 0.5581, 0, 0, 0, 0, 0],
+        "Kendall's Tau": [0.1281, 0.0635, 0.2716, 0.3864, 0, 0, 0, 0, 0],
+        "Spearman": [0.1352, 0.0667, 0.2867, 0.4157, 0, 0, 0, 0, 0]
     }
     df2 = pd.DataFrame(TE)
 
     SG = {
         "TeamId": TeamId,
         "Methods": Methods,
-        "Accuracy": [0.6504, 0.6014, 0.7162, 0.7441, 0.7684735750360749, 0.7659274997877937, 0.7702904570919278, 0.7707237554112554],
-        "Kendall's Tau": [0.3957, 0.2688, 0.5092, 0.5001, 0.5139446977332496, 0.5635917219315821, 0.5789961063044075, 0.5704551232357526],
-        "Spearman": [0.4188, 0.2817, 0.5403, 0.5405, 0.5610788011671747, 0.6164421350125108, 0.6242002118163157, 0.6148419886082258],
+        "Accuracy": [0.6504, 0.6014, 0.7162, 0.7441, 0.7684735750360749, 0.7659274997877937, 0.7702904570919278, 0.7707237554112554, 0.7171193287921227],
+        "Kendall's Tau": [0.3957, 0.2688, 0.5092, 0.5001, 0.5139446977332496, 0.5635917219315821, 0.5789961063044075, 0.5704551232357526, 0.5678532047471645],
+        "Spearman": [0.4188, 0.2817, 0.5403, 0.5405, 0.5610788011671747, 0.6164421350125108, 0.6242002118163157, 0.6148419886082258, 0.6019919123404138],
     }
     df3 = pd.DataFrame(SG)
 
     NFQA = {
         "TeamId": TeamId,
         "Methods": Methods,
-        "Accuracy": [0.5935, 0.5817, 0.7000, 0.7203, 0, 0, 0, 0],
-        "Kendall's Tau": [0.2332, 0.2389, 0.4440, 0.4235, 0, 0, 0, 0],
-        "Spearman": [0.2443, 0.2492, 0.4630, 0.4511, 0, 0, 0, 0]
+        "Accuracy": [0.5935, 0.5817, 0.7000, 0.7203, 0, 0, 0, 0, 0],
+        "Kendall's Tau": [0.2332, 0.2389, 0.4440, 0.4235, 0, 0, 0, 0, 0],
+        "Spearman": [0.2443, 0.2492, 0.4630, 0.4511, 0, 0, 0, 0, 0]
     }
     df4 = pd.DataFrame(NFQA)