1230

app.py

@@ -268,7 +268,7 @@ elif page == "LeaderBoard":
     TeamId = ["baseline", "baseline", "baseline", "baseline", 
     'ISLab', 'ISLab', 'ISLab', 'ISLab', 
     'default5', 'default5', 'default5', 'default5',
-    'KNUIR', 'KNUIR', 'KNUIR', 'KNUIR'],
+    'KNUIR', 'KNUIR', 'KNUIR', 'KNUIR']
     Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", 
     "llama3-1_baseline5", "llama3-1_baseline6", "llama3-1-baseline7", "llama3-2-baseline",
      "baselinev02", "baselinev72r1", "baselinev70r1", "baselinev72r2",
@@ -289,8 +289,9 @@ elif page == "LeaderBoard":
         "Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 
         0, 0, 0, 0, 
         0.4200280894403279, 0.5723981513727318, 0.3392536955889527, 0.6542301178956093, 
-        0.02673703949665616, 0.3132279427962962, 0.19244600211698878, 0.3697144425033483],
+        0.02673703949665616, 0.3132279427962962, 0.19244600211698878, 0.3697144425033483]
     }
+
     df1 = pd.DataFrame(DG)
 
     TE = {
@@ -307,7 +308,7 @@ elif page == "LeaderBoard":
         "Spearman": [0.1352, 0.0667, 0.2867, 0.4157,
         0.2033137543983765, 0.35189638758373964, 0, 0,
         0.44950359766748854, 0.4567231163496956, 0.3284040387552273, 0.5061135134678696,
-        0.04302709609666947, 0.3758784332521168, 0.2019542748712654, 0.25105320709917717],
+        0.04302709609666947, 0.3758784332521168, 0.2019542748712654, 0.25105320709917717]
     }
     df2 = pd.DataFrame(TE)
 
@@ -325,7 +326,7 @@ elif page == "LeaderBoard":
         "Spearman": [0.4188, 0.2817, 0.5403, 0.5405,
          0.5830423197486431, 0.6276373633425562, 0.324348752437819, 0.6664032039425867,
         0.5603311161969196, 0.5987990693735654, 0.6200483357955027, 0.6021636544977567,
-        0.0, 0.530151784406405, 0.0, 0.5767282714406644],
+        0.0, 0.530151784406405, 0.0, 0.5767282714406644]
     }
     df3 = pd.DataFrame(SG)
 
@@ -343,7 +344,7 @@ elif page == "LeaderBoard":
         "Spearman": [0.2443, 0.2492, 0.4630, 0.4511,
          0, 0, 0, 0,
           0.5214865171404164, 0.4479941149402397, 0.424528242404003, 0.49907660929552167,
-           0.41802883351668096, 0.31033689944001186, 0.1096152564140644, 0.43265604612874153],
+           0.41802883351668096, 0.31033689944001186, 0.1096152564140644, 0.43265604612874153]
     }
     df4 = pd.DataFrame(NFQA)
 

test.py

@@ -1,30 +1,34 @@
-data = {
-        ('', 'teamId'): ['baseline', 'baseline', 'baseline', 'baseline'],
-        ('', 'methods'): ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o-mini"],
-        ('', 'overall'): [0,0,0,0],
-        ('Dialogue Generation', 'accuracy'): [0.5806, 0.5483, 0.6001, 0.6472],
-        ('Dialogue Generation', "kendall's tau"): [0.3243, 0.1739, 0.3042, 0.4167],
-        ('Dialogue Generation', 'spearman'): [0.3505, 0.1857, 0.3264, 0.4512],
-        ('Text Expansion', "accuracy"): [0.5107, 0.5050, 0.5461, 0.5581],
-        ('Text Expansion', "kendall's tau"): [0.1281, 0.0635, 0.2716, 0.3864],
-        ('Text Expansion', 'spearman'): [0.1352, 0.0667, 0.2867, 0.4157],
-        ('Summary Generation', 'accuracy'): [0.6504, 0.6014, 0.7162, 0.7441],
-        ('Summary Generation', "kendall's tau"): [0.3957, 0.2688, 0.5092, 0.5001],
-        ('Summary Generation', 'spearman'): [0.4188, 0.2817, 0.5403, 0.5405],
-        ('Non-Factoid QA', "accuracy"): [0.5935, 0.5817, 0.7000, 0.7203],
-        ('Non-Factoid QA', "kendall's tau"): [0.2332, 0.2389, 0.4440, 0.4235],
-        ('Non-Factoid QA', 'spearman'): [0.2443, 0.2492, 0.4630, 0.4511]
+import pandas as pd
+
+TeamId = ["baseline", "baseline", "baseline", "baseline", 
+    'ISLab', 'ISLab', 'ISLab', 'ISLab', 
+    'default5', 'default5', 'default5', 'default5',
+    'KNUIR', 'KNUIR', 'KNUIR', 'KNUIR'],
+Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", 
+"llama3-1_baseline5", "llama3-1_baseline6", "llama3-1-baseline7", "llama3-2-baseline",
+    "baselinev02", "baselinev72r1", "baselinev70r1", "baselinev72r2",
+    'bert-base-uncased', 'gpt35turbo', 'logisticRegression', 'paraphrase-MiniLM-L6-v2']
+
+DG = {
+        "TeamId": TeamId,
+        "Methods": Methods,
+        "Accuracy": [0.5806, 0.5483, 0.6001, 0.6472, 
+        0, 0, 0, 0, 
+        0.631700513538749, 0.7111356209150326, 0.6176633986928104, 0.735954715219421, 
+        0.5073529411764706, 0.5104038281979459, 0.5405182072829132, 0.5156874416433239],
+        "Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 
+        0, 0, 0, 0, 
+        0.38961572200778516, 0.5285302196320519, 0.31022946186879186, 0.5974703857412484, 
+        0.024753688574416864, 0.2838365040871617, 0.18291748486237186, 0.334110095650077],
+        "Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 
+        0, 0, 0, 0, 
+        0.4200280894403279, 0.5723981513727318, 0.3392536955889527, 0.6542301178956093, 
+        0.02673703949665616, 0.3132279427962962, 0.19244600211698878, 0.3697144425033483]
     }
 
-overall = [0, 0, 0, 0]
-for d in data:
-    if d != ('', 'teamId') and d != ('', 'methods') and d != ('', 'overall'):
-        for i in range(4):
-                overall[i] += data[d][i]
-overall = [i / (3*4) for i in overall]
-data[('', 'overall')] = overall
-for d in data:
-    if d != ('', 'teamId') and d != ('', 'methods'):
-        for col in range(len(data[d])):
-            data[d][col] = "{:.4f}".format(data[d][col])
-print(data)
+for key, value in DG.items():
+    print(len(value))
+
+df1 = pd.DataFrame(DG)
+
+print(df1)