陈俊杰
commited on
Commit
•
5fb8238
1
Parent(s):
79cd51c
1225
Browse files
app.py
CHANGED
@@ -265,43 +265,43 @@ elif page == "LeaderBoard":
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"Spearman (Non-Factoid QA)": [],
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}
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-
TeamId = ["baseline", "baseline", "baseline", "baseline", 'ISLab', 'ISLab', 'ISLab', 'ISLab', 'default5']
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Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", "llama3-1_baseline5", "llama3-1_baseline6", "llama3-1-baseline7", "llama3-2-baseline", "llm"]
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# teamId 唯一标识码
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DG = {
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"TeamId": TeamId,
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"Methods": Methods,
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"Accuracy": [0.5806, 0.5483, 0.6001, 0.6472, 0, 0, 0, 0, 0.6504481792717087],
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-
"Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 0, 0, 0, 0, 0.4034134076281578],
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"Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 0, 0, 0, 0, 0.4303514807222638],
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}
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df1 = pd.DataFrame(DG)
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TE = {
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"TeamId": TeamId,
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"Methods": Methods,
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"Accuracy": [0.5107, 0.5050, 0.5461, 0.5581, 0.5067545088210725, 0.4766805549971185, 0, 0, 0.511188817632316],
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-
"Kendall's Tau": [0.1281, 0.0635, 0.2716, 0.3864, 0.18884532500063825, 0.31629653258509166, 0, 0, 0.10828008098753536],
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"Spearman": [0.1352, 0.0667, 0.2867, 0.4157, 0.2033137543983765, 0.35189638758373964, 0, 0, 0.11421806788123415],
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}
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df2 = pd.DataFrame(TE)
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SG = {
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"TeamId": TeamId,
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"Methods": Methods,
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-
"Accuracy": [0.6504, 0.6014, 0.7162, 0.7441, 0.7518953983108395, 0.7870818213649097, 0.6187623875307698, 0.8003185213479332, 0.7007955341227401],
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-
"Kendall's Tau": [0.3957, 0.2688, 0.5092, 0.5001, 0.5377072309689559, 0.5709963447418871, 0.30897221697376714, 0.6064826537169805, 0.4819411311747811],
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-
"Spearman": [0.4188, 0.2817, 0.5403, 0.5405, 0.5830423197486431, 0.6276373633425562, 0.324348752437819, 0.6664032039425867, 0.5076789062134682],
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}
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df3 = pd.DataFrame(SG)
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NFQA = {
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"TeamId": TeamId,
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"Methods": Methods,
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-
"Accuracy": [0.5935, 0.5817, 0.7000, 0.7203, 0, 0, 0, 0, 0.5922294372294372],
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"Kendall's Tau": [0.2332, 0.2389, 0.4440, 0.4235, 0, 0, 0, 0, 0.1701874070113157],
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"Spearman": [0.2443, 0.2492, 0.4630, 0.4511, 0, 0, 0, 0, 0.18058287732894646],
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}
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df4 = pd.DataFrame(NFQA)
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"Spearman (Non-Factoid QA)": [],
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}
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+
TeamId = ["baseline", "baseline", "baseline", "baseline", 'ISLab', 'ISLab', 'ISLab', 'ISLab', 'default5', 'default5']
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Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", "llama3-1_baseline5", "llama3-1_baseline6", "llama3-1-baseline7", "llama3-2-baseline", "llm", "baselinev00"]
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# teamId 唯一标识码
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DG = {
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"TeamId": TeamId,
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"Methods": Methods,
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+
"Accuracy": [0.5806, 0.5483, 0.6001, 0.6472, 0, 0, 0, 0, 0.6504481792717087, 0.5816503267973856],
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+
"Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 0, 0, 0, 0, 0.4034134076281578, 0.25873621367415467],
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"Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 0, 0, 0, 0, 0.4303514807222638, 0.28208851475394925],
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}
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df1 = pd.DataFrame(DG)
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TE = {
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"TeamId": TeamId,
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"Methods": Methods,
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+
"Accuracy": [0.5107, 0.5050, 0.5461, 0.5581, 0.5067545088210725, 0.4766805549971185, 0, 0, 0.511188817632316, 0.5369175431336809],
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+
"Kendall's Tau": [0.1281, 0.0635, 0.2716, 0.3864, 0.18884532500063825, 0.31629653258509166, 0, 0, 0.10828008098753536, 0.26854542496891],
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+
"Spearman": [0.1352, 0.0667, 0.2867, 0.4157, 0.2033137543983765, 0.35189638758373964, 0, 0, 0.11421806788123415, 0.2820788649343955],
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}
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df2 = pd.DataFrame(TE)
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SG = {
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"TeamId": TeamId,
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"Methods": Methods,
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+
"Accuracy": [0.6504, 0.6014, 0.7162, 0.7441, 0.7518953983108395, 0.7870818213649097, 0.6187623875307698, 0.8003185213479332, 0.7007955341227401, 0.7519048414820473],
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+
"Kendall's Tau": [0.3957, 0.2688, 0.5092, 0.5001, 0.5377072309689559, 0.5709963447418871, 0.30897221697376714, 0.6064826537169805, 0.4819411311747811, 0.4874144871543796],
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"Spearman": [0.4188, 0.2817, 0.5403, 0.5405, 0.5830423197486431, 0.6276373633425562, 0.324348752437819, 0.6664032039425867, 0.5076789062134682, 0.539580429716673],
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}
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df3 = pd.DataFrame(SG)
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NFQA = {
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"TeamId": TeamId,
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"Methods": Methods,
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"Accuracy": [0.5935, 0.5817, 0.7000, 0.7203, 0, 0, 0, 0, 0.5922294372294372, 0.7146378968253966],
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+
"Kendall's Tau": [0.2332, 0.2389, 0.4440, 0.4235, 0, 0, 0, 0, 0.1701874070113157, 0.44019963513470517],
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"Spearman": [0.2443, 0.2492, 0.4630, 0.4511, 0, 0, 0, 0, 0.18058287732894646, 0.4592548426675891],
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}
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df4 = pd.DataFrame(NFQA)
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