陈俊杰
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Commit
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Parent(s):
83caa2d
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Browse files- .DS_Store +0 -0
- app.py +14 -14
- baseline_example/.DS_Store +0 -0
.DS_Store
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Binary files a/.DS_Store and b/.DS_Store differ
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app.py
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@@ -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']
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Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", 'gpt4o-mini-baseline', 'gpt4o-mini-baseline2']
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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],
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"Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 0, 0],
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"Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 0, 0]
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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, 0],
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"Kendall's Tau": [0.1281, 0.0635, 0.2716, 0.3864, 0, 0],
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"Spearman": [0.1352, 0.0667, 0.2867, 0.4157, 0, 0]
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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, 0.7684735750360749, 0.7659274997877937],
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"Kendall's Tau": [0.3957, 0.2688, 0.5092, 0.5001, 0.5139446977332496, 0.5635917219315821],
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"Spearman": [0.4188, 0.2817, 0.5403, 0.5405, 0.5610788011671747, 0.6164421350125108],
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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],
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"Kendall's Tau": [0.2332, 0.2389, 0.4440, 0.4235, 0, 0],
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"Spearman": [0.2443, 0.2492, 0.4630, 0.4511, 0, 0]
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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']
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Methods = ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o", 'gpt4o-mini-baseline', 'gpt4o-mini-baseline2', 'llama3-1-baseline', 'llama3-1-baseline2']
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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],
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+
"Kendall's Tau": [0.3243, 0.1739, 0.3042, 0.4167, 0, 0, 0, 0],
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"Spearman": [0.3505, 0.1857, 0.3264, 0.4512, 0, 0, 0, 0]
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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, 0, 0, 0],
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+
"Kendall's Tau": [0.1281, 0.0635, 0.2716, 0.3864, 0, 0, 0, 0],
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"Spearman": [0.1352, 0.0667, 0.2867, 0.4157, 0, 0, 0, 0]
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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, 0.7684735750360749, 0.7659274997877937, 0.7702904570919278, 0.7707237554112554],
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"Kendall's Tau": [0.3957, 0.2688, 0.5092, 0.5001, 0.5139446977332496, 0.5635917219315821, 0.5789961063044075, 0.5704551232357526],
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"Spearman": [0.4188, 0.2817, 0.5403, 0.5405, 0.5610788011671747, 0.6164421350125108, 0.6242002118163157, 0.6148419886082258],
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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],
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"Kendall's Tau": [0.2332, 0.2389, 0.4440, 0.4235, 0, 0, 0, 0],
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"Spearman": [0.2443, 0.2492, 0.4630, 0.4511, 0, 0, 0, 0]
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}
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df4 = pd.DataFrame(NFQA)
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baseline_example/.DS_Store
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Binary file (6.15 kB). View file
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