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@@ -89,9 +89,6 @@ Average: 75.9% without mmlu
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  Average: 49.08%
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  ### GPT4ALL
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-
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- Here is the generated table in the desired format using the JSON data provided:
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-
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  | Task | Version | Metric | Value | | Stderr |
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  |------------|---------|--------|-------|---|--------|
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  | arc\_challenge | 0 | acc | 66.29 | _ | 1.38 |
@@ -107,7 +104,32 @@ Average: 49.08%
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  | | | acc\_norm | 84.87 | _ | 0.84 |
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  | winogrande | 0 | acc | 81.06 | _ | 1.10 |
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  ### Training hyperparameters
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  Average: 49.08%
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  ### GPT4ALL
 
 
 
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  | Task | Version | Metric | Value | | Stderr |
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  |------------|---------|--------|-------|---|--------|
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  | arc\_challenge | 0 | acc | 66.29 | _ | 1.38 |
 
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  | | | acc\_norm | 84.87 | _ | 0.84 |
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  | winogrande | 0 | acc | 81.06 | _ | 1.10 |
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+ Average: 68.75%
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+
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+ ### AGIEVAL
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+
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+ Here is the converted table in the required format, including multiplication of all values by 100 and calculating the average for the value column:
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+
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+ | Task | Version | Metric | Value | StdErr |
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+ | --- | --- | --- | --- | --- |
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+ | agieval\_aqua\_rat | | acc | 28.35 | 2.83 |
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+ | | | acc\_norm | 26.38 | 2.77 |
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+ | agieval\_logiqa\_en | | acc | 38.25 | 1.91 |
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+ | | | acc\_norm | 38.09 | 1.90 |
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+ | agieval\_lsat\_ar | | acc | 23.91 | 2.82 |
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+ | | | acc\_norm | 23.48 | 2.80 |
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+ | agieval\_lsat\_lr | | acc | 52.75 | 2.21 |
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+ | | | acc\_norm | 53.92 | 2.21 |
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+ | agieval\_lsat\_rc | | acc | 66.91 | 2.87 |
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+ | | | acc\_norm | 67.29 | 2.87 |
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+ | agieval\_sat\_en | | acc | 78.64 | 2.86 |
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+ | | | acc\_norm | 78.64 | 2.86 |
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+ | agieval\_sat\_en\_without\_passage | | acc | 45.15 | 3.48 |
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+ | | | acc\_norm | 44.17 | 3.47 |
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+ | agieval\_sat\_math | | acc | 33.18 | 3.18 |
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+ | | | acc\_norm | 31.36 | 3.14 |
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+ Average: 47.44%
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  ### Training hyperparameters
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