open-llm-leaderboard-old/details_AA051612__A0125

该数据集是在Open LLM Leaderboard上对模型AA051612/A0125进行评估运行时自动创建的。数据集由63个配置组成，每个配置对应一个评估任务。数据集包含一次运行的结果，每次运行在每个配置中表示为特定的分割，train分割始终指向最新的结果。此外，名为results的配置存储了所有运行的聚合结果，用于计算和显示Open LLM Leaderboard上的聚合指标。README还提供了如何使用`datasets`库中的`load_dataset`函数加载运行细节的示例。

原始信息汇总

数据集概述

该数据集是在模型 AA051612/A0125 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集包含 63 个配置，每个配置对应一个评估任务。

数据集结构

• 创建来源：数据集从 1 次运行中创建。每个运行可以在每个配置中作为一个特定的分片找到，分片名称使用运行的时间戳。
• 分片命名：每个分片使用运行的时间戳命名。
• 最新结果："train" 分片始终指向最新的结果。
• 结果汇总：一个额外的配置 "results" 存储所有运行的汇总结果，用于计算和显示 Open LLM Leaderboard 上的汇总指标。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AA051612__A0125", "harness_winogrande_5", split="train")

最新结果

以下是 2024-01-25T17:35:27.963132 运行的最新结果：

python { "all": { "acc": 0.853843249835467, "acc_stderr": 0.023020303121600947, "acc_norm": 0.8632142115707717, "acc_norm_stderr": 0.023353666335398204, "mc1": 0.42472460220318237, "mc1_stderr": 0.01730400095716748, "mc2": 0.6026945746890666, "mc2_stderr": 0.015339327539715458 }, "harness|arc:challenge|25": { "acc": 0.6467576791808873, "acc_stderr": 0.013967822714840055, "acc_norm": 0.697098976109215, "acc_norm_stderr": 0.013428241573185347 }, "harness|hellaswag|10": { "acc": 0.6534554869547898, "acc_stderr": 0.004748965717214275, "acc_norm": 0.8500298745269866, "acc_norm_stderr": 0.0035631244274585 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.8666666666666667, "acc_stderr": 0.029365879728106857, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.029365879728106857 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9276315789473685, "acc_stderr": 0.021085011261884105, "acc_norm": 0.9276315789473685, "acc_norm_stderr": 0.021085011261884105 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8981132075471698, "acc_stderr": 0.01861754975827669, "acc_norm": 0.8981132075471698, "acc_norm_stderr": 0.01861754975827669 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9583333333333334, "acc_stderr": 0.016710315802959976, "acc_norm": 0.9583333333333334, "acc_norm_stderr": 0.016710315802959976 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.84, "acc_stderr": 0.036845294917747094, "acc_norm": 0.84, "acc_norm_stderr": 0.036845294917747094 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.8728323699421965, "acc_stderr": 0.025403262004794074, "acc_norm": 0.8728323699421965, "acc_norm_stderr": 0.025403262004794074 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.7352941176470589, "acc_stderr": 0.043898699568087785, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.043898699568087785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.8808510638297873, "acc_stderr": 0.021178168405396817, "acc_norm": 0.8808510638297873, "acc_norm_stderr": 0.021178168405396817 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.7543859649122807, "acc_stderr": 0.0404933929774814, "acc_norm": 0.7543859649122807, "acc_norm_stderr": 0.0404933929774814 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.9172413793103448, "acc_stderr": 0.022959752132687583, "acc_norm": 0.9172413793103448, "acc_norm_stderr": 0.022959752132687583 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.8227513227513228, "acc_stderr": 0.019667770001273677, "acc_norm": 0.8227513227513228, "acc_norm_stderr": 0.019667770001273677 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.6031746031746031, "acc_stderr": 0.043758884927270585, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.043758884927270585 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.67, "acc_stderr": 0.047258156262526094, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526094 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9419354838709677, "acc_stderr": 0.01330413811280927, "acc_norm": 0.9419354838709677, "acc_norm_stderr": 0.01330413811280927 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.8078817733990148, "acc_stderr": 0.02771931570961478, "acc_norm": 0.8078817733990148, "acc_norm_stderr": 0.02771931570961478 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.9575757575757575, "acc_stderr": 0.01573880284887258, "acc_norm": 0.9575757575757575, "acc_norm_stderr": 0.01573880284887258 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9747474747474747, "acc_stderr": 0.01117803212271851, "acc_norm": 0.9747474747474747, "acc_norm_stderr": 0.01117803212271851 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9792746113989638, "acc_stderr": 0.010281417011909029, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.010281417011909029 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8923076923076924, "acc_stderr": 0.015717188416273085, "acc_norm": 0.8923076923076924, "acc_norm_stderr": 0.015717188416273085 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.7185185185185186, "acc_stderr": 0.027420019350945277, "acc_norm": 0