open-llm-leaderboard-old/details_ResplendentAI__Aura_Uncensored_l3_8B

该数据集是在Open LLM Leaderboard上对模型ResplendentAI/Aura_Uncensored_l3_8B进行评估时自动创建的。数据集由63个配置组成，每个配置对应一个评估任务。数据集是从1次运行中生成的，每次运行在每个配置中表示为特定的拆分，train拆分始终指向最新结果。一个名为results的额外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`函数加载运行中的详细信息的示例。还包括了2024-04-21运行的最新结果，显示了不同任务的各种准确率指标。

原始信息汇总

数据集概述

数据集简介

该数据集是在对模型 ResplendentAI/Aura_Uncensored_l3_8B 进行评估运行期间自动创建的，用于 Open LLM Leaderboard。

数据集结构

• 数据集包含 63 个配置，每个配置对应一个评估任务。
• 数据集从 1 次运行中创建，每次运行的详细信息可以在每个配置中找到，以运行的时间戳命名的特定分片形式存储。
• "train" 分片始终指向最新的结果。
• 一个额外的 "results" 配置存储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。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ResplendentAI__Aura_Uncensored_l3_8B", "harness_winogrande_5", split="train")

最新结果

以下是 2024-04-21T03:36:54.010175 运行的最新结果：

python { "all": { "acc": 0.6606918627657457, "acc_stderr": 0.031454688612242016, "acc_norm": 0.6744233417508984, "acc_norm_stderr": 0.03229594697849892, "mc1": 0.3598531211750306, "mc1_stderr": 0.016801860466677147, "mc2": 0.5093958707621045, "mc2_stderr": 0.015103871939292203 }, "harness|arc:challenge|25": { "acc": 0.5827645051194539, "acc_stderr": 0.014409825518403082, "acc_norm": 0.6228668941979523, "acc_norm_stderr": 0.014163366896192594 }, "harness|hellaswag|10": { "acc": 0.5956980681139216, "acc_stderr": 0.004897534686686318, "acc_norm": 0.7949611631149174, "acc_norm_stderr": 0.004029048890501029 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7302631578947368, "acc_stderr": 0.03611780560284898, "acc_norm": 0.7302631578947368, "acc_norm_stderr": 0.03611780560284898 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7547169811320755, "acc_stderr": 0.026480357179895705, "acc_norm": 0.7547169811320755, "acc_norm_stderr": 0.026480357179895705 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7986111111111112, "acc_stderr": 0.03353647469713839, "acc_norm": 0.7986111111111112, "acc_norm_stderr": 0.03353647469713839 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.03656343653353159, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353159 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5, "acc_stderr": 0.04975185951049946, "acc_norm": 0.5, "acc_norm_stderr": 0.04975185951049946 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6085106382978723, "acc_stderr": 0.03190701242326812, "acc_norm": 0.6085106382978723, "acc_norm_stderr": 0.03190701242326812 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6140350877192983, "acc_stderr": 0.04579639422070434, "acc_norm": 0.6140350877192983, "acc_norm_stderr": 0.04579639422070434 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.43386243386243384, "acc_stderr": 0.025525034382474887, "acc_norm": 0.43386243386243384, "acc_norm_stderr": 0.025525034382474887 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7967741935483871, "acc_stderr": 0.02289168798455494, "acc_norm": 0.7967741935483871, "acc_norm_stderr": 0.02289168798455494 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8383838383838383, "acc_stderr": 0.02622591986362927, "acc_norm": 0.8383838383838383, "acc_norm_stderr": 0.02622591986362927 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.02399150050031303, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.02399150050031303 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.40370370370370373, "acc_stderr": 0.029914812342227627, "acc_norm": 0.40370370370370373, "acc_norm_stderr": 0.029914812342227627 }, "harness|hendrycksTest-high_school_micro