open-llm-leaderboard-old/details_vitruv__vitruv_1

该数据集是在模型 vitruv/vitruv_1 在 Open LLM Leaderboard 上的评估运行期间自动创建的。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 1 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，还有一个 results 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` 函数加载数据集的示例，并包含了特定运行的最新结果。

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 vitruv/vitruv_1 进行评估运行期间自动创建的，评估结果展示在 Open LLM Leaderboard 上。

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，每次运行可以在每个配置中找到特定的分割，分割名称使用运行的时间戳。
• "train" 分割始终指向最新的结果。
• 一个额外的配置 "results" 存储所有运行的聚合结果，用于计算和显示聚合指标在 Open LLM Leaderboard 上。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_vitruv__vitruv_1", "harness_winogrande_5", split="train")

最新结果

以下是 2024-03-06T02:40:06.932046 运行的最新结果：

python { "all": { "acc": 0.48098814582864774, "acc_stderr": 0.034444629168415404, "acc_norm": 0.48708235528126465, "acc_norm_stderr": 0.03523822694795442, "mc1": 0.25458996328029376, "mc1_stderr": 0.01525011707915648, "mc2": 0.4122590137037498, "mc2_stderr": 0.014277193708018924 }, "harness|arc:challenge|25": { "acc": 0.4726962457337884, "acc_stderr": 0.014589589101985994, "acc_norm": 0.4991467576791809, "acc_norm_stderr": 0.014611369529813276 }, "harness|hellaswag|10": { "acc": 0.5605457080262896, "acc_stderr": 0.0049530634047914536, "acc_norm": 0.7605058753236407, "acc_norm_stderr": 0.004259025448541507 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5333333333333333, "acc_stderr": 0.043097329010363554, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.40789473684210525, "acc_stderr": 0.039993097127774734, "acc_norm": 0.40789473684210525, "acc_norm_stderr": 0.039993097127774734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.539622641509434, "acc_stderr": 0.030676096599389177, "acc_norm": 0.539622641509434, "acc_norm_stderr": 0.030676096599389177 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.23, "acc_stderr": 0.042295258468165065, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939098, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.47398843930635837, "acc_stderr": 0.03807301726504511, "acc_norm": 0.47398843930635837, "acc_norm_stderr": 0.03807301726504511 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108101, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108101 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.043727482902780064, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.043727482902780064 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4206896551724138, "acc_stderr": 0.0411391498118926, "acc_norm": 0.4206896551724138, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3439153439153439, "acc_stderr": 0.024464426625596437, "acc_norm": 0.3439153439153439, "acc_norm_stderr": 0.024464426625596437 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.29365079365079366, "acc_stderr": 0.040735243221471255, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.040735243221471255 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5774193548387097, "acc_stderr": 0.02810096472427264, "acc_norm": 0.5774193548387097, "acc_norm_stderr": 0.02810096472427264 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3448275862068966, "acc_stderr": 0.03344283744280458, "acc_norm": 0.3448275862068966, "acc_norm_stderr": 0.03344283744280458 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6181818181818182, "acc_stderr": 0.03793713171165634, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.03793713171165634 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6666666666666666, "acc_stderr": 0.033586181457325226, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.033586181457325226 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836184, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836184 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.025124653525885117, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.025124653525885117 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02730914058823018, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02730914058823018 }, "harness