open-llm-leaderboard-old/details_luffycodes__llama-class-shishya-7b-ep3

该数据集是在模型 luffycodes/llama-class-shishya-7b-ep3 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，每个配置对应一个被评估的任务。它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。一个名为 results 的额外配置存储了该次运行的所有聚合结果，这些结果用于计算和显示 Open LLM Leaderboard 上的聚合指标。README 还提供了一个示例，展示了如何使用 Python 中的 datasets 库加载运行中的详细信息。

原始信息汇总

数据集概述

数据集简介

该数据集是在评估模型 luffycodes/llama-class-shishya-7b-ep3 在 Open LLM Leaderboard 上的运行过程中自动创建的。

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。
• 数据集由 1 次运行创建，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。
• 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_luffycodes__llama-class-shishya-7b-ep3", "harness_winogrande_5", split="train")

最新结果

以下是 最新结果 的摘要： python { "all": { "acc": 0.46443607121383107, "acc_stderr": 0.03429100219375034, "acc_norm": 0.4715546923049019, "acc_norm_stderr": 0.03522731903306309, "mc1": 0.1835985312117503, "mc1_stderr": 0.013553186376880936, "mc2": 0.27936842078964386, "mc2_stderr": 0.013103829305712317 }, "harness|arc:challenge|25": { "acc": 0.3848122866894198, "acc_stderr": 0.014218371065251098, "acc_norm": 0.40784982935153585, "acc_norm_stderr": 0.014361097288449701 }, "harness|hellaswag|10": { "acc": 0.5943039235212109, "acc_stderr": 0.004900227226433388, "acc_norm": 0.7703644692292372, "acc_norm_stderr": 0.0041973886269400665 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.044619604333847415, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847415 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464243, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464243 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5, "acc_stderr": 0.04068942293855797, "acc_norm": 0.5, "acc_norm_stderr": 0.04068942293855797 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5471698113207547, "acc_stderr": 0.030635627957961816, "acc_norm": 0.5471698113207547, "acc_norm_stderr": 0.030635627957961816 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4930555555555556, "acc_stderr": 0.04180806750294938, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.04180806750294938 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.41040462427745666, "acc_stderr": 0.03750757044895537, "acc_norm": 0.41040462427745666, "acc_norm_stderr": 0.03750757044895537 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364396, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364396 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.40425531914893614, "acc_stderr": 0.03208115750788684, "acc_norm": 0.40425531914893614, "acc_norm_stderr": 0.03208115750788684 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.41379310344827586, "acc_stderr": 0.04104269211806232, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.023517294335963286, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.023517294335963286 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.24603174603174602, "acc_stderr": 0.038522733649243156, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.038522733649243156 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5193548387096775, "acc_stderr": 0.028422687404312107, "acc_norm": 0.5193548387096775, "acc_norm_stderr": 0.028422687404312107 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.593939393939394, "acc_stderr": 0.03834816355401181, "acc_norm": 0.593939393939394, "acc_norm_stderr": 0.03834816355401181 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5808080808080808, "acc_stderr": 0.03515520728670417, "acc_norm": 0.5808080808080808, "acc_norm_stderr": 0.03515520728670417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6476683937823834, "acc_stderr": 0.03447478286414357, "acc_norm": 0.6476683937823834, "acc_norm_stderr": 0.03447478286414357 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.40512820512820513, "acc_stderr": 0.024890471769938145, "acc_norm": 0.40512820512820513, "acc_norm_stderr": 0.024890471769938145 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2518518518518518, "acc_stderr": 0.026466117538959916, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.026466117538959916 }, "har