open-llm-leaderboard-old/details_Charlie911__vicuna-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general

该数据集是在Open LLM Leaderboard上对模型Charlie911/vicuna-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general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拆分，train拆分始终指向最新结果。此外，还有一个名为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Python中的datasets库加载运行详情的示例。

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 Charlie911/vicuna-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general 进行评估时自动创建的，用于 Open LLM Leaderboard。

数据集组成

• 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，每个运行可以在每个配置中找到特定的拆分，拆分名称使用运行的时间戳。
• "train" 拆分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 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_Charlie911__vicuna-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general", "harness_winogrande_5", split="train")

最新结果

以下是 2024-03-01T00:49:28.631373 运行的最新结果：

python { "all": { "acc": 0.5095487673377654, "acc_stderr": 0.03422621033953083, "acc_norm": 0.51521649322945, "acc_norm_stderr": 0.03497868199028634, "mc1": 0.3317013463892289, "mc1_stderr": 0.016482148810241473, "mc2": 0.5033295578435169, "mc2_stderr": 0.0156709243513642 }, "harness|arc:challenge|25": { "acc": 0.5025597269624573, "acc_stderr": 0.014611199329843784, "acc_norm": 0.5332764505119454, "acc_norm_stderr": 0.014578995859605806 }, "harness|hellaswag|10": { "acc": 0.5839474208325035, "acc_stderr": 0.004918951019183894, "acc_norm": 0.7741485759808803, "acc_norm_stderr": 0.00417287228298421 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5037037037037037, "acc_stderr": 0.04319223625811331, "acc_norm": 0.5037037037037037, "acc_norm_stderr": 0.04319223625811331 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.46710526315789475, "acc_stderr": 0.040601270352363966, "acc_norm": 0.46710526315789475, "acc_norm_stderr": 0.040601270352363966 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.539622641509434, "acc_stderr": 0.03067609659938918, "acc_norm": 0.539622641509434, "acc_norm_stderr": 0.03067609659938918 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5138888888888888, "acc_stderr": 0.04179596617581, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.04179596617581 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.48554913294797686, "acc_stderr": 0.03810871630454764, "acc_norm": 0.48554913294797686, "acc_norm_stderr": 0.03810871630454764 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.18627450980392157, "acc_stderr": 0.03873958714149352, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.03873958714149352 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.451063829787234, "acc_stderr": 0.032529096196131965, "acc_norm": 0.451063829787234, "acc_norm_stderr": 0.032529096196131965 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278006, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278006 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.42758620689655175, "acc_stderr": 0.04122737111370331, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.04122737111370331 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30423280423280424, "acc_stderr": 0.023695415009463087, "acc_norm": 0.30423280423280424, "acc_norm_stderr": 0.023695415009463087 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.043435254289490965, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.043435254289490965 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5387096774193548, "acc_stderr": 0.028358634859836935, "acc_norm": 0.5387096774193548, "acc_norm_stderr": 0.028358634859836935 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.39901477832512317, "acc_stderr": 0.03445487686264715, "acc_norm": 0.39901477832512317, "acc_norm_stderr": 0.03445487686264715 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6242424242424243, "acc_stderr": 0.03781887353205982, "acc_norm": 0.6242424242424243, "acc_norm_stderr": 0.03781887353205982 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6161616161616161, "acc_stderr": 0.034648816750163396, "acc_norm": 0.6161616161616161, "acc_norm_stderr": 0.034648816750163396 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7305699481865285, "acc_stderr": 0.03201867122877794, "acc_norm": 0.7305699481865285, "acc_norm_stderr": 0.03201867122877794 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.48717948717948717, "acc_stderr": 0.025342671293807264, "acc_norm": 0.48717948717948717, "acc_norm_stderr": 0.025342671293807264 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073,