open-llm-leaderboard-old/details_Charlie911__llama2-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general

该数据集是在模型 Charlie911/llama2-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general 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。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 分割始终指向最新结果。一个额外的配置 results 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 库加载数据集详细信息的示例。

原始信息汇总

数据集概述

该数据集是在对模型 Charlie911/llama2-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general 进行评估运行期间自动创建的，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 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__llama2-MultiLoRA-sharegpt-mmlu-drop-ffn-1.0general", "harness_winogrande_5", split="train")

最新结果

以下是 2024-03-02T04:55:08.179555 运行的最新结果：

python { "all": { "acc": 0.4704110115706505, "acc_stderr": 0.034505950889987654, "acc_norm": 0.4753525435626209, "acc_norm_stderr": 0.035288165057582505, "mc1": 0.24724602203182375, "mc1_stderr": 0.01510240479735965, "mc2": 0.38750630913194245, "mc2_stderr": 0.013510604029586733 }, "harness|arc:challenge|25": { "acc": 0.4931740614334471, "acc_stderr": 0.014610029151379813, "acc_norm": 0.5315699658703071, "acc_norm_stderr": 0.014582236460866978 }, "harness|hellaswag|10": { "acc": 0.5884285998805019, "acc_stderr": 0.0049111251010646425, "acc_norm": 0.7858992232622983, "acc_norm_stderr": 0.004093587404303691 }, "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": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.40789473684210525, "acc_stderr": 0.03999309712777471, "acc_norm": 0.40789473684210525, "acc_norm_stderr": 0.03999309712777471 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4641509433962264, "acc_stderr": 0.030693675018458003, "acc_norm": 0.4641509433962264, "acc_norm_stderr": 0.030693675018458003 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4722222222222222, "acc_stderr": 0.04174752578923185, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.04174752578923185 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4277456647398844, "acc_stderr": 0.037724468575180255, "acc_norm": 0.4277456647398844, "acc_norm_stderr": 0.037724468575180255 }, "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": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.41702127659574467, "acc_stderr": 0.032232762667117124, "acc_norm": 0.41702127659574467, "acc_norm_stderr": 0.032232762667117124 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2719298245614035, "acc_stderr": 0.04185774424022056, "acc_norm": 0.2719298245614035, "acc_norm_stderr": 0.04185774424022056 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708628, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708628 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147126, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147126 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.4967741935483871, "acc_stderr": 0.02844341422643833, "acc_norm": 0.4967741935483871, "acc_norm_stderr": 0.02844341422643833 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3694581280788177, "acc_stderr": 0.033959703819985726, "acc_norm": 0.3694581280788177, "acc_norm_stderr": 0.033959703819985726 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "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": 0.4898989898989899, "acc_stderr": 0.03561625488673745, "acc_norm": 0.4898989898989899, "acc_norm_stderr": 0.03561625488673745 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6787564766839378, "acc_stderr": 0.033699508685490674, "acc_norm": 0.6787564766839378, "acc_norm_stderr": 0.033699508685490674 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.45897435897435895, "acc_stderr": 0.025265525491284295, "acc_norm": 0.45897435897435895, "acc_norm_stderr": 0.025265525491284295 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3