open-llm-leaderboard-old/details_davzoku__frankencria-llama2-12.5b-v1.3-m.2

该数据集是在模型 davzoku/frankencria-llama2-12.5b-v1.3-m.2 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 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 分割始终指向最新结果。一个额外的 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

数据集摘要

该数据集是在模型 davzoku/frankencria-llama2-12.5b-v1.3-m.2 在 Open LLM Leaderboard 上的评估运行期间自动创建的。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。

数据集结构

数据集包含以下配置：

• harness_arc_challenge_25
• harness_gsm8k_5
• harness_hellaswag_10
• harness_hendrycksTest_5

每个配置包含多个数据文件，分为不同的分割，如 2024_02_14T15_39_16.700250 和 latest。

数据加载示例

以下是加载数据集的示例代码： python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_davzoku__frankencria-llama2-12.5b-v1.3-m.2", "harness_winogrande_5", split="train")

最新结果

以下是 最新结果 的摘要： python { "all": { "acc": 0.4617722987057413, "acc_stderr": 0.03442751213597903, "acc_norm": 0.4686971200766007, "acc_norm_stderr": 0.03527698452163269, "mc1": 0.3219094247246022, "mc1_stderr": 0.016355567611960404, "mc2": 0.5030678363563933, "mc2_stderr": 0.01589753382807047 }, "harness|arc:challenge|25": { "acc": 0.5110921501706485, "acc_stderr": 0.01460779491401305, "acc_norm": 0.5503412969283277, "acc_norm_stderr": 0.014537144444284743 }, "harness|hellaswag|10": { "acc": 0.6093407687711612, "acc_stderr": 0.0048690101522807505, "acc_norm": 0.7916749651463851, "acc_norm_stderr": 0.004052804959005537 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3925925925925926, "acc_stderr": 0.04218506215368879, "acc_norm": 0.3925925925925926, "acc_norm_stderr": 0.04218506215368879 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.48026315789473684, "acc_stderr": 0.04065771002562605, "acc_norm": 0.48026315789473684, "acc_norm_stderr": 0.04065771002562605 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4867924528301887, "acc_stderr": 0.030762134874500476, "acc_norm": 0.4867924528301887, "acc_norm_stderr": 0.030762134874500476 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5, "acc_stderr": 0.04181210050035455, "acc_norm": 0.5, "acc_norm_stderr": 0.04181210050035455 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "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": 0.36416184971098264, "acc_stderr": 0.03669072477416907, "acc_norm": 0.36416184971098264, "acc_norm_stderr": 0.03669072477416907 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "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": 0.39574468085106385, "acc_stderr": 0.031967586978353627, "acc_norm": 0.39574468085106385, "acc_norm_stderr": 0.031967586978353627 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.044346007015849245, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.044346007015849245 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.328042328042328, "acc_stderr": 0.024180497164376896, "acc_norm": 0.328042328042328, "acc_norm_stderr": 0.024180497164376896 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.25396825396825395, "acc_stderr": 0.03893259610604674, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.03893259610604674 }, "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": 0.535483870967742, "acc_stderr": 0.028372287797962935, "acc_norm": 0.535483870967742, "acc_norm_stderr": 0.028372287797962935 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3054187192118227, "acc_stderr": 0.03240661565868407, "acc_norm": 0.3054187192118227, "acc_norm_stderr": 0.03240661565868407 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5515151515151515, "acc_stderr": 0.038835659779569286, "acc_norm": 0.5515151515151515, "acc_norm_stderr": 0.038835659779569286 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5757575757575758, "acc_stderr": 0.035212249088415845, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.035212249088415845 }, "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": 0.41794871794871796, "acc_stderr": 0.02500732988246122, "acc_norm": 0.41794871794871796, "acc_norm_stderr": 0.02500732988246122 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.02671