open-llm-leaderboard-old/details_nbeerbower__flammen6-mistral-7B

该数据集是在模型nbeerbower/flammen6-mistral-7B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次运行的数据，每次运行在每个配置中表示为特定的拆分。train拆分始终指向最新的结果。一个名为results的额外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

该数据集是在对模型 nbeerbower/flammen6-mistral-7B 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_nbeerbower__flammen6-mistral-7B", "harness_winogrande_5", split="train")

最新结果

以下是 2024-03-14T21:05:30.920430 运行 的最新结果：

python { "all": { "acc": 0.6463686254143772, "acc_stderr": 0.032083108489198105, "acc_norm": 0.6463981879688413, "acc_norm_stderr": 0.03274198265924435, "mc1": 0.46266829865361075, "mc1_stderr": 0.01745464515097059, "mc2": 0.6347674012321349, "mc2_stderr": 0.015145748610941845 }, "harness|arc:challenge|25": { "acc": 0.6561433447098977, "acc_stderr": 0.013880644570156218, "acc_norm": 0.6919795221843004, "acc_norm_stderr": 0.013491429517292038 }, "harness|hellaswag|10": { "acc": 0.6886078470424218, "acc_stderr": 0.004621163476949209, "acc_norm": 0.869946225851424, "acc_norm_stderr": 0.0033567515689037672 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7236842105263158, "acc_stderr": 0.036390575699529276, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.036390575699529276 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305527, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305527 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7967741935483871, "acc_stderr": 0.02289168798455496, "acc_norm": 0.7967741935483871, "acc_norm_stderr": 0.02289168798455496 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479049, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479049 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768776, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768776 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6358974358974359, "acc_stderr": 0.024396672985094767, "acc_norm": 0.6358974358974359, "acc_norm_stderr": 0.024396672985094767 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524565, "acc_norm": 0.3296296296296296, "acc_norm_stderr":