open-llm-leaderboard-old/details_aloobun__Reyna-Mini-1.8B-v0.1

该数据集是在模型 aloobun/Reyna-Mini-1.8B-v0.1 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，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 aloobun/Reyna-Mini-1.8B-v0.1 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，每个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_aloobun__Reyna-Mini-1.8B-v0.1", "harness_winogrande_5", split="train")

最新结果

以下是 2024-02-15T07:29:36.560907 运行的最新结果：

python { "all": { "acc": 0.44766652106081417, "acc_stderr": 0.03438060993883449, "acc_norm": 0.4545350911182196, "acc_norm_stderr": 0.03520914160548039, "mc1": 0.26560587515299877, "mc1_stderr": 0.015461027627253595, "mc2": 0.4140207828143034, "mc2_stderr": 0.014035709599911956 }, "harness|arc:challenge|25": { "acc": 0.33361774744027306, "acc_stderr": 0.013778687054176546, "acc_norm": 0.35238907849829354, "acc_norm_stderr": 0.013960142600598675 }, "harness|hellaswag|10": { "acc": 0.44991037641904, "acc_stderr": 0.004964679845918436, "acc_norm": 0.6041625174268074, "acc_norm_stderr": 0.004880303863138508 }, "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": 0.3925925925925926, "acc_stderr": 0.04218506215368879, "acc_norm": 0.3925925925925926, "acc_norm_stderr": 0.04218506215368879 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4342105263157895, "acc_stderr": 0.040335656678483184, "acc_norm": 0.4342105263157895, "acc_norm_stderr": 0.040335656678483184 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5018867924528302, "acc_stderr": 0.030772653642075664, "acc_norm": 0.5018867924528302, "acc_norm_stderr": 0.030772653642075664 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4305555555555556, "acc_stderr": 0.04140685639111502, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.04140685639111502 }, "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": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3988439306358382, "acc_stderr": 0.037336266553835096, "acc_norm": 0.3988439306358382, "acc_norm_stderr": 0.037336266553835096 }, "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": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4, "acc_stderr": 0.03202563076101737, "acc_norm": 0.4, "acc_norm_stderr": 0.03202563076101737 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159393, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159393 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4896551724137931, "acc_stderr": 0.04165774775728763, "acc_norm": 0.4896551724137931, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.024870815251057096, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.024870815251057096 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03718489006818115, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03718489006818115 }, "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": 0.4774193548387097, "acc_stderr": 0.02841498501970786, "acc_norm": 0.4774193548387097, "acc_norm_stderr": 0.02841498501970786 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3645320197044335, "acc_stderr": 0.0338640574606209, "acc_norm": 0.3645320197044335, "acc_norm_stderr": 0.0338640574606209 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6181818181818182, "acc_stderr": 0.037937131711656344, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.037937131711656344 }, "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": 0.5440414507772021, "acc_stderr": 0.035944137112724366, "acc_norm": 0.5440414507772021, "acc_norm_stderr": 0.035944137112724366 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.358974358974359, "acc_stderr": 0.024321738484602354, "acc_norm": 0.358974358974359, "acc_norm_stderr": 0.024321738484602354 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066468, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066468 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.41596638655462187,