open-llm-leaderboard-old/details_KaeriJenti__kaori-34b-v3

该数据集是在Open LLM Leaderboard上对模型KaeriJenti/kaori-34b-v3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。此外，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`datasets`库中的`load_dataset`函数加载运行详情的示例。

原始信息汇总

数据集概述

数据集创建

• 数据集是在模型 KaeriJenti/kaori-34b-v3 在 Open LLM Leaderboard 上的评估运行期间自动创建的。

数据集结构

• 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一个特定的分割，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，用于计算和显示聚合指标在 Open LLM Leaderboard 上。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KaeriJenti__kaori-34b-v3", "harness_winogrande_5", split="train")

最新结果

• 这些是最新结果，来自 2023-12-23T20:18:59.235180 的运行。

python { "all": { "acc": 0.6942406707070711, "acc_stderr": 0.030569400022157234, "acc_norm": 0.702379645260744, "acc_norm_stderr": 0.031160918444313776, "mc1": 0.37821297429620565, "mc1_stderr": 0.01697633590754687, "mc2": 0.5236714287384441, "mc2_stderr": 0.015221488743934338 }, "harness|arc:challenge|25": { "acc": 0.613481228668942, "acc_stderr": 0.014230084761910471, "acc_norm": 0.6424914675767918, "acc_norm_stderr": 0.014005494275916573 }, "harness|hellaswag|10": { "acc": 0.6059549890460068, "acc_stderr": 0.004876459434619799, "acc_norm": 0.795857398924517, "acc_norm_stderr": 0.004022499210760733 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6814814814814815, "acc_stderr": 0.040247784019771096, "acc_norm": 0.6814814814814815, "acc_norm_stderr": 0.040247784019771096 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8289473684210527, "acc_stderr": 0.03064360707167709, "acc_norm": 0.8289473684210527, "acc_norm_stderr": 0.03064360707167709 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.82, "acc_stderr": 0.03861229196653696, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653696 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7773584905660378, "acc_stderr": 0.025604233470899098, "acc_norm": 0.7773584905660378, "acc_norm_stderr": 0.025604233470899098 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565666, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565666 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4803921568627451, "acc_stderr": 0.04971358884367406, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.04971358884367406 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7063829787234043, "acc_stderr": 0.029771642712491227, "acc_norm": 0.7063829787234043, "acc_norm_stderr": 0.029771642712491227 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7241379310344828, "acc_stderr": 0.03724563619774632, "acc_norm": 0.7241379310344828, "acc_norm_stderr": 0.03724563619774632 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5476190476190477, "acc_stderr": 0.02563425811555495, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.02563425811555495 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.043902592653775614, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.043902592653775614 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.832258064516129, "acc_stderr": 0.021255464065371318, "acc_norm": 0.832258064516129, "acc_norm_stderr": 0.021255464065371318 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5566502463054187, "acc_stderr": 0.03495334582162934, "acc_norm": 0.5566502463054187, "acc_norm_stderr": 0.03495334582162934 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8787878787878788, "acc_stderr": 0.02325315795194208, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.02325315795194208 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9430051813471503, "acc_stderr": 0.016731085293607565, "acc_norm": 0.9430051813471503, "acc_norm_stderr": 0.016731085293607565 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7128205128205128, "acc_stderr": 0.022939925418530616, "acc_norm": 0.7128205128205128, "acc_norm_stderr": 0.022939925418530616 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37407407407407406, "acc_stderr": 0.02950286112895529, "acc_norm": 0.37407407407407406, "acc_norm_stderr": 0.02950286112895529 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7899159663865546, "acc_stderr": 0.02