open-llm-leaderboard-old/details_TriadParty__deepmoney-34b-200k-base

该数据集是在模型 TriadParty/deepmoney-34b-200k-base 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。数据集由 2 次运行创建，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，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` 库中的 `load_dataset` 函数加载数据集的示例。

原始信息汇总

数据集概述

数据集简介

该数据集是在评估模型TriadParty/deepmoney-34b-200k-base在Open LLM Leaderboard上的运行过程中自动创建的。

数据集结构

• 配置数量：63个配置，每个配置对应一个评估任务。
• 数据来源：数据集由2次运行生成，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。
• 最新结果："train"分割始终指向最新结果。
• 结果汇总：一个额外的配置"results"存储所有运行结果的汇总，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_TriadParty__deepmoney-34b-200k-base", "harness_winogrande_5", split="train")

最新结果

以下是最新结果的摘要： python { "all": { "acc": 0.7256546951493829, "acc_stderr": 0.028872391946942647, "acc_norm": 0.7403326146455504, "acc_norm_stderr": 0.029642883506415262, "mc1": 0.3072215422276622, "mc1_stderr": 0.01615020132132301, "mc2": 0.4593002272815368, "mc2_stderr": 0.014606974103928553 }, "harness|arc:challenge|25": { "acc": 0.60580204778157, "acc_stderr": 0.014280522667467327, "acc_norm": 0.6399317406143344, "acc_norm_stderr": 0.014027516814585188 }, "harness|hellaswag|10": { "acc": 0.6435968930491934, "acc_stderr": 0.00477957440277138, "acc_norm": 0.8386775542720574, "acc_norm_stderr": 0.0036707636737929607 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6962962962962963, "acc_stderr": 0.039725528847851375, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.039725528847851375 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8355263157894737, "acc_stderr": 0.030167533468632726, "acc_norm": 0.8355263157894737, "acc_norm_stderr": 0.030167533468632726 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.78, "acc_stderr": 0.04163331998932262, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932262 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8188679245283019, "acc_stderr": 0.023702963526757798, "acc_norm": 0.8188679245283019, "acc_norm_stderr": 0.023702963526757798 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8611111111111112, "acc_stderr": 0.028919802956134912, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.028919802956134912 }, "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": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.049598599663841815, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.049598599663841815 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7617021276595745, "acc_stderr": 0.027851252973889774, "acc_norm": 0.7617021276595745, "acc_norm_stderr": 0.027851252973889774 }, "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": 0.7379310344827587, "acc_stderr": 0.036646663372252565, "acc_norm": 0.7379310344827587, "acc_norm_stderr": 0.036646663372252565 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6005291005291006, "acc_stderr": 0.02522545028406793, "acc_norm": 0.6005291005291006, "acc_norm_stderr": 0.02522545028406793 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.57, "acc_stderr": 0.04975698519562427, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562427 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8935483870967742, "acc_stderr": 0.01754510295165663, "acc_norm": 0.8935483870967742, "acc_norm_stderr": 0.01754510295165663 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6403940886699507, "acc_stderr": 0.03376458246509568, "acc_norm": 0.6403940886699507, "acc_norm_stderr": 0.03376458246509568 }, "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": 0.8424242424242424, "acc_stderr": 0.028450388805284357, "acc_norm": 0.8424242424242424, "acc_norm_stderr": 0.028450388805284357 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9141414141414141, "acc_stderr": 0.01996022556317289, "acc_norm": 0.9141414141414141, "acc_norm_stderr": 0.01996022556317289 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9792746113989638, "acc_stderr": 0.010281417011909029, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.010281417011909029 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7923076923076923, "acc_stderr": 0.020567539567246797, "acc_norm": 0.7923076923076923, "acc_norm_stderr": 0.020567539567246797 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.42962962962962964, "acc_stderr": 0.030182099804387266, "acc_norm": 0.42962962962962