open-llm-leaderboard-old/details_deepseek-ai__deepseek-math-7b-base

该数据集是在模型 deepseek-ai/deepseek-math-7b-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。它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 库加载运行细节的示例。

原始信息汇总

数据集概述

数据集摘要

该数据集是在评估模型deepseek-ai/deepseek-math-7b-base在Open LLM Leaderboard上的运行过程中自动创建的。

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，用于计算和显示在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_deepseek-ai__deepseek-math-7b-base", "harness_winogrande_5", split="train")

最新结果

以下是2024-03-15T10:41:41.444832运行的最新结果：

python { "all": { "acc": 0.5720441323525545, "acc_stderr": 0.0345237727785387, "acc_norm": 0.5737206040031878, "acc_norm_stderr": 0.0352334023126239, "mc1": 0.2631578947368421, "mc1_stderr": 0.01541524174023702, "mc2": 0.4071269130958089, "mc2_stderr": 0.01426178868135068 }, "harness|arc:challenge|25": { "acc": 0.48976109215017066, "acc_stderr": 0.014608326906285019, "acc_norm": 0.5221843003412969, "acc_norm_stderr": 0.014597001927076136 }, "harness|hellaswag|10": { "acc": 0.5126468830910177, "acc_stderr": 0.00498818498834529, "acc_norm": 0.6948814977096196, "acc_norm_stderr": 0.004595165551383618 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4148148148148148, "acc_stderr": 0.042561937679014075, "acc_norm": 0.4148148148148148, "acc_norm_stderr": 0.042561937679014075 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395269, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395269 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5735849056603773, "acc_stderr": 0.030437794342983045, "acc_norm": 0.5735849056603773, "acc_norm_stderr": 0.030437794342983045 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6180555555555556, "acc_stderr": 0.040629907841466674, "acc_norm": 0.6180555555555556, "acc_norm_stderr": 0.040629907841466674 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956913, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956913 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.03765746693865151, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.03765746693865151 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542129, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6595744680851063, "acc_stderr": 0.030976692998534422, "acc_norm": 0.6595744680851063, "acc_norm_stderr": 0.030976692998534422 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.047028804320496165, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.047028804320496165 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6482758620689655, "acc_stderr": 0.0397923663749741, "acc_norm": 0.6482758620689655, "acc_norm_stderr": 0.0397923663749741 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5634920634920635, "acc_stderr": 0.025542846817400492, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.025542846817400492 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.667741935483871, "acc_stderr": 0.026795560848122794, "acc_norm": 0.667741935483871, "acc_norm_stderr": 0.026795560848122794 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5763546798029556, "acc_stderr": 0.03476725747649037, "acc_norm": 0.5763546798029556, "acc_norm_stderr": 0.03476725747649037 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6484848484848484, "acc_stderr": 0.037282069986826503, "acc_norm": 0.6484848484848484, "acc_norm_stderr": 0.037282069986826503 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6919191919191919, "acc_stderr": 0.03289477330098616, "acc_norm": 0.6919191919191919, "acc_norm_stderr": 0.03289477330098616 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836184, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836184 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.030213340289237927, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.03021334