five

open-llm-leaderboard-old/details_cloudyu__60B_MoE_Coder_v3

收藏
Hugging Face2024-02-10 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_cloudyu__60B_MoE_Coder_v3
下载链接
链接失效反馈
官方服务:
资源简介:
该数据集是在评估模型cloudyu/60B_MoE_Coder_v3时自动创建的,包含63个配置,每个配置对应一个评估任务。数据集由1次运行生成,每次运行的结果存储为特定的分割,分割名称使用运行的时间戳。此外,数据集还包含一个名为“results”的配置,用于存储所有运行的聚合结果,并用于计算和显示在Open LLM Leaderboard上的聚合指标。

该数据集是在评估模型cloudyu/60B_MoE_Coder_v3时自动创建的,包含63个配置,每个配置对应一个评估任务。数据集由1次运行生成,每次运行的结果存储为特定的分割,分割名称使用运行的时间戳。此外,数据集还包含一个名为“results”的配置,用于存储所有运行的聚合结果,并用于计算和显示在Open LLM Leaderboard上的聚合指标。
提供机构:
open-llm-leaderboard-old
原始信息汇总

数据集概述

该数据集是在对模型 cloudyu/60B_MoE_Coder_v3 进行评估运行期间自动创建的,用于 Open LLM Leaderboard

数据集组成

  • 该数据集包含 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。

最新结果

以下是 2024-02-10T04:01:02.016455 运行的最新结果

json { "all": { "acc": 0.7507692268285616, "acc_stderr": 0.028851716073379694, "acc_norm": 0.7546718582183198, "acc_norm_stderr": 0.029402819641764666, "mc1": 0.5042839657282742, "mc1_stderr": 0.017502858577371258, "mc2": 0.6700593362662586, "mc2_stderr": 0.014408380056133315 }, "harness|arc:challenge|25": { "acc": 0.6834470989761092, "acc_stderr": 0.013592431519068079, "acc_norm": 0.71160409556314, "acc_norm_stderr": 0.013238394422428175 }, "harness|hellaswag|10": { "acc": 0.658832901812388, "acc_stderr": 0.004731324409133276, "acc_norm": 0.8544114718183629, "acc_norm_stderr": 0.003519724163310883 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7111111111111111, "acc_stderr": 0.03915450630414251, "acc_norm": 0.7111111111111111, "acc_norm_stderr": 0.03915450630414251 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.881578947368421, "acc_stderr": 0.02629399585547494, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.02629399585547494 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7886792452830189, "acc_stderr": 0.025125766484827845, "acc_norm": 0.7886792452830189, "acc_norm_stderr": 0.025125766484827845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02628055093284806, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02628055093284806 }, "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": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.45, "acc_stderr": 0.04999999999999998, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999998 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7341040462427746, "acc_stderr": 0.03368762932259433, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.03368762932259433 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5294117647058824, "acc_stderr": 0.049665709039785295, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.049665709039785295 }, "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": 0.7531914893617021, "acc_stderr": 0.028185441301234095, "acc_norm": 0.7531914893617021, "acc_norm_stderr": 0.028185441301234095 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.543859649122807, "acc_stderr": 0.046854730419077895, "acc_norm": 0.543859649122807, "acc_norm_stderr": 0.046854730419077895 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7310344827586207, "acc_stderr": 0.036951833116502325, "acc_norm": 0.7310344827586207, "acc_norm_stderr": 0.036951833116502325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7248677248677249, "acc_stderr": 0.023000086859068652, "acc_norm": 0.7248677248677249, "acc_norm_stderr": 0.023000086859068652 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9, "acc_stderr": 0.017066403719657255, "acc_norm": 0.9, "acc_norm_stderr": 0.017066403719657255 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6502463054187192, "acc_stderr": 0.03355400904969566, "acc_norm": 0.6502463054187192, "acc_norm_stderr": 0.03355400904969566 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781668, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781668 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9292929292929293, "acc_stderr": 0.018263105420199488, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.018263105420199488 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9585492227979274, "acc_stderr": 0.014385432857476434, "acc_norm": 0.9585492227979274, "acc_norm_stderr": 0.014385432857476434 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8102564102564103, "acc_stderr": 0.0198801654065888, "acc_norm": 0.8102564102564103, "acc_norm_stderr": 0.0198801654065888 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.45925925925925926, "acc_stderr": 0.030384169232350825, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.030384169232350825 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8403361344537815, "acc_stderr": 0.023793353997528802, "acc_norm": 0.8403361344

5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作