five

open-llm-leaderboard-old/details_JaeyeonKang__CCK_Gony_v3

收藏
Hugging Face2024-01-26 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_JaeyeonKang__CCK_Gony_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Open LLM Leaderboard上对模型JaeyeonKang/CCK_Gony_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。数据集由1次运行创建,每次运行在每个配置中作为一个特定的分割,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存储了运行的所有聚合结果,用于计算和显示Open LLM Leaderboard上的聚合指标。文件还提供了如何使用Python代码加载运行细节的示例,并包含了特定运行的最新结果。

该数据集是在Open LLM Leaderboard上对模型JaeyeonKang/CCK_Gony_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。数据集由1次运行创建,每次运行在每个配置中作为一个特定的分割,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存储了运行的所有聚合结果,用于计算和显示Open LLM Leaderboard上的聚合指标。文件还提供了如何使用Python代码加载运行细节的示例,并包含了特定运行的最新结果。
提供机构:
open-llm-leaderboard-old
原始信息汇总

数据集概述

该数据集是在模型JaeyeonKang/CCK_Gony_v3Open LLM Leaderboard上的评估运行期间自动创建的。数据集包含63个配置,每个配置对应一个评估任务。

数据集结构

  • 配置数量:63个配置
  • 数据来源: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。
  • 最新结果:"train"分割总是指向最新的结果。
  • 结果汇总:一个额外的配置"results"存储所有运行的汇总结果,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_JaeyeonKang__CCK_Gony_v3", "harness_winogrande_5", split="train")

最新结果

以下是2024-01-26T06:24:47.762718运行的最新结果:

python { "all": { "acc": 0.7097203818503772, "acc_stderr": 0.030355271247042948, "acc_norm": 0.7137772249925107, "acc_norm_stderr": 0.03093852986113917, "mc1": 0.576499388004896, "mc1_stderr": 0.017297421448534744, "mc2": 0.7332871379338723, "mc2_stderr": 0.014493155381350617 }, "harness|arc:challenge|25": { "acc": 0.689419795221843, "acc_stderr": 0.01352229209805305, "acc_norm": 0.7133105802047781, "acc_norm_stderr": 0.013214986329274779 }, "harness|hellaswag|10": { "acc": 0.7057359091814379, "acc_stderr": 0.004547798964126658, "acc_norm": 0.8870742879904402, "acc_norm_stderr": 0.00315855127052641 }, "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": 0.6888888888888889, "acc_stderr": 0.03999262876617721, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.03999262876617721 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7828947368421053, "acc_stderr": 0.03355045304882925, "acc_norm": 0.7828947368421053, "acc_norm_stderr": 0.03355045304882925 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7849056603773585, "acc_stderr": 0.02528839450289137, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.02528839450289137 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8055555555555556, "acc_stderr": 0.03309615177059006, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.03309615177059006 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "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": 0.7514450867052023, "acc_stderr": 0.03295304696818318, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818318 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "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": 0.6851063829787234, "acc_stderr": 0.03036358219723817, "acc_norm": 0.6851063829787234, "acc_norm_stderr": 0.03036358219723817 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6052631578947368, "acc_stderr": 0.04598188057816542, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.04598188057816542 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03960933549451208, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03960933549451208 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4973544973544973, "acc_stderr": 0.02575094967813039, "acc_norm": 0.4973544973544973, "acc_norm_stderr": 0.02575094967813039 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8483870967741935, "acc_stderr": 0.020402616654416762, "acc_norm": 0.8483870967741935, "acc_norm_stderr": 0.020402616654416762 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5960591133004927, "acc_stderr": 0.03452453903822032, "acc_norm": 0.5960591133004927, "acc_norm_stderr": 0.03452453903822032 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8, "acc_stderr": 0.031234752377721164, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721164 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8787878787878788, "acc_stderr": 0.023253157951942088, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.023253157951942088 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9585492227979274, "acc_stderr": 0.014385432857476461, "acc_norm": 0.9585492227979274, "acc_norm_stderr": 0.014385432857476461 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6897435897435897, "acc_stderr": 0.023454674889404288, "acc_norm": 0.6897435897435897, "acc_norm_stderr": 0.023454674889404288 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3962962962962963, "acc_stderr": 0.029822619458534, "acc_norm": 0.3962962962962963, "acc_norm_stderr": 0.029822619458534 }, "harness

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