five

open-llm-leaderboard-old/details_chatty123__mistral_rank8_dpo

收藏
Hugging Face2024-04-15 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_chatty123__mistral_rank8_dpo
下载链接
链接失效反馈
官方服务:
资源简介:
该数据集是在模型 chatty123/mistral_rank8_dpo 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。数据集是从1次运行中创建的,每次运行在每种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。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。文件还提供了如何使用Python代码加载运行细节的示例,并列出了特定运行的最新结果。

该数据集是在模型 chatty123/mistral_rank8_dpo 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。数据集是从1次运行中创建的,每次运行在每种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。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。文件还提供了如何使用Python代码加载运行细节的示例,并列出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

数据集摘要

该数据集是在模型 chatty123/mistral_rank8_dpoOpen LLM Leaderboard 上的评估运行期间自动创建的。

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。
  • 额外的 "results" 配置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。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chatty123__mistral_rank8_dpo", "harness_winogrande_5", split="train")

最新结果

以下是 2024-04-15T17:29:51.038786 运行的最新结果

python { "all": { "acc": 0.603147483881691, "acc_stderr": 0.03328681418310232, "acc_norm": 0.6079132670868433, "acc_norm_stderr": 0.03396199446250826, "mc1": 0.5348837209302325, "mc1_stderr": 0.01746084997587397, "mc2": 0.6861248999232029, "mc2_stderr": 0.01529536413773913 }, "harness|arc:challenge|25": { "acc": 0.5819112627986348, "acc_stderr": 0.014413988396996076, "acc_norm": 0.6305460750853242, "acc_norm_stderr": 0.014104578366491887 }, "harness|hellaswag|10": { "acc": 0.6671977693686517, "acc_stderr": 0.004702533775930293, "acc_norm": 0.8511252738498307, "acc_norm_stderr": 0.003552374531305201 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.04793724854411021, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411021 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6736111111111112, "acc_stderr": 0.03921067198982266, "acc_norm": 0.6736111111111112, "acc_norm_stderr": 0.03921067198982266 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5895953757225434, "acc_stderr": 0.037507570448955356, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.037507570448955356 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5234042553191489, "acc_stderr": 0.032650194750335815, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.032650194750335815 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594963, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594963 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.37566137566137564, "acc_stderr": 0.024942368931159795, "acc_norm": 0.37566137566137564, "acc_norm_stderr": 0.024942368931159795 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795132, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795132 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6774193548387096, "acc_stderr": 0.026593084516572274, "acc_norm": 0.6774193548387096, "acc_norm_stderr": 0.026593084516572274 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7151515151515152, "acc_stderr": 0.03524390844511781, "acc_norm": 0.7151515151515152, "acc_norm_stderr": 0.03524390844511781 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646826, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646826 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8497409326424871, "acc_stderr": 0.025787723180723872, "acc_norm": 0.8497409326424871, "acc_norm_stderr": 0.025787723180723872 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.558974358974359, "acc_stderr": 0.025174048384000745, "acc_norm": 0.558974358974359, "acc_norm_stderr": 0.025174048384000745 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness|hendry

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