five

open-llm-leaderboard-old/details_ChaoticNeutrals__RP_Vision_7B

收藏
Hugging Face2024-03-23 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_ChaoticNeutrals__RP_Vision_7B
下载链接
链接失效反馈
官方服务:
资源简介:
数据集`Evaluation run of ChaoticNeutrals/RP_Vision_7B`是在模型`ChaoticNeutrals/RP_Vision_7B`在Open LLM Leaderboard上的评估运行中自动生成的。该数据集包含63个配置,每个配置对应一个评估任务。数据集由1次运行生成,每次运行的结果存储在特定的分割中,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,并用于在Open LLM Leaderboard上计算和显示聚合指标。

数据集`Evaluation run of ChaoticNeutrals/RP_Vision_7B`是在模型`ChaoticNeutrals/RP_Vision_7B`在Open LLM Leaderboard上的评估运行中自动生成的。该数据集包含63个配置,每个配置对应一个评估任务。数据集由1次运行生成,每次运行的结果存储在特定的分割中,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,并用于在Open LLM Leaderboard上计算和显示聚合指标。
提供机构:
open-llm-leaderboard-old
原始信息汇总

数据集概述

数据集简介

该数据集是在模型ChaoticNeutrals/RP_Vision_7B的评估运行期间自动创建的,用于Open LLM Leaderboard

数据集结构

  • 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。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ChaoticNeutrals__RP_Vision_7B", "harness_winogrande_5", split="train")

最新结果

以下是2024-03-23T05:58:51.196010运行的最新结果:

python { "all": { "acc": 0.6501517888097911, "acc_stderr": 0.032048221712222776, "acc_norm": 0.6507990701154603, "acc_norm_stderr": 0.03270238816915129, "mc1": 0.5263157894736842, "mc1_stderr": 0.017479241161975457, "mc2": 0.6850422329465207, "mc2_stderr": 0.015032479220010228 }, "harness|arc:challenge|25": { "acc": 0.6877133105802048, "acc_stderr": 0.013542598541688067, "acc_norm": 0.7064846416382252, "acc_norm_stderr": 0.013307250444941108 }, "harness|hellaswag|10": { "acc": 0.7117108145787692, "acc_stderr": 0.004520406331084043, "acc_norm": 0.8781119298944433, "acc_norm_stderr": 0.0032648787375868854 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105654, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932262, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932262 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6042553191489362, "acc_stderr": 0.03196758697835362, "acc_norm": 0.6042553191489362, "acc_norm_stderr": 0.03196758697835362 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305527, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305527 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268545, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268545 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6820512820512821, "acc_stderr": 0.02361088430892786, "acc_norm": 0.6820512820512821, "acc_norm_stderr": 0.02361088430892786 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616248, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616248 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc":

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