five

open-llm-leaderboard-old/details_lloorree__kssht-castor-70b

收藏
Hugging Face2023-09-18 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_lloorree__kssht-castor-70b
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 lloorree/kssht-castor-70b 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集由 61 个配置组成,每个配置对应一个评估任务。它包含 1 次运行的数据,每次运行在每个配置中表示为特定的分割,使用运行的时间戳命名。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,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。

该数据集是在模型 lloorree/kssht-castor-70b 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集由 61 个配置组成,每个配置对应一个评估任务。它包含 1 次运行的数据,每次运行在每个配置中表示为特定的分割,使用运行的时间戳命名。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,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。
提供机构:
open-llm-leaderboard-old
原始信息汇总

数据集概述

数据集简介

该数据集是在对模型 lloorree/kssht-castor-70b 进行评估运行时自动创建的,评估结果展示在 Open LLM Leaderboard 上。

数据集组成

  • 数据集包含 61 个配置,每个配置对应一个评估任务。
  • 数据集由 1 次运行创建,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_lloorree__kssht-castor-70b", "harness_truthfulqa_mc_0", split="train")

最新结果

以下是 2023-09-18T23:54:47.734205 运行 的最新结果:

python { "all": { "acc": 0.7025630433354887, "acc_stderr": 0.03070323641112233, "acc_norm": 0.7065431366848456, "acc_norm_stderr": 0.03067233267965294, "mc1": 0.40024479804161567, "mc1_stderr": 0.01715160555574914, "mc2": 0.5630669446354012, "mc2_stderr": 0.014865953800030475 }, "harness|arc:challenge|25": { "acc": 0.6501706484641638, "acc_stderr": 0.01393680921215829, "acc_norm": 0.6953924914675768, "acc_norm_stderr": 0.01344952210993249 }, "harness|hellaswag|10": { "acc": 0.6857199761003784, "acc_stderr": 0.004632797375289762, "acc_norm": 0.8753236407090221, "acc_norm_stderr": 0.003296764320821918 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8486842105263158, "acc_stderr": 0.02916263159684399, "acc_norm": 0.8486842105263158, "acc_norm_stderr": 0.02916263159684399 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565666, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565666 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.35294117647058826, "acc_stderr": 0.047551296160629475, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.047551296160629475 }, "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": 0.6978723404255319, "acc_stderr": 0.030017554471880557, "acc_norm": 0.6978723404255319, "acc_norm_stderr": 0.030017554471880557 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.04692008381368909, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.04692008381368909 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03960933549451207, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03960933549451207 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4365079365079365, "acc_stderr": 0.0255428468174005, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.0255428468174005 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8193548387096774, "acc_stderr": 0.021886178567172523, "acc_norm": 0.8193548387096774, "acc_norm_stderr": 0.021886178567172523 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5566502463054187, "acc_stderr": 0.03495334582162933, "acc_norm": 0.5566502463054187, "acc_norm_stderr": 0.03495334582162933 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8303030303030303, "acc_stderr": 0.029311188674983134, "acc_norm": 0.8303030303030303, "acc_norm_stderr": 0.029311188674983134 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8737373737373737, "acc_stderr": 0.023664359402880236, "acc_norm": 0.8737373737373737, "acc_norm_stderr": 0.023664359402880236 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9430051813471503, "acc_stderr": 0.016731085293607555, "acc_norm": 0.9430051813471503, "acc_norm_stderr": 0.016731085293607555 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7128205128205128, "acc_stderr": 0.022939925418530616, "acc_norm": 0.7128205128205128, "acc_norm_stderr": 0.022939925418530616 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074

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