five

open-llm-leaderboard-old/details_PistachioAlt__Noromaid-Bagel-7B-Slerp

收藏
Hugging Face2023-12-29 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_PistachioAlt__Noromaid-Bagel-7B-Slerp
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PistachioAlt/Noromaid-Bagel-7B-Slerp在Open LLM Leaderboard上的评估过程中自动生成的。数据集包含63个配置,每个配置对应一个被评估的任务。它包含一次运行的数据,每次运行在每个配置中表示为特定的分割,分割名称由运行的时间戳命名。train分割始终指向最新的结果。一个额外的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。README还提供了一个Python代码片段用于加载数据集,并列出了特定运行的最新结果。

该数据集是在模型PistachioAlt/Noromaid-Bagel-7B-Slerp在Open LLM Leaderboard上的评估过程中自动生成的。数据集包含63个配置,每个配置对应一个被评估的任务。它包含一次运行的数据,每次运行在每个配置中表示为特定的分割,分割名称由运行的时间戳命名。train分割始终指向最新的结果。一个额外的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。README还提供了一个Python代码片段用于加载数据集,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

数据集简介

该数据集是在评估模型PistachioAlt/Noromaid-Bagel-7B-SlerpOpen LLM Leaderboard上的运行过程中自动创建的。

数据集组成

  • 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次运行创建,每次运行的详细结果存储在特定的分割中,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_PistachioAlt__Noromaid-Bagel-7B-Slerp", "harness_winogrande_5", split="train")

最新结果

以下是2023-12-29T17:27:18.779306运行的最新结果:

python { "all": { "acc": 0.6422049061069124, "acc_stderr": 0.032410788947613685, "acc_norm": 0.6464619875380062, "acc_norm_stderr": 0.03305989898949311, "mc1": 0.3659730722154223, "mc1_stderr": 0.01686294168408838, "mc2": 0.5288372703003257, "mc2_stderr": 0.015191217388559787 }, "harness|arc:challenge|25": { "acc": 0.6126279863481229, "acc_stderr": 0.01423587248790987, "acc_norm": 0.6450511945392492, "acc_norm_stderr": 0.013983036904094087 }, "harness|hellaswag|10": { "acc": 0.6489743079067914, "acc_stderr": 0.004763155068744877, "acc_norm": 0.8458474407488548, "acc_norm_stderr": 0.0036035695286784127 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6578947368421053, "acc_stderr": 0.03860731599316091, "acc_norm": 0.6578947368421053, "acc_norm_stderr": 0.03860731599316091 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493857, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493857 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416906, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416906 }, "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": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944437, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944437 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7548387096774194, "acc_stderr": 0.02447224384089552, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.02447224384089552 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.032250781083062896, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.032250781083062896 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6487179487179487, "acc_stderr": 0.024203665177902803, "acc_norm": 0.6487179487179487, "acc_norm_stderr": 0.024203665177902803 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897

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