five

open-llm-leaderboard/details_yeontaek__Platypus2-13B-LoRa-v2

收藏
Hugging Face2023-08-29 更新2024-03-04 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard/details_yeontaek__Platypus2-13B-LoRa-v2
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yeontaek/Platypus2-13B-LoRa-v2进行评估时自动创建的。数据集由61个配置组成,每个配置对应一个评估任务。数据集从1次运行中创建,每次运行可以在每个配置中找到特定的分割,分割以运行的时间戳命名。train分割始终指向最新的结果。此外,还有一个名为results的配置存储了所有运行的聚合结果,并用于计算和显示Open LLM Leaderboard上的聚合指标。
提供机构:
open-llm-leaderboard
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yeontaek/Platypus2-13B-LoRa-v2Open LLM Leaderboard上的运行过程中自动创建的。

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_yeontaek__Platypus2-13B-LoRa-v2", "harness_truthfulqa_mc_0", split="train")

最新结果

以下是2023-08-29T11:20:59.240376的最新结果: python { "all": { "acc": 0.571991245483798, "acc_stderr": 0.034294067141786025, "acc_norm": 0.5761375119651778, "acc_norm_stderr": 0.03427336583128381, "mc1": 0.28151774785801714, "mc1_stderr": 0.01574402724825605, "mc2": 0.4191985438925104, "mc2_stderr": 0.014270484892545822 }, "harness|arc:challenge|25": { "acc": 0.5563139931740614, "acc_stderr": 0.014518421825670444, "acc_norm": 0.5947098976109215, "acc_norm_stderr": 0.014346869060229328 }, "harness|hellaswag|10": { "acc": 0.6179047998406691, "acc_stderr": 0.004849065962692132, "acc_norm": 0.8241386178052181, "acc_norm_stderr": 0.003799241408502969 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4962962962962963, "acc_stderr": 0.04319223625811331, "acc_norm": 0.4962962962962963, "acc_norm_stderr": 0.04319223625811331 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5986842105263158, "acc_stderr": 0.039889037033362836, "acc_norm": 0.5986842105263158, "acc_norm_stderr": 0.039889037033362836 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6150943396226415, "acc_stderr": 0.02994649856769995, "acc_norm": 0.6150943396226415, "acc_norm_stderr": 0.02994649856769995 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5722543352601156, "acc_stderr": 0.03772446857518026, "acc_norm": 0.5722543352601156, "acc_norm_stderr": 0.03772446857518026 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.047240073523838876, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.047240073523838876 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4297872340425532, "acc_stderr": 0.03236214467715564, "acc_norm": 0.4297872340425532, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.043727482902780064, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.043727482902780064 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4482758620689655, "acc_stderr": 0.04144311810878151, "acc_norm": 0.4482758620689655, "acc_norm_stderr": 0.04144311810878151 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3386243386243386, "acc_stderr": 0.02437319786798306, "acc_norm": 0.3386243386243386, "acc_norm_stderr": 0.02437319786798306 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6709677419354839, "acc_stderr": 0.02672949906834996, "acc_norm": 0.6709677419354839, "acc_norm_stderr": 0.02672949906834996 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.458128078817734, "acc_stderr": 0.03505630140785741, "acc_norm": 0.458128078817734, "acc_norm_stderr": 0.03505630140785741 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7090909090909091, "acc_stderr": 0.03546563019624336, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.03546563019624336 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646836, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646836 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8186528497409327, "acc_stderr": 0.02780703236068609, "acc_norm": 0.8186528497409327, "acc_norm_stderr": 0.02780703236068609 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5307692307692308, "acc_stderr": 0.025302958890850154, "acc_norm": 0.5307692307692308, "acc_norm_stderr": 0.025302958890850154 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.027309140588230172, "acc_norm": 0.277777777

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