five

open-llm-leaderboard-old/details_cstr__Spaetzle-v8-7b

收藏
Hugging Face2024-03-11 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_cstr__Spaetzle-v8-7b
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[cstr/Spaetzle-v8-7b](https://huggingface.co/cstr/Spaetzle-v8-7b)在[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/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2次运行中创建的,每次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。此外,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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)上的聚合指标。

该数据集是在模型[cstr/Spaetzle-v8-7b](https://huggingface.co/cstr/Spaetzle-v8-7b)在[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/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2次运行中创建的,每次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。此外,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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)上的聚合指标。
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 cstr/Spaetzle-v8-7bOpen LLM Leaderboard 上的评估运行期间自动创建的。数据集包含 63 个配置,每个配置对应一个评估任务。

数据集结构

数据集由 2 次运行创建,每次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。

额外配置

一个额外的配置 "results" 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。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cstr__Spaetzle-v8-7b", "harness_winogrande_5", split="train")

最新结果

以下是 2024-03-11T05:33:29.178933 运行 的最新结果:

python { "all": { "acc": 0.6501215241389848, "acc_stderr": 0.03205644100318208, "acc_norm": 0.6504026367782804, "acc_norm_stderr": 0.03271458640117835, "mc1": 0.4847001223990208, "mc1_stderr": 0.017495304473187902, "mc2": 0.6405116114057202, "mc2_stderr": 0.015232534705181737 }, "harness|arc:challenge|25": { "acc": 0.6638225255972696, "acc_stderr": 0.013804855026205763, "acc_norm": 0.6868600682593856, "acc_norm_stderr": 0.013552671543623492 }, "harness|hellaswag|10": { "acc": 0.6773551085441147, "acc_stderr": 0.004665327309399188, "acc_norm": 0.866759609639514, "acc_norm_stderr": 0.003391398293613439 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952928, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952928 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544057, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544057 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6820809248554913, "acc_stderr": 0.0355068398916558, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.0355068398916558 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370332, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370332 }, "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": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7645161290322581, "acc_stderr": 0.024137632429337717, "acc_norm": 0.7645161290322581, "acc_norm_stderr": 0.024137632429337717 }, "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": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8, "acc_stderr": 0.03123475237772117, "acc_norm": 0.8, "acc_norm_stderr": 0.03123475237772117 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.028606204289229872, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.028606204289229872 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3888888888888889, "acc_stderr": 0.029723278961476664, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.029723278961476664 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "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