open-llm-leaderboard-old/details_ethzanalytics__pythia-31m

该数据集是在模型ethzanalytics/pythia-31m的评估运行中自动创建的。数据集由64个配置组成，每个配置对应一个评估任务。数据集是从1次运行中创建的，每次运行都可以在特定配置中找到，分割以运行的时间戳命名。train分割始终指向最新结果。此外，results配置存储了所有运行的聚合结果，并用于计算和显示在Open LLM Leaderboard上的聚合指标。

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 ethzanalytics/pythia-31m 进行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 64 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。
• 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 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_ethzanalytics__pythia-31m_public", "harness_winogrande_5", split="train")

最新结果

以下是 2023-11-13T13:01:31.225551 运行 的最新结果：

python { "all": { "acc": 0.2486090533214635, "acc_stderr": 0.030580280893238346, "acc_norm": 0.24951095231696532, "acc_norm_stderr": 0.031375786973211, "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015023, "mc2": 0.49102256781530107, "mc2_stderr": 0.015750842651440947, "em": 0.0006291946308724832, "em_stderr": 0.0002568002749723811, "f1": 0.013650377516778552, "f1_stderr": 0.0006539918270891778 }, "harness|arc:challenge|25": { "acc": 0.1697952218430034, "acc_stderr": 0.010971775157784212, "acc_norm": 0.21843003412969283, "acc_norm_stderr": 0.012074291605700985 }, "harness|hellaswag|10": { "acc": 0.26309500099581756, "acc_stderr": 0.004394136724172986, "acc_norm": 0.26996614220274845, "acc_norm_stderr": 0.00443034623465038 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.25925925925925924, "acc_stderr": 0.03785714465066655, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.03785714465066655 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21132075471698114, "acc_stderr": 0.025125766484827842, "acc_norm": 0.21132075471698114, "acc_norm_stderr": 0.025125766484827842 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304134, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.21, "acc_stderr": 0.04093601807403326, "acc_norm": 0.21, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2138728323699422, "acc_stderr": 0.03126511206173044, "acc_norm": 0.2138728323699422, "acc_norm_stderr": 0.03126511206173044 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2851063829787234, "acc_stderr": 0.029513196625539355, "acc_norm": 0.2851063829787234, "acc_norm_stderr": 0.029513196625539355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.23448275862068965, "acc_stderr": 0.035306258743465914, "acc_norm": 0.23448275862068965, "acc_norm_stderr": 0.035306258743465914 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.1746031746031746, "acc_stderr": 0.0339549002085611, "acc_norm": 0.1746031746031746, "acc_norm_stderr": 0.0339549002085611 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031103, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031103 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0317852971064275, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0317852971064275 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.24, "acc_stderr": 0.04292346959909281, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909281 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.20606060606060606, "acc_stderr": 0.031584153240477086, "acc_norm": 0.20606060606060606, "acc_norm_stderr": 0.031584153240477086 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.30303030303030304, "acc_stderr": 0.032742879140268674, "acc_norm": 0.30303030303030304, "acc_norm_stderr": 0.032742879140268674 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.22797927461139897, "acc_stderr": 0.030276909945178263, "acc_norm": 0.22797927461139897, "acc_norm_stderr": 0.030276909945178263 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2230769230769231, "acc_stderr": 0.02110773012724398, "acc_norm": 0.2230769230769231, "acc_norm_stderr