open-llm-leaderboard-old/details_AI-Sweden-Models__gpt-sw3-1.3b

该数据集是在Open LLM Leaderboard上对AI-Sweden-Models/gpt-sw3-1.3b模型进行评估时自动生成的。数据集包含64个配置，每个配置对应一个评估任务。数据集由一个或多个运行的结果组成，每个运行作为一个特定的分割存储，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。此外，还有一个results配置，用于存储运行的所有聚合结果，这些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Hugging Face datasets库加载数据集的示例。

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 AI-Sweden-Models/gpt-sw3-1.3b 进行评估运行时自动创建的，用于 Open LLM Leaderboard。

数据集组成

• 数据集包含 64 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，用于计算和显示聚合指标。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b_public", "harness_winogrande_5", split="train")

最新结果

以下是 2023-11-19T11:42:51.452519 运行的最新结果：

python { "all": { "acc": 0.26488474204014834, "acc_stderr": 0.031111618585053954, "acc_norm": 0.2662369199123101, "acc_norm_stderr": 0.031927997249928834, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871117, "mc2": 0.3996656760993288, "mc2_stderr": 0.014244979717903544, "em": 0.0008389261744966443, "em_stderr": 0.000296496298980123, "f1": 0.04081061241610719, "f1_stderr": 0.001194792794486935 }, "harness|arc:challenge|25": { "acc": 0.27303754266211605, "acc_stderr": 0.01301933276263575, "acc_norm": 0.3037542662116041, "acc_norm_stderr": 0.013438909184778755 }, "harness|hellaswag|10": { "acc": 0.3951404102768373, "acc_stderr": 0.004878816961012043, "acc_norm": 0.5039832702648874, "acc_norm_stderr": 0.0049896230687787955 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2740740740740741, "acc_stderr": 0.03853254836552003, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.03853254836552003 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.19736842105263158, "acc_stderr": 0.03238981601699397, "acc_norm": 0.19736842105263158, "acc_norm_stderr": 0.03238981601699397 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.25660377358490566, "acc_stderr": 0.026880647889051996, "acc_norm": 0.25660377358490566, "acc_norm_stderr": 0.026880647889051996 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.3125, "acc_stderr": 0.038760854559127644, "acc_norm": 0.3125, "acc_norm_stderr": 0.038760854559127644 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.040201512610368445, "acc_norm": 0.2, "acc_norm_stderr": 0.040201512610368445 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2832369942196532, "acc_stderr": 0.034355680560478746, "acc_norm": 0.2832369942196532, "acc_norm_stderr": 0.034355680560478746 }, "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": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.31063829787234043, "acc_stderr": 0.03025123757921317, "acc_norm": 0.31063829787234043, "acc_norm_stderr": 0.03025123757921317 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.25517241379310346, "acc_stderr": 0.03632984052707842, "acc_norm": 0.25517241379310346, "acc_norm_stderr": 0.03632984052707842 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2804232804232804, "acc_stderr": 0.023135287974325618, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.023135287974325618 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2777777777777778, "acc_stderr": 0.040061680838488774, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.040061680838488774 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24838709677419354, "acc_stderr": 0.02458002892148101, "acc_norm": 0.24838709677419354, "acc_norm_stderr": 0.02458002892148101 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.24630541871921183, "acc_stderr": 0.03031509928561773, "acc_norm": 0.24630541871921183, "acc_norm_stderr": 0.03031509928561773 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.2, "acc_stderr": 0.040201512610368445, "acc_norm": 0.2, "acc_norm_stderr": 0.040201512610368445 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21212121212121213, "acc_stderr": 0.03192271569548299, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.03192271569548299 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.29292929292929293, "acc_stderr": 0.032424979581788166, "acc_norm": 0.29292929292929293, "acc_norm_stderr": 0.032424979581788166 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.22797927461139897, "acc_stderr": 0.030276909945178267, "acc_norm": 0.22797927461139897, "acc_norm_stderr": 0.030276909945178267 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.31025641025641026, "acc_stderr": 0.023454674889404288, "acc_norm": 0.31025641025641026, "acc_norm_stderr": 0.023454674889404288 }, "harness|hendrycksTest-