open-llm-leaderboard/details_revolutionarybukhari__Llama-2-7b-chat-finetune-AUTOMATE

该数据集是在评估模型revolutionarybukhari/Llama-2-7b-chat-finetune-AUTOMATE时自动创建的，包含64个配置，每个配置对应一个评估任务。数据集由1次运行生成，每次运行的结果存储为特定的分割，分割名称使用运行的时间戳。此外，数据集还包含一个名为"results"的配置，用于存储所有运行的聚合结果。README还提供了如何加载数据的示例代码，并展示了最新的评估结果。

原始信息汇总

数据集概述

数据集摘要

该数据集是在对模型revolutionarybukhari/Llama-2-7b-chat-finetune-AUTOMATE进行评估运行期间自动创建的，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。
• 数据集从1次运行中创建，每个运行可以在每个配置中作为一个特定的分片找到，分片名称使用运行的时间戳。
• "train"分片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_revolutionarybukhari__Llama-2-7b-chat-finetune-AUTOMATE_public", "harness_winogrande_5", split="train")

最新结果

以下是2023-11-12T17:51:35.598056运行的最新结果：

python { "all": { "acc": 0.48664997345720373, "acc_stderr": 0.03427289794847252, "acc_norm": 0.4932299888431757, "acc_norm_stderr": 0.03508094254293674, "mc1": 0.2876376988984088, "mc1_stderr": 0.015846315101394812, "mc2": 0.44729919889234016, "mc2_stderr": 0.015286276115878357, "em": 0.010906040268456376, "em_stderr": 0.0010636334198498001, "f1": 0.06768770973154396, "f1_stderr": 0.0017077194500790263 }, "harness|arc:challenge|25": { "acc": 0.4906143344709898, "acc_stderr": 0.014608816322065, "acc_norm": 0.5307167235494881, "acc_norm_stderr": 0.014583792546304037 }, "harness|hellaswag|10": { "acc": 0.5622385978888668, "acc_stderr": 0.004950973231188739, "acc_norm": 0.7559251145190201, "acc_norm_stderr": 0.004286594977390899 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.46710526315789475, "acc_stderr": 0.040601270352363966, "acc_norm": 0.46710526315789475, "acc_norm_stderr": 0.040601270352363966 }, "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": 0.539622641509434, "acc_stderr": 0.030676096599389184, "acc_norm": 0.539622641509434, "acc_norm_stderr": 0.030676096599389184 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5347222222222222, "acc_stderr": 0.04171115858181618, "acc_norm": 0.5347222222222222, "acc_norm_stderr": 0.04171115858181618 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4046242774566474, "acc_stderr": 0.03742461193887248, "acc_norm": 0.4046242774566474, "acc_norm_stderr": 0.03742461193887248 }, "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": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.42127659574468085, "acc_stderr": 0.03227834510146268, "acc_norm": 0.42127659574468085, "acc_norm_stderr": 0.03227834510146268 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.37719298245614036, "acc_stderr": 0.045595221419582166, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.045595221419582166 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.041665675771015785, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.023517294335963286, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.023517294335963286 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.24603174603174602, "acc_stderr": 0.03852273364924314, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.03852273364924314 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.532258064516129, "acc_stderr": 0.028384747788813332, "acc_norm": 0.532258064516129, "acc_norm_stderr": 0.028384747788813332 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.35960591133004927, "acc_stderr": 0.03376458246509566, "acc_norm": 0.35960591133004927, "acc_norm_stderr": 0.03376458246509566 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5696969696969697, "acc_stderr": 0.03866225962879077, "acc_norm": 0.5696969696969697, "acc_norm_stderr": 0.03866225962879077 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5909090909090909, "acc_stderr": 0.03502975799413007, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.03502975799413007 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7098445595854922, "acc_stderr": 0.03275264467791516, "acc_norm": 0.7098445595854922, "acc_norm_stderr": 0.03275264467791516 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4230769230769231, "acc_stderr": 0.02504919787604234, "acc_norm":