open-llm-leaderboard-old/details_CHIH-HUNG__llama-2-13b-Fintune_1_17w-gate_up_down_proj

该数据集是在模型CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj在Open LLM Leaderboard上的评估运行期间自动创建的。数据集由61个配置组成，每个配置对应一个被评估的任务。数据集包含1次运行的数据，每次运行在每个配置中表示为特定的分割，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，还有一个名为results的配置，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。README还提供了如何使用Python中的datasets库加载运行细节的示例。

原始信息汇总

数据集概述

数据集来源

• 数据集是在评估模型 CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj 在 Open LLM Leaderboard 上的自动创建的。

数据集结构

• 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，用于计算和显示在 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_CHIH-HUNG__llama-2-13b-Fintune_1_17w-gate_up_down_proj", "harness_truthfulqa_mc_0", split="train")

最新结果

• 这些是最新结果来自 2023-09-04T21:15:30.560082 的示例：

python { "all": { "acc": 0.5475267614652215, "acc_stderr": 0.034609984402304776, "acc_norm": 0.5517087027206374, "acc_norm_stderr": 0.03459190488664288, "mc1": 0.25703794369645044, "mc1_stderr": 0.015298077509485076, "mc2": 0.369230031760143, "mc2_stderr": 0.013637900924748413 }, "harness|arc:challenge|25": { "acc": 0.5213310580204779, "acc_stderr": 0.014598087973127106, "acc_norm": 0.5614334470989761, "acc_norm_stderr": 0.014500682618212865 }, "harness|hellaswag|10": { "acc": 0.6037641904003187, "acc_stderr": 0.0048811488668741845, "acc_norm": 0.8103963353913562, "acc_norm_stderr": 0.003911862797736198 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464242, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464242 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5526315789473685, "acc_stderr": 0.0404633688397825, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.0404633688397825 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5886792452830188, "acc_stderr": 0.030285009259009794, "acc_norm": 0.5886792452830188, "acc_norm_stderr": 0.030285009259009794 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6041666666666666, "acc_stderr": 0.04089465449325583, "acc_norm": 0.6041666666666666, "acc_norm_stderr": 0.04089465449325583 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5144508670520231, "acc_stderr": 0.03810871630454764, "acc_norm": 0.5144508670520231, "acc_norm_stderr": 0.03810871630454764 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.451063829787234, "acc_stderr": 0.032529096196131965, "acc_norm": 0.451063829787234, "acc_norm_stderr": 0.032529096196131965 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278005, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278005 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3492063492063492, "acc_stderr": 0.024552292209342658, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.024552292209342658 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949097, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949097 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6580645161290323, "acc_stderr": 0.026985289576552742, "acc_norm": 0.6580645161290323, "acc_norm_stderr": 0.026985289576552742 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4236453201970443, "acc_stderr": 0.03476725747649037, "acc_norm": 0.4236453201970443, "acc_norm_stderr": 0.03476725747649037 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6787878787878788, "acc_stderr": 0.036462049632538115, "acc_norm": 0.6787878787878788, "acc_norm_stderr": 0.036462049632538115 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7070707070707071, "acc_stderr": 0.03242497958178816, "acc_norm": 0.7070707070707071, "acc_norm_stderr": 0.03242497958178816 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7772020725388601, "acc_stderr": 0.030031147977641538, "acc_norm": 0.7772020725388601, "acc_norm_stderr": 0.030031147977641538 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4794871794871795, "acc_stderr": 0.02532966316348994, "acc_norm": 0.4794871794871795, "acc_norm_stderr": 0.02532966316348994 }, "harness|hendrycksTest-high_school_mathematics|5