open-llm-leaderboard-old/details_SciPhi__SciPhi-Self-RAG-Mistral-7B-32k

该数据集是在评估模型SciPhi/SciPhi-Self-RAG-Mistral-7B-32k时自动创建的，评估是在Open LLM Leaderboard上进行的。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，results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上的聚合指标。

原始信息汇总

数据集概述

数据集摘要

该数据集是在模型SciPhi/SciPhi-Self-RAG-Mistral-7B-32k在Open LLM Leaderboard上的评估运行期间自动创建的。

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_SciPhi__SciPhi-Self-RAG-Mistral-7B-32k", "harness_winogrande_5", split="train")

最新结果

以下是2023-11-20T07:34:10.299317运行的最新结果：

python { "all": { "acc": 0.6025602935621713, "acc_stderr": 0.03298893084060298, "acc_norm": 0.6108691668755563, "acc_norm_stderr": 0.033697327638679664, "mc1": 0.3011015911872705, "mc1_stderr": 0.016058999026100612, "mc2": 0.45630119540652325, "mc2_stderr": 0.015576968550952539, "em": 0.2489513422818792, "em_stderr": 0.004428237695563259, "f1": 0.31078754194630925, "f1_stderr": 0.004386404073454112 }, "harness|arc:challenge|25": { "acc": 0.5341296928327645, "acc_stderr": 0.0145773113152311, "acc_norm": 0.5733788395904437, "acc_norm_stderr": 0.014453185592920293 }, "harness|hellaswag|10": { "acc": 0.6132244572794264, "acc_stderr": 0.004860162076330984, "acc_norm": 0.8044214299940251, "acc_norm_stderr": 0.003958347934520328 }, "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": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6513157894736842, "acc_stderr": 0.0387813988879761, "acc_norm": 0.6513157894736842, "acc_norm_stderr": 0.0387813988879761 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319878, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319878 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7083333333333334, "acc_stderr": 0.03800968060554859, "acc_norm": 0.7083333333333334, "acc_norm_stderr": 0.03800968060554859 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.0498887651569859, "acc_norm": 0.44, "acc_norm_stderr": 0.0498887651569859 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "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": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5063829787234042, "acc_stderr": 0.032683358999363366, "acc_norm": 0.5063829787234042, "acc_norm_stderr": 0.032683358999363366 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.38596491228070173, "acc_stderr": 0.045796394220704334, "acc_norm": 0.38596491228070173, "acc_norm_stderr": 0.045796394220704334 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923992, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923992 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7258064516129032, "acc_stderr": 0.0253781399708852, "acc_norm": 0.7258064516129032, "acc_norm_stderr": 0.0253781399708852 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4729064039408867, "acc_stderr": 0.03512819077876106, "acc_norm": 0.4729064039408867, "acc_norm_stderr": 0.03512819077876106 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7323232323232324, "acc_stderr": 0.03154449888270286, "acc_norm": 0.7323232323232324, "acc_norm_stderr": 0.03154449888270286 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8393782383419689, "acc_stderr": 0.02649905770139746, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.02649905770139746 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6128205128205129, "acc_stderr": 0.024697216930878937, "acc_norm": 0.6128205128205129, "acc_norm_