open-llm-leaderboard-old/details_conceptofmind__LLongMA-2-7b-16k

该数据集是在模型 conceptofmind/LLongMA-2-7b-16k 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集由 60 个配置组成，每个配置对应一个评估任务。数据集包含一次运行的结果，每次运行作为一个特定的分割存储，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，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 库加载数据集的示例，并包含了特定运行的最新结果。

原始信息汇总

数据集概述

该数据集是在对模型 conceptofmind/LLongMA-2-7b-16k 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 60 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 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_conceptofmind__LLongMA-2-7b-16k", "harness_truthfulqa_mc_0", split="train")

最新结果

这些是最新结果（来自 2023-08-22T18:10:15.438623 运行）的示例： python { "all": { "acc": 0.38930534904833497, "acc_stderr": 0.03491718638238362, "acc_norm": 0.39334911896029395, "acc_norm_stderr": 0.03490530736130865, "mc1": 0.26805385556915545, "mc1_stderr": 0.015506204722834555, "mc2": 0.3905977414272289, "mc2_stderr": 0.01382437830936997 }, "harness|arc:challenge|25": { "acc": 0.4803754266211604, "acc_stderr": 0.014600132075947085, "acc_norm": 0.5221843003412969, "acc_norm_stderr": 0.014597001927076143 }, "harness|hellaswag|10": { "acc": 0.5653256323441546, "acc_stderr": 0.004947010937455345, "acc_norm": 0.7620991834295957, "acc_norm_stderr": 0.004249278842903415 }, "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": 0.4222222222222222, "acc_stderr": 0.04266763404099582, "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3355263157894737, "acc_stderr": 0.038424985593952694, "acc_norm": 0.3355263157894737, "acc_norm_stderr": 0.038424985593952694 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.37735849056603776, "acc_stderr": 0.029832808114796, "acc_norm": 0.37735849056603776, "acc_norm_stderr": 0.029832808114796 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.375, "acc_stderr": 0.04048439222695598, "acc_norm": 0.375, "acc_norm_stderr": 0.04048439222695598 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.32947976878612717, "acc_stderr": 0.03583901754736411, "acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.03583901754736411 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3617021276595745, "acc_stderr": 0.0314108219759624, "acc_norm": 0.3617021276595745, "acc_norm_stderr": 0.0314108219759624 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.04339138322579861, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.04339138322579861 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.3793103448275862, "acc_stderr": 0.040434618619167466, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.040434618619167466 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.023068188848261114, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.023068188848261114 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.18253968253968253, "acc_stderr": 0.0345507101910215, "acc_norm": 0.18253968253968253, "acc_norm_stderr": 0.0345507101910215 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.36451612903225805, "acc_stderr": 0.02737987122994324, "acc_norm": 0.36451612903225805, "acc_norm_stderr": 0.02737987122994324 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.27586206896551724, "acc_stderr": 0.03144712581678242, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.03144712581678242 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.47878787878787876, "acc_stderr": 0.03900828913737302, "acc_norm": 0.47878787878787876, "acc_norm_stderr": 0.03900828913737302 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.3333333333333333, "acc_stderr": 0.03358618145732523, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.03358618145732523 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5544041450777202, "acc_stderr": 0.03587014986075659, "acc_norm": 0.5544041450777202, "acc_norm_stderr": 0.03587014986075659 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.31794871794871793, "acc_stderr": 0.023610884308927865, "acc_norm": 0.31794871794871793, "acc_norm_stderr": 0.023610884308927865 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25555555555555554, "acc_stderr": 0.02659393910184407, "acc_norm": 0.25555555555555554, "acc_norm_stderr": 0.02659393910184407 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3067226890756303, "acc_stderr