open-llm-leaderboard-old/details_oh-yeontaek__llama-2-70B-LoRA-assemble

该数据集是在模型 oh-yeontaek/llama-2-70B-LoRA-assemble 在 Open LLM Leaderboard 上进行评估时自动创建的。数据集由 61 个配置组成，每个配置对应一个评估任务。它包含 1 次运行的结果，每次运行在每个配置中表示为特定的分割。train 分割始终指向最新的结果。一个名为 results 的额外配置存储了所有运行的聚合结果，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 Python 中的 datasets 库加载运行中的详细信息的示例。

原始信息汇总

数据集概述

该数据集是在评估模型oh-yeontaek/llama-2-70B-LoRA-assemble在Open LLM Leaderboard上的运行过程中自动创建的。

数据集组成

• 数据集包含61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_oh-yeontaek__llama-2-70B-LoRA-assemble", "harness_truthfulqa_mc_0", split="train")

最新结果

以下是2023-09-14T11:41:03.022396运行的最新结果： python { "all": { "acc": 0.6934330265245879, "acc_stderr": 0.031312838620430335, "acc_norm": 0.697335554746802, "acc_norm_stderr": 0.03128337547678218, "mc1": 0.46511627906976744, "mc1_stderr": 0.01746084997587397, "mc2": 0.6479539766332348, "mc2_stderr": 0.014916593992436448 }, "harness|arc:challenge|25": { "acc": 0.6851535836177475, "acc_stderr": 0.01357265770308495, "acc_norm": 0.7184300341296929, "acc_norm_stderr": 0.013143376735009022 }, "harness|hellaswag|10": { "acc": 0.6707827126070504, "acc_stderr": 0.00468968597815517, "acc_norm": 0.867755427205736, "acc_norm_stderr": 0.0033806414709899157 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343603, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343603 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.73, "acc_stderr": 0.04461960433384741, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7358490566037735, "acc_stderr": 0.027134291628741702, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.027134291628741702 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6275862068965518, "acc_stderr": 0.040287315329475576, "acc_norm": 0.6275862068965518, "acc_norm_stderr": 0.040287315329475576 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4656084656084656, "acc_stderr": 0.025690321762493844, "acc_norm": 0.4656084656084656, "acc_norm_stderr": 0.025690321762493844 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8290322580645161, "acc_stderr": 0.021417242936321582, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.021417242936321582 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5320197044334976, "acc_stderr": 0.035107665979592154, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.035107665979592154 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8242424242424242, "acc_stderr": 0.02972094300622445, "acc_norm": 0.8242424242424242, "acc_norm_stderr": 0.02972094300622445 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8939393939393939, "acc_stderr": 0.021938047738853113, "acc_norm": 0.8939393939393939, "acc_norm_stderr": 0.021938047738853113 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.927461139896373, "acc_stderr": 0.018718998520678178, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678178 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6948717948717948, "acc_stderr": 0.023346335293325887, "acc_norm": 0.6948717948717948, "acc_norm_stderr": 0.023346335293325887 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.028406