open-llm-leaderboard-old/details_glenn2__gemma-7b-lora-distilabel-intel-orca-dpo-pairs

该数据集是在模型 glenn2/gemma-7b-lora-distilabel-intel-orca-dpo-pairs 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集由 63 个配置组成，每个配置对应一个评估任务。数据集是从 1 次运行中生成的，每次运行在每个配置中表示为特定的拆分，拆分的名称使用运行的时间戳。train 拆分始终指向最新的结果。一个名为 results 的额外配置存储了运行的所有聚合结果，这些结果用于计算和显示 Open LLM Leaderboard 上的聚合指标。README 还提供了一个示例，展示了如何使用 Python 中的 datasets 库加载运行中的详细信息。

原始信息汇总

数据集概述

数据集简介

该数据集是在评估模型 glenn2/gemma-7b-lora-distilabel-intel-orca-dpo-pairs 在 Open LLM Leaderboard 上的运行过程中自动创建的。

数据集结构

• 配置数量：63个配置，每个配置对应一个评估任务。
• 创建来源：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。
• 最新结果："train" 分割总是指向最新的结果。
• 结果汇总：一个额外的配置 "results" 存储所有运行的汇总结果，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_glenn2__gemma-7b-lora-distilabel-intel-orca-dpo-pairs", "harness_winogrande_5", split="train")

最新结果

以下是 2024-02-24T06:43:06.352968 运行的最新结果：

python { "all": { "acc": 0.41805479594650774, "acc_stderr": 0.03448086724298717, "acc_norm": 0.4216629246844621, "acc_norm_stderr": 0.03523967626934255, "mc1": 0.22399020807833536, "mc1_stderr": 0.014594964329474202, "mc2": 0.3310156474833476, "mc2_stderr": 0.013466118840446744 }, "harness|arc:challenge|25": { "acc": 0.4684300341296928, "acc_stderr": 0.014582236460866973, "acc_norm": 0.49146757679180886, "acc_norm_stderr": 0.014609263165632182 }, "harness|hellaswag|10": { "acc": 0.5313682533359888, "acc_stderr": 0.004979952166595542, "acc_norm": 0.7177853017327226, "acc_norm_stderr": 0.004491574539441884 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "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": 0.4473684210526316, "acc_stderr": 0.04046336883978251, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04046336883978251 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4679245283018868, "acc_stderr": 0.030709486992556545, "acc_norm": 0.4679245283018868, "acc_norm_stderr": 0.030709486992556545 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4583333333333333, "acc_stderr": 0.04166666666666665, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.44508670520231214, "acc_stderr": 0.03789401760283647, "acc_norm": 0.44508670520231214, "acc_norm_stderr": 0.03789401760283647 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.17647058823529413, "acc_stderr": 0.0379328118530781, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.0379328118530781 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4, "acc_stderr": 0.03202563076101735, "acc_norm": 0.4, "acc_norm_stderr": 0.03202563076101735 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.43448275862068964, "acc_stderr": 0.04130740879555497, "acc_norm": 0.43448275862068964, "acc_norm_stderr": 0.04130740879555497 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2804232804232804, "acc_stderr": 0.02313528797432562, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.02313528797432562 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2698412698412698, "acc_stderr": 0.03970158273235172, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.03970158273235172 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.4774193548387097, "acc_stderr": 0.028414985019707868, "acc_norm": 0.4774193548387097, "acc_norm_stderr": 0.028414985019707868 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.39901477832512317, "acc_stderr": 0.03445487686264716, "acc_norm": 0.39901477832512317, "acc_norm_stderr": 0.03445487686264716 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.41818181818181815, "acc_stderr": 0.03851716319398394, "acc_norm": 0.41818181818181815, "acc_norm_stderr": 0.03851716319398394 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5303030303030303, "acc_stderr": 0.0355580405176393, "acc_norm": 0.5303030303030303, "acc_norm_stderr": 0.0355580405176393 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5647668393782384, "acc_stderr": 0.03578038165008585, "acc_norm": 0.5647668393782384, "acc_norm_stderr": 0.03578038165008585 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.40512820512820513, "acc_stderr": 0.024890471769938145, "acc_norm": 0.40512820512820513, "acc_norm_stderr": 0.024890471769938145 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "