open-llm-leaderboard/details_codellama__CodeLlama-34b-hf

该数据集是在模型 codellama/CodeLlama-34b-hf 在 Open LLM Leaderboard 上的评估运行期间自动创建的。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 4 次运行中生成的，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。一个名为 results 的额外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

数据集摘要

该数据集是在对模型 codellama/CodeLlama-34b-hf 进行评估运行期间自动创建的，用于 Open LLM Leaderboard。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 4 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。

数据集结构

数据集包含以下配置：

• harness_arc_challenge_25
• harness_drop_3
• harness_gsm8k_5
• harness_hellaswag_10
• harness_hendrycksTest_5

每个配置包含多个分割，每个分割对应不同的运行时间戳，例如：

• 2023_08_26T05_33_43.008439
• 2024_01_05T02_39_47.564010
• 2024_02_18T18_31_41.422822
• latest

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_codellama__CodeLlama-34b-hf", "harness_winogrande_5", split="train")

最新结果

以下是 最新结果 的摘要： python { "all": { "acc": 0.5492276017664751, "acc_stderr": 0.034136689819192864, "acc_norm": 0.5535771168570393, "acc_norm_stderr": 0.0348496967279896, "mc1": 0.2460220318237454, "mc1_stderr": 0.015077219200662568, "mc2": 0.39113618393918814, "mc2_stderr": 0.01395474555566057 }, "harness|arc:challenge|25": { "acc": 0.5034129692832765, "acc_stderr": 0.014611050403244077, "acc_norm": 0.5409556313993175, "acc_norm_stderr": 0.014562291073601229 }, "harness|hellaswag|10": { "acc": 0.5585540728938458, "acc_stderr": 0.004955447564694052, "acc_norm": 0.7582154949213304, "acc_norm_stderr": 0.004272893583742263 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464244, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464244 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5723684210526315, "acc_stderr": 0.04026097083296562, "acc_norm": 0.5723684210526315, "acc_norm_stderr": 0.04026097083296562 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5132075471698113, "acc_stderr": 0.030762134874500482, "acc_norm": 0.5132075471698113, "acc_norm_stderr": 0.030762134874500482 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5208333333333334, "acc_stderr": 0.041775789507399935, "acc_norm": 0.5208333333333334, "acc_norm_stderr": 0.041775789507399935 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4624277456647399, "acc_stderr": 0.0380168510452446, "acc_norm": 0.4624277456647399, "acc_norm_stderr": 0.0380168510452446 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3235294117647059, "acc_stderr": 0.046550104113196177, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.046550104113196177 }, "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": 0.48936170212765956, "acc_stderr": 0.03267862331014063, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.03267862331014063 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.39473684210526316, "acc_stderr": 0.045981880578165414, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.045981880578165414 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.041665675771015785, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41534391534391535, "acc_stderr": 0.0253795249107784, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.0253795249107784 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6129032258064516, "acc_stderr": 0.027709359675032488, "acc_norm": 0.6129032258064516, "acc_norm_stderr": 0.027709359675032488 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4088669950738916, "acc_stderr": 0.034590588158832314, "acc_norm": 0.4088669950738916, "acc_norm_stderr": 0.034590588158832314 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.703030303030303, "acc_stderr": 0.0356796977226805, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.0356796977226805 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7070707070707071, "acc_stderr": 0.032424979581788145, "acc_norm": 0.7070707070707071, "acc_norm_stderr": 0.032424979581788145 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7461139896373057, "acc_stderr": 0.0314102478056532, "acc_norm": 0.7461139896373057, "acc_norm_stderr": 0.0314102478056532 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5076923076923077, "acc_stderr": 0.02534800603153477, "acc_norm": 0.5076923076923077, "acc_norm_stderr": 0.02534800603153477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc