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

该数据集是在模型 codellama/CodeLlama-34b-Instruct-hf 在 Open LLM Leaderboard 上的评估过程中自动生成的。数据集由 64 个配置组成，每个配置对应一个被评估的任务。它由 4 次运行创建，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 还提供了一个如何使用 Python 中的 datasets 库加载运行细节的示例。

原始信息汇总

数据集概述

数据集简介

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

数据集结构

• 配置数量：64个配置，每个配置对应一个评估任务。
• 运行次数：数据集由4次运行创建，每次运行在每个配置中作为一个特定的分割存在，分割名称使用运行的时间戳。
• 训练分割："train" 分割始终指向最新的结果。
• 结果配置：一个额外的配置 "results" 存储所有运行的聚合结果，用于计算和显示聚合指标。

数据加载示例

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

最新结果

以下是 2023-12-10T07:31:59.292506 运行的最新结果：

python { "all": { "acc": 0.5550583840264827, "acc_stderr": 0.03405562001199965, "acc_norm": 0.5588404318554717, "acc_norm_stderr": 0.03476259213185152, "mc1": 0.28518971848225216, "mc1_stderr": 0.015805827874454895, "mc2": 0.44437538633055657, "mc2_stderr": 0.014550940721814704 }, "harness|arc:challenge|25": { "acc": 0.5093856655290102, "acc_stderr": 0.014608816322065, "acc_norm": 0.5426621160409556, "acc_norm_stderr": 0.01455810654392406 }, "harness|hellaswag|10": { "acc": 0.5637323242381995, "acc_stderr": 0.004949080334816024, "acc_norm": 0.7691694881497709, "acc_norm_stderr": 0.004205030476886528 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "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": 0.5921052631578947, "acc_stderr": 0.03999309712777472, "acc_norm": 0.5921052631578947, "acc_norm_stderr": 0.03999309712777472 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.49056603773584906, "acc_stderr": 0.0307673947078081, "acc_norm": 0.49056603773584906, "acc_norm_stderr": 0.0307673947078081 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5138888888888888, "acc_stderr": 0.04179596617581, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.04179596617581 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4508670520231214, "acc_stderr": 0.03794012674697028, "acc_norm": 0.4508670520231214, "acc_norm_stderr": 0.03794012674697028 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.047240073523838876, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.047240073523838876 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.49361702127659574, "acc_stderr": 0.03268335899936336, "acc_norm": 0.49361702127659574, "acc_norm_stderr": 0.03268335899936336 }, "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": 0.5241379310344828, "acc_stderr": 0.041618085035015295, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3915343915343915, "acc_stderr": 0.025138091388851102, "acc_norm": 0.3915343915343915, "acc_norm_stderr": 0.025138091388851102 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6096774193548387, "acc_stderr": 0.027751256636969576, "acc_norm": 0.6096774193548387, "acc_norm_stderr": 0.027751256636969576 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3891625615763547, "acc_stderr": 0.034304624161038716, "acc_norm": 0.3891625615763547, "acc_norm_stderr": 0.034304624161038716 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6727272727272727, "acc_stderr": 0.036639749943912434, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.036639749943912434 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7222222222222222, "acc_stderr": 0.03191178226713549, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.03191178226713549 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7616580310880829, "acc_stderr": 0.03074890536390989, "acc_norm": 0.7616580310880829, "acc_norm_stderr": 0.03074890536390989 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5153846153846153, "acc_stderr": 0.02533900301010651, "acc_norm": 0.5153846153846153, "acc_norm_stderr": 0.02533900301010651 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.02889774874113114, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.02889774874