open-llm-leaderboard-old/details_meta-llama__Meta-Llama-3-70B-Instruct

该数据集是在评估模型meta-llama/Meta-Llama-3-70B-Instruct时自动创建的，评估过程在Open LLM Leaderboard上进行。数据集由63个配置组成，每个配置对应一个评估任务。数据集从1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。此外，results配置存储了所有运行的聚合结果，并用于计算和显示Open LLM Leaderboard上的聚合指标。

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 meta-llama/Meta-Llama-3-70B-Instruct 在 Open LLM Leaderboard 上的自动创建的。

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。
• 训练分割："train" 分割总是指向最新的结果。
• 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_meta-llama__Meta-Llama-3-70B-Instruct_private", "harness_winogrande_5", split="train")

最新结果

以下是 2024-04-21T11:59:48.701689 运行 的最新结果：

python { "all": { "acc": 0.7976491631613839, "acc_stderr": 0.026683740033223404, "acc_norm": 0.8000817296751432, "acc_norm_stderr": 0.02721621913130329, "mc1": 0.44063647490820074, "mc1_stderr": 0.01737969755543745, "mc2": 0.618101503499271, "mc2_stderr": 0.015389077757884843 }, "harness|arc:challenge|25": { "acc": 0.681740614334471, "acc_stderr": 0.013611993916971453, "acc_norm": 0.7141638225255973, "acc_norm_stderr": 0.013203196088537372 }, "harness|hellaswag|10": { "acc": 0.6579366660027883, "acc_stderr": 0.004734311435009196, "acc_norm": 0.8569010157339175, "acc_norm_stderr": 0.0034945810763985165 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7703703703703704, "acc_stderr": 0.036333844140734636, "acc_norm": 0.7703703703703704, "acc_norm_stderr": 0.036333844140734636 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9342105263157895, "acc_stderr": 0.02017493344016284, "acc_norm": 0.9342105263157895, "acc_norm_stderr": 0.02017493344016284 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8679245283018868, "acc_stderr": 0.02083771543069401, "acc_norm": 0.8679245283018868, "acc_norm_stderr": 0.02083771543069401 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.815028901734104, "acc_stderr": 0.0296056239817712, "acc_norm": 0.815028901734104, "acc_norm_stderr": 0.0296056239817712 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5980392156862745, "acc_stderr": 0.04878608714466996, "acc_norm": 0.5980392156862745, "acc_norm_stderr": 0.04878608714466996 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.8170212765957446, "acc_stderr": 0.025276041000449972, "acc_norm": 0.8170212765957446, "acc_norm_stderr": 0.025276041000449972 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.7280701754385965, "acc_stderr": 0.04185774424022056, "acc_norm": 0.7280701754385965, "acc_norm_stderr": 0.04185774424022056 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7793103448275862, "acc_stderr": 0.034559302019248124, "acc_norm": 0.7793103448275862, "acc_norm_stderr": 0.034559302019248124 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.708994708994709, "acc_stderr": 0.02339382650048487, "acc_norm": 0.708994708994709, "acc_norm_stderr": 0.02339382650048487 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.626984126984127, "acc_stderr": 0.04325506042017086, "acc_norm": 0.626984126984127, "acc_norm_stderr": 0.04325506042017086 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.59, "acc_stderr": 0.04943110704237101, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237101 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.896774193548387, "acc_stderr": 0.017308381281034537, "acc_norm": 0.896774193548387, "acc_norm_stderr": 0.017308381281034537 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6896551724137931, "acc_stderr": 0.03255086769970103, "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03255086769970103 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.92, "acc_stderr": 0.027265992434429086, "acc_norm": 0.92, "acc_norm_stderr": 0.027265992434429086 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706467, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706467 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9292929292929293, "acc_stderr": 0.018263105420199488, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.018263105420199488 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9844559585492227, "acc_stderr": 0.008927492715084341, "acc_norm": 0.9844559585492227, "acc_norm_stderr": 0.008927492715084341 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8384615384615385, "acc_stderr": 0.018659703705332976, "acc_norm": 0.8384615384615385, "acc_norm_stderr": 0.018659703705332976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.5444444444444444, "acc_stderr": 0.03036486250482443, "acc_norm": 0.