open-llm-leaderboard-old/details_Korabbit__Llama-2-7b-chat-hf-afr-200step-flan-v2

该数据集是在模型 Korabbit/Llama-2-7b-chat-hf-afr-200step-flan-v2 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，每个配置对应一个被评估的任务。它包含 1 次运行的结果，每次运行都作为一个特定的分割表示，分割名称由运行的时间戳命名。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

该数据集是在对模型 Korabbit/Llama-2-7b-chat-hf-afr-200step-flan-v2 进行评估运行期间自动创建的，评估结果发布在 Open LLM Leaderboard 上。

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。
• 数据来源：数据集来自1次运行，每个运行结果以时间戳命名的特定分片存储。
• 最新结果："train" 分片始终指向最新的结果。
• 汇总结果：额外配置 "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_Korabbit__Llama-2-7b-chat-hf-afr-200step-flan-v2", "harness_winogrande_5", split="train")

最新结果

以下是 2023-12-06T16:38:37.454092 运行的最新结果：

python { "all": { "acc": 0.48616292304115616, "acc_stderr": 0.03429063635606904, "acc_norm": 0.49078740973720447, "acc_norm_stderr": 0.03504500095644619, "mc1": 0.3011015911872705, "mc1_stderr": 0.016058999026100616, "mc2": 0.4541594429045071, "mc2_stderr": 0.015593505840237026 }, "harness|arc:challenge|25": { "acc": 0.49402730375426623, "acc_stderr": 0.014610348300255795, "acc_norm": 0.5264505119453925, "acc_norm_stderr": 0.014590931358120169 }, "harness|hellaswag|10": { "acc": 0.5935072694682334, "acc_stderr": 0.004901747426331731, "acc_norm": 0.780422226648078, "acc_norm_stderr": 0.0041311457711038875 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4222222222222222, "acc_stderr": 0.04266763404099582, "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.48026315789473684, "acc_stderr": 0.040657710025626036, "acc_norm": 0.48026315789473684, "acc_norm_stderr": 0.040657710025626036 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5433962264150943, "acc_stderr": 0.03065674869673943, "acc_norm": 0.5433962264150943, "acc_norm_stderr": 0.03065674869673943 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5347222222222222, "acc_stderr": 0.04171115858181618, "acc_norm": 0.5347222222222222, "acc_norm_stderr": 0.04171115858181618 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4046242774566474, "acc_stderr": 0.03742461193887248, "acc_norm": 0.4046242774566474, "acc_norm_stderr": 0.03742461193887248 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4085106382978723, "acc_stderr": 0.03213418026701576, "acc_norm": 0.4085106382978723, "acc_norm_stderr": 0.03213418026701576 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3684210526315789, "acc_stderr": 0.04537815354939392, "acc_norm": 0.3684210526315789, "acc_norm_stderr": 0.04537815354939392 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.29894179894179895, "acc_stderr": 0.023577604791655802, "acc_norm": 0.29894179894179895, "acc_norm_stderr": 0.023577604791655802 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.24603174603174602, "acc_stderr": 0.03852273364924314, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.03852273364924314 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5290322580645161, "acc_stderr": 0.028396016402761005, "acc_norm": 0.5290322580645161, "acc_norm_stderr": 0.028396016402761005 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3694581280788177, "acc_stderr": 0.033959703819985726, "acc_norm": 0.3694581280788177, "acc_norm_stderr": 0.033959703819985726 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5878787878787879, "acc_stderr": 0.03843566993588717, "acc_norm": 0.5878787878787879, "acc_norm_stderr": 0.03843566993588717 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6060606060606061, "acc_stderr": 0.03481285338232963, "acc_norm": 0.6060606060606061, "acc_norm_stderr": 0.03481285338232963 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7253886010362695, "acc_stderr": 0.03221024508041153, "acc_norm": 0.7253886010362695, "acc_norm_stderr": 0.03221024508041153 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4282051282051282, "acc_stderr": 0.025088301454694834, "acc_norm": 0.4282051282051282, "acc_norm_stderr": 0.025088301454694834 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2