open-llm-leaderboard-old/details_CausalLM__35b-beta-long

该数据集是在评估模型CausalLM/35b-beta-long时自动创建的，包含63个配置，每个配置对应一个评估任务。数据集由1次运行生成，每次运行的结果存储为特定配置中的一个分割，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，用于在Open LLM Leaderboard上计算和显示聚合指标。

原始信息汇总

数据集概述

数据集简介

该数据集是在对模型 CausalLM/35b-beta-long 进行评估运行期间自动创建的，用于 Open LLM Leaderboard。

数据集组成

• 该数据集包含 63 个配置，每个配置对应一个评估任务。
• 数据集从 1 次运行中创建，每个运行可以在每个配置中找到一个特定的拆分，拆分名称使用运行的时间戳。
• "train" 拆分始终指向最新的结果。
• 额外的 "results" 配置存储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。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CausalLM__35b-beta-long", "harness_winogrande_5", split="train")

最新结果

以下是 2024-04-16T09:32:17.527371 运行的最新结果：

python { "all": { "acc": 0.7455440334315395, "acc_stderr": 0.028664988412464665, "acc_norm": 0.7481173089488518, "acc_norm_stderr": 0.029218891133886506, "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.5865716438215435, "mc2_stderr": 0.015329956873040422 }, "harness|arc:challenge|25": { "acc": 0.6501706484641638, "acc_stderr": 0.01393680921215829, "acc_norm": 0.6757679180887372, "acc_norm_stderr": 0.013678810399518826 }, "harness|hellaswag|10": { "acc": 0.6857199761003784, "acc_stderr": 0.004632797375289756, "acc_norm": 0.8743278231428002, "acc_norm_stderr": 0.003308020824426784 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8355263157894737, "acc_stderr": 0.03016753346863273, "acc_norm": 0.8355263157894737, "acc_norm_stderr": 0.03016753346863273 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909282, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8150943396226416, "acc_stderr": 0.02389335183446432, "acc_norm": 0.8150943396226416, "acc_norm_stderr": 0.02389335183446432 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8958333333333334, "acc_stderr": 0.02554523921025691, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.02554523921025691 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939098, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7109826589595376, "acc_stderr": 0.03456425745086999, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.03456425745086999 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6680851063829787, "acc_stderr": 0.03078373675774565, "acc_norm": 0.6680851063829787, "acc_norm_stderr": 0.03078373675774565 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6140350877192983, "acc_stderr": 0.045796394220704355, "acc_norm": 0.6140350877192983, "acc_norm_stderr": 0.045796394220704355 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.696551724137931, "acc_stderr": 0.038312260488503336, "acc_norm": 0.696551724137931, "acc_norm_stderr": 0.038312260488503336 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5105820105820106, "acc_stderr": 0.02574554227604548, "acc_norm": 0.5105820105820106, "acc_norm_stderr": 0.02574554227604548 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677172, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677172 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8548387096774194, "acc_stderr": 0.020039563628053297, "acc_norm": 0.8548387096774194, "acc_norm_stderr": 0.020039563628053297 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6600985221674877, "acc_stderr": 0.033327690684107895, "acc_norm": 0.6600985221674877, "acc_norm_stderr": 0.033327690684107895 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8787878787878788, "acc_stderr": 0.02548549837334323, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.02548549837334323 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.898989898989899, "acc_stderr": 0.021469735576055332, "acc_norm": 0.898989898989899, "acc_norm_stderr": 0.021469735576055332 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9326424870466321, "acc_stderr": 0.018088393839078922, "acc_norm": 0.9326424870466321, "acc_norm_stderr": 0.018088393839078922 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7461538461538462, "acc_stderr": 0.022066054378726257, "acc_norm": 0.7461538461538462, "acc_norm_stderr": 0.022066054378726257 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4222222222222222, "acc_stderr": 0.03011444201966811, "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.03011444201966811 }, "harness|hendrycksTest-high_school_

搜集汇总

背景与挑战

背景概述

该数据集记录了CausalLM/35b-beta-long模型在Open LLM Leaderboard上的评估结果，包含63个任务配置和最新运行数据，适用于模型性能分析和比较。