open-llm-leaderboard-old/details_alchemonaut__QuartetAnemoi-70B-t0.0001

该数据集是在评估模型alchemonaut/QuartetAnemoi-70B-t0.0001时自动生成的，用于在Open LLM Leaderboard上进行评估。数据集包含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。此外，数据集还包含一个名为“results”的配置，用于存储所有运行的聚合结果，并在Open LLM Leaderboard上显示聚合指标。

原始信息汇总

数据集概述

该数据集是在对模型 alchemonaut/QuartetAnemoi-70B-t0.0001 进行评估运行期间自动创建的，用于 Open LLM Leaderboard。

数据集组成

• 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，每次运行的详细信息可以在每个配置中找到，以运行的时间戳命名的特定分片形式存储。
• "train" 分片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_alchemonaut__QuartetAnemoi-70B-t0.0001", "harness_winogrande_5", split="train")

最新结果

以下是 2024-02-04T09:33:24.428024 运行的最新结果：

python { "all": { "acc": 0.7529733519925265, "acc_stderr": 0.028560689453846086, "acc_norm": 0.7560953011110824, "acc_norm_stderr": 0.029110491550081063, "mc1": 0.5373317013463892, "mc1_stderr": 0.017454645150970588, "mc2": 0.6953224067002621, "mc2_stderr": 0.014718923922056508 }, "harness|arc:challenge|25": { "acc": 0.6919795221843004, "acc_stderr": 0.013491429517292038, "acc_norm": 0.7337883959044369, "acc_norm_stderr": 0.012915774781523214 }, "harness|hellaswag|10": { "acc": 0.7132045409281019, "acc_stderr": 0.004513409114983832, "acc_norm": 0.8889663413662617, "acc_norm_stderr": 0.0031353173122281234 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6962962962962963, "acc_stderr": 0.03972552884785137, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.03972552884785137 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8355263157894737, "acc_stderr": 0.03016753346863271, "acc_norm": 0.8355263157894737, "acc_norm_stderr": 0.03016753346863271 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.8, "acc_stderr": 0.04020151261036844, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036844 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8, "acc_stderr": 0.024618298195866514, "acc_norm": 0.8, "acc_norm_stderr": 0.024618298195866514 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.875, "acc_stderr": 0.02765610492929436, "acc_norm": 0.875, "acc_norm_stderr": 0.02765610492929436 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7514450867052023, "acc_stderr": 0.03295304696818318, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818318 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7489361702127659, "acc_stderr": 0.02834696377716245, "acc_norm": 0.7489361702127659, "acc_norm_stderr": 0.02834696377716245 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5877192982456141, "acc_stderr": 0.04630653203366596, "acc_norm": 0.5877192982456141, "acc_norm_stderr": 0.04630653203366596 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7379310344827587, "acc_stderr": 0.036646663372252565, "acc_norm": 0.7379310344827587, "acc_norm_stderr": 0.036646663372252565 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5370370370370371, "acc_stderr": 0.025680564640056882, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.025680564640056882 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8741935483870967, "acc_stderr": 0.01886583428803001, "acc_norm": 0.8741935483870967, "acc_norm_stderr": 0.01886583428803001 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6551724137931034, "acc_stderr": 0.033442837442804574, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.033442837442804574 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781675, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781675 }, "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": 0.9430051813471503, "acc_stderr": 0.016731085293607558, "acc_norm": 0.9430051813471503, "acc_norm_stderr": 0.016731085293607558 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7794871794871795, "acc_stderr": 0.0210206726808279, "acc_norm": 0.7794871794871795, "acc_norm_stderr": 0.0210206726808279 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4148148148148148, "acc_stderr": 0.030039842454069283, "acc_norm": 0.4148148148148148, "acc_norm_stderr": 0.030039842454069283 }, "harness|hendrycksTest-high_school_microeconomics|5": { "