open-llm-leaderboard-old/details_KnutJaegersberg__Deacon-34b-qlora-adapter

该数据集是在模型 KnutJaegersberg/Deacon-34b-qlora-adapter 在 Open LLM Leaderboard 上的评估运行期间自动生成的。数据集由 63 个配置组成，每个配置对应一个被评估的任务。它包含一次运行的结果，每次运行作为每个配置中的一个特定分割，以运行的时间戳命名。train 分割始终指向最新的结果。一个额外的 results 配置存储了运行的所有聚合结果，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

该数据集是在评估模型 KnutJaegersberg/Deacon-34b-qlora-adapter 在 Open LLM Leaderboard 上的运行过程中自动创建的。

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。
• 一个额外的配置 "results" 存储所有运行的聚合结果，用于计算并在 Open LLM Leaderboard 上显示聚合指标。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Deacon-34b-qlora-adapter", "harness_winogrande_5", split="train")

最新结果

以下是 2023-12-10T07:35:32.492424 运行的最新结果：

python { "all": { "acc": 0.7583327240683659, "acc_stderr": 0.02818417949184259, "acc_norm": 0.7633703000884471, "acc_norm_stderr": 0.028709251183600685, "mc1": 0.40636474908200737, "mc1_stderr": 0.0171938358120939, "mc2": 0.5621149830422679, "mc2_stderr": 0.015167725368215625 }, "harness|arc:challenge|25": { "acc": 0.6143344709897611, "acc_stderr": 0.014224250973257179, "acc_norm": 0.6484641638225256, "acc_norm_stderr": 0.013952413699600938 }, "harness|hellaswag|10": { "acc": 0.655646285600478, "acc_stderr": 0.004741859753178431, "acc_norm": 0.8556064528978291, "acc_norm_stderr": 0.003507699935074239 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7481481481481481, "acc_stderr": 0.03749850709174021, "acc_norm": 0.7481481481481481, "acc_norm_stderr": 0.03749850709174021 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9013157894736842, "acc_stderr": 0.024270227737522715, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.024270227737522715 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7924528301886793, "acc_stderr": 0.02495991802891127, "acc_norm": 0.7924528301886793, "acc_norm_stderr": 0.02495991802891127 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02628055093284808, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02628055093284808 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7167630057803468, "acc_stderr": 0.03435568056047875, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.03435568056047875 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5, "acc_stderr": 0.04975185951049946, "acc_norm": 0.5, "acc_norm_stderr": 0.04975185951049946 }, "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": 0.7659574468085106, "acc_stderr": 0.027678452578212387, "acc_norm": 0.7659574468085106, "acc_norm_stderr": 0.027678452578212387 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5526315789473685, "acc_stderr": 0.04677473004491199, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6613756613756614, "acc_stderr": 0.02437319786798306, "acc_norm": 0.6613756613756614, "acc_norm_stderr": 0.02437319786798306 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8741935483870967, "acc_stderr": 0.018865834288029997, "acc_norm": 0.8741935483870967, "acc_norm_stderr": 0.018865834288029997 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.645320197044335, "acc_stderr": 0.03366124489051449, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.03366124489051449 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706473, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706473 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02239078763821677, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02239078763821677 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9792746113989638, "acc_stderr": 0.010281417011909042, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.010281417011909042 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7948717948717948, "acc_stderr": 0.020473233173551965, "acc_norm": 0.7948717948717948, "acc_norm_stderr": 0.020473233173551965 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.45925925925925926, "acc_stderr": 0.030384169232350825, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.030384169232350825 }, "harness|hendrycksTest-high_school_microeconomics|5":