open-llm-leaderboard-old/details_tricktreat__Llama-2-7b-chat-hf-guanaco-freeze-embed-tokens-q-v-proj-lora

该数据集是在特定模型在Open LLM Leaderboard上的评估运行期间自动创建的。它包含63个配置，每个配置对应一个评估任务。数据集是从一次运行中创建的，每次运行都作为一个特定的分割，以运行的日期时间戳命名。此外，还有一个名为results的配置，存储了运行的聚合结果，用于在Leaderboard上计算和显示聚合指标。数据集提供了跨各种任务和配置的详细性能指标。

原始信息汇总

数据集概述

数据集描述

该数据集是在评估模型 tricktreat/Llama-2-7b-chat-hf-guanaco-freeze-embed-tokens-q-v-proj-lora 在 Open LLM Leaderboard 上的自动创建的。

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。
• 数据集来自 1 次运行，每个运行在每个配置中都有一个特定的分割，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_tricktreat__Llama-2-7b-chat-hf-guanaco-freeze-embed-tokens-q-v-proj-lora", "harness_winogrande_5", split="train")

最新结果

以下是 2024-04-16T04:38:13.243008 运行的最新结果：

python { "all": { "acc": 0.46892131898965095, "acc_stderr": 0.03444062283174299, "acc_norm": 0.47499636851082255, "acc_norm_stderr": 0.03522696874836134, "mc1": 0.2876376988984088, "mc1_stderr": 0.015846315101394816, "mc2": 0.4250572562925463, "mc2_stderr": 0.015188747225079806 }, "harness|arc:challenge|25": { "acc": 0.4812286689419795, "acc_stderr": 0.014601090150633964, "acc_norm": 0.515358361774744, "acc_norm_stderr": 0.014604496129394913 }, "harness|hellaswag|10": { "acc": 0.5830511850229038, "acc_stderr": 0.00492046593606861, "acc_norm": 0.7651862178848835, "acc_norm_stderr": 0.004230160814469385 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.37777777777777777, "acc_stderr": 0.04188307537595853, "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.04188307537595853 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.47368421052631576, "acc_stderr": 0.040633027314866725, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.040633027314866725 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4490566037735849, "acc_stderr": 0.030612730713641092, "acc_norm": 0.4490566037735849, "acc_norm_stderr": 0.030612730713641092 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4861111111111111, "acc_stderr": 0.041795966175810016, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.041795966175810016 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4161849710982659, "acc_stderr": 0.03758517775404947, "acc_norm": 0.4161849710982659, "acc_norm_stderr": 0.03758517775404947 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.27450980392156865, "acc_stderr": 0.04440521906179327, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.04440521906179327 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.425531914893617, "acc_stderr": 0.03232146916224469, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.03232146916224469 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2982456140350877, "acc_stderr": 0.043036840335373146, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.043036840335373146 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4413793103448276, "acc_stderr": 0.04137931034482758, "acc_norm": 0.4413793103448276, "acc_norm_stderr": 0.04137931034482758 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.023068188848261117, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.023068188848261117 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.25396825396825395, "acc_stderr": 0.03893259610604675, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.03893259610604675 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5258064516129032, "acc_stderr": 0.028406095057653326, "acc_norm": 0.5258064516129032, "acc_norm_stderr": 0.028406095057653326 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3842364532019704, "acc_stderr": 0.03422398565657551, "acc_norm": 0.3842364532019704, "acc_norm_stderr": 0.03422398565657551 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.41, "acc_stderr": 0.04943110704237101, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237101 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5696969696969697, "acc_stderr": 0.03866225962879077, "acc_norm": 0.5696969696969697, "acc_norm_stderr": 0.03866225962879077 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5808080808080808, "acc_stderr": 0.03515520728670417, "acc_norm": 0.5808080808080808, "acc_norm_stderr": 0.03515520728670417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7202072538860104, "acc_stderr": 0.03239637046735704, "acc_norm": 0.7202072538860104, "acc_norm_stderr": 0.03239637046735704 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4641025641025641, "acc_stderr": 0.025285585990017838, "acc_norm": 0.4641025641025641, "acc_norm_stderr": 0.025285585990017838 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25