open-llm-leaderboard-old/details_KnutJaegersberg__YaYi-30b-EverythingLM
收藏Hugging Face2024-02-01 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_KnutJaegersberg__YaYi-30b-EverythingLM
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资源简介:
该数据集是在Open LLM Leaderboard上对模型KnutJaegersberg/YaYi-30b-EverythingLM进行评估时自动创建的。数据集由63个配置组成,每个配置对应一个评估任务。数据集是从1次运行中创建的,每次运行在每个配置中作为一个特定的分割,分割名称使用运行的时间戳。train分割始终指向最新的结果。此外,还有一个名为results的配置,存储了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。README还提供了如何使用Python代码加载运行中的详细信息的示例,并列出了特定运行的最新结果。
该数据集是在Open LLM Leaderboard上对模型KnutJaegersberg/YaYi-30b-EverythingLM进行评估时自动创建的。数据集由63个配置组成,每个配置对应一个评估任务。数据集是从1次运行中创建的,每次运行在每个配置中作为一个特定的分割,分割名称使用运行的时间戳。train分割始终指向最新的结果。此外,还有一个名为results的配置,存储了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。README还提供了如何使用Python代码加载运行中的详细信息的示例,并列出了特定运行的最新结果。
提供机构:
open-llm-leaderboard-old
原始信息汇总
数据集概述
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。
- 数据集由1次运行生成,每个运行的结果存储在特定的分割中,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_KnutJaegersberg__YaYi-30b-EverythingLM", "harness_winogrande_5", split="train")
最新结果
- 最新结果来自2024-02-01T23:16:21.173986的运行,包含多个任务的评估指标。
- 示例结果: python { "all": { "acc": 0.6767860816427482, "acc_stderr": 0.03218516670791061, "acc_norm": 0.6894497700980339, "acc_norm_stderr": 0.032885991003254615, "mc1": 0.3378212974296206, "mc1_stderr": 0.016557167322516872, "mc2": 0.4973644577114843, "mc2_stderr": 0.01544476842939492 }, "harness|arc:challenge|25": { "acc": 0.35238907849829354, "acc_stderr": 0.01396014260059868, "acc_norm": 0.3796928327645051, "acc_norm_stderr": 0.014182119866974872 }, "harness|hellaswag|10": { "acc": 0.47649870543716394, "acc_stderr": 0.004984266543053121, "acc_norm": 0.6105357498506274, "acc_norm_stderr": 0.004866322258335992 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6592592592592592, "acc_stderr": 0.040943762699967946, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.040943762699967946 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952929, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952929 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768077, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.039420826399272135, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.039420826399272135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.035331333893236574, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.035331333893236574 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.6470588235294118, "acc_stderr": 0.04755129616062947, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.04755129616062947 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6936170212765957, "acc_stderr": 0.03013590647851756, "acc_norm": 0.6936170212765957, "acc_norm_stderr": 0.03013590647851756 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583706, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583706 }, "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": 0.6164021164021164, "acc_stderr": 0.0250437573185202, "acc_norm": 0.6164021164021164, "acc_norm_stderr": 0.0250437573185202 }, "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": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7225806451612903, "acc_stderr": 0.025470196835900055, "acc_norm": 0.7225806451612903, "acc_norm_stderr": 0.025470196835900055 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6748768472906403, "acc_stderr": 0.03295797566311271, "acc_norm": 0.6748768472906403, "acc_norm_stderr": 0.03295797566311271 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7090909090909091, "acc_stderr": 0.03546563019624336, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.03546563019624336 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026552207828215293, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026552207828215293 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7927461139896373, "acc_stderr": 0.02925282329180363, "acc_norm": 0.7927461139896373, "acc_norm_stderr": 0.02925282329180363 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7102564102564103, "acc_stderr": 0.023000628243687964, "acc_norm": 0.7102564102564103, "acc_norm_stderr": 0.023000628243687964 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.5444444444444444, "acc_stderr": 0.03036486250482443, "acc_norm": 0.5444444444444444, "acc_norm_stderr": 0.03036486250482443 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.773109243697479, "acc_stderr": 0.02720537153827948, "acc_norm": 0.773109243697479, "acc_norm_stderr": 0.02720537153827948 },



