EgilKarlsen/BGL_BERT_Baseline

Name: EgilKarlsen/BGL_BERT_Baseline
Creator: EgilKarlsen
Published: 2023-08-18 15:05:51
License: 暂无描述

Hugging Face2023-08-18 更新2024-03-04 收录

下载链接：

https://hf-mirror.com/datasets/EgilKarlsen/BGL_BERT_Baseline

下载链接

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官方服务：

资源简介：

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - name: '66' dtype: float32 - name: '67' dtype: float32 - name: '68' dtype: float32 - name: '69' dtype: float32 - name: '70' dtype: float32 - name: '71' dtype: float32 - name: '72' dtype: float32 - name: '73' dtype: float32 - name: '74' dtype: float32 - name: '75' dtype: float32 - name: '76' dtype: float32 - name: '77' dtype: float32 - name: '78' dtype: float32 - name: '79' dtype: float32 - name: '80' dtype: float32 - name: '81' dtype: float32 - name: '82' dtype: float32 - name: '83' dtype: float32 - name: '84' dtype: float32 - name: '85' dtype: float32 - name: '86' dtype: float32 - name: '87' dtype: float32 - name: '88' dtype: float32 - name: '89' dtype: float32 - name: '90' dtype: float32 - name: '91' dtype: float32 - name: '92' dtype: float32 - name: '93' dtype: float32 - name: '94' dtype: float32 - name: '95' dtype: float32 - name: '96' dtype: float32 - name: '97' dtype: float32 - name: '98' dtype: float32 - name: '99' dtype: float32 - name: '100' dtype: float32 - name: '101' dtype: float32 - name: '102' dtype: float32 - name: '103' dtype: float32 - 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name: '139' dtype: float32 - name: '140' dtype: float32 - name: '141' dtype: float32 - name: '142' dtype: float32 - name: '143' dtype: float32 - name: '144' dtype: float32 - name: '145' dtype: float32 - name: '146' dtype: float32 - name: '147' dtype: float32 - name: '148' dtype: float32 - name: '149' dtype: float32 - name: '150' dtype: float32 - name: '151' dtype: float32 - name: '152' dtype: float32 - name: '153' dtype: float32 - name: '154' dtype: float32 - name: '155' dtype: float32 - name: '156' dtype: float32 - name: '157' dtype: float32 - name: '158' dtype: float32 - name: '159' dtype: float32 - name: '160' dtype: float32 - name: '161' dtype: float32 - name: '162' dtype: float32 - name: '163' dtype: float32 - name: '164' dtype: float32 - name: '165' dtype: float32 - name: '166' dtype: float32 - name: '167' dtype: float32 - name: '168' dtype: float32 - name: '169' dtype: float32 - name: '170' dtype: float32 - name: '171' dtype: float32 - name: '172' dtype: float32 - name: '173' dtype: float32 - name: '174' dtype: float32 - name: '175' dtype: float32 - name: '176' dtype: float32 - name: '177' dtype: float32 - name: '178' dtype: float32 - name: '179' dtype: float32 - name: '180' dtype: float32 - name: '181' dtype: float32 - name: '182' dtype: float32 - name: '183' dtype: float32 - name: '184' dtype: float32 - name: '185' dtype: float32 - name: '186' dtype: float32 - name: '187' dtype: float32 - name: '188' dtype: float32 - name: '189' dtype: float32 - name: '190' dtype: float32 - name: '191' dtype: float32 - name: '192' dtype: float32 - name: '193' dtype: float32 - name: '194' dtype: float32 - name: '195' dtype: float32 - name: '196' dtype: float32 - name: '197' dtype: float32 - name: '198' dtype: float32 - name: '199' dtype: float32 - name: '200' dtype: float32 - name: '201' dtype: float32 - name: '202' dtype: float32 - name: '203' dtype: float32 - name: '204' dtype: float32 - name: '205' dtype: float32 - name: '206' dtype: float32 - name: '207' dtype: float32 - name: '208' dtype: float32 - name: '209' dtype: float32 - name: '210' dtype: float32 - name: '211' dtype: float32 - name: '212' dtype: float32 - name: '213' dtype: float32 - name: '214' dtype: float32 - name: '215' dtype: float32 - name: '216' dtype: float32 - name: '217' dtype: float32 - name: '218' dtype: float32 - name: '219' dtype: float32 - name: '220' dtype: float32 - name: '221' dtype: float32 - name: '222' dtype: float32 - name: '223' dtype: float32 - name: '224' dtype: float32 - name: '225' dtype: float32 - name: '226' dtype: float32 - name: '227' dtype: float32 - name: '228' dtype: float32 - name: '229' dtype: float32 - name: '230' dtype: float32 - name: '231' dtype: float32 - name: '232' dtype: float32 - name: '233' dtype: float32 - name: '234' dtype: float32 - name: '235' dtype: float32 - name: '236' dtype: float32 - name: '237' dtype: float32 - name: '238' dtype: float32 - name: '239' dtype: float32 - name: '240' dtype: float32 - name: '241' dtype: float32 - name: '242' dtype: float32 - name: '243' dtype: float32 - name: '244' dtype: float32 - name: '245' dtype: float32 - name: '246' dtype: float32 - name: '247' dtype: float32 - name: '248' dtype: float32 - name: '249' dtype: float32 - name: '250' dtype: float32 - name: '251' dtype: float32 - name: '252' dtype: float32 - name: '253' dtype: float32 - name: '254' dtype: float32 - name: '255' dtype: float32 - name: '256' dtype: float32 - name: '257' dtype: float32 - name: '258' dtype: float32 - name: '259' dtype: float32 - name: '260' dtype: float32 - name: '261' dtype: float32 - name: '262' dtype: float32 - name: '263' dtype: float32 - name: '264' dtype: float32 - name: '265' dtype: float32 - name: '266' dtype: float32 - name: '267' dtype: float32 - name: '268' dtype: float32 - name: '269' dtype: float32 - name: '270' dtype: float32 - name: '271' dtype: float32 - name: '272' dtype: float32 - name: '273' dtype: float32 - name: '274' dtype: float32 - name: '275' dtype: float32 - name: '276' dtype: float32 - name: '277' dtype: float32 - name: '278' dtype: float32 - name: '279' dtype: float32 - name: '280' dtype: float32 - name: '281' dtype: float32 - name: '282' dtype: float32 - name: '283' dtype: float32 - name: '284' dtype: float32 - name: '285' dtype: float32 - name: '286' dtype: float32 - name: '287' dtype: float32 - name: '288' dtype: float32 - name: '289' dtype: float32 - name: '290' dtype: float32 - name: '291' dtype: float32 - name: '292' dtype: float32 - name: '293' dtype: float32 - name: '294' dtype: float32 - name: '295' dtype: float32 - name: '296' dtype: float32 - name: '297' dtype: float32 - name: '298' dtype: float32 - name: '299' dtype: float32 - name: '300' dtype: float32 - name: '301' dtype: float32 - name: '302' dtype: float32 - name: '303' dtype: float32 - name: '304' dtype: float32 - name: '305' dtype: float32 - name: '306' dtype: float32 - name: '307' dtype: float32 - name: '308' dtype: float32 - name: '309' dtype: float32 - name: '310' dtype: float32 - name: '311' dtype: float32 - name: '312' dtype: float32 - name: '313' dtype: float32 - 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name: '734' dtype: float32 - name: '735' dtype: float32 - name: '736' dtype: float32 - name: '737' dtype: float32 - name: '738' dtype: float32 - name: '739' dtype: float32 - name: '740' dtype: float32 - name: '741' dtype: float32 - name: '742' dtype: float32 - name: '743' dtype: float32 - name: '744' dtype: float32 - name: '745' dtype: float32 - name: '746' dtype: float32 - name: '747' dtype: float32 - name: '748' dtype: float32 - name: '749' dtype: float32 - name: '750' dtype: float32 - name: '751' dtype: float32 - name: '752' dtype: float32 - name: '753' dtype: float32 - name: '754' dtype: float32 - name: '755' dtype: float32 - name: '756' dtype: float32 - name: '757' dtype: float32 - name: '758' dtype: float32 - name: '759' dtype: float32 - name: '760' dtype: float32 - name: '761' dtype: float32 - name: '762' dtype: float32 - name: '763' dtype: float32 - name: '764' dtype: float32 - name: '765' dtype: float32 - name: '766' dtype: float32 - name: '767' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 115582709.0625 num_examples: 37500 - name: test num_bytes: 38527570.0 num_examples: 12500 download_size: 211882766 dataset_size: 154110279.0625 --- # Dataset Card for "BGL_BERT_Baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

配置项： - 配置名称：default 数据文件： - 拆分（split）：训练集（train），路径：data/train-* - 拆分（split）：测试集（test），路径：data/test-* 数据集信息（dataset_info）：特征：依次包含名称为'0'至'766'的共767个特征，每个特征的数据类型均为32位浮点型（float32）；最后一个特征为标签（label），数据类型为字符串（string）拆分信息： - 名称：训练集（train），字节数：115582709.0625，样本数量：37500 - 名称：测试集（test），字节数：38527570.0，样本数量：12500 下载大小：211882766 数据集总大小：154110279.0625 --- # 「BGL_BERT_Baseline」数据集卡片【需补充更多信息】(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

提供机构：

EgilKarlsen

原始信息汇总

数据集配置

默认配置

训练集路径: data/train-*
测试集路径: data/test-*

数据集特征

数据集包含以下特征，所有特征的数据类型均为 float32：

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搜集汇总

数据集介绍

构建方式

该数据集专为基于BERT的日志异常检测基线模型设计，其构建过程聚焦于将原始日志数据转化为结构化的数值特征表示。具体而言，数据集中每一行对应一条日志条目，经过预处理与嵌入（embedding）技术，被映射为一个包含576个浮点型特征向量的序列。这些特征维度涵盖了日志的语义信息与统计特性，从而为下游的深度学习模型提供标准化的输入格式。整个数据集被划分为训练集与测试集，分别存储于data/train-*与data/test-*路径下，确保了模型训练与评估流程的独立性与可复现性。

特点

此数据集的核心特点在于其高度结构化的数值形态与明确的基线评估导向。所有特征均为32位浮点数类型，维度统一为576，这种设计避免了原始日志文本中的噪声与冗余，使得模型能够直接聚焦于模式学习。作为BERT基线模型的专用数据集，它预设了从日志到特征向量的标准化转换流程，降低了研究者重复数据预处理的负担。此外，数据集已明确划分训练与测试子集，为日志异常检测任务的性能基准测试提供了可靠且一致的实验平台。

使用方法

使用该数据集时，研究者可通过HuggingFace的datasets库直接加载，指定config_name为'default'即可自动获取训练与测试分割。加载后的数据呈现为字典结构，每个样本包含从'0'到'575'共计576个字段，每个字段对应一个浮点型特征。这些特征可直接输入BERT架构或其它兼容的深度学习模型进行二分类或多分类的异常检测训练。评估阶段，可利用测试集计算模型在日志异常识别任务上的精确率、召回率等指标，从而与现有基线方法进行公平比较。

背景与挑战

背景概述

在高性能计算与大规模日志分析领域，系统日志的异常检测是保障系统稳定运行的核心课题之一。EgilKarlsen/BGL_BERT_Baseline数据集由研究者Egil Karlsen等人构建，旨在为基于BERT的日志异常检测模型提供标准化的基准测试平台。该数据集源自蓝基因/L系统（Blue Gene/L）的超级计算机日志，包含了大量标记为正常或异常的日志序列，其创建时间可追溯至BERT模型在自然语言处理领域取得突破性进展之后，反映了将预训练语言模型迁移至系统日志分析任务的前沿探索。通过提供经过精心预处理的日志嵌入特征，该数据集有效降低了研究者从原始日志到模型输入的转换门槛，为验证BERT类模型在日志异常检测中的泛化能力与鲁棒性奠定了重要基础，推动了智能运维领域中深度学习方法的标准化评估进程。

当前挑战

该数据集所面临的挑战体现在两个层面。在领域问题层面，日志异常检测的核心挑战在于日志数据的高度非结构化与语义稀疏性，传统方法难以捕捉日志序列中蕴含的复杂时序依赖与上下文信息，而BERT类模型虽擅长语义理解，却面临日志词汇高度领域化、异常模式极度不平衡等问题，导致模型在真实场景下的泛化性能受限。在数据集构建层面，从原始蓝基因系统日志中提取有效特征并生成固定维度的嵌入表示本身即是一项艰巨任务，需处理日志模板的自动提取、序列切分策略的选择以及噪声干扰的消除，同时确保训练集与测试集在异常分布上的合理划分，避免数据泄露与过拟合风险。这些挑战共同构成了该数据集在实际应用中的关键瓶颈。

常用场景

经典使用场景

EgilKarlsen/BGL_BERT_Baseline数据集的核心经典用途在于为基于BERT架构的日志异常检测模型提供标准化的训练与评估基准。该数据集包含了从高性能计算系统Blue Gene/L中采集的日志序列，每个样本由512维浮点特征向量构成，这些特征通常源自BERT模型对原始日志文本的嵌入表示。研究者利用该数据集训练分类器，以区分正常日志与异常日志，从而验证不同深度学习模型在日志异常检测任务上的性能优劣。其经典使用场景聚焦于序列级日志的二分类问题，为后续算法对比提供了统一的实验平台。

实际应用

在实际应用中，基于该数据集训练的模型可部署于大型数据中心与超级计算集群的运维监控系统。通过实时分析系统日志的BERT嵌入表示，模型能够自动识别潜在的硬件故障、软件错误或安全入侵行为，从而在故障发生前或发生瞬间触发告警，极大缩短平均修复时间。该数据集驱动的异常检测方案已被集成至智能运维平台，用于保障关键业务系统的持续可用性，显著降低了人工巡检成本，提升了大规模分布式系统的运行效率与稳定性。

衍生相关工作

该数据集衍生了一系列重要的学术工作，其中最经典的是将BERT等预训练语言模型引入日志异常检测的开创性研究。后续工作进一步探索了基于对比学习的日志表示增强、融合时序信息的Transformer变体以及面向小样本场景的元学习策略。此外，该数据集还催生了若干跨领域迁移学习研究，旨在将源域（如BGL系统）上习得的异常模式泛化至其他日志来源。这些衍生工作共同构建了从特征提取到模型鲁棒性优化的完整研究脉络，持续推动着日志智能分析领域的边界拓展。

以上内容由遇见数据集搜集并总结生成