基于ALBERT-BiGRU-BCEWithLogitsLoss的多标签分类方法研究

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中图分类号：TP391 文献标志码：A 文章编号：2095-2945（2025）24-0054-04

Abstract：Thequalificationreviewoftherecruitmentprocessinvolvesmultipledimensions，whichincreasesthecomplexity anddificultyofclassifcation.Tosolvethisproblem，thispaperproposesamulti-labeltextclasificationmodelbasedon ALBERT-BiGRU-BCEWithLogitsLos.First，theALBERTpre-trainedmodelisusedtoextractglobalsemanticfeaturesofhetext toimprovethemodelsabilitytocapturetextsemanticinformation.ThesefeaturesaretheninputintoBiGRU，andthroughits two-waycontextcapturecapabilities，thecontextdependenciesofthetextarefurtherextracted.Finall，basedonthe BCEWithLogitsLosslossfunction，theprobabilitycalculationandclasificationofthelogsoutputaredirectlycarredout， simplifyingthemodelstructureandimprovingcomputationalstabilityExperimentalresultsprovetheefectivenessofthismethod and can effectively improve the efficiency and accuracy of qualification review.

Keywords： multi-tag clasification; ALBERT; BiGRU;BCEWithLogitsLoss;Natural Language Processing

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