Organizational success in todays competitive employment market depends on choosing the right staff. This work evaluates software engineer profiles using an automated staff selection method based on advanced natural language processing (NLP) techniques. A fresh dataset was generated by collecting LinkedIn profiles with important attributes like education, experience, skills, and self-introduction. Expert feedback helped transformer models including RoBERTa, DistilBERT, and a customized BERT variation, LastBERT, to be adjusted. The models were meant to forecast if a candidate's profile fit the selection criteria, therefore allowing automated ranking and assessment. With 85% accuracy and an F1 score of 0.85, RoBERTa performed the best; DistilBERT provided comparable results at less computing expense. Though light, LastBERT proved to be less effective, with 75% accuracy. The reusable models provide a scalable answer for further categorization challenges. This work presents a fresh dataset and technique as well as shows how transformer models could improve recruiting procedures. Expanding the dataset, enhancing model interpretability, and implementing the system in actual environments will be part of future activities.

翻译：在当今竞争激烈的就业市场中，组织的成功取决于能否选拔合适的人员。本研究基于先进的自然语言处理（NLP）技术，采用一种自动化人员选拔方法对软件工程师档案进行评估。通过收集包含教育背景、工作经验、技能和自我介绍等重要属性的LinkedIn档案，构建了一个全新的数据集。专家反馈有助于对包括RoBERTa、DistilBERT以及一个定制化的BERT变体LastBERT在内的Transformer模型进行微调。这些模型旨在预测候选人的档案是否符合选拔标准，从而实现自动化的排名与评估。RoBERTa以85%的准确率和0.85的F1分数表现最佳；DistilBERT以较低的计算成本提供了可比的结果。尽管轻量，但LastBERT被证明效率较低，准确率为75%。这些可复用的模型为更广泛的分类任务提供了可扩展的解决方案。本研究不仅提出了新的数据集和方法，还展示了Transformer模型优化招聘流程的潜力。未来的工作将包括扩展数据集、提升模型可解释性以及在真实环境中部署该系统。