Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform  

作　　者：Xiaoyun Zhu 

机构地区：[1]Yunnan Open University,Kunming 650500,Yunnan Province,China

出　　处：《Journal of Contemporary Educational Research》2024年第11期227-232,共6页当代教育研究（百图）

摘　　要：Leveraging the Baidu Qianfan model platform,this paper designs and implements a highly efficient and accurate scoring system for subjective questions,focusing primarily on questions in the field of computer network technology.The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques,such as supervised fine-tuning.In the data preparation phase,a comprehensive collection of subjective data related to computer network technology is gathered,cleaned,and labeled.During model training and evaluation,optimal hyperparameters and tuning strategies are applied,resulting in a model capable of scoring with high accuracy.Evaluation results demonstrate that the proposed model performs well across multiple dimensions-content,expression,and development scores-yielding results comparable to those of manual scoring.

关 键 词：Subjective score Natural language processing Deep learning Baidu Qianfan large model platform Supervised fine-tuning Model training and evaluation 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]