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

Abstract

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.