Developed a machine learning–driven system using PyTorch, Transformers, and scikit-learn to classify messy, multilingual procurement item descriptions into UNSPSC codes. The system preprocesses mixed-language and noisy inputs, applies semantic similarity and ensemble methods, and outputs accurate segment, family, class, and commodity predictions. It enables fast, automated categorization of thousands of procurement items daily, improving efficiency and consistency in data processing.