This repository contains trained models and data for automatic classification of Swedish university courses into disciplinary domains (utbildningsområden, UO), developed as part of a master's project at Stockholm University. Contents: bert_binary_model.zip Fine-tuned KB-BERT model for multi-label classification (90.9% subset accuracy, 93.6% micro-F1) bert_distributional_model.zip KB-BERT model for direct percentage distribution prediction (92.4% top-1 accuracy, 1.44 MAE) tfidf_baseline.zip TF-IDF + Linear SVC baseline pipeline (87.9% subset accuracy) corpus_raw.csv Original course plan corpus (9,770 course versions, 4,880 unique courses) corpus_preprocessed.csv Cleaned and deduplicated training data with train/validation splits boglind_2026_su_admin_course_classification.pdf Report Data Source:2023 extract from Ladok (Swedish national student information system) provided by Stockholm University Administration. Contains course titles, descriptions, learning objectives, and UO classifications. No personal data about students or instructors. Models:All transformer models use KB-BERT (KB/sentence-bert-swedish-cased). The distributional model was trained with KL-divergence loss to directly predict percentage allocations across 10 disciplinary domains. Implementation: https://github.com/fboglind/lis070-su-admin-project