Advancements in Large Language Models (LLMs) blur the distinction between human and machine-generated text (MGT), raising concerns about misinformation and academic dishonesty. Existing MGT detection methods often fail to generalize across domains and generator models. We address this by framing MGT detection as a text classification task using transformer-based models. Utilizing Distil-RoBERTa-Base, we train four classifiers (binary and multi-class, with and without class weighting) on the RAID dataset (Dugan et al., 2024). Our systems placed first to fourth in the COLING 2025 MGT Detection Challenge Task 3 (Dugan et al., 2025). Internal in-domain and zero-shot evaluations reveal that applying class weighting improves detector performance, especially with multi-class classification training. Our best model effectively generalizes to unseen domains and generators, demonstrating that transformer-based models are robust detectors of machine-generated text.