Setfit for Automated Essay Scoring: Extending Longformer to a Sentence Transformer
Leon Krug, Jannik Bundeli, Jannine Meier, Елена Борисовна Назаренко · 2025
Automated Essay Scoring (AES) is a challenging task requiring models to evaluate writing quality with human-like consistency while being computationally efficient and scalable. Current approaches often rely on computationally intensive prompt engineering or resource-heavy transformer models, limiting their practical application. In this study, we present a novel prompt-free approach using SetFit for AES that achieves competitive accuracy while significantly reducing computational overhead. Unlike traditional transformer-based models such as DeBERTa, SetFit enables sentence transformer fine-tuning with contrastive learning, making it suitable for essay scoring even in low-data regimes. To handle full-length essays, we extended Longformer into a custom sentence transformer with a 4096-token context window, overcoming the 512-token limitation of standard transformer architectures. Our fine-tuned model, published on Hugging Face, has gained over$\mathbf{6, 0 0 0}$downloads, indicating strong community interest in efficient, prompt-free approaches. Our results demonstrate that SetFit with an extended Longformer sentence transformer achieves competitive performance, making it a viable alternative to computationally expensive models. Beyond essay scoring, our approach shows promise for other longform text analysis tasks such as legal document review, research paper assessment, and content quality evaluation in educational contexts. This work contributes to the growing exploration of efficient NLP methods for educational assessment, offering a practical alternative to resource-intensive prompt-based solutions.