  Clinical patient notes are critical for documenting patient interactions,
diagnoses, and treatment plans in medical practice. Ensuring accurate
evaluation of these notes is essential for medical education and certification.
However, manual evaluation is complex and time-consuming, often resulting in
variability and resource-intensive assessments. To tackle these challenges,
this research introduces an approach leveraging state-of-the-art Natural
Language Processing (NLP) techniques, specifically Masked Language Modeling
(MLM) pretraining, and pseudo labeling. Our methodology enhances efficiency and
effectiveness, significantly reducing training time without compromising
performance. Experimental results showcase improved model performance,
indicating a potential transformation in clinical note assessment.
