The class diagram plays an important role in software development. As these diagrams are created using software requirement text, it helps to improve communication between the developers and the stakeholders. Thus, the automatic extraction of class diagrams enhances the speed of software development procedures. The research carried out in this direction mostly relies on rule-based methodologies and deep learning models. These methodologies have their drawbacks, such as the fact that large rulebased systems are complex to handle, whereas the word embeddings used in deep learning models are context-independent. Thus, the presented research work strives to extract the class diagram entities from the natural language text by employing a transformer-based model, as the embeddings generated by these models are context-dependent. The results have been compared with the existing procedure, and an ablation study has also been carried out to find out the relevance of each step in the extraction procedure. The analysis involved examining the true positive, false positive, and false negative rates for specific class diagram elements in separate case studies. As a result, an enhancement of 9–7% has been observed in the procedures used for extracting the resulting class diagrams.