Static code analysis (SCA) tools are widely used as effective ways to detect bugs and vulnerabilities in software systems. However, the reports generated by these tools often contain a large number of non-actionable findings, which can overwhelm developers to the point of ignoring them altogether -- this phenomenon is known as "alert fatigue". In this paper, we combat alert fatigue by proposing STAF: Sentence Transformer-based Actionability Filtering. Our approach leverages a transformer based architecture with sentence embeddings to classify findings into actionable and non-actionable categories. Evaluating STAF on a large dataset of reports from Java projects, we demonstrate that our method can effectively reduce the number of non-actionable findings while maintaining a high level of accuracy in identifying actionable issues. The results show that our approach can improve the usability of static analysis tools reaching an F1 score of 89%, outperforming existing methods for SCA warning filtering by at least 11% in a within-project setting and by at least 6% in a cross-project setting. By providing a more focused and relevant set of findings, we aim to enhance the overall effectiveness of static analysis in software development.