Our primary objective is to achieve a pioneering measurement of the challenging process in Large Hadron Collider (LHC) data to extract new physics contributions in the context of the Standard Model Effective Field Theory (SMEFT) framework. By leveraging the power of multi-head attention mechanism within Transformer encoders, we developed an innovative approach to efficiency capture long-range dependencies and contextual information in sequences of particle-collision-event final-state objects. This new technique enhances our ability to extract SMEFT parameters that are not well constrained by other measurements and deepens our understanding of fundamental interactions within the Higgs-boson sector. This presentation showcases the versatility of Transformer networks beyond their original domain and presents new opportunities for advanced data-driven physics research at the LHC.