This paper addresses the challenge of achieving fully distributed event-triggered bipartite consensus in discrete-time nonlinear multi-agent systems (MASs) characterized by unknown dynamic models and antagonistic interactions. It begins by transforming the bipartite consensus issue into a standard consensus problem through a combined measurement error function. A dynamic linearization model is subsequently established for the input and the combined measurement error function, easing the strongly connected requirement of MASs' communication topology. To enhance performance, an event-triggered model-free sliding-mode bipartite consensus algorithm is proposed, designed to boost convergence speed, reduce steady-state error, and relieve communication burden. The convergence of the proposed method is rigorously proven, allowing for control of fine-tuned specific practical needs. Simulation studies are conducted to verify the effectiveness of the proposed scheme.