In this study, we address the challenge of using energy-based models to produce high-quality, label-specific data in complex structured datasets. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing, which affects the diversity of synthetic data and increases generation times. To address these issues, we use a novel training algorithm that exploits non-equilibrium MCMC effects. This approach improves the model's ability to correctly classify samples and generate high-quality samples in only a few sampling steps. The effectiveness of this method is demonstrated learning three datasets with Restricted Boltzmann Machines: handwritten digits for visualization, a human mutation genome dataset classified by continental origin, and sequences of an enzyme protein family categorized by experimental biological function.