  We present the first steps in the development of a new class of hadronization
models utilizing machine learning techniques. We successfully implement,
validate, and train a conditional sliced-Wasserstein autoencoder to replicate
the Pythia generated kinematic distributions of first-hadron emissions, when
the Lund string model of hadronization implemented in Pythia is restricted to
the emissions of pions only. The trained models are then used to generate the
full hadronization chains, with an IR cutoff energy imposed externally. The
hadron multiplicities and cumulative kinematic distributions are shown to match
the Pythia generated ones. We also discuss possible future generalizations of
our results.
