artm_model.py

def get_artm_model(topic_names, dictionary):
    
    # model
    model_artm = artm.ARTM(num_topics=len(topic_names),
                           topic_names=topic_names, 
                           cache_theta=True,
                           seed=1,
                           scores=[artm.PerplexityScore(name='PerplexityScore', dictionary=dictionary)],
                           regularizers=[artm.SmoothSparseThetaRegularizer(name='SparseTheta', tau=-0.2),
                                        artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=-0.2),
                                        artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi', tau=2.5e+5)]
    # metrics
    model_artm.scores.add(artm.TopTokensScore(name='TopTokensScore', num_tokens=5))
                           
    model_artm.initialize(dictionary)
    return model_artm