from turftopic import FASTopic

# Model specification
topic_size  = 10
model = FASTopic(n_components = topic_size,       # train for 10 topics
                 vectorizer = bigram_vectorizer,  # generate bigrams in topics
                 random_state = 32).fit(docs)     # set random state 

# Fit model to corpus
topic_data = model.prepare_topic_data(docs)