train_fasttext.py

from gensim.models import FastText

embed_model = FastText(vector_size=meta_hyper['vector_size'], 
                       window=meta_hyper['window'], 
                       min_count = meta_hyper['min_count'], 
                       alpha= meta_hyper['alpha'], 
                       workers=meta_hyper['CPU'])

embed_model.build_vocab(tokenized_data)

import time
start = time.time()

embed_model.train(tokenized_data, total_examples=len(tokenized_data), epochs=meta_hyper['epochs'])  

end = time.time()

embed_model.save('fasttext_{}.mdl'.format(meta_hyper['vector_size']))