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tokenizer = DeepcutTokenizer(ngram_range=(1,3), max_df=1.0, min_df=0.0)
X = tokenizer.fit_tranform(tokenized_df)
X_test = tokenizer.transform(test_df[:10]) # use built tokenizer vobalurary to transform new text
print(X_test.shape)
tokenizer.save_model(dirpath+'tokenizer.pickle') # save the tokenizer to use later
environments
saving part
load part
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