def get_tf(self, text, ones=False):
tokens = [i for i in word_tokenize(text) if i in self.df_dict]
lemmaFreq = FreqDist(tokens)
sum_doc = np.array([lemmaFreq[i] for i in lemmaFreq]).sum()
lemmaFreq = sorted(lemmaFreq.items(), key=operator.itemgetter(1))
lemmaFreq.reverse()
term_freq = [(i[0], i[1], round(i[1] / sum_doc, 5)) for i in lemmaFreq]
# if ones:
# term_freq = [(i[0], i[1], round(i[1] / sum_doc, 5)) for i in lemmaFreq if i[1] == 1]
#print(term_freq)
# else:
# term_freq = [(i[0], i[1], round(i[1] / sum_doc, 5)) for i in lemmaFreq if i[1] > 1]
#print(term_freq)
return term_freq
