NLTK n-gram model

ngram.py

from nltk.corpus import brown
from nltk.lm import MLE
from nltk import bigrams
from nltk.lm.preprocessing import padded_everygram_pipeline

def get_model(order=2):
  lm = MLE(order)
  train, vocab = padded_everygram_pipeline(order=2, text=brown.sents())
  lm.fit(train, vocab)
  return lm

model = get_model()
model.perplexity('prints the perplexity of this sentence')