new_contents: from keras.layers.core import Dense, Reshape
  from keras.layers.embeddings import Embedding 
  from keras.models import Sequential
  from keras.preprocessing.text import *
  from keras.preprocessing.sequence import skipgrams
  
  text = "I love green eggs and ham ."
  
  tokenizer = Tokenizer()
  tokenizer.fit_on_texts([text])
  
  word2id = tokenizer.word_index
  id2word = {v:k for k, v in word2id.items()}
  
  wids = [word2id[w] for w in text_to_word_sequence(text)]
  pairs, labels = skipgrams(wids, len(word2id))
  print(len(pairs), len(labels))
  for i in range(10):
  	print("({:s} ({:d}), {:s} ({:d})) -> {:d}".format(
  														id2word[pairs[i][0]], pairs[i][0],
  														id2word[pairs[i][1]], pairs[i][1],
  														labels[i]
  														))
  
  vocab_size = 5000
  embed_size = 300
  
  word_model = Sequential()
  word_model.add(Embedding(vocab_size, embed_size,
  						embeddings_initializer="glorot_uniform",
  						input_length = 1))
  word_model.add(Reshape((embed_size, )))
  
  context_model = Sequential()
  context_model.add(Embedding(vocab_size, embed_size,
  						embeddings_initializer="glorot_uniform",
  						input_length = 1))
  context_model.add(Reshape((embed_size, )))
  
  model = Sequential()
  model.add(word_model)
  model.add(context_model)
  model.add(Dense(1, kernel_initializer="glorot_uniform", activation = "sigmoid"))
  model.compile(loss="mse", optimizer="adam")
  
  
subject: add word2vec skipgrams