title: Normalize Fasttext word embedding vector generated by model
body: <p>I'm currently using FastText to get the word embeddings for some input text data in order to catch similarity among them and give those embedding as input to a neural network for a NER task.
  I've first tried to use the fastText .vec file and I was able to get good similarity result from them, then I've tried to use the .bin model file so that I can get embeddings also from out-of-vocabulary terms but I've noticed that the vectors generated from the .vec and the .bin file are different.
  The .vec file contains embeddings clipped to a range of [-1,1] while this is not true for the embedding contained in the .bin file.
  Using the .bin file I get very bad results so I'm trying to undestand if this is due to the fact the these vectors (.bin) are not normalized.</p>
  
  <p>Is there a good way to normalize these vectors in a range [-1, 1] using Python?</p>
  
  <p>Thanks in advance.</p>