title: Use a word embedding model instead of OneHot
body: <p>I'm doing the pre-processing of a dataset composed of numerical and text values. And I'd like some suggestions on how to deal with textual data in the most efficient way. My issue is that my text data represent random cities/provinces/regions. So, regarding the size of my dataset, I can have up to 1M different cities (same for 3 other features). Because of this number, OneHotEncoding isn't a good idea for sure. So, I have implemented a fasttext model to convert these words to vector.</p>
  <p>I'd like to have your opinion on what vector size is the best. Knowing that I have already 55 numerical features, and these data will fed an auto encoder.</p>