This project is based on the ideas in Facebook's FastText but implemented in Tensorflow. However, it is not an exact replica of fastText. Classification is done by embedding each word, taking the mean embedding over the full text and classifying that using a linear classifier. The embedding is trained with the classifier. You can also specify to use 2+ character ngrams. These ngrams get hashed then embedded in a similar manner to the orginal words. Note, ngrams make training much slower but only make marginal improvements in performance, at least in English.