The second major focus of fastText is creating word embeddings for the input text. During training, fastText looks at the supplied text corpus and forms a high-dimensional vector space model, where it tries to encapsulate as much meaning as possible. The aim of creating the vectors space is that the vectors of similar words should be near to each other. In fastText, these word vectors are then saved in two files, similar to what you have seen in text classification: a .bin file and a .vec file.
In this section, we will look at the creation and use of word vectors using the fastText command line.
