Edit: I see that I didn't answer your question at all. However I'll leave it here for people who are not that familiar with ML If I'm not completely wrong, the

Edit: I see that I didn't answer your question at all. However I'll leave it here for people who are not that familiar with ML If I'm not completely wrong, these are so called latent factors of words. That pretty much means computer representations of the meaning of a word. Words with similar meanings would have similar factors, for example the word "Rome" and the word "Italy" will probably in one or more of these dimensions be quite similar. These vectors usually take a lot of time to train if done properly, and they come out quite similar anyway, so having them precomputed makes it easier for other people without the resources of fb to do NLP. Another cool thing is that they are available for so many languages, this is the first time I've seen precomputed vectors for my native language. TLDR: computer representations of words, which makes it easier for people to make machine learning models.