FastText using pre-trained word vector for text classification

Question

I am working on a text classification problem, that is, given some text, I need to assign to it certain given labels.

I have tried using fast-text library by Facebook, which has two utilities of interest to me:

A) Word Vectors with pre-trained models

B) Text Classification utilities

However, it seems that these are completely independent tools as I have been unable to find any tutorials that merge these two utilities.

What I want is to be able to classify some text, by taking advantage of the pre-trained models of the Word-Vectors. Is there any way to do this?

Answer 1

FastText's native classification mode depends on you training the word-vectors yourself, using texts with known classes. The word-vectors thus become optimized to be useful for the specific classifications observed during training. So that mode typically wouldn't be used with pre-trained vectors.

If using pre-trained word-vectors, you'd then somehow compose those into a text-vector yourself (for example, by averaging all the words of a text together), then training a separate classifier (such as one of the many options from scikit-learn) using those features.