StarSpace | Learning embeddings for classification , retrieval | Machine Learning library

The way StarSpace uses the labels highly depends on the trainMode you are using. The labelDoc format is useful when you go for a trainMode that just relies on labels (trainMode 1 through 4) where it may be the same thing to use a fastText format specifying the __label__ prefix but some trainModes benefit from labelDoc format (i.e. trainMode 1 or 3) to use a whole sentence as a label element for that trainMode.

So to clarify that, if you are performing a text classification task(as explained in this example labelDoc wouldn't have any input recognized but on the other hand, as you stated, using fastText format will breakdown all non-labeled text as input and learn to predict the __label__ tags.

And an example for labelDoc format would be developing a content based recommender system (as explained in this example) every tab separated sentence is used at LHS or RHS during training time. But if you go on a collaborative approach (the content of the articles or wherever you sentences come from is not taken in account) it can be trained either with fastText (specifying the __label__ prefix) or labelDoc file format as labels are picked randomly during training time for LHS or RHS. (This second example is explained here).