I agree, and not just for text classification. Its a good baseline for anything that looks like text (if you train a model from scratch), e.g., URLs with some b

I agree, and not just for text classification. Its a good baseline for anything that looks like text (if you train a model from scratch), e.g., URLs with some basic preprocessing. This is because FastText uses character n-grams; the consequence of this is substring similarities are exploited. Some things to note: (1) FastText comes with a classifier but I have often seen good results with using a difference classifier like LightGBM or a SVM. (2) If you want other word embeddings to compare with as well, magnitude [1] is an easy to use library. (3) FastText now supports multiple languages [2]. [1] https://github.com/plasticityai/magnitude#pre-converted-magnitude-formats-of-popular-embeddings-models https://github.com/plasticityai/magnitude#pre-converted-magn... [2] https://fasttext.cc/docs/en/crawl-vectors.html#models https://fasttext.cc/docs/en/crawl-vectors.html#models