  We introduce functorial language models: a principled way to compute
probability distributions over word sequences given a monoidal functor from
grammar to meaning. This yields a method for training categorical compositional
distributional (DisCoCat) models on raw text data. We provide a
proof-of-concept implementation in DisCoPy, the Python toolbox for monoidal
categories.
