I was just about to undergo adding custom teach for sklearn but I saw that it was on your roadmap, and you have new releases often … so can’t hurt to ask!
Can confirm it won’t be in 1.4, sadly.
The plan is to support other libraries by adding wrappers in Thinc, that take e.g. a scikit-learn model, and gives it the Thinc interface. The key part of the interface is:
class Model: def begin_update(self, batch_of_data, drop=0.0): '''Run the model over a batch of data, returning a callback to update the weights.''' def finish_update(batch_of_d_output, sgd=None): return batch_of_d_input def to_bytes(self): '''Serialize the current weights to a binary string''' return byte_string def to_disk(self, path): '''Serialize the current weights to disk.''' pass
I think these should be fairly easy to implement for scikit-learn, but I don’t use the library very much myself, so it might depend on which models you’re using?