Unsupervised short sentences clustering with fastai

[uw198162 (Cosimo Streppone)]

It seems that this post could be quite useful for what I’m trying to do:

NPL: Using fastai word embeddings to cluster unlabeled documents

I have a question on the best way to use fastai word embeddings to cluster unlabeled documents. Say for example I have 100 documents that result in a vocabulary of 500 words. Using the excellent fastai LM learner I can create word embeddings for the 500 word vocab such that I have 500 vectors of length 400 representing the 500 distinct words found in the corpus. learn.model._modules['0'].weights.shape -> torch.Size([500, 400]) I can then take each document and substitute the word vector for …

Will just leave it here in case someone else stumbles upon my request.