title: Fine tune spaCy's word embeddings
body: <p>Spacy has great parsing capacities and it's API is very intuitive for the most part. Is there any way from the Spacy API to fine tune its word embedding models? In particular, I would like to keep Spacy's tokens and give them a vector when possible.</p>
  
  <p>The only thing I've come across for now is to train the embeddings using gensim (but then I wouldn't know how to load the embeddings from spacy to gensim) and then load then back to spacy, as in: <a href="https://www.shanelynn.ie/word-embeddings-in-python-with-spacy-and-gensim/" rel="nofollow noreferrer">https://www.shanelynn.ie/word-embeddings-in-python-with-spacy-and-gensim/</a>. This doesn't help for the first part: training on spacy tokens.</p>
  
  <p>Any help appreciated.</p>