# Load pretrained embedding model on TF-Hub to calculate Word Mover's Distance (WMD) on Gensim or spaCy

I'd like to calculate Word Mover's Distance with Universal Sentence Encoder on TensorFlow Hub embedding.

I have tried the example on spaCy for WMD-relax, which loads 'en' model from spaCy, but I couldn't find another way to feed other embeddings.

In gensim, it seems that it only accepts load_word2vec_format file (file.bin) or load file (file.vec).

As I know, someone has written a Bert to token embeddings based on pytorch, but it's not generalized to other models on tf-hub.

Is there any other approach to transfer pretrained models on tf-hub to spaCy format or word2vec format?