adding_similarity_to_spacy.py

import spacy
import numpy

# load no model - this one: https://spacy.io/models/nb
nlp = spacy.load('nb_core_news_sm')

with open(w2v_path+'/w2vtrained.txt', 'rb') as file_:
    header = file_.readline()

    nr_row, nr_dim = header.split()
    print(nr_row) #b'658624' <- n words
    print(nr_dim) #b'300'<- n dim of vector
    
    # trick: https://github.com/explosion/spaCy/issues/2914
    nlp.vocab.vectors.resize((int(nr_row), int(nr_dim)))
    #nlp.vocab.reset_vectors(width = int(nr_dim))
    
    for line in file_:
        line = line.rstrip().decode('utf8')

        pieces = line.rsplit(' ', int(nr_dim))

        word = pieces[0]
        vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
        nlp.vocab.set_vector(word, vector)

# save
nlp.to_disk("nb_core_news_sm_w_vec")


# load model with vectors
import spacy
nlp = spacy.load('./nb_core_news_sm_w_vec')

# now we can do: 

doc1= nlp("Jeg hater måker")
doc2 = nlp("Jeg hater båter")

print(doc1.similarity(doc2))