gistfile1.txt

#!/usr/bin/env python
# coding: utf8
"""Load vectors for a language trained using fastText
https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
Compatible with: spaCy v2.0.0+
"""
from __future__ import unicode_literals
import plac
import numpy

import spacy
from spacy.language import Language


lang = "nb"
vectors_loc = "wiki.no.vec"

if lang is None:
    nlp = Language()
else:
    # create empty language class – this is required if you're planning to
    # save the model to disk and load it back later (models always need a
    # "lang" setting). Use 'xx' for blank multi-language class.
    nlp = spacy.blank(lang)
    
with open(vectors_loc, 'rb') as file_:
    header = file_.readline()
    nr_row, nr_dim = header.split()
    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)  # add the vectors to the vocab
        
nlp.to_disk('norsk.model')