old_contents: #!/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
  
  
  @plac.annotations(
      vectors_loc=("Path to vectors", "positional", None, str),
      lang=("Optional language ID. If not set, blank Language() will be used.",
            "positional", None, str))
  def main(vectors_loc, lang=None):
      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(' ', 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
      # test the vectors and similarity
      text = 'class colspan'
      doc = nlp(text)
      print(text, doc[0].similarity(doc[1]))
  
  
  if __name__ == '__main__':
      plac.call(main)
  
new_contents: #!/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
  
  
  @plac.annotations(
      vectors_loc=("Path to vectors", "positional", None, str),
      lang=("Optional language ID. If not set, blank Language() will be used.",
            "positional", None, str))
  def main(vectors_loc, lang=None):
      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
      # test the vectors and similarity
      text = 'class colspan'
      doc = nlp(text)
      print(text, doc[0].similarity(doc[1]))
  
  
  if __name__ == '__main__':
      plac.call(main)
  
subject: Make sure nr_dim is an int