              lexeme.cluster = 0
          lex_added += 1
      nlp.vocab.cfg.update({'oov_prob': oov_prob})
  
      if len(vectors_data):
          nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
      if prune_vectors >= 1:
 
</s> remove     probs, oov_prob = read_freqs(freqs_loc)
 </s> add     probs, oov_prob = read_freqs(freqs_loc) if freqs_loc is not None else ({}, -20) </s> remove     if not freqs_loc.exists():
 </s> add     if freqs_loc is not None and not freqs_loc.exists(): </s> remove     print("Reading vectors...")
    with vectors_loc.open() as f:
        shape = tuple(int(size) for size in f.readline().split())
        vectors_data = numpy.zeros(shape=shape, dtype='f')
        vectors_keys = []
        for i, line in enumerate(tqdm(f)):
            pieces = line.split()
            word = pieces.pop(0)
            vectors_data[i] = numpy.array([float(val_str) for val_str in pieces], dtype='f')
            vectors_keys.append(word)
 </s> add     print("Reading vectors from %s" % vectors_loc)
    f = open_file(vectors_loc)
    shape = tuple(int(size) for size in next(f).split())
    vectors_data = numpy.zeros(shape=shape, dtype='f')
    vectors_keys = []
    for i, line in enumerate(tqdm(f)):
        pieces = line.split()
        word = pieces.pop(0)
        vectors_data[i] = numpy.asarray(pieces, dtype='f')
        vectors_keys.append(word) </s> remove def init_model(lang, output_dir, freqs_loc, clusters_loc=None, vectors_loc=None, prune_vectors=-1):
 </s> add def init_model(lang, output_dir, freqs_loc=None, clusters_loc=None, vectors_loc=None, prune_vectors=-1):
    for word in vector_keys:
        if word not in nlp.vocab:
            lexeme = nlp.vocab[word]
            lexeme.is_oov = False
            lex_added += 1
              lexeme.cluster = 0
          lex_added += 1
      nlp.vocab.cfg.update({'oov_prob': oov_prob})
    for word in vector_keys:
        if word not in nlp.vocab:
            lexeme = nlp.vocab[word]
            lexeme.is_oov = False
            lex_added += 1
  
      if len(vectors_data):
          nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
      if prune_vectors >= 1:
keep keep add keep keep keep keep
 Support .gz and .tar.gz files in spacy init-model 
spacy/cli/init_model.py