      return nlp
  
  
  def read_vectors(vectors_loc):
      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)
      return vectors_data, vectors_keys
  
  
  def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
      print("Counting frequencies...")
 
</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 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):
    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)
      return nlp
  
  
  def read_vectors(vectors_loc):
    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)
    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)
    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)
    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)
    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)
    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)
    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)
    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)
    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)
    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)
      return vectors_data, vectors_keys
  
  
  def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
      print("Counting frequencies...")
keep keep keep keep replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Support .gz and .tar.gz files in spacy init-model 
spacy/cli/init_model.py