text: # coding: utf8
  from __future__ import unicode_literals
  
  import numpy
  
  from spacy.lang.en import English
  from spacy.vocab import Vocab
  
  
  def test_issue4725():
      # ensures that this runs correctly and doesn't hang or crash because of the global vectors
      vocab = Vocab(vectors_name="test_vocab_add_vector")
      data = numpy.ndarray((5, 3), dtype="f")
      data[0] = 1.0
      data[1] = 2.0
      vocab.set_vector("cat", data[0])
      vocab.set_vector("dog", data[1])
  
      nlp = English(vocab=vocab)
      ner = nlp.create_pipe("ner")
      nlp.add_pipe(ner)
      nlp.begin_training()
      docs = ["Kurt is in London."] * 10
      for _ in nlp.pipe(docs, batch_size=2, n_process=2):
          pass
  