text: from spacy.util import compounding, minibatch
  
  TRAIN_DATA = [
      ("Replay motion XXX", {"tags": ["VB", "NN", "NNP"]}),
      ("Turn right and then left", {"tags": ["VB", "RB", "CC", "RB", "RB"]}),
  ]
  
  with nlp.disable_pipes(*(pipe for pipe in nlp.pipe_names if pipe != "tagger")):
      for _ in range(100):
          batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
          for batch in batches:
              texts, annotations = zip(*batch)
              nlp.update(
                  texts,  # batch of texts
                  annotations,  # batch of annotations
                  drop=0.5,  # dropout - make it harder to memorise data
                  # losses=losses,
              )
  