      ner1.add_label("C")
      ner1.add_label("B")
      ner1.add_label("A")
      ner1.begin_training([])
      ner2 = EntityRecognizer(Vocab()).from_bytes(ner1.to_bytes())
      assert ner1.moves.n_moves == ner2.moves.n_moves
      for i in range(ner1.moves.n_moves):
          assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)
  
  
 
</s> remove     ner1 = EntityRecognizer(Vocab())
 </s> add     ner1 = EntityRecognizer(Vocab(), default_ner()) </s> remove     ner2 = EntityRecognizer(doc.vocab)
 </s> add     ner2 = EntityRecognizer(doc.vocab, default_ner()) </s> remove         ner2 = EntityRecognizer(vocab)
 </s> add         ner2 = EntityRecognizer(vocab, default_ner()) </s> remove     ner2 = EntityRecognizer(doc2.vocab)
 </s> add     ner2 = EntityRecognizer(doc2.vocab, default_ner()) </s> remove     "pipe_cls,n_moves", [(DependencyParser, 5), (EntityRecognizer, 4)]
 </s> add     "pipe_cls,n_moves,model", [(DependencyParser, 5, default_parser()), (EntityRecognizer, 4, default_ner())]
    ner2 = EntityRecognizer(Vocab(), default_ner())

    # the second model needs to be resized before we can call from_bytes
    ner2.model.resize_output(ner1.moves.n_moves)
    ner2.from_bytes(ner1.to_bytes())
      ner1.add_label("C")
      ner1.add_label("B")
      ner1.add_label("A")
      ner1.begin_training([])
    ner2 = EntityRecognizer(Vocab(), default_ner())

    # the second model needs to be resized before we can call from_bytes
    ner2.model.resize_output(ner1.moves.n_moves)
    ner2.from_bytes(ner1.to_bytes())
      assert ner1.moves.n_moves == ner2.moves.n_moves
      for i in range(ner1.moves.n_moves):
          assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)
  
  
keep keep keep keep replace keep keep keep keep keep
 Default settings to configurations (#4995)

* fix grad_clip naming

* cleaning up pretrained_vectors out of cfg

* further refactoring Model init's

* move Model building out of pipes

* further refactor to require a model config when creating a pipe

* small fixes

* making cfg in nn_parser more consistent

* fixing nr_class for parser

* fixing nn_parser's nO

* fix printing of loss

* architectures in own file per type, consistent naming

* convenience methods default_tagger_config and default_tok2vec_config

* let create_pipe access default config if available for that component

* default_parser_config

* move defaults to separate folder

* allow reading nlp from package or dir with argument 'name'

* architecture spacy.VocabVectors.v1 to read static vectors from file

* cleanup

* default configs for nel, textcat, morphologizer, tensorizer

* fix imports

* fixing unit tests

* fixes and clean up

* fixing defaults, nO, fix unit tests

* restore parser IO

* fix IO

* 'fix' serialization test

* add *.cfg to manifest

* fix example configs with additional arguments

* replace Morpohologizer with Tagger

* add IO bit when testing overfitting of tagger (currently failing)

* fix IO - don't initialize when reading from disk

* expand overfitting tests to also check IO goes OK

* remove dropout from HashEmbed to fix Tagger performance

* add defaults for sentrec

* update thinc

* always pass a Model instance to a Pipe

* fix piped_added statement

* remove obsolete W029

* remove obsolete errors

* restore byte checking tests (work again)

* clean up test

* further test cleanup

* convert from config to Model in create_pipe

* bring back error when component is not initialized

* cleanup

* remove calls for nlp2.begin_training

* use thinc.api in imports

* allow setting charembed's nM and nC

* fix for hardcoded nM/nC + unit test

* formatting fixes

* trigger build 
spacy/tests/parser/test_add_label.py