      @meta.setter
      def meta(self, value):
          self._meta = value
  
      # Conveniences to access pipeline components
      # Shouldn't be used anymore!
      @property
      def tensorizer(self):
          return self.get_pipe("tensorizer")
 
</s> remove     that takes the nlp object and **cfg arguments and returns the initialized
    component.
 </s> add     that takes the nlp & model objects and **cfg arguments, and returns the
    initialized component. </s> remove     model = masked_language_model(nlp.vocab, model)
 </s> add     model = build_masked_language_model(nlp.vocab, model) </s> remove     def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
 </s> add     def __init__(self, Vocab vocab, model, **cfg): </s> remove     @classmethod
    def Model(cls, **cfg):
        embed_width = cfg.get("embed_width", 300)
        hidden_width = cfg.get("hidden_width", 128)
        type_to_int = cfg.get("type_to_int", dict())

        model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg)
        return model

    def __init__(self, vocab, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg): </s> remove         return None if self.model in (None, True, False) else self.model.tok2vec
 </s> add         return self.model.tok2vec
    @property
    def config(self):
        return self._config

      @meta.setter
      def meta(self, value):
          self._meta = value
  
    @property
    def config(self):
        return self._config

      # Conveniences to access pipeline components
      # Shouldn't be used anymore!
      @property
      def tensorizer(self):
          return self.get_pipe("tensorizer")
keep keep keep add 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/language.py