                  raise KeyError(Errors.E108.format(name=name))
              else:
                  raise KeyError(Errors.E002.format(name=name))
          factory = self.factories[name]
          return factory(self, **config)
  
      def add_pipe(
          self, component, name=None, before=None, after=None, first=None, last=None
      ):
          """Add a component to the processing pipeline. Valid components are
 
</s> remove         self, vocab=True, make_doc=True, max_length=10 ** 6, meta={}, **kwargs
 </s> add         self, vocab=True, make_doc=True, max_length=10 ** 6, meta={}, config=None, **kwargs </s> remove         self.require_model()
 </s> add  </s> remove     model = masked_language_model(nlp.vocab, model)
 </s> add     model = build_masked_language_model(nlp.vocab, model)
        default_config = self.defaults.get(name, None)

        # transform the model's config to an actual Model
        model_cfg = None
        if "model" in config:
            model_cfg = config["model"]
            if not isinstance(model_cfg, dict):
                user_warning(Warnings.W099.format(type=type(model_cfg), pipe=name))
                model_cfg = None
            del config["model"]
        if model_cfg is None and default_config is not None:
            user_warning(Warnings.W098)
            model_cfg = default_config["model"]
        model = None
        if model_cfg is not None:
            self.config[name] = {"model":  model_cfg}
            model = registry.make_from_config({"model": model_cfg}, validate=True)["model"]
        return factory(self, model, **config)
                  raise KeyError(Errors.E108.format(name=name))
              else:
                  raise KeyError(Errors.E002.format(name=name))
          factory = self.factories[name]
        default_config = self.defaults.get(name, None)

        # transform the model's config to an actual Model
        model_cfg = None
        if "model" in config:
            model_cfg = config["model"]
            if not isinstance(model_cfg, dict):
                user_warning(Warnings.W099.format(type=type(model_cfg), pipe=name))
                model_cfg = None
            del config["model"]
        if model_cfg is None and default_config is not None:
            user_warning(Warnings.W098)
            model_cfg = default_config["model"]
        model = None
        if model_cfg is not None:
            self.config[name] = {"model":  model_cfg}
            model = registry.make_from_config({"model": model_cfg}, validate=True)["model"]
        return factory(self, model, **config)
  
      def add_pipe(
          self, component, name=None, before=None, after=None, first=None, last=None
      ):
          """Add a component to the processing pipeline. Valid components are
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/language.py