  
      # Update tag map with provided mapping
      nlp.vocab.morphology.tag_map.update(tag_map)
  
      if vectors:
          msg.text(f"Loading vector from model '{vectors}'")
          _load_vectors(nlp, vectors)
  
      # Multitask objectives
      multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)]
      for pipe_name, multitasks in multitask_options:
          if multitasks:
              if pipe_name not in pipeline:
 
</s> remove                 pipe_cfg = {}
            nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
 </s> add                 raise ValueError(f"Component {pipe} currently not supported.")
            pipe_cfg = util.load_config(config_loc, create_objects=False)
            if vectors:
                pretrained_config = {'@architectures': 'spacy.VocabVectors.v1', 'name': vectors}
                pipe_cfg["model"]["tok2vec"]["pretrained_vectors"] = pretrained_config

            if pipe == "parser":
                pipe_cfg["learn_tokens"] = learn_tokens
            elif pipe == "textcat":
                pipe_cfg["exclusive_classes"] = not textcat_multilabel
                pipe_cfg["architecture"] = textcat_arch
                pipe_cfg["positive_label"] = textcat_positive_label

            pipe = nlp.create_pipe(pipe, config=pipe_cfg)
            nlp.add_pipe(pipe) </s> remove                 pipe_cfg = {
                    "exclusive_classes": not textcat_multilabel,
                    "architecture": textcat_arch,
                    "positive_label": textcat_positive_label,
                }
 </s> add                 config_loc = default_dir / "textcat_defaults.cfg" </s> remove         if self.model not in (True, False, None):
 </s> add         if self.model.has_dim("nO"):
  
      # Update tag map with provided mapping
      nlp.vocab.morphology.tag_map.update(tag_map)
  




      # Multitask objectives
      multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)]
      for pipe_name, multitasks in multitask_options:
          if multitasks:
              if pipe_name not in pipeline:
keep keep keep keep replace replace replace 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/cli/train.py