          if name not in disable:
              config = meta.get("pipeline_args", {}).get(name, {})
              factory = factories.get(name, name)
              component = nlp.create_pipe(factory, config=config)
              nlp.add_pipe(component, name=name)
      return nlp.from_disk(model_path, exclude=disable)
  
 
</s> remove def create_nlp_from_config(lang, vectors, pipeline):
    lang_class = spacy.util.get_lang_class(lang)
    nlp = lang_class()
    if vectors is not None:
        spacy.cli.train._load_vectors(nlp, vectors)
    for name, component_cfg in pipeline.items():
        factory = component_cfg.pop("factory")
        component = nlp.create_pipe(factory, config=component_cfg)
        nlp.add_pipe(component, name=name)
    return nlp


 </s> add  </s> remove             doc = proc(doc, **component_cfg.get(name, {}))
 </s> add             try:
                doc = proc(doc, **component_cfg.get(name, {}))
            except KeyError:
                raise ValueError(Errors.E109.format(name=name)) </s> remove         self.cfg.setdefault("cnn_maxout_pieces", 2)
 </s> add         # TODO: remove - put in config
        self.cfg.setdefault("maxout_pieces", 2)
            if nlp_config.get(name, None):
                model_config = nlp_config[name]["model"]
                config["model"] = model_config
          if name not in disable:
              config = meta.get("pipeline_args", {}).get(name, {})
              factory = factories.get(name, name)
            if nlp_config.get(name, None):
                model_config = nlp_config[name]["model"]
                config["model"] = model_config
              component = nlp.create_pipe(factory, config=config)
              nlp.add_pipe(component, name=name)
      return nlp.from_disk(model_path, exclude=disable)
  
keep keep add 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/util.py