      E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
      E108 = ("As of spaCy v2.1, the pipe name `sbd` has been deprecated "
              "in favor of the pipe name `sentencizer`, which does the same "
              "thing. For example, use `nlp.create_pipeline('sentencizer')`")
      E109 = ("Model for component '{name}' not initialized. Did you forget to "
              "load a model, or forget to call begin_training()?")
      E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
      E111 = ("Pickling a token is not supported, because tokens are only views "
              "of the parent Doc and can't exist on their own. A pickled token "
              "would always have to include its Doc and Vocab, which has "
              "practically no advantage over pickling the parent Doc directly. "
 
</s> remove     E053 = ("Could not read meta.json from {path}")
 </s> add     E053 = ("Could not read {name} from {path}") </s> remove 
    @classmethod
    def Model(cls, nr_class=1, exclusive_classes=None, **cfg):
        if nr_class == 1:
            exclusive_classes = False
        if exclusive_classes is None:
            raise ValueError(
                "TextCategorizer Model must specify 'exclusive_classes'. "
                "This setting determines whether the model will output "
                "scores that sum to 1 for each example. If only one class "
                "is true for each example, you should set exclusive_classes=True. "
                "For 'multi_label' classification, set exclusive_classes=False."
            )
        if "embed_size" not in cfg:
            cfg["embed_size"] = util.env_opt("embed_size", 2000)
        if "token_vector_width" not in cfg:
            cfg["token_vector_width"] = util.env_opt("token_vector_width", 96)
        if cfg.get("architecture") == "bow":
            return build_bow_text_classifier(nr_class, exclusive_classes, **cfg)
        else:
            if "tok2vec" in cfg:
                tok2vec = cfg["tok2vec"]
            else:
                config = {
                    "width": cfg.get("token_vector_width", 96),
                    "embed_size": cfg.get("embed_size", 2000),
                    "pretrained_vectors": cfg.get("pretrained_vectors", None),
                    "window_size": cfg.get("window_size", 1),
                    "cnn_maxout_pieces": cfg.get("cnn_maxout_pieces", 3),
                    "subword_features": cfg.get("subword_features", True),
                    "char_embed": cfg.get("char_embed", False),
                    "conv_depth": cfg.get("conv_depth", 4),
                    "bilstm_depth": cfg.get("bilstm_depth", 0),
                }
                tok2vec = Tok2Vec(**config)
                return build_simple_cnn_text_classifier(
                    tok2vec,
                    nr_class,
                    exclusive_classes,
                    **cfg
                )

    @property
    def tok2vec(self):
        if self.model in (None, True, False):
            return None
        else:
            return self.model.tok2vec

    def __init__(self, vocab, model=True, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg): </s> remove     model = masked_language_model(nlp.vocab, model)
 </s> add     model = build_masked_language_model(nlp.vocab, model)
    E109 = ("Component '{name}' could not be run. Did you forget to "
            "call begin_training()?")
      E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
      E108 = ("As of spaCy v2.1, the pipe name `sbd` has been deprecated "
              "in favor of the pipe name `sentencizer`, which does the same "
              "thing. For example, use `nlp.create_pipeline('sentencizer')`")
    E109 = ("Component '{name}' could not be run. Did you forget to "
            "call begin_training()?")
    E109 = ("Component '{name}' could not be run. Did you forget to "
            "call begin_training()?")
      E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
      E111 = ("Pickling a token is not supported, because tokens are only views "
              "of the parent Doc and can't exist on their own. A pickled token "
              "would always have to include its Doc and Vocab, which has "
              "practically no advantage over pickling the parent Doc directly. "
keep keep keep keep 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/errors.py