      with nlp.disable_pipes(*other_pipes):  # only train textcat
          optimizer = nlp.begin_training()
          if init_tok2vec is not None:
              with init_tok2vec.open("rb") as file_:
                  textcat.model.tok2vec.from_bytes(file_.read())
          print("Training the model...")
          print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
          batch_sizes = compounding(4.0, 32.0, 1.001)
          for i in range(n_iter):
              losses = {}
 
</s> remove         textcat.model.tok2vec.from_bytes(tok2vec_weights)
 </s> add         textcat.model.get_ref("tok2vec").from_bytes(tok2vec_weights) </s> remove     # Load in pretrained weights
 </s> add     # Load in pretrained weights (TODO: this may be broken in the config rewrite) </s> remove             if self.model is True:
                self.model, cfg = self.Model(**self.cfg)
            else:
                cfg = {}
 </s> add  </s> remove     assert losses["textcat"] < 0.00001
 </s> add     assert losses["textcat"] < 0.01 </s> remove         self.require_model()
 </s> add 
                textcat.model.get_ref("tok2vec").from_bytes(file_.read())
      with nlp.disable_pipes(*other_pipes):  # only train textcat
          optimizer = nlp.begin_training()
          if init_tok2vec is not None:
              with init_tok2vec.open("rb") as file_:
                textcat.model.get_ref("tok2vec").from_bytes(file_.read())
          print("Training the model...")
          print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
          batch_sizes = compounding(4.0, 32.0, 1.001)
          for i in range(n_iter):
              losses = {}
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 
examples/training/train_textcat.py