  
  
  def train_textcat(nlp, n_texts, n_iter=10):
      textcat = nlp.get_pipe("textcat")
      tok2vec_weights = textcat.model.tok2vec.to_bytes()
      (train_texts, train_cats), (dev_texts, dev_cats) = load_textcat_data(limit=n_texts)
      print(
          "Using {} examples ({} training, {} evaluation)".format(
              n_texts, len(train_texts), len(dev_texts)
          )
 
</s> remove         self.require_model()
 </s> add  </s> remove                 pipe_cfg = {"learn_tokens": learn_tokens}
 </s> add                 pipe_cfg["learn_tokens"] = learn_tokens </s> remove                 textcat.model.tok2vec.from_bytes(file_.read())
 </s> add                 textcat.model.get_ref("tok2vec").from_bytes(file_.read())
    tok2vec_weights = textcat.model.get_ref("tok2vec").to_bytes()
  
  
  def train_textcat(nlp, n_texts, n_iter=10):
      textcat = nlp.get_pipe("textcat")
    tok2vec_weights = textcat.model.get_ref("tok2vec").to_bytes()
      (train_texts, train_cats), (dev_texts, dev_cats) = load_textcat_data(limit=n_texts)
      print(
          "Using {} examples ({} training, {} evaluation)".format(
              n_texts, len(train_texts), len(dev_texts)
          )
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/pretrain_textcat.py