          for multitask in self._multitasks:
              multitask.update(examples, drop=drop, sgd=sgd)
          # The probability we use beam update, instead of falling back to
          # a greedy update
          beam_update_prob = self.cfg.get('beam_update_prob', 0.5)
          if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() < beam_update_prob:
              return self.update_beam(examples, self.cfg.get('beam_width', 1),
                      drop=drop, sgd=sgd, losses=losses, set_annotations=set_annotations,
                      beam_density=self.cfg.get('beam_density', 0.001))
  
          set_dropout_rate(self.model, drop)
          # Chop sequences into lengths of this many transitions, to make the
 
</s> remove             beam_width = self.cfg.get('beam_width', 1)
 </s> add             beam_width = self.cfg['beam_width'] </s> remove             beam_width = self.cfg.get('beam_width', 1)
 </s> add             beam_width = self.cfg['beam_width']
        beam_update_prob = self.cfg['beam_update_prob']
        if self.cfg['beam_width'] >= 2 and numpy.random.random() < beam_update_prob:
            return self.update_beam(examples, self.cfg['beam_width'],
          for multitask in self._multitasks:
              multitask.update(examples, drop=drop, sgd=sgd)
          # The probability we use beam update, instead of falling back to
          # a greedy update
        beam_update_prob = self.cfg['beam_update_prob']
        if self.cfg['beam_width'] >= 2 and numpy.random.random() < beam_update_prob:
            return self.update_beam(examples, self.cfg['beam_width'],
        beam_update_prob = self.cfg['beam_update_prob']
        if self.cfg['beam_width'] >= 2 and numpy.random.random() < beam_update_prob:
            return self.update_beam(examples, self.cfg['beam_width'],
        beam_update_prob = self.cfg['beam_update_prob']
        if self.cfg['beam_width'] >= 2 and numpy.random.random() < beam_update_prob:
            return self.update_beam(examples, self.cfg['beam_width'],
                      drop=drop, sgd=sgd, losses=losses, set_annotations=set_annotations,
                      beam_density=self.cfg.get('beam_density', 0.001))
  
          set_dropout_rate(self.model, drop)
          # Chop sequences into lengths of this many transitions, to make the
keep keep keep keep 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/syntax/nn_parser.pyx