          heads.append(head)
          deps.append(dep)
          ents.append(ent)
      doc = Doc(Vocab(), words=[t[1] for t in annot_tuples])
      parser = DependencyParser(doc.vocab)
      parser.moves.add_action(0, "")
      parser.moves.add_action(1, "")
      parser.moves.add_action(1, "")
      parser.moves.add_action(4, "ROOT")
      for i, (head, dep) in enumerate(zip(heads, deps)):
 
</s> remove     ner = EntityRecognizer(doc.vocab)
 </s> add     ner = EntityRecognizer(doc.vocab, default_ner()) </s> remove     ner2 = EntityRecognizer(doc.vocab)
 </s> add     ner2 = EntityRecognizer(doc.vocab, default_ner()) </s> remove     ner1 = EntityRecognizer(doc1.vocab)
 </s> add     ner1 = EntityRecognizer(doc1.vocab, default_ner()) </s> remove     ner = EntityRecognizer(en_vocab)
 </s> add     ner = EntityRecognizer(en_vocab, default_ner()) </s> remove     def get_loss(self, examples, scores):
 </s> add     def _examples_to_truth(self, examples):
    parser = DependencyParser(doc.vocab, default_parser())
          heads.append(head)
          deps.append(dep)
          ents.append(ent)
      doc = Doc(Vocab(), words=[t[1] for t in annot_tuples])
    parser = DependencyParser(doc.vocab, default_parser())
      parser.moves.add_action(0, "")
      parser.moves.add_action(1, "")
      parser.moves.add_action(1, "")
      parser.moves.add_action(4, "ROOT")
      for i, (head, dep) in enumerate(zip(heads, deps)):
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 
spacy/tests/parser/test_arc_eager_oracle.py