  
      # 2. test blocking behaviour
      nlp2 = English()
      doc2 = nlp2("I live in New York")
      ner2 = EntityRecognizer(doc2.vocab)
  
      # set "New York" to a blocked entity
      doc2.ents = [(0, 3, 5)]
      assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
      assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
 
</s> remove     ner1 = EntityRecognizer(doc1.vocab)
 </s> add     ner1 = EntityRecognizer(doc1.vocab, default_ner()) </s> remove     ner2 = EntityRecognizer(doc.vocab)
 </s> add     ner2 = EntityRecognizer(doc.vocab, default_ner()) </s> remove def test_change_number_features():
    # Test the default number features
    nlp = English()
    ner = nlp.create_pipe("ner")
    nlp.add_pipe(ner)
    ner.add_label("PERSON")
    nlp.begin_training()
    assert ner.model.lower.get_dim("nF") == ner.nr_feature
    # Test we can change it
    nlp = English()
    ner = nlp.create_pipe("ner")
    nlp.add_pipe(ner)
    ner.add_label("PERSON")
    nlp.begin_training(
        component_cfg={"ner": {"nr_feature_tokens": 3, "token_vector_width": 128}}
    )
    assert ner.model.lower.get_dim("nF") == 3
    # Test the model runs
    nlp("hello world")


def test_overfitting():
 </s> add def test_overfitting_IO():
    ner2 = EntityRecognizer(doc2.vocab, default_ner())
  
      # 2. test blocking behaviour
      nlp2 = English()
      doc2 = nlp2("I live in New York")
    ner2 = EntityRecognizer(doc2.vocab, default_ner())
  
      # set "New York" to a blocked entity
      doc2.ents = [(0, 3, 5)]
      assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
      assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
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_ner.py