      for _, annotations in TRAIN_DATA:
          for ent in annotations.get("entities"):
              ner.add_label(ent[2])
  
      # get names of other pipes to disable them during training
      pipe_exceptions = ["simple_ner"]
 
</s> remove         ner = nlp.get_pipe("ner")
 </s> add         ner = nlp.get_pipe("simple_ner") </s> remove     if "ner" not in nlp.pipe_names:
        ner = nlp.create_pipe("ner")
 </s> add     if "simple_ner" not in nlp.pipe_names:
        ner = nlp.create_pipe("simple_ner") </s> remove         return self.model.tok2vec
 </s> add         return self.model.get_ref("tok2vec") </s> remove                     drop=0.5,  # dropout - make it harder to memorise data
 </s> add                     drop=0.0,  # dropout - make it harder to memorise data </s> remove         for subbatch in subdivide_batch(batch):
 </s> add         for subbatch in subdivide_batch(batch, accumulate_gradient):
            print("Add label", ent[2])
      for _, annotations in TRAIN_DATA:
          for ent in annotations.get("entities"):
            print("Add label", ent[2])
              ner.add_label(ent[2])
  
      # get names of other pipes to disable them during training
      pipe_exceptions = ["simple_ner"]
keep add keep keep keep keep
 Adapt parser and NER for transformers (#5449)

* Draft layer for BILUO actions

* Fixes to biluo layer

* WIP on BILUO layer

* Add tests for BILUO layer

* Format

* Fix transitions

* Update test

* Link in the simple_ner

* Update BILUO tagger

* Update __init__

* Import simple_ner

* Update test

* Import

* Add files

* Add config

* Fix label passing for BILUO and tagger

* Fix label handling for simple_ner component

* Update simple NER test

* Update config

* Hack train script

* Update BILUO layer

* Fix SimpleNER component

* Update train_from_config

* Add biluo_to_iob helper

* Add IOB layer

* Add IOBTagger model

* Update biluo layer

* Update SimpleNER tagger

* Update BILUO

* Read random seed in train-from-config

* Update use of normal_init

* Fix normalization of gradient in SimpleNER

* Update IOBTagger

* Remove print

* Tweak masking in BILUO

* Add dropout in SimpleNER

* Update thinc

* Tidy up simple_ner

* Fix biluo model

* Unhack train-from-config

* Update setup.cfg and requirements

* Add tb_framework.py for parser model

* Try to avoid memory leak in BILUO

* Move ParserModel into spacy.ml, avoid need for subclass.

* Use updated parser model

* Remove incorrect call to model.initializre in PrecomputableAffine

* Update parser model

* Avoid divide by zero in tagger

* Add extra dropout layer in tagger

* Refine minibatch_by_words function to avoid oom

* Fix parser model after refactor

* Try to avoid div-by-zero in SimpleNER

* Fix infinite loop in minibatch_by_words

* Use SequenceCategoricalCrossentropy in Tagger

* Fix parser model when hidden layer

* Remove extra dropout from tagger

* Add extra nan check in tagger

* Fix thinc version

* Update tests and imports

* Fix test

* Update test

* Update tests

* Fix tests

* Fix test

Co-authored-by: Ines Montani <ines@ines.io> 
examples/training/train_ner.py