      hidden_width: StrictInt,
      maxout_pieces: StrictInt,
      nO=None,
  ):
      token_vector_width = tok2vec.get_dim("nO")
      tok2vec = chain(
 
</s> remove     tok2vec = chain(tok2vec, list2array())
    tok2vec.set_dim("nO", token_vector_width)
 </s> add     tok2vec = chain(
        tok2vec,
        with_array(Linear(hidden_width, token_vector_width)),
        list2array(),
    )
    tok2vec.set_dim("nO", hidden_width) </s> remove         upper = model.upper
 </s> add         upper = model.get_ref("upper") </s> remove         nO=hidden_width,
 </s> add         nO=hidden_width if use_upper else nO, </s> remove @architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model.embed:width}
 </s> add @architectures = "spacy.HashEmbedCNN.v1"
width = 128
depth = 4
embed_size = 7000
maxout_pieces = 3
window_size = 1
subword_features = true
pretrained_vectors = null </s> remove [nlp.pipeline.tok2vec]
factory = "tok2vec"

[nlp.pipeline.tok2vec.model]
@architectures = "spacy.Tok2Vec.v1"

[nlp.pipeline.tok2vec.model.extract]
@architectures = "spacy.Doc2Feats.v1"
columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]

[nlp.pipeline.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
columns = ${nlp.pipeline.tok2vec.model.extract:columns}
width = 96
rows = 2000
use_subwords = true
pretrained_vectors = null

[nlp.pipeline.tok2vec.model.embed.mix]
@architectures = "spacy.LayerNormalizedMaxout.v1"
width = ${nlp.pipeline.tok2vec.model.embed:width}
maxout_pieces = 3

[nlp.pipeline.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = ${nlp.pipeline.tok2vec.model.embed:width}
window_size = 1
maxout_pieces = 3
depth = 2

 </s> add 
    use_upper=True,
      hidden_width: StrictInt,
      maxout_pieces: StrictInt,
    use_upper=True,
      nO=None,
  ):
      token_vector_width = tok2vec.get_dim("nO")
      tok2vec = chain(
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> 
spacy/ml/models/parser.py