              default_tensorizer_config,
              default_tok2vec_config,
          )
  
          self.defaults = {
              "tagger": default_tagger_config(),
              "parser": default_parser_config(),
              "ner": default_ner_config(),
 
</s> remove         with nlp.use_params(optimizer.averages):
            dev_examples = list(
                corpus.dev_dataset(
                    nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True
                )
 </s> add         dev_examples = list(
            corpus.dev_dataset(
                nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True </s> remove             n_words = sum(len(ex.doc) for ex in dev_examples)
            start_time = timer()
            scorer = nlp.evaluate(dev_examples)
            end_time = timer()
            wps = n_words / (end_time - start_time)
            scores = scorer.scores
            # Calculate a weighted sum based on score_weights for the main score
            weights = cfg["score_weights"]
            weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
            scores["speed"] = wps
 </s> add         )
        n_words = sum(len(ex.doc) for ex in dev_examples)
        start_time = timer()
            
        if optimizer.averages:
            with nlp.use_params(optimizer.averages):
                scorer = nlp.evaluate(dev_examples, batch_size=32)
        else:
            scorer = nlp.evaluate(dev_examples, batch_size=32)
        end_time = timer()
        wps = n_words / (end_time - start_time)
        scores = scorer.scores
        # Calculate a weighted sum based on score_weights for the main score
        weights = cfg["score_weights"]
        weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
        scores["speed"] = wps </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         nO=hidden_width,
 </s> add         nO=hidden_width if use_upper else nO, </s> remove         nP=maxout_pieces,
 </s> add         nP=maxout_pieces
            default_simple_ner_config
              default_tensorizer_config,
              default_tok2vec_config,
            default_simple_ner_config
          )
  
          self.defaults = {
              "tagger": default_tagger_config(),
              "parser": default_parser_config(),
              "ner": default_ner_config(),
keep add keep keep 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/language.py