      b = model.ops.alloc2f(nO, nP)
      pad = model.ops.alloc4f(1, nF, nO, nP)
  
      ops = model.ops
      scale = float(ops.xp.sqrt(1.0 / (nF * nI)))
      W = normal_init(ops, W.shape, mean=scale)
      model.set_param("W", W)
      model.set_param("b", b)
      model.set_param("pad", pad)
  
      ids = ops.alloc((5000, nF), dtype="f")
 
</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     model.initialize()
 </s> add  </s> remove         nO=hidden_width,
 </s> add         nO=hidden_width if use_upper else nO,
    W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
      b = model.ops.alloc2f(nO, nP)
      pad = model.ops.alloc4f(1, nF, nO, nP)
  
      ops = model.ops
    W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
    W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
      model.set_param("W", W)
      model.set_param("b", b)
      model.set_param("pad", pad)
  
      ids = ops.alloc((5000, nF), dtype="f")
keep keep keep keep replace replace 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/ml/_precomputable_affine.py