              orth_variant_level=cfg["orth_variant_level"],
              gold_preproc=cfg["gold_preproc"],
              max_length=cfg["max_length"],
              ignore_misaligned=True,
          )
          for batch in util.minibatch_by_words(train_examples, size=cfg["batch_size"]):
              yield batch
  
  
  def create_evaluation_callback(nlp, optimizer, corpus, cfg):
      def evaluate():
 
</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                 if batch:
                    yield batch
                return
            batch_size -= count_words(example.doc)
            batch.append(example)
 </s> add                 if oversize:
                    examples = iter(oversize)
                    oversize = []
                    if batch:
                        yield batch
                    break
                else:
                    if batch:
                        yield batch
                    return
            n_words = count_words(example.doc)
            if n_words < (batch_size + tol_size):
                batch_size -= n_words
                batch.append(example)
            else:
                oversize.append(example) </s> remove def subdivide_batch(batch):
    return [batch]
 </s> add def subdivide_batch(batch, accumulate_gradient):
    batch = list(batch)
    batch.sort(key=lambda eg: len(eg.doc))
    sub_len = len(batch) // accumulate_gradient
    start = 0
    for i in range(accumulate_gradient):
        subbatch = batch[start : start + sub_len]
        if subbatch:
            yield subbatch
        start += len(subbatch)
    subbatch = batch[start : ]
    if subbatch:
        yield subbatch </s> remove         while batch_size >= 0:
 </s> add         if oversize:
            example = oversize.pop(0)
            n_words = count_words(example.doc)
            batch.append(example)
            batch_size -= n_words
        while batch_size >= 1: </s> remove         for subbatch in subdivide_batch(batch):
 </s> add         for subbatch in subdivide_batch(batch, accumulate_gradient):
        ))
        random.shuffle(train_examples)
        batches = util.minibatch_by_words(train_examples, size=cfg["batch_size"])
        for batch in batches:
              orth_variant_level=cfg["orth_variant_level"],
              gold_preproc=cfg["gold_preproc"],
              max_length=cfg["max_length"],
              ignore_misaligned=True,
        ))
        random.shuffle(train_examples)
        batches = util.minibatch_by_words(train_examples, size=cfg["batch_size"])
        for batch in batches:
        ))
        random.shuffle(train_examples)
        batches = util.minibatch_by_words(train_examples, size=cfg["batch_size"])
        for batch in batches:
              yield batch
  
  
  def create_evaluation_callback(nlp, optimizer, corpus, cfg):
      def evaluate():
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/cli/train_from_config.py