          if (step - best_step) >= patience:
              break
  
  
  def subdivide_batch(batch):
      return [batch]
  
  
  def setup_printer(training, nlp):
      score_cols = training["scores"]
      score_widths = [max(len(col), 6) for col in score_cols]
 
</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         for subbatch in subdivide_batch(batch):
 </s> add         for subbatch in subdivide_batch(batch, accumulate_gradient): </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         scores = self.model.ops.flatten(scores)
        tag_index = {tag: i for i, tag in enumerate(self.labels)}
        cdef int idx = 0
        correct = numpy.zeros((scores.shape[0],), dtype="i")
        guesses = scores.argmax(axis=1)
        known_labels = numpy.ones((scores.shape[0], 1), dtype="f")
        for ex in examples:
            gold = ex.gold
            for tag in gold.tags:
                if tag is None:
                    correct[idx] = guesses[idx]
                elif tag in tag_index:
                    correct[idx] = tag_index[tag]
                else:
                    correct[idx] = 0
                    known_labels[idx] = 0.
                idx += 1
        correct = self.model.ops.xp.array(correct, dtype="i")
        d_scores = scores - to_categorical(correct, n_classes=scores.shape[1])
        d_scores *= self.model.ops.asarray(known_labels)
        loss = (d_scores**2).sum()
        docs = [ex.doc for ex in examples]
        d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
 </s> add         loss_func = SequenceCategoricalCrossentropy(names=self.labels)
        truths = [eg.gold.tags for eg in examples]
        d_scores, loss = loss_func(scores, truths)
        if self.model.ops.xp.isnan(loss):
            raise ValueError("nan value when computing loss")
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
          if (step - best_step) >= patience:
              break
  
  
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
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
  
  
  def setup_printer(training, nlp):
      score_cols = training["scores"]
      score_widths = [max(len(col), 6) for col in score_cols]
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