          if len(self.pipeline):
              docs = list(docs)
          end_time = timer()
          for i, (doc, eg) in enumerate(zip(docs, examples)):
              if verbose:
                  print(doc)
              eg.predicted = doc
          results = scorer.score(examples)
          n_words = sum(len(eg.predicted) for eg in examples)
          results["speed"] = n_words / (end_time - start_time)
          return results
 
</s> remove             kwargs.setdefault("verbose", verbose)
 </s> add  </s> remove         verbose: bool = False,
 </s> add  </s> remove         scores: Iterable[str] = tuple(),
        default_score_weights: Dict[str, float] = SimpleFrozenDict(),
 </s> add  </s> remove     docs: Union[Iterable[Doc], Doc],
 </s> add     docs: Union[Iterable[Union[Doc, Span]], Doc, Span], </s> remove     scores = nlp.evaluate(dev_dataset, verbose=False)
 </s> add     scores = nlp.evaluate(dev_dataset)
            util.logger.debug(doc)
          if len(self.pipeline):
              docs = list(docs)
          end_time = timer()
          for i, (doc, eg) in enumerate(zip(docs, examples)):
            util.logger.debug(doc)
            util.logger.debug(doc)
              eg.predicted = doc
          results = scorer.score(examples)
          n_words = sum(len(eg.predicted) for eg in examples)
          results["speed"] = n_words / (end_time - start_time)
          return results
keep keep keep keep replace replace keep keep keep keep keep
 Update docs, types and API consistency 
spacy/language.py