          initial ones. This is useful for keeping a pretrained model on-track,
          even if you're updating it with a smaller set of examples.
  
          docs (iterable): A batch of `Doc` objects.
          drop (float): The droput rate.
          sgd (callable): An optimizer.
          RETURNS (dict): Results from the update.
  
          EXAMPLE:
              >>> raw_text_batches = minibatch(raw_texts)
 
</s> remove         drop (float): The droput rate.
 </s> add         drop (float): The dropout rate. </s> remove     drop (float): The droput rate.
 </s> add     drop (float): The dropout rate. </s> remove     logger.info("STEP 1: loading model from {}".format(nlp_dir))
    nlp, kb = read_nlp_kb(nlp_dir, kb_path)
 </s> add     logger.info("STEP 1a: Loading model from {}".format(nlp_dir))
    nlp = spacy.load(nlp_dir)
    logger.info("STEP 1b: Loading KB from {}".format(kb_path))
    kb = read_kb(nlp, kb_path) </s> remove             pass  # ignore namespaces at the beginning of the string
 </s> add             pass  # ignore the entity if it points to a "meta" page </s> remove     # If the docs in the data require further processing with an entity linker, set el_pipe
 </s> add     """
    Evaluate the ent.kb_id_ annotations against the gold standard.
    Only evaluate entities that overlap between gold and NER, to isolate the performance of the NEL.
    If the docs in the data require further processing with an entity linker, set el_pipe.
    """
        drop (float): The dropout rate.
          initial ones. This is useful for keeping a pretrained model on-track,
          even if you're updating it with a smaller set of examples.
  
          docs (iterable): A batch of `Doc` objects.
        drop (float): The dropout rate.
          sgd (callable): An optimizer.
          RETURNS (dict): Results from the update.
  
          EXAMPLE:
              >>> raw_text_batches = minibatch(raw_texts)
keep keep keep keep replace keep keep keep keep keep
 KB extensions and better parsing of WikiData (#4375)

* fix overflow error on windows

* more documentation & logging fixes

* md fix

* 3 different limit parameters to play with execution time

* bug fixes directory locations

* small fixes

* exclude dev test articles from prior probabilities stats

* small fixes

* filtering wikidata entities, removing numeric and meta items

* adding aliases from wikidata also to the KB

* fix adding WD aliases

* adding also new aliases to previously added entities

* fixing comma's

* small doc fixes

* adding subclassof filtering

* append alias functionality in KB

* prevent appending the same entity-alias pair

* fix for appending WD aliases

* remove date filter

* remove unnecessary import

* small corrections and reformatting

* remove WD aliases for now (too slow)

* removing numeric entities from training and evaluation

* small fixes

* shortcut during prediction if there is only one candidate

* add counts and fscore logging, remove FP NER from evaluation

* fix entity_linker.predict to take docs instead of single sentences

* remove enumeration sentences from the WP dataset

* entity_linker.update to process full doc instead of single sentence

* spelling corrections and dump locations in readme

* NLP IO fix

* reading KB is unnecessary at the end of the pipeline

* small logging fix

* remove empty files 
spacy/language.py