      lr=("Learning rate (default 0.005)", "option", "n", float),
      l2=("L2 regularization", "option", "r", float),
      train_inst=("# training instances (default 90% of all)", "option", "t", int),
      dev_inst=("# test instances (default 10% of all)", "option", "d", int),
  )
  def main(
      dir_kb,
      output_dir=None,
      loc_training=None,
 
</s> remove     descriptions_from_wikipedia=("Flag for using wp descriptions not wd", "flag", "wp"),
    limit=("Optional threshold to limit lines read from dumps", "option", "l", int),
    lang=("Optional language for which to get wikidata titles. Defaults to 'en'", "option", "la", str),
 </s> add     descr_from_wp=("Flag for using wp descriptions not wd", "flag", "wp"),
    limit_prior=("Threshold to limit lines read from WP for prior probabilities", "option", "lp", int),
    limit_train=("Threshold to limit lines read from WP for training set", "option", "lt", int),
    limit_wd=("Threshold to limit lines read from WD", "option", "lw", int),
    lang=("Optional language for which to get Wikidata titles. Defaults to 'en'", "option", "la", str), </s> remove     # for testing, get all pos instances, whether or not they are in the kb
    dev_data = training_set_creator.read_training(
 </s> add     # for testing, get all pos instances (independently of KB)
    dev_data = wikipedia_processor.read_training( </s> remove                         context_docs.append(doc)
 </s> add                         sentence_docs.append(ent.sent.as_doc()) </s> remove         context_encodings, bp_context = self.model.begin_update(context_docs, drop=drop)
        loss, d_scores = self.get_similarity_loss(scores=context_encodings, golds=golds, docs=None)
 </s> add         sentence_encodings, bp_context = self.model.begin_update(sentence_docs, drop=drop)
        loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds, docs=None)
    labels_discard=("NER labels to discard (default None)", "option", "l", str),
      lr=("Learning rate (default 0.005)", "option", "n", float),
      l2=("L2 regularization", "option", "r", float),
      train_inst=("# training instances (default 90% of all)", "option", "t", int),
      dev_inst=("# test instances (default 10% of all)", "option", "d", int),
    labels_discard=("NER labels to discard (default None)", "option", "l", str),
  )
  def main(
      dir_kb,
      output_dir=None,
      loc_training=None,
keep keep keep add 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 
bin/wiki_entity_linking/wikidata_train_entity_linker.py