      This entity vector will be stored in the KB, for further downstream use in the entity model.
      """
  
      DROP = 0
      EPOCHS = 5
      STOP_THRESHOLD = 0.04
  
      BATCH_SIZE = 1000
  
      def __init__(self, nlp, input_dim, desc_width):
          self.nlp = nlp
          self.input_dim = input_dim
 
</s> remove     def __init__(self, nlp, input_dim, desc_width):
 </s> add     # Set min. acceptable loss to avoid a 'mean of empty slice' warning by numpy
    MIN_LOSS = 0.01

    # Reasonable default to stop training when things are not improving
    MAX_NO_IMPROVEMENT = 20

    def __init__(self, nlp, input_dim, desc_width, epochs=5): </s> remove                         # this will set the prior probabilities to 0 (just like in training) if their weight is 0
                        prior_probs = xp.asarray([[c.prior_prob] for c in candidates])
                        prior_probs *= self.cfg.get("prior_weight", 1)
 </s> add                         # this will set all prior probabilities to 0 if they should be excluded from the model
                        prior_probs = xp.asarray([c.prior_prob for c in candidates])
                        if not self.cfg.get("incl_prior", True):
                            prior_probs = xp.asarray([[0.0] for c in candidates])
      This entity vector will be stored in the KB, for further downstream use in the entity model.
      """
  
      DROP = 0



      BATCH_SIZE = 1000
  
      def __init__(self, nlp, input_dim, desc_width):
          self.nlp = nlp
          self.input_dim = input_dim
keep keep keep keep replace replace replace keep keep keep keep keep
 CLI scripts for entity linking (wikipedia & generic) (#4091)

* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* turn kb_creator into CLI script (wip)

* proper parameters for training entity vectors

* wikidata pipeline split up into two executable scripts

* remove context_width

* move wikidata scripts in bin directory, remove old dummy script

* refine KB script with logs and preprocessing options

* small edits

* small improvements to logging of EL CLI script 
bin/wiki_entity_linking/train_descriptions.py