          self.vocab = vocab
          self.model = True
          self.kb = None
          self.cfg = dict(cfg)
          self.sgd_context = None
          if not self.cfg.get("context_width"):
              self.cfg["context_width"] = 128
  
      def set_kb(self, kb):
          self.kb = kb
  
      def require_model(self):
 
</s> remove     def __init__(self, Vocab vocab, entity_vector_length):
 </s> add     def __init__(self, Vocab vocab, entity_vector_length=64): </s> remove             self.sgd_context = self.create_optimizer()
 </s> add  </s> remove             self.encoder = (
                Affine(hidden_with, orig_width)
 </s> add             self.encoder = Affine(hidden_with, orig_width)
            self.model = self.encoder >> zero_init(
                Affine(orig_width, hidden_with, drop_factor=0.0)
          self.vocab = vocab
          self.model = True
          self.kb = None
          self.cfg = dict(cfg)



  
      def set_kb(self, kb):
          self.kb = kb
  
      def require_model(self):
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 
spacy/pipeline/pipes.pyx