  
      el_pipe = nlp.create_pipe(name="entity_linker", config={"context_width": 64})
      el_pipe.set_kb(mykb)
      el_pipe.begin_training()
      el_pipe.context_weight = 0
      el_pipe.prior_weight = 1
      nlp.add_pipe(el_pipe, last=True)
  
      # test whether the entity links are preserved by the `as_doc()` function
      text = "She lives in Boston. He lives in Denver."
      doc = nlp(text)
 
</s> remove     el_pipe = nlp.create_pipe(name="entity_linker", config={"context_width": 64})
 </s> add     el_pipe = nlp.create_pipe(name="entity_linker") </s> remove         type_to_int = self.cfg.get("type_to_int", dict())

 </s> add  </s> remove     EPOCHS = 5
    STOP_THRESHOLD = 0.04

 </s> add  </s> remove             while loss > self.STOP_THRESHOLD and start < len(descriptions):
 </s> add             while to_continue and start < len(descriptions): </s> remove     print(" * _get_entity_frequencies", datetime.datetime.now())
 </s> add     print(now(), " *  get entity frequencies:")
    el_pipe.incl_context = False
    el_pipe.incl_prior = True
  
      el_pipe = nlp.create_pipe(name="entity_linker", config={"context_width": 64})
      el_pipe.set_kb(mykb)
      el_pipe.begin_training()
    el_pipe.incl_context = False
    el_pipe.incl_prior = True
    el_pipe.incl_context = False
    el_pipe.incl_prior = True
      nlp.add_pipe(el_pipe, last=True)
  
      # test whether the entity links are preserved by the `as_doc()` function
      text = "She lives in Boston. He lives in Denver."
      doc = nlp(text)
keep keep keep keep 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/tests/pipeline/test_entity_linker.py