  
  class EntityLinker(Pipe):
      """Pipeline component for named entity linking.
  
      DOCS: TODO
      """
      name = 'entity_linker'
      NIL = "NIL"  # string used to refer to a non-existing link
  
      @classmethod
 
</s> remove 
 </s> add     E152 = ("The `nlp` object should have a pre-trained `ner` component.")
    E153 = ("Either provide a path to a preprocessed training directory, "
            "or to the original Wikipedia XML dump.")
    E154 = ("Either the `nlp` model or the `vocab` should be specified.")
    E155 = ("The `nlp` object should have access to pre-trained word vectors, cf. "
            "https://spacy.io/usage/models#languages.") </s> remove     E018 = ("Can't retrieve string for hash '{hash_value}'.")
 </s> add     E018 = ("Can't retrieve string for hash '{hash_value}'. This usually refers "
            "to an issue with the `Vocab` or `StringStore`.") </s> remove     mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1])
    mykb.add_entity(entity="Q2", freq=0.2, entity_vector=[2])
    mykb.add_entity(entity="Q3", freq=0.5, entity_vector=[3])
 </s> add     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
    mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
    mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
    DOCS: https://spacy.io/api/entitylinker
  
  class EntityLinker(Pipe):
      """Pipeline component for named entity linking.
  
    DOCS: https://spacy.io/api/entitylinker
      """
      name = 'entity_linker'
      NIL = "NIL"  # string used to refer to a non-existing link
  
      @classmethod
keep keep keep keep 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