  
      # test the content of the candidates
      assert mykb.get_candidates("adam")[0].entity_ == "Q2"
      assert mykb.get_candidates("adam")[0].alias_ == "adam"
      assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 0.2)
      assert_almost_equal(mykb.get_candidates("adam")[0].prior_prob, 0.9)
  
  
  def test_preserving_links_asdoc(nlp):
      """Test that Span.as_doc preserves the existing entity links"""
 
</s> remove     mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1])
    mykb.add_entity(entity="Q2", freq=0.8, entity_vector=[1])
 </s> add     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
    mykb.add_entity(entity="Q2", freq=8, entity_vector=[1]) </s> remove     assert 0.6999 < candidates[0].entity_freq < 0.701
 </s> add     assert 6.999 < candidates[0].entity_freq < 7.01 </s> remove     assert 0.199 < candidates[1].entity_freq < 0.201
 </s> add     assert 1.99 < candidates[1].entity_freq < 2.01 </s> remove                     type_vector = [0 for i in range(len(type_to_int))]
                    if len(type_to_int) > 0:
                        type_vector[type_to_int[gold_ent.label_]] = 1

                    # store data
                    entity_encodings.append(entity_encoding)
                    context_docs.append(doc)
                    type_vectors.append(type_vector)

                    if self.cfg.get("prior_weight", 1) > 0:
                        priors.append([prior_prob])
                    else:
                        priors.append([0])
 </s> add                 for kb_id, value in kb_dict.items():
                    # Currently only training on the positive instances
                    if value:
                        context_docs.append(doc) </s> remove     mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1, 2, 3])
 </s> add     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1, 2, 3])
    assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12)
  
      # test the content of the candidates
      assert mykb.get_candidates("adam")[0].entity_ == "Q2"
      assert mykb.get_candidates("adam")[0].alias_ == "adam"
    assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12)
      assert_almost_equal(mykb.get_candidates("adam")[0].prior_prob, 0.9)
  
  
  def test_preserving_links_asdoc(nlp):
      """Test that Span.as_doc preserves the existing entity links"""
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/tests/pipeline/test_entity_linker.py