      candidates = sorted(kb.get_candidates('double07'), key=lambda x: x.entity_)
      assert len(candidates) == 2
  
      assert candidates[0].entity_ == 'Q007'
      assert 0.6999 < candidates[0].entity_freq < 0.701
      assert candidates[0].entity_vector == [0, 0, 7]
      assert candidates[0].alias_ == 'double07'
      assert 0.899 < candidates[0].prior_prob < 0.901
  
      assert candidates[1].entity_ == 'Q17'
 
</s> remove     assert 0.199 < candidates[1].entity_freq < 0.201
 </s> add     assert 1.99 < candidates[1].entity_freq < 2.01 </s> remove     assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 0.2)
 </s> add     assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12) </s> remove             if cnt % 500000 == 0:
                print(datetime.datetime.now(), "processed", cnt, "lines of WikiData dump")
 </s> add             if cnt % 1000000 == 0:
                print(
                    datetime.datetime.now(), "processed", cnt, "lines of WikiData JSON dump"
                ) </s> remove         while line:
            if cnt % 5000000 == 0:
                print(now(), "processed", cnt, "lines of Wikipedia dump")
 </s> add         while line and (not limit or cnt < limit):
            if cnt % 25000000 == 0:
                print(now(), "processed", cnt, "lines of Wikipedia XML dump") </s> remove     with bz2.open(wikidata_file, mode='rb') as file:
 </s> add     with bz2.open(wikidata_file, mode="rb") as file:
    assert 6.999 < candidates[0].entity_freq < 7.01
      candidates = sorted(kb.get_candidates('double07'), key=lambda x: x.entity_)
      assert len(candidates) == 2
  
      assert candidates[0].entity_ == 'Q007'
    assert 6.999 < candidates[0].entity_freq < 7.01
      assert candidates[0].entity_vector == [0, 0, 7]
      assert candidates[0].alias_ == 'double07'
      assert 0.899 < candidates[0].prior_prob < 0.901
  
      assert candidates[1].entity_ == 'Q17'
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/serialize/test_serialize_kb.py