              )
              >> flatten_add_lengths
              >> Pooling(mean_pool)
              >> Residual(zero_init(Maxout(hidden_width, hidden_width)))
              >> zero_init(Affine(context_width, hidden_width))
          )
  
          model.tok2vec = tok2vec
  
      model.tok2vec = tok2vec
 
</s> remove 
    model.tok2vec = tok2vec
    model.tok2vec.nO = context_width
    model.nO = 1
 </s> add         model.nO = context_width </s> remove         tok2vec = (
            Tok2Vec(
 </s> add         tok2vec = Tok2Vec( </s> remove         model = (
            Affine(entity_width, entity_width + context_width + 1 + ner_types)
            >> Affine(1, entity_width, drop_factor=0.0)
            >> logistic
        )

 </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)
            >> zero_init(Affine(context_width, hidden_width, drop_factor=0.0))
              )
              >> flatten_add_lengths
              >> Pooling(mean_pool)
              >> Residual(zero_init(Maxout(hidden_width, hidden_width)))
            >> zero_init(Affine(context_width, hidden_width, drop_factor=0.0))
          )
  
          model.tok2vec = tok2vec
  
      model.tok2vec = tok2vec
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/_ml.py