  
      def _build_network(self, orig_width, hidden_with):
          with Model.define_operators({">>": chain}):
              # very simple encoder-decoder model
              self.encoder = (
                  Affine(hidden_with, orig_width)
              )
              self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
          self.sgd = create_default_optimizer(self.model.ops)
  
      def _update(self, vectors):
 
</s> remove             self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
 </s> add  </s> remove         predictions, bp_model = self.model.begin_update(np.asarray(vectors), drop=self.DROP)
 </s> add         predictions, bp_model = self.model.begin_update(
            np.asarray(vectors), drop=self.DROP
        ) </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     context_width = cfg.get("context_width")
    entity_width = cfg.get("entity_width")
 </s> add     context_width = cfg.get("entity_width")
            self.encoder = Affine(hidden_with, orig_width)
            self.model = self.encoder >> zero_init(
                Affine(orig_width, hidden_with, drop_factor=0.0)
  
      def _build_network(self, orig_width, hidden_with):
          with Model.define_operators({">>": chain}):
              # very simple encoder-decoder model
            self.encoder = Affine(hidden_with, orig_width)
            self.model = self.encoder >> zero_init(
                Affine(orig_width, hidden_with, drop_factor=0.0)
            self.encoder = Affine(hidden_with, orig_width)
            self.model = self.encoder >> zero_init(
                Affine(orig_width, hidden_with, drop_factor=0.0)
              )
              self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
          self.sgd = create_default_optimizer(self.model.ops)
  
      def _update(self, vectors):
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 
bin/wiki_entity_linking/train_descriptions.py