  
          context_encodings = self.model.tok2vec(docs)
          xp = get_array_module(context_encodings)
  
          type_to_int = self.cfg.get("type_to_int", dict())
  
          for i, doc in enumerate(docs):
              if len(doc) > 0:
                  # currently, the context is the same for each entity in a sentence (should be refined)
                  context_encoding = context_encodings[i]
                  for ent in doc.ents:
 
</s> remove         context_encodings = self.model.tok2vec(docs)
 </s> add         context_encodings = self.model(docs) </s> remove                     type_vector = [0 for i in range(len(type_to_int))]
                    if len(type_to_int) > 0:
                        type_vector[type_to_int[ent.label_]] = 1
 </s> add  </s> remove         entity_encodings = []

        priors = []
        type_vectors = []

        type_to_int = self.cfg.get("type_to_int", dict())
 </s> add  </s> remove                 for kb_id, value in kb_dict.items():
                    entity_encoding = self.kb.get_vector(kb_id)
                    prior_prob = self.kb.get_prior_prob(kb_id, mention)

                    gold_ent = ents_by_offset["{}_{}".format(start, end)]
                    if gold_ent is None:
                        raise RuntimeError(Errors.E147.format(method="update", msg="gold entity not found"))
 </s> add 
  
          context_encodings = self.model.tok2vec(docs)
          xp = get_array_module(context_encodings)
  


          for i, doc in enumerate(docs):
              if len(doc) > 0:
                  # currently, the context is the same for each entity in a sentence (should be refined)
                  context_encoding = context_encodings[i]
                  for ent in doc.ents:
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 
spacy/pipeline/pipes.pyx