                          final_tensors.append(context_encoding)
                      else:
                          random.shuffle(candidates)
  
                          # this will set the prior probabilities to 0 (just like in training) if their weight is 0
                          prior_probs = xp.asarray([[c.prior_prob] for c in candidates])
                          prior_probs *= self.cfg.get("prior_weight", 1)
                          scores = prior_probs
  
                          if self.cfg.get("context_weight", 1) > 0:
                              entity_encodings = xp.asarray([c.entity_vector for c in candidates])
                              if len(entity_encodings) != len(prior_probs):
 
</s> remove                         if self.cfg.get("context_weight", 1) > 0:
 </s> add                         # add in similarity from the context
                        if self.cfg.get("incl_context", True): </s> remove                             mention_encodings = [list(context_encoding) + list(entity_encodings[i])
                                                 + list(prior_probs[i]) + type_vector
                                                 for i in range(len(entity_encodings))]
                            scores = self.model(self.model.ops.asarray(mention_encodings, dtype="float32"))
 </s> add                              # cosine similarity
                            sims = xp.dot(entity_encodings, context_enc_t) / (norm_1 * norm_2)
                            scores = prior_probs + sims - (prior_probs*sims) </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                     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 
                        # this will set all prior probabilities to 0 if they should be excluded from the model
                        prior_probs = xp.asarray([c.prior_prob for c in candidates])
                        if not self.cfg.get("incl_prior", True):
                            prior_probs = xp.asarray([[0.0] for c in candidates])
                          final_tensors.append(context_encoding)
                      else:
                          random.shuffle(candidates)
  
                        # this will set all prior probabilities to 0 if they should be excluded from the model
                        prior_probs = xp.asarray([c.prior_prob for c in candidates])
                        if not self.cfg.get("incl_prior", True):
                            prior_probs = xp.asarray([[0.0] for c in candidates])
                        # this will set all prior probabilities to 0 if they should be excluded from the model
                        prior_probs = xp.asarray([c.prior_prob for c in candidates])
                        if not self.cfg.get("incl_prior", True):
                            prior_probs = xp.asarray([[0.0] for c in candidates])
                        # this will set all prior probabilities to 0 if they should be excluded from the model
                        prior_probs = xp.asarray([c.prior_prob for c in candidates])
                        if not self.cfg.get("incl_prior", True):
                            prior_probs = xp.asarray([[0.0] for c in candidates])
                          scores = prior_probs
  
                          if self.cfg.get("context_weight", 1) > 0:
                              entity_encodings = xp.asarray([c.entity_vector for c in candidates])
                              if len(entity_encodings) != len(prior_probs):
keep keep keep keep replace 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