                                  alias=previous_alias,
                                  entities=selected_entities,
                                  probabilities=prior_probs,
                              )
                          except ValueError as e:
                              print(e)
                  total_count = 0
                  counts = []
                  entities = []
 
</s> remove         entity_encodings = []

        priors = []
        type_vectors = []

        type_to_int = self.cfg.get("type_to_int", dict())
 </s> add  </s> remove             while loss > self.STOP_THRESHOLD and start < len(descriptions):
 </s> add             while to_continue and start < len(descriptions): </s> remove             if losses is not None:
                losses[self.name] += loss
            return loss
        return 0
 </s> add         loss, gradients = get_cossim_loss(yh=scores, y=entity_encodings)
        loss = loss / len(entity_encodings)
        return loss, gradients </s> remove     with bz2.open(wikidata_file, mode='rb') as file:
 </s> add     with bz2.open(wikidata_file, mode="rb") as file: </s> remove             context_encodings, bp_context = self.model.tok2vec.begin_update(context_docs, drop=drop)
            mention_encodings = [list(context_encodings[i]) + list(entity_encodings[i]) + priors[i] + type_vectors[i]
                                 for i in range(len(entity_encodings))]
            pred, bp_mention = self.model.begin_update(self.model.ops.asarray(mention_encodings, dtype="float32"), drop=drop)
 </s> add     def get_similarity_loss(self, docs, golds, scores):
        entity_encodings = []
        for gold in golds:
            for entity, kb_dict in gold.links.items():
                for kb_id, value in kb_dict.items():
                    # this loss function assumes we're only using positive examples
                    if value:
                        entity_encoding = self.kb.get_vector(kb_id)
                        entity_encodings.append(entity_encoding)
                            cnt += 1
                                  alias=previous_alias,
                                  entities=selected_entities,
                                  probabilities=prior_probs,
                              )
                            cnt += 1
                          except ValueError as e:
                              print(e)
                  total_count = 0
                  counts = []
                  entities = []
keep keep keep add 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/kb_creator.py