                      doc_vector = self._get_doc_embedding(doc)
                      batch.append(doc_vector)
  
                  loss = self._update(batch)
                  print(i, batch_nr, loss)
                  processed += len(batch)
  
                  batch_nr += 1
                  start = start + self.BATCH_SIZE
                  stop = min(stop + self.BATCH_SIZE, len(descriptions))
 
</s> remove             while loss > self.STOP_THRESHOLD and start < len(descriptions):
 </s> add             while to_continue and start < len(descriptions): </s> remove         for i in range(self.EPOCHS):
 </s> add         for i in range(self.epochs): </s> remove                                             print("alias (" + lang + "):", item["value"])
 </s> add                                             print(
                                                "alias (" + lang + "):", item["value"]
                                            )
                if batch_nr % 25 == 0:
                    print("loss:", loss)
                      doc_vector = self._get_doc_embedding(doc)
                      batch.append(doc_vector)
  
                  loss = self._update(batch)
                if batch_nr % 25 == 0:
                    print("loss:", loss)
                  processed += len(batch)
  
                  batch_nr += 1
                  start = start + self.BATCH_SIZE
                  stop = min(stop + self.BATCH_SIZE, len(descriptions))
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 
bin/wiki_entity_linking/train_descriptions.py