              for i in range(self.length):
                  self.c[i].ent_type = 0
                  self.c[i].ent_iob = 0  # Means missing.
              cdef attr_t ent_type
              cdef int start, end
              for ent_info in ents:
                  ent_type, start, end = get_entity_info(ent_info)
                  if ent_type is None or ent_type < 0:
 
</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  </s> remove         for i in range(self.EPOCHS):
 </s> add         for i in range(self.epochs): </s> remove             while loss > self.STOP_THRESHOLD and start < len(descriptions):
 </s> add             while to_continue and start < len(descriptions): </s> remove                         # 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)
 </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]) </s> remove         while line:
            if cnt % 5000000 == 0:
                print(now(), "processed", cnt, "lines of Wikipedia dump")
 </s> add         while line and (not limit or cnt < limit):
            if cnt % 25000000 == 0:
                print(now(), "processed", cnt, "lines of Wikipedia XML dump")
                self.c[i].ent_kb_id = 0
              for i in range(self.length):
                  self.c[i].ent_type = 0
                self.c[i].ent_kb_id = 0
                  self.c[i].ent_iob = 0  # Means missing.
              cdef attr_t ent_type
              cdef int start, end
              for ent_info in ents:
                  ent_type, start, end = get_entity_info(ent_info)
                  if ent_type is None or ent_type < 0:
keep add keep 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/tokens/doc.pyx