      ]
      ruler.add_patterns(patterns)
      nlp.add_pipe(ruler)
  
      el_pipe = nlp.create_pipe(name="entity_linker", config={"context_width": 64})
      el_pipe.set_kb(mykb)
      el_pipe.begin_training()
      el_pipe.context_weight = 0
      el_pipe.prior_weight = 1
      nlp.add_pipe(el_pipe, last=True)
 
</s> remove     el_pipe.context_weight = 0
    el_pipe.prior_weight = 1
 </s> add     el_pipe.incl_context = False
    el_pipe.incl_prior = True </s> remove         for i in range(self.EPOCHS):
 </s> add         for i in range(self.epochs): </s> remove                                 cp_dicts = [cp['mainsnak']['datavalue'].get('value') for cp in claim_property
                                            if cp['mainsnak'].get('datavalue')]
                                cp_values = [cp_dict.get('id') for cp_dict in cp_dicts if isinstance(cp_dict, dict)
                                             if cp_dict.get('id') is not None]
 </s> add                                 cp_dicts = [
                                    cp["mainsnak"]["datavalue"].get("value")
                                    for cp in claim_property
                                    if cp["mainsnak"].get("datavalue")
                                ]
                                cp_values = [
                                    cp_dict.get("id")
                                    for cp_dict in cp_dicts
                                    if isinstance(cp_dict, dict)
                                    if cp_dict.get("id") is not None
                                ] </s> remove         descriptions = description_list.copy()   # copy this list so that shuffling does not affect other functions
 </s> add         # copy this list so that shuffling does not affect other functions
        descriptions = description_list.copy()
        to_continue = True
    el_pipe = nlp.create_pipe(name="entity_linker")
      ]
      ruler.add_patterns(patterns)
      nlp.add_pipe(ruler)
  
    el_pipe = nlp.create_pipe(name="entity_linker")
      el_pipe.set_kb(mykb)
      el_pipe.begin_training()
      el_pipe.context_weight = 0
      el_pipe.prior_weight = 1
      nlp.add_pipe(el_pipe, last=True)
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 
spacy/tests/pipeline/test_entity_linker.py