                          found_link = False
                          if parse_sitelinks:
                              site_value = obj["sitelinks"].get(site_filter, None)
                              if site_value:
                                  site = site_value['title']
                                  if to_print:
                                      print(site_filter, ":", site)
                                  title_to_id[site] = unique_id
                                  found_link = True
  
 
</s> remove                                         print("label (" + lang + "):", lang_label["value"])
 </s> add                                         print(
                                            "label (" + lang + "):", lang_label["value"]
                                        ) </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                                     cp_id = cp['mainsnak'].get('datavalue', {}).get('value', {}).get('id')
                                    cp_rank = cp['rank']
 </s> add                                     cp_id = (
                                        cp["mainsnak"]
                                        .get("datavalue", {})
                                        .get("value", {})
                                        .get("id")
                                    )
                                    cp_rank = cp["rank"] </s> remove                                         print("description (" + lang + "):", lang_descr["value"])
 </s> add                                         print(
                                            "description (" + lang + "):",
                                            lang_descr["value"],
                                        ) </s> remove     with bz2.open(wikidata_file, mode='rb') as file:
 </s> add     with bz2.open(wikidata_file, mode="rb") as file:
                                site = site_value["title"]
                          found_link = False
                          if parse_sitelinks:
                              site_value = obj["sitelinks"].get(site_filter, None)
                              if site_value:
                                site = site_value["title"]
                                  if to_print:
                                      print(site_filter, ":", site)
                                  title_to_id[site] = unique_id
                                  found_link = 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 
bin/wiki_entity_linking/wikidata_processor.py