              frequency_list.append(freq)
              filtered_title_to_id[title] = entity
  
      print(len(title_to_id.keys()), "original titles")
      print("kept", len(filtered_title_to_id.keys()), " with frequency", min_entity_freq)
  
      print()
      print(" * train entity encoder", datetime.datetime.now())
      print()
      encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
 
</s> remove     print(" * train entity encoder", datetime.datetime.now())
 </s> add     print(now(), " * train entity encoder:") </s> remove     encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
 </s> add     encoder = EntityEncoder(nlp, input_dim, entity_vector_length) </s> remove     print(" * get entity embeddings", datetime.datetime.now())
 </s> add     print(now(), " * get entity embeddings:") </s> remove     print()
    print(" * adding", len(entity_list), "entities", datetime.datetime.now())
 </s> add     print(now(), " * adding", len(entity_list), "entities") </s> remove     print(" * _get_entity_frequencies", datetime.datetime.now())
 </s> add     print(now(), " *  get entity frequencies:")
    kept_nr = len(filtered_title_to_id.keys())
    print("kept", kept_nr, "entities with min. frequency", min_entity_freq)
              frequency_list.append(freq)
              filtered_title_to_id[title] = entity
  
      print(len(title_to_id.keys()), "original titles")
    kept_nr = len(filtered_title_to_id.keys())
    print("kept", kept_nr, "entities with min. frequency", min_entity_freq)
  
      print()
      print(" * train entity encoder", datetime.datetime.now())
      print()
      encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
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/kb_creator.py