  
  
  cdef class KnowledgeBase:
  
      def __init__(self, Vocab vocab, entity_vector_length=64):
          self.vocab = vocab
          self.mem = Pool()
          self.entity_vector_length = entity_vector_length
 
</s> remove     def __init__(self, Vocab vocab, entity_vector_length):
 </s> add     def __init__(self, Vocab vocab, entity_vector_length=64): </s> remove         self.sgd_context = None
        if not self.cfg.get("context_width"):
            self.cfg["context_width"] = 128
 </s> add  </s> remove     def __init__(self, nlp, input_dim, desc_width):
 </s> add     # Set min. acceptable loss to avoid a 'mean of empty slice' warning by numpy
    MIN_LOSS = 0.01

    # Reasonable default to stop training when things are not improving
    MAX_NO_IMPROVEMENT = 20

    def __init__(self, nlp, input_dim, desc_width, epochs=5):
    """A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
    to support entity linking of named entities to real-world concepts.

    DOCS: https://spacy.io/api/kb
    """
  
  
  cdef class KnowledgeBase:
    """A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
    to support entity linking of named entities to real-world concepts.

    DOCS: https://spacy.io/api/kb
    """
  
      def __init__(self, Vocab vocab, entity_vector_length=64):
          self.vocab = vocab
          self.mem = Pool()
          self.entity_vector_length = entity_vector_length
keep keep add 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/kb.pyx