  def now():
      return datetime.datetime.now()
  
  
  def read_prior_probs(wikipedia_input, prior_prob_output):
      """
      Read the XML wikipedia data and parse out intra-wiki links to estimate prior probabilities.
      The full file takes about 2h to parse 1100M lines.
      It works relatively fast because it runs line by line, irrelevant of which article the intrawiki is from.
      """
 
</s> remove     _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None)
 </s> add     _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=limit) </s> remove     lang = 'en'
    site_filter = 'enwiki'
 </s> add     lang = "en"
    site_filter = "enwiki"
def read_prior_probs(wikipedia_input, prior_prob_output, limit=None):
  def now():
      return datetime.datetime.now()
  
  
def read_prior_probs(wikipedia_input, prior_prob_output, limit=None):
      """
      Read the XML wikipedia data and parse out intra-wiki links to estimate prior probabilities.
      The full file takes about 2h to parse 1100M lines.
      It works relatively fast because it runs line by line, irrelevant of which article the intrawiki is from.
      """
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/wikipedia_processor.py