  import datetime
  from os import listdir
  from random import shuffle
  
  from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
  
  from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
  
 
</s> remove         lowest_mse = None
 </s> add         highest_prob = None </s> remove         self.entity_encoder = self._simple_encoder(in_width=300, out_width=96)
        self.article_encoder = self._simple_encoder(in_width=300, out_width=96)
 </s> add         self.entity_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
        self.article_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
import numpy as np
  import datetime
  from os import listdir
  from random import shuffle
import numpy as np
  
  from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
  
  from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
  
keep keep add keep keep keep keep keep
 implement loss function using dot product and prob estimate per candidate cluster 
examples/pipeline/wiki_entity_linking/train_el.py