code: def _load_network(self):
      
      self.embeddings = load_embeddings(self.lang, type='cw', normalize=True)
      self.model = load_ner_model(lang=self.lang, version=2)
      first_layer, second_layer = self.model
  
      def predict_proba(input_):
        hidden = np.tanh(np.dot(first_layer, input_))
        hidden = np.hstack((hidden, np.ones((hidden.shape[0], 1))))
        output =  (second_layer *  hidden).sum(axis=1)
        output_ = 1.0/(1.0 + np.exp(-output))
        probs = output_/output_.sum()
        return probs
      return predict_proba
docs: Building the predictor out of the model.