class_docstr: Named entity extractor.
class_docstr_tok: ["Named","entity","extractor","."]
docstr: Building the predictor out of the model.
docstr_tok: ["Building","the","predictor","out","of","the","model","."]
code: def _load_network(self):
      """Building the predictor out of the model."""
      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
  
imports: import numpy
  from six.moves import range
  from ..decorators import memoize
  from ..load import load_embeddings, load_ner_model, load_pos_model, load_unified_pos_model
label_desc: Use image node_id 2 for calling the NEChunker obj's underlying member method code with example usage: obj._load_network() and returns: predict_proba, probs