old_contents: from rasa_nlu import Interpreter
  
  import cloudpickle
  import spacy
  from rasa_nlu.featurizers.spacy_featurizer import SpacyFeaturizer
  from rasa_nlu.extractors.spacy_entity_extractor import SpacyEntityExtractor
  
  
  class SpacySklearnInterpreter(Interpreter):
  
      def __init__(self, entity_extractor=None, intent_classifier=None, language_name='en', **kwargs):
          self.nlp = spacy.load(language_name, tagger=False, parser=False, entity=False, matcher=False)
          self.featurizer = SpacyFeaturizer(self.nlp)
          with open(intent_classifier, 'rb') as f:
              self.classifier = cloudpickle.load(f)
  
          self.extractor = SpacyEntityExtractor(self.nlp, entity_extractor)
  
      def get_intent(self, text):
          X = self.featurizer.create_bow_vecs([text])
          return self.classifier.predict(X)[0]
  
      def parse(self, text):
          intent = self.get_intent(text)
          entities = self.extractor.extract_entities(self.nlp, text)
  
          return {'text': text, 'intent': intent, 'entities': entities}
  
new_contents: from rasa_nlu import Interpreter
  
  import cloudpickle
  import spacy
  from rasa_nlu.featurizers.spacy_featurizer import SpacyFeaturizer
  from rasa_nlu.extractors.spacy_entity_extractor import SpacyEntityExtractor
  
  
  class SpacySklearnInterpreter(Interpreter):
  
      def __init__(self, entity_extractor=None, intent_classifier=None, language_name='en', **kwargs):
          self.nlp = spacy.load(language_name, parser=False, entity=False, matcher=False)
          self.featurizer = SpacyFeaturizer(self.nlp)
          with open(intent_classifier, 'rb') as f:
              self.classifier = cloudpickle.load(f)
  
          self.extractor = SpacyEntityExtractor(self.nlp, entity_extractor)
  
      def get_intent(self, text):
          X = self.featurizer.create_bow_vecs([text])
          return self.classifier.predict(X)[0]
  
      def parse(self, text):
          intent = self.get_intent(text)
          entities = self.extractor.extract_entities(self.nlp, text)
  
          return {'text': text, 'intent': intent, 'entities': entities}
  
subject: load tagger for spacy nlp obj when running model