class_docstr: Word2Vec service
  
      Args:
          word2vec_model_path (str): the local path to the Word2Vec pre-trained model
class_docstr_tok: ["Word2Vec","service","Args",":","word2vec_model_path","(","str",")",":","the","local","path","to","the","Word2Vec","pre-trained","model"]
docstr:     Get the cosign similarity distance between the np words (only if 2)
  
  Args:
      noun_phrase (str): the noun-phrase
  
  Returns:
      float: cosign similarity distance between the np words (only if 2)
docstr_tok: ["Get","the","cosign","similarity","distance","between","the","np","words","(","only","if","2",")","Args",":","noun_phrase","(","str",")",":","the","noun-phrase","Returns",":","float",":","cosign","similarity","distance","between","the","np","words","(","only","if","2",")"]
code: def get_similarity_score(self, noun_phrase):
      """
          Get the cosign similarity distance between the np words (only if 2)
  
      Args:
          noun_phrase (str): the noun-phrase
  
      Returns:
          float: cosign similarity distance between the np words (only if 2)
      """
      candidates = noun_phrase.split(" ")
      if len(candidates) < 2:
          candidates.extend(candidates[0])
      if len(candidates) > 2:
          return -1
      try:
          if not self.model:
              return -1
          return self.model.similarity(candidates[0], candidates[1])
      except KeyError:
          return -1
  
imports: import nltk
  import nltk.corpus
  import numpy
  import requests
  from gensim.models.keyedvectors import KeyedVectors
  from nltk.corpus import wordnet
  from nltk.stem.snowball import SnowballStemmer
  from nlp_architect.utils.generic import license_prompt
label_desc: Use image node_id 4 for calling the Word2Vec obj's underlying member method code with example usage: obj.get_similarity_score(noun_phrase) and returns: unknown, self, unknown, unknown