text: #!/usr/bin/env python
  # encoding: utf-8
  
  # Copyright (c) 2021 Grant Hadlich
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  # THE SOFTWARE. 
  import sys
  sys.path.append("../sentimentmodels")
  sys.path.append("./sentimentmodels")
  
  from vader import VaderModel
  from naive_bayes import NaiveBayesModel
  from transformer import TransformerModel
  from flair_model import FlairModel
  from distilbert import DistilBertModel
  from textblob_model import TextBlobModel
  
  def get_models():
      models = [TransformerModel(), VaderModel(), NaiveBayesModel(), FlairModel(), DistilBertModel(), TextBlobModel()]
  
      return models
  
  if __name__ == "__main__":
  
      text = "Sold! Enjoying with an ice cold @GuinnessIreland right now; Much love, Happy St. Patrick’s Day, and many thanks, folks! 🍀🍻🤘"
  
      for model in get_models():
          pred, score = model.predict(text)
  
          print("Model: " + model.name() + " Text: \"" + text + "\" is " + pred + " with a score of " + str(round(100*abs(float(score)), 2)) + "%")
  