input: Write a python function to build a sentiment analysis model to predict whether a given sentence is positive or negative.
  assert sentiment_model(['I love this movie', 'I hate this movie'])==[1, 0]
  assert sentiment_model(['This is the best!', 'This is the worst!'])==[1, 0]
  assert sentiment_model(['Amazing product', 'Terrible experience'])==[1, 0]
output: def sentiment_model(sentences):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Training data
      train_sentences = ['I love this movie', 'I hate this movie', 'This is the best!', 'This is the worst!', 'Amazing product', 'Terrible experience']
      train_labels = [1, 0, 1, 0, 1, 0]
  
      # Vectorize the sentences
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(train_sentences)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, train_labels)
  
      # Vectorize the input sentences
      X_test = vectorizer.transform(sentences)
  
      # Predict the sentiment
      predictions = model.predict(X_test)
      
      return predictions.tolist()