input: from typing import List
  
  def classify_string(input_string: str, data: List, target: List) -> int:
      """ Create a machine learning classifier using Multinomial Naive Bayes for categorizing user input.
      The classifier will be trained on the provided data and target, and will predict the category of the input string.
      >>> classify_string("example input", [["example", "input"], ["another", "example"]], [0, 1])
      1
      >>> classify_string("test string", [["test", "string"], ["different", "input"]], [1, 0])
      1
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(analyzer=lambda x: x), MultinomialNB())
      
      # Train the model
      model.fit(data, target)
      
      # Predict the category of the input string
      prediction = model.predict([input_string.split()])
      
      return prediction[0]