old_contents: import spacy
  from interfaces.SentenceOperation import SentenceOperation
  from tasks.TaskTypes import TaskType
  
  
  class QuantitativeQuestion(SentenceOperation):
      tasks = [TaskType.QUESTION_ANSWERING, TaskType.QUESTION_GENERATION]
      languages = ["en"]
  
      def __init__(self):
          super().__init__()
          self.nlp = spacy.load("en_core_web_sm")
          self.quant_ques = ['many','far','close','long','much','old']
  
      def filter(self, sentence: str = None) -> bool:
          tokenized = self.nlp(sentence, disable=["parser", "tagger", "ner"])
          if 'how' == tokenized[0].text.lower() and tokenized[1].text.lower() in self.quant_ques:
              return True
          else:
              return False
  
  
  
  
new_contents: import spacy
  from interfaces.QuestionAnswerOperation import QuestionAnswerOperation
  from tasks.TaskTypes import TaskType
  
  
  class QuantitativeQuestion(QuestionAnswerOperation):
      tasks = [TaskType.QUESTION_ANSWERING, TaskType.QUESTION_GENERATION]
      languages = ["en"]
  
      def __init__(self):
          super().__init__()
          self.nlp = spacy.load("en_core_web_sm")
          # Covers the broad types of quant questions: distance , age , measurable , un-measurable
          self.quant_ques = ['many','far','close','long','much','old']
  
      def filter(self,context:str = None,question: str = None,answers:str = None) -> bool:
          tokenized = self.nlp(question, disable=["parser", "tagger", "ner"])
          if 'how' == tokenized[0].text.lower() and tokenized[1].text.lower() in self.quant_ques:
              return True
          else:
              return False
  
  
  
  
subject: Update filter.py