class_docstr: Uses the GPT model to validate the similarities between pairs of related words.
class_docstr_tok: ["Uses","the","GPT","model","to","validate","the","similarities","between","pairs","of","related","words","."]
docstr: Constructor for GPTValidator.
  
  Args:
      target_score: The minimum score threshold for two words to be considered similar.
      criteria: A dictionary containing any specific criteria to be used in the validation process.
      model: The identifier of the GPT model to use for the validation.
docstr_tok: ["Constructor","for","GPTValidator",".","Args",":","target_score",":","The","minimum","score","threshold","for","two","words","to","be","considered","similar",".","criteria",":","A","dictionary","containing","any","specific","criteria","to","be","used","in","the","validation","process",".","model",":","The","identifier","of","the","GPT","model","to","use","for","the","validation","."]
code: def __init__(
      self,
      target_score: int,
      criteria: Dict[str, str] = None,
      model: str = "gpt-4",
  ) -> None:
      """
      Constructor for GPTValidator.
  
      Args:
          target_score: The minimum score threshold for two words to be considered similar.
          criteria: A dictionary containing any specific criteria to be used in the validation process.
          model: The identifier of the GPT model to use for the validation.
      """
      self._model = model
      self.criteria = criteria
      super().__init__(target_score)
  
imports: import os
  import re
  from abc import ABC, abstractmethod
  from collections.abc import Callable
  from typing import Dict, List, NamedTuple, Tuple, Union
  import numpy
  from logger_config import logger
  from openai import OpenAI
label_desc: Use image node_id 1 to create a new GPTValidator object from inherited base classes: QualityValidator with example: obj = GPTValidator(target_score, criteria, model)