class_docstr: interface to Zero Shot Topic Classifier
docstr: ```
  ZeroShotClassifier constructor
  
  Args:
    model_name(str): name of a BART NLI model
    device(str): device to use (e.g., 'cuda', 'cpu')
    quantize(bool): If True, faster quantization will be used
  ```
code: def __init__(
      self,
      model_name="facebook/bart-large-mnli",
      device=None,
      quantize=False,
  ):
      """
      ```
      ZeroShotClassifier constructor
  
      Args:
        model_name(str): name of a BART NLI model
        device(str): device to use (e.g., 'cuda', 'cpu')
        quantize(bool): If True, faster quantization will be used
      ```
      """
      if "mnli" not in model_name and "xnli" not in model_name:
          raise ValueError(
              "ZeroShotClasifier requires an MNLI or XNLI model"
          )
  
      super().__init__(device=device, quantize=quantize)
      from transformers import (
          AutoModelForSequenceClassification,
          AutoTokenizer,
      )
  
      self.tokenizer = AutoTokenizer.from_pretrained(model_name)
      self.model = AutoModelForSequenceClassification.from_pretrained(
          model_name
      ).to(self.torch_device)
      if quantize:
          self.model = self.quantize_model(self.model)
  
file_path: ktrain/ktrain/text/zsl/core.py
imports: import math
  import warnings
  import numpy
  from ...None import utils
  from ...torch_base import TorchBase
label_desc: Use image node_id 1 to create a new ZeroShotClassifier object from inherited base classes: TorchBase with example: obj = ZeroShotClassifier(model_name, device, quantize)