class_docstr: TextClassificationModel to be trained on two- or three-class textual entailment data, to be used for zero shot intent recognition.
class_docstr_tok: ["TextClassificationModel","to","be","trained","on","two-","or","three-class","textual","entailment","data",",","to","be","used","for","zero","shot","intent","recognition","."]
code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      super().__init__(cfg=cfg, trainer=trainer)
  
imports: import os
  from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.nlp.data.zero_shot_intent_recognition.zero_shot_intent_dataset import ZeroShotIntentDataset, ZeroShotIntentInferenceDataset, calc_class_weights_from_dataloader
  from nemo.collections.nlp.models import TextClassificationModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new ZeroShotIntentModel object from inherited base classes: TextClassificationModel with example: obj = ZeroShotIntentModel(cfg, trainer)