docstr: Generate the card data to be used for the `ArgillaModelCard`.
  
  Args:
      card_data_kwargs: Extra arguments provided by the user when creating the `ArgillaTrainer`.
  
  Returns:
      TRLModelCardData: Container for the data to be written on the `ArgillaModelCard`.
docstr_tok: ["Generate","the","card","data","to","be","used","for","the","`","ArgillaModelCard","`",".","Args",":","card_data_kwargs",":","Extra","arguments","provided","by","the","user","when","creating","the","`","ArgillaTrainer","`",".","Returns",":","TRLModelCardData",":","Container","for","the","data","to","be","written","on","the","`","ArgillaModelCard","`","."]
code: def get_model_card_data(
      self, **card_data_kwargs
  ) -> "TRLModelCardData":
      """
      Generate the card data to be used for the `ArgillaModelCard`.
  
      Args:
          card_data_kwargs: Extra arguments provided by the user when creating the `ArgillaTrainer`.
  
      Returns:
          TRLModelCardData: Container for the data to be written on the `ArgillaModelCard`.
      """
      from argilla.client.feedback.integrations.huggingface.model_card import (
          TRLModelCardData,
      )
  
      if not card_data_kwargs.get("tags"):
          if isinstance(self._task, TrainingTaskForSFT):
              tags = ["supervised-fine-tuning", "sft"]
          elif isinstance(self._task, TrainingTaskForRM):
              tags = ["reward-modeling", "rm"]
          elif isinstance(self._task, TrainingTaskForPPO):
              tags = ["proximal-policy-optimization", "ppo"]
          elif isinstance(self._task, TrainingTaskForDPO):
              tags = ["direct-preference-optimization", "dpo"]
  
          card_data_kwargs.update({"tags": tags + ["TRL", "argilla"]})
  
      return TRLModelCardData(
          model_id=self._model,
          task=self._task,
          update_config_kwargs={
              **self.training_args_kwargs,
              **self.trainer_kwargs,
          },
          **card_data_kwargs,
      )
  
file_path: argilla/src/argilla/client/feedback/training/frameworks/trl.py
imports: import logging
  from typing import TYPE_CHECKING, Dict, List, Optional, Union
  from argilla.client.feedback.training.base import ArgillaTrainerSkeleton
  from argilla.client.feedback.training.schemas.base import TrainingTaskForDPO, TrainingTaskForPPO, TrainingTaskForRM, TrainingTaskForSFT
  from argilla.training.utils import filter_allowed_args
  from argilla.utils.dependency import require_dependencies, requires_dependencies
label_desc: Use image node_id 9 for calling the ArgillaTRLTrainer obj's underlying member method code with example usage: obj.get_model_card_data() and returns: TRLModelCardData