code: def __init__(
      self,
      dataset: "FeedbackDataset",
      task: TrainingTaskForSentenceSimilarity,
      prepared_data=None,
      model: str = None,
      seed: int = None,
      train_size: Optional[float] = 1,
      cross_encoder: bool = False,
  ) -> None:
      require_dependencies("sentence-transformers")
  
      super().__init__(
          dataset=dataset,
          task=task,
          prepared_data=prepared_data,
          model=model,
          train_size=train_size,
          seed=seed,
      )
  
      # The prepared_data is lost in the TrainerSkeleton, is this intended?
      self._prepared_data = prepared_data
      self._cross_encoder = cross_encoder
  
      if model is None:
          if not self._cross_encoder:
              self._model = "sentence-transformers/all-MiniLM-L6-v2"
          else:
              self._model = "cross-encoder/ms-marco-MiniLM-L-6-v2"
  
          self._logger.info(
              f"No model was selected, using pre-defined `{'Cross-Encoder' if self._cross_encoder else 'Bi-Encoder'}` with `{self._model}`."
          )
  
      else:
          self._model = model
  
      if isinstance(self._prepared_data, tuple):
          self._train_dataset = self._prepared_data[0]
          self._eval_dataset = self._prepared_data[1]
      else:
          self._train_dataset = self._prepared_data
          self._eval_dataset = None
  
      self._loss_cls = None
      self.init_training_args()
  
file_path: argilla/src/argilla/client/feedback/training/frameworks/sentence_transformers.py
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from argilla.client.feedback.training.base import ArgillaTrainerSkeleton
  from argilla.client.feedback.training.schemas.base import TrainingTaskForSentenceSimilarity
  from argilla.training.utils import filter_allowed_args, get_default_args
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 1 to create a new ArgillaSentenceTransformersTrainer object from inherited base classes: ArgillaTrainerSkeleton with example: obj = ArgillaSentenceTransformersTrainer(dataset, task, prepared_data, model, seed, train_size, cross_encoder)