def __init__(
        self,
        model: Union["SentenceTransformer", str] = "TaylorAI/bge-micro-v2",
        show_progress: Optional[bool] = True,
        **kwargs,
    ):
        """
        Initialize the SentenceTransformersExtractor.

        Args:
            model (Optional[Union["SentenceTransformer", str]]): The SentenceTransformer model to use.
                Defaults to "TaylorAI/bge-micro-v2".
            show_progress (Optional[bool]): Whether to show the progress bar during encoding.
                Defaults to True.
            **kwargs: Additional keyword arguments to pass to the init of the SentenceTransformer model.

        Examples:
        >>> import argilla_v1 as rg
        >>> from argilla_v1.client.feedback.integrations.textdescriptives import SentenceTransformersExtractor
        >>> ds = rg.FeedbackDataset(...)
        >>> tde = SentenceTransformersExtractor()
        >>> updated_ds = tde.update_dataset(ds)
        >>> updated_records = tde.update_records(ds.records)
        """
        require_dependencies("sentence_transformers")
        from sentence_transformers import SentenceTransformer

        self.show_progress = show_progress
        if isinstance(model, str):
            self.model = SentenceTransformer(model, **kwargs)
        else:
            if kwargs:
                warnings.warn(
                    "kwargs for initializing SentenceTransformer are ignored when passing a SentenceTransformer instance."
                )
            self.model = model
        self.embedding_dim = self.model.get_sentence_embedding_dimension()

        Initialize the SentenceTransformersExtractor.

        Args:
            model (Optional[Union["SentenceTransformer", str]]): The SentenceTransformer model to use.
                Defaults to "TaylorAI/bge-micro-v2".
            show_progress (Optional[bool]): Whether to show the progress bar during encoding.
                Defaults to True.
            **kwargs: Additional keyword arguments to pass to the init of the SentenceTransformer model.

        Examples:
        >>> import argilla_v1 as rg
        >>> from argilla_v1.client.feedback.integrations.textdescriptives import SentenceTransformersExtractor
        >>> ds = rg.FeedbackDataset(...)
        >>> tde = SentenceTransformersExtractor()
        >>> updated_ds = tde.update_dataset(ds)
        >>> updated_records = tde.update_records(ds.records)
        
argilla-v1/src/argilla_v1/client/feedback/integrations/sentencetransformers.py