class_docstr: This class extracts a number of basic text descriptives from FeedbackDataset
  records using the TextDescriptives library and adds them as record metadata.
docstr: 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 as rg
  >>> from argilla.client.feedback.integrations.textdescriptives import SentenceTransformersExtractor
  >>> ds = rg.FeedbackDataset(...)
  >>> tde = SentenceTransformersExtractor()
  >>> updated_ds = tde.update_dataset(ds)
  >>> updated_records = tde.update_records(ds.records)
docstr_tok: ["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","as","rg",">",">",">","from","argilla.client.feedback.integrations.textdescriptives","import","SentenceTransformersExtractor",">",">",">","ds","=","rg.FeedbackDataset","(","...",")",">",">",">","tde","=","SentenceTransformersExtractor","(",")",">",">",">","updated_ds","=","tde.update_dataset","(","ds",")",">",">",">","updated_records","=","tde.update_records","(","ds.records",")"]
code: 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 as rg
      >>> from argilla.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()
  
file_path: argilla/src/argilla/client/feedback/integrations/sentencetransformers.py
imports: import warnings
  from typing import List, Optional, Union
  from argilla.client.feedback.dataset.local.dataset import FeedbackDataset
  from argilla.client.feedback.dataset.remote.dataset import RemoteFeedbackDataset
  from argilla.client.feedback.schemas.records import FeedbackRecord
  from argilla.client.feedback.schemas.remote.records import RemoteFeedbackRecord
  from argilla.client.feedback.schemas.vector_settings import VectorSettings
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 1 to create a new SentenceTransformersExtractor object  with example: obj = SentenceTransformersExtractor(model, show_progress)