docstr: Initialize the ArgillaSpaCyTrainer class.
  
  Args:
      freeze_tok2vec: A flag indicating whether to freeze the tok2vec weights
          during training. Defaults to False.
      **kwargs: Additional arguments for ArgillaSpaCyTrainerBase.
  
  Examples:
      >>> from argilla import ArgillaSpaCyTrainer
      >>> trainer = ArgillaSpaCyTrainer(freeze_tok2vec=True)
docstr_tok: ["Initialize","the","ArgillaSpaCyTrainer","class",".","Args",":","freeze_tok2vec",":","A","flag","indicating","whether","to","freeze","the","tok2vec","weights","during","training",".","Defaults","to","False",".","*","*","kwargs",":","Additional","arguments","for","ArgillaSpaCyTrainerBase",".","Examples",":",">",">",">","from","argilla","import","ArgillaSpaCyTrainer",">",">",">","trainer","=","ArgillaSpaCyTrainer","(","freeze_tok2vec=True",")"]
code: def __init__(self, freeze_tok2vec: bool = False, **kwargs) -> None:
      """
      Initialize the ArgillaSpaCyTrainer class.
  
      Args:
          freeze_tok2vec: A flag indicating whether to freeze the tok2vec weights
              during training. Defaults to False.
          **kwargs: Additional arguments for ArgillaSpaCyTrainerBase.
  
      Examples:
          >>> from argilla import ArgillaSpaCyTrainer
          >>> trainer = ArgillaSpaCyTrainer(freeze_tok2vec=True)
      """
      self.freeze_tok2vec = freeze_tok2vec
      super().__init__(**kwargs)
  
imports: import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Literal, Optional, Union
  from pydantic import BaseModel
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 1 to create a new ArgillaSpaCyTrainer object from inherited base classes: _ArgillaSpaCyTrainerBase with example: obj = ArgillaSpaCyTrainer(freeze_tok2vec)