class_docstr: BertEmbedding is a simple wrapped class of TransformerEmbedding.
  If you need load other kind of transformer based language model, please use the TransformerEmbedding.
docstr: Args:
      model_folder: path of checkpoint folder.
      kwargs: additional params
docstr_tok: ["Args",":","model_folder",":","path","of","checkpoint","folder",".","kwargs",":","additional","params"]
code: def __init__(self, model_folder: str, **kwargs: Any):
      """
  
      Args:
          model_folder: path of checkpoint folder.
          kwargs: additional params
      """
      self.model_folder = model_folder
      vocab_path = os.path.join(self.model_folder, "vocab.txt")
      config_path = os.path.join(self.model_folder, "bert_config.json")
      checkpoint_path = os.path.join(
          self.model_folder, "bert_model.ckpt"
      )
      kwargs["vocab_path"] = vocab_path
      kwargs["config_path"] = config_path
      kwargs["checkpoint_path"] = checkpoint_path
      kwargs["model_type"] = "bert"
      super(BertEmbedding, self).__init__(**kwargs)
  
imports: import os
  from typing import Dict, Any
  from kashgari.embeddings.transformer_embedding import TransformerEmbedding
label_desc: Use image node_id 2 to create a new BertEmbedding object from inherited base classes: TransformerEmbedding with example: obj = BertEmbedding(model_folder)