class_docstr: A modified wrapper for a public python BPE tokenizer. The modifications allow encoding directly into the formats
  required for finetune. Particularly with respect to formatting with multiple inputs.
class_docstr_tok: ["A","modified","wrapper","for","a","public","python","BPE","tokenizer",".","The","modifications","allow","encoding","directly","into","the","formats","required","for","finetune",".","Particularly","with","respect","to","formatting","with","multiple","inputs","."]
code: def __init__(
      self, encoder_path=None, vocab_path=VOCAB_PATH, lower_case=False
  ):
      super().__init__(encoder_path=encoder_path, vocab_path=vocab_path)
      self.lower_case = lower_case
  
      self.tokenizer = FullTokenizer(
          vocab_file=self.vocab_path, do_lower_case=self.lower_case
      )
  
      self.start_token = self.tokenizer.convert_tokens_to_ids(
          ["[CLS]"]
      )[0]
      self.delimiter_token = self.tokenizer.convert_tokens_to_ids(
          ["[SEP]"]
      )[0]
      self.mask_token = self.tokenizer.convert_tokens_to_ids(
          ["[MASK]"]
      )[0]
      self.end_token = self.delimiter_token
  
imports: import os
  import numpy
  import finetune
  from finetune.encoding.input_encoder import EncodedOutput, BaseEncoder
  from finetune.base_models.bert.tokenizer import FullTokenizer
label_desc: Use image node_id 1 to create a new BERTEncoder object from inherited base classes: BaseEncoder with example: obj = BERTEncoder(encoder_path, vocab_path, lower_case)