class_docstr: 处理 **CMRC2018** 的数据，处理之后 :class:`~fastNLP.core.DataSet` 中新增的内容如下（原有的 field 仍然保留）：
  
  .. csv-table::
      :header: "context_len", "raw_chars",  "target_start", "target_end", "chars"
      
      492, "['范', '廷', '颂... ]", 30, 34, "[21, 25, ...]"
      491, "['范', '廷', '颂... ]", 41, 61, "[21, 25, ...]"
      ".", "...", "...","...", "..."
  
  ``raw_chars`` 列是 ``context`` 与 ``question`` 拼起来的结果（连接的地方加入了 ``[SEP]`` ）， ``chars`` 是转为
  index 的值， ``target_start`` 为答案开始的位置， ``target_end`` 为答案结束的位置（闭区间）； ``context_len``
  指示的是 ``chars`` 列中 context 的长度。
  
  :param max_len:
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code: def __init__(self, max_len=510):
      super().__init__()
      self.max_len = max_len
  
file_path: fastNLP/fastNLP/io/pipe/qa.py
imports: from copy import deepcopy
  from .pipe import Pipe
  from fastNLP.io.data_bundle import DataBundle
  from ..loader.qa import CMRC2018Loader
  from .utils import get_tokenizer
  from fastNLP.core.dataset import DataSet
  from fastNLP.core.vocabulary import Vocabulary
label_desc: Use image node_id 1 to create a new CMRC2018BertPipe object from inherited base classes: Pipe with example: obj = CMRC2018BertPipe(max_len)