class_docstr: @article{xu2017discourse,
  title={A discourse-level named entity recognition and relation extraction dataset for chinese literature text},
  author={Xu, Jingjing and Wen, Ji and Sun, Xu and Su, Qi},
  journal={arXiv preprint arXiv:1711.07010},
  year={2017}
  }
class_docstr_tok: ["@","article","{","xu2017discourse",",","title=","{","A","discourse-level","named","entity","recognition","and","relation","extraction","dataset","for","chinese","literature","text","}",",","author=","{","Xu",",","Jingjing","and","Wen",",","Ji","and","Sun",",","Xu","and","Su",",","Qi","}",",","journal=","{","arXiv","preprint","arXiv:1711.07010","}",",","year=","{","2017","}","}"]
code: def __init__(self):
      super().__init__(
          labels_origin=[
              1,
              2,
              3,
              4,
              5,
              6,
              7,
              8,
              9,
          ],
          labels_mapped=[
              "位于",
              "局部整体",
              "家人",
              "普遍特殊",
              "社会",
              "所有者",
              "使用",
              "制作",
              "邻近",
          ],  # Located, Part-Whole, Family, General-Special, Social, Ownership, Use, Create, Near
      )
  
file_path: OpenPrompt/openprompt/data_utils/ZH/relation.py
imports: from .processor import *
label_desc: Use image node_id 1 to create a new Chinese_Literature_NER_RE object from inherited base classes: CLSProcessor with example: obj = Chinese_Literature_NER_RE()