readme:   CGRE is a generation-based relation extraction model
  
    ·a SOTA chinese end-to-end relation extraction model,using bart as backbone.
  
    ·using the Distant-supervised data from cndbpedia,pretrained from the checkpoint of fnlp/bart-base-chinese.
    
    ·can perform SOTA in many chinese relation extraction dataset,such as DuIE~1.0,DuIE~2.0,HacRED,etc.
    
    ·easy to use,just like normal generation task.
    
    ·input is sentence,and output is linearlize triples,such as input:姚明是一名NBA篮球运动员 output:[subj]姚明[obj]NBA[rel]公司[obj]篮球运动员[rel]职业
  
  
  using model：
  
  from transformers import BertTokenizer, BartForConditionalGeneration
  
  model_name = 'fnlp/bart-base-chinese'
  
  tokenizer_kwargs = {
      "use_fast": True,
      "additional_special_tokens": ['<rel>', '<obj>', '<subj>'],
  } # if cannot see tokens in model card please open readme file
  
  tokenizer = BertTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
  
  
  