Update README.md

README.md

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 rebel-base-chinese-cndbpedia is a generation-based relation extraction model
 
-  ·a SOTA chinese end-to-end relation extraction model.
+  ·a SOTA chinese end-to-end relation extraction model,using bart as backbone.
   
-  ·using the training method of "REBEL：Relation Extraction By End-to-end Language generation"(EMNLP Findings 2021).
+  ·using the training method of <REBEL：Relation Extraction By End-to-end Language generation>(EMNLP Findings 2021).
   
-  ·using the Distant-supervised data from cndbpedia.
+  ·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 lic2019,lic2020,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(more details can read on REBEL paper)
+  ·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(more details can read on REBEL paper)
+  
+using model：
+ 
+from transformers import BertTokenizer, BartForConditionalGeneration
+# load tokenizer
+model_name = 'fnlp/bart-base-chinese'
+
+tokenizer_kwargs = {
+    "use_fast": True,
+    "additional_special_tokens": ['<rel>', '<obj>', '<subj>'],
+}
+
+tokenizer = BertTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
+
+#using model
+model = BartForConditionalGeneration.from_pretrained("fanxiao/rebel-base-chinese-cndbpedia")