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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)
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