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rebel-base-chinese-cndbpedia is a generation-based 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 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)
using model：
from transformers import BertTokenizer, BartForConditionalGeneration
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)
model = BartForConditionalGeneration.from_pretrained("fanxiao/rebel-base-chinese-cndbpedia")
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