| 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) | |
| model = BartForConditionalGeneration.from_pretrained('./CGRE_CNDBPedia-Generative-Relation-Extraction') | |
| inputs = tokenizer(sent, max_length=max_source_length, padding="max_length", truncation=True, return_tensors="pt") | |
| params = {"decoder_start_token_id":0,"early_stopping":False,"no_repeat_ngram_size":0,"length_penalty": 0,"num_beams":20,"use_cache":True} | |
| out_id = model.generate(inputs["input_ids"], attention_mask = inputs["attention_mask"], max_length=max_target_length, **params) | |