# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("joonhan/roberta-roa")
model = AutoModelForTokenClassification.from_pretrained("joonhan/roberta-roa")Quick Links
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- Fine-tunning "KLUE/roberta-large" model For CER(Company Entity Recognition) With Custom Dataset
- Custom Datasets are composed of news data
label_list = ['O',"B-PER","I-PER","B-ORG","I-ORG","B-COM","I-COM","B-LOC","I-LOC","B-DAT","I-DAT","B-TIM","I-TIM","B-QNT","I-QNT"]
refer_list = ['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14']
- EX: "B-PER" : 1 , "B-COM" : 5
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="joonhan/roberta-roa")