usage improved

README.md

@@ -27,7 +27,7 @@ from transformers import AutoTokenizer,AutoModelForTokenClassification
 tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation")
 model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation")
 s="子曰學而時習之不亦説乎有朋自遠方來不亦樂乎人不知而不慍不亦君子乎"
-p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))[0],dim=2)[0].tolist()[1:-1]]
+p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
 print("".join(c+"。" if q=="E" or q=="S" else c for c,q in zip(s,p)))
 ```