Update README.md

README.md

@@ -16,6 +16,7 @@ language:
 |모델     |korsts|klue-sts|glue(stsb)|stsb_multi_mt(en)|
 |:--------|------:|--------:|--------------:|------------:|
 |albert-small-kor-cross-encoder-v1    |0.8455    |0.8526    |0.8513           |0.7976|
+|klue-cross-encoder-v1    |0.8262    |0.8833    |0.8512           |0.7889|
 |**kpf-cross-encoder-v1**    |0.8799    |0.9133    |0.8626           |0.8027|  
 
 ## Usage and Performance
@@ -23,12 +24,12 @@ language:
 Pre-trained models can be used like this:
 ```
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('bongsoo/kpf-cross-encoder-v1')
-scores = model.predict([('대한민국', '파리'), ('대한민국', '서울')])
+model = CrossEncoder("bongsoo/kpf-cross-encoder-v1")
+scores = model.predict([('오늘 날씨가 좋다', '오늘 등산을 한다'), ('오늘 날씨가 흐리다', '오늘 비가 내린다')])
 print(scores)
 ```
 ```
-[0.02951439 0.56858784]
+[0.298039   0.70830244]
 ```
 
 The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.