Update README.md

README.md

@@ -113,11 +113,11 @@ print(f'*cosine_score:{cosine_scores[0]}')
 **MLM(Masked Langeuage Model) 훈련**
 - 입력 모델 : bert-base-multilingual-cased(vocab(119,548개))
 - 말뭉치 : 훈련 : bongsoo/moco-corpus-kowiki2022(7.6M) , 평가: bongsoo/bongevalsmall(200)
-- HyperParameter : LearningRate : 5e-5, epochs: 8, batchsize: 32, max_token_len : 128
-- vocab : 152,537개 (기존 119,548 에 32,989 신규 vocab 추가)
+- HyperParameter : **LearningRate : 5e-5, epochs: 8, batchsize: 32, max_token_len : 128**
+- vocab : **152,537개** (기존 119,548 에 32,989 신규 vocab 추가)
 - 출력 모델 : mbertV2.0 (size: 813MB)
 - 훈련시간 : 90h/1GPU (24GB/19.6GB use)
-- loss : 훈련loss: 2.258400, 평가loss: 3.102096, perplexity: 19.78158([bongsoo/bongeval](https://huggingface.co/datasets/bongsoo/bongeval):1,500개)
+- loss : **훈련loss: 2.258400, 평가loss: 3.102096, perplexity: 19.78158**([bongsoo/bongeval](https://huggingface.co/datasets/bongsoo/bongeval):1,500개)
 - 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/bert/bert-MLM-Trainer-V1.2.ipynb) 참조
 <br>perplexity 평가 코드는 [여기](https://github.com/kobongsoo/BERT/blob/master/bert/bert-perplexity-eval-V1.2.ipynb) 참조