updated README.md

README.md

@@ -37,10 +37,15 @@ print(embeddings)
 
 KorNLI 학습 데이터셋으로 학습한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다.
 
-<!--- Describe how your model was evaluated -->
-모델|학습 데이터|Cosine Pearson|Cosine Spearman|Euclidean Pearson|Euclidean Spearman|Manhattan Pearson|Manhattan Spearman|Dot Pearson|Dot Spearman|
-|:----:|:------------------------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
-SKT-KoBERT|NLI|82.03|82.36|80.06|79.85|80.08|79.91|75.76|74.72 
+- Cosine Pearson: 82.03
+- Cosine Spearman: 82.36
+- Euclidean Pearson: 80.06
+- Euclidean Spearman: 79.85
+- Manhattan Pearson: 80.08
+- Manhattan Spearman: 79.91
+- Dot Pearson: 75.76
+- Dot Spearman: 74.72
+
 
 ## Training
 The model was trained with the parameters:
@@ -88,4 +93,10 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+- Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korsts: New benchmark datasets for korean natural language understanding. arXiv
+preprint arXiv:2004.03289
+- Reimers, Nils and Iryna Gurevych. “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.” ArXiv abs/1908.10084 (2019)
+- [Ko-Sentence-BERT-SKTBERT](https://github.com/BM-K/KoSentenceBERT-SKT)
+- [KoBERT](https://github.com/SKTBrain/KoBERT)<!--- Describe where people can find more information -->
+
+