updated README.md

README.md

@@ -6,7 +6,7 @@ tags:
 - sentence-similarity
 ---
 
-# {MODEL_NAME}
+# ko-sbert-multitask
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -14,19 +14,19 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
 
 ## Usage (Sentence-Transformers)
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+모델을 사용하기 위해서는 `ko-sentence-transformers` 를 설치해야 합니다.
 
 ```
-pip install -U sentence-transformers
+pip install -U ko-sentence-transformers
 ```
 
 Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('jhgan/ko-sbert-multitask')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -37,8 +37,17 @@ print(embeddings)
 
 <!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
+KorSTS, KorNLI 학습 데이터셋으로 멀티 태스크 학습을 진행한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다.
+
+- Cosine Pearson: 83.78
+- Cosine Spearman: 84.02
+- Euclidean Pearson: 81.68
+- Euclidean Spearman: 81.81
+- Manhattan Pearson: 81.61
+- Manhattan Spearman: 81.72
+- Dot Pearson: 79.16
+- Dot Spearman: 78.69
 
 ## Training
 The model was trained with the parameters:
@@ -97,4 +106,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+<!--- Describe where people can find more information -->