Update README.md

README.md

@@ -27,7 +27,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('bespin-global/klue-sroberta-base-continue-learning-by-mnr)
+model = SentenceTransformer("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -53,8 +53,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('bespin-global/klue-sroberta-base-continue-learning-by-mnr')
-model = AutoModel.from_pretrained('bespin-global/klue-sroberta-base-continue-learning-by-mnr')
+tokenizer = AutoTokenizer.from_pretrained("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
+model = AutoModel.from_pretrained("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,7 +77,8 @@ print(sentence_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})
-![]("https://media.vlpt.us/images/jaehyeong/post/ea83f369-ce37-4e7e-b727-18c3ba7f6174/%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202022-03-01%20%E1%84%8B%E1%85%A9%E1%84%8C%E1%85%A5%E1%86%AB%201.06.52.png")
+
+<img src="https://media.vlpt.us/images/jaehyeong/post/ea83f369-ce37-4e7e-b727-18c3ba7f6174/%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202022-03-01%20%E1%84%8B%E1%85%A9%E1%84%8C%E1%85%A5%E1%86%AB%201.06.52.png">
 
 
 ## Training