Update README.md

README.md

@@ -15,6 +15,8 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
 
 <!--- Describe your model here -->
 
+This model is [snunlp/KR-SBERT-V40K-klueNLI-augSTS](https://huggingface.co/snunlp/KR-SBERT-V40K-klueNLI-augSTS) with max input length 512.
+
 ## Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
@@ -29,7 +31,7 @@ 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('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
+model = SentenceTransformer('smartmind/ko-sbert-augSTS-maxlength512')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -55,8 +57,8 @@ 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('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
-model = AutoModel.from_pretrained('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
+tokenizer = AutoTokenizer.from_pretrained('smartmind/ko-sbert-augSTS-maxlength512')
+model = AutoModel.from_pretrained('smartmind/ko-sbert-augSTS-maxlength512')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')