Update README.md

README.md

@@ -6,7 +6,8 @@ tags:
 - sentence-similarity
 ---
 
-# {MODEL_NAME}
+# kpf-sbert-128d-v1
+- kpf bert 모델 출력을 128 차원으로 줄이고, nli(3)+sts(10)+nli(3)+sts(10) 훈련시킴
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -26,7 +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('{MODEL_NAME}')
+model = SentenceTransformer('bongsoo/kpf-sbert-128d-v1')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -77,7 +78,7 @@ Parameters of the fit()-Method:
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 72, 'do_lower_case': True}) with Transformer model: BertModel 
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
   (2): Dense({'in_features': 768, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
 )
@@ -85,4 +86,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+bongsoo