Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# {pritamdeka/S-Biomed-Roberta-snli-multinli-stsb}
 
 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.
 
@@ -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('{MODEL_NAME}')
+model = SentenceTransformer('{pritamdeka/S-Biomed-Roberta-snli-multinli-stsb}')
 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('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('{pritamdeka/S-Biomed-Roberta-snli-multinli-stsb}')
+model = AutoModel.from_pretrained('{pritamdeka/S-Biomed-Roberta-snli-multinli-stsb}')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -123,4 +123,16 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+<!--- Describe where people can find more information -->
+
+To cite the wonderful work of sentence transformers use the citation given below.
+``` 
+
+@article{reimers2019sentence,
+  title={Sentence-bert: Sentence embeddings using siamese bert-networks},
+  author={Reimers, Nils and Gurevych, Iryna},
+  journal={arXiv preprint arXiv:1908.10084},
+  year={2019}
+}
+
+```