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Update README.md

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@@ -7,7 +7,7 @@ tags:
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  - transformers
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  ---
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- # {MODEL_NAME}
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  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.
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@@ -27,7 +27,7 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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- model = SentenceTransformer('{MODEL_NAME}')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
@@ -53,8 +53,8 @@ def mean_pooling(model_output, attention_mask):
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
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- model = AutoModel.from_pretrained('{MODEL_NAME}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
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  - transformers
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  ---
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+ # pritamdeka/S_PubMedBert_MS_MARCO
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  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.
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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+ model = SentenceTransformer('pritamdeka/S_PubMedBert_MS_MARCO')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
 
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('pritamdeka/S_PubMedBert_MS_MARCO')
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+ model = AutoModel.from_pretrained('pritamdeka/S_PubMedBert_MS_MARCO')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')