Update README.md

README.md

@@ -7,9 +7,9 @@ tags:
 - transformers
 ---
 
-# {pritamdeka/PubMedBERT-mnli-snli-scinli-stsb}
+# pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-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.
+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. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings.
 
 <!--- Describe your model here -->
 
@@ -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('{pritamdeka/PubMedBERT-mnli-snli-scinli-stsb}')
+model = SentenceTransformer('pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-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('{pritamdeka/PubMedBERT-mnli-snli-scinli-stsb}')
-model = AutoModel.from_pretrained('{pritamdeka/PubMedBERT-mnli-snli-scinli-stsb}')
+tokenizer = AutoTokenizer.from_pretrained('pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb')
+model = AutoModel.from_pretrained('pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')