updated README

README.md

@@ -7,7 +7,7 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# nthakur/contriever-base-msmarco
 
 This is a port of the [Contriever MSMARCO Model](https://huggingface.co/facebook/contriever-msmarco) to [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('nthakur/contriever-base-msmarco')
 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('nthakur/contriever-base-msmarco')
+model = AutoModel.from_pretrained('nthakur/contriever-base-msmarco')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -76,7 +76,7 @@ print(sentence_embeddings)
 
 <!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=nthakur/contriever-base-msmarco)