Update README.md

README.md

@@ -9,7 +9,7 @@ datasets:
 - unicamp-dl/mmarco
 ---
 
-# LazarusNLP/mmarco-indobert-base
+# LazarusNLP/s-indobert-base-mmarco
 
 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.
 
@@ -29,7 +29,7 @@ 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('LazarusNLP/mmarco-indobert-base')
+model = SentenceTransformer('LazarusNLP/s-indobert-base-mmarco')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -55,8 +55,8 @@ 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('LazarusNLP/mmarco-indobert-base')
-model = AutoModel.from_pretrained('LazarusNLP/mmarco-indobert-base')
+tokenizer = AutoTokenizer.from_pretrained('LazarusNLP/s-indobert-base-mmarco')
+model = AutoModel.from_pretrained('LazarusNLP/s-indobert-base-mmarco')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -78,7 +78,7 @@ 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=LazarusNLP/mmarco-indobert-base)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=LazarusNLP/s-indobert-base-mmarco)
 
 
 ## Training