fixed README

README.md

@@ -31,7 +31,7 @@ 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('serafim-335m-portuguese-pt-sentence-encoder')
+model = SentenceTransformer('PORTULAN/serafim-335m-portuguese-pt-sentence-encoder')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -57,8 +57,8 @@ 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('serafim-335m-portuguese-pt-sentence-encoder')
-model = AutoModel.from_pretrained('serafim-335m-portuguese-pt-sentence-encoder')
+tokenizer = AutoTokenizer.from_pretrained('PORTULAN/serafim-335m-portuguese-pt-sentence-encoder')
+model = AutoModel.from_pretrained('PORTULAN/serafim-335m-portuguese-pt-sentence-encoder')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -80,7 +80,7 @@ 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=serafim-335m-portuguese-pt-sentence-encoder)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=PORTULAN/serafim-335m-portuguese-pt-sentence-encoder)
 
 
 ## Training