fixed README

README.md

@@ -10,7 +10,7 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# Serafim 100m Portuguese (PT) Sentence Encoder
 
 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.
 
@@ -30,7 +30,7 @@ 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('PORTULAN/serafim-100m-portuguese-pt-sentence-encoder-ir')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -56,8 +56,8 @@ 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('PORTULAN/serafim-100m-portuguese-pt-sentence-encoder-ir')
+model = AutoModel.from_pretrained('PORTULAN/serafim-100m-portuguese-pt-sentence-encoder-ir')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -79,7 +79,7 @@ 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=PORTULAN/serafim-100m-portuguese-pt-sentence-encoder-ir)
 
 
 ## Training