Update README.md

README.md

@@ -26,9 +26,9 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis", "Pizza adalah makanan khas Italia", "Saya kuliah di Carneige Melon University"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('firqaaa/indo-sbert-finetuned-snli-mnli-id')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -51,11 +51,12 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis", "Pizza adalah makanan khas Italia", "Saya kuliah di Carneige Melon University"]
+
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('firqaaa/indo-sbert-finetuned-snli-mnli-id')
+model = AutoModel.from_pretrained('firqaaa/indo-sbert-finetuned-snli-mnli-id')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')