Upload README.md

README.md

@@ -83,7 +83,7 @@ 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 = ["Kini olu ilu England", "Kini eranko ti o gbona julọ ni agbaye?"]
 
 model = SentenceTransformer('0xnu/pmmlv2-fine-tuned-yoruba')
 embeddings = model.encode(sentences)
@@ -105,7 +105,7 @@ def mean_pooling(model_output, attention_mask):
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["Kini olu ilu England", "Kini eranko ti o gbona julọ ni agbaye?"]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('0xnu/pmmlv2-fine-tuned-yoruba')