Update README.md

README.md

@@ -60,6 +60,10 @@ model = AutoModel.from_pretrained("govtech/lionguard-2-lite", trust_remote_code=
 
 # Download model from the 🤗 Hub
 embedding_model = SentenceTransformer("google/embeddinggemma-300m")
+
+# Text to classify
+texts = ["hello", "world"]
+
 # Add prompt instructions to generate embeddings that are optimized to classify texts according to preset labels
 formatted_texts = [f"task: classification | query: {c}" for c in texts]
 embeddings = embedding_model.encode(formatted_texts) # NOTE: use encode() instead of encode_documents()