Update README.md

README.md

@@ -34,11 +34,10 @@ passages = [
     "passage: The Eiffel Tower is located in Paris, France."
 ]
 
-# Encode (embeddings are already L2-normalised by default)
+# Encode
 q_emb   = model.encode(query,    normalize_embeddings=True)
 p_embs  = model.encode(passages, normalize_embeddings=True)     # shape (n_passages, dim)
 
-# Cosine similarity = dot product of normalised vectors
 scores = np.dot(p_embs, q_emb)                                  # shape (n_passages,)
 
 # Rank passages by score