Update README.md

README.md

@@ -250,8 +250,7 @@ For every training pair:
 Original: (Sentence_A, Sentence_B, score)
 Augmented: (Sentence_B, Sentence_A, score)
 
-Both are included. This doubles the effective dataset and enforces bidirectional similarity.
-
+Both are included, doubling the effective training set from ~8k to ~16k pairs. Note: because cosine similarity is symmetric by construction (sim(A,B) = sim(B,A) regardless of training), this augmentation's main effect is increasing the volume of training pairs rather than teaching the model a new bidirectional property. The performance gains reported in Section 4 are real and measured directly; the ablation isolating "more data" from "swap specifically" is planned as future work.
 ### 5.3 Hyperparameters
 
 <table>