Update README.md

README.md

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 ## Model — short description
 
+![PyTorch](https://img.shields.io/badge/PyTorch-%3E%3D1.8-orange) ![Transformers](https://img.shields.io/badge/Transformers-compatible-blue) ![Hugging%20Face](https://img.shields.io/badge/Hugging%20Face-model-blueviolet) ![License: MIT](https://img.shields.io/badge/License-MIT-green) ![Model size](https://img.shields.io/badge/Model--size-120M-lightgrey)
+
 STEALTH is a **120M-parameter** transformer encoder trained to produce **encryption-invariant** sentence embeddings. It learns a topology-preserving mapping from encrypted text embeddings to a plaintext embedding space using the **Semantic Isomorphism Enforcement (SIE)** multi-objective loss. 
 
 ## Model specs
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 * **Embedding dim (output):** 256. 
 * **Tokenizer:** encryption-aware byte-level tokenizer. 
 
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+## Highlights
+
+* ✅ **Privacy-first**: Operates on ciphertext without requiring decryption.
+* ✅ **Topology preserving**: SIE loss aligns encrypted and plaintext embeddings while preserving semantic distances.
+* ✅ **Robust training**: Multi-key augmentation (multiple ciphertext variants per plaintext) improves invariance and generalization.
+* ✅ **Practical**: Small model footprint (120M) for efficient deployment in constrained environments.
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