Update README.md

README.md

@@ -12,7 +12,7 @@ license: apache-2.0
 </div>
 
 We introduce **Charm** , a novel tokenization approach that preserves **C**omposition, **H**igh-resolution,
-**A**spect **R**atio, and **M**ulti-scale information simultaneously. By preserving critical aesthetic information, <em> Charm </em> achieves significant performance improvement across different image aesthetic and quality assessment datasets.
+**A**spect **R**atio, and **M**ulti-scale information simultaneously. By preserving critical information, <em> Charm </em>  works like a charm for image aesthetic and quality assessment 🌟.
 
 
 ### Quick Inference
@@ -80,4 +80,5 @@ model = backbone(training_dataset='tad66k', device='cpu')
 prediction = model.predict(tokens, pos_embed, mask_token)
 ```
 
+
 **Note:** For the training code, check our [GitHub Page](https://github.com/FBehrad/Charm/).