Update README.md

README.md

@@ -29,5 +29,5 @@ outputs = vision_tower(**inputs)
 logits_per_image = outputs.pooler_output  # this is the image-text similarity score
 ```
 
-There's still a slight different where hf's CLIPVision model uses a [CLS] embedding as pool embedding while SigLIP uses global attention pooler to get the final latent feature.
+There's still a slight difference where hf's CLIPVision model uses a [CLS] embedding as pool embedding while SigLIP uses global attention pooler to get the final latent feature.