Update processing_granite_vision_embedding.py

processing_granite_vision_embedding.py

@@ -23,7 +23,7 @@ def floor_by_factor(number: float, factor: int) -> int:
 
 class GraniteVisionEmbProcessor(LlavaNextProcessor):
     """
-    Processor for ColPali.
+    Processor for GraniteVisionEmb.
     """
 
     visual_prompt_prefix: ClassVar[str] = "<|user|>\n<image>\nDescribe the image.\n"
@@ -300,7 +300,7 @@ class GraniteVisionEmbProcessor(LlavaNextProcessor):
             images: List[Image.Image],
     ) -> BatchFeature:
         """
-        Process images for ColPali.
+        Process images.
         """
         # texts_doc = [self.apply_chat_template(self.format_data_wo_role(self.visual_prompt_prefix, img),tokenize=False ) for img in images]
         texts_doc = [self.visual_prompt_prefix for _ in images]
@@ -394,7 +394,7 @@ class GraniteVisionEmbProcessor(LlavaNextProcessor):
     ) -> torch.Tensor:
         """
         Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector
-        query embeddings (`qs`) and passage embeddings (`ps`). For ColPali, a passage is the
+        query embeddings (`qs`) and passage embeddings (`ps`). For us, a passage is the
         image of a document page.
 
         Because the embedding tensors are multi-vector and can thus have different shapes, they