Update README.md

README.md

@@ -27,17 +27,17 @@ You can use the raw model for encoding document images into a vector space, but
 Here is how to use this model in PyTorch:
 
 ```python
-from transformers import BeitFeatureExtractor, BeitForMaskedImageModeling
+from transformers import BeitImageProcessor, BeitForMaskedImageModeling
 import torch
 from PIL import Image
 
 image = Image.open('path_to_your_document_image').convert('RGB')
 
-feature_extractor = BeitFeatureExtractor.from_pretrained("microsoft/dit-base")
+processor = BeitImageProcessor.from_pretrained("microsoft/dit-base")
 model = BeitForMaskedImageModeling.from_pretrained("microsoft/dit-base")
 
 num_patches = (model.config.image_size // model.config.patch_size) ** 2
-pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
 # create random boolean mask of shape (batch_size, num_patches)
 bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()