Update code example

README.md

@@ -23,7 +23,7 @@ You can use the raw model for optical character recognition (OCR) on single text
 Here is how to use this model in PyTorch:
 
 ```python
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoFeatureExtractor, XLMRobertaTokenizer
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
 import requests
 
@@ -31,17 +31,12 @@ import requests
 url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'
 image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
 
-# For the time being, TrOCRProcessor does not support the small models, so the following temporary solution can be adopted
-# processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-printed')
-feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/trocr-small-printed')
-tokenizer = XLMRobertaTokenizer.from_pretrained('microsoft/trocr-small-printed')
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-printed')
 model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-printed')
-# pixel_values = processor(images=image, return_tensors="pt").pixel_values
-pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
 
 generated_ids = model.generate(pixel_values)
-# generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 
 ### BibTeX entry and citation info