Update README.md

README.md

@@ -35,12 +35,13 @@ The abstract of the paper states that:
 You should ask specific questions to the model in order to get consistent generations. Here we are asking the model whether the sum of values that are in a chart are greater than the largest value.
 
 ```python
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
 import requests
 from PIL import Image
 
-model = Pix2StructForConditionalGeneration.from_pretrained('ybelkada/matcha-chartqa')
-processor = Pix2StructProcessor.from_pretrained('ybelkada/matcha-chartqa')
+processor = Pix2StructProcessor.from_pretrained('google/matcha-chartqa')
+model = Pix2StructForConditionalGeneration.from_pretrained('google/matcha-chartqa')
+
 url = "https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png"
 image = Image.open(requests.get(url, stream=True).raw)
 
@@ -52,7 +53,7 @@ print(processor.decode(predictions[0], skip_special_tokens=True))
 
 To run the predictions on GPU, simply add `.to(0)` when creating the model and when getting the inputs (`inputs = inputs.to(0)`)
 
-## Converting from T5x to huggingface
+# Converting from T5x to huggingface
 
 You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_original_pytorch_to_hf.py) script as follows:
 ```bash