Update README.md

README.md

@@ -9,43 +9,32 @@ The model can be used as follows:
 ```python
 
 import requests
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 from PIL import Image
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 from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
 
 loc = "ydshieh/flax-vit-gpt2-coco-en"
 
 feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
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 tokenizer = AutoTokenizer.from_pretrained(loc)
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 model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
 
 # We will verify our results on an image of cute cats
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 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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 with Image.open(requests.get(url, stream=True).raw) as img:
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     pixel_values = feature_extractor(images=img, return_tensors="np").pixel_values
 
 def generate_step(pixel_values):
 
     output_ids = model.generate(pixel_values, max_length=16, num_beams=4).sequences
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     preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
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     preds = [pred.strip() for pred in preds]
 
     return preds
 
 preds = generate_step(pixel_values)
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 print(preds)
 
 # should produce
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 # ['a cat laying on top of a couch next to another cat']
 
 ```