Update README.md

README.md

@@ -22,9 +22,10 @@ It produces optimal results in a reasonable time. Moreover, its implementation w
 from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
 
 # Model
-model = VisionEncoderDecoderModel.from_pretrained("nttdataspain/vit-gpt2-coco-lora")
-tokenizer = AutoTokenizer.from_pretrained("nttdataspain/vit-gpt2-coco-lora")
-feature_extractor = ViTFeatureExtractor.from_pretrained("nttdataspain/vit-gpt2-coco-lora")
+model_id = "nttdataspain/vit-gpt2-coco-lora"
+model = VisionEncoderDecoderModel.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_id)
 
 # Predict function
 def predict_prompts(list_images, max_length=16):