chunked/nltk_chunking/tasks_visual_question_answering/chunk_62.txt

processor = ViltProcessor.from_pretrained("MariaK/vilt_finetuned_200")
image = Image.open(example['image_id'])
question = example['question']
prepare inputs
inputs = processor(image, question, return_tensors="pt")
model = ViltForQuestionAnswering.from_pretrained("MariaK/vilt_finetuned_200")
forward pass
with torch.no_grad():
     outputs = model(**inputs)
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])
Predicted answer: down

Zero-shot VQA
The previous model treated VQA as a classification task.