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def transforms(example_batch):
    images = [x for x in example_batch["image"]]
    captions = [x for x in example_batch["text"]]
    inputs = processor(images=images, text=captions, padding="max_length")
    inputs.update({"labels": inputs["input_ids"]})
    return inputs
train_ds.set_transform(transforms)
test_ds.set_transform(transforms)

With the dataset ready, you can now set up the model for fine-tuning.