| ).convert("RGB") | |
| words = ["hello", "world"] | |
| boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes | |
| encoding = processor(image, words, boxes=boxes, return_tensors="pt") | |
| print(encoding.keys()) | |
| dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image']) | |
| Use case 3: token classification (training), apply_ocr=False | |
| For token classification tasks (such as FUNSD, CORD, SROIE, Kleister-NDA), one can also provide the corresponding word | |
| labels in order to train a model. |