| ', | |
| 'question_type': 'none of the above', | |
| 'question_id': 262148000, | |
| 'image_id': '/root/.cache/huggingface/datasets/downloads/extracted/ca733e0e000fb2d7a09fbcc94dbfe7b5a30750681d0e965f8e0a23b1c2f98c75/val2014/COCO_val2014_000000262148.jpg', | |
| 'answer_type': 'other', | |
| 'label': {'ids': ['at table', 'down', 'skateboard', 'table'], | |
| 'weights': [0.30000001192092896, | |
| 1.0, | |
| 0.30000001192092896, | |
| 0.30000001192092896]}} | |
| The features relevant to the task include: | |
| * question: the question to be answered from the image | |
| * image_id: the path to the image the question refers to | |
| * label: the annotations | |
| We can remove the rest of the features as they won't be necessary: | |
| dataset = dataset.remove_columns(['question_type', 'question_id', 'answer_type']) | |
| As you can see, the label feature contains several answers to the same question (called ids here) collected by different human annotators. |