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',
'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. |