Hi guys,
I’m trying to concatenate two datasets that share some common features.
But these two datasets have features in a different order.
It’s like:
DatasetDict({
    train: Dataset({
        features: ['__index_level_0__', 'answers', 'context', 'document_id', 'id', 'question', 'title'],
        num_rows: 3952
    })
    validation: Dataset({
        features: ['__index_level_0__', 'answers', 'context', 'document_id', 'id', 'question', 'title'],
        num_rows: 240
    })
})

and
DatasetDict({
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 60407
    })
    validation: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 5774
    })
})

so I erased uncommon features like: __index_level_0__, document_id, etc by using .remove_columns
Now the two DatasetDicts have the same features. However the order is different.
1st DatasetDict: features: ['answers', 'context', 'question', 'title'],
2nd DatasetDict: features: ['title', 'context', 'question', 'answers'],
So when I try to concatenate them by using datasets.concatenate_datasets([1stDatasetDict, 2ndDatasetDict]),
I get an error that says:

ValueError: Features must match for all datasets