If you are working on a multi-class classification, the targets should be the class indices and should not be one-hot encoded.
E.g. if your use case uses 5 targets, the valid values would be [0, 1, 2, 3, 4].
For your input data, you could use a mapping, such that e.g. manipuri maps to 0, odissi maps to 1, etc.
The Dataset.__getitem__(self, index) method is called by the DataLoader with an index for each sample in the range [0, len(dataset)] and is responsible to load and return the sample for the current index.