Some further information that I hope is useful.
Running this:-
train_dataset.encodings

Returns this:-
0s
train_dataset.encodings
{'input_ids': tensor([[  101,   155,  1942,  ...,     0,     0,     0],
        [  101, 27900,  7641,  ...,     0,     0,     0],
        [  101,   155,  1942,  ...,     0,     0,     0],
        ...,
        [  101,   109,  7414,  ...,     0,     0,     0],
        [  101,  2809,  1141,  ...,     0,     0,     0],
        [  101,  1448,  1111,  ...,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        ...,
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0]])}

Running this:-
train_dataset.labels

Returns this:-
0s
train_dataset.labels
10      1
147     0
342     0
999     2
811     2
       ..
1095    2
1130    0
1294    1
860     1
1126    1
Name: sentiment, Length: 1040, dtype: int64