If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
probably want:
To pad each sentence to the maximum length there is in your batch.
To truncate each sentence to the maximum length the model can accept (if applicable).
To return tensors.
You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
```py
batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
print(batch)
{'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
                      [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
                      [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
