1 indicates a value that should be attended to, while 0 indicates a padded value. This attention mask is
in the dictionary returned by the tokenizer under the key "attention_mask":
```python
padded_sequences["attention_mask"]
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
```
Token Type IDs
Some models' purpose is to do classification on pairs of sentences or question answering.
These require two different sequences to be joined in a single "input_ids" entry, which usually is performed with the
help of special tokens, such as the classifier ([CLS]) and separator ([SEP]) tokens. For example, the BERT
model builds its two sequence input as such:
```python
[CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
```
We can use our tokenizer to automatically generate such a sentence by passing the two sequences to tokenizer as two
