```python
encoded_dict["token_type_ids"]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
The first sequence, the "context" used for the question, has all its tokens represented by a 0, whereas the
second sequence, corresponding to the "question", has all its tokens represented by a 1.
Some models, like [XLNetModel] use an additional token represented by a 2.
Position IDs
Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
each token. Therefore, the position IDs (position_ids) are used by the model to identify each token's position in
the list of tokens.
They are an optional parameter. If no position_ids are passed to the model, the IDs are automatically created as
absolute positional embeddings.
Absolute positional embeddings are selected in the range [0, config.max_position_embeddings - 1]. Some models use
