batch, you probably want to pad them all to the same length, truncate them to the maximum length the model can accept
and get tensors back. You can specify all of that to the tokenizer:
```py
pt_batch = tokenizer(
...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
...     padding=True,
...     truncation=True,
...     max_length=512,
...     return_tensors="pt",
... )
===PT-TF-SPLIT===
tf_batch = tokenizer(
...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
...     padding=True,
...     truncation=True,
...     max_length=512,
...     return_tensors="tf",
... )
```
The padding is automatically applied on the side expected by the model (in this case, on the right), with the padding
token the model was pretrained with. The attention mask is also adapted to take the padding into account:
