from_pretrained method, since we need to use the same vocab as when the model was pretrained.
To apply these steps on a given text, we can just feed it to our tokenizer:
```py
inputs = tokenizer("We are very happy to show you the 🤗 Transformers library.")
```
This returns a dictionary string to list of ints. It contains the ids of the tokens, as
mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
attention mask that the model will use to have a better understanding of the sequence:
```py
print(inputs)
{'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102],
 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
You can pass a list of sentences directly to your tokenizer. If your goal is to send them through your model as a
