model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
Using the tokenizer
We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
words (or part of words, punctuation symbols, etc.) usually called tokens. There are multiple rules that can govern
that process (you can learn more about them in the tokenizer summary), which is why we need
to instantiate the tokenizer using the name of the model, to make sure we use the same rules as when the model was
pretrained.
The second step is to convert those tokens into numbers, to be able to build a tensor out of them and feed them to
the model. To do this, the tokenizer has a vocab, which is the part we download when we instantiate it with the
