model = DistilBertForSequenceClassification(config)
===PT-TF-SPLIT===
from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4 * 512)
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
model = TFDistilBertForSequenceClassification(config)
```
For something that only changes the head of the model (for instance, the number of labels), you can still use a
pretrained model for the body. For instance, let's define a classifier for 10 different labels using a pretrained body.
Instead of creating a new configuration with all the default values just to change the number of labels, we can instead
pass any argument a configuration would take to the [from_pretrained] method and it will update the default
