parameters such as the hidden dimension, dropout rate, etc for DistilBERT. If you do core modifications, like changing
the hidden size, you won't be able to use a pretrained model anymore and will need to train from scratch. You would
then instantiate the model directly from this configuration.
Below, we load a predefined vocabulary for a tokenizer with the
[~DistilBertTokenizer.from_pretrained] method. However, unlike the tokenizer, we wish to initialize
the model from scratch. Therefore, we instantiate the model from a configuration instead of using the
[DistilBertForSequenceClassification.from_pretrained] method.
```py
from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4 * 512)
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
