Training a classification model with native PyTorch

The Trainer class is very powerful, and we have the HuggingFace team to thank for providing such a useful tool. However, in this section, we will fine-tune the pre-trained model from scratch to see what happens under the hood. Let's get started:

1. First, let's load the model for fine-tuning. We will select DistilBERT here since it is a small, fast, and cheap version of BERT:

   from transformers import DistilBertForSequenceClassification
   model = DistilBertForSequenceClassification.from_pre-trained('distilbert-base-uncased')

2. To fine-tune any model, we need to put it into training mode, as follows:

   model.train()

3. Now, we must load the tokenizer:

   from transformers import DistilBertTokenizerFast
   tokenizer = DistilBertTokenizerFast.from_pre-trained('bert-base-uncased')

4. Since the Trainer class organized the entire process for us, we did not deal with optimization and other training settings in...