Fine-tuning a BERT model for single-sentence binary classification

In this section, we will discuss how to fine-tune a pre-trained BERT model for sentiment analysis by using the popular IMDb sentiment dataset. Working with a GPU will speed up our learning process, but if you do not have such resources, you can work with a CPU as well for fine-tuning. Let's get started:

1. To learn about and save our current device, we can execute the following lines of code:

   from torch import cuda
   device = 'cuda' if cuda.is_available() else 'cpu'

2. We will use the DistilBertForSequenceClassification class here, which is inherited from the DistilBert class, with a special sequence classification head at the top. We can utilize this classification head to train the classification model, where the number of classes is 2 by default:

   from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
   model_path= 'distilbert-base-uncased'
   tokenizer...