logging, gradient accumulation, and mixed precision.
First, let's define our model:
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
```
This will issue a warning about some of the pretrained weights not being used and some weights being randomly
initialized. That's because we are throwing away the pretraining head of the BERT model to replace it with a
classification head which is randomly initialized. We will fine-tune this model on our task, transferring the knowledge
of the pretrained model to it (which is why doing this is called transfer learning).
Then, to define our [Trainer], we will need to instantiate a
[TrainingArguments]. This class contains all the hyperparameters we can tune for the
