[bug] nan validation loss when finetuning BERT sentiment analysis classifier via Trainer API

### Enter the chapter number

Chapter 14

### Enter the page number

_No response_

### What is the cell's number in the notebook

Cell 120

### Enter the environment you are using to run the notebook

Colab

### Describe your issue

I'm running chapter 14 in colab, specifically the sections "Task-specific classes" and "The Trainer API" (cells 112-120), and the trainer is reporting a `nan` validation loss with accuracy between 40-50%, i.e., something completely breaks.

It seemed very odd, since validation metric looks indeed correct and the loss is backed into the Trainer object itself.

### Enter what you expected to happen

_No response_

### If you found a workaround, describe it here

Since I was skeptical the issue was with the metric/loss, I checked the float16.

First I tried to remove the conversion, but the T4 of colab was forcing to extremely reduce the batch size making 1 epoch incredibly long. Then I realised that Trainer class offers flags to specify the model weights dtype.

Overall, I resolved the problem by commenting out the `dtype=torch.float16` in the instantiation of the pretrained model in cell 112

```
bert_for_binary_clf = BertForSequenceClassification.from_pretrained(
"bert-base-uncased",
num_labels=2,
#dtype=torch.float16 #<---- remove this
).to(device)
```
and adding type casting using the TrainerArguments options, based on the [official documentation](https://huggingface.co/docs/transformers/v5.4.0/en/main_classes/trainer#transformers.TrainingArguments.fp16) in cell 119

```
train_args = TrainingArguments(
output_dir="my_imdb_model", num_train_epochs=2,
per_device_train_batch_size=128,
per_device_eval_batch_size=128,
eval_strategy="epoch",
logging_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",
report_to="none",
fp16=True. # <--- add this
)
```

With these two changes I get a validation loss of around 0.3 and (more important) the validation accuracy is around 0.9 (as originally in the notebook).