That’s a good question. cc @sgugger, would be great if the several notebooks also include an inference part. I had to look into several notebooks before finding out you can access the trained model using trainer.model. Here’s how to do inference on a new, unseen sentence:
sentence = “You look good today.” 
# encode sentence (i.e. create input_ids, attention_mask)
encoding = tokenizer(sentence)
# make sure the keys of the "encoding" dict are on the same device as the model
encoding = {k: v.to(trainer.args.device) for k, v in encoding.items()}
# forward pass through the model
with torch.no_grad():
    outputs = trainer.model(**encoding)
logits = outputs.logits 
print("Predicted class index:", logits.argmax(-1))

The predicted class index will be either a zero or a one (I guess one represents positive).