Checkout this docs 5.
First, you won’t need to pass labels for predictions and for prediction put the code under torch.no_grad() to avoid calculating gradients.
The returned logits have shape [batch_size, num_classes], you can then apply argmax to get the index highest scored class.
classes = ["NEGATIVE", "POSITIVE"]
enc = tokenizer("positive text", return_tensors="pt")
with torch.no_grad():
  logits = model(**enc, return_dict=True).logits

pred_class_idx = torch.argmax(logits).item()
classes[pred_class_idx]

you can also apply softmax on logits to get the probabilities.
and better yet, just use pipeline as shown in the docs above, which does all of this for you.