other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
Labels
The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
predictions and the expected value (the label).
These labels are different according to the model head, for example:
For sequence classification models (e.g., [BertForSequenceClassification]), the model expects a
  tensor of dimension (batch_size) with each value of the batch corresponding to the expected label of the
  entire sequence.
For token classification models (e.g., [BertForTokenClassification]), the model expects a tensor
  of dimension (batch_size, seq_length) with each value corresponding to the expected label of each individual
