

Splinter was trained to predict answers spans conditioned on a special [QUESTION] token. These tokens contextualize
  to question representations which are used to predict the answers. This layer is called QASS, and is the default
  behaviour in the [SplinterForQuestionAnswering] class. Therefore:
Use [SplinterTokenizer] (rather than [BertTokenizer]), as it already
  contains this special token. Also, its default behavior is to use this token when two sequences are given (for
  example, in the run_qa.py script).
If you plan on using Splinter outside run_qa.py, please keep in mind the question token - it might be important for
  the success of your model, especially in a few-shot setting.
Please note there are two different checkpoints for each size of Splinter. Both are basically the same, except that
  one also has the pretrained weights of the QASS layer (tau/splinter-base-qass and tau/splinter-large-qass) and one
  doesn't (tau/splinter-base and tau/splinter-large). This is done to support randomly initializing this layer at
  fine-tuning, as it is shown to yield better results for some cases in the paper.