The academic paper which describes XLNet in detail and provides full results on a number of tasks can be found here: https://arxiv.org/abs/1906.08237.
Instructions and user guide will be added soon.
XLNet is a generalized autoregressive BERT-like pretraining language model that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order. It can learn dependency beyond a fixed length without disrupting temporal coherence by using segment-level recurrence mechanism and relative positional encoding scheme introduced in Transformer-XL. XLNet outperforms BERT on 20 NLP benchmark tasks and achieves state-of-the-art results on 18 tasks including question answering, natural language inference, sentiment analysis, and document ranking.