readme: # XLNet Fine-tuned on SQuAD 2.0 Dataset
  [XLNet](https://arxiv.org/abs/1906.08237) jointly developed by Google and CMU and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for question answering down-stream task.
  
  ## Training Results (Metrics)
  ```
  {
      "HasAns_exact": 74.7132253711201
      "HasAns_f1": 82.11971607032643
      "HasAns_total": 5928
      "NoAns_exact": 73.38940285954584
      "NoAns_f1": 73.38940285954584
      "NoAns_total": 5945
      "best_exact": 75.67590331003116
      "best_exact_thresh": -19.554906845092773
      "best_f1": 79.16215426779269
      "best_f1_thresh": -19.554906845092773
      "epoch": 4.0
      "exact": 74.05036637749515
      "f1": 77.74830934598614
      "total": 11873
  }
  ```
  
  ## Results Comparison
  | Metric | Paper     | Model     |
  | ------ | --------- | --------- |
  | **EM** | **78.46** | **75.68** (-2.78)      |
  | **F1** | **81.33** | **79.16** (-2.17)|
  
  Better fine-tuned models coming soon.
  
  ## How to Use
  ```
  from transformers import XLNetForQuestionAnswering, XLNetTokenizerFast
  
  model = XLNetForQuestionAnswering.from_pretrained('jkgrad/xlnet-base-squadv2)
  tokenizer = XLNetTokenizerFast.from_pretrained('jkgrad/xlnet-base-squadv2')
  ```