readme: ## BERT-large finetuned on squad v2.
  
  F1 on dev (from paper)[https://arxiv.org/pdf/1810.04805v2.pdf] is 81.9, we reach 81.58.
  
  ```
  {'exact': 78.6321906847469,
  'f1': 81.5816656803201,
  'total': 11873,
  'HasAns_exact': 73.73481781376518,
   'HasAns_f1': 79.64222615088413,
   'HasAns_total': 5928,
   'NoAns_exact': 83.51555929352396,
   'NoAns_f1': 83.51555929352396,
   'NoAns_total': 5945,
   'best_exact': 78.6321906847469,
   'best_exact_thresh': 0.0,
   'best_f1': 81.58166568032006,
   'best_f1_thresh': 0.0,
   'epoch': 1.59}
  ```
  
  
  ```
  python run_qa.py \
    --model_name_or_path bert-large-uncased \
    --dataset_name squad_v2 \
    --do_train \
    --do_eval \
    --save_steps 2500 \
    --eval_steps 2500 \
    --evaluation_strategy steps \
    --per_device_train_batch_size 12 \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir bert-large-uncased-squadv2 \
    --version_2_with_negative 1
    ```