readme: This model is [BERT base uncased](https://huggingface.co/bert-base-uncased) trained on SQuAD v2 as:
  
  ```
  export SQUAD_DIR=../../squad2
  python3 run_squad.py 
      --model_type bert 
      --model_name_or_path bert-base-uncased 
      --do_train 
      --do_eval 
      --overwrite_cache 
      --do_lower_case 
      --version_2_with_negative 
      --save_steps 100000 
      --train_file $SQUAD_DIR/train-v2.0.json 
      --predict_file $SQUAD_DIR/dev-v2.0.json 
      --per_gpu_train_batch_size 8 
      --num_train_epochs 3 
      --learning_rate 3e-5 
      --max_seq_length 384 
      --doc_stride 128 
      --output_dir ./tmp/bert_fine_tuned/
  ```
  
  Performance on a dev subset is close to the original paper:
  
  ```
  Results: 
  {
      'exact': 72.35932872655479, 
      'f1': 75.75355132564763, 
      'total': 6078, 
      'HasAns_exact': 74.29553264604812, 
      'HasAns_f1': 81.38490892002987, 
      'HasAns_total': 2910, 
      'NoAns_exact': 70.58080808080808, 
      'NoAns_f1': 70.58080808080808, 
      'NoAns_total': 3168, 
      'best_exact': 72.35932872655479, 
      'best_exact_thresh': 0.0, 
      'best_f1': 75.75355132564766, 
      'best_f1_thresh': 0.0
  }
  ```
  
  We are hopeful this might save you time, energy, and compute. Cheers!