readme: This model is [Distilroberta base](https://huggingface.co/distilroberta-base) trained on SQuAD v2 as:
  
  ```
  export SQUAD_DIR=../../squad2
  python3 run_squad.py 
      --model_type robberta 
      --model_name_or_path distilroberta-base 
      --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/distilroberta_fine_tuned/
  ```
  
  Performance on a dev subset is close to the original paper:
  
  ```
  Results: 
  {
      'exact': 70.9279368213228, 
      'f1': 74.60439802429168, 
      'total': 6078, 
      'HasAns_exact': 67.62886597938144, 
      'HasAns_f1': 75.30774267754136, 
      'HasAns_total': 2910, 
      'NoAns_exact': 73.95833333333333, 
      'NoAns_f1': 73.95833333333333, 'NoAns_total': 3168, 
      'best_exact': 70.94438960184272, 
      'best_exact_thresh': 0.0, 
      'best_f1': 74.62085080481161, 
      'best_f1_thresh': 0.0
  }
  ```
  
  We are hopeful this might save you time, energy, and compute. Cheers!