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!