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