This model is Distilbert base uncased trained on SQuAD v2 as:
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type distilbert
--model_name_or_path distilbert-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/distilbert_fine_tuned/
Performance on a dev subset is close to the original paper:
Results:
{
'exact': 64.88976637051661,
'f1': 68.1776176526635,
'total': 6078,
'HasAns_exact': 69.7594501718213,
'HasAns_f1': 76.62665295288285,
'HasAns_total': 2910,
'NoAns_exact': 60.416666666666664,
'NoAns_f1': 60.416666666666664,
'NoAns_total': 3168,
'best_exact': 64.88976637051661,
'best_exact_thresh': 0.0,
'best_f1': 68.17761765266337,
'best_f1_thresh': 0.0
}
We are hopeful this might save you time, energy, and compute. Cheers!