| datasets: | |
| - squad_v2 | |
| # BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0 | |
| BERT model with [10 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), [fine-tuned for MLM](https://huggingface.co/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616) on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0. | |
| ## Training the model | |
| ```bash | |
| python run_squad.py | |
| --model_type bert | |
| --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616 | |
| --train_file 'train-v2.0.json' | |
| --predict_file 'dev-v2.0.json' | |
| --do_train | |
| --do_eval | |
| --do_lower_case | |
| --version_2_with_negative | |
| --max_seq_length 384 | |
| --per_gpu_train_batch_size 10 | |
| --learning_rate 3e-5 | |
| --num_train_epochs 2 | |
| --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2 | |