---
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