| # BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16) | |
| BERT model with [4 Transformer layers and hidden embedding of size 256](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4), 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 on CORD-19 dataset (as released on 2020/06/16). | |
| ## Training the model | |
| ```bash | |
| python run_language_modeling.py | |
| --model_type bert | |
| --model_name_or_path google/bert_uncased_L-4_H-256_A-4 | |
| --do_train | |
| --train_data_file {cord19-200616-dataset} | |
| --mlm | |
| --mlm_probability 0.2 | |
| --line_by_line | |
| --block_size 256 | |
| --per_device_train_batch_size 20 | |
| --learning_rate 3e-5 | |
| --num_train_epochs 2 | |
| --output_dir bert_uncased_L-4_H-256_A-4_cord19-200616 | |