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# BERT L-4 H-256 fine-tuned on MLM (CORD-19 2020/06/16)
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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).
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## Training the model
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```bash
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python run_language_modeling.py
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--model_type bert
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--model_name_or_path google/bert_uncased_L-4_H-256_A-4
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--do_train
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--train_data_file {cord19-200616-dataset}
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--mlm
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--mlm_probability 0.2
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--line_by_line
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--block_size 256
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--per_device_train_batch_size 20
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--learning_rate 3e-5
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--num_train_epochs 2
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--output_dir bert_uncased_L-4_H-256_A-4_cord19-200616
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