The fine-tuning process was performed on a single P100 GPUs (16GB). The hyperparameters are:
max_seq_length=512
per_device_train_batch_size=16
gradient_accumulation_steps=1
total train batch size (w. parallel, distributed & accumulation) = 16
learning_rate=3e-5
num_train_epochs=3
"eval_exact_match": 78.55250709555345
"eval_f1": 85.91482799690257
Bioformer's performance is on par with
DistilBERT (EM/F1: 77.7/85.8),
although Bioformer was pretrained only on biomedical texts.
In our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faster than DistilBERT.
