| [bioformer-cased-v1.0](https://huggingface.co/bioformers/bioformer-cased-v1.0) fined-tuned on the [SQuAD1](https://rajpurkar.github.io/SQuAD-explorer) dataset for 3 epochs. | |
| 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 | |
| ``` | |
| ## Evaluation results | |
| ``` | |
| "eval_exact_match": 78.55250709555345 | |
| "eval_f1": 85.91482799690257 | |
| ``` | |
| Bioformer's performance is on par with [DistilBERT](https://arxiv.org/pdf/1910.01108.pdf) (EM/F1: 77.7/85.8), | |
| although Bioformer was pretrained only on biomedical texts. | |
| ## Speed | |
| In our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faster than DistilBERT. | |