| # BERT-tiny model finetuned with M-FAC | |
| This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC. | |
| Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). | |
| ## Finetuning setup | |
| For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering](https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering) and just swap Adam optimizer with M-FAC. | |
| Hyperparameters used by M-FAC optimizer: | |
| ```bash | |
| learning rate = 1e-4 | |
| number of gradients = 1024 | |
| dampening = 1e-6 | |
| ``` | |
| ## Results | |
| We share the best model out of 5 runs with the following score on SQuAD version 2 validation set: | |
| ```bash | |
| exact_match = 50.29 | |
| f1 = 52.43 | |
| ``` | |
| Mean and standard deviation for 5 runs on SQuAD version 2 validation set: | |
| | | Exact Match | F1 | | |
| |:----:|:-----------:|:----:| | |
| | Adam | 48.41 ± 0.57 | 49.99 ± 0.54 | | |
| | M-FAC | 49.80 ± 0.43 | 52.18 ± 0.20 | | |
| Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py) and running the following bash script: | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 python run_qa.py \ | |
| --seed 42 \ | |
| --model_name_or_path prajjwal1/bert-tiny \ | |
| --dataset_name squad_v2 \ | |
| --version_2_with_negative \ | |
| --do_train \ | |
| --do_eval \ | |
| --per_device_train_batch_size 12 \ | |
| --learning_rate 1e-4 \ | |
| --num_train_epochs 2 \ | |
| --max_seq_length 384 \ | |
| --doc_stride 128 \ | |
| --output_dir out_dir/ \ | |
| --optim MFAC \ | |
| --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' | |
| ``` | |
| We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). | |
| Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). | |
| A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). | |
| ## BibTeX entry and citation info | |
| ```bibtex | |
| @article{frantar2021m, | |
| title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, | |
| author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, | |
| journal={Advances in Neural Information Processing Systems}, | |
| volume={35}, | |
| year={2021} | |
| } | |
| ``` | |