bert tiny finetuned mnli EU AI Act Compliance Profile

BERT-tiny model finetuned with M-FAC

This model is finetuned on MNLI 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.

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/text-classification and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer:

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 MNLI validation set:

matched_accuracy = 69.55
mismatched_accuracy = 70.58

Mean and standard deviation for 5 runs on MNLI validation set:

| | Matched Accuracy | Mismatched Accuracy | |:----:|:-----------:|:----------:| | Adam | 65.36 ± 0.13 | 66.78 ± 0.15 | | M-FAC | 68.28 ± 3.29 | 68.98 ± 3.05 |

Results can be reproduced by adding M-FAC optimizer code in https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py and running the following bash script:

CUDA_VISIBLE_DEVICES=0 python run_glue.py \
  --seed 42 \
  --model_name_or_path prajjwal1/bert-tiny \
  --task_name mnli \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 1e-4 \
  --num_train_epochs 5 \
  --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

Your obligations as a bert tiny finetuned mnli deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Library