How to Fine-tune BERT-tiny Model Using M-FAC

September 13, 2024

In the landscape of Natural Language Processing (NLP), fine-tuning robust models like BERT is crucial for getting state-of-the-art results. This guide aims to illustrate how to fine-tune the BERT-tiny model using the M-FAC (Matrix-Free Approximation of Curvature) optimizer on the SQuAD version 2 dataset. Whether you’re a novice or seasoned developer, we will walk you through the steps clearly.

Understanding M-FAC

M-FAC is an advanced second-order optimizer that enhances the training process by estimating second-order information while using significantly less memory. Think of it as a chef who knows how to perfectly adjust ingredients—a balance between sweetness and saltiness—to create a delicious dish, without wasting any of the key flavors (or resources) available.

External References

For more technical specifications on M-FAC, refer to the NeurIPS 2021 paper available at this link.

Fine-tuning Setup

To ensure a fair comparison between the M-FAC optimizer and the default Adam baseline, we will set up our model in a defined framework. For detailed steps, you can refer to the Hugging Face GitHub repository here.

Hyperparameters for M-FAC Optimizer

Learning rate = 1e-4
Number of gradients = 1024
Dampening = 1e-6

Results Overview

In our experimentation with 5 runs, we obtained impressive results on the SQuAD version 2 validation set. Here are the key scores:

Exact Match (M-FAC): 49.80 ± 0.43
F1 Score (M-FAC): 52.18 ± 0.20

In comparison, the Adam optimizer resulted in Exact Match of 48.41 ± 0.57 and F1 Score of 49.99 ± 0.54. This indicates that M-FAC performed slightly better in terms of F1 score, which is significant for question answering tasks.

Reproducing the Results

You can replicate these results by incorporating M-FAC code from the Hugging Face repository. Execute the following bash commands:

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

Troubleshooting

If you encounter issues during the fine-tuning process, consider these troubleshooting tips:

Ensure that you have installed all required libraries and dependencies.
Verify your CUDA device settings if you receive memory-related errors.
Examine the learning rate and other hyperparameters; slight modifications may lead to better performance.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.