Deep learning has ushered in a new era of natural language processing (NLP), and one remarkable player on this field is SqueezeBERT. It’s not just another variant of BERT; this model is designed for efficiency without sacrificing the performance that BERT is known for. Let’s dive into how to work with SqueezeBERT, from pretraining to finetuning!

What is SqueezeBERT?

SqueezeBERT is a pretrained model, specifically squeezebert-mnli, that has been crafted for the English language using techniques like masked language modeling (MLM) and Sentence Order Prediction (SOP). It’s been fine-tuned on the Multi-Genre Natural Language Inference (MNLI) dataset. Similar to BERT-base, SqueezeBERT substitutes conventional pointwise fully-connected layers with grouped convolutions, enhancing its efficiency. In fact, it is 4.3 times faster than BERT-base-uncased on devices like Google Pixel 3!

Pretraining the Model

Pretraining Data

• BookCorpus – A diverse dataset with unpublished books.
• English Wikipedia – A well-established source of knowledge.

Pretraining Procedure

The SqueezeBERT model is pretrained via MLM and SOP tasks. If you wish to pretrain your own model solely with MLM, that’s viable. Here’s an analogy to understand its architecture: think of SqueezeBERT as a well-trained baker who uses unique tools (grouped convolutions) instead of conventional ones, enabling them to whip up delightful pastries (language predictions) much faster!

The authors leverage the LAMB optimizer with specifics like:

• Global batch size: 8192
• Learning rate: 2.5e-3
• Warmup proportion: 0.28

The model is pretrained for 56,000 steps on a maximum sequence length of 128 and an additional 6,000 steps with a maximum sequence length of 512.

Finetuning SqueezeBERT

Finetuning SqueezeBERT can be approached in two ways:

• Finetuning without bells and whistles: Train SqueezeBERT on individual GLUE tasks after pretraining.
• Finetuning with bells and whistles: Use a distilled MNLI-finetuned model as a student model to finetune on other GLUE tasks.

For more detailed hyperparameters, check the appendix of the SqueezeBERT paper.

How to Finetune SqueezeBERT

To finetune SqueezeBERT on the MRPC (Microsoft Research Paraphrase Corpus) text classification task, you can use the following command:

.utils/download_glue_data.py
python examples/text-classification/run_glue.py \
    --model_name_or_path squeezebert-base-headless \
    --task_name mrpc \
    --data_dir ./glue_data/MRPC \
    --output_dir ./models/squeezebert_mrpc \
    --overwrite_output_dir \
    --do_train \
    --do_eval \
    --num_train_epochs 10 \
    --learning_rate 3e-05 \
    --per_device_train_batch_size 16 \
    --save_steps 20000

Troubleshooting

If you encounter issues during pretraining or finetuning, consider the following:

• Check if all dependencies are properly installed.
• Ensure that the paths to datasets are correctly set in your command.
• Review GPU availability or adjust batch sizes if you face memory issues.

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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.