How to Fine-Tune BERT on QNLI Using BERT-of-Theseus Compression

May 23, 2021

If you’ve ever wondered how to take the powerful BERT (Bidirectional Encoder Representations from Transformers) model and adapt it to specific tasks, then you’re in the right place! In this guide, we will walk you through the process of fine-tuning a BERT model that has already been trained on the SQUAD v2 dataset. We will then adapt it for the QNLI task while employing compression through a method known as BERT-of-Theseus.

Understanding the QNLI Task

The Question Answering Natural Language Inference (QNLI) task is designed to assess the model’s ability to understand questions and their corresponding answers. The goal is to predict whether a given passage contains the answer to the question posed. Think of it like having a library full of books (our dataset), and you need to find out if a specific book answers a question you have. The BERT model will act as a knowledgeable librarian that can quickly point you to where the answers might be found.

Step 1: Getting the Dataset

Before diving into training, we first need to gather our dataset. Follow these commands in your terminal:

bash
wget https://raw.githubusercontent.com/rhythmcao/QNLI/master/data/QNLI/train.tsv
wget https://raw.githubusercontent.com/rhythmcao/QNLI/master/data/QNLI/test.tsv
wget https://raw.githubusercontent.com/rhythmcao/QNLI/master/data/QNLI/dev.tsv
mkdir QNLI_dataset
mv *.tsv QNLI_dataset

Step 2: Training the Model

Once you have the dataset, it’s time to train the model. We’ll be using a Tesla P100 GPU along with 25GB of RAM to ensure efficient processing. Here’s the command you’ll need:

bash
!python content/BERT-of-Theseus/run_glue.py \
   --model_name_or_path deepset/bert-base-cased-squad2 \
   --task_name qnli \
   --do_train \
   --do_eval \
   --do_lower_case \
   --data_dir content/QNLI_dataset \
   --max_seq_length 128 \
   --per_gpu_train_batch_size 32 \
   --per_gpu_eval_batch_size 32 \
   --learning_rate 2e-5 \
   --save_steps 2000 \
   --num_train_epochs 50 \
   --output_dir content/output_dir \
   --evaluate_during_training \
   --replacing_rate 0.7 \
   --steps_for_replacing 2500

The parameters in this command allow for flexibility in how the model is trained. For example, adjusting the batch size or learning rate can lead to different outcomes. Think of this as tweaking the ingredients in a recipe – a little more salt here, a bit less sugar there can make all the difference!

Step 3: Evaluating the Model

Once the training is complete, it’s important to evaluate the performance of your model. You can compare the accuracy metrics across different models:

BERT-base: 91.2%
BERT-of-Theseus: 88.8%
bert-uncased-finetuned-qnli: 87.2%
DistillBERT: 85.3%

Troubleshooting

If you run into issues during training or evaluation, here are a few troubleshooting tips:

Check your GPU allocation; ensure you have sufficient resources.
Inspect your dataset for issues, such as missing files or improper formatting.
Ensure all required libraries or packages are properly installed.
Experiment with the hyperparameters like learning rate or batch size if you encounter convergence issues.

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Conclusion

Fine-tuning a BERT model on the QNLI task using the BERT-of-Theseus compression technique can greatly enhance the performance of your natural language processing applications. With the provided steps, you should be well-equipped to embark on your own journey of model fine-tuning!

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.