Local Setup

This guide will take you step-by-step through the process of fine-tuning BERT on the SQuAD dataset. You can run the code locally on your machine, or use a GPU Notebook server, such as Google Colab, to speed up the training process.

What you’ll need

Before you start, make sure you have the following installed on your machine:

• Python 3.8 or higher

Procedure

1. Clone the repository:

   user:~$ git clone https://github.com/dpoulopoulos/bert-qa-finetuning.git
   

2. Navigate to the project directory:

   user:~$ cd bert-qa-finetuning
   

3. Create a Python virtual environment:

   user:~/bert-qa-finetuning$ python -m venv .venv
   

4. Activate the virtual environment:

   user:~/bert-qa-finetuning$ source .venv/bin/activate
   

5. Install the required packages:

   user:~/bert-qa-finetuning$ pip install -r requirements.txt
   

6. Run the Jupyter Notebook server and select the bert-squad.ipynb file:

   user:~/bert-qa-finetuning$ jupyter notebook
   

7. Follow the instructions in the notebook to fine-tune BERT on the SQuAD dataset.

Next steps

Congratulations! You’ve successfully fine-tuned BERT on the SQuAD dataset. You can now use the model to solve Question-Answering tasks on your own data.

If you have access to a Kubeflow cluster, you can also leverage Kubeflow Pipelines to scale and automate the experiment. Check the Kubeflow Pipelines guide for more information.