BERT-sentiment-analysis

An exploration into training a BERT model for sentiment classification using Metal Performance Shaders.

Setting Up and Running the Code

You can skip this part if you are not interested in doing this experiment for yourself, but are interested in the results.

Setup

Must install miniforge3 so that we can train models using Mac ARM architecture:

Install the latest for OS X arm64

Next, run:

conda create --name myenv python=3.11
conda activate myenv

Make sure the conda environment is active before running any code in this project.

Then to install our needed packages from 🤗

pip install transformers accelerate datasets

Install pytorch nightly to allow Metal Performance Shaders usage:

conda install pytorch-nightly::pytorch torchvision torchaudio -c pytorch-nightly

Training the Model

If you would like to run the model for yourself, run the following:

python training.py

Play around with the training parameters and see what happens.

Playground

playground.py allows you to mess around with trained models. To play around with your own models, please update PATH_TO_MODEL with the path to your trained model and TEXTS with strings to classify.