The Complete Guide To Sentiment Analysis with Ludwig — Part II

In the second part of a three-part series on sentiment analysis with Ludwig, the authors focus on using Transformer encoders like BERT to enhance model performance. BERT, a state-of-the-art Transformer model, is implemented in Ludwig with minimal configuration changes from previous models, such as Parallel CNN and Bi-LSTM. It is recommended to use smaller learning rates and batch sizes due to BERT's high memory demands. Despite the longer training times, BERT achieves superior accuracy compared to the other models, as evidenced by its performance on the SST-5 dataset. The authors use Ludwig's tools to evaluate and compare models, highlighting BERT's better accuracy but longer prediction latency. Through various visualizations, the study shows how BERT and other models perform on test data, allowing users to choose based on accuracy and speed trade-offs. The series encourages readers to explore further by optimizing hyperparameters in the upcoming part and engaging with the Ludwig community for deeper insights.