Sentence Classification Using Transfer Learning with BERT

The rise of machine learning is enhancing various products in the software industry in a variety of ways by applying a diverse set of algorithms. One such application is to verify whether a sentence is grammatically correct or not. We find the use of such applications in Writing Assistance tools, Language Learning platforms, Automated Scoring platforms, Content Moderation platforms, and Email platforms. In our research, we present Sentence Classification using Transfer Learning with BERT (Bidirectional Encoder Representations from Transformers). We leverage transfer learning with the Hugging Face Transformers library to fine-tune the BERT model, incorporating hyperparameter sweeps via Weights and Biases to optimize learning rate, batch size, and the number of epochs. The process involves downloading and preprocessing the dataset, tokenizing sentences with the BERT tokenizer, and preparing input data with special tokens, padding, and attention masks. The dataset is split into training and validation sets, and a custom training loop is implemented to fine-tune the model, logging performance metrics to Weights and Biases. Additionally, a test function is provided to predict the grammaticality of example sentences using the fine-tuned model.