Hands-On Transfer Learning with BERT: Solving the CoLA Classification Task

In the previous lecture, we explored how Transfer Learning is applied using the BERT model to solve natural language classification problems. The approach is similar to techniques used in computer vision, but with important differences. In this session, we put theory into practice by implementing a Python solution for the CoLA (Corpus of Linguistic Acceptability) task. This problem requires classifying whether an English sentence is linguistically acceptable, covering grammar and broader language rules. We leverage Hugging Face models and libraries for tokenization, padding, data loaders, and training with Adam optimizer. Key concepts include Transfer Learning, the GLUE benchmark, CoLA, NLP, tokenization, padding, collation, and data loaders. The full Colab notebook is available via the provided link.