Transfer learning and fine-tuning in practice

Transfer learning and fine-tuning are powerful techniques in the field of ML, particularly within NLP, to enhance the performance of models on specific tasks. This section will provide a detailed explanation of these concepts in practice.

Transfer learning

Transfer learning is the process of taking a pre-trained model that’s been trained on a large dataset (often a general one) and adapting it to a new, typically related task. The idea is to leverage the knowledge the model has already acquired, such as understanding language structures or recognizing objects in images, and apply it to a new problem with less data available. In NLP, transfer learning has revolutionized the way models are developed. Previously, most NLP tasks required a model to be built from scratch, a process that involved extensive data collection and training time. With transfer learning, you can take a pre-trained model and adapt it to a new task with relatively...