ULMFiT (Universal Language Model Fine-tuning)

ULMFiT (Universal Language Model Fine-tuning) is a method for teaching computers to understand and work with text more effectively. It involves first training a language model on a large amount of general text to grasp language patterns. Then, this pre-trained model is fine-tuned on a smaller, specific dataset related to a particular task, like classifying emails or analyzing sentiment. This approach leverages broad language knowledge and adapts it efficiently, resulting in accurate results even with limited task-specific data, making NLP tasks faster and more effective.