Transfer learning has revolutionized the field of Natural Language Processing (NLP) by allowing practitioners to leverage pre-trained models for their own tasks, thus significantly reducing training time and computational resources. In this article, we will discuss the concept of transfer learning, explore some popular pre-trained models, and demonstrate how to use these models for text classification with a real-world example. We'll be using the Hugging Face Transformers library for our implementation.
The Emergence of Transfer Learning in NLP
In the early days of NLP, traditional machine learning models such as Naive Bayes, logistic regression, and support vector machines were popular for solving text-related tasks. However, these models typically required large amounts of labeled data and carefully engineered features to achieve good performance.