Transfer learning in NLP involves leveraging pre-trained models that have learned general language representations on large datasets and fine-tuning them for specific tasks. This approach has become the standard in modern NLP, drastically reducing the data and computational requirements for building task-specific models.
Pre-trained models like BERT, GPT, and T5 are trained on massive corpora using tasks like language modeling or masked language modeling. These tasks enable the models to learn grammar, syntax, semantics, and even some world knowledge. When fine-tuned on a smaller labeled dataset, these models adapt their pre-trained knowledge to the target task, such as sentiment analysis or question answering.
Transfer learning improves efficiency and performance, especially in low-resource settings. Instead of training models from scratch, developers can use pre-trained models from libraries like Hugging Face Transformers or TensorFlow Hub and customize them for their needs. This paradigm has led to significant advancements in NLP and democratized access to state-of-the-art techniques for developers.