Transfer learning and fine-tuning have become essential techniques in natural language processing (NLP), particularly with the advent of large language models (LLMs). Transfer learning leverages pre-trained models on vast corpora of text, allowing them to capture general linguistic patterns. Fine-tuning involves adapting these pre-trained models to specific tasks or domains by training on smaller, task-specific datasets. Hugging Face's Transformers library has simplified this process, offering a wide range of pre-trained models like BERT, GPT, and T5, which can be fine-tuned for tasks such as text classification, question answering, and summarization. This approach significantly reduces computational costs and time while improving performance on specialized NLP tasks.
Transfer learning in NLP involves leveraging knowledge from pre-trained language models to enhance performance on specific downstream tasks. Instead of training a model from scratch, transfer learning allows the model first to learn general language representations from large corpora and then fine-tune it for tasks like sentiment analysis, named entity recognition, or machine translation. Explore these resources to get a small taste of Transfer Learning:
Transformers are a powerful deep learning architecture that has revolutionized natural language processing (NLP). Unlike traditional models, transformers use self-attention mechanisms to capture relationships between words in a sentence, regardless of their distance from each other. This allows transformers to handle long-range dependencies and understand context more effectively. Introduced in the paper "Attention is All You Need," transformers form the backbone of state-of-the-art models like BERT and GPT. Some resources (explore some or all) to understand the ideas behind a transformer:
Basic:
Advanced (Optional - but Highly Recommended):