Author: Suresh Rajasekaran

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.

Generative AI refers to a class of artificial intelligence models that are capable of creating new data samples resembling the original data they were trained on. These models learn the underlying patterns and distributions of the data, enabling them to generate novel instances with similar properties. Some popular generative AI techniques include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based language models.

In the context of drug discovery, generative AI has emerged as a powerful tool in recent years, offering a more efficient and effective approach to identifying and optimizing new drug candidates. By leveraging advanced techniques like GANs and VAEs, researchers can explore vast chemical spaces, predict molecular properties, and accelerate the drug development process. In this article, we'll delve into the use of generative models in drug discovery, providing code snippets to demonstrate their implementation.

In recent years, large language models (LLMs) like GPT-4 have gained significant attention due to their incredible capabilities in natural language understanding and generation. However, to tailor an LLM to specific tasks or domains, custom training is necessary. This article offers a detailed, step-by-step guide on custom training LLMs, complete with code samples and examples.

Prerequisites

Before diving in, ensure you have: