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Offered By: IBMSkillsNetwork

Train Sentiment-Aware LLMs with Reinforcement Learning & PPO

Explore the integration of reinforcement learning with human feedback using Proximal Policy Optimization (PPO) to build sentiment-aware LLMs. In this hands-on project, you’ll build "Happy" and "Pessimistic" LLMs by leveraging sentiment analysis with IMDb data. By the end, you will have trained, evaluated, and compared models capable of delivering sentiment-driven responses, providing practical experience with reinforcement learning for real-world applications.

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Guided Project

Machine Learning

4.5
(4 Reviews)

At a Glance

Explore the integration of reinforcement learning with human feedback using Proximal Policy Optimization (PPO) to build sentiment-aware LLMs. In this hands-on project, you’ll build "Happy" and "Pessimistic" LLMs by leveraging sentiment analysis with IMDb data. By the end, you will have trained, evaluated, and compared models capable of delivering sentiment-driven responses, providing practical experience with reinforcement learning for real-world applications.

Imagine you are an AI engineer who wants to train a "Happy LLM" and a "Pessimistic LLM" to train customer service agents. You have a reward function trained on the sentiment classifier from the IMDb dataset, and you will now use Reinforcement Learning (RL). RL is a subfield of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward. The agent, in this case, will be the LLM, and the decisions will be about what text to output. Unlike supervised learning, which requires labeled input/output pairs, RL relies on the agent exploring the environment and learning from the feedback it receives in the form of rewards or penalties. This trial-and-error approach enables the agent to improve its decision-making strategy over time.

Proximal Policy Optimization (PPO) is one of the most effective and widely used RL algorithms. Introduced by OpenAI, PPO strikes a balance between simplicity and performance, making it a popular choice for training RL agents. PPO optimizes the policy directly and employs mechanisms to ensure the updates are not too drastic, thereby maintaining stability and reliability during training.

In this lab, you will be guided through the process of training an RL agent using the PPO algorithm with a focus on sentiment analysis. You will use the IMDb dataset, a large collection of movie reviews, to train your model. By the end of this lab, you will have a solid understanding of how to implement and train an RL agent using PPO, and you will be equipped with practical skills to apply RL techniques to other problems and datasets. 


A Look at the Project Ahead

After completing this lab you will be able to:

  • Apply the basics of reinforcement learning and proximal policy optimization (PPO).

  • Set up the environment and load the IMDb dataset for training.

  • Define and configure the PPO agent and tokenizer.

  • Implement the PPO training loop.

  • Generate and evaluate text responses from the trained model.

  • Compare the performance of two models on the dataset.

  • Save and load the trained model for future use.


Who should complete this project?

This project is designed for:

  • Aspiring AI and Machine Learning Engineers
  • NLP Enthusiasts
  • Data Scientists Looking to Enhance Skills in RL
  • AI Developers Interested in Customer Experience Applications


What You'll Need

Before starting this project, make sure you have the following:

  • Browser: The IBM Skills Network Labs platform is compatible with the latest versions of Chrome, Edge, Firefox, and Safari. Please ensure your browser is up-to-date for the best experience.

  • Python Knowledge: You should have a basic understanding of Python, including working with libraries and data structures.

  • Familiarity with Machine Learning: A foundational understanding of machine learning concepts, particularly reinforcement learning, will help you navigate through the lab efficiently.

  • Basic Knowledge of NLP: It's helpful if you are familiar with natural language processing (NLP) concepts, especially sentiment analysis.

Take the next step in your AI journey and dive into the world of reinforcement learning with PPO. By the end of this lab, you’ll have practical skills in training "Happy" and "Pessimistic" LLMs using sentiment analysis. Whether you're building customer service agents or exploring advanced NLP techniques, this project will equip you with the tools and knowledge to take your AI projects to the next level.

Estimated Effort

30 Minutes

Level

Intermediate

Skills You Will Learn

Artificial Intelligence, Deep Learning, Generative AI, LLM, Machine Learning, Python

Language

English

Course Code

GPXX0D4HEN

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