LLMs for Sentiment Analysis Training in Dublin, Ireland

This hands-on course focuses on using Large Language Models (LLMs) to perform sentiment analysis on text data. Participants will learn how LLMs like BERT, RoBERTa, and GPT can be fine-tuned or prompted for sentiment tasks. The course includes working with Hugging Face Transformers, preparing datasets, evaluating models, and deploying sentiment analysis systems in real-world applications.

Foundations of Sentiment Analysis and LLMs

Module 1: Introduction to Sentiment Analysis

• What is sentiment analysis?
• Use cases across industries
• Traditional vs LLM-based sentiment approaches

Module 2: Overview of Transformers and LLMs

• Introduction to BERT, RoBERTa, DistilBERT, GPT
• Tokenization, embeddings, and attention
• Hugging Face Transformers library overview

Module 3: Preparing Data for Sentiment Tasks

• Dataset formats (binary, multi-class)
• Common datasets (IMDb, SST-2, Amazon Reviews)
• Preprocessing: cleaning, labeling, tokenizing

Module 4: Inference with Pretrained LLMs

• Using BERT and RoBERTa for sentiment inference
• Zero-shot sentiment analysis with GPT and pipeline()
• Comparing outputs of different models

Fine-tuning and Evaluation

Module 5: Fine-Tuning Pretrained Models

• Fine-tuning BERT/RoBERTa on a labeled dataset
• Understanding model architecture for classification
• Setting up training loops with Transformers and Trainer API

Module 6: Evaluation and Error Analysis

• Metrics: accuracy, precision, recall, F1-score
• Confusion matrix and classification report
• Spotting overfitting and underfitting
• Analyzing misclassified examples

Module 7: Prompt-Based Sentiment Analysis

• What is prompt engineering?
• Few-shot prompting examples using OpenAI or Hugging Face models
• Comparing performance: fine-tuned vs prompted models

Module 8: Advanced Use Cases

• Multi-language sentiment analysis
• Domain-specific fine-tuning (e.g., finance, healthcare)
• Handling sarcasm, irony, and complex opinions

Deployment and Real-World Project

Module 9: Model Deployment Options

• Batch vs real-time prediction pipelines
• Exporting models (ONNX, TorchScript)
• Serving models via API using FastAPI or Gradio

Module 10: Real-World Sentiment Analysis Exercises

Module 11: Best Practices and Troubleshooting

• Managing bias and fairness in sentiment models
• Dealing with unbalanced datasets
• Efficient inference with quantization or distillation

Module 12: Wrap-Up and Next Steps

• Review of all tools and techniques covered
• Resources for continued learning (datasets, tutorials, papers)
• Optional knowledge check or quiz
• Final Q&A and feedback