This workshop was moved from 2/20.
Learn how modern NLP systems are built and fine-tuned using Hugging Face—powering real-world text understanding applications.
This workshop introduces how modern NLP systems work in practice, using Hugging Face tools to analyze, classify, and understand text. Students will gain an intuitive understanding of transformer-based models, common NLP tasks, and how these models are adapted to real-world use cases such as sentiment analysis, entity extraction, and question answering.
Speaker
Nelson Filipe Costa
Nelson is a researcher specializing in NLP and language model fine-tuning, with experience applying AI models in both academic research and government projects. He has published peer-reviewed work on discourse analysis and language modeling, developed NLP systems for the Government of Québec, and taught undergraduate AI courses. His work bridges modern NLP research with real-world deployment using open-source tools like Hugging Face.
Agenda
What modern NLP looks like today (from rules to transformers)
How pre-trained language models work and why Hugging Face matters
Core NLP tasks in practice: classification, NER, and QA
Fine-tuning vs prompting: when and why models are adapted
From workshop to PBL: what students actually build in the Hugging Face project
Q&A