HR teams generate vast amounts of text data—from résumés and job descriptions to performance notes and internal communications. With the right tools, AI engineers can build models that streamline hiring workflows, improve talent matching, and surface insights that drive better people decisions. In this hands-on session, you’ll explore how Hugging Face and JobBERT can be used to tackle real-world HR data challenges.
In this code-along, Jens-Joris Decorte, Founder's Associate at Techwolf, guides you through using the Hugging Face ecosystem to work with HR datasets and apply the JobBERT LLM to tasks like résumé parsing, job description matching, and semantic search. You’ll gain practical experience designing workflows and deploying models that solve core HR problems with modern NLP techniques.
Key Takeaways:
- Learn how to use the Hugging Face AI ecosystem to work with HR text data.
- Discover practical approaches for solving common HR data challenges with NLP.
- Get hands-on with the JobBERT LLM and apply it to real HR workflows.
