Learning Objectives

  • Understanding Transfer Learning in NLP

  • How the Transformers and Tokenizers libraries are organized and

  • How to use Transformers and Tokenizer for downstream tasks like text classification, NER and text generation

Instructor's Bio: Thomas Wolf, PhD

Thomas leads the Science Team at Huggingface Inc., a Brooklyn-based startup working on Natural Language Generation and Natural Language Understanding. After graduating from Ecole Polytechnique (Paris, France), he worked on laser-plasma interactions at the BELLA Center of the Lawrence Berkeley National Laboratory (Berkeley, CA). Got accepted for a PhD at MIT (Cambridge, MA) but ended up doing his PhD in Statistical/Quantum physics at Sorbonne University and ESPCI (Paris, France), working on superconducting materials for the French DARPA (DGA) and Thales. Thomas is interested in Natural Language Processing, Deep Learning, and Computational Linguistics. Much of his research is about Natural Language Generation (mostly) and Natural Language Understanding (as a tool for better generation).

Who will be interested in this course?

  • This course is for current and aspiring Data Scientists, NLP and ML Engineers, and AI Product Managers

  • Knowledge of following tools and concepts is useful:

  • Familiarity with Python and Jupyter notebooks

  • Basic understanding of Natural Language Processing techniques