Hugging Face Transformers: the basics. Practical coding guides SE1E1. NLP Models (BERT/RoBERTa)

### Practical Python Coding Guide - BERT in PyTorch In this first episode of the practical coding guide series, I discuss the basics of the Hugging Face Transformers Library. What is it? how does it work? what can you do with it? This episode focuses on high-level concepts, navigating their website and implementing some out-of-the-box functionality. Intro: 00:00 What is Hugging Face's Transformer Library: 1:12 Hugging Face models: 2:00 Navigating the Transformers documentation: 8:56 Coding with Transformers - installation: 11:55 Using pre-defined pipelines: 12:45 Implementing a model through PyTorch: 14:08 Tokenisers, Token IDs and Attention Masks: 16:28 Output from the model: 25:26 Outro: 27:26 This series attempts to offer a casual guide to Hugging Face and Transformer models focused on implementation rather than theory. Let me know if you enjoy them! In future episodes, I will be retraining a model from the Transformers Library (RoBERTa) on a downstream task: a multi-label classification problem. In an attempt to spot subtle sentiment attributes in online comments. Make sure to subscribe if you are interested. Check out my website: https://www.rupert.digital ----- Good learning material for theory (Transformers / BERT) Attention is all you need paper: https://arxiv.org/abs/1706.03762 BERT paper: https://arxiv.org/abs/1810.04805 RoBERTa paper: https://arxiv.org/abs/1907.11692 Jay Alanmar illustrated articles: https://jalammar.github.io/illustrated-transformer/ (check out his BERT one too) Chris McCormick: https://mccormickml.com/ (check out his youtube series on BERT / Transformers)