This course teaches you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub.
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Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the Hugging Face Hub, fine-tune it on a dataset, and share your results on the Hub!
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Chapters 5 to 8 provide a deep dive into the Datasets Library, the Tokenizers library, a walkthrough of all the common NLP use cases covered by the 🤗 Transformers library, and instructions on debugging and searching the official documentation.
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Chapter 9 provides an introduction to Gradio, a framework for building Machine Learning applications. This chapter gives a deep dive into building Gradio Interfaces and Blocks.
Future chapters are being developed and will be released in the near future.
I am working on the Pytorch version of the HuggingFace courses, although the syntax for the Tensorflow version is highly similar and I am familiar with Tensorflow syntax (received Tensorflow Developer Certification).