Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

DataCamp

Working with Hugging Face

via DataCamp

Overview

Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.

In today's rapidly evolving landscape of machine learning (ML) and artificial intelligence (AI), Hugging Face stands out as a vital platform, allowing anyone to leverage the latest advancements in their projects.

Explore the Hugging Face Hub



To begin, you'll navigate the Hugging Face Hub's vast model and dataset repository. You'll also discover the power of Large Language Models and Transformers, exploring the diverse range available. You'll discover how the models and datasets can be applied to tasks ranging from sentiment analysis to language translation. Furthermore, we'll extend our exploration to image and audio processing.

Master Pipelines for Text, Images, and Audio



Pipelines are the backbone of many ML and AI workflows. You'll start with the basics of the pipeline module and Auto classes from the transformers library. Then, you'll build pipelines for natural language processing tasks before moving on to image and audio processing, ensuring you have the tools to tackle a wide range of tasks efficiently.

Fine-Tune Models and Leverage Embeddings



Finally, you'll dive into different frameworks for fine-tuning, text generation, and embeddings. You'll go through a fine-tuning example before exploring the concept of embeddings in machine learning, understanding how they capture semantic information.

By the end of the course, you'll be equipped with the knowledge and skills to tackle a wide range of ML and AI tasks effectively using the Hugging Face Hub.

Syllabus

  • Getting Started with Hugging Face
    • Start your journey with the Hugging Face platform by understanding what Hugging Face is and common use cases. Then, you'll learn about the Hugging Face Hub including models and datasets available, how to search for them, navigate model, or dataset, cards, and download. Lastly, you'll learn about the high-level components of transformers and LLMs.
  • Building Pipelines with Hugging Face
    • It's time to dive into the Hugging Face ecosystem! You'll start by learning the basics of the pipeline module and Auto classes from the transformers library. Then, you'll learn at a high level what natural language processing and tokenization is. Finally, you'll start using the pipeline module for several text-based tasks, including text classification.
  • Building Pipelines for Image and Audio
    • In this chapter, you'll apply pipeline methodologies to new tasks using image and audio data. Specifically, you will learn ways to process these types of data in preparation for tasks such as classification, question and answering and automatic speech recognition.
  • Fine-tuning and Embeddings
    • Explore the different frameworks for fine-tuning, text generation, and embeddings. Start with the basics of fine-tuning a pre-trained model on a specific dataset and task to improve performance. Then, use Auto classes to generate the text from prompts and images. Finally, you will explore how to generate and use embeddings.

Taught by

Jacob Marquez

Reviews

Start your review of Working with Hugging Face

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.