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Working with Hugging Face

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

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4 Hours16 Videos57 Exercises

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Course Description

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.
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  1. 1

    Getting Started with Hugging Face

    Free

    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.

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    Introduction to Hugging Face
    50 xp
    What are Large Language Models?
    50 xp
    Use cases for Hugging Face
    100 xp
    Transformers and the Hub
    50 xp
    Transformer components
    50 xp
    Searching the Hub with Python
    100 xp
    Saving a model
    100 xp
    Working with datasets
    50 xp
    Inspecting datasets
    100 xp
    Loading datasets
    100 xp
    Manipulating datasets
    100 xp
  2. 2

    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.

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  3. 4

    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.

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In the following tracks

Developing AI Applications

Collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf
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Jordan Beecher

Audio Recorded By

Jacob Marquez's avatar
Jacob Marquez
Jacob Marquez HeadshotJacob Marquez

Data Scientist at Microsoft

Jacob H. Marquez is an insatiable learner and lifelong builder. He is a data scientist by day, answering audacious questions to support customer experience and company goals. He is a serial hobbyist by day and night: being an educator, building a coffee recommendation app, drinking coffee, writing on Medium, and amateur cycling and muay thai. He has a bachelor's in psychology and a master's in computational analytics (2024).
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