Course
Working with Hugging Face
BasicSkill Level
Updated 07/2025
PythonArtificial Intelligence2 hr8 videos26 Exercises2,050 XP31,131Statement of Accomplishment
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Start Building AI with Hugging Face
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 models, datasets, and functionality 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 load, use, and save models from Hugging Face, and download and manipulate datasets for model training.Build Pipelines for Text Applications
Utilize pre-trained models available on Hugging Face for text classification tasks, such as grammatical correctness and dynamic category assignment; summarize long passages of text using both extractive and abstractive summarization models, and even begin having conversations with documents!By the end of the course, you'll be equipped with the knowledge and skills to tackle a wide range of text-based ML and AI tasks effectively using Hugging Face.
What you'll learn
- Assess when AutoModel and AutoTokenizer classes provide advantages over standard pipelines for advanced NLP customization
- Define the Python steps needed to construct and execute transformers pipelines for text classification, summarization, and document question-answering
- Differentiate the computational and workflow implications of running inference locally versus through Hugging Face inference providers
- Identify the key metadata elements in Hugging Face model and dataset cards that influence model or data set selection
- Recognize the methods for loading, filtering, and querying datasets using the Hugging Face datasets library
Feels like what you want to learn?
Start Course for FreePrerequisites
Introduction to Functions in Python1
Getting Started with Hugging Face
Discover the power of Hugging Face, the go-to AI platform for developers and data scientists. Explore pre-trained models, access datasets, and set up workflows to launch your AI-powered solutions.
2
Building Pipelines with Hugging Face
Learn to build AI pipelines with Hugging Face. Master text classification, summarize content, and extract insights from documents, equipping you to tackle real-world challenges across industries.
Working with Hugging Face
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FAQs
Is this course suitable for beginners?
Yes. Chapter 1 starts with installing huggingface_hub and explains what Hugging Face is, so learners only need basic Python knowledge to follow along.
Which Hugging Face libraries and functions will I use?
You will work with huggingface_hub to browse models, transformers for pipelines, AutoModel and AutoTokenizer for custom workflows, and the datasets library to load and filter data.
What can I build after completing this course?
You can build text classifiers for binary, multi-class, and zero-shot tasks, generate summaries with controlled length, and create question-answering pipelines that read PDF documents.
Why does the course cover AutoModel and AutoTokenizer alongside pipelines?
Pipelines offer a simple syntax for common tasks, while AutoModel and AutoTokenizer give you the control to build custom NLP workflows when default pipelines are not enough.
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