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Course

Working with Hugging Face

Basic2 hr

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

Python2 hr8 videos26 Exercises2,050 XP35,496Statement of accomplishment

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Course Description

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.

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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

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Course outline

Working with Hugging Face

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