Working with Hugging Face
Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.
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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.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Associate AI Engineer for Developers
Go To TrackDeveloping AI Applications
Go To Track- 1
Getting Started with Hugging Face
FreeStart 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.
Introduction to Hugging Face50 xpWhat are Large Language Models?50 xpUse cases for Hugging Face100 xpTransformers and the Hub50 xpTransformer components50 xpSearching the Hub with Python100 xpSaving a model100 xpWorking with datasets50 xpInspecting datasets100 xpLoading datasets100 xpManipulating datasets100 xp - 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.
Pipelines with Hugging Face50 xpGetting started with pipelines100 xpUsing AutoClasses100 xpComparing models with the pipeline100 xpNLP and tokenization50 xpNormalizing text100 xpComparing tokenizer output100 xpText classification50 xpGrammatical correctness100 xpQuestion Natural Language Inference100 xpZero-shot classification100 xpSummarization50 xpSummarizing long text100 xpUsing min_length and max_length100 xpSummarizing several inputs100 xp - 3
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.
Processing and classifying images50 xpProcessing image data100 xpCreating an image classifier100 xpWhat about the original image?50 xpQuestion answering and multi-modal tasks50 xpDocument question and answering100 xpVisual question and answering100 xpAudio classification50 xpResampling audio files100 xpFiltering out audio files100 xpClassifying audio files100 xpAutomatic speech recognition50 xpInstantiating an ASR pipeline100 xpWord error rate100 xpIterating over a dataset100 xp - 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.
Fine-tuning a model50 xpPreparing a dataset100 xpBuilding the trainer100 xpUsing the fine-tuned model100 xpText generation50 xpThe process of generating text50 xpGenerating text from a text prompt100 xpGenerating a caption for an image100 xpEmbeddings50 xpUse cases for embeddings50 xpBenefits and challenges of embeddings100 xpGenerate embeddings for a sentence100 xpSemantic search50 xpSemantic search versus keyword search100 xpUsing semantic search100 xpCongratulations50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Associate AI Engineer for Developers
Go To TrackDeveloping AI Applications
Go To Trackcollaborators
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prerequisites
Introduction to Functions in Python
Jacob MarquezData 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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