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Hugging Face Transformers: Leverage Open-Source AI in Python
The Hugging Face Ecosystem
Exploring Hugging Face
Understanding Model Cards
The Transformers Library
Installing Transformers
Running Pipelines
Looking Under the Hood With Auto Classes
The Power of GPUs
Setting Up a Google Colab Notebook
Running Pipelines on GPUs
Conclusion
Frequently Asked Questions
Transformers is a powerful Python library created by Hugging Face that allows you to download, manipulate, and run thousands of pretrained, open-source AI models. These models cover multiple tasks across modalities like natural language processing, computer vision, audio, and multimodal learning.
Using pretrained open-source models can reduce costs, save the time needed to train models from scratch, and give you more control over the models you deploy.
By the end of this tutorial, you’ll understand that:
Hugging Face Transformers is a Python library for downloading and running pretrained open-source models, and version 5 runs on PyTorch only.
The pipeline() function runs a model in a few lines of code, and the model card on the Model Hub tells you which inputs each model needs.
Auto classes like AutoTokenizer and AutoModelForSequenceClassification give you access to the tokenizer and model layers inside a pipeline.
Passing device=0 to pipeline() runs a model on a GPU, which can make inference more than twenty times faster than on a CPU.
Throughout this tutorial, you’ll gain a conceptual understanding of Hugging Face’s AI offerings and learn how to work with the Transformers library through hands-on examples. When you finish, you’ll have the knowledge and tools you need to start using models for your own use cases. Before starting, you’ll benefit from having an intermediate understanding of Python and popular deep learning libraries like PyTorch.
Get Your Code: Click here to download the free sample code that shows you how to use Hugging Face Transformers to leverage open-source AI in Python.
Take the Quiz: Test your knowledge with our interactive “Hugging Face Transformers” quiz. You’ll receive a score upon completion to help you track your learning progress:
In this quiz, you'll test your understanding of the Hugging Face Transformers library. This library is a popular choice for working with transformer models in natural language processing tasks, computer vision, and other machine learning applications.
The Hugging Face Ecosystem
Before using Transformers, you’ll want to have a solid understanding of the Hugging Face ecosystem. In this first section, you’ll briefly explore everything that Hugging Face offers with a particular emphasis on model cards.
The Hugging Face Ecosystem
Exploring Hugging Face
Understanding Model Cards
The Transformers Library
Installing Transformers
Running Pipelines
Looking Under the Hood With Auto Classes
The Power of GPUs
Setting Up a Google Colab Notebook
Running Pipelines on GPUs
Conclusion
Frequently Asked Questions
Transformers is a powerful Python library created by Hugging Face that allows you to download, manipulate, and run thousands of pretrained, open-source AI models. These models cover multiple tasks across modalities like natural language processing, computer vision, audio, and multimodal learning.
Using pretrained open-source models can reduce costs, save the time needed to train models from scratch, and give you more control over the models you deploy.
By the end of this tutorial, you’ll understand that:
Hugging Face Transformers is a Python library for downloading and running pretrained open-source models, and version 5 runs on PyTorch only.
The pipeline() function runs a model in a few lines of code, and the model card on the Model Hub tells you which inputs each model needs.
Auto classes like AutoTokenizer and AutoModelForSequenceClassification give you access to the tokenizer and model layers inside a pipeline.
Passing device=0 to pipeline() runs a model on a GPU, which can make inference more than twenty times faster than on a CPU.
Throughout this tutorial, you’ll gain a conceptual understanding of Hugging Face’s AI offerings and learn how to work with the Transformers library through hands-on examples. When you finish, you’ll have the knowledge and tools you need to start using models for your own use cases. Before starting, you’ll benefit from having an intermediate understanding of Python and popular deep learning libraries like PyTorch.
Get Your Code: Click here to download the free sample code that shows you how to use Hugging Face Transformers to leverage open-source AI in Python.
Take the Quiz: Test your knowledge with our interactive “Hugging Face Transformers” quiz. You’ll receive a score upon completion to help you track your learning progress:
In this quiz, you'll test your understanding of the Hugging Face Transformers library. This library is a popular choice for working with transformer models in natural language processing tasks, computer vision, and other machine learning applications.
The Hugging Face Ecosystem
Before using Transformers, you’ll want to have a solid understanding of the Hugging Face ecosystem. In this first section, you’ll briefly explore everything that Hugging Face offers with a particular emphasis on model cards.