How Hugging Face Pipelines Work
Explore how Hugging Face pipelines provide a high-level API that connects pretrained models, tokenizers, and deep learning frameworks like PyTorch to perform AI tasks in NLP and computer vision. Understand the pipeline workflow from input processing to output generation, and discover how pipelines simplify complex model execution while allowing customization for different tasks and performance needs.
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By now, you should be familiar with Hugging Face and its key components.
But how do you actually use it to perform tasks like sentiment analysis, image classification, or question answering? This lesson introduces pipelines, the core interface that connects pretrained models, tokenizers, and deep learning frameworks to provide a simple, high-level API.
Pipelines act as a bridge between theoretical knowledge of Transformers and real-world applications, letting you go from input data to meaningful output in just a few lines of Python code.
What is a pipeline?
In Hugging Face, a pipeline is an abstraction that wraps three main components: the model, the tokenizer, and the framework wrapper (PyTorch, TensorFlow, or JAX). ...