How Hugging Face Pipelines Work
Understand how Hugging Face pipelines bridge theory and practice, simplifying model inference for NLP, vision, and multimodal tasks.
We'll cover the following...
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).
The model is a pretrained neural network, such as BERT for text or ViT for images.
The tokenizer converts raw data ...