Introduction to NLP Inference
Understand the fundamentals of NLP inference including core task categories such as classification, generation, extraction, and similarity. Explore how Hugging Face pipelines simplify text processing by handling tokenization, model execution, and output formatting, preparing you to apply pretrained models effectively without training.
We'll cover the following...
Natural language processing (NLP) allows machines to interpret, analyze, and generate human language.
Before applying Hugging Face pipelines to real tasks, it's essential to understand what inference means and how NLP tasks are organized. This lesson provides a practical foundation, enabling you to use pre-trained models effectively without delving into model training or deep learning internals.
This chapter provides an overview of performing common NLP tasks.
Training vs. inference
In machine learning, training and inference play very different roles.
Training is the heavy lifting part. Models learn patterns from massive datasets, adjust millions or billions of parameters, and require specialized hardware. It is slow, resource-intensive, and typically performed by research groups or engineering teams with deep expertise.
Inference, on the other hand, is the stage where you simply use an already-trained model.
You load the model, pass new text to it, and get predictions in seconds. This is where AI becomes practical for real products and workflows. Every spam filter, translation tool, or chatbot you use runs inference constantly, even though the underlying model may have been trained just once.
This course focuses entirely on inference because Hugging Face pipelines allow you to access state-of-the-art NLP models instantly, without training or optimization steps.
Fun fact: Hugging Face pipelines enable you to perform inference instantly without needing to touch training code or model internals.
Why do we focus on inference rather than training?
Core categories of NLP tasks
NLP tasks fall into several fundamental categories, each solving a different type of language problem. Understanding these categories makes it easier to choose the right pipeline.
1. Classification tasks
Classification assigns predefined labels to input text.
Sentiment analysis, spam detection, topic categorization, and intent recognition all fall under this category. Modern models understand nuance, domain-specific ...