دانلود کتاب مدل های زبان بزرگ عملی

One of the most common tasks in natural language processing, and machine learning in general, is classification. The goal of the task is to train a model to assign a label or class to some input text. Categorizing text is used across the world for a wide range of applications, from sentiment analysis and intent detection to extracting entities and detecting language. The impact of Large Language Models on categorization cannot be understated. The addition of these models has quickly settled as the default for these kinds of tasks. In this chapter, we will discuss a variety of ways to use Large Language Modeling for categorizing text. Due to the broad field of text categorization, a variety of techniques, as well as use cases, will be discussed. This chapter also serves as a nice introduction to LLMs as most of them can be used for classification. We will focus on leveraging pre-trained LLMs, models that already have been trained on large amounts of data and that can be used for categorizing text. Fine-tuning these models for categorizing text and domain adaptation will be discussed in more detail in Chapter 10. Let’s start by looking at the most basic application and technique, fully-supervised text classification.