In the previous post, you learned how to build a simple retrieval-augmented generation (RAG) system. RAG is a powerful approach for enhancing large language models with external knowledge and there are many variations in how to make it work better. In the following, you will see some advanced features and techniques to improve the performance […]
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the capabilities of large language models. By combining the strengths of retrieval systems with generative models, RAG systems can produce more accurate, factual, and contextually relevant responses. This approach is particularly valuable when dealing with domain-specific knowledge or when up-to-date information is required. In […]
Transformer models are the standard models to use for NLP tasks today. Almost all of the NLP tasks involve generating text but it is not the direct output of the model. You may expect the model to help you generate text that is coherent and contextually relevant. While partially this is related to the quality […]
Context vectors are powerful representations generated by transformer models that capture the meaning of words in their specific contexts. In our previous tutorials, we explored how to generate these vectors and some basic applications. Now, we’ll focus on building practical applications that leverage context vectors to solve real-world problems. In this tutorial, we’ll implement several […]
Context vectors are a powerful tool for advanced NLP tasks. They allow you to capture the contextual meaning of words, such as identifying the correct sense of a word in a sentence when it has multiple meanings. In this post, we will explore some example applications of context vectors. Specifically: You will learn how to […]
A context vector is a numerical representation of a word that captures its meaning within a specific context. Unlike traditional word embeddings that assign a single, fixed vector to each word, a context vector for the same word can change depending on the surrounding words in a sentence. Transformers are the tool of choice for […]
Text embeddings have revolutionized natural language processing by providing dense vector representations that capture semantic meaning. In the previous tutorial, you learned how to generate these embeddings using transformer models. In this post, you will learn the advanced applications of text embeddings that go beyond basic tasks like semantic search and document clustering. Specifically, you […]
In the transformers library, auto classes are a key design that allows you to use pre-trained models without having to worry about the underlying model architecture. It makes your code more concise and easier to maintain. For example, you can easily switch between different model architectures by just changing the model name; even the code […]
Text embeddings are numerical representations of text that capture semantic meaning in a way that machines can understand and process. These embeddings have revolutionized natural language processing by enabling computers to work with text more meaningfully than traditional bag-of-words or one-hot encoding approaches. In the following, you’ll explore how to generate high-quality text embeddings using […]
The transformers library provides a clean and well-documented interface for many popular transformer models. Not only it makes the source code easier to read and understand, it also provided a standardize way to interact with the model. You have seen in the previous post how to use a model such as DistilBERT for natural language […]
