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Building RAG Systems with Transformers - MachineLearningMastery.com
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 this post, you will explore…
Understanding Text Generation Parameters in Transformers - MachineLearningMastery.com
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 of the model, the generation…
Applications with Context Vectors - MachineLearningMastery.com
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 extract contextual keywords from a…
Generating and Visualizing Context Vectors in Transformers - MachineLearningMastery.com
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 generating context vectors today. In…
Example Applications of Text Embedding - MachineLearningMastery.com
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 will learn: How to build…
Using Auto Classes in the Transformers Library - MachineLearningMastery.com
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 to run the model is…
Text Embedding Generation with Transformers - MachineLearningMastery.com
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 transformer models from the Hugging…
Fine-Tuning DistilBERT for Question Answering - MachineLearningMastery.com
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 processing tasks. In this post,…
Advanced Q&A Features with DistilBERT - MachineLearningMastery.com
Question Answering (Q&A) is one of the signature practical applications of natural language processing. In a previous post, you have seen how to use DistilBERT for question answering by building a pipeline using the transformers library. In this post, you will deep dive into the technical details to see how you can manipulate the question for your own purpose. Specifically,…
A Gentle Introduction to Attention and Transformer Models - MachineLearningMastery.com
Transformer is a deep learning architecture that is very popular in natural language processing (NLP) tasks. It is a type of neural network that is designed to process sequential data, such as text. In this article, we will explore the concept of attention and the transformer architecture. Specifically, you will learn: What problems do the transformer models address What is…
Implementing Multilingual Translation with T5 and Transformers - MachineLearningMastery.com
Language translation is one of the most important tasks in natural language processing. In this tutorial, you will learn how to implement a powerful multilingual translation system using the T5 (Text-to-Text Transfer Transformer) model and the Hugging Face Transformers library. By the end of this tutorial, you’ll be able to build a production-ready translation system that can handle multiple language…
A Gentle Introduction to Transformers Library - MachineLearningMastery.com
Transformers is an architecture of machine learning models that uses the attention mechanism to process data. Many models are based on this architecture, like GPT, BERT, T5, and Llama. A lot of these models are similar to each other. While you can build your own models in Python using PyTorch or TensorFlow, Hugging Face released a library that makes it…
Understanding the DistilBart Model and ROUGE Metric - MachineLearningMastery.com
DistilBart is a typical encoder-decoder model for NLP tasks. In this tutorial, you will learn how such a model is constructed and how you can check its architecture so that you can compare it with other models. You will also learn how to use the pretrained DistilBart model to generate summaries and how to control the summaries' style. After completing…
Text Summarization with DistillBart Model - MachineLearningMastery.com
Text summarization represents a sophisticated evolution of text generation, requiring deep understanding of content and context. With encoder-decoder transformer models like DistilBart, you can now create summaries that capture the essence of longer text while maintaining coherence and relevance. In this tutorial, you'll discover how to implement text summarization using DistilBart. You'll learn through practical, executable examples, and by the…
Building LLM Applications with Hugging Face Endpoints and FastAPI - MachineLearningMastery.com
The combined use of FastAPI’s efficient handling of HTTP requests and Hugging Face’s powerful LLMs, helps developers quickly build AI-powered applications that respond to user prompts based on natural language generation.
Text Generation with GPT-2 Model - MachineLearningMastery.com
Text generation is one of the most fascinating applications of deep learning. With the advent of large language models like GPT-2, we can now generate human-like text that's coherent, contextually relevant, and surprisingly creative. In this tutorial, you'll discover how to implement text generation using GPT-2. You'll learn through hands-on examples that you can run right away, and by the…