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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…
Auto-Completion Style Text Generation with GPT-2 Model - MachineLearningMastery.com
Generating gibberish text is a simple programming exercise for beginners. But completing a sentence meaningfully would require a lot of work. The landscape of auto-completion technology has transformed dramatically with the introduction of neural approaches. With Hugging Face's transformers library, implementing text completion is only a few lines of code. In this comprehensive tutorial, you will implement several examples and…
A Complete Introduction to Using BERT Models - MachineLearningMastery.com
BERT model is one of the first Transformer application in natural language processing (NLP). Its architecture is simple, but sufficiently do its job in the tasks that it is intended to. In the following, we'll explore BERT models from the ground up --- understanding what they are, how they work, and most importantly, how to use them practically in your…
Understanding RAG Part IV: RAGAs & Other Evaluation Frameworks - MachineLearningMastery.com
Be sure to check out the previous articles in this series: Understanding RAG Part I: Why It’s Needed Understanding RAG Part II: How Classic RAG Works Understanding RAG Part III: Fusion Retrieval and Reranking Retrieval augmented generation (RAG) has played a pivotal role in expanding the limits and overcoming many limitations of standalone large language […]
3 Easy Ways to Fine-Tune Language Models - MachineLearningMastery.com
Language models have quickly become cornerstones of many business applications in recent years. Their usefulness has been proven by many people who interact with them daily. As language models continue to find their place in people’s lives, the community has made many breakthroughs to improve models’ capabilities, primarily through fine-tuning. Language model fine-tuning is a […]
5 Python Libraries to Build an Optimized RAG System - MachineLearningMastery.com
Retrieval augmented generation (RAG) has become a vital technique in contemporary AI systems, allowing large language models (LLMs) to integrate external data in real time. This approach empowers models to ground their responses in precise information extracted from relevant sources, leading to better performance in tasks such as question-answering, summarization, and content generation. By augmenting […]
The Roadmap for Mastering Machine Learning in 2025 - MachineLearningMastery.com
Machine learning (ML) is now a part of our daily lives, from the voice assistants on our mobiles to advanced robots performing tasks similar to humans. It has transformed many sectors like healthcare with tools to assist doctors in diagnosing diseases, the automobile industry by introducing self-driving cars, retail by enhancing customer experiences through personalized […]
Building a Custom Model Pipeline in PyCaret: From Data Prep to Production - MachineLearningMastery.com
Building a custom model pipeline in PyCaret can help make machine learning easier. PyCaret is able to automate many steps, including data preparation and model training. It can also allow you to create and use your own custom models. In this article, we will build a custom machine learning pipeline step by step using PyCaret. […]
RAG Hallucination Detection Techniques - MachineLearningMastery.com
Introduction Large language models (LLMs) are useful for many applications, including question answering, translation, summarization, and much more, with recent advancements in the area having increased their potential. As you are undoubtedly aware, there are times when LLMs provide factually incorrect answers, especially when the response desired for a given input prompt is not represented […]
5 Common Mistakes to Avoid When Training LLMs - MachineLearningMastery.com
Introduction Training large language models (LLMs) is an involved process that requires planning, computational resources, and domain expertise. Data scientists, machine learning practitioners, and AI engineers alike can fall into common training or fine-tuning patterns that could compromise a model’s performance or scalability. This article aims to identify five common mistakes to avoid when training […]
LLM Evaluation Metrics Made Easy - MachineLearningMastery.com
Metrics are a cornerstone element in evaluating any AI system, and in the case of large language models (LLMs), this is no exception. This article demystifies how some popular metrics for evaluating language tasks performed by LLMs work from inside, supported by Python code examples that illustrate how to leverage them with Hugging Face libraries […]