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…

MachineLearningMastery.com

08.04.2025

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…

MachineLearningMastery.com

10.04.2025

5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs - MachineLearningMastery.com

Are large language models LLMs replacing the most conventional machine learning solutions out there? Here’s why not.

MachineLearningMastery.com

10.04.2025

How to Perform Scikit-learn Hyperparameter Optimization with Optuna - MachineLearningMastery.com

In this article, we show how to combine both for a hyperparameter optimization task.

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Using Auto Classes in the Transformers Library - MachineLearningMastery.com

Example Applications of Text Embedding - MachineLearningMastery.com

5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs - MachineLearningMastery.com

How to Perform Scikit-learn Hyperparameter Optimization with Optuna - MachineLearningMastery.com

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