Iván Palomares Carrascosa
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🌐 kdnuggets.comSource
Learn, in a few easy steps, how to build a two-stage scikit-learn pipeline for multilingual text classification, guided by two pre-trained language models handled by Scikit-LLM for translation and zero-shot classification.
🌐 machinelearningmastery.comSource
In this article, you will learn how to build a side-by-side benchmark of local and cloud LLM classifiers using Scikit-Ollama and a Groq-backed Scikit-LLM pipeline.
🌐 kdnuggets.comSource
Learn how to build an architecture that blends Scikit-LLM estimators for text summarization with traditional scikit-learn methods into a single, unified pipeline for summarizing and classifying raw, messy text.
🌐 machinelearningmastery.comSource
In this article, you will learn how to compare two strategies for adapting a large language model to a specific domain: fine-tuning with LoRA versus training a scikit-learn classifier on frozen embeddings.
🌐 machinelearningmastery.comSource
In this article, you will learn how to build a robust JSON serialization pipeline in Python that handles custom data types, graceful error recovery, and schema migrations.
🌐 kdnuggets.comSource
Build standardized custom NER pipelines in a few simple steps, powered by the reasoning capabilities of LLMs.
🌐 machinelearningmastery.comSource
In this article, you will learn how to benchmark a deterministic 3-Tiered Graph-RAG system against a standard vector RAG pipeline on fact-dense queries, and what the results reveal about matching prompt complexity to model capacity.
🌐 machinelearningmastery.comSource
In this article, you will learn how to build a lightweight, end-to-end RAG pipeline using scikit-learn for vector retrieval and open-source models for embeddings and text generation, without the use of paid APIs or heavyweight frameworks.
🌐 machinelearningmastery.comSource
In this article, you will learn what Python descriptors are and how to use them to build three types of controlled attributes: validated, computed, and lazily loaded.
🌐 kdnuggets.comSource
This article discusses LLM explainability and outlines the advances, trends, and ongoing developments in this important field of study.