Categories: AI/ML Research

Multilingual Text Classification with Scikit-LLM and Multilingual Embeddings

In this article, you will learn how to build a multilingual text classification pipeline using multilingual large language model (LLM) embeddings and Scikit-learn, without training…

Feature Engineering with LLM Embeddings: Enhancing Scikit-learn Models

Large language model embeddings, or LLM embeddings, are a powerful approach to capturing semantically rich information in text and utilizing it to leverage other machine learning models — like those trained using Scikit-learn — in tasks that require deep contextual understanding of text, such as intent recognition or sentiment analysis.

July 18, 2025

In "AI/ML Research"

LLM Embeddings vs TF-IDF vs Bag-of-Words: Which Works Better in Scikit-learn?

Machine learning models built with frameworks like scikit-learn can accommodate unstructured data like text, as long as this raw text is converted into a numerical representation that is understandable by algorithms, models, and machines in a broader sense.

February 18, 2026

In "AI/ML Research"

Document Clustering with LLM Embeddings in Scikit-learn

Imagine that you suddenly obtain a large collection of unclassified documents and are tasked with grouping them by topic.

February 11, 2026

In "AI/ML Research"

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A central goal of autonomous reinforcement learning is continuous policy training without external resets. However,…

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