How to build a multilingual text classifier with Scikit-LLM and open multilingual embeddings

# Overview This guide explains how to construct a multilingual text classification pipeline without training separate models per language. The approach uses multilingual embeddings produced by an open LLM (BGE-M3) served locally via Ollama, with Scikit-LLM handling the embedding calls and scikit-learn providing the downstream classifier.

# Why use multilingual embeddings

# Environment and tooling The example stack in the article uses:

• Ollama to host models locally and avoid paid APIs.
• BGE-M3 as the multilingual embedding model.
• Scikit-LLM for seamless integration between embeddings and scikit-learn.
• scikit-learn (logistic regression in the example) for the downstream classifier.
• Reviews dataset as a real-world evaluation corpus.

The guide includes commands to install dependencies, start Ollama as a background process, pull the BGE-M3 model, and set Scikit-LLM configuration to point at http://localhost:11434/v1/ with a dummy key accepted by the local server.

# Data selection and sampling

# Pipeline steps (high-level)

• Start Ollama and pull the BGE-M3 model.
• Configure Scikit-LLM to use the local Ollama endpoint and provide a dummy key.
• Load and shuffle the multilingual dataset, selecting a balanced sample per language.
• Generate embeddings for each review using Scikit-LLM calling the local model.
• Train a simple scikit-learn classifier (logistic regression in the article) on the embeddings.
• Evaluate on a held-out test split to measure downstream classifier performance.

• Using a local Ollama instance avoids paid APIs and keeps the solution runnable in notebook environments.

# When this approach fits This pipeline suits projects that need a single classifier across multiple languages and where keeping inference costs and external API dependency low is important. It is also useful when you prefer to run models locally or within controlled environments.

# Limitations and next steps to consider The walkthrough focuses on a minimal, runnable example. For production use, consider embeddings caching, batching, model versioning, and monitoring for embedding drift. You may also scale the training sample and evaluate other classifiers or calibration steps depending on label distribution.

# Bottom line Generate multilingual embeddings once with a multilingual LLM, train a lightweight scikit-learn classifier on those embeddings, and you can classify texts across languages without separate per-language models. The article provides the concrete commands and code snippets to set up Ollama, pull BGE-M3, configure Scikit-LLM, and run the full pipeline on a sample multilingual review dataset.