Integrating LLM Translation into Your Scikit-Learn Pipelines with Scikit-LLM

Introduction

Text classification sometimes requires dealing with raw texts written in different languages, with the added challenge of normalizing all incoming data into a unified language before providing it to the downstream classification model. Thankfully, Scikit-LLM not only facilitates the construction of pipelines driven by large language models (LLMs) for tasks like zero-shot and few-shot classification. It also provides custom classes to adapt pre-trained models for other specialized language tasks like translation.

This article shows how to build an overarching scikit-learn pipeline for multilingual text classification, consisting of two major stages, both guided by Scikit-LLM-wrapped models: translation and zero-shot classification. The resulting pipeline I'll teach you to build has multiple advantages:

• It replaces the rigid keyword matching of traditional text classifiers with semantic reasoning, thanks to a pre-trained LLM wrapped for zero-shot classification
• Incoming texts are unified into a common language to enable effective classification with a single downstream model
• Zero-shot classification with Scikit-LLM addresses the cold-start data issue, needing only category names (class labels) along with a few associated training examples to start doing its job: no extra model training needed at all!

Building a Multilingual Translation and Zero-Shot Classification Pipeline

This example can be run entirely in a Google Colab notebook. It uses Groq's LLM API to rely entirely on free models. As a prerequisite, go to the Groq website, register, and generate your own API key. Once it is generated, carefully copy your API key (it is shown only once) and paste it as a new "Secret" variable in Google Colab, found under the key icon on the left-hand side of Colab. Here, you'll paste your copied API key and name the secret variable GROQ_API_KEY.

After that, we start our notebook by installing Scikit-LLM:

!pip install scikit-llm -q

Output:

Final results: