---
title: Homepage2vec
emoji: 🌐
colorFrom: purple
colorTo: blue
python_version: 3.10
sdk: gradio
sdk_version: 4.11.0
app_file: app.py
pinned: false
tags: ["llm", "gpt", "homepage2vec", "multi-lingual", "multi-label", "classification"]
---

# 🌐 Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification

This project was developed in collaboration with the Data Science Lab ([DLab](https://dlab.epfl.ch/)) at EPFL as part of the [Machine Learning](https://www.epfl.ch/labs/mlo/machine-learning-cs-433/) (CS-433) course. We thank [Prof. Robert West](https://people.epfl.ch/robert.west) for enabling the project and [Tiziano Piccardi](https://tizianopiccardi.github.io/) for his guidance and support throughout the project.

![Training Overview](images/training-overview.png)


## 📚 Abstract

Homepage2Vec, a state-of-the-art open-source model for multilingual, multilabel website classification, has proven powerful in accurately classifying website topics. However, it is limited by its initial training data, which on average only contains a single topic for a website. This study explores the use of Large Language Models (LLMs) for creating a high-quality finetuning dataset that more accurately reflects the topic diversity of a website. We assess various LLM-based labelers and select the best one through comparison to crowdsourced annotations. We generate two variants of a new 10,000-website dataset, `curlie-gpt3.5-10k` and `curlie-gpt4-10k`, for finetuning Homepage2Vec. We show that finetuning Homepage2Vec with these datasets improves its macro F1 from 38% to 42%. Finally, we release both LLM-annotated datasets publicly.