  Currently, publicly available models for website classification do not offer
an embedding method and have limited support for languages beyond English. We
release a dataset of more than two million category-labeled websites in 92
languages collected from Curlie, the largest multilingual human-edited Web
directory. The dataset contains 14 website categories aligned across languages.
Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model
for classifying and embedding websites based on their homepage in a
language-agnostic way. Homepage2Vec, thanks to its feature set (textual
content, metadata tags, and visual attributes) and recent progress in natural
language representation, is language-independent by design and generates
embedding-based representations. We show that Homepage2Vec correctly classifies
websites with a macro-averaged F1-score of 0.90, with stable performance across
low- as well as high-resource languages. Feature analysis shows that a small
subset of efficiently computable features suffices to achieve high performance
even with limited computational resources. We make publicly available the
curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec
model, and libraries
