**Estienne** is a text-segmentation model trained on Deberta.

In contrast with most text-segmentation approach, Estienne is based on token classification. Editorial structure are identified similarly to named-entity recognition.

Estienne was trained on 2,000 example of manually annotated texts, excerpted at random from three very large dataset collected by Pleias: Common Corpus (cultural heritage texts in the public domain), Marianne-OpenData (French/English administrative documents) and OpenScientificPile (scientific publications in free licenses, indexed on OpenAlex). Given the diversity of the corpus, Estienne should work out on diverse document formats in European languages.

Estienne supports the following segmentations:

The model is named in reference to the humanist Henri Estienne who introduced many practices of text segmentation still in use in scholarly edition today.