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README.md

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-**Estienne** is a text-segmentation model trained on Deberta.
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-In contrast with most text-segmentation approach, Estienne is based on token classification. Editorial structure are identified similarly to named-entity recognition.
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-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).
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-Given the diversity of the corpus, Estienne should work out on diverse document formats in European languages.
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-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.
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-## Use
-As Deberta remove newline by default and has no support for it in the tokenizer, they should be replaced by pilcrows (¶).
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-Estienne supports the following segmentations:
-* **Text**
-* **Separator** - actually a segmentation separator. They are generally based on newline (actually ¶) with some variations due to text segmentation understanding.
-* **Title**
-* **Table**
-* **Dialog** - any kind of speaker attributed intervention.
-* **Bibliography** - statement of a specific bibliographic reference, either in a bibliography section or a footnote.
-* **Contact** - personal information, can be especially useful in the context of PII removal.
-* **Paratext** - any non-meaningful text included in standard documents like header, page numbering, section recall, etc.
-* **Author** - author names and signatures.
-* **Date** - statement of date and time, common in letters and newspaper articles.
-* **Keyword** - list of keywords, especially common in scientific publications.
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-## Example