transformers safetensors camembert token-classification fr license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
---
language:
- fr
license: cc-by-nc-4.0
pipeline_tag: token-classification
widget:
- text: '* ALBI, (Géog.) ville de France, capitale de l''Albigeois, dans le haut Languedoc
    : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44.'
- text: HILPERHAUSEN, (Géog.) ville d'Allemagne en Franconie, sur la Werra, au comté
    de Henneberg, entre Cobourg & Smalcalde ; elle appartient à une branche de la
    maison de Saxe-Gotha. Long. 28. 15. lat. 50. 35. (D. J.)
---

# bert-base-french-cased-edda-ner-level-1


<!-- Provide a quick summary of what the model is/does. -->

This model is designed to identify and classify Named Entity Recognition with the prefix IO. 
It has been trained on the French *Encyclopédie ou dictionnaire raisonné des sciences des arts et des métiers par une société de gens de lettres (1751-1772)* edited by Diderot and d'Alembert (provided by the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu)).
Dataset: [https://huggingface.co/datasets/GEODE/GeoEDdA](https://huggingface.co/datasets/GEODE/GeoEDdA)


## Class labels

<!-- Provide a list of tag detected by the model. -->

The NER detected by this model are:
- **NC-Spatial**: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. `ville`, `la rivière`, `royaume`.
- **NP-Spatial**: a proper noun identifying the name of a place (spatial named entities), e.g. `France`, `Paris`, `la Chine`.
- **ENE-Spatial**: nested spatial entity , e.g. `ville de France`, `royaume de Naples`, `la mer Baltique`.
- **Relation**: spatial relation, e.g. `dans`, `sur`, `à 10 lieues de`.
- **Latlong**: geographic coordinates, e.g. Long. 19. 49. lat. 43. 55. 44.
- **NC-Person**: a common noun that identifies a person (nominal spatial entity), e.g. `roi`, `l'empereur`, `les auteurs`.
- **NP-Person**: a proper noun identifying the name of a person (person named entities), e.g. `Louis XIV`, `Pline`, `les Romains`.
- **ENE-Person**: nested people entity, e.g. `le czar Pierre`, `roi de Macédoine`
- **NP-Misc**: a proper noun identifying entities not classified as spatial or person, e.g. `l'Eglise`, `1702`, `Pélasgique`.
- **ENE-Misc**: nested named entity not classified as spatial or person, e.g. `l'ordre de S. Jacques`, `la déclaration du 21 Mars 1671`.
- **Head**: entry name
- **Domain-Mark**: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. `Géographie`, `Geog.`, `en Anatomie`.



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

This model was trained entirely on French encyclopedic entries and will likely not perform well on text in other languages or other corpora. 


## Acknowledgement


The authors are grateful to the [ASLAN project](https://aslan.universite-lyon.fr) (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR).
Data courtesy the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu), University of Chicago.
