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Teklia/spacy-home-nacr-multilingual
spacy-home-nacr-multilingual is a machine learning model from Teklia. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for Spacy. The card lists the license as mit.
This model detects Person and Location entities in Latin, Czech and German.
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Updated Sep 10, 2024
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From the Hugging Face model README
This model detects Person and Location entities in Latin, Czech and German.
The model has been trained using the Spacy v2 library on the HOME-NACR document annotations. The model is compatible with version 2.3.2 of Spacy and incompatible with versions 3.x.x.
The model achieves the following results on HOME-NACR:
| tag | predicted | matched | Precision | Recall | F1 | Support |
|---|---|---|---|---|---|---|
| PERS | 28,276 | 28,006 | 0.990 | 0.997 | 0.994 | 28,087 |
| LOC | 27,541 | 27,165 | 0.986 | 0.987 | 0.987 | 27,528 |
| All | 55,817 | 55,171 | 0.988 | 0.992 | 0.990 | 55,615 |
Please refer to the Spacy library to use this model.
@inproceedings{spacy2022,
author = {Monroc, Claire Bizon and Miret, Blanche and Bonhomme, Marie-Laurence and Kermorvant, Christopher},
title = {{A Comprehensive Study Of Open-Source Libraries For Named Entity Recognition On Handwritten Historical Documents}},
year = {2022},
isbn = {978-3-031-06554-5},
url = {https://doi.org/10.1007/978-3-031-06555-2_29},
doi = {10.1007/978-3-031-06555-2_29},
booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
pages = {429–444},
numpages = {16},
}