---
library_name: Spacy
license: mit
tags:
- Spacy
- Named entity recognition
metrics:
- P
- R
- F1
language:
- la
- de
- cs
version:
- Spacy v2
---

# Spacy - HOME-NACR Multilingual

This model detects Person and Location entities in Latin, Czech and German.

## Model description

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.

## Evaluation results

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  |

## How to use?

Please refer to the [Spacy library](https://pypi.org/project/spacy/2.3.5/) to use this model.

## Cite us!

```bibtex
@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    title = {{A Comprehensive Study Of Open-Source Libraries For Named Entity Recognition On Handwritten Historical Documents}},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},
}
```