README.md

---
library_name: transformers
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bm
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gn
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kg
- kk
- km
- kn
- ko
- ku
- ky
- la
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- om
- or
- pa
- pl
- ps
- pt
- qu
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- te
- th
- ti
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yo
- zh

license: agpl-3.0
tags:
- retrieval
- entity-retrieval
- named-entity-disambiguation
- entity-disambiguation
- named-entity-linking
- entity-linking
- text2text-generation
---

# Model Card for `impresso-project/nel-mgenre-multilingual`

The **Impresso multilingual named entity linking (NEL)** model is based on **mGENRE** (multilingual Generative ENtity REtrieval) proposed by [De Cao et al](https://arxiv.org/abs/2103.12528), a sequence-to-sequence architecture for entity disambiguation based on [mBART](https://arxiv.org/abs/2001.08210). It uses **constrained generation** to output entity names mapped to Wikidata/QIDs.

This model was adapted for historical texts and fine-tuned on the [HIPE-2022 dataset](https://github.com/hipe-eval/HIPE-2022-data), which includes a variety of historical document types and languages.

## Model Details

### Model Description

### Model Description

- **Developed by:** EPFL from the [Impresso team](https://impresso-project.ch). The project is an interdisciplinary project focused on historical media analysis across languages, time, and modalities. Funded by the Swiss National Science Foundation ([CRSII5_173719](http://p3.snf.ch/project-173719), [CRSII5_213585](https://data.snf.ch/grants/grant/213585)) and the Luxembourg National Research Fund (grant No. 17498891).
- **Model type:** mBART-based sequence-to-sequence model with constrained beam search for named entity linking
- **Languages:** Multilingual (100+ languages, optimized for French, German, and English)
- **License:** [AGPL v3+](https://github.com/impresso/impresso-pyindexation/blob/master/LICENSE)
- **Finetuned from:** [`facebook/mgenre-wiki`](https://huggingface.co/facebook/mgenre-wiki)
- 
### Model Architecture

- **Architecture:** mBART-based seq2seq with constrained beam search

## Training Details

### Training Data

The model was trained on the following datasets:

| Dataset alias | README | Document type | Languages |  Suitable for | Project | License |
|---------|---------|---------------|-----------| ---------------|---------------| ---------------|
| ajmc       | [link](documentation/README-ajmc.md)  | classical commentaries | de, fr, en | NERC-Coarse, NERC-Fine, EL | [AjMC](https://mromanello.github.io/ajax-multi-commentary/) | [![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) |
| hipe2020   | [link](documentation/README-hipe2020.md)| historical newspapers | de, fr, en | NERC-Coarse, NERC-Fine, EL | [CLEF-HIPE-2020](https://impresso.github.io/CLEF-HIPE-2020)| [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC_BY--NC--SA_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
| topres19th | [link](documentation/README-topres19th.md) | historical newspapers | en | NERC-Coarse, EL |[Living with Machines](https://livingwithmachines.ac.uk/) | [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC_BY--NC--SA_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
| newseye    | [link](documentation/README-newseye.md)|  historical newspapers | de, fi, fr, sv | NERC-Coarse, NERC-Fine, EL |  [NewsEye](https://www.newseye.eu/) |  [![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)|
| sonar      | [link](documentation/README-sonar.md) | historical newspapers  | de | NERC-Coarse, EL |  [SoNAR](https://sonar.fh-potsdam.de/)  | [![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)|


## How to Use

```python
from transformers import AutoTokenizer, pipeline

NEL_MODEL_NAME = "impresso-project/nel-mgenre-multilingual"
nel_tokenizer = AutoTokenizer.from_pretrained(NEL_MODEL_NAME)

nel_pipeline = pipeline("generic-nel", model=NEL_MODEL_NAME,
                        tokenizer=nel_tokenizer,
                        trust_remote_code=True,
                        device='cpu')

sentence = "Le 0ctobre 1894, [START] Dreyfvs [END] est arrêté à Paris, accusé d'espionnage pour l'Allemagne — un événement qui déch1ra la société fr4nçaise pendant des années."
print(nel_pipeline(sentence))
```

### Output Format

```python
[
    {
        'surface': 'Dreyfvs', 
        'wkd_id': 'Q171826', 
        'wkpedia_pagename': 'Alfred Dreyfus', 
        'wkpedia_url': 'https://fr.wikipedia.org/wiki/Alfred_Dreyfus', 
        'type': 'UNK', 
        'confidence_nel': 99.98, 
        'lOffset': 24, 
        'rOffset': 33}]
```
The type of the entity is `UNK` because the model was not trained on the entity type. The `confidence_nel` score indicates the model's confidence in the prediction.

## Use Cases

- Entity disambiguation in noisy OCR settings
- Linking historical names to modern Wikidata entities
- Assisting downstream event extraction and biography generation from historical archives

## Limitations

- Sensitive to tokenisation and malformed spans
- Accuracy degrades on non-Wikidata entities or in highly ambiguous contexts
- Focused on historical entity mentions — performance may vary on modern texts

## Environmental Impact

- **Hardware:** 1x A100 (80GB) for finetuning
- **Training time:** ~12 hours
- **Estimated CO₂ Emissions:** ~2.3 kg CO₂eq

## Contact

- Website: [https://impresso-project.ch](https://impresso-project.ch)

<p align="center">
  <img src="https://github.com/impresso/impresso.github.io/blob/master/assets/images/3x1--Yellow-Impresso-Black-on-White--transparent.png?raw=true" width="300" alt="Impresso Logo"/>
</p>