---
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:
  - v1.0.0
  - entity-disambiguation
  - named-entity-linking
  - entity-linking
  - text2text-generation
---

# Model Card for `emanuelaboros/historic-nel`

The 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.

Entity linking model for historical VOC/GLOBALISE archival data, linking named entities from early modern Dutch colonial sources to curated GLOBALISE/Dataverse authority records.
This model was fine-tuned on the [HIPE-2022 dataset](https://github.com/hipe-eval/HIPE-2022-data).

## How to Use

```python
from transformers import AutoTokenizer, pipeline

NEL_MODEL_NAME = "emanuelaboros/globalise-entity-linker"
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))
```