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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))
```
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