---
license: apache-2.0
---

## Overview
This model was presented at the [WMT24 Shared Task on Translation into Low-Resource Languages of Spain](https://www2.statmt.org/wmt24/romance-task.html) 
as a submission by the [Transducens](https://transducens.dlsi.ua.es/) team from the [Universitat d'Alacant](https://www.ua.es/). It is a many-to-many model 
capable of translating between several languages of the Iberian Peninsula.

**The model is based on [NLLB-1.3B](https://huggingface.co./facebook/nllb-200-1.3B), fine-tuned for the following languages:**
+ Spanish ↔ Asturian
+ Spanish ↔ Aragonese
+ Spanish ↔ Aranese
+ Spanish ↔ Galician
+ Spanish ↔ Catalan
+ Spanish ↔ Valencian
+ Catalan ↔ Aranese

**The new language tokens are:**
+ Aragonese: arg_Latn
+ Aranese: arn_Latn
+ Valencian: val_Latn


## Usage

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("Transducens/IbRo-nllb")
tokenizer = AutoTokenizer.from_pretrained("Transducens/IbRo-nllb")

tokenizer.src_lang = "spa_Latn"

sentence = "«Actualmente, tenemos ratones de cuatro meses de edad que antes solían ser diabéticos y que ya no lo son», agregó."
inputs = tokenizer(sentence, return_tensors="pt")

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["arg_Latn"])

print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True))
```

## Citation
If you use this model, please cite it as follows:
```
@inproceedings{wmt2024-galiano-jimenez,
    title = "Universitat d'{A}lacant's Submission to the {WMT} 2024 {S}hared {T}ask on {T}ranslating into {L}ow-{R}esource {L}anguages of {S}pain",
    author = "Galiano-Jim{\'e}nez, Aar{\'o}n and S{\'a}nchez-Cartagena, V{\'i}ctor M and P{\'e}rez-Ortiz, Juan Antonio and S{\'a}nchez-Mart{\'i}nez, Felipe",
    editor = "Koehn, Philipp  and Haddow, Barry  and Kocmi, Tom  and  Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami",
    publisher = "Association for Computational Linguistics",
}
```

## Acknowledgements
This model has been produced as part of the research project [Lightweight neural translation technologies for low-resource languages (LiLowLa)](https://transducens.dlsi.ua.es/lilowla/) (PID2021-127999NB-I00) funded by the Spanish Ministry of Science and Innovation (MCIN), the Spanish Research Agency (AEI/10.13039/501100011033) and the European Regional Development Fund A way to make Europe.