Downloads · 30 days
4.1K
22% of all-time downloads
Transducens/IbRo-nllb
IbRo-nllb is a machine learning model from Transducens. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This model was presented at the WMT24 Shared Task on Translation into Low-Resource Languages of Spain as a submission by the Transducens team from the Universitat d'Alacant. It is a many-to-many model capable of trans…
Downloads · 30 days
4.1K
22% of all-time downloads
All-time downloads
18.8K
Public
Parameters
1.4B
5.5 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors5.5 GB · 100%
From the Hugging Face model README
This model was presented at the WMT24 Shared Task on Translation into Low-Resource Languages of Spain as a submission by the Transducens team from the Universitat d'Alacant. 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, fine-tuned for the following languages:
The new language tokens are:
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))
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",
}
This model has been produced as part of the research project Lightweight neural translation technologies for low-resource languages (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.