license: cc0-1.0
language:
- is
tags:
- MaCoCu
Model description
XLMR-base-MaCoCu-is is a large pre-trained language model trained on Icelandic texts. It was created by continuing training from the XLM-RoBERTa-base model. It was developed as part of the MaCoCu project and only uses data that was crawled during the project. The main developer is Jaume Zaragoza-Bernabeu from Prompsit Language Engineering.
XLMR-base-MaCoCu-is was trained on 4.4GB of Icelandic text, which is equal to 688M tokens. It was trained for 40,000 steps with a batch size of 256. It uses the same vocabulary as the original XLMR-base model.
The training and fine-tuning procedures are described in detail on our Github repo.
Warning
This model has not been fully trained because it was intended for use as base of Bicleaner AI Icelandic model. If you need better performance, please use XLMR-MaCoCu-is.
How to use
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("MaCoCu/XLMR-base-MaCoCu-is")
model = AutoModel.from_pretrained("MaCoCu/XLMR-base-MaCoCu-is") # PyTorch
model = TFAutoModel.from_pretrained("MaCoCu/XLMR-base-MaCoCu-is") # Tensorflow
Data
For training, we used all Icelandic data that was present in the monolingual Icelandic MaCoCu corpus. After de-duplicating the data, we were left with a total of 4.4 GB of text, which equals 688M tokens.
Acknowledgements
The authors received funding from the European Union’s Connecting Europe Facility 2014- 2020 - CEF Telecom, under Grant Agreement No.INEA/CEF/ICT/A2020/2278341 (MaCoCu).
Citation
If you use this model, please cite the following paper:
@inproceedings{non-etal-2022-macocu,
title = "{M}a{C}o{C}u: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages",
author = "Ba{\~n}{\'o}n, Marta and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Garc{\'\i}a-Romero, Cristian and
Kuzman, Taja and
Ljube{\v{s}}i{\'c}, Nikola and
van Noord, Rik and
Sempere, Leopoldo Pla and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Rupnik, Peter and
Suchomel, V{\'\i}t and
Toral, Antonio and
van der Werff, Tobias and
Zaragoza, Jaume",
booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2022",
address = "Ghent, Belgium",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2022.eamt-1.41",
pages = "303--304"
}