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---
license: cc0-1.0
language:
- mt
tags:
- MaltBERTa
- MaCoCu
---
# Model description
**MaltBERTa** is a large pre-trained language model trained on Maltese texts. It was trained from scratch using the RoBERTa architecture. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main developer is [Rik van Noord](https://www.rikvannoord.nl/) from the University of Groningen.
MaltBERTa was trained on 3.2GB of text, which is equal to 439M tokens. It was trained for 100,000 steps with a batch size of 1,024.
The training and fine-tuning procedures are described in detail on our [Github repo](https://github.com/macocu/LanguageModels).
# How to use
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("RVN/MaltBERTa")
model = AutoModel.from_pretrained("RVN/MaltBERTa") # PyTorch
model = TFAutoModel.from_pretrained("RVN/MaltBERTa") # Tensorflow
```
# Data
For training, we used all Maltese data that was present in the [MaCoCu](https://macocu.eu/), Oscar and mc4 corpora. After de-duplicating the data, we were left with a total of 3.2GB of text. We ran experiments with only training on data that came from the .mt domain in Oscar and mc4, but got better performance by incorporating all data.
# Benchmark performance
We tested the performance of MaltBERTa on the UPOS and XPOS benchmark of the [Universal Dependencies](https://universaldependencies.org/) project. We compare performance to the strong multi-lingual models XLMR-base and XLMR-large, though note that Maltese was not one of the training languages for those models. We also compare to the recently introduced Maltese language models [BERTu](https://huggingface.co/MLRS/BERTu) and [mBERTu](https://huggingface.co/MLRS/mBERTu). For details regarding the fine-tuning procedure you can checkout our [Github](https://github.com/macocu/LanguageModels).
Scores are averages of three runs. We use the same hyperparameter settings for all models.
| | **UPOS** | **UPOS** | **XPOS** | **XPOS** |
|-----------------|:--------:|:--------:|:--------:|:--------:|
| | **Dev** | **Test** | **Dev** | **Test** |
| **XLM-R-base** | 93.6 | 93.2 | 93.4 | 93.2 |
| **XLM-R-large** | 94.9 | 94.4 | 95.1 | 94.7 |
| **BERTu** | 97.5 | 97.6 | 95.7 | 95.8 |
| **mBERTu** | 97.7 | 97.8 | 97.9 | 98.1 |
| **MaltBERTa** | 95.7 | 95.8 | 96.1 | 96.0 |
# Citation
If you use this model, please cite the following paper:
```bibtex
@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"
}
``` |