---
license: cc-by-4.0
language:
- sw
---


BERT base (cased) model trained on a subset of 125M tokens of cc100-Swahili for our work [Scaling Laws for BERT in Low-Resource Settings](https://youtu.be/dQw4w9WgXcQ) at ACL2023 Findings.

The model has 124M parameters (12L), with a vocab size of 50K.
It was trained for 500K steps with a sequence length of 512 tokens.

A bert-medium and bert-mini (8 and 4L) models are available at our [GitHub](https://github.com/orai-nlp/low-scaling-laws/tree/main/models).


Authors
-----------
Gorka Urbizu [1], Iñaki San Vicente [1], Xabier Saralegi [1],
Rodrigo Agerri [2] and Aitor Soroa [2]

Affiliation of the authors: 

[1] Orai NLP Technologies

[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU



Licensing
-------------

Copyright (C) by Orai NLP Technologies. 

The model is licensed under the Creative Commons Attribution 4.0. International License (CC BY 4.0). 

To view a copy of this license, visit [http://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/deed.eu).




Acknowledgements
-------------------
If you use this model please cite the following paper:

- G. Urbizu, I. San Vicente, X. Saralegi, R. Agerri, A. Soroa. Scaling Laws for BERT in Low-Resource Settings. Findings of the Association for Computational Linguistics: ACL 2023. July, 2023. Toronto, Canada



Contact information
-----------------------
Gorka Urbizu, Iñaki San Vicente: {g.urbizu,i.sanvicente}@orai.eus