|
--- |
|
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), and a vocab size of 50K. |
|
It was trained for 500K steps with a sequence length of 512 tokens. |
|
|
|
RESULTS |
|
|
|
TODO |
|
|
|
|
|
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 |
|
------------- |
|
|
|
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 |
