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license: cc-by-4.0 |
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language: |
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- sw |
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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. |
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The model has 124M parameters (12L), and a vocab size of 50K. |
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It was trained for 500K steps with a sequence length of 512 tokens. |
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Authors |
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Gorka Urbizu [1], Iñaki San Vicente [1], Xabier Saralegi [1], |
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Rodrigo Agerri [2] and Aitor Soroa [2] |
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Affiliation of the authors: |
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[1] Orai NLP Technologies |
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[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU |
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Licensing |
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Copyright (C) by Orai NLP Technologies. |
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The model is licensed under the Creative Commons Attribution 4.0. International License (CC BY 4.0). |
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To view a copy of this license, visit [http://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/deed.eu). |
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Acknowledgements |
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If you use this model please cite the following paper: |
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- 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 |
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Contact information |
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Gorka Urbizu, Iñaki San Vicente: {g.urbizu,i.sanvicente}@orai.eus |
