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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]() 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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Results |
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| | [bert-base-sw](https://huggingface.co/orai-nlp/bert-base-sw) | [bert-medium-sw](https://huggingface.co/orai-nlp/bert-medium-sw) | Flair | [mBERT](https://huggingface.co/bert-base-multilingual-cased) | [SwahBERT](https://github.com/gatimartin/SwahBERT#pre-trained-models) | |
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|-----------|--------------|----------------|-------|-------|---------------------------------| |
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| NERC | **92.09** | 91.63 | 92.04 | 91.17 | 88.60 | |
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| Topic | **93.07** | 92.88 | 91.83 | 91.52 | 90.90 | |
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| Sentiment | **79.04** | 77.07 | 73.60 | 69.17 | 71.12 | |
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| QNLI | 63.34 | 63.87 | 52.82 | 63.48 | **64.72** | |
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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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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 |
