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README.md

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 license: cc-by-4.0
+language:
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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), with a vocab size of 50K.
+It was trained for 500K steps with a sequence length of 512 tokens.
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+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
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+[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU
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+
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+Licensing
+-------------
+
+Copyright (C) by Orai NLP Technologies. 
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+The corpora, datasets and models created in this work, are 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).
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+
+
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+Acknowledgements
+-------------------
+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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+
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+Contact information
+-----------------------
+Gorka Urbizu, Iñaki San Vicente: {g.urbizu,i.sanvicente}@orai.eus