Update README.md

README.md

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 license: cc-by-4.0
+language:
+- sw
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+
+---
+license: cc-by-4.0
+language:
+- sw
+---
+
+
+BERT medium (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 51M parameters (8L), with a vocab size of 50K.
+It was trained for 500K steps with a sequence length of 512 tokens.
+
+
+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