Update README.md

README.md

@@ -48,7 +48,8 @@ Multilingual pretrained language models (mPLMs) acquire valuable, generalizable
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 To date, only ~31 out of 2,000 African languages are covered in existing language models. We ameliorate this limitation by developing <b>SERENGETI</b>, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages. 
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-<b>SERENGETI</b> outperforms other models on 11 datasets across eights tasks, achieving 82.27 average F<sub>1</sub>-score. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research.
+<b>SERENGETI</b> outperforms other models on 11 datasets across eights tasks, achieving 82.27 average F<sub>1</sub>-score. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research. 
+  Further details about the model is available in the [(paper)](https://aclanthology.org/2023.findings-acl.97/).
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 #  3. How to use Serengeti model