UBC-NLP
/

serengeti

@@ -51,7 +51,6 @@ Multilingual pretrained language models (mPLMs) acquire valuable, generalizable
 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.
 <br><br>
 <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/).
 </div>
 #  3. How to use Serengeti model

 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.
 <br><br>
 <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.
 </div>
 #  3. How to use Serengeti model