Update README.md

README.md

@@ -14,8 +14,7 @@ To train the model, we sample as uniformly as possible across languages while li
 We combine [WURA data](https://huggingface.co/datasets/castorini/wura) with high-quality English documents from [FineWeb-Edu](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1) and [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) which results into improved Lugha-Llama-Edu and Lugha-Llama-Maths models respectively.
 Our models consistently achieve the best performance amongst similary-sized baselines. 
 
-In a separate ablation experiment, we translate English education documents to Swahili to study whether the performance gains from FineWeb-Edu data is due to its content or English source language. 
-* Translated Swahili data 200M tokens: [FineWeb_Edu-swahili-translated](https://huggingface.co/datasets/princeton-nlp/fineweb_edu-swahili-translated)
+In a separate ablation experiment, we translate English education documents to Swahili to study whether the performance gains from FineWeb-Edu data is due to its content or English source language. [FineWeb_Edu-swahili-translated](https://huggingface.co/datasets/princeton-nlp/fineweb_edu-swahili-translated).
 
 
 We demonstrate the findings in our paper [Adapting Large Language Models for African Languages:
@@ -23,7 +22,7 @@ The Lugha-Llama Model]()
 
 Authors: [Happy Buzaaba](https://buzaabah.github.io/)\*, [Alexander Wettig](https://www.cs.princeton.edu/~awettig/)\*, [David Ifeoluwa Adelani](https://dadelani.github.io/), [Christiane Fellbaum](https://www.cs.princeton.edu/people/profile/fellbaum) (* equal contribution)
 
-Contact \textit({happy.buzaaba@, awettig@cs}princeton.edu)
+Contact *{happy.buzaaba@, awettig@cs}princeton.edu*
 
 
 ## Lugha-Llama models