Update README.md

README.md

@@ -107,7 +107,7 @@ This repo contains models, code and pointers to datasets from our paper: [TwHIN-
 ### Overview
 TwHIN-BERT is a new multi-lingual Tweet language model that is trained on 7 billion Tweets from over 100 distinct languages. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision (e.g., MLM), but also with a social objective based on the rich social engagements within a Twitter Heterogeneous Information Network (TwHIN).
 
-TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation **tasks such as predicting user to Tweet engagement.
+TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation** tasks such as predicting user to Tweet engagement.
 
 ## 1. Pretrained Models
 
@@ -142,7 +142,7 @@ TBD -->
 
 
 ## Citation
-If you use TwHIN-BERT or out datasets in your work, please cite, please cite the following:
+If you use TwHIN-BERT or out datasets in your work, please cite the following:
 ```bib
 @article{zhang2022twhin,
   title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},