updates

README.md

@@ -84,10 +84,10 @@ Please reach out to Alexandra DeLucia (aadelucia at jhu.edu) or open an issue if
 The language of Twitter differs significantly from that of other domains commonly included in large language model training. 
 While tweets are typically multilingual and contain informal language, including emoji and hashtags, most pre-trained 
 language models for Twitter are either monolingual, adapted from other domains rather than trained exclusively on Twitter, 
-or are trained on a limited amount of in-domain Twitter data.We introduce Bernice, the first multilingual RoBERTa language 
+or are trained on a limited amount of in-domain Twitter data. We introduce Bernice, the first multilingual RoBERTa language 
 model trained from scratch on 2.5 billion tweets with a custom tweet-focused tokenizer. We evaluate on a variety of monolingual 
 and multilingual Twitter benchmarks, finding that our model consistently exceeds or matches the performance of a variety of models 
-adapted to social media data as well as strong multilingual baselines, despite being trained on less data overall.We posit that it is 
+adapted to social media data as well as strong multilingual baselines, despite being trained on less data overall. We posit that it is 
 more efficient compute- and data-wise to train completely on in-domain data with a specialized domain-specific tokenizer.
 
 ## Training data