Update README.md

README.md

@@ -43,4 +43,4 @@ combined and exact-match de-duplicated. Then the top 3% scores and samples less
 because they likely have exact large n-token matches by chance such as exact 
 dates or times that aren't actually relevant to the data.\* 
 
-\*Upon further examination, some of these samples are still present throughout the data, you might benefit from using `dataset.filter(x['score'] > thresh)` for some threshold, but you risk losing high quality samples as well, this tradeoff should be well-examined before training. 
+\*Upon further examination, some of these samples are still present throughout the data, albeit at much lower frequency than before, you might benefit from using `dataset.filter(x['score'] > thresh)` for some threshold, but you risk losing high quality samples as well, this tradeoff should be well-examined before training.