Minor editing

article.md

@@ -41,7 +41,7 @@ For the data, I used the `im2latex-100k` dataset, which includes a total of roug
 Some preprocessing steps were done by Harvard NLP for the [`im2markup` project](https://github.com/harvardnlp/im2markup).
 To limit the scope of the project and simplify the task, I limited training data to only look at equations containing 100 LaTeX tokens or less.
 This covers most single line equations, including fractions, subscripts, symbols, etc, but does not cover large multi line equations, some of which can have up to 500 LaTeX tokens.
-GPU training was done using on Kaggle in roughly 3 hours.
+GPU training was done on a Kaggle GPU Kernel in roughly 3 hours.
 You can find the full training code on my Kaggle profile [here](https://www.kaggle.com/code/younghoshin/finetuning-trocr/notebook).
 
 ## What's next?