Update `README.md`

README.md

@@ -8,10 +8,10 @@ datasets:
 
 # MathBERTa base model
 
-Pretrained model on English language using a masked language modeling (MLM)
-objective. It was developed for [the ARQMath-3 shared task evaluation][1] at
-CLEF 2022 and first released in [this repository][2]. This model is case-sensitive:
-it makes a difference between english and English.
+Pretrained model on English language and LaTeX using a masked language modeling
+(MLM) objective. It was developed for [the ARQMath-3 shared task evaluation][1]
+at CLEF 2022 and first released in [this repository][2]. This model is
+case-sensitive: it makes a difference between english and English.
 
  [1]: https://www.cs.rit.edu/~dprl/ARQMath/
  [2]: https://github.com/witiko/scm-at-arqmath3
@@ -26,8 +26,8 @@ Like RoBERTa, MathBERTa has been fine-tuned with the Masked language modeling
 (MLM) objective. Taking a sentence, the model randomly masks 15% of the words
 and math symbols in the input then run the entire masked sentence through the
 model and has to predict the masked words and symbols. This way, the model
-learns an inner representation of the English language and the language of
-LaTeX that can then be used to extract features useful for downstream tasks.
+learns an inner representation of the English language and LaTeX that can then
+be used to extract features useful for downstream tasks.
 
  [3]: https://huggingface.co/roberta-base
  [7]: https://github.com/Witiko/scm-at-arqmath3/blob/main/02-train-tokenizers.ipynb