Update README.md

README.md

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 # TrelBERT
 
-TrelBERT is a BERT-based Language Model trained on data from Polish Twitter.
-It uses Masked Language Modeling objective. It is based on [HerBERT](https://aclanthology.org/2021.bsnlp-1.1) model and therefore released under the same license - CC BY 4.0.
+TrelBERT is a BERT-based Language Model trained on data from Polish Twitter using Masked Language Modeling objective. It is based on [HerBERT](https://aclanthology.org/2021.bsnlp-1.1) model and therefore released under the same license - CC BY 4.0.
 
 ## Training
 
@@ -23,7 +22,7 @@ The data we used for MLM fine-tuning was approximately 45 million Polish tweets.
 
 ### Preprocessing
 
-For each Tweet, the user handles that occur in the beginning of the text were removed, as they are not part of the message content but only represent who the user is replying to. The remaining user handles were replaced by "@anonymized_account". Links are replaced with a special @URL token.
+For each Tweet, the user handles that occur in the beginning of the text were removed, as they are not part of the message content but only represent who the user is replying to. The remaining user handles were replaced by "@anonymized_account". Links were replaced with a special @URL token.
 
 ## Tokenizer
 
@@ -48,11 +47,11 @@ We fine-tuned TrelBERT to [KLEJ benchmark](klejbenchmark.com) tasks and achieved
 |DYK|67.4|
 |PSC|95.7|
 |AR|86.1|
-|__avg__|__86.1__|
+|__average__|__86.1__|
 
-For fine-tuning to KLEJ tasks we used [Polish RoBERTa](https://github.com/sdadas/polish-roberta) scripts, which we modified to use `transformers` library. In the CBD task, we set the maximum sequence length to 128 and implemented the same preprocessing procedure as in the MLM phase.
+For fine-tuning to KLEJ tasks we used [Polish RoBERTa](https://github.com/sdadas/polish-roberta) scripts, which we modified to use `transformers` library. For the CBD task, we set the maximum sequence length to 128 and implemented the same preprocessing procedure as in the MLM phase.
 
-The model achieved 1st place in cyberbullying detection (CBD) task in the [KLEJ leaderboard](https://klejbenchmark.com/leaderboard). Overall, it reached 7th place, just below HerBERT model.
+Our model achieved 1st place in cyberbullying detection (CBD) task in the [KLEJ leaderboard](https://klejbenchmark.com/leaderboard). Overall, it reached 7th place, just below HerBERT model.
 
 ## Authors