Update README.md

README.md

@@ -52,16 +52,15 @@ model = AutoModelForMaskedLM.from_pretrained(model_name)
 
 ### Evaluation Overview
 
-Models were tested on the [`sud-resh-benchmark in Hugging Face`](https://huggingface.co/datasets/lawful-good-project/sud-resh-benchmark/tree/main) legal texts using a masked language modeling setup. Tokens were randomly masked at varying probabilities (10–40%), and models predicted them using their pre-trained heads.
+Models were tested on the [`sud-resh-benchmark`](https://huggingface.co/datasets/lawful-good-project/sud-resh-benchmark/tree/main) legal texts using a masked language modeling setup. Tokens were randomly masked at varying probabilities (10–40%), and models predicted them using their pre-trained heads.
 
-> **Note:** The models were **pre-trained on legal texts such as laws and statutes**, but **not specifically on judicial decisions**. The evaluation reflects how well they generalize to predicting masked tokens in Russian court rulings.
+> **Note:** The ruBERT-ruLaw model was **pre-trained on legal texts such as laws and statutes**, but **not specifically on judicial decisions**. The evaluation reflects how well it generalizes to predicting masked tokens in Russian court rulings.
 
 * **Top-1 Accuracy:** fraction of masked tokens predicted exactly.
 * **Top-5 Accuracy:** fraction of masked tokens predicted within the top 5 candidates.
 
 Results reflect performance across all masked tokens, aggregated for the dataset.
 
-
 ## MLM Accuracy Comparison
 
 |  MLM Probability  |  Metric  |  ruBERT-ruLaw  |  rubert-base-cased  |  legal-bert-base-uncased  |