Update README.md

README.md

@@ -60,7 +60,7 @@ This work presents the development of two question–answering (QA) datasets for
 
 The **SQuAD-sr-md** dataset contains around 7k examples and represents a manually refined subset of the original SQuAD-sr dataset.While SQuAD-sr is a significant resource for Serbian QA research, it was created through a translation-based approach using an adapted Translate–Align–Retrieve method,
 which introduced various linguistic inconsistencies. The manual revision process, although time-consuming, substantially improved the grammatical correctness, terminological consistency, and overall reliability of the dataset, making it more suitable for training extractive QA models. 
-We fine-tuned a several Serbian base encoder models on (i) our SQuAD-sr-md dataset and (ii) a subset of SQuAD-sr that corresponds exactly to the articles and QA pairs thatwere refined to create the SQuAD-sr-md. Evaluation conducted of the models highlighted the TeslaXLM base model as a particularly promising candidate for future extractive QA training. The results also indicate that the refinement of the dataset positively impacts model performance.
+We fine-tuned a several Serbian base encoder models on (i) our SQuAD-sr-md dataset and (ii) a subset of SQuAD-sr that corresponds exactly to the articles and QA pairs thatwere refined to create the SQuAD-sr-md. Evaluation of these models highlighted the TeslaXLM base model as a particularly promising candidate for future extractive QA training. The results also indicate that the refinement of the dataset positively impacts model performance.
 
 ### SQuAD-sr subset