Update README.md

README.md

@@ -58,7 +58,9 @@ Faculty of Mining and Geology, University of Belgrade, Serbia
 
 This work presents the development of two question–answering (QA) datasets for the Serbian language: **SQuAD-sr-md** and **SerbianQA-Gen**.  
 
-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. Evaluation conducted on several Serbian base encoder models highlighted the TeslaXLM base model as a particularly promising candidate for future extractive QA training.
+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.
 
 ### SQuAD-sr subset