Update README.md

README.md

@@ -20,7 +20,7 @@ It outperforms its multilingual counterparts, albeit being much smaller than oth
 VBART-XLarge is created by adding extra Transformer layers between the layers of VBART-Large. Hence it was able to transfer learned weights from the smaller model while doublings its number of layers.
 VBART-XLarge improves the results compared to VBART-Large albeit in small margins.
 
-This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for text paraphrasing task.
+This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for sentence-level text paraphrasing task.
 
 - **Developed by:** [VNGRS-AI](https://vngrs.com/ai/)
 - **Model type:**  Transformer encoder-decoder based on mBART architecture
@@ -51,7 +51,7 @@ The base model is pre-trained on [vngrs-web-corpus](https://huggingface.co/datas
 The fine-tuning dataset is a mixture of [OpenSubtitles](https://huggingface.co/datasets/open_subtitles), [TED Talks (2013)](https://wit3.fbk.eu/home) and [Tatoeba](https://tatoeba.org/en/) datasets.
 
 ### Limitations
-This model is fine-tuned for paraphrasing tasks. It is not intended to be used in any other case and can not be fine-tuned to any other task with full performance of the base model. It is also not guaranteed that this model will work without specified prompts.
+This model is fine-tuned for paraphrasing tasks and finetuned in sentence level only. It is not intended to be used in any other case and can not be fine-tuned to any other task with full performance of the base model. It is also not guaranteed that this model will work without specified prompts.
 
 ### Training Procedure  
 Pre-trained for 30 days and for a total of 708B tokens. Finetuned for 25 epoch.