Update apps/home.py

apps/home.py

@@ -27,38 +27,38 @@ def write():
     )
     st.markdown(
             """
-Welcome to our Huggingface space, where you can explore the capabilities of TURNA. 
-
-**Key Features of TURNA:**
-
-- **Powerful Architecture:** TURNA contains 1.1B parameters, and was pre-trained with an encoder-decoder architecture following the UL2 framework on 43B tokens from various domains.
-- **Diverse Training Data:** Our model is trained on a varied dataset of 43 billion tokens, covering a wide array of domains.
-- **Broad Applications:** TURNA is fine-tuned for a variety of generation and understanding tasks, including:
-    - Summarization
-    - Paraphrasing
-    - News title generation
-    - Sentiment classification
-    - Text categorization
-    - Named entity recognition
-    - Part-of-speech tagging
-    - Semantic textual similarity
-    - Natural language inference
-
-Explore various applications powered by **TURNA** using the **Navigation** bar.
-
-Refer to our [paper](https://arxiv.org/abs/2401.14373) for more details...
-
-### Citation
-```bibtex
-@misc{uludoğan2024turna,
-      title={TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation}, 
-      author={Gökçe Uludoğan and Zeynep Yirmibeşoğlu Balal and Furkan Akkurt and Melikşah Türker and Onur Güngör and Susan Üsküdarlı},
-      year={2024},
-      eprint={2401.14373},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
+            Welcome to our Huggingface space, where you can explore the capabilities of TURNA. 
+            
+            **Key Features of TURNA:**
+            
+            - **Powerful Architecture:** TURNA contains 1.1B parameters, and was pre-trained with an encoder-decoder architecture following the UL2 framework on 43B tokens from various domains.
+            - **Diverse Training Data:** Our model is trained on a varied dataset of 43 billion tokens, covering a wide array of domains.
+            - **Broad Applications:** TURNA is fine-tuned for a variety of generation and understanding tasks, including:
+                - Summarization
+                - Paraphrasing
+                - News title generation
+                - Sentiment classification
+                - Text categorization
+                - Named entity recognition
+                - Part-of-speech tagging
+                - Semantic textual similarity
+                - Natural language inference
+            
+            Explore various applications powered by **TURNA** using the **Navigation** bar.
+            
+            Refer to our [paper](https://arxiv.org/abs/2401.14373) for more details...
+            
+            ### Citation
+            ```bibtex
+            @misc{uludoğan2024turna,
+                  title={TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation}, 
+                  author={Gökçe Uludoğan and Zeynep Yirmibeşoğlu Balal and Furkan Akkurt and Melikşah Türker and Onur Güngör and Susan Üsküdarlı},
+                  year={2024},
+                  eprint={2401.14373},
+                  archivePrefix={arXiv},
+                  primaryClass={cs.CL}
+            }
+            ```
             """)
     st.markdown(
             """