Update apps/home.py

apps/home.py

@@ -23,54 +23,54 @@ def write():
 			"""
 			<h3 style="text-align:left;">a Turkish encoder-decoder language model</h3>
 			<p style="text-align:right;"><p>
-            """, unsafe_allow_html=True,
-    )
-    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}
-            }
-            ```
-            """)
-    st.markdown(
-            """
+			""", unsafe_allow_html=True,
+	)
+	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}
+			}
+			```
+			""")
+	st.markdown(
+			"""
 			<p style="text-align:left;">In this Huggingface space, you can test the TURNA language model. </p>
 			<p style="text-align:left;">The model contains 1.1B parameters, and was pre-trained with an encoder-decoder architecture following the UL2 framework on 43B tokens from various domains.  </p>
-            <p style="text-align:left;">TURNA was fine-tuned to carry out Turkish summarization, paraphrasing, news title generation, sentiment classification, text categorization, named entity recognition, part-of-speech tagging, semantic textual similarity and natural language inference tasks.  </p>
-            <p style="text-align:left;">Go to the <strong>Navigation</strong> bar to access our applications. </p>
-            <p style="text-align:left;">Refer to our <a href="https://arxiv.org/abs/2401.14373">paper</a>  for more details...  </p>
-            <p style="text-align:left;"><p>
-            <p style="text-align:right;"><em>TURNA can generate toxic content or provide erroneous information. Double-check before usage. </em><p>
+			<p style="text-align:left;">TURNA was fine-tuned to carry out Turkish summarization, paraphrasing, news title generation, sentiment classification, text categorization, named entity recognition, part-of-speech tagging, semantic textual similarity and natural language inference tasks.  </p>
+			<p style="text-align:left;">Go to the <strong>Navigation</strong> bar to access our applications. </p>
+			<p style="text-align:left;">Refer to our <a href="https://arxiv.org/abs/2401.14373">paper</a>  for more details...  </p>
+			<p style="text-align:left;"><p>
+			<p style="text-align:right;"><em>TURNA can generate toxic content or provide erroneous information. Double-check before usage. </em><p>
 			""",
 			unsafe_allow_html=True,
 		)
-        
+