Modified description

app.py

@@ -4,16 +4,44 @@ from transformers import pipeline
 import torch
 
 DESCRIPTION="""
-This is the space for the Language Modeling Group at TABILAB in Computer Engineering of Bogazici University.
 
-We released the first version of our Turkish language model TURNA.
+### a Turkish encoder-decoder language model 
 
-This model is based on an encoder-decoder T5 architecture with 1.1B parameters.
+Welcome to our Huggingface space, where you can explore the capabilities of TURNA. 
+			
+**Key Features of TURNA:**
 
-For more details, please refer to our paper.
+- **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
+
+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}
+}
+```
 
 **Note:** First inference might take time as the models are downloaded on-the-go.
 
+*TURNA can generate toxic content or provide erroneous information. Double-check before usage.*
+
 """
 
 
@@ -109,9 +137,13 @@ def turna(input, max_new_tokens, length_penalty,
 
 with gr.Blocks(theme="abidlabs/Lime") as demo:
     gr.Markdown("# TURNA 🐦")
+    gr.Image("images/turna-logo.png", scale=1)
+
     gr.Markdown(DESCRIPTION)
 
     
+
+    
     with gr.Tab("Sentiment Analysis"):
         gr.Markdown("TURNA fine-tuned on sentiment analysis. Enter text to analyse sentiment and pick the model (tweets or product reviews).")
         with gr.Column():