add markdown

app.py

@@ -171,24 +171,22 @@ def _cached_predictions(state):
 
 with gr.Blocks() as demo:
     gr.Markdown("""# Data Use Detector
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     This Space demonstrates our fine-tuned GLiNER model’s ability to spot **dataset mentions** and **relations** in any input text. It identifies dataset names via NER, then extracts relations such as **publisher**, **acronym**, **publication year**, **data geography**, and more.
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-    ---
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+    
     **How it works**  
     1. **NER**: Recognizes dataset names in your text.  
     2. **RE**: Links each dataset to its attributes (e.g., publisher, year, acronym).  
     3. **Visualization**: Highlights entities and relation spans inline.
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+    
     **Instructions**  
     1. Paste or edit your text in the box below.  
     2. Tweak the **NER** & **RE** confidence sliders.  
     3. Click **Submit** to see highlights.  
     4. Click **Get Model Predictions** to view the raw JSON output.
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+    
     **Resources**  
-    - **Model:** `rafmacalaba/gliner_re_finetuned-v3`  
+    - **Model:** [rafmacalaba/gliner_re_finetuned-v3](https://huggingface.co/rafmacalaba/gliner_re_finetuned-v3) 
     - **Paper:** _Large Language Models and Synthetic Data for Monitoring Dataset Mentions in Research Papers_ – ArXiv: [2502.10263](https://arxiv.org/pdf/2502.10263)  
     - [GLiNER GitHub Repo](https://github.com/urchade/GLiNER)  
     - [Project Docs](https://worldbank.github.io/ai4data-use/docs/introduction.html)