Update README.md

README.md

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 app_file: app.py
 pinned: false
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-Sure! Here’s the final, polished README.md text you can use for your Bias Bin project:
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 Bias Bin: Bias Detection and Mitigation in Language Models
 
 Bias Bin is an interactive Gradio-based web application for detecting and mitigating gender bias in narrative text. It uses a fine-tuned BERT model and counterfactual data augmentation techniques to highlight and analyze bias in NLP outputs.
 
-🚀 Live Demo: Try it on Hugging Face Spaces
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 🧠 Project Overview
 
 This tool allows users to:
@@ -28,9 +21,7 @@ This tool allows users to:
 	•	Visualize bias scores to understand model behavior.
 	•	Demonstrate bias mitigation through gender-swapped text examples.
 
-This project was developed as part of a university coursework in Deep Learning & Generative AI, focusing on fairness, explainability, and responsible AI.
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+This project was developed as part of a university coursework in Deep Learning & Generative AI.
 
 📁 Repository Contents
 	•	app.py – Main Python file to launch the Gradio web app.
@@ -38,39 +29,30 @@ This project was developed as part of a university coursework in Deep Learning &
 	•	fine_tuned_model.zip – Zip file containing the fine-tuned BERT model (must be extracted).
 	•	requirements.txt – List of Python dependencies.
 
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 ⚙️ Setup Instructions
 	1.	Clone the Repository
 
 git clone https://huggingface.co/spaces/aryn25/bias.bin
 cd bias.bin
 
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 	2.	Install Dependencies
 
 pip install -r requirements.txt
 
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 	3.	Extract the Model
 Unzip the fine_tuned_model.zip file and place the extracted folder in the project root.
 	4.	Run the App
 
 python app.py
 
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 	5.	Open in Browser
-Visit the Gradio URL printed in the terminal (e.g., http://127.0.0.1:7860/).
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+Visit the Gradio URL printed in the terminal 
 
 📊 Methodology
 	•	Model: Fine-tuned BERT classifier trained on gender-labeled narrative datasets.
 	•	Bias Detection: Uses counterfactual data augmentation by swapping gendered words (e.g., “he” → “she”).
 	•	Metrics: Bias scores are computed based on prediction discrepancies between original and counterfactual samples.
 
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 📚 References
 
 This project is built using foundational and peer-reviewed research on:
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 Full citation list available in the project report.
 
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 📌 Authors
 
 Created by Aryan N. Salge and team as part of the Deep Learning & Generative AI coursework at the National College of Ireland.
 
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 📄 License
 
 This project is for educational and research purposes. Please cite appropriately if you use or adapt the work.
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-Let me know if you’d like a version tailored for GitHub, Hugging Face README.md, or both!
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference