Update README.md

README.md

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+Sure! Here’s the final, polished README.md text you can use for your Bias Bin project:
 
+⸻
+
+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
+
+⸻
+
+🧠 Project Overview
+
+This tool allows users to:
+	•	Detect gender bias in input text using a BERT-based classification model.
+	•	Explore counterfactual predictions by swapping gendered terms.
+	•	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.
+
+⸻
+
+📁 Repository Contents
+	•	app.py – Main Python file to launch the Gradio web app.
+	•	Evaluation&Results.ipynb – Notebook with experiments, model evaluations, and visualizations.
+	•	fine_tuned_model.zip – Zip file containing the fine-tuned BERT model (must be extracted).
+	•	requirements.txt – List of Python dependencies.
+
+⸻
+
+⚙️ Setup Instructions
+	1.	Clone the Repository
+
+git clone https://huggingface.co/spaces/aryn25/bias.bin
+cd bias.bin
+
+
+	2.	Install Dependencies
+
+pip install -r requirements.txt
+
+
+	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
+
+
+	5.	Open in Browser
+Visit the Gradio URL printed in the terminal (e.g., http://127.0.0.1:7860/).
+
+⸻
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+📊 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.
+
+⸻
+
+📚 References
+
+This project is built using foundational and peer-reviewed research on:
+	•	BERT and Transformer models
+	•	Gender bias in NLP
+	•	Counterfactual data augmentation
+	•	Bias mitigation techniques
+
+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.
+
+⸻
+
+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