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README.md

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 short_description: AI vs Human text classifier
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+# 🤖 AI vs Human Text Classifier (RoBERTa)
+
+This project fine-tunes **RoBERTa** to classify text as either:
+- 🧑 Human-Written  
+- 🤖 AI-Generated  
+
+It was developed as a **Capstone Project** to explore the power of transformer-based models in detecting AI-generated content.
+
+---
+
+## 📌 Project Overview
+With the rapid rise of LLMs like GPT and other AI text generators, distinguishing between human-written and AI-generated text is becoming crucial in education, research, and online authenticity.  
+This project leverages **RoBERTa**, a transformer-based model, to build a binary text classifier.
+
+---
+
+## 🛠️ Features
+- Fine-tuned **RoBERTa-base** model  
+- Binary classification: `Human (0)` vs `AI (1)`  
+- Deployed with **Gradio** for easy interaction  
+- Model hosted on **Hugging Face Model Hub**  
+
+---
+
+## 📂 Dataset
+The dataset used in training contains two columns:
+- **Text** → the input text sample  
+- **Generated** → label (`0 = Human`, `1 = AI`)  
+
+---
+
+## 🚀 Training
+The model was fine-tuned on Google Colab using the Hugging Face `transformers` library.
+
+**Steps:**
+1. Load dataset (`Text`, `Generated`)  
+2. Preprocess using Hugging Face `AutoTokenizer`  
+3. Fine-tune RoBERTa with `Trainer` API  
+4. Evaluate using Accuracy, Precision, Recall, F1-score  
+
+---
+
+## 📊 Results
+Validation accuracy achieved: **~99%**  
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference