Update README.md

README.md

@@ -10,129 +10,97 @@ pinned: false
 license: apache-2.0
 short_description: Gradio
 ---
-GPT-2 TinyStories Generator 🏢 - FableWeaver AI
 
-Overview
 
-This project fine-tunes a GPT-2 model on the TinyStories dataset to generate structured, coherent, and engaging short narratives. The model is hosted on Hugging Face Spaces and provides a user-friendly interface for story generation.
-
-Features
-
-✅ Story Generation: Produces structured, child-friendly short stories.
-
-✅ Bias Monitoring: Ensures balanced gender and cultural representation.
-
-✅ Efficient Training: Fine-tuned on 200,000 training samples and 20,000 test samples.
+---
+title: GPT-2 TinyStories Generator
+emoji: 📖
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 5.13.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: A fine-tuned GPT-2 model for generating structured, child-friendly short stories.
+---
 
-✅ Grammar & Readability Enhancements: Integrated grammar-checking tools and text refinement.
+# GPT-2 TinyStories Generator 📖 FableWeaver AI
 
-✅ Optimized Performance: Uses loss tracking, sampling techniques, and bias mitigation strategies.
+## Overview
+This project fine-tunes a **GPT-2 model** on the **TinyStories dataset** to generate structured, coherent, and engaging short narratives. The model is hosted on **Hugging Face Spaces** and provides a user-friendly interface for story generation.
 
-🚀 System Architecture
+## Features
+- ✅ **Story Generation:** Produces structured, child-friendly short stories.
+- ✅ **Bias Monitoring:** Ensures balanced gender and cultural representation.
+- ✅ **Efficient Training:** Fine-tuned on **200,000 training samples** and **20,000 test samples**.
+- ✅ **Grammar & Readability Enhancements:** Integrated grammar-checking tools and text refinement.
+- ✅ **Optimized Performance:** Uses loss tracking, sampling techniques, and bias mitigation strategies.
 
+## 🚀 System Architecture
 The model is designed for easy interaction via Hugging Face Spaces and follows this workflow:
+1. **Data Preprocessing & Cleaning**
+   - Tokenization, formatting, and encoding normalization.
+   - Bias mitigation and balanced data preparation.
+2. **Model Fine-Tuning**
+   - Fine-tuned **GPT-2 (124M parameters)** using **Hugging Face Transformers**.
+   - Hyperparameter optimization (batch size, learning rate, weight decay).
+3. **Story Generation Pipeline**
+   - Uses **top-k filtering (k=50)**, **top-p nucleus sampling (p=0.9)**, and **temperature adjustments**.
+4. **Post-Processing & Bias Mitigation**
+   - Contextual reinforcement and diversity-aware storytelling.
+   - No-repeat n-gram settings and logical scene transitions.
+5. **Evaluation & Performance Monitoring**
+   - Tracked using **Weights & Biases (W&B)** and **TensorBoard**.
+   - Analyzed validation loss and coherence checks.
+
+## 🔹 Getting Started
+### Accessing the Model
+The model is available on **Hugging Face Spaces**: [GPT-2 TinyStories Generator](https://huggingface.co/spaces/cpv2280/gpt2-tinystories-generator)
+
+### Usage Instructions
+1. Visit the **Hugging Face Space** linked above.
+2. Enter a prompt (e.g., *"Once upon a time..."*) in the input field.
+3. Click **Generate** to receive an AI-generated short story.
+4. Modify the prompt and settings (temperature, top-k, top-p) for different results.
+
+## 📊 Training Details
+- **Model:** GPT-2 (124M)
+- **Dataset:** RonenEldan/TinyStories
+- **Training:** 3 epochs on Google Colab GPU (T4)
+- **Loss Metrics:**
+  - Training Loss: **3.08 → 2.86**
+  - Validation Loss: **1.46 → 1.40**
+
+## 📝 Evaluation & Observations
+- **Perplexity improved from 8.12 → 2.09**, indicating better text fluency.
+- **Validation loss decreased consistently**, suggesting effective generalization.
+- **Human evaluation highlighted minor inconsistencies**, such as abrupt scene shifts and simplistic narratives.
+
+## ⚖️ Ethical Considerations
+- **Bias Monitoring:** Pronoun analysis and diversity checks to ensure fairness.
+- **Harmful Content Mitigation:** Manually reviewed outputs for stereotypes.
+- **Text Processing Issues:** UTF-8 encoding applied to prevent character errors.
+
+## 🔮 Future Improvements
+- **Enhancing Creativity:** Fine-tune temperature and randomness settings.
+- **Genre-Specific Training:** Introduce theme-based datasets.
+- **Larger Model Training:** Experiment with **GPT-2 (355M)** for richer storytelling.
+
+## 🤝 Contributors
+- **Charla Pia Vella** (Project Developer)
+- **Affiliation:** ARI3333 Generative AI
+
+## 📜 License
+This project is released under the **Apache-2.0 License**.
+
+## 🎓 Acknowledgments
+- **OpenAI** for GPT-2
+- **Hugging Face** for the fine-tuning framework
+- **Ronen Eldan** for the TinyStories dataset
+
+📌 For more details, visit the [Hugging Face Space](https://huggingface.co/spaces/cpv2280/gpt2-tinystories-generator).
 
-Data Preprocessing & Cleaning
-
-Tokenization, formatting, and encoding normalization.
-
-Bias mitigation and balanced data preparation.
-
-Model Fine-Tuning
-
-Fine-tuned GPT-2 (124M parameters) using Hugging Face Transformers.
-
-Hyperparameter optimization (batch size, learning rate, weight decay).
-
-Story Generation Pipeline
-
-Uses top-k filtering (k=50), top-p nucleus sampling (p=0.9), and temperature adjustments.
-
-Post-Processing & Bias Mitigation
-
-Contextual reinforcement and diversity-aware storytelling.
-
-No-repeat n-gram settings and logical scene transitions.
-
-Evaluation & Performance Monitoring
-
-Tracked using Weights & Biases (W&B) and TensorBoard.
-
-Analyzed validation loss and coherence checks.
-
-🔹 Getting Started
-
-Accessing the Model
-
-The model is available on Hugging Face Spaces: GPT-2 TinyStories Generator
-
-Usage Instructions
-
-Visit the Hugging Face Space linked above.
-
-Enter a prompt (e.g., "Once upon a time...") in the input field.
-
-Click Generate to receive an AI-generated short story.
-
-Modify the prompt and settings (temperature, top-k, top-p) for different results.
-
-📊 Training Details
-
-Model: GPT-2 (124M)
-
-Dataset: RonenEldan/TinyStories
-
-Training: 3 epochs on Google Colab GPU (T4)
-
-Loss Metrics:
-
-Training Loss: 3.08 → 2.86
-
-Validation Loss: 1.46 → 1.40
-
-📝 Evaluation & Observations
-
-Perplexity improved from 8.12 → 2.09, indicating better text fluency.
-
-Validation loss decreased consistently, suggesting effective generalization.
-
-Human evaluation highlighted minor inconsistencies, such as abrupt scene shifts and simplistic narratives.
-
-⚖️ Ethical Considerations
-
-Bias Monitoring: Pronoun analysis and diversity checks to ensure fairness.
-
-Harmful Content Mitigation: Manually reviewed outputs for stereotypes.
-
-Text Processing Issues: UTF-8 encoding applied to prevent character errors.
-
-🔮 Future Improvements
-
-Enhancing Creativity: Fine-tune temperature and randomness settings.
-
-Genre-Specific Training: Introduce theme-based datasets.
-
-Larger Model Training: Experiment with GPT-2 (355M) for richer storytelling.
-
-🤝 Contributors
-
-Charla Pia Vella (Project Developer)
-
-Affiliation: ARI3333 Generative AI
-
-📜 License
-
-This project is released under the Apache-2.0 License.
-
-🎓 Acknowledgments
-
-OpenAI for GPT-2
-
-Hugging Face for the fine-tuning framework
-
-Ronen Eldan for the TinyStories dataset
-
-📌 For more details, visit the Hugging Face Space.