Update README.md

README.md

@@ -1,67 +1,33 @@
-## SET 1 - MAKEMORE (PART 1) 🔗
-
-[![Documentation](https://img.shields.io/badge/Documentation-Available-blue)](https://muzzammilshah.github.io/Road-to-GPT/Makemore-part1/)
-![Number of Commits](https://img.shields.io/github/commit-activity/m/MuzzammilShah/NeuralNetworks-LanguageModels-1?label=Commits)
-[![Last Commit](https://img.shields.io/github/last-commit/MuzzammilShah/NeuralNetworks-LanguageModels-1.svg?style=flat)](https://github.com/MuzzammilShah/NeuralNetworks-LanguageModels-1/commits/main)  
-![Project Status](https://img.shields.io/badge/Status-Done-success)
-
- 
-
-### **Overview**
-Introduced to the concept of a bigram character-level language model, this repository explores its **training**, **sampling**, and **evaluation** processes. The model evaluation was conducted using the **Negative Log Likelihood (NLL)** loss to assess its quality.
-
-The model was trained in two distinct ways, both yielding identical results:
-
-1. **Frequency-Based Approach**: Directly counting and normalizing bigram frequencies.
-2. **Gradient-Based Optimization**: Optimizing the counts matrix using a gradient-based framework guided by minimizing the NLL loss.
-
-This demonstrated that **both methods converge to the same result**, showcasing their equivalence in achieving the desired outcome.
-
- 
-
-### **🗂️Repository Structure**
-
-```plaintext
-├── .gitignore
-├── A-Main-Notebook.ipynb
-├── B-Main-Notebook.ipynb
-├── C-Main-Notebook.ipynb
-├── README.md
-├── notes/
-│   ├── A-main-makemore-part1.md
-│   ├── B-main-makemore-part1.md
-│   ├── C-main-makemore-part1.md
-│   └── README.md
-└── names.txt
-```
-
-- **Notes Directory**: Contains detailed notes corresponding to each notebook section.
-- **Jupyter Notebooks**: Step-by-step implementation and exploration of the bigram model.
-- **README.md**: Overview and guide for this repository.
-- **names.txt**: Supplementary data file used in training the model.
-
- 
-
-### **📄Instructions**
-
-To get the best understanding:
-
-1. Start by reading the notes in the `notes/` directory. Each section corresponds to a notebook for step-by-step explanations.
-2. Open the corresponding Jupyter Notebook (e.g., `A-Main-Notebook.ipynb` for `A-main-makemore-part1.md`).
-3. Follow the code and comments for a deeper dive into the implementation details.
-
- 
-
-### **⭐Documentation**
-
-For a better reading experience and detailed notes, visit my **[Road to GPT Documentation Site](https://muzzammilshah.github.io/Road-to-GPT/)**. 
-
-> **💡Pro Tip**: This site provides an interactive and visually rich explanation of the notes and code. It is highly recommended you view this project from there.
-
- 
-
-
-### **✍🏻Acknowledgments**
-Notes and implementations inspired by the **Makemore - Part 1** video by [Andrej Karpathy](https://karpathy.ai/).  
-
-For more of my projects, visit my [Portfolio Site](https://muhammedshah.com).
+---
+license: mit
+datasets:
+- MuzzammilShah/people-names
+language:
+- en
+model_name: Bigram Character-Level Language Model
+library_name: pytorch
+tags:
+- makemore
+- bigram
+- language-model
+- andrej-karpathy
+---
+
+# Bigram Character-Level Language Model: Makemore (Part 1)
+
+Introduced to the concept of a bigram character-level language model, this repository explores its **training**, **sampling**, and **evaluation** processes. The model evaluation was conducted using the **Negative Log Likelihood (NLL)** loss to assess its quality.
+
+## Overview
+The model was trained in two distinct ways, both yielding identical results:
+1. **Frequency-Based Approach**: Directly counting and normalizing bigram frequencies.
+2. **Gradient-Based Optimization**: Optimizing the counts matrix using a gradient-based framework guided by minimizing the NLL loss.
+
+This demonstrated that **both methods converge to the same result**, showcasing their equivalence in achieving the desired outcome.
+
+## Documentation
+For a better reading experience and detailed notes, visit my **[Road to GPT Documentation Site](https://muzzammilshah.github.io/Road-to-GPT/Makemore-part1/)**.
+
+## Acknowledgments
+Notes and implementations inspired by the **Makemore - Part 1** video by [Andrej Karpathy](https://karpathy.ai/).
+
+For more of my projects, visit my [Portfolio Site](https://muhammedshah.com).