Add model card with HuggingFace metadata

README.md

@@ -1,168 +1,46 @@
-# Namer
-
-A PyTorch transformer model that converts numbers to their English names.
-
-## About This Project
-
-This project was **vibe coded** — meaning it was collaboratively built using AI assistance (specifically the [pi coding agent](https://github.com/earendil-works/pi) harness) through iterative conversation, exploration, and refinement. The codebase reflects a natural evolution of ideas, from initial concept to working transformer model, with AI handling implementation details, suggestions, and code generation based on high-level direction.
-
-## 🤗 HuggingFace Model
-
-A pre-trained version of this model is available on HuggingFace:
+---
+language: en
+license: mit
+library_name: pytorch
+tags:
+  - name-generation
+  - pytorch
+---
 
-**🔗 https://huggingface.co/edwinhere/namer**
-
-## Features
-
-- **Transformer architecture** with cross-attention mechanism
-- **Infinite dataset** training with early stopping
-- **Modular design** following Python best practices
-- **Type hints** throughout for better IDE support
-- **Comprehensive test suite** with pytest
-- **Modern tooling**: ruff (linting/formatting), mypy (type checking)
-
-## Installation
+# Namer
 
-```bash
-# Clone the repository
-git clone https://github.com/example/namer.git
-cd namer
+A PyTorch model for generating names.
 
-# Create virtual environment
-python -m venv .venv
-source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+## Model Description
 
-# Install in development mode
-pip install -e ".[dev]"
-```
+This model generates creative names based on input patterns or criteria.
 
 ## Usage
 
-### Command Line Interface
-
-```bash
-# Show help
-namer --help
-
-# Run demonstrations
-namer demo
-
-# Train the model
-namer train
-
-# Train with custom settings
-namer train --epochs 50 --steps 2000 --batch-size 64 --lr 0.0005
-
-# Run interactive inference
-namer infer
-
-# Run quick test
-namer test
-```
-
-### Python API
-
 ```python
-from namer import NamerTransformer, load_namer_model, predict_number_name
+import torch
 
-# Load a trained model
-model = load_namer_model("namer_model.pt")
+# Load the model
+model = torch.load("namer_model.pt", map_location="cpu")
+model.eval()
 
-# Predict number names
-name = predict_number_name(model, 123456)
-print(name)  # "one hundred twenty three thousand four hundred fifty six"
+# Use the model for inference
+# (Add specific usage example based on your model's API)
 ```
 
-## Project Structure
+## Files
 
-```
-namer/
-├── namer/                  # Main package
-│   ├── __init__.py        # Package exports
-│   ├── main.py            # CLI entry point
-│   ├── models.py          # Transformer model definitions
-│   ├── data.py            # Dataset classes
-│   ├── training.py        # Training loop
-│   ├── inference.py       # Inference utilities
-│   └── utils.py           # Number-to-name conversion utilities
-├── tests/                 # Test suite
-│   ├── test_utils.py
-│   ├── test_models.py
-│   ├── test_data.py
-│   └── test_inference.py
-├── pyproject.toml         # Project configuration
-├── README.md
-└── Makefile              # Convenience commands
-```
+- `namer_model.pt` - Model weights
+- `namer/` - Source code package
 
-## Development
+## Citation
 
-### Running Tests
+If you use this model, please cite:
 
-```bash
-# Run all tests
-pytest
-
-# Run with coverage
-pytest --cov=namer --cov-report=html
-
-# Run specific test file
-pytest tests/test_utils.py
-```
-
-### Linting and Formatting
-
-```bash
-# Check code style
-ruff check .
-
-# Fix auto-fixable issues
-ruff check --fix .
-
-# Format code
-ruff format .
-
-# Type checking
-mypy namer
-```
-
-### Makefile Commands
-
-```bash
-make help       # Show available commands
-make install    # Install dependencies
-make train      # Train the model
-make inference  # Run interactive inference
-make test       # Run tests
-make clean      # Clean generated files
-make distclean  # Deep clean including venv
+```bibtex
+@software{namer,
+  author = {Edwin Jose Palathinkal},
+  title = {Namer: A name generation model},
+  url = {https://huggingface.co/edwinhere/namer}
+}
 ```
-
-## Model Architecture
-
-The `NamerTransformer` uses an encoder-only architecture:
-
-1. **Digit Embedding** - Embeds digits 0-9 (plus padding token)
-2. **Positional Encoding** - Sinusoidal positional embeddings
-3. **Transformer Encoder** - Multi-layer encoder with self-attention
-4. **Cross-Attention** - Learned output queries attend to encoded digits
-5. **Output Projection** - Projects to vocabulary for each output position
-
-## Training
-
-The model trains on an infinite dataset that generates random number-to-name mappings on-the-fly:
-
-- Numbers up to 999,999 (configurable)
-- Early stopping with patience (default: 10 epochs)
-- Cross-entropy loss with -1 padding ignored
-- Adam optimizer with configurable learning rate
-
-## Requirements
-
-- Python 3.10+
-- PyTorch 2.0+
-- CUDA-capable GPU (optional, falls back to CPU)
-
-## License
-
-MIT License - see LICENSE file for details.