Update model card with accurate description of integer-to-English conversion

README.md

@@ -3,35 +3,95 @@ language: en
 license: mit
 library_name: pytorch
 tags:
-  - name-generation
+  - text-generation
+  - number-to-text
   - pytorch
+  - transformer
 ---
 
 # Namer
 
-A PyTorch model for generating names.
+A PyTorch transformer model that converts **integers to their English names** (e.g., `42` → "forty two", `123` → "one hundred twenty three").
 
 ## Model Description
 
-This model generates creative names based on input patterns or criteria.
+Namer is a sequence-to-sequence transformer trained to read digits of a number and generate the corresponding English textual representation. It handles numbers from 0 up to billions, learning the patterns of English number naming conventions.
+
+**Example conversions:**
+| Integer | English Name |
+|---------|-------------|
+| 0 | zero |
+| 42 | forty two |
+| 123 | one hundred twenty three |
+| 1000 | one thousand |
+| 1234567 | one million two hundred thirty four thousand five hundred sixty seven |
 
 ## Usage
 
+### Quick Start
+
 ```python
 import torch
+from namer import load_namer_model, predict_number_name
+
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = load_namer_model("namer_model.pt", device)
+
+# Convert number to name
+name = predict_number_name(model, 42)
+print(f"42 -> '{name}'")  # Output: forty two
+```
+
+### Interactive Mode
 
-# Load the model
-model = torch.load("namer_model.pt", map_location="cpu")
-model.eval()
+```bash
+python -m namer infer
+```
+
+Then enter numbers to convert interactively.
+
+### API
+
+```python
+from namer.inference import predict_number_name
 
-# Use the model for inference
-# (Add specific usage example based on your model's API)
+# Single prediction
+name = predict_number_name(model, 123456)
+# Returns: "one hundred twenty three thousand four hundred fifty six"
 ```
 
+## Model Architecture
+
+- **Type**: Sequence-to-sequence transformer
+- **Input**: Digits of the integer (as token indices)
+- **Output**: English words representing the number
+- **Vocabulary**: English number words (zero-nineteen, twenty-ninety, hundred, thousand, million, billion, etc.)
+
 ## Files
 
-- `namer_model.pt` - Model weights
-- `namer/` - Source code package
+| File | Description |
+|------|-------------|
+| `namer_model.pt` | Trained model weights |
+| `namer/models.py` | Transformer architecture |
+| `namer/inference.py` | Prediction utilities |
+| `namer/utils.py` | Encoding/decoding utilities |
+
+## Training
+
+To train from scratch:
+
+```bash
+python -m namer train
+```
+
+## Installation
+
+```bash
+git clone https://huggingface.co/edwinhere/namer
+cd namer
+pip install -e .
+```
 
 ## Citation
 
@@ -40,7 +100,11 @@ If you use this model, please cite:
 ```bibtex
 @software{namer,
   author = {Edwin Jose Palathinkal},
-  title = {Namer: A name generation model},
+  title = {Namer: Integer to English Name Converter},
   url = {https://huggingface.co/edwinhere/namer}
 }
 ```
+
+## Links
+
+- [GitHub Repository](https://github.com/edwinhere/namer)