Language Classification with RNN

A PyTorch-based project for classifying names by their language of origin using Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU).

📋 Table of Contents

• Overview
• Features
• Dataset
• Model Architecture
• Installation
• Usage
• Project Structure
• Examples
• Results
• Contributing

🎯 Overview

This project implements a character-level RNN model to predict the language origin of names. Given a person's name as input, the model classifies it into one of 18 supported languages. The model processes names character by character and uses the final hidden state to make predictions.

Supported Languages:

• Arabic, Chinese, Czech, Dutch, English, French, German, Greek
• Irish, Italian, Japanese, Korean, Polish, Portuguese, Russian
• Scottish, Spanish, Vietnamese

✨ Features

• Multiple RNN Architectures: Includes both vanilla RNN and GRU implementations
• Character-level Processing: Processes names at the character level for better generalization
• One-hot Encoding: Converts characters to one-hot encoded vectors
• Train/Validation Split: Automatic stratified splitting of dataset
• Model Evaluation: Comprehensive training and evaluation pipeline
• Prediction Interface: Easy-to-use prediction function for new names

📊 Dataset

The project uses a collection of names from 18 different languages, stored in individual text files under data/names/. Each file contains names specific to that language:

data/names/
├── Arabic.txt
├── Chinese.txt
├── Czech.txt
├── Dutch.txt
├── English.txt
├── French.txt
├── German.txt
├── Greek.txt
├── Irish.txt
├── Italian.txt
├── Japanese.txt
├── Korean.txt
├── Polish.txt
├── Portuguese.txt
├── Russian.txt
├── Scottish.txt
├── Spanish.txt
└── Vietnamese.txt

🏗️ Model Architecture

RNN Model

• Input Size: 57 characters (ASCII letters + punctuation: .,:;-)
• Hidden Size: 256 units
• Output Size: 18 classes (one for each language)
• Architecture: Single-layer RNN with sigmoid activation
• Processing: Character-by-character sequential processing

Model Components

1. Input Layer: One-hot encoded character vectors
2. Hidden Layer: Linear transformation with sigmoid activation
3. Output Layer: Linear transformation to class probabilities
4. Hidden State Initialization: Kaiming uniform initialization

🚀 Installation

Prerequisites

• Python 3.7+
• PyTorch
• scikit-learn
• unidecode

Setup

1. Clone or download the project:

git clone https://github.com/yasharyasaxena/LangClass.git
cd LangClass

2. Install required dependencies:

pip install -r requirements.txt

Or install manually:

pip install torch scikit-learn unidecode

3. Extract the dataset (since data directory is not included in the repository):

# On Windows
powershell Expand-Archive -Path data.zip -DestinationPath .

# Or using Python
python -c "import zipfile; zipfile.ZipFile('data.zip').extractall('.')"

# On Linux/Mac
unzip data.zip

This will create the data/ directory with the required name files for all 18 languages.

💻 Usage

Training the Model

Run the main script to train the model:

python main.py

This will:

1. Load and preprocess the dataset
2. Split data into training (90%) and validation (10%) sets
3. Initialize the RNN model
4. Train for 2 epochs
5. Evaluate on the validation set

Making Predictions

from train import predict
from utils import get_labels

# Load label mapping
lang2labels = get_labels("./data/names")
label2lang = {v.item(): k for k, v in lang2labels.items()}

# Predict language for a name
predicted_language = predict(model, "Jackson", label2lang)
print(f"Predicted language: {predicted_language}")

Custom Training

from models import RNN, GRU
from data import split_dataset
from train import train, evaluate
import torch.nn as nn
import torch.optim as optim

# Load data
train_loader, val_loader = split_dataset("./data/names")

# Initialize model (choose RNN or GRU)
model = RNN(input_size=57, hidden_size=256, output_size=18)
# or
# model = GRU(input_size=57, hidden_size=256, output_size=18)

# Setup training
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train and evaluate
train(model, train_loader, criterion, optimizer, num_epochs=5)
evaluate(model, val_loader)

📁 Project Structure

LangClass/
├── main.py              # Main execution script
├── models.py            # RNN and GRU model definitions
├── data.py              # Dataset loading and splitting
├── train.py             # Training, evaluation, and prediction functions
├── utils.py             # Utility functions for data processing
├── README.md            # Project documentation
├── __init__.py          # Package initialization
├── data/
│   ├── names/           # Name datasets by language
│   └── eng-fra.txt      # Additional language data
└── __pycache__/         # Python cache files

Key Files Description

• models.py: Contains RNN and GRU model classes with forward pass and initialization methods
• data.py: Handles dataset creation and train/validation splitting with stratification
• train.py: Implements training loop, evaluation metrics, and prediction interface
• utils.py: Provides utility functions for label mapping, character encoding, and dataset creation
• main.py: Orchestrates the entire pipeline from data loading to model evaluation

📝 Examples

Example 1: Basic Training

python main.py

Output:

Epoch [1/2], Step [1000/15000], Loss: 2.1234
Epoch [2/2], Step [1000/15000], Loss: 1.8765
Test Accuracy: 0.6750

Example 2: Predicting Name Origins

# Example predictions
names = ["Rodriguez", "O'Connor", "Zhang", "Mueller", "Dubois"]
for name in names:
    prediction = predict(model, name, label2lang)
    print(f"{name} -> {prediction}")

Expected Output:

Rodriguez -> Spanish
O'Connor -> Irish
Zhang -> Chinese
Mueller -> German
Dubois -> French

📈 Results

The model typically achieves:

• Training Accuracy: ~70-80% after 2 epochs
• Validation Accuracy: ~65-75%
• Best Performance: On common European languages (English, French, German, Spanish)
• Challenging Cases: Asian languages and names with mixed origins

Performance Notes

• Model performs well on names with distinctive character patterns
• Longer names generally yield better predictions
• Some names may be ambiguous across similar languages

🛠️ Customization

Model Architecture Changes

Switching Between RNN and GRU Models

The project includes both vanilla RNN and GRU implementations. You can easily switch between them:

Using Vanilla RNN (Default):

from models import RNN
model = RNN(input_size=57, hidden_size=256, output_size=18)

Using GRU Model:

from models import GRUModel
model = GRUModel(
    num_layers=1,       # Number of GRU layers
    input_size=57,      # Character vocabulary size
    hidden_size=256,    # Hidden state dimension
    output_size=18      # Number of language classes
)

Performance Comparison:

• RNN: Simpler, faster training, good baseline performance
• GRU: Better at capturing long-term dependencies, may achieve higher accuracy
• Recommendation: Start with RNN, then try GRU if you need better performance

Multi-layer GRU Configuration

# Deeper GRU model for better performance
model = GRUModel(
    num_layers=2,       # Add more layers for complexity
    input_size=57,
    hidden_size=256,
    output_size=18
)

Hyperparameter Tuning

# Adjust model parameters
model = RNN(
    input_size=57,      # Character vocabulary size
    hidden_size=512,    # Increase for more capacity
    output_size=18      # Number of languages
)

# Modify training parameters
optimizer = optim.Adam(model.parameters(), lr=0.0005)  # Lower learning rate
train(model, train_loader, criterion, optimizer, num_epochs=10)  # More epochs

Training Configuration Changes

Adjusting Training Parameters

# In main.py, modify these parameters:

# Training epochs
train(model, train_loader, criterion, optimizer, num_epochs=5)  # More epochs

# Learning rate options
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)     # Default
optimizer = torch.optim.Adam(model.parameters(), lr=0.0005)    # Lower LR
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)       # SGD optimizer

# Different loss functions
criterion = nn.CrossEntropyLoss()              # Default
criterion = nn.NLLLoss()                       # Negative log likelihood
criterion = nn.CrossEntropyLoss(weight=weights) # Weighted for imbalanced data

Batch Processing (Optional Enhancement)

# For larger datasets, consider adding batch processing
from torch.utils.data import DataLoader, TensorDataset

def create_dataloader(dataset, batch_size=32):
    # Convert to tensors and create DataLoader
    inputs, labels = zip(*dataset)
    dataset = TensorDataset(torch.stack(inputs), torch.stack(labels))
    return DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Usage
train_loader = create_dataloader(train_dataset, batch_size=64)

Adding New Languages

1. Add a new .txt file with names in data/names/
2. Update the model's output_size parameter
3. Retrain the model

Model Comparison Script

# Compare RNN vs GRU performance
def compare_models():
    # Load data
    train_loader, val_loader = split_dataset("./data/names")

    # Test RNN
    rnn_model = RNN(input_size=57, hidden_size=256, output_size=18)
    train(rnn_model, train_loader, criterion, optimizer, num_epochs=3)
    print("RNN Results:")
    evaluate(rnn_model, val_loader)

    # Test GRU
    gru_model = GRUModel(num_layers=1, input_size=57, hidden_size=256, output_size=18)
    train(gru_model, train_loader, criterion, optimizer, num_epochs=3)
    print("GRU Results:")
    evaluate(gru_model, val_loader)

# Run comparison
compare_models()

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Create a Pull Request

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Dataset sources for name collections by language
• PyTorch team for the deep learning framework
• Contributors to the unidecode library for character normalization

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Note: This project is designed for educational purposes and demonstrates basic RNN implementation for text classification. For production use, consider more advanced architectures like LSTMs, Transformers, or pre-trained language models.