torchtextclassifiers 1.0.0

A text classification toolkit to easily build, train and evaluate deep learning text classifiers using PyTorch.

torchTextClassifiers

A unified, extensible framework for text classification with categorical variables built on PyTorch and PyTorch Lightning.

🚀 Features

• Complex input support: Handle text data alongside categorical variables seamlessly.
• Unified yet highly customizable:
  • Use any tokenizer from HuggingFace or the original fastText's ngram tokenizer.
  • Manipulate the components (TextEmbedder, CategoricalVariableNet, ClassificationHead) to easily create custom architectures - including self-attention. All of them are torch.nn.Module !
  • The TextClassificationModel class combines these components and can be extended for custom behavior.
• Multiclass / multilabel classification support: Support for both multiclass (only one label is true) and multi-label (several labels can be true) classification tasks.
• PyTorch Lightning: Automated training with callbacks, early stopping, and logging
• Easy experimentation: Simple API for training, evaluating, and predicting with minimal code:
  • The torchTextClassifiers wrapper class orchestrates the tokenizer and the model for you
• Additional features: explainability using Captum

📦 Installation

# Clone the repository
git clone https://github.com/InseeFrLab/torchTextClassifiers.git
cd torchtextClassifiers

# Install with uv (recommended)
uv sync

# Or install with pip
pip install -e .

📖 Documentation

Full documentation is available at: https://inseefrlab.github.io/torchTextClassifiers/ The documentation includes:

• Getting Started: Installation and quick start guide
• Architecture: Understanding the 3-layer design
• Tutorials: Step-by-step guides for different use cases
• API Reference: Complete API documentation

📝 Usage

Checkout the notebook for a quick start.

📚 Examples

See the examples/ directory for:

• Basic text classification
• Multi-class classification
• Mixed features (text + categorical)
• Advanced training configurations
• Prediction and explainability

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.