texar: a modular toolkit for NLP and text generation with support for multiple learning paradigms

What it solves

Texar simplifies the process of building, experimenting with, and prototyping machine learning models for natural language processing (NLP) and text generation. It provides a modular library of components that allows researchers and practitioners to quickly compose complex models without having to write everything from scratch.

How it works

Texar uses a principled decomposition of Learning, Inference, and Model Architecture. It offers a wide array of pre-trained models (like BERT, GPT2, and XLNet) and modular components such as encoders, decoders, and attention mechanisms. The toolkit is designed to be fully compatible with native TensorFlow and PyTorch APIs, allowing users to plug in external modules easily. It also supports various learning paradigms, including maximum likelihood, reinforcement learning, and adversarial learning.

Who it’s for

It is designed for ML researchers and practitioners who need a flexible, extensible toolkit for fast prototyping and experimentation in text generation and NLP.

Highlights

• Dual Framework Support: Maintains mostly identical interfaces for both TensorFlow and PyTorch versions.
• Modular Architecture: Components are modularized for maximum reuse and clean APIs.
• Pre-trained Model Library: Includes built-in support for popular models like BERT, GPT2, and XLNet.
• Versatile Learning: Supports multiple training methods including RL policy gradients and adversarial learning.
• Distributed Training: Capable of training models across multiple GPUs.