OpenNMT is an open source (MIT) initiative for neural machine translation and neural sequence modeling.
Since its launch in December 2016, OpenNMT has become a collection of implementations targeting both academia and industry. The systems are designed to be simple to use and easy to extend, while maintaining efficiency and state-of-the-art accuracy.
OpenNMT has currently 3 main implementations:
- OpenNMT-lua (a.k.a. OpenNMT): the original project developed with LuaTorch.
Full-featured, optimized, and stable code ready for quick experiments and production.
- OpenNMT-py: an OpenNMT-lua clone using the more modern PyTorch.
Initially created by the Facebook AI research team as an example, this implementation is easier to extend and particularly suited for research.
- OpenNMT-tf: a TensorFlow alternative.
The more recent project focusing on large scale experiments and high performance model serving using the latest TensorFlow features.
All versions are currently maintained.
Common features include:
- Simple general-purpose interface, requiring only source/target files.
- Highly configurable models and training procedures.
- Recent research features to improve system performance.
- Extensions to allow other sequence generation tasks such as summarization, image-to-text, or speech-recognition.
- Active community welcoming both academic and industrial requests and contributions.