Since its launch in December 2016, a lot happened around the OpenNMT initiative: new features, new use cases, new users, a new implementation in PyTorch, etc. Today, we continue in this dynamic and publish OpenNMT-tf, an experimental TensorFlow alternative of OpenNMT and OpenNMT-py.
OpenNMT-tf already supports a lot of the features of the existing versions but thanks to a modular design it allows several new model variants:
- mixed word/character embeddings
- arbitrarily complex encoder architectures (mixing RNN, CNN, self attention, etc.)
- hybrid encoder-decoder model (self attention encoder and RNN decoder)
- multi source inputs (e.g. source text + Moses translation for machine translation)
and all of the above can be used simultaneously! The project also makes use of some of the best TensorFlow features:
Testing, feedback, and contributions are highly wanted in these early stages (rough edges are to be expected!). Thank you!
Also see the OpenNMT website to learn how the 3 versions are related and what are their goal. All versions will remain supported.