KerasNLP is a repository of modular building blocks (layers, metrics, losses). Engineers working with applied natural language processing can leverage it to rapidly assemble training and inference pipelines that are both state-of-the-art and production-grade. Common use cases for application include sentiment analysis, named entity recognition, text generation, etc.
KerasNLP can be understood as a horizontal extension of the Keras API: they're new first-party Keras objects (layers, metrics, etc) that are too specialized to be added to core Keras, but that receive the same level of polish and backwards compatibility guarantees as the rest of the Keras API and that are maintained by the Keras team itself (unlike TFAddons).
Currently, KerasNLP is operating pre-release. Upon launch of KerasNLP 1.0, full API docs and code examples will be available.
If you'd like to contribute, please see our contributing guide.
Thank you to all of our wonderful contributors!