API reference for text and NLP libraries.

KerasNLP

API reference

The easiest way to get started processing text in TensorFlow is to use KerasNLP, a natural language processing library that provides modular components with state-of-the-art preset weights and architectures. You can use KerasNLP components out-of-the-box or customize them as needed. KerasNLP emphasizes in-graph computation for all workflows, so you can expect easy productionization using the TensorFlow ecosystem.

To install KerasNLP, see Installation.

TensorFlow Text

API reference

The tensorflow_text package provides a collection of text related classes and ops ready to use with TensorFlow. The library can perform the preprocessing regularly required by text-based models, and includes other features useful for sequence modeling not provided by core TensorFlow.

For installation details, refer to the the guide

TensorFlow Models - NLP

API reference

The TensorFlow Models repository provides implementations of state-of-the-art (SOTA) models. The tensorflow-models-official pip package includes many high-level functions and classes for building SOTA NLP models including nlp.layers, nlp.losses, nlp.models and nlp.tasks.

You can install the package with pip:

$pipinstalltensorflow-models-official# For the latest release
$#or
$pipinstalltf-models-nightly# For the nightly build

The NLP functionality is available in the tfm.nlp submodule.

importtensorflow_modelsastfm
tfm.nlp