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 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
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
You can install the package with
$ pip install tensorflow-models-official # For the latest release $ #or $ pip install tf-models-nightly # For the nightly build
The NLP functionality is available in the
import tensorflow_models as tfm tfm.nlp