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Introduction to TensorFlow Text

TensorFlow Text provides a collection of text related classes and ops ready to use with TensorFlow 2.0. 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.

The benefit of using these ops in your text preprocessing is that they are done in the TensorFlow graph. You do not need to worry about tokenization in training being different than the tokenization at inference, or managing preprocessing scripts.

Install TensorFlow Text

Install using pip

When installing TF Text with pip install, note the version of TensorFlow you are running, as you should specify the corresponding version of TF Text.

pip install -U tensorflow-text==<version>

Build from source

TensorFlow Text must be built in the same environment as TensorFlow. Thus, if you manually build TF Text, it is highly recommended that you also build TensorFlow.

If building on MacOS, you must have coreutils installed. It is probably easiest to do with Homebrew. First, build TensorFlow from source.

Clone the TF Text repo.

git clone  https://github.com/tensorflow/text.git

Finally, run the build script to create a pip package.