TensorFlow.Text has been launched by Google. It preprocesses the language model using Google’s open-source platform for machine learning (ML) and provides an end-to-end open-source platform.
A subgroup of Keras exists, but it is not as wide as TF.Text. We are currently in dialogue with them to fill the gaps that language engineers need, but they are not included in the core Keras API. I would be very surprised if additional Kerus layers are provided by TF.Text in the future.
TensorFlow has a broad range of operations that can be used to build models from video or pictures. There are large models that start with the text, and these language models require some preprocessing before the text can be input into the model,” says Robby Neale of TensorFlow.
He explained that TF.Text… was designed to alleviate this problem by providing ops for handling the preprocessing continually found in text-based models, as well as other features useful to language modeling not provided with core TensorFlow.
Clients of TF.Text can use tokens for breaking apart and examining text such as words, numbers, and accentuation. It can detect void area, Unicode text, and foreordained groups of word pieces or “workpieces”, which it has used before in programs’ pretraining techniques for language models, such as BERT. PIP can be used to introduce the library.
The beta arrival of TensorFlow 2.0, which has fewer APIs, deeper Keras reconciliation, and upgraded runtime for Eager Execution, saw the introduction of TF.Text.
TF.Text doesn’t represent the only library that uses ML Google has introduced recently. TensorFlow graphics was launched by Google last month to provide graphics and 3D modeling in an intensive learning effort.