KerasNLP is a natural language processing library that works natively with TensorFlow, JAX, or PyTorch. Built on Keras 3, these models, layers, metrics, and tokenizers can be trained and serialized in any framework and re-used in another without costly migrations.
KerasNLP supports users through their entire development cycle. Our workflows are built from modular components that have state-of-the-art preset weights when used out-of-the-box and are easily customizable when more control is needed.
This library is an extension of the core Keras API; all high-level modules are
Layers or
Models that receive that same level of polish
as core Keras. If you are familiar with Keras, congratulations! You already
understand most of KerasNLP.
See our Getting Started guide to start learning our API. We welcome contributions.
KerasNLP supports both Keras 2 and Keras 3. We recommend Keras 3 for all new users, as it enables using KerasNLP models and layers with JAX, TensorFlow and PyTorch.
To install the latest KerasNLP release with Keras 2, simply run:
pip install --upgrade keras-nlp
There are currently two ways to install Keras 3 with KerasNLP. To install the stable versions of KerasNLP and Keras 3, you should install Keras 3 after installing KerasNLP. This is a temporary step while TensorFlow is pinned to Keras 2, and will no longer be necessary after TensorFlow 2.16.
pip install --upgrade keras-nlp
pip install --upgrade keras>=3
To install the latest nightly changes for both KerasNLP and Keras, you can use our nightly package.
pip install --upgrade keras-nlp-nightly
Important
Keras 3 will not function with TensorFlow 2.14 or earlier.
Read Getting started with Keras for more information on installing Keras 3 and compatibility with different frameworks.
Fine-tune BERT on a small sentiment analysis task using the
keras_nlp.models API:
import os
os.environ["KERAS_BACKEND"] = "tensorflow" # Or "jax" or "torch"!
import keras_nlp
import tensorflow_datasets as tfds
imdb_train, imdb_test = tfds.load(
"imdb_reviews",
split=["train", "test"],
as_supervised=True,
batch_size=16,
)
# Load a BERT model.
classifier = keras_nlp.models.BertClassifier.from_preset(
"bert_base_en_uncased",
num_classes=2,
activation="softmax",
)
# Fine-tune on IMDb movie reviews.
classifier.fit(imdb_train, validation_data=imdb_test)
# Predict two new examples.
classifier.predict(["What an amazing movie!", "A total waste of my time."])For more in depth guides and examples, visit https://keras.io/keras_nlp/.
If you have Keras 3 installed in your environment (see installation above),
you can use KerasNLP with any of JAX, TensorFlow and PyTorch. To do so, set the
KERAS_BACKEND environment variable. For example:
export KERAS_BACKEND=jaxOr in Colab, with:
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_nlpImportant
Make sure to set the KERAS_BACKEND before import any Keras libraries, it
will be used to set up Keras when it is first imported.
We follow Semantic Versioning, and plan to
provide backwards compatibility guarantees both for code and saved models built
with our components. While we continue with pre-release 0.y.z development, we
may break compatibility at any time and APIs should not be consider stable.
KerasNLP provides access to pre-trained models via the keras_nlp.models API.
These pre-trained models are provided on an "as is" basis, without warranties
or conditions of any kind. The following underlying models are provided by third
parties, and subject to separate licenses:
BART, DeBERTa, DistilBERT, GPT-2, OPT, RoBERTa, Whisper, and XLM-RoBERTa.
If KerasNLP helps your research, we appreciate your citations. Here is the BibTeX entry:
@misc{kerasnlp2022,
title={KerasNLP},
author={Watson, Matthew, and Qian, Chen, and Bischof, Jonathan and Chollet,
Fran\c{c}ois and others},
year={2022},
howpublished={\url{https://github.com/keras-team/keras-nlp}},
}Thank you to all of our wonderful contributors!