Industry-strength Natural Language Processing extensions for Keras.
Project description
KerasNLP: Modular NLP Workflows for Keras
KerasNLP is a natural language processing library that works natively with TensorFlow, JAX, or PyTorch. Built on multi-backend Keras (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.
Quick Links
For everyone
For contributors
Installation
To install the latest official release:
pip install keras-nlp --upgrade
To install the latest unreleased changes to the library, we recommend using pip to install directly from the master branch on github:
pip install git+https://github.com/keras-team/keras-nlp.git --upgrade
Quickstart
Fine-tune BERT on a small sentiment analysis task using the
keras_nlp.models API:
import os
os.environ["KERAS_BACKEND"] = "jax" # Or "tensorflow", 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/.
Configuring your backend
Keras 3 is an upcoming release of the Keras library which supports
TensorFlow, Jax or Torch as backends. This is supported today in KerasNLP,
but will not be enabled by default until the official release of Keras 3. If you
pip install keras-nlp and run a script or notebook without changes, you will
be using TensorFlow and Keras 2.
If you would like to enable a preview of the Keras 3 behavior, you can do
so by setting the KERAS_BACKEND environment variable. For example:
export KERAS_BACKEND=jax
Or in Colab, with:
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_nlp
[!IMPORTANT] Make sure to set the
KERAS_BACKENDbefore import any Keras libraries, it will be used to set up Keras when it is first imported.
Until the Keras 3 release, KerasNLP will use a preview of Keras 3 on PyPI named keras-core.
[!IMPORTANT] If you set
KERAS_BACKENDvariable, you shouldimport keras_core as kerasinstead ofimport keras. This is a temporary step until Keras 3 is out!
To restore the default Keras 2 behavior, unset KERAS_BACKEND before
importing Keras and KerasNLP.
Compatibility
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.
Disclaimer
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.
Citing KerasNLP
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}},
}
Acknowledgements
Thank you to all of our wonderful contributors!
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