Industry-strength Natural Language Processing extensions for Keras.
Project description
KerasNLP: Multi-framework NLP Models
[!IMPORTANT] 📢 KerasNLP is becoming KerasHub! 📢 Read the announcement.
We have renamed the repo to KerasHub in preparation for the release, but have not yet released the new package. Follow the announcement for news.
KerasNLP is a natural language processing library that works natively with TensorFlow, JAX, or PyTorch. KerasNLP provides a repository of pre-trained models and a collection of lower-level building blocks for language modeling. Built on Keras 3, models can be trained and serialized in any framework and re-used in another without costly migrations.
This library is an extension of the core Keras API; all high-level modules are Layers and Models that receive that same level of polish as core Keras. If you are familiar with Keras, congratulations! You already understand most of KerasNLP.
All models support JAX, TensorFlow, and PyTorch from a single model definition and can be fine-tuned on GPUs and TPUs out of the box. Models can be trained on individual accelerators with built-in PEFT techniques, or fine-tuned at scale with model and data parallel training. See our Getting Started guide to start learning our API. Browse our models on Kaggle. We welcome contributions.
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Quickstart
Fine-tune BERT on IMDb movie reviews:
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.Classifier.from_preset(
"bert_base_en",
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."])
Try it out in a colab. For more in depth guides and examples, visit keras.io/keras_nlp.
Installation
To install the latest KerasNLP release with Keras 3, simply run:
pip install --upgrade keras-nlp
To install the latest nightly changes for both KerasNLP and Keras, you can use our nightly package.
pip install --upgrade keras-nlp-nightly
Note that currently, installing KerasNLP will always pull in TensorFlow for use
of the tf.data API for preprocessing. Even when pre-processing with tf.data,
training can still happen on any backend.
Read Getting started with Keras for more information on installing Keras 3 and compatibility with different frameworks.
[!IMPORTANT] We recommend using KerasNLP with TensorFlow 2.16 or later, as TF 2.16 packages Keras 3 by default.
Configuring your backend
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=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.
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, BLOOM, DeBERTa, DistilBERT, GPT-2, Llama, Mistral, 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-hub}},
}
Acknowledgements
Thank you to all of our wonderful contributors!
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