Skip to main content

Industry-strength Natural Language Processing extensions for Keras.

Project description

KerasNLP: Modular NLP Workflows for Keras

Python contributions welcome

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.

Quick Links

For everyone

For contributors

Installation

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.

Keras 2 Installation

To install the latest KerasNLP release with Keras 2, simply run:

pip install --upgrade keras-nlp

Keras 3 Installation

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.

Quickstart

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/.

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_BACKEND before 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, 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!

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

keras-nlp-nightly-0.9.0.dev2024031603.tar.gz (277.7 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file keras-nlp-nightly-0.9.0.dev2024031603.tar.gz.

File metadata

File hashes

Hashes for keras-nlp-nightly-0.9.0.dev2024031603.tar.gz
Algorithm Hash digest
SHA256 de08db59b9c7cfe67b02b6fcd9c26002600d5dcbbb812d97daeff640d041b1b9
MD5 6bfb92e3d5c54df7dc7c4b9731dd2d5f
BLAKE2b-256 0001b2040835f3554781579d676819211c3ed83aa17f4bcc5c2032849736a65c

See more details on using hashes here.

Provenance

File details

Details for the file keras_nlp_nightly-0.9.0.dev2024031603-py3-none-any.whl.

File metadata

File hashes

Hashes for keras_nlp_nightly-0.9.0.dev2024031603-py3-none-any.whl
Algorithm Hash digest
SHA256 7c8022688f6a0819a25e7448260563caf38dbc1fe702d84eb6abfdee7e8ad8aa
MD5 d98940acb0e2f7d306adf27c4527b596
BLAKE2b-256 fdee4afaec8ff8d7cb1553eb9927a81d28d41684f86dcff78be030028dafbc9e

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page