keras-nlp-nightly 0.7.0.dev2023111103

Industry-strength Natural Language Processing extensions for Keras.

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

• Home Page
• Developer Guides
• API Reference
• Getting Started guide

For contributors

• Contributing Guide
• Roadmap
• Style Guide
• API Design Guide
• Call for Contributions

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_BACKEND before 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_BACKEND variable, you should import keras_core as keras instead of import 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!