KerasNLP is a natural language processing library that supports users through their entire development cycle. Our workflows are built from modular components that have state-of-the-art preset weights and architectures when used out-of-the-box and are easily customizable when more control is needed. We emphasize in-graph computation for all workflows so that developers can expect easy productionization using the TensorFlow ecosystem.
This library is an extension of the core Keras API; all high-level modules are
Layers
or Models
that recieve 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 for example usage of our modular API starting with evaluating pretrained models and building up to designing a novel transformer architecture and training a tokenizer from scratch.
KerasNLP is new and growing! If you are interested in contributing, please check out our contributing guide.
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
Fine-tune BERT on a small sentiment analysis task using the
keras_nlp.models
API:
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")
# 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."])
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:
DistilBERT, RoBERTa, XLM-RoBERTa, DeBERTa, and GPT-2.
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}},
}