Industry-strength Natural Language Processing extensions for Keras.
Project description
KerasNLP: Modular NLP Workflows for Keras
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.
We are a new and growing project and welcome contributions.
Quick Links
For everyone
For contributors
Installation
To install the latest official release:
pip install keras-nlp tensorflow --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 tensorflow --upgrade
Quickstart
Fine-tune BERT on a small sentiment analysis task using the
keras_nlp.models API:
import keras_nlp
from tensorflow import keras
import tensorflow_datasets as tfds
imdb_train, imdb_test = tfds.load(
"imdb_reviews",
split=["train", "test"],
as_supervised=True,
batch_size=16,
)
classifier = keras_nlp.models.BertClassifier.from_preset(
"bert_base_en_uncased",
)
classifier.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=keras.optimizers.Adam(5e-5),
metrics=keras.metrics.SparseCategoricalAccuracy(),
jit_compile=True,
)
classifier.fit(
imdb_train,
validation_data=imdb_test,
epochs=1,
)
# Predict a new example
classifier.predict(["What an amazing movie, three hours of pure bliss!"])
For more in depth guides and examples, visit https://keras.io/keras_nlp/.
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:
DistilBERT, RoBERTa, XLM-RoBERTa, DeBERTa, and GPT-2.
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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