Industry-strength Natural Language Processing extensions for Keras.
Project description
KerasNLP: Modular NLP Workflows for Keras
KerasNLP is a natural language processing library that works natively with TensorFlow, JAX, or PyTorch. Built on Keras Core, these models, layers, metrics, callbacks, etc., can be trained and serialized in any framework and re-used in another without costly migrations. See "Using KerasNLP with Keras Core" below for more details on multi-framework KerasNLP.
KerasNLP 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.
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 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 --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
Using KerasNLP with Keras Core
As of version 0.6.0, KerasNLP supports multiple backends with Keras Core out
of the box. There are two ways to configure KerasNLP to run with multi-backend
support:
- Via the
KERAS_BACKENDenvironment variable. If set, then KerasNLP will be using Keras Core with the backend specified (e.g.,KERAS_BACKEND=jax). - Via the
.keras/keras.jsonand.keras/keras_nlp.jsonconfig files (which are automatically created the first time you import KerasNLP):- Set your backend of choice in
.keras/keras.json; e.g.,"backend": "jax". - Set
"multi_backend": Truein.keras/keras_nlp.json.
- Set your backend of choice in
Once that configuration step is done, you can just import KerasNLP and start using it on top of your backend of choice:
import keras_nlp
gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset("gpt2_base_en")
gpt2_lm.generate("My trip to Yosemite was", max_length=200)
Until Keras Core is officially released as Keras 3.0, KerasNLP will use
tf.keras as the default backend. To restore this default behavior, simply
unset KERAS_BACKEND and ensure that "multi_backend": False or is unset in
.keras/keras_nlp.json. You will need to restart the Python runtime for changes
to take effect.
Quickstart
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",
num_classes=2,
)
# 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/.
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!
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
Built Distribution
File details
Details for the file keras-nlp-0.6.2.tar.gz.
File metadata
- Download URL: keras-nlp-0.6.2.tar.gz
- Upload date:
- Size: 307.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 61477e111e07165ad9d18665aae482575b7ec4628abd90224b0e0ddf26ca86e0 |
|
| MD5 | b080d45beb6119a3ced6d48d56f59500 |
|
| BLAKE2b-256 | e0fe243cde187fa34f063614d55c19204efef874d1c8ec1cef0998cc4c009ca2 |
Provenance
File details
Details for the file keras_nlp-0.6.2-py3-none-any.whl.
File metadata
- Download URL: keras_nlp-0.6.2-py3-none-any.whl
- Upload date:
- Size: 590.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f67aab351607dfba0b4510204b4bc056ed52d734fa847b54f1776cadcb260e0e |
|
| MD5 | 443e537c52a99b7dcf18a877968ffc8e |
|
| BLAKE2b-256 | 37d4dfd85606db811af2138e97fc480eb7ed709042dd96dd453868bede0929fe |
