GitLocker: The Coding Marketplace

Description:

kerasnlp 0.14.4

KerasNLP: Multi-framework NLP Models

KerasNLP is a natural language processing library that works natively
with TensorFlow, JAX, or PyTorch. KerasNLP provides a repository of pre-trained
models and a collection of lower-level building blocks for language modeling.
Built on Keras 3, models can be trained and serialized in any framework
and re-used in another without costly migrations.
This library is an extension of the core Keras API; all high-level modules are
Layers and Models that receive that same level of polish as core Keras.
If you are familiar with Keras, congratulations! You already understand most of
KerasNLP.
All models support JAX, TensorFlow, and PyTorch from a single model
definition and can be fine-tuned on GPUs and TPUs out of the box. Models can
be trained on individual accelerators with built-in PEFT techniques, or
fine-tuned at scale with model and data parallel training. See our
Getting Started guide
to start learning our API. Browse our models on
Kaggle.
We welcome contributions.
Quick Links
For everyone

Home Page
Developer Guides
API Reference
Pre-trained Models

For contributors

Contributing Guide
Roadmap
Style Guide
API Design Guide
Call for Contributions

Quickstart
Fine-tune BERT on IMDb movie reviews:
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.Classifier.from_preset(
"bert_base_en",
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."])

Try it out in a colab.
For more in depth guides and examples, visit
keras.io/keras_nlp.
Installation
To install the latest KerasNLP release with Keras 3, simply run:
pip install --upgrade keras-nlp

To install the latest nightly changes for both KerasNLP and Keras, you can use
our nightly package.
pip install --upgrade keras-nlp-nightly

Note that currently, installing KerasNLP will always pull in TensorFlow for use
of the tf.data API for preprocessing. Even when pre-processing with tf.data,
training can still happen on any backend.
Read Getting started with Keras for more
information on installing Keras 3 and compatibility with different frameworks.

[!IMPORTANT]
We recommend using KerasNLP with TensorFlow 2.16 or later, as TF 2.16 packages
Keras 3 by default.

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, BLOOM, DeBERTa, DistilBERT, GPT-2, Llama, Mistral, 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!