Skip to content

update README #1886

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Sep 27, 2024
Merged

update README #1886

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6546f35
update README
divyashreepathihalli Sep 27, 2024
568d87b
update year
divyashreepathihalli Sep 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
55 changes: 29 additions & 26 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# KerasNLP: Multi-framework NLP Models
# KerasHub: Multi-framework Models
[![](https://github.com/keras-team/keras-hub/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-hub/actions?query=workflow%3ATests+branch%3Amaster)
![Python](https://img.shields.io/badge/python-v3.9.0+-success.svg)
[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/keras-team/keras-hub/issues)
Expand All @@ -10,16 +10,17 @@
> We have renamed the repo to KerasHub in preparation for the release, but have not yet
> released the new package. Follow the announcement for news.

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.
KerasHub is a library that supports natural language processing, computer
vision, audio, and multimodal backbones and task models, working natively with
TensorFlow, JAX, or PyTorch. KerasHub provides a repository of pre-trained
models and a collection of lower-level building blocks for these tasks. 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.
KerasHub.

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
Expand Down Expand Up @@ -55,7 +56,7 @@ Fine-tune BERT on IMDb movie reviews:
import os
os.environ["KERAS_BACKEND"] = "jax" # Or "tensorflow" or "torch"!

import keras_nlp
import keras_hub
import tensorflow_datasets as tfds

imdb_train, imdb_test = tfds.load(
Expand All @@ -65,8 +66,8 @@ imdb_train, imdb_test = tfds.load(
batch_size=16,
)
# Load a BERT model.
classifier = keras_nlp.models.Classifier.from_preset(
"bert_base_en",
classifier = keras_hub.models.Classifier.from_preset(
"bert_base_en",
num_classes=2,
activation="softmax",
)
Expand All @@ -82,34 +83,34 @@ For more in depth guides and examples, visit

## Installation

To install the latest KerasNLP release with Keras 3, simply run:
To install the latest KerasHub release with Keras 3, simply run:

```
pip install --upgrade keras-nlp
pip install --upgrade keras-hub
```

To install the latest nightly changes for both KerasNLP and Keras, you can use
To install the latest nightly changes for both KerasHub and Keras, you can use
our nightly package.

```
pip install --upgrade keras-nlp-nightly
pip install --upgrade keras-hub-nightly
```

Note that currently, installing KerasNLP will always pull in TensorFlow for use
Note that currently, installing KerasHub 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](https://keras.io/getting_started/) 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
> We recommend using KerasHub 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
you can use KerasHub with any of JAX, TensorFlow and PyTorch. To do so, set the
`KERAS_BACKEND` environment variable. For example:

```shell
Expand All @@ -122,7 +123,7 @@ Or in Colab, with:
import os
os.environ["KERAS_BACKEND"] = "jax"

import keras_nlp
import keras_hub
```

> [!IMPORTANT]
Expand All @@ -138,24 +139,26 @@ 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.
KerasHub provides access to pre-trained models via the `keras_hub.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
## Citing KerasHub

If KerasNLP helps your research, we appreciate your citations.
If KerasHub helps your research, we appreciate your citations.
Here is the BibTeX entry:

```bibtex
@misc{kerasnlp2022,
title={KerasNLP},
author={Watson, Matthew, and Qian, Chen, and Bischof, Jonathan and Chollet,
Fran\c{c}ois and others},
year={2022},
@misc{kerashub2024,
title={KerasHub},
author={Watson, Matthew, and Chollet, Fran\c{c}ois and Sreepathihalli,
Divyashree, and Saadat, Samaneh and Sampath, Ramesh, and Rasskin, Gabriel and
and Zhu, Scott and Singh, Varun and Wood, Luke and Tan, Zhenyu and Stenbit,
Ian and Qian, Chen, and Bischof, Jonathan and others},
year={2024},
howpublished={\url{https://github.com/keras-team/keras-hub}},
}
```
Expand Down