Address comments

CONTRIBUTING_MODELS.md

@@ -7,7 +7,7 @@ open to contributions!
 In this guide, we will walk you through the steps one needs to take in order to
 contribute a new pre-trained model to KerasHub. For illustration purposes, let's
 assume that you want to contribute the DistilBERT model. Before we dive in, we encourage you to go through
-[our getting started guide](https://keras.io/guides/keras_hub/getting_started/)
+[our getting started guide](https://keras.io/guides/keras_nlp/getting_started/)
 for an introduction to the library, and our
 [contribution guide](https://github.com/keras-team/keras-nlp/blob/master/CONTRIBUTING.md).
 
@@ -22,29 +22,29 @@ Keep this checklist handy!
 
 ### Step 2: PR #1 - Add XXBackbone
 
-- [ ] An `xx/xx_backbone.py` file which has the model graph \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_backbone.py)\].
-- [ ] An `xx/xx_backbone_test.py` file which has unit tests for the backbone \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_backbone_test.py)\].
+- [ ] An `xx/xx_backbone.py` file which has the model graph \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_backbone.py)\].
+- [ ] An `xx/xx_backbone_test.py` file which has unit tests for the backbone \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_backbone_test.py)\].
 - [ ] A Colab notebook link in the PR description which matches the outputs of the implemented backbone model with the original source \[[Example](https://colab.research.google.com/drive/1SeZWJorKWmwWJax8ORSdxKrxE25BfhHa?usp=sharing)\].
 
 ### Step 3: PR #2 - Add XXTokenizer
 
-- [ ] An `xx/xx_tokenizer.py` file which has the tokenizer for the model \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_tokenizer.py)\].
-- [ ] An `xx/xx_tokenizer_test.py` file which has unit tests for the model tokenizer \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_tokenizer_test.py)\].
+- [ ] An `xx/xx_tokenizer.py` file which has the tokenizer for the model \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_tokenizer.py)\].
+- [ ] An `xx/xx_tokenizer_test.py` file which has unit tests for the model tokenizer \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_tokenizer_test.py)\].
 - [ ] A Colab notebook link in the PR description, demonstrating that the output of the tokenizer matches the original tokenizer \[[Example](https://colab.research.google.com/drive/1MH_rpuFB1Nz_NkKIAvVtVae2HFLjXZDA?usp=sharing)].
 
 ### Step 4: PR #3 - Add XX Presets
 
-- [ ] An `xx/xx_presets.py` file with links to weights uploaded to a personal GCP bucket/Google Drive \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_presets.py)\].
+- [ ] An `xx/xx_presets.py` file with links to weights uploaded to a personal GCP bucket/Google Drive \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_presets.py)\].
 - [ ] A `tools/checkpoint_conversion/convert_xx_checkpoints.py` which is reusable script for converting checkpoints \[[Example](https://github.com/keras-team/keras-nlp/blob/master/tools/checkpoint_conversion/convert_distilbert_checkpoints.py)\].
 - [ ] A Colab notebook link in the PR description, showing an end-to-end task such as text classification, etc. The task model can be built using the backbone model, with the task head on top \[[Example](https://gist.github.com/mattdangerw/bf0ca07fb66b6738150c8b56ee5bab4e)\].
 
 ### Step 5: PR #4 and Beyond - Add XX Tasks and Preprocessors
 
 This PR is optional.
 
-- [ ] An `xx/xx_<task>.py` file for adding a task model like classifier, masked LM, etc. \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_classifier.py)\]
-- [ ] An `xx/xx_<task>_preprocessor.py` file which has the preprocessor and can be used to get inputs suitable for the task model \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_preprocessor.py)\].
-- [ ] `xx/xx_<task>_test.py` file and `xx/xx_<task>_preprocessor_test.py` files which have unit tests for the above two modules \[[Example 1](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_classifier_test.py) and [Example 2](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_preprocessor_test.py)\].
+- [ ] An `xx/xx_<task>.py` file for adding a task model like classifier, masked LM, etc. \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_classifier.py)\]
+- [ ] An `xx/xx_<task>_preprocessor.py` file which has the preprocessor and can be used to get inputs suitable for the task model \[[Example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_preprocessor.py)\].
+- [ ] `xx/xx_<task>_test.py` file and `xx/xx_<task>_preprocessor_test.py` files which have unit tests for the above two modules \[[Example 1](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_classifier_test.py) and [Example 2](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_preprocessor_test.py)\].
 - [ ] A Colab notebook link in the PR description, demonstrating that the output of the preprocessor matches the output of the original preprocessor \[[Example](https://colab.research.google.com/drive/1GFFC7Y1I_2PtYlWDToqKvzYhHWv1b3nC?usp=sharing)].
 
 ## Detailed Instructions
@@ -81,7 +81,7 @@ around by a class to implement our models.
 
 A model is typically split into three/four sections. We would recommend you to
 compare this side-by-side with the
-[`keras_hub.layers.DistilBertBackbone` source code](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_backbone.py)!
+[`keras_hub.layers.DistilBertBackbone` source code](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_backbone.py)!
 
 **Inputs to the model**
 
@@ -110,7 +110,7 @@ Standard layers used: `keras_hub.layers.TransformerDecoder`.
 
 The standard layers provided in Keras and KerasHub are generally enough for
 most of the usecases and it is recommended to do a thorough search
-[here](https://keras.io/api/layers/) and [here](https://keras.io/api/keras_hub/layers/).
+[here](https://keras.io/api/layers/) and [here](https://keras.io/api/keras_nlp/layers/).
 However, sometimes, models have small tweaks/paradigm changes in their architecture.
 This is when things might slightly get complicated.
 
@@ -165,12 +165,12 @@ All the underlying actual tokenization will be taken care of by the superclass.
 The important thing here is adding "special tokens". Most models have
 special tokens such as beginning-of-sequence token, end-of-sequence token,
 mask token, pad token, etc. These have to be
-[added as member attributes](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_tokenizer.py#L91-L105)
+[added as member attributes](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_tokenizer.py#L91-L105)
 to the tokenizer class. These member attributes are then accessed by the
 preprocessor layers.
 
 For a full list of the tokenizers KerasHub offers, please visit
-[this link](https://keras.io/api/keras_hub/tokenizers/) and make use of the
+[this link](https://keras.io/api/keras_nlp/tokenizers/) and make use of the
 tokenizer your model uses!
 
 #### Unit Tests
@@ -193,7 +193,7 @@ files. These files will then be uploaded to GCP by us!
 After wrapping up the preset configuration file, you need to
 add the `from_preset` function to all three classes, i.e., `DistilBertBackbone`,
 and `DistilBertTokenizer`. Here is an
-[example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_backbone.py#L187-L189).
+[example](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_backbone.py#L187-L189).
 
 The testing for presets is divided into two: "large" and "extra large".
 For "large" tests, we pick the smallest preset (in terms of number of parameters)
@@ -230,7 +230,7 @@ The preprocessor class might have a few intricacies depending on the model. For
 the DeBERTaV3 tokenizer does not have the `[MASK]` in the provided sentencepiece
 proto file, and we had to make some modifications [here](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/deberta_v3/deberta_v3_preprocessor.py). Secondly, we have
 a separate preprocessor class for every task. This is because different tasks
-might require different input formats. For instance, we have a [separate preprocessor](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/models/distil_bert/distil_bert_masked_lm_preprocessor.py)
+might require different input formats. For instance, we have a [separate preprocessor](https://github.com/keras-team/keras-nlp/blob/master/keras_hub/src/models/distil_bert/distil_bert_masked_lm_preprocessor.py)
 for masked language modeling (MLM) for DistilBERT.
 
 ## Conclusion

README.md

@@ -1,27 +1,30 @@
-# KerasHub: Multi-framework NLP Models
+# KerasNLP: Multi-framework NLP Models
 [![](https://github.com/keras-team/keras-nlp/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-nlp/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-nlp/issues)
 
 > [!IMPORTANT]
-> KerasHub is becoming KerasHub! Read the announcement [here](https://github.com/keras-team/keras-nlp/issues/1831).
+> KerasNLP is becoming KerasHub! Read the announcement
+> [in this issue](https://github.com/keras-team/keras-nlp/issues/1831). We have
+> renamed our repo to KerasHub in preparation for the release, but have not yet
+> released the new package. Follow the issue for news.
 
-KerasHub is a natural language processing library that works natively
-with TensorFlow, JAX, or PyTorch. KerasHub provides a repository of pre-trained
+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
-KerasHub.
+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](https://keras.io/guides/keras_hub/getting_started)
+[Getting Started guide](https://keras.io/guides/keras_nlp/getting_started)
 to start learning our API. Browse our models on
 [Kaggle](https://www.kaggle.com/organizations/keras/models).
 We welcome contributions.
@@ -30,9 +33,9 @@ We welcome contributions.
 
 ### For everyone
 
-- [Home Page](https://keras.io/keras_hub)
-- [Developer Guides](https://keras.io/guides/keras_hub)
-- [API Reference](https://keras.io/api/keras_hub)
+- [Home Page](https://keras.io/keras_nlp)
+- [Developer Guides](https://keras.io/guides/keras_nlp)
+- [API Reference](https://keras.io/api/keras_nlp)
 - [Pre-trained Models](https://www.kaggle.com/organizations/keras/models)
 
 ### For contributors
@@ -51,7 +54,7 @@ Fine-tune BERT on IMDb movie reviews:
 import os
 os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch"!
 
-import keras_hub
+import keras_nlp
 import tensorflow_datasets as tfds
 
 imdb_train, imdb_test = tfds.load(
@@ -61,7 +64,7 @@ imdb_train, imdb_test = tfds.load(
     batch_size=16,
 )
 # Load a BERT model.
-classifier = keras_hub.models.Classifier.from_preset(
+classifier = keras_nlp.models.Classifier.from_preset(
     "bert_base_en", 
     num_classes=2,
     activation="softmax",
@@ -74,38 +77,38 @@ classifier.predict(["What an amazing movie!", "A total waste of my time."])
 
 Try it out [in a colab](https://colab.research.google.com/gist/mattdangerw/e457e42d5ea827110c8d5cb4eb9d9a07/kerasnlp-quickstart.ipynb).
 For more in depth guides and examples, visit
-[keras.io/keras_hub](https://keras.io/keras_hub/).
+[keras.io/keras_nlp](https://keras.io/keras_nlp/).
 
 ## Installation
 
-To install the latest KerasHub release with Keras 3, simply run:
+To install the latest KerasNLP release with Keras 3, simply run:
 
 ```
-pip install --upgrade keras-hub
+pip install --upgrade keras-nlp
 ```
 
-To install the latest nightly changes for both KerasHub and Keras, you can use
+To install the latest nightly changes for both KerasNLP and Keras, you can use
 our nightly package.
 
 ```
-pip install --upgrade keras-hub-nightly
+pip install --upgrade keras-nlp-nightly
 ```
 
-Note that currently, installing KerasHub will always pull in TensorFlow for use
+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](https://keras.io/getting_started/) for more
 information on installing Keras 3 and compatibility with different frameworks.
 
 > [!IMPORTANT]
-> We recommend using KerasHub with TensorFlow 2.16 or later, as TF 2.16 packages
+> 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 KerasHub with any of JAX, TensorFlow and PyTorch. To do so, set the
+you can use KerasNLP with any of JAX, TensorFlow and PyTorch. To do so, set the
 `KERAS_BACKEND` environment variable. For example:
 
 ```shell
@@ -118,7 +121,7 @@ Or in Colab, with:
 import os
 os.environ["KERAS_BACKEND"] = "jax"
 
-import keras_hub
+import keras_nlp
 ```
 
 > [!IMPORTANT]
@@ -134,21 +137,21 @@ may break compatibility at any time and APIs should not be consider stable.
 
 ## Disclaimer
 
-KerasHub provides access to pre-trained models via the `keras_hub.models` API.
+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 KerasHub
+## Citing KerasNLP
 
-If KerasHub helps your research, we appreciate your citations.
+If KerasNLP helps your research, we appreciate your citations.
 Here is the BibTeX entry:
 
 ```bibtex
 @misc{kerasnlp2022,
-  title={KerasHub},
+  title={KerasNLP},
   author={Watson, Matthew, and Qian, Chen, and Bischof, Jonathan and Chollet, 
   Fran\c{c}ois and others},
   year={2022},

RELEASE_PROCESS.md

@@ -10,7 +10,7 @@ Our release process consists of two main components:
 
 - Adding a new release to the [keras-hub](https://pypi.org/project/keras-hub/)
   project on the Python Package Index (pypi).
-- Updating our documentation on [keras.io](https://keras.io/keras_hub/) to match
+- Updating our documentation on [keras.io](https://keras.io/keras_nlp/) to match
   the release.
 
 We follow [semantic versioning](https://semver.org/) for our releases, and

keras_hub/src/api_export.py

@@ -14,7 +14,7 @@
 
 import types
 
-import keras
+from keras.saving import register_keras_serializable
 
 try:
     import namex
@@ -23,22 +23,19 @@
 
 
 def maybe_register_serializable(path, symbol):
-    # If we have multiple export names, actually make sure to register these
-    # first. This makes sure we have a backward compat mapping of old serialized
-    # name to new class.
     if isinstance(path, (list, tuple)):
+        # If we have multiple export names, actually make sure to register these
+        # first. This makes sure we have a backward compat mapping of old
+        # serialized names to new class.
         for name in path:
             name = name.split(".")[-1]
-            keras.saving.register_keras_serializable(
-                package="keras_nlp", name=name
-            )(symbol)
+            register_keras_serializable(package="keras_nlp", name=name)(symbol)
+            register_keras_serializable(package="keras_hub", name=name)(symbol)
     if isinstance(symbol, types.FunctionType) or hasattr(symbol, "get_config"):
-        # We register twice, first with the old name, second with the new name,
-        # so loading still works under the old name.
-        # TODO replace keras_nlp with keras-hub after rename.
-        compat_name = "keras_nlp"
-        keras.saving.register_keras_serializable(package=compat_name)(symbol)
-        keras.saving.register_keras_serializable(package="keras_hub")(symbol)
+        # We register twice, first with keras_nlp, second with keras_hub,
+        # so loading still works for classes saved as "keras_nlp".
+        register_keras_serializable(package="keras_nlp")(symbol)
+        register_keras_serializable(package="keras_hub")(symbol)
 
 
 if namex:

keras_hub/src/utils/preset_utils_test.py

@@ -43,10 +43,10 @@ def test_preset_errors(self):
         backbone = BertBackbone.from_preset("bert_tiny_en_uncased")
         preset_dir = self.get_temp_dir()
         config = keras.utils.serialize_keras_object(backbone)
-        config["registered_name"] = "keras_nlp>BortBackbone"
+        config["registered_name"] = "keras_hub>BortBackbone"
         with open(os.path.join(preset_dir, CONFIG_FILE), "w") as config_file:
             config_file.write(json.dumps(config, indent=4))
-        with self.assertRaisesRegex(ValueError, "class keras_nlp>BortBackbone"):
+        with self.assertRaisesRegex(ValueError, "class keras_hub>BortBackbone"):
             BertBackbone.from_preset(preset_dir)
 
     def test_upload_empty_preset(self):