Update README.md with new model card content

README.md

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 library_name: keras-hub
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-This is a [`Bert` model](https://keras.io/api/keras_hub/models/bert) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-Model config:
-* **name:** bert_backbone
-* **trainable:** True
-* **vocabulary_size:** 30522
-* **num_layers:** 24
-* **num_heads:** 16
-* **hidden_dim:** 1024
-* **intermediate_dim:** 4096
-* **dropout:** 0.1
-* **max_sequence_length:** 512
-* **num_segments:** 2
-
-This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
+### Model Overview
+BERT (Bidirectional Encoder Representations from Transformers) is a set of language models published by Google. They are intended for classification and embedding of text, not for text-generation. See the model card below for benchmarks, data sources, and intended use cases.
+
+Weights and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
+
+## Links
+
+* [Bert Quickstart Notebook](https://www.kaggle.com/code/matthewdwatson/bert-quickstart)
+* [Bert API Documentation](https://keras.io/api/keras_hub/models/bert/)
+* [Bert Model Card](https://github.com/google-research/bert/blob/master/README.md)
+* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
+* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-hub
+pip install -U -q keras>=3
+```
+
+Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
+
+## Presets
+
+The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
+
+| Preset name            | Parameters | Description                                                                                     |
+|------------------------|------------|-------------------------------------------------------------------------------------------------|
+| `bert_tiny_en_uncased`   | 4.39M      | 2-layer BERT model where all input is lowercased.   |
+| `bert_small_en_uncased`  | 28.76M     | 4-layer BERT model where all input is lowercased.   |
+| `bert_medium_en_uncased` | 41.37M     | 8-layer BERT model where all input is lowercased.   |
+| `bert_base_en_uncased`   | 109.48M    | 12-layer BERT model where all input is lowercased.  |
+| `bert_base_en`           | 108.31M    | 12-layer BERT model where case is maintained.  |
+| `bert_base_zh`           | 102.27M    | 12-layer BERT model. Trained on Chinese Wikipedia.                                              |
+| `bert_base_multi`        | 177.85M    | 12-layer BERT model where case is maintained. |
+| `bert_large_en_uncased`  | 335.14M    | 24-layer BERT model where all input is lowercased. |
+| `bert_large_en`          | 333.58M    | 24-layer BERT model where case is maintained. |
+
+### Example Usage
+```python
+import keras
+import keras_hub
+import numpy as np
+```
+
+Raw string data.
+```python
+features = ["The quick brown fox jumped.", "I forgot my homework."]
+labels = [0, 3]
+
+# Pretrained classifier.
+classifier = keras_hub.models.BertClassifier.from_preset(
+    "bert_large_en_uncased",
+    num_classes=4,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+classifier.predict(x=features, batch_size=2)
+
+# Re-compile (e.g., with a new learning rate).
+classifier.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    optimizer=keras.optimizers.Adam(5e-5),
+    jit_compile=True,
+)
+# Access backbone programmatically (e.g., to change `trainable`).
+classifier.backbone.trainable = False
+# Fit again.
+classifier.fit(x=features, y=labels, batch_size=2)
+```
+
+Preprocessed integer data.
+```python
+features = {
+    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
+    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
+    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
+}
+labels = [0, 3]
+
+# Pretrained classifier without preprocessing.
+classifier = keras_hub.models.BertClassifier.from_preset(
+    "bert_large_en_uncased",
+    num_classes=4,
+    preprocessor=None,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+import keras
+import keras_hub
+import numpy as np
+```
+
+Raw string data.
+```python
+features = ["The quick brown fox jumped.", "I forgot my homework."]
+labels = [0, 3]
+
+# Pretrained classifier.
+classifier = keras_hub.models.BertClassifier.from_preset(
+    "hf://keras/bert_large_en_uncased",
+    num_classes=4,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+classifier.predict(x=features, batch_size=2)
+
+# Re-compile (e.g., with a new learning rate).
+classifier.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    optimizer=keras.optimizers.Adam(5e-5),
+    jit_compile=True,
+)
+# Access backbone programmatically (e.g., to change `trainable`).
+classifier.backbone.trainable = False
+# Fit again.
+classifier.fit(x=features, y=labels, batch_size=2)
+```
+
+Preprocessed integer data.
+```python
+features = {
+    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
+    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
+    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
+}
+labels = [0, 3]
+
+# Pretrained classifier without preprocessing.
+classifier = keras_hub.models.BertClassifier.from_preset(
+    "hf://keras/bert_large_en_uncased",
+    num_classes=4,
+    preprocessor=None,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+```