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 [`DebertaV3` model](https://keras.io/api/keras_hub/models/deberta_v3) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-Model config:
-* **name:** deberta_v3_backbone
-* **trainable:** True
-* **vocabulary_size:** 128100
-* **num_layers:** 12
-* **num_heads:** 6
-* **hidden_dim:** 384
-* **intermediate_dim:** 1536
-* **dropout:** 0.1
-* **max_sequence_length:** 512
-* **bucket_size:** 256
-
-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
+DeBERTaV3 encoder networks are a set of transformer encoder models published by Microsoft. DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder.
+
+Weights are released under the [MIT License](https://opensource.org/license/mit). Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
+
+## Links
+
+* [DeBERTaV3 Quickstart Notebook](https://www.kaggle.com/code/gabrielrasskin/debertav3-quickstart)
+* [DeBERTaV3 API Documentation](https://keras.io/api/keras_hub/models/deberta_v3/deberta_v3_classifier/)
+* [DeBERTaV3 Model Paper](https://arxiv.org/abs/2111.09543)
+* [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                                                                                                  |
+| :------------------------------- | :------------: | :-------------------------------------------------------------------------------------------------------- |
+| `deberta_v3_extra_small_en`    | 70.68M    | 12-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText.  |
+| `deberta_v3_small_en`          | 141.30M   | 6-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText.   |
+| `deberta_v3_base_en`           | 183.83M   | 12-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText. |
+| `deberta_v3_large_en`          | 434.01M   | 24-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText. |
+| `deberta_v3_base_multi`        | 278.22M   | 12-layer DeBERTaV3 model where case is maintained. Trained on the 2.5TB multilingual CC100 dataset.       |
+
+## Prompts
+
+DeBERTa's main use as a classifier takes in raw text that is labelled by the class it belongs to. In practice this can look like this:
+
+```python
+features = ["The quick brown fox jumped.", "I forgot my homework."]
+labels = [0, 3]
+```
+
+### 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.DebertaV3Classifier.from_preset(
+    "deberta_v3_extra_small_en",
+    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"),
+    "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.DebertaV3Classifier.from_preset(
+    "deberta_v3_extra_small_en",
+    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.DebertaV3Classifier.from_preset(
+    "hf://keras/deberta_v3_extra_small_en",
+    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"),
+    "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.DebertaV3Classifier.from_preset(
+    "hf://keras/deberta_v3_extra_small_en",
+    num_classes=4,
+    preprocessor=None,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+```