Update README.md with new model card content

README.md

@@ -1,16 +1,134 @@
 ---
 library_name: keras-hub
 ---
-This is a [`FNet` model](https://keras.io/api/keras_hub/models/f_net) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-Model config:
-* **name:** f_net_backbone
-* **trainable:** True
-* **vocabulary_size:** 32000
-* **num_layers:** 24
-* **hidden_dim:** 1024
-* **intermediate_dim:** 4096
-* **dropout:** 0.1
-* **max_sequence_length:** 512
-* **num_segments:** 4
-
-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
+FNet is a set of language models published by Google as part of the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824). FNet replaces the self-attention of BERT with an unparameterized fourier transform, dramatically lowering the number of trainable parameters in the model. FNet achieves training at 92-97% accuracy of BERT counterparts on GLUE benchmark, with faster training and much smaller saved checkpoints.
+
+Weights and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
+
+## Links
+
+* [FNet Quickstart Notebook](https://www.kaggle.com/code/matthewdwatson/fnet-quickstart/)
+* [FNet API Documentation](https://keras.io/api/keras_hub/models/f_net/)
+* [FNet Model Card](https://github.com/google-research/google-research/blob/master/f_net/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                                   |
+|----------------|------------|-----------------------------------------------|
+| `f_net_base_en`  | 82.86M     | 12-layer FNet model where case is maintained. |
+| `f_net_large_en` | 236.95M    | 24-layer FNet 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.FNetClassifier.from_preset(
+    "f_net_base_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"),
+    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
+}
+labels = [0, 3]
+
+# Pretrained classifier without preprocessing.
+classifier = keras_hub.models.FNetClassifier.from_preset(
+    "f_net_base_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.FNetClassifier.from_preset(
+    "f_net_base_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"),
+    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
+}
+labels = [0, 3]
+
+# Pretrained classifier without preprocessing.
+classifier = keras_hub.models.FNetClassifier.from_preset(
+    "f_net_base_en",
+    num_classes=4,
+    preprocessor=None,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+```