Update README.md with new model card content

README.md

@@ -1,6 +1,3 @@
----
-library_name: keras-hub
----
 ### Model Overview
 A RoBERTa encoder network.
 
@@ -39,7 +36,7 @@ __Arguments__
 ### Example Usage
 ```python
 import keras
-import keras_hub
+import keras_nlp
 import numpy as np
 ```
 
@@ -49,8 +46,8 @@ features = ["The quick brown fox jumped.", "I forgot my homework."]
 labels = [0, 3]
 
 # Pretrained classifier.
-classifier = keras_hub.models.RobertaClassifier.from_preset(
-    "roberta_base_en",
+classifier = keras_nlp.models.RobertaClassifier.from_preset(
+    "${VARIATION_SLUG}",
     num_classes=4,
 )
 classifier.fit(x=features, y=labels, batch_size=2)
@@ -77,10 +74,60 @@ features = {
 labels = [0, 3]
 
 # Pretrained classifier without preprocessing.
-classifier = keras_hub.models.RobertaClassifier.from_preset(
-    "roberta_base_en",
+classifier = keras_nlp.models.RobertaClassifier.from_preset(
+    "${VARIATION_SLUG}",
     num_classes=4,
     preprocessor=None,
 )
 classifier.fit(x=features, y=labels, batch_size=2)
-```
+```
+
+## Example Usage with HuggingFace uri
+
+```python
+import keras
+import keras_nlp
+import numpy as np
+```
+
+Raw string data.
+```python
+features = ["The quick brown fox jumped.", "I forgot my homework."]
+labels = [0, 3]
+
+# Pretrained classifier.
+classifier = keras_nlp.models.RobertaClassifier.from_preset(
+    "${VARIATION_SLUG}",
+    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_nlp.models.RobertaClassifier.from_preset(
+    "${VARIATION_SLUG}",
+    num_classes=4,
+    preprocessor=None,
+)
+classifier.fit(x=features, y=labels, batch_size=2)
+```