Update README.md with new model card content
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README.md
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---
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library_name: keras-hub
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---
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### Model Overview
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A RoBERTa encoder network.
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# Pretrained classifier.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"
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num_classes=4,
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)
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classifier.fit(x=features, y=labels, batch_size=2)
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# Pretrained classifier without preprocessing.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"
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num_classes=4,
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preprocessor=None,
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)
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classifier.fit(x=features, y=labels, batch_size=2)
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```
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## Example Usage with
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```python
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import keras
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# Pretrained classifier.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"
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num_classes=4,
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)
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classifier.fit(x=features, y=labels, batch_size=2)
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# Pretrained classifier without preprocessing.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"
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num_classes=4,
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preprocessor=None,
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)
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### Model Overview
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A RoBERTa encoder network.
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# Pretrained classifier.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"${VARIATION_SLUG}",
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num_classes=4,
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)
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classifier.fit(x=features, y=labels, batch_size=2)
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# Pretrained classifier without preprocessing.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"${VARIATION_SLUG}",
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num_classes=4,
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preprocessor=None,
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)
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classifier.fit(x=features, y=labels, batch_size=2)
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```
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## Example Usage with HuggingFace uri
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```python
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import keras
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# Pretrained classifier.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"${VARIATION_SLUG}",
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num_classes=4,
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)
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classifier.fit(x=features, y=labels, batch_size=2)
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# Pretrained classifier without preprocessing.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"${VARIATION_SLUG}",
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num_classes=4,
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preprocessor=None,
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)
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