Update README.md with new model card content
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README.md
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@@ -39,7 +39,7 @@ __Arguments__
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### Example Usage
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```python
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import keras
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import
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import numpy as np
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```
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labels = [0, 3]
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# Pretrained classifier.
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classifier =
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"roberta_base_en",
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num_classes=4,
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)
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labels = [0, 3]
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# Pretrained classifier without preprocessing.
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classifier =
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"roberta_base_en",
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num_classes=4,
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preprocessor=None,
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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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import
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import numpy as np
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```
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labels = [0, 3]
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# Pretrained classifier.
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classifier =
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"roberta_base_en",
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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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labels = [0, 3]
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# Pretrained classifier without preprocessing.
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classifier =
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"roberta_base_en",
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num_classes=4,
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preprocessor=None,
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)
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### Example Usage
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```python
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import keras
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import keras_nlp
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import numpy as np
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```
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labels = [0, 3]
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# Pretrained classifier.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"roberta_base_en",
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num_classes=4,
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)
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labels = [0, 3]
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# Pretrained classifier without preprocessing.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"roberta_base_en",
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num_classes=4,
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preprocessor=None,
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classifier.fit(x=features, y=labels, batch_size=2)
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```
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## Example Usage with Hugging Face URI
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```python
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import keras
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import keras_nlp
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import numpy as np
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```
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labels = [0, 3]
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# Pretrained classifier.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"hf://keras/roberta_base_en",
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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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labels = [0, 3]
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# Pretrained classifier without preprocessing.
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classifier = keras_nlp.models.RobertaClassifier.from_preset(
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"hf://keras/roberta_base_en",
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num_classes=4,
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preprocessor=None,
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)
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