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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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@@ -49,7 +49,7 @@ features = ["The quick brown fox jumped.", "I forgot my homework."]
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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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@@ -77,7 +77,7 @@ features = {
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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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@@ -89,7 +89,7 @@ classifier.fit(x=features, y=labels, batch_size=2)
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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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@@ -99,7 +99,7 @@ features = ["The quick brown fox jumped.", "I forgot my homework."]
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labels = [0, 3]
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# Pretrained classifier.
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classifier =
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"hf://keras/roberta_base_en",
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num_classes=4,
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)
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@@ -127,7 +127,7 @@ features = {
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labels = [0, 3]
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# Pretrained classifier without preprocessing.
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classifier =
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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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### Example Usage
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```python
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import keras
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import keras_hub
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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_hub.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_hub.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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```python
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import keras
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+
import keras_hub
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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_hub.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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labels = [0, 3]
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# Pretrained classifier without preprocessing.
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classifier = keras_hub.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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