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Update README.md with new model card content
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README.md
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library_name: keras-hub
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BERT (Bidirectional Encoder Representations from Transformers) is a set of language models published by Google. They are intended for classification and embedding of text, not for text-generation. See the model card below for benchmarks, data sources, and intended use cases.
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Weights and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
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| `bert_large_en_uncased` | 335.14M | 24-layer BERT model where all input is lowercased. |
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| `bert_large_en` | 333.58M | 24-layer BERT model where case is maintained. |
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```python
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import keras
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import keras_hub
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library_name: keras-hub
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## Model Overview
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BERT (Bidirectional Encoder Representations from Transformers) is a set of language models published by Google. They are intended for classification and embedding of text, not for text-generation. See the model card below for benchmarks, data sources, and intended use cases.
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Weights and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
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| `bert_large_en_uncased` | 335.14M | 24-layer BERT model where all input is lowercased. |
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| `bert_large_en` | 333.58M | 24-layer BERT model where case is maintained. |
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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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