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bert_base_multi

Text Classification
KerasHub
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Update README.md with new model card content

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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).
@@ -41,7 +41,7 @@ The following model checkpoints are provided by the Keras team. Full code exampl
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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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  ---
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  library_name: keras-hub
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  ---
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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