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--- |
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library_name: keras-hub |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- text-classification |
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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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## Links |
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* [Bert Quickstart Notebook](https://www.kaggle.com/code/matthewdwatson/bert-quickstart) |
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* [Bert API Documentation](https://keras.io/api/keras_hub/models/bert/) |
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* [Bert Model Card](https://github.com/google-research/bert/blob/master/README.md) |
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* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/) |
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* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/) |
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## Installation |
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Keras and KerasHub can be installed with: |
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``` |
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pip install -U -q keras-hub |
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pip install -U -q keras>=3 |
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``` |
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Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page. |
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## Presets |
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The following model checkpoints are provided by the Keras team. Full code examples for each are available below. |
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| Preset name | Parameters | Description | |
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|------------------------|------------|-------------------------------------------------------------------------------------------------| |
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| `bert_tiny_en_uncased` | 4.39M | 2-layer BERT model where all input is lowercased. | |
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| `bert_small_en_uncased` | 28.76M | 4-layer BERT model where all input is lowercased. | |
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| `bert_medium_en_uncased` | 41.37M | 8-layer BERT model where all input is lowercased. | |
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| `bert_base_en_uncased` | 109.48M | 12-layer BERT model where all input is lowercased. | |
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| `bert_base_en` | 108.31M | 12-layer BERT model where case is maintained. | |
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| `bert_base_zh` | 102.27M | 12-layer BERT model. Trained on Chinese Wikipedia. | |
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| `bert_base_multi` | 177.85M | 12-layer BERT model where case is maintained. | |
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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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import numpy as np |
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``` |
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Raw string data. |
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```python |
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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 = keras_hub.models.BertClassifier.from_preset( |
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"bert_large_en_uncased", |
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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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classifier.predict(x=features, batch_size=2) |
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# Re-compile (e.g., with a new learning rate). |
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classifier.compile( |
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
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optimizer=keras.optimizers.Adam(5e-5), |
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jit_compile=True, |
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) |
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# Access backbone programmatically (e.g., to change `trainable`). |
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classifier.backbone.trainable = False |
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# Fit again. |
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classifier.fit(x=features, y=labels, batch_size=2) |
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``` |
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Preprocessed integer data. |
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```python |
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features = { |
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"token_ids": np.ones(shape=(2, 12), dtype="int32"), |
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"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2), |
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"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2), |
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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.BertClassifier.from_preset( |
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"bert_large_en_uncased", |
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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 Hugging Face URI |
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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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Raw string data. |
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```python |
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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 = keras_hub.models.BertClassifier.from_preset( |
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"hf://keras/bert_large_en_uncased", |
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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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classifier.predict(x=features, batch_size=2) |
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# Re-compile (e.g., with a new learning rate). |
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classifier.compile( |
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
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optimizer=keras.optimizers.Adam(5e-5), |
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jit_compile=True, |
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) |
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# Access backbone programmatically (e.g., to change `trainable`). |
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classifier.backbone.trainable = False |
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# Fit again. |
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classifier.fit(x=features, y=labels, batch_size=2) |
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``` |
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Preprocessed integer data. |
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```python |
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features = { |
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"token_ids": np.ones(shape=(2, 12), dtype="int32"), |
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"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2), |
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"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2), |
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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.BertClassifier.from_preset( |
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"hf://keras/bert_large_en_uncased", |
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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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``` |
