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---
library_name: keras-hub
---
### Model Overview
DistilBert is a set of language models published by HuggingFace. They are efficient, distilled version of BERT, and 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.
Weights and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
## Links
* [DistilBert Quickstart Notebook](https://www.kaggle.com/code/matthewdwatson/distilbert-quickstart)
* [DistilBert API Documentation](https://keras.io/api/keras_hub/models/distil_bert/)
* [DistilBert Model Card](https://huggingface.co/distilbert/distilbert-base-uncased)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
## Installation
Keras and KerasHub can be installed with:
```
pip install -U -q keras-hub
pip install -U -q keras>=3
```
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.
## Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|-----------------------------|------------|--------------------------------------------------------|
| distil_bert_base_en_uncased | 66.36M | 6-layer model where all input is lowercased. |
| distil_bert_base_en | 65.19M | 6-layer model where case is maintained. |
| distil_bert_base_multi | 134.73M | 6-layer multi-linguage model where case is maintained. |
### Example Usage
```python
import keras
import keras_hub
import numpy as np
```
Raw string data.
```python
features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]
# Use a shorter sequence length.
preprocessor = keras_hub.models.DistilBertPreprocessor.from_preset(
"distil_bert_base_en_uncased",
sequence_length=128,
)
# Pretrained classifier.
classifier = keras_hub.models.DistilBertClassifier.from_preset(
"distil_bert_base_en_uncased",
num_classes=4,
preprocessor=preprocessor,
)
classifier.fit(x=features, y=labels, batch_size=2)
# Re-compile (e.g., with a new learning rate)
classifier.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=keras.optimizers.Adam(5e-5),
jit_compile=True,
)
# Access backbone programmatically (e.g., to change `trainable`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)
```
Preprocessed integer data.
```python
features = {
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)
}
labels = [0, 3]
# Pretrained classifier without preprocessing.
classifier = keras_hub.models.DistilBertClassifier.from_preset(
"distil_bert_base_en_uncased",
num_classes=4,
preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)
```
## Example Usage with Hugging Face URI
```python
import keras
import keras_hub
import numpy as np
```
Raw string data.
```python
features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]
# Use a shorter sequence length.
preprocessor = keras_hub.models.DistilBertPreprocessor.from_preset(
"hf://keras/distil_bert_base_en_uncased",
sequence_length=128,
)
# Pretrained classifier.
classifier = keras_hub.models.DistilBertClassifier.from_preset(
"hf://keras/distil_bert_base_en_uncased",
num_classes=4,
preprocessor=preprocessor,
)
classifier.fit(x=features, y=labels, batch_size=2)
# Re-compile (e.g., with a new learning rate)
classifier.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=keras.optimizers.Adam(5e-5),
jit_compile=True,
)
# Access backbone programmatically (e.g., to change `trainable`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)
```
Preprocessed integer data.
```python
features = {
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)
}
labels = [0, 3]
# Pretrained classifier without preprocessing.
classifier = keras_hub.models.DistilBertClassifier.from_preset(
"hf://keras/distil_bert_base_en_uncased",
num_classes=4,
preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)
```
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