keras/distil_bert_base_multi

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.

Links

• DistilBert Quickstart Notebook
• DistilBert API Documentation
• DistilBert Model Card
• KerasHub Beginner Guide
• KerasHub Model Publishing Guide

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the Keras 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

import keras
import keras_hub
import numpy as np

Raw string data.

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_multi",
    sequence_length=128,
)
# Pretrained classifier.
classifier = keras_hub.models.DistilBertClassifier.from_preset(
    "distil_bert_base_multi",
    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.

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_multi",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

Example Usage with Hugging Face URI

import keras
import keras_hub
import numpy as np

Raw string data.

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_multi",
    sequence_length=128,
)
# Pretrained classifier.
classifier = keras_hub.models.DistilBertClassifier.from_preset(
    "hf://keras/distil_bert_base_multi",
    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.

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_multi",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)