keras/deberta_v3_base_en

Model Overview

DeBERTaV3 encoder networks are a set of transformer encoder models published by Microsoft. DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder.

Weights are released under the MIT License. Keras model code is released under the Apache 2 License.

Links

• DeBERTaV3 Quickstart Notebook
• DeBERTaV3 API Documentation
• DeBERTaV3 Model Paper
• 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>=3

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
deberta_v3_extra_small_en	70.68M	12-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText.
deberta_v3_small_en	141.30M	6-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText.
deberta_v3_base_en	183.83M	12-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText.
deberta_v3_large_en	434.01M	24-layer DeBERTaV3 model where case is maintained. Trained on English Wikipedia, BookCorpus and OpenWebText.
deberta_v3_base_multi	278.22M	12-layer DeBERTaV3 model where case is maintained. Trained on the 2.5TB multilingual CC100 dataset.

Prompts

DeBERTa's main use as a classifier takes in raw text that is labelled by the class it belongs to. In practice this can look like this:

features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]

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]

# Pretrained classifier.
classifier = keras_hub.models.DebertaV3Classifier.from_preset(
    "deberta_v3_base_en",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, 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.DebertaV3Classifier.from_preset(
    "deberta_v3_base_en",
    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]

# Pretrained classifier.
classifier = keras_hub.models.DebertaV3Classifier.from_preset(
    "hf://keras/deberta_v3_base_en",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, 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.DebertaV3Classifier.from_preset(
    "hf://keras/deberta_v3_base_en",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)