DebertaV3Classifier model

[source]

DebertaV3Classifier class

keras_nlp.models.DebertaV3Classifier(
    backbone,
    num_classes,
    preprocessor=None,
    activation=None,
    hidden_dim=None,
    dropout=0.0,
    **kwargs
)

An end-to-end DeBERTa model for classification tasks.

This model attaches a classification head to a keras_nlp.model.DebertaV3Backbone model, mapping from the backbone outputs to logit output suitable for a classification task. For usage of this model with pre-trained weights, see the from_preset() method.

This model can optionally be configured with a preprocessor layer, in which case it will automatically apply preprocessing to raw inputs during fit(), predict(), and evaluate(). This is done by default when creating the model with from_preset().

Note: DebertaV3Backbone has a performance issue on TPUs, and we recommend other models for TPU training and inference.

Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The underlying model is provided by a third party and subject to a separate license, available here.

Arguments

• backbone: A keras_nlp.models.DebertaV3 instance.
• num_classes: int. Number of classes to predict.
• preprocessor: A keras_nlp.models.DebertaV3Preprocessor or None. If None, this model will not apply preprocessing, and inputs should be preprocessed before calling the model.
• activation: Optional str or callable. The activation function to use on the model outputs. Set activation="softmax" to return output probabilities. Defaults to None.
• hidden_dim: int. The size of the pooler layer.
• dropout: float. Dropout probability applied to the pooled output. For the second dropout layer, backbone.dropout is used.

Examples

Raw string data.

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

# Pretrained classifier.
classifier = keras_nlp.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_nlp.models.DebertaV3Classifier.from_preset(
    "deberta_v3_base_en",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

Custom backbone and vocabulary.

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

bytes_io = io.BytesIO()
ds = tf.data.Dataset.from_tensor_slices(features)
sentencepiece.SentencePieceTrainer.train(
    sentence_iterator=ds.as_numpy_iterator(),
    model_writer=bytes_io,
    vocab_size=10,
    model_type="WORD",
    pad_id=0,
    bos_id=1,
    eos_id=2,
    unk_id=3,
    pad_piece="[PAD]",
    bos_piece="[CLS]",
    eos_piece="[SEP]",
    unk_piece="[UNK]",
)
tokenizer = keras_nlp.models.DebertaV3Tokenizer(
    proto=bytes_io.getvalue(),
)
preprocessor = keras_nlp.models.DebertaV3Preprocessor(
    tokenizer=tokenizer,
    sequence_length=128,
)
backbone = keras_nlp.models.DebertaV3Backbone(
    vocabulary_size=30552,
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    intermediate_dim=512,
    max_sequence_length=128,
)
classifier = keras_nlp.models.DebertaV3Classifier(
    backbone=backbone,
    preprocessor=preprocessor,
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)

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from_preset method

DebertaV3Classifier.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_nlp.models.Task from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

For any Task subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

This constructor can be called in one of two ways. Either from a task specific base class like keras_nlp.models.CausalLM.from_preset(), or from a model class like keras_nlp.models.BertClassifier.from_preset(). If calling from the a base class, the subclass of the returning object will be inferred from the config in the preset directory.

Arguments

• preset: string. A built in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma generative task.
causal_lm = keras_nlp.models.CausalLM.from_preset(
    "gemma_2b_en",
)

# Load a Bert classification task.
model = keras_nlp.models.Classifier.from_preset(
    "bert_base_en",
    num_classes=2,
)

backbone property

keras_nlp.models.DebertaV3Classifier.backbone

A keras_nlp.models.Backbone model with the core architecture.

preprocessor property

keras_nlp.models.DebertaV3Classifier.preprocessor

A keras_nlp.models.Preprocessor layer used to preprocess input.