Keras 3 API documentation / KerasNLP / Models / Roberta / RobertaClassifier model

RobertaClassifier model

[source]

RobertaClassifier class

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

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

This model attaches a classification head to a keras_nlp.model.RobertaBackbone instance, mapping from the backbone outputs to logits suitable for a classification task. For usage of this model with pre-trained weights, see the from_preset() constructor.

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().

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.RobertaBackbone instance.
  • num_classes: int. Number of classes to predict.
  • preprocessor: A keras_nlp.models.RobertaPreprocessor 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. The dropout probability value, applied to the pooled output, and after the first dense layer.

Examples

Raw string data.

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

# Pretrained classifier.
classifier = keras_nlp.models.RobertaClassifier.from_preset(
    "roberta_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.RobertaClassifier.from_preset(
    "roberta_base_en",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

Custom backbone and vocabulary.

features = ["a quick fox", "a fox quick"]
labels = [0, 3]

vocab = {"<s>": 0, "<pad>": 1, "</s>": 2, "<mask>": 3}
vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
merges += ["Ġ f", "o x", "Ġf ox"]
tokenizer = keras_nlp.models.RobertaTokenizer(
    vocabulary=vocab,
    merges=merges
)
preprocessor = keras_nlp.models.RobertaPreprocessor(
    tokenizer=tokenizer,
    sequence_length=128,
)
backbone = keras_nlp.models.RobertaBackbone(
    vocabulary_size=20,
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    intermediate_dim=512,
    max_sequence_length=128
)
classifier = keras_nlp.models.RobertaClassifier(
    backbone=backbone,
    preprocessor=preprocessor,
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)

[source]

from_preset method

RobertaClassifier.from_preset()

Instantiate RobertaClassifier model from preset architecture and weights.

Arguments

  • preset: string. Must be one of "roberta_base_en", "roberta_large_en".
  • load_weights: Whether to load pre-trained weights into model. Defaults to True.

Examples

# Load architecture and weights from preset
model = RobertaClassifier.from_preset("roberta_base_en")

# Load randomly initialized model from preset architecture
model = RobertaClassifier.from_preset(
    "roberta_base_en",
    load_weights=False
)
Preset name Parameters Description
roberta_base_en 124.05M 12-layer RoBERTa model where case is maintained.Trained on English Wikipedia, BooksCorpus, CommonCraw, and OpenWebText.
roberta_large_en 354.31M 24-layer RoBERTa model where case is maintained.Trained on English Wikipedia, BooksCorpus, CommonCraw, and OpenWebText.

backbone property

keras_nlp.models.RobertaClassifier.backbone

A keras.Model instance providing the backbone sub-model.


preprocessor property

keras_nlp.models.RobertaClassifier.preprocessor

A keras.layers.Layer instance used to preprocess inputs.