Keras 3 API documentation / KerasNLP / Models / Albert / AlbertClassifier model

AlbertClassifier model

[source]

AlbertClassifier class

keras_nlp.models.AlbertClassifier(
    backbone, num_classes, preprocessor=None, activation=None, dropout=0.1, **kwargs
)

An end-to-end ALBERT model for classification tasks

This model attaches a classification head to a keras_nlp.model.AlbertBackbone backbone, 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().

Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind.

Arguments

  • backbone: A keras_nlp.models.AlertBackbone instance.
  • num_classes: int. Number of classes to predict.
  • preprocessor: A keras_nlp.models.AlbertPreprocessor 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.
  • dropout: float. The dropout probability value, applied after the 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.AlbertClassifier.from_preset(
    "albert_base_en_uncased",
    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"),
    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
    "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.AlbertClassifier.from_preset(
    "albert_base_en_uncased",
    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,
    unk_id=1,
    bos_id=2,
    eos_id=3,
    pad_piece="<pad>",
    unk_piece="<unk>",
    bos_piece="[CLS]",
    eos_piece="[SEP]",
    user_defined_symbols="[MASK]",
)
tokenizer = keras_nlp.models.AlbertTokenizer(
    proto=bytes_io.getvalue(),
)
preprocessor = keras_nlp.models.AlbertPreprocessor(
    tokenizer=tokenizer,
    sequence_length=128,
)
backbone = keras_nlp.models.AlbertBackbone(
    vocabulary_size=tokenizer.vocabulary_size(),
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    embedding_dim=128,
    intermediate_dim=512,
    max_sequence_length=128,
)
classifier = keras_nlp.models.AlbertClassifier(
    backbone=backbone,
    preprocessor=preprocessor,
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)

[source]

from_preset method

AlbertClassifier.from_preset()

Instantiate AlbertClassifier model from preset architecture and weights.

Arguments

  • preset: string. Must be one of "albert_base_en_uncased", "albert_large_en_uncased", "albert_extra_large_en_uncased", "albert_extra_extra_large_en_uncased".
  • load_weights: Whether to load pre-trained weights into model. Defaults to True.

Examples

# Load architecture and weights from preset
model = AlbertClassifier.from_preset("albert_base_en_uncased")

# Load randomly initialized model from preset architecture
model = AlbertClassifier.from_preset(
    "albert_base_en_uncased",
    load_weights=False
)
Preset name Parameters Description
albert_base_en_uncased 11.68M 12-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.
albert_large_en_uncased 17.68M 24-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.
albert_extra_large_en_uncased 58.72M 24-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.
albert_extra_extra_large_en_uncased 222.60M 12-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.

backbone property

keras_nlp.models.AlbertClassifier.backbone

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


preprocessor property

keras_nlp.models.AlbertClassifier.preprocessor

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