DistilBertTextClassifier model

[source]

DistilBertTextClassifier class

keras_nlp.models.DistilBertTextClassifier(
    backbone,
    num_classes,
    preprocessor=None,
    activation=None,
    hidden_dim=None,
    dropout=0.2,
    **kwargs
)

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

This model attaches a classification head to a keras_hub.model.DistilBertBackbone 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_hub.models.DistilBert instance.
• num_classes: int. Number of classes to predict.
• preprocessor: A keras_hub.models.DistilBertTextClassifierPreprocessor 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 after the first dense layer.

Examples

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.DistilBertTextClassifierPreprocessor.from_preset(
    "distil_bert_base_en_uncased",
    sequence_length=128,
)
# Pretrained classifier.
classifier = keras_hub.models.DistilBertTextClassifier.from_preset(
    "distil_bert_base_en_uncased",
    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.DistilBertTextClassifier.from_preset(
    "distil_bert_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]
vocab = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
vocab += ["The", "quick", "brown", "fox", "jumped", "."]
tokenizer = keras_hub.models.DistilBertTokenizer(
    vocabulary=vocab,
)
preprocessor = keras_hub.models.DistilBertTextClassifierPreprocessor(
    tokenizer=tokenizer,
    sequence_length=128,
)
backbone = keras_hub.models.DistilBertBackbone(
    vocabulary_size=30552,
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    intermediate_dim=512,
    max_sequence_length=128,
)
classifier = keras_hub.models.DistilBertTextClassifier(
    backbone=backbone,
    preprocessor=preprocessor,
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)

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from_preset method

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

Instantiate a keras_hub.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 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_hub.models.CausalLM.from_preset(), or from a model class like keras_hub.models.BertTextClassifier.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, saved weights will be loaded into the model architecture. If False, all weights will be randomly initialized.

Examples

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

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

backbone property

keras_nlp.models.DistilBertTextClassifier.backbone

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

preprocessor property

keras_nlp.models.DistilBertTextClassifier.preprocessor

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