Keras 3 API documentation / KerasNLP / Pretrained Models / DistilBert / DistilBertMaskedLM model

DistilBertMaskedLM model


DistilBertMaskedLM class

keras_nlp.models.DistilBertMaskedLM(backbone, preprocessor=None, **kwargs)

An end-to-end DistilBERT model for the masked language modeling task.

This model will train DistilBERT on a masked language modeling task. The model will predict labels for a number of masked tokens in the input data. 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 inputs can be raw string features during fit(), predict(), and evaluate(). Inputs will be tokenized and dynamically masked during training and evaluation. 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.



Raw string data.

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

# Pretrained language model.
masked_lm = keras_nlp.models.DistilBertMaskedLM.from_preset(
), batch_size=2)

# Re-compile (e.g., with a new learning rate).
# Access backbone programmatically (e.g., to change `trainable`).
masked_lm.backbone.trainable = False
# Fit again., batch_size=2)

Preprocessed integer data.

# Create preprocessed batch where 0 is the mask token.
features = {
    "token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1]] * 2),
    "mask_positions": np.array([[2, 4]] * 2)
# Labels are the original masked values.
labels = [[3, 5]] * 2

masked_lm = keras_nlp.models.DistilBertMaskedLM.from_preset(
), y=labels, batch_size=2)


from_preset method

DistilBertMaskedLM.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.


  • 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.


# Load a Gemma generative task.
causal_lm = keras_nlp.models.CausalLM.from_preset(

# Load a Bert classification task.
model = keras_nlp.models.Classifier.from_preset(
Preset name Parameters Description
distil_bert_base_en_uncased 66.36M 6-layer DistilBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus using BERT as the teacher model.
distil_bert_base_en 65.19M 6-layer DistilBERT model where case is maintained. Trained on English Wikipedia + BooksCorpus using BERT as the teacher model.
distil_bert_base_multi 134.73M 6-layer DistilBERT model where case is maintained. Trained on Wikipedias of 104 languages

backbone property


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

preprocessor property


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