XLMRobertaMaskedLM model

[source]

XLMRobertaMaskedLM class

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

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

This model will train XLM-RoBERTa 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() method.

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.

Arguments

• backbone: A keras_hub.models.XLMRobertaBackbone instance.
• preprocessor: A keras_hub.models.XLMRobertaMaskedLMPreprocessor or None. If None, this model will not apply preprocessing, and inputs should be preprocessed before calling the model.

Examples

Raw string inputs and pretrained backbone.

# Create a dataset with raw string features. Labels are inferred.
features = ["The quick brown fox jumped.", "I forgot my homework."]

# Pretrained language model
# on an MLM task.
masked_lm = keras_hub.models.XLMRobertaMaskedLM.from_preset(
    "xlm_roberta_base_multi",
)
masked_lm.fit(x=features, batch_size=2)

Re-compile (e.g., with a new learning rate)

.

masked_lm.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)

.

masked_lm.backbone.trainable = False Fit again

.

masked_lm.fit(x=features, batch_size=2)

Preprocessed integer data.
```python
__Create a preprocessed dataset 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_hub.models.XLMRobertaMaskedLM.from_preset(
    "xlm_roberta_base_multi",
    preprocessor=None,
)

masked_lm.fit(x=features, y=labels, batch_size=2)

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from_preset method

XLMRobertaMaskedLM.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.XLMRobertaMaskedLM.backbone

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

preprocessor property

keras_nlp.models.XLMRobertaMaskedLM.preprocessor

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