Keras
Keras 3 API 문서
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Pretrained Models
XLM-RoBERTa
XLMRobertaMaskedLM model

XLMRobertaMaskedLM model

source

XLMRobertaMaskedLM class 

keras_hub.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

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.

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`](/keras_site/ko/docs/api/keras_hub/base_classes/task/#task-class) 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**

causal_lm = keras_hub.models.CausalLM.from_preset( “gemma_2b_en”, )

model = keras_hub.models.TextClassifier.from_preset( “bert_base_en”, num_classes=2, )

| Preset name | Parameters | Description |
| --- | --- | --- |
| xlm\_roberta\_base\_multi | 277.45M | 12-layer XLM-RoBERTa model where case is maintained. Trained on CommonCrawl in 100 languages. |
| xlm\_roberta\_large\_multi | 558.84M | 24-layer XLM-RoBERTa model where case is maintained. Trained on CommonCrawl in 100 languages. |
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### `backbone` property

keras_hub.models.XLMRobertaMaskedLM.backbone

A [`keras_hub.models.Backbone`](/keras_site/ko/docs/api/keras_hub/base_classes/backbone/#backbone-class) model with the core architecture.
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### `preprocessor` property

keras_hub.models.XLMRobertaMaskedLM.preprocessor


A [`keras_hub.models.Preprocessor`](/keras_site/ko/docs/api/keras_hub/base_classes/preprocessor/#preprocessor-class) layer used to preprocess input.

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