diff --git a/keras_nlp/models/__init__.py b/keras_nlp/models/__init__.py
index a479c21004..3787d74ee1 100644
--- a/keras_nlp/models/__init__.py
+++ b/keras_nlp/models/__init__.py
@@ -90,6 +90,12 @@
 from keras_nlp.models.xlm_roberta.xlm_roberta_classifier import (
     XLMRobertaClassifier,
 )
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm import (
+    XLMRobertaMaskedLM,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm_preprocessor import (
+    XLMRobertaMaskedLMPreprocessor,
+)
 from keras_nlp.models.xlm_roberta.xlm_roberta_preprocessor import (
     XLMRobertaPreprocessor,
 )
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm.py b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm.py
new file mode 100644
index 0000000000..3b25655a1e
--- /dev/null
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm.py
@@ -0,0 +1,159 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""XLM-RoBERTa masked lm model."""
+
+import copy
+
+from tensorflow import keras
+
+from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.layers.masked_lm_head import MaskedLMHead
+from keras_nlp.models.roberta.roberta_backbone import roberta_kernel_initializer
+from keras_nlp.models.task import Task
+from keras_nlp.models.xlm_roberta.xlm_roberta_backbone import XLMRobertaBackbone
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm_preprocessor import (
+    XLMRobertaMaskedLMPreprocessor,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_presets import backbone_presets
+from keras_nlp.utils.keras_utils import is_xla_compatible
+from keras_nlp.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.models.XLMRobertaMaskedLM")
+class XLMRobertaMaskedLM(Task):
+    """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](https://github.com/facebookresearch/fairseq).
+
+    Args:
+        backbone: A `keras_nlp.models.XLMRobertaBackbone` instance.
+        preprocessor: A `keras_nlp.models.XLMRobertaMaskedLMPreprocessor` or
+            `None`. If `None`, this model will not apply preprocessing, and
+            inputs should be preprocessed before calling the model.
+
+    Example usage:
+
+    Raw string inputs and pretrained backbone.
+    ```python
+    # 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_nlp.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 programatically (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": tf.constant(
+            [[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
+        ),
+        "padding_mask": tf.constant(
+            [[1, 1, 1, 1, 1, 1, 1, 1]] * 2, shape=(2, 8)
+        ),
+        "mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2))
+    }
+    # Labels are the original masked values.
+    labels = [[3, 5]] * 2
+
+    masked_lm = keras_nlp.models.XLMRobertaMaskedLM.from_preset(
+        "xlm_roberta_base_multi",
+        preprocessor=None,
+    )
+
+    masked_lm.fit(x=features, y=labels, batch_size=2)
+    ```
+    """
+
+    def __init__(
+        self,
+        backbone,
+        preprocessor=None,
+        **kwargs,
+    ):
+        inputs = {
+            **backbone.input,
+            "mask_positions": keras.Input(
+                shape=(None,), dtype="int32", name="mask_positions"
+            ),
+        }
+        backbone_outputs = backbone(backbone.input)
+        outputs = MaskedLMHead(
+            vocabulary_size=backbone.vocabulary_size,
+            embedding_weights=backbone.token_embedding.embeddings,
+            intermediate_activation="gelu",
+            kernel_initializer=roberta_kernel_initializer(),
+            name="mlm_head",
+        )(backbone_outputs, inputs["mask_positions"])
+
+        # Instantiate using Functional API Model constructor.
+        super().__init__(
+            inputs=inputs,
+            outputs=outputs,
+            include_preprocessing=preprocessor is not None,
+            **kwargs,
+        )
+        # All references to `self` below this line
+        self.backbone = backbone
+        self.preprocessor = preprocessor
+
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            weighted_metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
+    @classproperty
+    def backbone_cls(cls):
+        return XLMRobertaBackbone
+
+    @classproperty
+    def preprocessor_cls(cls):
+        return XLMRobertaMaskedLMPreprocessor
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py
new file mode 100644
index 0000000000..2ed7baa0bf
--- /dev/null
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py
@@ -0,0 +1,184 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""XLM-RoBERTa masked language model preprocessor layer."""
+
+from absl import logging
+
+from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.layers.masked_lm_mask_generator import MaskedLMMaskGenerator
+from keras_nlp.models.xlm_roberta.xlm_roberta_preprocessor import (
+    XLMRobertaPreprocessor,
+)
+from keras_nlp.utils.keras_utils import pack_x_y_sample_weight
+
+
+@keras_nlp_export("keras_nlp.models.XLMRobertaMaskedLMPreprocessor")
+class XLMRobertaMaskedLMPreprocessor(XLMRobertaPreprocessor):
+    """XLM-RoBERTa preprocessing for the masked language modeling task.
+
+    This preprocessing layer will prepare inputs for a masked language modeling
+    task. It is primarily intended for use with the
+    `keras_nlp.models.XLMRobertaMaskedLM` task model. Preprocessing will occur in
+    multiple steps.
+
+    1. Tokenize any number of input segments using the `tokenizer`.
+    2. Pack the inputs together with the appropriate `"<s>"`, `"</s>"` and
+      `"<pad>"` tokens, i.e., adding a single `"<s>"` at the start of the
+      entire sequence, `"</s></s>"` between each segment,
+      and a `"</s>"` at the end of the entire sequence.
+    3. Randomly select non-special tokens to mask, controlled by
+      `mask_selection_rate`.
+    4. Construct a `(x, y, sample_weight)` tuple suitable for training with a
+      `keras_nlp.models.XLMRobertaMaskedLM` task model.
+
+    Args:
+        tokenizer: A `keras_nlp.models.XLMRobertaTokenizer` instance.
+        sequence_length: int. The length of the packed inputs.
+        truncate: string. The algorithm to truncate a list of batched segments
+            to fit within `sequence_length`. The value can be either
+            `round_robin` or `waterfall`:
+                - `"round_robin"`: Available space is assigned one token at a
+                    time in a round-robin fashion to the inputs that still need
+                    some, until the limit is reached.
+                - `"waterfall"`: The allocation of the budget is done using a
+                    "waterfall" algorithm that allocates quota in a
+                    left-to-right manner and fills up the buckets until we run
+                    out of budget. It supports an arbitrary number of segments.
+        mask_selection_rate: float. The probability an input token will be
+            dynamically masked.
+        mask_selection_length: int. The maximum number of masked tokens
+            in a given sample.
+        mask_token_rate: float. The probability the a selected token will be
+            replaced with the mask token.
+        random_token_rate: float. The probability the a selected token will be
+            replaced with a random token from the vocabulary. A selected token
+            will be left as is with probability
+            `1 - mask_token_rate - random_token_rate`.
+
+    Call arguments:
+        x: A tensor of single string sequences, or a tuple of multiple
+            tensor sequences to be packed together. Inputs may be batched or
+            unbatched. For single sequences, raw python inputs will be converted
+            to tensors. For multiple sequences, pass tensors directly.
+        y: Label data. Should always be `None` as the layer generates labels.
+        sample_weight: Label weights. Should always be `None` as the layer
+            generates label weights.
+
+    Examples:
+    ```python
+    # Load the preprocessor from a preset.
+    preprocessor = keras_nlp.models.XLMRobertaMaskedLMPreprocessor.from_preset(
+        "xlm_roberta_base_multi"
+    )
+
+    # Tokenize and mask a single sentence.
+    preprocessor("The quick brown fox jumped.")
+    # Tokenize and mask a batch of single sentences.
+    preprocessor(["The quick brown fox jumped.", "Call me Ishmael."])
+    # Tokenize and mask sentence pairs.
+    # In this case, always convert input to tensors before calling the layer.
+    first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
+    second = tf.constant(["The fox tripped.", "Oh look, a whale."])
+    preprocessor((first, second))
+    ```
+
+    Mapping with `tf.data.Dataset`.
+    ```python
+    preprocessor = keras_nlp.models.XLMRobertaMaskedLMPreprocessor.from_preset(
+        "xlm_roberta_base_multi"
+    )
+    first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
+    second = tf.constant(["The fox tripped.", "Oh look, a whale."])
+
+    # Map single sentences.
+    ds = tf.data.Dataset.from_tensor_slices(first)
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+
+    # Map sentence pairs.
+    ds = tf.data.Dataset.from_tensor_slices((first, second))
+    # Watch out for tf.data's default unpacking of tuples here!
+    # Best to invoke the `preprocessor` directly in this case.
+    ds = ds.map(
+        lambda first, second: preprocessor(x=(first, second)),
+        num_parallel_calls=tf.data.AUTOTUNE,
+    )
+    ```
+    ```
+    """
+
+    def __init__(
+        self,
+        tokenizer,
+        sequence_length=512,
+        truncate="round_robin",
+        mask_selection_rate=0.15,
+        mask_selection_length=96,
+        mask_token_rate=0.8,
+        random_token_rate=0.1,
+        **kwargs,
+    ):
+        super().__init__(
+            tokenizer,
+            sequence_length=sequence_length,
+            truncate=truncate,
+            **kwargs,
+        )
+
+        self.masker = MaskedLMMaskGenerator(
+            mask_selection_rate=mask_selection_rate,
+            mask_selection_length=mask_selection_length,
+            mask_token_rate=mask_token_rate,
+            random_token_rate=random_token_rate,
+            vocabulary_size=tokenizer.vocabulary_size(),
+            mask_token_id=tokenizer.mask_token_id,
+            unselectable_token_ids=[
+                tokenizer.start_token_id,
+                tokenizer.end_token_id,
+                tokenizer.pad_token_id,
+            ],
+        )
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "mask_selection_rate": self.masker.mask_selection_rate,
+                "mask_selection_length": self.masker.mask_selection_length,
+                "mask_token_rate": self.masker.mask_token_rate,
+                "random_token_rate": self.masker.random_token_rate,
+            }
+        )
+        return config
+
+    def call(self, x, y=None, sample_weight=None):
+        if y is not None or sample_weight is not None:
+            logging.warning(
+                f"{self.__class__.__name__} generates `y` and `sample_weight` "
+                "based on your input data, but your data already contains `y` "
+                "or `sample_weight`. Your `y` and `sample_weight` will be "
+                "ignored."
+            )
+
+        x = super().call(x)
+        token_ids, padding_mask = x["token_ids"], x["padding_mask"]
+        masker_outputs = self.masker(token_ids)
+        x = {
+            "token_ids": masker_outputs["token_ids"],
+            "padding_mask": padding_mask,
+            "mask_positions": masker_outputs["mask_positions"],
+        }
+        y = masker_outputs["mask_ids"]
+        sample_weight = masker_outputs["mask_weights"]
+        return pack_x_y_sample_weight(x, y, sample_weight)
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor_test.py b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor_test.py
new file mode 100644
index 0000000000..2b573204db
--- /dev/null
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor_test.py
@@ -0,0 +1,174 @@
+# Copyright 2022 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for XLM-RoBERTa masked language model preprocessor layer."""
+
+import io
+import os
+
+import pytest
+import sentencepiece
+import tensorflow as tf
+from absl.testing import parameterized
+from tensorflow import keras
+
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm_preprocessor import (
+    XLMRobertaMaskedLMPreprocessor,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_tokenizer import (
+    XLMRobertaTokenizer,
+)
+
+
+class XLMRobertaMaskedLMPreprocessorTest(
+    tf.test.TestCase, parameterized.TestCase
+):
+    def setUp(self):
+        bytes_io = io.BytesIO()
+        vocab_data = tf.data.Dataset.from_tensor_slices(
+            ["the quick brown fox", "the earth is round"]
+        )
+        sentencepiece.SentencePieceTrainer.train(
+            sentence_iterator=vocab_data.as_numpy_iterator(),
+            model_writer=bytes_io,
+            vocab_size=12,
+            model_type="WORD",
+            pad_id=0,
+            unk_id=1,
+            bos_id=2,
+            eos_id=3,
+            pad_piece="<pad>",
+            unk_piece="<unk>",
+            bos_piece="<s>",
+            eos_piece="</s>",
+            user_defined_symbols="[MASK]",
+        )
+        self.proto = bytes_io.getvalue()
+
+        self.tokenizer = XLMRobertaTokenizer(proto=self.proto)
+        self.preprocessor = XLMRobertaMaskedLMPreprocessor(
+            tokenizer=self.tokenizer,
+            # Simplify out testing by masking every available token.
+            mask_selection_rate=1.0,
+            mask_token_rate=1.0,
+            random_token_rate=0.0,
+            mask_selection_length=5,
+            sequence_length=12,
+        )
+
+    def test_preprocess_strings(self):
+        input_data = " brown fox quick"
+
+        x, y, sw = self.preprocessor(input_data)
+        self.assertAllEqual(
+            x["token_ids"], [0, 13, 13, 13, 2, 1, 1, 1, 1, 1, 1, 1]
+        )
+        self.assertAllEqual(
+            x["padding_mask"], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
+        )
+        self.assertAllEqual(x["mask_positions"], [1, 2, 3, 0, 0])
+        self.assertAllEqual(y, [7, 9, 11, 0, 0])
+        self.assertAllEqual(sw, [1.0, 1.0, 1.0, 0.0, 0.0])
+
+    def test_preprocess_list_of_strings(self):
+        input_data = [" brown fox quick"] * 13
+
+        x, y, sw = self.preprocessor(input_data)
+        self.assertAllEqual(
+            x["token_ids"], [[0, 13, 13, 13, 2, 1, 1, 1, 1, 1, 1, 1]] * 13
+        )
+        self.assertAllEqual(
+            x["padding_mask"], [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]] * 13
+        )
+        self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 0, 0]] * 13)
+        self.assertAllEqual(y, [[7, 9, 11, 0, 0]] * 13)
+        self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 0.0, 0.0]] * 13)
+
+    def test_preprocess_dataset(self):
+        sentences = tf.constant([" brown fox quick"] * 13)
+        ds = tf.data.Dataset.from_tensor_slices(sentences)
+        ds = ds.map(self.preprocessor)
+        x, y, sw = ds.batch(13).take(1).get_single_element()
+        self.assertAllEqual(
+            x["token_ids"], [[0, 13, 13, 13, 2, 1, 1, 1, 1, 1, 1, 1]] * 13
+        )
+        self.assertAllEqual(
+            x["padding_mask"], [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]] * 13
+        )
+        self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 0, 0]] * 13)
+        self.assertAllEqual(y, [[7, 9, 11, 0, 0]] * 13)
+        self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 0.0, 0.0]] * 13)
+
+    def test_mask_multiple_sentences(self):
+        sentence_one = tf.constant(" airplane")
+        sentence_two = tf.constant(" round")
+
+        x, y, sw = self.preprocessor((sentence_one, sentence_two))
+        self.assertAllEqual(
+            x["token_ids"], [0, 2, 2, 2, 13, 2, 1, 1, 1, 1, 1, 1]
+        )
+        self.assertAllEqual(
+            x["padding_mask"], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]
+        )
+        self.assertAllEqual(x["mask_positions"], [4, 0, 0, 0, 0])
+        self.assertAllEqual(y, [12, 0, 0, 0, 0])
+        self.assertAllEqual(sw, [1.0, 0.0, 0.0, 0.0, 0.0])
+
+    def test_no_masking_zero_rate(self):
+        no_mask_preprocessor = XLMRobertaMaskedLMPreprocessor(
+            self.preprocessor.tokenizer,
+            mask_selection_rate=0.0,
+            mask_selection_length=5,
+            sequence_length=12,
+        )
+        input_data = " quick brown fox"
+
+        x, y, sw = no_mask_preprocessor(input_data)
+        self.assertAllEqual(
+            x["token_ids"], [0, 11, 7, 9, 2, 1, 1, 1, 1, 1, 1, 1]
+        )
+        self.assertAllEqual(
+            x["padding_mask"], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
+        )
+        self.assertAllEqual(x["mask_positions"], [0, 0, 0, 0, 0])
+        self.assertAllEqual(y, [0, 0, 0, 0, 0])
+        self.assertAllEqual(sw, [0.0, 0.0, 0.0, 0.0, 0.0])
+
+    def test_serialization(self):
+        config = keras.utils.serialize_keras_object(self.preprocessor)
+        new_preprocessor = keras.utils.deserialize_keras_object(config)
+        self.assertEqual(
+            new_preprocessor.get_config(),
+            self.preprocessor.get_config(),
+        )
+
+    @parameterized.named_parameters(
+        ("tf_format", "tf", "model"),
+        ("keras_format", "keras_v3", "model.keras"),
+    )
+    @pytest.mark.large
+    def test_saved_model(self, save_format, filename):
+        input_data = tf.constant([" quick brown fox"])
+
+        inputs = keras.Input(dtype="string", shape=())
+        outputs = self.preprocessor(inputs)
+        model = keras.Model(inputs, outputs)
+
+        path = os.path.join(self.get_temp_dir(), filename)
+        model.save(path, save_format=save_format)
+
+        restored_model = keras.models.load_model(path)
+        outputs = model(input_data)[0]["token_ids"]
+        restored_outputs = restored_model(input_data)[0]["token_ids"]
+        self.assertAllEqual(outputs, restored_outputs)
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_test.py b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_test.py
new file mode 100644
index 0000000000..168a219da3
--- /dev/null
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_test.py
@@ -0,0 +1,134 @@
+# Copyright 2022 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for XLM-RoBERTa masked language model."""
+
+import io
+import os
+
+import pytest
+import sentencepiece
+import tensorflow as tf
+from absl.testing import parameterized
+from tensorflow import keras
+
+from keras_nlp.models.xlm_roberta.xlm_roberta_backbone import XLMRobertaBackbone
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm import (
+    XLMRobertaMaskedLM,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm_preprocessor import (
+    XLMRobertaMaskedLMPreprocessor,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_tokenizer import (
+    XLMRobertaTokenizer,
+)
+
+
+class XLMRobertaMaskedLMTest(tf.test.TestCase, parameterized.TestCase):
+    def setUp(self):
+        bytes_io = io.BytesIO()
+        vocab_data = tf.data.Dataset.from_tensor_slices(
+            ["the quick brown fox", "the slow brown fox"]
+        )
+        sentencepiece.SentencePieceTrainer.train(
+            sentence_iterator=vocab_data.as_numpy_iterator(),
+            model_writer=bytes_io,
+            vocab_size=5,
+            model_type="WORD",
+            pad_id=0,
+            unk_id=1,
+            bos_id=2,
+            eos_id=3,
+            pad_piece="<pad>",
+            unk_piece="<unk>",
+            bos_piece="<s>",
+            eos_piece="</s>",
+            user_defined_symbols="[MASK]",
+        )
+        self.proto = bytes_io.getvalue()
+
+        self.preprocessor = XLMRobertaMaskedLMPreprocessor(
+            XLMRobertaTokenizer(proto=self.proto),
+            sequence_length=5,
+            mask_selection_length=2,
+        )
+
+        self.backbone = XLMRobertaBackbone(
+            vocabulary_size=self.preprocessor.tokenizer.vocabulary_size(),
+            num_layers=2,
+            num_heads=2,
+            hidden_dim=2,
+            intermediate_dim=4,
+            max_sequence_length=self.preprocessor.packer.sequence_length,
+        )
+
+        self.masked_lm = XLMRobertaMaskedLM(
+            self.backbone,
+            preprocessor=self.preprocessor,
+        )
+
+        self.raw_batch = tf.constant(
+            ["the quick brown fox", "the slow brown fox"]
+        )
+        self.preprocessed_batch = self.preprocessor(self.raw_batch)[0]
+        self.raw_dataset = tf.data.Dataset.from_tensor_slices(
+            self.raw_batch
+        ).batch(2)
+        self.preprocessed_dataset = self.raw_dataset.map(self.preprocessor)
+
+    def test_valid_call_masked_lm(self):
+        self.masked_lm(self.preprocessed_batch)
+
+    def test_classifier_predict(self):
+        self.masked_lm.predict(self.raw_batch)
+        self.masked_lm.preprocessor = None
+        self.masked_lm.predict(self.preprocessed_batch)
+
+    def test_classifier_fit(self):
+        self.masked_lm.fit(self.raw_dataset)
+        self.masked_lm.preprocessor = None
+        self.masked_lm.fit(self.preprocessed_dataset)
+
+    def test_classifier_fit_no_xla(self):
+        self.masked_lm.preprocessor = None
+        self.masked_lm.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+            jit_compile=False,
+        )
+        self.masked_lm.fit(self.preprocessed_dataset)
+
+    def test_serialization(self):
+        config = keras.utils.serialize_keras_object(self.masked_lm)
+        new_classifier = keras.utils.deserialize_keras_object(config)
+        self.assertEqual(
+            new_classifier.get_config(),
+            self.masked_lm.get_config(),
+        )
+
+    @parameterized.named_parameters(
+        ("tf_format", "tf", "model"),
+        ("keras_format", "keras_v3", "model.keras"),
+    )
+    @pytest.mark.large
+    def test_saved_model(self, save_format, filename):
+        save_path = os.path.join(self.get_temp_dir(), filename)
+        self.masked_lm.save(save_path, save_format=save_format)
+        restored_model = keras.models.load_model(save_path)
+
+        # Check we got the real object back.
+        self.assertIsInstance(restored_model, XLMRobertaMaskedLM)
+
+        model_output = self.masked_lm(self.preprocessed_batch)
+        restored_output = restored_model(self.preprocessed_batch)
+
+        self.assertAllClose(model_output, restored_output)
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer.py b/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer.py
index d679ab9ba3..13a8a7bac5 100644
--- a/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer.py
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer.py
@@ -100,10 +100,11 @@ def __init__(self, proto, **kwargs):
         self.pad_token_id = 1  # <pad>
         self.end_token_id = 2  # </s>
         self.unk_token_id = 3  # <unk>
+        self.mask_token_id = self.vocabulary_size() - 1  # <mask>
 
     def vocabulary_size(self):
         """Get the size of the tokenizer vocabulary."""
-        return super().vocabulary_size() + 1
+        return super().vocabulary_size() + 2
 
     def get_vocabulary(self):
         """Get the size of the tokenizer vocabulary."""
@@ -112,10 +113,14 @@ def get_vocabulary(self):
                 tf.range(super().vocabulary_size())
             )
         )
-        return self._vocabulary_prefix + vocabulary[3:]
+        return self._vocabulary_prefix + vocabulary[3:] + ["<mask>"]
 
     def id_to_token(self, id):
         """Convert an integer id to a string token."""
+
+        if id == self.mask_token_id:
+            return "<mask>"
+
         if id < len(self._vocabulary_prefix):
             return self._vocabulary_prefix[id]
 
@@ -139,25 +144,9 @@ def tokenize(self, inputs):
         # Shift the tokens IDs right by one.
         return tf.add(tokens, 1)
 
-    def detokenize(self, inputs):
-        if inputs.dtype == tf.string:
-            return super().detokenize(inputs)
-
-        # Shift the tokens IDs left by one.
-        tokens = tf.subtract(inputs, 1)
-
-        # Correct `unk_token_id`, `end_token_id`, `start_token_id`, respectively.
-        # Note: The `pad_token_id` is taken as 0 (`unk_token_id`) since the
-        # proto does not contain `pad_token_id`. This mapping of the pad token
-        # is done automatically by the above subtraction.
-        tokens = tf.where(tf.equal(tokens, self.unk_token_id - 1), 0, tokens)
-        tokens = tf.where(tf.equal(tokens, self.end_token_id - 1), 2, tokens)
-        tokens = tf.where(tf.equal(tokens, self.start_token_id - 1), 1, tokens)
-
-        # Note: Even though we map `"<s>" and `"</s>"` to the correct IDs,
-        # the `detokenize` method will return empty strings for these tokens.
-        # This is a vagary of the `sentencepiece` library.
-        return super().detokenize(tokens)
+    def detokenize(self, ids):
+        ids = tf.ragged.boolean_mask(ids, tf.not_equal(ids, self.mask_token_id))
+        return super().detokenize(ids)
 
     @classproperty
     def presets(cls):
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer_test.py b/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer_test.py
index 912403fc4e..6c216403c7 100644
--- a/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer_test.py
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer_test.py
@@ -66,7 +66,7 @@ def test_unk_token(self):
     def test_detokenize(self):
         input_data = tf.constant([[4, 9, 5, 7]])
         output = self.tokenizer.detokenize(input_data)
-        self.assertEqual(output, tf.constant(["the quick brown fox"]))
+        self.assertEqual(output, tf.constant(["brown round earth is"]))
 
     def test_vocabulary(self):
         vocabulary = self.tokenizer.get_vocabulary()
@@ -84,9 +84,10 @@ def test_vocabulary(self):
                 "▁is",
                 "▁quick",
                 "▁round",
+                "<mask>",
             ],
         )
-        self.assertEqual(self.tokenizer.vocabulary_size(), 11)
+        self.assertEqual(self.tokenizer.vocabulary_size(), 12)
 
     def test_id_to_token(self):
         print(self.tokenizer.id_to_token(9))