Repository: keras-team/keras-nlp (python)

Issue:
Add an `add_prefix_space` Arg in `RobertaPreprocessor`
RoBERTa uses the BPE tokeniser. If you see the vocabulary (https://huggingface.co/roberta-base/blob/main/vocab.json), most tokens have this character at the beginning: "Ġ". This is because whitespace is considered a part of the token. Setting `add_prefix_space` to `True` will add a space at the beginning of the input sequences, so that the token is not mapped to UNK.

Have a look here for more details: https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaTokenizer.

Pull request:
Add an add_prefix_space Arg in BytePairTokenizer
Closes #436 

cc : @abheesht17 

Patch:
diff --git a/keras_nlp/models/__init__.py b/keras_nlp/models/__init__.py
index db70d43b..893d9300 100644
--- a/keras_nlp/models/__init__.py
+++ b/keras_nlp/models/__init__.py
@@ -16,6 +16,7 @@ from keras_nlp.models.albert.albert_backbone import AlbertBackbone
 from keras_nlp.models.albert.albert_preprocessor import AlbertPreprocessor
 from keras_nlp.models.albert.albert_tokenizer import AlbertTokenizer
 from keras_nlp.models.bart.bart_backbone import BartBackbone
+from keras_nlp.models.bart.bart_tokenizer import BartTokenizer
 from keras_nlp.models.bert.bert_backbone import BertBackbone
 from keras_nlp.models.bert.bert_classifier import BertClassifier
 from keras_nlp.models.bert.bert_preprocessor import BertPreprocessor
diff --git a/keras_nlp/models/albert/albert_classifier.py b/keras_nlp/models/albert/albert_classifier.py
index 20f6e1a8..a6b3eb41 100644
--- a/keras_nlp/models/albert/albert_classifier.py
+++ b/keras_nlp/models/albert/albert_classifier.py
@@ -22,6 +22,7 @@ from keras_nlp.models.albert.albert_backbone import albert_kernel_initializer
 from keras_nlp.models.albert.albert_preprocessor import AlbertPreprocessor
 from keras_nlp.models.albert.albert_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -173,6 +174,14 @@ class AlbertClassifier(Task):
         self.num_classes = num_classes
         self.dropout = dropout
 
+        # Default compilation
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     def get_config(self):
         config = super().get_config()
         config.update(
diff --git a/keras_nlp/models/bart/bart_backbone.py b/keras_nlp/models/bart/bart_backbone.py
index 32c52873..a66a6210 100644
--- a/keras_nlp/models/bart/bart_backbone.py
+++ b/keras_nlp/models/bart/bart_backbone.py
@@ -14,6 +14,8 @@
 
 """BART backbone model."""
 
+import copy
+
 import tensorflow as tf
 from tensorflow import keras
 
@@ -21,6 +23,8 @@ from keras_nlp.layers.position_embedding import PositionEmbedding
 from keras_nlp.layers.transformer_decoder import TransformerDecoder
 from keras_nlp.layers.transformer_encoder import TransformerEncoder
 from keras_nlp.models.backbone import Backbone
+from keras_nlp.models.bart.bart_presets import backbone_presets
+from keras_nlp.utils.python_utils import classproperty
 
 
 def bart_kernel_initializer(stddev=0.02):
@@ -247,3 +251,7 @@ class BartBackbone(Backbone):
     @property
     def token_embedding(self):
         return self.get_layer("token_embedding")
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)
diff --git a/keras_nlp/models/bart/bart_presets.py b/keras_nlp/models/bart/bart_presets.py
new file mode 100644
index 00000000..053eaddf
--- /dev/null
+++ b/keras_nlp/models/bart/bart_presets.py
@@ -0,0 +1,63 @@
+# 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.
+"""BART model preset configurations."""
+
+backbone_presets = {
+    "bart_base_en": {
+        "config": {
+            "vocabulary_size": 50265,
+            "num_layers": 6,
+            "num_heads": 12,
+            "hidden_dim": 768,
+            "intermediate_dim": 3072,
+            "dropout": 0.1,
+            "max_sequence_length": 1024,
+        },
+        "preprocessor_config": {},
+        "description": (
+            "Base size of BART where case is maintained. "
+            "Trained on a 160GB English dataset comprising BookCorpus, "
+            "English Wikipedia and CommonCrawl."
+        ),
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/bart_base_en/v1/model.h5",
+        "weights_hash": "5b59403f0cafafbd89680e0785791163",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/bart_base_en/v1/vocab.json",
+        "vocabulary_hash": "be4d3c6f3f5495426b2c03b334334354",
+        "merges_url": "https://storage.googleapis.com/keras-nlp/models/bart_base_en/v1/merges.txt",
+        "merges_hash": "75a37753dd7a28a2c5df80c28bf06e4e",
+    },
+    "bart_large_en": {
+        "config": {
+            "vocabulary_size": 50265,
+            "num_layers": 12,
+            "num_heads": 16,
+            "hidden_dim": 1024,
+            "intermediate_dim": 4096,
+            "dropout": 0.1,
+            "max_sequence_length": 1024,
+        },
+        "preprocessor_config": {},
+        "description": (
+            "Large size of BART where case is maintained. "
+            "Trained on a 160GB English dataset comprising BookCorpus, "
+            "English Wikipedia and CommonCrawl."
+        ),
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/bart_large_en/v1/model.h5",
+        "weights_hash": "6bfe7e591af8c5699ce6f9f18753af9a",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/bart_large_en/v1/vocab.json",
+        "vocabulary_hash": "cf410ee085c5c69c957bb1f6d8456596",
+        "merges_url": "https://storage.googleapis.com/keras-nlp/models/bart_large_en/v1/merges.txt",
+        "merges_hash": "75a37753dd7a28a2c5df80c28bf06e4e",
+    },
+}
diff --git a/keras_nlp/models/bart/bart_tokenizer.py b/keras_nlp/models/bart/bart_tokenizer.py
new file mode 100644
index 00000000..c5e2a25e
--- /dev/null
+++ b/keras_nlp/models/bart/bart_tokenizer.py
@@ -0,0 +1,120 @@
+# 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.
+
+"""BART tokenizer."""
+
+import copy
+
+from tensorflow import keras
+
+from keras_nlp.models.bart.bart_presets import backbone_presets
+from keras_nlp.tokenizers.byte_pair_tokenizer import BytePairTokenizer
+from keras_nlp.utils.python_utils import classproperty
+
+
+@keras.utils.register_keras_serializable(package="keras_nlp")
+class BartTokenizer(BytePairTokenizer):
+    """A BART tokenizer using Byte-Pair Encoding subword segmentation.
+
+    This tokenizer class will tokenize raw strings into integer sequences and
+    is based on `keras_nlp.tokenizers.BytePairTokenizer`. Unlike the
+    underlying tokenizer, it will check for all special tokens needed by BART
+    models and provides a `from_preset()` method to automatically download
+    a matching vocabulary for a BART preset.
+
+    This tokenizer does not provide truncation or padding of inputs. It can be
+    combined with a `keras_nlp.models.BartPreprocessor` layer for input
+    packing.
+
+    If input is a batch of strings (rank > 0), the layer will output a
+    `tf.RaggedTensor` where the last dimension of the output is ragged.
+
+    If input is a scalar string (rank == 0), the layer will output a dense
+    `tf.Tensor` with static shape `[None]`.
+
+    Args:
+        vocabulary: string or dict, maps token to integer ids. If it is a
+            string, it should be the file path to a json file.
+        merges: string or list, contains the merge rule. If it is a string,
+            it should be the file path to merge rules. The merge rule file
+            should have one merge rule per line. Every merge rule contains
+            merge entities separated by a space.
+
+    Examples:
+
+    Batched inputs.
+    >>> vocab = {"
": 0, "<pad>": 1, "
": 2, "<mask>": 3}
+    >>> vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
+    >>> merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
+    >>> merges += ["Ġ f", "o x", "Ġf ox"]
+    >>> tokenizer = keras_nlp.models.RobertaTokenizer(
+    ...     vocabulary=vocab, merges=merges
+    ... )
+    >>> tokenizer(["a quick fox", "a fox quick"])
+    <tf.RaggedTensor [[4, 5, 6], [4, 6, 5]]>
+
+    Unbatched input.
+    >>> vocab = {"
": 0, "<pad>": 1, "
": 2, "<mask>": 3}
+    >>> vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
+    >>> merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
+    >>> merges += ["Ġ f", "o x", "Ġf ox"]
+    >>> tokenizer = keras_nlp.models.RobertaTokenizer(
+    ...     vocabulary=vocab, merges=merges
+    ... )
+    >>> tokenizer("a quick fox")
+    <tf.Tensor: shape=(3,), dtype=int32, numpy=array([4, 5, 6], dtype=int32)>
+
+    Detokenization.
+    >>> vocab = {"
": 0, "<pad>": 1, "
": 2, "<mask>": 3}
+    >>> vocab = {**vocab, "a": 4, "Ġquick": 5, "Ġfox": 6}
+    >>> merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
+    >>> merges += ["Ġ f", "o x", "Ġf ox"]
+    >>> tokenizer = keras_nlp.models.RobertaTokenizer(
+    ...     vocabulary=vocab, merges=merges
+    ... )
+    >>> tokenizer.detokenize(tokenizer("a quick fox")).numpy().decode('utf-8')
+    'a quick fox'
+    """
+
+    def __init__(
+        self,
+        vocabulary,
+        merges,
+        **kwargs,
+    ):
+        super().__init__(
+            vocabulary=vocabulary,
+            merges=merges,
+            **kwargs,
+        )
+
+        # Check for necessary special tokens.
+        start_token = "
"
+        pad_token = "<pad>"
+        end_token = "
"
+        for token in [start_token, pad_token, end_token]:
+            if token not in self.get_vocabulary():
+                raise ValueError(
+                    f"Cannot find token `'{token}'` in the provided "
+                    f"`vocabulary`. Please provide `'{token}'` in your "
+                    "`vocabulary` or use a pretrained `vocabulary` name."
+                )
+
+        self.start_token_id = self.token_to_id(start_token)
+        self.pad_token_id = self.token_to_id(pad_token)
+        self.end_token_id = self.token_to_id(end_token)
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)
diff --git a/keras_nlp/models/deberta_v3/deberta_v3_classifier.py b/keras_nlp/models/deberta_v3/deberta_v3_classifier.py
index 3ea01dd1..660d0a99 100644
--- a/keras_nlp/models/deberta_v3/deberta_v3_classifier.py
+++ b/keras_nlp/models/deberta_v3/deberta_v3_classifier.py
@@ -26,6 +26,7 @@ from keras_nlp.models.deberta_v3.deberta_v3_preprocessor import (
 )
 from keras_nlp.models.deberta_v3.deberta_v3_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -199,6 +200,14 @@ class DebertaV3Classifier(Task):
         self.hidden_dim = hidden_dim
         self.dropout = dropout
 
+        # Default compilation
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     def get_config(self):
         config = super().get_config()
         config.update(
diff --git a/keras_nlp/models/distil_bert/distil_bert_classifier.py b/keras_nlp/models/distil_bert/distil_bert_classifier.py
index 6ed81d86..b20c556d 100644
--- a/keras_nlp/models/distil_bert/distil_bert_classifier.py
+++ b/keras_nlp/models/distil_bert/distil_bert_classifier.py
@@ -26,6 +26,7 @@ from keras_nlp.models.distil_bert.distil_bert_preprocessor import (
 )
 from keras_nlp.models.distil_bert.distil_bert_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -202,6 +203,13 @@ class DistilBertClassifier(Task):
         self.hidden_dim = hidden_dim
         self.dropout = dropout
 
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     def get_config(self):
         config = super().get_config()
         config.update(
diff --git a/keras_nlp/models/f_net/f_net_classifier.py b/keras_nlp/models/f_net/f_net_classifier.py
index 8f7f08a0..6e43b76f 100644
--- a/keras_nlp/models/f_net/f_net_classifier.py
+++ b/keras_nlp/models/f_net/f_net_classifier.py
@@ -23,6 +23,7 @@ from keras_nlp.models.f_net.f_net_backbone import f_net_kernel_initializer
 from keras_nlp.models.f_net.f_net_preprocessor import FNetPreprocessor
 from keras_nlp.models.f_net.f_net_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -121,6 +122,13 @@ class FNetClassifier(Task):
         self.num_classes = num_classes
         self.dropout = dropout
 
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     def get_config(self):
         config = super().get_config()
         config.update(
diff --git a/keras_nlp/models/opt/opt_backbone.py b/keras_nlp/models/opt/opt_backbone.py
index c88f208b..cdcb5062 100644
--- a/keras_nlp/models/opt/opt_backbone.py
+++ b/keras_nlp/models/opt/opt_backbone.py
@@ -30,7 +30,7 @@ def opt_kernel_initializer(stddev=0.02):
 
 @keras.utils.register_keras_serializable(package="keras_nlp")
 class OPTBackbone(Backbone):
-    """OPT encoder-decoder network.
+    """OPT decoder network.
 
     This class implements a Transformer-based decoder model as described in
     ["OPT: Open Pre-trained Transformer Language Models"](https://arxiv.org/abs/2205.01068).
@@ -45,15 +45,14 @@ class OPTBackbone(Backbone):
 
     Args:
         vocabulary_size: int. The size of the token vocabulary.
-        num_layers: int. The number of transformer encoder layers and
-            transformer decoder layers.
+        num_layers: int. The number of transformer decoder layers.
         num_heads: int. The number of attention heads for each transformer.
             The hidden size must be divisible by the number of attention heads.
         hidden_dim: int. The hidden size of the transformer decoder layers.
         intermediate_dim: int. The output dimension of the first Dense layer in
             a two-layer feedforward network for each transformer decoder layer.
-        dropout: float. Dropout probability for the Transformer encoder.
-        max_sequence_length: int. The maximum sequence length that this encoder
+        dropout: float. Dropout probability for the Transformer decoder.
+        max_sequence_length: int. The maximum sequence length that this decoder
             can consume. If None, `max_sequence_length` uses the value from
             sequence length. This determines the variable shape for positional
             embeddings.
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_classifier.py b/keras_nlp/models/xlm_roberta/xlm_roberta_classifier.py
index 0e250ff8..c50ea42b 100644
--- a/keras_nlp/models/xlm_roberta/xlm_roberta_classifier.py
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_classifier.py
@@ -24,6 +24,7 @@ from keras_nlp.models.xlm_roberta.xlm_roberta_preprocessor import (
     XLMRobertaPreprocessor,
 )
 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
 
 
@@ -196,6 +197,13 @@ class XLMRobertaClassifier(Task):
         self.hidden_dim = hidden_dim
         self.dropout = dropout
 
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     def preprocess_samples(self, x, y=None, sample_weight=None):
         return self.preprocessor(x, y=y, sample_weight=sample_weight)
 
diff --git a/keras_nlp/tokenizers/byte_pair_tokenizer.py b/keras_nlp/tokenizers/byte_pair_tokenizer.py
index 8d7e886c..602a9e31 100644
--- a/keras_nlp/tokenizers/byte_pair_tokenizer.py
+++ b/keras_nlp/tokenizers/byte_pair_tokenizer.py
@@ -196,6 +196,11 @@ class BytePairTokenizer(tokenizer.Tokenizer):
             should have one merge rule per line.
         sequence_length: int, defaults to None. If set, the output will be
             padded or truncated to the `sequence_length`.
+        add_prefix_space: bool, defaults to False. Whether or not to add an
+            initial space to the input. This tokenizer is whitespace aware,
+            and will tokenize a word with a leading space differently. Adding
+            a prefix space to the first word will cause it to be tokenized
+            equivalently to all subsequent words in the sequence.
 
     Examples:
 
@@ -230,6 +235,7 @@ class BytePairTokenizer(tokenizer.Tokenizer):
         vocabulary,
         merges,
         sequence_length=None,
+        add_prefix_space=False,
         **kwargs,
     ) -> None:
         assert_tf_text_installed(self.__class__.__name__)
@@ -268,6 +274,7 @@ class BytePairTokenizer(tokenizer.Tokenizer):
                 f"Received: `type(merges)={type(merges)}`"
             )
         self.sequence_length = sequence_length
+        self.add_prefix_space = add_prefix_space
 
         # Create byte <=> unicode mapping. This is useful for handling
         # whitespace tokens.
@@ -451,6 +458,9 @@ class BytePairTokenizer(tokenizer.Tokenizer):
         if not isinstance(inputs, (tf.Tensor, tf.RaggedTensor)):
             inputs = tf.convert_to_tensor(inputs)
 
+        if self.add_prefix_space:
+            inputs = tf.strings.join([" ", inputs])
+
         scalar_input = inputs.shape.rank == 0
         if scalar_input:
             inputs = tf.expand_dims(inputs, 0)


Test patch:
diff --git a/keras_nlp/models/albert/albert_classifier_test.py b/keras_nlp/models/albert/albert_classifier_test.py
index 6f586b9c..622e8bae 100644
--- a/keras_nlp/models/albert/albert_classifier_test.py
+++ b/keras_nlp/models/albert/albert_classifier_test.py
@@ -108,6 +108,9 @@ class AlbertClassifierTest(tf.test.TestCase, parameterized.TestCase):
         self.classifier_no_preprocessing.compile(jit_compile=jit_compile)
         self.classifier_no_preprocessing.predict(self.preprocessed_batch)
 
+    def test_albert_classifier_fit_default_compile(self):
+        self.classifier.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("jit_compile_false", False), ("jit_compile_true", True)
     )
diff --git a/keras_nlp/models/bart/bart_presets_test.py b/keras_nlp/models/bart/bart_presets_test.py
new file mode 100644
index 00000000..2015877d
--- /dev/null
+++ b/keras_nlp/models/bart/bart_presets_test.py
@@ -0,0 +1,139 @@
+# 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.
+# 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.
+"""Tests for loading pretrained model presets."""
+
+import pytest
+import tensorflow as tf
+from absl.testing import parameterized
+
+from keras_nlp.models.bart.bart_backbone import BartBackbone
+from keras_nlp.models.bart.bart_tokenizer import BartTokenizer
+
+
+@pytest.mark.large
+class BartPresetSmokeTest(tf.test.TestCase, parameterized.TestCase):
+    """
+    A smoke test for BART presets we run continuously.
+
+    This only tests the smallest weights we have available. Run with:
+    `pytest keras_nlp/models/bart/bart_presets_test.py --run_large`
+    """
+
+    def test_tokenizer_output(self):
+        tokenizer = BartTokenizer.from_preset(
+            "bart_base_en",
+        )
+        outputs = tokenizer("The quick brown fox.")
+        expected_outputs = [133, 2119, 6219, 23602, 4]
+        self.assertAllEqual(outputs, expected_outputs)
+
+    @parameterized.named_parameters(
+        ("preset_weights", True), ("random_weights", False)
+    )
+    def test_backbone_output(self, load_weights):
+        input_data = {
+            "encoder_token_ids": tf.constant([[0, 133, 2119, 2]]),
+            "encoder_padding_mask": tf.constant([[1, 1, 1, 1]]),
+            "decoder_token_ids": tf.constant([[0, 7199, 14, 2119, 2]]),
+            "decoder_padding_mask": tf.constant([[1, 1, 1, 1, 1]]),
+        }
+        model = BartBackbone.from_preset(
+            "bart_base_en", load_weights=load_weights
+        )
+        outputs = model(input_data)
+        if load_weights:
+            encoder_output = outputs["encoder_sequence_output"][0, 0, :5]
+            expected_encoder_output = [-0.033, 0.013, -0.003, -0.012, -0.002]
+            decoder_output = outputs["decoder_sequence_output"][0, 0, :5]
+            expected_decoder_output = [2.516, 2.489, 0.695, 8.057, 1.245]
+
+            self.assertAllClose(
+                encoder_output, expected_encoder_output, atol=0.01, rtol=0.01
+            )
+            self.assertAllClose(
+                decoder_output, expected_decoder_output, atol=0.01, rtol=0.01
+            )
+
+    @parameterized.named_parameters(
+        ("bart_tokenizer", BartTokenizer),
+        ("bart", BartBackbone),
+    )
+    def test_preset_docstring(self, cls):
+        """Check we did our docstring formatting correctly."""
+        for name in cls.presets:
+            self.assertRegex(cls.from_preset.__doc__, name)
+
+    @parameterized.named_parameters(
+        ("bart_tokenizer", BartTokenizer),
+        ("bart", BartBackbone),
+    )
+    def test_unknown_preset_error(self, cls):
+        # Not a preset name
+        with self.assertRaises(ValueError):
+            cls.from_preset("bart_base_en_clowntown")
+
+
+@pytest.mark.extra_large
+class BartPresetFullTest(tf.test.TestCase, parameterized.TestCase):
+    """
+    Test the full enumeration of our preset.
+
+    This tests every BART preset and is only run manually.
+    Run with:
+    `pytest keras_nlp/models/bart/bart_presets_test.py --run_extra_large`
+    """
+
+    @parameterized.named_parameters(
+        ("preset_weights", True), ("random_weights", False)
+    )
+    def test_load_bart(self, load_weights):
+        for preset in BartBackbone.presets:
+            model = BartBackbone.from_preset(preset, load_weights=load_weights)
+            input_data = {
+                "encoder_token_ids": tf.random.uniform(
+                    shape=(1, 1024),
+                    dtype=tf.int64,
+                    maxval=model.vocabulary_size,
+                ),
+                "encoder_padding_mask": tf.constant(
+                    [1] * 768 + [0] * 256, shape=(1, 1024)
+                ),
+                "decoder_token_ids": tf.random.uniform(
+                    shape=(1, 1024),
+                    dtype=tf.int64,
+                    maxval=model.vocabulary_size,
+                ),
+                "decoder_padding_mask": tf.constant(
+                    [1] * 489 + [0] * 535, shape=(1, 1024)
+                ),
+            }
+            model(input_data)
+
+    def test_load_tokenizers(self):
+        for preset in BartTokenizer.presets:
+            tokenizer = BartTokenizer.from_preset(preset)
+            tokenizer("The quick brown fox.")
diff --git a/keras_nlp/models/bart/bart_tokenizer_test.py b/keras_nlp/models/bart/bart_tokenizer_test.py
new file mode 100644
index 00000000..cd8fd624
--- /dev/null
+++ b/keras_nlp/models/bart/bart_tokenizer_test.py
@@ -0,0 +1,86 @@
+# 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.
+
+"""Tests for BART tokenizer."""
+
+import os
+
+import tensorflow as tf
+from absl.testing import parameterized
+from tensorflow import keras
+
+from keras_nlp.models.bart.bart_tokenizer import BartTokenizer
+
+
+class BartTokenizerTest(tf.test.TestCase, parameterized.TestCase):
+    def setUp(self):
+        vocab = {
+            "
": 0,
+            "<pad>": 1,
+            "
": 2,
+            "Ġair": 3,
+            "plane": 4,
+            "Ġat": 5,
+            "port": 6,
+            "Ġkoh": 7,
+            "li": 8,
+            "Ġis": 9,
+            "Ġthe": 10,
+            "Ġbest": 11,
+        }
+
+        merges = ["Ġ a", "Ġ t", "Ġ k", "Ġ i", "Ġ b", "Ġa i", "p l", "n e"]
+        merges += ["Ġa t", "p o", "r t", "o h", "l i", "Ġi s", "Ġb e", "s t"]
+        merges += ["Ġt h", "Ġai r", "pl a", "Ġk oh", "Ġth e", "Ġbe st", "po rt"]
+        merges += ["pla ne"]
+
+        self.tokenizer = BartTokenizer(vocabulary=vocab, merges=merges)
+
+    def test_tokenize(self):
+        input_data = " airplane at airport"
+        output = self.tokenizer(input_data)
+        self.assertAllEqual(output, [3, 4, 5, 3, 6])
+
+    def test_tokenize_batch(self):
+        input_data = tf.constant([" airplane at airport", " kohli is the best"])
+        output = self.tokenizer(input_data)
+        self.assertAllEqual(output, [[3, 4, 5, 3, 6], [7, 8, 9, 10, 11]])
+
+    def test_detokenize(self):
+        input_tokens = [[3, 4, 5, 3, 6]]
+        output = self.tokenizer.detokenize(input_tokens)
+        self.assertAllEqual(output, [" airplane at airport"])
+
+    def test_vocabulary_size(self):
+        self.assertEqual(self.tokenizer.vocabulary_size(), 12)
+
+    @parameterized.named_parameters(
+        ("tf_format", "tf", "model"),
+        ("keras_format", "keras_v3", "model.keras"),
+    )
+    def test_saved_model(self, save_format, filename):
+        input_data = tf.constant([" airplane at airport"])
+
+        inputs = keras.Input(dtype="string", shape=())
+        outputs = self.tokenizer(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)
+        self.assertAllEqual(
+            model(input_data),
+            restored_model(input_data),
+        )
diff --git a/keras_nlp/models/deberta_v3/deberta_v3_classifier_test.py b/keras_nlp/models/deberta_v3/deberta_v3_classifier_test.py
index 1b57b8aa..9266a901 100644
--- a/keras_nlp/models/deberta_v3/deberta_v3_classifier_test.py
+++ b/keras_nlp/models/deberta_v3/deberta_v3_classifier_test.py
@@ -106,6 +106,9 @@ class DebertaV3ClassifierTest(tf.test.TestCase, parameterized.TestCase):
         self.classifier_no_preprocessing.compile(jit_compile=jit_compile)
         self.classifier_no_preprocessing.predict(self.preprocessed_batch)
 
+    def test_debertav3_classifier_fit_default_compile(self):
+        self.classifier.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("jit_compile_false", False), ("jit_compile_true", True)
     )
diff --git a/keras_nlp/models/distil_bert/distil_bert_classifier_test.py b/keras_nlp/models/distil_bert/distil_bert_classifier_test.py
index e0d9f89c..a9a0fc06 100644
--- a/keras_nlp/models/distil_bert/distil_bert_classifier_test.py
+++ b/keras_nlp/models/distil_bert/distil_bert_classifier_test.py
@@ -109,6 +109,9 @@ class DistilBertClassifierTest(tf.test.TestCase, parameterized.TestCase):
         )
         self.classifier_no_preprocessing.fit(self.preprocessed_dataset)
 
+    def test_distilbert_classifier_fit_default_compile(self):
+        self.classifier.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("tf_format", "tf", "model"),
         ("keras_format", "keras_v3", "model.keras"),
diff --git a/keras_nlp/models/f_net/f_net_classifier_test.py b/keras_nlp/models/f_net/f_net_classifier_test.py
index cf4120fe..7593b809 100644
--- a/keras_nlp/models/f_net/f_net_classifier_test.py
+++ b/keras_nlp/models/f_net/f_net_classifier_test.py
@@ -107,6 +107,9 @@ class FNetClassifierTest(tf.test.TestCase, parameterized.TestCase):
         self.classifier_no_preprocessing.compile(jit_compile=jit_compile)
         self.classifier_no_preprocessing.predict(self.preprocessed_batch)
 
+    def test_fnet_classifier_fit_default_compile(self):
+        self.classifier.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("jit_compile_false", False), ("jit_compile_true", True)
     )
diff --git a/keras_nlp/models/xlm_roberta/xlm_roberta_classifier_test.py b/keras_nlp/models/xlm_roberta/xlm_roberta_classifier_test.py
index b1e7ea92..c8efb4fb 100644
--- a/keras_nlp/models/xlm_roberta/xlm_roberta_classifier_test.py
+++ b/keras_nlp/models/xlm_roberta/xlm_roberta_classifier_test.py
@@ -122,6 +122,9 @@ class XLMRobertaClassifierTest(tf.test.TestCase, parameterized.TestCase):
         )
         self.classifier_no_preprocessing.fit(self.preprocessed_dataset)
 
+    def test_xlmroberta_classifier_fit_default_compile(self):
+        self.classifier.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("tf_format", "tf", "model"),
         ("keras_format", "keras_v3", "model.keras"),
diff --git a/keras_nlp/tokenizers/byte_pair_tokenizer_test.py b/keras_nlp/tokenizers/byte_pair_tokenizer_test.py
index 56c2e136..d3cd6eb8 100644
--- a/keras_nlp/tokenizers/byte_pair_tokenizer_test.py
+++ b/keras_nlp/tokenizers/byte_pair_tokenizer_test.py
@@ -66,6 +66,24 @@ class BytePairTokenizerTest(tf.test.TestCase, parameterized.TestCase):
         )
         self.assertAllEqual(call_output, expected)
 
+    def test_tokenize_prefix_space(self):
+        input_data = ["brown.", "black."]
+        tokenizer = BytePairTokenizer(
+            vocabulary=VOCAB_PATH,
+            merges=MERGE_PATH,
+            dtype=tf.string,
+            add_prefix_space=True,
+        )
+        call_output = tokenizer(input_data)
+
+        expected = tf.ragged.constant(
+            [
+                ["Ġbrown", "."],
+                ["Ġblack", "."],
+            ]
+        )
+        self.assertAllEqual(call_output, expected)
+
     def test_tokenize_scalar_input(self):
         input_data = "brown."
         encoded = self.tokenizer.tokenize(input_data)