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keras/
xlm_roberta_base_multi

Name: xlm_roberta_base_multi
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Model Overview

An XLM-RoBERTa encoder network.

This class implements a bi-directional Transformer-based encoder as described in "Unsupervised Cross-lingual Representation Learning at Scale". It includes the embedding lookups and transformer layers, but it does not include the masked language modeling head used during pretraining.

The default constructor gives a fully customizable, randomly initialized RoBERTa encoder with any number of layers, heads, and embedding dimensions. To load preset architectures and weights, use the from_preset() constructor.

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.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

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.

Arguments

vocabulary_size: int. The size of the token vocabulary.
num_layers: int. The number of transformer 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 size of the transformer encoding layer.
intermediate_dim: int. The output dimension of the first Dense layer in a two-layer feedforward network for each transformer.
dropout: float. Dropout probability for the Transformer encoder.
max_sequence_length: int. The maximum sequence length this encoder can consume. The sequence length of the input must be less than max_sequence_length default value. This determines the variable shape for positional embeddings.

Example Usage

python

import keras
import keras_hub
import numpy as np

Raw string data.

python

features = ["The quick brown fox jumped.", "نسيت الواجب"]
labels = [0, 3]

# Pretrained classifier.
classifier = keras_hub.models.XLMRobertaClassifier.from_preset(
    "xlm_roberta_base_multi",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, batch_size=2)

# Re-compile (e.g., with a new learning rate).
classifier.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`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)

Preprocessed integer data.

python

features = {
    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

# Pretrained classifier without preprocessing.
classifier = keras_hub.models.XLMRobertaClassifier.from_preset(
    "xlm_roberta_base_multi",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

Example Usage with Hugging Face URI

python

import keras
import keras_hub
import numpy as np

Raw string data.

python

features = ["The quick brown fox jumped.", "نسيت الواجب"]
labels = [0, 3]

# Pretrained classifier.
classifier = keras_hub.models.XLMRobertaClassifier.from_preset(
    "hf://keras/xlm_roberta_base_multi",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, batch_size=2)

# Re-compile (e.g., with a new learning rate).
classifier.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`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)

Preprocessed integer data.

python

features = {
    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

# Pretrained classifier without preprocessing.
classifier = keras_hub.models.XLMRobertaClassifier.from_preset(
    "hf://keras/xlm_roberta_base_multi",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

keras/xlm_roberta_base_multi

Model Overview

Links

Installation

Presets

Example Usage

Example Usage with Hugging Face URI

keras/
xlm_roberta_base_multi