Keras 3 API documentation / KerasNLP / Models / XLMRoberta / XLMRobertaBackbone model

XLMRobertaBackbone model

[source]

XLMRobertaBackbone class

keras_nlp.models.XLMRobertaBackbone(
    vocabulary_size,
    num_layers,
    num_heads,
    hidden_dim,
    intermediate_dim,
    dropout=0.1,
    max_sequence_length=512,
    dtype=None,
    **kwargs
)

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.

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.
  • dtype: string or keras.mixed_precision.DTypePolicy. The dtype to use for model computations and weights. Note that some computations, such as softmax and layer normalization, will always be done at float32 precision regardless of dtype.

Examples

input_data = {
    "token_ids": np.ones(shape=(1, 12), dtype="int32"),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained XLM-R encoder.
model = keras_nlp.models.XLMRobertaBackbone.from_preset(
    "xlm_roberta_base_multi",
)
model(input_data)

# Randomly initialized XLM-R model with custom config.
model = keras_nlp.models.XLMRobertaBackbone(
    vocabulary_size=250002,
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    intermediate_dim=512,
    max_sequence_length=128
)
model(input_data)

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from_preset method

XLMRobertaBackbone.from_preset()

Instantiate XLMRobertaBackbone model from preset architecture and weights.

Arguments

  • preset: string. Must be one of "xlm_roberta_base_multi", "xlm_roberta_large_multi".
  • load_weights: Whether to load pre-trained weights into model. Defaults to True.

Examples

# Load architecture and weights from preset
model = keras_nlp.models.XLMRobertaBackbone.from_preset(
    "xlm_roberta_base_multi"
)

# Load randomly initialized model from preset architecture
model = keras_nlp.models.XLMRobertaBackbone.from_preset(
    "xlm_roberta_base_multi",
    load_weights=False
)
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.

token_embedding property

keras_nlp.models.XLMRobertaBackbone.token_embedding

A keras.layers.Embedding instance for embedding token ids.

This layer embeds integer token ids to the hidden dim of the model.