XLMRobertaMaskedLM
classkeras_nlp.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
keras_nlp.models.XLMRobertaBackbone
instance.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.
# 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 programmatically (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": 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_nlp.models.XLMRobertaMaskedLM.from_preset(
"xlm_roberta_base_multi",
preprocessor=None,
)
masked_lm.fit(x=features, y=labels, batch_size=2)
from_preset
methodXLMRobertaMaskedLM.from_preset()
Instantiate XLMRobertaMaskedLM model from preset architecture and weights.
Arguments
True
.Examples
# Load architecture and weights from preset
model = XLMRobertaMaskedLM.from_preset("xlm_roberta_base_multi")
# Load randomly initialized model from preset architecture
model = XLMRobertaMaskedLM.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. |
backbone
propertykeras_nlp.models.XLMRobertaMaskedLM.backbone
A keras.Model
instance providing the backbone sub-model.
preprocessor
propertykeras_nlp.models.XLMRobertaMaskedLM.preprocessor
A keras.layers.Layer
instance used to preprocess inputs.