FNetMaskedLM
classkeras_nlp.models.FNetMaskedLM(backbone, preprocessor=None, **kwargs)
An end-to-end FNet model for the masked language modeling task.
This model will train FNet 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()
constructor.
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.
Arguments
keras_hub.models.FNetBackbone
instance.keras_hub.models.FNetMaskedLMPreprocessor
or
None
. If None
, this model will not apply preprocessing, and
inputs should be preprocessed before calling the model.Examples
Raw string data.
features = ["The quick brown fox jumped.", "I forgot my homework."]
# Pretrained language model.
masked_lm = keras_hub.models.FNetMaskedLM.from_preset(
"f_net_base_en",
)
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.
# Create a preprocessed dataset where 0 is the mask token.
features = {
"token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
"segment_ids": np.array([[0, 0, 0, 1, 1, 1, 0, 0]] * 2),
"mask_positions": np.array([[2, 4]] * 2)
}
# Labels are the original masked values.
labels = [[3, 5]] * 2
masked_lm = keras_hub.models.FNetMaskedLM.from_preset(
"f_net_base_en",
preprocessor=None,
)
masked_lm.fit(x=features, y=labels, batch_size=2)
from_preset
methodFNetMaskedLM.from_preset(preset, load_weights=True, **kwargs)
Instantiate a keras_hub.models.Task
from a model preset.
A preset is a directory of configs, weights and other file assets used
to save and load a pre-trained model. The preset
can be passed as
one of:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./bert_base_en'
For any Task
subclass, you can run cls.presets.keys()
to list all
built-in presets available on the class.
This constructor can be called in one of two ways. Either from a task
specific base class like keras_hub.models.CausalLM.from_preset()
, or
from a model class like keras_hub.models.BertTextClassifier.from_preset()
.
If calling from the a base class, the subclass of the returning object
will be inferred from the config in the preset directory.
Arguments
True
, saved weights will be loaded into
the model architecture. If False
, all weights will be
randomly initialized.Examples
# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
"gemma_2b_en",
)
# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
"bert_base_en",
num_classes=2,
)
Preset name | Parameters | Description |
---|---|---|
f_net_base_en | 82.86M | 12-layer FNet model where case is maintained. Trained on the C4 dataset. |
f_net_large_en | 236.95M | 24-layer FNet model where case is maintained. Trained on the C4 dataset. |
backbone
propertykeras_nlp.models.FNetMaskedLM.backbone
A keras_hub.models.Backbone
model with the core architecture.
preprocessor
propertykeras_nlp.models.FNetMaskedLM.preprocessor
A keras_hub.models.Preprocessor
layer used to preprocess input.