case class MaskedLanguageModelModule(mlp: MaskedLanguageModelModule.MLP) extends GenericModule[(Variable, STen), Variable] with Product with Serializable
Masked Language Model Input of (embedding, positions) Embedding of size (batch, num tokens, embedding dim) Positions of size (batch, max num tokens) long tensor indicating which positions to make predictions on Output (batch, len(Positions), vocabulary size)
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- new MaskedLanguageModelModule(mlp: MaskedLanguageModelModule.MLP)
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- def apply[S](a: (Variable, STen))(implicit arg0: Sc[S]): Variable
Alias of forward
Alias of forward
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The implementation of the function.
The implementation of the function.
In addition of
xit can also use all thestate to compute its value.- Definition Classes
- MaskedLanguageModelModule → GenericModule
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- final def gradients(loss: Variable, zeroGrad: Boolean = true): Seq[Option[STen]]
Computes the gradient of loss with respect to the parameters.
Computes the gradient of loss with respect to the parameters.
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- final def learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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- val mlp: MaskedLanguageModelModule.MLP
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Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
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- def state: Seq[(Constant, PTag)]
List of optimizable, or non-optimizable, but stateful parameters
List of optimizable, or non-optimizable, but stateful parameters
Stateful means that the state is carried over the repeated forward calls.
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- MaskedLanguageModelModule → GenericModule
- final def synchronized[T0](arg0: => T0): T0
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(Since version 9)