Alignment
- XPM Configxpmir.letor.trainers.alignment.AlignmentLoss(*, weight)[source]
Bases:
Config,ABC,Generic[AlignementLossInput,AlignmentLossTarget]Submit type:
xpmir.letor.trainers.alignment.AlignmentLoss- weight: float = 1.0
Weight for this loss
- XPM Configxpmir.letor.trainers.alignment.AlignmentTrainer(*, hooks, model, batcher, sampler, batch_size, losses, target_model)[source]
Bases:
LossTrainerSubmit type:
xpmir.letor.trainers.alignment.AlignmentTrainerCompares two representations
Both the representations are expected to a be in a vector space
- hooks: List[xpmir.learning.context.TrainingHook] = []
Hooks for this trainer: this includes the losses, but can be adapted for other uses The specific list of hooks depends on the specific trainer
- model: xpmir.learning.optim.Module
If the model to optimize is different from the model passsed to Learn, this parameter can be used – initialization is still expected to be done at the learner level
- batcher: xpmir.learning.batchers.Batcher = xpmir.learning.batchers.Batcher.XPMValue()
How to batch samples together
- sampler: xpmir.learning.base.BaseSampler
The pairwise sampler
- batch_size: int = 16
Number of samples per batch
- losses: Dict[str, xpmir.letor.trainers.alignment.AlignmentLoss]
The loss function(s)
- target_model: xpmir.learning.optim.Module
Target model
- XPM Configxpmir.letor.trainers.alignment.MSEAlignmentLoss(*, weight)[source]
Bases:
AlignmentLoss[RepresentationOutput,RepresentationOutput]Submit type:
xpmir.letor.trainers.alignment.MSEAlignmentLossComputes the MSE between contextualized query representation and gold representation
- weight: float = 1.0
Weight for this loss