ApproxLossFunction

class lightning_ir.loss.approximate.ApproxLossFunction(temperature: float = 1)[source]

Bases: ListwiseLossFunction

Base class for approximate loss functions that compute ranks from scores.

__init__(temperature: float = 1) → None[source]

Initialize the ApproxLossFunction.

Parameters:

temperature (float) – Temperature parameter for scaling the scores. Defaults to 1.

Methods

__init__([temperature])

Initialize the ApproxLossFunction.

get_approx_ranks(scores, temperature)

Compute approximate ranks from scores.
abstract compute_loss(output: LightningIROutput, batch: TrainBatch) → torch.Tensor

Compute the loss based on the scores and targets in the output and batch.

Parameters:

  • output (LightningIROutput) – The output from the model containing scores.

  • batch (TrainBatch) – The training batch containing targets.

Returns:

The computed loss.

Return type:

torch.Tensor

static get_approx_ranks(scores: Tensor, temperature: float) → Tensor[source]

Compute approximate ranks from scores.

Parameters:

  • scores (torch.Tensor) – The input scores.

  • temperature (float) – Temperature parameter for scaling the scores.

Returns:

The computed approximate ranks.

Return type:

torch.Tensor

process_scores(output: LightningIROutput) → torch.Tensor

Process the scores from the output.

Parameters:

output (LightningIROutput) – The output from the model.

Returns:

The scores tensor.

Return type:

torch.Tensor

process_targets(scores: torch.Tensor, batch: TrainBatch) → torch.Tensor

Process the targets from the batch.

Parameters:

  • scores (torch.Tensor) – The scores tensor.

  • batch (TrainBatch) – The training batch.

Returns:

The processed targets tensor.

Return type:

torch.Tensor