sparse_scores

Computes dot product between anchor, positive and negative activations.

Parameters

• anchor_activations (torch.Tensor)

  Activations of the anchors.

• positive_activations (torch.Tensor)

  Activations of the positive documents.

• negative_activations (torch.Tensor)

  Activations of the negative documents.

• in_batch_negatives (bool) – defaults to False

  Whether to use in batch negatives or not. Defaults to True. Sum up with negative scores the dot product.

Examples

>>> from neural_cherche import models

>>> model = models.Splade(
...     model_name_or_path="raphaelsty/neural-cherche-sparse-embed",
...     device="mps"
... )

>>> anchor_activations = model(
...     ["Sports", "Music"],
...     query_mode=True,
... )

>>> positive_activations = model(
...    ["Sports", "Music"],
...    query_mode=False,
... )

>>> negative_activations = model(
...    ["Cinema", "Movie"],
...    query_mode=False,
... )

>>> sparse_scores(
...     anchor_activations=anchor_activations["sparse_activations"],
...     positive_activations=positive_activations["sparse_activations"],
...     negative_activations=negative_activations["sparse_activations"],
... )
{'positive_scores': tensor([470.4049, 435.0986], device='mps:0', grad_fn=<SumBackward1>), 'negative_scores': tensor([301.5698, 353.6218], device='mps:0', grad_fn=<SumBackward1>)}