colbert_scores

Computes the ColBERT scores between queries and documents embeddings. The score is computed as the sum of maximum similarities between the query and the document.

Parameters

• queries_embeddings (list | numpy.ndarray | torch.Tensor)

  The first tensor. The queries embeddings. Shape: (batch_size, num tokens queries, embedding_size)

• documents_embeddings (list | numpy.ndarray | torch.Tensor)

  The second tensor. The documents embeddings. Shape: (batch_size, num tokens documents, embedding_size)

• mask (torch.Tensor) – defaults to None

Examples

>>> import torch

>>> queries_embeddings = torch.tensor([
...     [[1.], [0.], [0.], [0.]],
...     [[0.], [2.], [0.], [0.]],
...     [[0.], [0.], [3.], [0.]],
... ])

>>> documents_embeddings = torch.tensor([
...     [[10.], [0.], [1.]],
...     [[0.], [100.], [1.]],
...     [[1.], [0.], [1000.]],
... ])

>>> scores = colbert_scores(
...     queries_embeddings=queries_embeddings,
...     documents_embeddings=documents_embeddings
... )

>>> scores
tensor([[  10.,  100., 1000.],
        [  20.,  200., 2000.],
        [  30.,  300., 3000.]])