capreolus.reranker.TK
¶
Module Contents¶
Classes¶
Adapted from https://github.com/sebastian-hofstaetter/transformer-kernel-ranking/blob/master/matchmaker/models/tk.py |
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Sebastian Hofstätter, Markus Zlabinger, and Allan Hanbury. 2019. TU Wien @ TREC Deep Learning ‘19 – Simple Contextualization for Re-ranking. In TREC ‘19. |
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class
capreolus.reranker.TK.
PositionalEncoding
(d_model, dropout=0.1, max_len=5000)[source]¶ Bases:
torch.nn.Module
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class
capreolus.reranker.TK.
TK_class
(extractor, config)[source]¶ Bases:
torch.nn.Module
Adapted from https://github.com/sebastian-hofstaetter/transformer-kernel-ranking/blob/master/matchmaker/models/tk.py TK is a neural IR model - a fusion between transformer contextualization & kernel-based scoring -> uses 1 transformer block to contextualize embeddings -> soft-histogram kernels to score interactions
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get_mu_matrix
(self, extractor)[source]¶ Returns a matrix of mu values that can be directly subtracted from the cosine matrix. This is the matrix mu in equation 5 in the paper (https://arxiv.org/pdf/2002.01854.pdf)
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get_mask
(self, embedding)[source]¶ Gets a mask of shape (seq_len, seq_len). This is an additive mask, hence masked elements should be -inf
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class
capreolus.reranker.TK.
TK
(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶ Bases:
capreolus.reranker.Reranker
Sebastian Hofstätter, Markus Zlabinger, and Allan Hanbury. 2019. TU Wien @ TREC Deep Learning ‘19 – Simple Contextualization for Re-ranking. In TREC ‘19.