capreolus.reranker
¶
Submodules¶
capreolus.reranker.CDSSM
capreolus.reranker.CEDRKNRM
capreolus.reranker.ConvKNRM
capreolus.reranker.DRMM
capreolus.reranker.DRMMTKS
capreolus.reranker.DSSM
capreolus.reranker.DUET
capreolus.reranker.DeepTileBar
capreolus.reranker.HINT
capreolus.reranker.KNRM
capreolus.reranker.PACRR
capreolus.reranker.POSITDRMM
capreolus.reranker.TFBERTMaxP
capreolus.reranker.TFCEDRKNRM
capreolus.reranker.TFKNRM
capreolus.reranker.TFVanillaBert
capreolus.reranker.TK
capreolus.reranker.birch
capreolus.reranker.common
capreolus.reranker.parade
capreolus.reranker.ptBERTMaxP
capreolus.reranker.ptparade
Package Contents¶
Classes¶
Base class for Reranker modules. The purpose of a Reranker is to predict relevance scores for input documents. Rerankers are generally supervised methods implemented in PyTorch or TensorFlow. |
- class capreolus.reranker.Reranker(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶
Bases:
capreolus.ModuleBase
Base class for Reranker modules. The purpose of a Reranker is to predict relevance scores for input documents. Rerankers are generally supervised methods implemented in PyTorch or TensorFlow.
- Modules should provide:
a
build_model
method that initializes the model useda
score
and atest
method that take a representation created by anExtractor
module as input and return document scoresa
load_weights
and asave_weights
method, if the base class’ PyTorch methods cannot be used