capreolus.reranker.TFKNRM
¶
Module Contents¶
Classes¶
TFKNRM_Class (extractor, config, **kwargs) |
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TFKNRM (config=None, provide=None, share_dependency_objects=False, build=True) |
TensorFlow implementation of KNRM. |
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class
capreolus.reranker.TFKNRM.
TFKNRM_Class
(extractor, config, **kwargs)[source]¶ Bases:
tensorflow.keras.Model
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call
(self, x, **kwargs)[source]¶ During training, both posdoc and negdoc are passed During eval, both posdoc and negdoc are passed but negdoc would be a zero tensor Whether negdoc is a legit doc tensor or a dummy zero tensor is determined by which sampler is used (eg: sampler.TrainDataset) as well as the extractor (eg: EmbedText)
Unlike the pytorch KNRM model, KNRMTF accepts both the positive and negative document in its forward pass. It scores them separately and returns the score difference (i.e posdoc_score - negdoc_score).
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class
capreolus.reranker.TFKNRM.
TFKNRM
(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶ Bases:
capreolus.reranker.Reranker
TensorFlow implementation of KNRM.
Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017. End-to-End Neural Ad-hoc Ranking with Kernel Pooling. In SIGIR‘17.