:mod:`capreolus.reranker.TFKNRM`
================================

.. py:module:: capreolus.reranker.TFKNRM


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   capreolus.reranker.TFKNRM.TFKNRM_Class
   capreolus.reranker.TFKNRM.TFKNRM



.. py:class:: TFKNRM_Class(extractor, config, **kwargs)

   Bases: :class:`tensorflow.keras.Model`

   .. method:: get_score(self, doc_tok, query_tok, query_idf)


   .. method:: call(self, x, **kwargs)

      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).



.. py:class:: TFKNRM

   Bases: :class:`capreolus.reranker.Reranker`

   .. attribute:: module_name
      :annotation: = TFKNRM

      

   .. attribute:: dependencies
      

      

   .. attribute:: config_spec
      

      

   .. method:: build_model(self)