logitorch.models.prover
=======================

.. py:module:: logitorch.models.prover


Classes
-------

.. autoapisummary::

   logitorch.models.prover.PRover


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

.. py:class:: PRover(pretrained_roberta_model: str, num_labels: int = 2)

   Bases: :py:obj:`torch.nn.Module`


   
   Initializes the PRover model.

   Args:
       pretrained_roberta_model: The path or name of the pretrained RoBERTa model.
       num_labels: The number of labels for classification.


   .. py:method:: forward(x, proof_offsets=None, node_labels=None, edge_labels=None, qa_labels=None, max_node_length=None, max_edge_length=None, device: str = 'cpu')

      Forward pass of the PRover model.

      Args:
          x: The input tensor.
          proof_offsets: The proof offsets.
          node_labels: The node labels.
          edge_labels: The edge labels.
          qa_labels: The QA labels.
          max_node_length: The maximum node length.
          max_edge_length: The maximum edge length.
          device: The device to run the model on.

      Returns:
          The model outputs.



   .. py:method:: predict(triples, rules, question, device: str = 'cpu')

      Predicts the label for a given question.

      Args:
          triples: The triples.
          rules: The rules.
          question: The question.
          device: The device to run the model on.

      Returns:
          The predicted label.



   .. py:attribute:: classifier


   .. py:attribute:: classifier_edge


   .. py:attribute:: classifier_node


   .. py:attribute:: config


   .. py:attribute:: encoder


   .. py:attribute:: naf_layer


   .. py:attribute:: num_labels
      :value: 2



   .. py:attribute:: num_labels_edge
      :value: 2



   .. py:attribute:: proofwriter_collator