logitorch.pl_models.prover
==========================

.. py:module:: logitorch.pl_models.prover


Classes
-------

.. autoapisummary::

   logitorch.pl_models.prover.PLPRover


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

.. py:class:: PLPRover(pretrained_model: str = 'roberta-base', learning_rate: float = 1e-05, weight_decay: float = 0.1, num_labels: int = 2)

   Bases: :py:obj:`lightning.pytorch.LightningModule`


   PyTorch Lightning module for the PRover model.

   Args:
       pretrained_model (str): Name of the pretrained model to use. Default is "roberta-base".
       learning_rate (float): Learning rate for the optimizer. Default is 1e-5.
       weight_decay (float): Weight decay for the optimizer. Default is 0.1.
       num_labels (int): Number of labels for the model. Default is 2.


   .. py:method:: configure_optimizers()

      Configure the optimizer and scheduler for training.

      Returns:
          Tuple of optimizer and scheduler.



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

      Forward pass of the PLPRover model.

      Args:
          x: Input data.
          proof_offsets: Proof offsets.
          node_labels: Node labels.
          edge_labels: Edge labels.
          qa_labels: QA labels.
          device (str): Device to use for computation. Default is "cpu".

      Returns:
          Output of the model.



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

      Make predictions using the PLPRover model.

      Args:
          triples: Triples data.
          rules: Rules data.
          question: Question data.
          device (str): Device to use for computation. Default is "cpu".

      Returns:
          Predicted output.



   .. py:method:: training_step(train_batch: Tuple[Dict[str, torch.Tensor], torch.Tensor], batch_idx: int) -> torch.Tensor

      Training step for the PLPRover model.

      Args:
          train_batch: Batch of training data.
          batch_idx: Index of the batch.

      Returns:
          Loss value.



   .. py:method:: validation_step(val_batch: Tuple[Dict[str, torch.Tensor], torch.Tensor], batch_idx: int) -> None

      Validation step for the PLPRover model.

      Args:
          val_batch: Batch of validation data.
          batch_idx: Index of the batch.



   .. py:attribute:: learning_rate
      :value: 1e-05



   .. py:attribute:: model


   .. py:attribute:: pretrained_model
      :value: 'roberta-base'



   .. py:attribute:: weight_decay
      :value: 0.1