logitorch.pl_models.proofwriter
===============================

.. py:module:: logitorch.pl_models.proofwriter


Classes
-------

.. autoapisummary::

   logitorch.pl_models.proofwriter.PLProofWriter


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

.. py:class:: PLProofWriter(pretrained_model: str = 'google/t5-v1_1-large', learning_rate: float = None, weight_decay=0.1)

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


   
   Initializes a PLProofWriter object.

   Args:
       pretrained_model (str, optional): The name or path of the pretrained model to use. Defaults to "google/t5-v1_1-large".
       learning_rate (float, optional): The learning rate for the optimizer. Defaults to None.
       weight_decay (float, optional): The weight decay for the optimizer. Defaults to 0.1.


   .. py:method:: configure_optimizers()

      Configures the optimizer and scheduler for training.

      Returns:
          Tuple[List[Optimizer], List[Dict[str, Any]]]: The optimizer and scheduler.



   .. py:method:: forward(x, y) -> transformers.modeling_outputs.SequenceClassifierOutput

      Performs a forward pass of the model.

      Args:
          x: The input data.
          y: The target data.

      Returns:
          SequenceClassifierOutput: The output of the model.



   .. py:method:: predict(context: str, question: str, num_beams: int = 5, max_length: int = 120, device: str = 'cpu')

      Generates predictions for the given context and question.

      Args:
          context (str): The context for the prediction.
          question (str): The question for the prediction.
          num_beams (int, optional): The number of beams for beam search decoding. Defaults to 5.
          max_length (int, optional): The maximum length of the generated sequence. Defaults to 120.
          device (str, optional): The device to use for prediction. Defaults to "cpu".

      Returns:
          The generated predictions.



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

      Performs a training step.

      Args:
          train_batch (Tuple[Dict[str, torch.Tensor], torch.Tensor]): The batch of training data.
          batch_idx (int): The index of the batch.

      Returns:
          torch.Tensor: The loss value.



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

      Performs a validation step.

      Args:
          val_batch (Tuple[Dict[str, torch.Tensor], torch.Tensor]): The batch of validation data.
          batch_idx (int): The index of the batch.



   .. py:attribute:: learning_rate
      :value: None



   .. py:attribute:: model


   .. py:attribute:: pretrained_model
      :value: 'google/t5-v1_1-large'



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