logitorch.models.ruletaker
==========================

.. py:module:: logitorch.models.ruletaker


Classes
-------

.. autoapisummary::

   logitorch.models.ruletaker.RuleTaker


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

.. py:class:: RuleTaker(num_labels: int = 2)

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


   RuleTaker is a PyTorch module for rule-based question answering using the Roberta model.

   Args:
       num_labels (int): The number of labels for classification. Default is 2.

   Attributes:
       num_labels (int): The number of labels for classification.
       encoder (RobertaForMultipleChoice): The Roberta model for multiple choice tasks.
       config (RobertaConfig): The configuration of the Roberta model.
       classifier (RobertaClassificationHead): The classification head of the Roberta model.
       tokenizer (RobertaTokenizer): The tokenizer for the Roberta model.

   Methods:
       forward(x, y=None): Performs forward pass of the RuleTaker model.
       predict(context, question, device="cpu"): Predicts the answer label for a given context and question.


   Initializes a RuleTaker instance.

   Args:
       num_labels (int): The number of labels for classification. Default is 2.



   .. py:method:: forward(x, y=None)

      Performs forward pass of the RuleTaker model.

      Args:
          x (dict): The input dictionary containing the context and question.
          y (Tensor): The target labels. Default is None.

      Returns:
          outputs (tuple): A tuple containing the logits and other outputs.




   .. py:method:: predict(context: str, question: str, device: str = 'cpu') -> int

      Predicts the answer label for a given context and question.

      Args:
          context (str): The context for the question.
          question (str): The question to be answered.
          device (str): The device to run the prediction on. Default is "cpu".

      Returns:
          pred (int): The predicted answer label.




   .. py:attribute:: classifier


   .. py:attribute:: config


   .. py:attribute:: encoder


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



   .. py:attribute:: tokenizer