:py:mod:`medcat.utils.relation_extraction.models`
=================================================

.. py:module:: medcat.utils.relation_extraction.models


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

Classes
~~~~~~~

.. autoapisummary::

   medcat.utils.relation_extraction.models.BertModel_RelationExtraction




.. py:class:: BertModel_RelationExtraction(pretrained_model_name_or_path, relcat_config, model_config)


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

   BertModel class for RelCAT


   .. py:attribute:: name
      :value: 'bertmodel_relcat'

      

   .. py:attribute:: log

      

   .. py:method:: __init__(pretrained_model_name_or_path, relcat_config, model_config)

      Class to hold the BERT model + model_config

      :param pretrained_model_name_or_path: path to load the model from,
                                            this can be a HF model i.e: "bert-base-uncased", if left empty, it is normally assumed that a model is loaded from 'model.dat'
                                            using the RelCAT.load() method. So if you are initializing/training a model from scratch be sure to base it on some model.
      :type pretrained_model_name_or_path: str
      :param relcat_config: relcat config.
      :type relcat_config: ConfigRelCAT
      :param model_config: HF bert config for model.
      :type model_config: BertConfig


   .. py:method:: get_annotation_schema_tag(sequence_output, input_ids, special_tag)

      Gets to token sequences from the sequence_ouput for the specific token
          tag ids in self.relcat_config.general.annotation_schema_tag_ids.

      :param sequence_output: hidden states/embeddings for each token in the input text
      :type sequence_output: torch.Tensor
      :param input_ids: input token ids
      :type input_ids: torch.Tensor
      :param special_tag: special annotation token id pairs
      :type special_tag: List

      :Returns: **torch.Tensor** -- new seq_tags


   .. py:method:: output2logits(pooled_output, sequence_output, input_ids, e1_e2_start)

      :param pooled_output: embedding of the CLS token
      :type pooled_output: torch.Tensor
      :param sequence_output: hidden states/embeddings for each token in the input text
      :type sequence_output: torch.Tensor
      :param input_ids: input token ids.
      :type input_ids: torch.Tensor
      :param e1_e2_start: annotation tags token position
      :type e1_e2_start: torch.Tensor

      :Returns: **torch.Tensor** -- classification probabilities for each token.


   .. py:method:: forward(input_ids = None, attention_mask = None, token_type_ids = None, position_ids = None, head_mask = None, encoder_hidden_states = None, encoder_attention_mask = None, Q = None, e1_e2_start = None, pooled_output = None)