medcat.utils.relation_extraction.models

Module Contents

Classes

BertModel_RelationExtraction

BertModel class for RelCAT
class medcat.utils.relation_extraction.models.BertModel_RelationExtraction(pretrained_model_name_or_path, relcat_config, model_config)

Bases: torch.nn.Module

BertModel class for RelCAT

Parameters:

  • pretrained_model_name_or_path (str) –

  • relcat_config (medcat.config_rel_cat.ConfigRelCAT) –

  • model_config (transformers.models.bert.configuration_bert.BertConfig) –

name = 'bertmodel_relcat'
log
__init__(pretrained_model_name_or_path, relcat_config, model_config)

Class to hold the BERT model + model_config

Parameters:

  • pretrained_model_name_or_path (str) – 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.

  • relcat_config (ConfigRelCAT) – relcat config.

  • model_config (BertConfig) – HF bert config for model.

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.

Parameters:

  • sequence_output (torch.Tensor) – hidden states/embeddings for each token in the input text

  • input_ids (torch.Tensor) – input token ids

  • special_tag (List) – special annotation token id pairs

Returns:

torch.Tensor – new seq_tags

Return type:

torch.Tensor

output2logits(pooled_output, sequence_output, input_ids, e1_e2_start)
Parameters:

  • pooled_output (torch.Tensor) – embedding of the CLS token

  • sequence_output (torch.Tensor) – hidden states/embeddings for each token in the input text

  • input_ids (torch.Tensor) – input token ids.

  • e1_e2_start (torch.Tensor) – annotation tags token position

Returns:

torch.Tensor – classification probabilities for each token.

Return type:

torch.Tensor

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)
Parameters:

  • input_ids (Optional[torch.Tensor]) –

  • attention_mask (Optional[torch.Tensor]) –

  • token_type_ids (Optional[torch.Tensor]) –

  • position_ids (Any) –

  • head_mask (Any) –

  • encoder_hidden_states (Any) –

  • encoder_attention_mask (Any) –

  • Q (Any) –

  • e1_e2_start (Any) –

  • pooled_output (Any) –

Return type:

Tuple[torch.Tensor, torch.Tensor]