medcat.utils.relation_extraction.ml_utils

Module Contents

Functions

split_list_train_test_by_class(data[, sample_limit, ...])

param data:

"output_relations": relation_instances, <-- see create_base_relations_from_doc/csv

load_bin_file(file_name[, path])

save_bin_file(file_name, data[, path])

save_state(model, optimizer, scheduler[, epoch, ...])

Used by RelCAT.save() and RelCAT.train()

load_state(model, optimizer, scheduler[, path, ...])

Used by RelCAT.load() and RelCAT.train()

save_results(data[, model_name, path, file_prefix])

load_results(path[, model_name, file_prefix])

create_tokenizer_pretrain(tokenizer, relcat_config)

This method simply adds the default special tokens that we ecounter.

create_dense_layers(relcat_config)

get_annotation_schema_tag(sequence_output, input_ids, ...)

Gets to token sequences from the sequence_ouput for the specific token

Attributes

logger

medcat.utils.relation_extraction.ml_utils.logger
medcat.utils.relation_extraction.ml_utils.split_list_train_test_by_class(data, sample_limit=-1, test_size=0.2, shuffle=True)
Parameters:

  • data (List) – “output_relations”: relation_instances, <– see create_base_relations_from_doc/csv for data columns

  • sample_limit (int) – limit the number of samples per class, useful for dataset balancing . Defaults to -1.

  • test_size (float) – Defaults to 0.2.

  • shuffle (bool) – shuffle data randomly. Defaults to True.

Returns:

Tuple[List, List] – train and test datasets

Return type:

Tuple[List, List]

medcat.utils.relation_extraction.ml_utils.load_bin_file(file_name, path='./')
Return type:

Any

medcat.utils.relation_extraction.ml_utils.save_bin_file(file_name, data, path='./')
medcat.utils.relation_extraction.ml_utils.save_state(model, optimizer, scheduler, epoch=1, best_f1=0.0, path='./', model_name='BERT', task='train', is_checkpoint=False, final_export=False)
Used by RelCAT.save() and RelCAT.train()

Saves the RelCAT model state. For checkpointing multiple files are created, best_f1, loss etc. score. If you want to export the model after training set final_export=True and leave is_checkpoint=False.

Parameters:

  • model (BaseModel_RelationExtraction) – BertModel_RelationExtraction | LlamaModel_RelationExtraction etc.

  • optimizer (torch.optim.AdamW, optional) – Defaults to None.

  • scheduler (torch.optim.lr_scheduler.MultiStepLR, optional) – Defaults to None.

  • epoch (int) – Defaults to None.

  • best_f1 (float) – Defaults to None.

  • path (str) – Defaults to “./”.

  • model_name (str) – . Defaults to “BERT”. This is used to checkpointing only.

  • task (str) – Defaults to “train”. This is used to checkpointing only.

  • is_checkpoint (bool) – Defaults to False.

  • final_export (bool) – Defaults to False, if True then is_checkpoint must be False also. Exports model.state_dict(), out into”model.dat”.

Return type:

None

medcat.utils.relation_extraction.ml_utils.load_state(model, optimizer, scheduler, path='./', model_name='BERT', file_prefix='train', load_best=False, relcat_config=ConfigRelCAT())

Used by RelCAT.load() and RelCAT.train()

Parameters:

  • model (BaseModel_RelationExtraction) – BaseModel_RelationExtraction, it has to be initialized before calling this method via (Bert/Llama)Model_RelationExtraction(…)

  • optimizer (_type_) – optimizer

  • scheduler (_type_) – scheduler

  • path (str, optional) – Defaults to “./”.

  • model_name (str, optional) – Defaults to “BERT”.

  • file_prefix (str, optional) – Defaults to “train”.

  • load_best (bool, optional) – Defaults to False.

  • relcat_config (ConfigRelCAT) – Defaults to ConfigRelCAT().

Returns:

Tuple (int, int) – last epoch and f1 score.

Return type:

Tuple[int, int]

medcat.utils.relation_extraction.ml_utils.save_results(data, model_name='BERT', path='./', file_prefix='train')
Parameters:

  • model_name (str) –

  • path (str) –

  • file_prefix (str) –

medcat.utils.relation_extraction.ml_utils.load_results(path, model_name='BERT', file_prefix='train')
Parameters:

  • model_name (str) –

  • file_prefix (str) –

Return type:

Tuple[List, List, List]

medcat.utils.relation_extraction.ml_utils.create_tokenizer_pretrain(tokenizer, relcat_config)

This method simply adds the default special tokens that we ecounter.

Parameters:
Returns:

BaseTokenizerWrapper_RelationExtraction – The same tokenizer.

Return type:

medcat.utils.relation_extraction.tokenizer.BaseTokenizerWrapper_RelationExtraction

medcat.utils.relation_extraction.ml_utils.create_dense_layers(relcat_config)
Parameters:

relcat_config (medcat.config_rel_cat.ConfigRelCAT) –

medcat.utils.relation_extraction.ml_utils.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