medcat.config_rel_cat

Module Contents

Classes

General	The General part of the RelCAT config
Model	The model part of the RelCAT config
Train	The train part of the RelCAT config
ConfigRelCAT	The RelCAT part of the config

class medcat.config_rel_cat.General(/, **data)

Bases: medcat.config.MixingConfig, medcat.config.BaseModel

The General part of the RelCAT config

Parameters:

data (Any) –

device: str = 'cpu'

The device to use (CPU or GPU).

NB! For these changes to take effect, the pipe would need to be recreated.

relation_type_filter_pairs: List = []

Map from category values to ID, if empty it will be autocalculated during training

vocab_size: medcat.config.Optional[int]

lowercase: bool = True

If true all input text will be lowercased

cntx_left: int = 15

Number of tokens to take from the left of the concept

cntx_right: int = 15

Number of tokens to take from the right of the concept

window_size: int = 300

Max acceptable dinstance between entities (in characters), care when using this as it can produce sentences that are over 512 tokens (limit is given by tokenizer)

limit_samples_per_class: int

Number of samples per class, this limit is applied for train samples, so if train samples are 100 then test would be 20.

addl_rels_max_sample_size: int = 200

Limit the number of ‘Other’ samples selected for training/test. This is applied per encountered medcat project, sample_size/num_projects.

create_addl_rels: bool = False

When processing relations from a MedCAT export/docs, relations labeled as ‘Other’ are created from all the annotations pairs available

create_addl_rels_by_type: bool = False

When creating the ‘Other’ relation class, actually split this class into subclasses based on concept types

tokenizer_name: str = 'bert'

The name of the tokenizer user.

NB! For these changes to take effect, the pipe would need to be recreated.

model_name: str = 'bert-base-uncased'

The name of the model used.

NB! For these changes to take effect, the pipe would need to be recreated.

log_level: int

The log level for RelCAT.

NB! For these changes to take effect, the pipe would need to be recreated.

max_seq_length: int = 512

The maximum sequence length.

NB! For these changes to take effect, the pipe would need to be recreated.

tokenizer_special_tokens: bool = False

Tokenizer.

NB! For these changes to take effect, the pipe would need to be recreated.

annotation_schema_tag_ids: List = [30522, 30523, 30524, 30525]

If a foreign non-MCAT trainer dataset is used, you can insert your own Rel entity token delimiters into the tokenizer, copy those token IDs here, and also resize your tokenizer embeddings and adjust the hidden_size of the model, this will depend on the number of tokens you introduce for example: 30522 - [s1], 30523 - [e1], 30524 - [s2], 30525 - [e2], 30526 - [BLANK], 30527 - [ENT1], 30528 - [ENT2], 30529 - [/ENT1], 30530 - [/ENT2] Please note that the tokenizer special tokens are supposed to be in pairs of two for example [s1] and [e1], [s2] and [e2], the [BLANK] is just an example placeholder token If you have more than four tokens here then you need to make sure they are present in the text, otherwise the pipeline will throw an error in the get_annotation_schema_tag() function.

tokenizer_relation_annotation_special_tokens_tags: List[str] = ['[s1]', '[e1]', '[s2]', '[e2]']

tokenizer_other_special_tokens: Dict[str, str]

The special tokens used by the tokenizer. The {PAD} is for Lllama tokenizer.

labels2idx: Dict[str, int]

idx2labels: Dict[int, str]

pin_memory: bool = True

If True the data loader will copy the tensors to the GPU pinned memory

seed: int = 13

The seed for random number generation.

NB! For these changes to take effect, the pipe would need to be recreated.

task: str = 'train'

The task for RelCAT.

language: str = 'en'

Used for Spacy lang setting

classmethod convert_keys_to_int(value)

__setattr__(key, value)

Implement setattr(self, name, value).

Parameters:

• key (str) –

• value (Any) –

class medcat.config_rel_cat.Model(/, **data)

Bases: medcat.config.MixingConfig, medcat.config.BaseModel

The model part of the RelCAT config

Parameters:

data (Any) –

class Config

extra = 'allow'

validate_assignment = True

input_size: int = 300

hidden_size: int = 768

The hidden size.

NB! For these changes to take effect, the pipe would need to be recreated.

hidden_layers: int = 3

hidden_size * 5, 5 being the number of tokens, default (s1,s2,e1,e2+CLS).

NB! For these changes to take effect, the pipe would need to be recreated.

model_size: int = 5120

The size of the model.

NB! For these changes to take effect, the pipe would need to be recreated.

dropout: float = 0.2

num_directions: int = 2

2 - bidirectional model, 1 - unidirectional

freeze_layers: bool = True

If we update the weights during training

padding_idx: int

emb_grad: bool = True

If True the embeddings will also be trained

ignore_cpos: bool = False

If set to True center positions will be ignored when calculating representation

llama_use_pooled_output: bool = False

If set to True, used only in Llama model, it will add the extra tensor formed from selecting the max of the last hidden layer

class medcat.config_rel_cat.Train(/, **data)

Bases: medcat.config.MixingConfig, medcat.config.BaseModel

The train part of the RelCAT config

Parameters:

data (Any) –

class Config

extra = 'allow'

validate_assignment = True

nclasses: int = 2

Number of classes that this model will output

batch_size: int = 25

batch size

nepochs: int = 1

Epochs

lr: float = 0.0001

Learning rate

stratified_batching: bool = False

Train the model with stratified batching

batching_samples_per_class: list = []

Number of samples per class in each batch example for batch size 64: [6,6,6,8,8,8,6,8,8]

batching_minority_limit: List[int] | int = 0

Maximum number of samples the minority class can have. Since the minority class elements need to be repeated, this is used to facilitate that example: batching_samples_per_class - [6,6,6,8,8,8,6,8,8]

batching_minority_limit - 6

adam_betas: Tuple[float, float] = (0.9, 0.999)

adam_weight_decay: float = 0

adam_epsilon: float = 1e-08

test_size: float = 0.2

gradient_acc_steps: int = 1

multistep_milestones: List[int] = [2, 4, 6, 8, 12, 15, 18, 20, 22, 24, 26, 30]

multistep_lr_gamma: float = 0.8

max_grad_norm: float = 1.0

shuffle_data: bool = True

Used only during training, if set the dataset will be shuffled before train/test split

class_weights: List[float] | None

enable_class_weights: bool = False

score_average: str = 'weighted'

What to use for averaging F1/P/R across labels

auto_save_model: bool = True

Should the model be saved during training for best results

class medcat.config_rel_cat.ConfigRelCAT(/, **data)

Bases: medcat.config.MixingConfig, medcat.config.BaseModel

The RelCAT part of the config

Parameters:

data (Any) –

class Config

extra = 'allow'

validate_assignment = True

general: General

model: Model

train: Train

classmethod load(load_path='./')

Load the config from a file.

Parameters:

load_path (str) – Path to RelCAT config. Defaults to “./”.

Returns:

ConfigRelCAT – The loaded config.

Return type:

ConfigRelCAT