Models

TOC

• Classifier models
  • Base Classifier
  • Huggingface Classifier
  • Setfit Classifier
• Embedding models
  • Base embedding model
  • Child label embedding model
  • Chunked embedding model
  • Ground up embedding model
  • Greedy ground up embedding model
  • Sentence based embedding model
• Hybrid models
  • Semi-supervised model
  • Semi-supervised selective model
• Zeroshot models
  • Base zeroshot model
  • Child based zeroshot model
  • Chunked zeroshot model
  • Sentence based zeroshot model

Base class

class src.models.base.Model(config: Config, logger: Logger, model_id: str)

Bases: object

Base model for all classes

_load_model(model_id: str) → None

This function enables custom model preperations before executing the classification

Parameters:

model_id – model_id to pull

Returns:

_prep_labels(taxonomy: Taxonomy | list[str]) → None

The function that prepares the labels, this converts them to the required format for further processing with a model. :param taxonomy: Taxonomy object where we will use the labels from :return:

add_labels(labels: list[str]) → None

This function enables the adding of extra labels to the models setup

Parameters:

labels – list of new labels to add/ set in place

Returns:

nothing

classify(text: str, multi_label: bool, **kwargs) → dict[str, float]

Abstract function that executes the text classificatoin

Parameters:

• text – the text to classify

• multi_label – boolean to identify if it is a multilabel problem

• kwargs – potential extra vars

Returns:

the results

property device

This property returns the device that the model is running on.

Returns:

torch device in use

Other

src.models.get_model(config: Config, logger: Logger, model_id: str, taxonomy: Taxonomy, specific_model_type: ModelType = None, **kwargs) → Model

This function provides you with an instantiated model based on the provided arguments and config.

Parameters:

• config – the global config object

• logger – the global logger object

• model_id – model it to use as base model weights

• taxonomy – the taxonomy to use for label predicitons

• specific_model_type – model flavour explicitly defined (overrule config)

• kwargs – extra kwargs used to initialize models

Returns:

An instance of Model class

src.models.get_topic_model(model_type: ModelType, config: Config, logger: Logger, dataset_builder: DatasetBuilder)

Model provided specifically for the topic models.

Parameters:

• model_type – the specific model type requested

• config – the global config object

• logger – the global logger object

• dataset_builder – the dataset builder object containing all the relevant information that could be used for the topic modeling

Returns:

An instance of the requested topic modeling