lisbet.training.core#
Training and fitting functions for LISBET.
Notes
[a] The dictionary of RNG seed could be refactored as a Pydantic model in the future.
- [b] The train/dev split is performed here and not in the input_pipeline module to
emphasize that the test set is frozen and won’t be used for hyper-parameters tuning.
- [c] When mixing datasets of different lengths, the training and evaluation loops will
stop after exhausting the shortest one. Please consider using random sampling.
Functions
|
Train a LISBET model. |
- lisbet.training.core.train(experiment_config)[source]#
Train a LISBET model.
This function orchestrates the full training pipeline for LISBET, including data loading, model construction, training, evaluation, and saving artifacts. All parameters match the CLI arguments exactly.
- Parameters:
experiment_config (
ExperimentConfig) – Configuration object containing all parameters for the training run. It includes data paths, model architecture, training hyperparameters, and task definitions. Must be a Pydantic model.- Returns:
model – The trained LISBET model instance.
- Return type:
Module
Notes
All arguments are exposed for CLI and documentation. For advanced usage, see the LISBET documentation.