async def _nlu_model_for_finetuning(
model_to_finetune: Text,
file_importer: TrainingDataImporter,
finetuning_epoch_fraction: float = 1.0,
called_from_combined_training: bool = False,
) -> Optional[Interpreter]:
path_to_archive = model.get_model_for_finetuning(model_to_finetune)
if not path_to_archive:
return None
rasa.shared.utils.cli.print_info(
f"Loading NLU model from {path_to_archive} for finetuning...", )
with model.unpack_model(path_to_archive) as unpacked:
_, old_nlu = model.get_model_subdirectories(unpacked)
new_fingerprint = await model.model_fingerprint(file_importer)
old_fingerprint = model.fingerprint_from_path(unpacked)
if not model.can_finetune(
old_fingerprint,
new_fingerprint,
nlu=True,
core=called_from_combined_training,
):
rasa.shared.utils.cli.print_error_and_exit(
"NLU model can not be finetuned.")
config = await file_importer.get_config()
model_to_finetune = Interpreter.load(
old_nlu,
new_config=config,
finetuning_epoch_fraction=finetuning_epoch_fraction,
)
if not model_to_finetune:
return None
return model_to_finetune
async def _core_model_for_finetuning(
model_to_finetune: Text,
file_importer: TrainingDataImporter,
finetuning_epoch_fraction: float = 1.0,
) -> Optional[Agent]:
path_to_archive = model.get_model_for_finetuning(model_to_finetune)
if not path_to_archive:
return None
rasa.shared.utils.cli.print_info(
f"Loading Core model from {path_to_archive} for finetuning...", )
with model.unpack_model(path_to_archive) as unpacked:
new_fingerprint = await model.model_fingerprint(file_importer)
old_fingerprint = model.fingerprint_from_path(unpacked)
if not model.can_finetune(old_fingerprint, new_fingerprint, core=True):
rasa.shared.utils.cli.print_error_and_exit(
"Core model can not be finetuned.")
config = await file_importer.get_config()
agent = Agent.load(
unpacked,
new_config=config,
finetuning_epoch_fraction=finetuning_epoch_fraction,
)
# Agent might be empty if no underlying Core model was found.
if agent.domain is not None and agent.policy_ensemble is not None:
return agent
return None
