def main(cfg: DictConfig) -> None:
logging.info(f'Config Params:\n {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = IntentSlotClassificationModel(cfg.model, trainer=trainer)
trainer.fit(model)
logging.info('Training finished!')
logging.info(
"================================================================================================"
)
if cfg.model.nemo_path:
model.save_to(cfg.model.nemo_path)
logging.info(
f'The model is saved into the `.nemo` file: {cfg.model.nemo_path}')
# after model training is done, you can load the model from the saved checkpoint
# and evaluate it on a data file.
logging.info(
"================================================================================================"
)
logging.info("Starting the testing of the trained model on test set...")
logging.info(
"We will load the latest model saved checkpoint from the training...")
# run this only on the master thread in case of multi-gpu training, for single gpu no difference
if trainer.checkpoint_callback is not None:
# retrieve the path to the last checkpoint of the training (you can use the best checkpoint instead)
checkpoint_path = os.path.join(
trainer.checkpoint_callback.dirpath,
trainer.checkpoint_callback.prefix + "end.ckpt")
# load a model from the checkpoint
eval_model = IntentSlotClassificationModel.load_from_checkpoint(
checkpoint_path=checkpoint_path)
# we will setup testing data reusing the same config (test section)
eval_model.setup_test_data(test_data_config=cfg.model.test_ds)
# we will reinitialize the trainer with a single GPU and no distributed backend for the evaluation
# we need to call the next line also to overcome an issue with a trainer reinitialization in PT
cfg.trainer.gpus = 1 if cfg.trainer.gpus != 0 else 0
cfg.trainer.accelerator = None
eval_trainer = pl.Trainer(**cfg.trainer)
eval_trainer.test(model=eval_model, ckpt_path=None, verbose=False)
logging.info("Testing finished!")
logging.info(
"================================================================================================"
)
def main(cfg: DictConfig) -> None:
logging.info(f'Config Params:\n {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = IntentSlotClassificationModel(cfg.model, trainer=trainer)
trainer.fit(model)
logging.info('Training finished!')
logging.info(
"================================================================================================"
)
if cfg.model.nemo_path:
model.save_to(cfg.model.nemo_path)
logging.info(
f'The model is saved into the `.nemo` file: {cfg.model.nemo_path}')
# after model training is done, you can load the model from the saved checkpoint
# and evaluate it on a data file or on given queries.
logging.info(
"================================================================================================"
)
logging.info("Starting the testing of the trained model on test set...")
logging.info(
"We will load the latest model saved checkpoint from the training...")
# run this only on the master thread in case of multi-gpu training, for single gpu no difference
if trainer.checkpoint_callback is not None:
# retrieve the path to the last checkpoint of the training in .nemo format
checkpoint_path = os.path.join(
trainer.checkpoint_callback.dirpath,
trainer.checkpoint_callback.prefix + ".nemo")
# load a model from the checkpoint
eval_model = IntentSlotClassificationModel.restore_from(
restore_path=checkpoint_path)
# we will setup testing data reusing the same config (test section)
eval_model.setup_test_data(test_data_config=cfg.model.test_ds)
# we will reinitialize the trainer with a single GPU and no distributed backend for the evaluation
# we need to call the next line also to overcome an issue with a trainer reinitialization in PT
cfg.trainer.gpus = 1 if cfg.trainer.gpus != 0 else 0
cfg.trainer.accelerator = None
eval_trainer = pl.Trainer(**cfg.trainer)
eval_trainer.test(model=eval_model, ckpt_path=None, verbose=False)
logging.info("Testing finished!")
logging.info(
"================================================================================================"
)
# run an inference on a few examples
logging.info(
"======================================================================================"
)
logging.info("Evaluate the model on given queries...")
# this will work well if you train the model on Assistant dataset
# for your own dataset change the examples appropriately
queries = [
'set alarm for seven thirty am',
'lower volume by fifty percent',
'what is my schedule for tomorrow',
]
pred_intents, pred_slots = eval_model.predict_from_examples(queries)
logging.info(
'The prediction results of some sample queries with the trained model:'
)
for query, intent, slots in zip(queries, pred_intents, pred_slots):
logging.info(f'Query : {query}')
logging.info(f'Predicted Intent: {intent}')
logging.info(f'Predicted Slots: {slots}')
logging.info("Inference finished!")
logging.info(
"=========================================================================================="
)