File size: 4,337 Bytes
7934b29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Sample command to run the script:
python multi_label_intent_slot_classification.py \
model.data_dir=/home/user/multiatis \
model.validation_ds.prefix=dev \
model.test_ds.prefix=dev \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
exp_manager.exp_dir=checkpoints
fast_dev_run=false will save checkpoints for the model
"""
import pytorch_lightning as pl
from omegaconf import DictConfig, OmegaConf
from nemo.collections.nlp.models import MultiLabelIntentSlotClassificationModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="conf", config_name="multi_label_intent_slot_classification_config")
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))
# initialize the model using the config file
model = MultiLabelIntentSlotClassificationModel(cfg.model, trainer=trainer)
# training
logging.info("================================================================================================")
logging.info('Starting training...')
trainer.fit(model)
logging.info('Training finished!')
# Stop further testing as fast_dev_run does not save checkpoints
if trainer.fast_dev_run:
return
# 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...")
# for evaluation and inference you can load the previously trained model saved in .nemo file
# like this in your code, but we will just reuse the trained model here
# eval_model = MultiLabelIntentSlotClassificationModel.restore_from(restore_path=checkpoint_path)
eval_model = model
# we will setup testing data reusing the same config (test section)
eval_model.update_data_dir_for_testing(data_dir=cfg.model.data_dir)
eval_model.setup_test_data(test_data_config=cfg.model.test_ds)
trainer.test(model=eval_model, ckpt_path=None, verbose=False)
logging.info("Testing finished!")
# Optimize Threshold
eval_model.optimize_threshold(cfg.model.test_ds, 'dev')
# run an inference on a few examples
logging.info("======================================================================================")
logging.info("Evaluate the model on the given queries...")
# this will work well if you train the model on ATIS dataset
# for your own dataset change the examples appropriately
queries = [
'i would like to find a flight from charlotte to las vegas that makes a stop in st. louis',
'on april first i need a ticket from tacoma to san jose departing before 7 am',
'how much is the limousine service in boston',
]
# We use the optimized threshold for predictions
pred_intents, pred_slots, pred_list = eval_model.predict_from_examples(queries, cfg.model.test_ds)
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 Intents: {intent}')
logging.info(f'Predicted Slots: {slots}')
logging.info("Inference finished!")
if __name__ == '__main__':
main()
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