File size: 2,086 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 |
# Copyright (c) 2021, 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.
# Please see tutorial at Nemo/tutorials/nlp/Entity_Linking_Medical.ipynb for
# more information on entity linking and self alignment pretraining.
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning import Trainer
from nemo.collections.nlp.models import EntityLinkingModel
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="umls_medical_entity_linking_config.yaml")
def main(cfg: DictConfig) -> None:
logging.info(f"\nConfig Params:\n{OmegaConf.to_yaml(cfg)}")
trainer = Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
logging.info(f"Loading weights from pretrained model {cfg.model.language_model.pretrained_model_name}")
model = EntityLinkingModel(cfg=cfg.model, trainer=trainer)
logging.info("===========================================================================================")
logging.info('Starting training...')
trainer.fit(model)
logging.info('Training finished!')
logging.info("===========================================================================================")
if cfg.model.nemo_path:
# '.nemo' file contains the last checkpoint and the params to initialize the model
model.save_to(cfg.model.nemo_path)
logging.info(f'Model is saved into `.nemo` file: {cfg.model.nemo_path}')
if __name__ == '__main__':
main()
|