Retrain a Pretrained Model Using A URI

import poutyne

from deepparse import download_from_public_repository
from deepparse.dataset_container import PickleDatasetContainer
from deepparse.parser import AddressParser

First, let’s download the train and test data from the public repository.

saving_dir = "./data"
file_extension = "p"
training_dataset_name = "sample_incomplete_data"
test_dataset_name = "test_sample_data"
download_from_public_repository(training_dataset_name, saving_dir, file_extension=file_extension)
download_from_public_repository(test_dataset_name, saving_dir, file_extension=file_extension)

Now let’s create a training and test container.

training_container = PickleDatasetContainer(os.path.join(saving_dir,
                                                         training_dataset_name + "." + file_extension))
test_container = PickleDatasetContainer(os.path.join(saving_dir,
                                                     test_dataset_name + "." + file_extension))

We will retrain the FastText version of our pretrained model.

path_to_your_uri = "s3://<path_to_your_bucket>/fasttext.ckpt"
address_parser = AddressParser(model_type="fasttext", device=0, path_to_retrained_model=path_to_your_uri)

Now, let’s retrain for 5 epochs using a batch size of 8 since the data is really small for the example. Let’s start with the default learning rate of 0.01 and use a learning rate scheduler to lower the learning rate as we progress.

# Reduce LR by a factor of 10 each epoch
lr_scheduler = poutyne.StepLR(step_size=1, gamma=0.1)

The retrained model best checkpoint (ckpt) will be saved in the S3 Bucket <path_to_your_bucket.

address_parser.retrain(training_container, logging_path="s3://<path_to_your_bucket/", train_ratio=0.8, epochs=5, batch_size=8, num_workers=2, callbacks=[lr_scheduler])

Now, let’s test our fine-tuned model using the best checkpoint (default parameter).

address_parser.test(test_container, batch_size=256)