Retrain a Pretrained Model Using A URI

import poutyne

from deepparse import download_worldwide_addresses_sample
from deepparse.dataset_container import ListDatasetContainer
from deepparse.parser import AddressParser

First, let’s download a small sample of Canadian addresses from the worldwide-addresses dataset. The dataset tags are already mapped to the deepparse tags. We use the first 1,000 addresses for training and the last 200 for testing.

addresses = download_worldwide_addresses_sample(country_code="ca", num_samples=1200)

Now let’s create a training and test container.

training_container = ListDatasetContainer(addresses[:1000])
test_container = ListDatasetContainer(addresses[1000:])

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)